Reviewerly/exHarmony huggingface dataset:
‘Lockdown’: Digital and Emergency eLearning Technologies—A Student Perspective

The COVID-19 pandemic and the subsequent emergency measures had a fundamental and disruptive impact on societies and, in particular, on the educational sector. The transition of the modality of educational delivery from face-to-face to online occurred within days; this research study considered the concepts of digital trust and digital access, using structuration, postmodernism, and critical social theory as lenses to understand Higher Education (HE) student experiences during the first wave of the lockdown. The study compared these aspects in Hungary, South Africa, and Wales (UK) with different digital and network readiness indices. The digital development of the countries is presented using both the Digital Intelligence and Network Readiness indices. The research approach was cross-country, international, comparative, inductive, and quantitative. The research study found that there was a significant relationship between the countries’ GDP and their network readiness, impacting students’ online learning experiences. Significant differences were found between the participating universities in terms of digital access and digital trust; this research provides valuable insights for informing future pedagogical approaches and teaching best practice, specifically for residential universities. Understanding challenges and barriers to student learning experiences, and the impact of inequitable access to digital technologies and communication, is key for future pedagogical policy and practice. Several recommendations are made to inform practice.
Counter Mode of the Shannon Block Cipher Based on MPF Defined over a Non-Commuting Group

In this paper, we present a counter mode of a Shannon block cipher based on the matrix power function. We make use of the matrix power function to define a single round symmetric cipher. Continuing our previous research, we implement a non-commuting group the order of which is a power of two in order to define a CTR mode in its most classic interpretation. We explore the security of the newly defined scheme, first, by showing that our block cipher is perfectly secure and does not leak any information about the initial plaintext based on the ciphertext. Then, we define a sequence of security games that show how the CTR mode of our cipher can resist all passive attacks.
Flexible Information System Infrastructure Solutions for Small and Medium Enterprises

Digital transformation for businesses is a matter of great concern at the moment. One of the solutions for this could be based on cloud computing services and technologies that have never been growing faster and more mature than today. It’s very convenient to build a business that can go global in minutes. But that comes with a cost: cloud vendor-locked and requires knowledge for efficiency usage and cost management. For a business that is small and newborn, it doesn’t yet ready to go global, their customers primarily rely on the same state where they’re doing business, the data are required to be bound within the border, and a cloud computing solution is not suitable for them. Building information systems therefore needs an optimal solution: build at the right scale at the beginning, ready to move to the cloud or in hybrid mode. To do that, businesses need knowledge about designing and building infrastructure for information systems to meet that requirement. This study will focus on proposing a flexible solution for the design and in-depth analysis of infrastructure for such an information system.
A scalable species-based genetic algorithm for reinforcement learning problems

Abstract Reinforcement Learning (RL) methods often rely on gradient estimates to learn an optimal policy for control problems. These expensive computations result in long training times, a poor rate of convergence, and sample inefficiency when applied to real-world problems with a large state and action space. Evolutionary Computation (EC)-based techniques offer a gradient-free apparatus to train a deep neural network for RL problems. In this work, we leverage the benefits of EC and propose a novel variant of genetic algorithm called SP-GA which utilizes a species-inspired weight initialization strategy and trains a population of deep neural networks, each estimating the Q-function for the RL problem. Efficient encoding of a neural network that utilizes less memory is also proposed which provides an intuitive mechanism to apply Gaussian mutations and single-point crossover. The results on Atari 2600 games outline comparable performance with gradient-based algorithms like Deep Q-Network (DQN), Asynchronous Advantage Actor Critic (A3C), and gradient-free algorithms like Evolution Strategy (ES) and simple Genetic Algorithm (GA) while requiring far fewer hyperparameters to train. The algorithm also improved certain Key Performance Indicators (KPIs) when applied to a Remote Electrical Tilt (RET) optimization task in the telecommunication domain.
Indexed and fibered structures for partial and total correctness assertions

Abstract Hoare Logic has a long tradition in formal verification and has been continuously developed and used to verify a broad class of programs, including sequential, object-oriented, and concurrent programs. Here we focus on partial and total correctness assertions within the framework of Hoare logic and show that a comprehensive categorical analysis of its axiomatic semantics needs the languages of indexed and fibered category theory. We consider Hoare formulas with local, finite contexts, of program and logical variables. The structural features of Hoare assertions are presented in an indexed setting, while the logical features of deduction are modeled in the fibered one.
Psychological Traits and Investors' Cryptocurrency Behavior

