Reviewerly/exHarmony huggingface dataset:
Promoting Conciseness with Shared Setup and Teardown

Our concise-test automated test framework is starting to take shape. We’ve already got something that’s usable: we can use the it function to write test cases and the describe function to group them.
Block-Level Surrogate Models for Inference Time Estimation in Hardware-Aware Neural Architecture Search

Hardware-Aware Neural Architecture Search (HA-NAS) is an attractive approach for discovering network architectures that balance task accuracy and deployment efficiency. In an iterative search algorithm, inference time is typically determined at every step by directly profiling architectures on hardware. This imposes limitations on the scalability of search processes because access to specialized devices for profiling is required. As such, the ability to assess inference time without hardware access is an important aspect to enable deep learning on resource-constrained embedded devices. Previous work estimates inference time by summing individual contributions of the architecture’s parts. In this work, we propose using block-level inference time estimators to find the network-level inference time. Individual estimators are trained on collected datasets of independently sampled and profiled architecture block instances. Our experiments on isolated blocks commonly found in classification architectures show that gradient boosted decision trees serve as an accurate surrogate for inference time. More specifically, their Spearman correlation coefficient exceeds 0.98 on all tested platforms. When such blocks are connected in sequence, the sum of all block estimations correlates with the measured network inference time, having Spearman correlation coefficients above 0.71 on evaluated CPUs and an accelerator platform. Furthermore, we demonstrate the applicability of our Surrogate Model (SM) methodology in its intended HA-NAS context. To this end, we evaluate and compare two HA-NAS processes: one that relies on profiling via hardware-in-the-loop and one that leverages block-level surrogate models. We find that both processes yield similar Pareto-optimal architectures. This shows that our method facilitates a similar task-performance outcome without relying on hardware access for profiling during architecture search.
SENSEI: Scraper for ENhanced AnalySis to Evaluate Illicit Trends

Over the last years, we faced an exponential growth of illegal online market services in the Dark Web, making it easier than ever before of acquiring illicit goods online via a simple service interaction. To study and understand this emerging illegal services economy, we developed a trend analysis and (dark-)web services monitoring tool: SENSEI, which stands for ‘Scraper for ENhanced analySis to Evaluate Illicit trends’. SENSEI extracts specific service transaction trends and analyses the human behaviours behind, to produce symmetric insights on specific service transaction habits from both customers and vendors on the Dark Web. Moreover, a trend analysis tool is provided to discover and typify relationships among different criminal activities and hence provide evidence and support investigation activities and Law Enforcement Agencies (LEAs) detecting criminal operations.
MySQL Select Query Optimization Using Self-Join

&lt;p&gt; The challenge of query execution in MySQL involves the efficient and accurate processing of SQL queries to retrieve, manipulate, and store data in a database. It requires optimizing the query execution plan, identifying and resolving performance issues, and ensuring data integrity and security. The challenge lies in balancing the trade-offs between query complexity, database size, system resources, and user requirements. Choosing the right approach depends on the specific characteristics of the data and workload. Therefore, in this paper, we have applied various techniques to get optimized results. &lt;/p&gt;
A Study on the police exercise for legitimate rallies and demonstrators: Focusing on United States cases and theories

