Reviewerly/exHarmony huggingface dataset:
Learning Individualized Hyperparameter Settings

The performance of optimization algorithms, and consequently of AI/machine learning solutions, is strongly influenced by the setting of their hyperparameters. Over the last decades, a rich literature has developed proposing methods to automatically determine the parameter setting for a problem of interest, aiming at either robust or instance-specific settings. Robust setting optimization is already a mature area of research, while instance-level setting is still in its infancy, with contributions mainly dealing with algorithm selection. The work reported in this paper belongs to the latter category, exploiting the learning and generalization capabilities of artificial neural networks to adapt a general setting generated by state-of-the-art automatic configurators. Our approach differs significantly from analogous ones in the literature, both because we rely on neural systems to suggest the settings, and because we propose a novel learning scheme in which different outputs are proposed for each input, in order to support generalization from examples. The approach was validated on two different algorithms that optimized instances of two different problems. We used an algorithm that is very sensitive to parameter settings, applied to generalized assignment problem instances, and a robust tabu search that is purportedly little sensitive to its settings, applied to quadratic assignment problem instances. The computational results in both cases attest to the effectiveness of the approach, especially when applied to instances that are structurally very different from those previously encountered.
Noisy Quantum Channel Characterization Using Quantum Neural Networks

Channel noise is considered to be the main obstacle in long-distance quantum communication and distributed quantum networks. Here, employing a quantum neural network, we present an efficient method to study the model and detect the noise of quantum channels. Based on various types of noisy quantum channel models, we construct the architecture of the quantum neural network and the model training process. Finally, we perform experiments to verify the training effectiveness of the scheme, and the results show that the cost function of the quantum neural network could approach above 90% of the channel model.
A statistical approach for neural network pruning with application to internet of things

Abstract Pruning is showing huge potential for compressing and accelerating deep neural networks by eliminating redundant parameters. Along with more terminal chips integrated with AI accelerators for internet of things (IoT) devices, structured pruning is gaining popularity with the edge computing research area. Different from filter pruning and group-wise pruning, stripe-wise pruning (SWP) conducts pruning at the level of stripes in each filter. By introducing filter skeleton (FS) to each stripe, the existing SWP method sets an absolute threshold for the values in FS and removes the stripes whose corresponding values in FS could not meet the threshold. Starting with investigation into the process of stripe wise convolution, we use the statistical properties of the weights located on each stripe to learn the importance between those stripes in a filter and remove stripes with low importance. Our pruned VGG-16 achieves the existing results by a fourfold reduction in parameter with only 0.4% decrease in accuracy. Results from comprehensive experiments on IoT devices are also presented.
Streamlining Rental Property and Equipment Processes: Design and Evaluation of an SMS Notification Solution

Efficiently managing rental properties and equipment is vital for property owners, tenants, and equipment renters. This paper presents the design and evaluation of an SMS notification solution that integrates with a rental management system to streamline processes. The solution combines components like rental management software, SMS notification gateway, user interfaces, and mobile devices to enhance communication and automate rental processes. Evaluation criteria include usability, effectiveness, accuracy, portability, security, and maintainability. The solution demonstrates strong performance in all areas, providing intuitive interfaces, timely notifications, accurate information, compatibility with various devices, robust security measures, and maintainability for future enhancements. Overall, this solution proves valuable in optimizing operational efficiency and enhancing user experiences in rental property and equipment management.
Analysis of Polarization Detector Performance Parameters on Polarization 3D Imaging Accuracy

Three-dimensional (3D) reconstruction of objects using the polarization properties of diffuse light on the object surface has become a crucial technique. Due to the unique mapping relation between the degree of polarization of diffuse light and the zenith angle of the surface normal vector, polarization 3D reconstruction based on diffuse reflection theoretically has high accuracy. However, in practice, the accuracy of polarization 3D reconstruction is limited by the performance parameters of the polarization detector. Improper selection of performance parameters can result in large errors in the normal vector. In this paper, the mathematical models that relate the polarization 3D reconstruction errors to the detector performance parameters including polarizer extinction ratio, polarizer installation error, full well capacity and analog-to-digital (A2D) bit depth are established. At the same time, polarization detector parameters suitable for polarization 3D reconstruction are provided by the simulation. The performance parameters we recommend include an extinction ratio ≥ 200, an installation error ∈ [-1°, 1°], a full-well capacity ≥ 100 Ke-, and an A2D bit depth ≥ 12 bits. The models provided in this paper are of great significance for improving the accuracy of polarization 3D reconstruction.
PENGARUH PENGGUNAAN MODUL DIGITAL INTERAKTIF TERHADAP MINAT DAN HASIL BELAJAR MATERI PECAHAN KELAS IV SDN 2 KLESEM

