Reviewerly/exHarmony huggingface dataset:
Environment manipulation mechanism with free adaptability using a simple parallel link structure made of flexible materials

We have developed an adaptive flexible environment manipulation mechanism that can be installed in existing environments and has high interpersonal affinity. By incorporating a parallel link structure composed of flexible materials into the thrust generation mechanism, the mechanism can stably transport objects with large mass while maintaining flexibility. In this paper, we verify the transport capability of the device through experiments using the device we have fabricated.
Problem-based learning to design an autonomous paper air hockey robot

A practical class, " Robot Creative Practice", in which each student designs and builds an autonomous air hockey robot through repeated trial and error has been proposed. Paper materials were used for structural components which is very easy to redesign in order to be able to instruct approximately 20 students within a limited time and budget. The students conceived and designed the robot by using of the microcomputer and CAD tools that they had learned in the previous year. Their unique robots were reported at a presentation and students evaluated each other. In addition, other related subjects were also provided, resulting in a comprehensive and well-rounded robot engineering education. In this article, the preparation for the opening of the course and the progress of the course after its opening will be described.
理論検証のためのユニット構成型ロボットアームの開発

In Japan, where the birthrate is declining and the population is aging, robots are being used to perform alternative tasks. However, it has been difficult to realize a redundant arm with a tree structure by using a control theory suitable for a conventional single arm from the viewpoints of computational cost and modification of the control model. Therefore, we propose a robot arm control theory that uses exponential coordinates. This theory enables flexible control of tree-structured arms. On the other hand, it is rarely applied to actual arms because the computational cost for serial link arms is larger than that of conventional theories. The purpose of our research is to verify the characteristics of the control theory using exponential coordinates by applying it to an actual robot arm, and to evaluate its effectiveness. For this purpose, we develop Unit-configured robot arm for theory verification.
ベイズ最適化を用いた移動マニピュレータによるドア開け動作の生成

We propose a new method for solving the learning problem in the door opening task based on Bayesian optimization. The method is evaluated in a simulation of a door with rotating handle and an omnidirectional mobile robot with two 6-DoF manipulators. In this method, the objective function of Bayesian optimization runs a simulation where the robot attempts to open the door by moving the grip in a direction indicated by a five-dimension problem space. The value of the objective function is the time until the robot encounters forces or torques above a certain threshold. The results indicate that learning the sequence of movements necessary to open a door requires no more than 15 trials for each movement.
下水道圧送管内点検のための連結車輪型管内移動ロボットの開発

This paper reports the social implementation activities of the articulated wheeled in-pipe inspection robot “AIRo-7.1” to the suwer pipes. The proposed robot is waterproof and dustproof, and its adaptive inner diameter is 3 to 4 in. The torque and angle of the middle joint can be controlled by the motor current and potentiometer, respectively. The inspection target is focused to sewage force main pipes, and the actual equipment developed and the results in field tests are described.
Trial of Grasping a Plastic Bottle Using a Dual-Arm Collaborative Robot

Depending on the object, grasping and manipulation by a robot may be susceptible to disturbances consisting of complex elements related to the object and may not be able to grasp the object in a stable manner. In addition, If the object to be gripped is long or flexible, it is difficult to finely adjust the grip using the existing single arm which is the basis of industrial robots, and there is a risk that the object may not be gripped or may be damaged. Under the long-term goal of making it possible to stably grasp objects, even for difficult objects, we try to find a new approach and grasping method using a dual-arm robot as one of these methods. In this study, we attempt to develop a system that can grasp an object with both arms using a cooperative dual-arm robot and a depth camera.
Proposed improvement of conventional methods using deep learning for QR code embedding

The purpose of this research is to embed a QR Code into a common image using deep learning. Specifically, we created an embedding AI that embeds a QR Code into an image and a restoration AI that restores the QR Code from the embedded image, and evaluated their performance. In addition, since there is a problem that the QR Code cannot be output when blurring is applied to the image output from the embedding AI, we will also conduct experiments to obtain tolerance to this problem and evaluate the results.
指腹部高解像吸引触覚ディスプレイによる把持感覚の再現

Both cutaneous and kinesthetic sensations contribute to human dexterous manipulation performance. To realize skillful manipulation in VR environments, tactile and force senses must be reproduced with high realism. This study aims to develop a display in which tactile and force sensations can be presented independently by integrating a suction tactile display and a parallel-linked force display, which has low inertia, high rigidity, and high-speed operation. Design guidelines for the integration are reported, and a prototype of a suction tactile display for the integration is shown. A contact simulator with a deformable finger is also introduced for future cutaneous sensation presentation.
Development of VR Fly Ball Catching Training System Using Voice Instructions

