Reviewerly/exHarmony huggingface dataset:
Introduction to the Special Section on Data Science for Cyber-Physical Systems

introduction Introduction to the Special Section on Data Science for Cyber-Physical Systems Share on Editors: Francesco Piccialli University of Naples FEDERICO II, Italy University of Naples FEDERICO II, ItalyView Profile , Nik Bessis Edge Hill University, UK Edge Hill University, UKView Profile , Gwanggil Jeon Incheon National University, Incheon, South Korea Incheon National University, Incheon, South KoreaView Profile , Calton Pu Georgia Institute of Technology, USA Georgia Institute of Technology, USAView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 21Issue 2June 2021 Article No.: 28epp 1–7https://doi.org/10.1145/3464766Online:15 June 2021Publication History 0citation51DownloadsMetricsTotal Citations0Total Downloads51Last 12 Months51Last 6 weeks3 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
Energy-Based Comparison for Workflow Task Clustering Techniques

Cloud computing consumes a huge amount of electrical power which penalizes both the cloud service providers and environmental standards. Energy-performance tradeoff is still an open issue in cloud computing that targets minimizing energy consumption by the datacenters’ infrastructure while meeting the user QoS as well as defined by the Service Level Agreement. Scientific distributed applications generate complex workflows that may contain thousands of tasks deployed over the cloud datacenters for execution. These applications are high demanding in terms of computation capabilities and may lead to hosts’ overloading which may increase the energy consumption. In this study, we analyze the contribution of task clustering techniques on energy consumption by the cloud’s infrastructure. The most relevant scheduling polices will be setup according to horizontal and vertical task clustering schemes with the Montage scientific workflow to demonstrate the impact on energy saving. For this purpose, we used WorkflowSim: an open source cloud simulator providing a workflow-level support, task scheduling and clustering techniques. The simulations’ results highlight the impact of horizontal scheme in increasing the energy consumption.
An overview of Advanced Optical Flow Techniques for Copy Move Video Forgery Detection

The establishment of digital media such as video, photographs, and audio throughout the web in recent years has expanded the opportunity to shape digital forgeries. The technique developed by hackers recently, by implementing duplication operation or copy-move on top of the list. The central idea is to hide from view or incorporate an object in a video frame to create a new frame or scene and the process is well known and exploited for suppressing an evidence from being discovered. This survey paper focuses on techniques that uses optical flow that could be the key strategy for calculating the movement of picture power, that may be attributed to the movement of objects contained by the scene.
Doughnutie: An efficient and low‐latency cloud data center network architecture

Summary Data center network provides the supporting infrastructure for cloud computing. Torus interconnection network is widely employed in the cloud data center networks. However, its long network diameter and average path length (APL) are prohibitive factors for supporting the delay‐sensitive applications. With the aim of meeting latency, network capacity, fault‐tolerance, scalability, and energy‐efficiency requirements of cloud data center networks, we propose a new data center network architecture, named Doughnutie . Doughnutie is built by adding a number of flyway connections to the base Torus network to ameliorate its performance. Doughnutie does not follow a single principle for establishing all of its flyways and consequently, each part of the network can profit from all local, faraway, and neither‐local‐nor‐faraway types of connection. Doughnutie decreases both network diameter and APL, which in turn leads to higher communication efficiency and lower network latency. Moreover, an exclusively designed loop‐free load‐balanced adaptive routing algorithm, named Doughnuter , is proposed. Employing the Doughnuter routing algorithm results in more efficient utilization of all the Doughnutie network resources. Theoretical analysis and simulation results also prove the superiorities of Doughnutie in throughput, load balancing, fault tolerance, and energy‐efficiency.
Toward audience-aware argument generation

The maturity of the computational argumentation field, demonstrated with the first live debate between a machine and a human,1 triggers a demanding question: how can we build argumentation technologies that bring people together? We believe that an important part of the answer is to include the audience's beliefs into the process.
Context-aware Outstanding Fact Mining from Knowledge Graphs

