Miracle-dz/UniFAR-FTUs huggingface dataset:
Phishing in Depth – Modern Methods of Detection and Risk Mitigation

Nowadays, everyone is living in a digital world with various of virtual experiences and realities, but all of them may eventually cause real threats in our real world. Some of these threats have been born together with the first electronic mail service. Some of them might be considered as really basic and simple, compared to others that were developed and advanced in time to adapt themselves for the security defense mechanisms of the modern digital world. On a daily basis, more than 238.4 billion emails are sent worldwide, which makes more than 2.7 million emails per second, and these statistics are only from the publicly visible networks. Having that information and considering around 60% and above of all emails as threatening or not legitimate, is more than concerning. Unfortunately, even the modern security measures and systems are not capable to identify and prevent all the fraudulent content that is created and distributed every day. In this paper we will cover the most common attack vectors, involving the already mass email infrastructures, the required contra measures to minimize the impact over the corporate environments and what else should be developed to mitigate the modern sophisticated email attacks.
Tacking Regime Changes in the Markets

In our previous work, we showed that regime changes in the market are retrospectively detectable using historic data in directional changes (DC). In this paper, we build on such results and show that DC indicators can be used for market tracking - using data up to the present - to understand what is going on in the market. In particular, we wanted to track the market to see whether the market is entering an abnormally volatile regime. The proposed approach used DC indicator values observed in the past to model the normal regime of a market (in which volatility is normal) or an abnormal regime (in which volatility is abnormally high). Given a particular value observed in the current market, we used a naive Bayes model to calculate independently two probabilities: one for the market being in the normal regime and one for it being in the abnormal regime. These two probabilities were combined to decide which regime the market was in, two decision rules were examined: a Simple Rule and a Stricter Rule. We used DJIA, FTSE 100 and S&P 500 data from 2007 to 2010 to build the Bayes model. The model was used to track the S&P 500 market from 2010 to 2012, which saw two spells of abnormal regimes, as identified by our previous work, with the benefit of hindsight. The tracking method presented in this paper, with either decision rule, managed to pick up both spells of regime changes accurately. The tracking signals could be useful to market participants. This study potentially lays the foundation of a practical financial early warning system.
EFFECT OF EXPERIMENTAL UTILITY IN CDIO COURSES: COMPARISON OF SIMULATED SOFTWARE PLATFORM TO SYNTHETIC HARDWARE TOOLKIT

According to the CDIO requirements, implementation of theoretical knowledge in the textbook through experimental exercises is one of the most important teaching modality in the CDIO courses. [4] Taking Digital Image Processing course of Chengdu University of Information Technology as research target, there are two categories of experimental utilities: software and hardware. Three subjects (image enhancement, image compression and color image processing) of this course were implemented using matlab 7.1 and image processing hardware toolkit (designed specifically in High speed Image Processing Centre of Chengdu University of Information Technology) [3] Two groups of students from 3 rd year undergraduate students are compared through questionnaire investigation about teaching effects and the academic scores tested as the quantitative evaluation. This study demonstrates how students benefit through using software and hardware utilities to implement CDIO experiments, and to find out what is the respective advantage for these two methods in order to make further improvement on teaching.
Evaluation of Cache Compression for Elbrus Processors

Data compression in cache memory allows increasing the effective capacity, which improves the hit rate and only insignificantly affects power consumption and die area. In this paper, the results of the first research on cache compression in processors with Elbrus architecture are presented. Base+Delta (B+Δ) and Base-Delta-Immediate (BΔI) compression algorithms are selected for hardware implementation for their high efficiency and lower decompression latency compared to other algorithms. The modified versions of these algorithms, B+Δ* and BΔI*, which allow to reduce implementation complexity and further shorten the latency, are presented. Additionally, a new set of compression schemes for modified algorithms (labeled as BΔI*-HL algorithm) is proposed to account for width of the interfaces and internal data buses. The algorithms were implemented using Verilog HDL and evaluated on FPGA prototype of Elbrus-8C2 processor and SPEC CPU2000 benchmark suite. The results show that BΔI* demonstrates almost equal of greater compression ration as the original BÄI algorithm while has significantly lower implementation complexity. The algorithm BΔI*-HL is proposed as the most suitable for hardware implementation with average compressed lines share of 24.4% and mean compression ratio of 1.246.
Controller and Gateway Partition Placement in SDN-Enabled Integrated Satellite-Terrestrial Network