We focused on the motion of catching a fly ball in the outfield. In order to develop a training system using voice instructions, we examined the effects of voice instructions on the catching behavior of the fly ball and the most appropriate voice instructions among several voice instructions. The voice instructions were effective in catching the fly ball. It was found that the most effective instruction for experienced players was the instruction with front and back information, while the most effective instruction for inexperienced players was the instruction with a large amount of information. It is considered that changing the method of voice instructions according to the level of experience will lead to effective training. We propose a method of wearing VR with less burden on the neck and a safe method of using VR to understand the surrounding environment while using VR.
Data Security In Cloud Computing Using RC6 Encryption and Steganography Algorithms

The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.
An Optimized Sign Language Recognition Using Convolutional Neural Networks (CNNs) and Tensor-Flow

Sign language is an essential means of communication for people with hearing disabilities. However, there is often a communication gap between hearing and non-hearing individuals, which can lead to social exclusion and discrimination. To bridge this gap, They proposed a deep learning-based sign language interpreter using TensorFlow. The system can recognize different signs and interpret them into text or speech output, allowing non-hearing individuals to communicate with hearing individuals more effectively. To evaluate the system on a publicly available sign language dataset and achieve an accuracy of 95% for recognizing signs and 90% for interpreting them into text or speech output. The results demonstrate the effectiveness of the approach and highlight the potential of deep learning for improving accessibility and inclusion for people with hearing disabilities. The main result of this research is to get the accuracy and accurate prediction of the model that they will use for transfer learning with the dataset
DDPG: Cloud Data Centre Hybrid Optimisation for Virtual Machine Migration and Job Scheduling

The rising energy consumption in cloud data centres (DCs) has made efficient resource management crucial, since it leads to higher prices for cloud customers and negative environmental effects. Virtual machine (VM) consolidation techniques have been put out in an effort to overcome this difficulty. Their goal is to minimise energy use by reallocating VMs through migration. In this study, we propose a unique method for optimising resource utilisation and energy consumption in cloud DCs through virtual machine consolidation, based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The suggested DDPG approach takes use of the DDPG algorithm's capacity to manage continuous action spaces, which makes it a good fit for handling challenging VM migration and task scheduling issues. By taking into account two goal functions in cloud DCs at the same time, DDPG seeks to find the best solution for both work scheduling and VM placement. In particular, when DDPG is used in place of the comparable approaches, there is a noteworthy 62 Kwh reduction in energy usage and an astounding 78% drop in resource utilisation.
An Adaptive Deep Learning Technique for Breast Cancer Diagnosis Based on Dataset

The cancer of breast is one of the most frequent forms of cancer in females, and diagnosing it at an early stage is essential for receiving good treatment. Imaging using ultrasound is a technology that does not include any risky or intrusive procedures and may be used to diagnosis of cancer of breast in its earlier stages. Detailed information on the internal anatomy of the breast may be gleaned from ultrasound scans of the breast tissue. This paves the way for the identification of anomalies, which may be an indication of the existence of cancer. Convolutional neural-networks (CNN) is kind of deep-learning technique that are particularly effective when used to the processes of picture recognition and classification. In the context of breast cancer prediction utilizing ultrasound pictures, CNNs may be used to assess and categorize images of breast tissue as either benign or malignant. This is accomplished by comparing the characteristics of the breast tissue to a database of known normal breast tissue. The CNN deep learning methodology is presented in this research as a method for predicting breast cancer based on an ultrasound picture. Python Spyder IDE 3.7 is the program that is used in the simulation process.
Comparative Analysis of Cryptographic Algorithms for Data Security in Cloud Computing: A Performance Evaluation

In the modern era, people are sharing their data online. securing this data in today's world is a crucial task i.e., anyone can get the data without the concern of the respective authority. Users store their sensitive/non-sensitive data on the cloud only by trusting the Terms & Conditions and the user agreement offered by the service provider. Cloud service providers are known to reject the responsibility for any data breaches that may happen. This paper proposes a method to strengthen the confidentiality of data in cloud. We have done comparative study between several cryptographic algorithms on the basis of their encryption and decryption time, performing ondifferent data formats like text, image, and videos.
Detection of Online Humiliation Through Social Media Platforms Using AI Inspired Algorithms