An Outstanding Fact (OF) is an attribute that makes a target entity stand out from its peers. The mining of OFs has important applications, especially in Computational Journalism, such as news promotion, fact-checking, and news story finding. However, existing approaches to OF mining: (i) disregard the context in which the target entity appears, hence may report facts irrelevant to that context; and (ii) require relational data, which are often unavailable or incomplete in many application domains. In this paper, we introduce the novel problem of mining Context-aware Outstanding Facts (COFs) for a target entity under a given context specified by a context entity. We propose FMiner, a context-aware mining framework that leverages knowledge graphs (KGs) for COF mining. FMiner generates COFs in two steps. First, it discovers top-k relevant relationships between the target and the context entity from a KG. We propose novel optimizations and pruning techniques to expedite this operation, as this process is very expensive on large KGs due to its exponential complexity. Second, for each derived relationship, we find the attributes of the target entity that distinguish it from peer entities that have the same relationship with the context entity, yielding the top-l COFs. As such, the mining process is modeled as a top-(k,l) search problem. Context-awareness is ensured by relying on the relevant relationships with the context entity to derive peer entities for COF extraction. Consequently, FMiner can effectively navigate the search to obtain context-aware OFs by incorporating a context entity. We conduct extensive experiments, including a user study, to validate the efficiency and the effectiveness of FMiner.
Systems of Socialization and Education of Students at the University of the Digital Age

AnnotationThe work is devoted to the consideration of the peculiarities of the organization of the processes of socialization and education of students in the era of the digital transformation of society based on the creation, competent and systematic use of a modern information and educational space. The authors note that such an environment is saturated with information and communication technologies and contributing to the adaptation of future specialists in the rapidly changing digital world, and the formation of their information culture.
A model‐driven approach for semantic web service modeling using web service modeling languages

Abstract The service‐oriented software engineering approach has been increasingly utilized to design and develop complex distributed systems. Exploiting semantic web technologies to increase utilization of the web services at the semantic level leads to create semantic web services (SWSs). This paper proposes a model‐driven architecture (MDA) to model the SWS and to transform it from a high‐level modeling language, such as the Unified Modeling Language (UML), to a low‐level semantic description, such as the Web Service Modeling Language (WSML). To annotate all aspects of the SWS, a UML profile is provided by extending the UML metamodel. Some stereotypes and tagged values are also defined to support WSML. Therefore, the structure aspects of the SWS are modeled with a class diagram based on stereotypes that are defined in the UML profile. The logical and behavioral aspects of the SWS are modeled and expressed using activity diagrams, sequence diagrams, and Object Constraint Language (OCL). Experimental results show that the proposed approach not only increases the level of independence, accuracy, expressiveness, understandability, and machine processability but also significantly reduces the complexity and heterogeneity. Moreover, the proposed method is also evaluated in comparison with the previous methods of the literature, and the results will show that the proposed method outperforms the other methods in terms of expressiveness, understandability, scalability, level of independence, and accuracy.
Informal Learning with Extended Reality Environments: Current Trends in Museums, Heritage, and Tourism

This chapter discusses the capabilities of extended reality technology in informal learning environments, such as museums and cultural heritage sites. Recent developments in extended reality technologies have led to increased integration into these informal learning spaces and have heightened the need for a systematic investigation into the affordances of modern multimedia representations spanning tangible to virtual mediums. These affordances vary across settings, ranging from personalizing learning experiences, increasing engagement through interactive activities, and augmenting exhibits with rich multimedia content. The chapter critically appraises several affordances of extended reality technologies while expanding on these notions by outlining the cognitive theory of multimedia learning to inform practical instructional design principles. Drawing on case examples from their own research with tangible and virtual museum objects in these settings, the authors explore and discuss how extended reality technologies can serve to both collect and analyze ambient data in real time. The authors claim that ambient data will become increasingly prevalent in such settings and critical to gain insights into visitor experience and promote meaningful and engaging learning experiences. They discuss the implications of their findings for practice and suggest directions for future research.
RALSA: Design and Implementation

International large-scale assessments (ILSAs) provide invaluable information for researchers and policy makers. Analysis of their data, however, requires methods that go beyond the usual analysis techniques assuming simple random sampling. Several software packages that serve this purpose are available. One such is the R Analyzer for Large-Scale Assessments (RALSA), a newly developed R package. The package can work with data from a large number of ILSAs. It was designed for user experience and is suitable for analysts who lack technical expertise and/or familiarity with the R programming language and statistical software. This paper presents the technical aspects of RALSA—the overall design and structure of the package, its internal organization, and the structure of the analysis and data preparation functions. The use of the data.table package for memory efficiency, speed, and embedded computations is explained through examples. The central aspect of the paper is the utilization of code reuse practices to the achieve consistency, efficiency, and safety of the computations performed by the analysis functions of the package. The comprehensive output system to produce multi-sheet MS Excel workbooks is presented and its workflow explained. The paper also explains how the graphical user interface is constructed and how it is linked to the data preparation and analysis functions available in the package.
Enhancing the Value of Counterfactual Explanations for Deep Learning

