Reviewerly/exHarmony huggingface dataset:
Research on the Role of Computer in the Management of Books and Reference Materials

Abstract With the development of network, computer has been applied to all walks of life, such as library management. Through the computer, the library can automatically manage all kinds of books and documents, which can constantly innovate the management system. By promoting the innovation of library management, we can improve the quality and efficiency of library management, which will realize the informatization and modernization of library management. Through modern management, the library can meet the reading needs of the public, which will better meet the public service. With the advent of the era of big data, the traditional book management is no longer suitable for modern library management, and network resources have become the main direction. Therefore, we must comply with the development of the times in order to better inherit knowledge. Therefore, the computer has an important role in the management of books and materials, such as changing the management concept, breaking the traditional reading habits, improving the efficiency of library management. First of all, this paper analyzes the necessity of library information management. Then, this paper analyzes the function of computer information. Finally, some suggestions are put forward.
Formation of an Individual Development Trajectory of a Specialist

The development of the digital economy has led to the transformation of most markets due to a more active transition from manual to automated labor. Competition is increasing and specialists with non-standard and in-demand skills in the usage of digital technologies have become the most valuable. The main purpose of the research is the elaboration of a conceptual model for building an “individual trajectory of a professional development”, implemented on a web service basis. To achieve this goal, the existing process of interaction between subjects of the educational environment, business and public authorities has been studied. The model of a «Web service for building an individual trajectory of personal development» is proposed. The architecture of a model is a flexible digital platform consisting of many separate platforms that will provide universal access to the main participants and will allow us to create a certain profile of an individual with its own ID to collect and organize all the necessary information. The model is based on a managing the development of individual, personal and professional abilities of an individual and allows the state to mobilize and use the full potential of human resources, processes and information technologies in the digital economy.
2nd International Workshop on Industrial Recommendation Systems (IRS)

Recommendation systems are used widely across many industries, such as e-commerce, multimedia content platforms and social networks, to provide suggestions that a user will most likely consume or connect; thus, improving the user experience. This motivates people in both industry and research organizations to focus on personalization or recommendation algorithms, which has resulted in a plethora of research papers. While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment. These key factors include but are not limited to: business metric definition and evaluation, recommendation quality control, data and model scalability, model interpretability, model robustness and fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc. The gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia's contributions for industrial recommendation systems. This workshop aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue through which academic researchers become aware of the additional factors that may affect the adoption of an algorithm into real production systems, and how well it will perform if deployed. Industrial researchers will also benefit from sharing the practical insights, approaches, and frameworks as well.
Containers vs. virtual machines: performance comparison

Server virtualization is a technological innovation widely used in information technology (IT) companies. Virtualization provides a platform to run different operating system services in the cloud. It makes it easy to build multiple virtual machines on a single basic physical machine, either in the form of hypervisors or containers. To host many microservices applications, emerging technology has introduced a model consisting of different operations performed by smaller individual deployed services. Therefore, the demand for low-cost virtualization techniques is developing rapidly. There are many lightweight virtualization technologies; Docker is one of them, which is an open-source platform. This technology enables developers and system administrators to build, create, and run applications using the Docker engine. This document provides performance evaluation of Docker containers and virtual machines using standard benchmark tools such as Sysbench, Phoronix, and Apache, including CPU performance, memory performance, storage read/write performance, load test and measurement of operating speed.
Feasibility of Fine Needle Aspiration Cytology in the Diagnosis of Breast Lumps

Background: most patients with breast mass are anxious (to start with) regarding method of final diagnosis. If FNAC is done by dependable hands it proves to be the least invasive and may lead (if positive) to one stage surgery. Aim of the study: The study aimed principally to evaluate the feasibility and accuracy of Fine Needle Aspiration Cytology (FNAC) in the diagnosis of breast lumps by comparing the results with the histopathological diagnosis. Methodology: data were prospectively collected from 103 female patients within a period of 5 years (Feb.2009 to Jan.2014). All patients had palpable breast lumps for which initially aspiration cytology of the lump has been done followed by partial or total surgical lump excision for final histopathological diagnosis and to compare the results.
An Improved Cloud Particles Optimizer for Function Optimization

