Reviewerly/exHarmony huggingface dataset:
Opinion Summarisation using Bi-Directional Long-Short Term Memory

This generation, people depend on multiple resources to stay up-to-date whether be it social media or a new product available on the market. To be able to develop a model that can effectively summarise a long paragraph or a long comment posted on a website can be useful to grasp the primary information provided in that article or review hence reducing the time input to make a decision or to come to an understanding of the data. In this paper we make use of a deep learning model that helps us to get the summaries from the lengthy reviews provided by the customers. Summarisation is the method of getting a summary from a sequence of texts or files that helps us understand the basic content of the information within them. Opinion summarisation is the method of getting opinions from a set of sentences. It can be done through extractive and abstractive methods. Here we use abstractive summarisation that learns from the input dataset and comes through with thee summary with the best possible result. Our paper describes an abstractive method to get the summaries from a dataset using Bi-directional Long-Short Term Memory. We make use of an Attention layer to increase the performance of the model and help to improve the efficiency of the model. The model effectiveness is evaluated using ROUGE and BLUE scores.
A Survey on Cache Timing Channel Attacks for Multicore Processors

Cache timing channel attacks has attained a lot of attention in the last decade. These attacks exploits the timing channel created by the significant time gap between cache and main memory accesses. It has been successfully implemented to leak the secret key of various cryptography algorithms. The latest advancements in cache attacks also exploit other micro-architectural components such as hardware prefetchers, branch predictor, and replacement engine, in addition to the cache memory. Detection of these attacks is a difficult task as the attacker process running in the processor must be detected before significant portion of the attack is complete. The major challenge for mitigation and defense mechanisms against these attacks is maintaining the system performance while disabling or avoiding these attacks. The overhead caused by detection, mitigation and defense mechanism must not be significant to system’s performance. This paper discusses the research carried out in three aspects of cache security: cache timing channel attacks, detection techniques of these attacks, and defense mechanisms in details.
Blending Android Programming Learning Assistance System into Online Android Programming Course

Effective learning method is essential for the attainment of the learning objectives. In pandemic situation when courses are conducted online, creativity and innovation in designing learning methods become more imperative. Learning Android programming via online course is much harder for students and teachers. Without a real face-to-face meeting, their interactions become limited and this condition produces several prominent issues. This paper presents the implementation of Android Programming Learning Assistance System (APLAS) as a self-learning tool to address the issues in online Android programming course. APLAS is blended into the course that used two tools, Learning Management System (LMS) and online meeting tool. Findings reveal that the blending of APLAS into an online course of Android programming is effective to improve students' achievement and makes it easier for them to learn Android programming. Students also show high enthusiasm and attractiveness to the course. Preference for being collectivist learners was also evident.
Neighbours and Kinsmen: Hateful Users Detection with Graph Neural Network

With a massive rise of user-generated web content on social media, the amount of hate speech is also increasing. Countering online hate speech is a critical yet challenging task. Previous research has primarily focused on hateful content detection. In this study, we shift the attention from hateful content detection towards hateful users detection. Note, hateful users detection can benefit from users' tweets, profiles, social relationships, but the real benefit is that it can be aided by Graph Neural Networks (GNN). Typical Graph Neural Networks, such as GraphSAGE, only considers local neighbourhood information and samples the neighbourhood uniformly, thus they lack the ability to capture long-range relationships or to differentiate neighbours of a node. In this paper, we present HateGNN – a GNN-based method to address these two limitations. Our proposed method relies on the notion of latent neighbourhood, as well as systematic sampling of the neighbourhood nodes. The experimental results demonstrate that HateGNN outperforms state-of-the-art baselines in the task of detecting hateful users. We also provide a detailed analysis to demonstrate the efficacy of the proposed method.
Comparative Evaluation of Digital Writing and Art in Real and Immersive Virtual Environments

