Reviewerly/exHarmony huggingface dataset:
Group Leakage Overestimates Performance: A Case Study in Keystroke Dynamics

Keystroke dynamics is a powerful behavioral biometric capable of user authentication based on typing patterns. As larger keystroke datasets become available, machine learning and deep learning algorithms are becoming popular. Knowledge of every possible impostor is not known during training which means that keystroke dynamics is an open set recognition problem. Treating open set recognition problems as closed set (assuming samples from all impostors are present) can cause models to incur data leakage, which can provide unrealistic overestimates of performance. It is a common problem in machine learning and can cause models to report higher accuracies than would be expected in the real world. In this paper, we outline open set recognition and discuss how, if not handled properly, it can lead to data leakage. The performance of common machine learning methods, such as SVM and MLP are investigated with and without leakage to clearly demonstrate the differences in performance. A synthetic dataset and a publicly available keystroke dynamics fixed-text dataset are used for research transparency and reproducibility.
Pancreas Volumetry in UK Biobank: Comparison of Models and Inference at Scale

The UK Biobank imaging sub-study enables large-scale measurement of pancreas volume, an important biomarker in metabolic disease, including diabetes. Previous methods utilised a pancreas-specific (PS) 3D MRI UK Biobank acquisition to automatically measure pancreas volume. This may lead to a clinically significant underestimation of volume, due to partial coverage of the pancreas in these acquisitions. To address this, we propose a pipeline for the accurate measurement of pancreas volume using stitched whole-body (WB) 3D MRI UK Biobank acquisitions and deep learning-based segmentation. We implement and compare the performance of six different U-Net-like model architectures, leveraging attention layers, recurrent layers, and residual blocks. Furthermore, we investigate pancreas volumetry in 42,313 subjects, separated by sex, and present novel results concerning the change in pancreas volume throughout the course of a day (diurnal variation). To the best of our knowledge, this is the largest pancreas volumetry study to date and the first to propose a pipeline using the whole-body UK Biobank MRI acquisitions to measure pancreas volume.
Relation Extraction: A Brief Survey on Deep Neural Network Based Methods

Knowledge, data, algorithms and computing power are the foundations of artificial intelligence (AI), for which knowledge is the most powerful support. An effective way to acquire knowledge, called Relation Extraction (RE), is beneficial to knowledge graph completion, path inference, logical rule reasoning, and many other AI tasks. In this survey, we provide a comprehensive review on deep neural networks (DNNs) based RE covering some main research topics about: 1) general framework, 2) supervised and distant supervised RE, 3) future research directions. General framework provides a full-view summarization on traditional methods, main component and basic conceptions of DNN-based RE. For supervised and distant supervised RE, this part summarizes DNN-based RE methods into a new taxonomies on these topics. In the end, to facilitate future research on RE, we also discuss some obstacles.
RNN-Based Learning of Nonlinear Dynamic System Using Wireless IIoT Networks

We consider the recurrent neural network (RNN)-based remote state estimation for nonlinear dynamic systems with unknown state dynamics. The nonlinear dynamic plant is monitored by multiple distributed IIoT sensors over a random access wireless network with shared common spectrum. We focus on the remote state estimation algorithm design so as to achieve remote state estimation stability subject to noninvertible nonlinear sensor state observations, imperfect channel state information (CSI) at the remote estimator, and various wireless impairments, such as multisensor interference, wireless fading, and additive channel noise. Utilizing a state diffeomorphism, the original system is transformed into a canonical form with a linear rank deficient observation matrix. We propose a novel RNN remote state estimator based on the pole placement design associated with the transformed rank deficient state measurement matrices. We further propose a novel online training algorithm such that the RNN at the remote estimator can not only address the divergence issue over wireless networks but also effectively learn the unknown nonlinear plant dynamics despite rank deficiency and imperfect CSI. Using the Lyapunov drift analysis approach, we establish closed-form sufficient requirements on the communication resources needed to achieve almost sure stability of both state estimation and RNN online training in the high signal-to-noise ratio (SNR) regime. As a result, our proposed scheme is asymptomatic optimal for large SNR in the sense that both the plant state and the unknown plant nonlinearity can be perfectly recovered at the remote estimator. The proposed scheme is also compared with various baselines and we show that significant performance gains can be achieved.
Double event joint location method considering P-wave arrival time system errors

