Reviewerly/exHarmony huggingface dataset:
Achieving Reliability in Cloud Computing by a Novel Hybrid Approach

Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing's challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system's ability to continue performing its intended task in the presence of defects. Fault-tolerance challenges include heterogeneity and a lack of standards, the need for automation, cloud downtime reliability, consideration for recovery point objects, recovery time objects, and cloud workload. The proposed research includes machine learning (ML) algorithms such as naïve Bayes (NB), library support vector machine (LibSVM), multinomial logistic regression (MLR), sequential minimal optimization (SMO), K-nearest neighbor (KNN), and random forest (RF) as well as a fault-tolerance method known as delta-checkpointing to achieve higher accuracy, lesser fault prediction error, and reliability. Furthermore, the secondary data were collected from the homonymous, experimental high-performance computing (HPC) system at the Swiss Federal Institute of Technology (ETH), Zurich, and the primary data were generated using virtual machines (VMs) to select the best machine learning classifier. In this article, the secondary and primary data were divided into two split ratios of 80/20 and 70/30, respectively, and cross-validation (5-fold) was used to identify more accuracy and less prediction of faults in terms of true, false, repair, and failure of virtual machines. Secondary data results show that naïve Bayes performed exceptionally well on CPU-Mem mono and multi blocks, and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction. In the case of greater accuracy and less fault prediction, primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity. Sequential minimal optimization has good time complexity with minor differences in random forest accuracy and fault prediction. We decided to modify sequential minimal optimization. Finally, the modified sequential minimal optimization (MSMO) algorithm with the fault-tolerance delta-checkpointing (D-CP) method is proposed to improve accuracy, fault prediction error, and reliability in cloud computing.
Smart filtering for user discovery and availing balance storage space continuity with faster big data service

The expanding use and advancement of database extraction technology seriously threaten the safety of people's private details. Privacy-preserving database miners (PPDM), a new area of data analysis, have recently received a lot of attention. The fundamental tenet of PPDM will be to alter the content, so that database extraction techniques are being carried out successfully without endangering the confidentiality of delicate facts in the database. In reality, an unintentional revelation of delicate details will even occur during file collection, content publication, and details (that is, content gathering reviews) delivery. However, the majority of existing research on PPDM is focused on determining how to minimize the security threat caused by content extraction processes. In such an essay, we consider the security concerns raised by data gathering from a larger angle and look into several defense mechanisms for private communications. Specifically, we classify consumers that participate in database extraction systems into four separate categories: data providers, data collectors, data miners, as well as policymakers. To break down massive content platforms into four consecutive modules—data creation, data processing, data retention, and data analytics—we provide an organized methodology. Such four parts form a big database revenue loop.
Intelligent Image Super-Resolution for Vehicle License Plate in Surveillance Applications

Vehicle license plate images are often low resolution and blurry because of the large distance and relative motion between the vision sensor and vehicle, making license plate identification arduous. The extensive use of expensive, high-quality vision sensors is uneconomical in most cases; thus, images are initially captured and then translated from low resolution to high resolution. For this purpose, several traditional techniques such as bilinear, bicubic, super-resolution convolutional neural network, and super-resolution generative adversarial network (SRGAN) have been developed over time to upgrade low-quality images. However, most studies in this area pertain to the conversion of low-resolution images to super-resolution images, and little attention has been paid to motion de-blurring. This work extends SRGAN by adding an intelligent motion-deblurring method (termed SRGAN-LP), which helps to enhance the image resolution and remove motion blur from the given images. A comprehensive and new domain-specific dataset was developed to achieve improved results. Moreover, maintaining higher quantitative and qualitative results in comparison to the ground truth images, this study upscales the provided low-resolution image four times and removes the motion blur to a reasonable extent, making it suitable for surveillance applications.
An Artificial Neural Network Model for Project Effort Estimation

