Reviewerly/exHarmony huggingface dataset:
TRAVERSING SONIC TERRITORIES (TST)

What happens when musicians improvising on acoustic instruments sample and exchange their sound libraries? How can such a transgression of sonic territories contribute to an expanded understanding of one’s own sonic identity? And could this b/lending of identities point to a more ambiguous yet vibrant field of intra-play? Departing from these questions, this project intends to challenge our idea of sonic identity as a personal subject-oriented entity, and consequently investigate how a collaborative sharing of sampled sounds, can contribute to an expanded understanding of the sounds we play and are played by. Individual idiomatic approaches to one’s own instrument are thus interfered as we transgress habitual boundaries for action possibilities and musical imagination. The practice circulates from the duo of Torben Snekkestad and Søren Kjærgaard toward external collaborators, where the sharing process involves different approaches to audio sampling and mapping, embedding and embodying, listening and playing with each other’s sonic material to a point where authorship, origin, instrument and sonic identity is diffracted.
Discrete-time filtering algorithms based on the modified weighted Gram – Schmidt orthogonalization for discrete-time stochastic systems with multiplicative and additive noises

Discrete-time stochastic systems with multiplicative and additive noises describe a wide class of mathematical models of complex systems, for example, industrial-technological, energy, economic, telecommunication, aerospace systems, etc. An important class of algorithms for processing measure ment information in complex systems are Kalman-type discrete-time filtering algorithms. Purpose. Construction of new discrete-time filtering algorithms for discrete-time linear systems with multiplicative and additive noises based on numerically stable modified weighted Gram – Schmidt orthogonalization (MWGS). Methodology. The methods of computational linear algebra were used, namely, the direct procedu re of MWGS-orthogonalization, the theory of Kalman filtering, methods of scientific programming in MATLAB. Findings. New LD-algorithms for discrete-time filtering in covariance and informational form for discrete-time stochastic systems with multiplicative and additive noises are constructed. The algorithms have an extended array form allowing updates for all necessary filter values. The method employs numerically stable modified weighted Gram – Schmidt orthogonalization. Algebraic equiva lence of LD-filters to Kalman-type covariance and informational algorithms for linear discrete time stochastic systems with multiplicative and additive noises is proved. The conducted numerical experiments have shown the effectiveness of the proposed algorithms using the example of solving the problem of parametric estimation of a model of almost rectilinear motion, as well as their savings in computation time compared to previously constructed UD filters. Value. New discrete-time filtering LD-algorithms can be used as a reliable computational alterna tive to the Kalman-type “standard algorithms” since they have the property being numerically stable to machine round-off errors due to the use of the MWGS orthogonalization computational procedure at each iteration of the algorithm. The results can be used to solve problems of measurement information processing in discrete-time systems with multiplicative and additive noise.
Caracterização dos estilos de aprendizagem em alunos do Bacharelado Técnico usando o questionário Honey-Alonso

The objective of the research was to characterize the learning styles that predominate in the technical high school students of the Oswaldo Guayasamín Educational Unit of the city of Santo Domingo, through the CHAEA Questionnaire for the improvement of their formative process. The methodology was non-experimental, descriptive, explanatory and field under a mixed research approach using for this purpose an internationally validated Instrument by the authors Honey and Alonso (2012), which was adapted to the Ecuadorian context through the opinion of 3 experts and a KR20 reliability of 0.80, the sample by convenience was applied to 174 students of first, second and third year of high school through Google Forms and the data were processed with the SPSS V.23 and Excel program. The results confirmed the moderate preference of the active style with 112 students; in consideration of gender, the male population has a preference for the active style (74), while the female population shares the same (38) but tends towards the pragmatic (36). In conclusion, the prevalence of the active learning style in technical education accompanied by the equipment for practices, complements student learning.
Trajectory Planning Framework Combining Replanning and Hierarchical Planning

To enable autonomous vehicles to respond quickly to changes in the surrounding environment and to generate safe and stable trajectory, we propose a safety-based hierarchical trajectory planning method that divides the planning module into two parts: the planner and the replanning monitor. In the planner, we first propose a fast linear trajectory planning method that uses the mass point model instead of the vehicle model and linearizes the collision avoidance constraint using the Big-M method for linear programming (Big-M) to obtain a linear programming model. The complete vehicle kinematic model is then built in the nonlinear programming stage, the collision constraints and cost functions are refined, and the rough solution of the linear programming is brought into it to obtain the exact solution. The replanning monitor is divided into safety and comfort monitors. The safety monitor will always pay attention to the changes in the surrounding environment and calculate whether the vehicle is in danger of collision in the future period, while the comfort monitor is more concerned with the comfort of driving the vehicle; when the monitor requirements are not met, replanning will be carried out. By simulating the driving environment, the proposed algorithm can form a safe and comfortable trajectory, which verifies the rationality of the proposed method.
The Existence of Artificial Intelligence in the Future

