Reviewerly/exHarmony huggingface dataset:
Modelling program verification tools for software engineers

In software engineering, models are used for many different things. In this paper, we focus on program verification, where we use models to reason about the correctness of systems. There are many different types of program verification techniques which provide different correctness guarantees. We investigate the domain of program verification tools, and present a concise megamodel to distinguish these tools. We also present a data set of almost 400 program verification tools. This data set includes the category of verification tool according to our megamodel, practical information such as input/output format, repository links, and more. The categorisation enables software engineers to find suitable tools, investigate similar alternatives and compare them. We also identify trends for each level in our megamodel based on the categorisation. Our data set, publicly available at https://doi.org/10.4121/20347950, can be used by software engineers to enter the world of program verification and find a verification tool based on their requirements.
Soft issues to focus on for ensuring sound communication in software project team

IT departments use project teams to bring key people together to achieve specific goals.Yet many struggle to achieve this effectively because of poor communication withinteams. Research done in this area indicates that management and team leaders haveto focus on specific soft issues to support the effective functioning of software projectteams. In this research project the authors investigated the impact of a large numberof soft issues on sound communication within project teams. Only four of these softissues, namely those of mutual support, cooperation, commitment and a knowledgebase, were found to be of vital importance to ensure that communication within softwareproject teams will remain on a sound basis during the course of such a software project.
A data sharing method for internet of drones based on federated learning

The transmission of massive data between connected drones makes it an important issue to ensure the efficient data sharing and user privacy security. The introduction of federated learning can effectively solve the problem of privacy protection. However, it is difficult to maintain a continuous and stable synchronous communication mechanism in the process of training. There are still problems of data redundancy and low sharing efficiency. Thus, this paper proposes a Multi-level Asynchronous Federated Learning (MAFL) architecture, realizing efficient data sharing in the Internet of Drones (IoD). The MAFL architecture deploys different federated learning training participation strategies for different IoD entities. The drone entity is deployed with the distributed local training by using initiative inquiry mechanism. For the edge entity of IoD, on the one hand, it enhances the performance of drone local training by designing data set delivery scheme; On the other hand, the weighted aggregation training is deployed to improve the convergence speed and accuracy of MAFL. For the central entity of IoD, the global aggregation training is deployed to accelerate the synchronization of MAFL. The simulation analysis shows that the MAFL can support training of different entities with fast convergence and high accuracy in IoD.
Text-based Image Editing for Food Images with CLIP

Recently, the large-scale language-image pre-trained model, such as CLIP, has drawn much attention due to its remarkable ability for various tasks, including classification and image synthesis. The combination of CLIP and GAN can be used for text-based image manipulation and text-based image synthesis.Several models of a combination of CLIP and GAN have been proposed so far. However, their effectiveness in the food image domain has not been examined comprehensively yet. In this paper, we reported the results of the experiments on text-based food image manipulation using VQGAN-CLIP and discussed the possibility of food image manipulation by texts.
Harmonic Intelligence: Towards New Security Policies AI Based

The paper highlights an essential aspect, which is probably not yet thoroughly investigated except by “insiders”. The concept of security as a 360° security, a whole that goes from the public to the private, passing through cyber and artificial intelligence, looking at aspects that go from the national to the local level; therefore, we could say macro-security and micro-security, where several elements work together towards the same objective.
Instructions for COVID-19 self-tests

Abstract This paper summarises a cross-disciplinary project that explored ways of making instructions, funded as part of the UK COVID-19 rapid-response initiative. The project explored ways of making instructions for COVID-19 Lateral Flow Tests easy for lay people to use. Our method comprised rapid design decision making, where we used existing research, good practice in information design and consultation with diagnostic experts as part of the design process. Iterative review by a panel of users informed the development of prototype instructions: small studies investigated user preference for diagrams, and gathered feedback on the graphic articulation of the procedural steps involved in carrying out the test.
Recommendation CM/REC(2017)5 of the Council of Europe and an Analysis of eVoting Protocols

Electronic voting via the Internet holds a number of promises, particularly in relation to citizens abroad and it may also replace postal voting, which is subject to postal delays and insecure lines of communication. This paper conducts an in-depth analysis of two prominent eVoting protocols, namely the Token protocol and the Envelope protocol explaining their strengths and weaknesses in relation to the Recommendations of the Council of Europe CM/Rec(2017)5. The findings show that Envelope protocols do not fulfil the requirements of the Recommendations, whereas Token protocols can fulfil the requirements, providing certain technical provisions are met.
Virtual universes: the various impacts of conspicuous alternative realities

