Reviewerly/exHarmony huggingface dataset:
Effectiveness for OBE-oriented College Students’ Blended English Learning Community in Smart Environment

In view of the low efficiency of college students' autonomous independent learning, this research was aimed at building the OBE-oriented college students' blended English learning community in a smart environment.Its effectiveness is tested by a 12-week experiment of joint learning for CET-4.The results showed that (1) in the process of joint learning for CET-4, the average score for each part and the total average score of the students in the experimental group have been continuously improved, (2) compared with the initial mock CET-4, in the final CET-4, the average score for listening, the average score for reading, the average score for writing and translation, the total average score and the pass rate of the students in the experimental group have been more significantly improved than those in the control group.
Too much is too bad: The effect of media coverage on the price volatility of cryptocurrencies

This study investigates the influence of information excess due to the increased media coverage on the price volatility of cryptocurrencies. News coverages may serve as either signals or noise in cryptocurrency markets characterized by an insufficient understanding of the fundamental value of assets and a high level of strategic complementarity. In a game-theoretic model, we show that the number of news coverages, either related or unrelated to the fundamentals, increases the price volatility of assets in a nascent financial market. We tested our hypotheses using a unique dataset of 358,118 observations of 500 cryptocurrencies and 36,572 media coverages between 2014 and 2017, the early period of cryptocurrency with the rise of public attention. The results show that cryptocurrency price volatility increases in the number of unrelated news for both major and minor coins. The volatility even increases with the number of related news in minor coins. These results have important implications for investors and entrepreneurs about the effect of misinformation in nascent markets.
Game Theory Based Energy-aware Virtual Machine Placement towards Improving Resource Efficiency in Homogeneous Cloud Data Center

In recent years, cloud data centers (CDCs) have grown into an essential component of enterprise's computing infrastructure. However, the upswing in the demand for cloud-based services is accompanied by the consumption of a considerable amount of energy. Thus, developing efficient management approaches to reduce energy consumption in CDCs becomes a top priority. Although a number of methods have already been developed for lowering the energy usage of CDCs, but they largely focus on cutting down the number of active physical machine (PM) and hardly address the issue of resource utilization efficiency in a multi-resource setting. In a dynamic environment, which often leads to frequent system overloading and sometimes causing a violation of service level agreement (SLA) in terms of quality of service (QoS) degradation. In order to tackle all the problems, a novel game theoretic approach, namely GB-VMP has been proposed in this paper to place the virtual machines (VMs) onto the PMs by leveraging the maximization of utilization across all the resource dimensions in an uniform way. The game finds the optimal mapping of VM to PM that requires minimal PMs to be in active state. It enables each of the PMs to be accommodated by a large number of VMs without causing any system overload. As a result, an energy efficient solution has been delivered while utilizing all the resources in a best possible way. Extensive experiments have been carried out on a simulated cloud setup to evaluate the effectiveness of the proposed approach in comparison to two well-known VMP strategies, IRB-VMP and BFD. The experimental results illustrates that GB- VMP outperforms the other strategies in terms of energy consumption and resource efficiency.
Distributed Multitarget Tracking Using Chernoff Fusion in SMC-CPHD Filtering

The multisensor random finite set (RFS) based multitarget tracking filters like probability hypothesis density (PHD) filter have been widely developed. Many existing fusion algorithms are restricted to scalability or computational efficiency. In this paper, we develop a distributed sensor fusion algorithm for multitarget tracking using sequential Monte Carlo cardinalized probability hypothesis density (SMC-CPHD) filter with the Chernoff fusion method, which is quite suitable for nonlinear fusion process. We illustrate the CPHD filter at first, then we introduce the Chernoff fusion in log-linear form, which is a generalization of the Covariance Intersection scheme, and we provide a combination and implementation method for the Chernoff fusion and SMC-CPHD filter. The performance of the Chernoff fusion algorithm for SMC-CPHD filter is demonstrated through a simulation of multitarget tracking scenario. The results show that the proposed distributed multitarget tracking algorithm is efficient both in estimation accuracy and computational load.
Real-time Path Planning Algorithms for Autonomous UAV

With the increasing complexity of UAV missions, path planning, as a key issue, is receiving more and more attention. Currently, most of the literatures related to this problem are concerned about off-line path planning. However, the dynamic and complex environment with uncertainty makes it more challenging for UAVs to complete their missions autonomously, safely and quickly, which calls for path planning in real time. In the paper, several representative algorithms for UAV real-time path planning are reviewed, from perspective on path searching and trajectory optimization. Therein, Artificial Potential Field (APF) method, Markov Decision Process (MDP) based method and Artificial Neural Network (ANN) algorithm are set forth, while their performance, fusion and improvement are analyzed. Finally, we propose a series of challenging real-time path planning problems for future research.
Bidirectional diffeomorphic network for 3D deformable medical image registration

