Reviewerly/exHarmony huggingface dataset:
SimSR: Simple Distance-Based State Representations for Deep Reinforcement Learning

This work explores how to learn robust and generalizable state representation from image-based observations with deep reinforcement learning methods. Addressing the computational complexity, stringent assumptions and representation collapse challenges in existing work of bisimulation metric, we devise Simple State Representation (SimSR) operator. SimSR enables us to design a stochastic approximation method that can practically learn the mapping functions (encoders) from observations to latent representation space. In addition to the theoretical analysis and comparison with the existing work, we experimented and compared our work with recent state-of-the-art solutions in visual MuJoCo tasks. The results shows that our model generally achieves better performance and has better robustness and good generalization.
Cyber Supply Chain Risk Management (C‐SCRM) a System Security Engineering Role in the Future of Systems Engineering

ABSTRACT The future of Systems Engineering will include a holistic and integrated approach to managing system security to deliver cyber resilient and secure systems. This article presents Cyber Supply Chain Risk Management (C‐SCRM), a System Security Engineering Role, as an overlay to the 11 Security Concepts outlined in the IS21 paper entitled Security in the Future of Systems Engineering (FuSE), a Roadmap of Foundation Concepts. C‐SCRM is the process, tools, technology, and techniques by which global supply chain cyber threats and vulnerabilities undergo evaluation, how stakeholders assess likely system mission impacts, and select mitigations to reduce the risks. C‐SCRM performs a program System Security Engineering role in relation to procurements and subcontracts throughout the entire supply chain. This important new role requires proficiencies in system security engineering, supply chain risk management, software &amp; firmware assurance, microelectronics ecosystem, and systems engineering. This new role's unique and differentiating responsibilities address the risk spectrum from counterfeit to maliciously modified components. C‐SCRM includes use cases as well as, more distinctively, the misuse cases not covered by other existing roles. C‐SCRM is responsible for not only ensuring the components integrated into subassemblies function as intended, but also for considering attack surfaces which may present opportunities for adversaries to gain access, affect system performance, deny service, or allow data exfiltration.
Safeguarding Cloud Services Sustainability by Dynamic Virtual Machine Migration with Re-allocation Oriented Algorithmic Approach

Data centres are networking platforms which exhibit virtual machine workload execution in a dynamic manner. As the users’ requests are of enormous magnitude, it manifests as overloaded physical machines resulting in quality of service degradation and SLA violations. This challenge can be negotiated by exercising a better virtual machine allocation by dint of re-allocating a subset of active virtual machines at a suitable destined server by virtual machine migration. It is exhibited as improved resource utilization with enhanced energy efficiency along with addressing the challenge of impending server overloading resulting in downgraded services. The aforesaid twin factors of enhanced energy consumption and enhanced resource utilization can be suitably addressed by combining them together as a single objective function by utilizing cost function based best-fit decreasing heuristic. It enhances the potentials for aggressively migrating large capacity applications like image processing, speech recognition, and decision support systems. It facilitates a seamless and transparent live virtual machine migration from one physical server to another along with taking care of cloud environment resources. The identification of most appropriate migration target host is executed by applying modified version of best-fit decreasing algorithm with respect to virtual machine dynamic migration scheduling model. By executing the selection algorithm, the hotspot hosts in cloud platform are segregated. Subsequently, virtual machine-related resource loads are identified in descending order with respect to hotspots. The resource loads pertaining to non-hotspot hosts are identified in ascending order. Next, the traversing manoeuvring in non-hotspot hosts queue is exercised for identification of the most appropriate host to be reckoned as migration target host.
Questions of the relationship between rational nutrition and quality of life in Russia

The subject of the research is the attitude of students to rational nutrition as a component of the quality of life. The purpose of this article is to identify the attitude of students to rational nutrition as a component of the quality of life in order to develop recommendations for improving its level. Methodology. Within the framework of the research conducted by the author of the article, the following theories, methods, technologies and approaches were used: qualitative analysis of the works of domestic and foreign scientists devoted to the study of the sociology of nutrition, the concept of rationality and rational choice theories, as well as concepts of post-industrial society; methods of analysis and synthesis, classification and generalization in highlighting the general and particular in determining the quality problem the lives and places of rational nutrition in the life of students; methods of sociological research such as focus group. Results. In the presented article, the authors analyzed the directions of studying rationality, nutrition and quality of life as an economic category. The article offers recommendations to improve its level in a post-industrial society. Application. The conclusions and results obtained can be proposed for further study, analysis and evaluation of the place of rational nutrition in the life of students, including representatives of educational institutions.
Relevance of Artificial Intelligence in the Hospitality and Tourism Industry

