Reviewerly/exHarmony huggingface dataset:
A Study of the Roles of Leadership Styles and Attitudes with Social Responsibility for the 4th Industrial Revolution

The 4 th Industrial Revolution (FIR) has already begun.Corporations have to fulfill their social responsibilities to the FIR.This study explores the leadership and attitudes required in the FIR era for companies to fulfill their social responsibilities.In the FIR era, workers should have certain attitudes, such as self-efficacy, expectancy, and acceptance of change.If workers have these attitudes, they will be able to make a difference in the future.This study suggests the leadership styles that are needed to improve these attitudes.Participation, creativity, delegation of leadership, and inspirational motivation of leaders involves organizational change and improvement of organizational performance.This paper demonstrates the kind of leadership that is more important for each attitude through statistical analysis.Furthermore, when workers have these attitudes, not only the future, but also their current performance can be improved.To explain this relationship, the paper describes the effect of attitudes on job satisfaction.If workers have certain attitudes, their current and future performance will improve.As a result of the research, all four leadership styles had positive effects on attitudes toward FIR, and attitudes showed a mediating effect on the relationship between leadership styles and job satisfaction.In addition, it has been proven that the role of participative and delegating leadership, which is more memberoriented leadership, is more important.Leadership will enable workers to have a particular attitude through member-centered leadership, and workers will be able to increase current and future performance.Through these efforts, companies will be able to increase their performance in the current and FIR era, and fulfill their social responsibilities more faithfully.Therefore, in the FIR era, companies will play an important role in the development of society, and create new values.
One Improved RLWE-based FHE and Fast Private Information Retrieval

With the rapid development of cloud computing, it raises real questions on privacy protection, which greatly limits the use of cloud computing.However, fully homomorphic encryption (FHE) can make cloud computing consistent with privacy.In this paper, we propose a simpler FHE scheme based on ring LWE problem, with a smaller size of ciphertext and a lower noise-expansion factor for homomorphic multiplication.Then based on our optimized RLWE-based FHE scheme, we propose a fast single-database private information retrieval protocol, combining with batching and number theoretic transform technology.
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In this correspondence, we investigate the covering radius of various types of repetition codes over Z p k ( k ≥ 2 ) with respect to the Lee distance. We determine the exact covering radius of the various repetition codes, which have been constructed using the zero divisors and units in Z p k . We also derive the lower and upper bounds on the covering radius of block repetition codes over Z p k .
Central Control over Distributed Service Function Path

Service Function Chaining (SFC) supports services through linking an ordered list of functions.There may be multiple instances of the same function, which provides a challenge to select available instances for all the functions in an SFC and generate a specific Service Function Path (SFP).Aiming to solve the problem of SFP selection, we propose an architecture consisting of distributed SFP algorithm and central control mechanism.Nodes generate distributed routings based on the first function and destination node in each service request.Controller supervises all of the distributed routing tables and modifies paths as required.The architecture is scalable, robust and quickly reacts to failures because of distributed routings.Besides, it enables centralized and direct control of the forwarding behavior with the help of central control mechanism.Simulation results show that distributed routing tables can generate efficient SFP and the average cost is acceptable.Compared with other algorithms, our design has a good performance on average cost of paths and load balancing, and the response delay to service requests is much lower.
Interferometer-based Photonic Circuit Classifier Showing &gt;90% Accuracy for Well-known Iris Dataset without Utilizing Nonlinear Activation Function

We demonstrate that interferometer-based photonic circuits can perform classification by only phase control even without activation functions, which can classify well-known Iris dataset with &gt;90% accuracy in simulation, showing simple photonic implementation for machine learning.
Envisioning and Questioning Near Future Urban Robotics

Robotic services, which have started to appear in urban environments, are going to transform our society.Designers of these robots are not only required to meet technical and legal challenges, but also address the potential social, political, and ethical consequences of their design choices.In this paper, we present a workshop format with its related tools intended for enabling speculation about such possible futures and fostering reflection on potential socio-ethical implications that might support/oppose these futures.We report the results and discussion of one particular workshop case, in which the implementation of two particular robotic services for a city was envisioned and questioned, i.e., surveillance and delivery of goods.By discussing the results, we illustrate how such a workshop format might be beneficial for setting the agenda for a more conscious design of urban robots and orienting future research towards meaningful themes related to the emerging coexistence scenarios between citizens and robots.
CBDC and Monetary Sovereignty

