Reviewerly/exHarmony huggingface dataset:
Robust Reading Comprehension With Linguistic Constraints via Posterior Regularization

In spite of the great advancements of machine reading comprehension (RC), existing RC models are still vulnerable and not robust to different types of adversarial examples. Neural models over-confidently predict wrong answers to semantic different adversarial examples, while over-sensitively predict wrong answers to semantic equivalent adversarial examples. Existing methods which improve the robustness of such neural models merely mitigate one of the two issues but ignore the other. In this article, we address the over-confidence issue and the over-sensitivity issue existing in current RC models simultaneously with the help of external linguistic knowledge. We first incorporate external knowledge to impose different linguistic constraints (entity constraint, lexical constraint, and predicate constraint), and then regularize RC models through posterior regularization. Linguistic constraints induce more reasonable predictions for both semantic different and semantic equivalent adversarial examples, and posterior regularization provides an effective mechanism to incorporate these constraints. Our method can be applied to any existing neural RC models including state-of-the-art BERT models. Extensive experiments show that our method remarkably improves the robustness of base RC models, and is better to cope with these two issues simultaneously.
Character Recognition using Machine Learning and Deep Learning - A Survey

Digitization of machine printed or handwritten text documents have become very popular with the advancements in computing and technology. Humans have tried to automatized their work by replacing themselves with machines. The transformation from manual to automatization gave rise to several research areas and text recognition is one among them. Deep learning and machine learning techniques have been proved to be very suitable for optical character recognition. In this work, an up-to-date overview of four machine learning and deep learning architectures, viz., Support vector machine, Artificial neural network, Naive Bayes and Convolutional neural network have been discussed in detail.
ALONE: A Dataset for Toxic Behavior Among Adolescents on Twitter

The convenience of social media has also enabled its misuse, potentially resulting in toxic behavior. Nearly 66% of internet users have observed online harassment, and 41% claim personal experience, with 18% facing severe forms of online harassment. This toxic communication has a significant impact on the well-being of young individuals, affecting mental health and, in some cases, resulting in suicide. These communications exhibit complex linguistic and contextual characteristics, making recognition of such narratives challenging. In this paper, we provide a multimodal dataset of toxic social media interactions between confirmed high school students, called ALONE (AdoLescents ON twittEr), along with descriptive explanation. Each instance of interaction includes tweets, images, emoji and related metadata. Our observations show that individual tweets do not provide sufficient evidence for toxic behavior, and meaningful use of context in interactions can enable highlighting or exonerating tweets with purported toxicity.
Identifying physical-layer attacks for IoT security: An automatic modulation classification approach using multi-module fusion neural network

The Internet of Things (IoT) is increasingly flourishing with establishing ubiquitous connections in every aspect of our daily lives and industrial production. However, the active physical-layer threats such as spoofing and jamming largely degrades the communication performance and makes the IoT network messy. Automatic modulation classification (AMC), which identifies the modulation formats of the active attack, plays a vital role in physical-layer security of IoT. In this paper, a multi-module fusion neural network (MMFN) is proposed to identify the signal modulations using a novel feature named pixel-coloring constellation images (PCCs). Specifically, a pixel-coloring constellation projection algorithm (PCCP) is proposed to convert raw modulated signals into PCCs, which are the inputs of the following MMFN. The MMFN is proposed to extract discriminative features and make the final classification decisions. In MMFN, multi-module fusion strategy is adopted to possess a strong representation ability and the characterization module is designed to balance the tradeoff between the number of parameters and the dimensions of extracted representative features. Simulation results exhibit that the proposed MMFN scheme achieves 90% classification accuracy at 1 dB signal to noise ratio (SNR) when the symbol length is set as 2048, and it outperforms the recent deep learning based AMC methods, which demonstrates its superior classification performance.
Techniques, Applications, and Issues in Mining Large-Scale Text Databases

The discovery of knowledge from large-scale text data or semi-structured data is very difficult. In text mining, useful information is extracted out of such large text corpus which fulfills a user current information need. This process is being exploited by various organizations for quality improvement, business need, and understanding user behavior. The text available in unstructured and semi-structured form can come through sources such as medical, financial, market, scientific, and others documents. Text mining applies quantitative approach to analyze massive amount of textual data and tries to solve information overload problem. The main objective is to review text mining techniques, application areas, and existing issues.
Mining blocks in a row: A statistical study of fairness in Bitcoin mining

