Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Artifact Detection in PPG Signals Based on Gramian Angular Field and 2-D-CNN

Due to the presence of motion artifacts (MAs), heart rate monitoring using PPG sensors in daily life and physical exercise is challenging, and there have been many studies on MA removal algorithms. However, most studies do not consider the quality evaluation of PPG signal before the MA removal. In this way, removing the MA directly regardless of whether there is motion artifact signal is not only a waste of computing resources, but also easy to introduce new noise. In this paper, the MA detection in PPG signal is performed by dividing the original signal into 6s signal segments and calculating the amplitude mean difference function (AMDF). Then the obtained AMDF is converted into a 2-D image through the Gramian Angular Field (GAF), and then classified by the Convolutional Neural Networks (CNN) classifier, so as to distinguish the contaminated signal and clean signal. In the subsequent processing, only the contaminated signal needs to remove the MAs, and the clean signal segment can be directly used for heart rate estimation. In this study, we achieve a classification accuracy of 0.966 in the local database, and a classification accuracy of 0.946 in the BIDMC PPG and Respiration Dataset published by PhysioNet. With the combination of feature extraction and SVM classifier, the proposed method has significantly improved the results.
A Provably Secure Two-Factor Authentication Scheme for USB Storage Devices

Universal Serial Bus (USB) is widely used, for example to facilitate hot-swapping and plug-and-play. However, USB ports can be exploited by an adversary to extract private or personal data from the connected devices. Hence, a number of organizations and workplaces have prohibited their employees from using USB devices, and there have been efforts to design secure USB storage device schemes to more effectively resist different known security attacks. However, designing such schemes is challenging. For example, in this article we revisit the Wei et al.’s scheme, and demonstrate that it is vulnerable to attacks such as password guessing and user impersonation. We also explain that the scheme does not verify the correctness of user’s input in the login phase, which is another design flaw. Then, we present an improved scheme and prove it secure in the random oracle model.
A Mobile Edge-Based CrowdSensing Framework for Heterogeneous IoT

In this article, we consider the problem of distributed offloading in mobile crowdsensing (MCS) by the means of mobile edge computing(MEC). Deploying MEC in MCS can help address many challenges the centralized MCS solutions are facing such as delays in answering real-time requirements due to the centralized nature of the solution, discovering and selecting non-connected devices in the Area of Interest (AoI), and dealing with the complexity of data computation. Specifically, we propose to improve the selection of crowdsourced workers by opting for a distributed mechanism, where the selection is partially offloaded to the Local Edge Nodes (LENs). The proposed framework, OffSEC, relies on a) a Mobile edge computing architecture that defines the Main Edge Node (MEN) and LENs responsible for selecting the local workers available in the AoI and b) a two-layer selection mechanism that helps to offload the selection of crowdsourced workers to the identified LENs. To do this, nodes in the area of interest are first clustered based on their locations, and then for each cluster, one LEN is identified based on the closeness metrics. MEN is then nominated based on a greedy selection. Finally, LENs discover the available nodes in their cluster, including heterogeneous IoT nodes and workers that are not necessarily connected to the Edge server, and select the final list of workers that maximize the quality of service (QoS). The process of selection is dynamic as it is updated according to the requested task. The proposed OffSEC is evaluated using a real dataset and is compared to a centralized approach. The results show that OffSEC outperforms the benchmark by maximizing the QoS of the sensing activities and improving the quality of the collected data readings (QoDR).
Evaluation of multiple model control strategies for pumping stations in a water distribution system

Water distribution systems represent complex infrastructure and there are many aspects to consider for ensuring the quality of service and reliability in metropolitan areas. There are multiple stages of distribution, while advanced control strategies are required for efficient operation of pumping stations. The proposed solution aims to uncover some of the challenges in the implementation of multiple-model control strategies for pumping stations, in the context of complex water distribution systems. The evaluation of multiple control structures, involving both traditional and advanced learning-based supervisors reveals some general characteristics in the context of an experimental water network.
A Personalized Classifier for Human Motion Activities With Semi-Supervised Learning

