Reviewerly/exHarmony huggingface dataset:
Electroencephalographic evidence for the involvement of mirror-neuron and error-monitoring related processes in virtual body ownership

The illusion that an artificial or virtual object becomes part of one’s body has been demonstrated and productively investigated in the past two decades. Empirical and theoretical accounts of this phenomenon suggest that the body ownership illusion relies not on a single process, but rather on the alignment of the biological and the alternative bodies across multiple aspects. However, the portrayal of these aspects and the demarcation of their neurophysiological correlates has yet to be established. Our study examines electroencephalographic (EEG) markers of two extensively studied systems in the context of virtual body ownership illusion: the mirror-neuron system (MNS) and the error-monitoring system (EMS). We designed an experimental manipulation of brief involuntary and unexpected virtual hand bounces, which triggers both systems, and examined how the response of EEG markers of these systems to this manipulation is modulated by three aspects of body ownership: agency, visuotactile synchronicity, and semantic congruence between the participant’s hands and its virtual representation. We found evidence for enhanced MNS-related power suppression at the Mu band in the synchronous and semantic congruence conditions. On the other hand, the EMS-related Pe/P300 wave was reduced by semantic congruence. This Pe/P300 effect was stronger among participants who exhibited higher acceptance of the spatial illusion and an increased tendency for affective empathy. Mu power and Pe/P300 were not correlated, suggesting a dissociation between the distinct aspects of body ownership they probe. The findings suggest that synchronicity and semantic congruence induce sensorimotor sensitivity to the alternative body, whereas the latter parameter also buffers minor erroneous virtual motions. These neurophysiological markers may be added to the arsenal of body ownership probes and incorporated into VR rehabilitation protocols.
Sketch-based interaction and modeling: where do we stand?

Abstract Sketching is a natural and intuitive communication tool used for expressing concepts or ideas which are difficult to communicate through text or speech alone. Sketching is therefore used for a variety of purposes, from the expression of ideas on two-dimensional (2D) physical media, to object creation, manipulation, or deformation in three-dimensional (3D) immersive environments. This variety in sketching activities brings about a range of technologies which, while having similar scope, namely that of recording and interpreting the sketch gesture to effect some interaction, adopt different interpretation approaches according to the environment in which the sketch is drawn. In fields such as product design, sketches are drawn at various stages of the design process, and therefore, designers would benefit from sketch interpretation technologies which support these differing interactions. However, research typically focuses on one aspect of sketch interpretation and modeling such that literature on available technologies is fragmented and dispersed. In this paper, we bring together the relevant literature describing technologies which can support the product design industry, namely technologies which support the interpretation of sketches drawn on 2D media, sketch-based search interactions, as well as sketch gestures drawn in 3D media. This paper, therefore, gives a holistic view of the algorithmic support that can be provided in the design process. In so doing, we highlight the research gaps and future research directions required to provide full sketch-based interaction support.
Coordinated Motion Planning: Reconfiguring a Swarm of Labeled Robots with Bounded Stretch

We develop constant-factor approximation algorithms for minimizing the execution time of a coordinated parallel motion plan for a relatively dense swarm of homogeneous robots in the absence of obstacles. In our first model, each robot has a specified start and destination on the square grid, and in each round of coordinated parallel motion, every robot can move to any adjacent position that is either empty or simultaneously being vacated by another robot. In this model, our algorithm achieves constant stretch factor: if every robot starts at distance at most $d$ from its destination, then the total duration of the overall schedule is $O(d)$, which is optimal up to constant factors. Our result holds for distinguished robots (each robot has a specific destination), identical (unlabeled) robots, and most generally, classes of different robot types (where each destination specifies a required type of robot). We also show that finding the optimal coordinated parallel motion plan is NP-hard, justifying approximation algorithms. In our second model, each robot is a unit-radius disk in the plane, and robots can translate continuously in parallel subject to not intersecting, i.e., having disk centers at $L_2$-distance at least $2$. We prove the same result---constant-factor approximation algorithm to minimizing execution time via constant stretch factor---when the pairwise $L_{\infty}$-distance between disk centers is at least $2\sqrt{2}=2.8284\dots$. On the other hand, for $N$ densely packed disks at distance at most $2+\delta$ for a sufficiently small $\delta>0$, we prove that a stretch factor of $\Omega(N^{1/4})$ is sometimes necessary (when densely packed), while a stretch factor of $\mathcal{O}(N^{1/2})$ is always possible.
Decision support system for diagnosing Rheumatic-Musculoskeletal Disease using fuzzy cognitive map technique

