Reviewerly/exHarmony huggingface dataset:
Using Opinion Mining as an educational analytic: An integrated strategy for the analysis of students’ feedback

The analysis of students’ feedback written in natural language has been poorly considered in academic institutions, looking more frequently at students’ ratings as a base to evaluate courses and instructors. Statistical text analyses offer the possibility of exploring text collections from a quantitative viewpoint. Particularly interesting is Opinion Mining (OM), a family of techniques at the crossroads of Statistics, Linguistics and Computer Science. OM allows evaluating the sentiment of individual opinions, highlighting their semantic orientation. In an educational context, this approach allows processing students’ comments and creating powerful analytics. This paper aims at introducing readers to OM, presenting a strategy to compute the sentiment polarity of students’ comments. After explaining the rationale of the proposal and its mathematical formalisation, a toy example is presented to show how it works in practice. A discussion about theoretical and empirical implications offers some hints about its potentiality in a learning environment.
Comparing immersive Virtual Reality to mobile applications in foreign language learning in higher education: a quasi-experiment

Virtual Reality applications are predicted to create a paradigm shift in education, but there is little empirical evidence of their educational value. Virtual Reality affordances to support language learning have not yet been realized. This quasi-experimental study investigated the effect of a Virtual Reality application on foreign language learning and compared engagement, engrossment, and immersion between two versions of the application (Virtual Reality and mobile). Twenty experimental group undergraduate students used a head-mounted Virtual Reality display and twenty control group students used a mobile application for learning Italian as a foreign language. Data sources included: (a) a 10 open-ended questions pre-test and post-test measuring vocabulary skills, and (b) a validated questionnaire with 21 seven-point Likert scale items measuring engagement, engrossment, and immersion. Findings revealed a statistically significant difference in the experimental group students' vocabulary performance when students' pre-test and post-test scores were compared. Engagement, engrossment, and immersion received relatively high scores by Virtual Reality participants. The study did not find a statistically significant difference between the two conditions. The study provides preliminary empirical data indicating that Virtual Reality applications can be both effective and engaging for language learning, even though they were not found to be superior to mobile applications.
λ<i>DNN</i>: Achieving Predictable Distributed DNN Training With Serverless Architectures

Serverless computing is becoming a promising paradigm for Distributed Deep Neural Network (DDNN) training in the cloud, as it allows users to decompose complex model training into a number of <i>functions</i> without managing virtual machines or servers. Though provided with a simpler resource interface (i.e., function number and memory size), inadequate function resource provisioning (either under-provisioning or over-provisioning) easily leads to <i>unpredictable</i> DDNN training performance in serverless platforms. Our empirical studies on AWS Lambda indicate that, such <i>unpredictable performance</i> of serverless DDNN training is mainly caused by the resource bottleneck of Parameter Servers (PS) and small local batch size. In this article, we design and implement <i><inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math><alternatives><mml:math><mml:mi>&#x03BB;</mml:mi></mml:math><inline-graphic xlink:href="xu-ieq1-3054656.gif"/></alternatives></inline-formula>DNN</i>, a cost-efficient function resource provisioning framework to provide predictable performance for serverless DDNN training workloads, while saving the budget of provisioned functions. Leveraging the PS network bandwidth and function CPU utilization, we build a <i>lightweight</i> analytical DDNN training performance model to enable our design of <i><inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math><alternatives><mml:math><mml:mi>&#x03BB;</mml:mi></mml:math><inline-graphic xlink:href="xu-ieq2-3054656.gif"/></alternatives></inline-formula>DNN</i> resource provisioning strategy, so as to guarantee DDNN training performance with serverless functions. Extensive prototype experiments on AWS Lambda and complementary trace-driven simulations demonstrate that, <i><inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math><alternatives><mml:math><mml:mi>&#x03BB;</mml:mi></mml:math><inline-graphic xlink:href="xu-ieq3-3054656.gif"/></alternatives></inline-formula>DNN</i> can deliver predictable DDNN training performance and save the monetary cost of function resources by up to 66.7 percent, compared with the state-of-the-art resource provisioning strategies, yet with an acceptable runtime overhead.
A novel fractional nonautonomous chaotic circuit model and its application to image encryption

