Reviewerly/exHarmony huggingface dataset:
Implementasi Algoritma Floyd Warshall dalam Pencarian Rute Terpendek Lokasi Tower Base Transceiver Station (BTS) pada PT Citra Akses Indonusa

PT Citra Akes Indonusa is a company that operates on the expertise of information technology services in Banten Province. One of the services provided by the company requires the construction of a Base Transceiver Station (BTS) tower. The tower requires maintenance if there is damage to the network signal. As a result, the shortest route is needed to make it easier for employees to reach the tower location to be more effective. There are eight BTS tower locations in Tangerang Regency and three BTS tower locations in Tangerang City. The process of finding the shortest route in this study uses the Floyd Warshall algorithm, which is unique in finding the shortest route by comparing each edge of all edges that are passed. The process of testing the shortest route is done by selecting the starting point, then selecting several BTS tower locations. Next, the shortest route will be searched using the Floyd Warshall algorithm from each point of destination for the BTS tower location, then the BTS tower location will be selected first and so on until the last destination. The effectiveness of this shortest route search involves a comparison of the routes presented by Google Maps. Based on the results of 30 randomized trials on BTS tower locations, the average shortest route effectiveness was 25.54% compared to the route generated by Google Maps. This is due to the selection at each BTS tower destination location so as to make the route more effective.
Which phish is captured in the net? Understanding phishing susceptibility and individual differences

Abstract Phishing research presents conflicting findings regarding the psychological predictors of phishing susceptibility. The present work aimed to resolve these discrepancies by utilizing a diverse online sample and email set. Participants completed a survey that included an email classification task and measured several individual differences, including phishing awareness, age, impulsivity, curiosity, and personality (the Big‐5). Phishing susceptibility was measured by participants' ability to distinguish between phishing and legitimate emails. Three regression analyses were performed to predict email discrimination ability; (1) an age model, (2) a deficient self‐regulation model (i.e., impulsivity, response times, and curiosity), and (3) a personality (i.e., the Big‐5) model. Overall, phishing susceptibility was predicted by younger age, higher levels of impulsivity and extraversion, lower levels of openness to experience and agreeableness, and quick responses. The present work identifies several populations that are particularly vulnerable to phishing attacks and may require targeted training interventions.
Attention Based Evolutionary Approach for Image Classification

Lately, evolutionary algorithms have gained traction due to their ability to produce state-of-the-art deep learning architectures for a given data set, even though they require considerable amount of compute resources, they are a heavily researched domain because of the complexities involved in designing deep learning architectures. Currently, none of the evolutionary approaches available have incorporated the attention mechanism, which is a proven technique to improve the performance of image classification and language models. This paper posits a neuroevolutionary technique coupled with the use of Convolution Block Attention Module for image classification. As technology progresses, it’s inevitable that there will be massive advancements leading to cheaper and more available computing making evolutionary approaches a promising avenue to develop task specific deep learning models. The proposed approach evolves a topology that achieves a high fitness of 87.44%, using fewer parameters as compared to previous approaches. This results in a superior fitness score compared to most past approaches, despite being evolved for just few generations.
Single Node Acceleration of Generative Adversarial Networks using HPC for Image Analytics

Generative Adversarial Networks (GAN) are approaches that are utilized for data augmentation, which facilitates the development of more accurate detection models for unusual or unbalanced datasets. Computer-assisted diagnostic methods may be made more reliable by using synthetic pictures generated by GAN. Generative adversarial networks are challenging to train because too unpredictable training dynamics may occur throughout the learning process, such as model collapse and vanishing gradients. For accurate and faster results the GAN network need to trained in parallel and distributed manner. We enhance the speed and precision of the Deep Convolutional Generative Adversarial Networks (DCGAN) architecture by using its parallelism and executing it on High-Performance Computing platforms. The effective analysis of a DCGAN in Graphic Processing Unit and Tensor Processing Unit platforms in which each layer execution pattern is analyzed. The bottleneck is identified for the GAN structure for each execution platforms. The Central Processing Unit is capable of processing neural network models, but it requires a great deal of time to do it. Graphic Processing Unit in contrast, side, are a hundred times quicker than CPUs for Neural Networks, however, they are prohibitively expensive compared to CPUs. Using the systolic array structure, TPU performs well on neural networks with high batch sizes but in GAN the shift between CPU and TPU is huge so it does not perform well.
Classification Classification of COVID-19 Monthly Cases Using Artificial Neural Network (ANN) Method

