Reviewerly/exHarmony huggingface dataset:
Software Fault Prediction for Imbalanced Data: A Survey on Recent Developments

The method of recognizing faults in a software system is acknowledged as software fault prediction. Software faults predicted in prior stages help in the management of resources and time required during software testing and maintenance. The identified software module can be fixed ahead of time, saving time and money near the end of the software development process. Over the years, various supervised machine learning-based techniques for fault prediction have been suggested. These models’ accuracy is based on the training datasets. The models are created and trained using a labeled dataset consisting of multiple independent variables like lines of codes, the complexity of the software, the size of the software, etc., and a dependent binary variable, either true or false. But the fault dataset may have some concerns like a class overlapping problem, class imbalance problem, null values, etc. Recent research in software fault prediction focuses on data quality. An imbalanced dataset is one in which one of the class data is present in the majority and another class data is present in the minority. Models built using imbalanced datasets are biased which results in inaccurate predictions. Therefore, balancing the dataset is important. In this paper, the most recent software fault prediction algorithms, which focus on class imbalance issues are discussed. A comparative presentation is presented in this paper, which would benefit the scholar in selecting the best techniques of fault prediction based on different datasets and algorithms. According to the survey, SMOTE is the most commonly used data sampling technique for dealing with data quality issues.
Proof of Transaction (PoTx) Based Traceability System for an Agriculture Supply Chain

Tracing the origin of the product in the agriculture supply chain offers food safety, identifying the root cause of the food hazard if any, information connectivity between the participating stakeholders and to achieve customer trust. Blockchain is a distributed ledger technology whose transactions can be recorded in a decentralised and immutable ledger. Several applications were developed in the last few years for blockchain based - traceability systems. Most of the work concentrated on information retrieval and system scalability. Still, promising research has not been seen for a blockchain traceability system with user identification methods. This work presents a blockchain system with user identification and access control mechanisms to fill the gap for each supply chain participant. In addition, the system also proposes a novel Proof of Transaction (PoTx) consensus algorithm designed to achieve scalability with reduced communication overhead and computation power. A novel PoTx algorithm is developed by choosing a random validator from a consensus group based on the transaction count to enforce the fault-tolerance of the system. The proposed system with user identification method allows to prevent and identify product adulteration while tracing the agriculture product.
A multi-strategy enhanced African vultures optimization algorithm for global optimization problems

Abstract The African vultures optimization algorithm (AVOA) is a recently proposed metaheuristic inspired by the African vultures’ behaviors. Though the basic AVOA performs very well for most optimization problems, it still suffers from the shortcomings of slow convergence rate and local optimal stagnation when solving complex optimization tasks. Therefore, this study introduces a modified version named enhanced AVOA (EAVOA). The proposed EAVOA uses three different techniques namely representative vulture selection strategy, rotating flight strategy, and selecting accumulation mechanism, respectively, which are developed based on the basic AVOA. The representative vulture selection strategy strikes a good balance between global and local searches. The rotating flight strategy and selecting accumulation mechanism are utilized to improve the quality of the solution. The performance of EAVOA is validated on 23 classical benchmark functions with various types and dimensions and compared to those of nine other state-of-the-art methods according to numerical results and convergence curves. In addition, three real-world engineering design optimization problems are adopted to evaluate the practical applicability of EAVOA. Furthermore, EAVOA has been applied to classify multi-layer perception using XOR and cancer datasets. The experimental results clearly show that the EAVOA has superiority over other methods.
NLBSE'22 tool competition

We report on the organization and results of the first edition of the Tool Competition from the International Workshop on Natural Language-based Software Engineering (NLBSE'22). This year, five teams submitted multiple classification models to automatically classify issue reports as bugs, enhancements, or questions. Most of them are based on BERT (Bidirectional Encoder Representations from Transformers) and were fine-tuned and evaluated on a benchmark dataset of 800k issue reports. The goal of the competition was to improve the classification performance of a baseline model based on fastText. This report provides details of the competition, including its rules, the teams and contestant models, and the ranking of models based on their average classification performance across the issue types.
Accounting for digital currencies

The purpose of this paper is twofold: (i) to investigate some of the main issues surrounding the classification of digital currencies, and (ii) to identify the accounting practices and standards tied to digital currencies. This paper discusses two different types of digital currencies, including: central bank digital currencies (CBDCs) and privately issued cryptocurrencies such as Bitcoin. The findings of this study suggest that current accounting standards do not precisely cover the accounting treatment of digital currencies, even though the estimated value of market capitalisation of cryptocurrency in 2022 was USD 200 billion. This conceptual paper identifies the imminent need for an accounting standard to provide guidance on the identification, classification, measurement, and presentation of digital currencies. In the interim, existing accounting standards can be amended to incorporate digital currencies to avoid inconsistent global accounting approaches.
Inclusive AR/VR: accessibility barriers for immersive technologies

