Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Credit Card Fraud Detection Using Machine Learning Techniques

Big Data technologies concern several critical areas such as Healthcare, Finance, Manufacturing, Transport, and E-Commerce. Hence, they play an indispensable role in the financial sector, especially within the banking services which are impacted by the digitalization of services and the evolvement of e-commerce transactions. Therefore, the emergence of the credit card use and the increasing number of fraudsters have generated different issues that concern the banking sector. Unfortunately, these issues obstruct the performance of Fraud Control Systems (Fraud Detection Systems & Fraud Prevention Systems) and abuse the transparency of online payments. Thus, financial institutions aim to secure credit card transactions and allow their customers to use e-banking services safely and efficiently. To reach this goal, they try to develop more relevant fraud detection techniques that can identify more fraudulent transactions and decrease frauds. The purpose of this article is to define the fundamental aspects of fraud detection, the current systems of fraud detection, the issues and challenges of frauds related to the banking sector, and the existing solutions based on machine learning techniques.
Adaptive Reward Computation in Reinforcement Learning-Based Continuous Integration Testing

Reinforcement learning (RL) has been applied to prioritizing test cases in Continuous Integration (CI) testing, where the reward plays a crucial role. It has been demonstrated that historical information-based reward function can improve the effectiveness of the test case prioritization (TCP). However, the inherent character of frequent iterations in CI can produce a considerable accumulation of historical information, which may decrease TCP efficiency and result in slow feedback. In this paper, the partial historical information is considered in the reward computation, where sliding window techniques are adopted to capture the possible efficient information. Firstly, the fixed-size sliding window is introduced to set a fixed length of recent historical information for each CI test. Then dynamic sliding window techniques are proposed, where the window size is continuously adaptive to each CI testing. Two methods are proposed, the test suite-based dynamic sliding window and the individual test case-based dynamic sliding window. The empirical studies are conducted on fourteen industrial-level programs, and the results reveal that under limited time, the sliding window-based reward function can effectively improve the TCP effect, where the NAPFD (Normalized Average Percentage of Faults Detected) and Recall of the dynamic sliding windows are better than that of the fixed-size sliding window. In particular, the individual test case-based dynamic sliding window approach can rank 74.18% failed test cases in the top 50% of the sorting sequence, with 1.35% improvement of NAPFD and 6.66 positions increased in TTF (Test to Fail).
Near-Optimal PI Controllers of STATCOM for Efficient Hybrid Renewable Power System

Connecting different renewable energy sources (RESs) to the electrical grids is presently being urged to fulfill the enormous need for electric power and to decrease traditional sources’ ecological related issues, the so-called hybrid systems. Unfortunately, these hybrid systems suffer from the possible negative environmental impacts of the wind gusts in wind energy conversion systems (WECSs) that may degrade the overall system performance. Additionally, various severe faults may disconnect some RESs from the hybrid system, like three-phase faults. In this paper, the static synchronous compensator (STATCOM) is considered for both improving the performance of a hybrid system, contains WECS and photovoltaics (PVs) against wind gusts and maintaining the continuous operations of RESs during three-phase fault occur at the point of common coupling (PCC) between the RESs and the grid. The STATCOM is stimulated by two PI controllers regulating the reactive power flow between the STATCOM and the hybrid system at PCC and, consequently, regulating the voltage at PCC. A metaheuristic optimizer optimally schedules these two PI controllers based on whale optimization algorithm (WOA). The impartial comparison between the WOA dynamic performance and the particle swarm optimization as another optimization algorithm verifies the efficiency of the WOA for the near-optimal gain scheduling of the PI controller gains.
17.2 A Masterless Fault-Tolerant Hybrid Dickson Converter with 95.3% Peak Efficiency 20V-to-60V Input and 3.3V Output for 48V Multi-Phase Automotive Applications

Autonomous-driving features have increased the load power demand of processors, which in turn, have resulted in the development of a 48V bus in electric vehicles. The resulting high and wide input-voltage range provides an opportunity for hybrid switched-capacitor (SC) converters, which enable efficient high-conversion-ratio high-density power delivery. Any failure in the power delivery to computers and control systems in the context of autonomous driving is catastrophic, which motivates the need for a fault-tolerant (FT) converter to satisfy the functional safety requirements.
Weighted network overlapping community partition based on node membership

Aiming at the problem of random selection of core nodes in the existing algorithm of overlapping communities, which leads to low accuracy of community division, this paper is based on the idea of optimizing node selection and then accurately dividing overlapping communities, this paper proposes an algorithm of dividing overlapping communities based on the degree of membership for the weighted network. Firstly, the common neighbor nodes are introduced, and based on the overlap ratio of node neighborhoods, the node overlap strength and node unit overlap strength are defined. Then select the core community, it can objectively reflect the importance of the core community in the networks9; Secondly, the degree of membership of nodes relative to the core community is calculated, and the network overlapping community is preliminarily divided. Finally, the extended module degree is used to optimize the sub-communities initially divided to realize the overlapping community division. Experiments show that the algorithm improves the accuracy of overlapping community partition.
Quality Approach to Analyze the Causes of Failures in MOOC

