Reviewerly/exHarmony huggingface dataset:
Multi-view graph structure learning using subspace merging on Grassmann manifold

Many successful learning algorithms have been recently developed to represent graph-structured data. For example, Graph Neural Networks (GNNs) have achieved considerable successes in various tasks such as node classification, graph classification, and link prediction. However, these methods are highly dependent on the quality of the input graph structure. One used approach to alleviate this problem is to learn the graph structure instead of relying on a manually designed graph. In this paper, we introduce a new graph structure learning approach using multi-view learning, named MV-GSL (Multi-View Graph Structure Learning), in which we aggregate different graph structure learning methods using subspace merging on Grassmann manifold to improve the quality of the learned graph structures. Extensive experiments are performed to evaluate the effectiveness of the proposed method on two benchmark datasets, Cora and Citeseer. Our experiments show that the proposed method has promising performance compared to single and other combined graph structure learning methods.
An FPGA implementation of the VESA Display Stream Compression decoder

A HW architecture for the implementation of the DSC decoder is proposed. It demands 1 cycle/pixel (cpp) for 4:4:4 chroma sub-sampling format and 0.5 cpp for 4:2:2 or 4:2:0 formats. Also, it supports 3:1 lossless compression ratio and operates with sub-line latency. To achieve the above, specific optimizations have been applied at the algorithmic and design levels to efficiently distribute the operations on the pipeline stages and improve frequency. Its implementation on a Kintex Ultrascale+ device achieves at 262 MHz for 10-bit input sample, and it can process 32 High Dynamic Range (HDR) frames per second (Fps) for 4:4:4 chroma format and 60 HDR Fps for 4:2:2 or 4:2:0 formats.
Robotics: From GOFAI to Situated Cognition and Behavior-Based Robotics

The fourth edition of this popular text has been significantly rewritten to make it more accessible to students and easier for instructors to use. It remains distinctive in presenting a unified narrative of cognitive science as a field of inquiry in its own right. Thematically organized, Cognitive Science underscores the problems and solutions of cognitive science rather than more narrowly examining individually the subjects that contribute to it - psychology, neuroscience, linguistics, and so on. The generous use of examples, illustrations, and applications demonstrates how theory and experiment can be applied to unlock the mysteries of the human mind. Drawing upon cutting-edge research, the text has been updated and enhanced with a new chapter on emotions and the emerging field of affective science. An extensive online set of resources is available to aid both instructors and students.
Multi-cloud Terminal Stability Enhancement Framework of Sanquan Education Digital Oriented Student Management Information Platform

From a full-staff, whole-process, and all-round perspective, build a joint force mechanism for educating people, design a second classroom project system, and build a second classroom information management platform. Accurately grasp the basic information of students, realize real-time query and big data analysis, and finally achieve all-round quality education for students. This information platform will effectively improve the scientific and effective management of students in colleges and universities in my country. Build an informatization platform with the concept of “three comprehensive education” as the connotation to improve the scientific, normative and informatization service level of college student management. The encoder part is used to extract the multi-cloud terminal stability enhancement framework. In order to capture the long-range context information between images, quickly obtain the main features of the image and suppress the secondary features
Edge Computing Offloading Strategy for Fast Diagnosis of Image of Substation

Since devices of the Internet of Things in power transmission and transformation system do not have the function of accurate identification of defects in video images, it is difficult to meet the requirements of intelligent analysis of video images for online intelligent inspection of substation. Method of image rapid diagnosis for substation is proposed in this paper, heavy power task such as image recognition of the edge being offloaded, putting forward the task offloading model and decision mechanism, formulating the offloading scheduling strategy based on deep learning, proposing offline algorithm and online algorithm to solve the scheduling problem. Finally, the paper simulates the diagnosis of inspection image of substation, and gives the system energy consumption and calculation delay under different task offloading modes, which can meet the requirements of devices fault identification and diagnosis under scenario of substation video inspection.
Big Data techniques applied to stakeholder data for non-governmental organizations

Non-governmental organizations, foundations, and various social actors promote the mobilization of the sustainable development goals set by the United Nations and contribute to improving the population's quality of life. Frequently, the search for opportunities to generate agreements and social innovation projects is done manually by accessing the web and selecting, according to the search, those calls that best align with the interests of each social actor. These activities are performed repeatedly by one person spending a lot of time, affecting productivity and timeliness in finding and implementing the social partnerships. Considering the technological advances and known Big Data techniques, an opportunity is identified to improve the results of these activities by automating the operations. This paper presents an alternative to optimize the searching and segmentation of possible projects. Through the implementation of web scraping techniques according to keywords in social topics and clustering algorithms for the creation of groups of initiatives according to similar features, to generate partnerships between actors and social calls, which ultimately impact the generation of alliances and networks with a common cause, benefiting more communities.
An anomaly detection method of environmental sensor data flow based on fuzzy PID control

