Reviewerly/exHarmony huggingface dataset:
Learning to Forget: A Critical Review of Knowledge Management and Knowledge Exchange Initiatives in the Detective Service

KM and KE have recently become commonly used terms in law enforcement agencies. However, implementing KM initiatives successfully in the SAPS still poses a challenge. This paper reviews factors that influence the success or failure of KM and KE initiatives as manifested in the SAPS, with emphasis on the DS. Both KM and KE initiatives are of critical importance to solve criminal cases. The consulted literature review highlighted various dimensions that are critically influential in the implementation of KM and KE in the DS. These are the negligence of the FP, during which the building blocks for a successful schooling career are laid; and once detectives are recruited, the lack of a mentorship programme and training curriculum, which lack coherence, connection, as well as depth of understanding that accompanies systematic critical thinking.
Feature Learning for Nonlinear Dimensionality Reduction toward Maximal Extraction of Hidden Patterns

Dimensionality reduction (DR) plays a vital role in the visual analysis of high-dimensional data. One main aim of DR is to reveal hidden patterns that lie on intrinsic low-dimensional manifolds. However, DR often overlooks important patterns when the manifolds are distorted or masked by certain influential data attributes. This paper presents a feature learning framework, FEALM, designed to generate a set of optimized data projections for nonlinear DR in order to capture important patterns in the hidden manifolds. These projections produce maximally different nearest-neighbor graphs so that resultant DR outcomes are significantly different. To achieve such a capability, we design an optimization algorithm as well as introduce a new graph dissimilarity measure, named neighbor-shape dissimilarity. Additionally, we develop interactive visualizations to assist comparison of obtained DR results and interpretation of each DR result. We demonstrate FEALM’s effectiveness through experiments and case studies using synthetic and real-world datasets.
LabelVizier: Interactive Validation and Relabeling for Technical Text Annotations

With the rapid accumulation of text data produced by data-driven techniques, the task of extracting "data annotations"—concise, high-quality data summaries from unstructured raw text—has become increasingly important. The recent advances in weak supervision and crowd-sourcing techniques provide promising solutions to efficiently create annotations (labels) for large-scale technical text data. However, such annotations may fail in practice because of the change in annotation requirements, application scenarios, and modeling goals, where label validation and relabeling by domain experts are required. To approach this issue, we present LabelVizier, a human-in-the-loop workflow that incorporates domain knowledge and user-specific requirements to reveal actionable insights into annotation flaws, then produce better-quality labels for large-scale multi-label datasets. We implement our workflow as an interactive notebook to facilitate flexible error profiling, in-depth annotation validation for three error types, and efficient annotation relabeling on different data scales. We evaluated our workflow in assisting the validation and relabelling of technical text annotation with two use cases and four expert reviews. The results show that LabelVizier is applicable in various application scenarios, and users with different knowledge backgrounds have diverse preferences for the tool usage.
Sentiments Analysis and Feedback among Three Cohorts in Learning Software Engineering Modules

Computing Science (CS) and software programming skillsets are widely used in many fields. The acquisition of software engineering skills is important and crucial to prepare students for the software industry. Learning of professional software development (PSD) knowledge involves various educational approaches. This study examines the feedback in students’ learning journey and preparedness for the industry across three years of the CS degree programme. The research is carried out to evaluate the current bottlenecks of learning in the PSD course. Surveys are conducted to collect feedback of 72 CS students from three different cohorts, namely freshmen, penultimate and final study year. Their sentiments are identified and analyzed using the quantitative and qualitative data gathered. Observed from the results obtained, most of the final year students have perceived attainment of favourable skills needed for the industry, after their internship experience where they applied their skillsets. A significant rise in positive sentiments from graduating students suggests that the PSD module taught is relevant and applicable for the software industry. However, the sentiments among the penultimate year students are different and show that there might be an issue with the current course delivery, which needs to be improved. In addition, this paper suggests possible improvement opportunities that could be explored by eliciting current challenges faced in the current syllabus. These include the specification variances for projects, timely introduction of topics, flexibility in marking rubrics, and introduction of in-demand practices.
The regulation of and through information technology: Towards a conceptual ontology for IS research

