Reviewerly/exHarmony huggingface dataset:
Attribute-Guided Coupled GAN for Cross-Resolution Face Recognition

In this paper, we propose a novel attribute-guided cross-resolution (low-resolution to high-resolution) face recognition framework that leverages a coupled generative adversarial network (GAN) structure with adversarial training to find the hidden relationship between the low-resolution and high-resolution images in a latent common embedding subspace. The coupled GAN framework consists of two sub-networks, one dedicated to the low-resolution domain and the other dedicated to the high-resolution domain. Each sub-network aims to find a projection that maximizes the pair-wise correlation between the two feature domains in a common embedding subspace. In addition to projecting the images into a common subspace, the coupled network also predicts facial attributes to improve the cross-resolution face recognition. Specifically, our proposed coupled framework exploits facial attributes to further maximize the pair-wise correlation by implicitly matching facial attributes of the low and high-resolution images during the training, which leads to a more discriminative embedding subspace resulting in performance enhancement for cross-resolution face recognition. The efficacy of our approach compared with the state-of-the-art is demonstrated using the LFWA, Celeb-A, SCFace and UCCS datasets.
Many-level Image Thresholding using a Center-Based Differential Evolution Algorithm

Image thresholding is a crucial image processing task. Most of the time, it plays a pivotal role in an image processing chain, therefore, any error in image thresholding can propagate to other steps such as edge detection, area/volume estimation, or object recognition. Multi-level image thresholding is a popular method for image segmentation, dividing an image into homogeneous regions. Conventional algorithms are timeconsuming due to utilising an exhaustive search, especially when the number of threshold levels increases. On the other hand, population-based metaheuristic algorithms have been successfully applied to this problem. In this paper, we propose a center-based differential evolution (DE) algorithm for high-dimensional multilevel image thresholding (many-level image thresholding). While DE has been shown to yield satisfactory performance for various real-world optimisation problems, in our algorithm, DE is further boosted with a center-based sampling strategy. We evaluate our algorithm on a set of benchmark images on high-dimensional search spaces and with regards to an entropy-based objective function and peak signal-to-noise ratio (PSNR). The obtained results demonstrate that the proposed algorithm can improve upon the performance of other metaheuristic image thresholding techniques.
Balancing the Influence of Evolutionary Operators for Global optimization

The proper use of evolutionary operators is crucial to find optimal solutions in a search space. Moreover, the diversity of the population affects the performance of Evolutionary Algorithms (EAs). This article introduces an EA called BWEAD which balances the influence of the operators. The proposal also performs a statistical analysis of the population when the diversity is low and decides which solutions might be replaced. Then BWEAD is able to explore the search space and exploit the prominent regions. The BWEAD has been tested over the CEC2014 set of benchmark functions. The experiments provide competitive results showing an improvement of 30% in 30-dimensional and 50-dimensional functions in comparison with state-of-the-art algorithms, overcoming some addressed instances and providing evidence of its capabilities on complex optimization problems.
MFEA-IG: A Multi-Task Algorithm for Mobile Agents Path Planning

Mobile agent path planning (MAPP) problem is a typical optimization problem. When we consider multiple agents path planning simultaneously, problems can be seen as multi-task optimization (MTO) problems. The Multi-factorial evolutionary algorithm (MFEA) is one promising technique for MTO problems. Within the MFEA some selective individuals that contain useful knowledge are transferred among independent tasks to enhance the convergence. In this work, we investigate what information, except to the selective individuals, should be transferred under the framework of the MFEA. In particular, the difference between the individuals and the estimated optimal solution of the corresponding task is used to calculate individual_gradient (IG), which is introduced into the MFEA as additional knowledge for transferring. Empirical studies on nine benchmarks validate the effectiveness of IG based MFEA (MFEA-IG). Moreover, we apply the MFEA-IG to MAPP problems. Simulation results show that the MFEA-IG outperforms the original MFEA and single task EA.
Energy Efficient Virtual Machine Consolidation Using Water Wave Optimization

With the unprecedented growth of Cloud Computing, data centers around the world have increased exponentially. The energy utilization in these data centers is becoming a concern, so the need for energy-efficient algorithms in the cloud has been on the top agenda for quite a while. Cloud providers use different energy management strategies to minimize energy utilization and to maximize ROI (Return On Investment) such as energy-efficient virtual machine (VM) placement. We have taken the problem for VM consolidation and worked on it for an energy-efficient algorithm. VM Consolidation uses live migrations of VMs during the execution of cloudlets so that underloaded physical servers can be switched off by transferring those VMs to other physical machines. VM consolidation in the cloud environment is a proven NP-hard problem. We have used Water Wave optimization (WWO) which is a meta-heuristic algorithm. Original WWO proposed for continuous space and so we have modified the parameters of the algorithm to apply for our problem of VM consolidation. Our approach produces a near-optimal solution using an objective function that minimizes energy consumption and increases the number of switched off servers.
Improving Arabic Text Classification Using P-Stemmer

