Reviewerly/exHarmony huggingface dataset:
CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation

We present the full-resolution correspondence learning for cross-domain images, which aids image translation. We adopt a hierarchical strategy that uses the correspondence from coarse level to guide the fine levels. At each hierarchy, the correspondence can be efficiently computed via PatchMatch that iteratively leverages the matchings from the neighborhood. Within each PatchMatch iteration, the ConvGRU module is employed to refine the current correspondence considering not only the matchings of larger context but also the historic estimates. The proposed Co-CosNet v2, a GRU-assisted PatchMatch approach, is fully differentiable and highly efficient. When jointly trained with image translation, full-resolution semantic correspondence can be established in an unsupervised manner, which in turn facilitates the exemplar-based image translation. Experiments on diverse translation tasks show that CoCosNet v2 performs considerably better than state-of-the-art literature on producing high-resolution images.
Low-Illumination Image Enhancement Algorithm Based on Improved Multi-Scale Retinex and ABC Algorithm Optimization

In order to solve the problems of poor image quality, loss of detail information and excessive brightness enhancement during image enhancement in low light environment, we propose a low-light image enhancement algorithm based on improved multi-scale Retinex and Artificial Bee Colony (ABC) algorithm optimization in this paper. First of all, the algorithm makes two copies of the original image, afterwards, the irradiation component of the original image is obtained by used the structure extraction from texture via relative total variation for the first image, and combines it with the multi-scale Retinex algorithm to obtain the reflection component of the original image, which are simultaneously enhanced using histogram equalization, bilateral gamma function correction and bilateral filtering. In the next part, the second image is enhanced by histogram equalization and edge-preserving with Weighted Guided Image Filtering (WGIF). Finally, the weight-optimized image fusion is performed by ABC algorithm. The mean values of Information Entropy (IE), Average Gradient (AG) and Standard Deviation (SD) of the enhanced images are respectively 7.7878, 7.5560 and 67.0154, and the improvement compared to original image is respectively 2.4916, 5.8599 and 52.7553. The results of experiment show that the algorithm proposed in this paper improves the light loss problem in the image enhancement process, enhances the image sharpness, highlights the image details, restores the color of the image, and also reduces image noise with good edge preservation which enables a better visual perception of the image.
A Survey on the Fairness of Recommender Systems

Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people’s daily lives. Since recommendations involve allocations of social resources (e.g., job recommendation), an important issue is whether recommendations are fair. Unfair recommendations are not only unethical but also harm the long-term interests of the recommender system itself. As a result, fairness issues in recommender systems have recently attracted increasing attention. However, due to multiple complex resource allocation processes and various fairness definitions, the research on fairness in recommendation is scattered. To fill this gap, we review over 60 papers published in top conferences/journals, including TOIS, SIGIR, and WWW. First, we summarize fairness definitions in the recommendation and provide several views to classify fairness issues. Then, we review recommendation datasets and measurements in fairness studies and provide an elaborate taxonomy of fairness methods in the recommendation. Finally, we conclude this survey by outlining some promising future directions.
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning and Deep Learning has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most Machine Learning-based techniques and Deep Learning-based techniques are deployed in the “black-box” manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users’ confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security.
Game Level Generation from Gameplay Videos

We present an unsupervised process to generate full video game levels from a model trained on gameplay video. The model represents probabilistic relationships between shapes properties, and relates the relationships to stylistic variance within a domain. We utilize the classic platformer game Super Mario Bros. to evaluate this process due to its highly-regarded level design. We evaluate the output in comparison to other data-driven level generation techniques via a user study and demonstrate its ability to produce novel output more stylistically similar to exemplar input.
Simulating boson sampling in lossy architectures

Photon losses are among the strongest imperfections affecting multi-photon interference. Despite their importance, little is known about their effect on boson sampling experiments. In this work we show that using classical computers, one can efficiently simulate multi-photon interference in all architectures that suffer from an exponential decay of the transmission with the depth of the circuit, such as integrated photonic circuits or optical fibers. We prove that either the depth of the circuit is large enough that it can be simulated by thermal noise with an algorithm running in polynomial time, or it is shallow enough that a tensor network simulation runs in quasi-polynomial time. This result suggests that in order to implement a quantum advantage experiment with single-photons and linear optics new experimental platforms may be needed.
fclust: An R Package for Fuzzy Clustering