This article stems from the question of who should be exercised when police authority is to be put in place due to certain reasons, such as assault and intimidation, after multiple assemblies and demonstrations have all been legally held. In particular, in the case where multiple assemblies are held and the assembly of one side causes the assembly of the other side to be illegal, the constitutional basis for determining whether the police power should be exercised against the group that caused the violence or intimidation or the group that committed the violence or intimidation are interested in Traditionally, it can be said that discussions on the freedom of expression have been discussed on the content or scope of protection from the viewpoint that the intervention of the ‘state’ should be minimal, centering on the speaker, the subject of expression. This is explained by the fact that all kinds of expression that contribute to the discovery of truth must be guaranteed in principle according to the theory of the ‘free market of ideas’, and even though it differs from the majority opinion, the right to express itself cannot be denied. On the other hand, today's speaker's right to express begins with extreme ‘hate speech’, ‘false information’ (fake news), and furthermore, in the realm of advertisements seen on buses and subways (in the case of the so-called ‘The Captive Audience Doctrine’). The need to consider the rights of ‘listeners’ is also being raised. In particular, when a counter-assembly is a problem, there is a relatively high possibility that the harm to expression cannot be resolved by waiting enough time, so there is room for a modified interpretation of the right to plural assembly. Regarding this background, this article briefly introduces the results of previous studies on the German case, followed by the ‘Heckler's right to veto’ theory is being considered. While raising the epistemology that protection of listeners who have different positions from those of the speaker is also necessary, ‘Heckler's right to veto’, which means ‘the right to refuse to receive the speaker's expression’, has been established in the United States. I would like to look at how it is being discussed in the area of assembly and demonstration, and pay attention to the fact that this theory can be an opportunity to bring about a change in the legal theory centered on the rights of speakers.
A study on countermeasures related to malicious comments on the Internet

Comments on the Internet are the result of responding behavior of recipients(readers) to posts in the Internet space and are written in the form of messages, whereas malicious comments are intentionally posted content that criticizes or ridicules the publisher or a specific person regardless of the content. can Therefore, malicious comments include abusive language or slander against others, invasion of privacy, promotion of violence or speculative behavior, obscene language, and spamming posts. However, the problem related to malicious comments has occurred since the use of the Internet became common, and it is not a problem of yesterday or today. Of course, in the past, the issue of malicious comments in media articles related to public figures (politicians, famous celebrities, sports stars, etc.) was mainly discussed, but recently, malicious comments against the general public have also become a social problem due to the influence of interactive media platforms. is beginning to be recognized. Accordingly, discussions on countermeasures related to malicious comments on posts mediated through media platforms have recently emerged as a hot potato in our society. In this paper, based on the content and implications of Japan's “Service Provider Liability Limitation Act”, which was recently revised as part of a consideration of countermeasures related to malicious comments on the Internet, appropriate regulatory types for service providers related to malicious comments were derived. So, we considered the desirable way to move forward.
Safe batch constrained deep reinforcement learning with generative adversarial network

Batch-constrained reinforcement learning constrains the learned policy to be close to the behavior policy, which holds a tremendous promise for alleviating the distributional shift in offline reinforcement learning. Existing batch-constrained techniques rely on perturbation models to adjust the actions generated from the generative model to maximize the estimated value function. However, the perturbation model deviates from the distribution of the offline datasets and introduces a new distribution drift problem, which affects the performance of the learned policies. In addition, since offline reinforcement learning cannot learn by trial and error, the final policies are often prone to failure in reality or make unsafe decisions when trained with a noisy or small size dataset. To address the above issues, this paper employs constrained generative adversarial network to generate actions with given states. Specifically, we train the generator to maximize the estimated value and constrain the state-action pairs to follow the dataset distribution. The perturbation model is trained to maximize the probability of the perturbed actions belonging to the dataset and minimize the likelihood of taking dangerous actions. Moreover, we utilize safety critics to predict the risk of the actions under a state. Experimental results show that the proposed method is effective and can choose safe actions while maintaining a high performance in offline settings.
Analyzing the Characteristics of Japanese Super Science High School (SSH) using Semantic Network Analysis

