Reviewerly/exHarmony huggingface dataset:
Chatbot for Online Customer Service

Chatbot has become popular in recent years due to the advancements in artificial intelligence and other underlying technologies. Likewise, increased internet interactivity and smarter mobile devices have specifically attracted more consumers to pursue superior and personalized customer service. The aim of this chapter was therefore to better understand the use of chatbots by online businesses to shed light on its effect on customer service satisfaction. The commitment trust theory served as the underlying theoretical foundation for the conceptual framework of this study. It explored the relationships among trust, commitment, service quality, and technology towards the use of chatbots. Subsequently, customer engagement gained has influenced the knowledge sharing and the referral to other customers. This chapter presented an integrative framework for predicting the use of chatbots to enhance customer bonding with firms. The main contribution was the list of antecedents needed to improve customer engagement in the implementation of chatbots.
An efficient identity tracing scheme for blockchain-based systems

The enhancement of anonymity for blockchain users received much attention. However, in some cases tracing user’s identity is also very important, especially to expose illegal transactions. In this paper, we propose an identity tracing scheme and implement it using a simple and efficient proof method. In particular, we design a new signature scheme that is used to generate a user’s certificate, and present a binding method for the user’s certificate and trading public key. Users only need to register once with the supervision center, and then the center may trace sender’s identity of the transactions on the blockchain. Our scheme is suitable to blockchain-based systems that explicitly use public/private key pairs, and does not affect the anonymity property of the original system.
Effectiveness of Immersive VR in STEM Education

Virtual Reality (VR) allows users to immerse themselves in a virtual, computer-generated environment, with this technology having the potential to solve many issues that the education industry faces by bringing new perspectives and features. Especially due to the SARS-CoV-2 pandemic, online education is becoming more prominent and VR can improve e-learning by creating an immersive experience of classrooms and subjects. This literature review looks at how immersive VR can aid the teaching of STEM education from the primary school to university level. The technology of VR, with its advantages and limitations, is evaluated and the implementation of this technology in education systems is discussed. Moreover, real-life case studies are presented and evaluated to show the pragmatic application of VR previously and currently in STEM education. This paper concludes that VR possesses immense potential in the field of education and promotes a more widespread use of this technology.
Emotional consequences and attention rewards: the social effects of ratings on Reddit

Rating features on social media platforms affect visibility algorithms and act as symbolic markers of evaluation. This paper addresses the social effects of content ratings through a case study of Reddit. Reddit is a social news site on which users in topic-based communities (subreddits) create posts upon which others upvote, downvote, and comment. Vote scores indicate convergence with, and divergence from, community norms. Analysing data from the platform's three most popular subreddits, we ask: How do rating features afford emotional expression and content engagement? Findings from a Variable-Lag Granger Causality model show that for a portion of Reddit users (14.5%), vote scores predict subsequent emotional expression, with upvotes preceding positive sentiments and downvotes preceding negative sentiments. This is the first systematic test of how ratings influence emotional expression on a social media platform. Findings also show that downvoted content receives higher levels of engagement than upvoted content. Together, these findings suggest a paradox in which divergence from community norms, as indicated by vote score patterns, have emotional consequences and attention rewards.
Camera-Based Batch Normalization: An Effective Distribution Alignment Method for Person Re-Identification

Person re-identification (ReID) aims at matching identities across disjoint cameras. Its fundamental difficulty lies in associating images across individual cameras, where a key clue, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , identity appearance, is prone to the environmental factors of cameras and, consequently, subject to distinct image distributions due to the environmental differences between cameras. To associate images from training cameras, ReID methods strongly demand expensive inter-camera annotations for learning the relations between the distribution of these cameras, yet trained models are still not guaranteed to transfer well to unseen cameras. This problem significantly limits the application of ReID. This paper rethinks the working mechanism of conventional ReID approaches and puts forward a new solution. With an effective operator named Camera-based Batch Normalization (CBN), we guarantee an invariant input distribution independent of all cameras. Thus, the training and testing procedures are always conducted under the same input distribution. This alignment brings three benefits. First, ReID models enjoy better abilities to generalize across testing scenarios with unseen cameras and transfer across multiple training sets. Second, it makes better use of intra-camera annotations, which have been undervalued before due to the lack of cross-camera information. Ideally, the cost of inter-camera annotations can be largely reduced. Third, cross-modality tasks can be better defined through aligning visible/infrared cameras’ distributions. Experiments on a wide range of ReID tasks demonstrate the effectiveness of our approach.
A Secured Smart National Identity Card Management Design using Blockchain