Fuzzy clustering methods discover fuzzy partitions where observations can be softly assigned to more than one cluster.The package fclust is a toolbox for fuzzy clustering in the R programming language.It not only implements the widely used fuzzy k-means (FkM) algorithm, but also many FkM variants.Fuzzy cluster similarity measures, cluster validity indices and cluster visualization tools are also offered.In the current version, all the functions are rewritten in the C++ language allowing their application in large-size problems.Moreover, new fuzzy relational clustering algorithms for partitioning qualitative/mixed data are provided together with an improved version of the so-called Gustafson-Kessel algorithm to avoid singularity in the cluster covariance matrices.Finally, it is now possible to automatically select the number of clusters by means of the available fuzzy cluster validity indices.
Peeking behind objects: Layered depth prediction from a single image

While conventional depth estimation can infer the geometry of a scene from a single RGB image, it fails to estimate scene regions that are occluded by foreground objects. This limits the use of depth prediction in augmented and virtual reality applications, that aim at scene exploration by synthesizing the scene from a different vantage point, or at diminished reality. To address this issue, we shift the focus from conventional depth map prediction to the regression of a specific data representation called Layered Depth Image (LDI), which contains information about the occluded regions in the reference frame and can fill in occlusion gaps in case of small view changes. We propose a novel approach based on Convolutional Neural Networks (CNNs) to jointly predict depth maps and foreground separation masks used to condition Generative Adversarial Networks (GANs) for hallucinating plausible color and depths in the initially occluded areas. We demonstrate the effectiveness of our approach for novel scene view synthesis from a single image.
Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network

Hepatic granuloma develops in the early stage of liver cirrhosis which can seriously injury liver health. At present, the assessment of medical microscopic images is necessary for various diseases and the exploiting of artificial intelligence technology to assist pathology doctors in pre-diagnosis is the trend of future medical development. In this article, we try to classify mice liver microscopic images of normal, granuloma-fibrosis1 and granuloma-fibrosis2, using convolutional neural networks (CNNs) and two conventional machine learning methods: support vector machine (SVM) and random forest (RF). On account of the included small dataset of 30 mice liver microscopic images, the proposed work included a preprocessing stage to deal with the problem of insufficient image number, which included the cropping of the original microscopic images to small patches, and the disorderly recombination after cropping and labeling the cropped patches In addition, recognizable texture features are extracted and selected using gray the level co-occurrence matrix (GLCM), local binary pattern (LBP) and Pearson correlation coefficient (PCC), respectively. The results established a classification accuracy of 82.78% of the proposed CNN based classifiers to classify 3 types of images. In addition, the confusion matrix figures out that the accuracy of the classification results using the proposed CNNs based classifiers for the normal class, granuloma-fibrosis1, and granuloma-fibrosis2 were 92.5%, 76.67%, and 79.17%, respectively. The comparative study of the proposed CNN based classifier and the SVM and RF proved the superiority of the CNNs showing its promising performance for clinical cases.
Scalar quantum field theories as a benchmark for near-term quantum computers

Quantum field theory (QFT) simulations are a potentially important application for noisy intermediate scale quantum (NISQ) computers. The ability of a quantum computer to emulate a QFT therefore constitutes a natural application-centric benchmark. Foundational quantum algorithms to simulate QFT processes rely on fault-tolerant computational resources, but to be useful on NISQ machines, error-resilient algorithms are required. Here we outline and implement a hybrid algorithm to calculate the lowest energy levels of the paradigmatic $1+1$--dimensional ${\ensuremath{\phi}}^{4}$ interacting scalar QFT. We calculate energy splittings and compare results with experimental values obtained on currently available quantum hardware. We show that the accuracy of mass-renormalization calculations represents a useful metric with which near-term hardware may be benchmarked. We also discuss the prospects of scaling the algorithm to full simulation of interacting QFTs on future hardware.
Digital-analog quantum computation

