Reviewerly/exHarmony huggingface dataset:
Human Motion Tracking Algorithm Based on Image Segmentation Algorithm and Kinect Depth Information

Human motion recognition has an important application value in scenarios such as intelligent monitoring and advanced human-computer interaction, and it is an important research direction in the field of computer vision. Traditional human motion recognition algorithms based on two-dimensional cameras are susceptible to changes in light intensity and texture. The advent of depth sensors, especially the Kinect series with good performance and low price released by Microsoft, enables extensive research based on depth information. However, to a large extent, the depth information has not overcome these problems based on two-dimensional images. This article introduces the research background and significance of human motion recognition technology based on depth information, introduces in detail the research methods of human motion recognition algorithms based on depth information at home and abroad, and analyzes their advantages and disadvantages. The public dataset is introduced. Then, based on the depth information, a method of human motion recognition is proposed and optimized. A moving human body image segmentation method based on an improved two-dimensional Otsu method is proposed to solve the problem of inaccurate and slow segmentation of moving human body images using the two-dimensional Otsu method. In the process of constructing the threshold recognition function, this algorithm not only uses the cohesion of the pixels within the class but also considers the maximum variance between the target class and the background class. Then, the quantum particle swarm algorithm is used to find the optimal threshold solution of the threshold recognition function. Finally, the optimal solution is used to achieve accurate and fast image segmentation, which increases the accuracy of human body motion tracking by more than 30%.
Fast 4D On-the-Fly Tomography for Observation of Advanced Pore Morphology (APM) Foam Elements Subjected to Compressive Loading

Observation of dynamic testing by means of X-ray computed tomography (CT) and in-situ loading devices has proven its importance in material analysis already, yielding detailed 3D information on the internal structure of the object of interest and its changes during the experiment. However, the acquisition of the tomographic projections is, in general, a time-consuming task. The standard method for such experiments is the time-lapse CT, where the loading is suspended for the CT scan. On the other hand, modern X-ray tubes and detectors allow for shorter exposure times with an acceptable image quality. Consequently, the experiment can be designed in a way so that the mechanical test is running continuously, as well as the rotational platform, and the radiographic projections are taken one after another in a fast, free-running mode. Performing this so-called on-the-fly CT, the time for the experiment can be reduced substantially, compared to the time-lapse CT. In this paper, the advanced pore morphology (APM) foam elements were used as the test objects for in-situ X-ray microtomography experiments, during which series of CT scans were acquired, each with the duration of 12 s. The contrast-to-noise ratio and the full-width-half-maximum parameters are used for the quality assessment of the resultant 3D models. A comparison to the 3D models obtained by time-lapse CT is provided.
Web Scraper Dan Graphql API Untuk Data Perguruan Tinggi Di Indonesia Berdasarkan Website Kementerian Ristekdikti (Studi Kasus: Website Kementerian Ristekdikti)

The urge to obtain and transmit information is one of the reasons for current technological developments, such as the need for students to access higher education reporting forums or Forlap Dikti. Forlap Dikti is a website page developed by the Ministry of Research, Technology and Higher Education that contains data on student academic activities based on reporting data from universities in Indonesia. Therefore we need a support application to be able to facilitate and expedite the need for access to information on the Forlap Dikti website page using the android application. This study aims to: (1) Help collect student data as material for presenting the required information, (2) Design an application as a medium for finding information on student data at Bina Darma University, (3) With an efficient web scraping function it also helps in data analysis. This application uses puppeteer for scraping data from websites, App Inventor and GraphQL API to display student data.
Analysis of The Effectiveness of Online Electronic Learning System Using Data Traffic Network Performance Management to Succeed Merdeka Learning – Merdeka Campus During the Covid-19 Pandemic

The problems in the Covid-19 pandemic have a major influence on the field of education with the use of technology to support the teaching and learning process to facilitate students who do home learning activities.The proposed concept of freedom of learning is a more comprehensive concept such as portfolio and assignments such as group assignments, writings, and so on that are done in full online by adding additional features such as Teaching and Learning Activities and Assessment through information technology media.(E-Learning / Learning Management System).The method proposed in this research is the Peer Connection Queue (PCQ) method on mikrotik operating systems.PCQ method is a program to manage network traffic in Quality of Services (QoS).Bandwidth management methods.The hypothesis formulated is to create bandwidth management with PCQ so that bandwidth sharing is automatically and evenly distributed to multi clients.Therefore, in this research finally formulated into the goal of E-Learning effectiveness analysis using bandwidth management analysis method, which will be measured and analyzed in this research is throughput, delay, jitter, and packet loss.So that the final result of this research obtained the feasibility of the teaching and learning process that is carried out effectively.
Sirius: Static Program Repair with Dependence Graph-Based Systematic Edit Patterns

