Reviewerly/exHarmony huggingface dataset:
A Comprehensive Defense Approach Targeting The Computer Vision Based Cheating Tools in FPS Video Games

Video games is one of the most popular multimedia forms and generate higher profits than the traditional film industry. In the meantime, with the advances of deep learning, computer vision algorithms have become more powerful for analyzing the video content and have been applied in the FPS video games as an advanced cheating tools, which have taken the video games industry by storm. Such algorithms, including the object detection and human pose estimations, could analyze and understand the video content in each frame and further help the player to automatically identify and aim at the enemies with extremely fast reaction. Compared to the classic cheating tools, computer-vision-based cheating tools are harder to detect and defend against because they do not need to manipulate the software or the system but purely simulate how a well trained and skilled human gamer plays the video game. In this paper, we propose a proactive and comprehensive defense approach, which generates perturbations that are not perceptible to humans yet can still mislead the computer vision algorithms. More specifically, this comprehensive approach includes two parts, the defense approach aims to fail the computer vision-based cheating tools to detect the in-game characters while the penalty approach aims to fool the computer vision-based cheating tools to detect the fake regions as in-game characters, which not only worsen the cheating experience but also serve as a trigger for detecting the cheating behavior. In this work, we first implement the object detection based cheating tools as the evaluation environment. Then, we implement our proposed defense, penalty and comprehensive approaches and evaluate the performance with four popular video games. The results show that our comprehensive approach obtains a high success rate with minor impact to user experience quality.
The Impact of CBLT Approaches on Students' Learning Achievements for ESP Courses

ESP courses in higher education are seen as one of the efforts to provide undergraduates with skills beneficial to their careers to meet the growing demand of young professionals. The study assessed the impact of CBLT approaches on the student’s learning achievement in ESP courses. The objective of the study was to find out the impact of CBLT on students’ learning achievement in ESP courses. This study employed a qualitative method design that applied a case study design. The data source in this study was thirty students from different study programs who implemented CBLT approaches in learning ESP courses at Bosowa University. The study applied a questionnaire utilizing the five-point Likert Scale to see their responses on the student’s learning achievement in ESP courses through CBLT approaches. The interview results applied the Nvivo R1-5 to know in depth their responses on learning ESP courses through CBLT approaches. Three types of data coding are used namely, open coding, axial coding, and selective coding. Findings revealed that firstly, students believed that they could improve their English capability in learning ESP courses. Students were able to communicate in real life. Secondly, the findings revealed that students’ learning achievement on CBLT implementation can motivate students to learn ESP courses. Students believed that CBLT offers many opportunities for communication skills on ESP and use specific English in their disciplines.
PENDAMPINGAN PELAKSANAAN PEMBELAJARAN TERINTEGRASI BAHASA IBU KELOMPOK A DI TK NEGERI HARAPAN KOELODA

It has been identified that mother tongue is the language that children understand and use in their daily lives. Children communicate using this language before being introduced to other languages. The high intensity of the use of the mother tongue by children means that children do not easily follow and understand explanation or material given by teachers at school using a different language, namely Indonesian. The use of different language ​​for children creates problems in the learning process and is a concern for teachers to determine the appropriate language of instruction. One of the languages ​​of instruction is the mother tongue. The aim of this service activity is to provide assistance to teachers in implementing mother language-based learning. The methods of implementing service were observation and face-to-face discussions. This service activity was carried out at the Koeloda State Kindergarten as one of the target schools for using mother tongue in learning activities. The result of the mentoring activities is that the learning process becomes more dynamic where children are very enthusiastic in playing and learning activities so that they are able to create encouraging learning situation and provide learning motivation for children through the use of the language that the child has mastered.
A Longitudinal Study of the Quality of Employment for Workers with Disabilities: Focusing on Differences between Mild and Severe Disabilities

