Reviewerly/exHarmony huggingface dataset:
Deep Joint Estimation Network for Satellite Video Super-Resolution With Multiple Degradations

Super-resolution (SR) for satellite video data has been a hot research topic in the field of remote sensing video analysis. The existing satellite video SR methods assume that the blur kernel in the imaging degradation model is known. However, the blur kernel in real satellite videos is usually unknown, which inevitably results in poor performance when the true blur kernel is not consistent with a predefined blur kernel. To address this issue, this article proposes a deep joint estimation network for satellite video SR (JENSVSR), which jointly estimates blur kernels and SR frames. Specifically, JENSVSR is composed of a video SR subnetwork and a blur kernel estimation subnetwork. On one hand, the video SR subnetwork makes use of multiple video frames to generate super-resolved satellite frames. To effectively fuse information from adjacent frames, an alignment and fusion module is proposed in the feature space. On the other hand, the blur estimation subnetwork is also proposed to predict blur kernels. The two subnetworks are coupled by cross-task feature fusion modules (CTFFMs) to achieve joint estimation rather than two-step independent estimation. The performance of our proposed method is evaluated on synthetic and real satellite videos. The experimental results show that our proposed method is superior to the current state-of-the-art SR methods.
Telecom-band hyperentangled photon pairs from a fiber-based source

Hyperentanglement, the simultaneous and independent entanglement of quantum particles in multiple degrees of freedom, is a powerful resource that can be harnessed for efficient quantum information processing. In photonic systems, the two degrees of freedom (DoF) often used to carry quantum and classical information are polarization and frequency, thanks to their robustness in transmission, both in free space and in optical fibers. Telecom-band hyperentangled photons generated in optical fibers are of particular interest because they are compatible with existing fiber-optic infrastructure, and can be distributed over fiber networks with minimal loss. Here, we experimentally demonstrate the generation of telecom-band biphotons hyperentangled in both the polarization and frequency DoFs using a periodically-poled silica fiber and observe entanglement concurrences above 0.95 for both polarization and frequency DOFs. Furthermore, by concatenating a Hong-Ou-Mandel interference test for frequency entanglement and full state tomography for polarization entanglement in a single experiment, we can demonstrate simultaneous entanglement in both the polarization and frequency DOFs. The states produced by our hyperentanglement source can enable protocols such as dense coding and high-dimensional quantum key distribution.
The role of mobile characteristics on mobile money innovations

This study focuses on linkages between bank accounts and supply-side mobile money drivers for mobile money innovations. It seeks to understand how bank accounts can be complemented with mobile subscription and mobile connectivity dynamics (i.e., mobile connectivity coverage and mobile connectivity performance) for mobile money innovations. The empirical evidence is based on quadratic Tobit regressions. First, there are positive net relationships from the roles of mobile subscriptions and mobile connectivity coverage in modulating bank accounts for mobile money innovations. Second, mobile connectivity performance does not significantly modulate bank accounts for mobile money innovations. Third, given the negative marginal relationships associated with the positive net relationships, thresholds for complementary policies in mobile money supply factors that are worthwhile for bank accounts to stimulate mobile money innovations are provided. The thresholds are: (1) mobile subscription rates of 87.50%, 80.50%, and 98.50% of the adult population for respectively, mobile money accounts, the mobile used to send money, and the mobile used to receive money, and (2) mobile connectivity coverages of 64.00%, 69.33%, and 78.00% for respectively, mobile money accounts, the mobile used to send money, and the mobile used to receive money.
Quality Management in Pedagogical Education based on the Diagnostics of Teachers’

The paper presents the methodology of education quality management based on the identification of teachers’ professional deficits. The methodology for evaluating the formation of the subject and methodological competencies is described. Assessment materials are developed in accordance with the requirements of the professional standard of the teacher and the Federal state educational standards of basic, primary and secondary education. It is supposed to implement an automated identification of professional deficits within teaching staff. The article presents empirical data on the results of the approbation of this technique. 624 teachers from rural and urban educational organizations of the Republic of Bashkortostan and 90 students of pedagogical specialties of the university participated in the testing. The analysis of the approbation results of the methodology and assessment materials showed that the developed methodology is applicable to identify professional deficits of teachers and students of teacher-training institutions for further construction of individual educational trajectories of the approbation participants.
Batch processing and data streaming Fourier-based convolutional neural network accelerator

