Global Database Encryption Market Analysis 2020-2025: by Key Players with Countries, Type, Application and Forecast Till 2025 – PRnews Leader

This report besides representing detailed synopsis of the current Database Encryption Market scenario, this section of the report also includes versatile details on the overall ecosystem, key trends, Market catalysts as well as threats and challenges that seem to significantly impact revenue generation in the Database Encryption Market.

Post persistent observation and research initiatives, this new research presentation on Global Database Encryption Market has been recently released to ensure optimum scavenging of the Global Database Encryption Market to make vital conclusions.

Access the PDF sample of the Database Encryption Market report @ https://www.orbisresearch.com/contacts/request-sample/2510040?utm_source=Atish

The key players covered in this studyInternational Business Machines CorporationSymantec CorporationIntel Security (Mcafee)Microsoft CorporationOracle CorporationNetapp, Inc.Hewlett-Packard CompanyVormetricSophos LtdGemalto

The report shows discernable light on pertinent Market elements such as segment specific performance. The report meticulously gauges into past and current performance status of various segments to understand past growth outlook as well as current milestones that result in accurate forecast predictions about Global Database Encryption Market.

Make an enquiry of Database Encryption Market report @ https://www.orbisresearch.com/contacts/enquiry-before-buying/2510040?utm_source=Atish

Additional report components entail real-time status of segment categorization. For superlative reader comprehension, this versatile report segregates key Market components into product and application based compartments. Further, the report aptly explains regional segmentation highlighting major growth hotspots along with relevant developments in the regions.

Market segment by Type, the product can be split intoTransparent EncryptionColumn-level EncryptionFile-system EncryptionApplication- Level EncryptionKey Management

Market segment by Application, split intoSMBsEnterprises

Browse the complete Database Encryption Market report @ https://www.orbisresearch.com/reports/index/global-database-encryption-market-size-status-and-forecast-2019-2025?utm_source=Atish

A distinctive DROT analysis section is also included in the report to closely scout for teeming Market opportunities, major threats and challenges that tend to shun growth through the forecast span.

A close review of opportunity mapping as well as barrier analysis across specific growth pockets allow Market participants to augment future-ready investment decisions.

A dedicated section on pandemic crisis and effective management guide have also been included in the report to comply with reader discretion.

About Us:Orbis Research (orbisresearch.com) is a single point aid for all your Market research requirements. We have vast database of reports from the leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required Market research study for our clients.

Contact Us:Hector CostelloSenior Manager Client Engagements4144N Central Expressway,Suite 600, Dallas,Texas 75204, U.S.A.Phone No.: +1 (972)-362-8199 ; +91 895 659 5155

Excerpt from:
Global Database Encryption Market Analysis 2020-2025: by Key Players with Countries, Type, Application and Forecast Till 2025 - PRnews Leader

Application-level Encryption Market Global Industry Analysis, Size, Share, Growth, Trends, and Forecast, 2020-2030 – Yahoo Finance UK

