Reviewerly/exHarmony huggingface dataset:
Tile-based multiple parallel triangle rasterization algorithm implemented in FPGA

Abstract With the progress of rendering technology, the industry has put forward higher requirements for GPU performance while rasterization algorithm plays an important role in GPU performance. In this paper, we first review some of the current mainstream rasterization algorithms. Then we propose a triangle rasterization algorithm—tile-based multiple parallel midpoint rasterization algorithm. This algorithm combines the characteristics of midpoint traversal and tiled traversal. At last, we use FPGA to implement the algorithm in order to verify the feasibility of the algorithm. And we use the triangle data set used in previous papers to simulate in Modelsim software. The results show that this algorithm can improve the rasterization speed and the highest frequency of this design is 200MHZ.
CV-Mora Based Lip Sync Facial Animations for Japanese Speech

To generate authentic real-time facial animations using face mesh data, which corresponds to fifty-six consonant and vowel (CV) types of morae that form the basis of Japanese speech, we propose a new method. Our method produces facial expressions by the weighted addition of fifty-three face meshes based on the real-time mapping of voice streaming to registered morae. In the user study, results showed that facial expressions produced during Japanese speech were more natural using our method than those using popular methods to generate real-time English-based Oculus lip sync and volume intensity-based facial animations.
Quantum Regression Model for the Prediction of Surface Plasmon Resonance Sensor Behaviour

In this paper, we made a pioneering effort for the first time to implement Quantum Neural Network regressor model to predict the sensing behavior of Surface plasmon resonance (SPR) sensor and compared the performance of the proposed model with two traditional algorithms namely Support Vector Regressor (SVR) and Artificial Neural Network (ANN) regressor. The proposed trained quantum regressor model is crucial and efficient enough as it could be used to predict the trend of the target value that is confinement loss of the SPR biosensor.
Period finding and prime factorization using classical wave superposition

Prime factorization is a procedure of determining the prime factors of a given number N that requires super-polynomial time for conventional digital computers. Peter Shor developed a polynomial-time algorithm for quantum computers. Period finding is the key part of the algorithm, which is accomplished with the help of quantum superposition of states and quantum entanglement. The period finding can be also accomplished using classical wave superposition. In this study, we present experimental data obtained on a multi-port spin wave interferometer made of Y3Fe2(FeO4)3. Number 817 was factorized by a sequence of phase measurements. We also present the results of numerical modeling on the prime factorization of larger numbers 334597,1172693,3377663,and9363239. The results of numerical modeling reveal significant shortcomings of the period-based approach. The major problems are associated with an inability to predict the period of the modular function, significant overhead over classical digital computers in some cases, and phase accuracy requirements. We argue that the same problems are inherent in classical analog and quantum computers.
Real Time Egocentric Object Segmentation for Mixed Reality: THU-READ Labeling and Benchmarking Results

Egocentric segmentation has attracted recent interest in the computer vision community due to their potential in Mixed Reality (MR) applications. While most previous works have been focused on segmenting egocentric human body parts (mainly hands), little attention has been given to egocentric objects. Due to the lack of datasets of pixel-wise annotations of egocentric objects, in this paper we con-tribute with a semantic-wise labeling of a subset of 2124 images from the RGB-D THU-READ Dataset. We also report benchmarking results using Thundernet, a real-time seman-tic segmentation network, that could allow future integration with end-to-end MR applications. A comparison with depth-based segmentation and a useful discussion regarding the suitability of the different algorithms in MR is also given.
Perception of Symmetry of Actual and Modulated Self-Avatar Gait Movements During Treadmill Walking

In virtual reality, it is possible to simulate one&#x0027;s visual self-representation by mapping one&#x0027;s body movements to those of an avatar. Accepting the virtual body as part of one&#x0027;s own body creates an ownership illusion. This study aimed to assess the perception threshold between a subject&#x0027;s actual gait movements and those of their modulated self-avatar during treadmill walking. Preliminary results on two subjects suggest that healthy subjects can detect the mismatch, but differences may exist between subjects.
Development of a Secured Safety Monitoring System for Miners Based on Lora Standard

In this article, we describe the issues of building a secure Internet of things system using the LoRa protocol. The relevance of this topic comes from the increased interest in Internet of Things devices and systems in mining industry, especially for safety monitoring. Such kind of systems provide a reliable way of collecting and storing data from lightweight devices over a large area.
Time Series–Based Detection on Tailgating Fare Evasions Using Human Pose Estimation

