Reviewerly/exHarmony huggingface dataset:
An experimental study on hybrid control of a solar tracking system to maximize energy harvesting in Jordan

Due to its capacity to maximize the power produced by photovoltaic (PV) panels, solar tracking systems have grown in popularity. However, erratic weather conditions, like cloudy or overcast days, change the panel's preferred orientation. On sunny days, the panel is turned to face the sun's radiation; however, on cloudy or overcast days, diffused radiation alters the optimal position. This has increased the necessity for new search algorithms that control the orientation of the panel to generate the most power feasible regardless of the weather conditions. This study aims at developing a sun-tracking system that can adjust the solar panel's orientation to generate the maximum possible electrical output from solar energy in Jordan, regardless of the local climate. To achieve this; a hybrid controller has been created that combines an open-loop sun monitoring system with a dynamic feedback controller built on an active search algorithm. The suggested algorithm is built on the real-time measurement of the PV panels' waning solar irradiance by using small solar cell 0.6 W. The system switches to the recently proposed circular path finding method to locate the Maximum Power Point Tracking (MPPT) if the instantaneous irradiance level falls below a predetermined threshold (DNI irradiance). As a result of the testing phase, the generated power of a solar panel increased by about 35%, confirming the effectiveness of the newly proposed sun tracker hybrid control system. During experimental test, positive irradiance current noises are appeared, using average filter taking the mean of 100 values for one measurement.
3D-unified spatial-temporal graph for group activity recognition

Early group activity recognition is typically conducted in a 2D scenario. This paper proposes a group activity recognition method in a 3D space. In practical applications, differences in camera angle and mutual shielding during the peak flow of people will affect the extraction of individual characteristics and the calculation of individual relationships. Therefore, a method using 3D relationships is proposed. We first detect the 3D backbone of pedestrians and calculate the movement relationship of individuals in the group based on it. Second, we calculate the 3D-unified spatial–temporal graphs (3D-USTG) of the group and the individuals in the group using global-G3D (G-G3D). The experimental results demonstrate that the proposed method can effectively solve the problems caused by occlusion and shooting angle compared with a 2D method, which indicates that the proposed method can achieve better results when applied to intersections of different specifications. Additionally, the proposed method achieves strong performance metrics with two publicly available datasets for group activity recognition.
NONLOCAL LOW RANK REGULARIZATION METHOD FOR FRACTAL IMAGE CODING UNDER SALT-AND-PEPPER NOISE

Image denoising has been a fundamental problem in the field of image processing. In this paper, we tackle removing impulse noise by combining the fractal image coding and the nonlocal self-similarity priors to recover image. The model undergoes a two-stage process. In the first phase, the identification and labeling of pixels likely to be corrupted by salt-and-pepper noise are carried out. In the second phase, image denoising is performed by solving a constrained convex optimization problem that involves an objective functional composed of three terms: a data fidelity term to measure the similarity between the underlying and observed images, a regularization term to represent the low-rank property of a matrix formed by nonlocal patches of the underlying image, and a quadratic term to measure the closeness of the underlying image to a fractal image. To solve the resulting problem, a combination of proximity algorithms and the weighted singular value thresholding operator is utilized. The numerical results demonstrate an improvement in the structural similarity (SSIM) index and peak signal-to-noise ratio.
MSHHOTSA: A variant of tunicate swarm algorithm combining multi-strategy mechanism and hybrid Harris optimization

This paper proposes a novel hybrid algorithm, named Multi-Strategy Hybrid Harris Hawks Tunicate Swarm Optimization Algorithm (MSHHOTSA). The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. Firstly, inspired by the idea of the neighborhood and thermal distribution map, the hyperbolic tangent domain is introduced to modify the position of new tunicate individuals, which can not only effectively enhance the convergence performance of the algorithm but also ensure that the data generated between the unknown parameters and the old parameters have a similar distribution. Secondly, the nonlinear convergence factor is constructed to replace the original random factor c1 to coordinate the algorithm's local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. Finally, the swarm update mechanism of the HHO algorithm is introduced into the position update of the TSA algorithm, which further balances the local exploitation and global exploration performance of the MSHHOTSA. The proposed algorithm was evaluated on eight standard benchmark functions, CEC2019 benchmark functions, four engineering design problems, and a PID parameter optimization problem. It was compared with seven recently proposed metaheuristic algorithms, including HHO and TSA. The results were analyzed and discussed using statistical indicators such as mean, standard deviation, Wilcoxon's rank sum test, and average running time. Experimental results demonstrate that the improved algorithm (MSHHOTSA) exhibits higher local convergence, global exploration, robustness, and universality than BOA, GWO, MVO, HHO, TSA, ASO, and WOA algorithms under the same experimental conditions.
Abstractive Financial News Summarization via Transformer-BiLSTM Encoder and Graph Attention-Based Decoder

