Reviewerly/exHarmony huggingface dataset:
Learning recurrent residual regressors for single image super-resolution

Example regression-based single image super-resolution (SR) technique has been recognized as an effective way to produce a high-quality image with finer details from one low-resolution (LR) input. However, most current popular approaches usually establish the mappings from the LR feature space to the final HR one in one-pass scheme, which is insufficient to represent the complicated mapping relationship well. In this paper, we propose a novel single image SR framework by learning a group of linear residual regressors in a boosting manner so as to alleviate the gap between the underlying mappings and estimated mappings. In the training stage, we begin with the learning of a set of linear regressors by integrating the K-SVD dictionary learning algorithm and the ridge regression, and then further improve the HR estimate accuracy by learning multi-round residual regressors from the estimated errors in a cascade manner. Accordingly, in the testing stage more details can be gradually added into the input LR image by applying the learned multi-round residual regressors to SR reconstruction. The proposed SR method is fundamentally coarse-to-fine. Experimental results carried out on six publicly available datasets indicate that the proposed SR framework achieves promising performance in comparing with other state-of-the-art competitors in terms of both subjective and objective equality assessments.
Multilevel Thresholding for Image Segmentation Using an Improved Electromagnetism Optimization Algorithm

Image segmentation is considered one of the most important tasks in image processing, which has severalapplications in different areas such as; industry agriculture, medicine, etc. In this paper, we develop theelectromagnetic optimization (EMO) algorithm based on levy function, EMO-levy, to enhance the EMOperformance for determining the optimal multi-level thresholding of image segmentation. In general, EMOsimulates the mechanism of attraction and repulsion between charges to develop the individuals of a population.EMO takes random samples from search space within the histogram of image, where, each sample represents eachparticle in EMO. The quality of each particle is assessed based on Otsu’s or Kapur objective function value. Thesolutions are updated using EMO operators until determine the optimal objective functions. Finally, this approachproduces segmented images with optimal values for the threshold and a few number of iterations. The proposedtechnique is validated using different standard test images. Experimental results prove the effectiveness andsuperiority of the proposed algorithm for image segmentation compared with well-known optimization methods
Exploring the role of work identity and work locus of control in information security awareness

A growing body of research evidence has been focused on exploring aspects of individual differences in the context of human factors and adherence to organisational information security. The present study aimed to extend this research by exploring three individual variables related directly to the individual's perceived control within the workplace, their commitment to current work identity, and the extent to which they are reconsidering commitment to work. A total 1003 participants aged between 18–65 (Mean = 40.29; SD = 12.28), who were in full or part-time employment took part in the study. The results demonstrated that work locus of control acted as a significant predictor for total scores on a measure of information security awareness. Those individuals who demonstrated more externality had weaker engagement in accepted information security within the workplace. The findings from the current study are discussed in the context of potential links to counterproductive work behaviours, as well as presenting possible practical routes for intervention strategies to help mitigate poor engagement in information security awareness.
Improved self-adaptive bat algorithm with step-control and mutation mechanisms

The bat algorithm (BA) is a new search optimization algorithm, inspired by bats' echolocation behavior. However, it is prone to fall into local optima and has low solution accuracy. This study proposes an improved self-adaptive bat algorithm (SABA) with adaptive step-control and mutation mechanisms. The step-control mechanism uses two frequencies to adapt the step sizes used for the global and local searches, and the mutation mechanism could improve the algorithm's ability to avoid local optima. SABA's parameters are analyzed to ensure convergence. Its optimization and convergence performance are experimentally studied using 12 unimodal and multimodal functions; compared with a range of baseline algorithms, it can effectively avoid local optima and exhibits high solution accuracy. Further, its practical performance is evaluated using engineering optimization problems.
An Innovative Genetic Algorithm for the Quantum Circuit Compilation Problem