Microseismic (MS) source location is a core technology in MS monitoring that helps to infer the mechanical state of the rock mass and prevent dynamic disasters. However, when considering P-wave arrival time system errors (PATSEs), conventional location methods encounter the problem of high inversion dimensions and do not consider the influence of large P-wave arrival time errors. Therefore, we proposed a low-dimension double event joint location (DEJL) method that considers the PATSEs and introduces data field theory to reduce the influence of large P-wave arrival time errors. First, the P-wave travel-time equation of double events was constructed to eliminate the earthquake occurrence time and PATSEs, and the high-dimension inversion was reduced to a set of equations containing only six parameters. Then, 2000 P-wave arrival time combinations from more than six sensors were selected, and a Newton iterative based algorithm was proposed to solve the location point of each combination. Finally, the location point with the largest potential value of the data field was considered as the location result. The Institute of Mine Seismology acquisition system, placed in the Yongshaba mine in Guizhou Province, China, was selected to conduct the tests. P-wave arrival time datasets were obtained from two synthetic locations and eight blasting events. The results showed that the average location error of the blasting events using the proposed method is 32.59 m, which is lower than that of the homogeneous velocity-based location results. Thus, we determined that the proposed method offers a good MS location accuracy and broad application potential.
LIFTS

This PhD project aims to automatically learn transition systems capturing the behaviour of a whole family of software-based systems. Reasoning at the family level yields important economies of scale and quality improvements for a broad range of systems such as software product lines, adaptive and configurable systems. Yet, to fully benefit from the above advantages, a model of the system family's behaviour is necessary. Such a model is often prohibitively expensive to create manually due to the number of variants. For large long-lived systems with outdated specifications or for systems that continuously adapt, the modelling cost is even higher. Therefore, this PhD proposes to automate the learning of such models from existing artefacts. To advance research at a fundamental level, our learning target are Featured Transition Systems (FTS), an abstract formalism that can be used to provide a pivot semantics to a range of variability-aware state-based modelling languages. The main research questions addressed by this PhD project are: (1) Can we learn variability-aware models efficiently? (2) Can we learn FTS in a black-box fashion? (i.e., with access to execution logs but not to source code); (3) Can we learn FTS in a white/grey-box testing fashion? (i.e., with access to source code); and (4) How do the proposed techniques scale in practice?
A count sketch maximal weighted residual Kaczmarz method for solving highly overdetermined linear systems

In this paper, combining count sketch and maximal weighted residual Kaczmarz method, we propose a fast randomized algorithm for highly overdetermined linear systems. Convergence analysis of the new algorithm is provided. Numerical experiments show that, for the same accuracy, our method behaves better in computing time compared with the maximal weighted residual Kaczmarz algorithm.
Assistive Smart Home Environment using Head Gestures and EEG Eye Blink Control Schemes

Field of Assistive Smart Homes has emerged with the aim of enabling the physically challenged, the elderly or those with constraint motion and to restore their capability of performing necessary daily life tasks by providing required assistance using modern technological tools. Objective of this work is to study possibility of using various available technological tools to enable such people perform independently in main stream life by giving them control of their environment and movement. The said objective is achieved using hybrid physiological gestures, such as, head movement and eye blinks, as even quadriplegic patients can perform these gestures easily. The orientation or movement of head is sensed by a head set embedded with an Inertial Measurement Unit (IMU) and Linear Discriminant Analysis (LDA) is used to recognize the intended command. Eye blinks are detected by sensing Electroencephalography (EEG) signals. After pre-processing, EEG signals are classified on the basis of various signal properties and converted into commands. With the combination of head orientation sensing and eye blink signals, a hierarchy of control commands is generated to control lights, fans, security lock and wheel chair movement. Using the prototype headset, the home environment is simulated and verified in Matlab environment and a GUI is designed for ease of user. The results show the feasibility of the designed system in real time, with average system accuracy of approximately 81.48%, making this design a good and reasonably priced choice for implementation in Assistive Smart Homes, especially in developing countries with low per capita income.
Permutation-based optimization using a generative adversarial network

This paper presents a new method to deal with permutation-based problems using a Generative Adversarial Network. Generative adversarial Networks (GANs) are used in many fields as models that mimic the trained data distribution. However, dealing with permutation space in GANs has not been yet investigated in detail. To reach this objective, we propose using GANs to solve the permutation-based optimization problem using an Estimation of Distribution Algorithm. We use a Random Key method to translate the generated data from continuous values to permutations. The Experiment aims to investigate the quality of solutions provided by the proposed method in two ways. Firstly, we evaluate the solutions obtained directly from the GANs, and in the second one, we use a local search method to improve the quality of the solution. In the end, We perform a set of experiments on instances of the known TSP problems. Also, we notice that the obtained results promise to extend the work on other classes of problems.
Fishing out the Phishing Websites

