Miracle-dz/UniFAR-FTUs huggingface dataset:
IDSP: A New Identity-Based Security Protocol for Cluster-Based Wireless Sensor Networks

Communication security in Cluster-Based Wireless Sensor Network (CWSN) is considered as a challenging task facing this type of network. The dynamic nature of CWSN and its deployment in open areas making these networks vulnerable to different kinds of cyber-attacks that can adversely affect its own functioning. Moreover, the resource-constrained nature of sensor devices make it impractical to apply conventional security schemes in CWSN, which require high overhead of computation, communication and memory storage. To deal with this issue of communication security, we propose an efficient secure protocol in CWSN called IDentity-based Security Protocol (IDSP), which guarantees secure communication between Cluster Head (CH) and Base Station (BS), as well as communication between CH and Sensor Node (SN). IDSP is based on Identity-Based Cryptography (IBC) that doesn’t require Public Key Infrastructure (PKI) or complicated certificate management. The cornerstone of our protocol is to provide a perfect trade-off between three key factors: (i) ensuring a good protection level, especially against various cyber-attacks that may target CWSN, (ii) proposing an efficient process regarding energy consumption and key storage, (iii) introducing a key exchange mechanism in IDSP which is lightweight and uncomplicated. Moreover, our protocol treats public key authentication problem. The proposed protocol is tested in Cooja network simulator with WiSMote platform, and the performance results showed that IDSP is efficient, lightweight and secure.
An ensemble learning approach for the classification of remote sensing scenes based on covariance pooling of CNN features

This paper aims at presenting a novel ensemble learning approach based on the concept of covariance pooling of CNN features issued from a pretrained model. Starting from a supervised classification algorithm, named multilayer stacked covariance pooling (MSCP), which exploits simultaneously second order statistics and deep learning features, we propose an alternative strategy which employs an ensemble learning approach among the stacked convolutional feature maps. The aggregation of multiple learning algorithm decisions, produced by different stacked subsets, permits to obtain a better predictive classification performance. An application for the classification of large scale remote sensing images is next proposed. The experimental results, conducted on two challenging datasets, namely UC Merced and AID datasets, improve the classification accuracy while maintaining a low computation time. This confirms, besides the interest of exploiting second order statistics, the benefit of adopting an ensemble learning approach.
Estimation of Measurement-Noise Variance for Variable-Step-Size NLMS Filters

Least-mean-square (LMS) filters are a well-studied processing technique that adapts iteratively to an unknown process. It has been proven that the parameters of the LMS filter converge to the optimum (Wiener) solution. Unfortunately, this is only possible if the adaptation steps are infinitely small. Small steps, however, result in slow convergence. The challenge is to vary the step size such that they are large, when the LMS filter coefficients are far from their optimal values and to lower the step size, when the adaptive system is approaching the optimum. State-of-the-art approaches to variable-step-size determination take estimates of the measurement noise into account for optimal performance. This paper proposes a new technique for the estimation of the measurement-noise variance, which also can deal with sudden changes of the unknown system. Based on investigations with a broad range of experimental conditions in terms of test signals and different measurement-noise levels, it is shown that the proposed estimation technique is robust to changes of the unknown system and outperforms other methods.
Factors influencing digital government adoption in Zimbabwe

D-government has demonstrated to be a game-changing tool for improving government-to-citizen (G2C) interactions and service offering across the world. Despite achieving commendable results in the developed world, the adoption of d-government systems by citizens in developing nations remains sluggish and constrained by several factors. This study investigates the effects of effort expectancy, cost of access, intention to use and information quality on citizens’ decisions to adopt and use d-government systems in Zimbabwe. Underpinned by the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) as a theoretical lens, this study proposes a research model. To test the model, survey data was collected from 489 randomly selected respondents using self-administered questionnaires. Regression The findings reveal that effort expectancy, cost of access and information quality have a positive significant influence on intention to use; whereas the intention to use positively influence use behaviour. Findings of this study have implications decision-makers and practitioners involved in digital government matters and endeavouring to improve its adoption by citizens.
A mutation screen of NR2E1 in patients with aniridia, Peters' anomaly and related eye disorders

