Reviewerly/exHarmony huggingface dataset:
Review of Secure and Optimized Techniques for Data Storage in Cloud Computing

Because of its effectiveness, dependability, and low cost, cloud services are now widely used by a wide range of businesses and individuals. The widespread usage of cloud services has also led to the development of security measures to protect cloud users concerns. Secure cloud computing ensures that the right users may access the data and services. However, protecting cloud-based infrastructures has required a different strategy than the old models, which has led to the emergence of ML-based optimization techniques, despite cryptographic techniques being the primary support in ensuring security. With real-time data processing and threat intelligence, ML may improve the entire security of an IT organization. In this paper the major thrust is on the various secure approaches that have been implemented for the cloud data storage and its security. Apart from it the various ML techniques and its associated optimization approaches that are being utilized in the cloud storage and security have been highlighted. Moreover the comparative analysis of the ML techniques and cryptographic algorithms has been done based on the parameters like accuracy, data security, computational efficiency, time efficiency, throughput etc for inference gathering. It has been inferred that the integration of ML with the cryptographic approaches leads to more efficient techniques for the cloud storage and retrieval.
Powering Multi-Task Federated Learning with Competitive GPU Resource Sharing

Federated learning (FL) nowadays involves compound learning tasks as cognitive applications’ complexity increases. For example, a self-driving system hosts multiple tasks simultaneously (e.g., detection, classification, etc.) and expects FL to retain life-long intelligence involvement. However, our analysis demonstrates that, when deploying compound FL models for multiple training tasks on a GPU, certain issues arise: (1) As different tasks’ skewed data distributions and corresponding models cause highly imbalanced learning workloads, current GPU scheduling methods lack effective resource allocations; (2) Therefore, existing FL schemes, only focusing on heterogeneous data distribution but runtime computing, cannot practically achieve optimally synchronized federation. To address these issues, we propose a full-stack FL optimization scheme to address both intra-device GPU scheduling and inter-device FL coordination for multi-task training. Specifically, our works illustrate two key insights in this research domain: (1) Competitive resource sharing is beneficial for parallel model executions, and the proposed concept of “virtual resource” could effectively characterize and guide the practical per-task resource utilization and allocation. (2) FL could be further improved by taking architectural level coordination into consideration. Our experiments demonstrate that the FL throughput could be significantly escalated.
Employing Futuristic Autobiographies to envision emerging human-agent interactions: The case of intelligent companions for stress management

Technology-supported stress management is one of the most promising and practically important application areas for intelligent companions, that is, digital assistants evoking empathy and personal attachment. In this paper, we employ the method of Futuristic Autobiographies (FABs) to elicit participants’ attitudes and reflections regarding an imaginary digital assistant for stress management, implemented as an intelligent companion. For the purposes of the study, we developed six FABs, highlighting a range of potential issues related to the use of intelligent companions. The participants (N=17) provided their responses to the FABs by completing a survey, and a subset of the participants (N=5) also took part in follow-up online interviews. A thematic analysis of the results revealed six main themes: objectivity of the digital assistant, human-likeness, context-specificity of assistant's behavior, user's control, stress management, and user privacy. The themes, as well as the implications of the results for the design and use of intelligent stress management companions, are discussed.
Channel-Wise Correlation Calibrates Attention Module for Convolutional Neural Networks

It is well known in image recognition that global features represent the overall and have the ability to generalize an entire object, while local features can reflect the details, both of which are important for extracting more discriminative features. Recent research has shown that the performance of convolutional neural networks can be improved by introducing an attention module. In this paper, we propose a simple and effective channel attention module named layer feature that meets channel attention module (LC module, LCM), which combines the layer global information with channel dependence to calibrate the correlation between channel features and then adaptively recalibrates channel-wise feature responses. Compared with the traditional channel attention methods, the LC module utilizes the most significant information that needs to be focused on in the overall features to refine the channel relationship. Through empirical studies on CIFAR-10, CIFAR-100, and mini-ImageNet, this work proved its superiority compared to other attention modules in different DCNNs. Furthermore, we performed the two-dimensional visualization of the feature map through the class activation map and intuitively analyzed the effectiveness of the model.
Home-based Immersive Web Rehabilitation Gaming with Audiovisual Sensors

