Reviewerly/exHarmony huggingface dataset:
Calculi, countermodel generation and theorem prover for strong logics of counterfactual reasoning

Abstract We present hypersequent calculi for the strongest logics in Lewis’ family of conditional systems, characterized by uniformity and total reflexivity. We first present a non-standard hypersequent calculus, which allows a syntactic proof of cut elimination. We then introduce standard hypersequent calculi, in which sequents are enriched by additional structures to encode plausibility formulas and diamond formulas. Proof search using these calculi is terminating, and the completeness proof shows how a countermodel can be constructed from a branch of a failed proof search. We then describe tuCLEVER, a theorem prover that implements the standard hypersequent calculi. The prover provides a decision procedure for the logics, and it produces a countermodel in case of proof search failure. The prover tuCLEVER is inspired by the methodology of leanTAP and it is implemented in Prolog. Preliminary experimental results show that the performances of tuCLEVER are promising.1
Numerical simulation of reservoir computing with magnetic nanowire lattices without inversion symmetry

We demonstrate the performance of magnetic nanowire lattices for reservoir computing using micromagnetic simulation. Domain walls are injected into nanowires as the input and are driven by clocking magnetic fields. The static magnetization states of wires are used as the reservoir states. The distribution of node weights shows that the device saves the memory as a shift register, and the domain walls provide the nonlinear computational power for reservoir computing. The maximum short-term memory capacity of 5 and the parity-check capacity of 5 are achieved. We expect the maximum capacities can be boosted by increasing the number of asymmetric structures and the length of the wires.
Homophily - a Driving Factor for Hate Speech on Twitter

In this paper, we hypothesize that homophily is a powerful driver in the generation of hate speech on Twitter. Prior works have shown the role of homophily in information diffusion, sustenance of online guilds and contagion in product adoption. We observe that familiarity computation techniques used in the literature such as the presence of an edge, and the number of mutual friends between a pair of users, have ample scope of improvement. We address the limitations of existing familiarity computation methods by employing a variational-graph-auto-encoder to capture a user’s position in the network and utilizing this representation to compute familiarity. We empirically demonstrate the presence of homophily on a dataset from Twitter. Further, we investigate how homophily varies with different hateful forms, such as hate manifesting in topics of gender, race, colour etc. Our results indicate higher homophily in users associating with topics of racism, sexism and nationalism.
Visual Analytics Tools for Polycode Stimuli Eye Gaze Tracking in Virtual Reality

This paper is devoted to extending the previously created unified pipeline for conducting eye-tracking- based experiments in a virtual reality environment. In the previous work, we proposed using SciVi semantic data mining platform, Unreal Engine and HTC Vive Pro Eye head-mounted display to study reading process in the immersive virtual reality. The currently proposed extension enables to handle so-called polycode stimuli: compound visual objects, which consist of individual parts carrying different semantics for the viewer. To segment polycode stimuli extracting areas of interest (areas, where the informant’s eye gaze is being tracked) we adopt Creative Maps Studio vector graphics editor. To integrate Creative Maps Studio into the existing pipeline we created plugins for SciVi platform to load and handle the segmented stimuli, place them in the virtual reality scenes, collect corresponding eye gaze tracking data and perform visual analysis of the data collected. To analyze the eye gaze tracks, we utilize a circular graph that allows comprehensive visualization of hierarchical areas of interest (mapping them to color- coded graph nodes grouped into the hierarchy with a help of multilevel circular scale) and corresponding eye movements (mapped to the graph edges). We tested our pipeline on two different stimuli: the advertising poster and the painting “The Appearance of Christ Before the People” by A. Ivanov (1857).
Extraction of Aerosol Optical Extinction Properties From a Smartphone Photograph to Measure Visibility

Airborne particulate matter participates in the scattering of light, thus leading to visibility degradation. The widespread use of smartphones provides an opportunity to determine the level of this degradation from a smartphone photograph perspective by extraction of the image features representing aerosol optical extinction properties (AOEPs). This article presents novel algorithms to measure visibility through the extraction of AOEPs (i.e., the local medium transmission rate and the local medium extinction coefficient) from a single photograph. Among them, the transmission rate is derived based on the refined photograph’s dark channel prior and the extinction coefficient from the improved transmission map and depth map. Furthermore, to address the complexity of urban scenes, we draw on visual features manifested on a photograph in various weather conditions, analyze the influence of scene structures on extracted features, and identify the combination of extracted features to improve the estimated AOEPs. We also exploit the suitability of these two AOEPs and develop a method to automatically select an appropriate property from them based on the scene structure of a given photograph. The proposed algorithm is first validated through experiments using public databases. Then, two experiments, one on a city scale and the other on a national scale, are conducted using smartphone photographs crawled from the Internet to evaluate the accuracy of visibility estimation. Experimental results show that the proposed algorithms can estimate the atmospheric visibility in real time; therefore, this study provides an effective method of monitoring environments with crowdsourced data.
Hybrid Improvements in Multimodal Analysis for Deep Video Understanding

