Reviewerly/exHarmony huggingface dataset:
Augmented reality-based accurate teaching system and method for industrial robots

Aiming at the problems of cumbersome and low efficiency in the teaching process of the existing industrial robot by using the teaching box, this paper establishes an accurate teaching system for industrial robots based on augmented reality and conducts algorithmic research on the 3D tracking registration, motion simulation, and hand recognition technologies of this system. First, the 3D registration method based on multiple markers is proposed to improve the accuracy of virtual object registration in real space, then the robot forward and inverse solution algorithms are elaborated and applied to the motion simulation and online control process of the virtual industrial robot; finally, the Mediapipe open source deep learning network model is applied to hand interaction to achieve immersive human-robot interaction. Experimental results show that this method can achieve an average trajectory accuracy of 1.05mm and a maximum trajectory accuracy of 2.24mm, meeting the accuracy requirements of robot welding and spraying applications.
Filling Deterministic Approximate Gap in Denoising Diffusion Probabilistic Models with Energy

Diffusion probabilistic models (DPMs) have shown the best generative quality and admit impressive likelihood meanwhile. However, its variants take very long generative processes during sampling, because of the small noise scale assumption, say, sets the variance of the forward diffusion kernel to be very small. Under which, the generative denoising processes are of tractable Gaussian form. To shorten the generative process, we explore the non-Gaussian reverse kernel under the large noise scale assumption and analysis the deterministic approximate gap of Gaussian denoising process. To fill the gap, we enhance the standard DPM with conditional energy and introduce variantional sampler to construct tractable generative process. The new probabilistic model, termed denoising diffusion energy-based model (DDEBM), are trained with two stage adversarial optimization. Experiments show our models demonstrate competitive generative performance with only four steps.
Evaluation of Physical Layer Security in Underwater Acoustic OFDM System Using Time-Reversal

Physical layer security (PLS) is a new approach of information security and has great potential for underwater acoustic (UWA) communication. Time-reversal (TR) technique has characteristics of temporal compression and spatial focusing. It can be applied to orthogonal frequency division multiplexing (OFDM) system to achieve PLS. This paper focuses on the evaluation of physical layer security in multi-input-single-output (MISO) UWA OFDM system and compares to the system without using TR technique to clarify the advantages of TR technique. For evaluation, we propose a scheme of UWA TR OFDM system and give a received signal model on the block transmission. Simulation results show that the secrecy rate of UWA OFDM system is significantly improved when applying TR technique. And the eavesdropper cannot obtain effective information.
Multivariate Beta Normality Scores Approach for Deep Anomaly Detection in Images Using Transformations

In this work, we propose a novel anomaly detection approach in images based on normality scores using transformations. By applying various transformations to the input image such as rotation and flipping, we train a classifier to predict the transformation label applied to the images. Then, we represent the output of the classifier by a softmax vector. Thanks to the flexibility of multivariate Beta in fitting the data compared to other conventional distributions such as the Dirichlet distribution, we approximate the softmax vector by this general form of the Beta distribution to construct the normality scores. Moreover, we use the Maximum Likelihood to estimate the parameters of the proposed distribution. To show the power and the effectiveness of our approach, we conduct experiments of detecting anomalies in various public datasets. Furthermore, the proposed method is compared with state-of-the-art techniques and results demonstrate its superiority in terms of Area Under Receiver Operating characteristics (AUROC).
Parallel Convolution and Attention for Pose Estimation

Multi-scale feature fusion modules exhibit excellent performance in CNNs. However, these convolution modules suffer from inherent receptive field constraints for human pose estimation (HPE). Recently, transformer blocks have been introduced into HPE to exploit global information and capture correlations between different joints. But relying solely on the transformer block is insufficient and computationally expensive. We propose a parallel convolution and attention(PCA) module, which takes advantage of both local and global representations for efficient feature extraction. Furthermore, we propose a network based on the PCA module for HPE, termed PCApose. Experiments on MS-COCO demonstrate that, under comparable performance, PCApose reduces computation by half compared to Transpose [1]. Our model strikes a balance between computational burden and accuracy.
Sign Language Recognition Based On Facial Expression and Hand Skeleton