일본의 슈퍼 사이언스 하이스쿨(SSH)은 2002년부터 실시한 ‘과학기술･이과 좋아지기 계획(科学技術･理科大好きプラン)’하에 만들어진 것으로, 지정된 학교별로 과학 중점의 독자적인 커리큘럼을 구성해 운영하고 있다. 한국에서는 우수 과학기술 인재양성을 목표로 이를 벤치마킹한 과학중점학교(SCS)가 2009년부터 현재까지 운영되고 있다. 본 연구에서는 145개 SSH 지정교의 2021년도 ‘연구개발 실시보고서’에 등장한 단어들의 언어 네트워크 분석을 통해 SSH의 특징을 확인하였다. 분석 결과, ‘외부 기관과의 협업’, ‘글로벌 활동과 발표 능력 강조’, ‘수업의 내실화’, ‘탐구 역량의 강화’ 등이 주요 성과로 나타났다. 해당 성과들은 중간평가의 평가항목으로 제시된 내용과 대부분 일치했으며, 이는 평가항목이 SSH 운영의 가이드라인으로 작용했음을 보여준다. 연구개발의 과제에 대한 언어 네트워크에서는 ‘탐구의 평가’가 강조되고 있음을 확인할 수 있었는데, 탐구 활동의 평가 방법 개선이 향후 SSH의 핵심 과제임을 보여준다. 연구를 통해 분석된 SSH의 성과와 과제는 운영 주체의 변화와 함께 새로운 출발점에 서게 된 SCS에도 시사점을 준다. ‘학교 평가 방식 개선’과 ‘탐구 활동 평가 방법 개발’은 SCS에서도 중점적으로 논의되어야 할 사항이며, 이는 SCS와 함께 발전해야 할 한국의 과학교육에도 함의를 갖는다.‘Science and Technology Improvement Plans’ in Japan have been implemented since 2002. As one of its outcomes, Super Science High Schools (SSH) were introduced to organize and operate their own science-focused curriculum. In Korea, Science Core Schools (SCS), which benchmarked SSH, have been in operation since 2009 to foster students with scientific and technological talents. In this study, the characteristics of SSH have been analyzed using Semantic Network Analysis of the words in 2021 ‘R&D Implementation Reports’ of 145 SSHs. The results of the analysis show that the major achievements of SSH were ‘cooperation with external institutions’, ‘emphasis on global activities and presentation ability’, ‘internalization of classes’ and ‘strengthening exploration capabilities’. These achievements were mostly consistent with what was presented in the evaluation criteria of the midterm evaluation. This suggests that the evaluation criteria served as the guideline to operate SSH. In the Semantic Network Analysis of ‘Tasks for R&D’, it was found that ‘evaluation of inquiry’ was emphasized. This implies that improving the evaluation methods of inquiry activity is a key task for future SSH. The achievements and challenges of SSH analyzed through this study have implications for SCS as well. ‘Improvement of school evaluation tools’ and ‘development of inquiry activity evaluation methods’ are the matters to be considered seriously in the SCS as well. As Korean science education pays attention to SCS, this study has implications for both SCS and Korean science education.
Intellectual Property In the Era of AI, Blockchain and Web 3.0

It was over two hundred years ago when the founders provided in the U.S. Constitution that Congress shall have the power, “To promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Right to their respective Writings and Discoveries.” During just three decades, the global explosion in Internet usage has enhanced worker productivity and the availability of information to remote and economically disadvantaged global population segments unimagined before. Continued increases in computer processing speeds when combined with decreased production costs has resulted in vast new technological advancements.Intellectual Property (IP) law, like almost all areas of law, struggles to keep pace with rapidly evolving technologies. Topics unimagined just several decades ago, such as: artificial intelligence (AI); blockchain; non-fungible tokens (NFTs); quantum computing; virtual currencies, and others present novel challenges to IP law. This is the subject matter of our research and this paper. We believe that our coverage of these matters represents a valuable contribution to IP literature.
FXAM: A Unified and Fast Interpretable Model for Predictive Analytics