This special issue addresses a largely neglected area of Information Systems (IS) research – the regulation of and through Information Technology (IT). As with other human technologies, IT artefacts present risks and can harm individuals, groups, organisations, economies, and society: However, this remains a largely unexplored topic in IS research. Nevertheless, regulators, social commentators, the media, and the public have voiced their concerns about such risks, particularly those related to artificial intelligence (AI), cybersecurity, privacy, digital assets (e.g. cryptocurrencies), and the market dominance of digital platforms. Accordingly, regulations have been instituted or proposed to regulate IT artefacts in and across several business sectors. Additionally, in response to the informational challenges posed by a complex web of laws and regulations, regulators and business organisations have implemented IT artefacts to transform regulatory and supervisory processes or to enhance organisational risk management and compliance reporting capabilities. This special issue addresses the research challenges related to emergent issues surrounding the regulation of and through IT. In this editorial, we take stock of where the field currently stands. We advance a conceptual ontology of IT regulation to guide future research by specifying several taken-for-granted core concepts. By rendering the concepts, categories, and their relationships explicit, the model and its related research questions provide a firm foundation to generate a cumulative body of research on the regulation of and through IT.
A Two-Phase Constraint-Handling Technique for Constrained Optimization

A two-phase constraint-handling technique is integrated into the evolutionary algorithms to solve constrained optimization problems (called TPDE) in this article. In phase one, denoted as the exploration phase, an exterior penalty function method with the dynamic penalty coefficients is developed to compare any two candidate solutions, which aims to push the population into the feasible region. To reduce the computational burden, in phase two, denoted as the exploitation phase, an interior penalty function method with the dynamic penalty coefficients is developed, which enhances the search ability by using the information of constraints in feasible solutions. During the optimization process, differential evolution is adopted as the search algorithm to produce the offspring population. Experiment results on four benchmark test suites, namely, IEEE CEC 2006, IEEE CEC 2010, IEEE CEC 2017, and IEEE CEC 2020, indicate that TPDE is competitive with other popular algorithms.
SEAHORS: Spatial Exploration of ArcHaeological Objects in R Shiny

This paper presents SEAHORS, an R shiny application available as an R package, dedicated to the intra-site spatial analysis of piece-plotted archaeological remains.This opensource script generates 2D and 3D scatter and density plots for archaeological objects located with cartesian coordinates.Many different GIS software already exist for this, but they mostly require specific skills and training to be used and are rarely designed for the particular needs of archaeological applications.The goal of SEAHORS is to make the two and three-dimensional intra-site spatial exploration of archaeological data as user-friendly as possible, in order to give the opportunity to researchers not familiar with GIS and R software to utilise such approaches.SEAHORS has an easily accessible interface and can import data from text and Excel files (.csv and .xls/xlsxrespectively) without preformatting.The application includes functions to concatenate columns and to merge databases, for example when spatial data (XYZ coordinates) and analytical data (e.g.taxonomical attribution of faunal remains, typo-technological attributes of artifacts, etc.) are stored in separate files.SEAHORS can generate five types of plots: 3D, 2D and density plots, as well as 2D plots by slices (i.e.subdivisions according to a third dimension) and 2D plots with a modification of the angle of projection (i.e. to explore spatial organization without the constraints of the field grid orientation).SEAHORS has visualization tools with several sorting and formatting options (color, size, etc.) applicable to coordinates and all possible analytical variables (i.e.levels, spits, identified species, taphonomical alterations, etc.).Orthophotos can also be imported and directly used in the program.The application also allows the grouping of objects into new variables by selecting items on the interactive 2D plots.We present an overview of the application's functions by using the case study of the Cassenade Paleolithic site (France).
A novel hybrid ensemble convolutional neural network for face recognition by optimizing hyperparameters

Abstract A fully fledged face recognition system consists of face detection, face alignment, and face recognition. Facial recognition has been challenging due to various unconstrained factors such as pose variation, illumination, aging, partial occlusion, low resolution, etc . The traditional approaches to face recognition have some limitations in an unconstrained environment. Therefore, the task of face recognition is improved using various deep learning architectures. Though the contemporary deep learning techniques for face recognition systems improved overall efficiency, a resilient and efficacious system is still required. Therefore, we proposed a hybrid ensemble convolutional neural network (HE-CNN) framework using ensemble transfer learning from the modified pre-trained models for face recognition. The concept of progressive training is used for training the model that significantly enhanced the recognition accuracy. The proposed modifications in the classification layers and training process generated best-in-class results and improved the recognition accuracy. Further, the suggested model is evaluated using a self-created criminal dataset to demonstrate the use of facial recognition in real-time. The suggested HE-CNN model obtained an accuracy of 99.35, 91.58, and 95% on labeled faces in the wild (LFW), cross pose LFW, and self-created datasets, respectively.
Methods of Social Interaction Learning for Students of Non-Profit Organizations

