Reviewerly/exHarmony huggingface dataset:
Quaternion-based color image completion via logarithmic approximation

In color image processing, the objective of image completion is to restore missing entries from the incomplete observation image. Recent improvements have assisted in resolving the rank minimization issue, thereby promoting the realization of completion. This paper adopts the new quaternion matrix logarithmic norm to approximate rank in accordance with the quaternion matrix framework. Unlike the traditional matrix completion method, which handles RGB channels separately, the quaternion-based method avoids the destruction of the structure of images by placing the color image in a pure quaternion matrix. Furthermore, the logarithmic norm induces a more accurate rank surrogate. Based on the logarithmic norm, it is possible to exploit not only the factorization strategy but also the truncated technique, thus achieving successful image restoration. The alternating minimization framework renders it possible to optimize the two strategies, and mathematically detailed validation of the convergence analysis is provided. The experimental results demonstrate that the use of logarithmic surrogates in the quaternion domain is a superior strategy for solving the problem of color image completion.
Comparative Analysis of the Blockchain Consensus Algorithm Between Proof of Stake and Delegated Proof of Stake

Blockchain is a technology used as a public ledger that is accessible to everyone without central authority control. This technology has the ability to run smart contracts as well as register users. There are several consensus algorithms used in blockchains. Many researchers are looking into the consensus algorithm in order to ensure the security and authenticity of transaction information in blockchains. In determining which consensus algorithm research areas should be examined, several existing papers from other researchers have been analyzed. This study is motivated by the need to determine the best consensus key. Thus, this study will examine the comparison of the two consensus algorithms which are Proof of Stake (PoS) and Delegated Proof of Stake (DPoS) algorithms. A further focus of this research study is on the parameters that are being used by these two algorithms. Analyses are done based on the number of miners and the number of created blocks to determine how long is necessary to produce a new block. The measurements were conducted through the simulation tool, which is Ethereum in a private network. The impact of parameters was assessed and analyzed based on the results. In addition, we conclude by determining their scalability as well as their efficiency. As the results show, DPoS is much better than PoS when it comes to producing a new block, with a percentage difference of 68%-73% between them. For future work, the algorithm studied can be extended to the Dual Delegated Proof of Stake (DDPoS) and the relationship and comparison between Delegated Proof of Stake (DPoS) and Dual Delegated Proof of Stake (DDPoS) can be discussed. Furthermore, the key parameters of the analysis can be extended as well such as the power of the consumptions.
Self‐Organization of Remote Reservoirs: Transferring Computation to Spatially Distant Locations

Soft materials generate rich and diverse dynamics that can be used as computational resources based on the framework of physical reservoir computing. Herein, a method that exploits the dynamic coupling between soft structures and a water medium to allow for the transfer of computation to spatially distant locations is proposed. This technique is implemented by introducing the concept of remote reservoirs that can autonomously alter their physical constituents in real time rather than using reservoirs with predefined, fixed physical constituents. These remote reservoirs self‐organize by including many ad hoc physical substrates in the environment, making the framework more flexible for soft robotic applications. Using a simple experimental platform consisting of silicone rubber strips containing embedded flexible strain sensors, it is demonstrated that the dynamics of passive silicone rubber strips located at a distance from the actuation point can be successfully exploited for computation through generalized synchronization realized via the medium of water. Future application scenarios are illustrated, in which the proposed technique is applied in emergency situations as a communication tool for transferring a message to a location that humans cannot easily access. For example, the technique could be applied to communicate with people trapped in submerged caves.
DCA-CycleGAN: Unsupervised single image dehazing using Dark Channel Attention optimized CycleGAN

Single image dehazing has great significance in computer vision. In this paper, we propose a novel unsupervised Dark Channel Attention optimized CycleGAN (DCA-CycleGAN) to deal with the challenging scene with uneven and dense haze concentration. Firstly, the DCA-CycleGAN adopts the dark channel as input and then generate attention through a DCA subnetwork to handle the nonhomogeneous haze. Secondly, in addition to the conventional global discriminator, we also leverage two local discriminators to enhance the dehazing performance on the local dense haze, and a new local adversarial loss calculated strategy is been proposed. Specifically, the dehazing generator consists of two subnetworks: an auto-encoder and a dark channel attention subnetwork. The auto-encoder consists of an encoder, a feature transformation module, and a decoder. The dark channel attention subnetwork has the same structure as the encoder and the feature transformation module to ensure the same receptive field, which utilizes the dark channel to generate attention map and fine-tune the auto-encoder. Experimental results against several state-of-the-art methods demonstrate that our method can generate better visual effects, and is effective.
The semantic PHD filter for multi-class target tracking: From theory to practice

