Reviewerly/exHarmony huggingface dataset:
MCSRec: Modeling Cognitive Similarity in Sequential Recommendation with Social Networks

Combining user social relationships in sequence recommendation helps model users' potential preferences and improves the performance of the recommendation system. However, recommendation with social networks faces two challenging problems, and has not been well-studied in most existing works. The first is cognitive differences. Even users with similar preferences face the same recommended objects, they will make different choices due to cognitive differences. Therefore, modeling the cognitive similarity of users is crucial. The second is the influence strength of her friends might be different. To solve the above problems, this paper proposes a new deep learning model called MCSRec. Specifically, based on users' long short- term personal preferences, design a memory cognitive module to model the cognitive similarity between users and their friends. Then, after obtaining friends' preferences which are similar to users' cognition, model social influence with a graph attention network. The experimental results on three public data sets prove the effectiveness of our proposed MCSRec model on several competitive baselines, including state-of-the-art models.
Exploration on Evaluation Method for Thematic Learning Website

The research is based on the characteristics of thematic learning website, from four dimensions of resource area, task area, interaction area and website running to explore on a feasible evaluation method for a thematic learning website and gives the evaluation standard for two grades of “excellent” and “pass”. Furthermore, further work is put forward. Firstly, evaluation on usability of thematic learning website should be designed. Secondly, students' interest in tasks exploratory and the acquisition of learning ability in a quantitative way are needed to be reflected in evaluation standard for thematic learning website.
New Teaching Model of Structural Mechanics Course for Civil Engineering Majors in the Information Age

In the new era of undergraduate education in China, around the purpose of cultivating first-class talents, it is necessary for the majority of educators to actively explore the deep integration of online and offline teaching. Based on the above background, the purpose of this research is to study the new teaching model of structural mechanics for civil engineering majors in the information age. This study introduces the construction of the online course of structural mechanics and the mixed teaching of structural mechanics for the structural mechanics teaching team of civil engineering majors in the university, and compares the structural mechanics of students participating in hybrid teaching with traditional teaching by statistical analysis of a large number of first-line construction data Final exam scores, compared with the final exam scores of the material mechanics course of the prerequisite course, analyze the advantages and disadvantages of mixed teaching on the improvement of students' academic performance; in addition, use various platforms to analyze the factors that affect the effect of mixed teaching, with a view to The continuous improvement of mixed teaching provides useful reference. Studies have shown that face-to-face teaching, online learning and classroom tests are the main factors that affect the effectiveness of mixed teaching. Among them, face-to-face teaching plays a key role in mixed teaching. Giving play to the guiding role of face-to-face teaching in classrooms, better completing online learning, and maintaining a good learning progress can help students better master the course learning content, which is conducive to the implementation of student-centered education concepts.
Self-Supervised Denoising Image Filter Based on Recursive Deep Neural Network Structure

The purpose of this paper is to propose a novel noise removal method based on deep neural networks that can remove various types of noise without paired noisy and clean data. Because this type of filter generally has relatively poor performance, the proposed noise-to-blur-estimated clean (N2BeC) model introduces a stage-dependent loss function and a recursive learning stage for improved denoised image quality. The proposed loss function regularizes the existing loss function so that the proposed model can better learn image details. Moreover, the recursive learning stage provides the proposed model with an additional opportunity to learn image details. The overall deep neural network consists of three learning stages and three corresponding loss functions. We determine the essential hyperparameters via several simulations. Consequently, the proposed model showed more than 1 dB superior performance compared with the existing noise-to-blur model.
DSAG: A Mixed Synchronous-Asynchronous Iterative Method for Straggler-Resilient Learning

We consider straggler-resilient learning. In many previous works, e.g., in the coded computing literature, straggling is modeled as random delays that are independent and identically distributed between workers. However, in many practical scenarios, a given worker may straggle over an extended period of time. We propose a latency model that captures this behavior and is substantiated by traces collected on Microsoft Azure, Amazon Web Services (AWS), and a small local cluster. Building on this model, we propose DSAG, a mixed synchronous-asynchronous iterative optimization method, based on the stochastic average gradient (SAG) method, that combines timely and stale results. We also propose a dynamic load-balancing strategy to further reduce the impact of straggling workers. We evaluate DSAG for principal component analysis, cast as a finite-sum optimization problem, of a large genomics dataset, and for logistic regression on a cluster composed of 100 workers on AWS, and find that DSAG is up to about 50% faster than SAG, and more than twice as fast as coded computing methods, for the particular scenario that we consider.
Preprocessing of normative documents for interactive question answering