In an increasingly digitalized world, issuers of private digital currency can weaken central banks’ ability to stabilize the economy. By continuing to make central bank money attractive as a payment instrument in a digital world, a central bank digital currency (CDBC) could help to maintain a country’s monetary sovereignty.
Application of generative deep learning models for approximation of image distribution density

Generative neural network models for visual concept learning and the problem of approximating image distribution density are studied. A criterion for an image to belong to a simulated class is introduced based on an estimate of the probability in the space of latent variables and reconstruction errors. Several generative deep learning models are compared. Quality estimates of the solution to the problem of one-class classifications for a set of images of handwritten digits are experimentally obtained.
Technique to Thwart Brute-Force Attack : A Survey

Most encryption techniques have one essential problem that is they are vulnerable to brute-force attacks. Techniques used to prevent brute force attacks are increasing password length, password complexity, limit login attempts, using captcha, two factor authentication etc. A new mechanism to prevent against brute-force attack was introduced and this mechanism is known as Deception Model. Deception-based security mechanisms focus on altering adversaries perception in a way that can confuse them and waste their time and resources. This technique exploit adversaries biases and present them with a plausible looking but fake data. This technique can be used to serve for encrypting e-mail messages, human-generated messages and documents that are stored at different platforms. This paper will offer an detailed study of the current situation of Deception Model.
Semantic Dependency Graph Parsing of Financial Domain Questions Based on Deep Learning

Abstract In order to effectively solve the problems of limited accuracy of semantic parsing and ambiguous semantic recognition in the current Question-Answer System based on Chinese Knowledge Map in intention recognition. This paper presents a method about Dependency Reduction in language dependency analysis(DR-BLSTM-CRF) based on Semantic Dependency Graph Parsing(SDGP), Bidirectional Long Short-term Memory(BLSTM) model and Conditional Random Field(CRF) of Xunfei Open Platform. The semantic dependency graph dependency reduction method is as follows:1) Semantic Dependency Graph Parsing of interrogative sentences based on Web API of Xunfei Open Platform to obtain a sentence representation containing semantic dependency information. 2) Named Entity Recognition (NER) algorithm combined with BLSTM and CRF is used to identify the named entity of the interrogative sentence to gain a sequence containing character label information, and then combine the analysis results of semantic dependency graph to obtain a more accurate semantic dependency graph through dependency reduction. The experimental results show that the precision, recall and F1 value of the model proposed in this paper are 33.4%, 33.9% and 34.2% higher than those of LTP in terms of the semantic dependency analysis on the 140,000 self-built data sets of financial domain questions. The model can effectively analyze the semantic dependency of financial domain questions.
UKRR: a collaborative collection management success story

<em>It is worth noting that all the material in this paper is adapted from, and appears in greater length in, the UKRR Final Report</em>.<sup>1</sup> This article summarizes the achievements of the United Kingdom Research Reserve (UKRR) project, which was established in 2007 to explore whether, through collaborative documentation, preservation and deduplication of low-use print journals, it would be possible to realize benefits through the generation of space savings across the UK’s Higher Education libraries. In total, the project received £11,581,672 in funding from HEFCE (now Research England). UKRR was managed by Imperial College London in partnership with the British Library (BL) and between 2007 and 2019, 35 further libraries participated in the project. UKRR ran in three phases and has now transitioned to a service delivered by the BL which is available to libraries across the UK. During all three phases of the project, UKRR supported the processing of nearly 130,000 metres of print journal materials and enabled the release of nearly 98,000 metres of shelf space across the 36 libraries. Print copies of scarce titles were preserved, nearly 10,000 individual journal issues from 8,000 journal titles helped fill gaps in the BL’s own collection and data for over 300,000 individual journal issues was enhanced in the BL’s catalogue. When calculating the capital and recurrent value of the space released amongst the participating libraries, it is estimated that the project delivered £4.04 in savings for every £1.00 of funding received.
Doppia coniugazione regolare del passato remoto in italiano contemporaneo

Abstract The Italian preterite ( passato remoto , PR) is the only verb tense that has two series of equivalent endings for a single group of verbs ending in - ere . In the PR’s actual use, one notes, however, a strong preference for choosing one of the two inflexional paradigms of a given verb, which remains unattested in Italian grammatical studies. With this article, then, I intend to examine 1) what the actual usage is of the regular inflected forms of the PR in the largest corpora of written Italian; 2) how the regular second conjugation of the PR is presented in the most important Italian dictionaries. I believe that the results of this examination provide an important contribution regarding the formation of grammatical rules pertaining to the PR, but they also contribute to the debate on grammatical norms and on the actual usage of the Italian language.
Potensi Sekam Padi dan Jerami sebagai Alternatif Material Akustik