Predictive analytics aims to build machine learning models to predict behavior patterns and use predictions to guide decision-making. Predictive analytics is human involved, thus the machine learning model is preferred to be interpretable. In literature, Generalized Additive Model (GAM) is a standard for interpretability. However, due to the one-to-many and many-to-one phenomena which appear commonly in real-world scenarios, existing GAMs have limitations to serve predictive analytics in terms of both accuracy and training efficiency. In this paper, we propose FXAM (Fast and eXplainable Additive Model), a unified and fast interpretable model for predictive analytics. FXAM extends GAM's modeling capability with a unified additive model for numerical, categorical, and temporal features. FXAM conducts a novel training procedure called Three-Stage Iteration (TSI). TSI corresponds to learning over numerical, categorical, and temporal features respectively. Each stage learns a local optimum by fixing the parameters of other stages. We design joint learning over categorical features and partial learning over temporal features to achieve high accuracy and training efficiency. We prove that TSI is guaranteed to converge to the global optimum. We further propose a set of optimization techniques to speed up FXAM's training algorithm to meet the needs of interactive analysis. Thorough evaluations conducted on diverse data sets verify that FXAM significantly outperforms existing GAMs in terms of training speed, and modeling categorical and temporal features. In terms of interpretability, we compare FXAM with the typical post-hoc approach XGBoost+SHAP on two real-world scenarios, which shows the superiority of FXAM's inherent interpretability for predictive analytics.
Degree of Information Security at Telecommunications Sector in Yemen

This study aimed to determine the degree of information security (confidentiality, integrity, availability) at telecommunications sector in Yemen. To achieve this, the descriptive method was followed and a questionnaire was used for collecting data from the study sample at telecommunications sector, which includes the six telecommunication companies and Public Telecom Corp. The sample was selected by the complete census method. The questionnaire forms were distributed to (356) participants; however, only (218) forms were valid for analysis. The data were analyzed by the Statistical Package for the Social Sciences (SPSS) program. The study findings revealed that the degree of information security at telecommunications sector in Yemen was high; the degree of confidentiality was the highest, followed by integrity, then availability. The study recommended that the information security should be reinforced, and procedures for protecting information from disclosure or modification, as well as activities for authorized access to information and services should be supported.&#x0D; Keywords: information security, telecommunication sector, Yemen.&#x0D;
Denoising Fundus Images of Diabetic Retinopathy Using Natural Neighborhood Kriging

The procedure for removing noise in digital fundus images is using mask of a healthy subject. A low-pass filter or smoothing operation encouraged in recent applications. The convolving of original image with mask of the subject shall derive the value of the pixel harmonizes with neighbors. Image filtering is one of the standard process in image processing system, however choice of filters are based on the complexity of images. Kriging is a inpainting method that applied for correcting images with noise. Natural Neighbor Kriging is the procedure where non-geometric (tessellations) spaces with gaps or noise can be filled with common pixel color(s). In this paper we present Voronoi diagrams are superimposed with Delaunay triangulation and inpainting in the RoI is accomplished. The methods proposed are proved effective for large data sets, where there are much number of alternatives to find neighbours in the sampled image or the RoI.
A Hybrid Method for Characters Recognition using Ant Colony Feature Selection, KNN and Reducts

This work addresses the development of a hybrid method for feature selection and a strategy to classify quite large datasets of handwritten characters. The divide and conquer paradigm, generally, is used to divide a big problem into minor problems. This research applied this concept to recognize handwritten uppercase letters and numbers. As a result, a big problem is split into two nodes or subproblems, one for numbers and one for letters. Then, letters are divided into two nodes representing the straight and curved ones. The division can be called the binary decision tree and allows to obtain a subset with the minimal features of each node called reduct. Here, an improvement of reducts is proposed using the ant colony algorithm as the embedded method. The application of these methods had the following result and conclusions. For each node, subsets of fewer features were obtained with high performance in the classification, considering the morphology of each letter. It is crucial to highlight that the distribution of the samples affects the performance of the classifier and the strategy improves the performance of the reduct.
Face Detection with Improved Faster-R-CNN