Massive Open Online Courses (MOOC) have become popular around the world as a free way of online learning. However, one of the crucial problems associated with MOOC is their low completion rate. The analysis of data obtained from the forums and the social media groups associated with MOOCS provides a helpful mean to understand the behavior of the learners. The idea is to examine the correlation between the sentiment level reported on the basis of the forum messages and the rate of students dropping out of the courses. Moreover, a good number of quality tools are used on the domain of Education. Therefore, we propose in this paper to combine the Sentiment Analysis (Machine learning approach) of the forum posts and the ISHIKAWA method (Quality approach) to handle these issues. The aim is to predict the main causes of MOOCs’ failures
4.7 A 91mW 90fps Super-Resolution Processor for Full HD Images

Super resolution is the process of reconstructing a high-resolution (HR) image from a low-resolution (LR) one. Super-resolution technology enables high-resolution video streaming, image zoom-in, and far object recognition. Fig. 4.7. 1 shows such an application scenario. The details of the videos/images can be reconstructed and projected to a higher-resolution screen, thereby providing a better visual experience. A hardware accelerator is needed to speed up the super-resolution process to support real-time high-resolution video streaming. Conventionally, dictionary-based approaches, such as ANR/GR [1] and A+ [2], convert the LR image into the HR one from learned mapping functions. Neural network (NN)-based algorithms generate better-quality super-resolution images by extracting features from training [3]. However, the complexity of the dictionary-based and the NN-based algorithms is excessively high, making them unsuitable for high-speed applications [4]. A rapid and accurate image super resolution (RAISR) algorithm [4] is proposed to achieve comparable quality with a much faster processing speed when compared to the previous solutions. It employs pre-learned filters to enhance the image quality based on bicubic interpolation. A pre-learned filter (also known as kernel) is selected by a hash function to address the structure-related details.
Parameters Characterization of Dielectric Materials Samples in Microwave and Millimeter-Wave Bands

In this work, an optimized broadband method using multilayer transmission lines to characterize dielectric permittivity and loss tangent of material samples is presented. For this purpose, a microstrip line loaded with a piece of the selected dielectric to be characterized is used. From two-port measurements, and using different length lines, the propagation constant can be obtained. To minimize random errors and to improve the accuracy, an overdetermination of the method increasing the number of lines measured and a criterion to choose the optimal line lengths is considered. First, the measurement method itself is applied to uncovered microstrip lines and an accurate model of the substrate is obtained. Second, the lines are covered with several materials, made by fused deposition modeling (FDM) additive manufacturing technique, such as acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), high impact polystyrene (HIPS), thermoplastic polyurethane (TPU), copolyester (CPE), FLEX, polyethylene terephthalate glycol (PETG), and nylon. A model of the transmission line considering the cover is developed, and an electromagnetic (EM) simulator is used to indirectly determine the cover material electrical parameters. Results show excellent agreement with EM simulations in the 0.1–67-GHz frequency band, so they assess the suitability of the proposed method.
Significance of Predictive Motion Control

This research paper emphasizes on the development of an advanced controller for the adaptive cruise control system. The performance of headway or time gap control plays a pivotal role in an automated longitudinal motion. The controller stability in a traffic follow mode is predominantly governed by the longitudinal motion profile of the preceding vehicle. The measurement of acceleration of the preceding vehicle using heterogeneous sensors are challenging pertaining to the shortfall of longitudinal motion control through classical mechanism. Thus, a predictive model with defined constraints of vehicle dynamics has been designed to demonstrate the improved system performance during preceding target follow mode. A novel approach of predictive control adopting multifarious states of cruise and target follow mode is discussed to address the deficit of classical control. Furthermore, the non-linear tracking differentiator theory acts as an observer model for the estimation of an acceleration profile of the host vehicle. The integrated CarMaker vehicle dynamics model in conjunction with the controller establishes a podium for evaluating system performance in a closed loop simulation. The target trajectory estimation and motion characterization are the determinants of target selection which further aids to conduct smooth and consistent actuation. The performance of the conventional control is compared with proposed adaptive model predictive control under standard yardsticks of adaptive cruise control operation to demonstrate an improved system efficiency.
Recognizing Emotional States With Wearables While Playing a Serious Game

