Reviewerly/exHarmony huggingface dataset:
Cross-Platform Profiling and Tuning Framework for Design and Development of Heterogeneous Applications

&lt;p&gt;Parallel computing with heterogeneous platforms that include multi-core CPUs, GPGPUs, traditional GPUs and FPGAs are increasingly being employed to meet high performance demands. However, the application developer must thoroughly understand the application to parallelize tasks. It is known that careful architecture specific adjustments are required for tuning an application to effectively utilise the underlying heterogeneous devices. A cross-platform expandable profiling framework is presented that can be used to highlight bottlenecks in the application and guide design changes by providing both coarse and fine grain application statistics. Machine learning models are trained to understand the application behaviour and underlines features relating to performance. While code instrumentation is used to unlock individual code statistics of processes and kernels. The presented framework is applied to a variety of applications by profiling and tuning various benchmarks and real-life cases studies such as collision detection. Through these case studies, comparisons are made with the current industrial tools and other state of the art tuning approaches. The results highlight the unique parameters that can be extracted from the proposed framework and effectiveness of the framework due to the notable performance increase achieved.&lt;/p&gt;
Digital transitional justice: unpacking the black box

Artificial intelligence (AI) has transformed public policy and governance with machine learning (ML) facilitating decision-making in security, criminal justice, and surveillance. AI has also become increasingly opaque and autonomous, as recent technological advancements have led to the development of AI-enabled tools that require almost no human input. However, despite these developments, transitional justice practice and scholarship has not yet engaged in a sustained manner with the question of what these developments mean for the field. This contribution will highlight how transitional justice, with its focus on dealing with the legacy of large-scale and systemic atrocity, is faced with dilemmas presented by new digital justice spaces, and it will also explore how these dilemmas will grow as digital spaces continue to expand. By drawing upon a spatial framework, this chapter will explore these emergent digital spaces and unpack AI’s impact on transitional justice practice and research.
Topology preserving maps as aggregations for Graph Convolutional Neural Networks

In Graph Convolutional Neural Networks, the capability of learning the representation of graph nodes comes at hand when dealing with graph analysis tasks, such as predicting node properties. Furthermore, node-level representations can be aggregated to obtain a single graph-level representation and predictor. This work explores an alternative route for defining the aggregation function compared to existing approaches. We propose a graph aggregator that exploits Generative Topographic Mapping (GTM) to transform a set of node-level representations into a single graph-level one. The integration of GTM in a GCNN pipeline allows to estimate node representation probability densities and projects them in a low-dimensional space, while retaining the information about their mutual similarity and topology. A novel dedicated training procedure is specifically designed to learn from these reduced representations instead of the complete initial data. Experimental results on several graph classification datasets show that this approach achieves competitive predictive performances with respect to the commonly adopted aggregation architectures present in the literature while holding a well-grounded theoretical framework.
Citation Analysis of Serials in Graduate-Students’ Thesis: A Functional Tool for Effective Serials Management in University Libraries

Serials being important to students and researchers as they contain the most current and relevant information that can be used for academic and research purposes needs to be assessed periodically to determine if they are still relevant to the users. This study therefore examines the analysis of serials citations in graduate-students’ theses as a functional tool for effective management of serials in university libraries using theses submitted from 2013 to 2021 in Library and information Science of four government owned universities in eastern region of Nigeria as case in point. The study was meant to provide answers to three research questions which formed the guide while a descriptive survey design was applied with a population of 203 which stood as the total number of masters’ degree theses produced by the four universities within the period under study. The main instruments used in collecting data for this study were the researcher-designed checklists with which 8445 serials citations were collected and analyzed using descriptive statistics of mode, mean and range and data presented in tables, charts and graphs using frequencies and percentile. The outcome of the study revealed that journals with 76% citations were the most cited against conference proceedings/reports with 20% and the least cited being government publications with only .5% citations. It was further established that multi authorship is the most cited authorship pattern in the graduates’ theses whereas the average age of serials cited were within the age bracket of 0 - 18. The conclusion drawn is that with the most cited type(s) of serials, serials titles, ages of serials and preferred authorship pattern known through citations analysis of serials in research reports, the university librarian is better placed as to knowing which serials to select, acquire and how to have them organized for easy accessibility and retrieval by users as well as the amount of money to be earmarked for their acquisitions which invariably will bring about prudent management of the library budget in this period of shrinking budget for academic libraries.The study therefore recommended among other things that for effective application of serials citations analysis as a functional tool for effective serial management, staff of serials unit most partner documentation unit as to ensuring proper citations analysis of serials submitted to the library by every department as this can be used as a guide for the unit to identify the core serials for selections, acquisitions and also as a guide for total serials collections maintenance.
Network Security Situation Prediction Based on TCAN-BiGRU Optimized by SSA and IQPSO

