Reviewerly/exHarmony huggingface dataset:
Information needs and presentation in agile software development

Agile software companies applying the DevOps approach require collaboration and information sharing between practitioners in various roles to produce value. Adopting new development practices affects how practitioners collaborate, requiring companies to form a closer connection between business strategy and software development. However, the types of information management, sales, and development needed to plan, evaluate features, and reconcile their expectations with each other need to be clarified. To support practitioners in collaborating and realizing changes to their practices, we investigated what information is needed and how it should be represented to support different stakeholders in their tasks. Compared to earlier research, we adopted a holistic approach—by including practitioners throughout the development process—to better understand the information needs from a broader viewpoint. We conducted six workshops and 12 semi-structured interviews at three Finnish small and medium-sized enterprises from different software domains. Thematic analysis was used to identify information-related issues and information and visualization needs for daily tasks. Three themes were constructed as the result of our analysis. Visual information representation catalyzes stakeholder discussion, and supporting information exchange between stakeholder groups is vital for efficient collaboration in software product development. Additionally, user-centric data collection practices are needed to understand how software products are used and to support practitioners’ daily information needs. We also found that a passive way of representing information, such as a dashboard that would disturb practitioners only when attention is needed, was preferred for daily information needs. The software engineering community should consider reviewing the information needs of practitioners from a more holistic view to better understand how tooling support can benefit information exchange between stakeholder groups when making product development decisions and how those tools should be built to accommodate different stakeholder views.
DLRegion: Coverage-guided fuzz testing of deep neural networks with region-based neuron selection strategies

Deep Learning (DL) systems have been increasingly applied to safety-critical scenarios, such as autonomous driving or medical diagnoses. However, it often exhibits erroneous behaviors that can cause serious consequences. Therefore, it is important to ensure the quality of the DL systems through systematically testing. Recent research works have proposed many testing techniques for deep neural networks (DNNs), among which the coverage-guided fuzz testing has achieved remarkable results. The neuron selection strategy is the key ingredient of the technique. It can affect the technique's effectiveness. However, current neuron selection strategies did not utilize the output distribution of each neuron on the training data, which can characterize the behavior of the neuron. This paper introduces DLRegion, a coverage-guided fuzz testing technique of DNNs with region-based neuron selection strategies. DLRegion can expose erroneous behaviors of DNNs while maximizing coverage. DLRegion first incorporates a seed selection strategy based on the level of confidence in the classification of the inputs to select seed inputs to mutate. DLRegion also proposes region-based neuron selection strategies, which utilize the region where the output value of each neuron is in its output distribution to select valuable neurons to activate for covering more internal states. Empirical studies on three well-known datasets, guided by five existing criteria demonstrate that: (1) the proposed seed selection strategy very effectively improves coverage and defect detection; (2) selecting neurons in different regions has obvious differences in achieving model coverage and detecting defects; (3) DLRegion outperforms other existing techniques by coverage under different criteria and effectively identifies the quantity and diversity of defects; (4) the effectiveness of DLRegion in improving the model robustness. DLRegion not only can cover more internal logic of the DNNs but also can effectively detect DNNs' erroneous behaviors. Moreover, DLRegion can improve the robustness of the model.
Scored Anonymous Credentials

Securely maintaining “credits” of users judging their behavior in past authenticated sessions is vital to encourage user participation, but doing it over anonymous credentials is non-trivial, especially when users would avoid claiming negative credit and escape from blocklisting. Prevalent designs impose an authentication cost linear in the blocklist size or a stringent requirement of sequential and timely judgment of each session without retrospective adjustment, as a single unjudged session curbs the authentication of all users. We propose scored anonymous credentials, a new design storing a number of active sessions with volatile scores downgradable before finalized. Sessions can be judged in any order and at varying times without affecting all users. Any backlog of unjudged sessions only affects the users behind them. We achieve efficiency and flexibility using verifiable shuffle, which is hardly used in existing anonymous blocklisting/reputation enforcement systems.
Metaverse changing realm of the business world: a bibliometric snapshot