Augmented and virtual reality (AR/VR) hold significant potential to transform how we communicate, collaborate, and interact with others. However, there has been a lack of work to date investigating accessibility barriers in relation to immersive technologies for people with disabilities. To address current gaps in knowledge, we led two multidisciplinary Sandpits with key stakeholders (including academic researchers, AR/VR industry specialists, people with lived experience of disability, assistive technologists, and representatives from national charities and special needs colleges) to collaboratively explore and identify existing challenges with AR and VR experiences. We present key themes that emerged from Sandpit activities and map out the interaction barriers identified across a spectrum of impairments (including physical, cognitive, visual, and auditory disabilities). We conclude with recommendations for future work addressing the challenges highlighted to support the development of more inclusive AR and VR experiences.
Finding the best learning to rank algorithms for effort-aware defect prediction

Effort-Aware Defect Prediction (EADP) ranks software modules or changes based on their predicted number of defects (i.e., considering modules or changes as effort) or defect density (i.e., considering LOC as effort) by using learning to rank algorithms. Ranking instability refers to the inconsistent conclusions produced by existing empirical studies of EADP. The major reason is the poor experimental design, such as comparison of few learning to rank algorithms, the use of small number of datasets or datasets without indicating numbers of defects, and evaluation with inappropriate or few metrics. To find a stable ranking of learning to rank algorithms to investigate the best ones for EADP, We examine the practical effects of 34 algorithms on 49 datasets for EADP. We measure the performance of these algorithms using 7 module-based and 7 LOC-based metrics and run experiments under cross-release and cross-project settings, respectively. Finally, we obtain the ranking of these algorithms by performing the Scott-Knott ESD test. When module is used as effort, random forest regression performs the best under cross-release setting, and linear regression performs the best under cross-project setting among the learning to rank algorithms; (2) when LOC is used as effort, LTR-linear (Learning-to-Rank with the linear model) performs the best under cross-release setting, and Ranking SVM performs the best under cross-project setting. This comprehensive experimental procedure allows us to discover a stable ranking of the studied algorithms to select the best ones according to the requirement of software projects.
Causal knowledge graph construction and evaluation for clinical decision support of diabetic nephropathy

Many important clinical decisions require causal knowledge (CK) to take action. Although many causal knowledge bases for medicine have been constructed, a comprehensive evaluation based on real-world data and methods for handling potential knowledge noise are still lacking.The objectives of our study are threefold: (1) propose a framework for the construction of a large-scale and high-quality causal knowledge graph (CKG); (2) design the methods for knowledge noise reduction to improve the quality of the CKG; (3) evaluate the knowledge completeness and accuracy of the CKG using real-world data.We extracted causal triples from three knowledge sources (SemMedDB, UpToDate and Churchill's Pocketbook of Differential Diagnosis) based on rule methods and language models, performed ontological encoding, and then designed semantic modeling between electronic health record (EHR) data and the CKG to complete knowledge instantiation. We proposed two graph pruning strategies (co-occurrence ratio and causality ratio) to reduce the potential noise introduced by SemMedDB. Finally, the evaluation was carried out by taking the diagnostic decision support (DDS) of diabetic nephropathy (DN) as a real-world case. The data originated from a Chinese hospital EHR system from October 2010 to October 2020. The knowledge completeness and accuracy of the CKG were evaluated based on three state-of-the-art embedding methods (R-GCN, MHGRN and MedPath), the annotated clinical text and the expert review, respectively.This graph included 153,289 concepts and 1,719,968 causal triples. A total of 1427 inpatient data were used for evaluation. Better results were achieved by combining three knowledge sources than using only SemMedDB (three models: area under the receiver operating characteristic curve (AUC): p < 0.01, F1: p < 0.01), and the graph covered 93.9 % of the causal relations between diseases and diagnostic evidence recorded in clinical text. Causal relations played a vital role in all relations related to disease progression for DDS of DN (three models: AUC: p > 0.05, F1: p > 0.05), and after pruning, the knowledge accuracy of the CKG was significantly improved (three models: AUC: p < 0.01, F1: p < 0.01; expert review: average accuracy: + 5.5 %).The results demonstrated that our proposed CKG could completely and accurately capture the abstract CK under the concrete EHR data, and the pruning strategies could improve the knowledge accuracy of our CKG. The CKG has the potential to be applied to the DDS of diseases.
Clustering by fast detection of main density peaks within a peak digraph