Counterfactual examples can be used to explain a specific clinical prediction from a deep learning model by identifying what kind of feature changes would produce a different result, i.e. flipping the prediction's classification. On-going research seeks to refine the metrics for discovering counterfactual examples, given a specific input to a deep learning model. Our work enhances this by using feature importance to reveal how much individual feature changes in the counterfactual example contribute to flipping the prediction's classification, compared with the original. Our approach does not depend on the specific metrics used for generating the counterfactual examples, so it is general. It can be used either to gain further insight when the counterfactual examples have already been generated or to influence the generation of the counterfactual examples. We illustrate this novel approach with a healthcare example.
A Power Data Reconstruction Method Based on Super-Resolution Generative Adversarial Network

The smart grid is rapidly developing to become highly connected and automated. These advancements have been mainly attributed to the ubiquitous data communication in the grid. However, low sampling frequency will limit the utilization degree of data because low frequency measurement power data contains little information. The existing methods of reconstructing the low-frequency sampling data into the high-frequency sampling data have poor accuracy of data reconstruction since most of them failed to capture the characteristics of power data. This paper proposes a novel method based on super-resolution generative adversarial network (SRGAN) to address this issue. First, we convert power data into data-images. Furthermore, the data-images are used to train the SRGAN model. Finally, the trained generator can be used to reconstruct the low-frequency sampling data into the high-frequency sampling data. Numerical experiments have been carried out based on photovoltaic (PV) power generation time-series data from the State Grid Corporation of China with separately reconstruction of the irradiance and PV power datas. The results demonstrate the superior performance of the proposed method compared with a series of state-of-the-art methods.
Opening the Black Box of Deep Neural Networks in Physical Layer Communication

Deep Neural Network (DNN)-based physical layer techniques are attracting considerable interest due to their potential to enhance communication systems. However, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques and their cost in terms of computational complexity. We further investigate and also experimentally validate how information is flown in a DNN-based communication system under the information theoretic concepts.
Processing 3D Data from Laser Sensor into Visual Content Using Pattern Recognition

This paper is focused on processing data from a laser 3D scanner working on the triangulation principle. Generated point cloud processed by an algorithm in a programming language python using mainly OpenCV library to generate 2D data - images. This type of generated image achieves better pixel stability compared to images captured by camera vision, which are highly dependent on lighting conditions. At present, mainly inspection systems using camera vision, which are used to capture images being analyzed. This paper describes processing scanning surface of an object performed at an experimental inspection stand. The object of interest is an automobile's tire-like object. The main goal is to develop an inspection system for tire manufacturing. The first Part-Task is developing a matching system for combination more source data and merges them. Generation template data and compare them with inspected scanned data by the same methods. This process is performed by pattern recognition, where the possible methods applicable to these specific data types are described. The result of this work is the preparation of the 3D and 2D data and their analysis for further experiments in the inspection system. Especially, for the analysis of specific principles to identify abnormalities in the geometry of the scanned surface are used. The final system that will be used in mass production will be a compromise between conditions such as the price of hardware, the time to evaluate the possibility of finding abnormalities and analyze them.
An Intelligent System to Ensure Interoperability for the Dairy Farm Business Model

Picking reliable partners, negotiating synchronously with all partners, and managing similar proposals are challenging tasks for any manager. This challenge is even harder when it concerns small and medium enterprises (SMEs) who need to deal with short budgets and evident size limitations, often leading them to avoid handling very large contracts. This size problem can only be mitigated by collaboration efforts between multiple SMEs, but then again this brings back the initially stated issues. To address these problems, this paper proposes a collaborative negotiation system that automates the outsourcing part by assisting the manager throughout a negotiation. The described system provides a comprehensive view of all negotiations, facilitates simultaneous bilateral negotiations, and provides support for ensuring interoperability among multiple partners negotiating on a task described by multiple attributes. In addition, it relies on an ontology to cope with the challenges of semantic interoperability, it automates the selection of reliable partners by using a lattice-based approach, and it manages similar proposals by allowing domain experts to define a satisfaction degree for each SME. To showcase this method, this research focused on small and medium-size dairy farms (DFs) and describes a negotiation scenario in which a few DFs are able to assess and generate proposals.
Attack to Quantum Cryptosystems Through RF Fingerprints From Photon Detectors