The novel paradigm, Software-Defined Network (SDN), being incorporated in the integrated satellite-terrestrial networks, has become an emerging architecture and has attracted considerable attention recently. Meanwhile, the joint controller and gateway placement problem is of fundamental importance for designing the integrated networks with flexible and efficient management capabilities. Therefore, how to achieve network reliability maximization with network latency constraint in the joint placement problem is worthy of in-depth study. In this paper, we adopt network partition scheme and propose Simulated Annealing Partition-based K-Means (SAPKM) to further ameliorate the problem. Compared with previous works, extensive numerical results are provided to validate the efficacy of SAPKM for deriving reasonable solutions with near-optimal performance on network reliability, propagation latency, and much lower computational complexity.
DISCRIMINATION OF VENTRICULAR FIBRILLATION BASED ON CHAOTIC CHARACTERISTICS OF ELECTROCARDIOGRAM SIGNALS

Ventricular Fibrillation (VF) is a dangerous abnormality in the heart activity. During the VF, well known shape of electrocardiogram (ECG) signal changes to a pseudo-noise waveform. Recent researches have depicted that VF is not a noisy signal. The characteristics of VF and chaotic signals are the same. In this research, these characteristics were studied and used for discriminating the VF signal from the other electrocardiogram signals. Three types of electrocardiogram signals including VF, Tachycardia and Normal ECG were used for training and testing a back propagation neural network. We used these signals in three stages. At the first stage, the power spectrum of signals was used for training and testing the neural network. Time Series signals were used in the second stage. The result of the first experience was better than the second. At the third stage, we used surrogate technique to enrich the training signals in the time domain. The surrogate technique is a method which has been used in the chaotic systems. By using these new generated signals for training the neural network, the results of classification were extremely improved. Furthermore, the results of simulations showed that the chaotic dynamic of VF signal is a time dependant one.
Model Classification-and-Selection Assisted Robust Receiver for OFDM Systems

This paper devises a robust receiver for OFDM systems in the presence of residual timing offsets and unknown channel prior information. The proposed receiver constructs typical receiver models and resorts to the model selection technique to choose the best-matched receiver model to improve the channel estimation and signal detection. The typical receiver models are classified by considering the channel delay spread and the level of timing offset. Based on the receiver model selected by the Bayesian model selection algorithm, the channel length and timing offset parameters in the receiver model can provide the effective channel statistical information to make the channel estimator adapt to the altered circumstances and thus more accurate. Furthermore, the effective interference variance parameters in the selected receiver model are used to enhance the channel estimation and refine the soft symbol detection. The simulation results show that the proposed receiver achieves a significant performance gain compared to the existing methods. It is also shown that the proposed scheme makes the receiver robust to the diverse channel conditions and the timing offset states at a cost of the only a moderate increase in complexity.
Separation of blood in microchannel bends

One of the basic operations of miniature assay devices is the separation of blood and blood components in microchannels. In this study we investigated and developed a microfluidic chip with microchannel bend structures, which are used as a novel separation technique ideally combining micro and macro world specific separation processes. Experimental measurements of separation efficiency with human whole blood were done on prototype polymer chips. The results show that this technique is an alternative to existing micro blood separation processes.
The Invariance of Spectral-Kolmogorov-Type Statistics for Estimating Genomic Similarity

Accurate and efficient comparison of genetic sequences is an important undertaking that has applications in medicine as well as informing the hierarchical clustering of organisms. Genomic comparison is important for full genomic sequences across individuals of the same or different species as in phylogenies and also within organisms as in pedigrees. Given the enormity of the different genomes and their respective sizes, such comparisons are well-known to be computationally intensive and we are motivated to find more efficient and accurate means for the genomic comparison problem. This paper introduces a metric that is computed via the proposed methodology of comparing the empirical distributions of the observed k-mers among one or more genetic sequences. This metric is in fact a Kolmogorov-Smirnoff-like statistic since it is the supremum of differences in the empirical distribution functions. Specifically, genetic sequences are represented as quaternary or radix-4 encoded sequences that allow the metric to be computed and the metric is shown to produce similar clusterings when computed via spectral coefficients. Further, we investigate the use of spectral methods, in particular the Walsh-Hadamard spectrum, of the quaternary-encoded genetic sequence and observe computed maximal spectral densities as a basis of comparison. The invariance of the Kolmogorov-Smirnoff-like statistic when it is computed in the Walsh-Hadamard domain can enable faster comparison computations through the use of spectral properties. For example, the convolution of two sequences becomes a simple multiplication in the spectral domain.
Gait Recognition Based on Constrained Mutual Subspace Method with CNN Features