For special systems such as nonlinear large time delay, there is a problem that the control effect of conventional PID controller is not ideal. Therefore, a fuzzy-PID control method is proposed by combining the advantages of fuzzy control and conventional PID control. By combining different basic features, this method makes the meaning of alarm clearer, and has better scalability and success. At the same time, it provides a good platform for information interaction of anomaly detection. The electronic devices in the computer control system mostly work in high-voltage environment. The working intensity of the sensor is very high, but its high precision determines that its fault characteristic pole signal is easily interfered. In the hydraulic control system, the technical state of the sensor in the detection link directly affects the working performance of the system. Fuzzy controller can control complex and unclear systems simply and effectively. Therefore, combining the advantages of traditional PID and controller, and considering the characteristics of fuzzy control, the idea of fuzzy PID and controller is put forward. The simulation experiment on the actual power load data proves that this method can accurately detect the abnormal data online and provide correct abnormal data correction.
Personas of stem students completing online instructions during the COVID-19 pandemic

During the COVID-19 pandemic, instructors quickly transitioned to online teaching to protect themselves and their students. Due to this abrupt transition and lack of communication between instructor and student, instructors had little time to create online teaching methods that successfully adapt and teach the material to best meet the student's needs. Some students did well with online teaching methods, while others seemed to struggle to engage with the material in classes. Without immediate face-to-face feedback from students, we believe that background information such as learning styles, goals, motivations, and expectations of the potential students- will help teachers improve and increase classroom engagement and comprehension. We surveyed and interviewed STEM introductory-level courses online to collect data to create personas in STEM students. Personas are "life-like models whose characteristics are driven by the various goals and motivations of real or potential users." We used persona methodology to survey the diverse learning group in a STEM classroom to create four personas found in STEM classrooms. These personas represent real-life students enrolling in introductory STEM classrooms, allowing instructors to use these personas as a novel tool to design online courses that better engage undergraduate students across STEM disciplines.
Research and Design of Mine Microseismic Signal Acquisition System

Aiming at the problems that the existing mine microseismic signal acquisition system is expensive and versatile, a multi-channel microseismic signal acquisition system is designed. The system uses underground high frequency seismometer to pick up microseismic signals, and uses the acquisition circuit to realize the amplification, digital-to-analog conversion and digital filtering of the microseismic signal, and the filtered signals are sent to the upper computer through USART in real time by STM32 for analysis and processing. The test results show that the system can accurately collect and record the micro-seismic signals of three channels picked up by the underground high-frequency seismograph. The system has the advantages of good stability, high reliability and low cost.
NPP APCS Archive Data Scheme Optimization of Data Size and Query Execution Time for Implementation of Archive Conversion Module from Binary Data Format to PostgreSQL Database Format

This document describes some approaches to optimization of the NPP APCS archives data scheme and its implementation in the software conversion module. This module converts two binary archive formats, developed in ICS of RAS, to PostgreSQL database format.
Effects

Graphical effects are important in most games. Whether it's just a little sparkle effect or an explosion. Graphical effects can be done using sprites, built-in effects, or the Effects layers. The built-in effects allow for quick prototyping of a game, and can even be used in a finished project. Some examples would be
Screen space indirect lighting with visibility bitmask

Horizon-based indirect illumination efficiently estimates a diffuse light bounce in screen space by analytically integrating the horizon angle difference between samples along a given direction. Like other horizon-based methods, this technique cannot properly simulate light passing behind thin surfaces. We propose the concept of a visibility bitmask that replaces the two horizon angles by a bit field representing the binary state (occluded/un-occluded) of N sectors uniformly distributed around the hemisphere slice. It allows light to pass behind surfaces of constant thickness while keeping the efficiency of horizon-based methods. It can also do more accurate ambient lighting than bent normal by sampling more than one visibility cone. This technique improves the visual quality of ambient occlusion, indirect diffuse, and ambient light compared to previous screen space methods while minimizing noise and keeping a low performance overhead.
Performance Analysis of Supervised Learning Algorithms on Different Applications

