Reviewerly/exHarmony huggingface dataset:
Poses Estimation Technology for Physical Activity Monitoring

In recent years there has been an increase in the execution of physical exercise at home, which in most cases implies self-taught movement due to the lack of a monitor or guide. This leads to context physical stress due to a lack of knowledge, causing an increase in the number of injuries among those who realise exercise. On the other hand, the population living in the so-called “empty Spain” does not have access to sports and rehabilitation centers that ensure personal well-being. This paper details the development of a physical exercise platform that implements collaborative training rooms with a trainer-student relationship, as well as the use of pose estimation techniques for the correction of exercise execution.
Is Blockchain a Disruptive Innovation? a Systematic Literature Review

Indubitably, blockchain comprises one of the most ground-breaking technologies nowadays. Thanks to its unique properties (e.g., disintermediation, immutability, transparency, traceability), it presents great potential in all the fields of the economy. In an attempt to highlight its huge impact, many authors have characterized it as disruptive innovation. Nevertheless, within the scientific literature, the disruptive innovations satisfy specific theoretical principles. Furthermore, they require special activities from the managers, as otherwise their companies cannot survive. By utilizing the Christensen’s theory as an interpretive lens, this paper performs a systematic literature review and reveals that all the applications developed through this technology should not be labelled as disruptive innovations. Therefore, the academics should be very careful when they name a blockchain-based application as disruptive innovation and the practitioners should examine every single blockchain-based solution in terms of disruptiveness before devising their strategy. To the best of authors’ knowledge, this paper constitutes the first systematic literature review that examines the blockchain technology from the angle of Christensen’s theory.
MATHEMATICAL MODELS ONTOLOGY OF TECHNOLOGICAL OBJECTS FOR INDUSTRIAL ENTERPRISES. PART 2. ONTOLOGY IMPLEMENTATION AND VERIFICATION

The article proposes the implementation of an applied ontology of mathematical models of technological objects for the design of a subsystem of decision-making support, which issues recommendations for the mathematical apparatus in relation to the development goals for the automated management of a food enterprise. It consists of 46 entities with corresponding relationships, attributes, and axioms, and is also implemented in the OWL language by the Protege open platform tools, taking into account existing standards and recommendations. In the structure of the ontology, the mathematical model is represented as branches of subclasses with the corresponding sets of attributes, characterized by their relationship to the higher-level model. Within this ontology, 17 types of relationships are presented. Applied ontology passed two stages of verification: structural - based on generally accepted estimates; logical - by testing queries and manually checking the correctness of the results. In particular, examples of model selection using an ontology for virtual sensors are given. The use of the proposed ontology in the structure of the management decision support subsystem increases the efficiency of these decisions, the validity of management actions and the efficiency of the technological component of the enterprise. Also, the ontology can be integrated into the ontology of industrial enterprises tasks or other domain ontologies.
THE STATUS OF ARGUMENTS IN ABSTRACT ARGUMENTATION FRAMEWORKS. A TABLEAUX METHOD

Dung's argumentation frameworks are formalisms widely used to model interaction among arguments.Although their study has been profusely developed in the field of Artificial Intelligence, it is not common to see its treatment among those less connected to computer science within the logical-philosophical community.In this paper we propose to bring to that audience a proof-theory for argument justification based on tableaux, very similar to those the Logic students are familiar with.The tableaux enable to calculate whether an argument or subset of arguments are accepted or rejected in accordance to Dung's preferred and grounded extension-based semantics.Soundness and completeness results regarding those semantics are provided.
Learning and identity testing of Markov chains

In this chapter, we provide an overview of the two inference problems of learning and identity testing of a Markov chain based on a single trajectory of observations started from an arbitrary state. The learning problem is concerned with the estimation of the state transition probabilities of the process, while the testing problem deals with determining whether the unknown Markov chain is identical to or far from a given reference chain. We analyze both tasks from within the minimax framework and with respect to several competing notions of distance. We observe that the sample complexities depend on the number of states and often also on the stationary and mixing properties of the Markov chains. We further proceed to compare advantages and drawbacks of the different contrast functions we consider.
Testing Different Log Bases for Vector Model Weighting Technique