&lt;p class="Abstract"&gt;Technology that is increasingly developing produces equipments in the form of information, communication, production, transportation and entertainment facilities. Most of the equipments produce noises. Noise can be reduced by using acoustic dampers. This study aims to determine the potential for sound absorption from rice husks and straw as natural acoustic materials. Materials used include rice husks, rice straw, and resin. 12 cylindrical samples were made. Samples that have been made are tested using a sound absorption measuring instrument that is the impedance tube to measure the value of the sample sound absorption coefficient. The test results show the value of the sound absorption coefficient (α) can be influenced by the thickness. At a frequency of 500 Hz the value of the sound absorption coefficient (α) for each sample is 0.034dB; 0.030dB; 0.030dB; 0.027dB; 0.026dB; 0.027dB; 0.025dB; 0.025dB; 0.025dB; 0.024dB; 0.024dB; 0.023dB; 0.023dB. The sample material is good in research on materials 1 and 2 which have a sound absorption of 0.034 dB and 0.030 dB.&lt;/p&gt;
QoE-based Reinforcement Learning at MEC for Improving 4K Streaming Quality

4K or ultra-high-definition (UHD) will be the standard for video streaming in the next decade. In this research, we carry a study on dynamic adaptive streaming over HTTP (DASH) which is a crucial adaptive algorithm. More specifically, we employ asynchronous reinforcement learning (RL) algorithm in the form of quality of experience (QoE). In fact, QoE is an important factor to evaluate the efficiency of streaming transmission models. Hence, we made every streaming request must be passed through a MEC server, and the MEC make a decision to retrieve segments for clients. Experiment result shows that our proposed QoE RL algorithm achieves higher quality compared to our previous research on the same context of content-awareness.
Sports Game Management Method Based on Intelligent Computer Computing

With the development of social science and technology, the popularization rate of artificial intelligence is getting higher and higher. People apply artificial intelligence to all aspects of life and provide convenience for people’s lives. The management of sports competitions is an important part of sports competitions. It is necessary to ensure the quality of sports competitions on the principles of fairness, impartiality, and openness, to ensure the discipline of the competition, to maintain the order of the stadium, and the safety and security of traditional sports competition performance management systems. The management results are relatively unreliable and require more human resources. Therefore, this paper proposes a method for managing sports games based on intelligent machine calculations, and uses this method to design a management system. The system uses a network platform to transmit data. By analyzing user needs and functional requirements, the network platform is designed to implement data transmission between the system and the client, and the system is tested. The research results show that this paper is designed based on the intelligent computer computing method of sports competition management. The system results management results are reliable and the system performance is strong.
Technologies for automatic testing of a software package for realistic computer graphics

The article describes the technology of automatic software testing in relation to industrial systems of computer graphics and optical simulation. Test automation becomes vital in the face of limited resources with the frequent release of product versions, which often occur among software product manufacturers. There are presented both methods of regression testing the computational kernel of such systems, and methods of testing the user interface. Scripting mechanism based on Python is used for regression testing, its multithreading capabilities which allow significant decreasing of testing time are also described. Python allows two ways of parallelization – multithreading and multiprocessing, both of them are considered. Due to the stochastic methods used in optical simulation calculation results may differ from time to time, which complicates regression testing. In this case, it is proposed to apply some (in each case - your own) threshold when comparing the simulation results. Separately automated testing of user interface which was elaborated basing on the AutoIt tool is described. The approach for testing the user interface of systems implemented in the form of plugins to existing CAD/PDM complexes, the source code of which is closed and not available to the authors of automatic tests, are described as well.
HolDCSim: A Holistic Simulator for Data Centers

A comprehensive data center simulation infrastructure, which models major hardware and system components and offers interfaces to manage the interplay between computation and communication resources, is critical in advancing future research for more effective performance and energy optimization in these environments. In this paper, we present HolDCSim, a light-weight, holistic, extensible, event-driven data center simulation platform that effectively models both server and network architectures. HolDCSim can be used in a variety of data center system studies including job/task scheduling, resource provisioning, global and local server farm power management, and performance analysis. We demonstrate the design of our simulation infrastructure, and illustrate the usefulness of our framework with a case study that analyzes server-network performance and energy efficiency. We also perform validation studies for our simulator on a physical testbed with Intel Xeon-based processors and Cisco network switches.
A Machine Learning Approach to Extract Keyphrases from Bengali Document using CNN-Bidirectional LSTM