The authors provide a crisp yet in-depth summary of the relevance of artificial intelligence in the hospitality and tourism industry. Focusing on artificial intelligence, the chapter draws the attention of the reader toward its usage and role in the lives of the customers and service providers in the hospitality sector. It also focuses on the costs involved in the incorporation of artificial intelligence in the hospitality industry. Some of the salient features of artificial intelligence in the hospitality sector have been discussed in the chapter by elucidating the benefits of artificial intelligence like financial benefits, technological benefits, and resource management benefits. This chapter sought to project the various factors affecting the role of artificial intelligence in the industry and how the hospitality and tourism industry adopts the technology with time, demand, and expectations of the customer. Both perspectives have been highlighted, i.e., from the customer’s point of view as well as the managerial insights. The overall intention and thrust were to provide the reader with perspectives of the importance of advancing stages of artificial intelligence in the chosen industry along with a few contrasting thoughts as artificial intelligence cannot replace the essence of human touch completely as it is one of a kind of a servicescape involving humans as the most crucial and critical link.
Stakeholder Alignment/Capability Based Security Engineering: Capability Engineering vs “Problemeering” and “Solutioneering” – Prioritizing Stakeholder Needs Over Requirements

ABSTRACT When building systems, it is tempting to start with the stakeholder requirements and jump straight into implementing a preconceived solution and then mapping it to the requirements as best one can. This is “solutioneering.” To avoid this, systems engineers build models of the requirements, elaborating them into use cases, activity diagrams, IBDs, and other more concrete modeling steps. However, part of a system engineer's job is to validate these requirements against the stakeholder needs, capabilities, and goals to ensure you build the right system. This assumes that the stakeholder knows and understands their needs and problems, which we have dubbed “Problemeering.” The systems engineer can never take the requirements at face value, assume that they are correct and have captured the stakeholder needs and can solve their problems. This is true across many cross‐cutting aspects and domains, requiring a Subject Matter Expert (SME). This is especially true regarding security issues where is it unlikely that stakeholders are aware of the current threat landscape and risks and can fully elaborate them. This will require the knowledge and experience of a cyber‐security SME who can adequately analyze the system vulnerabilities from the right perspective. In this article, we will detail how Capability‐Based security engineering ensures the articulation of the true needs of the stakeholder so that the engineers ultimately implement the needs in the delivered system.
Trends of Artificial Intelligence in Revenue Management of Hotels

The authors provide an in-depth summary of artificial intelligence’s role in the revenue management of hotels across the globe. Focusing on artificial intelligence, the chapter draws the reader’s attention toward its usage and role in the lives of the customers and service providers in the hotel industry, which ultimately leads to the revenue management system. The chapter focuses on the current trends of artificial intelligence to generate revenue for hotels more innovatively. Some of the finest and latent features of artificial intelligence in the revenue management system have been discussed in the chapter by elucidating few examples of few renowned hotels in the world. The revenue management system of the hotel industry has been discussed briefly in the chapter. The essence of the chapter lies in the role and importance of the ever-changing and emerging trends of artificial intelligence in the hotel industry, particularly for revenue generation. Though artificial intelligence cannot replace the emotional intelligence provided by humans, it is the need of the hour to enhance and sustain in the innovative and digital era. AI rules across manufacturing and production sectors, but it also plays a strategic role in the service industry.
Making the Puzzle Pieces Fit – Utilizing UAF to Model a Cybersecurity SoS

ABSTRACT While there are security bundles on the market, most IT departments operate with a wide range of products (an average of 75) just to secure their network (Zurkus 2016). Companies developed the security products independently and offer little to no cross‐product integration. Although many people treat cybersecurity infrastructures as systems, it is far better to view them as a System of Systems (SoS) as many different parts of an organization develop, manage, and operate the systems they must protect. So, for system engineers attempting to secure systems, utilizing the Unified Architecture Framework (UAF) to create a map of these systems and software and how they must integrate is a far better practice than SysML. Not only is UAF specifically designed to handle an SoS, but it includes Security Viewpoints which can more easily represent the complex interactions between individual security systems. In this article, we will briefly discuss an introduction to UAF, explain its benefits when modeling integrated security for complex systems of systems, offer solutions to the most common issues encountered when attempting to model the interactions between security elements, and demonstrate how they address the FuSE concepts of security orchestration and architectural agility.
A Benchmark of Entropy Coders for the Compression of Genome Sequencing Data