This study proposes the MLP type ANN method with the influence of Cross-Validation evaluation using three K-Fold tests, namely 3, 5, and 8. The data used are the data that relate to COVID-19 cases issued from the Bandung Public Health Office, climate report from Bandung Meteorological, Climatological, and Geophysical Agency (BMKG), population data from Bandung Population Office, citizen’s educational history from the Bandung Education Office and the West Java Open Data website. The data also was gathered from 151 sub-districts in Bandung City, with a total of 22 attributes collected from November 2020 to December 2021. The ANN method is included in the deep learning process. Therefore, the number of hidden layers utilized has a significant impact on the performance of the model being constructed. The implementation of Cross-Validation evaluation with K=8 results in an accuracy value of up to 98% and an error metric measurement of 0.3404 for MAE and 0.5994 for RMSE. This study's objective is to provide information on the optimal K-Fold Cross Validation parameters used in this ANN method to provide better performance during building a classification model for confirmed Covid-19 patients.
Research on the Factors Affecting the Learning Satisfaction of A Learning Investment Perspective - based on the Tracking Study of Learning and Development among Chinese University Students at C University

The quality of talent cultivation is the lifeline of higher education. In order to comply with the development requirements of the new era and implement the "14th Five Year Plan" work requirements of "focusing on high-quality development, grasping reform to promote implementation, and uniting efforts to open up new opportunities", C University actively introduces the research tool and research ideas of "Chinese college students' learning and development tracking research", and regards learning engagement as including behavior, psychology The organic integration of elements such as social and cultural contexts, focusing on the interrelationships and interactions between various elements, while placing students, learning, and schools in a social reality environment and making targeted improvements.
[Research on eye movement data classification using support vector machine with improved whale optimization algorithm].

When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.支持向量机在进行不同眼动模式分类任务时受参数影响较大，针对这一问题，本文提出一种基于改进鲸鱼算法优化支持向量机的算法以提升眼动数据分类性能。根据眼动数据特点，本研究先提取注视、眼跳相关的57个特征，再利用近邻相关（ReliefF）算法进行特征筛选。针对鲸鱼算法收敛精度低，易陷入局部最小值等问题，本文引入惯性权重平衡局部搜索和全局搜索，加快算法收敛速度，同时利用差分变异策略增加个体多样性，跳出局部最优。本文对8个测试函数进行实验，结果表明改进鲸鱼算法具有最佳的收敛精度和收敛速度。最后，本文将改进鲸鱼算法优化支持向量机模型应用于自闭症眼动数据分类任务，公开数据集实验结果表明，相较于传统的支持向量机方法，本文方法的眼动数据分类准确率有着较大提升，相较于标准鲸鱼算法和其他优化算法，本文方法优化后的模型具有更高的分类精度，为眼动模式识别提供了新思路与方法。未来或可利用眼动仪获取的眼动数据，结合本文方法辅助医疗诊断。.
Spectacles for hearing-impaired and multi-language users realized with SABIC's non-halogenated flame-retardant ULTEM™ 1000 resin

Hearing loss compensation and spoken language translation can reportedly be provided now using an unlikely technology – augmented reality (AR) eyeglasses from Beijing LLVision Technology Co., Ltd., which display voice translations in text on the lenses in real time. The company's LEION Hey glasses feature legs (or temples) molded from SABIC's non-halogenated flame-retardant ULTEM™ 1000 resin. ULTEM resin reportedly enables thin-wall molding of the hollow legs, which contain both a lithium-ion battery and a computer chip to enable rapid, multi-language translations. SABIC's material is claimed to have helped LLVision minimize the weight of the legs to give users all-day comfort, while providing high strength that enables key components to be integrated into the legs with maximum functionality.
UPDATING THE FOUNDATIONS OF THE RUSSIAN STATEHOOD IN HIGHER EDUCATION INSTITUTIONS

The relevance of the reform of higher education in the humanities within the framework of the project "Spiritual and Moral Code of Russia" in terms of the mandatory introduction of the course "Fundamentals of Russian Statehood" into the educational process of Russian universities in order to form a unified position among students on the national foundations of state power is substantiated
EFFICIENT GENDER DETECTION USING DEEP CONVOLUTION NEURAL NETWORKS