Early, intensive, and repetitive physical rehabilitation is critical, but it can be difficult to keep patients motivated and engaged. The use of games in immersive reality makes physiotherapy more enjoyable and engaging. During play, many unpleasant emotions can occur as a result of interacting with the joyful activities of the game. This difficulty can be overcome by adjusting game features to the players' emotions and body gestures. This work contributes to the Sensor Enabled Affective Computing for Enhancing Medical Care (SenseCare) project for remote home healthcare applications, and its related SenseCare KM-EP (Knowledge Management-Ecosystem Portal) Affective Computing (AC) platform. In this paper, we propose web-based Augmented Reality (AR) and Virtual Reality (VR) games for home rehabilitation deploying audiovisual sensors (e.g., Camera, Microphone, Kinect), to monitor the patient's emotional well-being and calculate body estimation gestures during the gameplay session, with the aim of giving a quick return to the therapist. In parallel with SenseCare's methods for audiovisual monitoring of gamers, methods for using VR and AR web-based games using the WebXR API are also introduced, and customer satisfaction was determined through an online survey, showing an 81.8% satisfaction rate.
Qualitative Research In Online Language Learning: What Can It Do?

This article explores the theoretical foundations of qualitative research in online language learning. It will look at the distinction between offline and online language learning and discuss whether different ways of knowledge generation are appropriate for those different learning environments. Quantitative and qualitative methodologies will be examined and their fit with various learning theories evaluated. Fundamental theoretical differences between epistemologies supporting a realist ontology and those favouring relativist ontologies will be presented and set in the context of online language learning research. Finally, an argument will be presented that in a sociocultural framework, going beyond quantitative research approaches is necessary to adequately understand the experiences of learners and teachers who share a common interest in novel digital environments.
Data Analytics

This chapter provides a comprehensive and unified view of data analytics. Data analytics is the process of analyzing the raw data in order to draw inferences about the information in hand. Data analysis techniques are primarily used to get an insight which further facilitates enhancement of the sector under consideration. These techniques are beneficial for optimizing a process under consideration and also for increasing the overall efficiency of a system. These techniques also act as performance boosters as their implementation in the business models help in reduction of costs by considerable amount. It is the most important for any organization as it facilitates better decision-making approaches and also provides an analysis of customer trends as well as satisfaction which further leads to improved products as well as services. It also helps in effective marketing of the products and services. Data analytics has widespread application in various sectors. Various tools are used for carrying out data analytics jobs. All this is discussed in the chapter.
Feature extraction framework based on contrastive learning with adaptive positive and negative samples

Feature extraction is an efficient approach for alleviating the issue of dimensionality in high-dimensional data. As a popular self-supervised learning method, contrastive learning has recently garnered considerable attention. In this study, we propose a unified feature extraction framework based on contrastive learning with adaptive positive and negative samples (CL-FEFA) that is suitable for unsupervised, supervised, and semi-supervised feature extraction. CL-FEFA constructs adaptively positive and negative samples from the result of feature extraction, which makes them more appropriate and accurate. Meanwhile, the discriminative features are extracted based on adaptive positive and negative samples, which will make the intra-class embedded samples more compact and the inter-class embedded samples more dispersed. In the process, using the potential structure information of subspace samples to dynamically construct positive and negative samples can make our framework more robust to noisy data. Furthermore, it is proven that CL-FEFA actually maximizes the mutual information of positive samples, which captures non-linear statistical dependencies between similar samples in potential structure space and thus can act as a measure of true dependence. This also provides theoretical support for its advantages in feature extraction. The final numerical experiments prove that the proposed framework has a strong advantage over traditional feature extraction methods and contrastive learning methods.
Sliding window based ON/OFF flow watermarking on Tor

To analyze traffic in the anonymity network, an active network flow watermark scheme, the ON/OFF watermarking, is proposed to embed timing information into the network flow. This scheme exploits a technique that drops packets at prescribed time intervals to create a watermark sequence at the sending side of a flow, then extracts inter-packet delay at the receiving side, and computes the L1 distance between generated sequences to identify the network flow. A sliding window based L1 distance computation algorithm is proposed to detect flow watermark at a low overhead. Experimental results demonstrate that this timing-based flow watermarking scheme can identify watermarks efficiently and is resistant to timing disturbances in Tor.
The Application of Knowledge Management Practices in Corrosion Management for Oil and Gas Industry: A Revıew

