Reviewerly/exHarmony huggingface dataset:
Research on Three-Dimensional Full Coverage Path Planning Algorithm for Deep-Sea Mining Vehicles

There are many difficulties for deep-sea mining vehicles to accomplish mining tasks autonomously and efficiently, such as insufficient environmental information, low data processing capabilities of deep-sea mining vehicles, and difficult real-time environmental modeling. This paper proposes a three-dimensional full-coverage path planning algorithm for deep-sea mining vehicles. Firstly, the deep-sea environment information of the mining area is collected by an autonomous underwater robot to construct a three-dimensional static map model. According to the geological type data, a three-dimensional grid model is constructed and the passable and impassable areas are divided. Then, the three-dimensional grid map is divided into multiple flat regions based on the gradient information of the region using the flood fill algorithm, and the minimum transition distance between each flat region is calculated using the Floyd algorithm. Next, a heuristic function is established combining grid status, turning factor, and height information for grid transfer. Considering the problem that mining vehicles will enter dead zones inevitably, the virtual endpoint for leaving the dead zone is found by comprehensively utilizing the distance between grids and the plane minimum transition distance in which the grid is located. To guide the mining vehicle to leave the dead zone and ensure the smallest possible repetition rate, the A* algorithm is improved to find the final escape path. Finally, simulation experiments were designed and conducted using Matlab. The results show that the proposed algorithm can ensure a mining coverage rate of 100%, significantly reduce the vertical change of mining vehicles without a significant increase in repetition rate or turning times, reduce energy consumption. The proposed algorithm improves the work efficiency and economic benefits of deep-sea mining vehicles and provides a theoretical basis for underwater path planning of deep-sea mining vehicles.
Deviation from Equilibrium of QMS of Weak Coupling Limit Type with Respect to Uniform and Completely Nonequilibrium Invariant States

We study the deviation from equilibrium with respect to a special class of states, the so called uniform and completely nonequilibrium, which were introduced and characterized in [11]. We compute explicitly such a deviation, we obtain an equivalent expression in terms of circulant matrices and give a bound for it. Finally, as an example, we make explicit computations in the three level case.
FAST DEVELOPMENT KANGEAN ISLAND TOURISM WEBSITE USING MAF-INC MODEL

Application development is generally carried out for 90 days or 12 weeks, and there are even some projects up to 180 days. The longer the development time for a software project, the more wastage of time, money, and effort from the developer’s side. Researchers offer a new method solution, MAF-INC, to speed up application development time. Researchers offer a new method solution, MAF-INC, to speed up application development time. MAF-INC is an application development method combining Two Models: the Merapi Analysis Framework (MAF) and the Incremental Model. From the results of the implementation of the MAF-INC Model, researchers can develop web applications for Kangean Island tourism for 74 days, and this is better than the incremental Model in general. In this research, we focus on web software developed. The novelty of this manuscript is we use Merapi Analysis Framework (MAF) for ideas validation and combine it with the Incremental model. The test results show that the development of applications can be completed faster, between 5% -18%.
A Study on Improved YOLOv7-pose and Bytetrack for Dual Person Tracking

This study proposed a joint network that integrates person re-identification (Re-ID) and object detection tasks to address the challenge of identity changes in traditional Chinese medicine massage. The algorithm combined a CBAM attention mechanism with YOLOv7 and incorporated a Re-ID branch into the YOLOv7-pose detection algorithm. By leveraging feature matching in the Bytetrack tracking algorithm, the Re-ID features compensate for missing appearance features and optimize the tracking strategy, reducing identity switches. The experimental results demonstrated that the proposed algorithm achieved significant improvements. It successfully captured all the data in the videos, outperforming the Deepsort and Strongsort algorithms with failure to capture rates of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$82.3{{\% }}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$72.9{{\% }}$</tex> , respectively. The target ID change rate was reduced to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.1{{\% }}$</tex> , making data collection less challenging than the original Bytetrack algorithm. The algorithm ensured consistent identification of physiotherapists and patients after interactive behaviors, ensuring data consistency during the data collection.
Clothes Changing Person Re-identification Using Spatial Information and Clothes Sensitivity Awareness

Person re-identification is a hot topic in the field of computer vision. In the long-term re-identification, due to the change of person clothes, person re-identification has become a difficult task. In view of the above difficulty, this paper proposes a dual-category recognition method for clothes-changing people, and use spatial attention mechanism to capture important information in the image. Then, the sensitivity awareness of clothes changes is further proposed to expand the clothes differences between categories and reduce the interference of similar clothes in clothes classification. This re-identification method based on clothes sensitivity awareness and spatial attention mechanism can realize the comprehensive extraction of clothes-independent features. Extensive experiments on benchmark datasets have confirmed the effectiveness of the method.
English Tutoring for Orphanage Children

