Reviewerly/exHarmony huggingface dataset:
Research on the Optimization of University Performance Management Information System in the Big Data Era

In modern society, the rapid development of knowledge economy has made human resources the core of a country’s economic and social modernization. Under such circumstances, human resource management has attracted more and more attention and attention. As the foundation of all aspects of human resource management, performance management refers to the weaknesses of the management, control, decision-making and incentive processes within the system. Lack of management methods. Therefore, this article attempts to explore and consider optimizing the performance evaluation system of college students on the basis of the existing theoretical research results, hoping to establish a relatively scientific student performance evaluation index system. This article makes extensive use of management theory, teaching theory and performance management evaluation theory to analyze and study the status quo of student work management and performance evaluation system in my country’s colleges and universities through questionnaire surveys. And found some of the existing major problems in the evaluation of college student management performance, and then used relevant theoretical knowledge, such as analytic hierarchy process and fuzzy comprehensive evaluation model to appropriately adjust various indicators and weights to the college student management performance evaluation system, and Defines a relatively high-level general guidance student evaluation performance evaluation system. At the same time, through the application of the evaluation system of student work performance in the actual management of students, it has proved its feasibility in reality. After analyzing the evaluation results, we also put forward more sensible suggestions and improvement measures to promote the development and improvement of student work management. Through the method of this article, the performance information system of universities in the era of big data has increased by 23% compared with traditional universities.
Boocture: Automatic Educational Videos Hierarchical Indexing with eBooks

Books and online lecture videos are usually perceived as alternative learning resources, yet potentially could be complementary to each other. While MOOC or online lecture videos enable an informal, attractive and rich visual format content empowered by animations and friendlier language, Books present a deeper and thorough representation of knowledge with a carefully designed hierarchical indexing system. This paper presents Boocture-a solution for bridging the gap between the two mediums by aligning pairs of video segments with book content at multiple the levels of Chapter, sub-chapter and subtopic using machine learning algorithms. Experiments included aligning book content with video segments for different subjects, including comparison to human based validation.
A Survey on Line Segmentation Techniques for Indic Scripts

Optical Character Recognition (OCR) from handwritten documents is a very challenging task of document image processing (DIP). It composed of various crucial pre-processing steps, out of those, text line segmentation is the most essential which not only helps the process of character recognition but also non-trivial for tasks like word recognition, alignment of texts or images, authentication of specific fields and then extraction of those which are very useful application nowadays. In this paper, a short survey of the available methods on text line segmentation of handwritten documents is presented. Comparative study of different state-of-the-art methods is presented to provide a broad view of the current scenario of line segmentation in the area of DIP. It is our belief that potential researchers in this field will be benefited immensely by this survey.
Face identification in a video file based on hybrid intelligence technique-review

Abstract With the advancement in technology, the importance of facial recognition in the video camera or in the images has increased due to its effectiveness in criminal identification and the identification of wanted people by using different algorithms and theories, where different hybrid and non-hybrid techniques have been developed to recognize the face through its features. In this paper, the most important technologies that have been developed in the field of face detection are revealed, as the paper provides a detailed explanation of the most prominent systems that have been developed in this field in terms of time, accuracy, results and databases used for this purpose.
Sinhala Conversational Interface for Appointment Management and Medical Advice

This paper proposes an intelligent conversational user interface to assist Sinhala speaking users to make appointments with doctors and to obtain medical advices. This Sinhala Conversational Interface for Appointment Management and Medical Advice (SCI-AMMA) consists of Speech Recognition unit, Query Processing unit, Dialog Management unit, Voice Synthesizer unit, and User Information Management unit to handle user requests and maintain a meaningful dialogue. The SCI-AMMA gets the users' speech utterances and recognize the language content of it for further processing. Language content is further processed using query processing unit to identify users' intent. To fulfil the users' intent, a reply is generated from Dialogue Management Unit. This reply/answer will be delivered to the user by means of a voice synthesizer. The proposed system is successfully implemented using state of the art technology stack including Flutter, Python, Protégé and Firebase. Performance of the system is demonstrated using several sample scenarios/dialogues.
Toward a Cognitive Control Framework for Explainable Robotics