Importance of data security differs from sector to sector. However, data of sensitive nature are increasing day by day and thereby, it requires utmost attention towards the protection of data. Organizations from all over the world are getting involved to deploy the best cyber defense mechanism. Primary concerns for the organizations includes three common factors: people, processes, and technology. Smart national identity cards (smart cards) are immensely sensitive in nature. In Bangladesh, smart cards are being used for 22 services, which include banking, passports, driving licenses, etc. In addition, smart cards also hold bio-metric details for the cardholder such as retina scan, ten finger impressions, picture, and so on. Most of them are confidential data and prone to privacy breach. To address this issue, this paper propose a model for smart card management system using blockchain technology. In recent years, blockchain earned popularity among researchers to protect data from unlawful altercation. Adaptation of our proposed model will help the government of Bangladesh to secure citizens' private information and bring transparency on their information management. Furthermore, it will provide access control capability to citizens in case of third-party information access requests.
Low-Cost Implementation of Bilinear and Bicubic Image Interpolation for Real-Time Image Super-Resolution

Super-resolution imaging (S.R.) is a series of techniques that enhance the resolution of an imaging system, especially in surveillance cameras where simplicity and low cost are of great importance. S.R. image reconstruction can be viewed as a three-stage process: image interpolation, image registration, and fusion. Image interpolation is one of the most critical steps in the S.R. algorithms and has a significant influence on the quality of the output image. In this paper, two hardware-efficient interpolation methods are proposed for these platforms, mainly for the mobile application. Experiments and results on the synthetic and real image sequences clearly validate the performance of the proposed scheme. They indicate that the proposed approach is practically applicable to real-world applications. The algorithms are implemented in a Field Programmable Gate Array (FPGA) device using a pipelined architecture. The implementation results show the advantages of the proposed methods regarding area, performance, and output quality.
Will Dependency Conflicts Affect My Program's Semantics?

Java projects are often built on top of various third-party libraries. If multiple versions of a library exist on the classpath, JVM will only load one version and shadow the others, which we refer to as <i>dependency conflicts</i> . This would give rise to <i>semantic conflict</i> (SC) issues, if the library APIs referenced by a project have identical method signatures but inconsistent semantics across the loaded and shadowed versions of libraries. SC issues are difficult for developers to diagnose in practice, since understanding them typically requires domain knowledge. Although adapting the existing test generation technique for dependency conflict issues, <small>Riddle</small> , to detect SC issues is feasible, its effectiveness is greatly compromised. This is mainly because <small>Riddle</small> randomly generates test inputs, while the SC issues typically require specific arguments in the tests to be exposed. To address that, we conducted an empirical study of 316 real SC issues to understand the characteristics of such specific arguments in the test cases that can capture the SC issues. Inspired by our empirical findings, we propose an automated testing technique <small>Sensor</small> , which synthesizes test cases using ingredients from the project under test to trigger inconsistent behaviors of the APIs with the same signatures in conflicting library versions. Our evaluation results show that <small>Sensor</small> is effective and useful: it achieved a <inline-formula><tex-math notation="LaTeX">$Precision$</tex-math></inline-formula> of 0.898 and a <inline-formula><tex-math notation="LaTeX">$Recall$</tex-math></inline-formula> of 0.725 on open-source projects and a <inline-formula><tex-math notation="LaTeX">$Precision$</tex-math></inline-formula> of 0.821 on industrial projects; it detected 306 semantic conflict issues in 50 projects, 70.4 percent of which had been confirmed as real bugs, and 84.2 percent of the confirmed issues have been fixed quickly.
Ownership and purchase intention of crypto-assets: survey results

The paper employs surveys among Austrian households to study ownership and purchase intentions of crypto-assets. About 1.6% of Austrians own crypto-assets and about 5% can be viewed as potential adopters. Owners, on average, have higher financial knowledge and are more risk-tolerant than non-owners. Distrust in banks or in conventional currencies is not found to be an important driver of ownership. Intentions to adopt are strongly affected by profit expectations and by beliefs that crypto-assets offer advantages for payments—most adopters or potential adopters hold both beliefs. Perceptions of high volatility or the risk of fraud and online theft dampen the demand for crypto-assets.
SCBS: A Short Certificate-Based Signature Scheme With Efficient Aggregation for Industrial-Internet-of-Things Environment