Sign language is a visual language used by the deaf and dumb community to communicate. However, for most recognition methods based on monocular cameras, the recognition accuracy is low and the robustness is poor. Even if the effect is good on some data, it may perform poorly in other data with different interference due to the inability to extract effective features. To solve these problems, we propose a sign language recognition network that integrates skeleton features of hands and facial expression. Among this, we propose a hand skeleton feature extraction based on coordinate transformation to describe the shape of the hand more accurately. Moreover, by incorporating facial expression information, the accuracy and robustness of sign language recognition are finally improved, which was verified on A Dataset for Argentinian Sign Language and SEU’s Chinese Sign Language Recognition Database (SEUCSLRD).
DF4RT: Deep Forest for Requirements Traceability Recovery Between Use Cases and Source Code

Nowadays, many supervised learning techniques have been applied to requirements traceability recovery (RTR). However, the performance of these supervised learning techniques is still far from satisfactory, and exploring a more effective model is necessary. This paper proposes a new deep forest model for RTR(DF4RT) with a novel composition to improve the model's performance. The proposed model incorporates three feature representation methods, which not only information retrieval and query quality but also add distance. The DF4RT model is evaluated on four open-source projects and compared with nine state-of-the-art tracing approaches. The experimental results show that DF4RT improves precision by 94%, recall by 58%, and F-measure by 72% on average. We also conduct ablation experiments to explore the impact of the different features. It is the first time that deep forest is employed in requirements traceability. Our approach is effective for RTR with good interpretability, few parameters, and good performance in small-scale data.
Device-Free and Training-Free Hand Gesture Recognition with Acoustic Signal

Hand gesture recognition is an essential Human Computer Interaction (HCI) mechanism for users to control smart devices. While traditional device-based methods support acceptable recognition performance, the recent advance in wireless sensing could enable device-free hand gesture recognition. However, two severe limitations are serious environmental interference and high-cost hardware, which hamper the wide deployment. This paper proposes a novel system TaGesture, which employ the inaudible acoustic signal to realize device-free and training-free hand gesture recognition with a pair of commercial speaker and microphone array. We address unique technical challenges, such as proposing a novel acoustic hand tracking smoothing algorithm with Interaction Multiple Model (IMM) Kalman Filter to address the issue of localization angle ambiguity, and designing a classification algorithm to realize acoustic-based hand gesture recognition without training. Comprehensive experiments are conducted to evaluate TaGesture. Results show that it can achieve a total accuracy of 97.5% for acoustic-based hand gesture recognition, and support the furthest sensing range of up to 3 m.
A GPU-Accelerated Framework for Standard White-Box Cryptographic Algorithms in Unattended IoT Devices

White-box cryptography is widely used in Internet of Things (IoT) devices to ensure data confidentiality. However, the traditional implementations of white-box cryptographic algorithms (WBCAs) are inefficient and impractical for IoT devices that require fast encryption and decryption of large amounts of data. To address this challenge, we propose a framework that can accelerate WBCAs employed by IoT devices with Graphics Processing Units (GPUs). Our framework leverages the inherent multithreading capabilities of GPUs to simultaneously perform a large number of table lookups required in WBCAs. Additionally, we employ pipelined execution strategies to enhance performance further. To demonstrate the effectiveness of our framework, we apply it to two well-known WBCAs and evaluate its performance on two IoT devices. The experimental results show that our framework can improve performance by up to 70 times compared with the execution on a single-threaded CPU.
A Deep Q-Network-Based Algorithm for Obstacle Avoidance and Target Tracking for Drones

This paper introduces a novel algorithm, refer to NEWDQN, which is based on the deep Q-network (DQN) framework. The primary objective of this algorithm is to optimize the successful rate both in autonomous drone obstacle avoidance and target tracking tasks, while this algorithm can also improve the drawbacks of the previous algorithm in convergence. Furthermore, the algorithm endows the drone with environment perception capabilities and incorporates a direction-based reward-penalty function into the reward function, enhancing the drone's generalization ability and overall performance. Extensive simulations demonstrate that compared to conventional DQN and Double DQN (DDQN) algorithms, NEWDQN exhibits faster convergence speed, shorter tracking paths, and more robust adaptability to different environments.
Human Pain Relief by Using Augmented Reality of Noxious Stimulation