The oil and gas industry is a largest capital-intensive industry with assets ranging from wells, risers, drilling rigs, and offshore platforms in the upstream segment, to pipelines, liquefied natural gas (LNG) terminals, and refineries in the midstream and downstream segments. The integrity of these assets can be compromised by corrosion and the impact of various corrosion management (CM) is implemented to reduce the cost of corrosion (COC). Therefore, the critical step in reducing COC is not only dependent on the implementation of effective technical corrosion control but also incorporating the knowledge management (KM) domain. KM practices such as knowledge creation, acquisition, sharing, application, knowledge storage and retrieval, and communities of practice on CM programs and supported by organizational culture are expected to contributing in COC reduction. This study examines the recent development of KM practice applications in CM for the oil and gas industry, how the KM element could be embedded in Corrosion Management Program for reducing cost of corrosion and the likely Corrosion Knowledge Management framework suitable for that purpose. The results are expected to contribute to the establishement of corrosion knowledge management with proposed framework for reducing COC.
Game Theory–Based Parameter Tuning for Energy-Efficient Path Planning on Modern UAVs

Present-day path planning algorithms for UAVs rely on various parameters that need to be tuned at runtime to be able to plan the best possible route. For example, for a sampling-based algorithm, the number of samples plays a crucial role. The dimension of the space that is being searched to plan the path, the minimum distance for extending a path in a direction, and the minimum distance that the drone should maintain with respect to obstacles while traversing the planned path are all important variables. Along with this, we have a choice of vision algorithms, their parameters, and platforms. Finding a suitable configuration for all these parameters at runtime is very challenging because we need to solve a complicated optimization problem, and that too within tens of milliseconds. The area of theoretical exploration of the optimization problems that arise in such settings is dominated by traditional approaches that use regular nonlinear optimization techniques often enhanced with AI-based techniques such as genetic algorithms. These techniques are sadly rather slow, have convergence issues, and are typically not suitable for use at runtime. In this article, we leverage recent and promising research results that propose to solve complex optimization problems by converting them into approximately equivalent game-theoretic problems. The computed equilibrium strategies can then be mapped to the optimal values of the tunable parameters. With simulation studies in virtual worlds, we show that our solutions are 5-21% better than those produced by traditional methods, and our approach is 10× faster.
On Extension of Evaluation Algorithms in Keyed-Homomorphic Encryption

Homomorphic encryption (HE) is public key encryption that enables computation over ciphertexts without decrypting them, while it is known that HE cannot achieve IND-CCA2 security. To overcome this issue, the notion of keyed-homomorphic encryption (KH-PKE) was introduced, which has a separate homomorphic evaluation key and can achieve stronger security (Emura et al., PKC 2013). The contributions of this paper are twofold. First, the syntax of KH-PKE assumes that homomorphic evaluation is performed for single operations, and its security notion called KH-CCA security was formulated based on this syntax. Consequently, if the homomorphic evaluation algorithm is enhanced in a way of gathering up sequential operations as a single evaluation, then it is not obvious whether or not KH-CCA security is preserved. In this paper, we show that KH-CCA security is in general not preserved under such modification, while KH-CCA security is preserved when the original scheme additionally satisfies circuit privacy. Secondly, Catalano and Fiore (ACM CCS 2015) proposed a conversion method from linearly HE schemes into two-level HE schemes, the latter admitting addition and a single multiplication for ciphertexts. In this paper, we extend the conversion to the case of linearly KH-PKE schemes to obtain two-level KH-PKE schemes.
Security, Privacy Challenges and Solutions for Various Applications in Blockchain Distributed Ledger for Wireless-Based Communication Networks

Blockchain is a secure computing technology which could be used in a variety of applications like finance, supply chain management, land registry, health care, and education. To obtain services from various service providers, users or consumers register with the Registration Center Authority (RAC) via., wireless communications networks. However, RAC has flaws such as insider threat and Single Point of Failure (SPoF). To circumvent these problems, if user registration based on blockchain for any service is required through wireless communication networks and also blockchain networks has the Distributed Ledger (DL) for storing identity information about the users or consumers. In this chapter, we will discuss about the security and privacy issues that come with each of the applications mentioned above and also some of the key challenges in each applications such as data confidentiality, integrity, authentication, access control, and privacy. Public Key Infrastructure (PKI) is giving the solution for data confidentiality, integrity, and authentication. Access control is processed by the access control policies (to restrict the user permission) and the privacy is achieved through secure authentication.
Promoting Digital Campus to Smart Campus Based on Artificial Intelligence