The accuracy of historical situation values is required for traditional network security situation prediction (NSSP). There are discrepancies in the correlation and weighting of the various network security elements. To solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network is proposed, which is optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO). This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data. Furthermore, a prediction model of TCAN-BiGRU is established respectively for each subsequence. TCAN uses the TCN to extract features from the network security situation data and the improved channel attention mechanism (CAM) to extract important feature information from TCN. BiGRU learns the before-after status of situation data to extract more feature information from sequences for prediction. Besides, IQPSO is proposed to optimize the hyperparameters of BiGRU. Finally, the prediction results of the subsequence are superimposed to obtain the final predicted value. On the one hand, IQPSO compares with other optimization algorithms in the experiment, whose performance can find the optimum value of the benchmark function many times, showing that IQPSO performs better. On the other hand, the established prediction model compares with the traditional prediction methods through the simulation experiment, whose coefficient of determination is up to 0.999 on both sets, indicating that the combined prediction model established has higher prediction accuracy.
Tackling Faceless Killers: Toxic Comment Detection to Maintain a Healthy Internet Environment

According to BBC News, online hate speech increased by 20% during the COVID-19 pandemic. Hate speech from anonymous users can result in psychological harm, including depression and trauma, and can even lead to suicide. Malicious online comments are increasingly becoming a social and cultural problem. It is therefore critical to detect such comments at the national level and detect malicious users at the corporate level. To achieve a healthy and safe Internet environment, studies should focus on institutional and technical topics. The detection of toxic comments can create a safe online environment. In this study, to detect malicious comments, we used approximately 9,400 examples of hate speech from a Korean corpus of entertainment news comments. We developed toxic comment classification models using supervised learning algorithms, including decision trees, random forest, a support vector machine, and K-nearest neighbors. The proposed model uses random forests to classify toxic words, achieving an F1-score of 0.94. We analyzed the trained model using the permutation feature importance, which is an explanatory machine learning method. Our experimental results confirmed that the toxic comment classifier properly classified hate words used in Korea. Using this research methodology, the proposed method can create a healthy Internet environment by detecting malicious comments written in Korean.
Spoofing Detection for Speaker Verification with Glottal Flow and 1D Pure Convolutional Networks

Automatic Speaker Verification Systems are subject to attacks, these attacks aim to fool the system into accepting as valid the identity of a speaker when in fact it is the audio produced either by a voice conversion system that actually turns the voice of an identity thief into the voice of his victim, or by a speech synthesizer which parameters have been tuned to produce the voice of a specific individual whose identity is attempting to be stolen. A spoof detector decides wether the speech signal that is being used for verifying the identity of an individual is genuine or spoof. We use a 1D Pure Convolutional Neural Network (1DPCNN) with two classes (genuine and spoof). “Pure Convolutional” means all the layers of the neural network are convolutional or pooling, there are no dense layers, so the classifier block is also made of convolutional layers with a Global Maxpooling strategy. From the Speech signal we detect the voiced segments, which are those produced while the vocal cords vibrate, from those voiced segments we extract the glottal flow which is a signal far less complex than the speech signal and that is known to vary between speakers. We tested our technique with the dataset from the ASVSpoof 2015 challenge, using 7000 audio files for training, 4000 audio files for validation, and 30,000 audio files for testing. We achieved an accuracy of 91.4% with the test set.
Research on the rehabilitation of children with autism based on speech recognition

This paper is mainly implemented through neural network algorithms, using Python language, for identifying, processing the speech of children with autism, and then comparing with the speech database established in advance to obtain the final speech results, on this premise, to achieve human-computer interaction, to achieve the purpose of practicing dialogue with children with autism or guiding them to speak, in order to restore the language function of children with autism.
Cloud Offloading Technique based on an Optimized Ant Colony Algorithm

Cloud computing is both a service model and a computing model. As a service model, users have certain requirements for their service quality, either to obtain the most efficient service in the shortest time or to obtain the most fluent service at the lowest cost. Virtualization technology encapsulates a large number of heterogeneous computing, storage and network resources for users to access and use. A cloud resource scheduling algorithm based on improved ant colony algorithm is proposed. Time cost constraint function is created to redefine the pheromone, and a load adaptive function is introduced to calculate the ant transition probability. Simulation of cloud resource scheduling on the Cloud Sim cloud simulation platform shows that when the cost is unchanged, the optimized parameter combination enables the scheduling algorithm to complete the scheduling task more efficiently, with the lowest scheduling time and cost.
Leukocyte Recognition Using a Modified AlexNet and Image to Image GAN Data Augmentation