A novel fractional nonautonomous system is proposed by introducing fractional order meminductor-memristor based circuit. Four circuit models are presented by different arrangements for the elements and the dynamic behaviors for each circuit are explored. It is observed that the four systems exhibit chaotic and hyperchaotic behaviors which have been verified using Lyapunov exponents. Bifurcation diagrams and phase portraits are employed to examine the effects of parameters variation on the qualitative dynamics of each model. An image encryption scheme is presented based on pseudo chaos orbit generated by two interval extensions of chaotic circuit model. The security analysis is carried out to verify the robustness and efficiency of the encryption scheme against possible attacks.
Anomaly Detection With Particle Filtering for Online Video Surveillance

With growing security threats, many online and offine frameworks have been proposed for anomaly detection in video sequences.However, existing online anomaly detection techniques are either computationally very expensive or lack desirable accuracy.This research work proposes a novel particle filtering based framework for online anomaly detection which detects video frames with anomalous activities based upon the posterior probability of activities in a video sequence.The proposed method also detects anomalous regions in anomalous video frames.We propose novel prediction and measurement models to accurately detect anomalous video frames and anomalous regions in video frames.Novel prediction model for particle prediction and likelihood model for assigning weights to these particles are proposed.These models efficiently utilise variable sized cell structure which creates variable sized sub-regions of scenes in video frames.Furthermore, they efficiently extract and utilise information from the video frame in the form of size, motion and location features.The proposed framework is tested on UCSD and LIVE datasets and compared with the existing state-of-the-art algorithms in the literature.The proposed anomaly detection algorithm outperforms the state-of-the art algorithms in terms of reduced Equal Error Rate (EER) with comparatively lesser processing time.
Emoji-powered Sentiment and Emotion Detection from Software Developers’ Communication Data

Sentiment and emotion detection from textual communication records of developers have various application scenarios in software engineering (SE). However, commonly used off-the-shelf sentiment/emotion detection tools cannot obtain reliable results in SE tasks and misunderstanding of technical knowledge is demonstrated to be the main reason. Then researchers start to create labeled SE-related datasets manually and customize SE-specific methods. However, the scarce labeled data can cover only very limited lexicon and expressions. In this article, we employ emojis as an instrument to address this problem. Different from manual labels that are provided by annotators, emojis are self-reported labels provided by the authors themselves to intentionally convey affective states and thus are suitable indications of sentiment and emotion in texts. Since emojis have been widely adopted in online communication, a large amount of emoji-labeled texts can be easily accessed to help tackle the scarcity of the manually labeled data. Specifically, we leverage Tweets and GitHub posts containing emojis to learn representations of SE-related texts through emoji prediction. By predicting emojis containing in each text, texts that tend to surround the same emoji are represented with similar vectors, which transfers the sentiment knowledge contained in emoji usage to the representations of texts. Then we leverage the sentiment-aware representations as well as manually labeled data to learn the final sentiment/emotion classifier via transfer learning. Compared to existing approaches, our approach can achieve significant improvement on representative benchmark datasets, with an average increase of 0.036 and 0.049 in macro-F1 in sentiment and emotion detection, respectively. Further investigations reveal that the large-scale Tweets make a key contribution to the power of our approach. This finding informs future research not to unilaterally pursue the domain-specific resource but try to transform knowledge from the open domain through ubiquitous signals such as emojis. Finally, we present the open challenges of sentiment and emotion detection in SE through a qualitative analysis of texts misclassified by our approach.
A Survey on State-of-the-Art Denoising Techniques for Brain Magnetic Resonance Images

The accuracy of the magnetic resonance (MR) image diagnosis depends on the quality of the image, which degrades mainly due to noise and artifacts. The noise is introduced because of erroneous imaging environment or distortion in the transmission system. Therefore, denoising methods play an important role in enhancing the image quality. However, a tradeoff between denoising and preserving the structural details is required. Most of the existing surveys are conducted on a specific MR image modality or on limited denoising schemes. In this context, a comprehensive review on different MR image denoising techniques is inevitable. This survey suggests a new direction in categorizing the MR image denoising techniques. The categorization of the different image models used in medical image processing serves as the basis of our classification. This study includes recent improvements on deep learning-based denoising methods alongwith important traditional MR image denoising methods. The major challenges and their scope of improvement are also discussed. Further, many more evaluation indices are considered for a fair comparison. An elaborate discussion on selecting appropriate method and evaluation metric as per the kind of data is presented. This study may encourage researchers for further work in this domain.
Performance evaluation of Hyperledger Fabric-enabled framework for pervasive peer-to-peer energy trading in smart Cyber–Physical Systems

