Reviewerly/exHarmony huggingface dataset:
A proposal for an online imitation method of path-tracking behavior by end-to-end learning of vision and action

The purpose of this study is to make a robot to acquire path-following behavior with fewer learning steps by end-to-end learning using camera images as input. Our past studies have proposed an online imitation method of path-following behavior of vision and action. We added a function to select a path so that the robot can move in any direction, and verified the effectiveness of the function in simulations. However, the number of learning steps increased significantly when we added the path selection function, and it was desired to reduce the number of learning steps for experiments in a real environment. Therefore, we propose two methods to reduce the number of learning steps and verify their effectiveness through experiments. Furthermore, we verify the validity of the methods through experiments using a real robot.
Design of Sphere Transport Mechanism Based on Roller-Sphere Kinematics Properties

Efficient transportation of objects by robots is required due to the declining working-age population. This study focuses on sphere transport as a means of efficient object transport by robots. To date, there is no methodology for mechanism design based on kinematics properties for sphere transport by robots. Therefore, we propose a method for designing a sphere transport mechanism based on kinematic properties. The paper presents the requirements and methodology for designing a sphere transport mechanism based on kinematic properties.
Image Processing Based Detection of Contaminated Areas

This paper describes the development of a system to prevent the spread of COVID-19 infection, which has become prevalent in recent years. The purpose of this system is to display a heat map of areas at risk of infection. By visualizing risk areas as a heat map, those who perform disinfection work in public facilities can work efficiently and accurately. This system can also contribute to the prevention of the spread of viruses by allowing users of facilities to avoid areas with high-risk areas. By using two different methods, background differencing and OpenPose, we propose three systems and compare them through experiments. It thereby illustrates the advantages of each system.
PENDEKATAN KOMPUTASI UNTUK MENGATASI MALARIA DARI Coscinium fenestratum

Malaria is a disease with signs of fever and is caused by Plasmodium. One way to inhibit the growth of plasmodium is using Dihydroorotate dehydrogenase inhibitors. The method used in this study was docking with laptop aspire ES 14 equipment and PLANTS software for docking and the Discovery Studio for visualization and the materials used were compounds contained in C. fenestratum. This research was carried out using protein code 6VTY which removed water molecules and ligands that were not linked to the active site, and continued with the docking of the compounds contained in C. fenestratum. The results of this study are the Berberine docking score -82.2408; Berberrubine -84.6866 and Thalifendine -81.1591, the result of ligand interaction with Dihydroorotate dehydrogenase, the three ligands occupy the catalytic site of the enzyme, namely HIS185, PHE227, ARG 265 and CYS 175. The conclusion in this study is Berberrubine with a docking score of -82.488 is compounds that have the potential as Dihydroorotate dehydrogenase inhibitors
Visual support system for playing Angklung

Students are engaged in problem-based learning for special needs education. This paper proposes a visual support system for playing instruments in order to share musical experiences with participants. Prototype system has been developed for a traditional Indonesian music ensemble called as Angklung. The system can read a pre-created music file and create a musical score. It can color-code the musical scale to be played and present the timing of percussion. The tempo of the music can also be changed. Through the problem solving in the field, the students are able to broaden their perspectives on international and special needs.
インフレータブルモビリティの表面に実装可能なやわらかインターフェースの開発

In this paper, we propose a lightweight and flexible operating interface that can be implemented on inflatable mobility surfaces. It utilizes a capacitive touch sensor composed of a conductive fabric and a resistor. The sensor has three electrodes and can provide multiple inputs on a single wire. These features enables folding and fitting to curved surfaces, which are not possible with conventional interfaces.
Efficient and High-Speed CGRA Accelerator for Cryptographic Applications

Efficient cryptographic hardware architectures must exhibit both high performance and flexibility to effectively meet the diverse demands of data security in multi-domain applications. However, current research faces challenges in achieving a balance between high processing speed and flexibility, leading to low hardware efficiency and limitations in applicability. Therefore, this paper proposes the ultra-efficient crypto-processor (UECP), a coarse-grained reconfigurable arrays (CGRA) accelerator designed to support various cryptographic algorithms, offering excellent performance and hardware efficiency. To significantly enhance performance and configuration efficiency, the UECP implements two optimal techniques: a 4x4 pipelined processing element array (PEA) and an effective configurable arithmetic logic unit (C-ALU). The performance evaluation of the UECP on the Xilinx ZCU102 FPGA at the system-on-chip level demonstrates a throughput ranging from 1.45 to 4.35 Gbps, power consumption of 0.357 W, and remarkable energy efficiency of 12.19 Gbps/W. The UECP on FPGA outperforms modern embedded CPUs such as ARM Cortex A53 and ARM Cortex A57 by 6.23-24.1 $\times$ in various algorithm computations. Moreover, when compared to current low-flexibility FPGA-based designs, the flexible UECP demonstrates a throughput superiority of 1.58- 10.71$\times$. Finally, the ASIC synthesis comparison results show that the UECP outperforms related works with 7.16-22$\times$ higher throughput, 7.92-22.5$\times$ better area efficiency, and 1.76-15.36$\times$ superior energy delay products (EDP).
Concept Drift Detection in Data Stream: Ensemble Learning Method for Detecting Gradual Instances