A leukogram is essential in the diagnosis of people’s diseases. It can help in the discovery of diseases such as infection, arthritis, leukemia, etc. Deep learning techniques found in the state of the art solve leukocyte classification with the use of complex models including too many components. In this work, we solve leukocyte classification with only one deep learning model, a modified AlexNet. We use transfer learning and a new Generative Adversarial Network (GAN) as a data augmentation method for a new dataset that seeks a similar approach to a blood smear as opposed to the datasets available in the literature. Results indicate that the modified AlexNet can achieve a validation balanced precision classification of 98.17%, and it is ranked 4th place compared to state-of-the-art models considered in this work and 2nd place considering models with only one deep learning model. These performances indicate that the proposed model can solve the classification task and it could be implemented in equipment with reduced computational requirements as an embedded system.
Research on 3D visual reconstruction technology for open digital culture content construction

This paper first briefly introduces the open digital cultural museum and the application potential of oblique photography technology in its content construction; then summarizes the principle of oblique photography, and puts forward process flow of oblique photography technology in the specific construction of AR/VR content; finally, it summarizes the shooting skills applicable to different scenarios and shows the modeling results, provide finally Solutions to common problems are proposed so that people can build better 3D panoramas.
Text removal method combining text location and image inpainting

In view of the text in image or video on the visual interference and privacy issues, a text removal method combining text location and image inpainting is proposed. Firstly, the method inputs the image with text into the text location network to identify the text contour area, and then generates the masked image to simulate image damage part according to the text contour area. Finally, the damaged image is repaired through the damage reconstruction network to realize text removal. At the same time, in order to ensure the reliability of the text removal, the complementary fusion layer is proposed to ensure the non-text area in image do not change. The experimental results show that compared with the traditional texture and patch repair algorithm our method has a better repair effect. Compared with the improved generative adversarial network method, our method is more close to the original image features.
A new eyeball optical axis reconstruction method for head-mounted eye-tracking system

Gaze tracking can be divided into two-dimensional (2-D) gaze-estimation method and three-dimensional (3-D) gazeestimation method. 3-D gaze-estimation method can simplify user calibration and track gaze under full free head movement. To solve the shortage of traditional 3-D gaze-estimation method, this paper proposes a 3-D eyeball optical axis (OA) reconstruction method for single-camera multi-source head-mounted system. The method directly calculates the vector of the OA from the pupil image on the basis of the corneal center, and then reconstructs the eyeball OA. The results of simulation experiments show the effectiveness of the method.
Lightweight object detection network for helmets on the road

In this work, a lightweight object detection network based on YOLOv5 is proposed to solve the detection problem of nonmotor vehicle helmet wearing on the road. By replacing the original CSPDarknet53 backbone network with MobileNetv3, the number of network model parameters is reduced and the computing speed is increased. With further introduction of the criss-cross Attention, it improves the accuracy of the simplified network and improves the detection reliability. Compared with YOLOv5 network, the detection efficiency is improved 140%(FPS) at the cost of reducing 17.1%mAP, hence the task of helmet detection on the road can run in real time on the edge computing equipment.
Design and Implementation of Tour Guide Robot for Red Education Base

This article focuses on the development of red tourism and the construction of red education bases, and designs a tour guide robot that combines with the Internet of Things for guiding and promoting red education bases.The motion control part of this design is completed by STM32, and the control system adopts the ROS (Robot Operating System) framework, achieving functions such as map construction, path planning, navigation, obstacle avoidance, and voice guidance.The robot also implements the Internet of Things to improve human-machine interaction and user experience.
A Study on Investment Determinants of Webtoon Content Companies

This study attempted to provide ideas in establishing investment strategies by analyzing the determinants of investment in domestic webtoon company. The Delphi survey method was adopted targeting investment entities of various webtoon companies. As a result of the analysis, major investment determinants considering the characteristics of the industry were the current status of securing webtoon IP, the possibility of IP expansion, flexible thinking and execution and the competence and recognition of corporate-owned artist. On the other hand, it was found that the rationality of distribution of the industry, which includes illegal distribution of webtoon, was not considered in investment determinants. These findings suggest that company should have the ability to produce high-quality content that can expand IP based on the establishment of a creative environment to attract investment. This study is meaningful in that it presented a direction to establish an environment to revitalize investment in the webtoon industry by analyzing the determinants of investment decisions in webtoon content company.
Underwater image restoration based on color correction and reweighted graph total variation prior