This study aims to determine the effect of interactive digital module learning media on interest and learning outcomes in class IV SDN 2 Klesem material. The research approach used in this research is quantitative research, with a one-group pretest-posttest design. The sample in this study were 27 fourth grade students at SDN 2 Klesem consisting of 11 female students and 16 male students. The instrument of interest in this study uses a questionnaire and learning outcomes use test scores. Data analysis techniques using the t test with a significance level of 5%. Based on the results of the t test analysis (Hypothesis 1 test), the t count (8.387) &gt; t table (2.056) is obtained, and the p value (0.000) &lt; 0.05, these results conclude that there is an effect of using interactive digital modules on interest in learning fraction material class IV SDN 2 Klesem. The results of the t test analysis (Hypothesis 2 test) obtained t count value (11.760) &gt; t table (2.056), and p value (0.000) &lt; 0.05, these results concluded that there was an effect of using interactive digital modules on learning outcomes for class fraction material IV SDN 2 Klesem
Redefining politeness: Power and status in the digital age

Politeness refers to socio-cultural norms, conventions, or rituals in society. From a critical sociolinguistic perspective, the discourse of politeness is inseparable from social status and power. For instance, people who have a high social standing associate with a high level of politeness. This condition certainly has implications for a social hierarchy. Status is characterized by the presence of a high social position, while power is related to the hegemony of the powerful person to his subordinates. Previous research has shown that power is closely related to society characterized by educated, knowledgeable, and elderly people in society. However, in today’s digital age, those indicators have shifted to wealth, position, and fame. It is for this reason that relations of power and status in politeness need to be redefined.
An Asymmetric Collision-Free Optimal Trajectory Planning Method for Three DOF Industrial Robotic Arms

To improve the speed and dynamic adaptability of robotic arm trajectory planning, a collision-free optimal trajectory planning method combining non-uniform adaptive time meshing and bounding box collision detection was proposed. First, the dynamics and objective function of the asymmetric industrial robotic arm with three degrees of freedom (DOF) was formulated in the form of the dynamic optimization problem. Second, the control vector parameterization (CVP) was improved to enhance the computational performance of the problem. The discrete grid was adaptively adjusted according the trend of control variables. Then, a quick and effective collision detection strategy was used to avoid obstacles and to speed up calculation efficiency. The non-collision constraint is built by transforming the collision detection into the distance between two points, and then is combined into the dynamic optimization problem. The solution of the new optimization problem with the improved CVP leads to the higher calculation performance and the avoidance of obstacles. Lastly, the Siemens Manutec R3 robotic arm is taken as an example to verify the effectiveness of the planning method. The approach not only reduces computation time but also maintains accurate calculations, so that optimal trajectory can be selected from symmetric paths near the obstacles. When weights were set as λ1 = λ2 = 0.5, the solution efficiency was improved by 33%, and the minimum distance between the robotic arm and obstacle could be 0.08 m, which ensured that there was no collision.
Antecedents of psychological safety in agile software development teams

