Reviewerly/exHarmony huggingface dataset:
Research on The Construction of Course Knowledge Graph of High School Information Technology

Since Google proposed the concept of the knowledge graph in 2012, the research interest of the knowledge graph has been increased year by year. From the construction of more and more general knowledge graph to the hot research of domain knowledge graph in recent years, it will found that people have more in-depth research on knowledge, more refined granularity, and more stringent requirements on quality. In teaching, it is also essential to construct a course knowledge graph to assist in practical education. This article takes the new curriculum standard for the high school information technology compulsory book one "Data and Computing" and compulsory book two "Information System and Society" as examples. Based on the textbook organization, it designed a course knowledge graph that includes three types of knowledge units: chapters, sections, and knowledge points, and three semantic relationships: sequences, contains, and connects. Through the analysis of the basic structure and specific construction process of the course knowledge graph, this paper explores that the course knowledge graph can well support systematic learning, personalized learning, and other learning situations.
State Feedback Controller for type-2 Fuzzy Large-Scale System

The main objective of this paper is to design a type-2 fuzzy state feedback controller for continuous-time large-scale nonlinear systems modeled by type-2 fuzzy Takagi-Sugeno (T-S). Every nonlinear subsystem in the large-scale systems, described by the type-2 Fuzzy Takagi Sugeno T-S model, including the terms of the interconnection of the subsystems. Needful stability conditions in terms of linear matrix inequalities (LMIs) derive base on the Lyapunov stability theory, such that the closed-loop system is lead to asymptotically stable. Also, at the end of paper for showing the effectiveness of the obtained method, a numerical example of a double inverted pendulum system is demonstrated.
RETRACTED: Identification of microbial pollution sources in high-humidity buildings based on deep learning and image particle recognition

The presence of microorganisms in the air poses challenges to the hospital's control, commercial Indoor Air Quality (IAQ), and residential building projects. This work analyzes microbial control agencies for power generation, transmission, and air and proposes engineering control methods for ventilation commonly used to control microbial contamination of indoor air. Mathematical computational models of building ventilation systems are used to study microbial contamination control. Alone, the encompassing first-individual picture can be semantically connected with comparative circumstances before, individual chronicle space, and social design representation. All in all, first-individual recordings permit you to follow regular interests and connections to gatherings of people. It learns to use FPGAs to capture the egocentrism and visual implications of future orbital motor groups. Based on Deep Neural Network (DNN), deep learning architectures have successfully image recognition tasks of microbial particles. These architectures use a cascade of convolutional layers and activation functions. The number of layers for training, the number of neurons in each layer, the selection of activation functions, and the setting of optimization algorithms are significant. DNNs have been used to build cognitive feature extraction implementations and have been reported to achieve good results.
Programmable Axial Spectral Evolution of Space-Time Wavepackets

Controllable axial spectral encoding is shown via selective amplitude-modulation of a space-time wave packet. Arbitrarily-varying spectra along the optic axis could act as a ‘spectral stamp,’ with potential applications in axial rangefinding or remote-sensing.
Sea Wave Detection System using Web-Based Decision Tree Algorithm

Indonesia is a country that is prone to earthquakes. This phenomenon has existed since two tectonic plates crossed Indonesian territory. Also, Indonesia is flanked by two vast oceans, the Pacific Ocean and India. Therefore the tsunami posed a severe threat to the coastal areas of the Indonesian archipelago as well. Currently, Indonesia already has a sea wave early detection system. The system is called a buoy made by NOAA (National Oceanic and Atmospheric Administration). The device can detect wave heights and convey information when a tsunami occurs and works in real-time. Unfortunately, these tools cost a high price, and the maintenance is quite complicated, and the maintenance costs are relatively expensive. Based on these problems, we need a system that can handle the problem. The system is designed using the Decision Tree C4.5 Algorithm to provide predictions for tourists, fishers, and the surrounding community based on IoT parameters. The system test results show that the C4.5 algorithm has accuracy of around 98% to 100%.
Deep Neural Network and Weighted Ridge Regression Based Pixel Prediction Technique

Lossless image compression consists of two processes, pixel prediction and residue encoding. Recently, deep neural network (DNN) has been widely adopted in signal prediction. In this work, we apply a DNN architecture together with the information of surrounding pixels weighted by their similarities for pixel prediction. Simulations show that, with the proposed algorithm, the pixels can be predicted more precisely, which is very helpful for lossless image compression.
EP-FPG applied to RSSI-Based Wireless Indoor Localization