Genomic sequencing data contain three different data fields: read names, quality values, and nucleotide sequences. In this work, a variety of entropy encoders and com-pression algorithms were benchmarked in terms of compression-decompression rates and times separately for each data field as raw data from FASTQ files (implemented in the Fastq analysis script) and in MPEG-G uncompressed descriptor symbols de-coded from MPEG-G bitstreams (implemented in the symbols analysis script). The result of this benchmark is then compared to the performance of CABAC, which is the encoder used in first edition of the ISO /IEC MPEG-G standard for all types of descriptors, since CABAC was the best performing in terms of compression rates for the three types of data, thus providing overall better compression rates compared to other entropy coders in total. However, in some use cases encoding and decoding speed might be of higher interest than compression, and for specific datasets, types of data, or descriptor streams, other entropy coders might provide higher speed and/or better compression performance than CABAC.
Human Activity Recognition Using LSTM with Feature Extraction Through CNN

Human activity recognition is important for detecting anomalies from videos. The analysis of auspicious activities using videos is increasingly important for security, surveillance, and personal archiving. This research paper has given a model which can recognize activities in random videos. The architecture has been designed by using BiLSTM layer which helps to learn a system based on time dependencies. To convert every frame into a featured vector, the pre-trained GoogLeNet network has been used. The evaluation has been done by using a public HMDB51 data set. The accuracy achieved by using the model is 93.04% for ten classes and 63.96% for 51 classes from same data set only. Then, this network is compared with other state-of-the-art method, and it proves to be a better approach for the recognition of activities.
Artificial Neural Network and Math Behind It

Article is structured in such a way that the reader could easily understand from the roots of an artificial neuron to its applications in between gaining knowledge on how an artificial neuron exists. It starts with the history of neuron, and the explanation goes on by describing the architecture of artificial neuron and on to the functioning of neuron. It also explains the basic math involved in constructing an artificial neuron by describing the activation functions used in the network, and the mathematical models helps in building the neural network. It also talks about the structure of feed forward network, loss function imposing an error on the output, also the gradient descent, and the back propagation method involving the computation of cost function with respect to the network parameters. As a conclusion, paper describes the importance of artificial neural networks in the upcoming artificial intelligence and many more applications behind it.
An Efficient Approach to Stamp Verification

Stamps have become one of the most important security features in big companies where huge amounts of documents need processing daily. The stamp attached to a document is used to determine the authenticity of that document so that it is necessary to identify whether a stamp is forged or genuine. However, nowadays, it is easier for the general public to forge stamps. This paper presents a practical approach for stamp verification, based on the three stages process similar to some previous work: stamp segmentation, classification stamp or non-stamp, and stamp authenticity verification. In each stage, this work tries and tests new algorithms/methods to give a new way of solving the problem in each stage. Firstly, in our approach, an unsupervised learning machine method is implemented to detect all the objects in the input image, so all the regions including stamps and text are extracted. Next, two separate models of support vector machine classification are constructed. The first one is to distinguish between stamps and other objects in a document. The second model will determine the object which was classified as stamps in the first model whether it is genuine or not. The results show that this approach can perform the stamp verification tasks effectively.
Threats and Challenges of Artificial Intelligence in the Healthcare Industry

The authors provide a crisp yet in-depth summary of the relevance of artificial intelligence (AI) in providing healthcare solutions. Artificial intelligence is a relatively new concept in the field of health care. AI aids in the prediction of disease patients for medical procedures. Patients, pharma companies, health services, insurance companies, and medical institutions benefit from AI’s application in health care. Artificial intelligence supports different concepts, counting computing, computer program improvement, and information exchange. Machine learning, profound learning, normal dialect generation, discourse acknowledgment, robots, and biometric distinguishing proof are illustrations of artificial intelligence’s innovation. Artificial intelligence is used in a variety of areas, including health care, manufacturing, and business. It is also used in the automotive industry. The authors have discussed the current scenario of artificial intelligence and the threats and challenges posed for AI in the healthcare industry.
x3: Lossless Data Compressor

x3<sup>1</sup> is a lossless optimizing dictionary-based data compressor. The algorithm uses a combination of a dictionary, context modeling, and arithmetic coding. Optimization adds the ability to find the most appropriate parameters for each file. Even without optimization, x3 can compress data with a compression ratio comparable to the best dictionary compression methods like LZMA, zstd, or Brotli.
A Case Study of Reengineering Switchboard Defect Inspection Processes and Establishing a Production Information System