Globe Newswire

The global market for Automated Guided Vehicles (AGVs) is projected to reach US$2. 6 billion by 2025 driven by the strong focus shed on automation and production efficiency in the era of smart factory and industry 4.New York, Oct. 24, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Automated Guided Vehicle Industry" - https://www.reportlinker.com/p05797945/?utm_source=GNW 0. Smart manufacturing is the new revolution that will mark the next generation of production. Self-aware, self-optimized, self-configured equipment with the ability to wirelessly communicate with each other; advanced networking; real-time controls; more closer alignment of business management and plant floor activities and supply chains; additive manufacturing are few of the disruptive smart factory trends currently underway. Among these, "automation" lies at the heart of the smart factory concept. Almost like the beating heart, automation provides the foundation for the creation of knowledge-embedded manufacturing operations. Automating labor intensive operations is the starting point for automation, and as the most labor intensive process in manufacturing and warehousing, material handling is at the bottom of the automation pyramid. Defined as the movement of products and materials throughout the manufacturing process, material handling is labor intensive and expensive. Benefits of automating material handling include reduced human role in unproductive, repetitive and labor intensive tasks and the ensuing freeing up of resources for other core activities; greater throughput capability; better space utilization; increased production control; inventory control; improved stock rotation; reduced operation cost; improved worker safety; reduced losses from damage; and reduction in handling costs. The importance of production efficiency can be put into perspective by the fact that over 55% of factory operation managers earmark budgets for investing in resources that support achievement of higher production rates. Technology innovation in automation, in this regard, is a key factor to boost productivity. Benefiting from increased investments in factory automation are AGVs, the workhorse of every processing and manufacturing plant. AGVs help automate repetitive, labor intensive tasks such as carrying pallets, rolls, racks, carts, and containers. Modern AGVs have come a long way from simple materials handlers to the current intelligent autonomous robots that find their way across the plant floor using advanced auditory or visual or environmental stimulus. On the plant floor, these highly functional smart robots ensure on-time delivery of parts to the production/assembly line. Benefits offered by AGVs include round the clock transit of materials; traceability that ensures that the plant manager remains informed of the material movement around the facility; ability to time-stamp pick-up, transit and delivery of items which can help in route optimization and improvements; ability to integrate information generated by AGVs into enterprise resource planning (ERP) or materials resource planning (MRP) systems; among others. AGVs are moving from wired operation to wireless operations. Benefits of wireless AGVs include enhanced plant safety; operational flexibility; superior delivery speeds as it eliminates bottlenecks and obstruction in movements commonly experienced with wired operation; ability to operate AGV fleets on the plant floor; overthrows the need to modify or reconstruct the factor environment which is often needed for wired AGVs; changes in route plans can be easily executed as wireless AGVs can record and store images, identify obstructions and calculate their position; ability to autonomously take decisions about the best route in a manner that avoids collisions. The United States and Europe represent large markets worldwide with a combined share of 52.3% of the global market. China ranks as the fastest growing market with a CAGR of 9.4% over the analysis period supported by the Made in China (MIC) 2025 initiative that aims to bring the country`s massive manufacturing and production sector into the forefront of global technology competitiveness. Inspired by Germany`s "Industry 4.0", MIC 2025 will enhance adoption of automation, digital and IoT technologies. Faced with new and changing economic forces, the Chinese government through this initiative is stepping up investments in cutting edge robotics, automation and digital IT technologies to competitively integrate into the global manufacturing chain dominated by industrialized economies such as EU, Germany and the United States and move from being a low cost competitor to a direct added-value competitor.Read the full report: https://www.reportlinker.com/p05797945/?utm_source=GNW I. INTRODUCTION, METHODOLOGY & REPORT SCOPE II. EXECUTIVE SUMMARY 1. MARKET OVERVIEW Automated Guided Vehicle (AGV) Types of Automated Guided Vehicles Forklift Truck Tow Vehicle Pallet Truck Other Types of AGVs Navigation Technology in AGVs Applications of AGVs Advantages of AGVs Standards for AGVs Focus on Automation & Production Efficiency, the Foundation for Growing Investments in AGV Tow Vehicles: The Largest Vehicle Type Segment in the Global AGV Market Asia-Pacific to Spearhead Future Growth, while the US and Europe Dominate AGV Market Global Economic Outlook Real GDP Growth Rates in % by Country/Region for the Years 2017 through 2020 Global Competitor Market Shares Automated Guided Vehicle Competitor Market Share Scenario Worldwide (in %): 2019 Impact of Covid-19 and a Looming Global Recession 2. FOCUS ON SELECT PLAYERS ABB (Switzerland) Amerden Inc. (USA) Balyo, Inc. (USA) Bastian Solutions, Inc. (USA) Daifuku Co., Ltd. (Japan) Hyster-Yale Materials Handling, Inc. (USA) Jungheinrich AG (Germany) John Bean Technologies Corporation (USA) KION Group AG (Germany) Dematic Group (USA) Konecranes India Pvt. Ltd. (India) KUKA AG (Germany) Swisslog Holding AG (Switzerland) Murata Machinery, Ltd. (Japan) Seegrid Corporation (USA) SSI Schaefer - Fritz Schaefer GmbH (Germany) Toyota Industries Corporation (Japan) Vanderlande Industries (The Netherlands) Universal Robots A/S (Denmark) 3. MARKET TRENDS & DRIVERS Focus on Implementing High Standards of Safety at Workplaces Favors AGV Market Forklifts-related Accidents Raise Need for AGVs: Percentage Breakdown of Number of Fatalities Attributed to Forklift by Type of Accident in the US AGVs: The Future of Manufacturing World AGVs Assist in Enhancing Operational Efficiency of Factories AGV Systems Promise to Transform the Logistics Marketplace AGVs Transforming Intralogistic Processes in Factory Automation Space Rapidly Growing Logistics Industry Presents Favorable Outlook for AGVs Market Global Logistics Market Revenues in US$ Billion for the Years 2019, 2021, 2023 and 2025 AGVs Offer Significant Benefits for Warehousing Operations With Global E-Commerce Sales Skyrocketing, Emergence of E- Commerce Warehouses and Need to Automate Supply Chain Spurs Investments into AGVs Robust Growth Anticipated for E-Commerce: An Opportunity to Tap for AGVs Market Global B2C E-Commerce Sales in US$ Trillion for the Years 2017, 2019, 2021 and 2023 Global E-Commerce Market as a % of Retail Sales for the Period 2017-2023 Leading Retail E-Commerce Countries Worldwide: Ranked by Sales in $ Billion for 2019E Rapid Adoption of Automation Technologies in Material Handling Processes Fuel AGVs Market Growth AGVs Emerge as Important Constituents of Industry 4.0 Revolution and Shift towards Smart Manufacturing Facilities As Industry 4.0 and Logistics 4.0 Come Together, AGVs to Play an Even Greater Role Advancements Lead to Expanded Opportunities for AGVs AGV System in Healthcare Settings: An Area of Growth AGVs Increase Productivity in Maritime Industry Automotive Industry: A Major End-Use Market for AGVs Enabling Flexible and Efficient Production Operations: A Significant Advantage of AGVs in Automotive Industry Navigation Technologies for AGVs in Automotive Assembly Lines Automotive Companies Take the Lead in Automation of Automated Guided Vehicles Increase in Automobile Production: An Opportunity for AGVs Market Global Passenger Cars Production (In Million Units) by Geographic Region/Country for the Years 2017, 2019, 2022 AGVs Adoption in Food & Beverage Manufacturing Bolstered by Flexibility, Scalability and Safety Advantages and Ability to Address Labor Shortage Top Reasons for Adoption of AGVs in Food & Beverage Manufacturing Global Sales of Processed Foods in US$ Billion for Years 2014, 2016, 2018 and 2020 Global Beverages Market in US$ Trillion for the Years 2018, 2020 & 2022 Charging of Battery-Operated AGVs Becomes a Challenge for Warehouses With Electrically-driven AGVs Becoming Integral to Logistics Applications, Need for Sophisticated Charging Systems Gains Prominence Growing Significance of AGVs in Process Automation Gives Rise to Safety Concerns Emergence of Advanced Navigation Systems to Result in Greater Scalability and Flexibility of AGVs Advances in Motion Control Technology to Play a Critical Role in Enhancing Efficiency and Reducing Footprint of AGVs New Technologies with Tremendous Potential for AGVs Market AGVs to Benefit from the Increasing Implementation of AI in Factories Smart Technologies such as AI, IoT and Machine Learning Foster Development of Faster and Smarter AGVs AGVs Address the Needs of SMEs Issues Related to the Use of AGVs for Material Handling Applications Autonomous Vehicles: The Ultimate Future of Material Handling Automation Innovations & Advancements Numerous Benefits Provided by AGVs to Drive their Adoption ILIAD Project Seeks to Integrate Artificial Intelligence into Autonomous Forklifts ASTI and 5TONIC Team Up to Research on Application of 5G Technology to AGVs Rocla Develops New AGV Solution, Rocla ART, for Warehousing Operations Mecfor Unveils AGV Prototype for Use in Aluminum Smelters 4. GLOBAL MARKET PERSPECTIVE Table 1: World Current & Future Analysis for Automated Guided Vehicle by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 2: World Historic Review for Automated Guided Vehicle by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 3: World 15-Year Perspective for Automated Guided Vehicle by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets for Years 2012, 2020 & 2027 Table 4: World Current & Future Analysis for Forklift Truck by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 5: World Historic Review for Forklift Truck by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 6: World 15-Year Perspective for Forklift Truck by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 7: World Current & Future Analysis for Tow Vehicle by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 8: World Historic Review for Tow Vehicle by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 9: World 15-Year Perspective for Tow Vehicle by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 10: World Current & Future Analysis for Pallet Truck by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 11: World Historic Review for Pallet Truck by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 12: World 15-Year Perspective for Pallet Truck by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 13: World Current & Future Analysis for Other Types by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 14: World Historic Review for Other Types by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 15: World 15-Year Perspective for Other Types by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 16: World Current & Future Analysis for Lead by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 17: World Historic Review for Lead by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 18: World 15-Year Perspective for Lead by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 19: World Current & Future Analysis for Nickel by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 20: World Historic Review for Nickel by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 21: World 15-Year Perspective for Nickel by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 22: World Current & Future Analysis for Lithium Ion by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 23: World Historic Review for Lithium Ion by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 24: World 15-Year Perspective for Lithium Ion by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 25: World Current & Future Analysis for Other Battery Types by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 26: World Historic Review for Other Battery Types by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 27: World 15-Year Perspective for Other Battery Types by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 28: World Current & Future Analysis for Laser Guidance by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 29: World Historic Review for Laser Guidance by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 30: World 15-Year Perspective for Laser Guidance by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 31: World Current & Future Analysis for Vision Guidance by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 