Reviewerly/exHarmony huggingface dataset:
Zero-Shot Scene Graph Generation with Knowledge Graph Completion

Limited by the incomprehensive training samples, existing scene graph generation (SGG) methods perform poorly on predicting zero-shot (i.e., unseen) subject-predicate-object triples. To address this problem, we propose a general SGG framework to improve their zero-shot performance. The main idea of our method is to generate the information of zero-shot triples before the training of the predicate classifier and thus make the original zero-shot triples non-zero-shot. Specifically, the missing information of zero-shot triples is generated by our proposed knowledge graph completion strategy and then integrated with visual features of images. Therefore, the predicate classification of zero-shot triples is no longer just regarded as a single visual classification task but also transformed into a prediction task of missing links in a knowledge graph. The experiments on the dataset Visual Genome demonstrate that our proposed method outperforms the state-of-the-art methods in popular zero-shot metrics (i.e., zR@N, ng-zR@N) for all popular SGG tasks.
Text-Guided Image Manipulation Based on Sentence-Aware and Word-Aware Network

Text-guided image manipulation aims to use the given text description to modify the semantic content of the corresponding part in the input image. Although researchers have been obtained satisfactory performance in this field, they only 1) utilize the global sentence information at the initial modification stage and 2) exploit the fixed word information for regional adjustment in the subsequent modification process, hindering the improvement of image manipulation quality. Motivated by the mentioned issues, this paper proposes a novel approach to improve the performance of text-guided image manipulation by using sentence-aware and word-aware network. Concretely, we utilize global sentence information throughout the image manipulation process to improve the semantic consistency with the input text. On the other hand, we employ the dynamic selection method to dynamically adjust the word information corresponding to the regional image content to further improve the manipulation quality. As a result, our work surpasses the existing state-of-the-art methods on CUB and Oxford-102 flower datasets, demonstrating our effectiveness and superiority. In terms of Inception Score, our proposed method performs the most excellent performance. In terms of NIMA, the score of our method is closest to the score of the original dataset images, proving that our manipulated results are the most authentic.
Incomplete Multiview Clustering Using Normalizing Alignment Strategy With Graph Regularization

Matrix factorization has demonstrated promising performance in the incomplete multiview clustering (IMC) tasks. However, many algorithms require feature normalization operations to ensure the stability of model results, so either the convergence is unstable, or the objective function cannot fit the data well. Addressing these issues, we propose a novel IMC algorithm using a normalizing alignment strategy (IMCNAS) based on nonnegative matrix factorization. Specifically, the columns of the basis matrices are constrained into unit vector space, which integrates the feature normalization and the optimizing process, and makes the model converge fast and stable. On the other hand, this enables the model to fit the data better and produce more reasonable factorization results. Further, we develop a novel pairwise co-regularization to align incomplete multiple views more directly, without introducing a common consensus matrix like traditional centroid-based co-regularization. Graph regularization is also incorporated in the proposed model to utilize the geometrical information of data. We implement IMCNAS with a centroid-based regularization and a pairwise co-regularization respectively, and leads to two variants, i.e., IMCNAS-1 and IMCNAS-2. Both variants are optimized with multiplicative updating rules. Extensive experiments conducted on various real-world datasets comparing several state-of-the-art IMC methods verified the effectiveness of the proposed methods. The source code is available at: <uri>https://github.com/GuoshengCui/IMCNAS</uri>.
The use of <scp>Design Science</scp> and <scp>Agile Methodologies</scp> for improved information systems in the <scp>T</scp>anzanian <scp>Higher Education</scp> context

Abstract There was no known effective approach to implementing information systems in Tanzanian Higher Education Institutions (HEIs) because the requirements of users change frequently and HEIs vary in their requirements. Theoretically, the agile software development process and Design Science Research (DSR) could deliver software that suits such a context, yet no testing had been done priorly. Therefore, this study aimed on investigating the suitability of the combination of Agile Methodologies and the DSR in developing information systems for Tanzania's HEIs. The study used eXtreme Programming as one of the Agile Methodologies and the DSR to develop a timetabling software which easily integrates into the existing academic information system of the College of Business Education for better decision making. Likewise, the evaluation of the developed artifact confirmed that the resulting product raised the user satisfaction with the information system in HEIs. In this regard, the current study advances the frontier of knowledge on using the DSR framework and Agile Methodologies in designing and developing software in Tanzania's HEIs and beyond.
Fundamental Concepts of Modeling Computer Security in Cyberphysical Systems