In order for a mobile robot to be able to effectively operate in complex, dynamic environments it must be capable of understanding both where and what the objects around them are. In this paper we introduce the semantic probability hypothesis density (SPHD) filter, which allows robots to simultaneously track multiple classes of targets despite measurement uncertainty, including false positive detections, false negative detections, measurement noise, and target misclassification. The SPHD filter is capable of incorporating a different motion model for each type of target and of functioning in situations where the number of targets is unknown and time-varying. To demonstrate the efficacy of the SPHD filter, we conduct both simulated and hardware tests with multiple target types containing both static and dynamic targets. We show that the SPHD filter allows effective tracking of multiple classes of targets even with detection error to some level, and performs better than a collection of PHD filters running in parallel, one for each target class. We also provide a detailed methodology that practitioners can use to fit the probabilistic sensor models necessary to run the SPHD filter.
Hybrid multi-document summarization using pre-trained language models

Abstractive multi-document summarization is a type of automatic text summarization. It obtains information from multiple documents and generates a human-like summary from them. In this paper, we propose an abstractive multi-document summarization method called HMSumm. The proposed method is a combination of extractive and abstractive summarization approaches. First, it constructs an extractive summary from multiple input documents, and then uses it to generate the abstractive summary. Redundant information, which is a global problem in multi-document summarization, is managed in the first step. Specifically, the determinantal point process (DPP) is used to deal with redundancy. This step also controls the length of the input sequence for the abstractive summarization process. This step has two effects. The first is to reduce the computational time. The second is to preserve the important parts of the input documents for an abstractive summarizer. We employ a deep submodular network (DSN) to determine the quality of the sentences in the extractive summary, and use BERT-based similarities to compute the redundancy. The obtained extractive summary is fed into BART and T5 pre-trained models to generate two abstractive summaries. We use the diversity of sentences in each summary to select one of them as the final abstractive summary. To evaluate the performance of HMSumm, we use both human evaluations and ROUGE-based assessments, and compare it with several state-of-the-art methods. We use DUC 2002, DUC 2004, Multi-News, and CNN/DailyMail datasets to evaluate the algorithms. The experimental results show that HMSumm outperforms the related state-of-the-art algorithms.
Predicting effect of floating photovoltaic power plant on water loss through surface evaporation for wastewater pond using artificial intelligence: A case study

Crises related to water scarcity and air pollution are major problems in the world today. An important issue regarding wasting of the water is surface evaporation from reservoirs. If the surface of the pond is shaded, evaporation is largely prevented. The use of solar panels on surface of water, simultaneously reduces water evaporation and increases the efficiency of electricity generation. In this study, an artificial intelligence method is used to investigate the effect of a floating photovoltaic system on water loss through surface evaporation in a wastewater pond. The nominated studied case is the Yazd wastewater pond near the city of Yazd in central Iran, the water level of which varies for different months. Amounts of evaporation are measured using the Penman-Monteith method. The simulation is performed with all independent variables used as inputs of the neural network, and the pond’s surface area as the dependent variable. After performing sensitivity analysis, the results show that the network with the best structure for predicting water level is the one built with 9 inputs, 35 hidden neurons, and 1 output, which achieve a mean square error of 4.64658E − 20 and correlation coefficient of 0.999. The predictions show that the surface area of the pond will reach its highest level, 322 ha, in November 2025. Between 2021 and 2025, the evaporation reduction due to the PV system will vary from 272.7 ha in January 2021 to 413.9 ha in November 2025. Overall, the floating PV system will reduce evaporation from the pond by up to 70%.
A pseudo-inverse decomposition-based self-organizing modular echo state network for time series prediction

