Reviewerly/exHarmony huggingface dataset:
Top Design of Digitalization Specialty in a Power Company Based on TOGAF Framework

Starting with the concept of top-level design, this paper analyzes and introduces the significance, ideas and contents of top-level design.And taking the digital professional system of an electric power company as an example, according to the strategic planning of the digital professional of the company, combined with the current situation of the digital professional, the top-level design of the digital professional business system of the company is carried out by means of TOGAF enterprise architecture model, including business process, organizational structure, digital support and so on.It simplifies the business process, effectively improves the digital professional level of the company, and plays a supporting role in the future digital transformation and the construction of new power systems of the power company.
USE OF LANGUAGE AS EVIDENCE IN THE JUDICIAL PROCESS ON GUS NUR’S HATE SPEECH CASE IN THE INDONESIAN LEGAL CONTEXT

This study aims at examining how the language of Gus Nur (GN) is considered hate speech that caused him to undergo judicial process.For this aim, the research questions include which GN's statements are considered hate speech, why GN's statements become hate speech causing him to undergo judicial process, and what judicial processes GN experienced.To answer these questions, the data were obtained from the original text of GN's statements which he uploaded to his YouTube channel account on October 16, 2020.The collected data were then analyzed using the data analysis model of Miles and Huberman and the thematic analysis of Braun and Clark.The results show that because of his language considered as hate speech against Nahdlatul Ulama, one of the Islamic organizations in Indonesia, GN underwent such judicial processes as becoming the reported, the suspect, the accused and the convict.This means that GN has violated the provisions of Indonesian law regarding hate speech.Based on the results, the novelty of this study states that it provides a solid understanding of how language becomes hate speech in Indonesian legal context that no previous studies have discussed.In addition, this research provides an alternative model of doing a research on the forensic linguistics as the application of linguistics in field of law that it can become a reference for a further study.
Detecting Social Groups Using Low Mounted Camera in Mass Religious Gatherings

People often come in groups rather than individuals in public places. Automatic visual group detection could help in understanding the group patterns in such scenarios. Researchers have proposed various approaches to identify group patterns based on trajectories extracted from video. However, these pixel trajectories are used without considering perspective projection which results in erroneous data. Placing the camera at a higher point resulting in a top-down or bird’s eye view is often used as an alternative method to circumvent this problem. However, this may not be possible in all real-life scenarios. Thus, we propose an approach for developing simple group detection models based on real-world trajectories by applying projection transformation on the pixel-based trajectories, which can be applied to the data collected using low-mounted cameras. It was found that parameters like average distance between two pedestrians, average angle between their direction of motion for a specified time window is more evenly distributed while using pixel-based location rather than their real counterparts. It is observed that the model performance is improved with the increased time window. Also, simplistic models with real-world trajectories were able to achieve over 90% accuracy.
Pipelining of a Mobile SoC and an External NPU for Accelerating CNN Inference

Convolutional Neural Networks (CNN) algorithms are increasingly being deployed on edge devices with the co-growth of hardware and software. Deploying CNNs on resource-constrained devices often requires optimization of CPUs and GPUs. While a dedicated hardware such as a neural processing unit(NPU) has been successfully introduced, cooperative methods between CPU, GPU and NPU are still immature. In this paper, we propose two approaches to optimize the integration of a mobile system-on-chip(SoC) with an external neural processing unit(eNPU) to achieve harmonious pipelining and enhance inference speed and throughput. The first approach involves a BLAS library search scheme to allocate optimal libraries per layer on the host side, while the second approach optimizes performance by searching for model slice points. We utilize CPU-based NNPACK, OpenBLAS, and GPU-based CLBlast as computing libraries that are automatically allocated. The entire neural network is optimally split into two segments based on the characteristics of the neural network layers and hardware performance. We evaluated our algorithm on various mobile devices, including the Hikey-970, Hikey-960, and Firefly-rk3399. Through experiments, we show that the proposed pipeline inference method reduces latency by 10% and increases throughput by more than 17% compared to parallel execution on an eNPU and SoC.
YAMMER: a yoga movement exercise system with UHF RFID

Wireless sensor technology can effectively sense the impact of the human body on wireless links, which is conducive to promoting the evolution of wireless networks to intelligent sensor networks. It has good application prospects in bright space, security, rescue, human-computer interaction, etc. Motion recognition based on wireless sensing has recently been a research hotspot. Passive RFID tags are battery-free, portable, and have good privacy protection. They can be transmitted in a non-line-of-sight range. Because of these advantages, this paper uses a deep convolution neural network to classify activities based on RFID Doppler shift data. We designed a complete yoga movement recognition and evaluation system to evaluate the whole design, taking 12 everyday basic yoga movements as examples. We designed and implemented the prototype of our scheme using an Inpinj R420 reader and RFID tag. We invited 30 volunteers to collect data and conduct comprehensive experiments. The experimental results show that the average recognition accuracy of the essential yoga activity recognition we designed is 93.20%. It can also be applied to recognizing other activities and has broad application prospects.
Boosting Scene Graph Generation with Contextual Information

