Reviewerly/exHarmony huggingface dataset:
Image Motion Deblurring Based on Deep Residual Shrinkage and Generative Adversarial Networks

A network structure (DRSN-GAN) is proposed for image motion deblurring that combines a deep residual shrinkage network (DRSN) with a generative adversarial network (GAN) to address the issues of poor noise immunity and low generalizability in deblurring algorithms based solely on GANs. First, an end-to-end approach is used to recover a clear image from a blurred image, without the need to estimate a blurring kernel. Next, a DRSN is used as the generator in a GAN to remove noise from the input image while learning residuals to improve robustness. The BN and ReLU layers in the DRSN were moved to the front of the convolution layer, making the network easier to train. Finally, deblurring performance was verified using the GoPro, Köhler, and Lai datasets. Experimental results showed that deblurred images were produced with more subjective visual effects and a higher objective evaluation, compared with algorithms such as MPRNet. Furthermore, image edge and texture restoration effects were improved along with image quality. Our model produced slightly higher PSNR and SSIM values than the latest MPRNet, as well as increased YOLO detection accuracy. The number of required parameters in the DRSN-GAN was also reduced by 21.89%.
A brave new world of mediated online discourse

Is artificial intelligence up to the task of managing online discourse in social networks?
2 mm: A new technique for sorting data

In computation, sorting is highly essential to access and manipulate data efficiently. High-performance sorting algorithms have always been in demand so that computers may process data more quickly. Since the computer has become a vital tool in various domains of human life, the various researchers have investigated and presented numerous sorting algorithms to sort elements of list with minimal execution time and least space. As the volume of data increases, the urgency for efficient data processing algorithms also increases. In this novel work, we present a comparison-based sorting algorithm titled "2 mm" which is a modification of the sorting algorithm Min-Max Bidirectional Parallel Selection Sort (MMBPSS). The proposed algorithm follows divide and conquers method and uses constant space complexity i.e. O(n). Although 2 mm sort differs slightly from MMBPSS sort, its computational cost is very low and it reduces more than 50% comparison cost hence presents a more efficient solution to the sorting problems in the modern era. For performance evaluation and comparison, extensive experimentation has been performed that shows the better performance of the proposed method.
Dealing with overdispersion in multivariate count data

The problem of overdispersion in multivariate count data is a challenging issue. It covers a central role mainly due to the relevance of modern technology-based data, such as Next Generation Sequencing and textual data from the web or digital collections. A comprehensive analysis of the likelihood-based models for extra-variation data is presented. Particular attention is paid to the models feasible for high-dimensional data. A new approach together with its parametric-estimation procedure is proposed. It can be viewed as a deeper version of the Dirichlet-Multinomial distribution and it leads to important results allowing to get a better approximation of the observed variability. A significative comparison of the proposed model and existing strategies is made through two different simulation studies and an empirical data set, that confirm a better capability to describe overdispersion.
NetSync: a Network Adaptive and Deduplication-Inspired Delta Synchronization Approach for Cloud Storage Services

Delta sync (synchronization) is a key bandwidth-saving technique for cloud storage services. The representative delta sync utility, <monospace>rsync</monospace>, matches data chunks by sliding a search window byte-by-byte to maximize the redundancy detection for bandwidth efficiency. However, it is difficult for this process to cater to the forthcoming high-bandwidth cloud storage services which require lightweight delta sync that can well support large files. Moreover, <monospace>rsync</monospace> employs invariant chunking and compression methods during the sync process, making it unable to cater to services from various network environments which require the sync approach to perform well under different network conditions. Inspired by the Content-Defined Chunking (CDC) technique used in data deduplication, we propose NetSync, a network adaptive and CDC-based lightweight delta sync approach with less computing and protocol (metadata) overheads than the state-of-the-art delta sync approaches. Besides, NetSync can choose appropriate compressing and chunking strategies for different network conditions. The key idea of NetSync is (1) to simplify the process of chunk matching by proposing a fast weak hash called FastFP that is piggybacked on the rolling hashes from CDC, and redesigning the delta sync protocol by exploiting deduplication locality and weak/strong hash properties; (2) to minimize the sync time by adaptively choosing chunking parameters and compression methods according to the current network conditions. Our evaluation results driven by both benchmark and real-world datasets suggest NetSync performs <inline-formula><tex-math notation="LaTeX">$2\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>&#x00D7;</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq1-3145025.gif"/></alternatives></inline-formula>&#x2013;<inline-formula><tex-math notation="LaTeX">$10\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>10</mml:mn><mml:mo>&#x00D7;</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq2-3145025.gif"/></alternatives></inline-formula> faster and supports <inline-formula><tex-math notation="LaTeX">$30\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>30</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq3-3145025.gif"/></alternatives></inline-formula>&#x2013;<inline-formula><tex-math notation="LaTeX">$80\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>80</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq4-3145025.gif"/></alternatives></inline-formula> more clients than the state-of-the-art <monospace>rsync</monospace>-based WebR2sync+ and deduplication-based approach.
A learning-based approach for efficient visualization construction