Purpose Digital disruption has posed challenges to managers. Metaverse is one of the fastest-growing digital transformations (DTs) impacting business across the globe. There needs to be more literature on the academic prospect relating to metaverse. This article aims to analyze and evaluate the publications to highlight themes, opportunities and contemporary challenges of metaverse in business management to get insights about the challenges posed to managers through such transformation. Design/methodology/approach Over 74 articles from 2006 to 2022 were collected using the Scopus database. These were further analyzed to understand how previous research captures metaverse in the domain of business management and the amount to which has been studied in terms of publication productivity, country productivity, affiliation productivity and author productivity, Top-cited documents, keywords analysis, network visualization of keywords and bibliometric coupling. The authors also gathered literature related to metaverse in the field of business management and its uses for managers. Findings The authors' findings reveal that metaverse in business management has gained momentum in publications since 2022. Approximately 68% of total publications and 56% of total citations were from 2022. USA is the flagbearer of metaverse around the globe and the University of Georgia is leading in affiliation productivity. Network analysis through Vosviewer shows that keywords metaverse and virtual reality (VR) have the highest co-occurrences and link strength. Originality/value This article is relevant to those interested in how the world of virtual workspace is making a paradigm shift in management development. This study aims to review previous literary works through bibliometrics and visualization approaches comprehensively. Metaverse is changing the schema of the business world in terms of various strategies that this article addressed. Such studies have not been previously carried out.
Structure-guided virtual restoration for defective silk cultural relics

The digital restoration of damaged silk relics is a type of blind restoration problem that relies on limited prior information. Existing image restoration techniques face significant difficulties in repainting missing patterns of both structural and content. Silk cultural relics are prone to decay, mold, moth, fading, pollution, and adhesion due to long-term exposure to external factors such as temperature, humidity, and microorganisms. Thus, restoring cultural relic patterns is crucial for archeological and design industries. Virtual restoration, based on image inpainting technology, can tackle this problem. However, existing inpainting algorithms are not effective in defective areas lacking a structural trend and only relying on prior information. Moreover, the unique and irregular patterns of silk cultural relics pose a further challenge. To address these issues, we propose a structure-guided virtual restoration method. Concretely, to maintain global image consistency, we introduce an adaptive curve fitting algorithm to reconstruct missing structural lines. We then establish a new priority function to improve the filling order of patches to be repaired, and an adaptive selection of the sample block size is adopted based on the sparsity of the structure. Our method outperforms existing techniques by reconstructing the structural lines according to the pattern design, thus guiding image inpainting to avoid erroneous fillings and block effects. Results show that the restored images achieved an average SSIM of 0.977 and an average PSNR of 39.16. The proposed method for virtual restoration, utilizing curve fitting and image inpainting guided by the artifact's structure, has demonstrated significant improvements in the preservation of structure continuity and faithful reproduction of original patterns on the silk cultural relics.
A railway intrusion detection method based on decomposition and semi-supervised learning for accident protection

In recent years, video surveillance has become increasingly popular in railway intrusion detection. However, it is still quite challenging to detect the intruded objects efficiently and accurately because: (a) The backgrounds of video frames generated by the fixed cameras are similar and only few intrusive frames are available, resulting in a lack of diversity among video frames, and further leading to over fitting of the detection models during training; (b) The intrusion of small targets or targets far from the location of camera exhibits sparsity relative to the wide monitoring view of the camera, which challenges the detection of such targets in a complex background; (c) The extreme imbalance between non-intrusive frames and intrusive frames, as well as a large number of unlabeled frames, hinder the effective training of the detection model and weaken its capacity of generalization. To tackle the above issue, this article develops an effective intrusion detection method by combining low-rank and sparse decomposition (LRSD) and Semi-supervised Support Vector Domain Description (Semi-SVDD). Firstly, LRSD is used to decompose the monitored video into a background and a foreground. Then, based on the semantic segmentation method, we extract the mask of the track region in the decomposed background, which is used to mask the foreground. Next, by using both the labeled and unlabeled frames of the masked foreground, Semi-SVDD is established for the intrusion detection. Numerical results show that the removal of background interference and the combination of the labeled and unlabeled information help to improve the performance of the proposed method, and thus superior to benchmark methods.
Plan and generate: Explicit and implicit variational augmentation for multi-document summarization of scientific articles