Financial news summarization (FNS) has been an attractive research problem in recent years, which aims to generate a shorter highlight of the news article while preserving key factual aspects, emotions, and opinions, providing significant assistance in stock trading and investment decision-making. However, FNS faces two challenges compared to the common domain. Firstly, financial news involves professional qualitative and quantitative information and salient content always scatters across long-range interactions. Secondly, financial news contains latent causal relationships, where historical information in the early generated sequence can significantly affect the subsequent decoding process. To address these difficulties, we propose an enhanced Seq2Seq model named TLGA, where the hierarchical Transformer-BiLSTM encoder can capture long-range interactions and sequential semantics while the Graph Attention-based decoder can fully utilize the historical information of decoded tokens and capture key causal relations. Moreover, we propose history-enhanced attention to concentrate on salient input content based on history semantics, guiding our decoder to generate the summary around the corresponding contents. It is also the first attempt to reuse history information of previously generated summary sequences in FNS using the idea of the Graph Attention Mechanism. Additionally, we construct the LCFNS dataset with 430,820 news-summary pairs for the lack of large-scale high-quality datasets in FNS. Experimental results on two financial datasets and two benchmark datasets indicate that our model outperforms other baselines.
A CM-DEMATEL assessment of SME competitiveness factors

Competitiveness is a crucial dimension in accurate assessments of organizations' use of their resources and capabilities, which, if utilized effectively and efficiently, allow companies to achieve numerous competitive advantages. Firm level of competitiveness has assumed increasing importance, especially for small and medium-sized enterprises (SMEs), as it generates not only better organizational performance but also, in many cases, business activity survival. However, assessing competitiveness factors (CFs) is a complex, challenging endeavor given the multiple variables that influence companies' competitiveness. Thus, a comprehensive evaluation model is a necessary tool for decision makers who seek to assess their organization's competitiveness. To overcome previous research limitations, this study combines cognitive mapping and the decision-making trial and evaluation laboratory (DEMATEL) method to develop a complete, well-informed model that facilitates dynamic analyses of cause-and-effect relationships among CFs. Two in-person sessions were held with a panel of specialists in SME competitiveness, during which five clusters of CFs emerged from the discussion and knowledge shared. The results were subsequently validated by a neutral expert. The advantages and limitations of the proposed framework are also analyzed.
Automated Question Title Reformulation by Mining Modification Logs From Stack Overflow

In Stack Overflow, developers may not clarify and summarize the critical problems in the question titles due to a lack of domain knowledge or poor writing skills. Previous studies mainly focused on automatically generating the question titles by analyzing the posts’ problem descriptions and code snippets. In this study, we aim to improve title quality from the perspective of question title reformulation and propose a novel approach <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QETRA</monospace> motivated by the findings of our formative study. Specifically, by mining modification logs from Stack Overflow, we first extract title reformulation pairs containing the original title and the reformulated title. Then we resort to multi-task learning by formalizing title reformulation for each programming language as separate but related tasks. Later we adopt a pre-trained model T5 to automatically learn the title reformulation patterns. Automated evaluation and human study both show the competitiveness of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QETRA</monospace> after compared with six state-of-the-art baselines. Moreover, our ablation study results also confirm that our studied question title reformulation task is more practical than the direct question title generation task for generating high-quality titles. Finally, we develop a browser plugin based on <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QETRA</monospace> to facilitate the developers to perform title reformulation. Our study provides a new perspective for studying the quality of post titles and can further generate high-quality titles.
Optimizing the Production of Test Vehicles Using Hybrid Constrained Quantum Annealing