We propose an approach to underpin interactive visual exploration of large data volumes by training Learned Visualization Index (LVI). Knowing in advance the data, the aggregation functions that are used for visualization, the visual encoding, and available interactive operations for data selection, LVI allows to avoid time-consuming data retrieval and processing of raw data in response to user's interactions. Instead, LVI directly predicts aggregates of interest for the user's data selection. We demonstrate the efficiency of the proposed approach in application to two use cases of spatio-temporal data at different scales.
Semantic web Racer: Dynamic security testing of the web application against race condition in the business layer

The parallel execution of multiple threads of a web application will cause races if the web application is not synchronized correctly. Detecting the race condition in web applications depends on the application’s business logic. No logic-aware approach has been presented so far for detecting various race conditions in web applications. The existing approaches only detect part of server-side races or a few client-side race conditions. Most existing approaches result in DoS or they have a high vulnerability detection time. In this study, various race conditions existing in a web application, both on the server-side and on the client-side, are classified and described. In addition, we present Semantic Web Racer, a black-box approach for dynamic application security testing, to detect the business-layer vulnerability of the web application against race conditions both on the server-side and on the client-side. Semantic Web Racer detects race conditions by identifying the business logic of the web application. First, it identifies the business processes in the web application and, by defining a trace pattern for each type of race condition, identifies critical business processes. The detected critical processes are performed in the defined race window in both normal mode and race-prone mode, and the results are checked to identify vulnerabilities. The evaluation of well-known and widely used web applications and web pages shows that Semantic Web Racer can detect the business layer vulnerabilities of these applications against race conditions. Experiments showed that out of 38 detected race conditions by Semantic Web Racer in selected applications, 24 are new vulnerabilities that were not identified by related works. The amount of traffic generated to identify vulnerabilities has been improved by about 98.29% by identifying the business layer of the application. Thus, Semantic Web Racer does not result in DoS. Semantic Web Racer has improved race detection time by about 96.78%.
A composition-oriented aesthetic view recommendation network supervised by the simplified golden ratio theory

Composition is the most vital element in photography. Previous research on automatic image cropping or view recommendation merely studied the aesthetic value of the cropping image without explicitly regarded the rules of composition. This work proposes a composition-oriented aesthetic view recommendation network supervised by the simplified golden ratio theory (CAVR-Net), compared to other methods that output only aesthetic scores, which outputs a sequence of local views with aesthetic rank and composition category for an image and helps to enhance the interpretation of aesthetic perception. We adopt a model distillation technique (teacher–student framework) to train the student model CAVR-S supervised by two teacher models including a composition prediction network (CPN) and an aesthetic evaluation network (AEN). In addition, for the challenging candidate crops selection problem in the image cropping task, we propose a candidate crops extraction scheme based on the simplified golden ratio theory, which reduces the training cost and improves the model performance. The CAVR-Net achieves state-of-the-art performance on two benchmark datasets in the image cropping task and a real-time recommendation efficiency of 80+ fps in the view recommendation task. Our method has essential application value in intelligent photography guidance system or intelligent image analysis system based on image composition, such as photography guide, automatic post-processing, automatic extraction of video frames.
An optimal approach for text feature selection