본 연구는 경증 및 중증장애인 고용의 질에 종단적으로 영향을 미치는 요인을 살펴보고 비교하는 것을 목적으로 한다. 이를 위해 한국장애인고용패널조사 2차(2017년) ~ 5차(2020년) 자료를 활용하여 경증장애인 근로자 897명과 중증장애인 근로자 213명의 응답 자료를 패널 확률효과 모형으로 분석하였다. 연구결과, 첫째, 중증장애인은 경증장애인보다 고용의 질이 낮은 것으로 나타났다. 둘째, 경증 및 중증장애인 고용의 질에 주관적 사회경제적 지위, 학력, 컴퓨터 활용능력, 장애인 근로자 수, 직장 유형이 공통적으로 종단적 영향을 미치는 것으로 나타났다. 셋째, 중증과 다르게 경증장애인 고용의 질에만 성별, 배우자 유무, 영어 활용능력, 만성질환 보유 여부 변수가 종단적 영향을 미치는 것으로 나타났다. 그리고, 경증과 다르게 중증장애인 고용의 질에 대한 상대적 영향력 크기는 직장 유형, 컴퓨터 활용능력, 주관적 사회경제적 지위 등 순이었다. 본 연구결과를 토대로 장애인 고용의 질을 향상하기 위해 경증과 중증장애인 맞춤형 실천적⋅정책적 방안이 제공되어야 함을 제언하였다.This study aims to examine and compare the factors that longitudinally affect the quality of employment for people with mild and severe disabilities. For this purpose, we used data from the 2nd (2017) to 5th (2020) waves of the Korea Panel Survey of Employment for the Disabled(PSED) to analyze the responses of 897 workers with mild disabilities and 213 workers with severe disabilities, using a panel random effects model. The results found that, first, people with severe disabilities have lower quality of employment than people with mild disabilities. Second, subjective socioeconomic status, education, computer literacy, number of workers with disabilities, and type of workplace have common longitudinal effects on quality of employment for people with mild and severe disabilities. Third, the longitudinal effects of gender, spouse, English language proficiency, and chronic disease status on quality of employment for people with mild disabilities were different from people with mild disabilities. In addition, the relative magnitudes of the effects on employment quality for people with severe disabilities were different from those for people with mild disabilities, in the order of workplace type, computer literacy, and subjective socioeconomic status. Based on the results of this study, it is recommended that practical and policy measures customized for mild and severe disabilities should be provided to improve the quality of employment of people with disabilities.
Area Calculation of Binary Images Based on Overlapping Rectangular Subpatterns

Image representation is an important research topic in the field of computer vision, and pattern recognition. Also, applications based on image representation have an important role. Many image representation methods have been generated based on different division methods. The non-symmetry and antipacking pattern representation model (NAM) is an effective representation method. In this paper, we analyze the limitations of the non-overlapping NAM image representation and introduce an effective overlapping image representation algorithm, i.e., the ORNAMC representation algorithm for binary images. Based on the ORNAMC representation approach, we propose a new area calculation algorithm for binary images. When compared with the popular STC representation and the partial nonoverlapping NAM representation algorithm, theoretical analysis and experimental results presented in this paper show the superiority and effectiveness of our proposed ORNAMC-based algorithm.
AquaSense: Automated Sensitivity Analysis of Probabilistic Programs via Quantized Inference

We propose a novel tool, AquaSense, to automatically reason about the sensitivity analysis of probabilistic programs. In the context of probabilistic programs, sensitivity analysis investigates how the perturbation in the parameters of prior distributions affects the program’s result, i.e., the program’s posterior distribution. AquaSense leverages quantized inference, an efficient and accurate approximate inference algorithm that represents distributions of random variables with quantized intervals. AquaSense is the first tool to support sensitivity analysis of probabilistic programs that is at the same time symbolic, differentiable, and practical. Our evaluation compares AquaSense with an existing system PSense (a system that relies on fully symbolic inference). AquaSense can compute the sensitivity of all 45 parameters from 12 programs, compared to 11/45 that PSense computes. AquaSense is particularly effective on programs with continuous distributions: it achieves an average speedup of 18.10 $$\times $$ over PSense (which, in contrast, can solve only a handful of problems). Our evaluation shows that AquaSense computes exact results on discrete programs. On 91% of evaluated continuous parameters, AquaSense computed the sensitivity results within 40 s with high accuracy (below 5% error). The paper also discusses AquaSense’s performance-accuracy trade-offs, which can enable different operational points for programs with different input data sizes.
Self-training of nanophotonic electromagnetic simulator leveraging generative models

We propose a self-training process of an electromagnetic simulator implemented with generative models. Nanophotonic structures are generated without time-consuming electromagnetic simulations and are added to the original dataset to increase the accuracy of networks.
A Reconfigurable Photonic Computing Chip with Easily Scalable Topology

We present an easily scalable photonic computing topology with Mach–Zehnder interferometers based on a single tunable phase shifter to implement arbitrary matrices, achieving high recognition accuracies on MNIST handwritten digit tasks experimentally.
Digital Competence of College Teachers and Its Influencing Factors: Inputs for Teachers Technical Training

This research aims to investigate the level of digital competence and provide insights into the many aspects that impact the digital competence of teacher among instructors at many colleges in China. It provides a detailed examination of their ability to effectively use digital technology for teaching and learning purposes. The study used a mixed-methods methodology, integrating both survey and interview techniques, in order to get a comprehensive comprehension of educators' viewpoints and encounters. The results emphasize the need of implementing customized training programs in order to effectively handle the diverse range of digital skills among educators. This highlights the need of developing tailored professional development programs to enable educators to proficiently use digital technology into their instructional approaches. The findings of the study could help develop an effective improvement program to improve college teachers' digital competence. Ultimately, this study aimed to achieve the continuous and sustainable improvement in the quality of higher vocational education via improving college teachers' digital competence.
Parent's Perspective: The Influence of Home Learning Environment (HLE) on Digital Literacy Skills