Phishing is a cybercrime in which deceitful websites lure naive users and trick them into disclosing confidential information, such as social media passwords or financial data. Phishing websites are crafted such that they superficially appear similar to popular legitimate websites. This paper aims to detect such phishing websites by proposing a novel classifier that takes lexical-based, script-based, rule-based, and address-based features extracted from a website into account. A large-scale balanced dataset of 38,800 active phishing and legitimate websites is created, on which tree-based ensemble classifiers are trained, out of which the XGBoost (eXtreme Gradient Boosting) model performs the best with a testing accuracy of 99.6%. The classifier can detect zero-day phishing attacks without requiring any third- party features such as page rank. Several other benefits of using this model over the state-of-the-art techniques are discussed.
Impact of Filtering Generated Pseudo Bilingual Texts in Low-Resource Neural Machine Translation Enhancement: The Case of Persian-Spanish

Although the Neural Machine Translation (NMT) framework has already been shown effective in large training data scenarios, it is less effective for low-resource conditions. To improve NMT performance in a low-resource setting, we extend the high-quality training data by generating a pseudo bilingual dataset and then filtering out low-quality alignments using a quality estimation based on back-translation. We demonstrate that our approach yields significantly higher BLEU scores than those of a set of baselines.
Feature Vulnerability and Robustness Assessment against Adversarial Machine Learning Attacks

Whilst machine learning has been widely adopted for various domains, it is important to consider how such techniques may be susceptible to malicious users through adversarial attacks. Given a trained classifier, a malicious attack may attempt to craft a data observation whereby the data features purposefully trigger the classifier to yield incorrect responses. This has been observed in various image classification tasks, including falsifying road sign detection and facial recognition, which could have severe consequences in real-world deployment. In this work, we investigate how these attacks could impact on network traffic analysis, and how a system could perform misclassification of common network attacks such as DDoS attacks. Using the CICIDS2017 data, we examine how vulnerable the data features used for intrusion detection are to perturbation attacks using FGSM adversarial examples. As a result, our method provides a defensive approach for assessing feature robustness that seeks to balance between classification accuracy whilst minimising the attack surface of the feature space.
FRACTIONAL QUANTUM LOGISTIC MAP AND ITS APPLICATION IN IMAGE ENCRYPTION TECHNOLOGY

In this paper, a novel fractional quantum logistic map (FQLM) based on fractional difference is proposed and image encryption algorithm based on it are exploited. First, the FQLM is proposed and its dynamical behaviors are observed with bifurcation diagram, phase portraits and largest Lyapunov exponent plots. After that, the ECC is utilized to cover up the encryption keys and the FQLM is used for the permutation and diffusion process of image encryption. Finally, the cryptosystem is used in image encryption and the algorithm is analyzed in four respects, showing a distinct advantage of the proposed cryptosystem over some of existing ones.
Improving the Learning of Mechanics Through Augmented Reality

Abstract This study investigates to which extent students’ understanding of the physical phenomenon of torque can be improved through the use of visualization technology, in particular of augmented reality (AR). The students in the first-year course Mechanics I at KTH participated in the study by taking two tests on torque. In between those tests, a subgroup of students participated in a user study where they used an AR application to solve problems regarding torque. The results of the pre-test and the post-test indicate that the subgroup who used the app improved their understanding of torque to a greater extent than the ones who did not use the app. However, a larger sample space would be required to obtain a complete statistical characterization of the reported (qualitative and quantitative) improvement.
Spectral MVIR: Joint Reconstruction of 3D Shape and Spectral Reflectance

Reconstructing an object's high-quality 3D shape with inherent spectral reflectance property, beyond typical device-dependent RGB albedos, opens the door to applications requiring a high-fidelity 3D model in terms of both geometry and photometry. In this paper, we propose a novel Multi-View Inverse Rendering (MVIR) method called Spectral MVIR for jointly reconstructing the 3D shape and the spectral reflectance for each point of object surfaces from multi-view images captured using a standard RGB camera and low-cost lighting equipment such as an LED bulb or an LED projector. Our main contributions are twofold: (i) We present a rendering model that considers both geometric and photometric principles in the image formation by explicitly considering camera spectral sensitivity, light's spectral power distribution, and light source positions. (ii) Based on the derived model, we build a cost-optimization MVIR framework for the joint reconstruction of the 3D shape and the per-vertex spectral reflectance while estimating the light source positions and the shadows. Different from most existing spectral-3D acquisition methods, our method does not require expensive special equipment and cumbersome geometric calibration. Experimental results using both synthetic and real-world data demonstrate that our Spectral MVIR can acquire a high-quality 3D model with accurate spectral reflectance property.
Emotion Recognition Based on Piezoelectric Keystroke Dynamics and Machine Learning

