Reviewerly/exHarmony huggingface dataset:
Rank-One Prior: Real-Time Scene Recovery

Scene recovery is a fundamental imaging task with several practical applications, including video surveillance and autonomous vehicles, etc. In this article, we provide a new real-time scene recovery framework to restore degraded images under different weather/imaging conditions, such as underwater, sand dust and haze. A degraded image can actually be seen as a superimposition of a clear image with the same color imaging environment (underwater, sand or haze, etc.). Mathematically, we can introduce a rank-one matrix to characterize this phenomenon, i.e., rank-one prior (ROP). Using the prior, a direct method with the complexity O(N) is derived for real-time recovery. For general cases, we develop ROP + to further improve the recovery performance. Comprehensive experiments of the scene recovery illustrate that our method outperforms competitively several state-of-the-art imaging methods in terms of efficiency and robustness.
Towards Improving Privacy and Security of Identity Management Systems Using Blockchain Technology: A Systematic Review

An identity management system (IDMS) manages and organizes identities and credentials information exchanged between users, identity providers (IDPs), and service providers (SPs) to ensure confidentiality and enhance privacy of users’ personal data. Traditional or centralized IDMS rely on a third party to store a user’s personal information, authenticate the user, and organize the entire process. This clearly constitutes threats to the privacy of the user, in addition to other issues, such as single point of failure (SPOF), user tracking, and data availability issues. Blockchain technology has many useful features that can contribute to solving traditional IDMS issues, such as decentralization, immutability, and anonymity. Blockchain represents an attractive solution for many issues related to traditional IDMS, including privacy, third-party control, data leakage, and SPOF, supported by Distributed Ledger Technology (DLT) security features and powerful smart contracts technology. The current study presents a systematic literature review and analysis for recently proposed solutions that adopt the traditional centralized approach, as well as solutions based on blockchain technology. The study also aims to provide a deep understanding of proposed IDMS solutions and best practices, and highlight the research gaps and open issues related to IDMSs and users’ privacy. In particular, the current research focuses on analyzing the blockchain-based solutions and illustrating their strengths and weaknesses, as well as highlighting the promising blockchain technology framework that can be utilized to enhance privacy and solve security issues in a centralized IDMS. Such a study is an important step towards developing efficient solutions that address the pressing needs in the field.
Rethinking multi-exposure image fusion with extreme and diverse exposure levels: A robust framework based on Fourier transform and contrastive learning

Multi-exposure image fusion (MEF) is an important technique for generating high dynamic range images. However, most existing MEF studies focus on fusing a moderately over-exposed image and a moderately under-exposed image, and they are not robust in fusing images with extreme and diverse exposure levels. In this paper, we propose a robust MEF framework based on Fourier transform and contrastive learning. Specifically, we develop a Fourier transform-based pixel intensity transfer strategy to synthesize images with diverse exposure levels from normally exposed natural images and train an encoder–decoder network to reconstruct the original natural image. In this way, the encoder and decoder can learn to extract features from images with diverse exposure levels and generate fused images with normal exposure. We propose a contrastive regularization loss to further enhance the capability of the network in recovering normal exposure levels. In addition, we construct an extreme MEF benchmark dataset and a random MEF benchmark dataset for a more comprehensive evaluation of MEF algorithms. We extensively compare our method with fifteen competitive traditional and deep learning-based MEF algorithms on three benchmark datasets, and our method outperforms the other methods in both subjective visual effects and objective evaluation metrics. Our code, datasets and all fused images will be released.
Are you ready for robot services? Exploring robot-service adoption behaviors of hotel-goers

This research aims to provide a meaningful discussion on hotels that want to start robot services by confirming the effect of consumer’s self-identity with robot technology on their intention to adopt robot services in the future. An exploratory and confirmatory factor analytic approach along with a structural analysis was utilized. Our findings revealed that the developed model satisfactory accounted for guests’ decision-making process for robot service adoption. Self-identity along with care, moral obligation, willingness, and attitude were key contributors to boosting intention to adopt robot services. Customer innovativeness and their uncertainty regarding using robot services played a crucial moderating role. The self-identity-based model that reflects the will to introduce eco-friendly electric products was successfully expanded. This study provides a sufficient understanding about the customers’ adopting decision for robot services. The findings of the research motivate hotel managers to hire service robots according to technological trends in the hotel industry.
Subspace-based minority oversampling for imbalance classification

