Reviewerly/exHarmony huggingface dataset:
Empathy and Identification as an Online Technology of Blogger’s Communication in Digital Marketing

This article is about innovative technology of digital marketing, which allows communicating successfully with producers of goods with consumers at the stage of making a purchasing decision in order to increase sales of goods and services. The specifics of the purchasing decision-making process in the online environment at all its stages are described, the theoretical justification for the inclusion in the digital purchasing decision-making process of a blogger purposefully using the socio-psychological mechanisms of empathy and identification, is described. The result of it is an additional stimulating effect on the consumer when he became acquainted with a particular product. The results of the author’s empirical study proving the effect of these mechanisms in the process of making a purchasing decision are presented. It’s described that, provided to the subject to the actualization of the buyer’s conscious needs, the blogger creates the unconscious needs of the recipients that affect the consumer’s interest in the product, and, as a result, the level of willingness to purchase goods increases. At the same time, the corresponding emotion of the blogger is transmitted to the potential consumer, which leads to an increase in the likelihood of buying a product. The scientific novelty of this technology lies in the fact that for the first time it has been theoretically described and practically proved the feasibility of using socio-psychological mechanisms at certain stages of the decision to purchase in digital marketing. It’s empirically proven for the first time that the targeted use by a blogger, a reference person, of the totality of the socio-psychological mechanisms of empathy and identification at the time of contact with a potential buyer increases the buyer’s willingness to purchase the appropriate product. This is the substantial essence of this online digital marketing technology, which allows to increase sales in the future by influencing the consumer of a reference personality, acting as a symbolic intermediary between manufacturers of goods and representatives of the target audience in the process of making a purchasing decision. The article provides a theoretical justification for the action of this online marketing technology empirically proves the relationship between the using of this technology and the level of willingness of a potential consumer to make a purchase, gives practical recommendations to manufacturers of goods to increase sales.
Formal Verification of Memory Isolation for the TrustZone-based TEE

The trusted execution environment (TEE) is the security basis of embedded systems, which can provide a hardware-based isolated execution environment for security-sensitive components. Isolation of memory is a critical mechanism of TEE, the security of which plays a very important role in TEE's construction. In this paper, we present a formal verification of security properties about the memory isolation mechanism of TEE systems based on the ARM TrustZone, which is a hardware security technology commonly used on billions of ARM processors to create TEE. We establish a formal model of memory isolation, which consists of the formalization of ARMv8 architecture hardware components related to memory isolation and the formalization of a TrustZone monitor supporting world switch. We formally explicit and verify the correctness properties of memory management along with the information flow security properties of the memory isolation mechanism. The formalizations and verifications are all performed in the interactive theorem prover Isabelle/HOL.
The Amalgamation of Blockchain with Smart and Connected Vehicles: Requirements, Attacks, and Possible Solution

Smart and connected vehicles play a vital role in today's advanced and luxurious life. Vehicular Ad-hoc Network (VANET) allows communication between these vehicles regarding safety and control messages, road situations, and surrounding environments. VANET demands security requirements like authentication, availability, confidentiality, reliability, etc. These requirements have crucial attacks associated with it which hampers a system to work. Blockchain is an immutable, distributed, decentralized, smart contract-based technology that provides an efficient solution to VANET requirements and their corresponding attacks. This paper discusses various such requirements and attacks and their possible solutions with Blockchain. Apart from that, Blockchain also acts as a prominent solution to deal with the challenges being faced by electric and autonomous vehicles to come to reality. We have reviewed, analyzed, and discussed the proficient researcher's works and their contributions in this paper to unfold new directions in the present and future research. Further, we have analyzed and discussed the insurance, and forensic management is amalgamated with Blockchain by various researchers to create an automatic assessment protocol. Future possibilities for smart and connected vehicles with Blockchain could be a major objective of this paper.
Connecting latent relationships over heterogeneous attributed network for recommendation

Recently, deep neural network models for graph-structured data have been demonstrated to be influential in recommendation systems. Graph Neural Network (GNN), which can generate high-quality embeddings by capturing graph-structured information, is convenient for the recommendation. However, most existing GNN models mainly focus on the homogeneous graph. They cannot characterize heterogeneous and complex data in the recommendation system. Meanwhile, it is challenging to develop effective methods to mine the heterogeneity and latent correlations in the graph. In this paper, we adopt Heterogeneous Attributed Network (HAN), which involves different node types as well as rich node attributes, to model data in the recommendation system. Furthermore, we propose a novel graph neural network-based model to deal with HAN for Recommendation, called HANRec. In particular, we design a component connecting potential neighbors to explore the influence among neighbors and provide two different strategies with the attention mechanism to aggregate neighbors' information. The experimental results on two real-world datasets prove that HANRec outperforms other state-of-the-art methods.
Extending Contrastive Learning to Unsupervised Coreset Selection