Introduction: Stemming is an important preprocessing step in text classification, and could contribute in increasing text classification accuracy. Although many works proposed stemmers for English language, few stemmers were proposed for Arabic text. Arabic language has gained increasing attention in the previous decades and the need is vital to further improve Arabic text classification. Method: This work combined the use of the recently proposed P-Stemmer with various classifiers to find the optimal classifier for the P-stemmer in term of Arabic text classification. As part of this work, a synthesized dataset was collected. Result: The previous experiments show that the use of P-Stemmer has a positive effect on classification. The degree of improvement was classifier-dependent, which is reasonable as classifiers vary in their methodologies. Moreover, the experiments show that the best classifier with the P-Stemmer was NB. This is an interesting result as this classifier is wellknown for its fast learning and classification time. Discussion: First, the continuous improvement of the P-Stemmer by more optimization steps is necessary to further improve the Arabic text categorization. This can be made by combining more classifiers with the stemmer, by optimizing the other natural language processing steps, and by improving the set of stemming rules. Second, the lack of sufficient Arabic datasets, especially large ones, is still an issue. Conclusion: In this work, an improved P-Stemmer was proposed by combining its use with various classifiers. In order to evaluate its performance, and due to the lack of Arabic datasets, a novel Arabic dataset was synthesized from various online news pages. Next, the P-Stemmer was combined with Naïve Bayes, Random Forest, Support Vector Machines, KNearest Neighbor, and K-Star.
Multidimensional fiber echo state network analogue

Abstract Optical neuoromorphic technologies enable neural network-based signal processing through a specifically designed hardware and may confer advantages in speed and energy. However, the advances of such technologies in bandwidth and/or dimensionality are often limited by the constraints of the underlying material. Optical fiber presents a well-studied low-cost solution with unique advantages for low-loss high-speed signal processing. The fiber echo state network analogue (FESNA), fiber-based neuromorphic processor, has been the first technology suitable for multichannel high bandwidth (including THz) and dual-quadrature signal processing. Here we propose the multidimensional FESNA (MD-FESNA) processing by utilizing multi-mode fiber non-linearity. Thus, the developed MD-FESNA is the first neuromorphic technology which augments all aforementioned advantages of FESNA with multidimensional spatio-temporal processing. We demonstrate the performance and flexibility of the technology on the example of prediction tasks for hyperchaotic systems. These results will pave the way for a high-speed neuromorphic processing of multidimensional tasks, hardware for spatio-temporal neural networks and open new application venues for fiber-based spatio-temporal multiplexing.
Quantum earth mover’s distance, a no-go quantum Kantorovich–Rubinstein theorem, and quantum marginal problem

The quantum coupling of two given quantum states denotes the set of bipartite states whose marginal states are these given two states. In this paper, we provide tight inequalities to describe the structure of quantum coupling. These inequalities directly imply that the trace distance between two quantum states cannot be determined by the quantum analog of the earth mover’s distance, thus ruling out the equality version of the quantum Kantorovich–Rubinstein theorem for trace distance even in the finite-dimensional case. In addition, we provide an inequality that can be regarded as a quantum generalization of the Kantorovich–Rubinstein theorem. Then, we generalize our inequalities and apply them to the three tripartite quantum marginal problems. Numerical tests with a three-qubit system show that our criteria are much stronger than the known criteria: the strong subadditivity of entropy and the monogamy of entanglement.
What If Conversational Agents Became Invisible?

The popularity of conversational agents (CAs) in the form of AI speakers that support ubiquitous smart homes has increased because of their seamless interaction. However, recent studies have revealed that the use of AI speakers decreases over time, which shows that current agents do not fully support smart homes. Because of this problem, the possibility of unobtrusive, invisible intelligence without a physical device has been suggested. To explore CA design direction that enhances the user experience in smart homes, we aimed to understand each feature by comparing an invisible agent with visible ones embedded in stand-alone AI speakers. We conducted a drawing study to examine users' mental models formed through communicating with two different physical entities (i.e., visible and invisible CAs). From the drawings, interviews, and surveys, we identified how users' mental models and interactions differed depending on the presence of a physical entity. We found that a physical entity affected users' perceptions, expectations, and interactions toward the agent.
Quantum-Inspired Evolutionary Algorithm for Convolutional Neural Networks Architecture Search

