Reviewerly/exHarmony huggingface dataset:
VoteSumm: A Multi-Document Summarization Scheme Using Influential Nodes of Multilayer Weighted Sentence Network

This work proposes a sentence network-based approach for performing the task of multi-document text summarization. The sentences of the input set of documents are represented by the nodes of the network. Weighted edges are added between the nodes to represent the semantic similarity between the corresponding sentences. The network has a multilayer structure, where each layer corresponds to an individual input document. This helps in effective differentiation between the inter-document and intra-document edges. A hyperparameter, namely layering factor, has been used to alter the strength of inter-document connections through reinforcement or weakening. It is hypothesized that the summary sentence nodes must act as effective information spreaders in the sentence network. Summary generation is performed by identifying the influential nodes of the network using VoteRank scheme. A comparative study with different network measures, such as Weighted Degree, PageRank, Betweenness centrality, and Closeness centrality reveals the efficacy of the proposed VoteSumm technique for multi-document text summarization. Improved performance is observed when an additional pre-processing step of syntactic simplification is applied on the raw text. Performance is further improved when keyword information is included in the simplified texts.
Investigation on the Classification Accuracy of Harmonic Signal detection by Artificial Neural Network

Harmonic signals are produced by many natural sources as well as man-made sources. The detection of the harmonic signal can reveal a lot about the physical system. Various Fourier harmonic analysis based numerical methods are commonly used to detect such signals. However, recently there has been considerable interest in other non-Fourier-based methods as well, to determine the harmonics. In the present work we have studied the application of artificial neural network based machine learning in frequency identification of sinusoidal signals. We considered training sets comprising harmonic signals with randomised phase or randomised amplitude or combination of the both. Based on such training sets the trained network is then applied to detect the frequency of unknown harmonics. Here we performed an investigation on the relative advantages of the network trained using sets of harmonic signals with different features.
Mobile face detection algorithm inference traits

An ever-growing number of applications uses mobile face detection. However, most of the modern research papers focus on increasing detection quality while paying no attention to detection time. This means that many of the state-of-the-art algorithms are inapplicable on mo-bile due to excessively large detection time. The goal of this this paper is to adapt 5 of the face detection algorithms for inference on mobile devices and analyze their performance characteristics. These algorithms include es-tablished methods: Haar Cascades, LBP, HOG, as well as, novel neural-network-based algo-rithms: MTCNN, BlazeFace. The main research material. We conduct the experiments on three scenes typical for mobile face recognition systems: when there are no faces, 1 or 2 faces. For testing we have im-plemented an Android application. 2 widespread processors, namely Snapdragon 800 and 845, were selected for time measurements. Having tested the algorithms, we note that all them can run at real-time speeds for images of size 128x128 and only 2 of them (LBP, HOG) on 256x256 on the faster Snapdragon 845. On the slower Snapdragon 800 only BlazeFace, LBP, HOG can run at resolutions not higher than 128x128. We suggest not using Haar or LBP cascades in practice as their accuracy is quite low. Conclusions. Based on the research conducted, we suggest that for the practical use-cases the best algorithms are: 1) BlazeFace, which has stable and accurate predictions, how-ever, the method accepts only two image resolutions as input, in addition, higher inference time for empty images than for images with faces is untypical; 2) MTCNN, thanks to the cascaded architecture, conserves the resources when input frames have no faces. This algorithm is also the most adaptive and can run at resolutions as low as 32x32 given that the faces are quite large; 3) in case if inference time is of the most importance, we suggest using HOG-based algorithm. In this paper we have also shown that cascaded algorithm architecture dynamically changes execution time depending on image content and its complexity, which follows how we, humans, think. We hope that the novel practical results obtained, will increase the use of the above-described methods in mobile applications and will boost the development of the algorithm modifications.
FORMATION OF DISTANCE-EDUCATIONAL COMPETENCE OF STUDENTS-MUSICIANS IN HIGHER EDUCATION PEDAGOGICAL INSTITUTIONS

The article considers the problem of distance-educational competence formation of music students in higher education pedagogical institutions. The urgency of this problem for higher education is related to the introduction of electronic educational resources, information and communication technologies and the forming of specialists who can effectively use the information and educational environment, distance learning for their own professional development. It is noted that due to the pandemic, which led to forced self-isolation and limited personal contact, higher education institutions took the necessary measures for social distance, which encouraged students to be engaged in professional activities, study and even plan leisure activities without leaving home. This has become a particularly difficult task for students-musicians, adapting practical and individual lessons to the distance education system, taking into account the sound nature of music and the requirements for participants in the learning process to control, first of all, the quality of instrumental and vocal sound. It is found out that to ensure quality conditions for future professionals to acquire professional competencies for further instrumental performance, vocal-choral, conducting or music-pedagogical activities of a trained teacher with relevant competencies in distance learning, information and communication technologies is required. The article highlights such communication channels as social networks, messengers or programs for video communication, which are primarily used by teachers during the implementation of pedagogical activities remotely. With the help of these communication channels, you can organize the learning process in both individual and group formats. Effective information technologies and programs are highlighted, which provide an opportunity to implement practice-oriented learning of a student-musician in a distance form. Key words: distance learning, distance-educational competence, students- musicians, information and telecommunication technologies, music education.
Errors Made by The Presenters of Thesis Proposal Seminar in English Study Program

This study is aimed to find out the types of errors made by the presenters of thesis proposal seminar based on Communicative Effect Taxonomy and to find out whether or not global errors of Communicative Effect Taxonomy made by the presenters of thesis proposal seminar significantly hinder communication between the presenters and the audiences. This study used qualitative approach since the data of this study was taken from 4 students' presentations which were in the form of words rather than number and statistic. This study revealed that there were 188 errors committed by the students in which 179 and 9 erroneous utterances fall under local error and global error respectively. The local errors consist of 45 lexical errors, 93 morphological errors, and 41 syntactical errors. The global errors consist of 2 wrong order of major constituents, 1 missing, wrong, or misplaced sentence connectors, 1 missing, wrong, or misplaced sentence connectors, and 5 uncategorized global errors. As for the effect of global error to the audience, the writer found out that the global errors did not significantly hinder communication to the audience.
Effect of Parathyroid Hormone of Young Men: Mathematical Modelling by using Fuzzy Logic

Mathematical models are used in medical, physics, chemistry, engineering, geology, geography, computer and many other fields. The reason for use of mathematical models is because we get the easy answers. I have find out the Fuzzy survival and Fuzzy Hazard values in mathematical model of binary parameter Log-Logistic Distribution of PTH stages that arise in the body when taking Serum iPTH for young men.
Google Books Corpus and designing English for specific purposes materials

Designing language-learning materials that target the needs of specific groups of students can be a challenge because students' needs vary. There is a research gap in identifying learning materials that meet the needs of a specific group of students. The study objective is to utilize open educational resources to design English for specific purposes (ESP) materials. The study implements a corpus linguistics method by using the Google Books Corpus (GBC) to develop language-learning materials for engineering students. The application analyzed the corpora by frequency that was the default analysis procedure in GBC. The findings show the procedures to create English grammar materials for engineering students and engineers who need to improve their knowledge about the commonly used grammar structures in engineering texts. The results of analyzing GBC present five chapters: nouns, verbs, adjectives, adverbs, and prepositions that are commonly used in engineering texts using authentic examples. The findings demonstrate the importance of corpus technologies such as GBC in language learning classrooms. The implication is that language educators should utilize open educational resources such as GBC to provide students with learning materials that have authentic examples, commonly used words, and widely used grammar structures in the students' academic majors.
Towards A Vocalization Feedback Pipeline for Language Learners

Practice is essential for language learning. This is true for writing as well as speaking. However, in contrast to writing, it can be challenging to offer students sufficient time to practice speaking while receiving corrective feedback from a teacher. Considering the importance of corrective feedback for language mastery, automatic feedback systems could provide vital assistance through additional supervised speaking exercise. For this reason, this paper proposes an end-to-end feedback generation pipeline to correct grammar errors in unconstrained speech. The approach consists of four steps, converting raw speech files into transcripts with automatic speech recognition, removing disfluency, correcting grammatical errors, and preparing the feedback for presentation to the user. An explorative analysis of the pipeline with English language learners indicates that out-of-the-box automatic speech recognition models degrade in performance when used by language learners. However, training the model with only 15 minutes of learners’ speech decreases the word error rate almost by half.
DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction

In this work, we propose a new framework, called Document Image Transformer (DocTr), to address the issue of geometry and illumination distortion of the document images. Specifically, DocTr consists of a geometric unwarping transformer and an illumination correction transformer. By setting a set of learned query embedding, the geometric unwarping transformer captures the global context of the document image by self-attention mechanism and decodes the pixel-wise displacement solution to correct the geometric distortion. After geometric unwarping, our illumination correction transformer further removes the shading artifacts to improve the visual quality and OCR accuracy. Extensive evaluations are conducted on several datasets, and superior results are reported against the state-of-the-art methods. Remarkably, our DocTr achieves 20.02% Character Error Rate (CER), a 15% absolute improvement over the state-of-the-art methods. Moreover, it also shows high efficiency on running time and parameter count. The results will be available at https://github.com/fh2019ustc/DocTr for further comparison.
Virtual sports rehabilitation and monitoring system for the elderly based on intelligent interaction and embedded system

This article uses medical electrode pads as the ECG sensor, introduces the signal into the subsequent conditioning circuit, sticks to the electrode pads on both sides of the human body's chest and abdomen, and ensures a firm fit. After each signal acquisition module works normally, connect each module to the wireless body area network terminal node for system networking test. System testing includes not only signal acquisition testing and network communication testing, but also host computer software testing. The BCG signal is collected by a piezoelectric film sensor, which converts the mechanical vibration in the human body into a variable voltage output. The results show that the system can accurately collect and process data, maintain good communication with the software platform, and has high reliability.
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Older adults are slower at recognizing visual objects than younger adults. The same is true for recognizing that a letter string is a real word. People with Alzheimer's disease (AD) or Mild Cognitive Impairment (MCI) demonstrate even longer responses in written word recognition than elderly controls. Despite the general tendency towards slower recognition in aging and neurocognitive disorders, certain characteristics of words influence word recognition speed regardless of age or neuropathology (e.g., a word’s frequency of use). We present here a protocol for examining the influence of lexical characteristics on word recognition response times in a simple lexical decision experiment administered to younger and older adults and people with MCI or AD. In this experiment, participants are asked to decide as quickly and accurately as possible whether a given letter string is an actual word or not. We also describe mixed-effects models and principal components analysis that can be used to detect the influence of different types of lexical variables or individual characteristics of participants on word recognition speed.
Motion Planning and Control of Two Quadcopters with Cable-Suspended Point Mass Payload

Considering the low payload carrying capacity for a single quadcopter, one option to increase the payload carrying capacity is the use of more than one quadcopter. This work focuses on trajectory planning and control of two quadcopters with a cable-suspended point mass payload system using a leader–follower scheme. For safe and stable transportation of suspended payload, the quadcopters should be controlled to not lead to cable slackness. Accordingly, wrench closure analysis is carried out for the follower quadcopter with respect to the leader quadcopter, which helps design desired trajectory generation for the quadcopters. The performance of the proposed motion planning strategy is verified by conducting simulation in SIMSCAPE multibody package in MATLAB.
Realisation of large-scale photonic spiking hardware system

An efficient photonic hardware integration of neural networks can benefit us from the inherent properties of parallelism, high-speed data processing and potentially low energy consumption. In artificial neural networks (ANN), neurons are classified as static, single and continuous-valued. On contrary, information transmission and computation in biological neurons occur through spikes, where spike time and rate play a significant role. Spiking neural networks (SNNs) are thereby more biologically relevant along with additional benefits in terms of hardware friendliness and energy-efficiency. Considering all these advantages, we designed a photonic reservoir computer (RC) based on photonic recurrent spiking neural networks (SNN) i.e. a liquid state machine. It is a scalable proof-of-concept experiment, comprising more than 30,000 neurons. This system presents an excellent testbed for demonstrating next generation bio-inspired learning in photonic systems.
Aerial Object Detection Using Different Models of YOLO Architecture: A Comparative Study

Aerial object detection is a key to many functionalities like security systems, pedestrian counting, animal population estimation, security surveillance and many more. Previously used traditional machine learning approaches made use of handmade features and algorithms that failed to generalise on a larger data set. Therefore, deep learning models have outperformed the traditional machine learning models, especially in the computer vision field. Aerial object detection is a subdomain of object detection that has been a hot topic of interest in recent years. Transfer learning has emerged as one of the go-to methods to adapt models well on a small data set. This paper proposes a comparative study of the performance of three state-of-the-art object detection models on an aerial images data set using Transfer learning. The paper aims at studying how well a model can adapt to aerial images using transfer learning. The three models used are the YOLOv5m6, YOLOv5x6 and the YOLOv5l6 models based on the YOLO architecture. The data set used is the Vehicle Detection in Aerial Imagery (VEDAI) data set. The study finds out that the YOLOv5m6 model outperforms the other two models.
Proposal of Logistics Service Evaluation Framework from the Perspective of Service Purchase

In this study, we analyze the logistics service process for the improvement of win-win cooperation and competitiveness of the logistics industry ecosystem., and based on this, we propose an evaluation framework that can entrust the final logistics service. In the process of providing logistics services, an LLP may enter into a consignment contract with a partner (supplier) to perform a part of the logistics service. Therefore, this study proposes a method to analyze the various functions provided from the perspective of the process, rather than the industry classification based on the framework act on logistics policies. In addition, from the perspective of service purchase and supply, we propose an evaluation framework that can be referred to in the creation of a business ecosystem and strategic nurturing of the logistics industry. To this end, AHP analysis was performed through expert inverview to derive evaluation priorities in four aspects: the importance of the business, profit of the LLP, profit of the partner(supplier), and resilience.
A Simple and Flexible Global ID System for Authentication in Blockchain

In recent years, decentralized global ID systems have attracted significant attention. The concept of self-sovereign identity was established to realize a decentralized global ID, which is achieved by verifiable credentials and decentralized identifiers. However, there is a problem in using such decentralized global IDs for various remote services from the viewpoint of flexibility. In this paper, we propose a system which can give an "attribute" to global IDs dynamically by using only the basic function of a blockchain (i.e., using cryptocurrency transfer only). The main idea of our scheme is that an "attribute token" is assigned to global IDs or removed from global IDs using the token transmission function in a blockchain. Thus, we can set the credibility level and decentralization level of global IDs adaptively, and thus, we manage global IDs securely and flexibly. Furthermore, we conduct feasibility experiments with the proposed system and realize an attribute token assignment in Bitcoin and Ethereum cryptocurrencies. This result shows that our system can use almost all blockchain systems having a token transmission function.
Item Analysis of the Test of ‘Korean Culture’ of the Test of Teaching Korean as a Foreign Language: Based on Content Validity Improvement

The study aims to explore how to ensure content validity of the Test of Korean Culture, the 4th part of the Test of Teaching Korean as a Foreign Language(TOTKA). To achieve the study objective, appropriate content areas for item development of the test are suggested by analyzing opinions collected from a panel of experts in the field of Korean Language Teaching and Learning, the degree of content balance is investigated by calculating how frequently each content appears and, lastly, item difficulty and discrimination are analyzed utilizing item response theory. The results are as follows. First, modern Korean culture, Korean history and modern Korean society were judged as the most appropriate content areas. Second, the degree of content balance was found to be low. Especially, frequency of items with regard to modern Korean society was much lower than that of its counterparts. Third, extreme item difficulty and discrimination appeared frequently. The study finally suggested practical implications as to how to ensure better content validity of the Test of Korean Culture based on those research results.
A Study on the Intention to Use of Issue Tracking System for the Information Security Management System: Focused on Experts and Managers in Information Security

Recently, it is very difficult to flexibly and efficiently operate the ISMS to achieve the purpose of information security of companies, because service complexity is increasing and IT is also being changed very quickly. This study analyzed the intention to apply the Issue tracking system as a tool to efficiently operate the ISMS using the ‘UTAUT’ model because the ITS, a flexible collaboration tool, is applied to various ranges. As a result, it was confirmed that ‘Performance Expectancy’, ‘Social Influence’, and additional variables ‘Future Expansion Expectation’ had a significant effect on usefulness, and that usefulness and ‘Facilitating Conditions’ had a significant effect on ‘Behavioral Intention’. However, it was confirmed that ‘Effort Expectancy’ was not significantly related to ‘Use Behavior’. This study is different from previous studies in that the model was constructed by adding a belief variable based on trust to the existing ‘UTAUT’ model. The insufficient number of sample targets and not applying the moderating effect of the ‘UTAUT’ model are points to be supplemented through follow-studies.