Estimating the project effort remains a challenge for project managers and effort estimators. In the early phases of a project, having a high level of uncertainty and lack of experience cause poor estimation of the required work. Especially for projects that produce a highly customized unique product for each customer, it is challenging to make estimations. Project effort estimation has been studied mainly for software projects in the literature. Currently, there has been no study on estimating effort in customized machine development projects to the best of our knowledge. This study aims to fill this gap in the literature regarding project effort estimation for customized machine development projects. Additionally, this study focused on a single phase of a project, the automation phase, in which the machine is automated according to customer-specific requirements. Therefore, the effort estimation of this phase is crucial. In some cases, this is the first time that the company has experienced the requirements specific to the customer. For this purpose, this study proposed a model to estimate how much work is required to automate a machine. Insufficient effort estimation is one of the main reasons behind project failures, and nowadays, researchers prefer more objective approaches such as machine learning over expert-based ones. This study also proposed an artificial neural network (ANN) model for this purpose. Data from past projects were used to train the proposed ANN model. The proposed model was tested on 11 real-life projects and showed promising results with acceptable prediction accuracy. Additionally, a desktop application was developed to make this system easier to use for project managers.
Hybrid POI group recommender system based on group type in LBSN

Point of interest (POI) group recommender systems (GRSs) aim to suggest places for a group of users. Compared to recommender systems for individual users, GPRs are more challenging due to the complexity of groups formed by members with conflicting interests and preferences. To cope with this challenge, the current POI GRSs utilize strategies-based approaches, e.g., group history, aggregation of group members’ history, and further improved aggregation with the consideration of group members’ interactions. However, these GRSs still suffer from low accuracy and the cold-start problem for groups with insufficient historical information. Moreover, rare studies have attempted to compare/combine the different strategies for GRSs. In this paper, to achieve the collective advantages over existing GRSs, a hybrid method performs switching among different recommendation strategies based on the criteria of group type (persistent and ephemeral regarding group history) and homogeneity (similarity of group members’ interests and preferences) is proposed. A dataset from the city of Ankara that contains user and group check-ins, was extracted from the Foursquare Swarm application to evaluate the performance of the proposed method. This hybrid method outperforms the single strategies using various algorithms by improving at least 30% in [email protected] and 25% in [email protected] across all the test groups. The empirical results demonstrate that the introduced hybrid solution, which automatically identifies group type and homogeneity, can quickly and effectively go beyond the restrictions of the individual strategies for GRSs.
Feature subset selection using multimodal multiobjective differential evolution

The main aim of feature subset selection is to find the minimum number of required features to perform classification without affecting the accuracy. It is one of the useful real-world applications for different types of classification datasets. Different feature subsets may achieve similar classification accuracy, which can help the user to select the optimal features. There are two main objectives involved in selecting a feature subset: minimizing the number of features and maximizing the accuracy. However, most of the existing studies do not consider multiple feature subsets of the same size. In this paper, we have proposed an algorithm for multimodal multiobjective optimization based on differential evolution with respect to the feature subset selection problem. We have proposed the probability initialization method to identify the selected features with equal distribution in the search space. We have also proposed a niching technique to explore the search space and exploit the nearby solutions. Further, we have proposed a convergence archive to locate and store the optimal feature subsets. Exhaustive experimentation has been conducted on different datasets with varying characteristics to identify multiple feature subsets. We have also proposed an evaluation metric for the quantitative comparison of the proposed algorithm with the existing algorithms. Results have also been compared with existing algorithms in the objective space and in terms of classification accuracy, which shows the effectiveness of the proposed algorithm.
What’s Best for Customers: Empathetic Versus Solution-Oriented Service Robots

Abstract A promising application of social robots highlighted by the ongoing labor shortage is to deploy them as service robots at organizational frontlines. As the face of the firms, service robots are expected to provide cognitive and affective supports in response to customer inquiries. However, one question remains unanswered: Would having a robot with a high level of affective support be helpful when such a robot cannot provide a satisfactory level of cognitive support to users? In this study, we aim to address this question by showing that empathetic service robots can be beneficial, although the extent of such benefits depends on the quality of services they provide. Our in-person human–robot interaction study ( n = 55) shows that when a service robot can only provide a partial solution, it is preferable for it to express more empathetic behaviors, as users will perceive it to be more useful and will have a better customer experience. However, when a service robot is able to provide a full solution, the level of empathy displayed by it does not result in significant differences on perceived usefulness and customer experience. These findings are further validated in an online experimental study performed in another country ( n = 395).
Deep self-learning based dynamic secret key generation for novel secure and efficient hashing algorithm