ii TABLE OF CONTENTS . iii LIST OF TABLES . v LIST OF FIGURES . vi LIST OF ABBREVIATIONS . vii ACKNOWLEDGMENTS . viii CO-AUTHORSHIP STATEMENT . ix CHAPTER 1: INTRODUCTION . 1 1.1.1 Structure of the human eye. 1 1.1.2 Aniridia. 2 1.1. 3 Peters’ anomaly . 4 1.1. 4 NR2E1 . 7 1.1. 5 NR2E1 and PAX6 . 9 1.1. 5 NR2E1 and NR2E3 . 10 1.1. 6 NR2E3 and eye disorder . 12 1.1. 7 Hypothesis and thesis objectives . 14 1.2 REFERENCES . 15 CHAPTER 2: A MUTATION SCREEN OF NR2E1 IN PATIENTS WITH ANIRIDIA, PETERS’ ANOMALY, AND RELATED EYE DISORDERS . 26 2.
An Adaptive Video Acquisition Scheme for Object Tracking

In this paper, we propose an adaptive host-chip system for video acquisition constrained under a given bit rate to optimize object tracking performance. The chip is an imaging instrument with limited computational power consisting of a very high-resolution focal plane array (FPA) that transmits quadtree (QT)-segmented video frames to the host. The host has unlimited computational power for video analysis. We find the optimal QT decomposition to minimize a weighted rate distortion equation using the Viterbi algorithm. The weights are user-defined based on the class of objects to track. Faster R-CNN and a Kalman filter are used to detect and track the objects of interest respectively. We evaluate our architecture’s performance based on the Multiple Object Tracking Accuracy (MOTA).
Reducing Computational Complexity of Factor Graph-Based Belief Propagation Algorithm for Detection in Large-Scale MIMO Systems

In large-scale multiple-input multiple-output (LS-MIMO) systems, by exploiting hundreds of antennas at the base station, spectral efficiency, power efficiency, and link reliability can be enhanced significantly. However, by increasing the number of antennas, the computational complexity of the detectors makes the hardware implementation intractable, and therefore, LS-MIMO systems require sub-optimal low complexity detection algorithms. In this paper, two novel approaches for improving factor graphbased belief propagation with Gaussian approximation of interference (FG-BP-GAI) algorithm is proposed to reduce the computational complexity of the belief propagation (BP) based receiver without bit error rate (BER) degradation. More specifically, two novel techniques, namely odd Taylor series and odd least square, are proposed to approximate the a posteriori probability in the FG-BP-GAI policy with few polynomial terms of low degree. In the simulation results, the performance of our proposed algorithms are assessed and it is shown that our proposed improved FGBP-GAI policies can achieve lower computational complexity compared with the other approaches in the literature like MRF-BP algorithm without BER degradation.
An Evaluation of CNN-based Liver Segmentation Methods using Multi-types of CT Abdominal Images from Multiple Medical Centers

Automatic segmentation of CT images has recently been applied in several clinical liver applications. Convolutional Neural Networks (CNNs) have shown their effectiveness in medical image segmentation in general and also in liver segmentation. However, liver image quality may vary between medical centers due to differences in the use of CT scanners, protocols, radiation dose, and contrast enhancement. In this paper, we investigate three wells known CNNs, FCN-CRF, DRIU, and V-net, for liver segmentation using data from several medical centers. We perform qualitative evaluation of the CNNs based on Dice score, Hausdorff distance, mean surface distance and false positive rate. The results show that all three CNNs achieved a mean Dice score of over 90% in liver segmentation with typical contrast enhanced CT images of the liver. p-values from paired T-test on Dice score of the three networks using Mayo dataset are larger than 0.05 suggesting that no statistical significant difference in their performance. DRIU performs the best in term of processing time. The results also demonstrate that those CNNs have reduced performance in liver segmentation in the case of low-dose and non-contrast enhanced CT images. In conclusion, these promising results enable further investigation of alternative deep learning based approaches to liver segmentation using CT images.
The socioeconomic pattern of health and developmental outcomes among Aboriginal and Torres Strait Islander children