These days, it's impossible to imagine doing without the internet. Having access to a computer in real time is a clever, simple, and hilarious way to live. Alexa can quietly wake you up and initiate a variety of in-room conveniences to help you ease into your day. Another name for this is “real-time live computing,” and it facilitates a wide variety of communications and connections. Through online social networks, we may create a surrogate family that supports us even when we're thousands of miles apart. But there are always two sides to a story, and it was only a glimpse of the positive side. Because of this, young people are much more susceptible to online dangers. The primary objective of this dissertation was to review the literature on cybercrime, cyberbullying, and detection and prevention methods from the last several years. Second, we gathered data from over 35,000 tweets to feed to several intelligent machine learning algorithms, and then used five crucial ML algorithms to categorise and forecast whether tweets were offensive or not. Finally, a comparison of ML algorithms was made based on performance metrics.
Shining a Light on Solar Power Forecasting: Machine Learning Techniques for Unprecedented Accuracy

Accurate solar power forecasting is critical for efficient energy management and grid integration. This study looks into how to forecast solar power using weather forecast data and machine learning methods. Several machine learning models have been proposed to forecast solar power, including Artificial Neural Networks, Random Forests, Support Vector Regressions, Gradient Boosting, and Hybrid AI models. However, the proposed Deep Belief Networks (DBNs) model has several advantages over conventional machine learning models, including its capacity to learn intricate patterns and relationships in the data, lowering the requirement for manual feature engineering, and scalability for both short- and long-term forecasting. The comparison table and accuracy metrics show that the suggested model performs better in terms of accuracy than conventional models and takes less time to run. The proposed model provides valuable insights into the optimal design and implementation of solar power forecasting systems, supporting the transition to a sustainable energy future.
Navigating the Challenges of Security and Privacy in the Internet of Things Ecosystem

Internet of Things constitute a rebellion of the internet which is used to connect the devices over it so that they can share data and information to create the useful services and applications to ameliorate our grade of creation. With the help of sensors, IoT authorize the numerous gadgets and things on every side to be transmittable, identifiable and emplace. Even Though IoT escorted boundless advantages, it fabricates diverse provocations, particularly in security and privacy. To control these matters, make sure security and privacy of the object is the rudimentary precedence. Customers requires to have faith on IoT tools and associated facility. Furthermore, the IoT welfare must be contemplated to avert the IoT structure and its elements from generating an intolerable possibility of bruise or detriment and at the carbon time in view of communal conduct and moral utilization of IoT automation to validate efficacious certainty and security. This paper provides a consultation on IoT privacy and security issues. It begins with supplying an outline of the IoT structure, its construction and necessary attributes. This is gone along with talk through IoT safety summons, conditions and prime exercise to preserve IoT gadgets.
Extracting Photovoltaic Panel Parameters for Model and Simulation Purpose

The paper proposes an improved method to extract the parameters of a photovoltaic (PV) panel based on the data provided by the manufacturer in technical documents for the purpose of model and simulation. Manufacturers of PV panels, instead of I-V equation, usually provide only a few experimental data about electrical and thermal characteristics. Some of the parameters required for PV panel model cannot be found in manufacturer's datasheets. The two unknown parameters for modeling of a PV panel are series resistance Rs and parallel resistance Rp. Based on a published algorithm, which identifies the parameters of the nonlinear I-V equation by adjusting the curve at three points: open circuit, maximum power, and short circuit, this paper proposed an improved method to increase the accuracy and convergence speed of the previous algorithm by using two loops with golden-section search technique to determine the Rs and Rp.
A Real-Time Large Animal Detection Lightweight Network for Autonomous Driving on Highways

The perception module plays a crucial role in the autonomous driving system, which is a very complicated system. In contrast, current perception research in the area of autonomous driving primarily focuses on the recognition of vehicles, lanes, and traffic signs, ignoring other factors that may cause traffic accidents, and fails to consider the fact that many traffic accidents on highways are caused by wild or wandering caused by animals. We conducted the following studies to close this gap in knowledge: First, a dataset of 1050 photos for huge animals that could appear on highways was created. Next, we propose a more efficient Yolo model by improving its backbone, replacing the C3 module with C3Ghost. The number of parameters is decreased to fewer than 3.7 million, just 52.7% of Yolov5s, but the average accuracy for each type of animals (mAP% 0.5) has reached over 95%. Third, our GhostSort- YoloNet (GS-YoloNet) also incorporates the Deep Sort algorithm to achieve real-time ranging and speed assessment of numerous targets, which has a very big practical application promise.
A syntax‒aware deep‒learning model for biomedical semantic role labelling