Information retrieval systems retrieves relevant documents based on a query submitted by the user. The documents are initially indexed and the words in the documents are assigned weights using a weighting technique called TFIDF which is the product of Term Frequency (TF) and Inverse Document Frequency (IDF). TF represents the number of occurrences of a term in a document. IDF measures whether the term is common or rare across all documents. It is computed by dividing the total number of documents in the system by the number of documents containing the term and then computing the logarithm of the quotient. By default, we use base 10 to calculate the logarithm. In this paper, we are going to test this weighting technique by using a range of log bases from 0.1 to 100.0 to calculate the IDF. Testing different log bases for vector model weighting technique is to highlight the importance of understanding the performance of the system at different weighting values. We use the documents of MED, CRAN, NPL, LISA, and CISI test collections that scientists assembled explicitly for experiments in data information retrieval systems.
Development of a Collaborative Robotic Arm Oriented Towards Industry 4.0

Robotics is a growing area in the daily life of users and especially in industry. In recent years, many developments have been made to create and improve new techniques for handling objects with articulated robotic arms. The industrial sector is currently the most interested in including more automated processes. Thanks to the great evolution of artificial intelligence, image processing and the development of new algorithms, the detection of objects and collisions is much easier than it was a few years ago. Therefore, this article will present the development of an articulated robot, built completely from scratch and in which collision detection techniques and object detection techniques have been applied in a controlled environment.
Cloud Security

The objective of this chapter is present security-related concepts in the cloud. The reader will understand how security is provided in different service models, IaaS, PaaS and SaaS. For better understanding different kinds of security threats in cloud are presented. The reader will understand that implementing security is a shared responsibility and the cloud user is responsible for data security, compliance, regulatory and privacy requirements. In addition, the reader will also come to know about the security challenges that occur due to the integration of the IoT with cloud.
Quadratic Residual Multiplicative Filter Neural Networks for Efficient Approximation of Complex Sensor Signals

This study focuses on low-dimensional regression problems involving extremely complex sensor signals. We present FourierQResNet and GaborQResNet, two novel Quadratic Residual Multiplicative Filter Neural Networks (QRMFNNs). In terms of approximation accuracy, training convergence speed, and spectral bias handling, our empirical results from rigorous experimentation consistently show that these networks outperform conventional methods and existing neural network models. FourierQResNet and GaborQResNet are resistant to spectral bias, ensuring improved accuracy and reliability in signal approximation. These findings have far-reaching implications, particularly for industries such as healthcare, environmental monitoring, and industrial automation, where the precision and dependability of sensor data analysis are critical. This research significantly advances the field of sensor signal analysis and opens up new avenues for improving data interpretation reliability and accuracy in complex signal environments.
Cloud Computing Fundamentals

The objective of this chapter is to introduce the fundamentals of cloud computing. It covers the features of cloud computing, architecture of cloud computing, different service classes and deployment models of cloud computing.
The Digital Content Formation Labor Costs for Electronic Libraries and Examples of the Formation of Virtual Exhibitions

Scientific digital libraries are characterized by high requirements for the quality of digital copies of printed scientific sources, since any ambiguity or contamination within chemical formulas or mathematical expressions can lead to erroneous perception or misunderstanding of the meaning. Special requirements for digital copies are also imposed when digitizing rare editions and archival documents that are of scientific and historical value not only in their content, and in the notes of scientists in the margins of a book or archival document. Requirements for the quality of digitized materials determine the significant labor intensity of preparation; it is necessary to evaluate it when planning work on filling scientific libraries. The article presents a labor cost calculation methodology for creating integrated digital content for the digital library “Scientific Heritage of Russia” (DL SHR). The content of the DL SHR contains rare (out of print, hard-to find) books, 3D-museum objects and archival documents, which make digital copies of these materials very labour intensive. This needs to be assessed when planning the content filling for DL SHR. The developed technique includes the decomposition of the entire technological process into a number of operations performed by specialists of a certain profile (archivists, librarians, editors, scanners, etc.). Each phase is divided into several operations, and for every operation the time spent on this type of work is estimated. A unit of DL SHR content can be an archival document, a page of a book, a whole book, a biography of a scientist, etc. The assessment of the time period is carried out either according to published standards, or, in their absence, based on analysis of the experience of performing the operation when forming the content of the DL SHR. The article provides data on the calculation of time costs for individual operations of the formation of digital objects and their collections in relation to DL SHR, taking into account Russian standards and 15 years of experience.
Emotion-Aware Speaker Identification With Transfer Learning