Rheumatic-Musculoskeletal Disease (RMD) is a leading cause of disability worldwide. It causes inflammation of the connecting body structures, affects joints, tendons, ligaments, bones, and muscles, and is responsible for thousands of deaths per year worldwide. It is prevalent in Africa. In Nigeria, RMD constitutes 10–15% of rheumatology cases in most clinics, with a ratio of 2.4:1 (Female:Male), killing over 1652 people per year. The similarity of its symptoms with other diseases often cause misdiagnosis at an early stage among infected patients, and existing computational techniques used for diagnosis cannot address the confusability of the symptoms. Hence, there is an inability to determine the causality of symptoms. Moreover, a dearth of Rheumatologists prevents many RMD infected persons from obtaining an early and accurate diagnosis. Therefore, the need arises to develop a decision support system (DSS) for diagnosing RMD, such as by using a fuzzy cognitive map (FCM) technique. Our study focuses on the development and implementation of FCM-based DSS for RMD diagnosis (RMD-FCMDSS). RMD-FCMDSS serves as a software tool complementing physician decision-making. Our evaluation of RMD-FCMDSS suggests that it has an improved diagnostic value as compared to earlier traditional and conventional medical methods. Performance results indicate an 87% accuracy, 90% sensitivity, and 80% specificity. RMD-FCMDSS was tested on limited data due to a dearth of Rheumatologists in Nigeria and a limited population of rheumatic patients interacted with; thus in future work we shall need to increase the test sample. Hybridization of Fuzzy-Logic and Cognitive Mapping techniques helps to guarantee accuracy, specificity, sensitivity, and also speed diagnosis.
HiLLoC: lossless image compression with hierarchical latent variable models

We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model. We use this property, applying fully convolutional models to lossless compression, demonstrating a method to scale the VAE-based 'Bits-Back with ANS' algorithm for lossless compression to large color photographs, and achieving state of the art for compression of full size ImageNet images. We release Craystack, an open source library for convenient prototyping of lossless compression using probabilistic models, along with full implementations of all of our compression results.
AVARCIBER: a framework for assessing cybersecurity risks

The identification and assessment of risks are a fundamental part of cybersecurity. Determining the elements that participate in this field is difficult because there is no exclusive approach to cybersecurity. This document aims to provide a framework to identify and assess cybersecurity risks. For this, a systematic review of the studies related to cybersecurity risk taxonomies was carried out. The main elements of the proposed conceptual model and framework have been determined by applying the snowball technique. To validate the implementation of the proposed framework, a case study has been implemented at the Ecuadorian Social Security Institute. The first task was to consolidate the information in a baseline. Once the baseline was obtained, the defined framework has been applied. As a result, through the use of the proposed framework, the assessment process has improved the decision-making process regarding the importance and criticality of the risks and countermeasures that must be applied.
Deep Learning Model for Text Recognition in Images

Computer Vision and its applications are the core of industry digitization which is known as industry 4.0. For automating a process, texts embedded in images are considered as good source of information about that object. Reading text from natural images is still a challenging problem because of complicated background, size and space variations, irregular arrangements of texts. Detection and Recognition are the main stages of reading texts in the wild. In last few years, many researchers have provided many methods for recognizing texts in images. These methods have fine results on horizontal texts only but not on irregular arrangements of texts. This paper mainly focuses on deep learning model for text recognition in images (DL-TRI). The model addresses various cases of curved and perspective fonts and hard to recognize due to complex background.
Human face recognition with combination of DWT and machine learning

To enhance the accuracy of object recognition, various combination of recognition algorithms are used in recent literature. In this paper coherence of Discrete Wavelet Transform (DWT) is combined with four different algorithms: error vector of principal component analysis (PCA), eigen vector of PCA, eigen vector of Linear Discriminant Analysis (LDA) and Convolutional Neural Network (CNN) then combination of four results are done using entropy of detection probability and Fuzzy system. From this research the accuracy of recognition is found dependent on image and diversity of database. The combined method of the paper provides recognition rate of 89.56% for the worst case and 93.34% for the best case both can be said better in comparison with the previous works where individual method has been implemented on a specific set of images.
Co-design with Children: Using Participatory Design for Design Thinking and Social and Emotional Learning