The advent of the Internet of Things (IoT) has escalated the sharing of information among various smart devices many fold, irrespective of their geographical location. Recently, applications, such as e-healthcare, farm monitoring, border security, smart transportation, etc. have attracted wide attention from the research community. However, as devices in the Industrial-IoT (IIoT) environment share their information using the Internet, security issues, such as authentication, integrity, and confidentiality of data pose various challenges to the research community for the successful implementation of any solution. To handle these issues, several digital signature-based schemes have been designed in the past. However, because of the usage of the identity-based public-key cryptography (IDPKC) or certificate-less-based public-key cryptography (CLPKC), these schemes suffer from key escrow or secret key distribution problems. To eliminate these flaws, this article presents a short digital signature scheme without pairing in certificate-based setting with aggregation in IIoT environment. Besides, in IIoT environment, communication and computational costs are also considered as imperative challenges. In this regard, the pairing free construction, short length signature and aggregation make it a communication and computational efficient signature scheme. The performance comparison of the proposed scheme shows that our scheme causes less computational overhead and takes significantly less execution time as compared to the existing schemes, which is imperative for the resource limited IIoT devices. We also demonstrate that signature aggregation and verification cost is 6.67(n+2) ms which is much less in comparison to the verification cost ( 20.01n ms) of n short signatures.
Trustworthy Blockchain-Empowered Collaborative Edge Computing-as-a-Service Scheduling and Data Sharing in the IIoE

Owing to the technology of 5G and beyond, collaborative edge computing-as-a-service has enabled trillions of interconnected edge applications. It has also become a prospective paradigm for providing computing services by offloading computationally intensive assignments to mobile-edge servers or fog nodes due to terminals constrained computing and caching resources. Nevertheless, in this process, trust of computing-as-a-service scheduling and edge data sharing in heterogeneous systems is an unavoidable challenge of paramount importance. As a powerful tool that addresses security issues, blockchains can ensure the trustworthiness and irreversibility of computing data by consensus mechanisms. However, in the Industrial Internet of Energy (IIoE), the storage burden of a single blockchain has increased. Therefore, from the perspective of a stable real-time operation, we propose a multiedgechain structure that accommodates thousands of edge data and promotes on-chain data efficiency to achieve cross-chain edge data sharing for heterogeneous blockchain systems. Moreover, aiming at the profits of computing resource scheduling in the IIoE, a two-stage Stackelberg game strategy with an optimal scheduling demand and reward is provided considering the edge user’s preferences and risk factors. Finally, the simulation results verify the superiority of the proposed scheme, regarding the game equilibrium, utility optimization, and data sharing efficiency of cloud–edge collaboration.
Profiling adopters (and non-adopters) of a contact tracing mobile application: Insights from Australia

Many governments are using contact tracing mobile applications (CTMAs) yet public adoption of such systems has been relatively low. The main objective of this paper is to profile adopters (and non-adopters) of Australia’s COVIDSafe CTMA. We use latent profile analysis to examine predictors of CTMA download behaviour. Specifically, we draw on a representative Australian sample (N = 2575) to examine the interplay between age, education, income, dispositional desire for privacy and political ideology on download behaviour. We examine trust in government as a mediating mechanism between profiles and download behaviour. Our analysis produces seven profiles. Trust in government mediates the relationship between most profiles and download behaviour. A combination of wealth and education appear to be key explanatory factors of CTMA download behaviour. Two profiles -- comprising individuals with high income and education -- had the highest rates of download behaviour. Profiles with low download percentages comprised politically left-leaning participants with average to low income and education. Our findings clearly indicate the profiles of people who are (not) likely to download a CTMA. Practical ways to improve widespread adoption include providing structural support to the more vulnerable members of society, making clear the societal benefits of downloading CTMAs, and engaging in bipartisan promotion of such apps.
Robust Secret Image Sharing Scheme Against Noise in Shadow Images

The (k, n)-threshold secret image sharing scheme is an image protection method, whose security comes partly from precise mathematical calculations, and even a little change in shadow images will lead to a false recovered image.Thus, it is crucial to recover the secret image information in the presence of possible noise on shadow images, which has rarely been considered in previous work.In this paper, a robust (k, n)-threshold polynomial-based secret image sharing scheme(RPSIS) against noise on shadow images is proposed, which depends on the randomness of the sharing phase without any other techniques, such as steganography.Additionally, pixel expansion caused by the direct application of error correction codes is avoided.Experimental results and theoretical proof confirm the effectiveness of our scheme.The shadow images of the scheme are of the same size as secret image and the security of the scheme is also maintained with no information leakage.Even though the shadow images are modified by noise, the original secret image can be reconstructed without loss under the error correction capability, which provides more possibilities for the practical application of secret image sharing.
Designing Robust Control for Permanent Magnet Synchronous Motor: Fuzzy Based and Multivariable Optimization Approach