Traditionally, feature selection is conducted by first deriving a candidate list of features, then ranking and selecting the top features based on predefined threshold. These methods are highly dependent on the choice of the threshold, and therefore lead to sub-optimal text categorization results. In this paper, we address the selection problem by suggesting a one-step method designed to optimally select the subset of features. The selection is formulated mathematically as an optimization problem with the objective of maximizing classification accuracy while simultaneously deriving and choosing the most discriminative features. Our method, MFX, is applicable to many of the conventional methods, with two distinguishing aspects. First, it is based on considering all documents from the same category as one extended document, instead of analyzing individual documents. Second, it considers choosing the most discriminative terms that are frequent and common across all documents of the same category, and minimally present in other categories. Moreover, MFX is language-independent. It was tested on the well-known benchmark Reuters RCV1 dataset. To showcase its language independence, MFX was also tested on Arabic datasets extracted from Arabic news sources. The results indicated that MFX always performed similar to or better than other well-known feature selection methods. MFX with a Support Vector Machine (SVM) classifier was also shown to outperform recent text classification algorithms based on neural networks and word embeddings.
Sparse Matrix Ordering Method with a Quantum Annealing Approach and its Parameter Tuning

Quantum annealing realizes quantum computers specialized for combinatorial optimization problems (COPs). A COP is formulated as a Hamiltonian, and quantum annealing obtains a solution by finding the ground state of the Hamiltonian. The ease of finding a solution depends on the weights assigned to the cost and constraint functions when formulating the problem. In other words, parameter tuning is essential in solving problems with quantum annealing. In the present paper, the problem of searching an ordering that reduces the fill-in for a sparse direct solver is formulated as a Hamiltonian, and quantum annealing finds the solution to this problem. We discuss the necessity and effectiveness of parameter tuning for solving COPs with quantum annealing. The results after weight tuning show that we can improve the rate of an optimal solution obtained by a maximum of 94% for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${5\,\times \,5}$</tex> matrices, 68% for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${6\,\times \,6}$</tex> matrices, and 27% for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${7\,\times \,7}$</tex> matrices. Moreover, it is shown that giving high weights to the constraints we want to satisfy will not provide an optimal solution.
Deep Reinforcement Learning with DQN vs. PPO in VizDoom

VizDoom is a flexible and easy-to-use 3D reinforcement learning research platform based on the well-known Doom first-person shooter. The challenge is to create bots that compete in the DeathMatch track, making decisions based solely on visual in-formation from the screen. The paper offers a com-parison of different approaches with reinforcement learning: Q-learning and policy-gradient algorithms. We explore the distributed learning paradigm in re-inforcement learning, and also discuss the differences in speed and quality of convergence when adding an object detection module.
Secure Data Transmission and Image Encryption Based on a Digital-Redesign Sliding Mode Chaos Synchronization

In this paper, a novel image encryption algorithm based on chaotic synchronization is proposed. First, a digital-redesign sliding mode controller (SMC) is developed to guarantee the chaos synchronization. The digital redesign method makes it possible to transform a proposed continuous-time SMC to discrete-time SMC whilst maintaining the performance of the robust synchronization. Then, the secret keys are embedded in the state equations of the master chaotic system, such that the secret keys do not appear in the public channel, and utilize the chaotic synchronization to achieve secure communication for transmitting the secret keys from transmitter to receiver. Second, an image encryption algorithm integrating the S-box with chaotic synchronization is established, where the S-box is created by the secret key transmitted from the transmitter. Finally, a detailed analysis of the image encryption algorithm based on chaos synchronization is included to verify the feasibility and effectiveness of this proposed approach.
Context-Aware Emoji Prediction Using Deep Learning

Emojis are a succinct and visual way to express feelings, emotions, and thoughts during text conversations. Owing to the increase in the use of social media, the usage of emojis has increased drastically. There are various techniques for automating emoji prediction, which use contextual information, temporal information, and user-based features. However, the problem of personalised and dynamic recommendations of emojis persists. This paper proposes personalised emoji recommendations using the time and location parameters. It presents a new annotated conversational dataset and investigates the impact of time and location for emoji prediction. The methodology comprises a hybrid model that uses neural networks and score-based metrics: semantic and cosine similarity. Our approach differs from existing studies and improves the accuracy of emoji prediction up to 73.32% using BERT.
Analyzing the effect of feature mapping techniques along with the circuit depth in quantum supervised learning by utilizing quantum support vector machine