Every larger organisation must establish a set of normative documents to control its processes and describe solutions to common problems. These documents are usually formally written and hard to read. This leads to the necessity off different customer services. Nowadays, a lot of companies are developing chatbots to automate first-line customer support. If a company does not have a large question–answer dataset to build a chatbot, the answers can be automatically answered directly from the documents. However, we found that the automatic answering usually does not work well on the normative documents. In this paper, we describe a novel method for preprocessing of normative documents in order to use them for such automatic question answering. Our method efficiently exploits the strict document structure that is typical for normative documents. The method enabled us to increase the recall from 35% to 84% (for paragraph-size answers) on selected normative documents from university and bank domains.
Analysis of College Students' Creativity Consciousness Driven under the Background of Blockchain

Taking the current situation of contemporary college students' creativity consciousness as the research subject and based on the concept of creativity driven, 648 college students were randomly sampled, a questionnaire survey was conducted by the Creativity Consciousness Scale and the data was statistically analyzed by SPSS13.0. It was found that college students' creativity consciousness was generally not high but they had strong creativity emotions and motivations. What has an impact on college students' creativity consciousness is demographic variables such as gender, major and grade.
How Do Practitioners Interpret Conditionals in Requirements

Context: Conditional statements like If A and B then C are core elements for describing software requirements. However, there are many ways to express such conditionals in natural language and also many ways how they can be interpreted. We hypothesize that conditional statements in requirements are a source of ambiguity, potentially affecting downstream activities such as test case generation negatively. Objective: Our goal is to understand how specific conditionals are interpreted by readers who work with requirements. Method: We conduct a descriptive survey with 104 RE practitioners and ask how they interpret 12 different conditional clauses. We map their interpretations to logical formulas written in Propositional (Temporal) Logic and discuss the implications. Results: The conditionals in our tested requirements were interpreted ambiguously. We found that practitioners disagree on whether an antecedent is only sufficient or also necessary for the consequent. Interestingly, the disagreement persists even when the system behavior is known to the practitioners. We also found that certain cue phrases are associated with specific interpretations. Conclusion: Conditionals in requirements are a source of ambiguity and there is not just one way to interpret them formally. This affects any analysis that builds upon formalized requirements (e.g., inconsistency checking, test-case generation). Our results may also influence guidelines for writing requirements.
Research on Cloud Computing Data Center Management and Resource Virtualization Technology

Cloud computing data center is the core layer of cloud computing, and its performance has a very important impact on the upper layer of computing. However, at present, the cloud computing data center has the problems of high energy consumption and low resource utilization. In order to solve these problems, resource virtualization technology has been widely used and developed. In this paper, based on the dynamic management of virtual machine in cloud computing data center, the dynamic selection strategy of virtual machine is studied in depth, and the factors such as the size of host resource occupied by virtual machine, the migration time of virtual machine, the performance loss of virtual machine and the utilization rate of virtual machine resource are fully considered for comprehensive evaluation, and multi-objective decision analysis algorithm is adopted for phase analysis Finally, we can make a choice through the research of balance analysis. Experiments show that under the selection strategy proposed in this paper, the utilization rate of data resources is significantly reduced, and the performance of data resources is improved, which ultimately provides a useful contribution to the development of data center management technology.
Analysis of jejak bali virtual class using usability testing, including concurrent think aloud techniques and performance measurement techniques