The motion activities performed by a mobile or wearable device user are characteristic of the user and the accuracy of detection of motion activity context can be improved by incorporating personalized samples of motion activity. But obtaining personalized samples of motion activity from device user is not practically feasible. In this article, a semi-supervised method is presented for improving the classification accuracy of the mobile or wearable device user’s motion activities by deriving a classifier that is personalized to the user starting from a factory-set generalized classifier. The novelty lies in using information theoretic criterion to select personalized data samples from all the personalized data samples collected from the target user. This is useful in improving the motion activity detection accuracy, since each device user has a distinct gait personality as compared to the motion activity data patterns available in a generalized database that are used to train a generalized classifier. The average accuracy for 11 target users of a generalized classifier is 93.11% that increases to 96.50% using the proposed method when the device is used in in-hand mode. It is observed that the accuracy improvement with a personalized classifier over a generalized classifier is greater for those subjects whose accuracy is lower than others with the generalized classifier.
Low-Power Hardware Architecture for Depthwise Separable Convolution Unit Design

Depthwise separable convolution is useful for building small and lightweight networks. However, the hardware design of depthwise separable convolution unit has not been well studied. With an analysis, we find that many multiplications in depthwise separable convolution can be omitted. Based on this observation, in this paper, we present a novel hardware design to avoid unnecessary multiplications (by using the clock gating technique) for power saving. Experiments on MobileNetV1 model show that the proposed hardware unit can greatly reduce power consumption.
A Parallel and Pipelined Implementation of a Pascal-Simplex Based Two Asset Option Pricer on FPGA using OpenCL

With the resurgence of hardware for financial technology, several methods for accelerating financial option pricing using reconfigurable hardware have been investigated. This paper presents the first architecture and implementation of a two-asset option pricer based on Pascal’s simplex, which takes advantage of the parallelism and pipelining offered by FPGA technology. The theory that this architecture is constructed from is based on a recombining multinomial tree approach which in turn is a generalization of the binomial tree model. Furthermore, we show that while a significant difficulty exists in efficiently maintaining the intermediate values required for the computation, a solution exists in the form of FIFOs.Our implementation, on an Intel Stratix 10 GX FPGA, is based on the OpenCL framework and can compute 6250 two asset option prices per second for a time step of 100 and the pipelining of the option value computation show a 25 times improvement when a 50-step pipeline is created.
Sales Prediction Analysis in Determining New Minimarket Stores

PT XYZ is a company engaged in the retail minimarket business in Indonesia. In running its business, one of the key activities involved is opening a new minimarket store. The audit team will analyze proposals to predict sales of would-be new stores, however, the results of the predictions are often not following reality, so research is needed to predict sales more accurately. This study aims to analyze the prediction of minimarket stores sales using deep learning technique to determine new minimarket stores. The model can predict 53.18% stores that achieve the target prediction and 28.32% stores that have the potential to achieve the target in the future, which indicates an increase in predicting stores on target compared to the branch office method which only predicts 31.2% of stores that achieve the target and 31.62% of potential stores. Thus, the approval decision of new minimarket stores which predicted achieve its target can be more accurate than the branch office method. The audit team will use the model to predict the store sales and consider the result for approving the proposal. Factors that had a significant influence on sales were rack size, store age, distance between competitors, domain location, and store type.
Vitiligoid hypopigmentation associated with pembrolizumab in metastatic head and neck cancer

Abstract Cutaneous adverse events are common with the use of immunotherapy. Although only 5% of patients develop severe reactions, about half will develop mild to moderate cutaneous adverse events. Vitiligo has been seen in melanomas treated with checkpoint inhibitors (CPI). We describe the first known case of Vitiligoid irAE (immune-related adverse event) in a non-melanoma solid cancer treated with pembrolizumab.
Result-Based Re-computation for Error-Tolerant Classification by a Support Vector Machine

Support Vector Machines (SVMs) are widely used in Machine Learning (ML) to perform classification. For a given element, an SVM computes a value using a kernel function and several support vectors to determine its class. Unfortunately, the computational units can be affected by externally induced phenomena such as soft errors; therefore, an erroneous result can be obtained. This is an issue when an SVM is used in safety-critical applications in which a change of the classification result is not acceptable. To ensure that errors do not change the classification result, traditional protection schemes can be used. For example, if the SVM computation is done on a processor, calculations can be executed twice, and the results get compared. If they are different, the error is detected, and the calculation can be done for a third time, voting can then be utilized to obtain the most likely result. However, this approach incurs in a large cost for computing resources and may not be acceptable when an SVM is used in resource constrained platforms such as Internet of Things (IoT) devices. In this paper, Result-based Re-computation (RBR) is proposed; RBR is an efficient technique to protect SVMs from errors in the kernel function, which is the most complex part in the SVM implementation. RBR is based on the observation that the SVM result is a sum of kernel terms to detect the terms that can modify the classification result and only these terms must be re-computed. The evaluation results using several publicly available datasets show that compared to a traditional protection scheme, the proposed RBR reduces up to 95.58% of the re-computation needed to protect an SVM against errors. Impact Statement—Error tolerance is critical in safety-critical Artificial Intelligence (AI) applications due to their dramatic consequences and safety implications. However, conventional protection solutions always incur in a large computational cost that could become prohibitive in resource-constrained platforms and application domains such as Internet of Things devices. The Algorithm-based Error Tolerance (ABET) approach for Support Vector Machines (SVMs) proposed in this paper has a significant advantage in terms of low computational demands. This advantage makes it very attractive in practice. The proposed algorithm reduces computational time with up to 95.58%, when compared to a classic protection scheme. This saving will promote the use of ABET approaches in industry, especially in embedded low-power systems.
Analysis of Motion Sickness Associated Brain Activity Using fNIRS: A Driving Simulator Study