Decision-making through artificial neural networks with minimal latency is critical for numerous applications such as navigation, tracking, and real-time machine action systems. This requires machine learning hardware to process multidimensional data at high throughput. Unfortunately, handling convolution operations, the primary computational tool for data classification tasks, obeys challenging runtime complexity scaling laws. However, homomorphically implementing the convolution theorem in a Fourier optics display light processor can achieve a non-iterative <i><strong>O</strong></i>(1) runtime complexity for data inputs beyond 1,000 × 1,000 large matrices. Following this approach, here we demonstrate data streaming multi-kernel image batching using a Fourier Convolutional Neural Network (FCNN) accelerator. We show image batch processing of large-scale matrices as 2 million dot product multiplications performed by a digital light processing module in the Fourier domain. Furthermore, we further parallelize this optical FCNN system by exploiting multiple spatially parallel diffraction orders, achieving a 98x throughput improvement over state-of-the-art FCNN accelerators. A comprehensive discussion of the practical challenges associated with working at the edge of system capabilities highlights the problem of crosstalk and resolution scaling laws in the Fourier domain. Accelerating convolution by exploiting massive parallelism in display technology brings non-Van Neumann-based machine learning acceleration.
Self-regulation in Online Content Platforms and the Protection of Personality Rights

As public debate moves to private content platforms online, content moderationContent moderation by the Judiciary is no longer the most common, the most efficient or the most legitimate way of separating acceptable from unacceptable content. A new model of self-regulationRegulation-self-regulation of personality rightsPersonality rights is being implemented by such platforms, where enforcement—and sometimes review decisions—are performed by codeCode while users operate as reviewers as much as content producers and consumers. Platforms have freedom to design and implement decentralized moderation systems, but the rules set in their architecture and community guidelines must comply with procedural boundaries established by the efficacy of fundamental rightsFundamental rights between private parties. Judicial review plays a role not in evaluating the merits of content posted or shared online in these platforms, but rather in course-correction of the procedural rules that ensure self-regulationRegulation-self-regulation does not disproportionally restrict informational personality rightsPersonality rights such as speech, honor and privacy.
Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation

Unsupervised domain adaptive person re-identification (ReID) has been extensively investigated to mitigate the adverse effects of domain gaps. Those works assume the target domain data can be accessible all at once. However, for the real-world streaming data, this hinders the timely adaptation to changing data statistics and sufficient exploitation of increasing samples. In this paper, to address more practical scenarios, we propose a new task, Lifelong Un-supervised Domain Adaptive (LUDA) person ReID. This is challenging because it requires the model to continuously adapt to unlabeled data in the target environments while alleviating catastrophic forgetting for such a fine-grained person retrieval task. We design an effective scheme for this task, dubbed CLUDA-ReID, where the anti-forgetting is harmoniously coordinated with the adaptation. Specifically, a meta-based Coordinated Data Replay strategy is proposed to replay old data and update the network with a coordinated optimization direction for both adaptation and memorization. Moreover, we propose Relational Consistency Learning for old knowledge distillation/inheritance in line with the objective of retrieval-based tasks. We set up two evaluation settings to simulate the practical application scenarios. Extensive experiments demonstrate the effectiveness of our CLUDA-ReID for both scenarios with stationary target streams and scenarios with dynamic target streams.
A Hybrid Calibration Method for the Binocular Omnidirectional Vision System

Omnidirectional three-dimensional (3D) measurement based on the binocular omnidirectional vision system attracts increasing attention in computer vision. A 360&#x00B0; horizontal field of view (FOV) and 180&#x00B0; vertical FOV are obtained by the binocular omnidirectional vision system with only a pair of images. However, a nonlinear relationship exists between points on image planes and in 3D space for the binocular omnidirectional vision system, and traditional calibration methods are no longer applicable. This study proposes a hybrid calibration approach, which effectively fuses the unified camera model and the backpropagation neural network with a virtual 3D target, in which the neural network improved with genetic algorithm compensates all kinds of distortions and errors. First, the unified camera model is used to calibrate the intrinsic parameters, extrinsic parameters, and a transformation matrix. Second, the pixel coordinates on image planes are converted to 3D coordinates by 3D reconstruction through binocular vision. The errors between actual and calculated 3D coordinates are obtained. In addition, the neural network improved with genetic algorithm is established and trained with pixel coordinates on image planes and errors. Experimental results indicate that the average measurement errors in the X and Y directions of the central area are respectively reduced to 0.1057 and 0.1548 mm, and those of the edge area are reduced to 0.1992 and 0.2748 mm. Compared with mathematical and neural network methods, the hybrid calibration method provides more stable, robust, and accurate results. It is proven suitable for complex calibration scenes with large FOV, high distortion, and strong nonlinearity.
Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review

