Reviewerly/exHarmony huggingface dataset:
Plenary Panel 4 - Quantum Software &amp;amp; Services

Quantum computing is a new and emergent computation parading, and we need to understand how it is going to be used and impact in the current software and services.Furthermore, it is necessary explore how connect quantum and conventional computation to take advantage in both sides.Understanding that one of the key components of this technology is the quantum hardware (Quantum Processing Units or QPUs), the software and algorithm need to cover from the core hardware to the final user.This software stack from one side need to help to new quantum developers to have better tools to handle and improve the quantum hardware, also create new algorithms and models, and finally how we are going to integrate all these pieces with the current conventional applications.In this journey we are going to need to discover use the powerful of this quantum computation and how the users can connect and develop new kind of software and services around of it.This panel is going to help to understand the current state of the art in the Quantum field, from the software near to the quantum hardware to services integrated on the cloud.The panel is moderated by Frank Leymann and Ismael Faro.
Research on educational video summary extraction algorithm based on semantic analysis

Educational videos are growing exponentially, and efficiently obtaining core video clips has become an urgent problem. Keyframe extraction is an effective means of creating video summaries, which reduces the storage burden and improves video retrieval speed. To address the importance of text information in educational videos, we propose a new keyframe extraction algorithm based on inter-frame text semantic analysis - KFEVSA. Firstly, text recognition is performed based on OCR technology, and the sampled frames are stored separately according to text frameset and non-text frameset. Secondly, text- similarity and perceptual hashing algorithms are used to process the two framesets to generate their respective key framesets. Finally, the two sets of keyframes are combined in chronological order to generate the final video summary set. The experimental comparison on a benchmark dataset of educational video summaries created by multiple users shows that this method accurately describes the video content and generates fewer redundant frames than traditional methods.
Energy-Efficient Virtual Machine Placement in Data Centers Via Accelerated Genetic Algorithm with Improved Fitness Computation

Energy efficiency basically refers to how effectively energy can be utilised or consumed by any virtualised data centre giving cloud administrations. Since Genetic Algorithm (GA) is an acceptable method to solve the VM placement problem, this research formulates the Virtual Machine (VM) placement problem as a constrained optimisation problem. A considerable amount of residual resources is available for each new VM allocation, however they are distributed across multiple active PMs, rendering them inefficient. Our research focuses on improving a data centre’s energy and time efficiency by devising a technique to decrease PM’s residual resources and make them more valuable. As the most time consuming element of a GA is calculating the fitness function, this paper presents a new fitness function to simplify the fitness function calculation in GA. The results are compared to the standard GA and First Fit Decreasing (FFD) algorithm. The findings reveals a 8% energy savings for our GA compared to FFD and a 66% reduction in our GA execution time compared to the standard GA for virtualised data centres. The simulation experiments are conducted on small-, medium- and large-scale of the google data centre to demonstrate the effectiveness of our GA.
An 8-bit Fixed Point Quantization Method for Sparse MobileNetV2

Sparse MobileNetV2 has potential for embedded image processing applications due to its low computation complexity. However, it uses 32-bit float point number system for computing and storage. As a result, its computing efficiency can be further improved. In this paper, an 8-bit fixed point quantization method for Sparse MobileNetV2 is proposed for model size reduction and computing operation simplification, which improves throughput and shrinks memory consumption. Firstly, quantization objects are confirmed by analyzing the computation pattern of Sparse MobileNetV2. Then, after the statistical analysis of the distribution characteristic of parameters, the 8-bit fixed point quantization method is applied. Experimental results of image classification on remote sensing image dataset show that the memory consumption is reduced to 18.7% and the throughput increases by l.6x, while there is only a slight accuracy loss.
Background

This chapter presents theoretical background of the proposed approach: Knowledge Discovery and Decision Reduct techniques, Text Mining for Sentiment Analysis and Visualiztaion techniques for Data Analytics.
Advances in Green Cloud Computing

Cloud computing is witnessing tremendous growth at a time when climate change and reducing emissions from energy use is gaining attention. With the growth of the Cloud, however, comes an increase in demand for energy. There is growing global awareness about reducing greenhouse gas emissions and healthy environments. Green computing in general aims to reduce the consumption of energy and carbon emission and also to recycle and reuse the energy usage in a beneficial and efficient way. Cloud data centers consume inordinate amounts of energy and have significant CO2 emissions. This chapter gives an overview about green cloud computing and its evolution, surveys related work, discusses associated integrated green cloud architecture (green cloud framework), innovations, and technologies, discusses green cloud computing scenarios, and highlights future work and challenges that need to be addressed to sustain an eco-friendly cloud computing environment that is poised for significant growth.
Research and Design of Switch Control Module of Railway Signaling Based on Power Electronics Technology

Abstract According to the characteristics of railway operation, and studying the main structure of the interlocking system, a set of railway switch control module suitable is designed by using power electronic technology and modular design. This paper studied on the new-type pure electronic control of switch control circuit. The reliability and safety of the railway switch control module are analyzed by scientific means. Through field tests, it fully proves that the module has the advantage characteristics of small volume, fully functional, and stable performance etc. In a word, it has strong engineering practical value and conforms to the development trend of railway technology.
An extensive, multi-sensor dataset for event and fall detection in human activities that includes video recordings and detailed annotations

The thermal time constants were used to analyze the energy transfer mechanism in the laser medium. It can be applied briefly than the photoacoustic detection technique for the radiative quantum efficiency. The time constant was found to be 133 s and 169 s under lasing and nonlasing conditions, respectively, for 4 at.% ceramic Nd:YAG at a pump power of 2.5 W. The radiative quantum efficiency was calculated using the thermal time constants under lasing and nonlasing conditions. A radiative quantum efficiency of 24% was obtained under lasing.
Quantum Computers

The fundamental problem of communication is to ensure that, the process of communication should always help reproduce at one point either exactly or approximately a message selected at another point. Normally, every message has a meaning and purpose and possibly even a backdrop too.
An Encryption Methodology for Enabling the Use of Data Warehouses on the Cloud

A cloud data warehouse (cloud DW) is a subject-oriented, integrated, time-variant, voluminous, nonvolatile and multidimensional distributed database that is hosted in a cloud. A solution to ensure data confidentiality for a cloud DW is cryptography. In this article, the authors propose an encryption methodology for a cloud DW stored according to the star schema, considering both the data confidentiality maintenance of the DW and the capability of processing analytical queries directly over the encrypted DW. The proposed encryption methodology comprises an encryption strategy for DW called MV-HO (MultiValued and HOmomorphic) for the definition of how the different types of DW's attributes must be encrypted. The proposed MV-HO encryption strategy was compared with encryption strategies based on symmetric encryption, order preserving symmetric encryption and homomorphic encryption. Results indicated that MV-HO is the best solution found, as MV-HO is pareto-optimal with respect to other strategies investigated.
Digital Libraries, Copyright Limitations, and Access for Purposes of Subsequent Academic Publishing

The chapter aims to analyse the current European Union legislation on digital copyright from the perspective of the public interest in scientific research, by studying the digital exclusive rights framework granted to creators and to database owners and by contraposing it with the foreseen narrow field of public interest based limitations on exclusive rights. Concepts like digital libraries, fair use and limitations on exclusive rights, tpm and drm, copyleft and free/open source contents is analysed. The chapter concludes by identifying the necessity of a redefinition of a new internal balance of copyright law which can respect authors' and database owners' legitimate economic interests and simultaneously promote the dissemination and the access to works for scientific research and publishing purposes and the renewal of the creative cycle.
Improving the Number of Queries Supported by Differentially Private Mechanisms

Differential privacy is a widely accepted privacy conception. However, there is a limitation on the number of queries supported by differentially private mechanisms, and the limitation stops differentially private mechanisms from being widely used in business systems. In this paper, we propose two mechanisms to optimize the number of queries, namely approximate answer differentially private mechanism and nonzero mean differentially private mechanism. We analyze the two proposed mechanisms in terms of their privacy and utility. On the one hand, the two mechanisms satisfy E differential privacy. On the other hand, outputs of proposed mechanisms are indistinguishable over an interval so the number of queries is improved greatly. For utility, we show that the utility of the two proposed mechanisms is the same as that of the Laplace mechanism in the view of information theory.
Opportunity to Start Strong

This chapter investigates challenges faced by educators in the early elementary grades as well as opportunities to transform science education in these critical, early years of schooling. We studied kindergarten teacher's first attempt to integrate one-to-one technology in an inquiry-based science lesson, drawing on works of Randi Engle to analyze the framing of the students and the activity, as well as on the works of Peggy Ertmer and colleagues to investigate barriers encountered in the implementation process. We employed Mishra and Koehler's (2006) Technological Pedagogical Content Knowledge (TPACK) framework to understand the dynamic triad of teacher's pedagogy, content, and technology knowledge, need for successful technology implementation endeavors. While implementations of technology enhanced inquiry-based science lessons in early elementary grades present educators with some unique dilemmas, they also carry a potential for sparking the scientific curiosity of the youngest elementary school learners' and illuminating the years to come.
Steganography Using Substitution Principle

In this chapter, the author describes the various substitution-based image steganography techniques. Basically, there are four categories of substitution techniques: (1) least significant bit (LSB) substitution, (2) LSB array-based substitution, (3) group of bits substitution (GBS), and (4) adaptive LSB substitution. The LSB substitution-based techniques are very much insecure (detectable by RS analysis), so LSB array, GBS, and adaptive LSB substitution techniques are proposed in literature. If substitution principle is used in a steganography technique, then it must be evaluated by RS analysis.
Semi-supervised Feature Selection Method for Fuzzy Clustering of Emotional States from Social Streams Messages

Capturing the text content, especially when it reflects the human emotional states and feelings, is crucial in every decision-making process: from the item purchase to the marketing campaign, the user mood is becoming an essential peculiarity to always monitoring. This work proposed a new method based on a fuzzy clustering algorithm that takes into account human suggestions for feature selection. The method exploits two fuzzy indices, namely, the feature relevance that is initially provided by the human expertise and the feature incidence on a specific cluster. The Extended Fuzzy C-Means (EFCM) clustering is used to balance the two “dueling” indexes; a t-norm operator-based feature importance index enables the appropriate feature set selection. Experimental results on social message streams show the method’s effectiveness in supporting those emotions the human considers relevant in the textual context.
Stacks and elimination

A stack is a last-in-first-out (LIFO) pool: threads push items on, and pop items off, the top of the stack. This chapter considers the challenge of implementing scalable concurrent stacks when every operation would seem to contend for access to the top of the stack. It presents a classic lock-free stack implementation, in which all operations do indeed conflict, and then introduces a powerful technique called elimination, which reduces contention by enabling push and pop operations to rendezvous away from the top of the stack. Matching push and pop operations cancel out each other's effects, so they can both avoid the need to access the top of the stack. Because they reduce contention, elimination-based stacks are able to scale.
Cloud Auditor Loyalty Checking Process Using Dual Signature

We apply dual signature method. Providing security to the data from auditor during remote data possession checking by applying dual signature. Basically dual signature is a mechanism that is used to provide security during secure electronic transition protocol. The function of dual signature is to provide authenticity and integrity of the data. It links two message wished for two different recipient. In the case of providing security from auditor we use this methodology because it works on the basic of providing two links for two different recipients. In the case of dual signature customer wants to send order information to the trader and payment information to the bank. Here we use two links but not for the purpose of secure transaction but for the purpose of secure information exchange in remote possession checking.
Optimization of a Solar-powered Irrigation System

Optimization is now a crucial element in industrial applications involving sustainable alternative energy systems. During the design of such systems, the engineer/decision maker would often encounter noise factors when their system interacts with the environment (e.g., solar insolation and ambient temperature fluctuations). In this chapter, the sizing and design optimization of the solar powered irrigation system is considered. This problem is multivariate, noisy, nonlinear, and multiobjective (MO). This chapter is divided into two parts where two situations are considered during the optimization of the solar powered irrigation system. Part 1 is the MO design optimization of the mentioned system under constant weather conditions. Part 2 involves optimizing a more general form of the design problem by accounting for varying weather conditions, insolation, and ambient temperature. The details of the optimization procedures of the two cases are presented and discussed in this chapter.
An efficient bandwidth reservation policy for data-intensive applications in cloud

Recently, more and more data-intensive applications have been deployed in cloud platforms due to its cost-effective and elastic capability. Although computing resources (i.e., CPU and RAM) can be provisioned in on-demand manner, the network bandwidth has enforce certain admission controlling for providing desirable quality-of-service (QoS). As a result, data-intensive applications often suffer from high delay during the phase of bulk data transferring between computing nodes. In this paper, we present an integrated bandwidth reservation algorithm, which combines different admission controlling policies and minimises the finishing time for all jobs in an online manner. Performance evaluation on a real-world cloud platform shows that the proposed reservation algorithm can ensure efficient bulk data transferring even when the system is facing heavy workloads.
Online Environment Abnormal Expression Detection Based on Improved Autoencoder

Detecting the status of online learning and online working through human facial expressions and actions has been a hot topic in the field of Computer Vision in recent years. Anomalies have the characteristics of low occurrence probability, difficulty in definition and various types, so anomaly detection is extremely challenging. This paper is based on image rather than video to detect laptop users' abnormal expressions more quickly, which divided into three steps. Firstly, we use illumination compensation to improve the brightness of the original image, and then separate the face from the background by skin color segmentation. Secondly, we combine sparse coding and an additional encoder to extract face features based on autoencoder, which reduces the noise impact in the encoding process and enhances the robustness of the model. Finally, we put the extracted face features into one-class Support Vector Machine (SVM) classifier based on the radial basis function (RBF) for classification. This paper conducts experiments on three real datasets of learning, entertainment and working, which proves that the F1-score of our proposed model is up to 0.89157.
E-Assessment System for Open and Short Answer (Applied to a Course of Arabic Grammar in 7th Year in Tunisia)