The ability to explain and motivate the execution of actions is a key feature for complex robotic systems. In this paper, we propose an executive framework, endowed with a hierarchical representation of tasks, where robot actions and constraints can be directly associated with natural language explanations in order to facilitate the design of novel tasks and the understanding of executing ones. The executive system relies on a cognitive control paradigm where attentional regulations are exploited to both schedule and explain robotic activities during tasks execution. The framework has been deployed in an industrial scenario where multiple pick-carry-and-place tasks are to executed, showing how the proposed approach naturally supports explainability and legibility of the robot behaviors.
On the Technological Innovation Path of Artificial Intelligence in the Integration of Production and Education

A new round of technological and industrial revolution is coming, and artificial intelligence with information technology as the core will become a hot spot in economic development. The industrial innovation and scientific progress triggered by artificial intelligence will surely lead the direction of scientific and technological innovation, and vocational education closely related to economy and society must respond to and lead this orientation. From the perspective of the integration of industry and education, this paper analyzes its characteristics, and explores how to exert the thrust of industrial revolution brought by artificial intelligence in the process of integration of industry and education to realize scientific and technological innovation.
Heterogeneous Fair Resource Allocation and Scheduling for Big Data Streams in Cloud Environments

In this paper, Heterogeneous Fair Resource Allocation and Scheduling (HFRAS) for cloud based Big Data Streams, is proposed. In this algorithm, a weight value is determined for the user for each of the requested resource, based on the resource priorities. Then each task is assigned a task priority index (TPI) based on this weight value, task arrival time and expected end time (EET). The requested tasks are divided into various priority queues based on the TPI of the tasks assigned. Then tasks are sorted in the ascending order of TPI and scheduled in which the Dominant Resource Share (DRS) is determined for each user. Experimental results have shown that HFRAS attains lesser execution time, minimum response delay and maximum CPU utilization, when compared to the existing algorithm.
Pandora: A Cyber Range Environment for the Safe Testing and Deployment of Autonomous Cyber Attack Tools

Cybersecurity tools are increasingly automated with artificial intelligent (AI) capabilities to match the exponential scale of attacks, compensate for the relatively slower rate of training new cybersecurity talents, and improve of the accuracy and performance of both tools and users. However, the safe and appropriate usage of autonomous cyber attack tools–especially at the development stages for these cyber attack tools – is still largely an unaddressed gap. Our survey of current literature and tools showed that most of the existing cyber range designs are mostly using manual tools and have not considered augmenting automated tools or the potential security issues caused by the tools. In other words, there is still room for a novel cyber range design which allow security researchers to safely deploy autonomous tools and perform automated tool testing if needed. In this paper, we introduce Pandora, a safe testing environment which allows security researchers and cyber range users to perform experiments on automated cyber attack tools that may have strong potential of usage and at the same time, a strong potential for risks. Unlike existing testbeds and cyber ranges which have direct compatibility with enterprise computer systems and the potential for risk propagation across the enterprise network, our test system is intentionally designed to be incompatible with enterprise real-world computing systems to reduce the risk of attack propagation into actual infrastructure. Our design also provides a tool to convert in-development automated cyber attack tools into to executable test binaries for validation and usage realistic enterprise system environments if required. Our experiments tested automated attack tools on our proposed system to validate the usability of our proposed environment. Our experiments also proved the safety of our environment by compatibility testing using simple malicious code.
CISV: A Cross-Blockchain Information Synchronization and Verification Mode

Although the advanced technology stack of blockchain leads to the prosperity of its applications, the underlying mechanism of blockchain interoperability remains stagnant. Targeting at the efficiency and verification problems of existing cross-blockchain mechanisms, we design a Cross-Blockchain Information Synchronization and Verification (CISV) mode. In CISV, Cross-chain Information Synchronization (CIS) uses the design of touch block to provide high synchronization rate within low network delay, while Cross-chain Information Verification (CIV) applies ECC cryptography algorithm and Secure Multi-party Computation (SMC) to guarantee the accuracy and privacy of cross-chain information. Experiments executed on three mainstream blockchains (Ethereum, Hyperledger Fabric, and EOS) show that CISV has a reliable performance in blockchain interoperability with strong expansibility.
A Study on Internet-Based Autonomous Learning of College Students’ English Based on ID3 Algorithm

Renewal of knowledge, teachers should not only teach students English knowledge, make them master these knowledge, but also cultivate students’ ability to learn knowledge independently. The enthusiasm and autonomy of students in College English learning, ID3 algorithm is combined to make them interested in learning and actively participate in curriculum and knowledge learning. Connotation autonomous learning and research-based learning, this paper designs a research-based learning mode based on autonomous learning. This model advocates the combination of autonomous learning and research-based learning.
Feasibility Study on Vibration Frequency Measurement of Power Equipment Based on Event Vision Sensing Technology