Evolutionary algorithm is a popular and effective method in optimization field. As one of the optimization algorithms, cloud particles optimizer (CPEA) has shown to be effective in solving problems with different characteristics. However, due to the powerful local search ability, premature convergence is a significant disadvantage of CPEA optimizer. To alleviate this problem, an improved cloud particles optimizer named ICPEA is proposed. In ICPEA, the fluid operation is designed to explore the evolution direction, while the solid operation is employed to improve the exploitation efficiency. Moreover, the dynamical selection strategies of control parameters are employed to cope with premature convergence issues. In order to demonstrate the effectiveness of ICPEA, CEC2014 test suites are used for simulating. The experimental results affirm that ICPEA is a competitive optimizer compared to CPEA algorithm and several state-of-the-art optimizers.
A Comprehensive Approach to On-board Autonomy Verification and Validation

Deep space missions are characterized by severely constrained communication links. To meet the needs of future missions and increase their scientific return, future space systems will require an increased level of autonomy on-board. In this work, we propose a comprehensive approach to on-board autonomy. We rely on model-based reasoning, and we consider many important (on-line and off-line) reasoning capabilities such as plan generation, validation, execution and monitoring, runtime diagnosis, and fault detection, identification, and recovery. The controlled platform is represented symbolically, and the reasoning capabilities are seen as symbolic manipulation of such formal model. We have developed a prototype of our framework, and we have integrated it within an on-board Autonomous Reasoning Engine. Finally, we have evaluated our approach on three case-studies inspired by real-world projects and characterized it in terms of reliability, availability, and performance.
Identity and Attribute Preserving Thumbnail Upscaling

We consider the task of upscaling a low resolution thumbnail image of a person, to a higher resolution image, which preserves the person’s identity and other attributes. Since the thumbnail image is of low resolution, many higher resolution versions exist. Previous approaches produce solutions where the person’s identity is not preserved, or biased solutions, such as predominantly Caucasian faces. We address the existing ambiguity by first augmenting the feature extractor to better capture facial identity, facial attributes (such as smiling or not) and race, and second, use this feature extractor to generate high-resolution images which are identity preserving as well as conditioned on race and facial attributes. Our results indicate an improvement in face similarity recognition and lookalike generation as well as in the ability to generate higher resolution images which preserve an input thumbnail identity and whose race and attributes are maintained.
On the Power of Pre-Trained Text Representations

Recent years have witnessed the enormous success of text representation learning in a wide range of text mining tasks. Earlier word embedding learning approaches represent words as fixed low-dimensional vectors to capture their semantics. The word embeddings so learned are used as the input features of task-specific models. Recently, pre-trained language models (PLMs), which learn universal language representations via pre-training Transformer-based neural models on large-scale text corpora, have revolutionized the natural language processing (NLP) field. Such pre-trained representations encode generic linguistic features that can be transferred to almost any text-related applications. PLMs outperform previous task-specific models in many applications as they only need to be fine-tuned on the target corpus instead of being trained from scratch.
A Survey on Explainability in Artificial Intelligence

The world has been evolving with new technologies and advances everyday. With learning technologies, the research community can provide solutions in every aspect of life. However, it is found to lag behind the ability to explain its prediction. The current situation is such that these modern technologies can predict and decide upon various cases more accurately and speedily than a human, but has failed to provide an answer when the question of “how” it arrived at such a prediction or “why” one must trust its prediction, is put forward. To attain a deeper understanding of this rising trend, the authors surveyed a very recent and talked-about novel contribution, “explainability,” which would provide rich insight on a prediction being made by a model. The central premise of this chapter is to provide an overview of studies explored in the domain and obtain an idea of the current scenario along with the advancements achieved to date in this field. This survey aims to provide a comprehensive background of the broad spectrum of “explainability.”
Online Application for Bitcoin Price Visualization

This paper presents a solution for the simple visualization of Bitcoin price. Current applications are often complex and include an amount of information that might be useful to some people. However, if someone is only interested in knowing the price, then these applications are needlessly complicated. Our proposed solution aims to display only a simple and clear visualization of price in a selected period and comparison to historical data. In the paper, we describe how to acquire data from online sources and process and visualize them in a simple graphic chart. The prototype is developed as a web application to make it accessible on both desktop and mobile devices.
Thoughts on the Safety of College Sports Activities under the Normalization of Epidemic Prevention and Control

