Reviewerly/exHarmony huggingface dataset:
Feature-Guided Spatial Attention Upsampling for Real-Time Stereo Matching Network

In this article, we propose an end-to-end real-time stereo matching network (RTSMNet). RTSMNet consists of three modules. The global and local feature extraction (GLFE) module captures the hierarchical context information and generates the coarse cost volume. The initial disparity estimation module is a compact three-dimensional convolution architecture aiming to produce the low-resolution (LR) disparity map rapidly. The feature-guided spatial attention upsampling module takes the LR disparity map and the shared features from the GLFE module as guidance, first estimates residual disparity values and then an attention mechanism is developed to generate context-aware adaptive kernels for each upsampled pixel. The adaptive kernels emphasize higher attention weights on the reliable area, which can significantly reduce blurred edges and recover thin structures. The proposed networks achieve 66 ∼ 175 fps on a 2080Ti and 11 ∼ 42 fps on edge computing devices, with competitive accuracy compared to state-of-the-art methods on multiple benchmarks.
WLV-RIT at HASOC-Dravidian-CodeMix-FIRE2020: Offensive Language Identification in Code-switched YouTube Comments.

This paper describes the WLV-RIT entry to the Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) shared task 2020. The HASOC 2020 organizers provided participants with annotated datasets containing social media posts of code-mixed in Dravidian languages (Malayalam-English and Tamil-English). We participated in task 1: Offensive comment identification in Code-mixed Malayalam Youtube comments. In our methodology, we take advantage of available English data by applying cross-lingual contextual word embeddings and transfer learning to make predictions to Malayalam data. We further improve the results using various fine tuning strategies. Our system achieved 0.89 weighted average F1 score for the test set and it ranked 5th place out of 12 participants.
Towards Understanding Cryptocurrency Derivatives:A Case Study of BitMEX

Since 2018, the cryptocurrency trading landscape has evolved from a collection of spot markets (fiat for cryptocurrency) to a hybrid ecosystem featuring complex and popular derivatives products. In this paper we explore this new paradigm through a study of BitMEX, one of the first and most successful derivatives platforms for leveraged cryptocurrency trading. BitMEX trades on average over 3 billion dollars worth of volume per day, and allows users to go long or short Bitcoin with up to 100x leverage. We analyze the evolution of BitMEX products—both settled and perpetual offerings that have become the standard across other cryptocurrency derivatives platforms. We additionally utilize on-chain forensics, public liquidation events, and a site-wide chat room to describe the diverse ensemble of amateur and professional traders that forms this community. These traders range from wealthy agents running automated strategies, to individuals trading small, risky positions and focusing on very short time-frames. Finally, we discuss how derivative trading has impacted cryptocurrency asset prices, notably how it has led to dramatic price movements in the underlying spot markets.
Preimage Attacks Against Lightweight Scheme Xoodyak Based on Deep Learning

With the rapid progress in the artificial intelligence field, machine learning algorithms have been utilized to construct cryptographic schemes and conduct cryptanalysis. In this paper, we propose deep learning-based preimage attacks against variants of Xoodyak hash mode which is a lightweight scheme submitted to the NIST lightweight cryptography standardization project. Three attack models whose internal permutations are of reduced rounds are derived from the original Xoodyak hash mode. Deep neural networks (DNNs) for attack models of 1-round underlying permutations are trained so that the messages of given hash values can be predicted correctly with the networks. In valid attacks, the DNNs are of a low loss rate and high accuracy. This work is more of a tentative attempt to examine the effectiveness of deep learning algorithms employed in conventional preimage attacks. In conclusion, it shows that deep learning techniques make little difference in preimage attacks against Xoodyak hash mode. Compared to the full 12-round internal permutation, only 1 round is covered in the deep learning-based attacks.
A Noise-Tolerant Audio Encryption Framework Designed by the Application of S8 Symmetric Group and Chaotic Systems

The recent decade has witnessed an exponential surge of digital content, especially multimedia and its applications. The security requirements of these innovative platforms necessitate the significance of enhancing advanced encryption schemes. In this paper, a novel encryption scheme is presented for real-time audio applications. The framework of the proposed scheme is grounded on the principles of confusion and diffusion. The confusion incorporates nonlinearity by the application of Mordell elliptic curves (MEC) and a symmetric group of permutations S8. The endurance of the proposed scheme is further enriched through the application of chaotic maps. The proposed scheme is intended to cater requirements of real-time voice communications in defense applications particularly warzones. The adoption of a modular design and fusion of chaotic maps makes the algorithm viable for numerous real-time audio applications. The security can further be enriched by incorporating additional rounds and number of S-boxes in the algorithm. The security and resistance of the algorithm against various attacks are gaged through performance evaluation and security measurements. The audio encryption scheme has the ability to tolerate noise triggered by a channel or induced by an invader. The decryption was successful and the resultant output was audible for noisy data. The overall results depict that the proposed audio encryption scheme contains an excellent cryptographic forte with the minimum computational load. These characteristics allow the algorithm to be a hotspot for modern robust applications.
Willingness to accept social robots in museums: an exploratory factor analysis according to visitor profile