Echo state network (ESN) refers to a popular recurrent neural network with a largely and randomly generated reservoir for its rapid learning ability. However, it is difficult to design a reservoir that matches a specific task. To solve the structure design of the reservoir, a pseudo-inverse decomposition-based self-organizing modular echo state (PDSM-ESN) is proposed. PDSM-ESN is constructed by growing–pruning method, where the error and condition number are used, respectively. Since the self-organizing process may negatively affect the learning speed, the pseudo-inverse decomposition is adopted to improve learning speed, which means the output weights are learned by an iterative incremental method. Meanwhile, to solve the ill-posed problem, the modular sub-reservoirs corresponding to the high condition number are pruned. Simulation results indicate that PDSM-ESN has better prediction performance and run-time complexity compared with the traditional ESN models.
How blockchain impacts the supply chain finance platform business model reconfiguration

This paper aims to explore how blockchain facilitates supply chain finance (SCF) platform business model reconfiguration. We conduct a case study on a typical Chinese SCF platform to address research questions and propose a blockchain-enabled SCF platform business model framework. The results show that (1) the reconfiguration of the SCF platform business model is driven by internal factors (content, governance, transaction structure) and external factors (sector dynamics, geographical environment, technology) in the context of blockchain. (2) Blockchain influences the SCF platform business model reconfiguration through empowering the operation of SCF information, financial, material, and value flows. (3) The introduction of blockchain changes the business model of the SCF platform significantly, which is mainly reflected in the improved value proposition, expanded value network, strengthened value creation, and generated value appropriation. This study contributes to the existing literature on SCF, business model, and blockchain. The research also provides some important practical implications.
DualGNN: Dual Graph Neural Network for Multimedia Recommendation

One of the important factors affecting micro-video recommender systems is to model the multi-modal user preference on the micro-video. Despite the remarkable performance of prior arts, they are still limited by fusing the user preference derived from different modalities in a unified manner, ignoring the users tend to place different emphasis on different modalities. Furthermore, modality-missing is ubiquity and unavoidable in the micro-video recommendation, some modalities information of micro-videos are lacked in many cases, which negatively affects the multi-modal fusion operations. To overcome these disadvantages, we propose a novel framework for the micro-video recommendation, dubbed Dual Graph Neural Network (DualGNN), upon the user-microvideo bipartite and user co-occurrence graphs, which leverages the correlation between users to collaboratively mine the particular fusion pattern for each user. Specifically, we first introduce a single-modal representation learning module, which performs graph operations on the user-microvideo graph in each modality to capture single-modal user preferences on different modalities. And then, we devise a multi-modal representation learning module to explicitly model the user’s attentions over different modalities and inductively learn the multi-modal user preference. Finally, we propose a prediction module to rank the potential micro-videos for users. Extensive experiments on two public datasets demonstrate the significant superiority of our DualGNN over state-of-the-arts methods.
A Hybrid Blockchain-Based Log Management Scheme With Nonrepudiation for Smart Grids

In the era of Industry 4.0 and digital transformation, smart grid (SG) plays an important role in the construction of various smart places. Logs produced by electrical appliances recording power consumption within the grid contain private information and may be collected as evidence for the services of dispute resolution or digital forensics. Therefore, security of the logs is of paramount importance. Our work designs a blockchain-enabled log management scheme with nonrepudiation for SGs. In this article, we employ ciphertext-policy attribute-based encryption to establish a fine-grained access control of the logs, which addresses data confidentiality concern. In addition, we introduce a hybrid blockchain system generating novel signature chains, for achieving an efficient private log protection system with a lot of properties including data loss prevention, tamper resistance, immutability and nonrepudiation. Employing various cost-saving solutions, our scheme achieves an efficient communication with more functionalities and security features, compared with the predecessor works.
End-to-End Pedestrian Trajectory Forecasting with Transformer Network

Analysis of pedestrians’ motion is important to real-world applications in public scenes. Due to the complex temporal and spatial factors, trajectory prediction is a challenging task. With the development of attention mechanism recently, transformer network has been successfully applied in natural language processing, computer vision, and audio processing. We propose an end-to-end transformer network embedded with random deviation queries for pedestrian trajectory forecasting. The self-correcting scheme can enhance the robustness of the network. Moreover, we present a co-training strategy to improve the training effect. The whole scheme is trained collaboratively by the original loss and classification loss. Therefore, we also achieve more accurate prediction results. Experimental results on several datasets indicate the validity and robustness of the network. We achieve the best performance in individual forecasting and comparable results in social forecasting. Encouragingly, our approach achieves a new state of the art on the Hotel and Zara2 datasets compared with the social-based and individual-based approaches.
Does psychological distance and religiosity influence fraudulent customer behaviour?