Quantum Computing represents the next big step towards speed boost in computation, which promises major breakthroughs in several disciplines including Artificial Intelligence. This paper investigates the performance of a genetic algorithm to optimize the realization (compilation) of nearest-neighbor compliant quantum circuits. Currrent technological limitations (e.g., decoherence effect) impose that the overall duration (makespan) of the quantum circuit realization be minimized, and therefore the makespanminimization problem of compiling quantum algorithms on present or future quantum machines is dragging increasing attention in the AI community. In our genetic algorithm, a solution is built utilizing a novel chromosome encoding where each gene controls the iterative selection of a quantum gate to be inserted in the solution, over a lexicographic double-key ranking returned by a heuristic function recently published in the literature.Our algorithm has been tested on a set of quantum circuit benchmark instances of increasing sizes available from the recent literature. We demonstrate that our genetic approach obtains very encouraging results that outperform the solutions obtained in previous research against the same benchmark, succeeding in significantly improving the makespan values for a great number of instances.
Asymmetric Image Encryption and Authentication Based on Equal Modulus Decomposition in the Fresnel Transform Domain

A novel asymmetric image encryption and authentication scheme based on equal modulus decomposition (EMD) in the Fresnel transform domain is proposed.First, the Fresnel spectrum of the plaintext is sparsely sampled.Then a sparse presentation of the Fresnel spectrum is divided into two complex-valued masks with equal modulus based on EMD, both of which are necessary for decryption and authentication.The decrypted image renders little information on the original image visually.Furthermore, a nonlinear correlation is used to authenticate the decrypted image with the plaintext.In the scheme proposed here, the illuminating wavelength and the diffraction distance strengthen the security of the proposed scheme significantly, and the two complex-valued masks are both essential for decryption and authentication.The percentage of extracted pixels does not have to be determined deliberately.The feasibility and validity of the scheme have been proved by numerical simulations.
Reasoning over Streaming Data in Metric Temporal Datalog

We study stream reasoning in datalogMTL—an extension of Datalog with metric temporal operators. We propose a sound and complete stream reasoning algorithm that is applicable to a fragment datalogMTLFP of datalogMTL, in which propagation of derived information towards past time points is precluded. Memory consumption in our algorithm depends both on the properties of the rule set and the input data stream; in particular, it depends on the distances between timestamps occurring in data. This is undesirable since these distances can be very small, in which case the algorithm may require large amounts of memory. To address this issue, we propose a second algorithm, where the size of the required memory becomes independent on the timestamps in the data at the expense of disallowing punctual intervals in the rule set. Finally, we provide tight bounds to the data complexity of standard query answering in datalogMTLFP without punctual intervals in rules, which yield a new PSPACE lower bound to the data complexity of the full datalogMTL.
CISI-net: Explicit Latent Content Inference and Imitated Style Rendering for Image Inpainting

Convolutional neural networks (CNNs) have presented their potential in filling large missing areas with plausible contents. To address the blurriness issue commonly existing in the CNN-based inpainting, a typical approach is to conduct texture refinement on the initially completed images by replacing the neural patch in the predicted region using the closest one in the known region. However, such a processing might introduce undesired content change in the predicted region, especially when the desired content does not exist in the known region. To avoid generating such incorrect content, in this paper, we propose a content inference and style imitation network (CISI-net), which explicitly separate the image data into content code and style code. The content inference is realized by performing inference in the latent space to infer the content code of the corrupted images similar to the one from the original images. It can produce more detailed content than a similar inference procedure in the pixel domain, due to the dimensional distribution of content being lower than that of the entire image. On the other hand, the style code is used to represent the rendering of content, which will be consistent over the entire image. The style code is then integrated with the inferred content code to generate the complete image. Experiments on multiple datasets including structural and natural images demonstrate that our proposed approach out-performs the existing ones in terms of content accuracy as well as texture details.
Attention guided deep audio-face fusion for efficient speaker naming

Speaker naming has recently received considerable attention in identifying the active speaking character in a movie video, and face cue alone is generally insufficient to achieve reliable performance due to its significant appearance variations. In this paper, we treat the speaker naming task as a group of matched audio-face pair finding problems, and present an efficient attention guided deep audio-face fusion approach to detect the active speakers. First, we start with VGG-encoding of face images and extract the Mel-Frequency Cepstrum Coefficients from audio signals. Then, two efficient audio encoding modules, namely two-layer Long Short-Term Memory encoding and two-dimensional convolution encoding, are addressed to discriminate the high-level audio features. Meanwhile, we train an end-to-end audio-face common attention model to discriminate the face attention vector, featuring adaptively to accommodate various face variations. Further, an efficient factorized bilinear model is presented to deeply fuse the paired audio-face features, whereby the joint audio-face representation can be reliably obtained for speaker naming. Extensive experiments highlight the superiority of the proposed approach and show its very competitive performance with the state-of-the-arts.
Deep Neural Network Quantization via Layer-Wise Optimization Using Limited Training Data