In order to solve the problems of color distortion and blurring common in underwater images, this paper uses color channel compensation and grayscale equalization to correct color cast, and uses the total variation of the reweighted graph to estimate the fuzzy kernel to achieve image restoration. Based on the average gray value of the color channel, the color channel with serious information loss is compensated to reduce the deviation of the gray distribution between the color channels, and the gray distribution balance is further realized by segmented stretching. Combined with the reweighted graph total variation prior algorithm and the improved L-R unblinded restoration algorithm to restore the final image to remove blurring. Several sets of different data sets were selected to compare the proposed algorithm with several classical algorithms, and the evaluation results of subjective vision and three objective indicators were analyzed. The results show that the proposed algorithm can be applied to underwater image restoration, and the algorithm has good general applicability, which can effectively correct color cast, remove blur and improve image clarity.
An Analysis on the Research Trends of Career Capital: Focused on Journals Published in Korea and Abroad

이 연구의 목적은 경력자본(career capital)에 관한 연구동향을 파악하여 향후 연구방향 및 시사점을 제시하는 것이다. 이를 위해 2022년 1월까지 수행된 국내외 총 48편의 논문을 최종 선정하여 경력자본 연구의 동향, 목적, 대상등을 분석하였으며, 다음과 같은 연구결과를 도출하였다. 첫째, 양적연구 대부분이 개인 수준에서 횡단연구로 이루어졌으며, 경력자본의 하위요인은 대부분 DeFillippi와 Arthur(1994)가 개발한 knowing-how, knowing-why, knowing-whom을 기반으로 하였다. 또한 자주 사용된 경력자본 관련 변인은 국제경험, 자발적/비자발적 해외주재원 파견, 인지된 성과, 경력 만족이었다. 둘째, 질적연구는 대부분 다양한 대상별로 경력개발 과정을 파악하기 위한 목적으로 실시되었다. 셋째, 이론적 연구는 경력자본 관련 개념적 틀을 제시하거나 경력자본 변인들을 살펴본 문헌 연구가 주로 수행되었다. 넷째, 실천적 연구에서는 진로자본을 활용한 진로자기관리 모형을 개발하였다. 다섯째, 경력자본 연구의 연구대상은 대부분이 근로자로 나타났으며, 연구대상의 다양성은 해외 연구가 국내연구에 비해 높았다. 여섯째, 연구대상 논문의 키워드에 대한 텍스트마이닝(워드클라우드) 분석 결과, 주요 키워드들은 글로벌 경력개발 환경에서 벌어지는 현상과 관련성이 높은 것으로 나타났다. 마지막으로 이러한 연구결과를 바탕으로 향후 연구방향 및 시사점을 제시하였다. This study sought to explore recent research trends regarding career capital. This study selected a total of 48 articles that were published until January 2022 and analyzed research trends, samples, and research purposes, and derived the following results: First, the sub-factors of career capital were mostly based on the knowing-how, knowing-why, and knowing-whom parameters developed by DeFillippi and Arthur (1994) for quantitative studies. The most frequently used variables in quantitative research related to career capital were international experiences, self-initiated/assigned expatriate experiences, perceived performance, and career satisfaction. Second, qualitative research has been conducted to identify the career capital development process of specific study participants. Third, theoretical research has been conducted to present the conceptual frameworks related to career capital or to examine the variables related to career capital. Fourth, action research has been conducted to develop a career-self-management model using career capital. Fifth, most of the samples were workers. Finally, work cloud analysis showed that the main keywords of the articles were relevant to a global career market. Based on these results, this study presented future research implications and directions.
Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Dan Support Vector Machine

In order to prepare students to face the rapid development of technology, changes in work life and skills, students must be better prepared to face the progress of the times. Universities must be able to carry out innovative learning processes so that students achieve optimal learning outcomes which include aspects of knowledge, skills and attitudes. So the MBKM program was launched to answer these demands. However, MBKM has pros and cons in its implementation, so it is necessary to analyze and evaluate policies to improve performance through feedback from the public by conducting sentiment analysis of MBKM policies on twitter users from 2019 to 2022 with the hashtag #kampusmerdeka. This study used the Naïve Bayes and SVM algorithms to determine accuracy based on sentiment classification. The data used 1118 data with positive sentiment 618 data and negative sentiment 500 data. This study resulted in an accuracy of 86%, precision of 87% and recall of 80% with testing data using the Naïve Bayes algorithm. Then using the linear kernel SVM algorithm with the same testing data resulted in accuracy of 93%, precision of 100% and recall of 84%. Therefore, it is important to conduct studies to improve the MBKM program so that its implementation is clearly in accordance with existing procedures.