Reviewerly/exHarmony huggingface dataset:
Energy Saving with Zero Hot Spots: A Novel Power Control Approach for Sustainable and Stable Data Centers

Data centers with high energy consumption have become a threat to urban sustainability on electric energy. In contrast, hot spots in a data center are another threat to server stability, which leads to unsafe data storage and service provisioning to urban lives. However, state-of-the-art works cannot ensure sustainability and stability together because they fail to consider them holistically. For example, some existing works eliminate the hot spots by increasing cooling power, which results in lower sustainability. In contrast, others reduce energy consumption by saving the cooling power, which harms stability. Therefore, to balance the hot spot elimination and energy saving through power control remains challenging, this paper proposes a novel power control approach for energy saving with zero hot spots in data centers. Power control works when hot spots appear, or consumed energy is excess. Specifically, we formulated a total consumption minimization problem to characterize and analyze the optimal set points for power control, where the number of hot spots is zero and the energy consumption is low. Adding the interactional penalty models can determine the power control approach when the objective function obtains the optimal solution. We propose a Modified Differential Evolution algorithm (MDE) to solve the function quickly and accurately. It adopts adaptive parameters to reduce the computing time. Meanwhile, it avoids optimal local solutions by changing mutation operations. Further, simulation experiments using our optimal solution demonstrate that energy consumption saves about 13% on average with zero hot spots, compared with three typical approaches.
Indian customers’ acceptance of service robots in restaurant services

The present study attempts to develop and empirically test a theoretical model of service robot use acceptance (SRUA) that intends to explain Indian customers' willingness to accept the use of service robots in restaurant services by modifying the Artificially Intelligent (AI) Device Use Acceptance (AIDUA) model. The proposed model is grounded on the Cognitive Appraisal Theory, Unified Theory of Acceptance and Use of Technology (UTAUT) and Human–Robot Interaction (HRI) literature. The data collected through a survey generated 419 usable customer responses that were used to empirically test the proposed model using Structural Equation Modeling (SEM). Findings indicate that customers go through a three-stage process, which includes primary appraisal stage, secondary appraisal stage and the outcome stage, in determining their willingness to accept the use of service robots in restaurants. The results of the study also indicate significant moderating effects of customers' gender and previous experience with service robots in the proposed model. By providing an understanding about customers' perception towards service robots in Indian restaurant settings, the study is expected to serve as a base for successful diffusion of service robots to create positive dining experiences within the restaurant settings in India and other developing nations with similar cultural orientations.
Efficient GitHub Crawling Using the GraphQL API

The number of publicly accessible software repositories on online platforms is growing rapidly. With more than 128 million public repositories (as of March 2020), GitHub is the world’s largest platform for hosting and managing software projects. Where it used to be necessary to merge various data sources, it is now possible to access a wealth of data using the GitHub API alone. However, collecting and analyzing this data is not an easy endeavor. In this paper, we present Prometheus, a system for crawling and storing software repositories from GitHub. Compared to existing frameworks, Prometheus follows an event-driven microservice architecture. By separating functionality on the service level, there is no need to understand implementation details or use existing frameworks to extend or customize the system, only data. Prometheus consists of two components, one for fetching GitHub data and one for data storage which serves as a basis for future functionality. Unlike most existing crawling approaches, the Prometheus fetching service uses the GitHub GraphQL API. As a result, Prometheus can significantly outperform alternatives in terms of throughput in some scenarios.
Near-Gaze Visualisations of Empathic Communication Cues in Mixed Reality Collaboration

In this poster, we present a live 360° panoramic-video based empathic Mixed Reality (MR) collaboration system that shares various Near-Gaze non-verbal communication cues including gaze, hand pointing, gesturing, and heart rate visualisations in real-time. The preliminary results indicate that the interface with the partner's communication cues visualised close to the gaze point allows users to focus without dividing attention to the collaborator's physical body movements yet still effectively communicate. Shared gaze visualisations coupled with deictic languages are primarily used to affirm joint attention and mutual understanding, while hand pointing and gesturing are used as secondary. Our approach provides a new way to help enable effective remote collaboration through varied empathic communication visualisations and modalities which covers different task properties and spatial setups.
MetaPo: A Robotic Meta Portal for Interspace Communication

We introduce MetaPo, a mobile robot with spheric display, 360° media I/O and robotic hands for creating a unified model of interspace communication. MetaPo works as a portal between pairs of physical-physical, cyber-cyber and cyber-physical spaces to provide 1) panoramic communication for multiple remote users, and 2) immersive interspace migration with mobility functionality. The paper overviews our concept and first prototype of MetaPo with its hardware and software implementation.
Multi-granularity visual explanations for CNN