Abstract Optimization of pre-production vehicle configurations is one of the challenges in the automotive industry. Given a list of tests requiring cars with certain features, it is desirable to find the minimum number of cars that cover the tests and obey the configuration rules. In this paper, we model the problem in the framework of satisfiability and solve it utilizing the newly introduced hybrid constrained quadratic model (CQM) solver provided by D-Wave. The problem definition is based on the “Optimizing the Production of Test Vehicles” use-case given in the BMW quantum computing challenge. We formulate a constrained quadratic model for the problem and use a greedy algorithm to configure the cars. We benchmark the results obtained from the CQM solver with the results from the classical solvers like coin-or branch and cut and Gurobi solver. We conclude that the performance of the CQM solver is comparable to the classical solvers in optimizing the number of test vehicles, given the noise-prone quantum hardware. However, the CQM solver takes much more time, which prohibits obtaining useful quantum advantages. As an extension to the problem, we describe how the scheduling of the tests can be incorporated into the model.
A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning

The popularity of cryptocurrencies has skyrocketed in recent years, with blockchain technologies enabling the development of new digital assets. However, along with their advantages, such as lower transaction costs, increased security, and transactional transparency, cryptocurrencies have also become susceptible to various forms of market manipulation. The pump and dump (P&amp;D) scheme is of significant concern among these manipulation tactics. Despite the growing awareness of P&amp;D activities in cryptocurrency markets, a comprehensive survey is needed to explore the detection methods. This paper aims to fill this gap by reviewing the literature on P&amp;D detection in the cryptocurrency world. This survey provides valuable insights into detecting and classifying P&amp;D schemes in the cryptocurrency market by analyzing the selected studies, including their definitions and the taxonomies of P&amp;D schemes, the methodologies employed, their strengths and weaknesses, and the proposed solutions. Presented here are insights that can guide future research in this field and offer practical approaches to combating P&amp;D manipulations in cryptocurrency trading.
Artificial Intelligence, Bias, and Ethics

Although ChatGPT attempts to mitigate bias, when instructed to translate the gender-neutral Turkish sentences “O bir doktor. O bir hemşire” to English, the outcome is biased: “He is a doctor. She is a nurse.” In 2016, we have demonstrated that language representations trained via unsupervised learning automatically embed implicit biases documented in social cognition through the statistical regularities in language corpora. Evaluating embedding associations in language, vision, and multi-modal language-vision models reveals that large-scale sociocultural data is a source of implicit human biases regarding gender, race or ethnicity, skin color, ability, age, sexuality, religion, social class, and intersectional associations. The study of gender bias in language, vision, language-vision, and generative AI has highlighted the sexualization of women and girls in AI, while easily accessible generative AI models such as text-to-image generators amplify bias at scale. As AI increasingly automates tasks that determine life’s outcomes and opportunities, the ethics of AI bias has significant implications for human cognition, society, justice, and the future of AI. Thus, it is necessary to advance our understanding of the depth, prevalence, and complexities of bias in AI to mitigate it both in machines and society.
Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding

This study extends the recently-developed LaCAM algorithm for multi-agent pathfinding (MAPF). LaCAM is a sub-optimal search-based algorithm that uses lazy successor generation to dramatically reduce the planning effort. We present two enhancements. First, we propose its anytime version, called LaCAM*, which eventually converges to optima, provided that solution costs are accumulated transition costs. Second, we improve the successor generation to quickly obtain initial solutions. Exhaustive experiments demonstrate their utility. For instance, LaCAM* sub-optimally solved 99% of the instances retrieved from the MAPF benchmark, where the number of agents varied up to a thousand, within ten seconds on a standard desktop PC, while ensuring eventual convergence to optima; developing a new horizon of MAPF algorithms.
Unsupervised single-shot depth estimation using perceptual reconstruction

Abstract Real-time estimation of actual object depth is an essential module for various autonomous system tasks such as 3D reconstruction, scene understanding and condition assessment. During the last decade of machine learning, extensive deployment of deep learning methods to computer vision tasks has yielded approaches that succeed in achieving realistic depth synthesis out of a simple RGB modality. Most of these models are based on paired RGB-depth data and/or the availability of video sequences and stereo images. However, the lack of RGB-depth pairs, video sequences, or stereo images makes depth estimation a challenging task that needs to be explored in more detail. This study builds on recent advances in the field of generative neural networks in order to establish fully unsupervised single-shot depth estimation. Two generators for RGB-to-depth and depth-to-RGB transfer are implemented and simultaneously optimized using the Wasserstein-1 distance, a novel perceptual reconstruction term, and hand-crafted image filters. We comprehensively evaluate the models using a custom-generated industrial surface depth data set as well as the Texas 3D Face Recognition Database, the CelebAMask-HQ database of human portraits and the SURREAL dataset that records body depth. For each evaluation dataset, the proposed method shows a significant increase in depth accuracy compared to state-of-the-art single-image transfer methods.
CAN CYBER RISK OF HEALTH CARE FIRMS BE INSURED? A MULTINOMIAL LOGISTIC REGRESSION MODEL

