Reviewerly/exHarmony huggingface dataset:
On the Flow of Software Security Advisories

In this paper, we report results on a large scale measurement campaign to collect temporal information about events associated with software vulnerabilities. The data is curated so as to extract dates from each of the analyzed security advisories. The resulting time series are our object of study. From our measurements we were able to identify which role was assumed by different platforms (such as websites and forums) in the security landscape, including sources and aggregators of information about vulnerabilities. Then, we propose an analytical model to express the flow of information through security advisories across multiple platforms. The model is based on a queueing network, where each platform corresponds to a queue which adds a delay in the information propagation. Such delays, in turn, have an impact on the visibility of the information at different platforms. Leveraging the proposed model and the collected data, we assess how different system parameters, such as the delays incurred by each platform to propagate its messages, impact the overall flow of information across platforms.
Handwritten Digit String Recognition using Deep Autoencoder based Segmentation and ResNet based Recognition Approach

Recognition of isolated handwritten digits is a well-studied research problem and several models show high recognition accuracy on different standard datasets. But the same is not true while we consider recognition of handwritten digit strings although it has many real-life applications like bank cheque processing, postal code recognition, and numeric field understanding from filled-in form images. The problem becomes more difficult when digits in the string are not neatly written which is commonly seen in freestyle handwriting. The performance of any such model primarily suffers due to the presence of touching digits in the string. To handle these issues, in the present work, we first use a deep autoencoder based segmentation technique for isolating the digits from a handwritten digit string, and then we pass the isolated digits to a Residual Network (ResNet) based recognition model to obtain the machine-encoded digit string. The proposed model has been evaluated on the Computer Vision Lab (CVL) Handwritten Digit Strings (HDS) database, used in HDSRC 2013 competition on handwritten digit string recognition, and a competent result with respect to state-of-the-art techniques has been achieved.
Speech Acoustic Modelling from Raw Phase Spectrum

Magnitude spectrum-based features are the most widely employed front-ends for acoustic modelling in automatic speech recognition (ASR) systems. In this paper, we investigate the possibility and efficacy of acoustic modelling using the raw short-time phase spectrum. In particular, we study the usefulness of the raw wrapped, unwrapped and minimum-phase phase spectra as well as the phase of the source and filter components for acoustic modelling. Furthermore, we explore the effectiveness of simultaneous deployment of the vocal tract and excitation components of the raw phase spectrum using multi-head CNNs and investigate multiple information fusion schemes. This paves the way for developing an effective phase-based multi-stream information processing systems for speech recognition. The performance, even for wrapped phase with a noise-like shape, is comparable to or better than the magnitude-based classic features, and up to 4.8% WER has been achieved in the WSJ (Eval-92) task.
Deep Convolutional Neural Network Ensemble for Improved Malaria Parasite Detection

Malaria prognosis, performed through the identification of parasites using microscopy, is a vital step in the early initiation of treatment. Malaria inducing parasites such as Plasmodium falciparum are difficult to identify and thus have a high mortality rate. For these reasons, a deep convolutional neural network algorithm is proposed in this paper to aid in accurately identifying parasitic cells from red blood smears. By using a mixture of machine learning techniques such as transfer learning, a cyclical and constant learning rate, and ensemble methods, we have developed a model capable of accurately identifying parasitic cells within red blood smears. 14 networks pretrained from the ImageNet database are retrained with the fully connected layers replaced. A cyclical and constant learning rate are used to traverse local minima in each network. The output of each trained neural network is representing a single vote that is used in the classification process. Majority voting criteria are applied in the final classification decision between the candidate malaria cells. Several experiments were conducted to evaluate the performance of the proposed model. The NIH Malaria Dataset from the National Institute of Health, a dataset of 27,558 images formed from microscopic patches of red blood smears, is used in these experiments. The dataset is segmented into 80% training set, 10% validation set, and 10% test set. The validation set is used as the decision metric for choosing ensemble network architectures and the test set is used as the evaluation metric for each model. Different ensemble network architectures are experimented with and promising performance is observed on the test dataset with the best models achieving a test accuracy better than several state-of-the-art methodologies.
IA-SSLM: Irregularity-Aware Semi-Supervised Deep Learning Model for Analyzing Unusual Events in Crowds

Analyzing unusual events is significantly important for video surveillance to ensure people safety.These events are characterized by irregular patterns that do not conform to the expected behavior in the surveillance scenes.We present a novel irregularity-aware semi-supervised deep learning model (IA-SSLM) for detection of unusual events.While most existing works depend on the availability of large amount of labeled data for training, our proposed method utilizes a semi-supervised deep model to automatically learn feature representations from limited number of labeled data samples.Our method extracts meaningful information from both labeled and unlabeled data during the training stage to improve the performance.For this purpose, we explore the concept of consistency regularization and entropy minimization to output confident predictions on unlabeled data.For experimental analysis, we consider various standard and diverse datasets.The results show that our IA-SSLM method outperforms several reference methods using different performance metrics.
Robust White Matter Hyperintensity Segmentation On Unseen Domain

Typical machine learning frameworks heavily rely on an underlying assumption that training and test data follow the same distribution. In medical imaging which increasingly begun acquiring datasets from multiple sites or scanners, this identical distribution assumption often fails to hold due to systematic variability induced by site or scanner dependent factors. Therefore, we cannot simply expect a model trained on a given dataset to consistently work well, or generalize, on a dataset from another distribution. In this work, we address this problem, investigating the application of machine learning models to unseen medical imaging data. Specifically, we consider the challenging case of Domain Generalization (DG) where we train a model without any knowledge about the testing distribution. That is, we train on samples from a set of distributions (sources) and test on samples from a new, unseen distribution (target). We focus on the task of white matter hyperintensity (WMH) prediction using the multi-site WMH Segmentation Challenge dataset and our local in-house dataset. We identify how two mechanically distinct DG approaches, namely domain adversarial learning and mix-up, have theoretical synergy. Then, we show drastic improvements of WMH prediction on an unseen target domain.
Toward Trusted and Secure Communication Among Multiple Internal Modules in CAV

By equipping various sensors and analyzing sensed data, vehicles can perform automatic driving; these vehicles are known as connected and autonomous vehicles (CAVs). In CAVs, tampered data will result in incorrect driving decisions. Hence, secure data transmission should be ensured to enable correct life-critical decisions. Untrusted resource-constrained modules allow attackers to obtain private data from CAVs, such as the key. Benefitting from trusted computing, the proposed scheme can verify the trusted status of internal modules and achieve secure data transmission by adopting the remote attestation and hash message authentication code. The scheme is proven to be secure in the random oracle model under the computational Diffie–Hellman problem. Furthermore, we perform experiments and evaluate the performance using Intel Software Guard eXtensions, which provide part of the trusted computing function. The experimental results show that the scheme could be efficient and suitable for CAVs.
Deconvolution Feature Fusion for traffic signs detection in 5G driven unmanned vehicle

Real-time and accurate recognition of distant traffic signs in a wide visual range is one of the key technologies in 5G driven unmanned vehicles. Most earlier studies focused on improving accurately of short rang traffic signs detection in manned driving assistance system, while little attention has been paid to distant range traffic signs recognition in the unmanned vehicle. At the same time, it is difficult to detect long-distance traffic signs due to their small size. This paper is oriented to the 5G driven unmanned vehicle scene, proposes a novel framework of Deconvolution Feature Fusion based on the backbone of YOLOv3 (DFF-YOLOv3) to enhance the accuracy of distant traffic signs recognition in a wide visual range for unmanned driving. The proposed framework has combined the deep and shallow feature maps to form a fusion module with a wider range and higher accuracy of distant information in a fixed monocular camera. Specifically, the deep feature map is up-sampled by deconvolution and then merged with the shallow feature map. The convolution module is used to learn the feature, and then through the dimensionality reduction of the convolutional layer to form a deconvolution feature fusion module, which finally replaces the original prediction layer to detect the target. Experimental results are provided to validate the framework, which can improve the recognition accuracy of distant traffic signs without reducing the entire visual range in unmanned vehicles. The result shows that the mean accuracy prediction (mAP) of the proposed DFF-YOLOv3 on distant traffic signs is 74.8%, which is higher than other classic detection algorithms.
SnarkPack: Practical SNARK Aggregation

Zero-knowledge SNARKs (zk-SNARKs) are non-interactive proof systems with short and efficiently verifiable proofs that do not reveal anything more than the correctness of the statement. zk-SNARKs are widely used in decentralised systems to address privacy and scalability concerns. A major drawback of such proof systems in practice is the requirement to run a trusted setup for the public parameters. Moreover, these parameters set an upper bound to the size of the computations or statements to be proven, which results in new scalability problems. We design and implement SnarkPack, a new argument that further reduces the size of SNARK proofs by means of aggregation. Our goal is to provide an off-the-shelf solution that is practical in the following sense: (1) it is compatible with existing deployed SNARK systems, (2) it does not require any extra trusted setup. SnarkPack is designed to work with Groth16 scheme and has logarithmic size proofs and a verifier that runs in logarithmic time in the number of proofs to be aggregated. Most importantly, SnarkPack reuses the public parameters from Groth16 system. SnarkPack can aggregate 8192 proofs in 8.7 s and verify them in 163 ms, yielding a verification mechanism that is exponentially faster than other solutions. SnarkPack can be used in blockchain applications that rely on many SNARK proofs such as Proof-of-Space or roll-up solutions.
Deep learning regularization techniques to genomics data

Deep Learning algorithms have achieved a great success in many domains where large scale datasets are used. However, training these algorithms on high dimensional data requires the adjustment of many parameters. Avoiding overfitting problem is difficult. Regularization techniques such as L1 and L2 are used to prevent the parameters of training model from being large. Another commonly used regularization method called Dropout randomly removes some hidden units during the training phase. In this work, we describe some architectures of Deep Learning algorithms, we explain optimization process for training them and attempt to establish a theoretical relationship between L2-regularization and Dropout. We experimentally compare the effect of these techniques on the learning model using genomics datasets.
Cost-sensitive learning for semi-supervised hit-and-run analysis

Hit-and-run crashes not only degrade the morality, but also result in delays of medical services provided to victims. However, class imbalance problem exists as the number of hit-and-run crashes is much smaller than that of non-hit-and-run crashes. The missing label problem also exists in the crash analysis due to reasons like data barrier such that the information hidden in the unlabelled samples has not been effectively utilised. In this paper, a cost-sensitive semi-supervised logistic regression (CS3LR) model is proposed for hit-and-run analysis, in order to tackle class-imbalanced data distribution and missing label problem, based on the crash dataset of Victorian, Australia (2013–2019). By performing label estimation with logistic regression jointly utilising both labelled and unlabelled data with pseudo labels in a well-designed cost-sensitive semi-supervised maximum likelihood framework, the proposed model can obtain an unbiased likelihood parameter for hit-and-run prediction and analysis. Comparing the experimental results of CS3LR model with two logistic regression models and seven machine learning methods, better performance of CS3LR model is demonstrated. The most significant contributing factors to hit-and-run crashes extracted by CS3LR with only 10% labelled data show a high degree of consistency with the true contributing factors obtained by the supervised cost-sensitive logistic regression with complete hit-and-run labels. The effects of class-weighted ratio and hyper-parameter λ on the performance of hit-and-run crash prediction model have also been analysed. The results can further provide recommendations and implications on the policies and counter-measures for preventing hit-and-run collisions and crimes. The methodology proposed in this paper can also be employed to analyse crash data with other types of missing labels, such as crash severity.
Application of Delaunay Triangulation and Catalan Objects in Steganography

This paper presents a new method of steganography based on a combination of Catalan objects and Voronoi–Delaunay triangulation. Two segments are described within the proposed method. The first segment describes the process of embedding data and generating a complex stego key. The second segment explains the extraction of a hidden message. The main goal of this paper is to transfer a message via the Internet (or some other medium) using an image so that the image remains absolutely unchanged. In this way, we prevented the potential attacker from noticing some secret message hidden in that picture. Additionally, the complex stego key consists of three completely different parts (the image, the encrypted Delaunay triangulation, and the array Rk in Base64 code), which are very difficult to relate with each other. Finally, a few security analyses of the proposed method are conducted, as well as the corresponding steganalysis.
A hybrid multi-stage learning technique based on brain storming optimization algorithm for breast cancer recurrence prediction

Breast cancer disease is considered to be the second leading reason for death among women. Unfortunately, even if the treatment of cancer started soon after diagnosis, the cancer cells may remain in the body, and cancer may recur. Various Machine Learning (ML) methods to predict breast cancer recurrence were applied recently, and the ML methods' performance needs to be examined to determine the proper method for prediction. Usually, the datasets contain many features which may sometimes mislead the prediction process; as some features may lead to confusion or inaccurate prediction. Thus, in this study, two breast cancer recurrence datasets were statistically analyzed and further refined by Brain Storming Optimization algorithm (BSO). The proposed multi-stages technique consists of three main stages; first, the statistical feature selection methods (SFM) which statistically select the discriminative features based on importance ranking and features correlations and statistical hypothesis testing, to be passed to the second stage. The features are ranked based on their correlation with the class variable. The second stage namely the multi classifier (MC), which evaluates each method based on three classifiers and produces a combination of features performed by two SFM and three classifiers. In the third stage, the best combination of the selected features was recognized by the BSO algorithm to search for an optimal solution that produces the highest accuracy. In addition, the BSO algorithm has been modified to deal with the feature selection problem. The performance of the proposed technique was evaluated by stratified 10-fold cross-validation. As a result, the multi-stage learning technique showed to be effective in ranking the features and improved the classification accuracy for breast cancer recurrence.
Capsule Network-Based Text Sentiment Classification

To improve text sentiments classification issues, such as information loss and insensitivity to spatial information, this paper proposes a text sentiment classification model based on the capsule network (T-Caps), which uses the Transformer to extract low-level text features. The method iteratively updates capsule network parameters through optimized dynamic routing algorithms and global parameter sharing, and it obtains the relationship between local features of the text and the overall emotional polarity to save the information integrity of text features. By comparing with multiple models, we find that the Transformer has the strongest feature extraction capability. The experimental results show that our model is capable of extracting more discriminative semantic features and yields a significant performance gain compared to other baseline methods.
ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation

This paper presents ParaQA, a question answering (QA) dataset with multiple paraphrased responses for single-turn conversation over knowledge graphs (KG). The dataset was created using a semi-automated framework for generating diverse paraphrasing of the answers using techniques such as back-translation. The existing datasets for conversational question answering over KGs (single-turn/multi-turn) focus on question paraphrasing and provide only up to one answer verbalization. However, ParaQA contains 5000 question-answer pairs with a minimum of two and a maximum of eight unique paraphrased responses for each question. We complement the dataset with baseline models and illustrate the advantage of having multiple paraphrased answers through commonly used metrics such as BLEU and METEOR. The ParaQA dataset is publicly available on a persistent URI for broader usage and adaptation in the research community.
Threshold Moving Approaches for Addressing the Class Imbalance Problem and their Application to Multi-label Classification

In this paper, simple threshold moving techniques are proposed for the class imbalance problem. Decision thresholds are adjusted to match the class distribution of the training data to that of the predicted outcomes for unseen data. The proposed approaches are applied to multi-label classification wherein each instance may simultaneously belong to more than one label. Experimental results on two data sets confirm that the performance of the well-known binary relevance method can be improved when combined with the proposed threshold moving techniques.
Exposing Vulnerabilities of Deepfake Detection Systems with Robust Attacks

Recent advances in video manipulation techniques have made the generation of fake videos more accessible than ever before. Manipulated videos can fuel disinformation and reduce trust in media. Therefore detection of fake videos has garnered immense interest in academia and industry. Recently developed Deepfake detection methods rely on Deep Neural Networks (DNNs) to distinguish AI-generated fake videos from real videos. In this work, we demonstrate that it is possible to bypass such detectors by adversarially modifying fake videos synthesized using existing Deepfake generation methods. We further demonstrate that our adversarial perturbations are robust to image and video compression codecs, making them a real-world threat. We present pipelines in both white-box and black-box attack scenarios that can fool DNN-based Deepfake detectors into classifying fake videos as real. Finally, we study the extent to which adversarial perturbations transfer across different Deepfake detectors and create more accessible attacks using universal adversarial perturbations that pose a very feasible attack scenario since they can be easily shared amongst attackers. 1
Fabrication of heat-resistant grids and their application to deformation measurements using a sampling moiré method

Heat-resistant grids are important sensing elements for deformation measurements that use the sampling moiré method at high temperatures. To satisfy the fabrication demand for heat-resistant grids for the sampling moiré method, an etched-paint-film grid-fabrication technique using a laser-marking technique is proposed in this study. First, the important factors influencing the fabrication process are experimentally studied, and grids with a pitch of several hundred microns are successfully fabricated using the nickel-based superalloy GH4169. The experimental results at high temperatures show that the self-developed grids have high temperature resistance at up to 1050 °C. Furthermore, to minimize the thermal disturbance involved in sampling moiré measurements at high temperatures, an image grayscale-averaging technique is introduced to improve the measurement accuracy of the high-temperature deformation fields. As example applications, the elastic constants at 20 °C/650 °C and the thermal expansion coefficients (up to 1050 °C) of GH4169 are accurately measured using the sampling moiré method. The experimental results confirm the feasibility of the sampling moiré method and our self-developed heat-resistant grids for characterizing the thermomechanical parameters of materials. The technique for fabricating heat-resistant grids proposed in this work is suitable for various metal materials, and has good application potential for use in high-temperature deformation measurements.