In this paper, combined with the problems existing in the current campus and based on the advanced technology of artificial intelligence, we propose a five-tier framework model for building a smart campus, and describe the specific application practice of the framework in detail.
Secured Cloud Communication Using Lightweight Hash Authentication with PUF

Internet-of-Things (IoT) is an awaited technology in real-world applications to process daily tasks using intelligent techniques.The main process of data in IoT involves communication, integration, and coordination with other real-world applications.The security of transferred, stored, and processed data in IoT is not ensured in many constraints.Internet-enabled smart devices are widely used among populations for all types of applications, thus increasing the popularity of IoT among widely used server technologies.Smart grid is used in this article with IoT to manage large data.A smart grid is a collection of numerous users in the network with the fastest response time.This article aims to provide high authentication to the smart grid, which constitutes secure communication in cloud-based IoT.Many IoT devices are deployed openly in all places.This open-access is vulnerable toward cloning attacks.Authentication is a significant process that provides strength while attacking.The security of the cloud and IoT must be computationally high.A lightweight authentication using hashing technique is proposed considering the aforementioned condition.The main factor of the authentication involves physically unclonable functions, which are utilized in improving the performance of the authentication.The proposed approach is evaluated with the existing techniques.Results show that the performance of the proposed algorithm provides high robust security.
Project-Based Learning for Teaching “Diffie-Hellman Algorithm, Elgamal Variant”

The paper presents the application of the flipped classroom and project-based learning approaches used in the course “Telecommunication Security” intended for students from the specialty “Internet and Mobile Communications” at the University of Ruse when teaching the topic “Diffie-Hellman Algorithm, Elgamal Variant”. Project-based learning, one of the tendencies of Education 4.0, develops and improves the main 21st-century skills.
An Emotion-fused Medical Knowledge Graph and its Application in Decision Support

Traditional medical guidance becomes increasingly unsatisfactory, as the care of patients should be centered around not just clinical symptoms but also their values and preferences. A method is proposed, in this paper, to fuse clinical knowledge and patient preferences into an integrated knowledge graph. Objective data was extracted from semi-structured online medical service interfaces, and subjective emotional data from patient review pages. A prototype system was designed and implemented to demonstrate the feasibility of the method. The system can recommend a ranked list of doctors with the best matched clinical background as well as patient preferences. An evaluation was conducted via carrying out a survey of user groups upon the medical guidance options of a human nurse, the “We Doctor” system, and our prototype system.
Methods of Extracting Parameters of the Processor Caches

As attack scenarios and targets are constantly expanding, cache side-channel attacks have gradually penetrated into various daily applications and brought great security risks. The success of a cache side-channel attack relies heavily on the pre-knowledge of some important parameters of the target cache system. Existing methods for reading cache parameters have their limits. In this paper, a series of tests are proposed to extract cache parameters at runtime, which provides a method for launching existing cache side-channel attacks in some restricted cases and reduces the cost of attacks. They have been used to extract cache parameters on four processors using three different architectures, as well as in a restricted virtual machine environment. The extracted parameters match with the publicly available information, including some parameters unavailable from the CPUID instruction.
Towards An Efficient Searching Approach of ROS Message by Knowledge Graph

The Robot Operating System (ROS) has become the most popular robot development framework in the last few years, which has loosely coupled structure and provides remote communications between different component nodes. The ROS messages are critical to bridge the communication channels and clearly define the data structures. The developers can use the standardized or user-customized ROS message types to construct a communication channel between two component nodes uniquely. However, it becomes increasingly difficult for developers to find the required ROS message type from thousands of diverse ROS message types in ROS-based robotic software development. Finding the proper ROS message type is a non-trivial task because developers may hardly know the exact names of required ROS messages but only has a rough knowledge of the task domain features. To tackle this challenge, we construct a novel ROS Message Knowledge Graph (RMKG) with 4543 entities and 14320 relationships, including all ROS message types and message packages. We take the shortest path algorithm to search ROS message in RMKG by searching with ROS message feature or ROS message package and visualize the subgraph structure of the search results. Moreover, we develop a ROS message package library that supports fuzzy queries to find the required message package. A comprehensive evaluation of RMKG shows the high accuracy of our knowledge construction approach. A user study indicates that RMKG is promising in helping developers find suitable ROS message types for robotics software development tasks. An effect evaluation of message package fuzzy query shows the good effects of our fuzzy query method under different situations.