Artificial intelligence is arranged and communicates in a language structure to have a machine learning algorithm that picks up the faster solution in solving humanity’s problems. However, the separation between good and bad all comes down to the human’s intention and determination. Hence, it is crucial to look at the data patterns and come out with the most appropriate machine algorithm. The layering of nodes and information is forming to create a piece of new knowledge. It is a universal language algorithm that speaks with one another to convey the same message. In other words, dimensionality and data simplicity lure in more knowledge and understanding of the problem.
The Relationship Between Executive Function with Learning Agility in Police Officers

Background: Learning agility is the willingness to learn from experiences and apply those learnings to new situations. Agile knowledge profoundly impacts performance, so several companies are incorporating it into their learning strategy. In comparison, cognitive processes and behaviors indicate learning agility. Learning agility has been used in many companies as an essential consideration for selecting high-potential talent. The study aims: This study aimed to determine the relationship between executive function and learning agility in police officers. Method: The research method used is a correlational quantitative method with Spearman as a data analysis technique. The research participants are 107 police officers. Data collection on learning agility uses a learning agility scale (17 items, α=0,922), and executive function uses a neuropsychological test tool. Result: Based on the results of the analysis test between executive function and learning agility on police officers with the Digit Span Backwards, Stroop Test, Trail Making Test A &amp; B, Ruff's Five-point test, and the Verbal Fluency Test shows that there is no significant negative relationship between executive function and learning agility Conclusion: Based on the analysis of the data, it was found that there is an essential relationship between executive function and learning agility in police officers.
A Multivariate Time Series Anomaly Detection Model Based on Spatio-Temporal Dual Features

This paper proposes a novel unsupervised model for multivariate time series anomaly detection (TSAD), targeting the challenges of sparse and unlabeled abnormal data, as well as high dimensionality in IoT applications. The core of our model is to extract spatiotemporal dual features through a coherent architecture that captures both temporal dependencies and spatial correlations among multiple variables. Specifically, a deep autoencoder is employed to capture the spatial features of multivariate time series data, while a multi-scale sparse Transformer network is used to extract the temporal features. An anomaly detection module based on the dynamic threshold POT method is used for detect anomalies, and extensive experiments are conducted on publicly available datasets. The result on the SMAP dataset show that our proposed model improves the precision and F1 score by 11.3% and 5.48% respectively compared with the latest baseline method. On the NAB dataset, the F1 score is increased by 0.47%. On the SWaT dataset, the precision is improved by 0.62%.
Development and research of deep neural network fusion computer vision technology

Abstract Deep learning (DL) has revolutionized advanced digital picture processing, enabling significant advancements in computer vision (CV). However, it is important to note that older CV techniques, developed prior to the emergence of DL, still hold value and relevance. Particularly in the realm of more complex, three-dimensional (3D) data such as video and 3D models, CV and multimedia retrieval remain at the forefront of technological advancements. We provide critical insights into the progress made in developing higher-dimensional qualities through the application of DL, and also discuss the advantages and strategies employed in DL. With the widespread use of 3D sensor data and 3D modeling, the analysis and representation of the world in three dimensions have become commonplace. This progress has been facilitated by the development of additional sensors, driven by advancements in areas such as 3D gaming and self-driving vehicles. These advancements have enabled researchers to create feature description models that surpass traditional two-dimensional approaches. This study reveals the current state of advanced digital picture processing, highlighting the role of DL in pushing the boundaries of CV and multimedia retrieval in handling complex, 3D data.
SCGTS: Semantic Content Guiding Teacher-Student Network for Group Activity Recognition