A deep learning model for biomedical semantic role labeling was build. Semantic role labeling is a useful task that enables the computer to comprehend the key facts expressed in each sentence, and is a necessary first step in the resolution of several other semantic-related tasks, such as event extraction, entity extraction, and Q-A systems... Semantic role labeling is a domain-dependent task. In the biomedical field, semantics are transmitted via more intricate grammatical structures and dependencies in addition to being built on a predicate argument frameset that differs greatly from that of the general domain. To effectively account for these unique characteristics, three types of information were integrated into this deep learning model: Context knowledge obtained from a pre-trained language model trained on a substantial corpus of biomedical texts, dependencies derived from the dependency parse trees and sentence structure obtained from constituency parse trees. To handle grammatical information that is naturally represented as graphs, the Graph Attention Network which is well-known for its remarkable graph learning capabilities, was used. To further boost the model effectiveness, predicate indicator embedding was additionally included in the proposed model. According to experimental findings, the two above-indicated forms of syntactic information along with the predicate indicator embedding, could boost F1 by up to 20%.
The Relationship Between Variations in Teaching Skills of Tutors and Learning Motivation of Learning Package C Students in SPNF SKB Kota Pariaman

ABSTRACTThe aim of this research is to find out the description and relationship between skills in applying variations in tutor teaching and the learning motivation of residents studying Package C at SPNF SKB Kota Pariaman. This research uses quantitative correlational. The population of this research is all students registered in the package C pursuit program at SPNF SKB Kota Pariaman for the 2023/2024 academic year, namely 72 people. The sampling technique used cluster random sampling, so that the total sample that the researchers took was 50 residents studying package C at SPNF Kota Pariaman. Data collection techniques using questionnaire format and list of statements as data collection tools, data analysis techniques using percentages to find correlations using the product moment formula. The results of this research are: 1.) Skills in applying variations in tutor teaching to students studying at SPNF SKB Kota Pariaman are classified as less than optimal, 2.) Motivation of students studying Package C at SPNF SKB Kota Pariaman is classified as less than optimal and 3.) There is a significant relationship between skills in applying variations in tutor teaching with learning motivation for residents studying Package C at SPNF SKB Kota Pariaman.Keywords: Variasi Mengajar, Motivasi Belajar, SKB.
Shoebox Program To Improve The Speech Language Skills Of Children With Ttention Deficit Hyperactivity Disorder

Children with attention deficit and hyperactivity disorders can experience language and speech delays if not treated early. Based on initial observations in the field, it was found that a child with hyperactivity and attention deficit disorder or ADHD aged 3 years and 4 months was late in language development. At that age, children should be able to express desires, ask questions, answer simple questions, respond when called, and be interested in playing with their peers. From an early age, children need to receive intervention as soon as possible from their immediate environment, namely the family, so that children's obstacles can be minimized properly. For this reason, this research aims to develop a program that can improve the language skills of children with ADHD. This program is called Shoebox, which was developed based on the results of an assessment of children's language skills. The program will be implemented by the child's parents through a process of mentoring researchers first until the parents implement it independently. This research uses mixed research methods, namely qualitative research methods as the basis for program development, and quantitative research methods with SSR design to test the program's effectiveness. Data collection techniques are interviews, observation and documentation, and oral language behavior tests. The results of this research are a shoebox program which, based on effectiveness tests, shows that this program can improve speech-language skills in ADHD children
PELATIHAN PUBLIC SPEAKING UNTUK MENUNJANG KEMAMPUAN PRESENTASI SISWA DI DESA SINDANGJAYA

Ability in public speaking is an important part of every activity, especially activities in the academic environment. In reality, there are still few school students who have public speaking skills. Departing from this, the author conducts Public Speaking Training which will be held at MTs Fatahillah Sindangjaya Village. The methods used in this training are material delivery, discussion and simulation. This study aims to present sources of related topics in an effort to support presentation skills for students located in Sindangjaya Village. Data in this study was collected using interviews, web scraping and documentation. The results of this study show that students can understand how to speak and present well and correctly. It is hoped that this activity can increase the literacy of students' abilities in public speaking which can be used as a key to the advancement of a civilization in a country.