Reviewerly/exHarmony huggingface dataset:
ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">soft</i> autoweighted strategy and a doubly <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">soft</i> regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">proper</i> weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\theta$</tex-math></inline-formula> -norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\theta$</tex-math></inline-formula> . Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability; 2) it automatically learns a proper weight for each view, thereby reducing the influence of noises; 3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works.
Password-Authenticated Decentralized Identities

Password-authenticated identities, where users establish username-password pairs with individual servers and use them later on for authentication, is the most widespread user authentication method over the Internet. Although they are simple, user-friendly, and broadly adopted, they offer insecure authentication and position server operators as trusted parties, giving them full control over users’ identities. To mitigate these limitations, many identity systems have embraced public-key cryptography and the concept of decentralization. All these systems; however, require users to create and manage public-private keypairs. Unfortunately, users usually do not have the required knowledge and resources to properly handle cryptographic secrets, which arguably contributed to the failures of many end-user public-key infrastructures (PKIs). In fact, as of today, no end-user PKI, able to authenticate users to web servers, has a significant adoption rate. In this paper, we propose Password-authenticated Decentralized Identities (PDIDs), an identity and authentication framework where users can register their self-sovereign username-password pairs and use them as universal credentials. Our system provides a global namespace, human-meaningful usernames, and resilience against username collision attacks. A user’s identity can be used to authenticate the user to any server without revealing that server anything about the password, such that no offline dictionary attacks are possible against the password. We analyze PDIDs and implement it using existing infrastructures and tools. We report on our implementation and evaluation.
Revocable Policy-Based Chameleon Hash

Policy-based chameleon hash (PCH) is a cryptographic building block which finds increasing practical applications. Given a message and an access policy, for any chameleon hash generated by a PCH scheme, a chameleon trapdoor holder whose rewriting privileges satisfy the access policy can amend the underlying message without affecting the hash value. In practice, it is necessary to revoke the rewriting privileges of a trapdoor holder due to various reasons, such as change of positions, compromise of credentials, or malicious behaviours. In this paper, we introduce the notion of revocable PCH (RPCH) and formally define its security. We instantiate a concrete RPCH construction by putting forward a practical revocable attribute-based encryption (RABE) scheme which is adaptively secure under a standard assumption on prime-order pairing groups. As application examples, we show how to effectively integrate RPCH into mutable blockchain and sanitizable signature for revoking the rewriting privileges of any chameleon trapdoor holders. We implement our RPCH scheme and evaluate its performance to demonstrate its efficiency.
Promoting physical distancing during COVID-19: a systematic approach to compare behavioral interventions

In the wake of the COVID-19 pandemic, physical distancing behavior turned out to be key to mitigating the virus spread. Therefore, it is crucial that we understand how we can successfully alter our behavior and promote physical distancing. We present a framework to systematically assess the effectiveness of behavioral interventions to stimulate physical distancing. In addition, we demonstrate the feasibility of this framework in a large-scale natural experiment (N = 639) conducted during an art fair. In an experimental design, we varied interventions to evaluate the effect of face masks, walking directions, and immediate feedback on visitors' contacts. We represent visitors as nodes, and their contacts as links in a contact network. Subsequently, we used network modelling to test for differences in these contact networks. We find no evidence that face masks influence physical distancing, while unidirectional walking directions and buzzer feedback do positively impact physical distancing. This study offers a feasible way to optimize physical distancing interventions through scientific research. As such, the presented framework provides society with the means to directly evaluate interventions, so that policy can be based on evidence rather than conjecture.
All-in-One: A DRL-based Control Switch Combining State-of-the-art Navigation Planners

Autonomous navigation of mobile robots is an es-sential aspect in use cases such as delivery, assistance or logistics. Although traditional planning methods are well integrated into existing navigation systems, they struggle in highly dynamic en-vironments. On the other hand, Deep-Reinforcement-Learning-based methods show superior performance in dynamic obstacle avoidance but are not suitable for long-range navigation and struggle with local minima. In this paper, we propose a Deep-Reinforcement-Learning-based control switch, which has the ability to select between different planning paradigms based solely on sensor data observations. Therefore, we develop an interface to efficiently operate multiple model-based, as well as learning-based local planners and integrate a variety of state-of-the-art planners to be selected by the control switch. Subsequently, we evaluate our approach against each planner individually and found improvements in navigation performance especially for highly dynamic scenarios. Our planner was able to prefer learning-based approaches in situations with a high number of obstacles while relying on the traditional model-based planners in long corridors or empty spaces.
Hardware implementation of metaheuristics through LabVIEW FPGA