The interpretability of convolutional neural networks (CNNs) is attracting increasing attention. Class activation maps (CAM) intuitively explain the classification mechanisms of CNNs by highlighting important areas. However, as coarse-grained explanations, classical CAM methods are incapable of explaining the classification mechanism in detail. Inspired by the concept of granular computing theory, we propose a new CAM method to divide the highlighted areas into commonality saliency maps and specificity saliency maps for multi-granularity visualization. This method consists of three components. First, the universe is simplified to contain only a category pair. Then, neighborhood rough sets are used to divide the universe into three disjointed regions containing the commonality and specificity of category pairs by adaptive thresholds of optimal granularity. Finally, these three regions are used to generate multi-granularity saliency maps. This method well visualizes the multi-granularity classification mechanism of the CNN and further explains the misclassification. We compare this method to five representative CAM methods using two newly proposed fine-grained evaluation metrics and subjective observations. First, experiments demonstrate that the multi-granularity visualization method provides a more extensive and detailed explanation. Second, the adaptive thresholds can be adapted to different situations to obtain a reliable visualization explanation. Finally, in explaining of the adversarial attack, it visualizes the details that caused misclassification.
A New Corpus and Lexicon for Offensive Tamazight Language Detection

In this paper, we address the offensive language detection on Tamazight language, which is one of the under-resourced languages that are still in their infancy and lack of standard orthography. We are particularly interested in the Kabyle dialect, mainly spoken in some cities of northern Algeria (i.e. Tizi-ouzou and Bejaïa). We propose a new corpus of offensive Tamazight language (i.e. OTAM corpus) compiling 6.2k texts, as well as a new lexicon of offensive and abusive Tamazight words with 12.6k entries. We have evaluated several baseline classifiers of machine learning and deep learning, where the results showed that we could produce acceptable results without features engineering.
Reconfigurable Noise-Assisted Logic Gates Exploiting Nonlinear Transformation of Input Signals

We demonstrate the direct implementation of all basic logical operations utilizing a single bistable system driven by nonlinearly transformed input signals, in the presence of noise. Exploiting the hopping between the dynamical states of the bistable system, assisted by the noise floor, in response to the transformed inputs, allows the implementation of the full set of logic operations. So this idea can form the basis of the design of a dynamical computing element that can be rapidly morphed to yield any desired logic gate by varying just a single control parameter. Further, the results are verified in electronic circuit experiments, demonstrating the robustness of the concept and the potential of this idea to be realized in wide-ranging systems.
RDIS: Random Drop Imputation With Self-Training for Incomplete Time Series Data

Time-series data with missing values are a common occurrence in various fields, including healthcare, meteorology, and robotics. The process of imputation aims to fill in the missing values with valid values. Most imputation methods implicitly train models due to the presence of missing values. In this paper, we propose Random Drop Imputation with Self-training (RDIS), a novel training method for time-series data imputation models. In RDIS, we generate extra missing values by applying a random drop to the observed values in incomplete data. We can explicitly train the imputation models by filling in the missing values. Moreover, we utilize self-training with pseudo values to exploit the original missing values. To enhance the quality of pseudo values, we set a threshold and filter them based on entropy calculation. To evaluate the effectiveness of RDIS for imputing time-series data, we test it across several imputation models and obtain competitive results on three real-world datasets.
Optimal Bayesian estimation of Gaussian mixtures with growing number of components

We study Bayesian estimation of finite mixture models in a general setup where the number of components is unknown and allowed to grow with the sample size. An assumption on growing number of components is a natural one as the degree of heterogeneity present in the sample can grow and new components can arise as sample size increases, allowing full flexibility in modeling the complexity of data. This however will lead to a high-dimensional model which poses great challenges for estimation. We novelly employ the idea of a sample size dependent prior in a Bayesian model and establish a number of important theoretical results. We first show that under mild conditions on the prior, the posterior distribution concentrates around the true mixing distribution at a near optimal rate with respect to the Wasserstein distance. Under a separation condition on the true mixing distribution, we further show that a better and adaptive convergence rate can be achieved, and the number of components can be consistently estimated. Furthermore, we derive optimal convergence rates for the higher-order mixture models where the number of components diverges arbitrarily fast. In addition, we suggest a simple recipe for using Dirichlet process (DP) mixture prior for estimating the finite mixture models and provide theoretical guarantees. In particular, we provide a novel solution for adopting the number of clusters in a DP mixture model as an estimate of the number of components in a finite mixture model. Simulation study and real data applications are carried out demonstrating the utilities of our method.
Bi-MGAN: Bidirectional T1-to-T2 MRI images prediction using multi-generative multi-adversarial nets