The traditional deformation registration methods had achieved a great registration results, but they were limited by the long registration time, the need of labels, and poor practicality. In recent years, unsupervised deep learning methods based on convolutional neural networks (CNN) had developed rapidly, which can achieve high accuracy and spend less time. However, these methods did not use bidirectional registration and did not constrain the local smoothness of the deformation field. We used bidirectional diffeomorphic registration network to improve the accuracy of registration while ensuring the topology-preserving registration. In addition, Jacobi determinant was introduced to improve the smoothness of the deformation field. We evaluated our method on a public dataset of 3D brain MRI images and achieved better registration accuracy, better smoothness, and less registration time than the same type of learning-based methods.
A Recurrent Neural Network for Image Deblocking Detection and Quality Enhancement

With the advancement of intelligent media, it is becoming much easier to control and adjust progressed video without leaving any visual snippets of data. Since video pressure is so prevalent in modern accounts, the use of deblocking systems to conceal the video adjusting may be a viable alternative. Modified JPEG (MJPEG) is a well-known video format in which each video outline or wound around the field of an electronicvideo gatheringis squeezed independently as a JPEG image. The change may use strong sight and sound deblocking techniques to cover the video changing follows by isolating the MJPEG video into JPEG picture follows. This research study proposes a novel approach to perceive deblocking, which can consequently learn portrayals that are dependent upon a critical learning structure. Initially Recurrent Neural Network (RNN) is trained based on the available datasets to become familiar with the features of deblocking techniques.. Some parts of the images are eliminated with the RNN by applying a proper assessed sliding-window to review the entire image. In order to secure the final discriminative component, a close-by pooling technique is used to set the created image representation.
Domain Adaptation Methods for Lab-to-Field Human Context Recognition

Human context recognition (HCR) using sensor data is a crucial task in Context-Aware (CA) applications in domains such as healthcare and security. Supervised machine learning HCR models are trained using smartphone HCR datasets that are scripted or gathered in-the-wild. Scripted datasets are most accurate because of their consistent visit patterns. Supervised machine learning HCR models perform well on scripted datasets but poorly on realistic data. In-the-wild datasets are more realistic, but cause HCR models to perform worse due to data imbalance, missing or incorrect labels, and a wide variety of phone placements and device types. Lab-to-field approaches learn a robust data representation from a scripted, high-fidelity dataset, which is then used for enhancing performance on a noisy, in-the-wild dataset with similar labels. This research introduces Triplet-based Domain Adaptation for Context REcognition (Triple-DARE), a lab-to-field neural network method that combines three unique loss functions to enhance intra-class compactness and inter-class separation within the embedding space of multi-labeled datasets: (1) domain alignment loss in order to learn domain-invariant embeddings; (2) classification loss to preserve task-discriminative features; and (3) joint fusion triplet loss. Rigorous evaluations showed that Triple-DARE achieved 6.3% and 4.5% higher F1-score and classification, respectively, than state-of-the-art HCR baselines and outperformed non-adaptive HCR models by 44.6% and 10.7%, respectively.
Tree Parity Machine-Based Symmetric Encryption: A Hybrid Approach

In a symmetric key encryption the sender and the receiver must possess the same pre-distributed key in order to encrypt or decrypt the exchanged messages. Exchanging symmetric keys is a challenging issue in cryptography. In this paper, we put forward a symmetric key encryption technique that does not require any common pre-shared “knowledge” between the parties. More specifically, we use a type of neural network called Tree Parity Machines (TPMs) which, when synchronized, enable two parties to reach a common state. The common state can be used to establish a common secret key. Our method makes use of the Tree Parity Machines to reach a common state between the parties communicating and encrypt the communications with an ElGamal-type encryption methodology. The advantage of our implementation is that the initial key exchange method is fast, lightweight and believed to become a post-quantum candidate. We have analyzed the randomness of the produced ciphertexts from our system using NIST randomness tests and the results are included in the paper. We also demonstrate security against chosen plaintext attacks.
Equilibrium Image Denoising With Implicit Differentiation