Convolutional neural networks (CNN) are widely used and effective deep learning methods for image classification tasks. But the architecture of CNN such as LetNet and AlexNet were designed elaborately by experts because designing the neural networks is time-consuming and requires expert knowledge. This paper proposed a quantum-inspired evolutionary algorithm to search the neural architectures. First, we encode CNNs into quantum chromosomes and distinguish these chromosomes from the Convolutional Layer, Pooling Layer, Fully-connected Layer and Disabled Layer with its range. Second, quantum chromosomes are updated by applying quantum gates and find the best individual with quantum genetic algorithm. Third, we can predict the network performance after a few steps of stochastic gradient descent by means of evaluation estimate strategy so that we can stop training the bad networks early, which can speed up evolutionary process. The proposed algorithm is examined and compared with some state-of-art methods for image classification in three benchmark datasets. The experimental results prove the proposed algorithm can search a strong classifier robustly. In addition, it performs better than the general evolutionary algorithm. More importantly, with the help of evaluation estimate strategy, it is substantially faster than the algorithms without evaluation estimate strategy which means we can take less time to search a good network for the given task.
Multi-Scale Collaborative Fireworks Algorithm

Fireworks Algorithm (FWA) is a special swarm intelligent optimization algorithm, which controls multiple subgroups of the population to search collaboratively. Instead of assigning fireworks to different local areas, we propose the multiscale collaborative firework algorithm (MSCFWA) which helps fireworks to search at coordinated scales. Since the collaboration of search scales is accomplished by restarting or adjusting fireworks whose local search are not making meaningful progress, fireworks in MSCFWA are able to exploit different local areas independently or cooperate in the same local area with different search scales. Experimental results show that the proposed strategy stably improved the overall optimization performance of fireworks algorithm on the benchmark functions of the CEC'13 competition significantly. It also shows outstanding efficiency compared with typical swarm intelligence optimization algorithms and evolutionary algorithms.
A novel error-correcting output codes based on genetic programming and ternary digit operators

The key to the success of an Error-Correcting Output Code (ECOC) algorithm is the effective codematrix, which represents a set of class reassignment schemes for decomposing a multiclass problem into a set of binary class problems. This paper proposes a new method, which uses Ternary digit Operators based Genetic Programming (GP) to generate effective ECOC codematrix (TOGP-ECOC for short). In our GP, each terminal node stores a ternary digit string, representing a column and a related feature subset; each non-terminal node represents a ternary digit operator, which produces a new column based on its child nodes. In this way, each individual is interpreted as an ECOC codematrix along with a set of corresponding feature subsets, serving the solution for the multiclass classification task. When a new individual is produced, a legality checking process is carried out to verify whether the transformed codematrix follows the ECOC constraints. The illegal one is corrected according to different strategies. Besides, a local optimization algorithm is designed to prune redundant columns and improve the performance of each individual. Our experiments compared TOGP-ECOC with some well known ECOC algorithms on various data sets, and the results confirm the superiority of our algorithm. Our source code is available at: https://github.com/MLDMXM2017/TOGP-ECOC.
Development of a method for quantifying relative importance of NPP cyber attack probability variables based on factor analysis and AHP

The cyber security of NPPs recently has become a major issue, and a utility in Korea is requested to follow cyber security controls and to perform cyber security risk management based on the regulatory guide RS-015. Since it is difficult to fully implement the controls with limited resources, it is necessary to find which controls are relatively more important than other controls. The aim of this study is to develop a method for quantifying the relative importance of NPP cyber attack probability variables. These variables were investigated, and rearrangement and classification of them was validated by the factor analysis method. The relative importance among the variables was quantified by the AHP method. By applying the relative importance to the regulatory guide, the controls that have higher priority to be implemented were identified. Considering the relatively more important controls, it is expected that the utility can follow cyber security controls efficiently.
Sociotechnical Transformative Effects of an ICT Project in Rural Bangladesh