32: World Historic Review for Vision Guidance by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 33: World 15-Year Perspective for Vision Guidance by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 34: World Current & Future Analysis for Other Navigation Technologies by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 35: World Historic Review for Other Navigation Technologies by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 36: World 15-Year Perspective for Other Navigation Technologies by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 37: World Current & Future Analysis for Transportation by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 38: World Historic Review for Transportation by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 39: World 15-Year Perspective for Transportation by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 40: World Current & Future Analysis for Distribution by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 41: World Historic Review for Distribution by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 42: World 15-Year Perspective for Distribution by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 43: World Current & Future Analysis for Storage by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 44: World Historic Review for Storage by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 45: World 15-Year Perspective for Storage by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 46: World Current & Future Analysis for Other Applications by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 47: World Historic Review for Other Applications by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 48: World 15-Year Perspective for Other Applications by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 49: World Current & Future Analysis for Automotive by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 50: World Historic Review for Automotive by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 51: World 15-Year Perspective for Automotive by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 52: World Current & Future Analysis for Food & Beverages by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 53: World Historic Review for Food & Beverages by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 54: World 15-Year Perspective for Food & Beverages by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 55: World Current & Future Analysis for Logistics by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 56: World Historic Review for Logistics by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 57: World 15-Year Perspective for Logistics by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 58: World Current & Future Analysis for Retail by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 59: World Historic Review for Retail by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 60: World 15-Year Perspective for Retail by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 Table 61: World Current & Future Analysis for Other Industries by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 Table 62: World Historic Review for Other Industries by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 63: World 15-Year Perspective for Other Industries by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific, Latin America, Middle East and Africa for Years 2012, 2020 & 2027 III. MARKET ANALYSIS GEOGRAPHIC MARKET ANALYSIS UNITED STATES Automated Guided Vehicle Manufacturing Market in the US: An Overview Competitive Landscape Market Analytics Table 64: USA Current & Future Analysis for Automated Guided Vehicle by Type - Forklift Truck, Tow Vehicle, Pallet Truck and Other Types - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 65: USA Historic Review for Automated Guided Vehicle by Type - Forklift Truck, Tow Vehicle, Pallet Truck and Other Types Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 66: USA 15-Year Perspective for Automated Guided Vehicle by Type - Percentage Breakdown of Value Sales for Forklift Truck, Tow Vehicle, Pallet Truck and Other Types for the Years 2012, 2020 & 2027 Table 67: USA Current & Future Analysis for Automated Guided Vehicle by Battery Type - Lead, Nickel, Lithium Ion and Other Battery Types - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 68: USA Historic Review for Automated Guided Vehicle by Battery Type - Lead, Nickel, Lithium Ion and Other Battery Types Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 69: USA 15-Year Perspective for Automated Guided Vehicle by Battery Type - Percentage Breakdown of Value Sales for Lead, Nickel, Lithium Ion and Other Battery Types for the Years 2012, 2020 & 2027 Table 70: USA Current & Future Analysis for Automated Guided Vehicle by Navigation Technology - Laser Guidance, Vision Guidance and Other Navigation Technologies - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 71: USA Historic Review for Automated Guided Vehicle by Navigation Technology - Laser Guidance, Vision Guidance and Other Navigation Technologies Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 72: USA 15-Year Perspective for Automated Guided Vehicle by Navigation Technology - Percentage Breakdown of Value Sales for Laser Guidance, Vision Guidance and Other Navigation Technologies for the Years 2012, 2020 & 2027 Table 73: USA Current & Future Analysis for Automated Guided Vehicle by Application - Transportation, Distribution, Storage and Other Applications - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 74: USA Historic Review for Automated Guided Vehicle by Application - Transportation, Distribution, Storage and Other Applications Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 75: USA 15-Year Perspective for Automated Guided Vehicle by Application - Percentage Breakdown of Value Sales for Transportation, Distribution, Storage and Other Applications for the Years 2012, 2020 & 2027 Table 76: USA Current & Future Analysis for Automated Guided Vehicle by Industry - Automotive, Food & Beverages, Logistics, Retail and Other Industries - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 77: USA Historic Review for Automated Guided Vehicle by Industry - Automotive, Food & Beverages, Logistics, Retail and Other Industries Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 78: USA 15-Year Perspective for Automated Guided Vehicle by Industry - Percentage Breakdown of Value Sales for Automotive, Food & Beverages, Logistics, Retail and Other Industries for the Years 2012, 2020 & 2027 CANADA Table 79: Canada Current & Future Analysis for Automated Guided Vehicle by Type - Forklift Truck, Tow Vehicle, Pallet Truck and Other Types - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 80: Canada Historic Review for Automated Guided Vehicle by Type - Forklift Truck, Tow Vehicle, Pallet Truck and Other Types Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 81: Canada 15-Year Perspective for Automated Guided Vehicle by Type - Percentage Breakdown of Value Sales for Forklift Truck, Tow Vehicle, Pallet Truck and Other Types for the Years 2012, 2020 & 2027 Table 82: Canada Current & Future Analysis for Automated Guided Vehicle by Battery Type - Lead, Nickel, Lithium Ion and Other Battery Types - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 83: Canada Historic Review for Automated Guided Vehicle by Battery Type - Lead, Nickel, Lithium Ion and Other Battery Types Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 84: Canada 15-Year Perspective for Automated Guided Vehicle by Battery Type - Percentage Breakdown of Value Sales for Lead, Nickel, Lithium Ion and Other Battery Types for the Years 2012, 2020 & 2027 Table 85: Canada Current & Future Analysis for Automated Guided Vehicle by Navigation Technology - Laser Guidance, Vision Guidance and Other Navigation Technologies - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 86: Canada Historic Review for Automated Guided Vehicle by Navigation Technology - Laser Guidance, Vision Guidance and Other Navigation Technologies Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 87: Canada 15-Year Perspective for Automated Guided Vehicle by Navigation Technology - Percentage Breakdown of Value Sales for Laser Guidance, Vision Guidance and Other Navigation Technologies for the Years 2012, 2020 & 2027 Table 88: Canada Current & Future Analysis for Automated Guided Vehicle by Application - Transportation, Distribution, Storage and Other Applications - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 89: Canada Historic Review for Automated Guided Vehicle by Application - Transportation, Distribution, Storage and Other Applications Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 90: Canada 15-Year Perspective for Automated Guided Vehicle by Application - Percentage Breakdown of Value Sales for Transportation, Distribution, Storage and Other Applications for the Years 2012, 2020 & 2027 Table 91: Canada Current & Future Analysis for Automated Guided Vehicle by Industry - Automotive, Food & Beverages, Logistics, Retail and Other Industries - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 92: Canada Historic Review for Automated Guided Vehicle by Industry - Automotive, Food & Beverages, Logistics, Retail and Other Industries Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 93: Canada 15-Year Perspective for Automated Guided Vehicle by Industry - Percentage Breakdown of Value Sales for Automotive, Food & Beverages, Logistics, Retail and Other Industries for the Years 2012, 2020 & 2027 JAPAN Japan: A Growing Market for AGVs Market Analytics Table 94: Japan Current & Future Analysis for Automated Guided Vehicle by Type - Forklift Truck, Tow Vehicle, Pallet Truck and Other Types - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 95: Japan Historic Review for Automated Guided Vehicle by Type - Forklift Truck, Tow Vehicle, Pallet Truck and Other Types Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 96: Japan 15-Year Perspective for Automated Guided Vehicle by Type - Percentage Breakdown of Value Sales for Forklift Truck, Tow Vehicle, Pallet Truck and Other Types for the Years 2012, 2020 & 2027 Table 97: Japan Current & Future Analysis for Automated Guided Vehicle by Battery Type - Lead, Nickel, Lithium Ion and Other Battery Types - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 98: Japan Historic Review for Automated Guided Vehicle by Battery Type - Lead, Nickel, Lithium Ion and Other Battery Types Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 99: Japan 15-Year Perspective for Automated Guided Vehicle by Battery Type - Percentage Breakdown of Value Sales for Lead, Nickel, Lithium Ion and Other Battery Types for the Years 2012, 2020 & 2027 Table 100: Japan Current & Future Analysis for Automated Guided Vehicle by Navigation Technology - Laser Guidance, Vision Guidance and Other Navigation Technologies - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 101: Japan Historic Review for Automated Guided Vehicle by Navigation Technology - Laser Guidance, Vision Guidance and Other Navigation Technologies Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 102: Japan 15-Year Perspective for Automated Guided Vehicle by Navigation Technology - Percentage Breakdown of Value Sales for Laser Guidance, Vision Guidance and Other Navigation Technologies for the Years 2012, 2020 & 2027 Table 103: Japan Current & Future Analysis for Automated Guided Vehicle by Application - Transportation, Distribution, Storage and Other Applications - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 104: Japan Historic Review for Automated Guided Vehicle by Application - Transportation, Distribution, Storage and Other Applications Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 105: Japan 15-Year Perspective for Automated Guided Vehicle by Application - Percentage Breakdown of Value Sales for Transportation, Distribution, Storage and Other Applications for the Years 2012, 2020 & 2027 Table 106: Japan Current & Future Analysis for Automated Guided Vehicle by Industry - Automotive, Food & Beverages, Logistics, Retail and Other Industries - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 Table 107: Japan Historic Review for Automated Guided Vehicle by Industry - Automotive, Food & Beverages, Logistics, Retail and Other Industries Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2012 through 2019 Table 108: Japan 15-Year Perspective for Automated Guided Vehicle by Industry - Percentage Breakdown of Value Sales for Automotive, Food & Beverages, Logistics, Retail and Other Industries for the Years 2012, 2020 & 2027 CHINA China?s AGV Market: Experiencing Strong Growth Booming E-Commerce Market in China Spurs Demand for AGVs Robust Growth of E-Commerce Market Bolsters Growth in AGVs Market: Chinese Retail E-Commerce Market Size in US$ Billion Please contact our Customer Support Center to get the complete Table of ContentsRead the full report: https://www.reportlinker.com/p05797945/?utm_source=GNWAbout ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.__________________________CONTACT: Clare: clare@reportlinker.comUS: (339)-368-6001Intl: +1 339-368-6001