Recent efforts on learning-based image denoising approaches use unrolled architectures with a fixed number of repeatedly stacked blocks. However, due to difficulties in training networks corresponding to deeper layers, simply stacking blocks may cause performance degradation, and the number of unrolled blocks needs to be manually tuned to find an appropriate value. To circumvent these problems, this paper describes an alternative approach with implicit models. To our best knowledge, our approach is the first attempt to model iterative image denoising through an implicit scheme. The model employs implicit differentiation to calculate gradients in the backward pass, thus avoiding the training difficulties of explicit models and elaborate selection of the iteration number. Our model is parameter-efficient and has only one implicit layer, which is a fixed-point equation that casts the desired noise feature as its solution. By simulating infinite iterations of the model, the final denoising result is given by the equilibrium that is achieved through accelerated black-box solvers. The implicit layer not only captures the non-local self-similarity prior for image denoising, but also facilitates training stability and thereby boosts the denoising performance. Extensive experiments show that our model leads to better performances than state-of-the-art explicit denoisers with enhanced qualitative and quantitative results.
Towards a novel coherent Ising machine using symmetry breaking in a Kerr resonator

Computer technology is experiencing a shift as physical computing systems are becoming more sought after due to their ability to overcome limitations found in digital systems. The Ising machine is one such computing system with interest driven by its ability to solve combinatorial optimisation problems efficiently. Here we propose a novel Ising machine based on spontaneous symmetry-breaking in a Kerr optical resonator with an opto-electronic feedback mechanism. We will present numerical results demonstrating that our system may have inherent advantages over existing implementations due to the underlying symmetry-breaking mechanism.
A contemporary anti-phishing framework based on visual cryptography and steganography

In new technologies developing tremendously, and with this, the usage of online commodities has also seen a surge with a drastic daily increase in the number of users. Online support has also led to increased online attacks on the user's confidential data, leading to financial and social loss. Phishing is one such online attack using which an attacker impersonates any authentic organisation to deceive and take advantage of users to steal various private information like passwords, payment card details, etc. These days, attackers have easy access to new tools and techniques which can easily evade many existing anti-phishing techniques. To enhance the security of the user's confidential information and protect them from such phishing attacks, this paper proposes a new approach that uses visual cryptography and steganography. This approach will help the user keep its identity and login credentials confidential unless the website proves its trustworthiness.
AN EFFICIENT PRIORITY-BASED METHOD FOR HUMAN GENDER RECOGNITION IN STANDARD FACIAL IMAGES

Human gender plays an imperative role as a social construct and an essential form of an individual's personality.This is highly reflected in social communication, forensic science, surveillance, and targeted marketing.The task has always been practiced in conjunction with face recognition, as it yields high performance compared to other features, e.g., gait and sound.This is attributed to its richness with diverse useful clues for gender inspection.However, facial features were always used separately without any integration between them, and a definitive solution has not yet been found yet.We propose a robust feature fusion method that integrates various facial features, in addition to a set of new features.Furthermore, Haar-like features are used to extract the essential features, while the used new features are, i.e., beard, moustache, cheeks, forehead, and face.The scientific novelty of the research is the robust fusion of features, to achieve high recognition accuracy.The proposed fusion method is a priority-based that arranges classifiers' responses from the strongest to weakest according to their importance to yield a final decision.When the proposed technique compares the recent and standard benchmarks, it achieves high recognition accuracy.The experiments on various standard and challenging datasets widely adopted in the scientific community, namely LFW, DataHub, NIST, and Caltech WebFaces demonstrated a robust performance of 99.1% accuracy.
A Chinese text classification model based on radicals and character distinctions

Chinese characters are generally correlated with their semantic meanings, and the structure of radicals, in particular, can be a clear indication of how characters are related to each other.In the Chinese characters simplification movement, some different traditional characters have been transferred into one simplified character (many-to-one mapping), resulting in the phenomenon of 'one simplified character corresponding to many traditional characters.Compared to the simplified characters, the traditional characters contain richer structural information, which is also more meaningful to semantic understanding.Traditional approaches of text modelling often overlook the structural content of Chinese characters and the role of human cognitive behaviour in the process of text comprehension.Hence, we propose a Chinese text classification model derived from the construction methods and evolution of Chinese characters.The model consists of two branches: the simplified and the traditional, with an attention module based on the radical classification in each branch.Specifically, we first develop a sequential modelling structure to obtain sequence information of Chinese texts.Afterwards, an associated word module using the part head as a medium is designed to filter out keywords with high semantic differentiation among the auxiliary units.An attention module is then implemented to balance the importance of each keyword in a particular context.Our proposed method is conducted on three datasets to demonstrate validity and plausibility.
Grow-push-prune: Aligning deep discriminants for effective structural network compression