Self-supervised contrastive learning offers a means of learning informative features from a pool of unlabeled data. In this paper, we investigate another useful approach. We propose an entirely unlabeled coreset selection method. In this regard, contrastive learning, one of several self-supervised methods, was recently proposed and has consistently delivered the highest performance. This prompted us to choose two leading methods for contrastive learning: the simple framework for contrastive learning of visual representations (SimCLR) and the momentum contrastive (MoCo) learning framework. We calculated the cosine similarities for each example of an epoch for the entire duration of the contrastive learning process and subsequently accumulated the cosine similarity values to obtain the coreset score. Our assumption was that a sample with low similarity would likely behave as a coreset. Compared with existing coreset selection methods with labels, our approach reduced the cost associated with human annotation. In this study, the unsupervised method implemented for coreset selection achieved improvements of 1.25% (for CIFAR10), 0.82% (for SVHN), and 0.19% (for QMNIST) over a randomly selected subset with a size of 30%. Furthermore, our results are comparable to those of the existing supervised coreset selection methods. The differences between the proposed and the above mentioned supervised coreset selection method (forgetting events) were 0.81% on the CIFAR10 dataset, −2.08% on the SVHN dataset (the proposed method outperformed the existing method), and 0.01% on the QMNIST dataset at a subset size of 30%. In addition, our proposed approach exhibited robustness even if the coreset selection model and target model were not identical (e.g., using ResNet18 as a selection model and ResNet101 as the target model). Lastly, we obtained more concrete proof that our coreset examples are highly informative by showing the performance gap between the coreset and non-coreset samples in the coreset cross test experiment. We observed a pair of performance ((testing: non-coreset, training: coreset), (testing: coreset, training: non-coreset)), i.e. (94.27%, 67.39 %) for CIFAR10, (98.24%, 83.30%) for SVHN, and (99.89%, 93.07%) for QMNIST with a subset size of 30%.
Joint detection and tracking of non‐ellipsoidal extended targets based on cubature Kalman‐CBMeMBer sub‐random matrices filter

Joint detection and tracking of multiple extended targets (ETs) from image observations is a challenging radar technology; especially for extended stealth targets (ESTs). This work provides a new approach for the ESTs tracking under the non-linear Gaussian system based on track-before-detect (TBD) approach. The sequential Monte Carlo cardinality-balanced multi-target multi-Bernoulli (SMC-CBMeMBer) filter provides a good framework to cope with TBD approach. However, this filter suffers from the particles’ degradation problem seriously; especially for ETs tracking. Recently, the cubature Kalman (CK)-CBMeMBer filter which employs a third-degree spherical-radical cubature rule has been proposed to handle the non-linear models, the CK-CBMeMBer filter is more accurate and more principled in mathematical terms compared to SMC-CBMeMBer filter. To this point, the authors address a TBD of ESTs with extended CK-CBMeMBer filter based on random matrix model (RMM), which is an efficient way to track ellipsoidal ESTs. In RMM-ESTs scenarios, although the extension ellipsoid is efficient, it may not be accurate enough because of lacking useful information, such as size, shape, and orientation. Therefore, they introduce a filter composed of sub-ellipses; each one is represented by a RMM. The results confirm the effectiveness and robustness of the proposed filter.
DependLoc: A Dependency-based Framework For Bug Localization

As software systems are becoming larger and more complex, debugging poses great challenges to software developers and maintainers. Among various efforts on easing the burden of debugging, bug localization techniques are developed to help locate where a bug occurs in source code files automatically. Information retrieval and deep neural network techniques are often adopted in existing research to achieve bug localization through capturing the textual or semantic similarity between bug reports and source code files. At the same time, some domain- specific eatures in software engineering are also utilized to locate the buggy files. However, the dependency relationship between classes (A depends on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> if <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> references B) is not considered or utilized by existing approaches. In this work, we propose a novel framework DependLoc for bug localization which leverages the dependency relationship among source code files. DependLoc is based on the observation that buggy files may not be highly similar to a bug report but have a dependency relationship with one or more files that are quite similar to the bug report. DependLoc adopts a customized Ant Colony algorithm to quantify the intrinsic dependency relationship (called reference heat) and designs a segment-based encoder to learn this feature. Experimental results on six widely-used benchmark datasets for bug localization show that our approach outperforms the state-of-the-art methods, and Accuracy@10 is improved by 4% on average.
Operational non-Markovianity in a statistical mixture of two environments