The Deep Video Understanding Challenge (DVU) is a task that focuses on comprehending long duration videos which involve many entities. Its main goal is to build relationship and interaction knowledge graph between entities to answer relevant questions. In this paper, we improved the joint learning method which we previously proposed in many aspects, including few shot learning, optical flow feature, entity recognition, and video description matching. We verified the effectiveness of these measures through experiments.
Design and Realization of Computer Image Intelligent Recognition System

Abstract The application of intelligent image recognition technology in life is more and more extensive, especially in the field of computer and multimedia, the research of machine vision system is becoming more and more mature, and the demand of human society for information processing is constantly increasing. This article first analyzes the basic knowledge of digital images based on computer technology, including basic knowledge of digital images, basic knowledge of image filtering and image recognition algorithms. Secondly, this paper studies the design and implementation of computer image intelligent recognition system.
Joint Workload Scheduling Method in Geo-Distributed Data Centers Considering UPS Loss

The rapidly growing energy consumption of modern data centers has drawn widespread attention and extensive research. Among all of the current researches, reducing the electricity cost in geo-distributed internet data center (IDC) is a hot spot. In this paper, electricity price, power output of photovoltaic and energy storage system (ESS) in geo-distributed IDC are optimized in combine with the computational tasks scheduling, while considering the power loss of uninterruptible power supply (UPS). Case studies are simulated and provided, whose results have demonstrated the effectiveness of this proposed method.
Spectral Graph Theory-Based Recovery Method for Missing Harmonic Data

In large-scale applications, parts of harmonic data are inevitably lost during transmission. This study presents an approach for the recovery of missing harmonic data based on the spectral graph theory. The proposed methodology involves graph theory for constructing a Laplacian matrix and a graph signal reconstruction function, the merging <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means algorithm for building a priori information model, and the accelerated segmentation Bregman iterative algorithm for solving the reconstruction function. Compared with existing methods on data recovery in power systems, the method maintains a good recovery accuracy when the data correlation of measurement units is low and the prior information is little. The proposed method has good anti-noise performances and low computational complexity. The feasibility and accuracy of the proposed method are verified through simulation and field recorded data.
PMU Missing Data Recovery Using Tensor Decomposition

The paper proposes a new approach for the recovery of missing data from phasor measurement units (PMUs). The approach is based on the application of tensor decomposition to PMU data organized as three-dimensional tensors with respect to time, location and type of variables. The organization of the PMU data is in the form of a bus-oriented data structure and a branch-oriented data structure. Two versions of the approach are introduced. The first version uses polyadic tensor decomposition and the second version uses Tucker tensor decomposition. Several case studies are conducted validating the proposed approaches including measured PMU data in the New York transmission system. Comparison with existing approaches is performed to demonstrate the improvement in missing data recovery accuracy achieved by the new approach.
A Multi-feature Fusion Temporal Neural Network for Multi-hand Gesture Recognition using Millimeter-wave Radar Sensor

This paper proposes a multi-feature fusion method for multi-hand gesture recognition method using frequency modulated continuous wave(FMCW) radar sensor. First, the FMCW radar is applied to acquire the intermediate frequency (IF) signals of multi-hand gesture, and the two dimensions fast Fourier transform (2D-FFT) and Multiple Signal classification (MUSIC) algorithm are, respectively, applied to construct the range-Doppler mapping (RDM) and angle-time mapping (ATM) of multi-hand gestures. Then, the interferences in RDM and ATM caused by radar system and the external environment are suppressed. Finally, the multi-feature fusion temporal neural network is proposed to extract the temporal and spatial change information of multi-hand gesture. The experiments are carried out to validate the effectiveness of the proposed method, and the results show that the average recognition accuracy of the proposed method is 97.17%.
Research on improved full-factor deep information mining algorithm