With the continuous development of convolutional neural network, object detection technology has made great progress. As a representative of the two-stage object detection algorithm, faster-r-cnn achieves high precision in object detection tasks. However, there are still many difficulties and challenges using fater-r-cnn to detect faces. For example, tiny faces in the images are not detected, and side faces or occluded faces are prone to be detected as false detections. In response to those questions raised above, this paper improves faster-r-cnn algorithm in the following aspects: we replaced the feature extraction network in the faster-r-cnn algorithm and used feature pyramid networks, which is more beneficial to detect tiny faces. Besides, we used classification loss function which is more conducive to classification task, and for the special sizes of faces, we set the anchor ratio matching mechanism. In addition, we used suitable activation function to increase the nonlinear fitting ability of the whole network, and for the problem of the training set of WIDER FACE database, we cleaned it in order to get better experimental results. As a consequence, our method achieved 96.2% mAP on FDDB and 93.6%, 88.5%, 62.6% mAP respectively on the easy, medium, hard set of WIDER FACE, and our method could run at 10 FPS on Nvidia GTX 1080Ti GPU.
Improved Segmentation of Neurodegenerative Disease using Deep Auto Encoders

Previous research has demonstrated that there is a correlation between a person body mass index (BMI) and their brain volume, and that correlation is typically an inverse one. However, the Body Mass Index (BMI), which does not take into account the fact that different areas of fat carry varying degrees of metabolic risk, is not necessarily the best indicator of health. This is because it does not consider the fact that different areas of fat carry different degrees of risk. In order to determine whether or whether there is a connection between MR-based fat depots and the quantity of gray matter (GM) in the brain, which is known to decrease in individuals, who suffer from neurodegenerative disorders. In this paper, Deep Auto Encoders (DAE) are used as a segmentation model to segment the neurodegenerative diseases in patients. Initially, the images are collected from the database; it is then pre-processed and segmented using DAE. The simulation is carried out in python to test the efficacy of unsupervised DAE segmentation over various conventional segmentation models. The results of simulation shows that the proposed method achieves higher rate of accuracy than other methods.
Rat Swarm Optimizer (RSO): A Novel Swarm Intelligence-Based Optimization Algorithm for Tackling Difficult Optimization Problems

For the evolution of swarm metaheuristic algorithms motivated by all animals kingdom in the year 2006–21 spans, significant optimizers had introduced. By the replicate action mechanism, the study is sorted out considering the living things. The consolidated information showed that nearly 62% of the algorithm which is based on animal behavior is motivated from vertebral and about 38% from spineless animals. This study dispense for resolving the searching of optimization issues by an algorithm which is a biography-influenced algorithm known as Rat Swarm Optimization. The habit of the rats in the environment is one of the motivations of this algorithm optimizer. Mathematical expressions of these actions, explanations of some actual restrained engineering outlined issues have been discussed in this paper. For testing the exploitation, exploration the combining and mathematical exploration had done for prevention of the suggested algorithm. Observing the results and comparing them with all other superior optimization algorithms, the suggested Rat Swarm algorithm is impressive in resolving the actuality issues.
FAM3L: Feature-Aware Multi-Modal Metric Learning for Integrative Survival Analysis of Human Cancers

Survival analysis is to estimate the survival time for an individual or a group of patients, which is a valid solution for cancer treatments. Recent studies suggested that the integrative analysis of histopathological images and genomic data can better predict the survival of cancer patients than simply using single bio-marker, for different bio-markers may provide complementary information. However, for the given multi-modal data that may contain irrelevant or redundant features, it is still challenge to design a distance metric that can simultaneously discover significant features and measure the difference of survival time among different patients. To solve this issue, we propose a Feature-Aware Multi-modal Metric Learning method (FAM3L), which not only learns the metric for distance constraints on patients’ survival time, but also identifies important images and genomic features for survival analysis. Specifically, for each modality of data, we firstly design one feature-aware metric that can be decoupled into a traditional distance metric and a diagonal weight for important feature identification. Then, in order to explore the complex correlation across multiple modality data, we apply Hilbert-Schmidt Independence Criterion (HSIC) to jointly learn multiple metrics. Finally, based on the learned distance metrics, we apply the Cox proportional hazards model for prognosis prediction. We evaluate the performance of our proposed FAM3L method on three cancer cohorts derived from The Cancer Genome Atlas (TCGA), the experimental results demonstrate that our method can not only achieve superior performance for cancer prognosis, but also identify meaningful image and genomic features correlating strongly with cancer survival.