The advancement of deep models poses great challenges to real-world deployment because of the limited computational ability and storage space on edge devices. To solve this problem, existing works have made progress to prune or quantize deep models. However, most existing methods rely heavily on a supervised training process to achieve satisfactory performance, acquiring large amount of labeled training data, which may not be practical for real deployment. In this paper, we propose a novel layer-wise quantization method for deep neural networks, which only requires limited training data (1% of original dataset). Specifically, we formulate parameters quantization for each layer as a discrete optimization problem, and solve it using Alternative Direction Method of Multipliers (ADMM), which gives an efficient closed-form solution. We prove that the final performance drop after quantization is bounded by a linear combination of the reconstructed errors caused at each layer. Based on the proved theorem, we propose an algorithm to quantize a deep neural network layer by layer with an additional weights update step to minimize the final error. Extensive experiments on benchmark deep models are conducted to demonstrate the effectiveness of our proposed method using 1% of CIFAR10 and ImageNet datasets. Codes are available in: https://github.com/csyhhu/L-DNQ
Composable Modular Reinforcement Learning

Modular reinforcement learning (MRL) decomposes a monolithic multiple-goal problem into modules that solve a portion of the original problem. The modules’ action preferences are arbitrated to determine the action taken by the agent. Truly modular reinforcement learning would support not only decomposition into modules, but composability of separately written modules in new modular reinforcement learning agents. However, the performance of MRL agents that arbitrate module preferences using additive reward schemes degrades when the modules have incomparable reward scales. This performance degradation means that separately written modules cannot be composed in new modular reinforcement learning agents as-is – they may need to be modified to align their reward scales. We solve this problem with a Q-learningbased command arbitration algorithm and demonstrate that it does not exhibit the same performance degradation as existing approaches to MRL, thereby supporting composability.
Arabic named entity recognition using deep learning approach

Most of the Arabic Named Entity Recognition (NER) systems depend massively on external resources and handmade feature engineering to achieve state-of-the-art results. To overcome such limitations, we proposed, in this paper, to use deep learning approach to tackle the Arabic NER task. We introduced a neural network architecture based on bidirectional Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) and experimented with various commonly used hyperparameters to assess their effect on the overall performance of our system. Our model gets two sources of information about words as input: pre-trained word embeddings and character-based representations and eliminated the need for any task-specific knowledge or feature engineering. We obtained state-of-the-art result on the standard ANERcorp corpus with an F1 score of 90.6%.
System-environment correlations in qubit initialization and control

This paper presents numerical results on the coupling between qubits and a low-temperature bath. The results show that there exist a strong coupling that gives rise to Lamb shift and bath entanglement and uncover a trade-off between speed and accuracy. However, this does not pose an issue for the execution of quantum gates and these can be modeled using the standard, weak-coupling approach
Secure Smart Cameras by Aggregate-Signcryption with Decryption Fairness for Multi-Receiver IoT Applications

Smart cameras are key sensors in Internet of Things (IoT) applications and often capture highly sensitive information. Therefore, security and privacy protection is a key concern. This paper introduces a lightweight security approach for smart camera IoT applications based on elliptic-curve (EC) signcryption that performs data signing and encryption in a single step. We deploy signcryption to efficiently protect sensitive data onboard the cameras and secure the data transfer from multiple cameras to multiple monitoring devices. Our multi-sender/multi-receiver approach provides integrity, authenticity, and confidentiality of data with decryption fairness for multiple receivers throughout the entire lifetime of the data. It further provides public verifiability and forward secrecy of data. Our certificateless multi-receiver aggregate-signcryption protection has been implemented for a smart camera IoT scenario, and the runtime and communication effort has been compared with single-sender/single-receiver and multi-sender/single-receiver setups.
Detecting the latent associations hidden in multi-source information for better group recommendation