Scene graph generation (SGG) has been developed to detect objects and their relationships from the visual data and has attracted increasing attention in recent years. Existing works have focused on extracting object context for SGG. However, very few works have attempted to exploit implicit contextual correlations among relationships of the objects. Furthermore, most existing SGG schemes rely on high-level features to predict the predicates while overlooking the potential inherent association of low-level features with the object relationships. We present in this article a novel scheme to capture enhanced contextual information for both objects and relationships. We design a Dual-branch Context Analysis Transformer (DCAT) architecture to extract both object context and relationship context from the visual data with dual transformer branches and then effectively fuse both high-level and low-level features by an adaptive approach to facilitate relationship prediction. Specifically, we first conduct feature representation learning to enrich relation representations by the visual, spatial, and linguistic feature extractors. Next, two transformer branches are designed to leverage the modeling of global associative interaction and mine the hidden association among objects and relationships. Then, we devise a novel feature disentangling method to decouple contextualized high-level features with guidance from the visual semantics. Finally, we develop a refined attention module to perform low-level feature recalibration for the refinement of the final predicate prediction. Experiments on Visual Genome and Action Genome datasets demonstrate the effectiveness of DCAT for both image and video SGG settings. Moreover, we also test the quality of the generated image scene graphs to verify the generalizability on downstream tasks like sentence-to-graph retrieval and image retrieval.
A Study on the Improvement Direction of Korean Language Education for KSL Learners: Focusing on ATL of IB curriculum

본 연구는 KSL 학습자의 학업 수행 능력과 관련된 연구를 살펴보고, 이를 바탕으로 향후 KSL 학습자의 학업 수행 능력 향상을 위해서 나아가야 할 방향에 대해 제언하는 것을 목적으로 하였다. 이를 위해 KSL 학습자 대상의 교육과정, 평가 관련 연구 등을 살폈으며, 이를 통해 현재 교육의 문제와 한계를 지적하였다. 현재 KSL 학습자 대상 교육은 이들의 어휘 중심으로 이루어지고 있으며, 이들의 학업 수행 능력을 범위를 좁게 보고 있었다. 이에 대해 본 연구에는 세계적으로 인정 받는 IB – MYP 교육과정의 언어습득 교과 영역을 살펴보고 중학교 학습자를 위한 교재에서 다루는 외국어 교육의 내용과 범위에 대한 시사점을 얻었고 관련하여 제언을 제시하였다.The purpose of this study was to examine research related to KSL learners' academic performance ability and to suggest directions for improving KSL learners' academic performance in the future. To this end, the curriculum and evaluation-related studies for KSL learners were examined, and through this, the problems and limitations of the current education were pointed out. Currently, education for KSL learners is centered on their vocabulary, and their academic performance was viewed narrowly. In response, this study examined the world-recognized language acquisition subject areas of the IB-MYP curriculum and obtained implications for the content and scope of foreign language education covered in textbooks for middle school learners.
Investigating the Determinant of IT Qualification, IT Qualification’s Effects on the Employment &amp; Wage of College Graduates

The purpose of this study is to identify the current status of university graduates' holding of IT certificate and to determine the effect of holding qualification on employment status and wages. The subjects of analysis were university graduates in August 2018 and February 2019, the data were referenced from GOMS(Graduates Occupational Mobility Survey). For the research method, the Logistic regression analysis and Heckman's two-step analysis were used. The types of IT qualification covered in this study are: Engineer Information Processing, Industrial Engineer Information Processing, Computer Specialist in Spreadsheet &amp; Database, and MOS(Microsoft Office Specialist Qualification). The investigation results are as follows: First, it was found that IT qualification had a negative impact on employment. Second, IT qualification had a positive impact on the wage effect. These result may contribute to how college graduates should prepare for the IT qualification and what the qualification system implies.
A Qualitative Study on Participation Experience in Mentoring Program among Female Single-Parent Families