Passengers pass ticket barrier gates without paying in metro stations all over the world. This kind of behavior is called fare evasion, and it is troublesome and costly to prevent. As a typical type of fare evasion, tailgating refers to following a fare-paying passenger through the gate. It can be dangerous because the passenger risks being injured by the barrier gate. To detect tailgating fare evasions automatically, the existing surveillance cameras in stations can be utilized to provide a visual-based method at a low cost and efficiently. However, occlusion by crowds during rush hours can lower the accuracy of regular recognition methods based on convolutional neural networks. Moreover, the behavior of a tailgater can be similar to that of other fare-paying passengers if the positional relationship is not taken into account. Thus, we propose a tailgating recognition method that uses videos as input. First, the estimated human pose data in each frame is obtained, of which incomplete skeletons are retained. Second, the multiple persons appearing in adjacent frames are matched, after which a sequence of skeleton data is generated for each pedestrian. Third, a time series of the positional relationship between passengers and the ticket barrier gate is extracted and the passing interval of passengers is defined as the indicator for detecting tailgating. Our experiments showed that tailgaters could be distinguished effectively from fare-paying passengers, and the time series can cope with missing joints caused by occlusion or misidentification in a few frames.
A Multiobjective Allocation Method for High-Quality Higher Education Resources Based on Cellular Genetic Algorithm

Optimizing the allocation of higher education resources and improving the utilization efficiency of educational resources are of great significance to further promoting the balanced development of higher education quality. In order to rationalize and maximize the efficiency of the allocation of innovation and entrepreneurship education resources in colleges and universities and build an evaluation index system for the input and output of educational resources, this paper proposes to use the improved cytogenetic algorithm educational resources for resource allocation and utilization. The adaptive constraint processing technology is combined with the cytogenetic algorithm to avoid the algorithm falling into local optimum. Our method has achieved good results on high-dimensional functions. The utilization efficiency and allocation efficiency of innovation and entrepreneurship education resources in colleges and universities have increased by 21.33% and 18.92%, respectively, and tend to be in a balanced state, which can optimize the allocation of educational resources.
Teachers’ Role and Level of Awareness on Safe Internet Use and its Legal Implications in the Conduct of Online Classes during the Pandemic

The pandemic shifts the classroom environment to virtual classrooms which change the role of the teachers to address the needs of the learners to fit in online distance learning. These changes expose teachers and students to cybersecurity risks. The Data Privacy Act of 2012 was enacted to mitigate the cybersecurity risks in the Philippines. It upholds the security and protection of personal information and communication systems in the country. Thus, awareness of internet security and safety is an important issue in the conduct of online classes. This study aims to find out if there is a significant difference in teachers’ role and level of awareness on four dimensions of safe internet use in the conduct of online classes. The research utilized a quantitative research method that focused on survey design. The self-selection method was used to obtain 225 participants. The study shows that the conduct of online classes does not affect the perceived role of teachers and this revealed that they are more aware of the essential measures of online security. It also showed that their level of awareness on the four dimensions of safe internet use is significantly different. In addition, the study was not able to establish a significant relationship between teachers’ length of service and teachers’ level of awareness on protecting information privacy. It shows that regardless of the years' in-service, teachers are aware of their role as personal information controllers as stipulated in the Data Privacy Act of 2012. The results of this study will help the schools to raise awareness among teachers on the reliability of computer security software that can ensure the security of personal information and avoid security breaches.
Lightweight and Effective Human Pose Estimation Model Based on Multi-Angle Knowledge Distillation

Abstract In the field of human pose estimation, most of the existing methods focus on improving the generalization performance of the model, while ignoring the significant efficiency issues. This leads to an increasing amount of model parameters and needs to take up more and more computing resources, which greatly reduces the practical value of the model. In order to solve this problem, we propose a novel lightweight network structure called Effective and Lightweight Pose Network (ELPN). At the same time, for the sake of alleviating the difficulty of lightweight model training, we propose a Multi-Angle Pose Distillation (MAPD) model training method that can more effectively train particularly small pose network models. In quantitative experiments, our models have excellent performance on two mainstream benchmark datasets: the MPII and the COCO. In qualitative testing, our models can accurately locate the keypoints of complex human movements. These fully demonstrates the efficiency and effectiveness of our methods. Our models have the characteristics of high precision, small size and fast inference speed. It is a cost-effective model with greater practical value.
A Hybrid-ARQ Method with Adaptive Receiving Buffer Duration in Real Time Video Streaming

