Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Using Deep Learning Approach

Deep learning has made a great breakthrough in the field of speech and image recognition. Mature deep learning neural network has completely changed the field of nat ural language processing (NLP). Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the Internet and other media, sentiment analysis has become one of the most active research fields in natural language processing. This paper introduces three deep learning networks applied in IMDB movie reviews sent iment analysis. Dataset was divided to 50% positive reviews and 50% negative reviews. Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) neural networ ks are two main types, which are widely used in NLP tasks, while Convolutional Neural Networks (CNN) is often used in image recognition. The results have shown that, CNN n etwork model can achieve good classification effect when applied to sentiment analysis o f movie reviews. CNN have reported the accuracy of 88.22%, while RNN and LSTM hav e reported accuracy of 68.64% and 85.32% respectively.
A novel method for VR sickness reduction based on dynamic field of view processing

Abstract In this paper, we proposed a novel method for virtual reality (VR) sickness reduction based on dynamic field of view (FOV) processing. Dynamic FOV processing is performed based on the estimated VR sickness for each video frame. The level of sickness is estimated using VR sickness model, which is obtained by defining the relationship between the motion information and the measured VR sickness. For motion information analysis, subregion-based correspondence points tracking is used to efficiently remove outliers and prevent prediction error propagation. Amount of head dispersion is used as a quantitative VR sickness measure, which can be calculated from inertial measurement unit sensor in VR devices. The optimal FOV range was determined by experimentally validating a minimum FOV that can effectively reduce VR sickness with almost negligible loss in presence. The simulation results show a significant decrease of 37% compared to full FOV viewing, when FOV is dynamically varied between full and 60°.
Exploiting Global Camera Network Constraints for Unsupervised Video Person Re-Identification

Many unsupervised approaches have been proposed recently for the video-based re-identification problem since annotations of samples across cameras are time-consuming.However, higher-order relationships across the entire camera network are ignored by these methods, leading to contradictory outputs when matching results from different camera pairs are combined.In this paper, we address the problem of unsupervised video-based re-identification by proposing a consistent cross-view matching (CCM) framework, in which global camera network constraints are exploited to guarantee the matched pairs are with consistency.Specifically, we first propose to utilize the first neighbor of each sample to discover relations among samples and find the groups in each camera.Additionally, a cross-view matching strategy followed by global camera network constraints is proposed to explore the matching relationships across the entire camera network.Finally, we learn metric models for camera pairs progressively by alternatively mining consistent cross-view matching pairs and updating metric models using these obtained matches.Rigorous experiments on two widelyused benchmarks for video re-identification demonstrate the superiority of the proposed method over current state-of-theart unsupervised methods; for example, on the MARS dataset, our method achieves an improvement of 4.2% over unsupervised methods, and even 2.5% over one-shot supervision-based methods for rank-1 accuracy.
Integrating Knowledge Graphs for Analysing Academia and Industry Dynamics

Academia and industry are constantly engaged in a joint effort for producing scientific knowledge that will shape the society of the future. Analysing the knowledge flow between them and understanding how they influence each other is a critical task for researchers, governments, funding bodies, investors, and companies. However, current corpora are unfit to support large-scale analysis of the knowledge flow between academia and industry since they lack of a good characterization of research topics and industrial sectors. In this short paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which characterizes 14M papers and 8M patents according to the research topics drawn from the Computer Science Ontology. 4M papers and 5M patents are also classified according to the type of the author’s affiliations (academy, industry, or collaborative) and 66 industrial sectors (e.g., automotive, financial, energy, electronics) obtained from DBpedia. AIDA was generated by an automatic pipeline that integrates several knowledge graphs and bibliographic corpora, including Microsoft Academic Graph, Dimensions, English DBpedia, the Computer Science Ontology, and the Global Research Identifier Database.
Exploiting attack–defense trees to find an optimal set of countermeasures

Selecting the most pertinent countermeasures to secure a system is one of the ultimate goals of risk assessment. In this context, it is important to rely on modeling methods that the security experts are already familiar with, so that the solution can be smoothly adopted within industry.We propose a full-fledged framework, relying on attack-defense trees and integer linear programming, to find an optimal set of countermeasures. We use attack-defense trees formalized with directed acyclic graphs. This enables us to conveniently reason about attacker's actions that can contribute to several distinct attacks, and countermeasures that can block different ways of attacking. We provide a constructive way of extracting all reasonable behaviors of the two actors from such models. We then exploit this extracted information to formulate a generic solution, based on integer linear programming, to address a wide class of optimization problems. We show how to instantiate it for specific security-relevant optimization criteria. We cover deterministic and probabilistic cases. The framework has been implemented in a prototype tool, and validated in a real-life case study.
Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures

Perception plays a pivotal role in autonomous driving systems, which utilizes onboard sensors like cameras and LiDARs (Light Detection and Ranging) to assess surroundings. Recent studies have demonstrated that LiDAR-based perception is vulnerable to spoofing attacks, in which adversaries spoof a fake vehicle in front of a victim self-driving car by strategically transmitting laser signals to the victim's LiDAR sensor. However, existing attacks suffer from effectiveness and generality limitations. In this work, we perform the first study to explore the general vulnerability of current LiDAR-based perception architectures and discover that the ignored occlusion patterns in LiDAR point clouds make self-driving cars vulnerable to spoofing attacks. We construct the first black-box spoofing attack based on our identified vulnerability, which universally achieves around 80% mean success rates on all target models. We perform the first defense study, proposing CARLO to mitigate LiDAR spoofing attacks. CARLO detects spoofed data by treating ignored occlusion patterns as invariant physical features, which reduces the mean attack success rate to 5.5%. Meanwhile, we take the first step towards exploring a general architecture for robust LiDAR-based perception, and propose SVF that embeds the neglected physical features into end-to-end learning. SVF further reduces the mean attack success rate to around 2.3%.
Mi-Cho-Coq, a Framework for Certifying Tezos Smart Contracts

Tezos is a blockchain launched in June 2018. It is written in OCaml and supports smart contracts. Its smart contract language is called Michelson and it has been designed with formal verification in mind. In this article, we present Mi-Cho-Coq, a Coq framework for verifying the functional correctness of Michelson smart contracts. As a case study, we detail the certification of a Multisig contract with the Mi-Cho-Coq framework.
Microseismic records classification using capsule network with limited training samples in underground mining

Abstract The identification of suspicious microseismic events is the first crucial step in microseismic data processing. Existing automatic classification methods are based on the training of a large data set, which is challenging to apply in mines without a long-term manual data processing. In this paper, we present a method to automatically classify microseismic records with limited samples in underground mines based on capsule networks (CapsNet). We divide each microseismic record into 33 frames, then extract 21 commonly used features in time and frequency from each frame. Consequently, a 21 × 33 feature matrix is utilized as the input of CapsNet. On this basis, we use different sizes of training sets to train the classification models separately. The trained model is tested using the same test set containing 3,200 microseismic records and compared to convolutional neural networks (CNN) and traditional machine learning methods. Results show that the accuracy of our proposed method is 99.2% with limited training samples. It is superior to CNN and traditional machine learning methods in terms of Accuracy, Precision, Recall, F1-Measure, and reliability.
AdvMind: Inferring Adversary Intent of Black-Box Attacks

Deep neural networks (DNNs) are inherently susceptible to adversarial attacks even under black-box settings, in which the adversary only has query access to the target models. In practice, while it may be possible to effectively detect such attacks (e.g., observing massive similar but non-identical queries), it is often challenging to exactly infer the adversary intent (e.g., the target class of the adversarial example the adversary attempts to craft) especially during early stages of the attacks, which is crucial for performing effective deterrence and remediation of the threats in many scenarios.
Leveraging Wearable Sensors for Human Daily Activity Recognition with Stacked Denoising Autoencoders

Activity recognition has received considerable attention in many research fields, such as industrial and healthcare fields. However, many researches about activity recognition have focused on static activities and dynamic activities in current literature, while, the transitional activities, such as stand-to-sit and sit-to-stand, are more difficult to recognize than both of them. Consider that it may be important in real applications. Thus, a novel framework is proposed in this paper to recognize static activities, dynamic activities, and transitional activities by utilizing stacked denoising autoencoders (SDAE), which is able to extract features automatically as a deep learning model rather than utilize manual features extracted by conventional machine learning methods. Moreover, the resampling technique (random oversampling) is used to improve problem of unbalanced samples due to relatively short duration characteristic of transitional activity. The experiment protocol is designed to collect twelve daily activities (three types) by using wearable sensors from 10 adults in smart lab of Ulster University, the experiment results show the significant performance on transitional activity recognition and achieve the overall accuracy of 94.88% on three types of activities. The results obtained by comparing with other methods and performances on other three public datasets verify the feasibility and priority of our framework. This paper also explores the effect of multiple sensors (accelerometer and gyroscope) to determine the optimal combination for activity recognition.
IMPACT OF COVID-19 PANDEMIC ON ACCEPTANCE OF E-LEARNING SYSTEM IN JORDAN: A CASE OF TRANSFORMING THE TRADITIONAL EDUCATION SYSTEMS

Purpose of the study: This paper aimed at investigating factors influencing students’ intention to use e-learning within the context of Jordan. The proposed model integrated subjective norms with the extended Technology Acceptance Model (TAM).&#x0D; Methodology: The data collected 587 students from Jadara University. Data were analyzed using Partial Least Squares - Structural Equation Modelling (PLS-SEM).&#x0D; Main Findings: The result has confirmed the direct effect of all variables. Next, the result has also shown that the mediation effect of perceived usefulness and perceived ease of use between subjective norm and the behavioral intention to use the E-learning system was partially supported.&#x0D; Applications of this study: The results of the current work may contribute to the improvement of the existing literature in the e-learning acceptance fields, and can be applied as an essential input for the development of the recent e-learning training programs.&#x0D; Novelty/Originality of this study: The present work is intended to conclude a series of concrete research data as well as to contribute a new angle to the existing literature on students’ perception of the e-learning system.&#x0D;
Digital quantum simulation of non-equilibrium quantum many-body systems

Digital quantum simulation uses the capabilities of quantum computers to determine the dynamics of quantum systems, which are beyond the computability of modern classical computers. A notoriously challenging task in this field is the description of non-equilibrium dynamics in quantum many-body systems. Here, we use the IBM quantum computers to simulate the non-equilibrium dynamics of few spin and fermionic systems. Our results reveal that with a combination of error mitigation, noise extrapolation and optimized initial state preparation, one can tackle the most important drawbacks of modern quantum devices. The systems we simulate demonstrate the potential for large-scale quantum simulations of light–matter interactions in the near future.
Deep neural system for supporting tumor recognition of mammograms using modified GAN

This paper presents the autoencoder-generative adversarial network (AGAN) in the analysis of mammograms. The AGAN architecture is used to augment the data by generating additional representations of the mammogram images, enhancing this way the information of the analyzed problem. The images generated by this this deep network are appended to the original set of mammograms and fed to the input of convolutional neural network, which plays the role of the final classifier. The proposed system was used to recognize the mammograms belonging to two classes: normal and abnormal. The investigations were performed using a large database consisting of 11,218 regions of interest of mammographic images from the DDSM base. The results demonstrate the advantage of this proposed deep learning system over other known approaches to mammogram recognition. Our average accuracy in detecting abnormal cases (malignant plus benign versus healthy) was 89.71%, sensitivity 93.54%, specificity 80.58% and AUC = 0.9410. These results are among the best for this large database.
Latent Approach in Entertainment Industry Using Machine Learning

Nowadays, a huge amount of data is available everywhere.Therefore, we need to prioritize analysing this dataset which would help us in gaining some meaningful information for the development of an algorithm based on the analysis.These feet can be obtained by using Machine Learning, Data Mining, and Data Analysis.Machine Learning which is a part of Artificial Intelligence is used for designing algorithms based on trends of data, patterns and the relation found between them.ML has been used in various fields such as Marketing, gameplay, intrusion detection, bioinformatics, information retrieval, healthcare, entertainment and also on COVID -19 applications and so on.This paper presents an overview of the contribution of ML in Entertainment industry.
How to Learn Item Representation for Cold-Start Multimedia Recommendation?

The ability of recommending cold items (that have no behavior history) is a core strength of multimedia recommendation compared with behavior-only collaborative filtering. To learn effective item representation, a key challenge lies in the discrepancy between training and testing, since the cold items only exist in the testing data. This means that the signal used to represent an item varies during training and testing --- in the training stage, we can represent an item with both collaborative embedding and content embedding; whereas in the testing stage, we represent a cold item with content embedding only. Nevertheless, existing learning frameworks omit this critical discrepancy, resulting in suboptimal item representation for multimedia recommendation.
The Attacker’s Perspective on Automatic Speaker Verification: An Overview

Security of automatic speaker verification (ASV) systems is compromised by various spoofing attacks. While many types of non-proactive attacks (and their defenses) have been studied in the past, attacker's perspective on ASV, represents a far less explored direction. It can potentially help to identify the weakest parts of ASV systems and be used to develop attacker-aware systems. We present an overview on this emerging research area by focusing on potential threats of adversarial attacks on ASV, spoofing countermeasures, or both. We conclude the study with discussion on selected attacks and leveraging from such knowledge to improve defense mechanisms against adversarial attacks.
Blended learning with Edmodo: The effectiveness of statistical learning during the COVID-19 pandemic

&lt;span&gt;The condition of all countries in the world is devastated by the presence of a corona virus epidemic known as Corona Virus Diseases-19 (COVID-19). The efforts of the Indonesian government to break the chain of distribution of COVID-19 in the midst of society is to impose a policy of keeping distance, avoiding publicity, frequent hand washing and wearing masks. This also has an impact on the field of education where learning from home is also known as work from home (WFH). Learning is done with an online learning system that at least applies distance, physical social protection. Blended learning is one that is done using the Edmodo application, which can be done online at home. The purpose of this study is to find out how effective the use of the blended-learning learning model with Edmodo applications is to students of Informatics Engineering Education during the COVID-19 pandemic. This research is a quasi-experimental design with a pre-test and post-test control group. The results of this study indicate that the use of Edmodo's blended-learning learning model in Information Technology Education students during the COVID-19 pandemic period is effective and can be used as a virtual learning solution.&lt;/span&gt;
DeepHandsVR: Hand Interface Using Deep Learning in Immersive Virtual Reality

This paper proposes a hand interface through a novel deep learning that provides easy and realistic interactions with hands in immersive virtual reality. The proposed interface is designed to provide a real-to-virtual direct hand interface using a controller to map a real hand gesture to a virtual hand in an easy and simple structure. In addition, a gesture-to-action interface that expresses the process of gesture to action in real-time without the necessity of a graphical user interface (GUI) used in existing interactive applications is proposed. This interface uses the method of applying image classification training process of capturing a 3D virtual hand gesture model as a 2D image using a deep learning model, convolutional neural network (CNN). The key objective of this process is to provide users with intuitive and realistic interactions that feature convenient operation in immersive virtual reality. To achieve this, an application that can compare and analyze the proposed interface and the existing GUI was developed. Next, a survey experiment was conducted to statistically analyze and evaluate the positive effects on the sense of presence through user satisfaction with the interface experience.
Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification

Recently adversarial attacks on automatic speaker verification (ASV) systems attracted widespread attention as they pose severe threats to ASV systems. However, methods to defend against such attacks are limited. Existing approaches mainly focus on retraining ASV systems with adversarial data augmentation. Also, countermeasure robustness against different attack settings are insufficiently investigated. Orthogonal to prior approaches, this work proposes to defend ASV systems against adversarial attacks with a separate detection network, rather than augmenting adversarial data into ASV training. A VGG-like binary classification detector is introduced and demonstrated to be effective on detecting adversarial samples. To investigate detector robustness in a realistic defense scenario where unseen attack settings may exist, we analyze various kinds of unseen attack settings' impact and observe that the detector is robust (6.27\% EER_{det} degradation in the worst case) against unseen substitute ASV systems, but it has weak robustness (50.37\% EER_{det} degradation in the worst case) against unseen perturbation methods. The weak robustness against unseen perturbation methods shows a direction for developing stronger countermeasures.
Statistical Predictions in String Theory and Deep Generative Models

Generative models in deep learning allow for sampling probability distributions that approximate data distributions. We propose using generative models for making approximate statistical predictions in the string theory landscape. For vacua admitting a Lagrangian description this can be thought of as learning random tensor approximations of couplings. As a concrete proof-of-principle, we demonstrate in a large ensemble of Calabi-Yau manifolds that Kahler metrics evaluated at points in Kahler moduli space are well-approximated by ensembles of matrices produced by a deep convolutional Wasserstein GAN. Accurate approximations of the Kahler metric eigenspectra are achieved with far fewer than $h^{11}$ Gaussian draws. Accurate extrapolation to values of $h^{11}$ outside the training set are achieved via a conditional GAN. Together, these results implicitly suggest the existence of strong correlations in the data, as might be expected if Reid's fantasy is correct.
Parity Detection of Propagating Microwave Fields

The parity of the number of elementary excitations present in a quantum system provides important insights into its physical properties. Parity measurements are used, for example, to tomographically reconstruct quantum states or to determine if the decay of an excitation has occurred, information that can be used for quantum error correction in computation or communication protocols. Here, we demonstrate a versatile parity detector for propagating microwaves, which distinguishes between radiation fields containing an even or odd number n of photons, both in a single-shot measurement and without perturbing the parity of the detected field. We showcase applications of the detector for direct Wigner tomography of propagating microwaves and heralded generation of Schrödinger cat states. This parity detection scheme is applicable over a broad frequency range and may prove useful, for example, for heralded or fault-tolerant quantum communication protocols.1 MoreReceived 26 September 2019Accepted 24 January 2020DOI:https://doi.org/10.1103/PhysRevX.10.011046Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum channelsQuantum communicationQuantum information processingQuantum information with solid state qubitsQuantum networksQuantum Information