The purpose of this study is to explore in depth what female single-parent families experience while participating in the ‘Mentoring Program among Single-parent Families’. The mentoring program was conducted by Welfare Institute A in 2021 and participated as a family unit with their children. The mentoring program was conducted for 8 months from April to November 2021, and included pre-interviews, mentor group meetings, and team-specific mentoring activities(6 times, 3 hours each). After the mentoring program was completed, in-depth interviews were conducted with 10 female single parents who agreed to participate in the study. The collected data were approached by the thematic analysis method. As a result of analyzing the in-depth interviews, the 5 upper-topics and 19 sub-topics were derived. The 5 upper-topics derived from the interview are as follows: ‘the start of a meaningful meeting’, ‘emotional connectedness between female single parents’, ‘self-understanding and efforts for growth as a single parent mother’, ‘female single parents who have been shrunk social relationships, restarted relationships’, ‘development of my child’s social skills and peer relationships’. Each upper-topics was sbudivided into 3∼4 sub-topics. Based on the results of this study, paractical and policy suggestions were presented to support female single-parent families.
PENGARUH EFIKASI DIRI, TEMAN SEBAYA, DAN INTEGRITAS SISWA TERHADAP KECURANGAN AKADEMIK

This study aims to determine the effect of self-efficacy, peers, and student integrity on academic cheating. The study uses quantitative methods with survey methods, including validity tests, reliability tests, multiple linear regression analysis tests, normality tests, linearity tests, and hypothesis testing. The research was conducted at SMK Negeri 14 Jakarta, and a sample of 152 respondents was obtained using a random sampling approach. The findings of this study indicate that self-efficacy, peers, and student integrity have a partial and significant effect on academic cheating. Additionally, self-efficacy, peers, and student integrity have a simultaneous and significant effect on academic cheating. Together, self-efficacy, peers, and student integrity significantly influence academic cheating.
RDMI: Recursive Training-Based Diffusion Model for Multivariate Time Series Imputation

In this paper, we present a novel approach for imputing missing values in multivariate time series using a recursive training-based diffusion model. Our proposed framework incorporates meta-learning, self-conditioning, and recursive training as key components to enhance imputation performance. We evaluate the model on two publicly available real-world datasets and achieve an improvement in RMSE, MAE, CRPS, MAPE, and SMAPE compared to the state-of-the-art model. Additionally, our ablation study confirms that each proposed technique has a meaningful effect on MTS imputation.
Convolutional Transformer-Based Deblurring Model for X-Ray Images

Image deblurring is an important pre-processing for improving relevant computer vision tasks. In this paper, we are interested in conducting deblurring X-ray images. Using a convolutional transformer as the main building block, we build an AutoEncoder-style deblurring model for X-ray images. From the experiments using the public X-ray image dataset, we show that our model conducts the deblurring operation well. For example, in terms of structural similarity (SSIM) as a performance metric, our model improves SSIM by up to 27% compared to the blurry images.
Deep Learning Based Image Enhancement for Semiconductor SEM Image Using Paired Dataset

In the semiconductor industry, Scanning Electron Microscope (SEM) images have been commonly used for metrology and defect inspection. High-quality images are achieved by increasing frame averages, but this has a trade-off relationship with time cost. In this study, we obtained paired sets of 4-frame and 32-frame averaging semiconductor images under the same position and angle. Furthermore, we collected data from various patterns to enhance the accuracy and robustness of the experiments. We compared various IQE (Image Quality Enhancement) algorithms for the semiconductor SEM images. Several deep learning approaches were categorized and implemented with some modifications. The results were compared both quantitatively and qualitatively.
Handling Data Imbalance for Improving Blurriness Estimation using Convolutional Transformer

Image deblurring is important pre-processing for various computer vision tasks. In this paper, as one approach for improving image deblurring, we are interested in blurriness estimation. For this, we first propose a model for blurriness estimation. Adopting the convolutional Transformer, we try to extract meaningful features from a blurry image to be used for blurriness estimation. Then, using the proposed model, we examine the usefulness of the known techniques for handling data imbalance issue, that is widely observed in real-world scenarios. Through the experiments using the RealBlur dataset, we show that the weighted loss is not effective in solving the data imbalance issue. On the contrary, the oversampling technique is useful, particularly in improving the estimation performance of the rare data while slightly sacrificing the estimation performance of the prevalent data.
Fine-Tuning Pre-Trained Model for Consumer Fraud Detection from Consumer Reviews

Consumer fraud is a significant problem that requires accurate and prompt detection. However, existing approaches such as periodic government inspections and consumer reports are inefficient and cumbersome. This paper proposes a novel approach named CFD-BERT, to detect consumer fraud automatically based on the group intelligence from consumer reviews. By applying the correlation between consumer reviews and official regulations to accurately mine consumer fraud patterns, and fine-tuning a pretrained model BERT to better model their semantics, which can detect fraudulent behaviors. Experimental evaluations using real-world datasets confirms the effectiveness of CFD-BERT in fraud detection. To explore its potential application and usefulness in real world scenarios, an empirical study was conducted with CFD-BERT on 143,587 reviews from the last three months. The results confirmed that CFD-BERT can serve as an auxiliary tool to provide early warnings to relevant regulators and consumers.
Learnable Filter Components for Social Recommendation

