Reviewerly/exHarmony huggingface dataset:
Research on Robot Path Planning Based on Improved Genetic Algorithm

Aiming at the disadvantages of genetic algorithm in robot path planning, such as blindness and randomness of initial population, too many path turning points and easy to fall into local optimization, an improved genetic algorithm is proposed. The prior knowledge is used to initialize the path in the direction of the target point, and the continuous and obstruction-free collision path is further obtained to enhance convergence rate. A new fitness function is designed combining the path length and the number of turning points to improve the efficiency of path planning. Simulation results show that compared with the traditional genetic algorithm, the shortest path of the improved genetic algorithm can be shortened by 9.97%, and it is better than the traditional genetic algorithm in convergence rate and the number of turning points.
Robot Navigation in Complex Polygonal Workspaces Using Conformal Navigation Transformations

In this paper, a new transformation called the conformal navigation transformation is proposed to enable collision-avoidance navigation of the mobile robots on arbitrary polygonal environments. The properties of the proposed transformation and its ability to provide solutions to navigation problems for polygonal workspaces are studied. The definition of navigation functions is extended to workspaces with non-smooth boundary. Using the conformal navigation transformation and the extended navigation function, a provably correct feedback control law is derived for the motion control and obstacle avoidance of a kinematic mobile robot. In addition, a fast iterative construction method is presented to solve the proposed transformation on the complex polygonal workspaces, which reduces the multiply connected problem to multiple simply connected problems. Finally, the simulation study is presented to verify the effectiveness of the methodology.
Deep Reinforcement Learning-based Swing-Free Trajectories Planning Algorithm for UAV with a Suspended Load

UAV transportation has great application prospects, and can be used to transport some materials that need to be delivered quickly, such as food, medicine, and even organ transplantation, in the face of many special situations available using rope suspended load, but, when using suspension transport, the swing of the load will increase the instability of the flight. To address this situation, we propose a method based on Deep Q-Networks to generate the minimum swing trajectory of UAV suspended load. First, a training environment is established using the kinematic model of UAV suspended load to discretize the action, which will greatly accelerate the training speed; Finally, a step-by-step regional approximation exploration reward function is designed, and the final training simulation results verify the feasibility of the method, and the suspended load oscillation is controlled within a certain range during the whole transportation process.
TrainERAI - Live Gym Tracker using Artificial Intelligence

There is a correlation between our posture and our physical and emotional wellness. The detection of various human postures has been approached in a variety of ways. Determine a patient's resting position, for example, using posture analysis in the medical industry. Image processing using openCV and mediapipe python library for human posture estimation. Analyzing standing and sitting postures is made easier with an image processing-based technique. The benefits of fitness activities to a person's health are remarkable, but if they are done improperly, they may be ineffective and even detrimental. Exercise blunders happen when someone doesn't adopt the right posture. This suggested application makes use of pose estimation to identify the user's workout posture and then offers specific, tailored advice on what the user can do to correct their posture. A pose estimation module in python called mediapipe helps to detect all major joints of the human body. Then it calculates the angle between the joints and increases the counter for each repetition. Pose estimate uses a picture or video of the subject to determine the precise locations of the body's major joints. This computer vision technology recognizes human posture in films and displays important regions, such the elbow or shoulder, in the finished product. Another way to workout at home is through interactive games. The proposed application has an interactive boxing game built using pygame, which helps all age groups to workout and take care of their health. Overall the user can track all their details on workout on this application, like how much calories are burned and the daily goals, different workout planner for each individual.
A 10 step checklist for starting FAIR discussions in your community: Call for contributions

The article aims at starting a conversation around a 10-step checklist providing practical recommendations on how to facilitate community discussions around the creation of standards to implement FAIR.
Analysis and Evaluation of Heavy Metal Pollution in Kosasthalaiyar River near Ennore Creek Using Geo Statistical Approach