This article proposed a novel optimal robust control design approach to solve the uncertain parameter problem in the permanent magnet synchronous motor (PMSM) system by integrating the fuzzy set theory and system control theory. First, we adopt the prescribed fuzzy number to describe the time-varying but bounded uncertain parameter in the PMSM system. Second, based on the estimated bound of the uncertain parameter and the nominal dynamical model, we design a deterministic robust control scheme characterized by model-based control and feedback control. The control objective is to guarantee the PMSM system with uncertain parameters satisfies the desired motion equation with deterministic system performance: uniform boundedness (UB) and uniform ultimate boundedness (UUB). Third, using the fuzzy uncertain parameter, a performance index relevant to the steady-state performance and control cost is constructed. A parameters design optimization problem is formulated by minimizing the performance index, whose analytical solution is successfully solved and proved to always exists and is unique. As a result, the resulting PMSM system under the proposed control approach has two-level system performance: the guaranteed deterministic performance and optimal performance. Finally, the results of numerical simulation and experiment are presented for demonstration.
Response-Hiding Encrypted Ranges: Revisiting Security via Parametrized Leakage-Abuse Attacks

Despite a growing body of work on leakage-abuse attacks for encrypted databases, attacks on practical response-hiding constructions are yet to appear. Response-hiding constructions are superior in that they nullify access-pattern based attacks by revealing only the search token and the result size of each query. Response-hiding schemes are vulnerable to existing volume attacks, which are, however, based on strong assumptions such as the uniform query assumption or the dense database assumption. More crucially, these attacks only apply to schemes that cannot be deployed in practice (ones with quadratic storage and increased leakage) while practical response-hiding schemes (Demertzis et al. [SIGMOD’16] and Faber et al. [ESORICS’15]) have linear storage and less leakage. Due to these shortcomings, the value of existing volume attacks on response-hiding schemes is unclear.In this work, we close the aforementioned gap by introducing a parametrized leakage-abuse attack that applies to practical response-hiding structured encryption schemes. The use of non-parametric estimation techniques makes our attack agnostic to both the data and the query distribution. At the very core of our technique lies the newly defined concept of a counting function with respect to a range scheme. We propose a two-phase framework to approximate the counting function for any range scheme. By simply switching one counting function for another, i.e., the so-called "parameter" of our modular attack, an adversary can attack different encrypted range schemes. We propose a constrained optimization formulation for the attack algorithm that is based on the counting functions. We demonstrate the effectiveness of our leakage-abuse attack on synthetic and real-world data under various scenarios.
A Secure Storage and Sharing Scheme of Stroke Electronic Medical Records Based on Consortium Blockchain

The maintenance and sharing of electronic medical records are one of the essential tasks in the medical treatment combination. Traditional cloud-based electronic medical record storage system is difficult to realize data security sharing. The tamper resistance and traceability of blockchain technology provide the possibility for the sharing of highly sensitive medical data. This paper proposes a safe sharing scheme of stroke electronic medical records based on the consortium blockchain. The scheme adopts the storage method of ciphertext of medical records stored in the cloud and index of medical records stored on the blockchain. The privacy protection mechanism proposed in this paper innovatively combines proxy reencryption and searchable encryption which supports patient pseudoidentity search. The mechanism could achieve controllable sharing of medical records and precise search. According to the organizational characteristics of the stroke medical treatment combination, this paper proposes an improved Practical Byzantine Fault Tolerance mechanism to reach a consensus between consensus nodes. Then, the proposed scheme is analyzed and evaluated from three aspects of medical record integrity, user privacy, and data security. The results show that the scheme can not only ensure the privacy of patient identity information and private key data but also resist the tampering and deletion attacks of internal and external malicious nodes on the medical record data. Therefore, the proposed scheme is conducive to the improvement of the timeliness of stroke treatment and the safe sharing of electronic medical records in stroke medical treatment combination.
Attentive deep network for blind motion deblurring on dynamic scenes

Non-uniform blind motion deblurring is a challenging yet important problem in image processing that receives enduring attention in the last decade. The non-uniformity nature of motion blurring leads to great variations on the blurring effects across image regions and over different images, which makes it very difficult to train an end-to-end deblurring neural network (NN) with good generalization performance. This paper introduces an attention mechanism for the blind deblurring NN, including both spatial and channel attention, so as to effectively handle the significant spatial variations on blurring effects. In the attention mechanism, the spatial attention is introduced in both the encoder for discriminative exploitation of image edges and smooth regions and the decoder for discriminative treatment on different regions with different blurring effects. The channel attention is introduced for better generalization performance of the NN, as it allows adaptive weighting on intermediate features for a particular image. Building such an attention mechanism into a multi-scale encoder–decoder framework, an attentive NN is developed for practical non-uniform blind image deblurring. The experiments on several benchmark datasets show that the proposed NN can effectively restore the images degraded by spatially-varying blurring, with state-of-the-art performance.