Virtual reality (VR) experiences currently tend to focus on full body interactions. However, fine motor control in actions such as writing and drawing are seldom studied. Challenges include the inability to perceive fine details due to the low resolution of head mounted displays, the difficulty in simulating fine motor actions in virtual environments, tracking instabilities, latency issues, etc. State of the art VR has managed to address a host of such concerns, supporting a variety of input mechanisms for activities such as writing, sketching, immersive modeling, etc. With VR increasingly being applied in education and medical contexts where writing and note taking is a crucial, it is important to study how well humans can perform these tasks in VR. In a between-subjects empirical evaluation, we studied participants' fine motor coordination with several digital input based writing and artistic tasks performed both in virtual and real world settings, further examining the effects of providing a virtual self avatar on task performance. We integrated multiple tracking systems and applied inverse kinematics to animate the virtual body and simulate hand motions. We went on to compare how different the outputs of these digital input metaphors are to a real world pen and paper approach in an effort to ascertain where we currently stand in being able to support writing and note taking in virtual world contexts. Overall, it seems to be the case that while writing and artistic activities can be successfully supported in VR applications using specialized input devices, the accuracy with which users perform such tasks is significantly higher in the real world, highlighting the need for developments that support such fine motor tasks in VR.
Nature-Inspired Optimization Algorithms for Path Planning and Fuzzy Tracking Control of Mobile Robots

This paper proposes two applications of nature-inspired optimization algorithms (NIOAs) to solve a path planning problem and the optimal tuning of Proportional-Integral (PI)-fuzzy controllers as tracking controllers for nonholonomic wheeled mobile robots in static environments. Two optimization problems are solved by NIOAs and included first in an off-line path planning approach that generates the reference trajectory and next a cost-effective PI-fuzzy tracking controller tuning approach. The NIOAs applied to solve both optimization problems are Particle Swarm Optimization, Gravitational Search Algorithm, Charged System Search Algorithm, Grey Wolf Optimizer and Whale Optimization Algorithm.
Kalman Optimizer for Consistent Gradient Descent

Deep neural networks (DNN) are typically optimized using stochastic gradient descent (SGD). However, the estimation of the gradient using stochastic samples tends to be noisy and unreliable, resulting in large gradient variance and bad convergence. In this paper, we propose Kalman Optimizor (KO), an efficient stochastic optimization algorithm that adopts Kalman filter to make consistent estimation of the local gradient by solving an adaptive filtering problem. Our method reduces estimation variance in stochastic gradient descent by incorporating the historic state of the optimization. It aims to improve noisy gradient direction as well as accelerate the convergence of learning. We demonstrate the effectiveness of the proposed Kalman Optimizer under various optimization tasks where it is shown to achieve superior and robust performance. The code is available at https://github.com/Adamdad/Filter-Gradient-Decent.
Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition

Graph convolutional networks have been very successful in skeleton- based human action recognition where the sequence of skeletons is modeled as a graph. However, most of the graph convolutional network-based methods in this area train a deep feed-forward network with a fixed topology that leads to high computational complexity and restricts their application in low computation scenarios. In this paper, we propose a method to automatically find a compact and problem-specific topology for spatio-temporal graph convolutional networks in a progressive manner. Experimental results on two widely used datasets for skeleton-based human action recognition indicate that the proposed method has competitive or even better classification performance compared to the state-of-the-art methods while it has much lower computational complexity.
On a Guided Nonnegative Matrix Factorization

Fully unsupervised topic models have found fantastic success in document clustering and classification. However, these models often suffer from the tendency to learn less-than-meaningful or even redundant topics when the data is biased towards a set of features. For this reason, we propose an approach based upon the nonnegative matrix factorization (NMF) model, deemed Guided NMF, that incorporates user-designed seed word supervision. Our experimental results demonstrate the promise of this model and illustrate that it is competitive with other methods of this ilk with only very little supervision information.
Role of Cloud Computing to Support Big Data and Big Data Analytics for Education Par Excellence

Big data and big data analytics has been off-late used in higher education institutions to get insights about various aspects of teaching and learning and student related facts. Big data has also been utilized to plan and achieve preemptive and preventive capabilities by big data analysis which gives insights about various facts which otherwise remain hidden, enabling the management to take appropriate decisions at various critical junctures. This research paper discusses the role of cloud computing to support the implementation of big data in higher education for enhancing the teaching experience and supporting administration with a clear visualization of future facts by big data analytics. Cloud computing has been successfully implemented in higher education, this research paper showcases the implementation of cloud computing in higher education as a supporting infrastructure for big data and big data analytics.
Stop Building Castles on a Swamp! The Crisis of Reproducing Automatic Search in Evidence-Based Software Engineering

The evidence-based approach has increasingly been employed to synthesize empirical findings from the primary research in software engineering. Nevertheless, the reproducibility of evidence-based software engineering (EBSE) studies seems to be underemphasized. In our investigation into the automatic search of 311 sample studies, more than 50% of the search strings are not reusable; about 87.5% of the search activities (e.g., search field settings) are unrepeatable; and more than 95% of the whole automatic search implementations are unreproducible. Considering that searching is a cornerstone of an EBSE study, we are afraid that the reproducibility of the current secondary research could be worse than we can imagine. By analyzing and reporting the root causes of the aforementioned observations, we urge collaboration and cooperation among all the stakeholders in our community to improve the research reproducibility in EBSE.
An improved algorithm using weighted guided coefficient and union self‐adaptive image enhancement for single image haze removal

The visibility of outdoor images is usually significantly degraded by haze. Existing dehazing algorithms, such as dark channel prior (DCP) and colour attenuation prior (CAP), have made great progress and are highly effective. However, they all suffer from the problems of dark distortion and detailed information loss. This paper proposes an improved algorithm for single-image haze removal based on dark channel prior with weighted guided coefficient and union self-adaptive image enhancement. First, a weighted guided coefficient method with sampling based on guided image filtering is proposed to refine the transmission map efficiently. Second, the k-means clustering method is adopted to calibrate the original image into bright and non-bright colour areas and form a transmission constraint matrix. The constraint matrix is then marked by connected-component labelling, and small bright regions are eliminated to form an atmospheric light constraint matrix, which can suppress the halo effect and optimize the atmospheric light. Finally, an adaptive linear contrast enhancement algorithm with a union score is proposed to optimize restored images. Experimental results demonstrate that the proposed algorithm can overcome the problems of image distortion and detailed information loss and is more efficient than conventional dehazing algorithms.
News Text Classification Based on MLCNN and BiGRU Hybrid Neural Network

In the era of knowledge explosion, text classification is becoming increasingly crucial. At the same time, with the proposed Blockchain, it is of great research significance to actively explore the combination of Blockchain and AI, especially to apply text classification technology to the security classification of Blockchain technology. In this paper, we propose a hybrid neural network model (MLCNN & BiGRU-ATT) based on Multilayer Convolutional Neural Networks (MLCNN) and Bidirectional Gated Recurrent Unit (BiGRU) with Attention Mechanism in the news text classification field. GRU (Gate Recurrent Unit), a variant of LSTM (Long-Short Term Memory), has the natural advantages in processing time series tasks, which can readily capture the characteristics of text context information. Due to its prominent advantages in local feature extraction, CNN is also applied to NLP area, in which the researchers have made substantial progress. The experiment results reveal that our model has achieved higher accuracy on THUCNews dataset and Sougou news corpus classification.
Application of back-translation

Ambiguous requirements are problematic in requirement engineering as various stakeholders can debate on the interpretation of the requirements leading to a variety of issues in the development stages. Since requirement specifications are usually written in natural language, analyzing ambiguous requirements is currently a manual process as it has not been fully automated to meet the industry standards. In this paper, we used transfer learning by using ULMFiT where we pre-trained our model to a general-domain corpus and then fine-tuned it to classify ambiguous vs unambiguous requirements (target task). We then compared its accuracy with machine learning classifiers like SVM, Linear Regression, and Multinomial Naive Bayes. We also used back translation (BT) as a text augmentation technique to see if it improved the classification accuracy. Our results showed that ULMFiT achieved higher accuracy than SVM (Support Vector Machines), Logistic Regression and Multinomial Naive Bayes for our initial data set. Further by augmenting requirements using BT, ULMFiT got a higher accuracy than SVM, Logistic Regression, and Multinomial Naive Bayes classifier, improving the initial performance by 5.371%. Our proposed research provides some promising insights on how transfer learning and text augmentation can be applied to small data sets in requirements engineering.
A robust image representation method against illumination and occlusion variations

Matrix data has arised in many field, especially in the field of image processing and computer vision. In traditional approaches, the original images need to be vectorized to one-dimension vectors, which may destroy the inherent structure of images. A novel geometrical sparse representation (GSR) model with single image is introduced in this paper that solves a model to measure the similarity between the input image and the single dictionary image. Unlike the traditional sparse representation model, the proposed model does not need to vectorize the image, so as to preserve the inherent geometrical structure of the image. We further introduce a binary coding method to preserve the local patterns of the image and enhance the sparsity of the GSR coefficients. Our method is used for face images with variations of structural noise (occlusion, illumination, etc.), extensive experiments show that our method can be competitive with or even superior to the baseline methods.
Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining

We study the effects of two mechanisms which increase the efficacy of contact-tracing applications (CTAs) such as the mobile phone contact-tracing applications that have been used during the COVID-19 epidemic. The first mechanism is the introduction of user referrals. We compare four scenarios for the uptake of CTAs-(1) the p% of individuals that use the CTA are chosen randomly, (2) a smaller initial set of randomly-chosen users each refer a contact to use the CTA, achieving p% in total, (3) a small initial set of randomly-chosen users each refer around half of their contacts to use the CTA, achieving p% in total, and (4) for comparison, an idealised scenario in which the p% of the population that uses the CTA is the p% with the most contacts. Using agent-based epidemiological models incorporating a geometric space, we find that, even when the uptake percentage p% is small, CTAs are an effective tool for mitigating the spread of the epidemic in all scenarios. Moreover, user referrals significantly improve efficacy. In addition, it turns out that user referrals reduce the quarantine load. The second mechanism for increasing the efficacy of CTAs is tuning the severity of quarantine measures. Our modelling shows that using CTAs with mild quarantine measures is effective in reducing the maximum hospital load and the number of people who become ill, but leads to a relatively high quarantine load, which may cause economic disruption. Fortunately, under stricter quarantine measures, the advantages are maintained but the quarantine load is reduced. Our models incorporate geometric inhomogeneous random graphs to study the effects of the presence of super-spreaders and of the absence of long-distant contacts (e.g., through travel restrictions) on our conclusions.
Deep Learning for Human Action Recognition

The aim of this project is to develop a model for human actions such as running, jogging, walking, clapping, hand-waving and boxing. A series of videos is given for the layout, where an individual executes an event in each video. The action performed on that particular video will be the label of a video. This relationship must be learned by the model, and the label of an input (video) which he never saw can then be predicted. Technically, despite descriptions of these acts, the model would need to learn to distinguish between various human behaviors. There may be many content identification programs which can work on following jobs like Active object tracking for identifying an item such as a vehicle or a human from a CCTV picture and learning the patterns in the movement of humans when we are able to create a pattern that will guide us (humans) to perform a variety of activities.
Classification of Expert-Novice Level Using Eye Tracking And Motion Data via Conditional Multimodal Variational Autoencoder

Sensor data from wearable devices have been utilized to analyze differences between experts and novices. Previous studies attempted to classify the expert-novice level from sensor data based on supervised learning methods. However, these approaches need to collect enough training data covering various novices’ sensor patterns. In this paper, we propose a semi-supervised anomaly detection approach that requires only sensor data of experts for training and identifies those of novices as anomalies. Our proposed anomaly detection model named conditional multimodal variational autoencoder (CMVAE) has the following two technical contributions: (i) considering action information of persons and (ii) utilizing multimodal sensor data, i.e., eye tracking data and motion data in this case. The proposed method is evaluated on sensor data measured when expert and novice soccer players were shooting, dribbling, and doing soccer ball juggling. Experimental results show that CMVAE can more accurately classify the expert-novice level than previous supervised learning methods and anomaly detection methods using other VAEs.
Deep Learning Implementation of Facemask Detection

Since the end of 2019, several cases of inexplicable pneumonia have been discovered in some hospitals in Wuhan, which have been confirmed as acute respiratory infection caused by “COVID-19”. COVID-19 is a respiratory infectious disease, which is mainly transmitted through the respiratory tract, such as droplet transmission, contact transmission and aerosol transmission. The spread of the virus needs to be in a relatively confined space, when the coronavirus reaches a certain concentration, healthy people inhale or the virus contacts the mucosa before it may cause infection. The scientific use of masks can effectively reduce the risk of new coronavirus infection and is an important means for the public to protect their health. Medical masks can not only prevent the patient from spraying droplets, reduce the amount and speed of droplets, but also block virus-containing droplets and prevent the wearer from inhaling. This paper proposes a real-time mask detection method, which classifies the detection of masks, hand shields, sunglasses, and glasses into the same classification model. The actual test effect of the model is more robust than the training of a single category. At the same time, the network structure by using convolutional neural network innovatively add the attention mechanism. The collected pictures contain 24,000 images, and the images are uniformly cropped with 64*64 pixels, and attained an accuracy rate of 97.2% during the training of this model. If a person who is not wearing a mask will be detected, so this study is beneficial in combating the spread of the virus and preventing contact with the virus.