Magnetic Resonance Imaging (MRI) is a widely-used detection technology both in clinical treatment and image-guide therapy, and it can yield multi-modal medical images. However, MRI technique requires expensive costs and long-time process, which causes extra financial burden and low throughput. Therefore, it is necessary to estimate desired modality from acquired modality without real acquisition, which can effectively decrease financial cost and indirectly improve throughput. Considering that current unidirectional methods can only predict one modality fixedly, we propose a bidirectional prediction method by using multi-generative multi-adversarial nets (Bi-MGAN) to predict desired modality from acquired modality in paired and unpaired fashion. Our method simultaneously learns two nonlinear mappings between T1-weighted MRI and T2-weighted MRI image. Within it, pathological label is introduced to ensure invariance of pathological information. Then, deep and handcrafted features are fused to preserve consistency of context feature and anatomical structure. Besides, spectral normalization is utilized to control the performance of discriminator and accelerate learning process, and local regularization is used to reduce generalization error of predictors. Experimental results show that our Bi-MGAN has preserved pathological feature and anatomical structure comparing with other state-of-the-art methods. It gains significant increments on paired dataset and obtains competitive results on unpaired dataset. Specifically, it achieves average increment of 57.1% SSIM, 47.1% FSIM and 50.0% MSIM on paired SPLP dataset, and obtains 9.1% SSIM, 3.6% FSIM and 9.6% MSIM on unpaired Brain dataset comparing with others.
WaveSNet: Wavelet Integrated Deep Networks for Image Segmentation

In deep networks, the lost data details significantly degrade the performances of image segmentation. In this paper, we propose to apply Discrete Wavelet Transform (DWT) to extract the data details during feature map down-sampling, and adopt Inverse DWT (IDWT) with the extracted details during the up-sampling to recover the details. On the popular image segmentation networks, U-Net, SegNet, and DeepLabV3+, we design wavelet integrated deep networks for image segmentation (WaveSNets). Due to the effectiveness of the DWT/IDWT in processing data details, experimental results on CamVid, Pascal VOC, and Cityscapes show that our WaveSNets achieve better segmentation performances than their vanilla versions.
Revisiting Iso-Recursive Subtyping

The Amber rules are well-known and widely used for subtyping iso-recursive types. They were first briefly and informally introduced in 1985 by Cardelli in a manuscript describing the Amber language. Despite their use over many years, important aspects of the metatheory of the iso-recursive style Amber rules have not been studied in depth or turn out to be quite challenging to formalize. This article aims to revisit the problem of subtyping iso-recursive types. We start by introducing a novel declarative specification for Amber-style iso-recursive subtyping. Informally, the specification states that two recursive types are subtypes if all their finite unfoldings are subtypes . The Amber rules are shown to have equivalent expressive power to this declarative specification. We then show two variants of sound , complete and decidable algorithmic formulations of subtyping with respect to the declarative specification, which employ the idea of double unfoldings . Compared to the Amber rules, the double unfolding rules have the advantage of: (1) being modular; (2) not requiring reflexivity to be built in; (3) leading to an easy proof of transitivity of subtyping; and (4) being easily applicable to subtyping relations that are not antisymmetric (such as subtyping relations with record types). This work sheds new insights on the theory of subtyping iso-recursive types, and the new rules based on double unfoldings have important advantages over the original Amber rules involving recursive types. All results are mechanically formalized in the Coq theorem prover.
Classification of Virtual Private networks encrypted traffic using ensemble learning algorithms

Virtual Private Networks (VPNs) are one example of encrypted communication services commonly used to bypass censorship and access geographically locked services. This study performed VPN and non-VPN traffic analysis and developed a classification system based on the new techniques of machine learning classifiers known as stacking ensemble learning. The methods used for VPN and Non-VPN classification use three machine learning techniques: random forest, neural network, and support vector machine. To assess the proposed method's performance, we tested it on a dataset containing 61 features. The experiment results accurately prove the study's classifiers to differentiate between VPN and Non-VPN traffic. The accuracy level was approximately 99% in the training and testing phase. The study's classifiers also show the best standard deviation, with a 100% accuracy rate compared to other A.I. classifier methods.
SLAM Based Indoor Autonomous Navigation System For Electric Wheelchair