The well-known RF penetration method is implemented on the quantum cryptosystems. In this study, RF radiations from the single photon detectors in a quantum key distribution system are intercepted. It was found that the data contents of a quantum transmission system can be detected from the vicinity of the receiving parties by exploiting the radiations of avalanche effect of single photon detectors. We showed that any Geiger-mode avalanche photodetector acts like a downconverter that converts the optical-wavelength photons to radio-wavelength photons. In our experiment, the fingerprints of the avalanche radiations from commercial single photon detectors were fed to a trained deep learning neural network, and the bit content of quantum transmission was cloned with >99% accuracy up to a distance of 2 meters from the detectors. We also speculate on the enhancement of the key cloning accuracy and range of the eavesdropping.
A General Method for Calibrating Stochastic Radio Channel Models With Kernels

Calibrating stochastic radio channel models to new measurement data is challenging when the likelihood function is intractable. The standard approach to this problem involves sophisticated algorithms for extraction and clustering of multipath components, following which point estimates of the model parameters can be obtained using specialized estimators. We propose a likelihood-free calibration method using approximate Bayesian computation. The method is based on the maximum mean discrepancy, which is a notion of distance between probability distributions. Our method not only by-passes the need to implement any high-resolution or clustering algorithm but is also automatic in that it does not require any additional input or manual preprocessing from the user. It also has the advantage of returning an entire posterior distribution on the value of the parameters, rather than a simple point estimate. We evaluate the performance of the proposed method by fitting two different stochastic channel models, namely the Saleh–Valenzuela model and the propagation graph model, to both simulated and measured data. The proposed method is able to estimate the parameters of both the models accurately in simulations, as well as when applied to 60 GHz indoor measurement data.
Standard-complaint Gazebo warehouse modelling and validation

Modern robotics require high-quality simulations for robots and algorithms validation. Warehouses are the source of active robotic deployment due to high level of standardization. In this paper, we present a model of a warehouse designed entirely in accordance with GOST regulations and international practices for the ROS / Gazebo environment. It is compared with the existing solutions in terms of applicability and computational performance. Created environments are validated using robotic navigation experiments. As a result, the ready-to-use warehouse models with flexible modular design are presented.
Modern Approach for Strategic Development of Smart Universities: Digitalization and Knowledge Export

Nowadays the world economy follows the tendency of implementing new forms of educational activity and transition of higher education institutions to the form of open education facilities. The processes of globalization and digitalization make transfer of intercultural and cross border communications and knowledge export at the global educational market being very relevant. Challenges of modern society are also considered to be as drivers for the transition of higher education institutions to the open smart university system. It forces higher education employees to implement a synergy of all digital communication skills and acquire skills in new intellectual technologies of modern e-learning. The purpose of the study is to simulate and design the model for the training and assessment of professors’ skills to work in the international digital educational environment.
Word Suggestions for non-word Text Errors using Similarity Measure

Spelling errors are a common phenomenon in any text writing system. It is due to various factors like memorization, spelling construction and training, and word ambiguity of that language. With the advent of machines keyboard layout also one of the causes for spelling mistakes. Natural Language Processing (NLP) is a prominent research area in the territory of human language. A spelling checker is a key aspect of many applications such as the MT framework, information retrieval, Desktop applications, and Office automation framework, etc. There are various approaches to spelling checking like rule-based, data-driven, etc. With the commencement of machine learning, a new dimension was introduced as a similarity measure. In this paper, we proposed the cosine similarity measure which handles the nonword error in the Marathi language text. As cosine similarity measure is an important strategy of spelling checking by using this strategy, we enhanced the quality of suggestions for non-word errors. This algorithm also boosts the user's proficiency in the situation when the users impotent to measure the accurate spelling by themselves. We have harvested about 9, 29, 663 unique words from various sources. The system results in suggestion generation accuracy of about 86.61%.