Background: The burden of mental health problems among Aboriginal and Torres Strait Islander children is a major public health problem in Australia. While socioeconomic factors are implicated as important determinants of mental health problems in mainstream populations, their bearing on the mental health of Indigenous Australians remains largely uncharted across all age groups. Methods: We examined the relationship between the risk of clinically significant emotional or behavioural difficulties (CSEBD) and a range of socioeconomic measures for 3993 Indigenous children aged 4–17 years in Western Australia, using a representative survey conducted in 2000–02. Analysis was conducted using multivariate logistic regression within a multilevel framework. Results: Almost one quarter (24%) of Indigenous children were classified as being at high risk of CSEBD. Our findings generally indicate that higher socioeconomic status is associated with a reduced risk of mental health problems in Indigenous children. Housing quality and tenure and neighbourhood-level disadvantage all have a strong direct effect on child mental health. Further, the circumstances of families with Indigenous children (parenting quality, stress, family composition, overcrowding, household mobility, racism and family functioning) emerged as an important explanatory mechanism underpinning the relationship between child mental health and measures of material wellbeing such as carer employment status and family financial circumstances. Conclusions: Our results provide incremental evidence of a social gradient in the mental health of Aboriginal and Torres Strait Islander children. Improving the social, economic and psychological conditions of families with Indigenous children has considerable potential to reduce the mental health inequalities within Indigenous populations and, in turn, to close the substantial racial gap in mental health. Interventions that target housing quality, home ownership and neighbourhood-level disadvantage are likely to be particularly beneficial.
Suicide Prediction in Twitter Data using Mining Techniques: A Survey

Online social networks and the World Wide Web specifically Twitter, have expanded the network between individuals with an end goal that data can be spread to a huge number of individuals in a matter of minutes. This type of online collective contagion has given numerous advantages to society, for example, giving consolation and crisis administration in the quick fallout of catastrophic events. Be that as it may likewise represents a potential hazard to powerless Web clients who get this data and could consequently come to hurt. One example is the spread of suicidal ideation in online Social network like Twitter, which has risen in concern. In this Survey paper, we report the consequences and results of various machine classifiers worked with the point of arranging text identifying with suicide on Twitter. The classifier recognizes the more stressing content, for example, suicidal ideation, and other suicide related points, for example, detailing of a suicide, dedication, campaigning and bolster. It aims to refer and identify flippant references to suicide by using baseline classifier using emotive, lexical, psychological, and structural features from twitter. In this survey clustering, classification, martingale function, association rule, Natural language processing (NLP) and different machine learning techniques are used. This paper further discusses about the research challenges in this domain that initiates the scope for further research.
Min-Delay Margin/Error Detection and Correction for Flip-Flops and Pulsed Latches in 10-nm CMOS

Min-delay (MID) error rates increase dramatically under aggressive voltage and technology scaling, limiting VMIN. Pulsed latches offer significant clocking power savings over flip-flops but further aggravate MID failures. This letter proposes MID margin/error detection and correction (M2/EDAC) for flip-flops and pulsed latches to reduce VMIN guard bands for voltage noise, temperature variation, and aging, and to detect and correct rare MID failures. Statistical data collection from a prototype in 10-nm tri-gate CMOS shows up to 122-mV VMIN reduction. Reliable pulsed latches enabled by M2/EDAC offer 12%–18% total dynamic power savings for logic blocks in 10-nm CMOS.
Semantics-Aware Autoencoder

