Reviewerly/exHarmony huggingface dataset:
Real-Time Markerless Facial Landmark Detection Using Deep Learning

Locating facial landmarks like eyes, eyebrows, nose, lips, facial outline, etc. can be used as the foundation for generating facial deformations caused by expressions and head movements, which is very crucial in 3D animation, the VFX industry, the gaming industry, VR & AR. This project intends to focus on building a robust and real-time Facial Landmark detection model by improving upon existing models. Over the past few years, a lot of research has been done for detecting facial landmarks without the use of markers. Yet, there is a huge scope for improvement in terms of accuracy and speed. There are three major types of facial Landmark detection algorithms: Holistic methods, Constrained Local Model (CLM), and regression-based methods. Holistic methods try to build a Global representation of facial appearance accurately but require a lot of processing power and time. CLM Build a local representation of facial appearance whereas, regression-based methods simply capture the facial shape and appearance information. Classical regression techniques are fast but have many limitations, for instance, they fail to detect landmarks in occluded faces. Neural networks and deep learning have opened up new possibilities in building models that are efficient, robust, and dependable. Though deep learning models are processor-intensive tasks, recent improvements in network compression and pruning have made it possible to use deep learning models in real-time on fairly low specification computers. This study intends to examine new combinations of previous implementations, new ideas, and heuristic information. The proposed model will be better in terms of speed than other models and on par if not better in terms of accuracy.
EKD-BSP: Bug Report Severity Prediction by Extracting Keywords from Description

Bug severity is important for triagers. Recently, the text in the summary field (i.e., bug summary) of bug reports is usually used to extract features, and then bug report severity prediction models are constructed. In some bug reports, the bug summary may not contain enough useful information. While the text field in the description (i.e., bug description) of bug reports contains detailed information of the bug (e.g., steps to reproduce the bug, stack traces, and expected behavior). However, the bug description may contain irrelevant information. Motivated by the above findings, we propose a novel method EKD-BSP (Bug Report Severity Prediction by Extracting Keywords from Description), which uses the bug summary and the keywords extracted from the bug description to perform severity prediction. Our empirical study selects two large-scale open-source projects (i.e., Eclipse and Mozilla) as the empirical subjects. The empirical results show that EKD-BSP can improve the performance of F-measure by up to 5.19% after compared with the baselines.
QKPT: Securing Your Private Keys in Cloud With Performance, Scalability and Transparency

Private key (e.g., RSA key) protection is a significant issue for cloud but existing keyless or keyguard solutions suffer from performance, elasticity or applicability limitations. Recently, represented by Intel KPT, a novel keyguard architecture emerges to combine trusted platform module and crypto accelerator for achieving both security and performance. However, the straight use of KPT for private key protection may not be a good fit in cloud as it incurs challenges on protection capacity, key provisioning latency and transparency. Based on KPT-like hardware, we propose QKPT, a comprehensive key management system to bring your own private keys (BYOPK) into multi-tenant clouds. QKPT introduces a carefully-designed key wrapping layer to overcome these challenges. A small symmetric wrapping key (SWK) is generated for each tenant as the master key to resolve the former two challenges, while a special private key wrapping scheme is adopted to resolve the transparency limitation. Additionally, QKPT incorporates certificate trust to enhance the security of the SWK lifecycle and provides a hardened key server solution without expensive HSM. The evaluation shows that QKPT has a low runtime overhead ( <inline-formula><tex-math notation="LaTeX">$\leq$</tex-math></inline-formula> 1.2% for SSL/TLS handshakes) and still greatly outperforms the software baseline (3.5x-17x) owing to the crypto offloading.
Video frame deletion detection based on time–frequency analysis

With the emergence of diverse multimedia editing software, a great number of edited or tampered video resources appear on the Internet, some of which can mix with the genuine ones. Digital video authenticity is an important step to make the best use of these video resources. As a common video forgery operation, frame tampering can change the video content and confuse viewers by removing or inserting some specific frames. In this paper, we explore the traces created by compression process and propose a new method to detect frame tampering based on the high-frequency features of reconstructed DCT coefficients in the tampered sequences. Experimental results demonstrate that our proposed method can effectively detect frame tampering operation, and accurately locate the breakpoint of frame tampering in the streams. • We first derive the periodic characteristics in the high-frequency components of reconstructed frames left by compression. • Secondly, new detection features are extracted from the reconstructed DCT coefficients. • Finally, a coarse-to-fine location approach is proposed to precisely locate breakpoints.
Sensitivity Analysis for Vulnerability Mitigation in Hybrid Networks

The development of cyber-assured systems is a challenging task, particularly due to the cost and complexities associated with the modern hybrid networks architectures, as well as the recent advancements in cloud computing. For this reason, the early detection of vulnerabilities and threat strategies are vital for minimising the risks for enterprise networks configured with a variety of node types, which are called hybrid networks. Existing vulnerability assessment techniques are unable to exhaustively analyse all vulnerabilities in modern dynamic IT networks, which utilise a wide range of IoT and industrial control devices (ICS). This could lead to having a less optimal risk evaluation. In this paper, we present a novel framework to analyse the mitigation strategies for a variety of nodes, including traditional IT systems and their dependability on IoT devices, as well as industrial control systems. The framework adopts avoid, reduce, and manage as its core principles in characterising mitigation strategies. Our results confirmed the effectiveness of our mitigation strategy framework, which took node types, their criticality, and the network topology into account. Our results showed that our proposed framework was highly effective at reducing the risks in dynamic and resource constraint environments, in contrast to the existing techniques in the literature.
INQUISITIVE BISIMULATION

Abstract Inquisitive modal logic, InqML , is a generalisation of standard Kripke-style modal logic. In its epistemic incarnation, it extends standard epistemic logic to capture not just the information that agents have, but also the questions that they are interested in. Technically, InqML fits within the family of logics based on team semantics. From a model-theoretic perspective, it takes us a step in the direction of monadic second-order logic, as inquisitive modal operators involve quantification over sets of worlds. We introduce and investigate the natural notion of bisimulation equivalence in the setting of InqML . We compare the expressiveness of InqML and first-order logic in the context of relational structures with two sorts, one for worlds and one for information states, and characterise inquisitive modal logic as the bisimulation invariant fragment of first-order logic over various natural classes of two-sorted structures.
Flexible-CLmser: Regularized Feedback Connections for Biomedical Image Segmentation

The skip connections in U-Net pass features from the levels of encoder to the ones of decoder in a symmetrical way, which makes U-Net and its variants become state-of-the-art approaches for biomedical image segmentation. However, the U-Net skip connections are unidirectional without considering feedback from the decoder, while this paper exploits the feedback information to refine the segmentation. We develop a deep bidirectional network based on the least mean square error reconstruction (Lmser) self-organizing network, an early model that folds an autoencoder along the central hidden layer such that the neurons on the paired layers between encoder and decoder merge into one, equivalently forming bidirectional skip connections between encoder and decoder. We find that the feedback links indeed increase the segmentation accuracy, but may also bring certain noise into the segmentation. To tackle this problem, we present a gating and masking mechanism on the feedback connections to filter the irrelevant information. Experimental results on MoNuSeg, TNBC, and EM membrane datasets demonstrate that our method are robust and outperforms state-of-the-art methods.
A style for every age: A stylometric inquiry into crosswriters for children, adolescents and adults

In the field of children’s literature studies, much attention has been devoted to investigating differences between children’s and adult literature. Works of crosswriters, authors who write for both readerships in different works, are an excellent source for this research. This article applies stylometry, the computational method of analysing style, to the oeuvres of 10 Dutch and English crosswriters to trace potential differences in their individual style and similarities between the authors. The analyses also take into account the age of the intended reader (as listed in the paratext) and the publication date, to study the influence these aspects have on writing style. Four case studies zoom in on a specific author or age category of the intended readership to study general tendencies as well as outliers. The results from the stylometric analyses are complemented with peritextual information about the author’s view on style and writing for readerships of different ages. The main conclusion drawn from the case studies is that the style of the texts usually correlates more strongly with the age of the intended reader than with the time period in which it was written. Young adult literature clusters more closely with adult literature. The style associated with a younger readership is distinct in the oeuvres of most authors studied in this article and even transcends the differences between authors.
Label-dependent and event-guided interpretable disease risk prediction using EHRs

Electronic health records (EHRs) contain patients’ heterogeneous data that are collected from medical providers involved in the patient’s care, including medical notes, clinical events, laboratory test results, symptoms, and diagnoses. In the field of modern healthcare, predicting whether patients would experience any risks based on their EHRs has emerged as a promising research area, in which artificial intelligence (AI) plays a key role. To make AI models practically applicable, it is required that the prediction results should be both accurate and interpretable. To achieve this goal, this paper proposed a label-dependent and event-guided risk prediction model (LERP) to predict the presence of multiple disease risks by mainly extracting information from unstructured medical notes. Our model is featured in the following aspects. First, we adopt a label-dependent mechanism that gives greater attention to words from medical notes that are semantically similar to the names of risk labels. Secondly, as the clinical events (e.g., treatments and drugs) can also indicate the health status of patients, our model utilizes the information from events and uses them to generate an event-guided representation of medical notes. Thirdly, both label-dependent and event-guided representations are integrated to make a robust prediction, in which the interpretability is enabled by the attention weights over words from medical notes. To demonstrate the applicability of the proposed method, we apply it to the MIMIC-III dataset, which contains real-world EHRs collected from hospitals. Our method is evaluated in both quantitative and qualitative ways.
User Alignment across Dynamic Social Networks based on Heuristic Algorithm

User alignment across social networks, whose main goal is to fuse user information in different network platforms, is a fundamental task in social network analysis. It can benefit social network applications such as user recommendation and information diffusion. Attributed to the inherent dynamic characteristic of the social networks, aligning users in dynamic networks is a key issue in practice. However, most of the alignment models encounter model retraining when the network is updated, thus result in the consumption of time and resources. To address this problem, a heuristic algorithm is proposed to align users in a dynamic environment. Firstly, the attention mechanism is leveraged to obtain the local importance weight of the new node in a single network. Secondly, the anchor nodes are adopted as supervised information for heuristically learning the alignment task-driven local influence of new nodes. Finally, by preserving the second-order similarity of the network, the model aligns users across networks. Experimental results conducted on realworld datasets prove that the proposed model has a comparable performance but lower time complexity compared with several state-of-the-art algorithms.
Improving Automatic Sign Language Translation with Image Binarisation and Deep Learning

Sign Language Translation (SLT) has been widely investigated to provide a futuristic solution to tackle human speech and hearing disability. Recent deep learning-based SLT models have redefined computer vision-based detection and classification to automatically translate the hand-gestured based sign language (SL) into natural language (NL) with higher accuracy. Unlike the existing models that directly learn from the natural image-sets, in this paper, we propose a 2D Convolutional Neural Network (CNN) model with customised hyper-parameters to be trained with binary SL image-sets. We thus introduce a binarisation step to preprocess the images of size $28 \times 28$ to feed the model. Preliminary results of our model trained with binarised image-set demonstrate its potential with an impressive classification accuracy of 99.99% on the NVIDIA Tesla K80 GPU environment (Google Colab) for an automatic SLT system.
Automatic sentiment analysis of public opinion on nuclear energy

Abstract Opinion mining and sentiment analysis based on social media has been developed these years, especially with the popularity of social media and the development of machine learning. But in the community of nuclear engineering and technology, sentiment analysis is seldom studied, let alone the automatic analysis by using machine learning algorithms. This work concentrates on the public sentiment mining of nuclear energy in German-speaking countries based on the public comments of nuclear news in social media by using the automatic methodology, since compared with the news itself, the comments are closer to the public real opinions. The results showed that majority comments kept in neutral sentiment. 23% of comments were in positive tones, which were approximate 4 times those in negative tones. The concerning issues of the public are the innovative technology development, safety, nuclear waste, accidents and the cost of nuclear power. Decision tree, random forest and long short-term memory networks (LSTM) are adopted for the automatic sentiment analysis. The results show that all of the proposed methods can be applied in practice to some extent. But as a deep learning algorithm, LSTM gets the highest accuracy approximately 85.6% with also the best robustness of all.
Classification of Quranic Topics Using SMOTE Technique

This paper aims to classify the Quranic topics that differ in their number of verses by applying the SMOTE technique. SMOTE is used to rebalance samples of minority classes in these Quranic topics. Moreover, SMOTE is combined with many classifiers to choose the best technique for the Quranic classification. Also, the k-values of SMOTE were studied to select the best values for the Quranic datasets and improve the performance of imbalanced classification. The SMOTE was implemented with many classifiers to choose the best one. The results showed that the Voted Perceptron classifier was the best technique when implemented with the SMOTE method to classify the Quranic topics. Also, it is concluded that the best range of K numbers in SMOTE method is [1, 10], to obtain the higher performance of Quranic classification.
Multilingual Sentiment Analysis on Short Text Document Using Semi-Supervised Machine Learning