In this study, we propose the use of electroencephalography (EEG), electrooculography (EOG), and kinematic motion data captured through wearable sensors to classify emotional states, while individuals are playing a serious computer game (Whack-a-Mole). Twenty-one participants wore an OpenBCI headset and JINS MEME eyewear while playing the Whack-a-Mole game at three levels of difficulty. We used a variety of classifiers [i.e., a support vector machine (SVM), logistic regression (LR), random forest (RF), and ensemble classifier (EC)] to classify the participants’ emotional states based on their EEG, EOG, and kinematic motion data. The classifiers were trained using the International Affective Picture System (IAPS). The EC and RF showed the best results in terms of their overall performance. Using tenfold cross-validation for all the subjects, the accuracies obtained were 73% for Arousal and 80% for Valence. Our results suggest that EEG and EOG biosignals, as well as kinematic motion data acquired using off-the-shelf wearable sensors in combination with machine-learning techniques such as EC, can be used to classify emotional states, while the individuals were playing the Whack-a-Mole game.
YOLO-6D+: Single Shot 6D Pose Estimation Using Privileged Silhouette Information

The task of estimating the 6D pose of the object from a single RGB image is important for augmented reality and robotic grasping applications. In this work, we introduce YOLO-6D+, a new end-to-end deep network for 6D object pose estimation. In particular, we propose a novel silhouette prediction branch that outputs the predicted segmentation mask in our network, which can force underlying features to learn the silhouette information of the object. Furthermore, we introduce edge restrain loss, a new loss function that focuses on constraining the 3D shape of an object. We use a two-stage method: we predict 2D keypoints firstly and then 6D pose is estimated using the PnP algorithm. On the public LINEMOD dataset, we demonstrate the proposed approach can outperform the state-of-the-art YOLO-based single shot pose estimation approach [1] by 4.09% and 11.72% under the 2D projection metric and the ADD(-s) metric respectively.
A Variable Curvature Model for Multi-Backbone Continuum Robots to Account for Inter-Segment Coupling and External Disturbance

Multi-backbone continuum robots demonstrated potentials for dexterous manipulation with proper payload capability in minimally invasive surgeries. Most prior works assume constant curvature shapes of the continuum segments in the modeling and control of the multi-backbone continuum robots. The actuation coupling effects between adjacent continuum segments and the segments’ variable curvature shapes under environmental interactions have not been fully addressed by a static-kinematic model specifically for multi-backbone continuum robots. This letter hence proposes a variable curvature model for multi-backbone continuum robots with relatively low bending curvature based on the Cosserat rod theory. The model focuses on the major factors that affect the robot's shape: the length-prescribed push-pull actuation, the elastic elongation of the backbone rods, and the external loads. With five assumptions made to simplify the constraints in the multi-backbone continuum robot, a compact statics-kinematics formulation is derived with computational performance acceptable for real-time control. Experiments were conducted on a continuum robotic system to quantify the modeling accuracy and computational efficiency. The proposed model was shown to have substantially improved accuracy over the constant curvature model. The average computational time for solving the inverse kinematics was 0.7ms on a 2.6 GHz Intel i7-5600U platform, which is promising for real-time control.
Design of Nonresonant Metasurfaces for Broadband RCS Reduction

A design method using simple patches with nonresonant mode to achieve broadband phase-gradient metasurfaces of reflective type is proposed and an example to apply it for radar cross section (RCS) reduction is given. The method is based on treating the patch in an element as a pixel and the phase differences are obtained by setting the pixels to be empty or full. As only two states can be guaranteed for a single element, super cell concept and multilayer configuration are introduced to improve the performance. A design example of reflective RCS reduction structure with the −10 dB bandwidth of 5.3–18 GHz is given to prove the efficiency of the method.
2.4-GHz Bluetooth Low Energy Receiver Employing New Quadrature Low-Noise Amplifier for Low-Power Low-Voltage IoT Applications

In this article, a 2.4-GHz Bluetooth low-energy (BLE) receiver employing a new quadrature low-noise amplifier (LNA) is presented for low-power low-voltage Internet of Things (IoT) applications. The proposed quadrature LNA consists of a common-source (CS) amplifier with inductive degeneration, an active-type poly-phase filter (PPF), and an LC tank. The proposed active-type PPF between the CS amplifier and the LC tank provides quadrature generation, and the CS amplifier determines and optimizes the overall LNA performance parameters. Moreover, the proposed active-type PPF shares the dc bias current with the CS amplifier, thereby eliminating the necessity for additional current consumption. The implemented BLE receiver is composed of the quadrature LNA, a $G_{m}$ -stage, a double-balanced current-mode passive mixer, a transimpedance amplifier, and an active- RC complex bandpass filter. The proposed design was fabricated in a 65-nm CMOS process and mainly characterized in the BLE operating frequency bands. The receiver achieves a noise figure of 8.2 dB, a conversion gain of 49.5 dB, an image rejection ratio of more than 33 dB, and an IIP3 of −25.75 dBm. The active die area of the implemented receiver is 1.16 mm2, and it draws a bias current of 2.7 mA from a nominal supply voltage of 0.8 V.
An Empirical Study of Code Deobfuscations on Detecting Obfuscated Android Piggybacked Apps