The current study looked into the relationship between self-esteem and the use of cryptocurrencies and overconfidence bias. Three hundred thirty-seven investors from the northern portion of India total took part in the survey. AMOS 20.0 was used to create and test the conceptual framework. A questionnaire was used to extract data about the participants' investments in order to determine the level of their overconfidence bias and bitcoin participation. The outcomes confirmed the hypothesis. The results of the mediation study showed that there is direct and indirect influence of self-esteem on cryptocurrency investment behavior showing partial mediation effect.
Impact of perceived social support on pain perception in virtual reality

Social influences are important modulators of pain. Experimental studies demonstrated analgesic effects of explicit social support. As digital health care gains importance, this study investigated influences of social support by virtual characters and their perceived agency in virtual reality on pain. In a within-subjects design, participants experienced painful heat stimuli in VR. Either a virtual character provided verbal support prior to the pain stimuli or not (support vs. no support condition). In addition, participants were instructed that a real human (avatar condition) or a computer script (virtual agent) controlled the virtual character. However, in both conditions, the virtual character was computer-controlled. Main outcome measures were pain ratings and physiological pain responses. Virtual verbal support affected physiological responses in terms of "stress buffering", but not pain ratings. The agency manipulation was successful as the avatar yielded higher ratings of perceived human-likeness, helpfulness and realism compared to the virtual agent. However, agency of the character did not affect pain responses. This study revealed that virtual verbal support positively influences physiological pain responses independently of perceived agency. The findings have important clinical implications as virtual interactions and digital health interventions are increasingly relevant for pain treatment.
Validation of Affective Sentences: Extending Beyond Basic Emotion Categories

We use nonverbal and verbal emotion cues to determine how others are feeling. Most studies in vocal emotion perception do not consider the influence of verbal content, using sentences with nonsense words or words that carry no emotional meaning. These online studies aimed to validate 95 sentences with verbal content intended to convey 10 emotions. Participants were asked to select the emotion that best described the emotional meaning of the sentence. Study 1 included 436 participants and Study 2 included 193. The Simpson diversity index was applied as a measure of dispersion of responses. Across the two studies, 38 sentences were labelled as representing 10 emotion categories with a low degree of diversity in participant responses. Expanding current databases beyond basic emotion categories is important for researchers exploring the interaction between tone of voice and verbal content, and/or people's capacity to make subtle distinctions between their own and others' emotions.
A Thorough Investigation on Image Forgery Detection

Image forging is the alteration of a digital image to conceal some of the necessary or helpful information.It cannot be easy to distinguish the modified region from the original image in some circumstances.The demand for authenticity and the integrity of the image drive the detection of a fabricated image.There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files, including re-sampling or copy-moving.This work presents a high-level view of the forensics of digital images and their possible detection approaches.This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness.These methods have identified forgery and its type and compared it with state of the art.This work will help us to find the best forgery detection technique based on the different environments.It also shows the current issues in other methods, which can help researchers find future scope for further research in this field.
Challenges in the Transition towards a Quantum-safe Government

The computation power of quantum computers introduces new security threats in Public Key Infrastructure (PKI), a system used by many governments to secure their digital public services and communication. This calls for an inevitable need for governments to be quantum-safe (QS) by modifying their PKI systems to be resistant to the attacks of quantum computers. However, there is limited academic literature on a QS PKI system, and in this limited literature, the transition challenges are perceived as exclusively technological. This paper aims to create a structured overview of challenges when transitioning to a QS PKI system. We do this by reviewing literature and classifying the challenges using Technology-Organization-Environment (TOE) framework and using an expert workshop to explore the challenges in the context of the PKI system in the Dutch government. The main challenges in the technological context include no universal QS solution, legacy system, complex PKI interoperability, and vulnerable Root CA. The main challenges in the organizational context include knowledge gap, unclear governance, lack of urgency, and in-house management support. Furthermore, the main challenges in the environmental context include institutional void, stakeholder collaboration, lack of awareness, and policy guidance. The results indicate that the QS transition from the current PKI system is complex, and the challenges are socio-technical. For policy-makers, this implies that they should start early to prepare, whereas organizations are hardly aware of the process of QS transition and the topic of quantum computing is yet to develop the urgency in organizations.
A unique secure multimodal biometrics-based user anonymous authenticated key management protocol (SMUAAKAP) based on block chain mechanism for generic HIoTNs