The well-known Density Peak Clustering algorithm (DPC) proposed a heuristic center detection idea, i.e., to find density peaks as cluster centers. Nevertheless, such a center detection idea cannot work well on multi-peak clusters of complex shapes. Besides, DPC needs the distances between data, making it prohibitively time-consuming. To overcome these problems, a Main Density Peak Clustering algorithm (MDPC+)—clustering by fast detection of main density peaks within a peak digraph—is proposed, where a main density peak is the highest density peak in a cluster. MDPC+ can easily detect the real centers of multi-peak clusters based on its new center assumption. In MDPC+, the clustering problem is viewed as a graph cut problem and a specific graph structure is designed for non-peak and density peak allocation, respectively, so it can reasonably reconstruct clusters of complex shapes. Meanwhile, a satellite peak attenuation technique is embedded into MDPC+ to give it a high resistance to the interference of satellite peaks (i.e., non-center density peaks). Besides, MDPC+ only needs kNN distances of data as its input, so it is suitable for large datasets. Experimental results on both synthetic and real-world datasets demonstrate the superiority of MDPC+ in center detection, complex shape reconstruction, and running speed.
Explainable, Physics-Aware, Trustworthy Artificial Intelligence: A paradigm shift for synthetic aperture radar

The recognition or understanding of the scenes observed with a synthetic aperture radar (SAR) system requires a broader range of cues beyond the spatial context. These encompass but are not limited to the imaging geometry, imaging mode, properties of the Fourier spectrum of the images, or behavior of the polarimetric signatures. In this article, we propose a change of paradigm for explainability in data science for the case of SAR data to ground explainable artificial intelligence (XAI) for SAR. It aims to use explainable data transformations based on well-established models to generate inputs for AI methods, to provide knowledgeable feedback for the training process, and to learn or improve high-complexity unknown or unformalized models from the data.
A Survey on SAR and Optical Satellite Image Registration

After decades of research, automatic synthetic aperture radar (SAR)-optical registration remains an unsolved problem. SAR and optical satellites utilize different imaging mechanisms, resulting in imagery with dissimilar heterogeneous characteristics. Transforming and translating these characteristics into a shared domain has been the main challenge in SAR-optical matching for many years. Combining the two sensors will improve the quality of existing and future remote sensing applications across multiple industries. Several approaches have emerged as promising candidates in the search for combining SAR and optical imagery. In addition, recent research has indicated that machine learning-based approaches have great potential for filling the information gap posed by utilizing only one sensor type in Earth observation applications. However, several challenges remain, and combining them is a multi-step process where no one-size-fits-all approach is available. This article reviews traditional, state-of-the-art, and recent development trends in SAR-optical co-registration methods.
Sustainability in Blockchain: A Systematic Literature Review on Scalability and Power Consumption Issues

Blockchain is a peer-to-peer trustless network that keeps records of digital assets without any central authority. With the passage of time, the sustainability issue of blockchain is rising. This paper discusses two major sustainability issues of blockchain: power consumption and scalability. It discusses the challenge of power consumption by analyzing various approaches to estimating power consumption in the literature. A case study of bitcoin is presented for this purpose. The study presents a review of the growing energy consumption of bitcoin along with a solution for immersion cooling in blockchain mining. The second challenge addressed in this research is scalability. With the increase in network size, scalability issues are also increasing as the number of transactions per second is decreasing. In other words, blockchain is observing low throughput with its increase in size. The paper discusses research studies and techniques proposed in the literature. The paper then investigates how to scale blockchain for better performance.
Progressive Video Summarization via Multimodal Self-supervised Learning

Modern video summarization methods are based on deep neural networks that require a large amount of annotated data for training. However, existing datasets for video summarization are small-scale, easily leading to over-fitting of the deep models. Considering that the annotation of large-scale datasets is time-consuming, we propose a multimodal self-supervised learning framework to obtain semantic representations of videos, which benefits the video summarization task. Specifically, the self-supervised learning is conducted by exploring the semantic consistency between the videos and text in both coarse-grained and fine-grained fashions, as well as recovering masked frames in the videos. The multimodal framework is trained on a newly-collected dataset that consists of video-text pairs. Additionally, we introduce a progressive video summarization method, where the important content in a video is pinpointed progressively to generate better summaries. Extensive experiments have proved the effectiveness and superiority of our method in rank correlation coefficients and F-score <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Recent Versions and Applications of Sparrow Search Algorithm