In pattern classification, the class imbalance problem always occurs when the number of observations in some classes is significantly different from that of other categories, which leads to the learning bias in the classifiers. One possible solution to this problem is to re-balance the training set by over-sampling the minority class. However, over-samplings always push the classification boundaries to the majority part, thus the recall increases while the precision decreases. To avoid this situation and better handle the class imbalance problem, this paper proposes a new over-sampling method, namely Subspace-based Minority Over-Sampling (abbr. SMO). This approach considers that each category of samples is formed by common and unique characteristics, and such characteristics can be extracted by subspace. To obtain the balanced data, the common part is over-sampled for more accurately depicting the minority, and the unique part can be expanded by some generative methods. The balanced data are obtained by restoring the generated products of the subspace to the original space. The experimental results demonstrate that the SMO has the ability to model complex data distributions and outperforms both classical and newly designed over-sampling algorithms. Also, SMO can be used to generate simple images, and the generation results of MNIST can be clearly identified by both human vision and machine vision.
Generating Sierpinski gasket from matrix calculus in Dempster–Shafer theory

Graphics with fractal features are usually generated using the Iterated Function System (IFS). IFS can generate the entire family of Sierpinski gaskets by performing different operations on the attractors. As the most classical graphic, Sierpinski gasket can also be generated using mod(n,2). Dempster–Shafer Theory (DST), as a mathematical theory about evidence, models information on the all possible combination states (power set), which relates to 2n. In this paper, we explore the relationship between the Sierpinski gasket and matrix calculus in DST, which is the first time to connect fractal theory and DST from the perspective of geometry. In addition, based on the generation process of the matrices, we propose a method to generate the Sierpinski Gasket using the Kronecker product.
A fine-grained deep learning model using embedded-CNN with BiLSTM for exploiting product sentiments

As technology advances, Facebook, Twitter, and microblogging sites have become the most effective platforms for communication and information exchange. Through these forums, people can share their views and experiences. These platforms enable discussion about a certain product that can be a valuable resource used to inform any decision-making process. For such studies, the majority of advanced-level researchers employed deep learning and machine learning models in conjunction with natural language processing (NLP). In recent years, the use of pre-trained models, such as Glove and BERT, in aspect-based sentiment analysis (ABSA) has increased. In ABSA, the auxiliary information is required to distinguish each aspect of this fine-grained task. However, BERT's input format is restricted to a collection of words that cannot include more context knowledge. To address this problem, a BiLSTM and embedded CNN-based deep learning model has been presented for sentiment analysis at the aspect level. Initially, datasets were compiled from several sources. Then, an auxiliary feature was extracted using standard NLP. The auxiliary features were further refined and transformed into feature vectors based on the proposed embedded CNN model. Finally, a BiLSTM-based classification has been performed for sentiment classification. The experimental evaluation demonstrated that the performance of the suggested technique achieves on the SemEval dataset in terms of F1 score and accuracy by 81.7 and 83.3 percentage points, respectively, and on the product review dataset by 80.8 and 83.1 percentage points, respectively.
Systematic Literature Review of Stemming and Lemmatization Performance for Sentence Similarity

In today’s era, where the Internet is a huge part of people’s life, Information Retrieval (IR) is as important as ever for people to retrieve relevant information in a quick way. The sentence similarity task is an integral aspect of IR. Improving the performance of this task will also improve IR. To help achieve this, stemming and lemmatization was made. However, it is still unclear to many which one is the best to use for sentence similarity tasks. Therefore, the purpose of this study is to find which preprocessing technique (stemming and lemmatization) is best for sentence similarity tasks. In this study, the authors would like to conduct a Systematic Literature Review (SLR) on stemming and lemmatization based on many previous studies related to this topic. Previous studies have tried to assess and compare both preprocessing techniques using many evaluation methods, and it is found that a lot of factors go into deciding which preprocessing technique (stemming or lemmatization) is the best option. In general, the authors conclude lemmatization is considered the best option for sentence similarity tasks since it produces better results than stemming. However, if speed optimization is imperative, then stemming is the better option since its computational speed is faster.
Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization

An efficient optimization method is needed to address complicated problems and find optimal solutions. The gazelle optimization algorithm (GOA) is a global stochastic optimizer that is straightforward to comprehend and has powerful search capabilities. Nevertheless, the GOA is unsuitable for addressing multimodal, hybrid functions, and data mining problems. Therefore, the current paper proposes the orthogonal learning (OL) method with Rosenbrock’s direct rotation strategy to improve the GOA and sustain the solution variety (IGOA). We performed comprehensive experiments based on various functions, including 23 classical and IEEE CEC2017 problems. Moreover, eight data clustering problems taken from the UCI repository were tested to verify the proposed method’s performance further. The IGOA was compared with several other proposed meta-heuristic algorithms. Moreover, the Wilcoxon signed-rank test further assessed the experimental results to conduct more systematic data analyses. The IGOA surpassed other comparative optimizers in terms of convergence speed and precision. The empirical results show that the proposed IGOA achieved better outcomes than the basic GOA and other state-of-the-art methods and performed better in terms of solution quality.
Improved multiparty quantum private comparison based on quantum homomorphic encryption