Abstract Operational non-Markovianity of reduced time-evolution is studied when a quantum system is influenced by a statistical mixture of two environments. The operational non-Markovianity is evaluated in terms of a conditional past-future correlation function proposed by Budini (2018) [9] . As an example, the phase-damping channel and the stochastic dephasing channel are considered. The effect of the statistical mixture on the operational non-Markovianity is investigated.
New approach for implementing big datamart using NoSQL key-value stores

Nowadays, NoSQL technologies are gaining significant ground and considered as the future of data storage, especially when it comes to huge amount of data, which is the case of data warehouse solutions. NoSQL databases provide high scalability and good performance among relational ones, which are really time consuming and can't handle large data volume. The growing popularity of the term NoSQL these days and vaguely related phrases like big data make us think about using this technology in decision support systems. The purpose of this paper is to investigate the possibility to instantiate a big data mart under one of the most popular and least complicated types of NoSQL databases; namely key-value store, the main challenge is to make a good correlation between the old-school approach of data warehousing based on traditional databases that favor data integrity, and interesting opportunities offered by new generation of database management systems. The paper describes the transformation process from multidimensional conceptual schema to the logical model following three approaches, and outlines a list of strengths and weaknesses for each one based on practical experience under Oracle NoSQL Database.
Research on AI Application in the Field of Quadcopter UAVs

The automatic flight control of quadcopter UAVs is a multi-disciplinary, multi-field and multi-crossing comprehensive technology that involves automatic control technology, advanced sensor technology and computer technology. In view of the current development of UAVs, many permissions of UAVs are set by human, and the flight status and parameters of UAVs cannot be effectively controlled anytime, anywhere. Therefore, the AI (artificial intelligence) application in the field of UAVs is promising in the future. In this paper, with the mainstream quadcopter UAV as the entry point, a mathematical model for the control process was quadcopter UAVs was established, and the application level of AI technology in the UAV flight control was expounded.
Stick-Slip Classification Based on Machine Learning Techniques for Building Damage Assessment

Accelerometers in smart devices have been used to successfully provide valuable information such as early warnings of earthquake activity and health monitoring of buildings. The next important step of using the acceleration measurements from smart devices is to assess building seismic damage, which is a more challenging application. A main challenge is related to the sliding motions of smart devices, which prevents acceleration measurements from directly representing the movement of underlying building floors. To detect and remove sliding motions in acceleration measurements, this paper presents an accurate and robust accelerometer-based stick-slip motion classification framework based on machine learning techniques. Three types of machine learning algorithms are introduced, and their classification performance are compared; support vector machine (SVM), multilayer perception (MLP), and recurrent neural networks (RNN). For the SVM and MLP, three classification conditions are considered: feature selection, non-linear discriminating analysis and classifier comparison. For the RNN, three hyperparameters are considered to find the best performing classification algorithm. Each algorithm is trained and validated with experimental acceleration data from a shaking table test.
Performance Analyses for Applying Machine Learning on Bitcoin Miners

Bitcoin and cryptocurrency rely on peer-to-peer (P2P) networking. Incorporating intelligence on Bitcoin miners to analyze and control networking can improve the information delivery and defend against networking threats. However, applying machine learning (ML) for building intelligence introduces a challenge because miners participate in the resource-intensive distributed consensus protocol and the ML application can consume much computing resources. In this paper, we study the feasibility and the interplay between ML algorithms and mining operations. Our prototype-based experiments measure and compare the performance of the ML algorithms to evaluate the implementation overhead and efficiencies of the ML algorithms and their impacts on mining operations, i.e., the mining reduction when the ML algorithm is running in parallel.
Efficient Inverted Index Compression Algorithm Characterized by Faster Decompression Compared with the Golomb-Rice Algorithm

This article deals with compression of binary sequences with a given number of ones, which can also be considered as a list of indexes of a given length. The first part of the article shows that the entropy H of random n-element binary sequences with exactly k elements equal one satisfies the inequalities klog2(0.48·n/k)&lt;H&lt;klog2(2.72·n/k). Based on this result, we propose a simple coding using fixed length words. Its main application is the compression of random binary sequences with a large disproportion between the number of zeros and the number of ones. Importantly, the proposed solution allows for a much faster decompression compared with the Golomb-Rice coding with a relatively small decrease in the efficiency of compression. The proposed algorithm can be particularly useful for database applications for which the speed of decompression is much more important than the degree of index list compression.
Personalized Recommendation Algorithm for Mobile Based on Federated Matrix Factorization