Motion sickness is a common disease encountered in traditional vehicles as well as autonomous vehicles, which will negatively affect user acceptance. To make clear the pathogenesis of motion sickness, this study focused on drivers’ brain activity changes before and after motion sickness happens. Based on the six-degree-of-freedom driving simulator and noninvasive functional near-infrared spectroscopy (fNIRS), a database containing driving operation data synchronized with drivers’ brain activity record from 52 participants was collected under straight and curved driving conditions. The correlation analysis between motion sickness and changes of cerebral oxyhemoglobin concentration in the cerebral cortex was carried out based on this database. Results suggest that brain activity associated with motion sickness may differ under different driving conditions. However, the emergence of motion sickness responses is related to the occipital lobe under both driving conditions. Experimental results corroborate with several theoretical hypothesis about motion sickness in neuroscience. Consequently, this study proposes a new approach to research the mechanism of the correlation between motion sickness and cerebral cortex activity, which will contribute to developing the driving assistance system for preventing or alleviating motion sickness in autonomous vehicles.
Method for Estimating the Location of A Low-frequency Target in A Shallow Sea Based on A Single Vector Hydrophone

To estimate the location of a low-frequency target in the complex environment of shallow seas, both real-time properties and accuracy must be considered. In this paper, a multichannel information fusion method is proposed for estimating the location of a target based on a single vector hydrophone. First, a window signal fusion algorithm is proposed, which combines the EM algorithm to achieve adaptive signal extraction. The RNN used later realized the self-localization of the sound source. The results show that the fixed-dynamic window based on the steepest rising segment of Shannon entropy can divide a very short signal sample into a sufficient number of signal sets and the signal self-replenishment based on the EM algorithm can further strengthen the characteristics of different signal segments on the basis of replenishing the signal. Compared with other networks, the RNN has the highest accuracy and stability for this type of location estimation of an acoustic source based on the time-domain signal. The experimental results indicate that in the shallow sea environment, this model only depends on a single vector hydrophone to collect signals and rapidly locate the acoustic source, and the error radius is controlled within 1.5 m.
Finite-frequency Identification of the Quadcopter Translation Dynamics

The application of the finite-frequency identification method to a particular problem of linearized model identification of the quadcopter translation dynamics is considered in the paper. The transfer function of the horizontal translation dynamics is derived from three-dimensional differential equations of the translation velocity. Taking into account the aerodynamic drag leads to the transfer function, parameters of which depend on velocity. Experimental identification of the quadcopter flight dynamics is carried out.
Adaptable and Divergent Synthetic Benchmark Generation for Hardware Security

Benchmarking can drive the development of technologies by facilitating standardization of features for comparison of different methods. While hardware security has seen an exponential growth in innovation throughout the last decade, the lack of sufficient benchmarks for data-driven analysis is prominent. Researchers must currently rely on decades-old VLSI benchmarks, which in most cases were not designed with security evaluation in mind. Considering the present day computational power, these benchmarks lack in both quality and quantity for usage in hardware security topics such as obfuscation and hardware Trojans. Many advanced techniques, like statistical analysis and machine learning, require a large number of samples in order to sufficiently examine the feature space. In an attempt to resolve this issue, we have developed the first synthetic benchmark generation process flow. This paper describes our novel technique that utilizes linear optimization to generate an endless number of synthetic combinational benchmarks that are adaptable to user input constraints and divergent in quantifiable structural features from input reference benchmarks. Thus, our framework offers customization for generating richer and more challenging benchmarks for data-driven hardware security. Through experimentation, we verify that our benchmarks offers more structural variation than the current benchmark suites.
A Survey on Various Machine Learning Models in IOT Applications