Emotion recognition based on touch event's temporal and force information receives global interests. However, current consumer touch panels cannot provide user's accurate force data. Moreover, conventional studies extracting various features from complex missions, can't achieve real-time emotion detection. To address these two issues, in this paper, a piezoelectric based keystroke dynamic technique for quick emotion detection is presented. The high sensitivity of force detection is achieved for the nature of piezoelectric materials. Meanwhile, we simplify the mission to merely password entry and extract features from only time and pressure dimension, reducing the time spent for feature extraction and processing. The discrete model (PAD 3dimensional-model) for emotion classification is employed. International Affective Digitized Sounds (IADS) is applied to elicit emotions and a Chinese version of abbreviated PAD emotion scale is used to evaluate the degree of emotion induction. With Random Forest Classifier, a 4-emotion-classification (happiness, sadness, fear, disgust) with an average accuracy of 78.31% is achieved. The proposed technique improves the reliability and practicability of emotion recognition in realistic applications.
RETRACTED ARTICLE: Cancer detection using deep learning techniques

Breast cancer has become the most common form of cancer in world recently having overtaken cervical cancer in urban cities. Immense research has been carried out on breast cancer and several automated machines for detection have been formed, however, they are far from perfection and medical assessments need more reliable services. Computer Assisted Diagnostics programs have been developed over the past 2 decades to help radiologists interpret mammogram screening. Deep convolutional neural networks (CNN), which have surpassed human output since 2012, have been an immense success in image recognition. Deep CNNs will revolutionize the analysis of medical images. We propose a method for breast cancer detection based on Faster R-CNN, the most common frameworks for object detection. In a non-human interference mammogram, the device detects and categorizes malignant or benign lesions. The method proposed sets the current status of the INbreast database public classification scheme, AUC = 0.95. In the digital mammography challenge DREAM with AUC = 0.85, the method mentioned here was second. When the device is used as a sensor, the accuracy of the INbreast data set is extremely low with very false positive image points.
Cricket: A virtualization layer for distributed execution of CUDA applications with checkpoint/restart support

Abstract In high‐performance computing and cloud computing the introduction of heterogeneous computing resources, such as GPU accelerator have led to a dramatic increase in performance and efficiency. While the benefits of virtualization features in these environments are well researched, GPUs do not offer virtualization support that enables fine‐grained control, increased flexibility, and fault tolerance. In this article, we present Cricket: A transparent and low‐overhead solution to GPU virtualization that enables future research into other virtualization techniques, due to its open‐source nature. Cricket supports remote execution and checkpoint/restart of CUDA applications. Both features enable the distribution of GPU tasks dynamically and flexibly across computing nodes and the multitenant usage of GPU resources, thereby improving flexibility and utilization for high‐performance and cloud computing.
On Hard and Soft Decision Decoding of BCH Codes

The binary primitive BCH codes are cyclic and are constructed by choosing a subset of the cyclotomic cosets. Which subset is chosen determines the dimension, the minimum distance and the weight distribution of the BCH code. We construct possible BCH codes and determine their coderate, true minimum distance and the non-equivalent codes. A particular choice of cyclotomic cosets gives BCH codes which are, extended by one bit, equivalent to Reed-Muller codes, which is a known result from the sixties. We show that BCH codes have possibly better parameters than Reed-Muller codes, which are related in recent publications to polar codes. We study the decoding performance of these different BCH codes using information set decoding based on minimal weight codewords of the dual code. We show that information set decoding is possible even in case of a channel without reliability information since the decoding algorithm inherently calculates reliability information. Different BCH codes of the same rate are compared and different decoding performances and complexity are observed. Some examples of hard decision decoding of BCH codes have the same decoding performance as maximum likelihood decoding. All presented decoding methods can possibly be extended to include reliability information of a Gaussian channel for soft decision decoding. We show simulation results for soft decision list information set decoding and compare the performance to other methods.
An Anonymous Credential System with Constant-Size Attribute Proofs for CNF Formulas with Negations

To enhance the user's privacy in electronic ID, anonymous credential systems have been researched. In the anonymous credential system, a trusted issuing organization first issues a certificate certifying the user's attributes to a user. Then, in addition to the possession of the certificate, the user can anonymously prove only the necessary attributes. Previously, an anonymous credential system was proposed, where CNF (Conjunctive Normal Form) formulas on attributes can be proved. The advantage is that the attribute proof in the authentication has the constant size for the number of attributes that the user owns and the size of the proved formula. Thus, various expressive logical relations on attributes can be efficiently verified. However, the previous system has a limitation: the proved CNF formulas cannot include any negation. Therefore, in this paper, we propose an anonymous credential system with constant-size attribute proofs such that the user can prove CNF formulas with negations. For the proposed system, we extend the previous accumulator for the limited CNF formulas to verify CNF formulas with negations.