Group activity recognition refers to the process of comprehending the activity performed by multi-person in a video. However, most methods need predefined individual labels during training or testing, which is impractical and lacks intelligence. Moreover, they only consider visual features and ignore corresponding semantic information. To address these issues, a Semantic Content Guiding Teacher-Student (SCGTS) network is developed. SCGTS depends neither on predefined individual labels nor on any detection methods. It utilizes a large-scale language model as the teacher network to extract content features from textual descriptions of labels. The semantic content features are then used to supervise the training of the baseline network which serves as the student network. In this way, the student network is enforced to mimic the teacher network to extract visual features with semantic information. Experiments on 2 challenging benchmarks, including Volleyball and NBA, demonstrate SCGTS outperforms the baseline network and achieves the leading performance.
ALBA: Novel Anomaly Location-Based Authentication in IoMT Environment Using Unsupervised ML

Smartphones have become essential components in the Internet of Medical Things (IoMT), providing convenient interfaces and advanced technology that enable interaction with various medical devices and sensors. This makes smartphones serve as gateways for sensitive data that could potentially affect patients’ health and privacy if compromised, making them primary targets for cybersecurity threats. Authentication is crucial for IoMT security, as its effectiveness relies on its resistance to any conditions of environment, device, or user. In this paper, we propose the Anomaly Location-based Authentication (ALBA) method using GPS technology and a lightweight unsupervised ML algorithm with more stable features. Our experimental results showed that the model successfully identified anomalous locations across three distinct datasets, demonstrating the adaptability of ALBA.
An Educational Virtual Reality Game from an Embodied Cognitive Perspective -Take the Game Poetry as an Example

With the rapid development of virtual reality (VR) technology, it has become a trend to apply VR technology to assist teaching in the field of education. At present, the learning of traditional poetry is often in the form of book learning. The lack of physical and sensory interaction in the learning process leads to a lack of initiative and enthusiasm for learning, resulting in low learning efficiency. Based on this background, this paper develops a poetry learning game based on VR technology, which creates interactive scenarios based on the depiction of poems and allows users to learn poems in an immersive experience. Users can complete their poetry learning objectives by experiencing the games plots and completing game tasks. Based on embodied cognition theory, the game applies the strong sense of presence and immersion of VR technology to enhance the interaction between the users body and the poetry scene, thus improving the users knowledge and understanding of the poetry, deepening their memory of the poetry and enabling them to learn in a more effective and interesting form. The game uses Cinema4D to design and model the poetry scenes and the game engine Unity to realize the interaction with the user. Through the study of the game scenes and user evaluation, the experimental results show that the game achieves the design purpose and has an application value.
Traffic Rule Violation and Accident Detection Using CNN

Traffic rule violations and accidents are major sources of inconvenience and danger on the road. In this paper, we propose a convolutional neural network (CNN)-based approach for detecting these events in real-time video streams. Our approach uses a YOLO-based object detection model to detect vehicles and other objects in the video and an IOU-based accident detection module to identify potential accidents. We evaluate the performance of our approach on a large dataset of traffic video footage and demonstrate its effectiveness in detecting traffic rule violations and accidents in real time. Our approach is able to accurately detect a wide range of traffic rule violations, including wrong-side driving, signal jumping, and over-speed. It is also able to accurately track the movements of objects in the video and to identify potential accidents based on their trajectories. In addition to detecting traffic rule violations and accidents, our approach also uses an ANPR module to automatically read the license plate numbers of detected vehicles. This allows us to generate e-challans and punishments for traffic rule violations, providing a potential deterrent to future violations. Overall, our proposed approach shows promise as a tool for detecting and preventing traffic rule violations and accidents in real-time surveillance systems. By combining powerful object detection and motion analysis algorithms with an ANPR module, it is able to accurately and efficiently identify traffic rule violations and accidents, providing valuable information for traffic management and safety.
Trends in the Development of Choreographic Education in Ukraine in Conditions of Digitalization: Standards, Innovative Models

The aim of this work is to analyze the trends in the development of choreographic education in Ukraine in the context of digitalization, as well as to identify standards and innovative models for further development. Scientific methods of analysis, synthesis, and deduction were used to study the mentioned problems. The results demonstrated the current standards of training of choreographers in Ukraine, the peculiarities of the implementation of digitalization, and its impact on innovative methods of teaching choreography. The importance of using the European experience in the formation of cooperation between educational institutions and potential employers has been proved. The indicated borrowing of experience will make it possible to adapt the Ukrainian training conditions of choreographers to modern educational trends, harmonize individual stages of training and generally increase its level, quality and balance. In Ukraine, the unique approach to innovative choreographer training lies in imparting a diverse range of combinations, predominantly showcasing Ukrainian choreographic displays enriched with a harmonious blend of European traditions. The conclusions noted that further relevant trend of innovative development of choreography education in Ukraine will be attempts to harmonize the Ukrainian and European training systems.
Metrics for Evaluating Interface Explainability Models for Cyberattack Detection in IoT Data