Metaheuristic optimization methods have been implemented for solving several problems. However, when it is required to implement those algorithms in hardware to run online, there is not enough information. This paper describes how could be programmed and implemented those optimization algorithms. Moreover, a complete evaluation is shown as well as a comparative study regarding the most important metaheuristic optimization algorithms. Thus, this paper presents a comparison between five optimization algorithms implemented into a cRIO field-programmable gate array (LabVIEW FPGA) NI-9030 of National InstrumentsTM(NI). The algorithms implemented were particle swarm optimization (PSO), bat algorithm (BA), grey wolf optimizer (GWO), earthquake algorithm (EA), and Nelder–Mead algorithm (NM). To analyze hardware device utilization and execution time by each algorithm, synthesis results were presented. In addition, a set of ten benchmark functions was selected to compare performance between algorithms. Results show the feasibility of this approach for NI FPGA hardware. From device utilization results, GWO presents the lowest placed usage (29%) while NM shows the fastest execution time (0.683 ms). Nevertheless, PSO, GWO and EA show better performance between benchmark functions due their exploration characteristics which make possible to find a better solution.
A Novel Approach for Web Mining Taxonomy for High-Performance Computing

Web mining is a central part of data analysis. The fetching and discovering knowledge from the different web data in data mining mechanism is more important nowadays. Web usage mining customs data mining practice for the investigation of custom decoration from different data storages. In this article paper, introducing a new approach for web mining taxonomy for high-performance computing. The primary motivation of this research is on the data collection in different real-time web servers for implementation and analysis. This article is focussed the WebLog Expert lite 9.3 tools for our study.
Localization Using Time-Delay and Doppler Shift by Moving Monostatic Sensors

This article considers the localization of a stationary target using time-delay and Doppler shift measurements obtained by moving monostatic sensors that takesthe sensor motion during the localization period into consideration. Starting from the model equation of a previous work, we propose a new formulation of optimization through constrained weighted least-squares to solve for the target location by developing two new localization solutions, one in closed form and the other by semi-definite relaxation (SDR). The new formulation enables the reduction of the minimum number of auxiliary variables in the closed-form solution and allows additional constraints in the SDR solution, resulting in fewer sensors needed in the former and less relaxation error and higher noise tolerance in the latter. Simulations validate the better performance of the proposed solutions than the previous ones.
A Framework for Secure Healthcare System Using Blockchain and Smart Contracts

The term “electronic healthcare” refers to patient records that are stored in digital format. Patient-centered data allows any authorized individual to view the data from any place and at any time. E-health insurance leads to more social and health benefits as well as fewer health mistakes. An electronic health record is a digital repository of a patient's personal history created by one or more care delivery organizations. The term blockchain was formed from the terms block and chain, as well as the list of transactions known as a block, which is associated with the cryptographic technology. Each block is linked to the block header that came before it. A blockchain only stores one patient's record. This article outlines a strategy for safeguarding healthcare data. The suggested model's key features are interoperability and safe storage and access to patient data.
Single image rain removal using recurrent scale-guide networks

Recently, removing rain streaks from a single image has attracted a lot of attention because rain streaks can severely degrade the perceptual quality of the image and cause many practical vision systems to fail. Single image deraining can be served as a pre-processing step to improve the performance of high-level vision tasks such as object detection and video surveillance. In this paper, we propose recurrent scale-guide networks for single image deraining. Although the multi-scale strategy has been successfully applied to many computer vision problems, the correlation between different scales has not been explored in most existing methods. To overcome this deficiency, we propose two types of scale-guide blocks and develop two combinations between the blocks. One type of scale-guide block is that small scale guides the large, and the other is that large scale guides the small. Moreover, we extend the single-stage deraining model to the multi-stage recurrent framework and introduce the Long Short-Term Memory (LSTM) to link every stage. Extensive experiments verify that the scale-guide manner boosts the deraining performance and the recurrent style improves the deraining results. Experimental results demonstrate that the proposed method outperforms other state-of-the-art deraining methods on three widely used datasets: Rain100H, Rain100L, and Rain1200. The source codes can be found at https://supercong94.wixsite.com/supercong94.
Joint Optimization of DNN Partition and Scheduling for Mobile Cloud Computing