Abstract There is a problem that the amount of users’ preference data on the mobile is small, and users are unwilling to disclose the preference data for the recommendation system about mobile users, so the server can’t centrally train a large amount of users’ preference data for a personalized recommendation. This paper proposes a personalized federated matrix factorization algorithm by introducing a federated matrix factorization model. The algorithm introduces users’ and items’ biases to modify the predictive rating model on each mobile; At the same time, conformity is introduced to give different weights to the preference data. In the case that the preference data does not leave the mobile, but the user preference data of the multiparty mobiles is shared, the multi-party mobiles and the server jointly train personalized matrix factorization model. The experimental results show that the algorithm in this paper still has high recommendation accuracy under the premise of correct updating in the federated matrix factorization model that uses bias and conformity.
Second-Order Step-Size Tuning of SGD for Non-Convex Optimization

In view of a direct and simple improvement of vanilla SGD, this paper presents a fine-tuning of its step-sizes in the mini-batch case. For doing so, one estimates curvature, based on a local quadratic model and using only noisy gradient approximations. One obtains a new stochastic first-order method (Step-Tuned SGD), enhanced by second-order information, which can be seen as a stochastic version of the classical Barzilai-Borwein method. Our theoretical results ensure almost sure convergence to the critical set and we provide convergence rates. Experiments on deep residual network training illustrate the favorable properties of our approach. For such networks we observe, during training, both a sudden drop of the loss and an improvement of test accuracy at medium stages, yielding better results than SGD, RMSprop, or ADAM.
Pole-piece position distance identification of cylindrical lithium-ion battery through x-ray testing technology

Abstract In the preparation process of cylindrical lithium-ion batteries, a rigorous manufacturing process demands that the position distances between positive and negative pole-pieces must be kept within a reasonable range of variation. Otherwise, a too small position distance may cause safety problems, such as short circuits and thermal runaway. To inspect the position distances between positive and negative pole-pieces automatically, and to decrease the risk of safety and economic losses during the subsequent use, this paper proposes a method to identify the position distance defects of a cylindrical lithium-ion battery on the base of x-ray digital radiography (DR) images. According to this method, the DR image is firstly enhanced by the GPU-accelerated homomorphic filtering algorithm to intensify its contrast and detailed information. Then through the Shi-Tomasi corner detection algorithm, corners of all the positive and negative pole-pieces are preliminarily detected in a region of interest which is defined in advance. To delete the false corners and find the lost corners, one-dimension region growing and curve-fitting methods are adopted. Finally, the minimum position distance, repeated accuracy and alignment metrics are calculated at the base of the detected corners. The experimental results show that the corner positions of five 26 650 cylindrical lithium-ion batteries with different pole-piece structural characteristics can be effectively identified by the proposed method, which provides a useful approach to filtrate unqualified batteries during the process of manufacture.
Path Tracking and Control for Parallel Parking

This paper demonstrates path planning and motion control for an automated parallel parking. The ultrasonic sensor based parallel parking control algorithm provides parking assist to ease the effort of driver. The trajectory for parking maneuver is generated based on the path planning algorithm to assist vehicle in the identified space with single maneuver. The lateral control with longitudinal aid provides accurate maneuver of the vehicle in a parking space. The performances of proposed fuzzy and conventional lateral control are compared to highlight the benefit of the proposed integrated fuzzy based lateral control with longitudinal aid. The lateral controlled steering command is fed to the system to maneuver the host vehicle successfully in parallel parking space. The novel approach of fuzzy based integrated feedback and feed-forward control for parallel parking has been illustrated in the paper as an optimized resolution for trajectory tracking in limited space parking application. The model based development approach has been adopted to develop path plan and control, however the designed model gets integrated with vehicle dynamics of CarMaker to validate the parking efficiency. The case study of parallel parking has been demonstrated in the research paper to comprehend the significance of path planning and rule based motion control.
Contact Web Status Presentation for Freehand Grasping in MR-based Robot-teaching

Presenting realistic grasping interaction with virtual objects in mixed reality (MR) is one of the important issues to promote the use of MR, such as in the robot-teaching domain. However, the intended interaction is difficult to achieve in MR due to the difficulty of depth perception and the lack of haptic feedback. To make intended grasping interactions in MR easier to achieve, we propose visual cues (contact web) that represent the contact state between the user's hand and a MR-object. To evaluate the effect of the proposed method, we performed two experiments. The first experiment determines the grasp type and object to be used in the evaluation of the second experiment, and the second experiment measures the time taken to complete grasping tasks of the object. Both objective and subjective evaluations show that the proposed visual cues entailed a significant reduction in the time required to complete the task.