Reviewerly/exHarmony huggingface dataset:
Research on Multidimensional Causal Model of Environment in Macao Cotai City Ecological Reserve

(1) Background: The Macao Cotai City Ecological Reserve in China’s Guangdong-Hong Kong-Macao Greater Bay Area is essential for biodiversity, environmental enhancement, and sustainable growth. However, varying weather and intricate environmental issues complicate its oversight. A thorough environmental model, addressing all influential factors, is crucial. (2) Methods: This study employs the Bayesian network (BN) modeling technique to develop a multidimensional causal model for the environment of the Macao Cotai City Ecological Reserve. By utilizing environmental models, the interactions among the three sub-networks: meteorology, air quality, and water quality are analyzed. This facilitates the qualitative and quantitative evaluation of the cause-and-effect relationships between pollutants and the factors influencing such pollutants. Through these models, the aim is to collaboratively predict pollutant concentrations, facilitating informed environmental decision-making and management. (3) Results: The multidimensional causal model designed for the Macao Cotai City Ecological Reserve environment is adept at capturing intricate nonlinear relationships. It is proficient in managing uncertainty, offers high interpretability, and ensures efficient computations. (4) Conclusions: This study offers insights into the environmental systems of Macao Cotai City Ecological Reserve, supporting its management and sustainable development.
RF-Based Simultaneous Localization and Source Seeking for Multi-Robot Systems

This paper considers a radio-frequency (RF)-based simultaneous localization and source-seeking (SLASS) problem in multi-robot systems, where multiple robots jointly localize themselves and an RF source using distance-only measurements extracted from RF signals and then control themselves to approach the source. We design a Rao-Blackwellized particle filter-based algorithm to realize the joint localization of the robots and the source. We also devise an information-theoretic control policy for the robots to approach the source. In our control policy, we maximize the predicted mutual information between the source position and the distance measurements, conditioned on the robot positions, to incorporate the robot localization uncertainties. A projected gradient ascent method is adopted to solve the mutual information maximization problem. Simulation results show that the proposed SLASS framework outperforms two benchmarks in terms of the root mean square error (RMSE) of the estimated source position and the decline of the distances between the robots and the source, indicating more effective approaching of the robots to the source.
From Virtual Reality to the Emerging Discipline of Perception Engineering

This article makes the case that a powerful new discipline, which we term perception engineering, is steadily emerging. It follows from a progression of ideas that involve creating illusions, from historical paintings and film to modern video games and virtual reality. Rather than creating physical artifacts such as bridges, airplanes, or computers, perception engineers create illusory perceptual experiences. The scope is defined over any agent that interacts with the physical world, including both biological organisms (humans and animals) and engineered systems (robots and autonomous systems). The key idea is that an agent, called a producer, alters the environment with the intent to alter the perceptual experience of another agent, called a receiver. Most importantly, the article introduces a precise mathematical formulation of this process, based on the von Neumann–Morgenstern notion of information, to help scope and define the discipline. This formulation is then applied to the cases of engineered and biological agents, with discussion of its implications for existing fields such as virtual reality, robotics, and even social media. Finally, open challenges and opportunities for involvement are identified. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 7 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
A model-independent redundancy measure for human versus ChatGPT authorship discrimination using a Bayesian probabilistic approach

Abstract The academic and scientific world in general is increasingly concerned about their inability to determine and ascertain the identity of the writer of a text. More and more often the question arises as to whether a scientific article or work handed in by a student was actually produced by the alleged author of the questioned text. The role of artificial intelligence (AI) is increasingly debated due to its dangers of undeclared use. A current example is undoubtedly the undeclared use of ChatGPT to write a scientific text. The article promotes an AI model-independent redundancy measure to support discrimination between hypotheses on authorship of various multilingual texts written by humans or produced by intelligence media such as ChatGPT. The syntax of texts written by humans tends to differ from that of texts produced by AIs. This difference can be grasped and quantified even with short texts (i.e. 1800 characters). This aspect of length is extremely important, because short texts imply a greater difficulty of analysis to characterize authorship. To meet the efficiency criteria required for the evaluation of forensic evidence, a probabilistic approach is implemented. In particular, to assess the value of the redundancy measure and to offer a consistent classification criterion, a metric called Bayes factor is implemented. The proposed Bayesian probabilistic method represents an original approach in stylometry. Analyses performed over multilingual texts (English and French) covering different scientific and human areas of interest (forensic science and socio-psycho-artistic topics) reveal the feasibility of a successful authorship discrimination with limited misclassification rates. Model performance is satisfactory even with small sample sizes.
CCCS: Contrastive Cross-Language Code Search Using Code Graph Information