Affected by COVID-19, our college sports activities are also facing a series of challenges. In order to fight the epidemic and win this battle of prevention and control, it has triggered thinking about the safety of college sports activities. By understanding the significance of safety precautions for sports activities in colleges and universities and analyzing the challenges faced, the following suggestions and considerations are put forward:(1) Strengthen the education of the concept of safety and health in sports activities; (2) Scientifically design the technical solutions for safety and prevention of sports activities in colleges and universities; (3) Reasonably arrange the physical education curriculum teaching plan; (4) Carefully prepare appropriate teaching design; (5) Strictly control extracurricular sports activities.
Silicon Photonics for Artificial Intelligence and Neuromorphic Computing

Artificial intelligence and neuromorphic computing driven by neural networks has enabled many applications. Software implementations of neural networks on electronic platforms are limited in speed and energy efficiency. Neuromorphic photonics aims to build processors in which optical hardware mimic neural networks in the brain. © 2021 The Author(s)
A Self-Adaptive IoT-based Approach for Improving the Decision Making of Active Surgical Robots in Hospitals

In recent years, surgical robots have become instrumental tools for assisting surgeons in performing complex surgical procedures in hospitals. Unlike conventional surgical methods, robotic systems help surgeons, for example, to perform minimally invasive surgical procedures while enhancing the precision and control of operations (e.g. tiny incisions, wound sutures, endoscopic suturing, among others). To this extent, it is essential to consider several factors that may influence the feasibility and decision making of employing robotic systems in surgical procedures. In this paper, we propose an IoT-based self-adaptive approach that uses multi-criteria decision analysis methods (MCDA) for enhancing the decision making of operations involving surgical robots. Throughout this paper, we present experimental validation results in utilizing MCDA as an effective strategy for enhancing the decisions of employing robotic systems in surgical procedures.
Secure Outsourcing of Fuzzy Linear Regression in Cloud Computing

There are problems in which the use of linear regression is not sufficiently justified. In these cases, fuzzy linear regression can be used as a modeling tool. The problem of constructing a fuzzy linear regression can usually be reduced to a linear programming problem. One of the features of the resulting linear programming problem is that it uses a relatively large number of constraints in the form of inequalities with a relatively small number of variables. It is known that the problem of constructing a fuzzy linear regression is reduced to the problem of linear programming. If the user does not have enough computing power the resulting problem can be transferred to the cloud server. Two approaches are used for the confidential transfer of the problem to the server: the approach based on cryptographic encryption, and the transformational approach. The paper describes a protocol based on the transformational approach that allows for secure outsourcing of fuzzy linear regression.
Hierarchical and Multi-Level Cost Aggregation For Stereo Matching

Nowadays, convolutional neural networks based on deep learning have greatly improved the performance of stereo matching. To obtain higher disparity estimation accuracy in ill-posed regions, this paper proposes a hierarchical and multi-level model based on a novel cost aggregation module (HMLNet). This effective cost aggregation consists of two main modules: one is the multi-level cost aggregation which incorporates global context information by fusing information in different levels, and the other called the hourglass+ module utilizes sufficiently volumes in the same level to regularize cost volumes better. Also, we take advantage of disparity refinement with residual learning to boost robustness to challenging situations. We conducted comprehensive experiments on Sceneflow, KITTI 2012, and KITTI 2015 datasets. The competitive results prove that our approach outperforms many other stereo matching algorithms.
Research and Development of Synchronous Monitoring for Weight Discrimination and Arrangement Control System

This study aims to combine the technologies of virtual reality and human machine interface for the application to the practical training of mechatronics machines to help users' practical learning of mechatronics without restrictions to time and space. Mechatronics machines in schools are limited that learners have to take turns for operation learning in the practical training. It results in extremely long practice time. The self-designed human machine interface integrated virtual reality is applied to the online to offline monitoring of mechatronics machines in order to solve the teaching problems in machines. A 3D model of mechatronics machines is first established and then attached with materials and assembled in this study. The virtual reality software is further used for establishing the virtual scene and machine motion. The human machine interface is finally programmed to monitor and control the virtual reality machine and the physical machine. Leaners, through the offline model on the human machine interface, could learn various device names of machine, operation procedures, and the edition and design of control programs. Through the online model on the human machine interface, leaners could also learn the remote online to offline synchronous monitoring of virtual and real machines. The experimental results reveal that leaners, through offline learning, could rapidly familiarize the operation of real machines and the writing and design of control programs. It would reduce the training time and costs and personnel injury or machine damage caused by leaners' careless operation or control program errors.
Design of personalized teaching resource recommendation system based on wireless communication network