The traditional contact measurement method for measuring the vibration of power equipment usually does not have the convenience of engineering implementation, and it is difficult to measure the rotating parts and electrified equipment. Therefore, developing new non-contact vibration measurement technology has engineering value. In the paper, the transformer is taken as an example to analyze the vibration frequency to represent the state of the equipment, and then the application theory of the event visual sensing to perceive the equipment vibration frequency signal is proposed and elaborated; Finally, a event visual vibration frequency measurement system is built to carry out the feasibility test of the equipment vibration frequency test. The test results show that the event vision sensing technology can realize the non-contact measurement of equipment vibration frequency signal, and has the feasibility and potential of engineering application.
Report on the Impact of Artificial Intelligence on Project Management

Artificial intelligence and machine learning have found a wide range of business applications, but their impact is only just starting to be seen in project management.This study explores how our existing PM profession will change to be more suitable to AI inputs; and how project management will be forced to change because of the advent of AI, along with concrete, succinct and precise recommendations backed by demonstrable reasoning.
Pedagogical diagnostics as the basis of the knowledge correction system

The article is devoted to one of the poorly studied applications of pedagogical diagnostics - the correction of the knowledge of university students. An important new component of the diagnostic component is corrective diagnostics, which does not imply a controlling function. It is shown that this technology has the following main characteristics: consistency, complexity, integrity, scientific nature, conceptuality, developmental character, structuredness, hierarchy, consistency, variability and flexibility, procedurality, controllability, instrumentality, diagnostics, predictability.
Semantic Data Discovery from Social Big Data

Due to the large volume of data and information generated by a multitude of social data sources, it is a huge challenge to manage and extract useful knowledge, especially given the different forms of data, streaming data and uncertainty and ambiguity of data. Hence, there are still challenges in this area of BD analytics research to capture, store, process, visualise, query, and manipulate datasets to derive meaningful information that is specific to an application’s domain. This chapter attempts to address this problem by studying Semantic Analytics and domain knowledge modelling, and to what extent these technologies can be utilised toward better understanding to the social textual contents. In particular, the chapter gives an overview of semantic analysis and domain ontology followed by shedding light on domain knowledge modelling, inference, semantic storage, and publicly available semantic tools and APIs. Also, the theoretical notion of Knowledge Graphs is reported and their interlinking with SBD is discussed. The utility of the semantic analytics is demonstrated and evaluated through a case study on social data in the context of politics domain.
Crowd Density Estimation Based on Multi-Column Hybrid Convolutional Network

Abstract This paper studies an accurate counting model for dealing with highly crowded people, multi-column hybrid convolutional neural network model. The model is mainly composed of three parts. The first part uses the first ten layers of VGG-16 convolutional network for image feature extraction. The middle layer is a dilated convolution with three rows of “jaggy” dilation rates, and each row uses the Resnet-block connection method, which is used primarily to perceive human head features of different sizes. Compared with a variety of image up-sampling ways, in the third part of the model, this paper tries to use a combination of bilinear interpolation and convolution to up-sample image features, and research shows that this method effectively reduces the model error. In this experiment, the average absolute error (MAE), mean square error (MSE) and average relative error (MRE) are used as evaluation indicators, and experiments on the ShanghaiTech dataset proves that the network works well.
Existence of periodic solutions for a modified Elman neural network

For a modified Elman neural network with a periodic input, sufficient conditions for the existence of a periodic output are found by using Mawhin’s continuation theorem of coincidence degree theory. An example is given.
Modelling of Parallel Unsigned 2<sup>n</sup>-1 Modular Arithmetic Multiplier for RNS

Abstract Modular Multiplication operations are widely used in Digital crypto processors. Modulo multipliers is an essential block for Residue Number System (RNS) computation. Pointing to increase the performance of the RNS computation, the parallel unsigned modulo multiplier for 2 n -1 moduli is designed. A mathematical modelling, VLSI architecture and real-time verification are done in this work. Further, the modulo multipliers are described usingVerilog HDL, and the synthesize results for both FPGA and ASIC technologies are presented. Comparison is made based on the parameters such as Area, Power, Delay, PDP&amp; ADP using Cadence RTL Compiler with 180 nm, 90 nm and 45 nm TSMC CMOS Technologies.From the analysis indicate that the proposed multiplier provides a 16% area reduction and 40% speed improvement with a better PDP and ADP performance compared tothe existing modulo multipliers. Finally, the usefulness of 2 n -1 modulo multiplier in RNS environment is discussed.
Disaggregated Accelerator Management System for Cloud Data Centers