The so-called post-postmodern cyber era is spectacularly looming upon users of versatile digital communication platforms and omnipresent virtual reality dimensions. The ‘brave new times’ have also resulted in the rising importance of various computerized applications aiming for spreading mass persuasion, disinformation, propaganda, and fake news, primarily targeting users belonging to two distinguished age groups: teenagers, young adult and senior citizens. The pandemic and the war in East Europe have also demonstrated the enormous power and influence of informational warfare and cyber security operations, which may threat even the normal operation of democratic societies and jeopardize the right of millions for reliable and authentic information resources and knowledge. The article presents a designated educational module on media literacy with the objective to prepare both students and educators for the various challenges of the cyber era. The paper also highlights upon the most ardent threats, various challenges posed by social media sites and all the new phenomena endemic of these virtual reality platforms.
Opinion Spamming: Analyzing the Accuracy of Online Detection Tools

Over the past few years, we have seen a rise in spoofed content across the Internet, including fake news and fake reviews. Fake news is false information that imitates real news intending to damage a reputation or make financial gains. Similarly, fake online reviews, commonly referred to as opinion spamming, have impacted customers and retailers. The main problem is that there is no efficient way to distinguish between genuine and fake reviews; even humans find it difficult to do so. This paper compares two tools used to detect opinion spamming and analyzes their results. We have compiled our research using 100 reviews from travelers who evaluated Istanbul's top five best-value hotels.
The Holistic Approach to Cybersecurity in Academia

Academic institutions are increasingly implementing digital services and new technologies, for a modern and accessible educational environment. But the virtual environment creates unknown vulnerabilities that need to be addressed properly. The holistic approach to cybersecurity in academia as a system has major benefits. But the main question is how this can be done in practice, as long as universities are heterogeneous complex environments. The proposed solution can be used to implement a security system in academia, is compliant with the security standard ISO 27001 and developed by applying the scientific method of Security Requirement Engineering.
Harris hawks optimization based on global cross-variation and tent mapping

Harris hawks optimization (HHO) is a new meta-heuristic algorithm that builds a model by imitating the predation process of Harris hawks. In order to solve the problems of poor convergence speed caused by uniform choice position update formula in the exploration stage of basic HHO and falling into local optimization caused by insufficient population richness in the later stage of the algorithm, a Harris hawks optimization based on global cross-variation and tent mapping (CRTHHO) is proposed in this paper. Firstly, the tent mapping is introduced in the exploration stage to optimize random parameter q to speed up the convergence in the early stage. Secondly, the crossover mutation operator is introduced to cross and mutate the global optimal position in each iteration process. The greedy strategy is used to select, which prevents the algorithm from falling into local optimal because of skipping the optimal solution and improves the convergence accuracy of the algorithm. In order to investigate the performance of CRTHHO, experiments are carried out on ten benchmark functions and the CEC2017 test set. Experimental results show that the CRTHHO algorithm performs better than the HHO algorithm and is competitive with five advanced meta-heuristic algorithms.
Characterizations and constructions of n-to-1 mappings over finite fields

n-to-1 mappings have wide applications in many areas, especially in cryptography, finite geometry, coding theory and combinatorial design. In this paper, many classes of n-to-1 mappings over finite fields are studied. First, we provide a characterization of general n-to-1 mappings over Fpm by means of the Walsh transform. Then, we completely determine 3-to-1 polynomials with degree no more than 4 over Fpm. Furthermore, we obtain an AGW-like criterion for characterizing some close relationship between the n-to-1 property of a mapping over finite set A and that of another mapping over a subset of A. Finally, we apply the AGW-like criterion into several forms of polynomials and obtain some explicit n-to-1 mappings. Especially, three explicit constructions of the form xrh(xs) from the cyclotomic perspective, and several classes of n-to-1 mappings of the form g(xqk−x+δ)+cx are provided.
Comparing Feature Importance and Rule Extraction for Interpretability on Text Data

Complex machine learning algorithms are used more and more often in critical tasks involving text data, leading to the development of interpretability methods. Among local methods, two families have emerged: those computing importance scores for each feature and those extracting simple logical rules. In this paper we show that using different methods can lead to unexpectedly different explanations, even when applied to simple models for which we would expect qualitative coincidence. To quantify this effect, we propose a new approach to compare explanations produced by different methods.
Did Mary Shelley write<i>Frankenstein</i>? A stylometric analysis