Abstract To decrease end-to-end delay for streaming media over internet, the transmission method based on best-effort model is always employed in Real Time Communication (RTC) system. However, the packet-loss, jitter and congestion occur frequently because the quality of transmission cannot be guaranteed, which causes lag and splash of video. Therefore, an optimized hybrid Automatic Repeat Query (ARQ) solution with adaptive receiving buffer duration is proposed to guarantee the quality of video transmission. In the method, the loss rate was used to adjust the buffer duration and calculate the redundancy for Forward Error Correction (FEC). The results show that the method in this paper gains lower end-to-end delay and better Mean Opinion Score (MOS) values while ensuring higher fluency in weak network scenarios.
Comparative Study on Various Storage Optimisation Techniques in Machine Learning based Cloud Computing System

Cloud computing is gaining more popularity in various industries as it enables in creating better services to different individuals, companies and government. With the progressive aspect on the concept, it offers flexible solutions to the growing computing problems related to infrastructure, application and other aspects. Cloud computing enables in minimizing the investments on IT infrastructure as it offers better and extensive support in saving various data and information of the companies, government and individuals, enable in retrieving them as and when required. Through the application of cloud computing the users received better and reliable services from the service providers which enables in reducing the investments, optimizing the cost and enable in enhancing products of the company in the long run. The cloud computing is taking better aspects and is considered as the paramount aspect for the business and individuals. This study is more focused in making a comparative understanding in the optimization techniques using machine learning based cloud computing systems. This research is more focused in analysing the application of machine learning as a key technique which can be applied in order to estimate the request patterns of the clients related to cloud storage. The research further focuses on the machine learning techniques covering linear regression model, Artificial neural networks, Support vector machines etc. are being explored in order to understand the optimization techniques for services the users in a better manner. The research applies questionnaire method for collecting the information and extensive analysis using SPSS is applied for analysing the data.
Research on defect recognition method of substation inspection images based on faster R-CNN

Aiming at the problems of low efficiency and poor accuracy in manual identification of tens of thousands of substation inspection images in the power grid, one method and system for identifying defects in substation inspection image data based on Faster R-CNN is proposed. The inspection images are preprocessed through data analysis module, image annotation module, image cleaning module, firstly. A convolutional neural network (CNN) is used to perform feature extraction on the processed images to obtain the target features, secondly. A Region Proposal Network (RPN) is introduced, which generates multiple candidate anchor frames on the target feature images, thirdly. Region of Interest (ROI) pooling is performed on the multiple candidate anchor frames to obtain a feature matrix of a fixed size, fourthly. Then, the frame regression and classification recognition are applied to obtain the defect recognition result and target anchor frame position. Finally, high-precision identification of 25 types of defects of substation inspection images such as oil leakage, insulator damage, mark damage, metal corrosion, is realized. The average accuracy of the method is as high as 93.44%, which is about 27% and 23% higher than the traditional systems with R-CNN and Fast R-CNN algorithms. The time of single image recognition is shortened to (150ms) millisecond level. This method greatly saves the detection time, which can effectively reduce manpower input, improve work efficiency more effectively, and provide a foundation for the digital transformation of the power grid.
A new contrast enhancement method for Color dark and low-light images

Image contrast enhancement is a preprocessing phase that improves the performance of image processing applications such as pattern recognition and computer vision. Many images have poor quality due to low-luminance and low-contrast, which must be changed before further processing. It is significant for medical imaging because the low-light intensity makes it challenging to diagnose and analyze specific diseases accurately. In addition, when the depth information of a low-light image is unknown, the drawback of illumination enhancement becomes very challenging. Since Contrast enhancement of low-light images with non-uniform illumination is complex, it may lead to inefficient contrast enhancement methods. In other words, in such cases, if contrast enhancement methods are used to increase the contrast of dark areas, the bright regions become over-enhanced, which may lead to the disappearance of the details of these areas. Given this problem, in this paper, a new method is proposed to increase the image contrast that can improve the contrast in both areas of the image without adversely affecting the details of the image. The result represented the efficiency of proposed methods compared to other image enhancement methods based on full-reference and noreference metrics such as PSNR, MSE, NIQE, and BRISQUE.
Analyses of the Structure of Lumber Using Ultrasonic Waves

A mathematical model of the acoustic path of the shadow method of sounding wood is proposed. The effective wave propagation speed along the annual layers is determined. The angle of deflection of ultrasound when passing through a finely layered medium is experimentally determined and the parameters of the transducer for reliable ultrasound testing are determined.
Lexical expressions and grammatical markers for source of information: A contrast between German and Korean