The Bitcoin system keeps its ledger consistent in a blockchain by solving cryptographic problems, in a method called "Proof-of-Work".The conventional wisdom asserts that the mining protocol is incentive-compatible.However, Eyal and Sirer in 2014 have discovered a mining attack strategy called selfish mining (SM), in which a miner (or a mining pool) publishes the blocks it mines selectively instead of immediately.SM strategy would have the impact of wasting resources of honest miners.Scholars proposed various extensions of the SM strategy and approaches to defense the SM attack.Whether selfish mining occurs in practice or not, has been subject of extensive debate.For the first time, in this paper we propose a method to identify selfish miners by detecting anomalies in the properties of consecutive blocks' statistics.Furthermore, we extend our method to detect the mining cartels, in which miners secretly get together and share timely information.Our results provide evidence that these strategic behaviors take place in practice.
Intentionality and design in the data sonification of social issues

Data sonification is a practice for conducting scientific analysis through the use of sound to represent data. It is now transitioning to a practice for communicating and reaching wider publics by expanding the range of languages and senses for understanding complexity in data-intensive societies. Communicating to wider publics, though, requires that authors intentionally shape sonification in ways that consider the goals and contexts in which publics relate. It requires a specific set of knowledge and skills that design as a discipline could provide. In this article, we interpret five recent sonification projects and locate them on a scale of intentionality in how authors communicate socially relevant issues to publics.
An evolutionary approach for generating software models: The case of Kromaia in Game Software Engineering

In the context of Model-Driven Engineering applied to video games, software models are high-level abstractions that represent source code implementations of varied content such as the stages of the game, vehicles, or enemy entities (e.g., final bosses). In this work, we present our Evolutionary Model Generation (EMoGen) approach to generate software models that are comparable in quality to the models created by human developers. Our approach is based on an evolution (mutation and crossover) and assessment cycle to generate the software models. We evaluated the software models generated by EMoGen in the Kromaia video game, which is a commercial video game released on Steam and PlayStation 4. Each model generated by EMoGen has more than 1000 model elements. The results, which compare the software models generated by our approach and those generated by the developers, show that our approach achieves results that are comparable to the ones created manually by the developers in the retail and digital versions of the video game case study. However, our approach only takes five hours of unattended time in comparison to ten months of work by the developers. We perform a statistical analysis, and we make an implementation of EMoGen readily available.
Reducing the number of non-naïve participants in Mechanical Turk samples

Using participants who have been previously exposed to experimental stimuli (referred to as non-naïveté) can reduce effect sizes. The workforce of Amazon's Mechanical Turk is particularly vulnerable to this problem and solutions are usually cost and time inefficient and of mixed effectiveness. In response to this problem and its currently underwhelming solutions, we tested various participant recruitment strategies designed to recruit participants naïve to frequently used experimental stimuli. We collected samples using maximum HIT restrictions (50 for Experiment 1 and 2, 500 for Experiment 2) and TurkPrime's (now CloudResearch) naiveté feature and compared them to samples recruited with standard restrictions (95% HIT approval rating). In these comparisons, we replicated past findings where using nonnaïve (vs. naïve) participants has been shown to reduce effect sizes and affect performance on a variety of tasks (e.g., the Cognitive Reflection Test, a Public Goods Game). We demonstrate that restricting by the maximum number of HITs heavily reduces the number of "experienced" research subjects in samples but necessitates some sacrifice in data quality and collection speed. We discuss the pragmatics of our method, its limitations, and future directions for solving the problem of non-naïveté on Mechanical Turk. For those looking to avoid this issue, we recommend setting a maximum HIT restriction of 50 when recruiting participants.
Practical wisdom in an age of computerisation

All professions are facing some formidable challenges including the ‘big three’ – climate change, pandemics and increasing computerisation. The potential threats include new uncertainties about unexpected extreme events, widespread predictions of job losses and the dangers of inappropriate unsafe automation. The opportunities include new types of jobs and new understanding of what civil engineers do, how they do it together with large scale improvements in effectiveness. Meeting these challenges requires us first to understand better and re-evaluate the overview of the services we provide. Secondly, we need to get a better handle on how we structure our problems to encompass both the ‘big picture’ and the detail and to use that structuring to develop workflow modelling tools that handle new sources of uncertain evidence such as AI and the IoT. In this paper I argue that we need to change the conversation about AI to one about AAI or Assistive Artificial Intelligence whilst recognising that the term AI will probably remain in general use. To do that we have to understand and communicate what is special about professional engineering expertise. We need a principle of ingenuity that ‘value, nurture and develop practical wisdom’ because it contains the crucial necessary qualities of professional engineering that cannot be computerised in the foreseeable future.
Efficient classification model of web news documents using machine learning algorithms for accurate information