Software development often involves systematic edits, similar but nonidentical changes to many code locations, that are error-prone and laborious for developers. Mining and learning such systematic edit patterns (SEPs) from past code changes enable us to detect and repair overlooked buggy code that requires systematic edits. A recent study presented a promising SEP mining technique that is based on program dependence graphs (PDGs), while traditional approaches leverage syntax-based representations. PDG-based SEPs are highly expressive and can capture more meaningful changes than syntax-based ones. The next challenge to tackle is to apply the same code changes as in PDG-based SEPs to other code locations; detection and repair of overlooked locations that require systematic edits. Existing program transformation techniques cannot well address this challenge because (1) they expect many structural code similarities that are not guaranteed in PDG-based SEPs or (2) they work on the basis of PDGs but are limited to specific domains (e.g., API migrations). We present in this paper a general-purpose program transformation algorithm for applying PDG-based SEPs. Our algorithm identifies a small transplantable structural subtree for each PDG node, thereby adapting code changes from PDG-based SEPs to other locations. We construct a program repair pipeline Sirius that incorporates the algorithm and automates the processes of mining SEPs, detecting overlooked code locations (bugs) that require systematic edits, and repairing them by applying SEPs. We evaluated the repair performance of Sirius with a corpus of open source software consisting of over 80 repositories. The results indicate that Sirius greatly outperformed the state-of-the-art technique for syntax-based SEPs. Sirius achieved a precision of 0.710, recall of 0.565, and F1-score of 0.630, while those of the state-of-the-art technique were 0.470, 0.141, and 0.216, respectively.
Survey on Astroturfing Detection and Analysis from an Information Technology Perspective

With the development of the Internet, user comments produced an unprecedented impact on information acquisition, goods purchase, and other aspects. For example, the user comments can quickly render a topic the focus of discussion in social networks. It can promote the sales of goods in e-commerce, and it influences the ratings of books, movies, or albums. Among these network applications and services, “astroturfing,” a kind of online suspicious behavior, can generate abnormal, damaging, and even illegal behaviors in cyberspace that mislead public perception and bring a bad effect on Internet users and society. Hence, the manner of detecting and combating astroturfing behavior has become highly urgent, attracting interest from researchers both from information technology and sociology. In the current paper, we restudy it mainly from the perspective of information technology, summarize the latest research findings of astroturfing detection, analyze the astroturfing feature, classify the machine learning-based detection methods and evaluation criteria, and introduce the main applications. Different from the previous surveys, we also discuss the new future directions of astroturfing detection, such as cross-domain astroturfing detection and user privacy protection.
A Sequential Iterative Detection Framework for Gender and Age Classification

Recent tendency to identify the gender and age of user becomes increasingly demanding commodity, which is used to improve the service on the internet, such as multimedia providers, advertisement service so as to identify the most suitable and correct customer etc. Therefore, there is a huge number of studies to obtain such meta-data from image or speech signals. In this paper, we are going to propose a new approach, which is called sequential iterative detection framework to enhance the performance of the classifiers. The preliminary work using a Turkish dataset will be presented and discussed.
Management of Key Performance Indicators by Heads of Higher Education Institutions

New potential opportunities for heads of higher education institutions and their divisions to control and implement development management functions through a system of key performance indicators are considered. This technique, used in many companies in the commercial sector, is described in the text of the article and adapted to the needs and characteristics of educational institutions.Formation of the system the key performance indicators in the article are presented in terms of improving the system of management and motivation, administrative and teaching staff of higher education. The methodology proposed for the application was used in practice in order to adapt and change the over bureaucratized and inefficient management system of a private higher education institution with partial funding from the state budget.Along with the opportunities that arise with the implementation of this methodology, the article considers the potential risks and problems that maya ccompany the administration and management of higher education in the implementation of management through a system of key performance indicators.
Asymptotic Properties of Generalized Eigenfunctions for Multi-dimensional Quantum Walks