Purpose The aim of this exploratory study is to identify the factors that influence the acceptance of social robots in museum environments and determine if this influence depends on the visitor's profile (age, gender, education and occupation). Design/methodology/approach Data collected from an electronic questionnaire include 433 responses from Spanish visitors. The authors subjected the variables proposed by De Kervenoael et al. (2020) adapted to museums. The initial descriptive analysis only showed significant differences by age (under or over 30 years old). Based on these previous results, an exploratory factor analysis was carried out to test the applicability of the questionnaire to museums. After identifying the factors, the authors applied an ANOVA test to determine whether there are age-related differences between the factors related to robot acceptance in museums. Findings The authors developed a multidimensional instrument for measuring willingness to accept social robots in museum contexts. Willingness is determined by three factors: museum visitor experience (which is a factor specific to museums), empathy and personal engagement (which are both usually relevant in other tourist sectors as well). The younger individuals (under 30 years old) have a higher degree of acceptance than do visitors over 30. Originality/value Social robot use in museums is still very low, so the key factors for their acceptance have yet to be ascertained. The specific skills of social robots could prove to be a major draw for young people and contribute significantly to the future of museums.
Political optimizer with interpolation strategy for global optimization

Political optimizer (PO) is a relatively state-of-the-art meta-heuristic optimization technique for global optimization problems, as well as real-world engineering optimization, which mimics the multi-staged process of politics in human society. However, due to a greedy strategy during the election phase, and an inappropriate balance of global exploration and local exploitation during the party switching stage, it suffers from stagnation in local optima with a low convergence accuracy. To overcome such drawbacks, a sequence of novel PO variants were proposed by integrating PO with Quadratic Interpolation, Advance Quadratic Interpolation, Cubic Interpolation, Lagrange Interpolation, Newton Interpolation, and Refraction Learning (RL). The main contributions of this work are listed as follows. (1) The interpolation strategy was adopted to help the current global optima jump out of local optima. (2) Specifically, RL was integrated into PO to improve the diversity of the population. (3) To improve the ability of balancing exploration and exploitation during the party switching stage, a logistic model was proposed to maintain a good balance. To the best of our knowledge, PO combined with the interpolation strategy and RL was proposed here for the first time. The performance of the best PO variant was evaluated by 19 widely used benchmark functions and 30 test functions from the IEEE CEC 2014. Experimental results revealed the superior performance of the proposed algorithm in terms of exploration capacity.
Rank-Metric Codes Over Arbitrary Galois Extensions and Rank Analogues of Reed--Muller Codes

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 29 June 2020Accepted: 27 January 2021Published online: 03 May 2021Keywordsrank-metric codes, skew group algebras, Galois theory, Reed--Muller codes, Dickson matrices, decodingAMS Subject Headings11T71, 94B05, 16S35Publication DataISSN (online): 2470-6566Publisher: Society for Industrial and Applied MathematicsCODEN: sjaabq
Infidelity Induced by Ground-Rydberg Decoherence of the Control Qubit in a Two-Qubit Rydberg-Blockade Gate

For neutral atom qubits, the two-qubit gate is typically realized via the Rydberg blockade effect, which hints about the special status of the Rydberg level besides the regular qubit register states. Here, we carry out experimental and theoretical studies to reveal how the ground-Rydberg coherence of the control qubit atom affects the process of two-qubit Controlled-Z ($C_Z$) gate, such as the commonly used ground-Rydberg $\pi$-gap-$\pi$ pulse sequence originally proposed in Phys. Rev. Lett. \textbf{85}, 2208 (2000). We measure the decoherence of the control qubit atom after the $\pi$-gap-$\pi$ pulses and make a direct comparison with the typical decoherence time $\tau_{gr}$ extracted from Ramsey fringes of the ground-Rydberg transition. In particular, we observe that the control qubit atom subject to such pulse sequences experiences a process which is essentially similar to the ground-Rydberg Ramsey interference. Furthermore, we build a straightforward theoretical model to link the decoherence process of control qubit subject to $C_Z$ gate $\pi$-gap-$\pi$ pulse sequence and the $\tau_{gr}$, and also analyze the typical origins of decoherence effects. Finally, we discuss the $C_Z$ gate fidelity loss due to the limits imposed by the ground-Rydberg coherence properties and prospective for improving fidelity with new gate protocols.
Continuous authentication using deep neural networks ensemble on keystroke dynamics