Abstract Jejak Bali is a website created specifically by PUSTEKKOM to manage learning activities so Bali Provincial Education Office can carry out supervision and guidance for e-learning in Bali. Many obstacles were experienced by teachers and students when using Jejak Bali. It encouraged the design of a study looking at the usability of a system from a user perspective to increase user satisfaction. The methods used were usability testing technique, Concurrent Think Aloud, and Performance Measurement which measured the effectiveness, efficiency, and user satisfaction. The population of this study were teachers and students from SMAN 2 and SMKN 3 Denpasar. In measuring Performance Measurement and CTA, the sample used consisted of 40 participants including teachers and students as well as 86 users who used the questionnaire SUS. The results of the research were: (1) Jejak Bali, for teacher and student users, was still ineffective because there were still errors made while doing the assigned assignments, (2) Jejak Bali which was seen from the time when teacher and student users were working was still inefficient because of the differences of the significant time from the advanced group to the beginners, (3) Jejak Bali still did not meet user satisfaction as seen from the results of the SUS questionnaire recapitulation in which a teacher scored 49.0 and a student scored 48.3. Because the value obtained was below 60 in the percentile rank, so it can be concluded that Jejak Bali still did not meet usability good as seen from the results obtained.
Research on mechanical optimization design software based on database and configuration software

research-article Research on mechanical optimization design software based on database and configuration software Share on Author: Li'e Yang Yunnan Open University, China Yunnan Open University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 603–607https://doi.org/10.1145/3482632.3482759Online:22 November 2021Publication History 0citation5DownloadsMetricsTotal Citations0Total Downloads5Last 12 Months5Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The Chain of Self-Taught Knowledge Distillation Combining Output and Features

Knowledge distillation is a model compression technique that uses a cumbersome model to improve the accuracy of a lightweight model. However, if there is no cumbersome teacher model that performs much better than the lightweight student model, the effect of knowledge distillation is restricted. To tackle this dilemma, this paper propose a novel self-taught knowledge distillation framework in which teacher and student models have the same network structure. To extract the knowledge of the model itself, output and feature transfer are studied to align the behavior between the pre-trained teacher and the student trained from scratch. Furthermore, the distilled student model is used as a teacher model to guide the training of a new student model, and this process is repeated as a chain of knowledge transfer to allow the model achieve greater accuracy. The effectiveness of our framework has been demonstrated by extensive experiments on CIFAR100 and STL-10 datasets. Code will be available here: https://github.com/WangYunnan/chain-of-self-KD.
Flexible creation of a 3D-Map in an unknown environment by a robot

With the advent of collaborative robots, the demand for manipulators is constantly increasing, especially in SMEs. Hence, the risk of collisions and resulting damage due to programs written by inexperienced users is also simultaneously increasing. Due to the lack of 3D maps of the environment, there are no comprehensive support systems available to check programmed sequences for collision-free execution or to optimize them subsequently. This leads to longer process times and complicates commissioning, especially for inexperienced users. In this paper, a method is presented in which a collaborative robot can autonomously create a 3D map of its environment. Subsequently, the created environment map can be used to optimize existing processes, guarantee collision-free motions and support the operator during commissioning.
Comparative Research of Fluid Cryptographic Transformation Algorithms

The requirements for PRS generators for use in SSC algorithms are determined by several aspects: a large sequence of sequences being formed, that is, the key sequence generated by a particular generator must be much longer than the plaintext sequence, encrypted; statistical certainty when generated by a PRS generator does not differ in its statistical properties from a truly random sequence; cryptographic resistance against various cryptographic attacks; computational efficiency, i.e. the ability to quickly form PRS and encrypt messages of varying lengths. In this chapter, comparative studies of the statistical safety indicators of different SSC and their computational efficiency are conducted. Results of researches according to various methods of statistical testing and research of speed of formation of PRS are presented.
An n×n Parallel and All-optical Reservoir Computing System for Recognizing 128-bit Optical Packet Headers

an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n \times n$</tex> parallel all-optical reservoir computing system model based on a semiconductor laser is proposed, which can process n-channel signals at the same time, thus expanding the signal processing capacity of the reservoir computing system. In this paper, four-input and four-output parallel all-optical reservoir computing system is taken as an example to realize the simultaneous recognition of 4-channel 128-bit optical packet headers compatible with IPv6 protocol address, and the influence of feedback strengths on the signal recognition effect is analyzed.
Squeezed-vacuum-induced optical tristability in an atom-cavity system