Developers often search and reuse existing code snippets to improve software development efficiency during software development. Currently, researchers have proposed many code search methods. However, the search intent of existing methods is basically a natural language query. In order to support code migration and code refactoring, it is necessary to search relevant code snippets of another programming language with code snippets of one programming language. In this paper, we propose a C ontrastive C ross-language C ode S earch method using code graph information, called CCCS . CCCS first converts code snippets into high-dimensional vectors using pre-trained CodeBERT to extract the sequence features of code snippets. Next, the structural features of code snippets are extracted using a graph convolutional neural network. Finally, the model is trained using the contrastive learning method to optimize the vector representation of cross-language code snippets, enabling the model to distinguish code snippets from different programming languages with the same functionality. To evaluate the effectiveness of our method, we conducted comparison experiments and ablation experiments on a small-scale dataset and a large-scale dataset, respectively. The experimental results show that our method far outperforms the state-of-the-art baseline model in terms of MRR metrics.
The aesthetics of more-than-human design: speculative energy briefs for the Chthulucene

This paper is a theoretical contribution to HCI that considers the more-than-human (MTH) as an intrinsic part of human–computer interaction design. In particular, it focuses on MTH as central to responses to the climate crisis as manifested in energy-demand reduction and smart meters. This is explored by expanding on the notion of the ‘design event’, defined aesthetically as the patterns or conformation of unfolding and becoming of heterogenous human and non-human elements. It is with this version of the design event that the MTH can be more directly and effectively engaged. We do this with reference to environmental problems as signaled by Haraway’s speculative future ‘the Chthulucene’ – a worlding for liveable futures. The paper views design briefs in HCI, and design more broadly, as problematics for exploring and determining aesthetic-possible pathways for invention, which necessarily involves MTH elements. Three interrelated design briefs are presented that propose how practitioners might go about addressing energy-demand reduction and metering and provide a set of guidelines on how to devise and write speculative more-than-human briefs. This, the paper argues, involves becoming sensitive to speculative MTH compositions where novel forms of ‘sense making’ orient alternative possibilistic – idiotic – relations to energy.
Is There a Relationship Between Country Development and Citizens’ Level of Digital Skills?

This paper examines the relationship between the citizens' level of digital skills and the level of country development. Country development is measured by the Gross National Income per capita (GNI p.c.), while the level of digital skills is measured by the World Economic Forum. We gathered data from the World Bank databases for 2019 for 135 countries worldwide. For the analysis, we used correlation analysis. The correlation analysis results show a significant strong positive linear relationship between the level of digital skills and GNI p.c., i.e., the level of country development. The results highlight the important role of country development in developing an adequate level of the crucial skills of the 21st century – digital skills.
DEVELOPMENT OF MULTIMEDIA LEARNING BASED ON MICROSOFT POWER POINTS TO INCREASING THE RESULTS OF CLASS III STUDENTS' VOCATIONAL SCHOOL LEARNING ON THEME 4 "OBLIGATIONS AND RIGHTS"

Development of multimedia-based power point learning media to improve PPKn learning outcomes for class III students on theme 4 "My Obligations and Rights". thesis. Basic education. graduate programs. Medan State University. 2023. This study aims to: (1) Describe the development of PPKn multimedia-based learning media for grade III students on theme 4 "My Obligations and Rights". (2) testing the feasibility of PPKn multimedia-based learning media for class III students on theme 4 "My Obligations and Rights". (3) Observing the increase in the results of Civics learning for class III students at SD 101884 Limau Manis. This research is a development research using the ADDIE development model namely Analyze, Design, Development, Implementation, and Evaluation. The subjects of this study were class III students at SD 101884 Limau Manis for the academic year 2022/2023, a total of 23 students. The instruments used were media expert validation questionnaires and PPKn material expert validation questionnaires. The results of this study indicate that (1) the process of developing multimedia-based power point learning media to improve PPKn learning outcomes for class III students in class III on theme 4 "My Obligations and Rights". Multimodal literacy-based literacy activities include five steps, namely: Analyze (analyze), Design (design), Development (development), Implementation (implementation), and Evaluation (assessment). (2) teaching materials for literacy activities based on multimodal literacy are feasible to use with an average percentage reaching 95.05% and the criteria are very feasible. 91 "high" criteria.
THE FOUR FACTORS DOMINATING ONLINE STREAMING PLATFORMS AS WITNESSED BY NETFLIX IN THE WAKE OF GLOBALIZATION