The importance of machine learning (ML) in detecting cyberattacks lies in its ability to efficiently process and analyze large volumes of IoT data, which is critical in ensuring the security and privacy of sensitive information transmitted between connected devices. However, the lack of explainability of ML algorithms has become a significant concern in the cybersecurity community. Therefore, explainable techniques are developed to make ML algorithms more transparent, thereby improving trust in attack detection systems by its ability to allow cybersecurity analysts to understand the reasons for model predictions and to identify any limitation or error in the model. One of the key artifacts of explainability is interface explainability models such as impurity and permutation feature importance analysis, Local Interpretable Model-agnostic Explanations (LIME), and SHapley Additive exPlanations (SHAP). However, these models are not able to provide enough quantitative information (metrics) to build complete trust and confidence in the explanations they generate. In this paper, we propose and evaluate metrics such as reliability and latency to quantify the trustworthiness of the explanations and to establish confidence in the model’s decisions to accurately detect and explain cyberattacks in IoT data during the ML process.
Model-based Filtering Techniques for Recommendation Systems in Healthcare Domain

A drug recommendation system is a technology-based solution that assists healthcare professionals in suggesting appropriate medications for patients based on various factors such as medical history, symptoms, demographics, and drug effectiveness. The system utilizes advanced algorithms and techniques to analyse large datasets, including patient information, drug profiles, clinical studies, and drug-drug interactions, among others. This research study presents a drug recommendation system that utilizes model-based filtering techniques, specifically Singular Value Decomposition (SVD) and Non-Negative Matrix Factorization (NMF). The system aims to enhance the accuracy and effectiveness of drug recommendations by analysing large diabetes patient datasets containing patient information, drug profiles, and clinical studies. To evaluate the system's performance, three standard evaluation metrics are employed. Furthermore, a visual comparison of the evaluation metrics was presented using a bar graph, which clearly demonstrates the superiority of SVD over NMF. The findings contribute to the advancement of personalized healthcare and emphasize the importance of employing suitable algorithms for accurate medication suggestions.
Hybrid Entropy-based Weighted Graph Embedding Model for Multi-Document Text Summarization

Managing the ever-growing volume of textual information presents a pressing challenge in today's information-rich landscape. Multi-document summarization, a vital task in this context, faces formidable hurdles such as redundancy, information misalignment, and the critical need for context preservation. Recent advancements in natural language processing have introduced innovative techniques to tackle these challenges. Hence this research delves into these challenges and proposes an innovative solution the Hybrid entropy-based weighted graph embedding model based Elytron Vespid-B optimized BiLSTM model (HEWGEF+EVR BiLSTM). The objective is to significantly advance multi-objective multi-document text summarization techniques. These challenges are solved by employing standard methods for preprocessing multi-document data. The TF-IDF vectors are utilized to extract word significance, a fundamental step in content evaluation. The novel approach incorporates the Hybrid entropy-based weighted graph embedding model, which evaluates sentence-word correlations through entropy-based attribute elimination. This unique combination results in feature-rich representations of the text. The summarization process is enhanced with the Rank-based optimized BiLSTM classifier, equipped with a dedicated layer for sentence prioritization. This strategic approach optimizes sentence ranking, a pivotal aspect of effective summarization. Notably, the parameters of the BiLSTM classifier are optimally tuned using collaborative swarm optimization, merging wasp and beetle behaviors for optimal performance. The experimental results underscore the effectiveness of the proposed method across diverse sources. For the Daily Mail dataset (d1), achieved F1-scores (ROUGE-1), F1-scores (ROUGE-2), and F1-scores (ROUGE-L) of 0.70, 0.39, and 0.67, respectively. Similarly, for the DUC 2002 dataset (d2), the proposed model obtained F1-scores (ROUGE-1) of 0.73, F1-scores (ROUGE-2) of 0.35, and F1-scores (ROUGE-L) of 0.72. Likewise, for the DUC 2004 dataset (d3), the proposed model secured F1-scores (ROUGE-1) of 0.65, F1-scores (ROUGE-2) of 0.34, and F1-scores (ROUGE-L) of 0.67.