The ability to detect cyberattacks in industrial installations depends heavily on in-advance learning about potential threats and vulnerabilities, which is best done through extensive modeling. Three general types of modeling approaches exist, which are based on three pillars of science: theory, experiments, and simulation. The paper reviews the author’s take to integrate all three views. Using the theoretical approach, the author with coworkers previously applied the Non-Functional Requirements (NFR) method to security analysis of SCADA installations. The objective of the current work is to complement and enhance it with the use of simulation and practical experiments. With respect to simulation, building models with the Monterey Phoenix tool has been applied to an IEEE standard related to SCADA security. Experimental approaches to cybersecurity rely on applying penetration testing, with tools such as Nmap or Shodan that can be useful in studying security vulnerabilities. Here, we advocate a comprehensive approach, where software tools, such as those mentioned above, could complement theoretical analysis. Work is reported on building an NFR model for SCADA security for the laboratory example with three kinds of devices (valves, flowmeters and sensors), in terms of architectural properties of the SCADA system. A practical NFR model with the use of both Monterey Phoenix tool and respective penetration experiments has been developed.
Comparison of Traditional and Elliptic Curves Digital Signatures Providing the Same Security Level

The paper presents the efficiency comparison of different digital signature schemes taking into account both digital signatures based on algebraic groups of numbers and digital signatures based on elliptic curves. This efficiency comparison of two types of digital signatures is possible under the condition that for each scheme the appropriate key length was taken ensuring the same level of security. The paper show that the key length for classic digital signature needs to be greater than the key length for elliptic curve digital signature and thus different processing speed is observed.
Autonomous Systems Incidentally Controlled by a Remote Operator

Research and development on autonomous systems (AS) focuses the attention of ever more and more countable universities and companies. The autonomy of operation, with respect to the environment and also the complexity of tasks and processes the AS is implemented to operate and solve, requires a knowledge-based approach. However, in many cases, it is very difficult, or even impossible, to equip the system with a complete knowledge base that would allow the flawless operation of AS in any circumstances. Hence, the system can be faced with a situation or a task that it is unable to solve. The approach developed by the author’s team expands the idea of Fault-Tolerant systems. We broaden the meaning of a fault including here such situations, that cannot be solved by the AS on its own. If such a fault is detected, the system assesses its severity, basing on its Knowledge Base, and, once it is unable to solve the problem, contacts the remote operator who takes over the control of the system to overcome the problem. To facilitate remote control of the operator, a Virtual Teleportation technology is applied. Then the control is returned to the AS. This paper deals with the problems of combining autonomy with remote control of the system supported by Virtual Teleportation of the operator. Particular attention is paid to the detection of faults, especially those that are caused by an incomplete Knowledge Base. Additionally, some examples are given.
On the relative value of weak information of supervision for learning generative models: An empirical study

Weakly supervised learning is aimed to learn predictive models from partially supervised data, an easy-to-collect alternative to the costly standard full supervision. During the last decade, the research community has striven to show that learning reliable models in specific weakly supervised problems is possible. We present an empirical study that analyzes the value of weak information of supervision throughout its entire spectrum, from none to full supervision. Its contribution is assessed under the realistic assumption that a small subset of fully supervised data is available. Particularized in the problem of learning with candidate sets, we adapt Cozman and Cohen [1] key study to learning from weakly supervised data. Standard learning techniques are used to infer generative models from this type of supervision with both synthetic and real data. Empirical results suggest that weakly labeled data is helpful in realistic scenarios, where fully labeled data is scarce, and its contribution is directly related to both the amount of information of supervision and how meaningful this information is.
A novel data processing approach to detect fraudulent insurance claims for physical damage to cars

Some automobile insurance companies use computerized auto-detection systems to expedite claims payment decisions for insured vehicles. Claims suspected of fraud are evaluated using empirical data from previously investigated claims. The main objective of this manuscript is to demonstrate a novel data processing system and its potential for use in data classification. The data processing approach was used to develop a machine learning-based sentiment classification model to describe property damage fraud in vehicle accidents and the indicators of fraudulent claims. To this end, Singular Value Decomposition-based components and correlation-based composite variables were created. Machine learning models were then developed, with predictors and composite variables selected based on standard feature selection procedures. Five machine learning models were used: Boosted Trees, Classification and Regression Trees, Random Forests, Artificial Neural Networks, and Support Vector Machines. For all models, the models with composite variables achieved higher accuracy rates, and among these models, the artificial neural network was the model with the highest accuracy performance at 76.56%.
How to Make It as an AI