Abstract Online businesses incur significant losses because of fraudulent customer behaviour. This study investigates the factors motivating fraudulent customer intention on eBay. A conceptual framework is developed that extends the theory of planned behaviour with religiosity factors, social detection risk, ethical judgement, and moderating factor of perceived psychological distance. A quantitative methodology is adopted that collects data from 450 respondents. Covariance‐based structural equation modelling is used to analyse the proposed hypotheses. Results reveal that intrinsic and extrinsic religiously negatively influences attitude towards fraudulent customer behaviour on eBay. Attitude, subjective norms, and perceived behavioural control positively influence fraudulent customer intentions, while ethical judgement negatively impacts it. Psychological distance strengthened the relationship between attitude, perceived behavioural control, and fraudulent customer intentions on eBay. Additionally, the association between ethical judgement and fraud intention was weaker for individuals with higher perceived psychological distance. The study contributes to the literature and provides insights to online businesses and cyber security developers into factors motivating fraudulent customer behaviour and strategies that can be used to combat this growing threat.
PyNose: A Test Smell Detector For Python

Similarly to production code, code smells also occur in test code, where they are called test smells. Test smells have a detrimental effect not only on test code but also on the production code that is being tested. To date, the majority of the research on test smells has been focusing on programming languages such as Java and Scala. However, there are no available automated tools to support the identification of test smells for Python, despite its rapid growth in popularity in recent years. In this paper, we strive to extend the research to Python, build a tool for detecting test smells in this language, and conduct an empirical analysis of test smells in Python projects.We started by gathering a list of test smells from existing research and selecting test smells that can be considered language-agnostic or have similar functionality in Python’s standard Unittest framework. In total, we identified 17 diverse test smells. Additionally, we searched for Python-specific test smells by mining frequent code change patterns that can be considered as either fixing or introducing test smells. Based on these changes, we proposed our own test smell called Suboptimal Assert. To detect all these test smells, we developed a tool called PYNOSE in the form of a plugin to PyCharm, a popular Python IDE. Finally, we conducted a large-scale empirical investigation aimed at analyzing the prevalence of test smells in Python code. Our results show that 98% of the projects and 84% of the test suites in the studied dataset contain at least one test smell. Our proposed Suboptimal Assert smell was detected in as much as 70.6% of the projects, making it a valuable addition to the list.
An Efficient Certificate-Based Aggregate Signature Scheme for Internet of Drones

Internet of drones (IoD) is a network of small drones that leverages IoT infrastructure to deliver real-time data communication services to users. On the one hand, IoD is an excellent choice for a number of military and civilian applications owing to key characteristics like agility, low cost, and ease of deployment; on the other hand, small drones are rarely designed with security and privacy concerns in mind. Intruders can exploit this vulnerability to compromise the security and privacy of IoD networks and harm the information exchange operation. An aggregate signature scheme is the best solution for resolving security and privacy concerns since multiple drones are connected in IoD networks to gather data from a certain zone. However, most aggregate signature schemes proposed in the past for this purpose are either identity-based or relied on certificateless cryptographic methods. Using these methods, a central authority known as a trusted authority (TA) is responsible for generating and distributing secret keys of every user. However, the key escrow problem is formulated as knowing the secret key generated by the TA. These methods are hampered by key distribution issues, which restrict their applicability in a variety of situations. To address these concerns, this paper presents a certificate-based aggregate signature (CBS-AS) scheme based on hyperelliptic curve cryptography (HECC). The proposed scheme has been shown to be both efficient in terms of computation cost and unforgeable while testing its toughness through formal security analysis.
A Highly Secure IoT Firmware Update Mechanism Using Blockchain

Internet of Things (IoT) device security is one of the crucial topics in the field of information security. IoT devices are often protected securely through firmware update. Traditional update methods have their shortcomings, such as bandwidth limitation and being attackers' easy targets. Although many scholars proposed a variety of methods that are based on the blockchain technology to update the firmware, there are still demerits existing in their schemes, including large storage space and centralized stored firmware. In summary, this research proposes a highly secure and efficient protection mechanism that is based on the blockchain technology to improve the above disadvantages. Therefore, this study can reduce the need of storage space and improve system security. The proposed system has good performance in some events, including firmware integrity, security of IoT device connection, system security, and device anonymity. Furthermore, we confirm the high security and practical feasibility of the proposed system by comparing with the existing methods.