Specification of information sources in human speech – evidentiality – prevents misunderstandings and allows listeners to assess the validity of the information communicated. Some of the same linguistic forms used for evidentiality, including grammatical markers and lexical items, are also reported to have a ‘mirative’ function, and can be used by speakers to express their surprise. Importantly, languages differ in the type of forms they typically use for evidentiality. For instance, corpus analyses suggest that German uses lexical items, while Korean typically encodes evidentiality via grammaticalized sentence-ending (SE) markers. Little is known, however, about the extent to which such linguistic differences are reflected in speakers' use of these forms in verbal interaction. To fill this gap, we carried out an experimental study in which Korean and German speakers were presented with events in audio and/or visual modalities and asked to describe them verbally as well as to rate their degree of surprise about the events. The results revealed several major differences between the two languages. Korean speakers used a high frequency of evidential SE markers, particularly the hearsay marker -tay, while German speakers, who relied on lexical items, encoded evidentiality much less frequently. In Korean, grammatical markers of evidentiality showed pragmatic extensions: The hearsay marker -tay carried an overtone of mirativity. Korean speakers seldom used markers having to do with perception, -te(la) and -ney; instead, it was the neutral marker -e that correlated with the salience of the visual evidence. In contrast, German speakers encoded hearsay and visual perception with lexical verbs in comparable frequencies, slightly prioritizing visual evidence. In German, lexical expressions (sagen/hören ‘say/hear’, sehen ‘see’) did not show pragmatic extension, and unmarked sentences did not imply a visual information source. These and other findings offer important insight into the lexical-grammatical continuum in evidentiality and the relationship between language and cognition in general.
Design of four rotor aircraft with obstacle avoidance

The system uses TM4C123G as the core of quadrotor autonomous vehicle control, which consists of flight control module, power supply module, motor speed control module, optical flow sensing module, and target tracking identification module. The flight control module includes angle sensor, gyroscope, and TLS1401-LF module. The flight control processes the collected data through the chip (TM4C123G), and processes the data with PID control algorithm, while solving the PWM increment and decrement needed for the corresponding motor, adjusting the motor in time and adjusting the flight attitude. The binocular camera identifies the color of the pole tower and measures the distance, so that the distance between the aircraft and the nearest point of the pole tower is kept within 50±10cm. After detecting the red (green) tower as the center, fly around the tower clockwise (counter) for one week (top view). Finally, the OV7725 camera is used to identify the solid black circle mark of the landing point and land smoothly and accurately in the target area, thus realizing an efficient robot around the barrier.
Evolving interpretable strategies for zero-sum games

The present paper introduces Gesy, a genetic programming approach to script synthesis for zero-sum games. We will explore the sum-zero game context in Real-Time Strategy (RTS) games, where players must look for strategies (planning of actions) to maximize their gains or minimize their losses. The goal is to solve the script synthesis problem, which demands the synthesis of a computer program from a space of programs defined by a Domain-Specific Language (DSL). The synthesized program must encode a practical strategy for zero-sum games. Empirical results validate Gesy using the μRTS platform, an academic test bed game that presents the main features found in RTS commercial games. The results show that our method provides interpretable strategies that are competitive with state-of-the-art search-based approaches in terms of play strength. Moreover, once synthesized, scripts require only a tiny fraction of the time needed by search-based methods to decide on the agent’s next action.
Confucian free expression and the threat of disinformation

At present, there is a wide divergence in attitudes toward free speech in countries strongly influenced by Confucianism. Japan, Korea, and Taiwan have fairly robust rights of free expression. Mainland China does not, strongly restricting speech that the government judges threatens State interests. I argue that although traditional Confucian scholars supported many restrictions on expression, Confucian philosophers actually have good reason to want to protect expression about values. Subsequently, I consider how to address the problem of disinformation while preserving this Confucian right to free expression. I focus on the case of Taiwan, as the Confucian state facing the most serious disinformation campaigns from China. The goal of government and civil society actors has been to focus on correcting disinformation while preserving free access to information, though laws do provide for civil and even criminal penalties for intentional spread of false information. As many democratic societies are facing the problem of concerted disinformation campaigns that aim to sow confusion and increase discord among the populace, Taiwan’s successes here are worth studying. Yet the Confucian cultural background means people accept more government involvement in defining what is true and false than may be the case elsewhere, and the right to free speech is less absolute.