&lt;p&gt;The increasing industrialization and urbanization in recent times has led to a significant increase in heavy metal pollution in soil and sediment. The present study aims to evaluate the heavy metal contamination (HmC) in surface sediments collected from the Kosasthalaiyar River located between latitudes 13°12′03″N and 13°16′34″N and longitudes 80°18′6″E and 80°19′56″E. Ten sampling locations were selected in the study area and samples of surface sediments were collected and analyzed for 16 geochemical and heavy metal parameters, including Silver (Ag), Aluminium (Al), Calcium (Ca), Cadmium (Cd), Chloride, Cobalt (Co), Total Chromium (Cr), Copper (Cu), Mercury (Hg), Manganese (Mn), Molybdenum (Mo), Nickel (Ni), Lead (Pb), Silica (Si), Titanium (Ti), and Zinc (Zn). The study aims to determine the potential environmental hazards of heavy metals in the sediment through calculation of the potential environmental hazard index (RI). Assess the impact of human activities on HmC in the surface sediments using four pollution indices: the Geo-accumulation index (Igeo), the Contaminant factor (CF), the degree of contamination (DC), the potential contamination index (PCI), and the pollution load index (PLI) and Conduct a human health risk assessment (HHRA) of heavy metals in the sediment. The study results will provide insight into the contamination levels and help inform measures for controlling pollution and promoting sustainability in the area.&lt;/p&gt;&#x0D;
A Feasibility Study on the Application of Metaverse in Foreign Language Teaching

Since the beginning of the new century, with the continuous improvement of Internet technology, online education has gradually become a major trend of educational reform in the future. .The new coronavirus (COVID-19) outbreak since 2019 has accelerated this process of change. At this point, Metaverse, which allows users to experience new experiences beyond the boundary between reality and virtuality, began to gain attention. This study aims to find out the current status of metaverse use in Korea, analyze the characteristics of three platforms, ifland, and Zepeto on metaverse, and present the possibility of using metaverse according to the class goal and class character in foreign language education based on the experience of applying it to actual foreign language classes. As a result of the study, classes using the Gattern platform were the most and relatively effective, and due to the nature of the ifland platform, it will be available for presentation classes, and the Zepeto platform will be used as a platform relatively suitable for comparison and events rather than subject classes. In addition, through curriculum innovation, students
Digital Security Act 2018: From the Lens of Investigative Journalism and Freedom of Expression

The world is converging as a result of technological evolution and integration. The new digital era is emphatically influencing and transforming human lives. The staggering growth in technology has many implications at one side it brings blessings for the general mass on the other it makes individuals’ life a hex due to its misappropriation. To safeguard individuals from the realm of digital transgression and to ensure individuals right to privacy the government of Bangladesh enacted Digital Security Act in 2018. The act has both affirmative and antithetical connotations by supporting penalisation for digital offences conversely barring investigative journalisms and freedom of speech. The present paper is the reflection of the findings congregated from the systematic assessment of the secondary sources. It focuses on the limitations and challenges of Digital Security Act 2018 by particularly seeing it from the lenses of investigative journalism and freedom of expression. Moreover, it also tries to put light on whether the act has aberrated from its sole purpose that is to ensure security of individual presence in the digital platform. Also, the article manifests the need for an incontrovertible digital security law for the welfare of the citizens.
Breast Cancer Detection on Histopathological Data

The leading cause of cancer-related fatalities among women worldwide is breast cancer, which is a severe public health concern. The likelihood of survival can be considerably increased by early identification. Although the accurate diagnosis of malignant tumors from histopathological pictures is mostly dependent on radiologists' long-term expertise, specialists often disagree with their decisions. Computer-aided diagnosis adds a second option for visual diagnosis, potentially increasing the validity of professionals' decisions. Automatic and precise classification of breast cancer histopathological images is critical in clinical applications for identifying malignant tumors from histopathological images. Deep learning has achieved excellent results in several disciplines such as image classification, object recognition, and so on. Deep learning for the analysis of medical pictures has recently attracted the attention of numerous researchers. In this field, convolutional neural networks (CNNs) have become a well-known class of models. To this end, the paper proposes using the well-known CNN architectures ResNet50, AlexNet, and GoogleNet which have been trained from scratch, in conjunction with various preprocessing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), Image sharpening, Edge enhancement, and so on, for the task at hand. The optimum model amongst the proposed i.e., Resnet50 + Image Sharpening achieves an accuracy of 95%.
Byzantine Fault Tolerant Consensus Algorithm Based on Credit Model and Verifiable Random Function

Blockchain technology has the advantages of decentralization, data tampering and data transparency, so that the application field of this technology continues to expand. However, the current consensus algorithm applied in blockchain system has some problems, such as arbitrary selection of master nodes, high communication complexity and low consensus efficiency. An enhanced consensus algorithm (BV-PBFT) is suggested in this study. The consensus protocol is simplified, the communication complexity of the algorithm is reduced, and the effectiveness of consensus is improved. First, the credit value of nodes is calculated by the credit model, and according to the credit value of nodes, the nodes in the network are divided into three types of nodes with different responsibilities. Secondly, the random and verifiable characteristics of verifiable random function (VRF) are used to construct an anonymous master selection algorithm to improve the selection of master nodes. According to experimental findings, the suggested consensus algorithm, when compared to the PBFT method, minimizes the time complexity from to , significantly lowers communication overhead and consensus delay in the network, and increases consensus efficiency and throughput.
A blockchain-based Access Control Mechanism for IOT