Purpose: If material or product defects occur, additional products must be reproduced to the extent of the number of products carrying the defects. The reproduction of additional products requires re-establishing material inventory and understanding product requirements. This study introduces realistic material requirement planning and production delay minimization methods by considering the occurrence of defects.BRMethods: The study presents a reengineering method for quality control, process control, and production tracking and proposes traceability management processes to minimize material requirement planning and production delays. We proposed an improved work process that enables real-time information sharing between the production, material, and quality control departments to reduce production delays and enhance work efficiency.BRResults: This study devised a production information system applying reengineering. The developed system was applied to H company’s switchboard production to increase production efficiency.BRConclusion: As a result of applying the production information system, reflecting the improved work process to share standardized information for the switchboard production system of H company, information that was not previously shared in an integrated manner was shared, thereby accelerating business decision-making. In addition, based on the occurrence of defects, reproduction and inventory management were automatically updated, enabling accurate production management.
Machine Expertise in the Loop: Artificial Intelligence Decision-Making Inputs and Cyber Conflict

How will national cybersecurity stakeholders react to increasingly sophisticated artificial intelligence (AI) products in the decision-making loop? Diverse AI applications, already a component of military operations, stand to further revolutionize threat-intelligence analytics, curation, and presentation in years to come. In doing so, they portend heightened efficiency and higher tempos of security operations while minimizing risk potential. And yet, challenges abound. AI inputs to deliberative processes may be perceived as more or less robust, accurate, or generalizable according to a range of factors that influence decision-makers. Then these habits and preferences might be reintroduced to the information loop in several ways, as the algorithm, the design/implementation process, and institutions adapt to accommodate the human element. Malicious actors are even likely to target this action-reaction loop to influence AI systems. This two-phase cycle is perhaps most worrisome in relation to cyber conflict, where informational ambiguity, functionally diverse workforces, and an expansive and fragmented threat environment combine to produce an immense opportunity for baking bias into the loop. This paper presents the results of two experiments designed to explore different manifestations of AI systems in the cyber conflict decision-making loop. Though findings suggest that technical expertise positively impacts respondents’ ability to gauge the potential utility and credibility of an input (indicating that training can, in fact, overcome decision-maker bias), the perception of human agency in the loop even in the presence of AI inputs mitigates this cautionary effect and makes decision-makers more willing to operate on less overall information.
Minimizing Sequential Confusion Error in Speech Command Recognition

Speech command recognition (SCR) has been commonly used on resource constrained devices to achieve hands-free user experience. However, in real applications, confusion among commands with similar pronunciations often happens due to the limited capacity of small models deployed on edge devices, which drastically affects the user experience. In this paper, inspired by the advances of discriminative training in speech recognition, we propose a novel minimize sequential confusion error (MSCE) training criterion particularly for SCR, aiming to alleviate the command confusion problem. Specifically, we aim to improve the ability of discriminating the target command from other commands on the basis of MCE discriminative criteria. We define the likelihood of different commands through connectionist temporal classification (CTC). During training, we propose several strategies to use prior knowledge creating a confusing sequence set for similar-sounding command instead of creating the whole non-target command set, which can better save the training resources and effectively reduce command confusion errors. Specifically, we design and compare three different strategies for confusing set construction. By using our proposed method, we can relatively reduce the False Reject Rate~(FRR) by 33.7% at 0.01 False Alarm Rate~(FAR) and confusion errors by 18.28% on our collected speech command set.
The Utility of Trust: Interpersonal, Institutional, and Technological

Building on research establishing the value of trust in business, including interpersonal and institutional trust, I evaluate a new conceptualization of trust, technological trust, associated with technologies like blockchain. The point of doing so is to contribute to understanding what role such technologies should play in business, ethically speaking. I focus on the business of executing and authenticating financial transactions. Scholars have shown that technological trust does not fulfill certain, foundational functions associated with traditional conceptualizations of trust, such as motivating people to form transacting relationships in the first place. I consider the extent to which such characteristics affect technological trust’s overall utility. After conducting utilitarian evaluations of interpersonal, institutional, and technological trust via a series of novel thought experiments, I argue that institutional trust creates greater overall utility than interpersonal trust and technological trust has the potential to create greater overall utility than institutional trust. As long as certain conditions are met regarding the foundations of trust, then, businesses need not entrench in strategies related to interpersonal or institutional trust, ethically speaking; rather, they may invest resources to develop business strategies related to technological trust.