Wireless Localization based on Received Signal Strength Indication (WL-RSSI) consists of predicting the localization of a particular device given the radio signals it receives. WL-RSSI methods are suitable for specific scenarios where Global Positioning System (GPS) is unstable or unavailable, such as indoor localization. Developing more efficient WL-RSSI methods is necessary to supplement GPS localization in such applications. Feedforward neural network trained by hybrid Particle swarm optimization and Gravitational search algorithm (FPG) is an optimization strategy that aims at better exploring the network weight-space when compared to methods such as Backpropagation (BP). Feedforward neural network trained by hybrid Particle swarm optimization and Gravitational search algorithm (FPG) is a kind of machine learning model with better exploring ability in the solution search space compared with conventional neural network training methods such as Backpropagation (BP). This article investigates a method to solve the slow convergence problem of conventional FPG and further improve its performance. Extreme Learning Machines (ELMs) are used to pre-train initial particles of the FPG (EP-FPG). This article also presents the application of EP-FPG to classification and regression WL-RSSI problems. Experimental results demonstrate that the proposed EP-FPG performs better on WL-RSSI problems than conventional FPG and BP.
Tasks of computer graphics under conditions of fundamentalization of preparation of an informatics teacher

The article deals with computer graphics tasks related to the activities of the future informatics teacher in conditions of fundamentalization of education. Training of future informatics teachers in the context of the fundamentalization of education requires them to know the range of tasks related to computer graphics and the skills to solve them. In order to enhance the fundamental component of computer graphics, methods are proposed that rely on interprandial communications, as well as on in-depth training of computer graphics. In the course of reasoning, the authors come to the conclusion that the content of computer graphics should be enriched with mathematical foundations of computer graphics and as a result update the content of the computer graphics course with machine graphics algorithms. The basic principle of selecting the content of the course offered is the principle of the fundamentalization of education. Since the scope of application of computer graphics is extensive, in our opinion, the system of tasks and tasks on computer graphics is the most interesting. A feature of this system is the orientation towards solving fundamental problems of computer graphics. It was also revealed during the study that it is possible to reduce the tasks of the proposed system to a certain sequence of stages. The application of stages for a certain type of tasks affects the methods of solving them. Thus, the fundamental training of future informatics teachers in computer graphics requires them to know these stages and methods of solving fundamental computer graphics tasks.
A Note on Constructive Interpolation for the Multi-Modal Logic K

The Craig Interpolation Theorem is a well-known property in the mathematical logic curricula, with many domain applications, such as in the modularization of formal specifications and ontologies. This property states the following: given an implication, say formula ϕ implies another formula ψ, then there is a formula β, called the interpolant, in the common language of ϕ and ψ, such that ϕ also implies β, as well as β implies ψ. Although it is already known that the propositional multi-modal logic Km enjoys Craig interpolation, we are not aware of method providing an explicit construction of interpolants. We describe in this paper a constructive proof of the Craig interpolation property on the multi-modal logic Km. Interpolants can be explicitly computed from the proof. Furthermore, we also describe an upper bound for the computation of interpolants. The proof is based on the application of Maehara technique on a tree-hypersequent calculus. As a corollary of interpolation, we also show Beth definability and Robinson joint consistency.
Exploring the interface design of smartwatch application menu

This study presents a preliminary study of investigating the interface design of smartwatch application menu and highlights issues at the hierarchical level of a classified application menu interface. After investigating the current smartwatch application menu interface and interviewing interface design experts, the results show that current application menu interface are designed variously according to different product positioning. Therefore, there is no universal and proper information architecture are applied to take care of numerous application menu interface arrangement. Future work will focus on investigating the possibility of designing interface by classifying the application menu to improve the user experience.
Evaluation of a Network Switch with Whitelist-Based Packet Monitoring and Control in Hospital Networks

A "whitelist network switch (WLS)", which can monitor and filter packets based on a whitelist, is particularly effective for stronger information security in closed network environments, such as in factories. This paper focuses on the base network environment for a nurse call system within a large hospital. This network environment is of particular importance to security, and the types of network protocols used are limited. We note important points regarding the practical operation and effectiveness of WLS through two approaches: (i) test introduction into an existing, actual hospital network system; (ii) test introduction in an experimental nurse call system environment.
Machine-Checking the Universal Verifiability of ElectionGuard