The globalization process has increased everything's accessibility and connectivity. Through information and technology, globalization makes it easier to move products, services, cultures, innovations, and creative ideas. As one of the most popular online streaming services, Netflix is fostering cross-cultural exchanges as part of the increasing globalization of capitalism. The author uses written sources, information, and evidence in the form of quotes from written works, written papers, and written responses to articles, reports, publications, and statistical data that are pertinent to Netflix. The author will apply globalization theory and a transnational approach to investigate why individuals favor Netflix over alternative online streaming services. These two theories are implemented in elaborating four dominant factors that Netflix has influenced the new habit patterns in today's society. Market growth will always play a role in globalization-related economic and cultural activities and processes. As a result, four key elements affect Netflix’s market dominance: substitution, barriers to entry and potential competition, measures of profitability, and market share.
Reuse and Reproducibility: Describing Cross-Domain Research Data in the Science Project Climate Neutral and Smart Cities

The Climate Neutral and Smart Cities project is part of the EOSC Future WP 6.3, exploring the best approaches for sharing data within cross-domain research projects. This paper looks at the implications for metadata exchange in a cross-domain research project, as explored in the project prototype. Cross-domain standard metadata is meeded to support collaborative research teams combining a mix of expertise, particularly around data lineage.&amp;nbsp;
Collaborative Wheat Lodging Segmentation Semi-Supervised Learning Model Based on RSE-BiSeNet Using UAV Imagery

Lodging is a common natural disaster during wheat growth. The accurate identification of wheat lodging is of great significance for early warnings and post-disaster assessment. With the widespread use of unmanned aerial vehicles (UAVs), large-scale wheat lodging monitoring has become very convenient. In particular, semantic segmentation is widely used in the recognition of high-resolution field scene images from UAVs, providing a new technical path for the accurate identification of wheat lodging. However, there are still problems, such as insufficient wheat lodging data, blurred image edge information, and the poor accuracy of small target feature extraction, which limit the recognition of wheat lodging. To this end, the collaborative wheat lodging segmentation semi-supervised learning model based on RSE-BiseNet is proposed in this study. Firstly, ResNet-18 was used in the context path of BiSeNet to replace the original backbone network and introduce squeeze-and-excitation (SE) attention, aiming to enhance the expression ability of wheat lodging characteristics. Secondly, the segmentation effects of the collaborative semi-supervised and fully supervised learning model based on RSE-BiSeNet were compared using the self-built wheat lodging dataset. Finally, the test results of the proposed RSE-BiSeNet model were compared with classic network models such as U-Net, BiseNet, and DeepLabv3+. The experimental results showed that the wheat lodging segmentation model based on RSE-BiSeNet collaborative semi-supervised learning has a good performance. The method proposed in this study can also provide references for remote sensing UAVs, other field crop disaster evaluations, and production assistance.
Small objects tracking in satellite videos based on feature enhancement and motion estimation

Small objects tracking in satellite videos faces challenge due to their small size and object occlusion. A novel correlation filter-based tracker combined with feature enhancement and motion estimation is proposed. The guided filtering method is introduced to realize the edge-preserving detail enhancement. Meanwhile, to fully exploit the advantage of the edge-preserving enhanced images, the Laplacian of Gaussian (LOG) edge feature and the Histogram of Oriented Gradient (HOG) textual feature are simultaneously employed to obtain the fused response. Besides, since small objects in satellite videos are susceptible to be occluded, an adaptive Kalman filtering is designed to estimate the motion of the targets, and a decision-level fusion strategy is developed for the final trajectory determination of the small objects. The proposed tracker is evaluated on the SatSOT dataset compared with 9 state-of-the-art trackers. The results reveal that the precision of 73.67% and the success rate of 41.83% are respectively obtained, which demonstrated the effectiveness of the proposed method.
Enhanced Explanations for Knowledge-Augmented Clustering using Subgroup Discovery

Contemporary machine learning techniques are capable of extracting complex structure from data in a way that complements or exceeds manual examination, yet, as is welldocumented, many of these techniques suffer from a lack of interpretability. This paper extends previous work on explainable and interpretable machine learning, in particular on the ‘Knowledge-Augmented Clusters (KnAC)’ approach, allowing human users to benefit from uninterpretable ‘black box’ models to extract structure from datasets by clustering and to make this better understandable. One of the key functions of KnAC is to relate expert-annotated clusters to clusters that have been identified by a machine learning method, and then provide a comprehensible explanation, thus clarifying the relationships that KnAC discovered. Our novel contribution in this paper is to examine the usefulness of subgroup discovery as a way to generate comprehensible explanations within KnAC, and to compare this to the existing approach based on the XAI algorithm Anchors through a detailed evaluation. We find that the approach using subgroup discovery performs equally or better in our extensive experimentation testing this on six different datasets.
Evaluating Learning Algorithms for Keystroke Based User Authentication