During the last years, several studies have been proposed about user identification by means of keystroke analysis. Keystroke dynamics has a lower cost when compared to other biometric-based methods since such a system does not require any additional specific sensor, apart from a traditional keyboard, and it allows the continuous identification of the users in the background as well. The research proposed in this paper concerns (i) the creation of a large integrated dataset of users typing on a traditional keyboard obtained through the integration of three real-world datasets coming from existing studies and (ii) the definition of an ensemble learning approach, made up of basic deep neural network classifiers, with the objective of distinguishing the different users of the considered dataset by exploiting a proper group of features able to capture their typing style. After an optimization phase, in order to find the best possible base classifier, we evaluated the ensemble super-classifier comparing different voting techniques, namely majority and Bayesian, as well as training allocation strategies, i.e., random and K-means. The approach we propose has been assessed using the created very large integrated dataset and the obtained results are very promising, achieving an accuracy of up to 0.997 under certain evaluation conditions.
MSAP: Multi-Step Adversarial Perturbations on Recommender Systems Embeddings

Recommender systems (RSs) have attained exceptional performance in learning users' preferences and finding the most suitable products. Recent advances in adversarial machine learning (AML) in computer vision have raised interests in recommenders' security.It has been demonstrated that widely adopted model-based recommenders, e.g., BPR-MF, are not robust to adversarial perturbations added on the learned parameters, e.g., users' embeddings, which can cause drastic reduction of recommendation accuracy.However, the state-of-the-art adversarial method, named fast gradient sign method (FGSM), builds the perturbation with a single-step procedure. In this work, we extend the FGSM method proposing multi-step adversarial perturbation (MSAP) procedures to study the recommenders' robustness under powerful methods. Letting fixed the perturbation magnitude, we illustrate that MSAP is much more harmful than FGSM in corrupting the recommendation performance of BPR-MF. Then, we assess the MSAP efficacy on a robustified version of BPR-MF, i.e., AMF. Finally, we analyze the variations of fairness measurements on each perturbed recommender. Code and data are available at https://github.com/sisinflab/MSAP.
Virtual Reality in transit: how acceptable is VR use on public transport?

When travelling on public transport, passengers use devices such as mobile phones or laptops to pass the time. VR (Virtual Reality) head-mounted displays could provide advantages over these devices by delivering personal and private experiences that help the wearer escape their confined space. This paper presents the key factors that influence VR acceptance on different modes of public transport (from buses to aeroplanes), uncovered through two surveys (N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> =60, N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> =108). An initial analysis of responses revealed unique passenger needs and challenges currently preventing wider VR adoption, creating parameters for future research.
Supporting Physical and Mental Health Rehabilitation at Home with Virtual Reality Headsets and Force Feedback Gloves

The outbreak of COVID-19 has led to worldwide quarantines and telehealth has become the lifeline for patients prone to infection. We propose using a Virtual Reality (VR) system as a playful coping strategy for rehabilitation at home. We hypothesize such a system can improve the physical and mental well-being of the user during play. In this demonstration, we transform the therapy into a playful virtual cat bathing simulation using a VR headset coupled with force feedback gloves. This results in an engaging and feedback-rich scenario where users practice fine motor skills by progressively completing three cat-care tasks.
Communication-Censored Distributed Stochastic Gradient Descent

This article develops a communication-efficient algorithm to solve the stochastic optimization problem defined over a distributed network, aiming at reducing the burdensome communication in applications, such as distributed machine learning. Different from the existing works based on quantization and sparsification, we introduce a communication-censoring technique to reduce the transmissions of variables, which leads to our communication-censored distributed stochastic gradient descent (CSGD) algorithm. Specifically, in CSGD, the latest minibatch stochastic gradient at a worker will be transmitted to the server if and only if it is sufficiently informative. When the latest gradient is not available, the stale one will be reused at the server. To implement this communication-censoring strategy, the batch size is increasing in order to alleviate the effect of stochastic gradient noise. Theoretically, CSGD enjoys the same order of convergence rate as that of SGD but effectively reduces communication. Numerical experiments demonstrate the sizable communication saving of CSGD.
Symmetric continuous subgraph matching with bidirectional dynamic programming