The hash function is an efficient source of the integrity and authentication of input text and other data messages (image & audio-video) in the cryptography field. Existing hashing algorithms are time-consuming and vulnerable to collision attacks, birthday attacks, meet-in-the-middle attacks, and key search attacks. In this context, we propose an innovative and secure hash algorithm that uses self-learning neurons during the implementation phase. Firstly, the proposed deep learning hashing algorithm accepts the variable length (ln) of the input text and image messages (im) at the input layer 0 and divides the whole input (M) into four characters, represented as message pair (MP). If the last MP contains less than four characters, have been completed by the assumption of the special cases and processed via the OR logical operation. Subsequently, the proposed pattern matching-swapping methods have resolved the conflict of the repeated characters in each MP. Secondly, the proposed novel dynamic secret keys are generated for each MP by introducing a skip and select basic conditions by omitting the consecutive 0's or 1's in key selection. Thirdly, upon successfully processing the phases above, the proposed novel hashing algorithm generates collision-free hash values using forwarding and backward propagation. Experimental results show that our proposed hashing algorithm is efficient and significantly outperforms in terms of sensitivity generated in hash output, speed, and collision resistance compared to the existing state-of-the-art hashing algorithms.
Quadrotor Path Planning and Polynomial Trajectory Generation Using Quadratic Programming for Indoor Environments

This study considers the problem of generating optimal, kino-dynamic-feasible, and obstacle-free trajectories for a quadrotor through indoor environments. We explore methods to overcome the challenges faced by quadrotors for indoor settings due to their higher-order vehicle dynamics, relatively limited free spaces through the environment, and challenging optimization constraints. In this research, we propose a complete pipeline for path planning, trajectory generation, and optimization for quadrotor navigation through indoor environments. We formulate the trajectory generation problem as a Quadratic Program (QP) with Obstacle-Free Corridor (OFC) constraints. The OFC is a collection of convex overlapping polyhedra that model tunnel-like free connecting space from current configuration to goal configuration. Linear inequality constraints provided by the polyhedra of OFCs are used in the QP for real-time optimization performance. We demonstrate the feasibility of our approach, its performance, and its completeness by simulating multiple environments of differing sizes and varying obstacle densities using MATLAB Optimization Toolbox. We found that our approach has higher chances of convergence of optimization solver as compared to current approaches for challenging scenarios. We show that our proposed pipeline can plan complete paths and optimize trajectories in a few hundred milliseconds and within approximately ten iterations of the optimization solver for everyday indoor settings.
Towards a cognitive engineering of transactional services in IoT based systems

Cognitive computing is the capability of a system to mimic the ability of human brain to learn and adapt from the surroundings. Cognitive systems have decision-making capabilities based on new information, actions and outcomes. Similarly, Internet of Things (IoT) aims at making things smart, and enabling them to perform complex tasks. Reliability and flexibility are persistent challenges in the IoT context where the promise is managing a multitude of devices and delivering real-time responses for critical smart applications. A limited number of studies examine these challenges while considering cognitive capabilities of things and have failed to handle thing’s specificities in terms of communication bandwidth, power availability and storage capacity. Following the service oriented architecture (SOA), the functionality can be encapsulated as services. Thus, automating the management of transactional services in smart IoT ecosystem can be fulfilled through the coupling of transactional properties to cognitive things. This paper provides a comprehensive approach to alleviate reliability restrictions in cognitive IoT service compositions. The concept of cognitive faculty (CF) is introduced to leverage transactional properties of services and can be customized to specific requirements of IoT applications. A proof-of-concept is included in this paper based on a self-monitoring IoT application for diabetic patients.
Content Disarm and Reconstruction of RTF Files a Zero File Trust Methodology

Content Disarm and Reconstruction (CDR) is a zero-trust file methodology that proactively extracts threat attack vectors from documents and media files. While there is extensive literature on CDR that emphasizes its importance, a detailed discussion of how the CDR process works, its effectiveness and drawbacks is lacking. Therefore, this paper presents DeepCDR, the first CDR system in which the validation, the prevention rate, and the received visual quality effect of disarming and reconstruction are presented and measured. The effectiveness of the novel DeepCDR against a well-known dataset shows that it disarmed not only the malicious components, but the reconstructed file is also usable and functional. Since CDRs rely on understanding the file format, any CDR solution should handle each supported file type separately due to the vast difference in each format. Hence, this paper focuses on the Rich Text Format file type that is commonly exploited by attackers.
Cost-Efficient Fault-Tolerant Workflow Scheduling for Deadline-Constrained Microservice-Based Applications in Clouds