See original here:
Application-level Encryption Market Global Industry Analysis, Size, Share, Growth, Trends, and Forecast, 2020-2030 - Yahoo Finance UK

Ten years since WikiLeaks and Julian Assange published the Iraq War Logs – WSWS

Today marks a decade since WikiLeaks published the Iraq War Logs, the most comprehensive exposure of imperialist criminality and neo-colonial banditry since the Pentagon Papers of the 1970s revealed the scale of American military activities in Vietnam, and perhaps of all time.

In minute detail, the logs exposed all of the lies used to justify the occupation of Iraq, revealing it to be a brutal operation involving the daily murder of civilians, torture, innumerable acts of imperialist thuggery targeting an oppressed population, and cover-ups extending to the top of the US and allied military commands.

The material was painstakingly reviewed, contextualised, and its political implications explained, above all by Julian Assange and his small team of journalistic colleagues at WikiLeaks.

The logs were one of the most powerful applications of the WikiLeaks model that Assange had developed when he founded the organisation in 2006. The publication of leaked documents, kept hidden by the powers-that-be, would expose to the population the real military, economic and political relations, and the daily intrigues of governments that shaped world politics and so much of their lives. Only by knowing what was really occurring, could ordinary people take informed political action, including in the fight to end war.

Assange and WikiLeaks have never been forgiven by the US ruling elite, or its allies in Britain, Australia and internationally, for taking these Enlightenment ideals seriously and acting on them. Behind all of the lies and slanders used to undermine support for Assange, the real watchword of the campaign against the WikiLeaks founder is: He exposed our crimes, so we will destroy him.

Ten years after he revealed war crimes, of a scale and intensity not seen since the horrors of the Nazi regime, Assange is alone in a cell at Londons maximum-security Belmarsh Prison, a facility designed to detain terrorists and murderers. He faces extradition to the US, prosecution under the Espionage Act for publishing the truth, including the Iraq War Logs, and 175 years in a supermax prison.

Chelsea Manning, the courageous whistleblower who released the material, has been subjected to a decade-long nightmare involving imprisonment, what the United Nations deemed to be state torture and attempts to coerce her into giving false testimony against Assange, which she has heroically resisted.

But the gangsters who orchestrated the rape of Iraq remain free. George W. Bush has been politically rehabilitated, above all by the US Democrats and the corrupt liberal press, former British Prime Minister Tony Blair is still up to his neck in imperialist intrigues in the Middle East and his Australian counterpart John Howard is enjoying a quiet retirement.

This operation has above all relied upon the same pliant, corporate media that promoted the illegal invasion of Iraq, based on lies about weapons of mass destruction, and then embedded itself in the occupation forces that pillaged the country and looted its oil. Their complicity today is summed up by the fact that not a single major publication in the US, Britain or Australia has even taken note of the ten-year anniversary of the Iraq War Logs.

The significance of the logs, and the explosive impact they had on popular consciousness, however, must be recalled.

The publication comprised 391,832 field reports by the US army, from 2004 to 2009, making it the largest leak in the history of the American military. They recorded 109,000 Iraqi deaths.

At least 66,081 were described by the US army as civilians. This included some 15,000 fatalities that had been completely covered up by the US and its allies, who prior to the publication, claimed that they did not have a record of civilian deaths. Without WikiLeaks and Assange, the murders of these workers, students, young people and senior citizens, equivalent to the population of a small town, would never have been known.

The logs showed that the US military routinely described those it killed as insurgents, when they were known to be civilians. Such was the case in the infamous 2007 Apache helicopter attack in Baghdad, documented in WikiLeaks Collateral Murder video, which involved the slaughter of up to 19 civilians, including two Reuters journalists. A US army press release at the time had described a fictitious firefight with insurgents.

The war logs revealed that some 700 civilians had been gunned-down by US and allied troops for coming too close to a military checkpoint. They included children and the mentally-ill. On at least six occasions, the victims were rushing their pregnant wives to hospital to give birth.

The carnage was also perpetrated by the private contractors who operated as shock troops of the US occupation. One report described Blackwater employees firing indiscriminately into a crowd after an IED explosion. Another said US soldiers observed a Blackwater PSD shoot up a civ vehicle in Baghdad. The May, 2005 attack killed an innocent man and maimed his wife and daughter.

The logs showed that the US routinely handed over detainees to their puppet Iraqi security forces for torture. One report noted the presence of a hand cranked generator with wire clamps in a Baghdad police station, used to electrocute prisoners. The official policy of the Coalition troops, as revealed in the logs, was not to investigate such incidents.

Taken together, the revelations painted an undeniable picture of systemic criminality, involving the most powerful governments in the world, their militaries and proxies.

Testifying at British show-trial hearings for Assanges extradition last month, Professor John Sloboda, co-founder of Iraq Body Count, stated that the logs had brought the killings of Iraqi civilians to the largest global audience of any single release All of [the recorded civilian deaths] which were unique to the Logs in 2010 are still unique the Iraq War Logs remain the only source of those incidents.