Abstract This chapter describes the many continuities between artificial intelligence (AI) development and promotion. The chapter traces how success in AI is about building systems that are responsive to a wide range of sociotechnical system pressures that include scientific standards, clinical commitments, regulatory structures, and economic incentives. It details how these system pressures collide to create a fraught landscape for AI development and popularization. This chapter also explores how AI accuracy claims become the foundation of promotional claims and subsequent trust in new AI technologies. In so doing, the chapter investigates competing case studies in the development and promotion of diagnostic AI systems—a breast cancer detection system designed in an industry context and an open science approach to nail fungus diagnosis.
The Search for Ground Truth

Abstract This chapter investigates data collection and curation activities that precede the training of artificially intelligent systems. The chapter documents different approaches to annotating data to create what is called “ground truth” in machine learning. It begins with an exploration of different techniques for health artificial intelligence (AI) “groundtruthing.” This includes collection of data from electronic health records, the use of established diagnostic techniques, and custom annotations by healthcare professionals. The chapter then investigates different statistical techniques for validating AI performance and describes the results of an AI development simulation that models the search for ground truth and exposes serious limitations in the most common research designs.
Outlook

Abstract The final chapter concludes the book by briefly discussing various topics we haven’t touched upon (e.g. simulating non-Markovian open quantum systems, quantum advantages in thermodynamics, resource theories) and lists some prospects for exciting future research avenues (e.g. practical applications in thermoelectrics and beyond, finding accurate reduced system descriptions, finite-bath effects).
Review on Cryptography and Network Security Zero Knowledge Technique in Blockchain Technology

A huge amount of data has been generated every day across the world by organizations with the emergence of the World Wide Web, social networks, along with e-commerce applications. To safeguard the network along with data transmission that occurs over a wireless network, cryptography along with network security (NS) is utilized. In data transmission over a wireless untrustworthy network, securing data is a major concern. Providing security to the wireless sensor networks is highly significant; NS provides security not only to the end system but also to all over the network system. Consequently, NS, cryptography, present progress in NS, linear cryptanalysis (LC), together with differential cryptanalysis (DC) are explicated in this work. The outcomes of the encryption time (ET) versus file size for data encryption standard (DES), advanced encryption standard (AES), 3DES, Rivest-Shamir-Adleman (RSA), blowfish, and the decryption time (DT) versus file size for RSA, AES, modified RSA (MRSA), and nth degree truncated polynomial ring units (NTRU) are also evaluated in this paper.
Quantum Stochastic Processes

Abstract This chapter describes the basic features of open quantum systems, i.e. quantum systems that are affected by noise due to uncontrollable degrees of freedom of an environment or bath. This noise is responsible for effects such as dissipation,decoherence and irreversibility. We study the equilibrium states of open quantum systems and review tools from quantum measurement theory, which describe how to theory, that describe how to extract information from an (open) quantum system. We generalize these tools to multimulti-time statistics and define the notion of a quantum stochastic process and a quan tum Markov process. Finally, we study in which cases a quantum stochastic process looks classical.
Wearable Device-Based Data Collection and Feature Analysis Method for Outdoor Sports

In recent years, with the rapid popularization of smart phones and wearable smart devices, it is no longer difficult to obtain a large number of human motion data related to people's heart rate and geographical location, which has spawned a series of running fitness applications, leading to the national running wave and promoting the rapid development of the sports industry. Based on the long short-term memory cyclic neural network, this paper processes, identifies, and analyzes the motion data collected by wearable devices. Through massive data training, a set of accurate auxiliary models of outdoor sports is obtained to help optimize and improve the effect of outdoor sports. The results show that the method proposed in this paper has a higher degree of sports action and feature recognition and can better assist in the completion of outdoor sports.
Computer Network Vulnerability Detection and Semantic Data Analysis Optimization Based on Artificial Intelligence

In order to solve the network security vulnerabilities in the process of network information interaction, which affect the integrity and confidentiality of data. The computer network vulnerability detection and semantic data analysis optimization based on artificial intelligence are proposed, and the results show that the final accuracy of the test set is improved to 88.5%, but the false positive rate is as high as 18%. Based on the direct classification model, the code under test is compared with the vulnerability template, the model fuses the two direct classification models and reduces the false positive rate to less than 5% under the condition that the accuracy is basically the same.
Meritocracy Enabled by Technology, Grounded in Science

Abstract This chapter looks at how one can enable greater meritocracy in the job market by making use of technology and science. I describe the scientific tools Aspiring Minds uses to measure job skills and scale these assessments to millions of candidates using technology. This chapter describes the five pillars of Aspiring Mind’s work: Science of developing assessments, artificial intelligence and machine learning methods to develop measures commensurate with this science, other technologies to translate these into high-fidelity assessments available cost-effectively at scale, an orientation to publishing data to inform public policy, and a consideration toward fairness to develop assessments free from racial, gender, ethnic or other biases. The chapter calls for a team of social scientists, measurement scientists, and technology innovators to join hands in order to use these pillars to help design systems to realize the vision of meritocracy at scale.
The Data Economy