Gait recognition has various applications such as surveillance and criminal investigation since it can work even at a distance from a camera without the cooperation of subjects. In the conventional methods, especially when using silhouette images, a typical feature is Gait Energy Image (GEI), which is an average silhouette image of a given sequence. Although the GEI feature can represent the sequence compactly, it cannot capture a more detailed structure of the sequence. To address this issue and increase the robustness to the speed variations of walking, gait recognition based on Mutual Subspace Method (MSM) was proposed, where each image sequence is compactly represented by a subspace. In this paper, we enhance further the MSM based method by introducing two functions: 1) to add a feature extraction by projecting onto a generalized difference subspace, 2) to use Convolutional Neural Network (CNN) features, which are obtained from a fully connected layer of a learned CNN, as an input. The extended MSM with the projection is called Constrained MSM, which has been well known as a useful method for image set based recognition. The proposed method achieved the accuracy of 97.7% on an experiment with 1000 subjects from the OU-ISIR Large population datast.
An Overview of Resilient and Automatic Model-Based Testing Approaches for Automotive Industry

The need for automatic model-based testing is increasing day by day as model-driven development is an emerging trend in the software industry, especially the automotive industry. Traditional software testing lacks the support of dynamic requirements for a highly consistent software systems, such as, for automotive industry. Automatic Model-based testing can provide significant help to produced platform independent model, design once and run anywhere approach. Automatic model-based testing not only increases the software quality, reduce complexity and improve productivity by reusing approach automatically for the automotive industry but also provide a cost-effective solution. This paper provides an overview in a manner of survey for different model-based testing approaches and tools that can help in building efficient and effective software for vehicular systems.
Secrecy Performance Analysis of Relay Selection in Cooperative NOMA Systems

This paper investigates the security performance of two relay selection schemes for cooperative non-orthogonal multiple access (NOMA) systems, where $K$ randomly distributed relays are employed with either decode-and-forward (DF) or amplify-and-forward (AF) protocols. More particularly, two-stage relay selection (TRS) and optimal relay selection (ORS) schemes are taken into consideration. To characterize the secrecy behaviors of these RS schemes, new closed-form expressions of both exact and asymptotic secrecy outage probability (SOP) are derived. We confirm that the SOP of the TRS scheme is equal to that of the ORS scheme for DF/AF-based NOMA systems. Based on the analytical results, the secrecy diversity orders achieved by the pair of RS schemes for the DF/AF-based NOMA systems are $K$ , which are equal to the number of relays. It is shown that the secrecy diversity orders for the cooperative NOMA systems are determined by the number of the relays. The numerical results are presented to demonstrate that: 1) the secrecy performance of the AF-based NOMA system outperforms that of the DF-based NOMA system, when not all DF relays successfully decode the received information; 2) with the number of relays increasing, the SOP of these RS schemes for the DF-/AF-based NOMA systems becomes lower, and; 3) the TRS/ORS schemes are capable of achieving better secrecy outage behaviors compared with random RS and orthogonal multiple access-based RS schemes.
Motion and Noise Artifact Detection and Vital Signal Reconstruction in ECG/PPG based Wearable Devices

The design and development of wearable biosensor systems for health and wellness monitoring has garnered lots of attention in the scientific community and the industry during the past decade. When it comes to health and wellness monitoring, accurate measurements, analysis and estimations would be vital. One major challenge in achieving a high performance wearable health monitoring system is motion and noise artifacts. This consideration was the basis for this dissertation. This study presents different algorithms and signal processing techniques to address motion and noise artifact detection and vital signal reconstruction from electrocardiogram/photoplethysmogram recordings. Five vital signals including ECG, PPG, Heart Rate, HRV and SpO2 are in the scope of the present dissertation. Accurate estimation of Heart Rates and Oxygen Saturation from photoplethysmogram (PPG) and Heart Rate from electrocardiogram (ECG) signals during daily physical activity is a very challenging problem. On the other hand motion and noise artifact in PPG and ECG recordings introduce challenges in early detection of heart failure diseases including Atrial Fibrillation and congestive heart failure. We address the problem of motion and noise artifact in three categories (1) “Motion and Noise Corrupted Data Removal, and (2) “MA Corrupted Signal Usability Index Measurement” and (3) “Motion and Noise Corrupted Data Reconstruction”. Nine novel signal processing techniques (TDV, RepMA, RAFMA, IMAR, DyParaM, TifMA, OxiMA, SpaMa and SegMA) are introduced in this study to cope with the motion and noise artifact in PPG and ECG based wearable sensors. Out of nine methods, three methods belong to the first category, one from the second and four vital signal reconstruction techniques for the third problem. It will be shown that the proposed techniques are able to meet major aims of this study and to address the problem of motion and noise artifacts corresponding to each of the three scenarios. The results show that the algorithms presented in this study have potential for PPG or ECG based HR or SpO2 monitoring in wearable devices for fitness tracking and health monitoring during daily physical activities.
EFTWeb: An Application to Support Skills Trading within Education