A crucial task in intelligent applications, such as access control, human-computer cooperation, enforcement, marketing intelligence, and visual surveillance, is estimating gender from a facial image.Gender is a key facial characteristic that is important for social coordination.Human classification and identification have been used for a very long period in many different fields.The main goal of this work is to create an algorithm that accurately and efficiently determines a person's gender.In light of the expanding use of online social networking sites, automatic gender detection is now relevant to an expansion of its implementation in various software and hardware.It has been determined that using deep convolutional neural networks and learning and classification techniques.These gender classification jobs are expected to handle information at an estimated rate of growth.The method described in this work is straightforward and simple to use for human classification; it simply requires a webcam and a respectable computer system to operate.
Blood Cells Morphometry and Descriptive Morphology of Captive Changeable Hawk Eagles (Nisaetus chirratus) at Wildlife Rescue Centre Jogja

Providing qualitative and quantitative haematologic references of neglected captive wild animals is pivotal for the sanctuary health management and improvement. Changeable hawk eagles (Nisaetus chirratus) are abundantly kept in sanctuary in which no haematologic reference is ever reported. This study aimed to present the visual keys and morphometric references of changeable hawk eagles’ blood cells as the standard for further haematologic count. The peripheral blood smears were prepared and collected from eight changeable hawk eagles kept at Wildlife Rescue Centre Jogja then stained with 10-fold diluted Giemsa stain following the standard manners. All slides were inspected and captured under camera-equipped microscope which then were morphologically and morphometrically evaluated using ImageJ version 1.52a. As the changeable hawk eagles are naturally present in dark and bright morph, we statistically compared the blood cells morphometric parameters between morph-based groups. Changeable hawk eagles’ erythrocytes were oval shaped with occasional morphologic variation. Leukocyte consisted of polymorphonucleated granulocytes - with exception of basophils which lacked nuclear lobulation - and mononucleated agranulocytes. There were significant differences (P&lt;0.05) of all erythrocyte morphometric parameters, heterophils diameter, and lymphocytes diameter between dark and bright morph group. Overall, the morphologic properties of changeable hawk eagles’ blood cells were visually identical to other avian species though the blood cells morphometry might be comparatively different.
Role of Official Development Assistance, Foreign Direct Investment and Trade Openness in Reducing Income Inequality: An Empirical Analysis from Selected Asian Countries

Income inequality has been increasing steadily in developing economies. Many factors are responsible for such income inequality and deteriorated economic growth in Asian economies. For this reason, a lot of debate has been made about reducing income inequality. Though the existing study also takes into account the important role of official development assistance, foreign direct investment, and trade openness in reducing income inequality in selected Asian countries, the study found that official development aid, foreign direct investment, trade openness, and labour force participation have greatly reduced income disparity in 10 Asian nations. For growth, good production and exports require more jobs. Globalization and international ties should be prioritized. Finally, economies need a stable economic and political environment to attract aid and foreign investment and grow and expand.
Overview of Formal Methods

Chapter 16 discusses formal methods, which consist of a set of mathematical techniques to rigorously specify the requirements of a software system, and a formal approach allows software to be produced that formally satisfies its specification. Formal methods may be employed to rigorously state the requirements of the proposed system; they may be employed to derive a program from its mathematical specification; and they may provide a rigorous proof that the implemented program satisfies its specification. They have been applied mainly to the safety critical field.
Video Crawling Using Deep Learning

Since the amount of video data nowadays grows quickly, video crawlers are becoming more and more necessary. A video crawler makes it simple to find any object and get a video. This technique primarily tries to create reviews by choosing the most useful video content the user requests. Using deep learning, we have attempted to investigate and address various video synopsis and summary problems in this study. The related study will concentrate on comparable earlier exploratory work of video summarizing and what the author stated in their work, which was accomplished by performing experiments in the literature where they presented several techniques of video summary. In one of these experiments, one of the writers from the literature proposed employing the Delaunay Triangulation-based keyframe method of video summarization (DT). The frame content is clustered using a DT system after being first used as multidimensional point data. In the authors’ proposed method, shot finding occurs after uprooting the initial video frames. Key frames are identified utilizing the visual content of the video frame data by using the clustering process. They did it in a unique way to demonstrate the innovative approach. In their investigation, the authors used two assessment techniques: quantitative evaluation and video summary. An animated summary video is essential in this case. Effective video data storage, quick browsing, and retrieval are made possible without losing important information. By speeding up browsing through a large collection of video data and attaining efficient access and representation of the video material concerning specified things for identification, it lessens the strain of manual searching or object detection. examining the impact of video quality by seeing low-quality footage. All neural network settings may be adjusted to increase the video straggler’s accuracy. This study primarily focuses on recent developments in this field and provides a quick overview of current deep-learning-based video crawler techniques.
How beauty influencers on TikTok can affect the purchasing of cosmetics