research-article Design of personalized teaching resource recommendation system based on wireless communication network Share on Author: Suling Song Henan College of Industry & Information Technology, China Henan College of Industry & Information Technology, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 368Pages 1–8https://doi.org/10.1145/3469213.3472787Online:18 August 2021Publication History 0citation13DownloadsMetricsTotal Citations0Total Downloads13Last 12 Months13Last 6 weeks2 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
Blockchain Applications in Computer Communication

Blockchain technology was first introduced more than a decade ago, as a crypto coin alternative for the financial sector, in the form of Bitcoin. Since its introduction, the research community has identified the potential of blockchain, not only for the financial sector, but for many other areas, such as industry, manufacturing, logistics, supply chain, healthcare, and so on. Blockchain technology has gained its place in computer communication, too, especially in the Internet-Of-Things (IoT) domain. In this paper, we present the main characteristics of blockchain technology and find the places in computer communications where it can fit.
Self-Guided Adversarial Learning For Domain Adaptive Semantic Segmentation

Unsupervised domain adaptation has been introduced to generalize semantic segmentation models from labeled synthetic images to unlabeled real-world images. Although much effort was devoted to minimize the cross-domain gap, the segmentation results on real-world data remain highly unstable. In this paper, we discuss two main issues which hinder previous methods from achieving satisfactory results and propose a novel self-guided adversarial learning to leverage the capability of domain adaptation. Firstly, to deal with the unpredictable data variation in the real-world domain, we develop a self-guided adversarial learning method by selecting reliable target pixels as guidance to lead the adaptation of the other pixels. Secondly, to address the class-imbalanced issue, we devise the selection strategy in each class independently and incorporate this idea with a class-level adversarial learning in a unified framework. Experimental results show that the proposed method significantly improves the previous methods on several benchmark datasets.
Using the Context-Sensitive Policy Mechanism for Building Data Acquisition Systems in Large Scale Distributed Cyber-Physical Systems Built on Fog Computing Platforms

The article deals with the use of context-sensitive policies in the building of data acquisition systems in large scale distributed cyber-physical systems built on fog computing platforms. It is pointed out that the distinctive features of modern cyber-physical systems are their high complexity and constantly changing structure and behavior, which complicates the data acquisition procedure. To solve this problem, it is proposed to use an approach according to which the data acquisition procedure is divided into two phases: model construction and data acquisition, which allows parallel realization of these procedures. A distinctive feature of the developed approach is that the models are built in runtime automatically. As a top-level model, a multi-level relative finite state operational automaton is used. The automaton state is described using a multi-level structural-behavioral model, which is a superposition of four graphs: the workflow graph, the data flow graph, the request flow graph and the resource graph. To implement the data acquisition procedure using the model, the context-sensitive policy mechanism is used. The article discusses possible approaches to implementation of suggested mechanisms and describes an example of application.
Privacy-Preserving Healthcare Analytics of Trajectory Data

Technological advancements have led to generation and collection of big data from various data sources including mobile devices. For instance, to prevent, combat and detect COVID-19, citizens of many countries were encouraged to use contact tracing apps on their mobile devices. Collection of their trajectories can be analyzed and mined for social goods. At the same time, their privacy also needs to be preserved. In other words, the advent of COVID-19 has made releasing of patient records become imperative and yet privacy of individuals must be protected. Releasing spatio-temporal COVID-19 data plays a significant role in contact tracing and may help in reducing the spread of the disease due to likelihood of increasing adherence to social distancing and other health related guidelines by the people around the cluster of the released data. In this paper, we examine the problem of preserving privacy of spatio-temporal trajectory data and introduce a hierarchical temporal representative point (HTRP) differential privacy model. We evaluate our framework using a South Korean COVID-19 patient route dataset. Empirical results show a balance of utility and privacy provided by our framework with our HTRP for privacy-preserving healthcare data analytics.