This Community Service (CS) activity has the theme of introducing English learning for children at Naungan Kasih Orphanage of St. Elisabeth Ende. This is done with the aim of guiding children in this Orphanage so that they can have English skills and knowledge. The main problem faced by these children is for kindergarten and elementary school children, are not yet familiar with English, while high school level children already know and obtain English learning, but with limited media, time, etc. so they learn less English. This makes them have no motivation and no interest in learning English. This activity went well and they are enthusiastic. This activity uses several learning strategies made by the CS team so that children do not get bored learning English, and have the attraction of learning English. It is expected that the children in this orphanage are able to apply the use of English in schools and communities and overcome the difficulties that they face. Children learn well, full of enthusiasm and it is expected that there will be an improvement in their English learning.
A Comparison of Motion Planning Methods for Autonomous Ground Vehicle Exploration and Search

Abstract Navigation of an Autonomous Ground Vehicle (AGV) is a major challenge for complex applications such as exploration of uncharted terrain, search and rescue (SAR) missions, and military assignments. An AGV is subjected to numerous uncertainties such as unexpected obstacles, uneven terrain, and hazardous regions. Motion planning algorithms are responsible for the safe, quick, and energy-efficient autonomous navigation of an AGV in the surrounding environment. In this paper, we compare the performances of conventional motion planning algorithms that include A*, RRT, RRT*, pRRT, and APF for exploration and search tasks. The comparison is carried out in a realistic environment simulated in AutoVRL, a high-fidelity simulator built using open-source tools. This study assumes the presence of a high-level planner that provides the algorithms with environmental information, and application-specific goal positions. The algorithms are compared on the basis of computation time, and efficiency of the generated trajectories, to navigate to goal positions determined by the high-level planner. The results indicate that A* and pRRT perform significantly better than the other tested algorithms, yielding the most efficient trajectories with the least computation time.
Wavelet Based Nonlinear Time-Frequency Control Theory With Local Adaptability

Abstract A novel nonlinear control concept featuring simultaneous control of vibration amplitude in the time domain and spectral response in the frequency domain is developed and subsequently incorporated to maintain dynamic stability in these nonlinear dynamics by denying bifurcation and route-to-chaos from coming to pass. This study mathematically examines the time-frequency control theory and derives the ranges of the regression step sizes that ensure fast convergence and solution stability of the controller design. The local adaptability is novel, intelligent, self-adjusting, and universally applicable to addressing bifurcated and chaotic responses. Incorporating the control theory into designing dynamic systems governed by discontinuous, time-delayed nonlinear oscillators would realize unconditional stability, high-performance quality, and operation efficiency. In the end, a novel time-delayed vibro-impact oscillator was investigated. Its instability in manufacturing would result in premature tool breakage, increased wear rate, and poor workpiece quality. Improvements in the discontinuous system in performance and stability were consequential in implementing the wavelet-based time-frequency controller with parallel online modeling. The controller demonstrated the most unique property in the frequency domain. As was evident in the instantaneous frequency domain, the controlled motions of the oscillator were unconditionally stable, stationary, and periodic.
A Robot Motion Planning Approach Based on Adaptive Multi-Tree Sampling

Abstract Efficient motion planning methods are essential to ensure industrial robots to work properly and safely. Although sampling-based planning algorithms are viable ones, they often struggle to adapt to highly constrained and complex environments. This paper introduces a new robot motion planning approach for such environments, utilizing a multi rapidly-exploring random trees exploration structure. The approach combines the fast exploration property of RRT-based methods with the global exploration property of multi-tree structures. In the subtree generation, an information gain-based method is used to analyze the sampled information from multiple trees to compute the potential information gain at various subtree generation locations. By selecting the locations with higher information gain, our method can effectively improve the exploration quality of the environment. Furthermore, an adaptive local subtree planning method is developed, which relies on local structure information and dynamically updates the sampling distribution to maximize the possibility of forming feasible trajectories in narrow passages. The effectiveness of the proposed approach is tested in 2D, 4D, and 6D environments, along with a complex material picking scenario. These experiments demonstrate that the proposed approach surpasses the performance of other algorithms, particularly in those highly constrained and complex environments. Our study contributes to the development of advanced and highly-adaptive motion planning methods for robots in complex environments.
Linguistic Annotation Generation with ChatGPT: a Synthetic Dataset of Speech Functions for Discourse Annotation of Casual Conversations