Data stream mining faces a significant challenge known as concept drift, especially gradual drift, which can significantly reduce the accuracy of online learning models. To overcome this challenge, it is necessary to detect gradual concept drift precisely and sensitively to make the models adaptive to changes in data distribution. Adapting the model to changes in data distribution before generating new predictions is essential. This article introduces a new method named ERICS+3, an improved version of ERICS, which combines hyperparameters tuning and ensemble learning techniques to create a model for concept drift detection on each data instance of a stream. The experimental results from stream datasets demonstrate that our method is more efficient in detecting gradual concept drift than the original model.
Industrial Cybersecurity Game-Scenarios Based on the MITRE ATT&amp;CK Framework

In modern society, cybersecurity has become an increasingly crucial challenge. To prepare the next generation of professionals in this field, we propose interactive approach to teaching industrial cybersecurity concepts. However, it's important to recognize that people are often the weakest point in the security systems of these industries. Additionally, the lack of published cybersecurity training that focuses on Industrial Control Systems (ICS) is a significant challenge that needs to be addressed. There is currently a dearth of training materials available that are tailored specifically to the needs of ICS-based cybersecurity, which is a major issue. To address this problem, discovering new and innovative security training methods that specifically target ICS is necessary. Serious games have become increasingly popular among cybersecurity researchers in recent years as an innovative and engaging approach to educating users and enhancing their awareness and knowledge of cybersecurity-related issues. The purpose of this paper is to provide examples of industrial cybersecurity game scenarios based on the MITRE ATT&CK framework. Our target audience is students with minimal knowledge of industrial cybersecurity.
The Rise of Virtual Health Assistants: Chatbot-Based Healthcare Support and Counseling Using Recurrent Neural Networks (RNNs)

Health Assistant Bot is an advanced AI-powered virtual health assistant to enhances patients' ability to receive more individualized care. Health Assistant Bot's capabilities in symptom recognition, physician recommendation, and essential health information provision are discussed in this study, along with its creation and evaluation. Extensive testing and analysis have shown that the system is accurate, effective, and user-friendly. The problems of false information and data privacy are addressed by Health Assistant Bot, guaranteeing its ethical and responsible use in healthcare settings. By harnessing technology, Health Assistant Bot supplements human caretakers and gives patients more agency in their treatment. By invisibly integrating state-of-the-art technology into healthcare practices, the virtual assistant paves the way for care centered on the patient. The proposed model of RNN gave an accuracy of 90.20%, precision of 88.50%, recall of 88% and Fl Score of 89.20%. Embrace the transformative potential of the Health Assistant Bot, which encourages better healthcare outcomes through co-production and gives people more agencies.
Role of Ontology-Informed Machine Learning in Computer Vision

Ontologies are used in practically every major biological database to formalise and reason about domain information in the life sciences. Ontologies are increasingly employed to offer background information in similarity-based analysis and machine learning models. Combining ontologies with machine learning is still experimental. We discuss how semantic similarity measures and ontology embeddings can use ontologies' background knowledge and provide constraints to improve machine learning models. Ontological research can help in machine learning by introducing the broad aspects of the future scope. The ontology supports structured, semi-structured, and unstructured data types for building a piece of valuable information.
Unveiling the Veil of Deception: An Insightful Journey into Adversarial Attacks and Defence Mechanisms in Deep Learning Networks

In recent years, a surge of interest has been witnessed in the realms of Artificial Intelligence (AI) driven technologies. Fields such as Computer Vision, Autonomous Navigation, Natural Language Processing, among others, have experienced transformative breakthroughs. Yet, alongside these advancements, a susceptibility to adversarial perturbations has been unveiled. Machine Learning models, despite their sophistication, can be deceived into making erroneous inferences by insubstantial manipulations in the input, a situation recognized as adversarial attacks. These attacks pose significant limitations to the applicability of AI technology in critical security-conscious sectors. Consequently, fortifying AI systems against such malevolent attempts has emerged as a cardinal component in the evolution of AI. This research paper pivots on devising a model that safeguards against adversarial onslaughts, concurrently equipping users with the tools necessary to cultivate robust models. The long-term goal is not only to enhance the reliability of AI but also to further the responsible and secure use of AI in society.
Incorporation of Agents with Ontology to Develop a Healthcare Assessment Provision System