Search and substantiation of new form productivity for student non-formal education, which make it possible to compensate for the deficit of purposeful preparation for social interaction in the traditional (formal) system of higher education. Methodology: system-dialectical approach, which allows to overcome the existing fragmentation and the fragmentation of research results concerning the experience of student social interaction; the methods of scientific analysis of sources; pedagogical experience study; comparative analysis; classification; content analysis; mathematical processing of statistical data. Results: they substantiate the relevance of addressing social education of students in youth non-profit organizations, as one of the forms of non-formal education. The individual and group experience of social interaction and its promising lines of enrichment (subjectivity, reflexivity, co-existence) are proposed as a system-organized educational result. The content of education is determined based on the characteristics of social practices implemented in non-profit organizations. The results of the comparative study of the applied forms and methods of youth training in existing youth non-profit organizations in Russia and abroad (the Association of Trainers of the Russian Union of Youth, the All-Russian School of Personal Growth and Development of Student Self-Government "Progress", "The College of Trainers by T. Hoist" (Germany), "Freechild Institute" (USA). The methods of social training of their participants most widespread in non-profit organizations, the features and results of their application are determined. The conclusions are made about the relevance and prospects of student youth preparation optimization for active social interaction in non-formal education within the context of non-profit organizations based on the methodology of the system-dialectical approach to the enrichment of individual and group experience of social interaction.
BERT4Loc: BERT for Location—POI Recommender System

Recommending points of interest (POI) is a challenging task that requires extracting comprehensive location data from location-based social media platforms. To provide effective location-based recommendations, it is important to analyze users’ historical behavior and preferences. In this study, we present a sophisticated location-aware recommendation system that uses Bidirectional Encoder Representations from Transformers (BERT) to offer personalized location-based suggestions. Our model combines location information and user preferences to provide more relevant recommendations compared to models that predict the next POI in a sequence. Based on our experiments conducted on two benchmark datasets, we have observed that our BERT-based model surpasses baselines models in terms of HR by a significant margin of 6% compared to the second-best performing baseline. Furthermore, our model demonstrates a percentage gain of 1–2% in the NDCG compared to second best baseline. These results indicate the superior performance and effectiveness of our BERT-based approach in comparison to other models when evaluating HR and NDCG metrics. Moreover, we see the effectiveness of the proposed model for quality through additional experiments.
Effect of requirements specification using native language on external software quality

Abstract In the context of requirements specification in Global Software Development, aspects such as differences in culture, language and schedule affect software development teams; however, we do not know the effect of these issues. Compare the native language requirements with the foreign language requirements concerning external quality. We conducted a controlled experiment of one‐factor two treatments within‐subjects with 17 experimental subjects. Wilcoxon test indicates that there is evidence to reject the null hypothesis ( p ‐value = 0.008); there is a statistically significant difference. The external quality value obtained with native language requirements is superior to the external quality produced with the foreign language. The effect size equals an absolute value of 0.45, which corresponds to a medium effect. The language used in the requirements specification influences the external quality; using the native language in the requirements specification significantly increases the external quality. The result obtained in this research should be considered to evaluate the roles and English language skills of GSD team members and their effect on external software quality. We also suggest considering the English language skills of the experimental subjects in the experimental laboratories since language could influence the results of the experiments.
A Deep Learning-Based Target Recognition Method for Entangled Optical Quantum Imaging System

Quantum imaging has the characteristics of non-locality and strong anti-interference ability, and it has received much attention. However, the target after entangled optical quantum imaging loses its local appearance structure, and it is difficult to be recognized accurately. Therefore, this paper proposes a denoise and target recognition method for entangled optical quantum imaging system. Specifically, we design the restore conditional generative adversarial network (RestoreCGAN) to restore and reconstruct the missing edge contour structure of the target. Then, we design the two-stream feature fusion convolutional neural network (TSFFCNet) to extract deep semantic features and shallow features for target recognition. The experimental results show that RestoreCGAN outperforms the state-of-the-art methods in terms of both PSNR and SSIM. Moreover, the recognition accuracy of the RestoreCGAN in combination with the TSFFCNet reaches 97.42%. This proves that the deep learning method is effective for denoise and target recognition of entangled optical quantum imaging system.
Elastic Use of Far Memory for In-Memory Database Management Systems