In the current era of computation, machine learning is the most commonly used technique to find out a pattern of highly complex datasets. The present paper shows some existing applications, such as stock data mining, undergraduate admission, and breast lesion detection, where different supervised machine learning algorithms are used to classify various patterns. A performance analysis, in terms of accuracy, precision, sensitivity, and specificity is given for all three applications. It is observed that a support vector machine (SVM) is the commonly used supervised learning method that shows good performance in terms of performance metrics. A comparative analysis of SVM classifiers on the above-mentioned applications is shown in the paper.
Seismic media: art and geological co-creation in Ōtautahi Christchurch, Aotearoa New Zealand

In September 2010 the city of Ōtautahi Christchurch, New Zealand was rocked by the first of a sequence of 16,000 earthquakes. This essay considers art and creative practices that have responded to the experience of living in the earthquake-damaged city. In an environment transformed by seismicity, we argue that artists became co-creators with the planet, using its instability as a medium of art making, an active agent and long-term collaborator in creative practice. Those practices mediate the experience of seismicity, communicating the overwhelming energy of a faultline rupture in artworks that employ sound, touch, image, memory and emotion. We use the term seismic media to describe an expansive field encompassing the unstable earth as medium, the practice of mediating seismicity, and the media texts and objects that attempt to express it. In this essay we employ the concept of seismic media to discuss artworks created within the city of Ōtautahi Christchurch as it undergoes an extended period of rebuilding. We identify artists’ relationships with the material geology of a city with a focus on new geographical and aesthetic understandings. Seismic media enables people to work through the experiences of earthquakes and their long aftermath, while remembering the city as it was and imagining the city that could be.
Prospects for North Africa in 2023

Significance Inflation, debt and unemployment are recurrent themes across the region, even in energy exporters Libya and Algeria, which are enjoying elevated revenues from high energy prices. Egypt and Tunisia are grappling with high levels of debt, while Morocco is not far behind.
Collaborative Multi-agent System for Automatic Linear Text Segmentation

This paper proposes a collaborative multi-agent system for splitting documents into semantically coherent text chunks, labeling them according to a given segmentation structure. Diverse linear text segmentation methods can be incorporated into the system by introducing new agents, which allows to combine complementary approaches: domain-specific, supervised and unsupervised. The system must be supplied with a representative set of previously segmented documents from the target corpus, which are used both to train the supervised agents and to evaluate every agent within the system, similar to ensemble methods. The accuracy of each agent determines its weight in a subsequent aggregation phase, when a common solution is agreed on. The proposed approach presented promising results on segmenting documents from a juridical corpus.
Transformer-based On-Offline Hybrid Reinforcement Learning for Locomotion Tasks

In general, for a learner robot to learn an optimized behavior policy through reinforcement learning in locomotion tasks with continuous state and action spaces, a lot of trial and error experiences are required in the environment. To overcome the low data efficiency problem of online reinforcement learning, offline reinforcement learning methods using offline experience datasets are being actively studied in recent years. In this study, we propose a hybrid reinforcement learning framework that can effectively utilize online experience data in addition to offline datasets and then a Transformer-based policy network that reflects the temporal contextual information inherent in sequential experience data. In addition, to improve learning efficiency with the proposed hybrid reinforcement learning framework, a new priority sampling strategy is used to select a batch of training data from the trajectory replay buffer. Herein, we demonstrate the effectiveness and superiority of the proposed framework through various experiments on three different locomotion tasks provided by OpenAI Gym.
Investigation of Computer Incidents for Cyber-Physical Infrastructures in Industrial Control Systems

Cyber-physical infrastructures in industrial control systems, including critical infrastructure and manufacturing, heavily on cyber-physical infrastructures and embedded devices. As industrial control systems become more complex and network-centric, physical infrastructure becomes increasingly dispersed and vulnerable. In rapid growth within the competitive technology, we have seen an expressive increase in information security vulnerabilities against such systems, ranging from simple hacking tools to new generation intelligent attacks. With the growing reliance on industrial control networks and, of course, the growing number of attacks, the lack of cyber-security monitoring and forensic analysis of security incidents. Cyber-forensic analysis of ICS/SCADA systems is unlike standard forensic analysis of enterprise computer systems, the cyber-forensic specialist often has to be an expert in cyber-physical infrastructures systems, networks, and devices to determine where potential forensic evidence may be located. This paper discusses ICS/SCADA, typical attacks and vulnerabilities, problems with forensic analysis, and the development of forensic methodology/tools for such systems.
Website Security Study at Large: Vulnerability Analysis, Tools and Remedies