Extracting useful patient information from a multitude of web pages and traditional hospital systems, and afterwards displaying these search results in a comprehensible manner, is a challenging undertaking. This is achieved via the implementation of conventional methodologies that aid in the anticipation of infections and ailments, such as heart attacks and several others. However, a disheartening aspect is that these conventional approaches were inadequate in detecting early indications of the human health system. The resolution to this issue may be found by utilizing an agent-based integration method, facilitated by the utilization of ontologies. This research focuses on the integration of data linked to human health, both structured and unstructured, using a suggested system that utilizes ontological-based agents. These agents work together to coordinate and semantically integrate external data sources. The process of integrating data leads to the derivation of new knowledge based on a set of rules, so transforming it into a multi-agent decision support system. The algorithm under consideration demonstrates the capability to forecast individuals' health state by analyzing their performed activities. The study provides a comprehensive analysis of a methodology that focuses on human daily activities, specifically targeting the improvement of cardiovascular health in individuals.
Towards a New Ontology-Based Descriptive Language: OntoObject-Z

The ultimate goal of our current research work is to design a new ontology-based language (OntoObject-Z) suitable for descriptive specifications of hardware/software systems. OntoObject-Z should be able to support Model Driven Architecture (MDA) development methodology enabling system implementation through progressive refinement of descriptive specifications. A first motivation is to contribute to the effort carried out by the community of researchers sharing the idea that the description of the problem space (as opposed to the description of the solution space) must be addressed in “appropriate” languages. The second motivation is to avoid “model bias” in our stepwise refinement approach. Model bias is encountered when addressing the Computing Independent Model (CIM) of complex systems such as the Internet of Things (IoT), where prescriptive languages are still favored for reasons explained later. In this research paper, we present the characteristics and the basic ingredients of our language by adopting a qualitative research method supported by illustrative cases, by privileging readability, saving by the way space and time for the reader.
Convolutional Neural Networks-based Real-time Gaze Analysis with IoT Integration in User Experience Design

Internet of Things (IoT) technology in user experience design has transformed its use of digital devices and apps. This transformation includes real-time user gaze analysis, which may greatly improve user experience. It uses machine learning to provide real-time gaze analysis and IoT device integration for a smooth and customized user experience. Gaze tracking and analysis are very accurate using machine learning techniques, especially deep learning models. It uses Convolutional Neural Networks (CNNs) to identify and predict user gaze patterns. IoT devices may adjust to user preferences in real time by analyzing eye movements and focus spots, making them more intuitive and immersive. It discusses IoT infrastructure for gaze analysis and user experience improvement. Gaze data from IoT sensors and cameras is analyzed locally or in the cloud using machine learning algorithms. To improve user experience, insights are utilized to change screen content, illumination, and augmented reality feedback. Gaze analysis and IoT integration privacy and ethics are also addressed. This research addresses real-time gaze analysis systems challenges by combining CNNs with IoT. Problems with scalability and practical adaptation impact recent methods, such as attention-based models and cloud computing. The suggested system designs address these issues by seamlessly integrating CNN s with IoT, giving an efficient and context-aware solution for better user experience design. The might transform user experience design by making interfaces more users, flexible, and customized.
Artificial Bee Colony Method with Guide-Based Adaptability for Traversing Areas of Search

One of the well-known strategies for swarm intelligence that is used to resolve a variety of quantitative issues and practical improvement issues is the fake colony of bee's program. Even so, ABC occasionally exhibits delayed convergence and excels at discovery but fails at utilization. We suggest an improved code dubbed the dynamic but instead global autonomous pollinator strategy according to worldwide best guidance in order to enhance the computational efficiency. In this article, we suggest a fresh approach to revising the observer beekeepers' answer. Our approach uses the finest option available at the time to increase abuse potential. In order to improve the worldwide integration, the existing inhabitants is also produced using a random system. Additionally, the program initially focuses on worldwide investigation before switching to extraction as it nears its conclusion. We test the effectiveness of our suggested approach on two different groups of issues: the chaos time - series data forecast using the Stochastic technique, and mathematical standard expressions. The suggested approach can reach better quality answers with quicker completion than the traditional Accounting or some other enhanced techniques, according to research observations.
TSM: A Cloud Computing Task Scheduling Model