In many real datasets such as social media streams and cyber data sources, graphs change over time through a graph update stream of edge insertions and deletions. Detecting critical patterns in such dynamic graphs plays an important role in various application domains such as fraud detection, cyber security, and recommendation systems for social networks. Given a dynamic data graph and a query graph, the continuous subgraph matching problem is to find all positive matches for each edge insertion and all negative matches for each edge deletion. The state-of-the-art algorithm TurboFlux uses a spanning tree of a query graph for filtering. However, using the spanning tree may have a low pruning power because it does not take into account all edges of the query graph. In this paper, we present a symmetric and much faster algorithm SymBi which maintains an auxiliary data structure based on a directed acyclic graph instead of a spanning tree, which maintains the intermediate results of bidirectional dynamic programming between the query graph and the dynamic graph. Extensive experiments with real and synthetic datasets show that SymBi outperforms the state-of-the-art algorithm by up to three orders of magnitude in terms of the elapsed time.
Teachers’ Perceptions Analysis on Students’ Emotions in Virtual Classes during COVID19 Pandemic: A Lexical Availability Approach

Virtual education has grown exponentially in the past year due to the global COVID19 pandemic. In this context, the exploration of teachers’ perceptions of their students’ emotions when using ICTs has become more relevant. The aim of this study was two-fold, on the one hand, to analyze how teachers perceived their students’ emotions and, on the other hand, to analyze the emotions teachers wanted to modulate in their students when using ICTs. To this end, an interpretative and comparative study was implemented using the Lexical Availability technique. The sample was formed by 178 Chilean teachers who took a lexical availability test. The analysis included general vocabulary through network graphs and a comparison across gender, academic background, school type and knowledge area. The results made it possible to identify the latent mental lexicon of teachers, revealing significant differences in the perception of emotions, according to gender and ICTs use and according to gender and knowledge area. The study further projects the potential of lexical availability to determine the emotions required by Affective Informatics in the adaptability of educational systems and to make adjustments to the instructional design.
Rural Islandness as a Lens for (Rural) HCI

This article contributes to research that aims to better understand and describe the rural context for rural computing. We argue that the particularities of rurality are heightened by the experience of ‘islandness’. We report on our experiences of engaging on one small island as islanders established community radio using a novel platform. Data comes from 12 semi-structured interviews with community members and ethnographic field notes assembled through eight researcher visits to the island. Transcripts and notes were analysed using thematic analysis. We discuss how rural islandness as a socio-cultural lens influenced technology appropriation and factors to support participation. We explore the elements of rural islandness that can be used as an analytic tool for rural HCI and HCI more broadly, through three main contributions of rural islandness that we believe have not yet been sufficiently explored in HCI. These are (1) separateness, (2) pushing things ahead, and (3) publics and rural pluralities.
HyDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks

The behaviors of deep neural networks (DNNs) are notoriously resistant to human interpretations. In this paper, we propose Hypergradient Data Relevance Analysis, or HyDRA, which interprets the predictions made by DNNs as effects of their training data. Existing approaches generally estimate data contributions around the final model parameters and ignore how the training data shape the optimization trajectory. By unrolling the hypergradient of test loss w.r.t. the weights of training data, HyDRA assesses the contribution of training data toward test data points throughout the training trajectory. In order to accelerate computation, we remove the Hessian from the calculation and prove that, under moderate conditions, the approximation error is bounded. Corroborating this theoretical claim, empirical results indicate the error is indeed small. In addition, we quantitatively demonstrate that HyDRA outperforms influence functions in accurately estimating data contribution and detecting noisy data labels. The source code is available at https://github.com/cyyever/aaai_hydra.
Designing effective virtual reality environments for pain management in burn-injured patients

Abstract Burn patients engage in repetitive painful therapeutic treatments, such as wound debridement, dressing changes, and other medical processes high in procedural pain. Pharmacological analgesics have been used for managing pain, but with ineffective results and negative side effects. Studies on pain management for burn patients suggested that Virtual Reality can treat procedural pain. This paper describes the process of designing, testing, and deploying a Virtual Reality system into a hospital setting. Firstly, a workshop was conducted to identify the most suitable types of Virtual Reality contents for the needs of burn-injured patients. Then, an experimental study, with 15 healthy adults, explored the analgesic impact of the Virtual Reality contents. The pain was induced through a cold pressor. Finally, we deployed the Virtual Reality system into the hospital to examine its efficiency on burn-injured inpatients. This study presents factors for the effective design and deployment of Virtual Reality for burn-injured patients residing in a hospital. Those factors refer to the use of cartoonish features and a choice of content based on each patient’s interests to increase the positive emotions and the use of interactive features, portable equipment to reduce pain and increase the feasibility of the technology in clinical settings. Finally, our results indicated that the extension of the VR use after the therapeutic session could support more effective pain treatment. Trial registration number Protocol ID: AA8434.