Multilevel User Credibility Assessment in Social Networks

Online social networks are one of the largest platforms for disseminating both real and fake news. Many users on these networks, intentionally or unintentionally, spread harmful content, fake news, and rumors in fields such as politics and business. As a result, numerous studies have been conducted in recent years to assess the credibility of users. A shortcoming of most of existing methods is that they assess users by placing them in one of two categories, real or fake. However, in real-world applications it is usually more desirable to consider several levels of user credibility. Another shortcoming is that existing approaches only use a portion of important features, which downgrades their performance. In this paper, due to the lack of an appropriate dataset for multilevel user credibility assessment, first we design a method to collect data suitable to assess credibility at multiple levels. Then, we develop the MultiCred model that places users at one of several levels of credibility, based on a rich and diverse set of features extracted from users' profile, tweets and comments. MultiCred exploits deep language models to analyze textual data and deep neural models to process non-textual features. Our extensive experiments reveal that MultiCred considerably outperforms existing approaches, in terms of several accuracy measures

SAgric-IoT: an IoT-based platform and deep learning for greenhouse monitoring

The Internet of Things (IoT) and convolutional neural networks (CNN) integration is a growing topic of interest for researchers as a technology that will contribute to transforming agriculture. IoT will enable farmers to decide and act based on data collected from sensor nodes regarding field conditions and not purely based on experience, thus minimizing the wastage of supplies (seeds, water, pesticide, and fumigants). On the other hand, CNN complements monitoring systems with tasks such as the early detection of crop diseases or predicting the number of consumable resources and supplies (water, fertilizers) needed to increase productivity. This paper proposes SAgric-IoT, a technology platform based on IoT and CNN for precision agriculture, to monitor environmental and physical variables and provide early disease detection while automatically controlling the irrigation and fertilization in greenhouses. The results show SAgric-IoT is a reliable IoT platform with a low packet loss level that considerably reduces energy consumption and has a disease identification detection accuracy and classification process of over 90%

Toward an integrated disaster management approach: How artificial intelligence can boost disaster management

Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters

Cyber Security Concerns in Social Networking Service

Today’s world is unimaginable without online social networks. Nowadays, millions of people connect with their friends and families by sharing their personal information with the help of different forms of social media. Sometimes, individuals face different types of issues while maintaining the multimedia contents like, audios, videos, photos because it is difficult to maintain the security and privacy of these multimedia contents uploaded on a daily basis. In fact, sometimes personal or sensitive information could get viral if that leaks out even unintentionally. Any leaked out content can be shared and made a topic of popular talk all over the world within few seconds with the help of the social networking sites. In the setting of Internet of Things (IoT) that would connect millions of devices, such contents could be shared from anywhere anytime. Considering such a setting, in this work, we investigate the key security and privacy concerns faced by individuals who use different social networking sites differently for different reasons. We also discuss the current state-of-the-art defense mechanisms that can bring somewhat long-term solutions to tackling these threats

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML

The main cause of fatal accidents in the construction sector are falls from height (FFH) and the inappropriate use of a harness is commonly associated with these fatalities. Traditional methods, such as onsite inspections, safety communication, or safety training, are not enough to mitigate accidents caused by FFH associated with a poor management in the use of a harness. Although some technological solutions for the automated monitoring of workers could improve safety conditions, their use is not frequent due to the particularities of construction sites: complexity, dynamic environments, outdoor workplaces, etc. Then, the integration of expert knowledge with technology is a key issue. Fuzzy logic systems (FLS) and Internet of Things (IoT) present many potential benefits, such as real-time decisions being made based on FLS and data from sensors. In the current research, the development and test of an IoT system integrated with the Java Fuzzy Markup Language Library for FLS, to support experts’ decision making in FFH, is proposed. The proposal was checked in four construction scenarios based on working conditions with different levels of risk of FFH and obtained promising results.Universidad de Malaga Plan Propio-Universidad de MalagaSpanish GovernmentEuropean Commission RTI2018-098371-B-I0

Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML

The main cause of fatal accidents in the construction sector are falls from height (FFH) and the inappropriate use of a harness is commonly associated with these fatalities. Traditional methods, such as onsite inspections, safety communication, or safety training, are not enough to mitigate accidents caused by FFH associated with a poor management in the use of a harness. Although some technological solutions for the automated monitoring of workers could improve safety conditions, their use is not frequent due to the particularities of construction sites: complexity, dynamic environments, outdoor workplaces, etc. Then, the integration of expert knowledge with technology is a key issue. Fuzzy logic systems (FLS) and Internet of Things (IoT) present many potential benefits, such as real-time decisions being made based on FLS and data from sensors. In the current research, the development and test of an IoT system integrated with the Java Fuzzy Markup Language Library for FLS, to support experts’ decision making in FFH, is proposed. The proposal was checked in four construction scenarios based on working conditions with different levels of risk of FFH and obtained promising results

A Comprehensive Survey of In-Band Control in SDN: Challenges and Opportunities

Software-Defined Networking (SDN) is a thriving networking architecture that has gained popularity in recent years, particularly as an enabling technology to foster paradigms like edge computing. SDN separates the control and data planes, which are later on synchronised via a control protocol such as OpenFlow. In-band control is a type of SDN control plane deployment in which the control and data planes share the same physical network. It poses several challenges, such as security vulnerabilities, network congestion, or data loss. Nevertheless, despite these challenges, in-band control also presents significant opportunities, including improved network flexibility and programmability, reduced costs, and increased reliability. Benefiting from the previous advantages, diverse in-band control designs exist in the literature, with the objective of improving the operation of SDN networks. This paper surveys the different approaches that have been proposed so far towards the advance in in-band SDN control, based on four main categories: automatic routing, fast failure recovery, network bootstrapping, and distributed control. Across these categories, detailed summary tables and comparisons are presented, followed by a discussion on current trends a challenges in the field. Our conclusion is that the use of in-band control in SDN networks is expected to drive innovation and growth in the networking industry, but efforts for holistic and full-fledged proposals are still needed

FaaSdom: A Benchmark Suite for Serverless Computing

Serverless computing has become a major trend among cloud providers. With serverless computing, developers fully delegate the task of managing the servers, dynamically allocating the required resources, as well as handling availability and fault-tolerance matters to the cloud provider. In doing so, developers can solely focus on the application logic of their software, which is then deployed and completely managed in the cloud. Despite its increasing popularity, not much is known regarding the actual system performance achievable on the currently available serverless platforms. Specifically, it is cumbersome to benchmark such systems in a language- or runtime-independent manner. Instead, one must resort to a full application deployment, to later take informed decisions on the most convenient solution along several dimensions, including performance and economic costs. FaaSdom is a modular architecture and proof-of-concept implementation of a benchmark suite for serverless computing platforms. It currently supports the current mainstream serverless cloud providers (i.e., AWS, Azure, Google, IBM), a large set of benchmark tests and a variety of implementation languages. The suite fully automatizes the deployment, execution and clean-up of such tests, providing insights (including historical) on the performance observed by serverless applications. FaaSdom also integrates a model to estimate budget costs for deployments across the supported providers. FaaSdom is open-source and available at https://github.com/bschitter/benchmark-suite-serverless-computing.Comment: ACM DEBS'2

Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence

Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented