Reviewerly/exHarmony huggingface dataset:
Architectural Design of Trusted Platform for IaaS Cloud Computing

This article describes how the enormous potential benefits provided by the cloud services, made enterprises to show huge interest in adopting cloud computing. As the service provider has control over the entire data of an organization stored onto the cloud, a malicious activity, whether internal or external can tamper with the data and computation. This causes enterprises to lack trust in adopting services due to privacy, security and trust issues. Despite of having such issues, the consumer has no root level access right to secure and check the integrity of procured resources. To establish a trust between the consumer and the provider, it is desirable to let the consumer to check the procured platform hosted at provider side for safety and security. This article proposes an architectural design of a trusted platform for the IaaS cloud computing by the means of which the consumer can check the integrity of a guest platform. TCG's TPM is deployed and used on the consumer side as the core component of the proposed architecture and it is distributed between the service provider and the consumer.
A Traceable Scheme for Consortium Blockchain

Criminal activities on Blockchain (such as ransomware) have caused huge losses to society and users. The main reason for the above problem is that the strong privacy protection of the Blockchain makes criminals escape punishment. Therefore, the prerequisite for solving the above problem is that the regulator can first decide who should be punished. That is, tracing users’ identities is very important for addressing the above problem. In this paper, by striking a balance between centralization and decentralization, we propose a traceable scheme for consortium Blockchain. In our scheme, we design a registration mechanism to enable users to register with the committee. Moreover, we add oversight measures to restrict the rights of the regulator:
Research on Automatic News Text Summarization Technology Based on GPT2 Model

Automatic text summarization helps human beings to read news in fragmented time, realize the news "from long to short", and get important information quickly, accurately and comprehensively. In this paper, we use GPT2 model for automatic text summarization, and rewrite GPT2LMHeadModel in the transformers package, modify the loss calculation part, and conduct 5 and 10 epoch experiments on the original news data and the split word data respectively, and use ROUGE evaluation value as the evaluation index. Rouge-1 and Rouge-2 metrics are significantly improved compared with other experiments. This model solves the inadequate and unapproachable situation existing in automatic extraction of news headlines compared with the BERT benchmark model, and achieves a better portrayal of the summary text.
Variable Selection Method for Regression Models Using Computational Intelligence Techniques

Regression model (RM) is an important tool for modeling and analyzing data. It is one of the popular predictive modeling techniques which explore the relationship between a dependent (target) and independent (predictor) variables. The variable selection method is used to form a good and effective regression model. Many variable selection methods existing for regression model such as filter method, wrapper method, embedded methods, forward selection method, Backward Elimination methods, stepwise methods, and so on. In this chapter, computational intelligence-based variable selection method is discussed with respect to the regression model in cybersecurity. Generally, these regression models depend on the set of (predictor) variables. Therefore, variable selection methods are used to select the best subset of predictors from the entire set of variables. Genetic algorithm-based quick-reduct method is proposed to extract optimal predictor subset from the given data to form an optimal regression model.
A Depth Camera-based Warning System Design for Social distancing Detection

Nowadays, COVID-19 is raging around the world. Because of its highly contagious, people have to take many measures and change their daily lifestyle to face it. Keeping social distancing is particularly important for the prevention of COVID-19, especially for the administrator of public spaces, it makes sense to urge people to maintain social distancing. However, if the administrator directly carries out social distancing management, it will consume a lot of manpower and material resources, it is necessary to design a system that can automatically detect social distancing status in the areas. For the studies of social distancing detection, most of the related works use pixel analysis techniques based on images to obtain distance data, but this type of technique may produce large errors due to the difference camera angles. Therefore, in this paper, we plan to present a design of the social distancing detection and warning system by using the devices of high-precision depth camera and Android-based smart glasses. By using the depth camera, we can obtain the distance data more accurately to prevent misjudgment due to insufficient information acquisition, in addition, the using of smart glasses as the information terminal in order to provide relevant social distancing warning information to the area administrators more quickly and accurately. This system will not only benefit area administrators directly, but will also provide the basis for research in the area of social distancing risk in public places.
Multi-task sentiment classification model based on DistilBert and multi-scale CNN

Sentiment classification is an important topic in the field of natural language processing (NLP). The mainstream methods have also changed from the initial emotional dictionary to the later machine learning, and now most of them focus on deep learning. In sentiment classification tasks, comments in different fields have many similar expressions and relevant characteristics, which can effectively solve the problem of insufficient training data for current deep learning methods. In order to improve the classification effect of the model, we proposed a sentiment analysis model based on multi-task learning that Distillation Bidirectional Encoder Representations from Transformers combined with multi-scale convolution (MTL-DISTILBERT-MSCNN). It models comments in multiple fields together, and uses the advantages of the multi-task learning framework, the pre-training model DistilBert and the convolutional neural network (CNN) model to obtain the correlation between comments in multiple fields, the global features and local features of the text. We conduct experiments on 14 product review data sets and 2 movie review data sets. Using accuracy, F1-score and training speed as the evaluation criteria of the proposed model. By comparing single-task learning and other multi-task learning methods, the MTL-DISTILBERT-MSCNN model can be improved by 2%-5%, and the training efficiency is very high. In addition, we also adjusted the convolutional neural network of the model to further optimize the training effect of the model.
Solving complex problems with coevolutionary algorithms

tutorial Solving complex problems with coevolutionary algorithms Share on Authors: Krzysztof Krawiec Poznan University of Technology, Poland Poznan University of Technology, PolandView Profile , Malcolm Heywood Dalhousie University, Canada Dalhousie University, CanadaView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 832–858https://doi.org/10.1145/3377929.3389874Online:08 July 2020Publication History 0citation39DownloadsMetricsTotal Citations0Total Downloads39Last 12 Months17Last 6 weeks0 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
Healthcare SaaS Based on a Data Model With Built-In Security and Privacy

Cloud computing is a major technology enabler for providing efficient services at affordable costs by reducing the costs of traditional software and hardware licensing models. As it continues to evolve, it is widely being adopted by healthcare organisations. But hosting healthcare solutions on cloud is challenging in terms of security and privacy of health data. To address these challenges and to provide security and privacy to health data on the cloud, the authors present a Software-as-a-Service (SaaS) application with a data model with built-in security and privacy. This data model enhances security and privacy of the data by attaching security levels in the data itself expressed in the form of XML instead of relying entirely on application level access controls. They also present the performance evaluation of their application using this data model with different scaling indicators. To further investigate the adoption of IT and cloud computing in Indian healthcare industry they have done a survey of some major hospitals in India.
Mobile Technology Integration and English Language Learners

The purpose of this chapter is to report a case study examining the benefits and challenges of iPad use to help ELLs develop language proficiency. Based on the differentiated instruction framework, the author integrated iPads into a grade 4-5 ELL classroom and investigated learning impacts through classroom observations and interviews with the teacher and students. The findings of the study presented that iPad integration helped make learning engaging, provided various options appropriate for leaners' needs, and promoted learning outside of the classroom. Although important learning benefits were observed, several challenges were also reported such as a lack of appropriate ELL educational apps, a limitation of multitasking, and difficulties of monitoring students' learning progresses.
Design steps toy car for STEM to STEAM Education learning in Chiang Mai Rajabhat Demonstration School

Abstract The objectives of this study were designing a set of “Toy car” learning activity on the basis of STEM education and improved set of STEM activity. Development of a set of STEM activities was executed according to the engineering process. From the experiment and studied relate theory, the results were manual for teaching and student activity training. It was analysed Index of Objective Congruence (IOC) results found that STEM learning activity package was correct in content, learning activities and suitable for the purpose. The activity was tested by used to learning management with grade 5 and 6’s students in CMRU demonstration school. It found that the students’ satisfaction level toward STEM process was at a “good” level. It has value of 4.11 + 0.71 The STEM activity was improving to STEAM. It was presented in teacher training and found that the participants ’ satisfaction level toward STEAM process was at a “good” level. It has value of 3.98 ± 0.54 Participants, 89.60 % were confident that arts can be integral with science mathematics and this activity can be used to improve 21 st century learning skills.
Task Scheduling Based on Multi-level Hashing and HRRN in cloud computing

Task scheduling plays a crucial role in the cloud data center. An excellent job scheduling system can not only bring a high quality of user service, but also improve the resource utilization rate of the cloud service providers. This paper focuses on makespan and response time in task scheduling to improve quality of service. Under the framework of packing problem, this paper proposes a heterogeneous data center task scheduling algorithm based on multi-level hashing and High Response Ratio Next. Considering the different computing performance of physical hosts in heterogeneous data centers and the different resource demands of tasks, multi-level hashing is adopted to map the tasks to different queues waiting for execution, which can improve the speed of task allocation. When the tasks are waiting to be executed, the high response ratio next algorithm is executed in each queue to improve the service quality. Simulation is conducted by CloudSim, and the experimental results show that the proposed method is better than First-Come-First-Served (FCFS) and Shortest-Job-First (SJF) in the optimization of response time and makespan.
Piecewise Segmentation Occlusion Model for Image-Based Plenoptic Spectral Analysis

Visual occlusion of scene remains one of the most difficult challenges in plenoptic spectral anaylsis, especially in the case of mutual occlusion. This paper introduces a new piecewise segmentation occlusion model for image-based plenoptic spectral analysis by dividing occlusion and nonocclusion span. Compared to other works on plenoptic spectral analysis, we consider the fundamental problem of occlusion, where the light ray emitted from the object surface are analyzed from camera plane and image plane separately. We first treat the light field using Fourier transform equation, including occlusion and nonocclusion integral span. We then estimate the plenoptic bandwidth and the minimum sampling rate of the POF. We demonstrate results for various scenes, showing the variation in the occlusion spectrum relative to the nonocclusion spectrum and the consistency between the theoretical analysis and the simulation results. Furthermore, our reconstruction simulation result enables us to show that our framework performs well within classical method.
Adaptive Clipping Bound of Deep Learning with Differential Privacy

Deep learning has been extensively applied in many fields, such as image segmentation, voice recognition, automatic language translation. However, many malicious attackers attempt to attack the model which was trained to accomplish a deep learning assignment via various schemes. Recently, differential privacy technology has been proposed to defend against such attacks via sacrificing the accuracy of model. Therefore, many optimization methods have been proposed to reduce the overall privacy cost, and aim to seek a tradeoff between privacy and utility. In this paper, we propose an approach based on the cluster technology to get a tighter clipping bound for differential privacy deep learning model. In addition, we quantify the clipping bound with an objective function of standard deviation and prove our scheme in an analytically way. A large number of experiments setting on real-datasets demonstrate that our adaptive clipping bound method is better than the previous method which sets the clipping bound constantly.
Mobile Forensics

Nowdays to solve any crime and incident, mobile phone evidence plays a vital role. Digital forensics experts need specialized tools and techniques to extract the evidence from mobile phones for analysis. Extracting the evidence from mobile phones in a forensically sound manner has never been an easy task. The evidence extraction from mobile phones poses many challenges, as the entire process must ensure the integrity of evidence and its admissibility in the court of law. There are various tools and techniques available for mobile forensics which are classified based on its complexity and its physical characteristics. In this paper we are discussing various contemporary state-of-the art approach and advanced techniques used in mobile forensic. The different level of approach for mobile forensics, its advantages, disadvantages, cost and complexity are discussed in detail. The paper also highlights critical technical issues, research opportunities and challenges. [144]
Model-based evolutionary algorithms

tutorial Share on Model-based evolutionary algorithms Authors: Dirk Thierens Utrecht University, Utrecht, The Netherlands Utrecht University, Utrecht, The NetherlandsView Profile , Peter A. N. Bosman Centrum Wiskunde & Informatica - CWI, Amsterdam, The Netherlands Centrum Wiskunde & Informatica - CWI, Amsterdam, The NetherlandsView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 806–836https://doi.org/10.1145/3319619.3323386Published:13 July 2019Publication History 2citation90DownloadsMetricsTotal Citations2Total Downloads90Last 12 Months12Last 6 weeks1 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
Motivation of Adults in the System of Additional Education

The article considers the educational motivation of adults undergoing professional retraining or improving their skills in educational organizations of additional education as a multi-level system. The first level of this system is the employer’s motivation, which should have an impact on external motives (material prosperity and career development) and internal motives (personal self-improvement). The second level is the motivation on the part of the course manager, who represents the course and the teachers who implement it in a beneficial light. The final link in this system is the motivation on the part of teachers who teach specific disciplines, and the organizational and methodological conditions of motivation are based on the life experience of trainees, the priority of independent work, individualization of training, its activities, etc. The system of motivation should act in such a way that trainees continue self-education after graduation.
Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment

Root cause analysis in a large-scale production environment is challenging due to the complexity and scale of the services running across global data centers. It is often difficult to review the logs jointly for understanding production issues given the distributed nature of the system. Additionally, there could easily be millions of entities, each described by hundreds of features. In this paper we present a fast dimensional analysis framework that automates the root cause analysis on structured logs with improved scalability. We first explore item-sets, i.e. combinations of feature values, that could identify groups of samples with sufficient support for the target failures using the Apriori algorithm and a subsequent improvement, FP-Growth. These algorithms were designed for frequent item-set mining and association rule learning over transactional databases. After applying them on structured logs, we select the item-sets that are most unique to the target failures based on lift. We propose pre-processing steps with the use of a large-scale real-time database and post-processing techniques and parallelism to further speed up the analysis and improve interpretability, and demonstrate that such optimization is necessary for handling largescale production datasets. We have successfully rolled out this approach for root cause investigation purposes within Facebook's infrastructure. We also present the setup and results from multiple production use cases in this paper.
Random Forests

This chapter continues to build on the idea of ensembles of statistical learning procedures. Random forests is introduced, which is an extremely useful approach that extends and improves on bagging. As before, there is an ensemble of classification or regression trees and votes over trees to regularize. Additional randomness is introduced when at each potential partitioning for each tree, a random subset of prediction is selected for evaluation. This has a variety of benefits, some of which can be quite subtle. Also discussed are supplementary algorithms to random forests that allow a peek into the black box.
Wearable Interaction

This chapter describes multimodalMultimodal interfacesInterface and interactiveInteractive solutionsSolution for inputInput entry and outputOutput responsesResponse for wearableWearability technologiesTechnology. Focusing on the designDesign of wearable interactionInteraction for different formForm factorsFactors, this chapter discusses the suitability, benefitsBenefit, and drawbacksDrawbacks of each modalityModality. MultimodalMultimodal interfacesInterface are presented, from popularPopular head-mountedHead-mounted displaysDisplay and wrist-wornWrist-worn wearablesWearability, to customizedCustomized smartSmart textilesTextiles and alternative formForm factorsFactors. This chapter describes the interactionInteraction designDesign process, covering five interaction modalitiesModality and highlighting their benefitsBenefit and drawbacksDrawbacks for the userUser interactionInteraction depending on the userUser contextContext, applicationApplication domainDomain, and formForm factorFactors. The multiple modalitiesModality discussed are graphicsGraphic, tactileTactile, gestureGesture, audioAudio (voice-basedVoice interactionInteraction), and brain–computerComputer interfacesInterface. This chapter provides examplesExample of userUser interactionInteraction with illustrations of wrist-wornWrist-worn devicesDevices, head-mountedHead-mounted devicesDevices, smartSmart clothesClothes, and alternative formForm factorsFactors, suchChest-mounted asBack back-mounted devicesDevices and chest-mounted devicesDevices.
Statistical Modelling in the Age of Data Science

Twenty years after Leo Breiman's wake-up call on the use of data models, I reconsider his concerns, which were heavily influenced by problems in prediction and classification, in light of the much vaster class of problems of estimating effects and (conditional) associations.Viewed from this perspective, one realises that the statistical community's commitment to the use of data models continues to be dominant and problematic, but that algorithmic modelling (machine learning) does not readily provide a satisfactory alternative, by virtue of being almost exclusively focused on prediction and classification.The only successful way forward is to bridge the two cultures.It requires machine learning skills from the algorithmic modelling culture in order to reduce model misspecification bias and to enable pre-specification of the statistical analysis.It moreover requires data modelling skills in order to choose and construct interpretable effect and association measures that target the scientific question; in order to identify those measures from observed data under the considered sampling design by relating to minimal and well-understood assumptions; and finally, in order to reduce regularisation bias and quantify uncertainty in the obtained estimates by relating to asymptotic theory.
Blockchains

This chapter seeks to discredit the popular belief that blockchains will revolutionize or disrupt commerce. More specifically, it aims to clarify that blockchains as such cannot serve as a technology or ideology for the decentralization of online marketplaces. To this end, the chapter examines the interrelated concepts of decentralization, disintermediation, trustlessness, and immutability. It is necessary to understand what those terms actually mean and how they affect actual, commercial practices. The chapter commences with a broad description of blockchains and introduces the important division between public and private blockchains, to demonstrate that only the latter could potentially serve as a technology that could provide a user-friendly and secure transacting environment. It confronts the practical implications of decentralization, focusing on the fact that the absence of formalized control usually translates into an absence of formalized governance processes.