A quantum feature map encodes classical data to the quantum state space by using a quantum circuit. The repetition of such a circuit during encoding is a customize value known as depth. Encoding data to quantum state is a must step for applying Quantum machine learning (QML) to classical data. Utilizing different feature map techniques by varying several depths, this research uses a kernel-based quantum support vector machine (QSVM) to classify several datasets. The fundamental aim of such activities is to check whether feature map techniques can make any sense to supervised QML concerning their depths and the outcomes analysis concludes that maximum accuracy of any supervised QML model is obtained due to the selection of an essential feature map approach with appropriate circuit depth. The results also present that time consumption of any feature map technique increases linearly with the increase of feature map circuit depth. However, the outcome of this research will help anyone to estimate the feature map technique and circuit depth when executing QML.
Insurtech &amp; Blockchain: Implementation of Technology in Insurance Operations and its Environmental Impact

Abstract While the digital had reshaped our world, the insurance industry moves very slow comparing to other domains. Insurtech is the future of insurance industry, it seen as a subdivision of Fintechs; it refers to a wide range of technology that will be introduced to insurers operations to save streamline processes and costs. Indeed, as banking institutions began changing the customer behavior through the combination of technology and finance, to provide self-functionality, this paper research aims to examine and demonstrate the necessity of implementing technology in insurance sector, first to enhance the consumers’ expectations, and second to support the environment case against the use of countless amount of paper. This study combines different factors to find the better way to include technology in Moroccan insurance industry, as well as We discussed the challenges and limitation of the results. With everything taking in consideration there is no better time like the present for the Moroccan insurances incumbents to adopt the concept of Insurtech to their business models, furthermore with the consequences of the pandemic COVID19, which proves that we can do almost everything online.
Probabilistic Energy-to-Amplitude Mapping in a Tapered Superconducting Nanowire Single-Photon Detector

A spectrum-resolved photon detector is crucial for cutting-edge quantum optics, astronomical observation, and spectroscopic sensing. However, such an ability is rarely obtained because a direct linear conversion from weak single-photon energy to a readable electrical signal above the noise level without causing an avalanche is challenging. Here, we overcame these difficulties by building a probabilistic energy-to-amplitude mapping in a tapered superconducting nanowire single-photon detector and combining a computational reconstruction to obtain equivalent spectral resolving capacity. Distinguished dependence of pulse amplitude distributions on varied input spectra has been observed experimentally. As the energy-to-amplitude mapping is probabilistic, statistical measurements are required. By collecting around a few hundred photons, we have demonstrated wavelength perception over a wide spectral range from 600 to 1700 nm with a resolution of 100 nm. These findings represent a new approach to designing spectrum-sensitive SNSPDs for low-light spectroscopic applications.
FedVANET: Efficient Federated Learning with Non-IID Data for Vehicular Ad Hoc Networks

The vehicular ad hoc networks (VANETs) play a significant role in intelligent transportation systems (ITS). In recent years, federated learning (FL) has been widely used in VANETs to preserve the privacy-sensitive data, such as vehicle locations, drivers&#x0027; driving patterns, on-board camera data, etc. However, conventional FL faces the challenges of non-independent and identically distributed (Non-IID) data and high communication overheads in VANETs. To address these challenges, we propose a novel FL framework for VANETs, named FedVANET, where a hierarchical inner-cluster FL model and a weighted inter-cluster cycling update algorithm are, respectively, developed. Extensive experiments demonstrate the high efficiency of the FedVANET in inner-cluster communications, effectiveness in handling Non-IID data, and robustness in dynamic VANET topologies.
Adversarial Mask Generation for Preserving Visual Privacy

We present a privacy preserving machine learning method for images that separates task-relevant information from task-irrelevant information. Our primary hypothesis is that by revealing the minimal number of pixels required for a task we can provide the most privacy preserving guarantees. Specifically, we propose an adversarial method that masks out task-irrelevant information from an image for preserving privacy. The proposed method only uses task-specific label information and no privacy annotations such as identity of the subject, gender, race, etc., are required. We validate the proposed method on face attribute prediction on the CelebA dataset and emotion recognition on the FER+ dataset, showing that we can preserve visual privacy with little degradation in the task performance.