Digital quantum computing paradigm offers highly desirable features such as universality, scalability, and quantum error correction. However, physical resource requirements to implement useful error-corrected quantum algorithms are prohibitive in the current era of NISQ devices. As an alternative path to performing universal quantum computation, within the NISQ era limitations, we propose to merge digital single-qubit operations with analog multiqubit entangling blocks in an approach we call digital-analog quantum computing (DAQC). Along these lines, although the techniques may be extended to any resource, we propose to use unitaries generated by the ubiquitous Ising Hamiltonian for the analog entangling block and we prove its universal character. We construct explicit DAQC protocols for efficient simulations of arbitrary inhomogeneous Ising, two-body, and $M$-body spin Hamiltonian dynamics by means of single-qubit gates and a fixed homogeneous Ising Hamiltonian. Additionally, we compare a sequential approach where the interactions are switched on and off (stepwise DAQC) with an always-on multiqubit interaction interspersed by fast single-qubit pulses (banged DAQC). Finally, we perform numerical tests comparing purely digital schemes with DAQC protocols, showing a remarkably better performance of the latter. The proposed DAQC approach combines the robustness of analog quantum computing with the flexibility of digital methods.
Blockchain-based system for secure outsourcing of bilinear pairings

Secure computation outsourcing in Internet of Things (IoT) system is an ongoing research challenge, partly due to the resource-constrained nature of most (inexpensive) IoT devices. In this paper, we focus on the secure outsourcing of bilinear pairings (SOBP) (the most computationally expensive operation in pairing-based cryptographic protocols / algorithms). First, we analyze the limitations in existing SOBP-based schemes, such as the one-malicious model (Strong Assumption), a secure channel (Insufficiency), and a trusted server (Centralization). Then, we propose a novel blockchain-based system for SOBP based on a permissioned version (i.e., a blockchain ledger maintained by some permissioned nodes), designed to efficiently address the limitations. Finally, we prove the security of our proposed approach in the one untrusted program model and implement it on Ethereum (an open-source blockchain system) to show its utility.
Technical and Didactic Knowledge of the Moodle LMS in Higher Education. Beyond Functional Use

Higher education institutions at the international level have seen the need to adopt and integrate information and communication technologies to meet the opportunities and challenges of innovation in teaching and learning processes. This logic has led to the implementation of virtual learning environments called ‘Learning Management Systems’, the functionalities of which support flexible and active learning under a constructivist approach. This study measured didactic and technological use of Moodle and its implications in teaching from a quantitative approach by administering a questionnaire to a sample of 640 higher education teachers. Some guiding questions were as follows: Are teachers using the Moodle platform for didactic purposes? What strategies, resources and tools are teachers using, and what do they contribute to student-centred teaching? Are teaching strategies that are focused on collaboration, interaction and student autonomy promoted? The results coincide with those of other studies, confirming an instrumental and functional use of the platform, which is mainly being used as a repository for materials and information, while its pedagogical use remains limited. This is becoming a problem in higher education institutions, something that requires debate and reflection from a systemic perspective on the adoption and integration of technology in the classroom.
TS-I3D Based Hand Gesture Recognition Method With Radar Sensor

Aiming at the problems of the noise impact on the parametric image of hand gestures, the difficulty of gesture feature extraction, and the inefficient utilization of continuous gesture time sequential information, we propose a time sequential inflated 3 dimensions (TS-I3D) convolutional neural network approach for hand gesture recognition based on frequency modulated continuous wave (FMCW) radar sensor. Specifically, the FMCW radar is used to acquire the hand gesture data, and the range and speed of the gesture in each frame signal are calculated by 2 dimensions fast Fourier transform. Then, the range-Doppler map (RDM) is generated based on the relationship between motion parameters and frequency. The interference in RDM caused by people and the external environment is filtered out and the peak of hand gesture in RDM is further enhanced by wavelet transform. Finally, TS-I3D network is designed to extract the range and speed change information of the continuous gestures. The experimental results show that the average recognition accuracy rate of the hand gestures of the proposed method is 96.17%.
Heterogeneous hand gesture recognition using 3D dynamic skeletal data