Sentiment analysis is a task of identifying the sentiments in text which is often applied to analyzing text in social media, customer feedbacks, and product reviews. Various studies have explored how sentiment analysis can automatically done by using machine learning techniques. However, there has been few attempts in implementing sentiment analysis on multilingual text. Furthermore, most of the existing works uses labelled data to train and develop machine learning models for sentiment analysis. Using labelled data are often expensive and time consuming. In this study, a sentiment analysis model for multilingual text using semi-supervised machine learning was explored. The data used is composed of 50,788 tweets about COVID-19, these are cleaned by removing unnecessary characters, stop words, and emojis. After cleaning, the language of each tweet was identified, all tweets that are not written in Filipino or English were removed from the dataset. Afterwards, the tweets were all translated in English in preparation for the annotation phase. This study used an open-source tool, TextBlob, in annotating the tweets. TextBlob outputs the polarity of the text in vector representation. The TextBlob annotation were then validated by human experts through an inter-rater agreement. The level of agreement between the human annotations and TextBlob annotations have a substantial agreement with 0.78 Fleiss’ Kappa value. Classifier models were developed using various machine learning algorithms. Based on the results of the experiment, SVC is the best performing model with count vectorizer as feature with an accuracy, precision, recall, and F1-score of 95%. For future work, fine tuning hyperparameters to optimize the models can be considered.
Exponential Convergent Nonlinear Estimation of the Quantum Density Operator for a Quantum Mechanical Statistical System Using Metric Projection Operators

The fundamental element of quantum statistical mechanics is the quantum density operator. Once determined, this operator reveals the statistics of observables in a quantum process. In this paper, we present our results on the estimation and approximation of the quantum density operator from the Liouville-Von Neumann Quantum Master Equation description of a dynamical Quantum System. We illustrate these results with an application of the estimation of the density operator for a quantum qubit.
A new emotion analysis fusion and complementary model based on online food reviews

As the content of the food Review text becomes more and more complex and the types of the food Review text representation become more and more diverse, emotion analysis becomes more and more important in dealing with problems. At present are faced with the problem, this paper puts forward a new type of complementary fusion model, the text pretreatment by Bert training text model, make the content of the food review text more coherent, keep on convolutional neural network logic timing relationships, before and after the statement of text vector of the local feature extraction, using the method of bidirectional long short-term memory network (BLSTM) capture and text context information related to global long-range dependence, using the weighted formula of attention mechanism to improve the comment text statement attention emotional words and then with the weight of characteristic value after conditions with the airport of sorting the output. Through a comparative experiment on the data set of JD food review data set reviews, the results show that BCBLAC, the feature fusion model proposed in this paper, is much more accurate than the existing model in the food Review text language classification.
Unsupervised graph-clustering learning framework for financial news summarization

Financial news shows significant influence on the inflection point of stock market. To condense the news texts with exponential growth, Automatic Text Summarization(ATS) becomes urgent. However, ATS tailored for financial news summarization has not been explored. In this paper, we propose a Graph-Clustering framework(FinGC) to extract financial news summarization. Our framework jointly learns the graph embedding and performs clustering in an unsupervised way. By combining graph structure which contains cross-sentence relations into text embedding, FinGC enriches the representation of financial news and highlights important sentences that point to specific events. Clustering is incorporated to group the financial news in terms of diversity and non-redundancy. We evaluated our best FinGC model on Fin-News and standard Multi-News. Experiments demonstrate it achieves state-of-the-art performance on standard datasets by ROUGE scores. Finally, we validated our results with human evaluation and show that our model achieves human performance on financial news.
Cholera Disease Detection using Fuzzy Logic Technique

In this research paper, a fuzzy based system is presented for the diagnosis of cholera disease. It provides decision support platform to the scientists, researchers, physicians and healthcare practitioners in cholera disease area. The given fuzzy expert system contains major components as; the Fuzzification, Knowledge base, Inference engine and Defuzzification module. This system is implemented based on observations of patients, medical diagnosis and the expert’s knowledge. The system is developed based on Mamdani's fuzzy inference system. It does the reasoning and inference the data from the rules designed. In this method in order to get the decision results majorly the symptoms considered are like mild, moderate and severe. To do the experimental analysis and study thirty patients of cholera disease are selected and considered. The outcomes are calculated and checked with domain knowledge experts. This system will be helpful for making the cholera diagnosis as the medical practitioners can directly input the symptoms and will get the results to take the decision.
Analysis of GraphSum’s Attention Weights to Improve the Explainability of Multi-Document Summarization

Modern multi-document summarization (MDS) methods are based on transformer architectures. They generate state of the art summaries, but lack explainability. To overcome this, we analyze the attention weights of a graph-based MDS such as GraphSum. We compare GraphSum’s performance utilizing different textual units, i. e., sentences versus paragraphs, on two MDS benchmark datasets, namely WikiSum and MultiNews. Our experiments show that paragraph-level representations provide the best summarization performance. Subsequently, we focus on analyzing the paragraph-level attention weights of GraphSum’s multi-heads and decoding layers. Furthermore, we examine source origin information via text similarity over a ROUGE-based reference metric. We observe a high correlation between the attention weights and our reference metric, especially on the later decoding layers. Finally, we investigate if the generated summaries follow a pattern of positional bias. Our results show that there is a high correlation between the position of paragraphs in the input documents and the content provided for the generated summary.