Biblio

The agricultural sector and other Micro, Small, and Medium Enterprises in India operate with more than 90% migrant workers searching for better employment opportunities far away from their native places. However, inherent challenges are far more for the migrant workers, most prominently their Identity. To the best of our knowledge, available literature lacks a comprehensive study on identity management components for user privacy and data protection mechanisms in identity management architecture. Self-Sovereign Identity is regarded as a new evolution in digital identity management systems. Blockchain technology and distributed ledgers bring us closer to realizing an ideal Self-Sovereign Identity system. This paper proposes a novel solution to address identity issues being faced by migrant workers. It also gives a holistic, coherent, and mutually beneficial Identity Management Solution for the migrant workforce in the Indian perspective towards e-Governance and Digital India.

Higher education management has problems producing 100% of graduates capable of responding to the needs of industry while industry also is struggling to find qualified graduates that responded to their needs in part because of the inefficient way of evaluating problems, as well as because of weaknesses in the evaluation of problem-solving capabilities. The objective of this paper is to propose an appropriate classification model to be used for predicting and evaluating the attributes of the data set of the student in order to meet the selection criteria required by the industries in the academic field. The dataset required for this analysis was obtained from a private firm and the execution was carried out using Chimp Optimization Algorithm (COA) based Deep Belief Neural Network (COA-DBNN) and the obtained results are compared with various classifiers such as Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF). The proposed model outperforms other classifiers in terms of various performance metrics. This critical analysis will help the college management to make a better long-term plan for producing graduates who are skilled, knowledgeable and fulfill the industry needs as well.

Large-scale onboarding of industrial cyber physical systems requires efficiency and security. In situations with the dynamic addition of devices (e.g., from subcontractors entering a workplace), automation of the onboarding process is desired. The Eclipse Arrowhead framework, which provides a platform for industrial automation, requires reliable, flexible, and secure device onboarding to local clouds. In this paper, we propose a device onboarding method in the Arrowhead framework where decentralized authorization is provided by Power of Attorney. The model allows users to subgrant power to trusted autonomous devices to act on their behalf. We present concepts, an implementation of the proposed system, and a use case for scalable onboarding where Powers of Attorney at two levels are used to allow a subcontractor to onboard its devices to an industrial site. We also present performance evaluation results.

The rapid shift towards smart cities, particularly in the era of pandemics, necessitates the employment of e-learning, remote learning systems, and hybrid models. Building adaptive and personalized education becomes a requirement to mitigate the downsides of distant learning while maintaining high levels of achievement. Explainable artificial intelligence (XAI), machine learning (ML), and the internet of behaviour (IoB) are just a few of the technologies that are helping to shape the future of smart education in the age of smart cities through Customization and personalization. This study presents a paradigm for smart education based on the integration of XAI and IoB technologies. The research uses data acquired on students' behaviours to determine whether or not the current education systems respond appropriately to learners' requirements. Despite the existence of sophisticated education systems, they have not yet reached the degree of development that allows them to be tailored to learners' cognitive needs and support them in the absence of face-to-face instruction. The study collected data on 41 learner's behaviours in response to academic activities and assessed whether the running systems were able to capture such behaviours and respond appropriately or not; the study used evaluation methods that demonstrated that there is a change in students' academic progression concerning monitoring using IoT/IoB to enable a relative response to support their progression.

{On considering workplace thefts as a major problem, there is a requirement of designing a vandal proof door hardware and locking mechanism for ensuring the security of our property. So the door lock system with extra security features with a user friendly cost is suggested in this paper. When a stranger comes at the door, he/she has to pass three security levels for unlocking the solenoid locks present at the door and if he fails to do so, the door will remain locked. These three levels are of three extraordinary security features as one of them is using Fingerprint sensor, second is using a knocking pattern, and the last lock is unlocked by the preset pin/pattern entered by the user. Since, in addition to these features, there is one more option for the case of appearing of guest at the door and that is the Image capturing using web-camera present at the door and here the owner of the house is able to unlock all the locks if he wants the guest to enter the home. This all will be monitored by Node MCU}.