Quantum homomorphic encryption has obvious superiority in privacy protection. In this paper, we propose an improved multiparty quantum private comparison protocol based on quantum homomorphic encryption. Firstly, a trusted key center is introduced to securely assist the distribution of the encryption keys and update of decryption keys, while preventing a malicious server from launching an attack that announces a false result. It can significantly improve the security of the protocol while ensuring that all participants get the correct comparison results. Then, our protocol is different from the previous protocols that use the help of a semi-honest third-party. The third-party in our protocol can be almost dishonest and will faithfully perform the homomorphic evaluation on the encrypted data without decryption. Finally, by preparing single photons, multiple participants request homomorphic computations from an almost dishonest third-party in parallel and there is no need for detection of the decoy photon, which greatly reduces the consumption of quantum resources. In addition, the correctness of the protocol is verified by the IBM Q experimental platform.
Robots in retail: Rolling out the Whiz

Robots are increasingly being employed in retail settings to accomplish a wide variety of tasks. In the years ahead, it is expected that most retailers will employ robots in some capacity and that these robots will alter the role of employees and change the nature of customer experience. However, since this revolution is in its early stages, scholarship in this domain is largely forward looking in nature and focused on the future rather than the present. Our research seeks to enrich and extend this literature by examining a recent robot deployment (i.e., the Whiz) across a large Japanese retail chain (i.e., Daiei). Specifically, we report an interview with executives from both Daiei as well as Softbank Robotics (the manufacturer of the Whiz). This interview touches upon a number of interesting topics including, how this robot is currently being deployed, how employees and customers are responding to this robot, and how it impacts store operations and retailer performance. We then reflect upon this interview to offer a set of future research directions. Our article is also accompanied by a commentary by Guha and Grewal (2022, in this issue) that offers additional insights into robots in retail.
Laminating STRATH block chain technology- SWOT architectures to endure business strategy between digital transformation, firms and supply chains capabilities for sustainability

The study laminated a link between Digital Transformation (DT), firms and supply chains capabilities based on Blockchain Technology (BCT) and accordingly STRATH (STRAtegies and THreats) modelling is presented to unlock the utility of Blockchain Technology (BCT) in supply chains (SC). SWOT architectures are mined, threats are identified and expert's opinions are sought to embrace crucial strategies for elevating the fertility of BCT in SCM. Sixteen threats & six strategies are evaluated, where theoretical framework based on SWOT dimensions, STRATH model, FuDE-VIR approach, DEMATEL, and Delphi technique under the boundaries of BCT and SCM is presented to admire the novelty of present study. The study embedded two pitch structures, where in the first pitch, SWOT- BCT architectures are mined to understand current state of affairs related to BCT. In the second pitch, STRATH modelling is done with FuDE-VIR approach to react towards sustainable outcomes. The FuDE-VIR approach is developed to define crucial strategies for BCT implementation in SCM. The study documented BCT insights, cataloged contemporary facts to BCT and provided strategic guidelines for implementing BCT in company's landmarks. Eight threats components are found under cause group and eight threats components are found under effect group, where 'Ïnvolvement of external stakeholders' and 'Difficulty in changing organization culture' demonstrated the highest cause ranking behaviour. 'Financial constraints' and 'Security and privacy concerns' demonstrated the highest effect ranking behaviour. Efforts have been received towards 'promoting legal certainty and development of clear regulatory regime' as first level strategic driver and 'Permitting standardized customers and defining credit score to customers and partners working in value chain' as the second level strategic driver for admiring the utility of the BCT in SCM.
RETRACTED ARTICLE: Improving cloud efficiency through optimized resource allocation technique for load balancing using LSTM machine learning algorithm

