The Internet of Things (IoT) is affecting national innovation ecosystems and the approach of organizations to innovation and how they create and capture value in everyday business activities. The Internet of Things (IoT), is disruptive, and it will change the manner in which human resources are developed and managed, calling for a new and adaptive human resource development approach. The Classical Internet communication form is human-human. The prospect of IoT is that every object will have a unique way of identification and can be addressed so that every object can be connected. The communication forms will expand from human-human to human-human, human-thing, and thing-thing. This will bring a new challenge to how Human Resource Development (HRD) is practiced. This paper provides an overview of the Internet of Things and conceptualizes the role of HRD in the age of the Internet of Things. Keywords:
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The internet's key points of global control lie in the hands of a few people, primarily private organizations based in the United States. These control points, as they exist today, raise structural risks to the global internet's long-term stability. I argue: the problem isn't that these control points exist, it's that there is no popular governance over them. I advocate for a localist approach to internet governance: small internets deployed on municipal scales, interoperating selectively, carefully, with this internet and one another.
Time Slotted Channel Hopping (TSCH) behavioural mode has been introduced in IEEE 802.15.4e standard to address the ultra-high reliability and ultra-low power communication requirements of Industrial Internet of Things (IIoT) networks. Scheduling the packet transmissions in IIoT networks is a difficult task owing to the limited resources and dynamic topology. In this paper, we propose a phasic policy gradient (PPG) based TSCH schedule learning algorithm. The proposed PPG based scheduling algorithm overcomes the drawbacks of totally distributed and totally centralized deep reinforcement learning-based scheduling algorithms by employing the actor-critic policy gradient method that learns the scheduling algorithm in two phases, namely policy phase and auxiliary phase.
Security has become paramount in modern software services as more and more security breaches emerge, impacting final users and organizations alike. Trends like the Microservice Architecture bring new security challenges related to communication, system design, development, and operation. The literature presents a plethora of security-related solutions for microservices-based systems, but the spread of information difficult practitioners' adoption of novel security related solutions. In this study, we aim to present a catalogue and discussion of security solutions based on algorithms, protocols, standards, or implementations; supporting principles or characteristics of information security, considering the three possible states of data, according to the McCumber Cube. Our research follows a Systematic Literature Review, synthesizing the results with a meta-aggregation process. We identified a total of 30 primary studies, yielding 75 security solutions for the communication of microservices.
Exoskeletons and orthoses are wearable mobile systems providing mechanical benefits to the users. Despite significant improvements in the last decades, the technology is not fully mature to be adopted for strenuous and non-programmed tasks. To accommodate this insufficiency, different aspects of this technology need to be analysed and improved. Numerous studies have been trying to address some aspects of exoskeletons, e.g. mechanism design, intent prediction, and control scheme. However, most works have focused on a specific element of design or application without providing a comprehensive review framework. This study aims to analyse and survey the contributing aspects to the improvement and broad adoption of this technology. To address this, after introducing assistive devices and exoskeletons, the main design criteria will be investigated from a physical Human-Robot Interface (HRI) perspective. The study will be further developed by outlining several examples of known assistive devices in different categories. In order to establish an intelligent HRI strategy and enabling intuitive control for users, cognitive HRI will be investigated. Various approaches to this strategy will be reviewed, and a model for intent prediction will be proposed. This model is utilised to predict the gate phase from a single Electromyography (EMG) channel input. The outcomes of modelling show the potential use of single-channel input in low-power assistive devices. Furthermore, the proposed model can provide redundancy in devices with a complex control strategy.
As the globally increasing population drives rapid urbanisation in various parts of the world, there is a great need to deliberate on the future of the cities worth living. In particular, as modern smart cities embrace more and more data-driven artificial intelligence services, it is worth remembering that technology can facilitate prosperity, wellbeing, urban livability, or social justice, but only when it has the right analog complements (such as well-thought out policies, mature institutions, responsible governance); and the ultimate objective of these smart cities is to facilitate and enhance human welfare and social flourishing. Researchers have shown that various technological business models and features can in fact contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In the light of these observations, addressing the philosophical and ethical questions involved in ensuring the security, safety, and interpretability of such AI algorithms that will form the technological bedrock of future cities assumes paramount importance. Globally there are calls for technology to be made more humane and human-centered. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical (data and algorithmic) challenges to a successful deployment of AI in human-centric applications, with a particular emphasis on the convergence of these concepts/challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions. We believe such rigorous analysis will provide a baseline for future research in the domain.