Abstract This paper discusses leveraging design thinking techniques for involving children in serious game design in Japanese elementary schools. Our action research project approach accomplished two different goals: (1) to inculcate design thinking in pupils, and (2) to sensitize children on bullying victimization. Our approach uses a range of participatory design methods to distil design ideas from children and to support their design thinking aiming to boost children’s creative confidence and develop social and emotional skills. Key findings from our project are: (1) children made valuable design contributions including realistic bullying scenarios, language content, user interface design, storyline progression, character profiles, coping strategies etc., and (2) participatory design and design thinking stimulated ethical reasoning, reflection and empathy in children on bullying victimization. Our approach is unique in the current design thinking landscape, because it moves from designing “thing” (object) to designing “think” (bullying sensitization). Future research should focus on highlighting ways how participatory design and design thinking enrich and complement each other. The significance of our paper stems from the simple standpoint that those participating in a design should gain from participating in the design process. Takeaways for practitioners are: (1) building relationships with stakeholders, especially children, (2) empathy and user research techniques, (2) translating field data into usable insights, (3) idea-generation and rapid concept development, (4) product co-prototyping, (5) user engagement and co-creation, (6) multiple perspectives on effective communication.
Deep Extractive Text Summarization

With introduction of deep learning techniques their has been an increase in intelligent classification of text in many applications. Advances in automatic text summarization using deep learning technique is prime focus of research now a days. Earlier traditional approaches for extractive text summarization have been heavily dependent on human engineered features. However, it is a laborious and tedious task. In this paper, a data-driven approach has been used to generate extractive summaries using deep learning. Approach proposed uses paraphrasing techniques to classify sentences as a candidate sentence for inclusion in summary or not.
Conversational Search (Dagstuhl Seminar 19461).

Dagstuhl Seminar 19461 was held on 10-15 November 2019. 44 researchers in Information Retrieval and Web Search, Natural Language Processing, Human Computer Interaction, and Dialogue Systems were invited to share the latest development in the area of Conversational Search and discuss its research agenda and future directions. A 5-day program of the seminar consisted of six introductory and background sessions, three visionary talk sessions, one industry talk session, and seven working groups and reporting sessions. The seminar also had three social events during the program. This report provides the executive summary, overview of invited talks, and findings from the seven working groups which cover the definition, evaluation, modelling, explanation, scenarios, applications, and prototype of Conversational Search. The ideas and findings presented in this report should serve as one of the main sources for diverse research programs on Conversational Search.
Mining Correlated High Utility Itemsets in One Phase

High-utility itemset mining (HUIM) in transaction databases has been extensively studied to discover interesting itemsets from users' purchase behaviors. With this, business managers can adjust their sale strategies appropriately to increase profit. HUIM approaches usually focus on the utility values of itemsets, but rarely evaluate the correlation of items in itemsets. Many high-utility itemsets are weakly correlated and have no real meaning. To address this issue, we suggest an algorithm, called CoHUI-Miner, to efficiently find correlated high-utility itemsets. In the proposed algorithm, we use the database projection mechanism to reduce the database size and present a new concept, called the prefix utility of projected transactions, to eliminate itemsets which do not satisfy the minimum threshold in the mining process. Experimental evaluation on two types of datasets from sparse to dense ones shows that CoHUI-Miner can efficiently mine correlated high-utility itemsets with regard to both execution time and memory usage.
Model-Based Robust Deep Learning.

While deep learning has resulted in major breakthroughs in many application domains, the frameworks commonly used in deep learning remain fragile to artificially-crafted and imperceptible changes in the data. In response to this fragility, adversarial training has emerged as a principled approach for enhancing the robustness of deep learning with respect to norm-bounded perturbations. However, there are other sources of fragility for deep learning that are arguably more common and less thoroughly studied. Indeed, natural variation such as lighting or weather conditions can significantly degrade the accuracy of trained neural networks, proving that such natural variation presents a significant challenge for deep learning. In this paper, we propose a paradigm shift from perturbation-based adversarial robustness toward {\em model-based robust deep learning}. Our objective is to provide general training algorithms that can be used to train deep neural networks to be robust against natural variation in data. Critical to our paradigm is first obtaining a \emph{model of natural variation} which can be used to vary data over a range of natural conditions. Such models may be either known a priori or else learned from data. In the latter case, we show that deep generative models can be used to learn models of natural variation that are consistent with realistic conditions. We then exploit such models in three novel model-based robust training algorithms in order to enhance the robustness of deep learning with respect to the given model. Our extensive experiments show that across a variety of naturally-occurring conditions and across various datasets, deep neural networks trained with our model-based algorithms significantly outperform both standard deep learning algorithms as well as norm-bounded robust deep learning algorithms.
Practical interval type-2 fuzzy self-tuning of PID controller to servo permanent magnet synchronous motor