Because of lightweight, large-scale and dynamic access characteristics in IoT devices, the current centralized access control mechanism leads to many security problems such as failure of single point, privacy leakage and user authentication. With the advent of blockchain technology, it could be a better way to address these problems by decentralized, transparent and immutability properties of blockchain. Thus, a blockchain-based access control mechanism for IOT is proposed based on the ABAC access control model. Firstly, the model architecture and access control workflow are developed in detail, and the data format of transactions in the blockchain is defined so as to facilitate the release, update, and revocation of attributes and policies. And then, the smart contracts on chain are programed to ensure that the conditions they set on access and sharing will be enforced. Finally, experimental simulation demonstrates that the proposed scheme is efficient in policy-making and policy-querying. At the same time, the use of decentralized ABAC makes it much secure for decision-makers to control variables that ensure a fine-grained approach to access control.
Research on Empathic Remediation Mechanism of Chatbots Mediated by Social Presence and Trust

Chatbots are changing the way consumers interact with businesses. However, chatbots continue to be widely perceived to lack a human touch and inevitably fail to deliver service. This paper argues that emerging research on AI empathy will address the lack of human touch in chatbots and provide new ideas for remedying chatbots service. By combing research from different disciplines, we argue that the connotations of chatbots empathy can be understood through a three-tier structure of transpersonal thinking, empathic care, and emotional empathy. Additionally, we find that chatbots empathy contains three dimensions: cognitive empathy, emotional empathy and behavioral empathy. These three dimensions are not mutually exclusive but rather complement each other to form the overall empathy capacity of chatbots. Based on the mediating mechanisms of social presence and trust and the moderating mechanism of technology readiness, we summarize a framework for the impact of chatbots empathy on consumers' willingness to forgive. This paper can further enrich the research on chatbots service remediation and improve companies' understanding and use of empathic remediation.
Circuit Simulation and Optimization of Quantum Search Algorithm

At present, the scale of quantum computers in the real sense is still small, and quantum simulation has become one of the important ways of quantum theory research, grover quantum search algorithm is suitable for the search problem of disordered database. Firstly, according to the implementation principle of Grover algorithm and Boolean logic relationship, the design idea of multi-objective Oracle is analyzed, based on IBMQ quantum cloud platform, the quantum circuit of Grover algorithm with multi-objective items is simulated. Based on the characteristics of Grover algorithm and the simulation process of quantum gate, the action of multiple identical quantum gates is combined to reduce the update times of probability amplitude and improve the simulation efficiency. The libquantum quantum simulator is used for experiments and the target item is successfully searched, which proves the feasibility of the optimization method and provides reference for the simulation and optimization of other quantum algorithms.
Light Field Saliency Detection Based on Multi-modal Fusion

Compared with RGB images, light field images contain more abundant visual information, which is helpful to accurately detect salient objects in complex scenes. However, most of the existing light field saliency detection methods use single light field data or do not fully consider the differences and complementarities between different light field data, resulting in insufficient multi-modal fusion. To address these issues, a multi-modal feature fusion network is proposed, which makes full use of the rich visual information in the light field images to realize the accurate saliency object detection. The proposed network consists of two parallel subnets, which are used to process the micro-lens image array and all-foucs image respectively. Then the light field refinement module is used to refine the feature map extracted from the micro-lens array stream, and finally the multi-modal feature fusion is realized by the light field attention module to predict saliency objects more accurately. In order to verify the effectiveness of proposed method, extensive comparison with several existing light field saliency detection algorithms is carried on both Lytro-Illum and LFSD datasets. Experimental results show that the proposed method is superior to others in all evaluation metrics on Lytro-Illum dataset, and has desired generalization abilities on LFSD dataset.
Integrating Two-Level Reinforcement Learning Process for Enhancing Task Scheduling Efficiency in a Complex Problem-Solving Environment