The goal of this article is the development of an evaluation system based on the Arabic language. This article contains four parts. The first part is the corpus construction from the 7th year basic education classes' grammar book in Tunisia. Then, the second part is on the construction of the Concept Maps (CMaps) ontological for simple Arabic sentences from this corpus, where the automatic extraction of terms is completed. This extraction is based on two major approaches: linguistic and statistical. The third part in this article is the automatic instantiation. The last part is devoted to the application of the similarity measure chosen in the CMaps ontological fusion, which summarizes the various semantic links and which ends with a judgment according to the calculated score.
State-of-the-Art E-Learning Platforms Intended for Teaching and Learning

Distance learning experiments have been launched since 2010 in several Moroccan universities as part of an experimental approach. It, therefore, seems that a strategy must be put in place to give this choice of education its place in training and grant it the means necessary to achieve these objectives. The objective of this chapter has presented an overview, on free and proprietary e-learning platforms, on their functional architectures, as well as the types of e-learning devices that can create from platforms e-learning, and to deduce the state of the art of the e-learning provisions of Moroccan universities and to compare them with the e-learning provisions of European and American universities.
Graph-Based Semi-Supervised Learning With Big Data

This chapter presents some popular graph-based semi-supervised approaches. These techniques apply to classification and regression problems and can be extended to big data problems using recently developed anchor graph enhancements. The background necessary for understanding this Chapter includes linear algebra and optimization. No prior knowledge in methods of machine learning is necessary. An empirical demonstration of the techniques for these methods is also provided on real data set benchmarks.
R Package GeneralCorr Functions for Portfolio Choice

We explain the usage of the following new R functions in my package called ‘generalCorr’ Vinod (2021b). The function sudoCoefParcor() is for pseudo regression coefficients from kernel regressions. They are a nonlinear version of regression coefficients from standardized data. The functions decileVote(), momentVote(), exactSdMtx() compute exact computation of stochastic dominance from ECDF areas. dif4mtx() computes growth, change in growth, etc., up-to order 4 differencing of time series. The function bootDom12() reports bootstrap confidence intervals for any confidence level. We illustrate all these functions with a toy example of only seven observations. The last section has the code which produced various tables in this document.
The Role of U-FADE in Selecting Persuasive System Features

Although persuasive technology continues to impact behavioral and attitudinal interventions, research has demonstrated that existing design approaches are faced with limitations that impede their effective usage. Mostly, they become obsolete, as user needs change during the use of the persuasive application. This research therefore proposes the unified framework for analyzing, designing, and evaluating of persuasive systems (U-FADE). The proposed framework integrates concepts from various existing models to provide a systematic approach that facilitates persuasive design. It addresses the issue of changing needs of users by considering external and internal activities that may promote or impede persuasion before and during implementation.
Verifiable receipt-free electronic voting system based on mask ballot

A secure electronic voting system should be both verifiable and receipt-free based on good fairness and accuracy. A verifiable e-voting system offers voting credentials that allow users to verify their votes, therefore, there is a conflict between receipt-freeness and verifiability. In this paper, we proposed a secure e-voting scheme — a solution to the problems such as ballot trading and coercive voting — which satisfies both receipt-freeness and verifiability. In our scheme, we adopted the Florentine Square scheme to generate votes and utilized OT protocol to enhance the randomness of the votes. This makes it difficult for voters to provide vote credentials to others, thus avoiding collusion or cheating, as well as ensuring good receipt-freeness. Moreover, we employed ElGamal homomorphic encryption to guarantee the verifiability. In addition, we have conducted experiments on the scheme, and the results proved that the scheme is feasible.
From Information Systems Outsourcing to Cloud Computing

We are witnessing the business prospects of cloud computing becoming apparent as many cloud computing services have been made available online. Previously, outsourcing of computing resources employs a third party to set up, develop, and maintain the infrastructure within or outside the business organization. However, nowadays many business organizations all over the world see that outsourcing their computing resources through cloud computing is a highly viable option as it saves a business organization from the complicated setup and maintenance costs of computer infrastructure. There are three generally known types cloud computing, namely public cloud, private cloud, and hybrid cloud. There also three generally service models namely software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). This chapter highlights the development of cloud computing technology, benefits and issues of outsourcing an organization's computing resources through cloud computing.