The EFTWeb is a World Wide Web based system that allows the interaction between users and contents. The users can be students and teachers that place, modify and use available contents. The system provide the means to control security, intellectual property rights, and billing issues, giving both type of users the necessary tools to access the system, organise materials and use them. Although the core system relies in a database to store the system usage, it uses market concepts to support its operation, implementing client issues, security and credits as a reward currency. The paper describes the application and discusses the role that systems like this one can have in modifying the traditional skills relationship in education settings between teachers and students, both in their skills and roles.
Automatic Measurement of Visual Attention to Video Content using Deep Learning

Advances in automated face analysis have made possible webcam-based assessment of viewer emotion during presentation of commercials and other video content. A key assumption of this technology is that viewer emotion is in response to the media. Is that assumption warranted? Because viewer attention is seldom assessed, emotional responses could result from other sources, such as talking to a friend, enjoying a meal, or attending to a pet. We developed a CNN-LSTM approach that detects attention and nonattention to commercials using webcam and mobile devices in settings of viewer's choice. Because cultural variation in viewer response is likely, we included participants from both Western and Eastern countries. Participants were 28,911 adults (ages 18 to 69 years) in Europe, USA, Russia, and China. A total of 15,543 sessions (ca. 6.5 million video frames) was analyzed. Accuracy was quantified using a variety of metrics. Our approach outperformed baseline and achieved moderate to high accuracy that approached that of human annotators.
Reliability of Additively Printed Traces on Polymer Substrates Subjected to Mechanical Stretching

Stretchable Electronics are rapidly emerging and making their way into the novel electronic devices attracting with their application in healthcare and wearable technologies. They provide superior characteristics over the conventional rigid and flexible materials. For the last decade, much work has been done in developing and improving the electronics that can fold, stretch, and even bend. In addition, the ability to additively print interconnects or traces on a stretchable material offers numerous possibilities that can never be achieved by rigid and flexible electronics. As we dive into the world of stretchable and conformable electronics, new challenges that come with stretchable materials need to be addressed, such as in manufacturing, their operational temperature, electrical and mechanical properties. Biggest of them would be their reliability in sustaining the properties even when it is deformed or stretched. Many research groups have worked on the stretchable materials and come up with interesting results. In this study, we utilize the additively screen-printed interconnects on a stretchable polymer substrate subjected to mechanical stretching. The polymer is a type of polyurethane that have excellent stretchability and temperature that can sustain the curing temperature of the ink printed. The ink used in this study is stretchable silver paste that has excellent adhesion, conduction properties, and compatible with the polyurethane substrate used in this study and multiple synthetic fibers. The effect of the amount of stretching imposed on a stretchable substrate on their electrical properties has not been looked at yet. The experiment process involves mechanically stretching the screen-printed substrate, characterizing the stretchable interconnects with their initial resistance, following with the insitu measurement of resistance while mechanically stretching, analyze the change in resistance against the strain level imposed. The effect on resistance while stretching versus the amount of stretch imposed is quantified. The challenges faced during the manufacturing process of screen-printed stretchable interconnects on a polymer substrate is discussed and the resistance data while subjected to stretching is presented in this paper.
Ultrareliable SWIPT using Unscheduled Short Packet Transmissions

Large communication networks, e.g. Internet of Things (IoT), are known to be vulnerable to the co-channel interference from simultaneous transmissions. In the recent time, this problem has been extensively studied in various contexts. Due to a potentially very long duty cycle, orthogonal multiple access techniques are not well suited for such schemes. Instead, random medium access (RMA) seems promising, since it guarantees a lower bound for the network throughput even in presence of an infinite number of simultaneous transmissions while reducing the average length of the duty cycle. Such an RMA scheme is based on transmission of short data packets with unknown scheduling. Of course, a reliable symbol detection for this type of communication is very challenging not only due to a large amount of interference from the adjacent nodes, but also because of the uncertainty related to the presence or absence of overlapping packets. Interestingly, with increasing number of network nodes also the amount of energy, which can be harvested from the received signal, increases. This is especially beneficial for powering of a relay device, which may utilize the energy for further information processing and retransmission. In this paper, we address the design of a simultaneous information and power transfer scheme based on unscheduled short packet transmissions for ultrareliable communication.
Multi-Aspect System Analysis Using State Machines Extracted from Specifications in VDM–SL

Model-oriented formal specification languages such as VDM– SL is useful to describe functional requirements of the target systems. However, since single-aspect analysis is not enough to make reliable specifications, we also use other approaches such as model checking to analyze dynamic aspects of the system as a part of multi-aspect analysis. In this paper, we discuss our approach to extract state machines from specifications in VDM–SL. We can analyze both static and dynamic aspect by using these two different kinds of specification languages, model-oriented and state-machine languages in an integrated manner.