In the use of fire-fighting physics platform for fire alarm data correlation analysis, there are often problems such as too much data volume and insufficient accuracy of the analysis results. For such questions, this paper establishes a full-factor secondary mining mechanism for fire accidents based on the fire big data based on the correlation analysis algorithm and the clustering algorithm. The association algorithm is used to conduct full-factor primary mining on the fire-related factors in the data warehouse, and the common-sense accident attributes in the association rules are extracted. Then use the K-means clustering algorithm, where the cluster center is the relevant attribute in the fire accident record, and perform the second combined clustering of the accident elements to achieve in-depth information mining of all factors of the fire accident. Experimental results show that the improved full-factor deep information mining algorithm proposed in this paper can effectively filter 31.6% of meaningless mining results compared to the traditional single mining algorithm. It shows that the algorithm in this paper can more accurately dig out the relationship between data, and can provide more effective decision support for fire management and other work.
Models Self-Blend and A LA CARTE as a promising direction for the transformation of higher education

The article discusses possible options for the transition from the traditional form of organizing the educational process to blended learning at a university. The author reveals the advantages and disadvantages of the Self-Blend and A LA CARTE blended learning organization models, which allow combining traditional full-time education with independent work of students in a digital environment corresponding to a new generation of specialists. The advantages models are: the formation of educational routes for students, taking into account the goals of training, changes in the specialty and the labor market; great freedom of the student when he can choose a convenient time, place and pace of training; motivating students to study in traditional classes with a teacher, reducing the percentage of incomplete courses.
Multi‐label hybrid radar signal recognition based on a feature pyramid network and class activation mapping

The electromagnetic environment of modern battlefields becomes increasingly complex, and radar receivers may receive multiple radar signals simultaneously. However, current deep learning models can only predict a single class and cannot recognize multi-label mixed radar signals. In this study, a multi-label hybrid radar signal recognition framework based on the feature pyramid network (FPN) and class activation map (CAM) is proposed. The multi-label radar signals are recognized by calculating the average value of the CAM corresponding to each class. The proposed method can recognize, localize and separate mixed radar signals in time-frequency images, which improves the interpretability and transparency of the model. In addition, the FPN is adopted to improve the spatial resolution of the feature maps, and the Mixup data augmentation is utilized to improve the generalization performance of the model. Experiments with eight different modulation types of mixed radar signals show that the recognition accuracy of hybrid radar signals achieves 92.2% at 0 dB.
Experimental MALL Research in SLA

This chapter provides an historical and current review of Mobile-Assisted Language Learning to characterize (qualitatively and quantitatively) its contribution to experimental SLA research and related practice. In so doing, it focuses on the strengths and weaknesses of SLA MALL research and identifies opportunities for future work in the field. The chapter concludes with a summary of the unique qualities that distinguish MALL from static CALL methodologies and argues for the need to more systematically exploit these to fully realize the potential of MALL to support SLA through contextually situated, location-based, meaningful language usage at all levels, but especially for advanced learners
Semantically guided self‐supervised monocular depth estimation

Depth information plays an important role in the vision-related activities of robots and autonomous vehicles. An effective method to obtain 3D scene information is self-supervised monocular depth estimation, which utilizes large and diverse monocular video datasets during the training process without the need for ground-truth data. A novel multi-task learning strategy that uses semantic information to guide the monocular depth estimation method while maintaining self-supervision is proposed. An improved differential direct visual odometer (DDVO) combined with Pose-Net is applied for achieving better pose prediction. Minimum reprojection loss with auto-masking and semantic masking is used to remove the effects of low-texture areas and moving dynamic-class objects within scenes. Concurrently, the semantic masking is introduced into the DDVO pose predictor to filter moving objects and reduce the matching error between monocular sequence frames. In addition, PackNet is employed as the backbone of multi-task learning to further improve the accuracy of deep prediction. The proposed method produces state-of-the-art results for monocular depth estimation on the KITTI Eigen split benchmark, even outperforming supervised methods that have been trained using ground-truth depth.
D-GF-CNN Algorithm for Modulation Recognition

This paper presents a novel modulation recognition algorithm based on dilated convolutional neural network with a new defined GF regularization function, named as D-GF-CNN algorithm. Firstly, an asynchronous delay sampling (ADS) technique is introduced. Via the defined ADS, the received signal is converted into an asynchronous delay histogram (ADH). The ADH of different modulation signals has distinct characteristics, which provides great convenience for the neural network to identify the modulation mode. Then, the pixel point matrix of the ADH is convolved with the dilated convolution kernel of the convolutional neural network, and the automatic extraction of signal features is completed so that the manual feature extraction processing can be effectively avoided. Finally, a novel GF regularization function is given, which can improve the constraint ability of the loss function on the weight and effectively weaken the influence of network over-fitting on the modulation recognition accuracy. Theoretical analysis and simulation experiments show that the proposed algorithm provides several advantages, for example: (1) automatically extract features; (2) effectively prevent network over-fitting; (3) significantly improve recognition accuracy in the lower SNR scenarios.
A Customizable Architecture for Application-Centric Management of Context-Aware Applications