Task scheduling scenarios require the system designers to have complete information about the resources and their capabilities, along with the tasks and their application-specific requirements. An effective task-to-resource mapping strategy will maximize resource utilization under constraints, while minimizing the task waiting time, which will in-turn maximize the task execution efficiency. In this work, a two-level reinforcement learning algorithm for task scheduling is proposed. The algorithm utilizes a deep-intensive learning stage to generate a deployable strategy for task-to-resource mapping. This mapping is re-evaluated at specific execution breakpoints, and the strategy is re-evaluated based on the incremental learning from these breakpoints. In order to perform incremental learning, real-time parametric checking is done on the resources and the tasks; and a new strategy is devised during execution. The mean task waiting time is reduced by 20% when compared with standard algorithms like Dynamic and Integrated Resource Scheduling, Improved Differential Evolution, and Q-learning-based Improved Differential Evolution; while the resource utilization is improved by more than 15%. The algorithm is evaluated on datasets from different domains like Coronavirus disease (COVID-19) datasets of public domain, National Aeronautics and Space Administration (NASA) datasets and others. The proposed method performs consistently on all the datasets.
An Unsupervised Learning Approach for Smart Home Operational Policy Generation

With the rise of the Internet of Things (IoT), smart homes can provide intelligent services to monitor household appliances remotely and automate user tasks. However, a significant amount of human intervention is expected in the deployment and operation of such services, making it inconvenient for human users who are less tech-savvy. Therefore, such systems should be trained to learn user behavior patterns to automatically configure and adapt their actions according to the preferences and daily routines of the occupants with minimal user involvement to enhance user experience. This paper uses an unsupervised learning approach that can be integrated with a generative policy framework. It enables automatic operational policy generation by analyzing the continuous data from sensors and smart devices. In order to generate policies according to user preferences, our process infers users' behavior patterns by looking for the patterns in their daily routine activities. We compared the performance of our proposed learning approach with existing approaches used in generative policy frameworks. Evaluation results show that our proposed approach positively contributes to the automatic policy generation to automate user tasks in smart homes.
Geophysical assessment for gold mineralization potential over the southern part of Kebbi State using aeromagnetic data

The magnetic signatures over the southern part of Kebbi State and its environs were analyzed together with the geological settings of the area to delineate the structures that may host gold mineralization. The aeromagnetic data used was the survey carried out by Fugro airborne surveys between 2005 and 2010 on behalf of the Federal Government of Nigeria. The reduction to equator (RTE), first and second vertical derivatives (FVD and SVD), Centre for Exploration Targeting (CET), analytic signal (AS), source parameter imaging (SPI) and tilt derivative (TDR) techniques were applied to the magnetic data covering the area. The results of the AS technique revealed that the study area is characterized with high amplitudes of magnetic anomalies (above 0.048 nT/m) and these could be of ferromagnetic minerals such as gold. The FVD, SVD, CET and TDR techniques also helped in delineating the lineaments (such as faults, fractures or shears zones) believed to be associated with alteration zones which play an important role in determining gold mineralized zones. The direction of the orientation of these features/lineaments trended in the NE-SW direction. The faults, fractures or shears zones delineated represent veins of possible mineralization. The depth of occurrence to the causative bodies using SPI algorithms was found to be below 137 m. Structures delineated within the area, when compared with the geological setting of the area, correspond to quartz-mica schist, granite, biotite, gneiss, diorite, medium coarse-grained and biotite hornblende granite. Results from these techniques revealed alteration zones that may host gold. These regions correspond to the following areas: SE parts of Yauri and Shanga, Fakai, Ngaski, Zuru, Magama, Rijau, and the eastern part of Wasagu/Danko and Bukkuyum.
Improving Multi-class Text Classification Using Balancing Techniques

Social media platforms and micro-blogging websites have grown in popularity in recent years. These platforms are used to express persons’ thoughts and feelings regarding items, people, and events. This massive amount of textual data must be exploited. Sentiment analysis is one of the tools used to take advantage of this text data, in which we classify text into different classes such as positive, negative, neutral, or a number of star classes. It has been investigated by many researchers in several languages. Deep Learning approaches such as CNN, RNN, and LSTM applied on balanced datasets have given efficient results compared to classical machine learning approaches such as SVM, NB, and LR. Furthermore, the apparition of BERT has revolutionized the text classification field, even in sentiment analysis tasks. The main problem that the datasets which have been collected from social media platforms, certain classes dominate others, meaning that the datasets are imbalanced. As a result, classifiers lose efficiency. This paper addresses this issue by introducing an ensemble of mathematical balancing techniques to increase the efficiency of sentiment analysis models based on BERT scheme. The obtained results are significant, indicating that our two main metrics, AVG-Recall and F1-PN, are 17% and 19% higher, respectively, when compared to the classifiers’ results applied to the imbalanced dataset.