Web applications are regarded as a popular platform to exchange information with users. These applications have to be able to process Big-Data quickly and to serve users in a timely manner with accurate information posted in news portals which can be a huge challenge to overcome. Huge computation power is needed to crawl the web and process big-data and the methods are needed to be developed to reduce space and time complexity of this process. Data mining is considered to be a solution to mitigate the aforementioned challenges by extracting specific information based on explicit features. This paper proposes an efficient model for web that extracts news information and sorts news documents into four different categories business, technology & science, health and entertainment. Four different machine learning classifiers Support Vector Machine (SVM), K-Nearest Neighbors (kNN), Decision Tree (DT) and Long Short-Term Memory (LSTM) are compared. These classifiers are implemented separately and are then compared using accuracy and receiver operating characteristic curves. The attained results show that the accuracy of kNN was the worst at 88.72% and SVM was the best at 95.04%.
NetTrans: Neural Cross-Network Transformation

Finding node associations across different networks is the cornerstone behind a wealth of high-impact data mining applications. Traditional approaches are often, explicitly or implicitly, built upon the linearity and/or consistency assumptions. On the other hand, the recent network embedding based methods promise a natural way to handle the non-linearity, yet they could suffer from the disparate node embedding space of different networks. In this paper, we address these limitations and tackle cross-network node associations from a new angle, i.e., cross-network transformation. We ask a generic question: Given two different networks, how can we transform one network to another? We propose an end-to-end model that learns a composition of nonlinear operations so that one network can be transformed to another in a hierarchical manner. The proposed model bears three distinctive advantages. First (composite transformation), it goes beyond the linearity/consistency assumptions and performs the cross-network transformation through a composition of nonlinear computations. Second (representation power), it can learn the transformation of both network structures and node attributes at different resolutions while identifying the cross-network node associations. Third (generality), it can be applied to various tasks, including network alignment, recommendation, cross-layer dependency inference. Extensive experiments on different tasks validate and verify the effectiveness of the proposed model.
A Framework for User Adaptation and Profiling for Social Robotics in Rehabilitation

Physical rehabilitation therapies for children present a challenge, and its success—the improvement of the patient’s condition—depends on many factors, such as the patient’s attitude and motivation, the correct execution of the exercises prescribed by the specialist or his progressive recovery during the therapy. With the aim to increase the benefits of these therapies, social humanoid robots with a friendly aspect represent a promising tool not only to boost the interaction with the pediatric patient, but also to assist physicians in their work. To achieve both goals, it is essential to monitor in detail the patient’s condition, trying to generate user profile models which enhance the feedback with both the system and the specialist. This paper describes how the project NAOTherapist—a robotic architecture for rehabilitation with social robots—has been upgraded in order to include a monitoring system able to generate user profile models through the interaction with the patient, performing user-adapted therapies. Furthermore, the system has been improved by integrating a machine learning algorithm which recognizes the pose adopted by the patient and by adding a clinical reports generation system based on the QUEST metric.
Public Data Integrity Verification Scheme for Secure Cloud Storage

Although cloud storage provides convenient data outsourcing services, an untrusted cloud server frequently threatens the integrity and security of the outsourced data. Therefore, it is extremely urgent to design security schemes allowing the users to check the integrity of data with acceptable computational and communication overheads. In this paper, we first propose a public data integrity verification scheme based on the algebraic signature and elliptic curve cryptography. This scheme not only allows the third party authority deputize for users to verify the outsourced data integrity, but also resists malicious attacks such as replay attacks, replacing attack and forgery attacks. Data privacy is guaranteed by symmetric encryption. Furthermore, we construct a novel data structure named divide and conquer hash list, which can efficiently perform data updating operations, such as deletion, insertion, and modification. Compared with the relevant schemes in the literature, security analysis and performance evaluations show that the proposed scheme gains some advantages in integrity verification and dynamic updating.
Graph Attention Networks over Edge Content-Based Channels

Edges play a crucial role in passing information on a graph, especially when they carry textual content reflecting semantics behind how nodes are linked and interacting with each other. In this paper, we propose a channel-aware attention mechanism enabled by edge text content when aggregating information from neighboring nodes; and we realize this mechanism in a graph autoencoder framework. Edge text content is encoded as low-dimensional mixtures of latent topics, which serve as semantic channels for topic-level information passing on edges. We embed nodes and topics in the same latent space to capture their mutual dependency when decoding the structural and textual information on graph. We evaluated the proposed model on Yelp user-item bipartite graph and StackOverflow user-user interaction graph. The proposed model outperformed a set of baselines on link prediction and content prediction tasks. Qualitative evaluations also demonstrated the descriptive power of the learnt node embeddings, showing its potential as an interpretable representation of graphs.
Nonconvex regularizer and latent pattern based robust regression for face recognition