At present manual and electric wheelchairs are widely used in the market. Manual wheelchairs rely on other people's or their own power to drive wheelchairs, which consumes physical strength, while electric wheelchairs will cause driving fatigue and limited cognitive ability. This paper introduces an indoor electric wheelchair autonomous navigation system. The wheel encoder and IMU (Inertial Measurement Unit)are used for wheelchair pose estimation, which avoids the large error caused by using a single sensor for pose estimation, At the same time, the dynamic obstacles are scanned by Lidar, and the map is constructed by combining the surrounding walls and gmapping algorithm. Finally, the autonomous navigation based on move_base framework is realized. The system uses STM (STMicroelectronics) 32 series chip as the core of the bottom hardware processor, Raspberry pie as the upper processor, and embedded ROS (Robot Operating System) to easily and safely integrate with the existing electric wheelchair. Can well solve the elderly reaction, cognitive ability is limited by its defects such as their own and others' safety problem, and the electric wheelchair has a lot of the human-computer interaction interface, such as face, voice, gestures and so on, so as to enrich the daily life of the elderly, and implement more than one modal for subsequent intelligent wheelchair nursing platform provide strong technical support.
Feature Grouping for No-reference Image Quality Assessment

No-reference image quality assessment (NR-IQA) is to evaluate the quality of the acquired distorted images by simulating the human subjective vision of a model. Currently, most NR-IQA methods research shallow features of images and do not pay attention to detail-rich deep features; moreover, the information exchange between sub-features extracted from deep features plays an important role in quality-aware features. Therefore, this paper proposes a novel, grouping feature-based NR-IQA model, which relies on sub-features from different levels combined with attention mechanisms. It consists of four parts: backbone network, grouping proposal, attention module, and head network. The backbone network extracts distorted image-related features and performs top-down and bottom-up architectures to complete feature grouping. The image distortion quality information is generated by filtering through the group attention module to obtain the quality features, and then the final distorted image quality is obtained by regression. The experimental results show that the proposed grouping architecture model (FGIQNet) outperforms other advanced NR-IQA models in four IQA databases of synthetic distortion and real distortion, and achieves excellent performance in cross-database evaluation, which proves the effectiveness and generalizability of the proposed model.
Augmented Reality Image Quality Assessment Based on Visual Confusion Theory

With the development of multimedia technology, Augmented Reality (AR) has become a promising next-generation mobile platform. In order to acquire better AR mobile streaming experience, it is significant to study the evaluation of Quality of Experience (QoE) in AR. The primary value of AR is to promote the fusion of digital contents and real-world environments, however, studies on how this fusion will influence the Quality of Experience (QoE) of these two components are lacking. To achieve better QoE of AR, whose two layers are influenced by each other, it is important to evaluate its perceptual quality first. In this paper, we consider AR technologies as the superimposition of virtual scenes and real scenes, and introduce visual confusion as its basic theory. We first establish an ARIQA database to better simulate the real AR application scenarios, which contains 20 AR reference images, 20 background (BG) reference images, and 560 distorted images generated from AR and BG references by mixing reference images in pairs, as well as the correspondingly collected subjective quality ratings. We also design three types of full-reference (FR) IQA metrics to study whether we should consider the visual confusion when designing corresponding IQA algorithms. An ARIQA metric is finally proposed for better evaluating the perceptual quality of AR images. The dataset, benchmark study and proposed metric will be released to facilitate the future studies related to AR QoE assessment and AR communication.
LFDeNet: Light Field Depth Estimation Network Based on Hybrid Data Representation

This paper investigates an efficient CNN-based depth estimation for light field (LF) image in a hybrid data representation, named “LFDeNet.” We pay attention to the high dimensionality of LF data in the context of designing an efficient CNN structure for LF depth estimation task. Unlike most of existing CNN-based methods being applied on 2D subspace, i.e. 2D representation, we propose a hybrid CNN structure that works on different data representations to fully handle the 4D structure. Specifically, our network contains three novel feature extractor modules, allowing one to simultaneously exploit information from three types of 4D LF representation: spatial, angular, and epipolar image information. The experiments in this paper show that the proposed LFDeNet achieves competitive performance on the benchmark datasets and shows robustness with the occlusion and texture-less areas.
Resource Reservation for Graph-structured Multimedia Services in Computing Power Network

Computation-intensive multimedia services have developed greatly in the computing network environment where cloud, edge, and user terminals coexist. In face of high concurrent services requests and different demands of computing resources for a variety of multimedia services, how to efficiently coordinate the computing capabilities of edge nodes and cloud nodes on a large scale, reasonably decide the resource reservation scope for graph-structured multimedia services in order to avoid resource conflicts between competing services forms a new challenge. This paper proposes CORA, a resource reservation method for concurrent multimedia services, which considers the interaction of function modules within each graph-structured multimedia service and the resource competition among the multiple concurrent services. The simulation results show that our method can obtain reasonable sets of computing nodes for resource reservation, which can meet the demand for computing resources of concurrent services and avoid the conflict of resource selection between multiple services.