The concept of blockchain has sparked hopes in the field of communication security between IoT devices. In blockchain, all network nodes need to verify its legitimacy before accepting and recording it. Users of HIoTNs (Hierarchical IoT Networks) want to get real-time data from specific sensors in a specific implementation. Users need authentications at GNs for obtaining their required services. On successful authentications, parties on either sides of communication can create secret session keys for safe communications. Critical issues like this in HIoTNs are addressed in this work by its novel proposed multimodal-biometrics based lightweight user authentications. The scheme based on block chain technologies is called SMUAAKMPs (Secure Multimodal Biometrics-based User Anonymous Authenticated Key Management Protocols). ECDSAs (Elliptic Curve Digital Signature Algorithms) are utilised for devices and GNs, to generate public and private keys. In addition, multimodal biometric verifications are executed using fuzzy commitment approaches. The fuzzy commitment technique is then used to validate the biometric authentication using the segmented face. This work's detailed security analyses use ROR (Real-Or-Random) models where formal security verifications are done by software verification tool AVISPAs (Automated Validation of Internet Security Protocols and Applications). Informal security analyses show that the proposed SMUAAKMPs withstand multiple known attacks. The system also provides improved safety and functionalities at reduced costs in computations and communications when compared to UABKMPs, UAKMPs, and SMBUAKEPs.
Visual Analytics Approach for Crane Anomaly Detection Based on Digital Twin

Anomaly detection of crane operating status is the basis for ensuring its stable operation. The current detection method based on anomaly detection algorithms cannot clearly distinguish normal data from abnormal data. And the high dimensions of the crane operation data and irrelevant dimensions also affect the detection accuracy of anomaly detection algorithms. In this paper, a crane operating status anomaly detection method with visual analytics task flow is proposed. To achieve closed-loop control of cranes, this paper realizes accurate and efficient anomaly detection of crane operation status based on digital twin as the architecture, visual analytics process as the mainline, and anomaly detection algorithms as the basis. In addition, anomaly reasoning is implemented by the expert system. Finally, the accuracy and effectiveness of the method proposed in this paper were confirmed by the contrast experiment.
Data Structure Visualization as an Aid in Collaborative Game Design

This paper presents data structure visualization tools added to the computer framework for collaborative narration creation based on the graph rules hierarchy that was developed and tested in collaboration with students at the authors’ university. The data types taken into consideration include the game world, all objects and the relations between them, visual representation of the actions which can be performed in this world, and the visual schemas of the structure of the storylines possible in this world with these actions set. Patterns emerging from visualization indicate the characteristic of the design.
Asking about Technical Debt: Characteristics and Automatic Identification of Technical Debt Questions on Stack Overflow

Background: Q&A sites allow to study how users reference and request support on technical debt. To date only few studies, focusing on narrow aspects, investigate technical debt on Stack Overflow.
Bayesian Analysis of Bug-Fixing Time using Report Data

Background: Bug-fixing is the crux of software maintenance. It entails tending to heaps of bug reports using limited resources. Using historical data, we can ask questions that contribute to better-informed allocation heuristics. The caveat here is that often there is not enough data to provide a sound response. This issue is especially prominent for young projects. Also, answers may vary from project to project. Consequently, it is impossible to generalize results without assuming a notion of relatedness between projects.
To What Extent Cognitive-Driven Development Improves Code Readability?

Background: Cognitive-Driven Development (CDD) is a coding design technique that aims to reduce developers’ cognitive effort in understanding a given code unit (e.g., a class). By following CDD design practices, it is expected that the coding units to be smaller and, thus, easier to maintain and evolve. However, it is so unknown whether these smaller code units coded using CDD standards are easier to understand. Aims: This work aims to assess how much CDD improves code readability. Method: To achieve this goal, we conducted a two-phase study. We start by inviting professional software developers to vote (and justify their rationale) on the most readable pair of code snippets (from a set of 10 pairs); one of the pairs was coded using CDD practices. We received 133 answers. In the second phase, we applied the state-of-the-art readability model to the 10-pairs of CDD-driven refactorings. Results: We observed some conflicting results. On the one hand, developers perceived that seven (out of 10) CDD-driven refactorings were more readable than their counterparts; for two other CDD-driven refactorings, developers were undecided, while only in one of the CDD-driven refactorings, developers preferred the original code snippet. On the other hand, we noticed that only one CDD-driven refactorings has better performance readability, assessed by state-of-the-art readability models. Conclusions: Our results provide initial evidence that CDD could be an exciting approach for software design.
Analyzing the Relationship between Community and Design Smells in Open-Source Software Projects: An Empirical Study