We construct a distorted Fourier transformation associated with the multi-dimensional quantum walk. In order to avoid the complication of notations, almost all of our arguments are restricted to two-dimensional quantum walks (2DQWs) without loss of generality. The distorted Fourier transformation characterizes generalized eigenfunctions of the time evolution operator of the QW. The 2DQW which will be considered in this paper has an anisotropy due to the definition of the shift operator for the free QW. Then, we define an anisotropic Banach space as a modified Agmon-Hörmander’s $${\mathcal {B}}^*$$ space, and we derive the asymptotic behavior at infinity of generalized eigenfunctions in these spaces. The scattering matrix appears in the asymptotic expansion of generalized eigenfunctions.
Japanese Universities' Response to the Global Pandemic Crisis: Harnessing Collective Intelligence to Activate Adaptive and Collaborative Leadership Practices

Japanese organizations treat change as an evolutionary phenomenon to maintain harmony and hierarchical organizational structure. However, in the event of adversity, the hierarchical leader-follower dynamics become fluid as the need for collective intelligence arises. Individuals from all levels are mobilized as a unified community for action. This chapter attempts to offer insights into the role of collective intelligence in activating adaptive and collaborative leadership practices at Japanese universities in response to the global COVID-19 pandemic. The chapter draws on the personal experiences of the author as well as faculty members from various Japanese universities with an emphasis on collective thinking, adaptive and collaborative response to adversity, and the resulting organization-wide learning process.
Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC)-based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP’s potential in discovering novel biological relationships in integrative genomic studies.
Secure Matrix Operations for Machine Learning Classifications Over Encrypted Data in Post Quantum Industrial IoT

We tackle the problem where a server owns a trained Machine Learning (ML) model and a client/user has an unclassified query that he wishes to classify in secure and private fashion using the server’s model. During the process the server learns nothing, while the user learns only his final classification and nothing else. Since several ML classification algorithms, such as deep neural networks, support vector machines-SVM (and hyperplane decisions in general), Logistic Regression, Naïve Bayes, etc., can be expressed in terms of matrix operations, initially we propose novel secure matrix operations as our building blocks. On top of them we build our secure and private ML classification algorithms under strict security and privacy requirements. As our underlying cryptographic primitives are shown to be resilient to quantum computer attacks, our algorithms are also suitable for the post-quantum world. Our theoretical analysis and extensive experimental evaluations show that our secure matrix operations, hence our secure ML algorithms build on top of them as well, outperform the state of the art schemes in terms of computation and communication costs. This makes our algorithms suitable for devices with limited resources that are often found in Industrial IoT (Internet of Things)
Designing A User Interface and User Experience from Piring Makanku Application by Using Figma Application for Teens

This study is aimed to design a user interface for Piring Makanku application that can be used for a promotion medium to introduce Piring Makanku program issued by the Ministry of Health, for the teens aged 15-18. A Figma webapp was employed as a tool for designing this interface quickly and easily. Whereas A Lean UX method, which is one method in designing UX, was used to design the interface shortly and effectively. A usability test with Retrospective Thinking Aloud was applied to measure whether or not the design can be accepted by the teen users. Findings derived from the stages of the test revealed that the design can be accepted by teens with positive response of 95.56% out of the total 5 respondents. It can be concluded that the interface to have been designed becomes appropriate for development into Piring Makanku application layout.
HACCLE: metaprogramming for secure multi-party computation

Cryptographic techniques have the potential to enable distrusting parties to collaborate in fundamentally new ways, but their practical implementation poses numerous challenges. An important class of such cryptographic techniques is known as Secure Multi-Party Computation (MPC). Developing Secure MPC applications in realistic scenarios requires extensive knowledge spanning multiple areas of cryptography and systems. And while the steps to arrive at a solution for a particular application are often straightforward, it remains difficult to make the implementation efficient, and tedious to apply those same steps to a slightly different application from scratch. Hence, it is an important problem to design platforms for implementing Secure MPC applications with minimum effort and using techniques accessible to non-experts in cryptography.
The effect of distance metrics in a general purpose synthesizer: An empirical study on integer domain imperative programs