This paper reviews the latest versions and applications of sparrow search algorithm (SSA). It is a recent swarm-based algorithm proposed in 2020 rapidly grew due to its simple and optimistic features. SSA is inspired by the sparrow living style of foraging and the anti-predation behavior of sparrows. Since its establishment, it has been utilized for a plethora of optimization problems in different research topics, such as mechanical engineering, electrical engineering, civil engineering, power systems, industrial engineering, image processing, networking, environment, robotics, planing and scheduling, and healthcare. Initially, the growth of SSA and its theoretical features are highlighted in terms of the number of published articles, citations, topics covered, etc. After that, the different extended versions of SSA are reviewed, where the main variations of SSA are produced to avoid premature convergence and to boost the diversity aspects. These extended versions are modifications and hybridization summarized with more focus on the motivations behind establishing these versions. Multi-objective SSA is also presented as another version to deal with Multi-objective optimization problems. The critical analysis of the main research gaps in the convergence behaviour of SSA is discussed. Finally, the conclusion and the possible future expansions are recommended based on the research works accomplished in the literature.
An end to shadow banning? Transparency rights in the Digital Services Act between content moderation and curation

This paper offers a legal perspective on the phenomenon of shadow banning: content moderation sanctions which are undetectable to those affected. Drawing on recent social science research, it connects current concerns about shadow banning to novel visibility management techniques in content moderation, such as delisting and demotion. Conventional moderation techniques such as outright content removal or account suspension can be observed by those affected, but these new visibility often cannot. This lends newfound significance to the legal question of moderation transparency rights. The EU Digital Services Act (DSA) is analysed in this light, as the first major legislation to regulate transparency of visibility remedies. In effect, its due process framework prohibits shadow banning with only limited exceptions. In doing so, the DSA surfaces tensions between two competing models for content moderation: as rule-bound administration or as adversarial security conflict. I discuss possible interpretations and trade-offs for this regime, and then turn to a more fundamental problem: how to define visibility reduction as a category of content moderation actions. The concept of visibility reduction or ‘demotions’ is central to both the shadow banning imaginary and to the DSA's safeguards, but its meaning is far from straightforward. Responding to claims that demotion is entirely relative, and therefore not actionable as a category of content moderation sanctions, I show how visibility reduction can still be regulated when defined as ex post adjustments to engagement-based relevance scores. Still, regulating demotion in this way will not cover all exercises of ranking power, since it manifests not only in individual cases of moderation but also through structural acts of content curation; not just by reducing visibility, but by producing visibility.
RACER: Rapid Collaborative Exploration With a Decentralized Multi-UAV System

Although the use of multiple unmanned aerial vehicles (UAVs) has great potential for fast autonomous exploration, it has received far too little attention. In this article, we present a RApid Collaborative ExploRation (RACER) approach using a fleet of decentralized UAVs. To effectively dispatch the UAVs, a pairwise interaction based on an online hgrid space decomposition is used. It ensures that all UAVs simultaneously explore distinct regions, using only asynchronous and limited communication. Furthermore, we optimize the coverage paths of unknown space and balance the workloads partitioned to each UAV with a capacitated vehicle routing problem formulation. Given the task allocation, each UAV constantly updates the coverage path and incrementally extracts crucial information to support the exploration planning. A hierarchical planner finds exploration paths, refines local viewpoints, and generates minimum-time trajectories in sequence to explore the unknown space agilely and safely. The proposed approach is evaluated extensively, showing high exploration efficiency, scalability, and robustness to limited communication. Furthermore, for the first time, we achieve fully decentralized collaborative exploration with multiple UAVs in the real world. We will release our implementation as an open-source package.
Crowd Emotion Prediction for Human-Vehicle Interaction Through Modified Transfer Learning and Fuzzy Logic Ranking

In metropolitan environments, unmanned aerial vehicles (UAVs) equipped with video surveillance equipment can monitor crowd behavior and maintain public safety. In high-traffic areas where humans are more likely to make mistakes, a smart city needs modern technology to forecast the behavior of its residents. In order to improve citywide traffic flow, urban transportation systems (UTSs) monitor and learn how people behave in crowds. Using UAVs for video surveillance in smart cities, our research describes a unique way to assess crowd condition, which expands the scope of human-vehicle interactions. Moreover, we use fuzzy logic ranking to improve the system’s ability to detect anomalies in crowds. In order to improve decision-making, a novel deep transfer learning (DTL) technique is applied to the UAV’s received frames. A 98.5% accuracy rate, satisfactory performance, and robustness to population behavior are all characteristics of the proposed integrated model. In UTSs and urban areas, our novel intelligent system analyzes human behavior based on vehicle-human interactions. In areas with low and high traffic congestion, the modified ResNet (mResNet) architecture predicts the crowd’s condition based on fuzzy logic (FLA). Through decision-making based on accurate crowd conditions, best general paths can be selected using the algorithm.