Reducing the inference time of Deep Neural Networks (DNNs) is critical when running time sensitive applications on mobile devices. Existing research has shown that partitioning a DNN and offloading a part of its computation to cloud servers can reduce the inference time. The single DNN partition problem has been extensively investigated recently. However, in real-world applications, a mobile device usually generates multiple DNN inference jobs simultaneously, and little attention has been paid to this case. We aim to minimize the makespan of multiple DNNs by jointly optimizing their partitioning and scheduling. Our observations show that the local computation time on a mobile device follows an increasing function, while the communication workload for offloading is usually decreasing as more DNN layers are computed. Based on this, we first relax our problem on continuous domain and show that partitioning all line-structure DNNs at the same layer is sufficient for makespan optimization. Then, for the discrete domain, two types of partitions are sufficient when the time difference between two adjacent partition layers is not drastic, subject to a given condition. An algorithm based on the binary search that efficiently finds optimal partition layers is illustrated. We also extend our approach to general-structure DNNs and offer a heuristic solution. Experiments have been conducted to evaluate the performance of different partition and scheduling methods on sample DNNs. Results validate the optimality of our theoretical results.
The effect of face morphing on face image quality

Face morphing poses high security risk, which moti-vates the work on detection algorithms, as well as on anticipating novel morphing approaches. Using the statistical and perceptual image quality of morphed images in previous works has shown no clear correlation between the image quality and the realistic appearance. This motivated our study on the effect of face morphing on image quality and utility, we, therefore, applied eight general image quality metrics and four face-specific image utility metrics. We showed that MagFace (face utility metric) shows a clear difference between the bona fide and the morph images, regardless if they were digital or re-digitized. While most quality and utility metrics do not capture the artifacts introduced by the morphing process. Acknowledged that morphing artifacts are more apparent in certain areas of the face, we further investigated only these areas, for instance, tightly cropped face, nose, eyes, and mouth regions. We found that especially close to the eyes and the nose regions, using general image quality metrics as MEON and dipIQ can capture the image quality deterioration introduced by the morphing process.
Benchmarking Ultra-High-Definition Image Super-resolution

Increasingly, modern mobile devices allow capturing images at Ultra-High-Definition (UHD) resolution, which includes 4K and 8K images. However, current single image super-resolution (SISR) methods focus on super-resolving images to ones with resolution up to high definition (HD) and ignore higher-resolution UHD images. To explore their performance on UHD images, in this paper, we first introduce two large-scale image datasets, UHDSR4K and UHDSR8K, to benchmark existing SISR methods. With 70,000 V100 GPU hours of training, we benchmark these methods on 4K and 8K resolution images under seven different settings to provide a set of baseline models. Moreover, we propose a baseline model, called Mesh Attention Network (MANet) for SISR. The MANet applies the attention mechanism in both different depths (horizontal) and different levels of receptive field (vertical). In this way, correlations among feature maps are learned, enabling the network to focus on more important features.
Impact of Human Vulnerabilities on Cybersecurity