Abstract The novel Frankenstein was published anonymously in 1818 and was first credited to Mary Shelley in a French translation of 1821. Since its publication, several claims—both contemporaneous and recent—have been made suggesting that Frankenstein was written by Mary’s husband, Percy Bysshe Shelley. We review the background of this controversy and then apply modern techniques from computational stylometry to determine who the true author is. Based on our analysis, we find extremely substantial evidence that Mary Shelley is indeed the true author of Frankenstein, and it is very improbable that Percy Shelley played a heavy role in composing the text. While our finding confirms mainstream scholarly opinion regarding Frankenstein, our analysis is the first application of stylometric techniques to this question and provides strong objective grounds for favouring Mary Shelley by freeing the question from some of the politics which have traditionally accompanied it.
Spatial Features Enhancement on Facial Landmarks for Face Detection

Image processing focuses on improving image color quality. According to our research, low-pass and high-pass filters boost color images for face detection. We also chosen 621,107 pictures of 936 people, scaled them to 160 × 160 pixels, and modified the color format from RGB to YUV or BGR. The composition of BRG, YUV, and RGB with a low-pass or high-pass filter improves image quality. This study proposes six approaches, the best of which yields 95.12% accuracy compared to the original’s 94.15%. Next, measure accuracy using YouTube Faces (YTF) and Dlib’s face detector. In addition, the peak signal-to-noise ratio (PSNR) and the mean squared error (MSE) are used to measure image noise.
Aligning Small Datasets Using Domain Adversarial Learning: Applications in Automated in Vivo Oral Cancer Diagnosis

Deep learning approaches for medical image analysis are limited by small data set size due to factors such as patient privacy and difficulties in obtaining expert labelling for each image. In medical imaging system development pipelines, phases for system development and classification algorithms often overlap with data collection, creating small disjoint data sets collected at numerous locations with differing protocols. In this setting, merging data from different data collection centers increases the amount of training data. However, a direct combination of datasets will likely fail due to domain shifts between imaging centers. In contrast to previous approaches that focus on a single data set, we add a domain adaptation module to a neural network and train using multiple data sets. Our approach encourages domain invariance between two multispectral autofluorescence imaging (maFLIM) data sets of in vivo oral lesions collected with an imaging system currently in development. The two data sets have differences in the sub-populations imaged and in the calibration procedures used during data collection. We mitigate these differences using a gradient reversal layer and domain classifier. Our final model trained with two data sets substantially increases performance, including a significant increase in specificity. We also achieve a significant increase in average performance over the best baseline model train with two domains (p = 0.0341). Our approach lays the foundation for faster development of computer-aided diagnostic systems and presents a feasible approach for creating a robust classifier that aligns images from multiple data centers in the presence of domain shifts.
Fast and accurate light field saliency detection through deep encoding

Light field saliency detection – important due to utility in many vision tasks – still lacks speed and can improve in accuracy. Due to the formulation of the saliency detection problem in light fields as a segmentation task or a memorizing task, existing approaches consume unnecessarily large amounts of computational resources for training, and have longer execution times for testing. We solve this by aggressively reducing the large light field images to a much smaller three-channel feature map appropriate for saliency detection using an RGB image saliency detector with attention mechanisms. We achieve this by introducing a novel convolutional neural network based features extraction and encoding module. Our saliency detector takes 0.4 s to process a light field of size 9×9×512×375 in a CPU and is significantly faster than state-of-the-art light field saliency detectors, with better or comparable accuracy. Furthermore, model size of our architecture is significantly lower compared to state-of-the-art light field saliency detectors. Our work shows that extracting features from light fields through aggressive size reduction and the attention mechanism results in a faster and accurate light field saliency detector leading to near real-time light field processing.
Robustness Analysis on Graph Neural Networks Model for Event Detection

Event Detection (ED), which aims to identify trigger words from the given text and classify them into corresponding event types, is an important task in Natural Language Processing (NLP); it contributes to several downstream tasks and is beneficial for many real-world applications. Most of the current SOTA (state-of-the-art) models for ED are based on Graph Neural Networks (GNN). However, a few studies focus on the issue of GNN-based ED models’ robustness towards text adversarial attacks, which is a challenge in practical applications of EDs that needs to be solved urgently. In this paper, we first propose a robustness analysis framework for an ED model. Using this framework, we can evaluate the robustness of the ED model with various adversarial data. To improve the robustness of the GNN-based ED model, we propose a new multi-order distance representation method and an edge representation update method based on attention weights, then design an innovative model named A-MDL-EEGCN. Extensive experiments illustrate that the proposed model can achieve better performance than other models both on original data and various adversarial data. The comprehensive robustness analysis according to experimental results in this paper brings new insights into the evaluation and design of a robust ED model.
Policing the Pandemic