The separation and independent scalability of compute and memory is one of the crucial aspects for modern in-memory database systems (IMDBMSs) in the cloud. The new, cache-coherent memory interconnect Compute Express Link (CXL) promises elastic memory capacity through memory pooling. In this work, we adapt the well-known IMDBMS, SAP HANA, for memory pools by features of table data placement and operational heap memory allocation on far memory, and study the impact of the limited bandwidth and higher latency of CXL. Our results show negligible performance degradation for TPC-C. For the analytical workloads of TPC-H, a notable impact on query processing is observed due to the limited bandwidth and long latency of our early CXL implementation. However, our emulation shows it would be acceptably smaller with the improved CXL memory devices.
Mining relevant solutions for programming tasks from search engine results

Abstract Official documentation of software development technologies, for example, APIs, may not be sufficient for all developer needs, so searching on the Internet is a usual practice. Nonetheless, finding useful information may be challenging because the best solutions are not always among the first ranked pages. Developers need to read and discard irrelevant pages, that is, those without code examples or those that have content with little focus on the desired solution. This work aims at proposing an approach to mine relevant solutions for programming tasks from search engine results by removing irrelevant pages. The authors evaluated the top‐20 pages returned by the Google search engine, for 10 different queries, and observed that only 31% of the evaluated pages are relevant to developers. Then, the authors proposed and evaluated three different approaches to mine the relevant pages returned by the search engine. Google's search engine has been used as a baseline, and authors’ results have shown that it returns a reasonable number of irrelevant pages for developers, and the authors could establish an effective approach to remove irrelevant pages, suggesting that developers could benefit from a customised web search filter for development content.
Evaluating the impact of quality antecedents on university students’ e-learning continuance intentions: A post COVID-19 perspective

The COVID−19 pandemic forced service providers to resort to online servicescapes across diverse industries globally. However, with the epidemic curve flattening in most parts of the world and the pandemic showing indications to phasing out, educational institutions are now operating on the continuum between blended e-learning and full-scale e-learning services. The purpose of this study was to examine the impact of e-learning quality antecedents on university students’ e-learning continuance intentions in Zimbabwe post the COVID−19 pandemic. The study adopted a causal research design within the positivism research paradigm. The study population were undergraduate students using e-learning at three universities in Zimbabwe. A random sampling procedure was employed to collect data. A quantitative approach validated 311 responses using covariance-based Structural Equation Modelling (SEM) in SPSS AMOS. The study revealed positive and statistically significant relationships between three quality factors (system quality, instructor quality and information quality) and students’ e-learning continuance intentions (p < 0.001). However, the impact of support service quality on e-learning continuance intentions was not statistically significant. The paper flags the key roles of instructor quality, information quality and system quality as key determinants of students’ willingness to continue using e-learning systems in a post COVID−19 era. Universities were urged to design e-learning systems that engulf state of the art communication technology attributes to ensure robustness, flexibility, speed, security, visual appeal, compatibility, availability and user-friendliness. The study also recommends periodical reviews to motivation, resourcing, training and development strategies for e-learning instructors to enhance their delivery during e-learning service encounters.
Analysis-Based Optimization of Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification

Temporal dynamic convolution neural networks (TDY-CNNs) extract speaker embeddings considering the time-varying characteristics of speech and improve text-independent speaker verification performance. In this paper, we optimize TDY-CNNs based on the detailed analysis of the network architecture. The temporal dynamic convolution generates attention weight of basis kernels from features defined by concatenating average channel and frequency data, resulting in a reduction in network parameters by 26%. In addition, the temporal dynamic convolutions replace vanilla convolutions in earlier layers, while the optimized temporal dynamic convolutions of latter layers use a steady kernel regardless of time bin data. As a result, Opt-TDY-ResNet-34( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 0.50$ </tex-math></inline-formula> ) shows the best speaker verification performance with EER of 1.07% among speaker verification models without data augmentation including ResNet-based baseline networks and other state-of-the-art networks. Moreover, we validate that Opt-TDY-CNNs adapt to time-bin data through various methods. By comparing the inter and intra phoneme distance of attention weights, it was confirmed that the temporal dynamic convolution uses different kernels depending on the phoneme groups directly related to the time-bin data. In addition, by applying gradient-weighted class activation mapping (Grad-CAM) on speaker verification to obtain speaker activation map (SAM), we showed that temporal dynamic convolution extracts speaker information from frequency characteristics of time bins such as phonemes’ formant frequencies while vanilla convolution extracts vague outline of Mel-spectrogram.