Today, security is a major challenge linked with computer network companies that cannot defend against cyber-attacks. Numerous vulnerable factors increase security risks and cyber-attacks, including viruses, the internet, communications, and hackers. Internets of Things (IoT) devices are more effective, and the number of devices connected to the internet is constantly increasing, and governments and businesses are also using these technologies to perform business activities effectively. However, the increasing uses of technologies also increase risks, such as password attacks, social engineering, and phishing attacks. Humans play a major role in the field of cybersecurity. It is observed that more than 39% of security risks are related to the human factor, and 95% of successful cyber-attacks are caused by human error, with most of them being insider threats. The major human factor issue in cybersecurity is a lack of user awareness of cyber threats. This study focuses on the human factor by surveying the vulnerabilities and reducing the risk by focusing on human nature and reacting to different situations. This study highlighted that most of the participants are not experienced with cybersecurity threats and how to protect their personal information. Moreover, the lack of awareness of the top three vulnerabilities related to the human factor in cybersecurity, such as phishing attacks, passwords, attacks, and social engineering, are major problems that need to be addressed and reduced through proper awareness and training.
Share Your Data Carefree: An Efficient, Scalable and Privacy-Preserving Data Sharing Service in Cloud Computing

Benefiting from the powerful computing and storage capabilities of cloud services, data sharing in the cloud has been permeated across various applications including social networks, e-health and crowdsourcing transportation system. Intuitively, outsourcing data to untrusted cloud commonly raises concerns about data privacy breaches. To combat this, one approach is exploiting Broadcast Based Searchable Encryption (BBSE) for secure data sharing. Nevertheless, the latest proposed BBSE is still defective in either security or efficiency. In this article, we propose ESPD, an Efficient, Scalable and Privacy-preserving Data sharing framework over encrypted cloud dataset. Different from previous works, ESPD supports sharing target data to multiple users with distinct secret keys, and keeps a constant ciphertext length with the changes of the amount of system users. This feature significantly improves search efficiency and makes ESPD scalable in real-world scenarios. We show a formal analysis to prove the security of ESPD in terms of file privacy, keyword privacy and trapdoor privacy. Also, extensive experiments on real-world dataset are conducted to indicate the desirable performance of ESPD compared to other similar schemes.
Optimized Combination of e-commerce Platform Sales Model and Blockchain Anti-Counterfeit Traceability Service Strategy

In the e-commerce market, many e-commerce platforms act as resellers when selling products, and act as agents when selling other products.In the sales process, e-commerce platforms can either build their own blockchain anti-counterfeit traceability platforms or cooperate with third-party blockchain anti-counterfeit traceability platforms.This will generate four scenarios: (a) reseller, building its own platform (RE); (b) reseller, cooperating with a third-party platform (RO); (c) agent, building its own platform (ME); (d) agent, cooperating with a third-party platform (MO).Therefore, this paper constructs a differential game model under four modes to explore the interaction between the choice of sales mode and the choice of anti-counterfeit traceability service strategy.The results show that suppliers' profits are influenced by various aspects.On the one hand, in small-scale markets, the situation in which suppliers can realize higher profits evolves from ME to RO as the wholesale price increases , and in large-scale markets, suppliers are more profitable in the ME mode.On the other hand, with the increase of market scale and the decrease of unit price of anti-counterfeit traceability service of third-party platform, the situation that suppliers can achieve higher profit evolves from RE to RO and then to RE.For e-commerce platform, selfbuilt platform is a better choice.In the small-scale market, as the market size increases, the cost performance of anti-counterfeit traceability service decreases, and the best choice for e-commerce platform evolves from resale to agency sales, and in the large-scale market, the best choice for e-commerce platform is resale.
Intensity-Aware Single-Image Deraining With Semantic and Color Regularization

Rain degrades image visual quality and disrupts object structures, obscuring their details and erasing their colors. Existing deraining methods are primarily based on modeling either visual appearances of rain or its physical characteristics (e.g., rain direction and density), and thus suffer from two common problems. First, due to the stochastic nature of rain, they tend to fail in recognizing rain streaks correctly, and wrongly remove image structures and details. Second, they fail to recover the image colors erased by heavy rain. In this paper, we address these two problems with the following three contributions. First, we propose a novel PHP block to aggregate comprehensive spatial and hierarchical information for removing rain streaks of different sizes. Second, we propose a novel network to first remove rain streaks, then recover objects structures/colors, and finally enhance details. Third, to train the network, we prepare a new dataset, and propose a novel loss function to introduce semantic and color regularization for deraining. Extensive experiments demonstrate the superiority of the proposed method over state-of-the-art deraining methods on both synthesized and real-world data, in terms of visual quality, quantitative accuracy, and running speed.