Context-aware applications present common requirements (e.g., heterogeneity, scalability, adaptability, availability) in a variety of domains (e.g., healthcare, natural disaster prevention, smart factories). Besides, they do also present domain specific requirements, among which the application concept itself is included. Therefore, a platform in charge of managing their execution must be generic enough to cover common requirements, but it must also be adaptable enough to consider the domain aspects to meet the demands at application-level. Several approaches in the literature tackle some of these demands, but not all of them, and without considering the applications concept and the customization demands in different domains. This work proposes a generic and customizable management architecture that covers both types of requirements based on multi-agent technology and model-driven development. Multi-agent technology is used to enable the distributed intelligence needed to address many common requirements, whereas model-driven development allows to address domain specific particularities. On top of that, a customization methodology to develop specific platforms from this generic architecture is also presented. This methodology is assessed by means of a case study in the domain of eHealthCare. Finally, the performance of MAS-RECON is compared with the most popular tool for the orchestration of containerized applications.
Analyzing and Processing of Astronomical Images using Deep Learning Techniques

Deep Learning techniques are widely used in various use cases like image detection, pattern recognition, computer vision, and prediction, etc. These days, Convolutional neural networks(CNN) which is an efficient algorithm of Deep Learning are also extensively used in astronomical image processing. In this work, we implemented different architectural models like AlexNet, VGG16, ResNe50, InceptionV3,Xception to classify and predict the Redshift(z) values of unlabeled galaxy images taken from the EFIGI catalog and galaxy-zoo image dataset from the Kaggle website. In addition to these pre-built architectural models, we have introduced a novel, customized CNN classifier and Redshift(z) predictor models to study the behavior of CNN layers and to achieve reasonable accuracy by fine-tuning hyperparameters. Our customized CNN Classifier model achieved a considerably good accuracy of 92.3% with 87.3% validation loss in galaxy classification. Whereas in Redshift(z) prediction, our novel CNN Redshift(z) predictor model achieved a very low loss of 0.000158 when compared to other pre-built architectures.
Artificial‐Intelligence‐Enabled Reagent‐Free Imaging Hematology Analyzer

Quantitative Phase Imaging A reagent-free hematology analyzer is developed in article number 2000277 by Rishikesh Pandey, Renjie Zhou, and co-workers. The researchers employ a quantitative phase imaging in conjunction with a neural network to classify leukocytes. In the cover image, cells are illuminated by a laser and phase images are acquired by a custom-built microscope (indicated by the objective lens). An AI algorithm (denoted by the background data flow) is used for classifying cells.
Multimodal Web Page Segmentation Using Self-organized Multi-objective Clustering

Web page segmentation (WPS) aims to break a web page into different segments with coherent intra- and inter-semantics. By evidencing the morpho-dispositional semantics of a web page, WPS has traditionally been used to demarcate informative from non-informative content, but it has also evidenced its key role within the context of non-linear access to web information for visually impaired people. For that purpose, a great deal of ad hoc solutions have been proposed that rely on visual, logical, and/or text cues. However, such methodologies highly depend on manually tuned heuristics and are parameter-dependent. To overcome these drawbacks, principled frameworks have been proposed that provide the theoretical bases to achieve optimal solutions. However, existing methodologies only combine few discriminant features and do not define strategies to automatically select the optimal number of segments. In this article, we present a multi-objective clustering technique called MCS that relies on \( K \) -means, in which (1) visual, logical, and text cues are all combined in a early fusion manner and (2) an evolutionary process automatically discovers the optimal number of clusters (segments) as well as the correct positioning of seeds. As such, our proposal is parameter-free, combines many different modalities, does not depend on manually tuned heuristics, and can be run on any web page without any constraint. An exhaustive evaluation over two different tasks, where (1) the number of segments must be discovered or (2) the number of clusters is fixed with respect to the task at hand, shows that MCS drastically improves over most competitive and up-to-date algorithms for a wide variety of external and internal validation indices. In particular, results clearly evidence the impact of the visual and logical modalities towards segmentation performance.