Their significance is even starker when placed in a broader political context. In 2003, millions of people joined demonstrations against the invasion of Iraq, in the largest anti-war movement in human history.

The pseudo-left, Green and trade union forces that politically dominated the protests did everything they could to subordinate this movement to pro-war organisations, such as the Democratic Party in the US and the Labor Party in Australia, as well as impotent appeals to the United Nations. In 2008, they supported the election of US President Barack Obama, proclaiming that representative of Wall Street, who would be at war his entire eight years in office, as the bringer of peace.

WikiLeaks publication of the war logs cut through this suppression of the anti-war movement, raising the urgency of a renewed fight against imperialist militarism. In the process, young people around the world became aware, in many cases for the first time, of the horrors being perpetrated in Iraq, and were politically activated.

The New York Times and the Guardian partnered with WikiLeaks on the war logs. Their aim was to control the narrative and land a scoop. But as it became clear that the publications were contributing to a political radicalisation of workers and young people, and that WikiLeaks was facing the full force of the US state, they began to denounce Assange in the most slanderous terms.

Such is the basic reason for the venomous hostility of the entire political and media establishment towards Assange in every country, especially its pseudo-left and liberal contingents. He and WikiLeaks rocked the boat upon which their own privileged and selfish upper-middle class existence depends. The wars, moreover, had not been at all bad for their stock portfolios, contributing to the open support of this milieu for the imperialist attacks on Libya and Syria.

But the publication of the war logs was an imperishable contribution to humanity and the fight against imperialist war, for which Assange is rightly viewed as a hero by millions of workers and young people. Now, it is up to the international working class to spearhead the fight for Assanges freedom, the defence of all WikiLeaks staff and of democratic rights as a whole.

This is inseparable from the struggle against the escalating drive to war, including US threats of war against China and Russia, and the fight to put an end to the capitalist order that is responsible for imperialist violence and authoritarianism.

Read this article:
Ten years since WikiLeaks and Julian Assange published the Iraq War Logs - WSWS

Altruist: A New Method To Explain Interpretable Machine Learning Through Local Interpretations of Predictive Models – MarkTechPost

Artificial intelligence (AI) and machine learning (ML) are the digital worlds trendsetters in recent times. Although ML models can make accurate predictions, the logic behind the predictions remains unclear to the users. Lack of evaluation and selection criteria make it difficult for the end-user to select the most appropriate interpretation technique.

How do we extract insights from the models? Which features should be prioritized while making predictions and why? These questions remain prevalent. Interpretable Machine Learning (IML) is an outcome of the questions mentioned above. IML is a layer in ML models that helps human beings understand the procedure and logic behind machine learning models inner working.

Ioannis Mollas, Nick Bassiliades, and Grigorios Tsoumakas have introduced a new methodology to make IML more reliable and understandable for end-users.Altruist, a meta-learning method, aims to help the end-user choose an appropriate technique based on feature importance by providing interpretations through logic-based argumentation.

The meta-learning methodology is composed of the following components:

Paper: https://arxiv.org/pdf/2010.07650.pdf

Github: https://github.com/iamollas/Altruist

Related

Consulting Intern: Grounded and solution--oriented Computer Engineering student with a wide variety of learning experiences. Passionate about learning new technologies and implementing it at the same time.

Continued here:
Altruist: A New Method To Explain Interpretable Machine Learning Through Local Interpretations of Predictive Models - MarkTechPost

Efficient audits with machine learning and Slither-simil – Security Boulevard

by Sina Pilehchiha, Concordia University

Trail of Bits has manually curated a wealth of datayears of security assessment reportsand now were exploring how to use this data to make the smart contract auditing process more efficient with Slither-simil.

Based on accumulated knowledge embedded in previous audits, we set out to detect similar vulnerable code snippets in new clients codebases. Specifically, we explored machine learning (ML) approaches to automatically improve on the performance of Slither, our static analyzer for Solidity, and make life a bit easier for both auditors and clients.

Currently, human auditors with expert knowledge of Solidity and its security nuances scan and assess Solidity source code to discover vulnerabilities and potential threats at different granularity levels. In our experiment, we explored how much we could automate security assessments to:

Slither-simil, the statistical addition to Slither, is a code similarity measurement tool that uses state-of-the-art machine learning to detect similar Solidity functions. When it began as an experiment last year under the codename crytic-pred, it was used to vectorize Solidity source code snippets and measure the similarity between them. This year, were taking it to the next level and applying it directly to vulnerable code.

Slither-simil currently uses its own representation of Solidity code, SlithIR (Slither Intermediate Representation), to encode Solidity snippets at the granularity level of functions. We thought function-level analysis was a good place to start our research since its not too coarse (like the file level) and not too detailed (like the statement or line level.)

Figure 1: A high-level view of the process workflow of Slither-simil.

In the process workflow of Slither-simil, we first manually collected vulnerabilities from the previous archived security assessments and transferred them to a vulnerability database. Note that these are the vulnerabilities auditors had to find with no automation.

After that, we compiled previous clients codebases and matched the functions they contained with our vulnerability database via an automated function extraction and normalization script. By the end of this process, our vulnerabilities were normalized SlithIR tokens as input to our ML system.

Heres how we used Slither to transform a Solidity function to the intermediate representation SlithIR, then further tokenized and normalized it to be an input to Slither-simil:

Figure 2: A complete Solidity function from the contract TurtleToken.sol.

Figure 3: The same function with its SlithIR expressions printed out.

First, we converted every statement or expression into its SlithIR correspondent, then tokenized the SlithIR sub-expressions and further normalized them so more similar matches would occur despite superficial differences between the tokens of this function and the vulnerability database.

Figure 4: Normalized SlithIR tokens of the previous expressions.

After obtaining the final form of token representations for this function, we compared its structure to that of the vulnerable functions in our vulnerability database. Due to the modularity of Slither-simil, we used various ML architectures to measure the similarity between any number of functions.

Figure 5: Using Slither-simil to test a function from a smart contract with an array of other Solidity contracts.

Lets take a look at the function transferFrom from the ETQuality.sol smart contract to see how its structure resembled our query function:

Figure 6: Function transferFrom from the ETQuality.sol smart contract.

Comparing the statements in the two functions, we can easily see that they both contain, in the same order, a binary comparison operation (>= and <=), the same type of operand comparison, and another similar assignment operation with an internal call statement and an instance of returning a true value.

As the similarity score goes lower towards 0, these sorts of structural similarities are observed less often and in the other direction; the two functions become more identical, so the two functions with a similarity score of 1.0 are identical to each other.

Research on automatic vulnerability discovery in Solidity has taken off in the past two years, and tools like Vulcan and SmartEmbed, which use ML approaches to discovering vulnerabilities in smart contracts, are showing promising results.

However, all the current related approaches focus on vulnerabilities already detectable by static analyzers like Slither and Mythril, while our experiment focused on the vulnerabilities these tools were not able to identifyspecifically, those undetected by Slither.

Much of the academic research of the past five years has focused on taking ML concepts (usually from the field of natural language processing) and using them in a development or code analysis context, typically referred to as code intelligence. Based on previous, related work in this research area, we aim to bridge the semantic gap between the performance of a human auditor and an ML detection system to discover vulnerabilities, thus complementing the work of Trail of Bits human auditors with automated approaches (i.e., Machine Programming, or MP).