Keyphrases are single or multiple word phrases of a document which describe the principal topics of that document. These keyphrases help readers to get an overview of the document. In this paper, we proposed a system that uses the combination of Convolutional Neural Network and Bidirectional Long Short-Term Memory (BiLSTM) Recurrent Neural Network (RNN) to automatically detect keyphrases from a document. We also used some preprocessing steps to clean and generate candidates keyphrases to train the model. Convolutional Neural Network can analyze semantic meanings of sentences. Bidirectional LSTM can learn the relations among words in the sentences. A Bengali pre-trained word embedding is used in this work.
Construction and Application of Online Course Teaching in Intelligent Learning Environment

At present, the development of intelligent education is in full swing. As the cornerstone of intelligent education, intelligent learning is the inevitable trend of learning mode transformation driven by the wave of intelligent education. The purpose of this paper is to study the construction and application of online course teaching in the intelligent learning environment. Firstly, it studies the technical support of intelligent learning environment and the teaching application of intelligent learning environment. Secondly, it discusses cloud computing technology, Internet of things technology and learning analysis technology. The questionnaire is based on the actual learning experience of students. The questionnaire is divided into four dimensions: perceived usefulness (5 items), perceived ease of use (4 items), course satisfaction (6 items) and learning interaction (6 items). The purpose of the questionnaire survey is to obtain students’ learning experience comprehensively and truly, and to verify the feasibility and effectiveness of the research model. The experimental results show that the scores of the two dimensions of the questionnaire design are all around 4.20, with an average of 4.23, which shows that students have a good evaluation of the online learning environment based on the research model, recognize its convenient use of platform functions, effectively obtain learning information, present clear and accurate content, and communicate with teachers and peers flexibly.
Polarization Calibration Scheme for a Practical Handheld Free Space Quantum Key Distribution Link

Free space Quantum Key Distribution (QKD) links between terminals that move relative to each other pose a number of challenges. The beam-steering components (typically mirrors) and the relative movement lead to variable birefringence, and for particular configuration there will also be fixed birefringence. The paper proposes a polarization calibration scheme suitable for real-time operation. Simulation and experiment are performed to verify the proposed scheme.
RETRACTED: Research on Risk Causes and Preventive Strategies of Enterprise Internal Audit Inspection Based on Computer Simulation Technology

Abstract Enterprise internal audit institutions and internal auditors often need to face various internal audit risks arising from macro-policy environment, internal environment of enterprises and problems of auditors themselves when performing internal audit tasks. In recent years, with the improvement of relevant laws, the independence of internal audit management of enterprises has been weakened to a certain extent, which leads to the great risk of internal audit work in most enterprises in China. Therefore, enterprises must proceed from their own reality, through the improvement of the system, strengthen the management of the eyelashes, quality improvement and other preventive measures to achieve the purpose of reducing internal audit risks. This paper demonstrates the establishment and implementation of the simulation model for the specific problems of the internal audit of the enterprise, and explains the application of computer simulation technology in the internal auditing risk causes and prevention strategies.
Performance-driven Programming of Multi-TFLOP Deep Learning Accelerators*

Deep Neural Network (DNN) accelerator architecture have evolved rapidly in recent years demonstrating impressive peak processing efficiencies. However, little effort has been devoted towards developing systematic methodologies to program DNN accelerators to extract the best accelerator utilization across a range of DNN workloads. This becomes critical as DNN layers vary dramatically in their computational characteristics, necessitating them to be programmed differently to maximize overall performance. In this work, we address this challenge in the context of the RaPiD multi-TFLOP DNN accelerator proposed in [1], which comprises of a 2D-systolic array of processing elements, a 1D-array of special function units and a scratchpad memory. We develop DeepMatrix, a framework that enables systematic exploration of the design space to map DNNs to a given accelerator architecture, which can discover even non-intuitive optimization strategies to achieve high utilization. Specifically, given a DNN, it identifies how the computations need to be spatiotemporally sequenced, how much data needs to be staged at each level in the memory hierarchy and when data-transfers between memory hierarchies need to occur so that performance is maximized while meeting the constraints imposed by the hardware (processing power, memory capacity, bandwidth etc). DeepMatrix achieves this by building a parameterized design space of mapping configurations, and uses a design space exploration methodology to identify the best configuration. Across multiple large and practical DNNs (AlexNet, ResNet, VGG), we demonstrate DeepMatrix can yield 1.4x−2.8x improvement in performance over hand-tuned homogenous mapping.