A conventional data center that consists of monolithic-servers is confronted with limitations including lack of operational flexibility, low resource utilization, low maintainability, etc. Resource disaggregation is a promising solution to address the above issues. We propose a concept of disaggregated cloud data center architecture called Flow-in-Cloud (FiC) that enables an existing cluster computer system to expand an accelerator pool through a high-speed network. FlowOS-RM manages the entire pool resources, and deploys a user job on a dynamically constructed slice according to a user request. This slice consists of compute nodes and accelerators where each accelerator is attached to the corresponding compute node. This paper demonstrates the feasibility of FiC in a proof of concept experiment running a distributed deep learning application on the prototype system. The result successfully warrants the applicability of the proposed system.
Ontop4theWeb: SPARQLing the Web On-the-fly

Web data come in many different structures and formats that are not supported by Semantic Web tools. To leverage them, we propose a system, called Ontop4TheWeb, which allows for mapping Web data of various formats into virtual RDF triples and querying them on-the-fly without materializing them as RDF triples. We put our system into practice, querying with SPARQL diverse Web data sources that range from HTML tables to REST APIs, such as social media APIs. Our thorough experimental evaluation demonstrates the high efficiency of our approach, which goes beyond the current state-of-the-art in this area, in terms of both functionality and performance.
Emerging Trends of Big Data in Cloud Computing

In today's world where data is accumulating at an ever-increasing rate, processing of this big data was a necessity rather than a need. This required some tools for processing as well as analysis of the data that could be achieved to obtain some meaningful result or outcome out of it. There are many tools available in market which could be used for processing of big data. But the main focus on this chapter is on Apache Hadoop which could be regarded as an open source software based framework which could be efficiently deployed for processing, storing, analyzing, and to produce meaningful insights from large sets of data. It is always said that if exponential increase of data is processing challenge then Hadoop could be considered as one of the effective solution for processing, managing, analyzing, and storing this big data. Hadoop versions and components are also illustrated in the later section of the paper. This chapter majorly focuses on the technique, methodology, components, and methodologies adopted by Apache Hadoop software framework for big data processing.
Media students using mobile phones in the Arabian Gulf to improve English writing and video production skills

Purpose A variety of alternate technology-enhanced teaching approaches are now available to university students to broaden their learning experiences and complement conventional face-to-face teaching. This paper aims to outline a study conducted at an English Medium Instruction (EMI) University in the Arabian Gulf where students were studying media. The study explored an innovative teaching approach that sought to enhance the students’ interaction with mobile phone applications as part of their learning experiences during the course. Design/methodology/approach The focus of the study was on enhancing the students’ English writing skills such as vocabulary, spelling and grammar and on improving their technical skills such as in video production. The study collected both quantitative and qualitative data. Findings The results indicated that mobile phone applications were helpful in improving students’ journalistic writing skills where they had a good level of proficiency in English, more so than students with poor English who are more dependent on traditional learning methods. Students also benefitted from mobile phone video production workshops that were intensive and creative. Based on the results of this study, it is recommended that courses and labs in media courses have skilled technicians that can train students in creative mobile phone video production while faculty members need to be trained and proactively encouraged to use mobile phones for teaching and learning purposes. Originality/value wBased on the results of this study, it is recommended that courses and labs in media courses have skilled technicians that can train students in creative mobile phone video production while faculty members need to be trained and proactively encouraged to use mobile phones for teaching and learning purposes.
WhatFits- Deep Learning for Clothing Collocation

With the great development of 5G technology research and the constant development of the network shopping, clothing classification and clothing collocation recommendation based on clothing pictures can provide advices to the customer and help businesses to promote sales. Deep learning is a latest research achievement in the field of machine learning, it has a strong ability of image modeling and image representation, which makes breakthrough progress in the field of image processing. Based on image data of dressing commodities provided by Taobao.com, as well as the text data of both customers' historical behaviors and dressing outfits generated by fashion experts, we design and implement clothing collocation and recommendation through relevant technologies of data mining and deep learning.