We still face the challenge of data scarcity concerning the scale of smart contracts available for analysis and the frequency of interesting vulnerabilities appearing in them. We can focus on the ML model because its sexy but it doesnt do much good for us in the case of Solidity where even the language itself is very young and we need to tread carefully in how we treat the amount of data we have at our disposal.

Archiving previous client data was a job in itself since we had to deal with the different solc versions to compile each project separately. For someone with limited experience in that area this was a challenge, and I learned a lot along the way. (The most important takeaway of my summer internship is that if youre doing machine learning, you will not realize how major a bottleneck the data collection and cleaning phases are unless you have to do them.)

Figure 7: Distribution of 89 vulnerabilities found among 10 security assessments.

The pie chart shows how 89 vulnerabilities were distributed among the 10 client security assessments we surveyed. We documented both the notable vulnerabilities and those that were not discoverable by Slither.

This past summer we resumed the development of Slither-simil and SlithIR with two goals in mind:

We implemented the baseline text-based model with FastText to be compared with an improved model with a tangibly significant difference in results; e.g., one not working on software complexity metrics, but focusing solely on graph-based models, as they are the most promising ones right now.

For this, we have proposed a slew of techniques to try out with the Solidity language at the highest abstraction level, namely, source code.

To develop ML models, we considered both supervised and unsupervised learning methods. First, we developed a baseline unsupervised model based on tokenizing source code functions and embedding them in a Euclidean space (Figure 8) to measure and quantify the distance (i.e., dissimilarity) between different tokens. Since functions are constituted from tokens, we just added up the differences to get the (dis)similarity between any two different snippets of any size.

The diagram below shows the SlithIR tokens from a set of training Solidity data spherized in a three-dimensional Euclidean space, with similar tokens closer to each other in vector distance. Each purple dot shows one token.

Figure 8: Embedding space containing SlithIR tokens from a set of training Solidity data

We are currently developing a proprietary database consisting of our previous clients and their publicly available vulnerable smart contracts, and references in papers and other audits. Together theyll form one unified comprehensive database of Solidity vulnerabilities for queries, later training, and testing newer models.

Were also working on other unsupervised and supervised models, using data labeled by static analyzers like Slither and Mythril. Were examining deep learning models that have much more expressivity we can model source code withspecifically, graph-based models, utilizing abstract syntax trees and control flow graphs.

And were looking forward to checking out Slither-simils performance on new audit tasks to see how it improves our assurance teams productivity (e.g., in triaging and finding the low-hanging fruit more quickly). Were also going to test it on Mainnet when it gets a bit more mature and automatically scalable.

You can try Slither-simil now on this Github PR. For end users, its the simplest CLI tool available:

Slither-simil is a powerful tool with potential to measure the similarity between function snippets of any size written in Solidity. We are continuing to develop it, and based on current results and recent related research, we hope to see impactful real-world results before the end of the year.

Finally, Id like to thank my supervisors Gustavo, Michael, Josselin, Stefan, Dan, and everyone else at Trail of Bits, who made this the most extraordinary internship experience Ive ever had.

Recent Articles By Author

*** This is a Security Bloggers Network syndicated blog from Trail of Bits Blog authored by Nol Ponthieux. Read the original post at: https://blog.trailofbits.com/2020/10/23/efficient-audits-with-machine-learning-and-slither-simil/

See the article here:
Efficient audits with machine learning and Slither-simil - Security Boulevard

AI and machine learning: A gift, and a curse, for cybersecurity – Healthcare IT News

The Universal Health Services attack this past month has brought renewed attention to the threat of ransomware faced by health systems and what hospitals can do to protect themselves against a similar incident.

Security experts say that the attack, beyond being one of the most significant ransomware incidents in healthcare history, may also be emblematic of the ways machine learning and artificial intelligence are being leveraged by bad actors.

With some kinds of "early worms," said Greg Foss, senior cybersecurity strategist at VMware Carbon Black, "we saw [cybercriminals] performing these automated actions, and taking information from their environment and using it to spread and pivot automatically; identifying information of value; and using that to exfiltrate."

The complexity of performing these actions in a new environment relies on "using AI and ML at its core," said Foss.

Once access is gained to a system, he continued, much malware doesn't require much user interference.But although AI and ML can be used to compromise systems' security, Foss said, they can also be used to defend it.

"AI and ML are something that contributes to security in multiple different ways," he said. "It's not something that's been explored, evenuntil just recently."

One effective strategy involves user and entity behavior analytics, said Foss: essentially when a system analyzes an individual's typical behavior and flags deviations from that behavior.

For example, a human resource representative abruptly running commands on their host is abnormal behavior and might indicate a breach, he said.

AI and ML can also be used to detect subtle patterns of behavior among attackers, he said. Given that phishing emails often play on a would-be victim's emotions playing up the urgency of a message to compel someone to click on a link Foss noted that automated sentiment analysis can help flag if a message seems abnormally angry.

He also noted that email structures themselves can be a so-called tell: Bad actors may rely on a go-to structure or template to try to provoke responses, even the content itself changes.

Or, if someone is trying to siphon off earnings or medication particularly relevant in a healthcare setting AI and ML can help work in conjunction with a supply chain to point out aberrations.

Of course, Foss cautioned, AI isn't a foolproof bulwark against attacks. It's subject to the same biases as its creators, and "those little subtleties of how these algorithms work allow them to be poisoned as well," he said. In other words, it, like other technology, can be a double-edged sword.

Layered security controls, robust email filtering solutions, data control and network visibility also play a vital role in keeping health systems safe.

At the end of the day, human engineering is one of the most important tools: training employees to recognize suspicious behavior and implement strong security responses.

Using AI and ML "is only starting to scratch the surface," he said.

Kat Jercich is senior editor of Healthcare IT News.Twitter: @kjercichEmail: kjercich@himss.orgHealthcare IT News is a HIMSS Media publication.

See the original post here:
AI and machine learning: A gift, and a curse, for cybersecurity - Healthcare IT News

Machine Learning Might Guide the Arrow of Time in Microscopic Processes – Science Times

In a microscopic context, fluctuations can cause phenomena that directly violate the second law of thermodynamics, leading observers to find the arrow of time being blurry and vague. However, a new machine-learning algorithm could help researchers in the future.

The second law of thermodynamics explains, in non-equilibrium states, an asymmetry that drives physical systems from one state to another. This law, concerning the evolution of physical systems, has been associated with the principle of cause preceding effect, or systems moving forward and backward in time - known as causality, or the arrow of time.

Unfortunately, researchers viewing a microscopic process - as in video playback - encounter difficulties and they can't tell apart whether they are watching it play normally or backward.

(Photo: Steve Johnson via Pexels.com)A classic wall-mounted clock. Clocks are often used as symbols of time, the direction of which has become a point of interest in studies of microscopic nature.

RELATED: 3 Characteristics of Water That Seem to Defy the Laws of Physics

A research team from the University of Maryland created their own machine-learning algorithm that can help determine where this thermodynamic arrow of time points. The details of their study are published in the journal Nature Physics.