Abstract Data is an essential input to the digital economy. As we have seen in the case of connectivity, a digital economy and society cannot prosper without a flourishing data economy behind it. However, the data value chain poses difficult challenges to policymakers, giving rise to complex and novel issues. Several important economic structural features associated with data and lack of transparency or competition often lead data markets to malfunction. Public policy can intervene, fostering competition and realigning the incentives of market players with those of society to achieve higher welfare levels. The European Union’s institutions have been very active in the area, setting global standards for privacy protection with the 2018 General Data Protection Regulation (GDPR) and designing actions to support the development and uptake of data analytics in Europe.
The Concept of Normality

Abstract Though everyday usage of the word ‘normal’ is now habitual and its meaning is taken as common sense, it has a relatively recent provenance (late eighteenth century in English) and emerged squarely from specialist and professional areas—in particular, from statistical and biomedical disciplines. In fact, both in its various specialist connotations and, relatedly, in popular discourses, the concept of the ‘normal’ has a critical place in modern knowledge construction and dissemination. This chapter considers what habituation to the catchphrase ‘new normal’ in so many contexts means for the concept of the ‘normal’ itself. It is possible that continuous usage of the catchphrase, even if unthinkingly, could implicitly shift the ground of what is commonly understood as ‘normal’. This argument is woven through well-defined conceptualizations of the ‘normal’ with various consonant and contradistinctive terms in specific contexts (with reference to the work of Georges Canguilhem, Michel Foucault, Lennard Davis, Michael Warner, Julian Carter, Sarah Igo, Anna Creadick, and Peter Cryle and Elizabeth Stevens). From a different direction, the implications of ‘new normal’ in relation to formulations of ‘normal science’ and ‘post-normal science’ are also picked up.
Digital Government

Abstract Digitizing the public sector has become imperative for every country in the world. A digitized public administration is leaner, faster, and more efficient. It can better identify and anticipate its constituency’s issues; it is transparent and accountable. That is all the more necessary to enable governments to perform increasingly challenging tasks, such as dealing with shrinking public budgets, meeting citizens’ raising expectations, and relating with a highly dynamic surrounding economic environment. Transformation is, however, far from easy. Technological adoption, the modernization of infrastructure, and the upskilling of the public workforce are just the first steps. A digital public sector requires a radical cultural change for public employees and citizens alike. It begs for significant investment and efforts to establish a mutually beneficial communication flow between public administrations and citizens, business, and society at large. Of particular relevance is the use of data in the public sector. Data is a powerful enabler of innovation. But to unleash its full potential, it needs the proper governance framework.
Technology and Employment

Abstract Technology can improve workers’ productivity, but it can also make them redundant: if machines and algorithms can perform the same job faster and at a lower cost, why would anybody employ human workers? As it turns out, the previous industrial revolutions did not reduce the need for humans in labour markets. Instead, they induced a transformation of the jobs that workers were required to perform, creating new opportunities and tasks that only humans could pursue. It may be reasonable to expect that this time, with the Fourth Industrial Revolution brought about by technological progress, is not going to be any different. New jobs will be created, and technological adoption will not produce structural unemployment. However, even if this is the case, the transition is nevertheless costly and necessarily painful for a significant part of the working population. Thus, public policies are needed to protect workers temporarily displaced or in atypical employment contracts with wide and flexible social safety nets. Lifelong learning may be the best way to ensure a smooth transition to labour markets 4.0.
Reimagining Merit in India

Abstract India’s pernicious caste system severely constrains the educational and career outcomes of its former untouchable castes and the tribal communities, which together account for nearly a quarter of the country’s population. It renders millions inferior and apparently “meritless.” Despite seven decades of development, caste inequalities have remained entrenched partly because India failed to address the cognitive dimensions of discrimination. Although affirmative action has improved economic and social well-being of these groups, it suffers from design and implementation defects. As the beneficiaries are selected on the basis of identity, affirmative action ends up perpetuating social identities instead of attenuating them. In its current form, affirmative action is more akin to distributing fish than to teaching how to fish. Several new approaches rooted in social psychology and cognitive neuroscience focus on merit as an individual effort rather than a group trait. Some markers of merit are more unambiguous, including nurturing and celebrating individual merit in sports, math, chess, music, and so on. Additionally, target groups are encouraged to focus on STEM as well as entrepreneurship.