The in-depth collaboration of Cyber–Physical Systems (CPSs) and smart grids constitute the novel paradigm of distributed energy trading, in which computation and process control are managed in an adaptive Peer-to-Peer (P2P) manner. To further strengthen this collaboration, Hyperledger Fabric (HF) can be prominently considered as a mean to implement next-generation secure and intelligent communication. However, implementing real-world applications on this platform may concern performance issues. For the constructive exploration of these issues, initially, we design a novel P2P energy trading framework for improving resource utilization and consequently addressing the impending electricity crisis challenge. Thenceforward, we evaluate the results based on the different system operational parameters for establishing a proof-of-concept. For determining performance bottlenecks and best-configuration, these results are investigated independently by using the Nectar Research Cloud, thereby sustaining scalability. The proposed evaluation approach will largely contribute to determining the system operational-level parameters of enterprise applications that will utilize the HF platform as their communication tool-support. In addition, a benchmark is presented based on the Hyperledger Caliper tool to facilitate application designers and developers in the form of selecting an appropriate implementation model across the two latest stable HF model versions. The illustrative CPS-enabled energy trading scenario corroborates the feasibility of the proposed framework to foster the development of HF-assisted smart P2P energy trading mechanisms.
Forecasting Action Through Contact Representations From First Person Video

Human actions involving hand manipulations are structured according to the making and breaking of hand-object contact, and human visual understanding of action is reliant on anticipation of contact as is demonstrated by pioneering work in cognitive science. Taking inspiration from this, we introduce representations and models centered on contact, which we then use in action prediction and anticipation. We annotate a subset of the EPIC Kitchens dataset to include time-to-contact between hands and objects, as well as segmentations of hands and objects. Using these annotations we train the Anticipation Module, a module producing Contact Anticipation Maps and Next Active Object Segmentations - novel low-level representations providing temporal and spatial characteristics of anticipated near future action. On top of the Anticipation Module we apply Egocentric Object Manipulation Graphs (Ego-OMG), a framework for action anticipation and prediction. Ego-OMG models longer term temporal semantic relations through the use of a graph modeling transitions between contact delineated action states. Use of the Anticipation Module within Ego-OMG produces state-of-the-art results, achieving 1st and 2 place on the unseen and seen test sets, respectively, of the EPIC Kitchens Action Anticipation Challenge, and achieving state-of-the-art results on the tasks of action anticipation and action prediction over EPIC Kitchens. We perform ablation studies over characteristics of the Anticipation Module to evaluate their utility.
Towards Blockchain-Based Federated Machine Learning: Smart Contract for Model Inference

Federated learning is a branch of machine learning where a shared model is created in a decentralized and privacy-preserving fashion, but existing approaches using blockchain are limited by tailored models. We consider the possibility to extend a set of supported models by introducing the oracle service and exploring the usability of blockchain-based architecture. The investigated architecture combines an oracle service with a Hyperledger Fabric chaincode. We compared two logistic regression implementations in Go language—a pure chaincode and an oracle service—at various data (2–32 k instances) and network (3–13 peers) sizes. Experiments were run to assess the performance of blockchain-based model inference using 2D synthetic and EEG eye state datasets for a supervised machine learning detection task. The benchmarking results showed that the impact on performance is acceptable with the median overhead of oracle service reaching 2–4%, depending on the dimensionality of the dataset. The overhead tends to diminish at large dataset sizes with the runtime depending on the network size linearly, where additional peers increased the runtime by 6.3 and 6.6 s for 2D and EEG datasets, respectively. Demonstrated negligible difference between implementations justifies the flexible choice of model in the blockchain-based federated learning and other machine learning applications.
Approach for Importance–Performance Analysis of Product Attributes From Online Reviews

Abstract The importance–performance analysis (IPA) is a widely used technique to guide strategic planning for the improvement of customer satisfaction. Compared with surveys, numerous online reviews can be easily collected at a lower cost. Online reviews provide a promising source for the IPA. This paper proposes an approach for conducting the IPA from online reviews for product design. Product attributes from online reviews are first identified by latent Dirichlet allocation. The performance of the identified attributes is subsequently estimated by the aspect-based sentiment analysis of IBM Watson. Finally, the importance of the identified attributes is estimated by evaluating the effect of sentiments of each product attribute on the overall rating using an explainable deep neural network. A Shapley additive explanation-based method is proposed to estimate the importance values of product attributes with a low variance by combining the effect of the input features from multiple optimal neural networks with a high performance. A case study of smartphones is presented to demonstrate the proposed approach. The performance and importance estimates of the proposed approach are compared with those of previous sentiment analysis and neural network-based method, and the results exhibit that the former can perform IPA more reliably. The proposed approach uses minimal manual operation and can support companies to take decisions rapidly and effectively, compared with survey-based methods.
FAR-ASS: Fact-aware reinforced abstractive sentence summarization