Recommender Systems are widely adopted in nowadays services such as e-commerce websites, multimedia streaming platforms, and many others. They help users to find what they are looking for by suggesting relevant items leveraging their past preferences. Deep Learning models are very effective in solving the recommendation problem; as a matter of fact, many deep learning architectures have been proposed over the years. Even if deep learning models outperform many state-of-the-art algorithms, the worst disadvantage is about their interpretability: explaining the reason a specific item has been recommended to a user is quite a difficult task since the model is not interpretable. Accuracy in the recommendation is no more enough since users are also expecting a useful explanation for the suggested items. Users, on the other hand, want to know why. In this paper, we present SemAuto, a novel approach based on an Autoencoder Neural Network that makes it possible to semantically label neurons in hidden layers, thus paving the way to the model’s interpretability and consequently to the explanation of a recommendation. We tested our semantics-aware approach with respect to other state-of-the-art algorithms to prove the recommendation’s accuracy. Furthermore, we performed an extensive A/B test with real users to evaluate the explanation we generate.
Automated Suggestions of Security Enhancing Improvements for Software Architectures

Today, connectivity is demanded in almost every domain, e.g., the smart home domain and its connected smart household devices like TVs and fridges, or the industrial automation domain, connecting plants, controllers and sensors to the internet for purposes like condition monitoring. This trend amplifies the need for secure applications that can protect their sensitive data against manipulation and leaks. However, many applications are still built without considering security in its design phase, often it is perceived as too complicated and time consuming. This is a major oversight, as fixing vulnerabilities after release is often not feasible when major architecture redesigns are necessary. Therefore, the software developer has to make sure that the developed software architecture is secure. Today, there are some tools available to help the software developer in identifying potential security weaknesses of their architecture. However, easy and fast to use tools that support the software developer in improving their architecture's security are lacking. The goal of my thesis is to make security improvements easily applicable by non-security and non-architecture experts by proposing systematic, easy to use and automated techniques that will help the software developer in designing secure software architectures. To achieve this goal, I propose a method that enables the software developer to automatically find flaws and weaknesses, as well as appropriate improvements in their given software architecture during the design phase. For this method, I adopt Model-Based Development techniques by extending and creating Domain-Specific Languages (DSL) for specifying the architecture itself and possible architectural improvements. Using these DSLs, my approach automatically suggests security enhancing improvements for the architecture, promoting increased security of software architectures and as such for the developed applications as a whole.
Cross-domain Knowledge Transfer Schemes for 3D Human Action Recognition

Previous work in 3D human action recognition has been mainly confined to schemes in a single domain, exploiting in principle skeleton-tracking data, due to their compact representation and efficient modeling of the observed motion dynamics. However, in order to extend and adapt the learning process to multi-modal domains, inevitably the focus needs also to be put on cross-domain analysis. On the other hand, attention schemes, which have lately been applied to numerous application cases and exhibited promising results, can exploit the intra-affinity of the considered modalities and can then be used for performing intra-modality knowledge transfer, e.g. to transfer domain-specific knowledge of the skeleton modality to the flow one and vice verca. This study investigates novel cross-modal attention-based strategies to efficiently model global contextual information regarding the action dynamics, aiming to contribute towards increased overall recognition performance. In particular, a new methodology for transferring knowledge across domains is introduced, by taking advantage of the increased temporal modeling capabilities of Long Short Term Memory (LSTM) models. Additionally, extensive experiments and thorough comparative evaluation provide a detailed analysis of the problem at hand and demonstrate the particular characteristics of the involved attention-enhanced schemes. The overall proposed approach achieves state-of-the-art performance in the currently most challenging public dataset, namely the NTU RGB-D one, surpassing similar uni/multi-modal representation schemes.
Towards Device-Agnostic Mobile Cough Detection with Convolutional Neural Networks