Nowadays, most recommendation approaches used to suggest appreciate items for individual users. However, due to the social nature of human beings, group activities have become an integral part of our daily life, thus motivating the study on group recommendation. Unfortunately, most existing approaches used in group recommender systems make recommendations through aggregating individual preferences or individual predictive results rather than comprehensively investigating the social features that govern user choices made within a group. As a result, such approaches often fail to detect many latent factors that could potentially improve the performance of the group recommender systems. Therefore, we propose a new approach, random walks based on a topic model (RTM), for group recommendations through combining an integrated probabilistic topic model − a User Topic Model (UTM) with the Random Walk with Restart (RWR) method. The goal of the work in this paper is better identify group preferences by comprehensively detecting the latent associations among group members, in order to alleviate the data sparsity problem and improve the performance of group recommender systems. The UTM provides a latent framework of users, groups, and items by exploiting both the users’ preference profiles and the items’ content information, which together can describe group interests and item features in a more complete manner. This latent framework is then combined with RWR to predict the preference degrees of groups to unrated items. In particular, we develop two different recommendation strategies based on the proposed approach, and design a special random walk path for each developed recommendation strategy to comprehensively detect various latent associations. Finally, we conduct experiments to evaluate our approach and compare it with other state-of-the-art approaches using the real-world CAMRa2011 dataset. The results demonstrate the advantage of our approach over comparative ones.
IoT/Sensor-Based Infrastructures Promoting a Sense of Home, Independent Living, Comfort and Wellness

This paper presents the results of three interrelated studies concerning the specification and implementation of ambient assisted living (AAL)/Internet of Things (IoT)/sensor-based infrastructures, to support resident wellness and person-centered care delivery, in a residential care context. Overall, the paper reports on the emerging wellness management concept and IoT solution. The three studies adopt a stakeholder evaluation approach to requirements elicitation and solution design. Human factors research combines several qualitative human–machine interaction (HMI) design frameworks/methods, including realist ethnography, process mapping, persona-based design, and participatory design. Software development activities are underpinned by SCRUM/AGILE frameworks. Three structuring principles underpin the resident’s lived experience and the proposed ‘sensing’ framework. This includes (1) resident wellness, (2) the resident’s environment (i.e., room and broader social spaces which constitute ‘home’ for the resident), and (3) care delivery. The promotion of resident wellness, autonomy, quality of life and social participation depends on adequate monitoring and evaluation of information pertaining to (1), (2) and (3). Furthermore, the application of ambient assisted living technology in a residential setting depends on a clear definition of related care delivery processes and allied social and interpersonal communications. It is argued that independence (and quality of life for older adults) is linked to technology that enables interdependence, and specifically technology that supports social communication between key roles including residents, caregivers, and family members.
High-accuracy calibration of line-structured light vision sensor by correction of image deviation

The structured-light vision sensor calibration approach based on planar target fails to generate reliable and accurate results due to the inaccurate localization of feature points in outdoor complex lighting environments. To address this issue, a novel high-accuracy calibration method that corrects image deviation is proposed for line-structured light vision sensor in this paper. The mathematical solution for stripe point location uncertainty is put forward and the location uncertainty of target feature points and stripe points is established. Moreover, the location deviation of all points is computed through large-scale nonlinear multi-step optimization based on the constraints of uncertainty. After compensation, the planar target based calibration method is adopted to solve the light plane equation. Both simulative and physical experiments have carried out to evaluate the performance of the proposed method and the results show that the proposed method is robust under large feature points localization deviation and can achieve the same measurement accuracy as the planar target calibration method under ideal imaging conditions, which reduces the requirements of the calibration environment, and has important practical engineering application value.
A parallel and distributed stochastic gradient descent implementation using commodity clusters

Deep Learning is an increasingly important subdomain of artificial intelligence, which benefits from training on Big Data. The size and complexity of the model combined with the size of the training dataset makes the training process very computationally and temporally expensive. Accelerating the training process of Deep Learning using cluster computers faces many challenges ranging from distributed optimizers to the large communication overhead specific to systems with off the shelf networking components. In this paper, we present a novel distributed and parallel implementation of stochastic gradient descent (SGD) on a distributed cluster of commodity computers. We use high-performance computing cluster (HPCC) systems as the underlying cluster environment for the implementation. We overview how the HPCC systems platform provides the environment for distributed and parallel Deep Learning, how it provides a facility to work with third party open source libraries such as TensorFlow, and detail our use of third-party libraries and HPCC functionality for implementation. We provide experimental results that validate our work and show that our implementation can scale with respect to both dataset size and the number of compute nodes in the cluster.