Automatic summarization systems provide an effective solution to today's unprecedented growth of textual data. For real-world tasks, such as data mining and information retrieval, the factual correctness of generated summary is critical. However, existing models usually focus on improving the informativeness rather than optimizing factual correctness. In this work, we present a Fact-Aware Reinforced Abstractive Sentence Summarization framework to improve the factual correctness of neural abstractive summarization models, denoted as FAR-ASS. Specifically, we develop an automatic fact extraction scheme leveraging OpenIE (Open Information Extraction) and dependency parser tools to extract structured fact tuples. Then, to quantitatively evaluate the factual correctness, we define a factual correctness score function that considers the factual accuracy and factual redundancy. We further propose to adopt reinforcement learning to improve readability and factual correctness by jointly optimizing a mixed-objective learning function. We use the English Gigaword and DUC 2004 datasets to evaluate our model. Experimental results show that compared with competitive models, our model significantly improves the factual correctness and readability of generated summaries, and also reduces duplicates while improving the informativeness.
Bringing transparency and trustworthiness to loot boxes with blockchain and smart contracts

The ever-increasing reliance on loot boxes by the video game industry has attracted scrutiny from consumer groups and regulators. For example, this practice of selling random assortments of virtual items for a price has been criticized for its lack of transparency since, before purchasing a loot box, players do not necessarily know the possible items they can win and the associated probabilities. Even in the rare cases when the list of items and probabilities are available, there are no guarantees that a game is actually using that information when randomly drawing an item, which naturally results in a trust problem. We draw from decision theory to explain the transparency and trust issues faced by loot boxes, and from a recently proposed decision model by Pedersen et al. (2019) to motivate the use of blockchain technology in this context. Following the design science research framework (Peffers et al., 2007), we then explain how loot boxes can be coded as smart contracts running on a blockchain network, and why this solution effectively tackles the transparency and trust problems we mentioned above. We illustrate the use of the proposed smart contract by developing a decentralized application (DApp) that mimics the process of purchasing and opening a loot box. We carefully analyze our solution by considering its computational complexity, accuracy, security, and cost aspects. We further discuss topics related to governance and deployment so as to help the video game industry with a potential real-life implementation of our solution.
User trustworthiness in online social networks: A systematic review

The growing popularity of social networks and their easy acceptance of new users have the unintended consequence of fostering an environment where anonymous users can act in malicious ways. Although these platforms have many incentives to prevent such occurrences, they have not been able to cope with the sheer volume of information that must be processed. Moreover, the tendency of attackers to rapidly change strategies in response to defensive measures also poses a challenge. Hence, research on issues related to user trustworthiness on social networks is gaining traction, with many interesting studies conducted in recent years. In this work, we aim to review the present state of this field and present an analysis of the studies published between 2012 and 2020 that attempt to address this problem using various methodologies. Some of the solutions discussed in the literature can be described as bot identification protocols, while others focus on anti-spam protection, recognition of fake news, or rating the truthfulness of user-generated content. Many of these solutions offer tangible benefits in various respects, however none of them are able to provide comprehensive all-around protection against all possible types of attacks. Monitoring this scientific field is thus a key task, and this review will hopefully lead to a better understanding of the concept of online user trustworthiness by highlighting recent works that deal with this issue.
Optimism in the Face of Adversity: Understanding and Improving Deep Learning Through Adversarial Robustness

Driven by massive amounts of data and important advances in computational resources, new deep learning systems have achieved outstanding results in a large spectrum of applications.Nevertheless, our current theoretical understanding on the mathematical foundations of deep learning lags far behind its empirical success.Towards solving the vulnerability of neural networks, however, the field of adversarial robustness has recently become one of the main sources of explanations of our deep models.In this article, we provide an in-depth review of the field of adversarial robustness in deep learning, and give a selfcontained introduction to its main notions.But, in contrast to the mainstream pessimistic perspective of adversarial robustness, we focus on the main positive aspects that it entails.We highlight the intuitive connection between adversarial examples and the geometry of deep neural networks, and eventually explore how the geometric study of adversarial examples can serve as a powerful tool to understand deep learning.Furthermore, we demonstrate the broad applicability of adversarial robustness, providing an overview of the main emerging applications of adversarial robustness beyond security.The goal of this article is to provide readers with a set of new perspectives to understand deep learning, and to supply them with intuitive tools and insights on how to use adversarial robustness to improve it.