This project addresses a classic employment law question in Canada and elsewhere using machine learning approach: how do we know whether a worker is an employee or an independent contractor? This is a central issue for self-represented litigants insofar as these two legal categories entail very different rights and employment protections. In this interdisciplinary research study, we collaborated with the Conflict Analytics Lab to develop machine learning models aimed at determining whether a worker is an employee or an independent contractor. We present a number of supervised learning models including a neural network model that we implemented using data labeled by law researchers and compared the accuracy of the models. Our neural network model achieved an accuracy rate of 91.5%. A critical discussion follows to identify the key features in the data that influence the accuracy of our models and provide insights about the case outcomes.

Practical activities usually require the ability to simultaneously work with internal, distributed information resources and access to the Internet. The need to solve this problem necessitates the use of appropriate administrative and technical methods to protect information. Such methods relate to the idea of domain isolation. This paper considers the principles of implementation and properties of an "Endpoint Cloud Terminal" that is general-purpose software tool with built-in security instruments. This apparatus solves the problem by combining an arbitrary number of isolated and independent workplaces on one hardware unit, a personal computer.

Trust relationship prediction among people provides valuable supports for decision making, information dissemination, and product promotion in online social networks. Network embedding has achieved promising performance for link prediction by learning node representations that encode intrinsic network structures. However, most of the existing network embedding solutions cannot effectively capture the properties of a trust network that has directed edges and nodes with in/out links. Furthermore, there usually exist rich user attributes in trust networks, such as ratings, reviews, and the rated/reviewed items, which may exert significant impacts on the formation of trust relationships. It is still lacking a network embedding-based method that can adequately integrate these properties for trust prediction. In this work, we develop an AtNE-Trust model to address these issues. We firstly capture user embedding from both the trust network structures and user attributes. Then we design a deep multi-view representation learning module to further mine and fuse the obtained user embedding. Finally, a trust evaluation module is developed to predict the trust relationships between users. Representation learning and trust evaluation are optimized together to capture high-quality user embedding and make accurate predictions simultaneously. A set of experiments against the real-world datasets demonstrates the effectiveness of the proposed approach.

The labor market involves several untrusted actors with contradicting objectives. We propose a blockchain based system for labor market, which provides benefits to all participants in terms of confidence, transparency, trust and tracking. Our system would handle employment data through new Wavelet blockchain platform. It would change the job market enabling direct agreements between parties without other participants, and providing new mechanisms for negotiating the employment conditions. Furthermore, our system would reduce the need in existing paper workflow as well as in major internet recruiting companies. The key differences of our work from other blockchain based labor record systems are usage of Wavelet blockchain platform, which features metastability, directed acyclic graph system and Turing complete smart contracts platform and introduction of human interaction inside the smart contracts logic, instead of automatic execution of contracts. The results are promising while inconclusive and we would further explore potential of blockchain solutions for labor market problems.

Wireless networks are currently proliferated by multiple tiers and heterogeneous networking equipment that aims to support multifarious services ranging from distant monitoring and control of wireless sensors to immersive virtual reality services. The vast collection of heterogeneous network equipment with divergent radio capabilities (e.g. multi-GHz operation) is vulnerable to wireless network attacks, raising questions on the service availability and coverage performance of future multi-tier wireless networks. In this paper, we study the impact of black hole attacks on service coverage of multi-tier heterogeneous wireless networks and derive closed form expressions when network nodes are unable to identify and avoid black hole nodes. Assuming access to multiple bands, the derived expressions can be readily used to assess the performance gains following from the employment of different association policies and the impact of black hole attacks in multi-tier wireless networks.

This study looks at the question of how to reduce/eliminate employee Internet Abuse. Companies have used acceptable use policies (AUP) and technology in an attempt to mitigate employees' personal use of company resources. Research shows that AUPs do not do a good job at this but that technology does. Research also shows that while technology can be used to greatly restrict personal use of the internet in the workplace, employee satisfaction with the workplace suffers when this is done. In this research experiment we used technology not to restrict employee use of company resources for personal use, but to make the employees more aware of the current Acceptable Use Policy, and measured the decrease in employee internet abuse. The results show that this method can result in a drop from 27 to 21 percent personal use of the company networks.