Businesses in the cosmetics industry have struggled to preserve their market share, growth, and growing consumer preferences. According to Chatzigeorgious (2017), one strategy for these businesses to enhance their product sales is using beauty influencers (— for example, beauty product influencers on social media). This study thus aimed to examine the effect of TikTok beauty influencers on cosmetics sales. The report will demonstrate consumers’ attitudes as well as behaviors towards beauty influencers’ recommendations for cosmetics. The research employed quantitative research strategy survey methods. The research respondent entailed 100 individuals selected through the convenience sampling technique. It is also presented in the form of tables. The research findings showed that beauty influencers play a significant role in influencing customer purchasing decisions.
Spurious Platelet Counts Due to Cell Fragmentation in a Patient with Influenza B Virus

In clinical practice, thrombocytopenia is very dangerous for patients, because it has the risk of bleeding and may cause serious adverse consequences. Therefore, timely and accurate identification of spurious platelet counts is very important to improve patient safety.This study reported a case of spurious platelet counts in a patient with influenza B virus.Leukocyte fragmentation is responsible for the inaccurate platelet detection results by resistance method in this influenza B patient.In practical work, if abnormalities are found, blood smear staining and microscopic examination should be carried out in time, and clinical data should be combined to avoid the occurrence of adverse events and ensure patient safety.
Jeffreys Divergence-Based Regularization of Neural Network Output Distribution Applied to Speaker Recognition

A new loss function for speaker recognition with deep neural network is proposed, based on Jeffreys Divergence. Adding this divergence to the cross-entropy loss function allows to maximize the target value of the output distribution while smoothing the non-target values. This objective function provides highly discriminative features. Beyond this effect, we propose a theoretical justification of its effectiveness and try to understand how this loss function affects the model, in particular the impact on dataset types (i.e. in-domain or out-of-domain w.r.t the training corpus). Our experiments show that Jeffreys loss consistently outperforms the state-of-the-art for speaker recognition, especially on out-of-domain data, and helps limit false alarms.
Composition of Motion from Video Animation Through Learning Local Transformations

In this work, we solve the problem of motion representation in videos, according to local transformations applied to specific keypoints extracted from static the images. First, we compute the co-ordinates of the keypoints of the body or face through a pre-trained model, and then we introduce a convolutional neural network to estimate a dense motion field through optical flow. Next, we train a generative adversarial network that exploits the previous information to generate new images that resemble as much as possible the target frames. To reduce trembling and extract smooth movements, our model incorporates a low-pass spatio-temporal Gaussian filter. Results indicate that our method provides high performance and the movement of objects is accurate and robust.
Long-Tailed Image Recognition with Dynamic Re-Weighting

For long-tailed image recognition tasks, re-weighting is effective to alleviate data imbalance by assigning higher weights to tail categories. However, existing re-weighting methods typically adopt a static weighting scheme, which usually hurts the accuracy of head categories. To deal with this issue, this paper proposes a progress-relevant weighting scheme called dynamic re-weighting, in which the weight assigned to a particular category first increases and then decreases, proportional to the number of samples that have been used in that category. In addition, we introduce a head-to-tail loss to control the evolving of weights, which makes the model gradually transfer its attention from head categories to tail categories. We conduct experiments on long-tailed CIFAR/ImageNet datasets, and confirm that our method not only outperforms static re-weighting methods, but also improves the accuracy on tail categories without sacrificing the accuracy of head categories.
Noise-Disentanglement Metric Learning for Robust Speaker Verification

Automatic speaker verification (ASV) suffers from performance degradation in noisy environments. To solve this problem, we propose the noise-disentanglement metric learning to reduce the speaker-irrelevant noisy components and build a noise-invariant embedding space. Specifically, the disentanglement module, including the speaker encoder and re-construction module, is dedicated to decoupling speech signals. The speaker encoder is used to disentangle speaker-related components, and the reconstruction module increases the model’s ability to constrain the noise information by re-constructing the signal. In addition, distribution optimization is introduced to supervise the spatial structure of speaker embeddings under noisy environments. Experiments on Vox-Celeb1 indicate that the proposed method improves the performance of the speaker verification system in both clean and noisy conditions.