The healthcare sector is prone to Distributed Denial-of-Service and Ransomware attacks owing to unsecured networks and software. This results in stalling of outpatient and inpatient operations of a hospital. In this study, we propose an H-CRAM model that computes the risk of a cyber-attack based on the threat appraisal component of the Protection Motivation Theory (PMT) using multinomial logistic regression. We also hypothesize that training the healthcare staff, implementing IT governance, and intervening technology will decrease the probability of the occurrence of a cyber threat. The severity of the risk is computed using Collective Risk Modelling. Next, based on the coping appraisal component of PMT, Rational Choice Theory, and NIST guidelines, we propose that the CIO of a healthcare firm should first reduce the cyber-risk by investing in encrypting Electronic Health Records, Security Incident and Event Management (SIEM) and Security Orchestration, Automation and Response (SOAR) tools. Then pass the residual cyber risk to a cyber insurer.
TopoBERT: a plug and play toponym recognition module harnessing fine-tuned BERT

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Defense strategies for Adversarial Machine Learning: A survey

Adversarial Machine Learning (AML) is a recently introduced technique, aiming to deceive Machine Learning (ML) models by providing falsified inputs to render those models ineffective. Consequently, most researchers focus on detecting new AML attacks that can undermine existing ML infrastructures, overlooking at the same time the significance of defense strategies. This article constitutes a survey of the existing literature on AML attacks and defenses with a special focus on a taxonomy of recent works on AML defense techniques for different application domains, such as audio, cyber-security, NLP, and computer vision. The proposed survey also explores the methodology of the defense solutions and compares them using several criteria, such as whether they are attack- and/or domain-agnostic, deploy appropriate AML evaluation metrics, and whether they share their source code and/or their evaluation datasets. To the best of our knowledge, this article constitutes the first survey that seeks to systematize the existing knowledge focusing solely on the defense solutions against AML and providing innovative directions for future research on tackling the increasing threat of AML.
The temporal event-based model: Learning event timelines in progressive diseases

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80% with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.
A metaworld: Implications, opportunities and risks of the metaverse

Abstract Cyberspace has continued to change throughout the 1990s and 2000s, when the Internet became widely used. The concept of a massive, integrated, sustainable, and interconnected cyber world is the heart of the metaverse. The aim of the metaverse is to create a digital world that is analogous to the existing world. Thus, the most recent metaverse development is investigated in light of cutting‐edge technologies and metaverse ecosystems. To this end, a pilot survey to provide a first overview of upcoming challenges and opportunities of the metaverse is presented. The results provide researchers with a direction for future study as well as potential applications in the metaverse.
CAELUS: Classification of sky conditions from 1-min time series of global solar irradiance using variability indices and dynamic thresholds

Precise sky classification as a function of cloudiness is desirable or necessary in a variety of applications. CAELUS, a novel classification algorithm that relies on various thresholds to separate all possible sky conditions into six classes, is presented here. It uses global horizontal irradiance (GHI) measurements at 1-min resolution, from which a set of four indices is derived to characterize the magnitude and temporal variability of GHI. The algorithm also requires precise estimates of 1-min GHI under hypothetical cloudless conditions, and the solar zenith angle (limited to a maximum of 85°). Using 1-min GHI measurements from 54 BSRN high-quality radiometric stations, which cover all five primary Köppen-Geiger climate classes, CAELUS is used here to classify their sky conditions. The classification results, including the distribution of sky classes and the transitions between consecutive sky classes, are found consistent with the known characteristics of each primary Köppen-Geiger climate. Moreover, in each climate class, the detection of 1-min cloudless situations is found comparable to that provided by two dedicated and state-of-the-art methods—Reno-Hansen and Bright-Sun.