Microservices are becoming increasingly popular in the construction of cloud applications. On the basis of containers, microservice instances can be implemented with high scalability and maintainability. Due to the need of ensuring various quality of service (QoS) requirements and the two-layer resource structure of containers and virtual machines (VMs), microservice workflow scheduling in clouds is a challenging problem to address. This paper proposes a heuristic algorithm GSMS to minimize execution cost of a microservice-based workflow application while satisfying deadline and reliability constraints. GSMS adopts a greedy fault-tolerant scheduling strategy for replicas of each task to select appropriate resources that meet the sub-deadline and minimize the cost until the sub-reliability is guaranteed. Furthermore, a resource adjustment strategy is incorporated into GSMS to further improve resource utilization. By conducting extensive experiments with several realistic workflow applications, in comparison with existing algorithms, the effectiveness and efficiency of GSMS in achieving lower execution cost and meeting deadline and reliability requirements are validated.
Efficient Deep Learning Approach to Recognize Person Attributes by Using Hybrid Transformers for Surveillance Scenarios

Numerous deep perception technologies and methods are built on the foundation of pedestrian feature identification. It covers various fields, including autonomous driving, spying, and object tracking. A recent study area is the identification of personality traits that has attracted much interest in video surveillance. Identifying a person’s distinct areas is complex and plays an incredibly significant role. This paper presents a current method applied to networks of primary convolutional neurons to locate the area connected to the Person attribute. Using Individual Feature Identification, the features of a person, such as gender, age, fashion sense, and equipment, have received much attention in video surveillance analytics. This Article adopted a Conv-Attentional image transformer that broke down the most discriminating Attribute and region into multiple grades. The feed-forward system and conv-attention are the components of serial blocks, and parallel blocks have two attention-focused tactics: direct cross-layer attention and feature interpolation. It also provides a flexible Attribute Localization Module (ALM) to learn the regional aspects of each Attribute are considered at several levels, and the most discriminating areas are selected adaptively. We draw the conclusion that hybrid transformers outperform pure transformers in this instance. The extensive experimental results indicate that the proposed hybrid technique achieves higher results than the current strategies on four unique private characteristic datasets, i.e., RapV2, RapV1, PETA, and PA100K.
Passthrough Mixed Reality With Oculus Quest 2: A Case Study on Learning Piano

Mixed reality (MR) in standalone headsets has many advantages over other types of devices. With the recent appearance of the Passthrough of Oculus Quest 2, new possibilities open up. This work details the features of the current Passthrough and how its potential was harnessed and its drawbacks minimized for developing a satisfying MR experience. It has been applied to learning to play the piano as a use case. A total of 33 piano students participated in a study to compare participants’ interpretation outcomes and subjective experience when using a MR application for learning piano with two visualization modes (border lines on all the keys (Wireframe) versus solid color hiding the real keys (Solid)). The two visualization modes provided a satisfying experience. Even though there were no significant differences in the analyzed variables, the students preferred the Solid mode, indicating that short-distance Passthrough limitations should be minimized in application development.
Adaptive Modified Unbiased Minimum-Variance Estimation for Highly Maneuvering Target Tracking With Model Mismatch

Due to the complexity, time variability, and mutability of the noncooperative maneuvering target, the dynamic tracking model of the target does not match with the actual state, which reduces the tracking accuracy or even completely tracking loss. In this article, an expectation maximization-based adaptive modified unbiased minimum-variance estimation (EM-AMUMVE) algorithm is proposed for highly maneuvering target tracking with model mismatch. First, the virtual maneuvering noise and the first-order Markov process model are integrated to quantitatively describe the maneuvering acceleration according to the boundness of the maneuvering acceleration. Then, the maneuvering acceleration and state estimation are derived based on the unbiased minimum-variance criterion, and the expectation maximization (EM) is introduced in the updating procedure of the quadratic Bayesian filter to adaptively estimate the mean and covariance of virtual maneuvering noise and thus to improve the accuracy of maneuvering acceleration estimation. Finally, the optimality of the adaptive modified maneuvering acceleration estimation is theoretically proved, and the tracking scenarios of time-varying maneuvering target with model mismatch and reentry gliding trajectory of typical hypersonic vehicle hypersonic technology vehicle 2 (HTV-2) are built to demonstrate the validness, convergence, sensitivity, and engineering practicability of the proposed algorithm.