The main focus of the cyber-security community has been to make operating systems and communication networks more secure and harder for attackers to penetrate. The most frequently used web application and user web pages are today developed with the Web Content Management System (WCMS), as it allows user-friendly access, easy development and operation. Any malware that can penetrate the WCMS can significantly affect the system itself and the service the web pages offer. This paper presents the approach for identifying the vulnerabilities of the majority of Internet sites with WCMS applications and the remedies to be applied with use of an automated, fast and dynamic vulnerability detection tool. The state of the web sites vulnerability in Europe and the impact factors that influence the vulnerability to be present are presented and discussed.
DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation

Collaborative filtering is a popular approach for building an efficient and scalable recommender system. However, it has not unleashed its full potential due to the following problems. (1) Serious privacy concerns: collaborative filtering relies on aggregated user data to make personalized predictions, which means that the centralized server can access and compromise user privacy. (2) Expensive resources required: conventional collaborative filtering techniques require a server with powerful computing capacity and large storage space, so that the server can train and maintain the model. (3) Considering only one form of user feedback: most existing works aim to model user preferences based on explicit feedback (e.g., ratings) or implicit feedback (e.g., purchase history, viewing history) due to their heterogeneous representation; however, these two forms of feedback are abundant in most collaborative filtering applications, can both affect the model, and very few works studied the simultaneous use thereof. To solve the above problems, in this study we focus on implementing decentralized probabilistic matrix factorization for privacy-preserving recommendations. First, we explore the existing collaborative filtering algorithms and propose a probabilistic matrix co-factorization model. By integrating explicit and implicit feedback into a shared probabilistic model, the model can cope with the heterogeneity between these two forms of feedback. Further, we devise a decentralized learning method that allows users to keep their private data on the end devices. A novel decomposing strategy is proposed for users to exchange only non-private information, in which stochastic gradient descent is used for updating the models. Complexity analysis proves that our method is highly efficient with linear computation and communication complexity. Experiments conducted on two real-world datasets FilmTrust and Epinions show that our model gains a guarantee of convergence as the RMSE decreases quickly within 100 rounds of iterations. Compared with the state-of-the-art models, our model achieves lower model loss in rating prediction task and higher precision in item recommendation task.
Optimization of Simple Additive Weighting Method in Assessment of Research Reviewer Selection

Quality research will not be separated from controlling systems that require a review mechanism. This demand considers it necessary to form an assessment committee or reviewer that ensures that all processes proceed towards the target target. The internal reviewer selection process is carried out by looking at several requirements of each prospective reviewer. The selection process is carried out by looking at the requirements files one by one. For this reason, it is necessary to optimize the method that is able to manage the assessment data of prospective reviewers who have the highest rating value from the results of weight calculations. Decision making in determining internal reviewers requires a method that can provide optimal decision results in terms of relatively fast processing time. The decision support method applied in determining internal reviewers is Simple Additive Weighting (SAW). The reason for choosing the SAW method in this study, the method has a basic concept that is used to find weight values on the performance rating of each alternative on all attributes. The SAW method is commonly known as the weighted summation method. There are six criteria used and fifty-five records for alternatives used. The results of the SAW method ranking obtained by A20 have the highest preference value of 0.77. This study shows the optimality of the SAW method in providing decision results based on an accuracy test value of 80%.
An Asymptotic Analysis of Random Partition Based Minibatch Momentum Methods for Linear Regression Models

Momentum methods have been shown to accelerate the convergence of the standard gradient descent algorithm in practice and theory. In particular, the minibatch-based gradient descent methods with momentum (MGDM) are widely used to solve large-scale optimization problems with massive datasets. Despite the success of the MGDM methods in practice, their theoretical properties are still underexplored. To this end, we investigate the theoretical properties of MGDM methods based on the linear regression models. We first study the numerical convergence properties of the MGDM algorithm and further provide the theoretically optimal tuning parameters specification to achieve faster convergence rate. In addition, we explore the relationship between the statistical properties of the resulting MGDM estimator and the tuning parameters. Based on these theoretical findings, we give the conditions for the resulting estimator to achieve the optimal statistical efficiency. Finally, extensive numerical experiments are conducted to verify our theoretical results.
Binary classification with noisy quantum circuits and noisy quantum data

We study the effects of single-qubit noises in the quantum circuit and the corruption in the quantum training data to the performance of binary classification problem. We find that under the presence of errors, the measurement at a qubit is affected only by the noise in the corresponding qubit, and that the errors on other qubits do not affect this outcome. Furthermore, for the task where we fit a binary classifier using a quantum training data, we show that the noise in the data can work as a regularizer, implying that we can benefit from the noise in certain cases. We support our findings with simulations.