The field of keystroke-based authentication increasingly relies on AI technologies for increased robustness and accuracy. A number of such approaches have been recently proposed, with variable levels of success and computational demands. This paper aims to investigate the comparative performance of supervised and unsupervised learning using two algorithms, KNN and K-means++, and explore the impact to the keystroke-based user authentication. Three keystroke features are selected: dwell time, flight time and press-to-press latency. FAR, FRR and accuracy are used as the performance metrics of KNN while purity and silhouette coefficient are selected as the performance metrics of K-means++. The experimental results show that KNN is more suitable for the analysed scenarios and has a slightly higher accuracy of 74.58% than K-means++. We further propose a method of reprocessing the dataset based on modifying the outliers when unsupervised algorithm was also able to get very good performance with 0.8767 of purity.
Training People with Mathematical Literacy Ability by Building a Level- Based System of Trigonometric Functions in the Setting of a Virtual Factory

The waveform of inspection instruments can be used to determine the differences in the quality of products made in industrial processes; however, mathematical literacy skills are required to operate the inspection instruments. The spread of the COVID-19 pandemic has rapidly necessitated a reduction in cluster infections; therefore, it is imperative to accelerate the application of virtual factories to change the current training mode for employees. Through the metaverse concept, this study simulated the scenarios of a company's inspection room and designed a level-based system of mathematical problems for the operation of instruments in the virtual test room. This system can automatically determine and provide correct answers, which enables students to understand the relationships between electronic measuring instruments and trigonometric functions, alternating current and time variation functions, and the principles between electronic signals related to instruments for the electronics industry and mathematics. The survey results show that this system can effectively improve students' learning attitudes and attention in learning, which can help them become more motivated and interested in learning. The application of this system can achieve multiple goals, including developing students' perception of workplaces before taking a job, reducing connection conflicts during work, and indirectly lowering the costs of developing skills for employees.
CAN THE COMBINATION OF FACIAL FEATURES ENHANCE THE PERFORMANCE OF FACE RECOGNITION?

The field of computer vision and pattern recognition has shown great interest in facial recognition due to its wide range of applications. These applications span across historical and genealogical research, forensic science, searching for missing family members, analyzing social media, automatically managing and annotating image databases, and identifying kinship relationships. This research paper aims to address the challenges associated with facial recognition by introducing two innovative approaches: Fusion-based Classifier Combination (FCC) and Sequential CNN Deep learning-based face recognition (S-CNN). In the first part of the study, we assess the effectiveness of three techniques: Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and a hand-crafted learned technique called Compact Binary Facial Descriptors (CBFD). To overcome these challenges, we employ a classification step that utilizes a novel multi-classifier combination model. In the second part, we propose a novel method where we extract high-level features from multiple image regions treated as sequential data using ensemble of Convolutional Neural Networks (CNNs). These features are then fed into a Deep Neural Network (DNN) for facial recognition. The experimental results obtained from well-known face databases, including Labeled Faces in the Wild (LFW) and ORL, highlight the competitive performance of both the proposed multi-classifier combination model and the S-CNN deep learning model when compared to state-of-the-art methods .
Multi-tiered Reversible Data Privacy Protection Scheme for IoT Based on Compression Sensing and Digital Watermarking

Privacy preservation and low-cost data processing have become two critical issues in the era of Internet of Things (IoT) due to the widespread deployment of lightweight smart surveillance and sensors. In this paper, we propose a multi-tierd reversible data privacy preservation system based on compressive sensing (CS) and watermarking. The system anonymizes the region of interest (ROI) using an obfuscation matrix, while compressing and encrypting the entire document. CS provides the first-tier encryption for data documents, and the obfuscation matrix provides the second-tier encryption for sensitive parts of data documents. The system offers a multi-tiered privacy protection scheme where restricted-authorized users can only access non-sensitive data while fully authorized users can access the entire document. To implement the reversible elimination of the obfuscation matrix, two watermark embedding methodologies are proposed in CS domain in order. In both methods, the watermark generated by the obfuscation matrix is embedded in the encrypted CS measurement values, with the first methodology concentrating on the optimal data reconstruction quality and the second methodology working to balance storage space and data restoration quality. Extensive experimental results indicate the superiority of the proposed methodologies over other conventional reversible data privacy preservation schemes.