Revealing private and sensitive information on Social Network Sites (SNSs) like Facebook is a common practice which sometimes results in unwanted incidents for the users. One approach for helping users to avoid regrettable scenarios is through awareness mechanisms which inform a priori about the potential privacy risks of a self-disclosure act. Privacy heuristics are instruments which describe recurrent regrettable scenarios and can support the generation of privacy awareness. One important component of a heuristic is the group of people who should not access specific private information under a certain privacy risk. However, specifying an exhaustive list of unwanted recipients for a given regrettable scenario can be a tedious task which necessarily demands the user's intervention. In this paper, we introduce an approach based on decision trees to instantiate the audience component of privacy heuristics with minor intervention from the users. We introduce Disclosure- Acceptance Trees, a data structure representative of the audience component of a heuristic and describe a method for their generation out of user-centred privacy preferences.

The indoor air quality in industrial workplace buildings, e.g. air temperature, humidity and levels of carbon dioxide (CO2), play a critical role in the perceived levels of workers' comfort and in reported medical health. CO2 can act as an oxygen displacer, and in confined spaces humans can have, for example, reactions of dizziness, increased heart rate and blood pressure, headaches, and in more serious cases loss of consciousness. Specialized organizations can be brought in to monitor the work environment for limited periods. However, new low cost wireless sensor network (WSN) technologies offer potential for more continuous and autonomous assessment of industrial workplace air quality. Central to effective decision making is the data analytics approach and visualization of what is potentially, big data (BD) in monitoring the air quality in industrial workplaces. This paper presents a case study that monitors air quality that is collected with WSN technologies. We discuss the potential BD problems. The case trials are from two workshops that are part of a large on-shore logistics base a regional shipping industry in Norway. This small case study demonstrates a monitoring and visualization approach for facilitating BD in decision making for health and safety in the shipping industry. We also identify other potential applications of WSN technologies and visualization of BD in the workplace environments; for example, for monitoring of other substances for worker safety in high risk industries and for quality of goods in supply chain management.

Modern cyber systems and their integration with the infrastructure has a clear effect on the productivity and quality of life immensely. Their involvement in our daily life elevate the need for means to insure their resilience against attacks and failure. One major threat is the software monoculture. Latest research work demonstrated the danger of software monoculture and presented diversity to reduce the attack surface. In this paper, we propose ChameleonSoft, a multidimensional software diversity employment to, in effect, induce spatiotemporal software behavior encryption and a moving target defense. ChameleonSoft introduces a loosely coupled, online programmable software-execution foundation separating logic, state and physical resources. The elastic construction of the foundation enabled ChameleonSoft to define running software as a set of behaviorally-mutated functionally-equivalent code variants. ChameleonSoft intelligently Shuffle, at runtime, these variants while changing their physical location inducing untraceable confusion and diffusion enough to encrypt the execution behavior of the running software. ChameleonSoft is also equipped with an autonomic failure recovery mechanism for enhanced resilience. In order to test the applicability of the proposed approach, we present a prototype of the ChameleonSoft Behavior Encryption (CBE) and recovery mechanisms. Further, using analysis and simulation, we study the performance and security aspects of the proposed system. This study aims to assess the provisioned level of security by measuring the avalanche effect percentage and the induced confusion and diffusion levels to evaluate the strength of the CBE mechanism. Further, we compute the computational cost of security provisioning and enhancing system resilience.

Modern cyber systems and their integration with the infrastructure has a clear effect on the productivity and quality of life immensely. Their involvement in our daily life elevate the need for means to insure their resilience against attacks and failure. One major threat is the software monoculture. Latest research work demonstrated the danger of software monoculture and presented diversity to reduce the attack surface. In this paper, we propose ChameleonSoft, a multidimensional software diversity employment to, in effect, induce spatiotemporal software behavior encryption and a moving target defense. ChameleonSoft introduces a loosely coupled, online programmable software-execution foundation separating logic, state and physical resources. The elastic construction of the foundation enabled ChameleonSoft to define running software as a set of behaviorally-mutated functionally-equivalent code variants. ChameleonSoft intelligently Shuffle, at runtime, these variants while changing their physical location inducing untraceable confusion and diffusion enough to encrypt the execution behavior of the running software. ChameleonSoft is also equipped with an autonomic failure recovery mechanism for enhanced resilience. In order to test the applicability of the proposed approach, we present a prototype of the ChameleonSoft Behavior Encryption (CBE) and recovery mechanisms. Further, using analysis and simulation, we study the performance and security aspects of the proposed system. This study aims to assess the provisioned level of security by measuring the avalanche effect percentage and the induced confusion and diffusion levels to evaluate the strength of the CBE mechanism. Further, we compute the computational cost of security provisioning and enhancing system resilience.