This preliminary study aims to investigate the influence and factors affecting the home learning environment on students' digital literacy skills from the perspective of parents. The research employs a survey method with descriptive quantitative data analysis, involving 40 parents of students as par
On-chip photonic artificial intelligence accelerator

We propose an on-chip photonic accelerator scheme fabricable with CMOS-compatible processes. Spiral waveguide-based MZI meshes and nonlinear activation functions are applied. At the output, we first applied a grating radiator array for the image classification task.
Shape Reconstruction in Computer Vision

The fundamental job of shape reconstruction in computer vision is crucial for many different applications, including robotics, medical imaging, and autonomous systems. The state-of-the-art methods, difficulties, and potential future directions in the field of shape reconstruction are succinctly outlined in this abstract. Inferring the three-dimensional (3D) geometry of objects or scenes from two-dimensional (2D) photographs or point clouds is the process of shape reconstruction. The conventional approaches relied on depth sensors, structure from motion, and stereo vision. Convolutional neural networks (CNNs) and generative models, in particular, are deep learning techniques that have been used recently to achieve impressive results in shape reconstruction. This abstract addresses important procedures, such as point cloud processing, depth estimation, and multi-view stereo techniques. It emphasizes the benefits and drawbacks of each strategy while highlighting the necessity of combining various modalities for effective rebuilding. To improve reconstruction accuracy, context-aware algorithms and the inclusion of semantic data are also being investigated. Efforts are made to overcome concerns with scalability, ambiguous features, and occlusions in form reconstruction. Deep learning models' computational complexity is also examined, highlighting the necessity for effective algorithms that strike a compromise between accuracy and speed. In order to evaluate the effectiveness of reconstruction algorithms in an objective manner, the significance of benchmark datasets and assessment criteria is also emphasized.
Bitcoin Mixing Service Detection Based on Spatio-Temporal Information Representation of Transaction Graph

Coin mixing is a technique used to enhance Bitcoin’s anonymity and can be used to obfuscate the relationship among transaction input addresses. Due to this property, much of the criminal activity on Bitcoin uses coin-mixing techniques to launder money, making these illicit funds difficult to trace. Therefore, it is important to implement the detection of Bitcoin mixing services. Several methods for identifying bitcoin mixing services have been proposed, but balancing their efficiency and generality at the same time is a challenging task. In this paper, We propose STMD (Spatio-Temporal Mixing Detector), which combines local features and global features of Bitcoin transactions to identify coin-mixing transactions. On one hand, we extract and process the statistical features of neighboring nodes of the transaction as local features. On the other hand, we construct a global position encoding (GPE) containing spatio-temporal information of the transaction as global features. Additionally, we employ the attention mechanism to handle these two types of features, effectively combining them. Finally, we utilize linear layers to achieve the detection of coin-mixing transactions. The experimental results show that STMD performs better than existing methods on the same dataset; it also has a higher recall on the test set of other types of coin-mixing transactions, which reflects the generality of the model. In particular, We apply local features and global features for experiments separately and verify the necessity of the two features. The results of the model trained using only global features also outperform the existing methods, which shows that the global position encoding (GPE) we constructed is effective for mixed currency transaction identification.
SAR Ship Target Detection Using SAR Images

Object identification is one of the fundamental challenges in computer vision. Dealing with tiny, fuzzy, and widely dispersed objects is challenging despite tremendous improvements. This article introduces the detector in our model as a tool for detecting tiny objects in SAR (Synthetic Aperture Radar) photos. To overcome the aforementioned issues, the suggested model makes use of shallow layer information, spatial attention, and contextual information. To avoid missing the tiny object features, a shallow semantic information extraction model that incorporates low-level semantic data into the backbone has been designed. The original Neck has been replaced by an MSCFP (Multi-scale Context Feature Pyramid) in order to enhance the exploitation of lower levels of information and provide context information. The recognition of attention locations of various sizes is made possible by the introduction of a spatial attention system. Tests using open-source SSDD (SAR Ship Detection Dataset) datasets show that our model has effective identifying capabilities.
rShare: Alleviating long startup on the Cloud-rendering platform through de-systemization