Ubiquitous mobile devices have the potential to reduce the financial burden of healthcare systems by providing scalable and cost-efficient health monitoring applications. Coughing is a symptom associated with prevalent pulmonary diseases, and bears great potential for being exploited by monitoring applications. Prior research has shown the feasibility of cough detection by smartphone-based audio recordings, but it is still open as to whether current detection models generalize well to a variety of mobile devices to ensure scalability. We first conducted a lab study with 43 subjects and recorded 6737 cough samples and 8854 control sounds by 5 different recording devices. We then reimplemented two approaches from prior work and investigated their performance in two different scenarios across devices. We propose an efficient convolutional neural network architecture and an ensemble based classifier to reduce the cross-device discrepancy. Our approach produced mean accuracies in the range [85.9%, 90.9%], showing consistency across devices (SD=[1.5%, 2.7%]) and outperforming prior learning algorithms. Thus, our proposal is a step towards cost-efficient, ubiquitous, scalable and device-agnostic cough detection.
Online Multiscale-Data Classification Based on Multikernel Adaptive Filtering with Application to Sentiment Analysis

We present an online method for multiscale data classification, using the multikernel adaptive filtering framework. The target application is Twitter sentiment analysis, which is a notoriously challenging task of natural language processing. This is because (i) each tweet is typically short, and (ii) domainspecific expressions tend to be used. The efficacy of the proposed multiscale online method is studied with dataset of Twitter. Simulation results show that the proposed approach achieves a higher F1 score than the other online-classification methods, and also outperforms the nonlinear support vector machine.
Multi-mode Dynamic Proportional Energy Management Strategy for Battery-Supercapacitor Hybrid Energy Storage System of Tram Based on Pseudo-spectrum Method

In this paper, a self-adaptive multi-mode dynamic proportional control strategy for hybrid-energy-storage tramcar is proposed. With battery life and system efficiency as optimization objectives, power distribution between two sources is carried out by pseudo-spectral method. According to the optimization results, an on-line dynamic proportional EMS is extracted. Furthermore, in order to make the control strategy change with the actual situation, the EMS is improved to a self-adaptive dynamic proportional EMS. The simulation results show that compared with the traditional proportional control strategy, the multi-mode dynamic proportional control strategy greatly improves the battery life and efficiency.
Low-Power. High-Speed Adversarial Attack based 4:2 Compressor as Full Adder for Multipliers in FIR Digital Filters

In this research work, an adversarial attack has been proposed by imposing attack on $X_{4}$ and $C_{in}$ inputs of a trained machine learning-based 4:2 compressor. Due to the adversarial attack, the Sum and Carry expressions of 4:2 compressor have been reduced to Full Adder. When the supervised machine learning algorithm is used to train the neural network to act as 4:2 compressor, the neural network is acting as full adder after the attack. The proposed adversarial attack method is resulting in secure learning since the learning algorithm is performing well (the power and area values are decreasing as 4:2 compressor scales down to a full adder). This obtained Full Adder is placed in a Dadda multiplier such that the transistor count and power has been decreased by 80% and 62.57% respectively when compared with exact 4:2 compressor in the multiplier and also less with respect to other existing approximate multipliers. Hence, the power of the Arithmetic Logic Unit and intern processor power decreases. Finally, this multiplier (with Full Adder in it) is placed in a Finite Impulse Response (FIR) filter.
Low-power SAR ADCs: trends, examples and future

With the development of mobile devices and Internet-of-Things, the demand for low-power circuits has been growing rapidly. The Analog-to-Digital Converter (ADC) is a key building block in these systems. In this work, we review the progress of low-power ADCs over the years in terms of performance and limitations. From these limitations, it can be shown why sometimes very different approaches are used to minimize power consumption. Various state-of-the-art examples will be shown to illustrate the diversity of techniques and ideas within the field. Next, an outlook to the future is given by discussing various unsolved challenges and new opportunities.