The purpose of this article is to provide lessons from the field about an Information and Communication Technology for Development (ICT4D) project (Participatory Research and Ownership With Technology, Information and Change [PROTIC]) concerned with the use of mobile phones by women in remote villages in Bangladesh. The Bangladeshi government considers that the role of ICT in social and economic transformation is significant for the country’s development. International nongovernmental organizations (NGOs) also regard ICT as important but are challenged as how to use them effectively for their programs and how to deal with long-term sustainability, digital divides, gender, and cultural issues. This article considers the PROTIC project as a modeling force for innovation and pressure on established sociotechnical structures. In this analysis, we follow what Donner defines as the “interrelationship” perspective, as applied to ICT4D. In particular, the notions of niche, regime, and landscape will be used to frame the changes that a village-level project may activate or respond to at the micro, meso, and macro levels of sociotechnical interaction. A mixed methods approach has been implemented during the 4 years of the project to monitor its outcomes, including interviews with project participants, reports of monthly consultations and training with villagers, extensive surveys, analysis of the Facebook profile of the project, and field notes and interviews with local NGOs and international NGO staff. Results show that the women villagers have undergone a transformation in attitudes, skills, and practices associated with mobile phone use. Transformations at individual and community niche levels have in turn influenced the conceptual framework of local and international NGOs and have also contributed to the reorientation of other regime actors, such as universities, major NGOs, and the government. Methodological constraints as well as the complexity of conducting international fieldwork with multiple actors will also be discussed.
Fast automatic camera network calibration through human mesh recovery

Abstract Camera calibration is a necessary preliminary step in computer vision for the estimation of the position of objects in the 3D world. Despite the intrinsic camera parameters can be easily computed offline, extrinsic parameters need to be computed each time a camera changes its position, thus not allowing for fast and dynamic network re-configuration. In this paper we present an unsupervised and automatic framework for the estimation of the extrinsic parameters of a camera network, which leverages on optimised 3D human mesh recovery from a single image, and which does not require the use of additional markers. We show how it is possible to retrieve the real-world position of the cameras in the network together with the floor plane, exploiting regular RGB images and with a weak prior knowledge of the internal parameters. Our framework can also work with a single camera and in real-time, allowing the user to add, re-position, or remove cameras from the network in a dynamic fashion.
A data visualization exploration to facilitate a sustainable usage of premises in a Smart Campus context

The growing interest in the IoT in everyday scenarios has led to the transition from buildings to smart buildings. Such smart environments are equipped with sensors and IoT-based facilities, able to collect a huge amount of data that can be analyzed and can bring about a more sustainable usage of spaces. In this paper, we present the case study of a smart campus, where heterogeneous data, gathered both from sensors and official open data, have been exploited to facilitate the data visualization exploration of rich information about the occupant counts, towards a more sustainable and efficient use of its premises (including classrooms, labs, and meeting rooms).
Predicting atypical visual saliency for autism spectrum disorder via scale-adaptive inception module and discriminative region enhancement loss

Autism spectrum disorder (ASD) is a prevalent lifelong neurodevelopmental disorder. Individuals with ASD perform atypical attention toward the visual stimulus. Despite that the number and performance of visual attention models are ongoing, the atypical attention prediction stagnates. To better understand abnormal visual behaviors of individuals with ASD, this paper models the atypical visual saliency via a deep neural network. First, the multi-level side-out feature maps are extracted from images. Second, the representation of features is enhanced by a scale-adaptive coarse-and-fine inception module that is specialized in integrating multi-scale features. Consequently, the enhanced features are transmitted from deep layers to shallow layers, boosting the precision of saliency maps. Furthermore, to facilitate our model transferring from typical saliency prediction to atypical saliency prediction, a loss function, namely discriminative region enhancement loss, is designed to intensify the discrepancy between the atypical saliency and typical saliency, driving the model to fit the atypical pattern of visual attention. Qualitative and quantitative experiments demonstrate that the proposed model achieves state-of-the-art performance in terms of various metrics.
Video person re-identification with global statistic pooling and self-attention distillation

Most existing methods for video person re-identification apply spatial-temporal global average or attention pooling to aggregate frame-level feature and get video-level feature. The obtained video-level feature models only the first-order statistics of the appearance feature from holistic video, resulting in limited representation capability of the feature network. In this paper, we propose a novel Global Statistic Pooling network (GSPnet) which takes full advantage of the second-order information for enhancing modeling capability. Firstly, a novel global statistic pooling module is proposed to summarize both the first- and second-order statistics across frame-level feature, and then transfer them into a compact and robust video-level feature embedding. Secondly, a statistic-based attention block is incorporated into multiple stages of convolutional networks to fully explore the second-order representations from low- to high-level features. To enhance the representation learning ability and further boost re-identification (re-ID) performance, we also propose a multi-level self-attention distillation training scheme, which squeezes the knowledge learned in the deeper portion of the networks into the shallow ones. Extensive experimental results have demonstrated the effectiveness and superiority of our approach on four popular video person re-ID datasets.