WebGL paves the way for the great development of lightweight rendering applications (e.g., web games). However, the target audience of Cloud-rendering frameworks (e.g., Google Stadia and gRemote) mainly focuses on native applications, called traditional Cloud-rendering frameworks. Even though traditional frameworks cover all the rendering functionalities and both light- and heavy-weight applications can run on them, such frameworks face long startup latency, imposing a significant challenge on lightweight applications. To reduce the startup latency, many researchers render objects locally, but the system-level redundancy still affects the performance of lightweight rendering applications negatively (especially in the application-initialized stage). In this paper, we propose rShare, a new rendering framework dedicated to lightweight applications. To reduce the startup latency of lightweight applications, rShare desystemizes the entire user space into the transmitted protocol. To ensure the portability of the entire system, rShare establishes two vHALs (virtual Hardware Abstraction Layer) (i.e., peripheral vHAL and rendering vHAL) within the protocol. To boost the CPU running efficiency at the initialized stage, rShare establishes an optimized mechanism called flexTLB, allowing various page sizes to work together. The experiments show that rShare can improve the CPU executing efficiency by 20%∼50% and reduce 90% startup latency compared to the CARE, a traditional Cloud-rendering framework proposed by Intel.
A Study of Formulaic Language in Epic Texts from Synchronic Diachronic Perspective: Taking the Heroic epic “Jangar” as an Example

Every language and culture has a large amount of accumulation, which has been preserved in a unique way as different genres and language models have been passed down and developed. Formulaic language is a typical example of the inheritance and accumulation of ethnic culture in terms of language, especially in epic poems with typical ethnic cultural traditions, the use of formulaic language in narrative structures is particularly prominent. Taking the heroic epic ‘Jangar’ as an example, analyze the formulaic language and explore the transformation and evolution of highly stylized formal structures in the inheritance process of the epic “Jangar”, analyze the role and significance of programming language in existing epic texts.
PV power prediction method based on ground-based cloud map and hybrid neural network

Under cloudy weather, PV power will fluctuate dramatic due to the sporty cloud shading. However, most existing PV power prediction models do not utilize the cloud map sufficiently. In this paper, we propose a novel method to improve the prediction accuracy through combining cloud map features and hybrid neural network. Firstly, we extract the static and dynamic features of the ground-based cloud map using image processing techniques. Secondly, we establish an PV power prediction model based on the ensemble empirical mode decomposition-bi-long short-term memory (EEMD-BiLSTM) with numerical weather information and historical power data. Finally, in order to further improve the power prediction accuracy of cloudy weather, we build an error correction model using the cloud map features, based on the light gradient boosting machine (LightGBM) decision tree. Experimental results on the data from a PV plant in western China shows that our method can effectively improve the accuracy of PV power prediction under cloudy weather.
Neural named entity recognition in Chinese electricity texts based on Bert-BiLSTM-CRF model

Aiming at the problems for electricity policy researchers to obtain policies related to specific entities, a deep learning model based on Bert-BiLSTM-CRF is proposed to address the problem by recognizing related entities. The model encodes the power policy text with characters through BERT, extracts features from the characters vectors through the BiLSTM network, and generates label sequences through CRF decoding. The experiment reveals that the BERT-BiLSTM-CRF model outperforms other standard models for the recognition of named entities in electricity texts, with the highest F1 score of 88.47%.
Multi-scale dual self-attention refine network for semantic segmentation

Effective image semantic segmentation is an important task. In recent years, deep convolutional neural networks have played a major role in improving the effectiveness of semantic segmentation. However, previous works capture local contexts by multi-scale feature fusion which ignore the global dependencies. Moreover, as the layers deepen, the loss of feature is inevitable. Multi-Scale Dual Attention Refine Network (MSDARNet) is proposed. Specifically, we firstly append two types of attention modules on top of dilated FCN, which model the global semantic interdependencies in spatial and channel dimensions respectively. Secondly, multi-scale feature fusion is added to extract local dependencies in the above attention module. We conduct extensive experiments to validate the effectiveness of our network on three challenging semantic segmentation datasets and obtain great improvements. In particular, our MSDARNet attains mIoU accurary of 50.3% on NYUDv2, 71.1% on Person-Part.
PID parameter tuning of DC motor based on improved particle swarm optimization

Aiming at the problem of complex and time-consuming PID parameter tuning of Brushless DC reduction motor. A PID parameter tuning method based on improved particle algorithm is proposed. Firstly, the principle of orbiting each particle is proposed, which improves the inertia weight of traditional particle swarm optimization algorithm; Then, the particle swarm mutation method is proposed to ensure that there will be no local optimization in the iterative process. The transfer function of the motor is measured by experimental method, and then the modeling and simulation analysis are carried out using MATLAB. The experimental and simulation results show that the improved particle swarm optimization algorithm will not fall into local optimization compared with the ordinary particle swarm optimization algorithm. Compared with the parameters obtained by the PID parameter tuning and the parameters obtained by the Zn and AC tuning methods, the rising time is less, the time required for stabilization is shorter, and the deviation after stabilization is smaller. Using improved particle algorithm for PID parameter tuning can improve the accuracy of PID control and reduce the complexity and timeconsuming problem of PID parameter tuning.