ElectionGuard is an open source set of software components and specifications from Microsoft designed to allow the modification of a number of different e-voting protocols and products to produce public evidence (transcripts) which anyone can verify. The software uses ElGamal, homomorphic tallying and sigma protocols to enable public scrutiny without adversely affecting privacy. Some components have been formally verified (machine-checked) to be free of certain software bugs but there was no formal verification of their cryptographic security.Here, we present a machine-checked proof of the verifiability guarantees of the transcripts produced according to the ElectionGuard specification. We have also extracted an executable version of the verifier specification, which we proved to be secure, and used it to verify election transcripts produced by ElectionGuard. Our results show that our implementation is of similar efficiency to existing implementations.
A Hybrid Differential Algorithm for Image Enhancement

In order to improve the enhancement effect of fractional differential algorithm, according to the fact that 2nd integer order differential is beneficial to enhance image edge and texture details, and fractional-order differential (G-L differential) method is helpful to enhance and extract texture the texture details in image smoothing area, an hybrid fractional image enhancement algorithm combing fractional and integer order differential is proposed in this paper. This method overcomes the limitation that the fractional differential order can only be 0-1 in image enhancement, and expands the differential order to more than 2. And the enhancement effect of texture and edge is superior to other differential methods.
The Study On The Technology Of Spammer Detection For Social Network On Bayesian Model

The purpose of this study is to identify the water army group of Sina Weibo. By setting the ratio of the number of followers to the number of fans, the ratio of the year of registration to the year of topic publishing, the ratio of the number of blogs to the time of registration, the ratio of the number of original microblogs to the number of microblogs, the ratio of the number of followers to the time of registration, the ratio of the number of microblogs forwarded with comments to the number of microblogs forwarded, the ratio of microblogs that refer to others to the number of people's microblogs, and member or not . The eight key fields are substituted into the Bayesian model for analysis. At the same time, the neural network model and the decision tree model are used as the comparison groups. The results of this study show that the 8 features based on Bayesian model can better ensure the accuracy of the recognition.
Spoken Language Identification With Hierarchical Topic Model and Multi-levels Discriminative Cues

The discriminative cues and effective representations are two important factors for spoken language identification (LID). In this paper, the multi-levels language cues, including phonetic, phonotactic and prosodic, are studied, and a hierarchical representation based on supervised topic model is further designed to organize the multi-levels cues. The difference of languages is distinguished by using the distribution of prosodic topics and phonemic topics in each language. We built the proposed LID method and report the test results on NIST LRE07 datasets. Compared with the most advanced LID methods, our proposed method shows the competitive performance in the LID task. In particular, our method helps to capture robust discriminative information for short duration language identification.
Studi komparasi model pembelajaran scramble dan concept sentence terhadap keterampilan menulis rangkuman ditinjau dari motivasi menulis

The purpose of this study was to determine the Comparative Study of Scramble Learning  Model and Concept Sentence on Summary Writing Skills in terms of Learning Motivation.  (Experimental Research on Class V Semester 2 SD Students in Laweyan District, 2019/2020  Academic Year). The population of this study was all students in grade V SD Negeri in Laweyan  District, Surakarta City for the 2019/2020 school year, totaling 38 schools. The sample in this study  was grade V of some public elementary schools in Laweyan District, Surakarta City. The experimental class in this study was in class V SD Negeri Bumi 1 and SD Negeri Bratan III, while  the control class was in class V SD Negeri Pajang III and SD Negeri Tegalsari. Data collection  techniques in this study used a test (post-test) and non-test (questionnaire). The data analysis  technique used in this study was the Two Way Variance Analysis (Two Way Anava) with a  significant level of α = 0.05. The results showed that there were differences in summary writing skills between students who were taught by the Scramble learning model and students who were  taught with the Concept Sentence learning model, indicated by the acquisition of the Sig. = 0.000  <0.05. There are differences in summary writing skills between students who have high learning  motivation and students who have low learning motivation. This is indicated by the acquisition of  the Sig. = 0.000 <0.05. There is an interaction between the learning model and learning motivation  on summary writing skills indicated by the acquisition of Sig. = 0.000 <0.05.
A User Mobility-Based Rate Adaptation Approach for Dynamic HTTP Streaming

Abstract It is common for users to use smartphones to watch streaming videos when they are walking, taking a bus, or taking a subway. However, the current streaming rate adaptation algorithms are mostly based on the available network throughput or the player buffer occupancy, and the user mobility state is less considered. In order to improve the user experience quality under the mobile streaming media, we propose a user mobility-based rate adaptation approach, which classifies the user mobility state using the random forests algorithm and adopts different rate decision under different mobility states. The experimental results show that compared with popular approaches, our approach reduces the re-buffer events by 24% on average, and reduces the data traffic consumption by 19% on average.
Data Augmentation and D-vector Representation Methods for Speaker Change Detection

Speaker Change Detection (SCD) is the process that detects speaker changes during a conversation. The conversation can be divided into homogeneous segments using a typical SCD system or speaker diarization system in which the segments are partitioned according to a speaker identity. When the d-vectors are used to identify or verify the speakers with deep neural network model, they are often considered insufficient to train model for detecting the speaker changes by using only acoustic information. There are few dedicated datasets for system training, so the progress of the SCD study is slow and the performance is poor. Therefore, we presented data augmentation method based on TIMIT dataset to suit for the system, and we also proposed several methods to represent d-vectors for SCD systems and their preliminary results. In the proposed data augmentation method, the boundary information of speakers is transformed into probability according to the offset in a given frame and collected in the segment. To model the boundaries of the speakers, we concatenate two random speech sentences dedicated to speech recognition system. The preliminary experimental results, specifically recall percentage, shows the possibility of the proposed approaches. In the future, we will add linguistic information to the proposed classification system, or improve the system to use hybrid system of d-vector and frame vectors, or convolutional networks.
Multiple Learners Combination: Boosting

The following article presents Boosting, one of the most widely used methods of combining multiple learners in Machine Learning. Born as a heuristic method to show the equivalence between two learning classes, the idea behind Boosting is to build a strong learner from multiple weak learners by acting iteratively on the distribution of the training dataset. More in detail, after each iteration, the misclassified examples are “emphasized” in order to have a high probability of being used as training examples for the next classifier. After discussing the theoretical foundations and presenting an overview of the evolution of boosting in general, its best-known algorithm, AdaBoost, is presented.
Automedian sets of permutations: direct sum and shuffle

Given a set A⊆Sn of m permutations of {1,2,…,n} and a distance function d, the median problem consists in finding the set M(A) of all the permutations that are the “closest” of this set A. In this article we study the automedian case of the problem, i.e. when A=M(A), under the Kendall-τ distance. We show that automedian sets of permutations are closed under the direct sum operation and also, when some balancing properties are imposed on these sets, under the shuffle operation. These results allow us to derive a parallel algorithm that computes the medians of any separable set of permutations in O(k!+mn), where k is the length of its longest inseparable component.
Bitrate selection for MPEG-DASH considering QoE by queueing theory

In recent years, video streaming data have increased. Therefore, MPEG-DASH (Moving Picture Experts Group-Dynamic Adaptive Streaming over HTTP) [1], which is one of the protocols that adaptively changes the video bitrate according to the user's network throughput in video streaming services, was developed. In the research on DASH, it is noted that the loss of the segment, which is a video playing unit, affects the video quality of the multicast environment [2]. It has also been noted that the occurrence of buffer underruns, which means that video playback stops, leads to user dissatisfaction [3]. In this paper, we propose an evaluation function for QoE (quality of experience) considering segment loss and buffer underrun probability. Moreover, we derive an optimal video bitrate to maximize the QoE value. The characteristics are analyzed by the simulation using the M/D/1/K model of queueing theory.
Multi-scale Feature Fusion for 3D Saliency Detection

Abstract 3D saliency detection aims to take advantage of the disparity map, depth map and color information to automatically detect informative objects from natural scenes. Although studies have concentrated on this issue in recent years, there are challenges such as how to leverage disparity map or depth map effectively to compute depth-induced saliency, and how to fuse optimally multiple visual features and cues. A novel 3D saliency detection approach is proposed, which fuses local contrast, region contrast, texture feature, depth cue, and location cue into a unified saliency computation framework. Results show that the proposed approach achieves significant and consistent improvements on other advanced methods in the RGBD1000 datasets.
Research on Effective Cooperative Learning Strategies in Group Teaching of Practical Courses

Group teaching method is generally used in practical courses, but during the implementation there are some problems of formalization and simplification. In this article the connotation of effective cooperative learning was analyzed and the strategies of effective cooperative learning were discussed.
Transhierarchy

Transhierarchy is a method of navigating hypertext within a stable, hierarchical user interface. In a transhierarchy, a standard tree view is generated by traversing outbound links from a given node. Inbound links and their descendants are transcluded into the hierarchy, allowing continuous navigation to cross-referenced nodes. Transhierarchy is a practical solution to the problem of how to represent a directed graph in a strict hierarchy. It preserves connectivity, provides context during navigation, and is visually compact, making it a viable method for navigating hypertext on mobile devices. It does not require additional semantics, such as typed links.