Psychological safety continues to inspire researchers’ curiosity in various fields of study. It has been shown to enhance teams’ performance, efficiency, and learning, among other corollaries. Researchers are stretching the boundaries of these early findings to identify further effects of psychological safety. Recent work shows that psychological safety promotes knowledge sharing, norm clarity, and complements agile values. Studies show that psychological safety enhance agile values and practices, and some practitioners went as far as to claim “agile doesn’t work without psychological safety.” Yet, researchers have not explored its antecedents. In this study, we sought to understand how psychological safety materializes in agile software development teams. We opted for a two-phase mixed-methods study; an exploratory qualitative phase (18 interviews) followed by a quantitative phase (survey study, N = 365) to broaden the empirical coverage and test phase one’s findings. Our findings show that psychological safety is established in agile software teams when individuals, the team, and the leadership adopt and promote strategies conducive to promoting a psychologically safe workplace. While openness and no blame towards team members are the “butter and bread” of psychological safety, collective-decision making within the team and the leadership ownership remain the pillars of a psychologically safe workplace. Conversely, team autonomy, technical practices providing a safety net and slack time were not found to promote psychological safety. To institutionalize psychological safety in agile software teams, individuals, teams, and the leadership should consolidate their effort to adopt no blame, openness, collective decision-making in the team, and assuming the ownership of promoting a psychologically safe workplace.
Exploring Prior Knowledge from Human Mobility Patterns for POI Recommendation

Point of interest (POI) recommendation is an important task in location-based social networks. It plays a critical role in smart tourism and makes it more likely for tourists to have personalized travel experiences. However, most current recommendation methods are based on learning the users’ check-in history and the users’ relationship network in the social network to make recommendations.Therefore, urban crowds’ regular travel patterns cannot be effectively utilized. In this paper, we propose a POI recommendation algorithm (HMRec) based on prior knowledge of human mobility patterns to solve this problem. Specifically, we propose the Human Mobility Pattern Extraction (HMPE) framework, which utilizes graph neural networks as extractors for human mobility patterns. The framework incorporates attention mechanisms to capture spatio-temporal information in urban traffic patterns. HMPE employs downstream tasks and design upsampling modules to reconstruct representation vectors for task objectives, enabling end-to-end training of the framework and obtaining pre-trained parameters for the human mobility pattern extractor. Furthermore, we introduce the Human Mobility Recommendation (HMRec) algorithm, which improves feature cross-interactions in the breadth model and incorporates prior knowledge of human patterns. This ensures that the recommendation results align more closely with human travel patterns in urban environments. Comparative experiments conducted on the Foursquare dataset demonstrate that HMRec outperforms baseline models with an average performance improvement of approximately 3%. Finally, we discuss existing challenges and future research directions, including approaches to address the issue of data sparsity.
Dating hot spot to fraud hot spot: Targeting the social characteristics of romance fraud victims in England and Wales

Abstract Research Summary We found that romance fraud was rising year‐on‐year across every region in England and Wales, increasing 55% during the 3 years between October 2018 and October 2021. Fifty percent of all the romance fraud victims in the period resided in 17% of the places where romance fraud had occurred. A total of 439 locations (outward postcode areas) were identified as the “power few” in the first year of the data set. Of these 439 locations, 162 of them recurred in both of the following years, becoming chronic “hot spot” locations containing more than one in six of all reported romance fraud victims. The demography of victims in repeat locations differed considerably, but hot spots were more frequently predominantly populated by less affluent populations. Policy Implications We conclude that the current national one‐size‐fits‐all fraud prevention approach may not be the most efficient or effective way to reach those victims who most require crime prevention advice. The National Fraud Intelligence Bureau, based in the City of London Police, could adopt a tailored approach to providing preventative information to local police forces based on the year‐to‐year patterns in crime and the associated intelligence provided by sociodemographic data sources such as Acorn.
Deep Learning Methods in Image Matting: A Survey

Image matting is a fundamental technique used to extract a fine foreground image from a given image by estimating the opacity values of each pixel. It is one of the key techniques in image processing and has a wide range of applications in practical scenarios, such as in image and video editing. Deep learning has demonstrated outstanding performance in various image processing tasks, making it a popular research topic. In recent years, image matting methods based on deep learning have gained significant attention due to their superior performance. Therefore, this article presents a comprehensive overview of the deep learning-based image matting algorithms that have been proposed in recent years. This paper initially introduces frequently used datasets and their production methods, along with the basic principles of traditional image matting techniques. We then analyze deep learning-based matting algorithms in detail and introduce commonly used image matting evaluation metrics. Additionally, this paper discusses the application scenarios of image matting, conducts experiments to illustrate the limitations of current image matting methods, and outlines potential future research directions in this field. Overall, this paper can serve as a valuable reference for researchers that are interested in image matting.
Technological environment and sustainable performance of oil and gas firms: a structural equation modelling approach

Abstract This study draws from the dynamic capabilities theory to examine how sustainable performance measured along with financial, environmental, and social performance are impacted by technology infrastructure, technology knowledge, and technology applications. Copies of the questionnaire were administered to the Operations and Information Technology department staff of selected oil firms in the Niger Delta region of Nigeria. Out of the three hundred (300) copies of the questionnaire administered, two hundred and forty-three (243) were validly filled. Sixteen (16) of the responses were deleted due to the presence of an outlier in the dataset. The remaining two hundred and twenty-seven (227) responses were used for data analyses. The research model was estimated using structural equation modelling to establish relationships among the variables. The study found that technology infrastructure has a positive but insignificant impact on the sustainable performance of oil and gas firms. However, technology knowledge and application positively and significantly impact sustainable performance. The study recommends that oil and gas companies continually employ, reward, and train individuals to absorb and utilize acquired technologies to develop a standard for addressing the social and environmental problems in the Niger Delta region.
Classification of Anomaly Patterns in Integrated Energy Systems Based on Conditional Variational Autoencoder and Attention Mechanism

By studying the classification of anomaly patterns in integrated energy systems, a deeper understanding of their operational status can be gained, leading to improved reliability and efficiency. This can ultimately result in reduced energy consumption and carbon emissions, contributing to sustainability efforts. This paper proposes a classification method that employs a conditional variational autoencoder and attention mechanism for deep clustering to identify anomaly patterns and distinguish between normal and anomaly datasets. The proposed model effectively addresses the issue of detecting anomalous periods in traditional anomaly detection methods. To account for the periodicity and coupling relationships of different loads, the model integrates sliding windows and attention mechanisms to improve the accuracy of detecting anomaly patterns. Firstly, during the encoder stage, a spatial attention mechanism is incorporated to extract features at each time step of the model input. Secondly, during the decoder stage, a temporal attention mechanism is introduced to perform feature extraction among the multiple time-step hidden layer states of the model input. The proposed method is applied to a typical integrated energy system and compared with existing methods. The experimental results demonstrate the effectiveness of the proposed method in accurately classifying the normal and anomaly patterns of integrated energy systems due to the internal clustering evaluation index.
Regulating scientific and technological uncertainty: The precautionary principle in the context of human genomics and AI

Considered in isolation, the ethical and societal challenges posed by genomics and artificial intelligence (AI) are profound and include issues relating to autonomy, privacy, equality, bias, discrimination, and the abuse of power, amongst others. When these two technologies are combined, the ethical, legal and societal issues increase substantially, become much more complex, and can be scaled enormously, which increases the impact. Adding to these complexities, both genomics and AI-enabled technologies are rife with scientific and technological uncertainties, which makes the regulation of these technologies not only challenging in itself, but also creates legal uncertainties. In science, the precautionary principle has been used globally to govern uncertainty, with the specific aim to prevent irreversible harm to human beings. The regulation of uncertainties in AI-enabled technologies is based on risk as set out in the AI Regulation that was recently proposed by the European Commission. However, when genomics and artificial intelligence are combined, not only do uncertainties double, but the current regulation of such uncertainties towards the safe use thereof for humans seems contradictory, considering the different approaches followed by science and technology in this regard. In this article, I explore the regulation of both scientific and technological uncertainties and argue that the application of the precautionary principle in the context of human genomics and AI seems to be the most effective way to regulate the uncertainties brought about by the combination of these two technologies. Significance: The significance of this article rests in the criteria framework proposed for the determination of the applicability of the precautionary principle and lessons learnt from the European Union’s attempt to regulate artificial intelligence.
Changing of Requirements to the State Bibliographic Indexes: the New SIBID Standard GOST R 7.0.61—2022

The article presents the comments of the standard developer and the text of the SIBID national standard — GOST R 7.0.61—2022 “Current state bibliographical indices . General requirements and publisher’s presentation ”, which is the base document for the Russian state bibliography .