Background: Software smells reflect the sub-optimal patterns in the software. In a similar way, community smells consider the sub-optimal patterns in the organizational and social structures of software teams. Related work performed empirical studies to identify the relationship between community smells and software smells at the architecture and code levels. However, how community smells relate with design smells is still unknown.
Toward Compact Transformers for End-to-End Object Detection With Decomposed Chain Tensor Structure

DEtection TRansformer (DETR) is a recently proposed method that streamlines the detection pipeline and achieves competitive results against two-stage detectors such as Faster-RCNN. The DETR models get rid of complex anchor generation and post-processing procedures thereby making the detection pipeline more intuitive. However, the numerous redundant parameters in transformers make the computation and storage of the DETR models intensive, which seriously hinder them to be deployed on the resources-constrained devices. In this paper, to obtain a compact end-to-end detection framework, we propose to deeply compress the transformers with low-rank tensor decomposition. The basic idea of our tensor-based compression method is to represent the large-scale weight matrix in one network layer with a chain of low-order matrices. Furthermore, we show that redundant attention heads will hinder the performance of detection transformers. We thus propose a gated multi-head attention (GMHA) module to suppress the redundant attention information by normalizing the attention heads. In GMHA, each attention head has an independent gate to determine the passed attention value, thereby down-weighting the uninformative heads. The accuracy drop of the tensor-compressed DETR models can be mitigated by applying GMHA modules. Lastly, to obtain fully compressed DETR models, a low-bitwidth quantization technique is introduced for further reducing the model storage size. Based on the proposed methods, we can achieve significant parameter and model size reduction while maintaining high detection performance. We conduct extensive experiments on the COCO and PASCAL VOC datasets to validate the effectiveness of our tensor-compressed (tensorized) DETR models. The experimental results on the COCO benchmark show that we can attain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.7\times $ </tex-math></inline-formula> full model compression with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$482\times $ </tex-math></inline-formula> feed forward network (FFN) parameter reduction and only 0.6 points accuracy drop.
Quest_SA: Preprocessing Method for Closed-Ended Questionnaires Using Sentiment Analysis through Polarity

Sentiment analysis is a prominent research topic in natural language processing, with applications in politics, news, education, product review, and other sectors. Especially in the education sector, sentiment analysis can assist educators in finding students’ feelings about a course on time, altering the teaching plan appropriately and timely to improve the quality of education and teaching. For students, the sentiment analysis can identify emotions, academic performance, behaviour, and so on; the primary purpose of this research paper is to analyze students’ emotions, self-esteem, and efficacy based on closed-ended questionnaires. This paper proposes Quest_SA, which uses the sentiment analysis technique to identify students’ emotions based on the answer provided by a closed-ended questionnaire. The polarity value is assigned for each questionnaire scale. The students’ responses are then gathered using a closed-ended questionnaire, and the student’s emotions are classified using a polarity-based method of sentiment analysis. Finally, sentiment scores and emotion variance were used to evaluate the outcomes. According to the sentiment ratings, students have favourable sentiments and emotions such as unhappy, somewhat happy, and happy. The real-world closed-ended questionnaires such as emotional intelligence, Eysenck, personality, self-determination scale, self-efficacy, Rosenberg’s self-esteem, positive and negative affect schedule, and Oxford happiness questionnaires were used to examine the academic performance with the proposed sentiment analysis. This study inferred that the proposed sentiment analysis preprocessing method with polarity scores is as accurate as the standard value calculation.
Quick generation of crosswords using concatenation

We propose two crossword generation methods based on a crossword concatenation, word addition and crossword rotation operation. This can be viewed as an alternative to the method proposed by Bonomo, Lauf and Yampolskiy or Bulitko and Botea, who focus on generating matrices filled with letters and mutating them in order to make them into actual crosswords. The first one uses a combination of first improvement and best improvement local search methods. The choice on which one to use is made using the temperature calculated for a given turn. Second algorithm is a simulated annealing algorithm which uses best improvement search and word removal operation. The crosswords are evaluated using a goal function that includes the amount of intersections in a crossword and the density of letters in the crossword. Unfortunately, both of these solutions, while producing decent results, create puzzles unsolvable for humans in reasonable time. Because of that, we plan on implementing: a better goal function, targeted word removal and targeted word addition. We also plan to switch simulated annealing for a cuckoo search algorithm.