It is estimated that 3.5-10 percent of the population are afraid of injections. These people are at risk of vasovagal syncope, a condition in which the patient faints during the injection. To help overcome this response to the perceived pain of injections, this paper proposes a diffuse noxious inhibitory control effect that can be applied through Augmented Reality (AR) smartphone applications. Furthermore, we confirm that presenting ARs that evoke noxious stimulation in participants can influence pain perception. We investigate the pain perception and negative affect when presented with noxious ARs and a distraction AR. The results show that pain thresholds are significantly increased in each AR condition when a painful stimulation is presented to participants. However, no difference in pain reduction between the noxious and distraction ARs is observed. One reason for this result may come from a manipulation problem: exposure to ARs does not make all participants feel pain. For the group of participants who felt pain when exposed to the ARs, the magnitude of pain tended to be smaller in a noxious AR (fire) condition than in the distraction condition. There was no significant increase in negative affect in the fire AR condition compared with the other conditions. These results may imply that the proposed method could reduce pain through diffuse noxious inhibitory control without producing any extra discomfort.
Hand Gesture Classification Model for Intelligent Wheelchair with Improved Gesture Variance Compensation

The rapid increase in the elderly and disabled population has been identified as a growing socioeconomic problem. Due to reasons such as a lack of reliable caretakers and the need to empower the elderly and disabled population, it is important to have assistive devices. The interactive capabilities of these devices should match the nature of the interaction that prospective users would have with their companions. Humans communicate with each other in many modalities, such as speech, hand gestures, head gestures, gaze, etc. Hand gestures have been a popular modality that has been used in these interactive devices for speech and mobility-impaired wheelchair users. There have been many gesture models that have been developed recently for hand gesture-controlled wheelchair navigation. Natural hand gestures that are used in human-human interactions include both static and dynamic gestures. Therefore, it was logical to include both of these gestures in a navigational gesture model or hand gesture-controlled navigational system. However, hand tremors that are prevalent among the elderly and disabled community could affect the nature of the hand gesture. These tremors can vary from person to person, and hence the fixed ranges cannot be used for hand features. Hand features such as palm velocity, fingertip velocity, and others will have different ranges from person to person. Due to these reasons, a static hand gesture intended by the human user could be identified as a dynamic gesture. Further, this could lead to the misrecognition of gestures defined in gesture models. Therefore, a system is proposed in this paper to validate the gestures by considering the activity of hand features in 3D regions defined for the gesture. The accuracies of the improved system for static and dynamic gestures were 0.9849 and 0.9840, which were improvements from the accuracies of 0.8994 and 0.8479.
Information Technologies in Scientific Activities

Information technology (IT) has become an integral part of the modern scientific process, influencing all its aspects. However, there are numerous problems and challenges that arise in connection with the use of IT in scientific activities. The article is aimed at disclosure of the essence and analysis of information technologies (IT) in the field of scientific activity. The study solves the main problems, including the description of the concept of «information technology», the allocation of their categories and the study of the possibilities of their application in science. It is allocated that the classification of information technologies is carried out by the method of implementation in the information system; by the class of implemented technological operations; by the degree of coverage of management tasks; by type of user interface; by subject area. The article highlights the current aspects of the use of IT, emphasizing their contribution to the improvement of scientific research. Various options for the use of information technologies are analyzed, their role in improving the efficiency and in development of the scientific process is emphasized. The study confirms that IT is an effective tool for collecting, analyzing, and processing data in various industries, such as economics, education, medicine, law enforcement, and engineering. The authors of the article focus on the universality of the use of IT in scientific experiments. According to the results of the study, it is found that the use of information technologies in scientific activities includes the use of computers, software, information systems, electronic databases, Internet technologies and other means for collecting, processing, analyzing and exchanging scientific information. Information technologies in scientific activity contribute to the improvement of research efficiency, faster data exchange between scientists, as well as the development of new methods and approaches to solving scientific problems. It is noted that in order to maximize the benefits of IT in scientific activities, further improvement of infrastructure is needed, in particular the development of effective systems for collecting and processing data, as well as increasing the level of cyber protection to ensure the confidentiality and integrity of data.
Research on the Influencing Factors that May Lead to Obesity

The global obesity problem is getting more and more serious, which not only affects the quality of life but also induces a series of complications. In this study, multiple linear regression method was used to study the influencing factors of obesity, and their answers were linked to the literature review to draw conclusions. And their responses were linked to literature Review to draw conclusions. Calorie consumption monitoring, physical activity frequency, and vehicle use also have significant positive effects on obesity levels. Frequent consumption of vegetables, main meals, smoking times, and alcohol consumption will also have a significant negative impact on the obesity level. Due to the limitations of this data set and other reasons, the model test results will be inaccurate. In the future, people from other regions may be included, making the research more scientific. The research in this paper has a positive effect on the treatment of obesity, which will help people to find it earlier, treat it as soon as possible, and improve the survival rate and quality of life of patients.
Analysis of the Concepts and Searching for Mini Black Hole