"I learned about thermodynamics at small scales when I took a course on non-equilibrium statistical mechanics taught by Prof. Jarzysnki," explained Alireza Seif, one of the researchers behind the study, in a statement to Phys.org. He was referring to Christopher Jarzynski, from the Department of Physics at the University of Maryland and also an author in the study. Seif also shared that at the time, he was looking for applications of machine learning in physics, which has been a point of interest for recent studies.

Some applications of machine learning are in the classification of images into groups, and some are even being used for the classification of the phases of matter. It led Seif to try if the problem of determining the direction of the arrow of time can be identified as a classification problem. After discussing with Jarzysnki and Mohammad Hafezi, the three collaborated and found early success.

Sief explained that they used supervised learning and neural network training based on a set of simulated movies showing physical processes. Each file was labeled whether they were playing forward or backward. Additionally, the neural network returns a value - either zero or one - depending on the movie and the programmed parameters on the network such as weights and biases. Researchers then checked the value of these parameters that "minimizes the difference between the output of the neural network and the true labels," referring to the direction of the arrow.

Researchers then tested their neural network against a set of physical process videos, establishing that it can successfully distinguish the direction of the thermodynamic arrow of time. Furthermore, researchers also identified the dissipated work as the quantity to determine the direction.

Also, the researchers report in their study that they used a method called inceptionism. It was developed by Google software engineers, attempting to make neural networks display the results of its image generation and pattern recognition to the users. It allows users of a neural network to observe the progress made by the system.

Seif explained that previous works have quantified the physics behind the arrow of time within the context of nonequilibrium systems. "It is interesting that a well-known algorithm (logistic regression) that existed decades before these theorems lead to the same results."

RELATED:Deep Learning Model Outperforms NPC, Player Records in Gran Turismo

Check out more news and information on Thermodynamics in Science Times.

Go here to read the rest:
Machine Learning Might Guide the Arrow of Time in Microscopic Processes - Science Times

Bridging the Skills Gap for AI and Machine Learning – Integration Developers

Even as COVID-19 has slowed business investments worldwide, AI/ML spending is increasing. In a post for IDN, dotDatas CEO Ryohei Fujimaki, Ph.D, looks at the latest trends in AI/ML automation and how they will speed adoption across industries.

COVID-19 has impacted businesses across the globe, from closures to supply chain interruptions to resource scarcity. As businesses adjust to the new normal, many are looking to do more with less and find ways to optimize their current business investments.

In this resource-constrained environment, many types of business investments have slowed dramatically. That said, investments in AI and machine learning are accelerated, according to a recentAdweek survey.

Adweek found two-thirds of business executives say COVID-19 has not slowed AI projects. In fact, some 40% of respondents told Adweek that the pandemic has accelerated their AI/ML efforts. Reasons for the sustained and growing interest in AI/ML include decreasing costs, improving performance, and increasing efficiencies-all efforts to make up for time and output lost during the COVID-19 slowdown.

Despite the rosy outlook for AI/ML investments, it bears mentioning that businesses also admit they still struggle to scale these technologies beyond PoCs (proof of concepts). This is due to an ongoing talent shortage in the data science field a shortage that COVID has made even more acute.

Data science is an interdisciplinary approach that requires cross-domain expertise, including mathematics, statistics, data engineering, software engineering, and subject matter expertise.

The shortage of data scientists as well as data architects, machine learning engineers skilled in building, testing, and deploying ML models has created a big challenge for businesses implementing AI and ML initiatives, limiting the scale of data science projects and slowing time to production. The scarcity of data scientists has also created a quandary for organizations: how can they change the way they do data science, empowering the teams they already have?

The democratization of data science is very important and a current industry trend, but true democratization has never been easy for organizations. Analytics and data science leaders lament their team's ability to only manage a few projects per year. BI leaders, on the other hand, have been trying to embed predictive analytics in their dashboards but face the daunting task of learning how to build AI/ML models. What can organizations do, what tactics will help them to scale AI initiatives and bridge the gap between what is required and what's available?

Democratization of data science in a true sense is to empower teams with advanced analytical tools and automation technologies.

These tools can significantly simplify tasks that formerly could only be completed by data scientists. They are empowering business analysts, BI developers and data engineers to execute AI and machine learning projects. Further, they accelerate data science processes with very little training.

Notable among these offerings are:

This class of automation tools removes much of the time and expense to design and deploy AI-powered analytics pipelines and do so little cost and without high-priced technical staff.

Today, s typical data team is interdisciplinary and consists of data engineers, data analysts and data scientists. The data analyst and engineer are responsible for cleaning, formatting and preparing data for the data scientist who then uses analytics-ready data to build features and then build ML models using a trial and error approach.

Data science processes are complicated, highly manual, and iterative in nature. Depending on the maturity of the data pipelines, a data science project can take from 30 to 90 days to complete with nearly 80% of the effort spent on AI-focused data preparation and Feature Engineering.

Further, the AI-focused data preparation process requires an impressive amount of hacking skills from developers, data scientists and data engineers to clean, manipulate and transform the data to enable data scientists to execute feature engineering.

That said, the landscaping is changing. Tools are now surfacing to deliver AI automation to pre-process data, connect to data and automatically build features and ML models. These results eliminate the need for having a large team and doing it efficiently at the greatest possible speed.

In addition, feature engineering automation has vast potential to change the traditional data science process. Feature engineering involves the application of business knowledge, math, and statistics to transform data into a format that can be directly consumed by machine learning models.

It also can significantly lower skill barriers beyond ML automation alone, eliminating hundreds or even thousands of manually-crafted SQL queries, and ramps up the speed of the data science project even without a full light of domain knowledge).

Organizations with large data science teams will also find automation platforms very valuable. They free up highly-skilled resources from many of the manual and time-consuming efforts involved in data science and machine learning workflow and allow them to focus on more complex and challenging strategic tasks.

The trend is definitely to leverage automation technologies to speed-up the ML development process. By using AI automation technologies, BI and junior data scientist can automatically build models. This frees up time for experienced data scientists who take on more challenging business problems. While everyone seemed to focus on building automated ML models, the industry is definitely moving towards automating the entire AI/ML workflow.

This empowers data scientists to achieve higher productivity and drive greater business impact than ever before.

Another important tactic for bridging the skills gap in data science is ongoing skills training for the AI, data science and business intelligence teams.

Rather than hiring outside talent from an already shallow talent pool, companies are often better off investing time and resources in data-science training of their existing talent pool. These citizen data scientists can bridge the skill gap, address the labor shortage and enable companies to leverage the existing resources they already have.

There are many advantages to this approach.

Theidea is to build a team from inside the company versus hiring experts from outside. Any transformation is only going to succeed, provided it is embraced by the vast majority. Creating internal AI teams, empowering citizen data scientists and scaling pilot programs focused on AI is the right approach.

One of the most important of which is building data science skills across multiple teams to support data science's democratization across the organization. This strategy can be implemented by first identifying employees with existing programming, analytical and quantitative skills and then augmenting those skills with the required data science skills and tools training. Experienced data scientists can play the role of an evangelizer to share data science best practices and guide the citizen data scientists through the process.

AI and ML-driven innovation becomes indispensable as more enterprises transform themselves into data-driven organizations. Building a strong analytics team, while challenging in todays resource-scarce environment, is attainable by using appropriate automation tools. The benefits of this approach include:

These factors can not only help fill the skills gap but will help accelerate both data science and business innovation, delivering greater and broader business impact.