Most of today’s popular deep architectures are hand-engineered to be generalists. However, this design procedure usually leads to massive redundant, useless, or even harmful features for specific tasks. Unnecessarily high complexities render deep nets impractical for many real-world applications, especially those without powerful GPU support. In this paper, we attempt to derive task-dependent compact models from a deep discriminant analysis perspective. We propose an iterative and proactive approach for classification tasks which alternates between (1) a pushing step, with an objective to simultaneously maximize class separation, penalize co-variances, and push deep discriminants into alignment with a compact set of neurons, and (2) a pruning step, which discards less useful or even interfering neurons. Deconvolution is adopted to reverse ‘unimportant’ filters’ effects and recover useful contributing sources. A simple network growing strategy based on the basic Inception module is proposed for challenging tasks requiring larger capacity than what the base net can offer. Experiments on the MNIST, CIFAR10, and ImageNet datasets demonstrate our approach’s efficacy. On ImageNet, by pushing and pruning our grown Inception-88 model, we achieve more accurate models than Inception nets generated during growing, residual nets, and popular compact nets at similar sizes. We also show that our grown Inception nets (without hard-coded dimension alignment) clearly outperform residual nets of similar complexities.
Domain-Driven and Discourse-Guided Scientific Summarisation

Scientific articles tend to follow a standardised discourse that enables a reader to quickly identify and extract useful or important information. We hypothesise that such structural conventions are strongly influenced by the scientific domain (e.g., Computer Science, Chemistry, etc.) and explore this through a novel extractive algorithm that utilises domain-specific discourse information for the task of abstract generation. In addition to being both simple and lightweight, the proposed algorithm constructs summaries in a structured and interpretable manner. In spite of these factors, we show that our approach outperforms strong baselines on the arXiv scientific summarisation dataset in both automatic and human evaluations, confirming that a scientific article’s domain strongly influences its discourse structure and can be leveraged to effectively improve its summarisation. Our code can be found at: https://github.com/TGoldsack1/DodoRank .
Mixed Noise Removal for Hyperspectral Images Based on Global Tensor Low-Rankness and Nonlocal SVD-Aided Group Sparsity

In hyperspectral images (HSIs), mixed noise ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., Gaussian noise, impulse noise, stripe noise, and deadlines) contamination is a common phenomenon that greatly reduces the visual quality of the image. In recent years, methods combining global and non-local low-rankness have been widely used in the field of HSI denoising. However, most methods apply original space-based denoising strategies (low-rank tensor decomposition, total variation, and tensor sparse representation, etc.) directly to the modeling of non-local low-rank tensors in subspace, without fully exploiting the intrinsic and latent properties of the non-local similar tensors. In this paper, we propose a hybrid prior denoising method based on global tensor low-rankness and non-local SVD-aided group sparsity (GTL_NSGS). This method introduces a novel plug-and-play NSGS denoiser that uses singular value decomposition as assistance to successively explore self-similarity of spatial dimension, low-rankness of spectral dimension, and group sparsity of difference domain in subspace non-local similar tensors. Globally, we utilize the existing three-way log-based tensor nuclear norm (3DLogTNN) to approximate the HSI tensor fibered rank and introduce a difference continuity regularization to obtain a continuous smooth spectral basis. Finally, we combine the Alternating Direction Method of Multipliers (ADMM) with the Augmented Lagrangian Multiplication (ALM) algorithm to solve the proposed model effectively. Extensive experiments on simulated and real data sets demonstrate that the proposed method has superior performance in removing mixed noise compared to state-of-the-art denoisers.
FFR-SSD: feature fusion and reconstruction single shot detector for multi-scale object detection

Object detection is one of the most fundamental tasks toward image content understanding. Although numerous algorithms have been proposed, implementing effective and efficient object detection is still very challenging for now. To solve the challenges of small and multi-scale objects, we propose a hierarchical feature fusion and reconstruction method called feature fusion and reconstruction single shot detector (FFR-SSD). We first present a multi-scale visual attention model, which incorporates the channel-wise and space-wise information into the multi-branch feature enhancement module to improve the feature representation capacities. Second, a hierarchical feature map weighing mechanism is developed to fuse multi-layer feature maps, which contributes to describing the intact objects for the subsequent module. Third, we present an effective feature map reconstruction module to encourage the model to focus on pivotal information. Therefore, the overall contour information is preserved for the shallow enhanced response map and the semantic feature information is enriched for the deep enhanced feature map. Numerous experiments on two public benchmark datasets show that the proposed method achieves significant improvements over the state of the arts.