Speech is a natural communication method used by humans. Speaker identification (SI) technology based on human speech has been used as an entry point for many human-computer-interaction applications. The performance of SI models can degrade when dealing with expressive speech uttered in emotional situations because emotion databases do not have sufficient data on expressive speech to train SI models for various emotional states. Generally, SI models are trained using relatively more samples of “neutral” speech than samples of other emotion classes. In this study, we propose an emotion-aware SI (em-SI) method that uses an emotion-embedding vector generated from a pre-trained speech emotion recognition (SER) model along with the acoustic features of speech data. We assess the performance of this method using individual English and Korean corpora and confirm that the proposed method provides an improved performance on multilingual corpora. The evaluation results show that the SI accuracy of em-SI on the Korean Emotion Multimodal Database (KEMDy19) improved by 3.2%, and the average speaker verification (SV) performance in terms of the equal error rate (EER) was improved by 1.3% compared to that of the baseline SI model. The visualization of the embedding vector of em-SI shows that em-SI maps speech data to an embedding space where both SI and emotional information are simultaneously represented. Through the experiments conducted in this study, we confirmed that the em-SI model, which learns by integrating emotion and speaker embedding information, improved the performance of SI for expressive speech.
Enhancing Conversational Model With Deep Reinforcement Learning and Adversarial Learning

This paper develops a Chatbot conversational model that is aimed to achieve two goals: 1) utilizing contextual information to generate accurate and relevant responses, and 2) implementing strategies to make conversations human-like.We propose a supervised learning approach for model development and make use of a dataset consisting of multi-turn conversations for model training.In particular, we first develop a module based on deep reinforcement learning to maximize the utilization of contextual information serving as insurance for accurate response generation.Then, we incorporate the response generation process into an adversarial learning framework so as to make the generated response more human-like.Using these two phases in combination eventually results in a unified model that generates semantically appropriate responses that are also expressed naturally as human-generated in the conversation.We conducted various experiments and obtained a significant improvement compared to the baseline and other related studies.
A Dual Encoder–Decoder Network for Self-Supervised Monocular Depth Estimation

Depth estimation from a single image is a fundamental problem in the field of computer vision. With the great success of deep learning techniques, various self-supervised monocular depth estimation methods using encoder–decoder architectures have emerged. However, most previous approaches regress the depth map directly using a single encoder–decoder structure, which may not obtain sufficient features in the image and results in a depth map with low accuracy and blurred details. To improve the accuracy of self-supervised monocular depth estimation, we propose a simple but very effective scheme for depth estimation using a dual encoder–decoder structure network. Specifically, we introduce a novel global feature extraction network (GFN) to extract global features from images. GFN includes PoolAttentionFormer and ResBlock, which work together to extract and fuse hierarchical global features into the depth estimation network (DEN). To further improve the accuracy, we design two feature fusion mechanisms, including global feature fusion and multiscale fusion. The experimental results of various dual encoder–decoder combination schemes tested on the KITTI dataset show that our proposed one is effective in improving the accuracy of self-supervised monocular depth estimation, which reached 89.6% ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta &lt; {1.25}$ </tex-math></inline-formula> ).
A Framework to Improve the Confidence of Restaurants User Reviews Based on Facial Analysis

The quality reviews from users impact significantly restaurants’ reputation and their economic success. Their customers are influenced by other reviews of their experience with restaurant service and therefore, they tend to prefer restaurants already classified as excellent. Unfortunately, a significant number of users’ reviews are biased and non-trusted. These reviews misguide other users and lead to unexpected, sub-par experiences, leaving them frustrated and disappointed. To overcome this scenario, this work proposes a framework to improve the confidence of restaurant users’ reviews at scale. For that purpose, user ratings are evaluated if they follow their facial emotions at the moment they are submitting their reviews. The experimental results denote the feasibility of the proposed framework to improve the confidence of restaurant users’ reviews. Decomposing their components into loosely coupled services helps to manage the complexity and improve scalability, performance, and agility. The framework performance and scalability allow a large adoption and integration with third-party users’ review solutions.
Data and models

This chapter is about different electrical models and it describes the constant phase element as a mathematical tool and how to present data and measurement results and about the choice of statistical methods. It also covers neural networks, chaos theory, fractals, and wavelet analysis.
Design of a Deep Neural Network-Based Visual Data Processing System for Digital Media Optimization Applications