This paper is devoted to examining the hierarchical and multilayered taxonomy of Speech Functions, encompassing pragmatics, turn-taking, feedback, and topic switching in open-domain conversations. To evaluate the distinctiveness of closely related pragmatic classes, we conducted comparative analyses involving both expert annotators and crowdsourcing workers. We then carried out classification experiments on a manually annotated dataset and a synthetic dataset generated using ChatGPT. We looked into the viability of using ChatGPT to produce data for such complex topics as discourse. Our findings contribute to the field of prompt engineering techniques for linguistic annotation in large language models, offering valuable insights for the development of more sophisticated dialogue systems.
Role of AI in Enhancing IoT Security

Thе abstract for thе rеsеarch papеr on thе "Rolе of AI in Enhancing IoT Sеcurity" еncapsulatеs a profound еxploration into thе synеrgiеs bеtwееn Artificial Intеlligеncе (AI) and thе Intеrnеt of Things (IoT) with a spеcific focus on bolstеring cybеrsеcurity mеasurеs. As thе prolifеration of IoT dеvicеs continuеs to rеshapе our digital landscapе, sеcurity concеrns havе bеcomе paramount. This rеsеarch invеstigatеs thе transformativе potеntial of AI applications in fortifying thе sеcurity infrastructurе of IoT еcosystеms. Thе mеthodology involvеs a thorough litеraturе rеviеw to еstablish thе currеnt landscapе, followеd by an in-dеpth analysis of AI-drivеn solutions for IoT sеcurity challеngеs. Stakеholdеr pеrspеctivеs, particularly from AI and IoT еxpеrts, contributе qualitativе insights into thе practical implications and nuancеs of intеgrating thеsе tеchnologiеs. Simulations arе еmployеd to еmulatе divеrsе sеcurity scеnarios, offеring practical insights into thе rеsiliеncе of AI-еnhancеd sеcurity mеasurеs against potеntial cybеr thrеats. Thе analysis еxtеnds to еxisting sеcurity protocols, authеntication mеchanisms, and еncryption mеthods, aiming to discеrn thе strеngths and wеaknеssеs of currеnt mеasurеs. Thе synthеsis of thеsе findings positions thе rеsеarch within thе dynamic landscapе whеrе AI and IoT convеrgе, offеring a nuancеd undеrstanding of how AI can augmеnt and optimizе sеcurity protocols in thе incrеasingly intеrconnеctеd world. Thе rеsеarch concludеs with valuablе rеcommеndations and insights, еnvisioning a futurе whеrе AI plays a pivotal rolе in mitigating risks, dеtеcting anomaliеs, and adapting to thе еvolving thrеat landscapе within IoT еnvironmеnts. By shеdding light on thе symbiotic rеlationship bеtwееn AI and IoT sеcurity, this rеsеarch aspirеs to contributе practical solutions and stratеgic guidancе for harnеssing thе full potеntial of thеsе tеchnologiеs whilе safеguarding against еmеrging cybеr thrеats.
Philosophy of e-learning vs m-learning

Aims. This study aimed to provide an in-depth analysis of e-learning and m-learning philosophy in the context of technology use in education. This research aimed to provide a deep and defined understanding of the impact of e-learning and m-learning philosophy in the context of education today. Methodology. The methodology used for this research was a literature review, which was oriented by a systematic and analytical approach, to identify, analyse and evaluate the relevant writings and studies in the field of e-learning and m-learning philosophy. Initially, the field of the study was identified and defined, including the main topics and concepts related to e-learning and m-learning philosophy. Through the content analysis, the main trends, differences, and challenges arising from these two learning modalities have been identified. Results. The results of the literature review served as a basis for further research in the field of education and technology, aspiring to contribute to the development of a comprehensive teaching approach that aligns with the needs of our rapidly advancing technological era. Scientific Novelty. One innovative aspect was the increased analysis of the synergy between e-learning and m-learning. This analysis went beyond a simple examination of the benefits and challenges of each, but also explored the shared opportunities that arose from their combination and matching, that could serve as a model for the development of a common learning approach. Conclusion. In conclusion, e-learning and m-learning philosophies serve as attractive paradigms for the use of technology in education. E-learning offers opportunities for research and advanced learning through online resources, while m-learning encourages active participation and the use of mobile technology for a personalised learning experience. These paradigms have a profound impact on students' critical and analytical skills, promoting a more dynamic and interactive approach to learning.
Quran Wordnet: A Framework for Semantic and Sentiment Search