The COVID-19 pandemic posed an unprecedented three-fold challenge for the policing system. They were to enforce general lockdown while facilitating emergency movement, to ensure that people under quarantine and those under home isolation abided by all rules and to safeguard the frontline police staff from infection. The immediate job was to enforce the complete lockdown. Police staff was briefed on essential duty movements and on dealing with violators effectively. Innovative approaches like ‘e-naka checking’ for recording the movement of every vehicle for contact tracing and ‘Pharma Sentinel’, for intelligence on influenza-like illnesses from pharmacies were taken. An innovative venture called ‘COVID Sentinel Project’ roped in volunteers from the community. Volunteers would call people under quarantine every day and monitor their location, explain the dos and don’ts in vernacular language and offer psychological assistance. The project additionally led to some research output based on a feedback questionnaire. This was a successful example of governmental organizations (GO), non-governmental organizations (NGO) and technology convergence. This unique project won several national accolades, awards and attention. Moreover, for safeguarding Police personnel, extensive training, and medical check-ups were done through ‘COVID Cop Care’. This chapter brings into focus the measures taken up to fight and manage the virus from the perspective of the police force.
41 The interaction of gender and culture in the communication of emotion

Communicating emotions is a transactional process that can be understood by examining the practices of the expressor and the practices of the receiver (e.g., emotion perception or understanding). Emotion expression, perception, and understanding vary as a function of gender and culture, and importantly, the interplay of gender and culture, as these emotional processes are bound to the norms and expectations within sociocultural contexts. Importantly, sociocultural beliefs and values about emotions are transmitted via socialization starting early in life, affecting one’s emotional behaviors over the life course. Therefore, we examine these constructs taking a developmental approach to the socialization of emotion through gender and culture norms that are shaped by parent-child interactions and other interpersonal interactions. We also discuss the ways in which emotion communication varies by gender and culture in online communication (e.g., emoticon usage, online groups), given the increasing importance and prevalence of these platforms. Finally, we provide recommendations for future research, both conceptually and methodologically.
Prior Knowledge Constrained Adaptive Graph Framework for Partial Label Learning

Partial label learning (PLL) aims to learn a robust multi-class classifier from the ambiguous data, where each instance is given with several candidate labels, among which only one label is real. Most existing methods usually cope with such problem by utilizing a feature similarity graph to conduct label disambiguation. However, these methods construct the feature graph by only employing original features, while the influences of latent outliers and the contributions of label space are regrettably ignored. To tackle these issues, in this article, we propose a P rior Kn O wledge Cons T rained A daptive G raph Fram E work ( POTAGE ) for partial label learning, which utilizes an adaptive graph fused with label information to accurately describe the instance relationship and guide the desired model training. Compared with the feature-induced fixed graph, the adaptive graph is deemed to be more robust and accurate to reveal the intrinsic manifold structure within the data, and the embedding label information is expected to effectively alleviate the label ambiguities and enlarge the gap of label confidences between two instances from different classes. Extensive experiments demonstrate that POTAGE achieves state-of-the-art performance.
The Perspectives of EFL Students at Yarmouk University towards Using YouTube in Learning and Understanding English during Covid-19 Pandemic

The Covid-19 epidemic has forced several nations to adjust to new conditions in a variety of fields, including education. Jordan has made the decision to go from in-person instruction to online classes utilizing a variety of programs, including WhatsApp, Teams, and Zoom. During the pandemic, students used YouTube to learn and comprehend English. This study examines how watching YouTube videos affects students' English language proficiency and contrasts it with lectures delivered by professors through Zoom and WhatsApp. Additionally, it illustrates the challenges of using YouTube videos for online learning as well as possible solutions. To achieve the objectives of the study, the researchers use qualitative and quantitative method to be applied on 100 fourth-year college students from the department of English language and literature, College of Education, Yarmouk University. The researchers conclude that students consider YouTube as a learning tool as they have motivation for using YouTube videos to understand academic materials to the extent that they believe that YouTube videos help them to improve their performance and language skills more than the lectures given by teachers on Zoom and WhatsApp. The researchers find three Barriers of using YouTube videos in learning and understanding English identified from the students of English as a Foreign Language (EFL) at Yarmouk University during Covid-19 pandemic. The three barriers are lack of interpersonal contact, technological barriers and physical barriers. The researchers also suggest approaches to overcome those obstacles. A number of recommendations were also given in this publication in light of the study's findings.