Hand gestures are the most natural and intuitive non-verbal communication medium while interacting with a computer, and related research efforts have recently boosted interest. Additionally, the identifiable features of the hand pose provided by current commercial inexpensive depth cameras can be exploited in various gesture recognition based systems, especially for Human–ComputerInteraction. In this paper, we focus our attention on 3D dynamic gesture recognition systems using the hand pose information. Specifically, we use the natural structure of the hand topology – called later hand skeletal data – to extract effective hand kinematic descriptors from the gesture sequence. Descriptors are then encoded in a statistical and temporal representation using respectively a Fisher kernel and a multi-level temporal pyramid. A linear SVM classifier can be applied directly on the feature vector computed over the whole presegmented gesture to perform the recognition. Furthermore, for early recognition from continuous stream, we introduced a prior gesture detection phase achieved using a binary classifier before the final gesture recognition. The proposed approach is evaluated on three hand gesture datasets containing respectively 10, 14 and 25 gestures with specific challenging tasks. Also, we conduct an experiment to assess the influence of depth-based hand pose estimation on our approach. Experimental results demonstrate the potential of the proposed solution in terms of hand gesture recognition and also for a low-latency gesture recognition. Comparative results with state-of-the-art methods are reported.
A Survey on Opinion Mining: From Stance to Product Aspect

With the prevalence of social media and online forum, opinion mining, aiming at analyzing and discovering the latent opinion in user-generated reviews on the Internet, has become a hot research topic. This survey focuses on two important subtasks in this field, stance detection and product aspect mining, both of which can be formalized as the problem of the triple (target, aspect, opinion) extraction. In this paper, we first introduce the general framework of opinion mining and describe the evaluation metrics. Then, the methodologies for stance detection on different sources, such as online forum and social media are discussed. After that, approaches for product aspect mining are categorized into three main groups which are corpus level aspect extraction, corpus level aspect, and opinion mining, and document level aspect and opinion mining based on the processing units and tasks. And then we discuss the challenges and possible solutions. Finally, we summarize the evolving trend of the reviewed methodologies and conclude the survey.
Computational Intelligence for Affective Computing and Sentiment Analysis [Guest Editorial]

Emotions are intrinsically part of our mental activity and play a key role in communication and decision-making processes. Emotion is a chain of events made up of feedback loops. Feelings and behavior can affect cognition, just as cognition can influence feeling. Emotion, cognition, and action interact in feedback loops and emotion can be viewed in a structural model tied to adaptation. Besides being important for the advancement of AI, detecting and interpreting emotional information are key in multiple areas of computer science, e.g., human-computer interaction, e-learning, e-health, domotics, automotive, security, user profiling and personalization.
Enabling Privacy-Preservation in Decentralized Optimization

Decentralized optimization is crucial for the design, deployment, and functionality of many distributed systems. In this paper, we address the problem of privacy-preservation in decentralized optimization. In most existing decentralized optimization approaches, participating agents exchange and disclose intermediate estimate values to neighboring agents explicitly, which may not be desirable when the estimates contain sensitive information of individual agents. The problem is more acute when adversaries exist which try to steal information from other participating agents. To address this issue, we propose a novel approach that enables privacy-preservation in decentralized optimization by combining partially homomorphic cryptography with the unique properties of the decentralized optimization. We show that cryptographic techniques can be incorporated in a fully decentralized manner to enable privacy-preservation in decentralized optimization in the absence of any third party or aggregator. This is significant in that to our knowledge, all existing cryptographic-based optimization approaches rely on the assistance of a third-party or aggregator in order to protect the privacy of all parties. The approach is also applicable to the average consensus problem, which is finding broad applications in fields as diverse as robotic networks, distributed computing, and power systems. Numerical simulations confirm the effectiveness and low computational complexity of the proposed approach.
Social Media Cyberbullying Detection using Machine Learning

With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages.Social networks provides a rich environment for bullies to uses these networks as vulnerable to attacks against victims.Given the consequences of cyberbullying on victims, it is necessary to find suitable actions to detect and prevent it.Machine learning can be helpful to detect language patterns of the bullies and hence can generate a model to automatically detect cyberbullying actions.This paper proposes a supervised machine learning approach for detecting and preventing cyberbullying.Several classifiers are used to train and recognize bullying actions.The evaluation of the proposed approach on cyberbullying dataset shows that Neural Network performs better and achieves accuracy of 92.8% and SVM achieves 90.3.Also, NN outperforms other classifiers of similar work on the same dataset.