Schizophrenia is a severe psychiatric disorder that causes significant social and functional impairment. Currently, the diagnosis of schizophrenia is based on information gleaned from the patient's self-report, what the clinician observes directly, and what the clinician gathers from collateral informants, but these elements are prone to subjectivity. Utilizing computer vision to measure facial expressions is a promising approach to adding more objectivity in the evaluation and diagnosis of schizophrenia.We conducted a systematic review using PubMed and Google Scholar. Relevant publications published before (including) December 2021 were identified and evaluated for inclusion. The objective was to conduct a systematic review of computer vision for facial behavior analysis in schizophrenia studies, the clinical findings, and the corresponding data processing and machine learning methods.Seventeen studies published between 2007 to 2021 were included, with an increasing trend in the number of publications over time. Only 14 articles used interviews to collect data, of which different combinations of passive to evoked, unstructured to structured interviews were used. Various types of hardware were adopted and different types of visual data were collected. Commercial, open-access, and in-house developed models were used to recognize facial behaviors, where frame-level and subject-level features were extracted. Statistical tests and evaluation metrics varied across studies. The number of subjects ranged from 2-120, with an average of 38. Overall, facial behaviors appear to have a role in estimating diagnosis of schizophrenia and psychotic symptoms. When studies were evaluated with a quality assessment checklist, most had a low reporting quality.Despite the rapid development of computer vision techniques, there are relatively few studies that have applied this technology to schizophrenia research. There was considerable variation in the clinical paradigm and analytic techniques used. Further research is needed to identify and develop standardized practices, which will help to promote further advances in the field.
An Efficient Semi-Quantum Key Distribution Protocol and Its Security Proof

The idea of semi-quantum communication protocols provides a theoretical solution for the high costs of quantum resources. In this study, we propose an efficient semi-quantum key distribution protocol that allows Alice and quantum-capable restricted Bob to establish a secure key. Then, we demonstrate that the protocol is information-theoretic secure in the asymptotic scenario. As long as the channel noise is less than the noise tolerance of the protocol, Alice and Bob can withdraw a secure key. The proposed protocol has advantages over previous protocols regarding qubit efficiency and communication cost. Besides, our efficiency analysis is more fair and practical because noise factors are taken into account. Based on these features, it may have potential applications in future quantum communications.
Single-Image Super-Resolution Neural Network via Hybrid Multi-Scale Features

In this paper, we propose an end-to-end single-image super-resolution neural network by leveraging hybrid multi-scale features of images. Different from most existing convolutional neural network (CNN) based solutions, our proposed network depends on the observation that image features extracted by CNN contain hybrid multi-scale features: both multi-scale local texture features and global structural features. By effectively exploiting these multi-scale and local-global features, our network involves far fewer parameters, leading to a large decrease in memory usage and computation during inference. Our network benefits from three key modules: (1) an efficient and lightweight feature extraction module (EFblock); (2) a hybrid multi-scale feature enhancement module (HMblock); and (3) a reconstruction–restoration module (DRblock). Experiments on five popular benchmarks demonstrate that our super-resolution approach achieves better performance with fewer parameters and less memory consumption, compared to more than 20 SOTAs. In summary, we propose a novel multi-scale super-resolution neural network (HMSF), which is more lightweight, has fewer parameters, and requires less execution time, but has better performance than the state-of-the-art methods. Compared to SOTAs, this method is more practical and better suited to run on constrained devices, such as PCs and mobile devices, without the need for a high-performance server.
Service-Oriented Reliability Modeling and Autonomous Optimization of Reliability for Public Cloud Computing Systems

The ubiquitous adoption of public clouds has resulted in the bloom of big data, Internet of Things, and artificial intelligence (AI) and a great capacity for applications. However, the efficient operation of these applications is challenging due to the infrastructure heterogeneity, computing hierarchy, scale distribution, and stochastic user behaviors. Among these challenges, reliability has received intense interest in the cloud community. Most existing research on the reliability of clouds is on fault discovery or fault tolerance. However, a public cloud has a wider range of failures, which makes it difficult to analyze the association between reliability and other indicators. More accurate reliability modeling methods and advanced optimization approaches are needed to ensure the reliable operation of a public cloud. To address these challenges, this article models the reliability of cloud computing from the service perspective and reasonably divides cloud services into the request processing phase and request execution phase when facing multiple users and service types. Moreover, this article combines a variety of AI-based methods, such as autonomous scheduling mechanisms, anomaly detection, and autonomous learning improvement, which can effectively adapt to the dynamic and complex cloud service environment and ensure the service reliability of a public cloud. Numerical results clearly indicate that the proposed service reliability model and autonomous optimizations are efficient for recovering the reliability of cloud computing systems in terms of multiple AI-based failure prognostics and intelligent learning ability.
CVLNet: A Complex-Valued Lightweight Network for CSI Feedback