With the progression of time, individuals have an increasing desire and interest in the universe, and black holes, as perilous and enigmatic entities within the universe, naturally draw the attention of many individuals seeking to study them. Amongst all types of black holes, the mini black hole is particularly mysterious, and it remains unknown whether it even exists. This study will briefly introduce and integrate the concept of MBHs and their search. In fact, the life expectancy of a mini black hole is divided into four stages, which distinguishes it from other black holes. Ultimately, it will culminate in the production of short gamma ray bursts. Two methods for detecting mini black holes have been identified, one through gravitational waves and the other through Radio and X-Ray Sky. If the existence of MBHs is proven, some of the Universe's puzzles may be solved or explained. The paper's significance lies in its ability to provide a straightforward comprehension of mini black holes for individuals seeking further knowledge on the topic.
Comparison of Different AIED Models and Evaluation Methods

In the educational field, although machine which attempts to learn AI is still in their early stages, the approach has yet to show remarkable results when facing complex challenges without obvious cut-off points, such as grading students’ papers or exploring enormous and complicated data collections. AI can also be used to create virtual learning environments, intelligent testing systems, and automated grading systems. AI in educational fields refers to the application of AI technology to enhance and support the studying processes, such as tracking students’ behavior and constructing models that can accurately hypothesize students’ achievements. It can include the use of AI-powered tutoring systems, personalized learning platforms, and data analysis tools that can help teachers and administrators better understand student needs and progress. This paper mainly concentrates on the field of artificial intelligence tutoring and summarizes the methods by which intelligent tutors assess student performance by comparing some student models and the input, output, and model forms of methods for evaluating fine-grained interactions of intelligent tutors. This article also provides basic information and new perspectives for studying which methods to use to model and evaluate tutorial learning.
Efficient Hardware Architecture for Ultra-High Sampling Rate FFT Analysis of Acoustic Emission Signals

In the realm of ultra-high sampling rates, Fast Fourier Transform (FFT) stands as a cornerstone in analyzing acoustic emission signals. This manuscript presents an efficient hardware architecture tailored for executing FFT using the radix-2 Frequency Decimation Algorithm (R2DIF) and a channelled method facilitating effective data sharing via shift registers. The architecture employs an optimal rotation method leveraging the modified Digital Coordinate Rotation Computer Algorithm (mCORDIC) and Radix-2r, dependent on the coding scheme, to replace complex multipliers in FFT computation. The integration of m-CORDIC enhances computational efficiency, while Radix-2r facilitates a logarithmic reduction in adder steps, optimizing FFT execution for ultra-high sampling rates.
THE REPETITION OF ENGLISH PROVERBS

Repetition is one of the main linguistic tools that could be used in so many different ways to communicate ideas without stating that the repeated word is the importance. The repeated is significant to the addressee since they hear it and process twice or more. This paper has conveyed that proverbs in English can carry out different level of meanings. Repetition appears in so many forms. The researcher has showed the semantic and the phonological levels of repletion since they are the most noticeable ones in language. The researcher then took example of English proverbs and explain the various repetition forms and function. Therefore, the researcher has concluded that repetition is used in different forms that could semantic, formal or full.
Training in economic disciplines through the prism of the competency-based approach

The article presents the relationship between training and the competency-based approach as fundamental to modern education. It is pointed out that it finds its reflection in teaching and learning. Education in economic disciplines is seen as forming professional and basic competences necessary in all spheres of modern social and economic life. The main dimensions of the process are discussed and analyzed, emphasizing its use as a pedagogical tool to increase the effectiveness of training.
Shortened and punctured codes

The hull of a linear code is the intersection of the code with its orthogonal complement. We study the relation between the hulls of a linear code, its shortened codes and its punctured codes.
PEMBUATAN VIDEO PROMOSI PELATIHAN DI BALAI LATIHAN KERJA SAMBAS MENGGUNAKAN METODE MDLC

The workforce is highly needed in the country's economic development, where qualified labor can drive more productive development. The Training Center Unit of Sambas District Work Training Center is a facility that accommodates training activities which function to provide, enhance, and develop skills, productivity, discipline, work attitude, and work ethic. To attract public attention to participate in the training, an introduction to each training available at the Sambas District Work Training Center through visual media is required, in the form of promotional videos. This promotional video creation utilizes the MDLC method, which is a combination of visual media, sound, video, animation, and others. The MDLC method consists of six stages: Concept, Design, Material Collection, Production, Testing, and Distribution. The result of this research is the completion of a 5-minute promotional video in H264 1080p format, namely Mp4."