Abstract This paper implements a practical interval type-2 fuzzy self-tuning (IT2FST) of optimal PID (OPID) controller to servo permanent magnet synchronous motor (SPMSM). The proposed method IT2FST updates the OPID controller gains in an online manner to drive the SPMSM with better speed response during variable load and parameter uncertainty occurrence. In this work, the industrial SPMSM system comprises three-phase PMSM with internal break, drive and mechanical parts. Due to the incomplete real information of the SPMSM, nonlinear least square algorithm has been utilized for its model identification. A comparative analysis in a real time of the SPMSM with an OPID, type-1 fuzzy self-tuning and IT2FST for OPID controllers under the influence of parameter uncertainties and external load disturbances has been carried out. The real-time practical implementation results illustrated that the proposed IT2FST of OPID controller gives a simple opportunity to enhance the speed performance of the SPMSM than the other controllers.
Cross-Correlated Attention Networks for Person Re-Identification

Deep neural networks need to make robust inference in the presence of occlusion, background clutter, pose and viewpoint variations -to name a few- when the task of person re-identification is considered. Attention mechanisms have recently proven to be successful in handling the aforementioned challenges to some degree. However previous designs fail to capture inherent inter-dependencies between the attended features; leading to restricted interactions between the attention blocks. In this paper, we propose a new attention module called Cross-Correlated Attention (CCA); which aims to overcome such limitations by maximizing the information gain between different attended regions. Moreover, we also propose a novel deep network that makes use of different attention mechanisms to learn robust and discriminative representations of person images. The resulting model is called the Cross-Correlated Attention Network (CCAN). Extensive experiments demonstrate that the CCAN comfortably outperforms current state-of-the-art algorithms by a tangible margin. Modeling the inherentspatial relations between different attended regions within the deep architecture. Joint end-to-end cross correlated attention and representational learning. State-of-the-art results in terms of mAP and Rank-1 accuracies across several challenging datasets.
Stimulating Organizational Citizenship Behavior by Applying Organizational Commitment and Satisfaction

Purpose of the study: This study aims to apply organizational commitment and job satisfaction as an indicator to stimulate organizational citizenship behaviours.&#x0D; Methodology: The data collected by a questionnaire survey conducted among a group of hotel employees in Baghdad, Iraq. The 200 questionnaires distributed, the researchers received 149, were valid responses. The data were analyzed by AMOS software after ensuring validity, Reliability, and model fit.&#x0D; Main Findings: The results revealed that organizational commitment and job satisfaction play a positive and influential role in changing the organizational citizenship behaviours among hotel employees. Also, organizational commitment has a stronger impact on improving the behaviour of employees than job satisfaction.&#x0D; Research limitations/implications: The study contributes to human resource managers as it reveals the influential role of firms' commitment and job satisfaction on altering staff behaviours towards organizational citizenship. Therefore, it is understood that more variables need to be considered, such as psychological contract and employees' empowerment.&#x0D; Novelty/Originality of this study: The finding can fill the gap in the literature related to this article in the Iraqi setting, where studies about organizational citizenship is not existing in this context.
Unsupervised Genre-Based Multidomain Sentiment Lexicon Learning Using Corpus-Generated Polarity Seed Words

Sentiment lexicon learning is of paramount importance in sentiment analysis.One of the most considerable challenges in learning sentiment lexicons is their domain-specific behavior.Transferring knowledge acquired from a sentiment lexicon from one domain to another is an open research problem.In this study, we attempt to address this challenge by presenting a transfer learning approach that creates new learning insights for multiple domains of the same genre.We propose an unsupervised sentiment lexicon learning methodology scalable to new domains of the same genre.Incremental learning and the methodology learn polarity seed words from corpora of multiple automatically selected source domains.This process then transfers its genre-level knowledge of corpus-learned seed words to the target domains.The corpus-learned seed words are used for sentiment lexicon generation for multiple target domains of the same genre.The sentiment lexicon learning process is based on the latent semantic analysis technique and uses unlabeled training data from the source and target domains.The experiment was performed using 24 domains of the same genre, i.e., consumer product review.The proposed model displays the best results using standard evaluation measures compared with the competitive baselines.The proposed genre-based unsupervised approach achieves a maximum accuracy of 86% and outperforms methods recently presented in the literature.
On-Chip Programmable Nonlinear Optical Signal Processor and Its Applications

We introduce an integrated device based on microring resonator (MRR) assisted Mach-Zehnder interferometer (MZI) preceded by a tunable MZ coupler for nonlinear optical signal processing. The novel structure of this device provides both high programmability and switching contrast. These desirable features suggest its potential use as a multi-purpose optical processor providing different high-performance functionalities. We experimentally demonstrate two functionalities using the same device fabricated on a silicon-on-insulator substrate. One functionality is an all-optical thresholder enabling 40× signal contrast improvement, and the other functionality is clock-less pulse carving technique that converts long-pulse signals to short-pulse signals. With these functionalities, we discuss system-level applications of our device in optical interconnects and photonic neural networks.