The deep learning-based (DL-based) channel state information (CSI) feedback in the frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system has demonstrated its potential and efficiency. However, conventional neural networks cannot fully utilize the complex-valued nature of the downlink channel. In addition, multi-scale and multi-resolution features of CSI can be further explored. In this letter, we present a complex-valued lightweight neural network for CSI feedback named CVLNet. The CVLNet adopts the complex-valued neural network components in a multi-scale feature augmentation encoder and a multi-resolution X-shaped reconstruction decoder with a series of lightweight details. The experiment results show that the proposed CVLNet maintains the same-level parameters of the encoder with state-of-the-art (SOTA) lightweight networks while outperforming them with at most a 33.4% improvement in accuracy under severe compression rates.
Identity-based authenticated encryption with identity confidentiality

After two decades of research on signcryption, recently a new cryptographic primitive, named higncryption, was proposed at ACM CCS'16. Higncryption can be viewed as privacy-enhanced signcryption, which integrates public key encryption, digital signature and identity concealment (which is not achieved in signcryption) into a monolithic primitive. Here, identity concealment means that the transcript of protocol runs should not leak participants' identity information. In this work, we propose the first identity-based higncryption (IBHigncryption, for short). We present the formal security model for IBHigncryption, under which security proof of the proposed scheme is conducted. The most impressive feature of IBHigncryption, besides other desirable properties it offers, is its simplicity and efficiency, which might be somewhat surprising in retrospect. Our IBHigncryption has a much simpler setup stage with smaller public parameters and particularly no need of computing master public key. It is essentially as efficient as (if not more than) the fundamental CCA-secure Boneh-Franklin identity-based encryption scheme [14], and has significant efficiency advantage over the IEEE 1363.3 standard of identity-based signcryption [8].
Dynamic scene deblurring and image de-raining based on generative adversarial networks and transfer learning for Internet of vehicle

Abstract Extracting traffic information from images plays an increasingly significant role in Internet of vehicle. However, due to the high-speed movement and bumps of the vehicle, the image will be blurred during image acquisition. In addition, in rainy days, as a result of the rain attached to the lens, the target will be blocked by rain, and the image will be distorted. These problems have caused great obstacles for extracting key information from transportation images, which will affect the real-time judgment of vehicle control system on road conditions, and further cause decision-making errors of the system and even have a bearing on traffic accidents. In this paper, we propose a motion-blurred restoration and rain removal algorithm for IoV based on generative adversarial network and transfer learning. Dynamic scene deblurring and image de-raining are both among the challenging classical research directions in low-level vision tasks. For both tasks, firstly, instead of using ReLU in a conventional residual block, we designed a residual block containing three 256-channel convolutional layers, and we used the Leaky-ReLU activation function. Secondly, we used generative adversarial networks for the image deblurring task with our Resblocks, as well as the image de-raining task. Thirdly, experimental results on the synthetic blur dataset GOPRO and the real blur dataset RealBlur confirm the effectiveness of our model for image deblurring. Finally, as an image de-raining task based on transfer learning, we can fine-tune the pre-trained model with less training data and show good results on several datasets used for image rain removal.
Multi-Party Payment Channel Network Based on Smart Contract

Blockchain-based cryptocurrencies are severely limited in transaction throughput and latency. A promising solution to this issue is a payment channel, which allows trust-free payments between two peers without exhausting the resources of the blockchain. A linked payment channel network (PCN) enables payments between two peers through a series of intermediate nodes that forward and charge for the payments. However, most of existing proposals only use the shortest path as the path of the transaction, which causes the frequently reused channels to be exhausted quickly. In addition, most of existing PCNs are almost only designed for payments between two parties, which leads to limited application scenarios. When multiple payments use the same intermediate channel, the two-party PCNs cannot achieve simultaneous payments. In this paper, we propose a multi-party payment channel (MPC) network, a payment channel proposal that supports multiple payments using the same intermediate channel simultaneously, thereby greatly expanding the application scenarios of payment channels. In addition, our channel selection and transaction conversion strategies can also increase the success rate of transactions. We implement MPC network in the simulated blockchain network and lightning network based on Truffle, and a large number of experiments verify the effectiveness of our solution.