Abstract Context Program synthesis is the task of automatically finding a program that satisfies the user intention. In previous work, we have developed a program synthesizer that integrates genetic algorithm with model finder. A genetic algorithm uses a fitness function to calculate how “distant to a solution” a given candidate program is. Researchers argue that different distance metrics for a fitness function may reveal behavioral differences in the genetic algorithm. Objective We have chosen five distance metrics as fitness functions to check whether they affect the synthesis task of five different integer domain imperative toy‐programs which read/write integer values using fundamental syntactic constructs, such as while , if‐then‐else , and so forth. We have used input/output examples and sketches to constrain the search space of the candidate programs. Method A hypothesis test was proposed and experiments were conducted to observe the number of calls to the fitness function ( x ) and to measure the synthesis time ( ). Results Regarding x , the synthesizer found a solution for all five subjects after calling the fitness function the same amount of times. For , a one‐way ANOVA was performed with a significance level of 5% ( ). No significant differences were observed in both x and . Conclusion With these preliminary results, this study suggests that the discrete distance metric is the best choice, because it guides the search with the same effectiveness as the others and is not time consuming, and so forth. However, future experimentation with a larger search space will confirm or not this initial impression.
Comparative Study of Machine Learning Techniques for Breast Cancer Diagnosis

Breast cancer is a widely detected cancer in Indian women that is deadly in most cases. Its detection in early stages may stagnate tumor growth, and the patient survival rate also increases. Therefore, in this study we present two of the best machine learning algorithms – support vector machine (SVM) and K-nearest neighbor (KNN) – for the classification of masses into benign and malignant based on their geometric and texture features. However, the theoretical background for many algorithms of machine learning that are popular, such as random forest, Naïve Bayes, and decision tree, are also presented in this study. Both the considered classifiers were trained and tested using the standard Wisconsin Breast Cancer Database to demonstrate which classifier is best suited to predict breast cancer with accuracy in the early stages based on overall performance metrics, sensitivity, and area under the curve (AUC), etc. The results show that the performance of both classifiers is highly competitive, and cancer diagnosis can be used, paving the way for effective treatment.
Image Style Transfer Based on Generative Adversarial Network

Image style transfer refers to the transformation of the style of image, so that the image details are retained to the maximum extent while the style is transferred. Aiming at the problem of low clarity of style transfer images generated by CycleGAN network, this paper improves the CycleGAN network. In this paper, the network model of auto-encoder and variational auto-encoder is added to the structure. The encoding part of the auto-encoder is used to extract image content features, and the variational auto-encoder is used to extract style features. At the same time, the generating network of the model in this paper uses first to adjust the image size and then perform the convolution operation to replace the traditional deconvolution operation. The discriminating network uses a multi-scale discriminator to force the samples generated by the generating network to be more realistic and approximate the target image, so as to improve the effect of image style transfer.
An Automated Liver Vasculature Segmentation from CT Scans for Hepatic Surgical Planning

Liver vasculature segmentation is a crucial step for liver surgical planning. Segmentation of liver vasculature is an important part of the 3D visualisation of the liver anatomy. The spatial relationship between vessels and other liver structures, like tumors and liver anatomic segments, helps in reducing the surgical treatment risks. However, liver vessels segmentation is a challenging task, that is due to low contrast with neighboring parenchyma, the complex anatomy, the very thin branches and very small vessels. This paper introduces a fully automated framework consist of four steps to segment the vessels inside the liver organ. Firstly, in the preprocessing step, a combination of two filtering techniques are used to extract and enhance vessels inside the liver region, first the vesselness filter is used to extract the vessels structure, and then the anisotropic coherence enhancing diffusion (CED) filter is used to enhance the intensity within the tubular vessels structure. This step is followed by a smart multiple thresholding to extract the initial vasculature segmentation. The liver vasculature structures, including hepatic veins connected to the inferior vena cava and the portal veins, are extracted. Finally, the inferior vena cava is segmented and excluded from the vessels segmentation, as it is not considered as part of the liver vasculature structure. The liver vessel segmentation method is validated on the publically available 3DIRCAD datasets. Dice coefficient (DSC) is used to evaluate the method, the average DSC score achieved a score 68.5%. The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning.