Reviewerly/exHarmony huggingface dataset:
Evaluating parametric uncertainty using non-linear regression in fringe projection

Optical coordinate measurement systems, such as fringe projection systems, offer fast, high-density measurements of arbitrary surface topographies. The versatility, speed and information density of fringe projection measurements make them attractive as in-situ measurement devices and autonomous inspection systems. However, the complex nature of the measurement process makes evaluating uncertainty from a fringe projection measurement complex – even in the hypothetical simple case where the accuracy of a measurement is limited only by the accuracy in the quantities that define a measurement from an indication; named system parameters here. In this paper, by validating a series of assumptions, a method to explore the uncertainty in the system parameters of a fringe projection system is given. The results of this investigation imply the common distortion model (the Brown-Conrady model) is not specific enough to the camera or projector of a fringe projection system to evaluate its uncertainty.
Post-Quantum Digital Signatures in Transport Documents

Asymmetric cryptography plays a critical role in the security, authenticity and sustainability of widely used electronic documents on a level comparable to the security and authenticity of a traditional paper documents in transport and logistics. However, security of the majority of the asymmetric cryptographic techniques currently in use, including those used to confirm the validity of electronic documents, will be compromised due to the forthcoming development of quantum computers. The concept of a quantum computer is no longer just a theory. The race for supremacy in quantum technology is on among countries since it is the most drastic technology in the world. On a large enough quantum computer, an attacker can easily generate fake digital signatures by applying Shor’s algorithm. Digital communications and electronic transactions could be devoid of the guarantee of authenticity and non-repudiation if this vulnerability is not addressed. Post-quantum cryptography strives to provide useful techniques that are resistant to attacks utilizing quantum computers in order to address the threat to existing asymmetric cryptography. In this research, we examine the current post-quantum digital signature algorithms used to confirm the validity of an electronic document in addition to comprehending the utilization of digital signing. Overall, we conclude that in order to define the next generation of electronic documents and to address the quantum threat, it is crucial to standardize a quantum-resistant digital signature scheme.
Hand Gesture Recognition System Based on Textural Features

Gesture is an effective mean of communication between deaf or hearing-impaired people. Designing a hand gesture recognition system involves an efficient characterization step in order to reduce the amount of information contained in the images. We propose two descriptors known as Binary Pattern of Phase Congruency (BPPC) and Monogenic Binary Coding (MBC) to characterize our hand images. Three datasets are used for this purpose, the American, Arabic and dynamic dataset. Support vector Machine SVM and Radial Basis function Neural Network RBF NN are used to build our hand gesture recognition system. 94 to 96% of accuracy was obtained for the three datasets.
Reconfigurable KECCAK Hardware Design for Lattice-based Post-Quantum Cryptography on the RISCV Architecture

In this work, after analyzing the reconfigurable requirement and feasibility, a reconfigurable KECCAK hardware design for lattice-based post-quantum cryptography on the RISCV architecture is implemented. The padding module can be filled with any number of bits in different ways by configuring the length and address. The output module can satisfy any bit output of SHAKE function. In addition, by comparing RISCV original instruction, partial extension and full extension, it is verified that the fully extended architecture can improve the energy efficiency by at least 30 times.
Zero-Shot Learning for Real-Time Ultrasound Image Enhancement

Ultrasound(US) imaging has been widely used for clinical diagnosis. However, ultrasound images inherently suffer from speckle noise and low contrast. Although deep learning-based approaches have been proven to outperform traditional filtering algorithms in image enhancement tasks, they usually require high-quality images for training, which are unavailable in practice. In this paper, we develop a zero-shot learning framework for real-time ultrasound image enhancement. In our framework, relatively low-quality(LQ) and high-quality(HQ) images are beamformed using raw Synthetic Aperture Ultrasound (SAU) data with different channel numbers. The generated LQ-HQ image pairs are used to train two designed U-Net variants whereby one is for inference on GPU and the other with depth-wise separable convolution for inference on CPU and mobile devices. The trained models are directly used for enhancing noisy HQ US images without requiring speckle-free and high contrast targets for training. Experiment results indicate that our method performs more favorably against state-of-the-art image enhancement approaches in terms of both image quality and inference time.
Combined B-mode and Nakagami Images for Improved Discrimination of Breast Masses using Deep Learning

Although ultrasound has become an important screening tool for the non-invasive diagnosis of breast cancer, it is limited by intra- and inter-observer variability, and subjectivity in diagnosis. On the other hand, deep learning-based approaches have the potential for objective and automated diagnosis in a manner that is efficient and reproducible. In this study, we propose a deep learning methodology for the classification of benign and malignant breast lesions based on combined ultrasound B-mode and Nakagami images. We hypothesize that combining the images, which contain complementary information, will provide better classification performance in a deep learning framework than using the images by themselves. The study included 230 patients who had 152 benign and 78 malignant masses. Nakagami images were formed using a sliding window applied to the envelope data of each patient. A superposition approach was adopted to form fused images, where Nakagami images and B-mode images were superimposed onto each other at differing weights. A modified VGG-16 network was trained on the resulting images, and performance was evaluated on a separate test dataset containing 50 images. Models trained using fused images outperformed models trained using individual B-mode and Nakagami images. Furthermore, the AVCs obtained by models trained on fused images were found to be statistically significantly higher than models trained on individual images. The obtained results demonstrate the feasibility of combining information from Nakagami and B-mode images, and its potential to provide improved diagnosis for breast cancer.
A Feasibility Study of Low-frequency Ultrasound Tomography for Human Thorax

Ultrasound is widely used in biomedical imaging. High-frequency ultrasound at MHz is usually used for good resolution. However, ultrasound at this band cannot permeate human thorax, for it is strongly scattered and reflected by air inclusions in the thorax. We attempt to detect thorax using low-frequency ultrasound transmitting through thorax with travel-time tomography. The ultrasound we use is at a few tens of kHz, which mitigates the influence of scattering and reflection. Transducers are fixed around the thorax via a manual elastic belt. Each of them serves as transmitter in turn and the rest serve as receivers. The traveltime of signals is picked and the acoustic velocity distribution of thorax is computed, from which we can compute the acoustic velocity distribution of the subject's thorax. In the inversion result, the outline of lungs can be seen and the distribution differs when the subject breathes in and out. This result verifies that it is possible to imaging human thorax using low-frequency ultrasound.
Combatting online harassment by using transformer language models for the detection of emotions, hate speech and offensive language on social media

In these contemporary times, social media is omnipresent and most people adhere to at least one of these digital platforms. Social entertainment generates an enormous amount of data and this is an unparalleled opportunity for data scientists and linguistic experts. These factors have renewed the interest in Natural Language Processing techniques and as such, there is a continuous increase in the number of publications that deal with the topic of Tweet classification using machine learning models. In this paper, experiments performed by the TweetEval team from the University of Cardiff have been studied and expanded upon. These tasks include emotion detection, offensive language identification and hate speech detection. The decision was made to focus on these specific classification tasks as they directly relate to unsought behaviours such as online harassment. This research endeavour involved building and testing a transformer-based language model which is capable of matching the performance of TweetEval. The aim of this study is therefore to identify common limitations to such models and how these can be circumvented to effectively combat phenomenon such as cyberbullying and online abuse using machine learning. From the results that were obtained, the developed BERT model performed comparatively well to other similar algorithms for all tasks as the obtained results were an F1-Score of 0.51, 0.76 and 0.80 for hate speech, emotion detection and offensive language respectively.
Security in Web Applications: A Comparative Analysis of Key SQL Injection Detection Techniques

Over the years, technological advances have driven massive proliferation of web systems and businesses have harbored a seemingly insatiable need for Internet systems and services. Whilst data is considered as a key asset to businesses and that their security is of extreme importance, there has been growing cybersecurity threats faced by web systems. One of the key attacks that web applications are vulnerable to is SQL injection (SQLi) attacks and successful attacks can reveal sensitive information to attackers or even deface web systems. As part of SQLi defence strategy, effective detection of SQLi attacks is important. Even though different techniques have been devised over the years to detect SQLi attacks, limited work has been undertaken to review and compare the effectiveness of these detection techniques. As such, in order to address this gap in literature, this paper performs a review and comparative analysis of the different SQLi detection techniques, with the aim to detect SQLi attacks in an effective manner and enhance the security of web applications. As part of the investigation, seven SQLi detection techniques including machine learning based detection are reviewed and their effectiveness against different types of SQLi attacks are compared. Results identified positive tainting and adoption of machine learning among the most effective techniques and stored procedures based SQLi as the most challenging attack to detect.
No Signal Left to Chance: Driving Browser Extension Analysis by Download Patterns

Browser extensions are popular small applications that allow users to enrich their browsing experience. Yet browser extensions pose security concerns because they can leak user data and maliciously act on behalf of the user. Because malicious behavior can manifest dynamically, detecting malicious extensions remains a challenge for the research community, browser vendors, and web application developers. This paper identifies download patterns as a useful signal for analyzing browser extensions. We leverage machine learning for clustering extensions based on their download patterns, confirming at a large scale that many extensions follow strikingly similar download patterns. Our key insight is that the download pattern signal can be used for identifying malicious extensions. To this end, we present a novel technique to detect malicious extensions based on the public number of downloads in the Chrome Web Store. This technique fruitfully combines machine learning with security analysis, showing that the download patterns signal can be used to both directly spot malicious extensions and as input to subsequent analysis of suspicious extensions. We demonstrate the benefits of our approach on a dataset from a daily crawl of the Web Store over 6 months to track the number of downloads. We find 135 clusters and identify 61 of them to have at least 80% malicious extensions. We train our classifier and run it on a test set of 1,212 currently active extensions in the Web Store successfully detecting 326 extensions as malicious solely based on downloads. Further, we show that by combining this signal with code similarity analysis, using the 326 as a seed, we find an additional 6,579 malicious extensions.
An Optimal Distributed Scheduling Model for Data Centers and Renewable Energy Integrated Systems

Data centers (DCs) are large power consumers with demand response (DR) capabilities in both time and special dimensions. This paper proposes a scheduling model for renewable energy integrated systems with DCs to improve renewable energy utilization. To protect the data privacy of data center operators (DCOs), a two-level distributed algorithm is proposed in this work. Finally, the proposed model is verified on an improved IEEE 118-bus power system with renewable energy farms and DCs. Simulation results prove that DCs’ participation in DR has a positive effect on reducing renewable energy curtailment and the proposed algorithm has high solution accuracy on the premise of privacy data protection of DCOs.
Find Out: How Do Your Data Packets Travel?

In today’s communication-centric world, users generate and exchange a massive amount of data. The Internet helps user data to travel from one part of the world to another, via a complex set of network systems. These systems are intelligent, heterogeneous, and non-transparent to users. This paper presents an extensive, trace-driven study of user data traffic covering five years of observations, six large ISPs, 22 different autonomous systems, and a total of 12 countries. This work aims to make users aware of how their data travels in the Internet, as the interests of ISPs majorly influence the data traffic path. Although data traffic should prefer to travel through countries that share land borders, we found that the shortest land distance between the two countries does not impact data path selection. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Distillation Language Adversarial Network for Cross-lingual Sentiment Analysis

Cross-lingual sentiment analysis aims at tackling the lack of annotated corpus of variant low-resource languages by training a common classifier, to transfer the knowledge learned from the source language to target languages. Existing large-scale pre-trained language models have got remarkable improvements in cross-lingual sentiment analysis. However, these models still suffer from lack of annotated corpus for low-resource languages. To address such problems, we propose an end-to-end sentiment analysis architecture for cross-lingual sentiment analysis, named Distillation Language Adversarial Network (DLAN). Based on pre-trained model, DLAN uses adversarial learning with knowledge distillation to learn language invariant features without extra training data. We evaluate the proposed method on Amazon review dataset, a multilingual sentiment dataset. The results illustrate that DLAN is more effective than the baseline methods in cross-lingual sentiment analysis.
3D-NWA: A Nested-Winograd Accelerator for 3D CNNs

3D Convolutional neural networks (3D CNNs) perform better in some scenarios, such as video understanding and 3D medical image diagnosis. With the increase in the dimension and size of the convolution kernel, CNN's computational complexity and implementation difficulty increase severely. Winograd transformation can significantly reduce the number of multiplications in convolution operations. However, large convolution filters will bring numerical instability. In this article, we presented a novel method called 3D nested Winograd algorithm to address the problem. Compared with the state-of-art OLA-Winograd algorithm, the proposed algorithm reduces the multiplications by 1.72 to 5.83× for computing 5 × 5 × 5 to 9 × 9 × 9 convolutions. Finally, we demonstrate the efficiency of 3D-NWA on the FPGA platform (Xilinx VCU118) and achieve highest DSP efficiency up to 4.67× compared with the state-of-art accelerators.
Emoji Prediction from Arabic Sentence