Multimedia information processing is a universal technical demand in smart life, especially in applications of digital media optimization. However, there still lacks mature design of reliable visual data processing systems for this area. Because digital media interaction is featured with high dynamics and large business amount, which poses high requirement for processing ability and efficiency. To deal with this issue, this paper presents design of a deep neural network-based visual data processing system for digital media optimization applications. First of all, an overall mechanism algorithm that coordinates automatic workflow of the whole system is designed. Then, a BP neural network structure is adopted to realize intelligent resource classification according to different user behaviors and user preference. Through the strong ability of information processing brought by deep neural network, business scheduling and operation in digital media applications can be optimized. As for evaluation, the designed deep neural network is compared with a traditional vision processing algorithm to test the processing efficiency of visual data. The obtained results can reflect that the designed visual data processing system can work well in digital media optimization applications.
Cloud Governance, Management and Brokerage

The objective of this chapter is to introduce the basics of cloud governance, management and brokerage.
Trace Recovery: Attacking and Defending the User Privacy in Smart Meter Data Analytics

Energy consumption data is collected the service providers and shared with various stakeholders involved in a smart grid ecosystem. The fine-grained energy consumption data is immensely useful for maintaining and operating grid services. Further, these data can be used for future consumption prediction using machine learning and statistical models and market segmentation purposes. However, sharing and releasing fine-grained energy data or releasing predictive models trained on user-specific data induce explicit violations of private information of consumers [34, 41]. Thus, the service providers may share and release aggregated statistics to protect the privacy of users aiming at mitigating the privacy risks of individual users’ consumption traces. In this chapter, we show that an attacker can recover individual users’ traces of energy consumption data by exploiting regularity and uniqueness properties of individual consumption load patterns. We propose an unsupervised attack framework to recover hourly energy consumption time-series of users without any background information. We construct the problem of assigning aggregated energy consumption meter readings to individual users as a mathematical assignment problem and solve it by the Hungarian algorithm [30, 50]. We used two real-world datasets to demonstrate an attacker’s performance in recovering private traits of users. Our results show that an attacker is capable of recovering 70% of users’ energy consumption patterns with over 90% accuracy. Finally, we proposed few defense techniques, such as differential privacy and federated machine learning that may potentially help reduce an attacker’s capability to infer users’ private information.
Revisiting VM-Agnostic KVM vCPU Scheduler for Mitigating Excessive vCPU Spinning

In virtualized environments, virtual CPUs (vCPUs) are commonly oversubscribed on physical CPUs (pCPUs) to utilize CPU resources efficiently. However, excessive vCPU spinning, which occurs when a vCPU is waiting in a spin loop for an event from a descheduled vCPU, greatly degrades application performance in virtualized environments. VM-agnostic hypervisors aim to prevent excessive vCPU spinning by rescheduling vCPUs when an excessive spin is detected by hardware support for virtualization. We investigate the effectiveness of the KVM vCPU scheduler and show that it fails to avoid excessive vCPU spinning under various situations. We identify three problems: 1) scheduler mismatch, 2) aggressive limitation of candidate vCPUs, and 3) IPI context misuse. The first problem stems from the mismatch between the KVM vCPU scheduler and the Linux scheduler. The second and third problems come from failures in choosing candidate vCPUs to be scheduled next. Our in-depth analysis reveals simple modification to KVM (89 LoC) can mitigate excessive vCPU spinning. Our simple modification reduces excessive vCPU spinning by up to 96% and improves benchmark performance by up to 2.6×. Part of the proposed mitigation has been integrated with KVM from Linux KVM v5.13 onward.
Basics of Cloud Management

Having taught technological aspects, migration and monitoring aspects, the reader must be interested in knowing about how the cloud computing environment is managed. The objective of this chapter is to introduce the basics of cloud managementBasics of cloud management. By the end of the chapter the reader will understand different cloud management functionalities and how they are managed by cloud management platforms and tools.
Secure Image Encryption Model for Cloud-Based Healthcare Storage Using Hyperchaos and DNA Encoding

In recent years, security and privacy have drawn a lot of attention due to digital communications and the widespread usage of multimedia transmissions over unsecured channels. In every area of analytics, digital images are essential. Sensitive image information like fingerprints, medical records, or defense satellite images needs to be securely stored and processed in a cloud server. In this article, we come up with a novel image encryption model for medical images utilizing a combination of chaotic maps over the DNA-encoded image with bit plane scrambling. Hyperchaotic maps are used to generate confusion keys and Chaotic maps are used for diffusion keys generation. The robustness and resistance of the system to attacks like differential attacks and statistical attacks are shown and extensively analyzed in terms of the security of the proposed model.