In this paper we investigate the model of $N$ two-level atoms coupled to a squeezed vacuum inside a coherently driven cavity. By a linear stability analysis in the semiclassical description, we find that the optical tristability emerges in our system owing to the presence of the squeezed vacuum. Moreover, the transition among monostable, bistable, and tristable states can be realized by manipulating the squeezing phase or the squeezing strength of the squeezed vacuum. We also show the signatures of quantum tristability by using the $Q$ function and quantum trajectory methods for the situation of a single atom and low cavity photon number. Our paper offers a way to realize optical tristability in a two-level atomic system.
An Experimental Study for Comparing Different Method for Time Series Forecasting Prediction &amp; Anomaly Detection

Time series forecasting is used to detect some anomaly, that is, any unusual or unrequired events in network traffic, so that it can be removed while using the dataset for further processing. Anomaly detection is very helpful in reducing the operation call. This paper compares different models for detecting anomaly in computer networks using time series forecasting methods with reduced error rates.
Is the Internet in Indonesia Has a Good Sentiment from Netizen?

During this pandemic, the Internet has become a basic necessity that equals clothing, food, and shelter. People use the Internet for various activities such as ordering food, communicating, learning remotely (online), working, and social media, one of which is Instagram. On Instagram, people can upload pictures, videos, and even text if they already have an account. This feature is not only limited to an individual but can also be used by companies or, in this case, internet providers. On Instagram comments on internet provider accounts, there are often positive and negative comments that overlap in the comments section, making it difficult for providers to measure the satisfaction of their customers. Therefore in this report, researchers will use sentiment analysis with the Naïve Bayes algorithm and Support Vector Machine (SVM) to measure customer satisfaction through the many hate speeches in the Instagram comments section of internet provider accounts. The research will begin by collecting 1000 data on the internet provider Instagram comments (Text Mining), then the existing data will enter the sentiment analysis process. After that, the sentiment results will be inputted into two algorithms, namely Naïve Bayes and Support Vector Machine (SVM). Then the accuracy will be calculated using the K-Fold Cross Validation and Confusion Matrix to become a precise result. The research results will be in the form of a percentage of customer satisfaction and dissatisfaction based on the many hate speeches that each internet provider has. The results of the analysis of this study are expected to help internet providers in Indonesia know that many customers who are not satisfied with their services can improve the quality of their services.
Corrupted Image Enhancement Through WaveNet: A Hybrid Approach

A denoising method for medical images through hybrid technique is presented in this paper. The hybrid technique is the combination of wavelet and neural network (WaveNet). Proposed algorithm has been validated through benchmark image, and medical images both are degraded by the variety of noise density through Gaussian noise and visual property. The performances of denoised images are also analyzed with wavelet techniques and compare the results with proposed technique. The proposed strategy is developed in MATLAB platform. Simulation results are evidence for the proposed work
Design of Mobile Application for a Smart Home Nutrition System (SmartNS)

Every day there are more devices or objects that connect to the internet, these devices are found in different areas in the home, health, industry and others, this project is implemented in the internet of things for homes, it is a module called "nutrition", the which allows you to have a record of diets provided by a nutritionist, this allows reducing costs and food waste because with the mobile application called: Mobile application for smart appliances (EInt), you can obtain the products directly from a preferred provider. Given the situation current due to the COVID-19 pandemic, exits to non-essential situations have been reduced, this prototype pretends to be a support for users when making errand purchases and avoid going out as little as possible. The analysis and design of the device contains several agents, this article describes the nutrition agent, the food ontology, its semantic network.
Intelligent Teacher Assistant

The development of artificial intelligence is changing teachers’ roles, educational management, and evaluation. In traditional teaching and learning, teachers mainly lecture, transform knowledge, and instruct students to learn. Teachers take responsibility for designing content, implementing lesson design, and evaluating students. AI has the potential to support teachers in these tasks.
Safe and Robust Function Development for Urban Autonomous Driving Based on Agile Methodology and DevOps

With infinitely many situations that an autonomous vehicle could face in real life, it is beneficial to combine different development methodologies with the V-model for the development of autonomous driving functions in a more robust and efficient way. To meet the complex challenges of developing safe and robust functions for autonomous driving, IAV proposes an approach in which different stages of V-model are combined with agile software development methodology and DevOps philosophy. This leads to a process model, which is more flexible as compared to conventional V-model and has ability to take into the account the information obtained at later stages of project into the development process efficiently.