Video streaming is dominating the Internet. To compete with the performance of traditional cable and satellite options, content providers outsource the content delivery to third-party content distribution networks and brokers. However, no existing monitoring mechanism offers a multilateral view of a streaming service's performance. In other words, no auditing mechanism reflects the mutual agreement of content providers, content distributors and end-users alike about how well, or not, a service performs. In this paper, we present UgoVor, a system for monitoring multilateral streaming contracts, that is enforceable descriptions of mutual agreements among content providers, content distributors and end-users. Our key insight is that real-time multilateral micro-monitoring -- capable of accounting for every re-buffering event and the resolution of every video chunk in a stream -- is not only feasible, but an Internet-scalable task. To demonstrate this claim we evaluate UgoVor in the context of a 10-month long experiment, corresponding to over 25 years of streaming data, including over 430,000 streaming sessions with clients from over 1,300 unique ASes. Our measurements confirm that UgoVor can provide an accurate distributed performance consensus for Internet streaming, and can help radically advance existing performance-agnostic pricing model towards novel and transparent pay-what-you-experience ones.
We aim to counter the tendency for specialization in science by advancing a language that can facilitate the translation of ideas and methods between disparate contexts. The focus is on questions of "resource-theoretic nature". In a resource theory, one identifies resources and allowed manipulations that can be used to transform them. Some of the main questions are: How to optimize resources? What are the trade-offs between them? Can a given resource be converted to another one via the allowed manipulations? Because of their ubiquity, methods used to answer them in one context can be used to tackle corresponding questions in new contexts. The translation occurs in two stages. Firstly, methods are generalized to the abstract language. Then, one can determine whether potentially novel contexts can accommodate them. We focus on the first stage, by introducing two variants of an abstract framework in which existing and yet unidentified resource theories can be represented. Using these, the task of generalizing concrete methods is tackled in chapter 4 by studying the ways in which meaningful measures of resources may be constructed. One construction expresses a notion of cost (or yield) of a resource. Among other applications, it may be used to extend measures from a subset of resources to a larger domain. Another construction allows the translation of resource measures from one resource theory to another. Special cases include resource robustness and weight measures, as well as relative entropy based measures quantifying minimal distinguishability from freely available resources. We instantiate some of these ideas in a resource theory of distinguishability in chapter 5. It describes the utility of systems with probabilistic behavior for the task of distinguishing between hypotheses, which said behavior may depend on.
AI is increasingly used to aid decision-making about the allocation of scarce societal resources, for example housing for homeless people, organs for transplantation, and food donations. Recently, there have been several proposals for how to design objectives for these systems that attempt to achieve some combination of fairness, efficiency, incentive compatibility, and satisfactory aggregation of stakeholder preferences. This paper lays out possible roles and opportunities for AI in this domain, arguing for a closer engagement with the political philosophy literature on local justice, which provides a framework for thinking about how societies have over time framed objectives for such allocation problems. It also discusses how we may be able to integrate into this framework the opportunities and risks opened up by the ubiquity of data and the availability of algorithms that can use them to make accurate predictions about the future.
Rural connectivity is widely research topic for several years. In India, around 70% of the population have poor or no connectivity to access digital services. Different solutions are being tested and trialled around the world, especially in India. They key driving factor for reducing digital divide is exploring different solutions both technologically and economically to lower the cost for the network deployments and improving service adoption rate. In this survey, we aim to study the rural connectivity use-cases, state of art projects and initiatives, challenges, and technologies to improve digital connectivity in rural parts of India. The strengths and weakness of different technologies which are being tested for rural connectivity is analyzed. We also explore the rural use-case of 6G communication system which would be suitable for rural Indian scenario.
Machine learning techniques have deeply rooted in our everyday life. However, since it is knowledge- and labor-intensive to pursuit good learning performance, human experts are heavily engaged in every aspect of machine learning. In order to make machine learning techniques easier to apply and reduce the demand for experienced human experts, automatic machine learning~(AutoML) has emerged as a hot topic of both in industry and academy. In this paper, we provide a survey on existing AutoML works. First, we introduce and define the AutoML problem, with inspiration from both realms of automation and machine learning. Then, we propose a general AutoML framework that not only covers almost all existing approaches but also guides the design for new methods. Afterward, we categorize and review the existing works from two aspects, i.e., the problem setup and the employed techniques. Finally, we provide a detailed analysis of AutoML approaches and explain the reasons underneath their successful applications. We hope this survey can serve as not only an insightful guideline for AutoML beginners but also an inspiration for future researches.
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