Multi-Document Summarization of Scientific articles (MDSS) is a challenging task that aims to generate concise and informative summaries for multiple scientific articles on a particular topic. However, despite recent advances in abstractive models for MDSS, grammatical correctness and contextual coherence remain challenging issues. In this paper, we introduce EDITSum, a novel abstractive MDSS model that leverages sentence-level planning to guide summary generation. Our model incorporates neural topic model information as explicit guidance and sequential latent variables information as implicit guidance under a variational framework. We propose a hierarchical decoding strategy that generates the sentence-level planning by a sentence decoder and then generates the final summary conditioned on the planning by a word decoder. Experimental results show that our model outperforms previous state-of-the-art models by a significant margin on ROUGE-1 and ROUGE-L metrics. Ablation studies demonstrate the effectiveness of the individual modules proposed in our model, and human evaluations provide strong evidence that our model generates more coherent and error-free summaries. Our work highlights the importance of high-level planning in addressing intra-sentence errors and inter-sentence incoherence issues in MDSS.
Adversarial Perturbation Elimination with GAN Based Defense in Continuous-Variable Quantum Key Distribution Systems

Machine learning is being applied to continuous-variable quantum key distribution (CVQKD) systems as defense countermeasures for attack classification. However, recent studies have demonstrated that most of these detection networks are not immune to adversarial attacks. In this paper, we propose to implement typical adversarial attack strategies against the CVQKD system and introduce a generalized defense scheme. Adversarial attacks essentially generate data points located near decision boundaries that are linearized based on iterations of the classifier to lead to misclassification. Using the DeepFool attack as an example, we test it on four different CVQKD detection networks and demonstrate that an adversarial attack can fool most CVQKD detection networks. To solve this problem, we propose an improved adversarial perturbation elimination with a generative adversarial network (APE-GAN) scheme to generate samples with similar distribution to the original samples to defend against adversarial attacks. The results show that the proposed scheme can effectively defend against adversarial attacks including DeepFool and other adversarial attacks and significantly improve the security of communication systems.
Reactive Message Passing for Scalable Bayesian Inference

We introduce reactive message passing (RMP) as a framework for executing schedule-free, scalable, and, potentially, more robust message passing-based inference in a factor graph representation of a probabilistic model. RMP is based on the reactive programming style, which only describes how nodes in a factor graph react to changes in connected nodes. We recognize reactive programming as the suitable programming abstraction for message passing-based methods that improve robustness, scalability, and execution time of the inference procedure and are useful for all future implementations of message passing methods. We also present our own implementation ReactiveMP.jl, which is a Julia package for realizing RMP through minimization of a constrained Bethe free energy. By user-defined specification of local form and factorization constraints on the variational posterior distribution, ReactiveMP.jl executes hybrid message passing algorithms including belief propagation, variational message passing, expectation propagation, and expectation maximization update rules. Experimental results demonstrate the great performance of our RMP implementation compared to other Julia packages for Bayesian inference across a range of probabilistic models. In particular, we show that the RMP framework is capable of performing Bayesian inference for large-scale probabilistic state-space models with hundreds of thousands of random variables on a standard laptop computer.
Book Publishing in Russia in 2022

The authors provide the main statistics of the Russian book publishing in 2022, analyzing the output indicators of printed publications and trends in the publishing industry in Russia. 13 tables with comments present the number of titles of books and pamphlets , their print runs , main publishers and the most popular fiction authors for adults and children .
Learning on the Move: A Pedagogical Framework for State-of-the-Art Mobile Learning

Efforts in both technology and pedagogy concentrate on four central axes that together define the educational landscape of the future. Mobility, interactivity, artificial intelligence, technological learning tools like games, and augmented reality all fall under this category. Combining them requires creating a mobile-interactive model that takes into consideration the learner's availability and their convenient times. Technology is already being utilized in education, albeit various forms of it are used in different settings. Because of this, it is critical to integrate and combine them into pedagogical models that place a premium on the students' education. This article analyzes many kinds of technology and proposes a unified model that might serve as a basis for classroom education. In the end, it is emphasized how important it is to have intelligent tutoring systems in order to make tutoring broadly available, as well as how important it is to conduct technological experiments and apply the findings to 'teaching–learning models' that make use of multiple interaction patterns.
Study of NFT Marketplace in the Metaverse

The metaverse has opened up the next door in digital evolution. It has the ability to expand the domain of online services by providing a life-like online experience. The digital experience is powered by blockchain technology, tokenomics, and decentralization. The non-fungible token (NFT) is a unique token on the blockchain that is traded, bought, and sold on various NFT marketplaces. NFT marketplaces can also be curated in the metaverse, providing a more interactive experience. Key concepts and principles regarding metaverse, blockchain, decentralized applications, and user experience have been studied, and a recipe to understand and create a metaverse NFT marketplace has been presented in this paper.
A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking

This article addresses the telecommunications industry’s priority of ensuring information security during the transition to next-generation networks. It proposes an image encryption system that combines watermarking techniques and a discrete fractional sine chaotic map. The authors also incorporate the principles of blockchain to enhance the security of transmitted and received image data. The proposed system utilizes a newly developed sine chaotic map with a fractional difference operator, exhibiting long-term chaotic dynamics. The complexity of this map is demonstrated by comparing it with three other fractional chaotic maps from existing literature, using bifurcation diagrams and the largest Lyapunov exponent. The authors also show the map’s sensitivity to changes in initial conditions through time-series diagrams. To encrypt images, the authors suggest a method involving watermarking of two secret images and encryption based on blockchain technology. The cover image is watermarked with the two hidden images using discrete wavelet transformations. Then, the image pixels undergo diffusion using a chaotic matrix generated from the discrete fractional sine chaotic map. This encryption process aims to protect the image data and make it resistant to unauthorized access. To evaluate the algorithm, the authors perform statistical analysis and critical sensitivity analysis to examine its characteristics. They also analyse different attacks to assess the algorithm’s ability to resist such threats and maintain image quality after decryption. The results demonstrate that the proposed algorithm effectively defends against attacks and ensures image security.
Shifted Window Vision Transformer for Blood Cell Classification

Blood cells play an important role in the metabolism of the human body, and the status of blood cells can be used for clinical diagnoses, such as the ratio of different blood cells. Therefore, blood cell classification is a primary task, which requires much time for manual analysis. The recent advances in computer vision can be beneficial to free doctors from tedious tasks. In this paper, a novel automated blood cell classification model based on the shifted window vision transformer (SW-ViT) is proposed. The SW-ViT architecture is firstly pre-trained on the ImageNet dataset and fine-tuned on the blood cell images for classification. Two transfer strategies are employed to generate better classification results. One is to fine-tune the entire SW-ViT, and the other is to only fine-tune the linear output layer of the SW-ViT while all the other parameters are frozen. A public dataset named BCCD_Dataset (Blood Cell Count and Detection) is utilized in the experiments. The results show that the SW-ViT outperforms several state-of-the-art methods in terms of classification accuracy. The proposed SW-ViT can be applied in daily clinical diagnosis.
Smoothness and Monotonicity Constraints for Neural Networks Using ICEnet

Deep neural networks have become an important tool for use in actuarial tasks, due to the significant gains in accuracy provided by these techniques compared to traditional methods, but also due to the close connection of these models to the Generalized Linear Models (GLMs) currently used in industry. Whereas constraining GLM parameters relating to insurance risk factors to be smooth or exhibit monotonicity is trivial, methods to incorporate such constraints into deep neural networks have not yet been developed. This is a barrier for the adoption of neural networks in insurance practice since actuaries often impose these constraints for commercial or statistical reasons. In this work, we present a novel method for enforcing constraints within deep neural network models, and we show how these models can be trained. Moreover, we provide example applications using real-world datasets. We call our proposed method ICEnet to emphasize the close link of our proposal to the individual conditional expectation (ICE) model interpretability technique.
Towards semi-supervised ensemble clustering using a new membership similarity measure

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HFTL: Hierarchical Federated Transfer Learning for Secure and Efficient Fault Classification in Additive Manufacturing

The technology advancement is supported by additive manufacturing industries, especially 3D printing companies, that enable fast object prototyping and development in Industry 4.0. As 3D printed products are highly adopted in various fields, the final printed product must fulfill precise requirements without any defects. Therefore, an efficient framework that simultaneously learns and detects faults during the printing process is required. Unfortunately, most state-of-the-art studies utilize a centralized approach, which is inefficient for continuous model updates. This article presents a hierarchical federated transfer learning (HFTL) framework that employs edge, fog, and cloud concepts to enable an efficient model updating mechanism. The proposed HFTL uses a well-known DL model with a new classifier to significantly reduce the distributed training process while providing high detection and classification performance. Additionally, the fog server comprehensively exploits the data collection from several edge servers and performs local training. The extensive simulation results indicate that the performance of the proposed HFTL is more efficient, with 24% faster training time, effectively detects flaws in 3D printing products with 45% accuracy, and has a 59% F1-score improvement in non-IID data distribution compared to traditional FL architecture.