Nuclear norm based matrix regression (NMR) approaches have yielded encouraging recognition results in the presence of image-level noise. However, NMR methods have two shortcomings. One is that test samples are directly used in the reconstruction process, and this may degrade recognition performance, especially when the samples are severely corrupted by noise. The other is that these methods exploit the nuclear norm to characterize low-rank structural information of residual images, which may lead to suboptimal solutions. To overcome these two deficiencies, we present a robust regression model that coalesces a nonconvex regularizer with a latent pattern (NRLPR). The latent pattern is essentially obtained by removing noise from a test sample, and therefore it can be closer to the reconstructed sample than the test sample. Furthermore, the nonconvex regularizer penalizes larger values less while penalizing smaller values more, accordingly, it can efficiently approximate the rank function. Additionally, we integrate the iteratively reweighted least squares method and the alternating direction method of multipliers to devise an efficient iterative algorithm (IR-ADMM) for NRLPR. Meanwhile, a nonconvex function is used for the design of the classifier. Finally, numerous experiments demonstrate the superiority of the proposed methods over state-of-the-art approaches for handling structural and mixed noise.
Exploring neural and peripheral physiological correlates of simulator sickness

Abstract This article investigates neural and physiological correlates of simulator sickness (SS) through a controlled experiment conducted within a fully immersive dome projection system. Our goal is to establish a reliable, objective, and in situ measurable predictive indicator of SS. SS is a problem common to all types of visual simulators consisting of motion sickness‐like symptoms that may be experienced while and after being exposed to a dynamic, immersive visualization. It leads to ethical concerns and impaired validity of simulator‐based research. Due to the popularity of virtual reality devices, the number of people exposed to this problem is increasing and, therefore, it is crucial to find reliable predictors of this condition before any symptoms appear. Despite its relevance and the several theories about its origins, SS cannot yet be quantitatively modeled and predicted. Our results indicate that, while neural correlates did not materialize, physiological measures may be a solid early indicator of oncoming SS.
Private Computation with Individual and Joint Privacy

This paper considers the problem of single-server Private Computation (PC) in the presence of Side Information (SI). In this problem, there is a server that stores K i.i.d. messages, and a user who has a subset of M uncoded messages or a coded linear combination of them as side information, where the identities of these messages are unknown to the server. The user wants to privately compute a linear combination of a subset of D other messages by downloading information from the server, where the identities of these messages must be kept private individually or jointly. For each setting, we define the capacity as the supremum of all achievable download rates.We characterize the capacity of both PC with coded and un-coded SI when individual privacy is required, for all K, M, D. Our results indicate that both settings have the same capacity. In addition, we establish a non-trivial lower bound on the capacity of PC with coded SI when joint privacy is required, for a range of parameters K, M, D. This lower bound is the same as the lower bound we previously established on the capacity of PC with uncoded SI when joint privacy is required.
xGAIL: Explainable Generative Adversarial Imitation Learning for Explainable Human Decision Analysis

To make daily decisions, human agents devise their own "strategies" governing their mobility dynamics (e.g., taxi drivers have preferred working regions and times, and urban commuters have preferred routes and transit modes). Recent research such as generative adversarial imitation learning (GAIL) demonstrates successes in learning human decision-making strategies from their behavior data using deep neural networks (DNNs), which can accurately mimic how humans behave in various scenarios, e.g., playing video games, etc. However, such DNN-based models are "black box" models in nature, making it hard to explain what knowledge the models have learned from human, and how the models make such decisions, which was not addressed in the literature of imitation learning. This paper addresses this research gap by proposing xGAIL, the first explainable generative adversarial imitation learning framework. The proposed xGAIL framework consists of two novel components, including Spatial Activation Maximization (SpatialAM) and Spatial Randomized Input Sampling Explanation (SpatialRISE), to extract both global and local knowledge from a well-trained GAIL model that explains how a human agent makes decisions. Especially, we take taxi drivers' passenger-seeking strategy as an example to validate the effectiveness of the proposed xGAIL framework. Our analysis on a large-scale real-world taxi trajectory data shows promising results from two aspects: i) global explainable knowledge of what nearby traffic condition impels a taxi driver to choose a particular direction to find the next passenger, and ii) local explainable knowledge of what key (sometimes hidden) factors a taxi driver considers when making a particular decision.