Cloud offers online-based runtime computing services through virtualized resources, ensuring scalability and efficient resource utilization on demand. Resource allocation in the dynamic cloud environment poses challenges for providers due to fluctuating user demand and resource availability. Cloud service providers must dynamically and economically allocate substantial resources among dispersed users worldwide. Users, in turn, expect reliable and cost-effective computing services, requiring the establishment of Service Level Agreements (SLAs). Resource distribution uncertainty arises in view of the dynamicity of the cloud, where VMs, memory capacity requirement, processing power, and networking are allocated to user applications using virtualization technology. Resource allocation strategies must address issues such as insufficient provisioning, scarcity, competition, resources fragmentation. CPU scheduler plays a crucial role in task completion, by selecting job from queue considering specific requirements. The Task Scheduling Model (TSM) algorithm improves scheduling by considering expected execution time, standard deviation, and resource completion time, aiming to address resource imbalances and task waiting times. The research discusses previous work, presents experimental findings, describes the experimental setup and results, and concludes with future research directions.
Causality between violations and failure factors in cloud service and its analysis methods: a survey

Causality is the relation of inevitability between violations of service and failure-factors. It is evidence of accountability in cloud service because compared with correlation, the causality shows more probative capability in evidentiary capability. This paper surveys the causality in recent research, including the features, patterns, and hypotheses of causality, and also the methods of discovery and analysis. Most research focuses on a single causal feature and the patterns of causality in typical violations, such as long tails, lack of resources, etc., and need to explore further the differences in causal patterns in different situations. At last, the results are proposed in this paper, including a taxonomy and some challenges in recent research, and an approach to causality analysis.
Evaluation model of metacognitive ability based on multi-channel self-attention and BiGRU

Metacognition is the critical element of personalized online autonomous learning, but it is not easy to observe or obtain. It is difficult to be monitored continuously in the practice of teaching and learning. The existing model of metacognitive ability is still in theoretical research and lacks effective model construction technology to externalize metacognition. The online learning behavior data contains rich metacognitive information. In contrast, the previous methods based on statistical analysis or traditional machine learning cannot fully extract the internal temporal and semantic features implied in the data. This study uses the self-attention mechanism and the recurrent neural network sequence model to deeply explore and analyze learners' online learning behavior and interactive text. A new evaluation model of metacognitive ability is constructed to represent learners' metacognitive ability. The research takes natural online learners' behavior data as the object to carry out experimental verification and analysis. The results show that the model's accuracy in representing metacognitive ability reaches 85.21%, which verifies the model's effectiveness.
Multi-feature Chinese semantic role labeling combined with global information

Semantic role labeling serves as a crucial approach to obtaining semantic information and enabling shallow semantic analysis. Currently, the BiLSTM-CRF model is the primary method used for semantic role labeling. However, this model has many network parameters and is unable to effectively capture semantic information in long sentences. To address these issues, this paper proposes a CNN-BiLSTM-MaxPool-CRF fusion model for Chinese semantic role labeling. The model utilizes MaxPool to sample and extract the output of the BiLSTM network to optimize the network structure. Convolution kernels of differing sizes are utilized to capture local features of sentences, and these features are then combined through average pooling to form new feature vectors. These new features incorporate semantic information of the sentence context and are inputted into the model alongside multi-level linguistic feature groups such as part of speech and sentence phrase structure. Through multiple sets of experimental demonstrations, the method proposed in this paper has demonstrated significant improvements in the performance of the semantic role labeling model.
Automated Means of Testing Software Modules for Solving Volterra Integral Equations of the Second Kind

The article is part of the methodology of automated testing of software modules for solving Volterra integral levels of the second kind. For the implementation of automated testing, the Matlab software environment was selected, which has a wide range of software testing capabilities, in particular: functions for generating data sets for testing; functions for comparing test results; functions for generating test reports, etc. For the development of automated testing tools, the Unit Testing Framework was selected, which is a component of the MATLAB Test Framework, has many ready-made methods for checking the correctness of values and the formation of statistical errors. A set of test problems has been developed for Volterra integral equations of the second kind, which are divided into different types, including linear Volterra integral equations of the second kind, which consist of a kernel with power, exponential, hyperbolic, logarithmic, trigonometric, inverse trigonometric functions and their combination. Developed testing tools used for automated quality control of software modules built on the basis of left rectangle, right rectangle, trapezoidal, and Simpson methods. The developed set of test tasks covers a wide range of possible operating conditions of software modules. The results of the testing allowed to improve the existing software modules to achieve the set conditions for their operation. The conducted research should contribute to the development of more reliable and efficient software modules for solving Volterra integral levels of the second kind. The obtained results are the basis for further research, which will be used in the following directions: development of testing methods for more complex types of Volterra integral levels of the second kind, including the level with nonlinear and non-stationary kernels.