More here:
Bridging the Skills Gap for AI and Machine Learning - Integration Developers

Machine Learning and AI Can Now Create Plastics That Easily Degrade – Science Times

Plastic pollutionis one of the most pressing environmental issues, and the increase in the production of disposable plastics does not help at all. These plastics would often take many years before they degrade, which poisons the environment. This has prompted efforts from nations to create a global treaty to help reduce plastic pollution.

A combination of machine learning and artificial intelligence has accelerated the design of making materials, including plastics, with properties that quickly degrade without harming the environment and super-strong lightweight plastics for aircraft and satellites that would one day replace the metals being used.

The researchers from the Pritzker School of Molecular Engineering (PME) at the University of Chicago published their study in Science Advances on October 21, which shows a way toward designing polymers using a combination of modeling and machine learning.

This is done through computational structuring of almost 2,000 hypothetical polymers that are large enough to train neural links that understand a polymer's properties.

(Photo: Pixabay)Machine Learning and AI Can Now Create Plastics That Easily Degrade

People have been using products with polymer, like plastic bottles, for so long as this material is very common in many things in the daily lives of humans.

Polymers are materials that have amorphous and disordered structures that even techniques for studying metals and crystalline materials developed by scientists have a hard time defining it. They are made of large atoms arranged in a very long string that might compromise millions of monomers.

Moreover, the length and sequence can affect the polymer molecule's properties that may vary depending on which the atoms are arranged. Due to that, a trial-and-error method will not be ideal to use because it is only limited, and generating the needed data for a rational design strategy would be very demanding, Phys.orgreported.

Fortunately, machine learning could solve this problem as researchers set to answer whether machine learning and AI can predict the properties of polymers based on their sequence. If this might be the case, how large of a dataset would be needed to teach underlying algorithms.

Read Also: P&G Aims to Halve Its Use of Virgin Petroleum Plastics by 2030: Here's How It Plans to Do So

The researchers used almost 2,000 computationally structured polymers that have different sequences in creating the database. They also ran molecular simulations to predict its behavior.

Juan de Pablo, Liew Family Professor of Molecular Engineering and lead researcher, said that they are unsure how many are the different polymer sequences needed to learn its behavior as it could be millions. Fortunately, only a few hundred would do, which means that they can now follow the same technique ad create a database to train the machine learning network.

Then the researchers proceeded to use the data that was learned in making the actual design of the new molecules. They were able to demonstrate to specify a desired property from the polymer, and using machine learning generated a set of polymer sequences that lead to specific properties.

Through this, companies can now design products that save the environment and design polymers that do exactly what they want to do. For instance, they could create polymers that could someday replace the metals used in aerospace or those used in biomedical devices. It could allow engineers to more affordable and sustainable polymer materials.

Read More: Unique Enzyme Combination Could Reduce Global Plastic Waste

Check out more news and information on Plastic Pollutionon Science Times.

Read the rest here:
Machine Learning and AI Can Now Create Plastics That Easily Degrade - Science Times

Revolutionizing IoT with Machine Learning at the Edge | Perceive’s Steve Teig – IoT For All

In episode 88 of the IoT For All Podcast, Perceive Founder and CEO Steve Teig joins us to talk about how Perceive is bringing the next wave of intelligence to IoT through machine learning at the edge. Steve shares how Perceive developed Ergo, their chip announced back in March, and how these new machine learning capabilities will transform consumer IoT.

Steve Teig is an award-winning technologist, entrepreneur, and inventor on 388 US patents. Hes been the CTO of three EDA software companies, two biotech companies, and a semiconductor company of these, two went public during his tenure, two were acquired, and one is a Fortune 500 company. As the CEO and Founder of Perceive, Steve is leading a team building solutions and transformative machine learning technology for consumer edge devices.

To start the episode, Steve gave us some background on how Perceive got started. While serving as CTO of Xperi, Steve worked with a wide array of imaging and audio products and saw an opportunity in making the edge smart by leveraging machine learning at the edge. What if you could make gadgets themselves intelligent? Steve asked, thats what motivated me to pursue it technically and then commercially with Perceive.

At its core, Perceive builds chips and machine learning software for edge inference, providing data center class accuracy at the low power that edge devices, like IoT, require. The kinds of applications we go after, Steve said, are from doorbell cameras to home security cameras, to toys, to phones wherever you have a sensor, it would be cool to make that sensor understand its environment without sending data to the cloud.

Of the current solutions for device intelligence, Steve said you have two options and neither of them are ideal: first, you can send all of the data your sensor collects to someone elses cloud, giving up your privacy; or second, you can have a tiny chip that, while low power enough for your device, doesnt provide the computing power to provide answers you can actually trust.

We fix that problem by providing the kind of sophistication you would expect from the big cloud providers, but low enough power that you can run it at the edge, Steve said, saying that their chip is 20 to 100 times more power efficient than anything else currently in the market.

Steve also spoke to some of the use cases that Ergo enables. Currently, the main applications are doorbell cameras, home security cameras, and appliances. As we look forward, Steve said, being able to put really serious contextual awareness into gadgets opens up all kinds of applications. One of the examples he gave was a microwave that could identify both the user and the food to be heated, and adjust its settings to match that users preferences. Another example would be a robot vacuum cleaner that you could ask to find your shoes.

Changing gears, Steve shared Perceives philosophy on machine learning, saying that because they were looking to make massive improvements they had to start fresh. We had to start with the math. We really started from first principles. That philosophy has led to a number of new and proprietary techniques, both on the software and hardware side.

Moving more into the industry at large, Steve shared some observations in the smart home space during the pandemic. Those observations highlighted two somewhat conflicting viewpoints while there has been a broader interest in smart home technology, with people spending more time at home, people have also become more sensitive about their privacy. Steve also shared how Ergo handles data, in order to meet these security and privacy concerns.

To close out the episode, Steve shared some of the challenges his team faced while developing Ergo and what those challenges meant as he built out the team itself. He also shared some of his thoughts on the future of the smart home and consumer IoT space, with the introduction of these new machine learning capabilities.

Interested in connecting with Steve? Reach out to him on Linkedin!

About Perceive: Steve Teig, founder and CEO of Perceive, drove the creation of the company in 2018 while CTO of its parent company and investor, Xperi. Launching Perceive, Steve and his team had the ambitious goal of enabling state-of-the-art inference inside edge devices running at extremely low power. Adopting an entirely new perspective on machine learning and neural networks allowed Steve and his team to very quickly build and deploy the software, tools, and inference processor Ergo that make the complete Perceive solution.

(00:50) Intro to Steve

(01:25) How did you come to found Perceive?

(02:30) What does Perceive do? Whats your role in the IoT space?

(03:37) What makes your offering unique to the market?

(04:49) Could you share any use cases?

(09:41) How would you describe your philosophy when it comes to machine learning?

(11:37) What is Ergo and what does it do?

(12:39) What does a typical customer engagement look like?

(14:57) Have you seen any change in demand due to the pandemic?

(20:47) What challenges have you encountered building Perceive and Ergo?

(22:24) Where do you see the market going for smart home devices?

Read the rest here:
Revolutionizing IoT with Machine Learning at the Edge | Perceive's Steve Teig - IoT For All