Quran is a source of guidance with diverse and complete knowledge for mankind. Performing a subjective search in a Quran knowledge base is a non-trivial task. This article focuses on developing Quran WordNet and searching techniques that can provide appropriate results from the Quran knowledge base. Existing search engine techniques focus on keyword-based searching along with English WordNet, which led to often irrelevant results. In this framework, a synonym-based search is introduced to perform sentiment and semantic-based searches for retrieving the most relevant translation of verses. Both sentiment and semantic-based approaches improved the relevant retrieval of results and calculated the sentiment and polarity of the user query. A Quran WordNet will also be a baseline for developing vocabulary and separate ontology. Promising results are reported using Quran WordNet and Part of Speech Tagger for sentiment analysis and concept-based retrieval.
A Complex Annotation-Based Approach for the Arabic Syntactic Analyzer using NooJ Text Annotation Structures

In this paper, we introduce an approach to the Arabic Syntactic Analyzer using the NooJ Text Annotation Structures. Thus, we propose to model various texts constraints of Atomic Linguistic Units (ALUs) which has various ambiguous senses and occur with high frequency in Arabic text. The appropriate samples used in this piece of research show the importance of Segmentation and Part Of Speech tagging for each form; and show also the useful technique of the chunking process to disambiguate and to improve Arabic Syntactic and Semantic Analysis. With the TAS as a rule-based annotation strategy; we have evaluated our proposition on Arabic texts related to specific contexts, and we obtained encouraging results.
Leveraging Data Density and Sparsity for Efficient SVM Training on GPUs

Support Vector Machines (SVMs) are a widely adopted data mining algorithm for binary and multi-class classification due to their ability to handle high-dimensional and non-linearly separable problems. However, SVM training is computationally expensive because of the heavy kernel matrix computation on large training datasets. Although much effort has been made to accelerate the training of SVMs, we find that existing libraries still suffer from inappropriate matrix multiplication methods and inefficient memory access patterns. In this paper, we propose a series of optimization approaches to address these limitations, including (i) matrix partitioning based on column density to achieve efficient kernel matrix computation; (ii) optimizing high latency memory access patterns; and (iii) dynamically selecting more suitable matrix multiplication methods based on the training dataset characteristics. Our proposed methods demonstrate significant improvements in SVM training performance without sacrificing accuracy, achieving a maximum speedup of 52x over the state-of-the-art SVMs on GPUs. These results highlight the effectiveness of our optimization in improving SVM training efficiency.
Online Ensemble of Ensemble OVA Framework for Class Evolution with Dominant Emerging Classes

In real-world data stream mining, the composition of classes undergoes unpredictable changes, giving rise to the challenge of class evolution, encompassing class emergence, disappearance, and reoccurrence. However, most existing approaches require the storage of past data to adapt their model. While some studies have focused on online learning approaches, they are built on an underlying assumption that the number of instances in any single class is consistently less than the sum of other classes. This assumption becomes invalid when a class emerges with a dominant amount, e.g., news about a pandemic outbreak, harming the performance of existing methods. In this paper, we thoroughly investigate this scenario and propose a novel online ensemble of ensemble one-versus-all framework (EEOF) to handle class evolution adaptively. The novel ensemble of ensemble architecture boosts diversity in each one-versus-all classifier. A novel adaptive model adaptation method is also designed to balance the error feedback between the emerging class and the other classes. A confidence-triggered fallback mode is integrated to prevent performance drop due to a wrong decision regarding class disappearance. Experimental studies are conducted on both synthetic and real-world data streams to show that our method achieves higher accuracy in diverse class evolution scenarios compared with the state-of-the-art method, particularly when classes emerge with dominant amounts.
CVaDeS: A Conditional Variational Deep Survival Model for Survival Analysis

Transcriptome-based survival modeling is a critical yet complicated task in cancer treatment due to the strong association between the prognosis of a patient and the progression of tumors based on heterogeneous molecular mechanisms. An effective prognosis model needs to consider the uncertainty that naturally stems from the high molecular heterogeneity together with numerous unforeseeable factors related to prognosis, and requires a perfect match between the selected covariates and the modeling approach of the joint distribution between covariates and right-censored survival time. Unfortunately, these necessary factors have not been fully considered in existing survival models. In this paper, we propose a novel conditional variational deep survival model (CVaDeS for short) for survival analysis. CVaDeS accurately predicts the risk values of patients and effectively distinguishes high-risk and low-risk individuals. Also, the proposed model CVaDeS can be effectively combined with Cox feature selection for survival analysis. Extensive experimental results demonstrate that the combination of Cox feature selection with CVaDeS significantly improves the predictive performance of the model, surpassing baseline models.