Android piggybacked malware (i.e., apps that piggyback malicious code) are becoming ubiquitous in app stores. Malware writers often use obfuscation techniques to obfuscate piggybacked apps to evade detection by Android malware detectors. Previous studies in this field have focused on the impact of code obfuscations on the detection of piggybacked malware, but the impact of code deobfuscation on detecting obfuscated piggybacked apps has rarely been studied. Knowing about the impact of code deobfuscation can provide useful insights into obfuscated piggybacked apps and therefore the design of resilient Android malware detectors. In this paper we conduct an empirical study of code deobfuscations on detecting obfuscated Android piggybacked apps, focusing on three types of malware detectors: commercial anti-malware products, machine learning-based detectors, and similarity-based detectors. We observe that code deobfuscations can impact differently depending on the malware detectors. For example, some deobfuscation strategies can improve the precision of detecting obfuscated piggybacked apps. Also we observe that the examined deobfuscation tools (Simplify and Deguard) have a different impact on obfuscated piggybacked apps after deobfuscations.
Recent Data Augmentation Strategies for Deep Learning in Plant Phenotyping and Their Significance

Plant phenotyping concerns the study of plant traits resulted from their interaction with their environment. Computer vision (CV) techniques represent promising, noninvasive approaches for related tasks such as leaf counting, defining leaf area, and tracking plant growth. Between potential CV techniques, deep learning has been prevalent in the last couple of years. Such an increase in interest happened mainly due to the release of a data set containing rosette plants that defined objective metrics to benchmark solutions. This paper discusses an interesting aspect of the recent best-performing works in this field: the fact that their main contribution comes from novel data augmentation techniques, rather than model improvements. Moreover, experiments are set to highlight the significance of data augmentation practices for limited data sets with narrow distributions. This paper intends to review the ingenious techniques to generate synthetic data to augment training and display evidence of their potential importance.
25.4 A 20nm 6GB Function-In-Memory DRAM, Based on HBM2 with a 1.2TFLOPS Programmable Computing Unit Using Bank-Level Parallelism, for Machine Learning Applications

In recent years, artificial intelligence (AI) technology has proliferated rapidly and widely into application areas such as speech recognition, health care, and autonomous driving. To increase the capabilities of AI more powerful systems are needed to process a larger amount of data. This requirement has made domain-specific accelerators, such as GPUs and TPUs, popular; as they can provide orders of magnitude higher performance than state-of-the-art CPUs. However, these accelerators can only operate at their peak performance when they get the necessary data from memory as quickly as it is processed: requiring off-chip memory with a high bandwidth and a large capacity [1]. HBM has thus far met the bandwidth and capacity requirement [2] –[6], but recent AI technologies such as recurrent neural networks require an even higher bandwidth than HBM [7]–[8]. While a further increase in off-chip bandwidth can be accomplished by various techniques, it is often limited by power constraints at the chip or system level [9]. Hence, it is essential to decrease demand for off-chip bandwidth with unconventional architectures: such as processing-in-memory. In this paper, we present function-In-memory DRAM (FIMDRAM) that integrates a 16-wide single-instruction multiple-data engine within the memory banks and that exploits bank-level parallelism to provide $4 \times $ higher processing bandwidth than an off-chip memory solution. Second, we show techniques that do not require any modification to conventional memory controllers and their command protocols, which make FIMDRAM more practical for quick industry adoption. Finally, we conclude this paper with circuit- and system-level evaluations of our fabricated FIMDRAM.
Downloadable machine learning for compressed radiolocation applications in radio access networks

Machine learning (ML) is an important tool for enabling automation in radio access networks. Multiple ML-based applications utilize the device radio-fingerprint, for example to improve network functions such as paging and inter-frequency mobility. The measurements on the plurality of beams in NR can enable the network to get an improved radio-fingerprint in comparison to previous technologies (e.g. LTE), but with the cost of increased overhead in device measurements and signaling. In this paper, the device radio-fingerprint is denoted radiolocation. The increasing use of ML-based applications with detailed radiolocation not only increases the device signaling but also increases the input dimensionality and complexity of the ML model. Hence, it is essential to propose method(s) for reducing the dimensionality by compressing the radiolocation and reducing the overall signaling by downloading such compression method(s) to the device. In this paper, one such solution is proposed, and the challenges in downloading a compression model to the device is further highlighted. Moreover, the performances for two ML applications, namely secondary carrier prediction and beam prediction, with and without compressed radiolocation are compared. The results indicate that a compressed version of radiolocation can achieve prediction performance close to the uncompressed one.