Emoji prediction aims to identify the appropriate emoji or set of emojis associated with the text. With the help of a machine and deep learning, models can learn and comprehend rich representations of the contextual and emotional intent of written texts. However, each emoji has a large conceptual and emotional range. Accordingly, each emoji may be used in a variety of sentences. We can choose the proper emoji for each text by assessing its content and attitude. As a result, one of the challenges that have drawn academics' attention is predicting an emoji from a given textual input. Even though the majority of existing research on the emoji prediction task focuses on the English language, no study has been done on the Arabic language in the emoji prediction task from a whole sentence. Therefore, in this project, using deep learning LSTM, an attempt for the first time has been made to predict the emoji for Arabic text. Similarly, three machine learning algorithms were examined, and the performance of the models was analyzed. Moreover, we were able to achieve, with a deep learning model based on LSTM, an accuracy of 80% using the 20 most common emojis, which is much greater than the accuracy of the SVM, Naive Bayes, and Random Forest models, which were respectively 29%, 27%, and 26 %. Furthermore, due to the unavailability of an Arabic emoji prediction dataset, the dataset has been reconstructed by translating the text in the existing English emoji-text pair dataset into Arabic text.
Isolated Kannada Character Recognition using Densely Connected Convolutional Network

Handwritten Character Recognition and Identification are one of the most interesting problem statements in the present digitized world because of its variety of applications. It has leveraged its potential in reducing the manual work of converting the documents containing handwritten characters to machine-readable texts. Recognition of handwritten characters is challenging due to various reasons like high variance in the writing styles across the globe, poor quality of the handwritten text compared to the printed text and the size of the handwritten text. Kannada language has a history of over 1000 years. Kannada vowels and consonants are curvy and symmetric in nature and hence recognition in an offline system becomes difficult. Hence, recognition of Handwritten Kannada characters effectively serves as the main objective of this work. This work proposes a DenseNet121 based Character Recognition model that effectively recognizes the Handwritten Kannada characters. Transfer Learning is used to improve the overall performance of the model. The proposed model achieved a training accuracy of 96.7% and test accuracy of 96.28%, hence proving the effectiveness of the model.
Software Defect Detection Method Based on Graph Structure and Deep Neural Network

For software source code defect detection, in order to achieve higher generalization ability and higher detection accuracy, this paper proposes a defect detection method based on graph structure and deep neural network. Use the code representation methods of abstract syntax tree, program dependency graph and code property graph to generate the dependency relationship of code data, extract the defect candidate key nodes of source code, and slice the program. Word2vec and one hot coding methods combined with attention mechanism are used to extract the semantic feature information and sentence type information of the program statements. In the deep neural network part, the BiGRU network model with attention mechanism is selected for deep learning to extract context information, forward and backward sequence information. Compared with the existing defect detection tools by data on SARD and CVE dataset, the generalization ability and detection effect are significantly improved. When applied to software testing, this method can achieve more efficient testing capability.
Multilingual Neural Machine Transliteration with Adaptive Segmentation Schemes

The lack of training data and divergences in the phonological structures of languages pose challenges to machine transliteration. In this paper, we present a multilingual neu-ral machine transliteration framework to encourage knowledge transfer from high-resource languages and low-resource languages. In order to mitigate the phonological divergence issue, we propose to use different segmentation schemes that are adaptive to the source or the target language. Experiment results on public datasets demonstrate that multilingual neural machine transliteration significantly outperforms bilingual transliteration on both mid- and low-resource languages and that segmentation schemes have a great impact on transliteration quality.
A Novel Variational Digital Filtering Method

In this paper, a novel variational filtering processing method is proposed, which can realize lowpass/highpass filtering and bandpass/bandstop filtering of signals by constructing the corresponding variational minium problem with constrain and solving it by mean of the augmented Lagrangian and the alternate direction method of multipliers algorithm. The method presented eliminates the phase delay between output and input signal, while maintaining the low-distortion property of output signal. In the experiments, the variational filtering method is applied to simulated and actual signal to assess the validity of the approach. Results show that the proposed variational digital filtering method achieves low-distortion filtering. And compared with the commonly used zero-phase forward-backward Butterworth filter, the proposed method has a better filtering effect.
Transductive Graph-Attention Network for Few-shot Classification

Few-shot Classification aims to use a small number of labeled samples to learn a general model that can solve the problem of image classification. At present, Few-shot Classification methods can be divided into three categories: Data Augmentation, Model Optimization and Gradient Descent Algorithm Optimization. However, most of the proposed work still has the problem of insufficient feature extraction due to too little data, and it is difficult for the model to fully operate the extracted features. In order to address these problems, we propose a Transductive Graph-Attention Network for Few-shot Classification (TGAN). In particular, TGAN uses attention to construct the similarity graph structure between samples to describe the relationship between labeled samples and unlabeled samples, and then uses label propagation algorithm to accurately classify. Experiments show that TGAN has excellent performance on MiniImageNet datasets and TieredImageNet datasets.