This paper presents a recent survey on various machine learning (ML) methods that acts as a backbone for various Internet of Things (IoT) applications. The study brings out the definition of ML models and its significance over IoT and introduces both supervised and unsupervised machine learning models that support the architecture of IoT applications. The study provides the idea of ML adoption in various fields of IoT applications. The study also presents some inferences associated with the present study. Finally, the study presents the directions of future researches to carry out their research on the ML with IoT.
A Performance Evaluation of Queueing Systems by Machine Learning

This paper presents a performance evaluation of queueing systems by machine learning. The number of devices connected to the internet is going up in the Internet of Things (IoT) era. Hence, it is useful for designing telecommunication systems to evaluate a performance of general queuing systems which distributions of inter-arrival time and processing time are general. However, the exact solutions of general queueing systems have not been developed. Alternatively, we can apply the discrete simulation to evaluate the performance. However, it takes much time. In this paper, we propose the performance evaluation method by machine learning to solve the problems.
Highly Integrated ZVS Flyback Converter ICs With Pulse Transformer to Optimize USB Power Delivery for Fast-Charging Mobile Devices

Flyback converter with USB power delivery is widely used for fast-charging mobile devices. However, conventional solutions have issues such as lower efficiency, higher cost, a large number of external components, and lower loop gain bandwidth. In this article, a high-efficiency zero-voltage switching (ZVS) flyback converter with primary- and secondary-side control ICs is proposed to solve the aforementioned issues with high integration level. The proposed control replaces optocoupler to achieve ZVS and secondary-side-regulation (SSR) by a pulse transformer. The ICs further integrate the USB power delivery (PD) controller and the constant voltage (CV)/constant current (CC) loop compensation to reduce external components. The proposed control and small-signal model are illustrated and verified by the experiment. The control ICs were fabricated in a 180-nm BCD process. The implemented 20-V/3-A/60-W USB PD flyback converter achieved 93.5% maximum efficiency using Si power switches. The feasibility of the proposed converter is verified by a thorough test of programmable power supply (PPS) with various CC/CV levels and load/line transients.
A Fault-Tolerant Adaptive Routing Method Based on the Passage of Faulty Nodes

Toward the realization of dependable high-end consumer electronics products, it is indispensable to design an efficient fault-tolerant packet routing method to enhance the communication performance of Network-on-Chip (NoC)-based many core processors. In the recent study of fault-tolerant routing, a novel approach has been proposed [1]; packets can pass through faulty nodes, rather than detour them as almost all existing methods do. However, disadvantage of this method (called passage-y in this paper) is that routing paths are fixed deterministically once the source and destination nodes of the packets are decided. This will result in large communication latency due to the incapability of avoiding congestion. In this paper, we propose a fault-tolerant adaptive routing method which combines two existing methods, well known west-first and the novel passage-y. Simulation result reveals that our method reduces average communication latency by about 21.5%, compared with the previous passage-y method.
Self-Supervised Learning of Robot Manipulation

Robot arm manipulation with reinforcement learning is an active research area. Immitation learning from human is one of the most curious till date to researchers. Demonstration video based learning becomes a challenge when the view angle becomes an independent parameter. Using third person view of some similar task and robot’s third person view, many complex actions can be trained using time coherence property of similar features from TCN (Time Contrastive Network). Learning and controlling a manipulator using its dynamics very difficult and time consuming process. So in this paper we are using kinematic model based PID control for model based arm control. Model free control is implemented based on novel Reward based Random Exploration (RRE) algorithm. This model based and model free updates are memorized and uses it to complete the task.
Discriminative Analysis Dictionary Learning With Adaptive Graph Constraint for Image Classification

Discrimination of coefficient matrix plays an important role in discriminative analysis dictionary learning (ADL) model. However, the local geometric structure of the profiles(i.e., row vector of coefficient matrix) is seldom exploited to design discriminative terms in the discriminative ADL algorithms. In this paper, we proposed a discriminative ADL algorithm with adaptive graph constrained (DADL-AGC)model, which can adaptively preserve the local geometric structure information of profiles. First, we construct an adaptive graph constrained model by maximizing the information entropy of the similarity matrix of profiles. In this way, the coefficient matrix can preserve and inherit the local geometric information of analysis atoms and training samples by using the K-means method to initialize the analysis dictionary. Moreover, a robust linear classifier is simultaneously learned to improve the classification performance of our DADL-AGC algorithm. On the four deep features and hand-crafted features databases, experimental results demonstrate that our DADL-AGC algorithm can achieve better performance than seven ADL and synthesis dictionary learning algorithms.