The recommendation system based on graph neural networks has gained increasing attention due to its superior learning ability of various additional information. Among them, the recommendation system based on social network graphs integrates social network graphs with user-item-graph interactions, aiming to extract dynamic preference features from recorded user behavior data to obtain more accurate recommendation results. However, recorded user behavior data contains noise and exhibits skewed distributions, which may lead to slightly insufficient representation performance of graph neural network models. This article proposes a new approach to address this issue by applying filtering algorithms commonly used in signal processing to noise reduction in the frequency domain. In this study, we combine learnable filter components with an MLP architecture to learn user and item latent embeddings in modeling social network graphs and user-item interaction graphs, and ultimately predict ratings using the latent embeddings of users and items. The entire MLP architecture has low time complexity, and the adaptive filter components are able to effectively suppress noise information. The model proposed in this article has been applied to real datasets and outperformed other models, achieving a 3.2% increase in the MAE evaluation index.
StructNeRF: Neural Radiance Fields for Indoor Scenes With Structural Hints

Neural Radiance Fields (NeRF) achieve photo-realistic view synthesis with densely captured input images. However, the geometry of NeRF is extremely under-constrained given sparse views, resulting in significant degradation of novel view synthesis quality. Inspired by self-supervised depth estimation methods, we propose StructNeRF, a solution to novel view synthesis for indoor scenes with sparse inputs. StructNeRF leverages the structural hints naturally embedded in multi-view inputs to handle the unconstrained geometry issue in NeRF. Specifically, it tackles the texture and non-texture regions respectively: a patch-based multi-view consistent photometric loss is proposed to constrain the geometry of textured regions; for non-textured ones, we explicitly restrict them to be 3D consistent planes. Through the dense self-supervised depth constraints, our method improves both the geometry and the view synthesis performance of NeRF without any additional training on external data. Extensive experiments on several real-world datasets demonstrate that StructNeRF shows superior or comparable performance compared to state-of-the-art methods (e.g. NeRF, DSNeRF, RegNeRF, Dense Depth Priors, MonoSDF, etc.) for indoor scenes with sparse inputs both quantitatively and qualitatively.
Performance evaluation and energy consumption of a video surveillance system with distributed storage

Video monitoring systems are widely used as a tool for access control, crime and accident prevention, and internal security in various establishments. The storage of the data and images generated by these systems is a major challenge, as the number of devices, the quality of the images, and the required storage time have impacted the use of hardware resources and, consequently, electricity consumption. Evaluating the relationship between the number of cameras and the storage methods is crucial in proposing strategies that ensure reliability and availability at the lowest possible cost. It is important to verify the criticality levels of the images to be stored to determine which storage configuration will be used. The results indicate that the storage method (distributed or replicated volumes) has distinct impacts on hardware usage and electricity consumption. For example, it was possible to observe that the use of replicated volumes increases electrical consumption by about 6.17% with 100 cameras, compared to the distributed mode, and that the greatest impact on performance is in the utilization of the video management system CPU, independent of the storage mode.
Impediment in Adaptation of Algorithm Trading: A Case of Frontier Stock Exchange

The global financial markets have been significantly affected by the rapid change in technology. The study is an attempt to get to know the barriers to not adopting algorithmic trading in conventional stock exchanges. This research aims to plan and analytically proposed a model for explaining the reasons why frontier stock exchange traders and investors are hesitant to adopt algorithmic trading as a tool. The research includes variables; Lack of awareness, Trust, Lack of Government interest, unemployment, and unnecessary investment, which were extracted from previously available literature based on the theory of reason and technology acceptance model (TAM). A sample of 50 traders/investors from Pakistan stock markets was taken by using convenience sampling. Data was collected through a questionnaire and analyzed using correlation and linear regression techniques. The results show trust factor is the biggest hurdle in implementing Algorithm Trading which means countries like Pakistan which are following conventional methods for trading in stock markets have great doubts about the efficiency of Algorithm base trading because of the less human interaction and dependency on machines. Fear of miscalculation and the inexperience of data engineers are also one of the reasons conventional stock exchanges are reluctant to adopt algorithm trading. Similarly, variables like Lack of Government interest, unnecessary investment, and employment have a significant effect on the implementation of algorithm trading. Moreover, lack of awareness is the least significant factor, which shows the traders and investors in the Pakistan Stock Exchange are well aware of algorithm trading but the results cannot be generalized to the population due to a limited sample size of the study.