Abstract Allocating resources is crucial in large-scale distributed computing, as networks of computers tackle difficult optimization problems. Within the scope of this discussion, the objective of resource allocation is to achieve maximum overall computing efficiency or throughput. Cloud computing is not the same as grid computing, which is a version of distributed computing in which physically separate clusters are networked and made accessible to the public. Because of the wide variety of application workloads, allocating multiple virtualized information and communication technology resources within a cloud computing paradigm can be a problematic challenge. This research focused on the implementation of an application of the LSTM algorithm which provided an intuitive dynamic resource allocation system that analyses the heuristics application resource utilization to ascertain the best extra resource to provide for that application. The software solution was simulated in near real-time, and the resources allocated by the trained LSTM model. There was a discussion on the benefits of integrating these with dynamic routing algorithms, designed specifically for cloud data centre traffic. Both Long-Short Term Memory and Monte Carlo Tree Search have been investigated, and their various efficiencies have been compared with one another. Consistent traffic patterns throughout the simulation were shown to improve MCTS performance. A situation like this is usually impossible to put into practice due to the rapidity with which traffic patterns can shift. On the other hand, it was verified that by employing LSTM, this problem could be solved, and an acceptable SLA was achieved. The proposed model is compared with other load balancing techniques for the optimization of resource allocation. Based on the result, the proposed model shows the accuracy rate is enhanced by approximately 10–15% as compared with other models. The result of the proposed model reduces the error percent rate of the traffic load average request blocking probability by approximately 9.5–10.2% as compared to other different models. This means that the proposed technique improves network usage by taking less amount of time due, to memory, and central processing unit due to a good predictive approach compared to other models. In future research, we implement cloud data centre employing various heuristics and machine learning approaches for load balancing of energy cloud using firefly algorithms.
Fine-grained Augmentation for RF Fingerprinting under Impaired Channels

Critical infrastructures such as connected and au-tonomous vehicles, are susceptible to cyber attacks due to their mission-critical deployment. To ensure security by design, radio frequency (RF)-based security is considered as an effective technique for a wirelessly monitored or actuated critical infrastructure. For this purpose, this paper proposes a novel augmentation-driven deep learning approach to analyze unique transmitter fingerprints to determine the legitimacy of a user device or transmitter. An RF fingerprinting model is susceptible to various channel and environmental conditions that impact the learning performance of a machine/deep learning model. As data gathering cannot be considered as a feasible alternative, efficient solutions that can tackle the impact of varying channels on learning performance are emergent. This work aims to shed light on the RF fingerprinting problem from a different angle when 4G, 5G and WiFi data samples are collected from different transmitters by proposing a fine-grained augmentation approach to improve the learning performance of a deep learning model. Numerical results point out the promising RF fingerprinting performance when training data are augmented in a waveform-specific fine-grained manner as fingerprinting accuracy (87.94%) under the previously presented TDL/CDL augmentation can be boosted to 95.61% under previously unseen RF data instances.
Clinical-chatbot AHP evaluation based on “quality in use” of ISO/IEC 25010

Conversational agents are currently a valid alternative to humans in first-level interviews with users who need information, even in-depth, about services or products. In application domains such as health care, this technology can become pervasive only if the perceived "quality in use" is appropriate. How to measure chatbot quality is an open question. The international standard ISO/IEC 25010 proposes a set of characteristics (effectiveness, efficiency, satisfaction, freedom from risk, and context coverage) to be considered when the "quality in use" of a software system has to be measured.This study proposes a clinical chatbot comparison method based on quality. The proposed approach is based on Analytic Hierarchy Process methodology (AHP).Our contribution is twofold. First, we propose a set of measures for each characteristic of ISO/IEC 25010 according to three classes of functionality: providing information, providing prescriptions and process management. Moreover a quantitative method is proposed for making homogeneous the pairwise weights when the AHP is used for the "quality-in-use" comparison. As a case study, a comparison of two versions of a chatbot was performed.The results show that the proposed approach provides an effective reference base for performing quality comparisons of medical chatbots compliant with the ISO/IEC 25010 standard.
Design and development of software stack of an autonomous vehicle using robot operating system

In recent research activities, autonomous vehicles and self-driving technology have gained lot of attention among scientists. The idea of autonomous vehicles can be anticipated in the 1920s when the design of the first radio-controlled vehicles was in progress. Autonomous vehicles are going to be the trend of the future in this modern era of automation and technology. In this paper various autonomous driving aspects, highlighting the software stack and hardware components are discussed. The software architecture covers mainly robot operating system (ROS), machine learning (ML), deep learning (DL), and OpenCV frameworks, along with the calibration of sensors and cameras. The paper also discussed about simultaneous localization and mapping (SLAM) based-path tracking, computer vision-based controller, and intelligent object avoidance. Further, point cloud, ground, radius, and raycast filters was implemented to distinguish between the real-time objects, ground, and its own parts or obstacle shadows. The paper highlights the overall hardware modules responsible for controlling the car.