Miracle-dz/UniFAR-FTUs huggingface dataset:
Entropy based features in FAWT framework for automated detection of epileptic seizure EEG signals

Flexible analytic wavelet transform (FAWT) is suitable for the study of oscillatory signals like electroencephalogram (EEG) signals with versatile features such as shift in-variance, tunable oscillatory properties and flexible time-frequency domain. In this paper, we propose an automated method for the classification of seizure and non-seizure EEG signals using FAWT and entropy-based features such as Stein's unbiased risk estimator (SURE) entropy, log energy entropy, and Shannon entropy. The obtained features are given as input to robust energy-based least squares twin support vector machines (RELS-TSVM) for classification. The proposed method has been implemented on publicly available epilepsy database (Bonn University EEG database) and is comparable with the existing methods with a maximum accuracy of 100% for the classification of seizure and non-seizure EEG signals.
Design and Development of Quadcopter for Autonomous Water Vehicle Navigation and Load Management Monitoring System

This paper presents an autonomous water vehicle (WV) navigation and tracking based on drone technology. The key objective of this work is shown the significant utilization of aerial system in WV tracking and monitoring. Recently, aerial vehicles or drones are used vastly for commercial purposes. For example Amazon.com, a giant company uses the drone for parcel deliver. Nowadays, the GPS system is applied to WV for better monitoring and tracking. To make it more precise and assurance a quadrotor cop drone can be introduced. With this proposed model, the WVs in a specific waterway can be monitored and navigated properly with the quadcopter. The ground center can control the quadcopter with GPS and other telemetry devices. With drone surveillance technique, the control center can track the water vehicle easily and they can observe the load or weight on the WV. Mainly, water level sensor and Radio Frequency (RF) transmitter are applied on the boat to transmit data about boat load condition. Thus this system will also help up to detect the boat with overweight. The mission planner software is used for showing the live time navigation data of Quadcopter and basic C program is used for load monitoring. Along with a Quadcopter project is implemented to validate the system.
EEG Channel Selection using Fractal Dimension and Artificial Bee Colony Algorithm

The development of Brain Computer Interfaces (BCI) has attracted the attention of several research groups for solving different kind of problems in the field of medicine, device control, gaming, etc. However, there are several challenges that must be solved in order to have more feasible BCI applications. One of these challenges is related with the high dimensionality of the EEG recordings due to they are acquired from several channels to preserve high spatial accuracy. However, it is necessary to carefully select the channels that provide the most relevant information as well as the feature extraction technique in order to guarantee an acceptable accuracy. On the other hand, Swarm Intelligence techniques are based on biological processes of some species for their survival such as the food search task or natural selection process. These techniques have been widely applied in several optimizations problems obtaining acceptable results. In this paper, we described the high dimensionality challenge as an optimization problem in order to applied a swarm optimization technique, the Artificial Bee Colony (ABC) algorithm, to determine the set of channels that are more useful to discriminate different mental tasks. Furthermore, during the feature extraction stage, we applied fractal dimension methods to build the feature vector that will be used to train a classifier. Finally, the accuracy of the proposed methodology is tested classifying different motor tasks using the data set IVa from BCI international competition III.
Redditors in Recovery: Text Mining Reddit to Investigate Transitions into Drug Addiction

Increasing rates of opioid drug abuse and heightened prevalence of online support communities underscore the necessity of employing data mining techniques to better understand drug addiction using these rapidly developing online resources. In this work, we obtain data from Reddit, an online collection of forums, to gather insight into drug use/misuse using text data from users themselves. Specifically, using user posts, we trained 1) a binary classifier which predicts transitions from casual drug discussion forums to drug recovery forums and 2) a Cox regression model that outputs likelihoods of such transitions. In doing so, we found that utterances of select drugs and certain linguistic features contained in one’s posts can help predict these transitions. Using unfiltered drug-related posts, our research delineates drugs that are associated with higher rates of transitions from recreational drug discussion to support/recovery discussion, offers insight into modern drug culture, and provides tools with potential applications in combating the opioid crisis.
A Compact Microstripline-Fed On-Body Matched Antenna for Wireless Medical Applications

A novel design of a flexible on-body matched microstripline-fed antenna for biomedical applications is proposed in this article. The core feature of the proposed antenna refers to its tiny dimension, bendability, low temperature co-fired ceramics (LTCC) substrate layer, impressive return loss, higher bandwidth that makes it appropriate for on-body matched wireless medical applications. The dimension of the antenna is 13.86 mm × 13.49 mm × 0.35 mm where substrate layer, LTCC layer has the thickness of 0.326 mm and on the other hand, the thickness of the radiating patch is 0.012 mm. The performance of the antenna was analyzed to operate in the Industrial, Scientific and Medical (ISM) band (2.4–2.5 GHz). Three layers of the human phantom model have been proposed as well as biocompatibility test, SAR calculations, flexibility, and several performance measurements have been utilized using CST Microwave Studio software in both curved and planar states by following different types of dielectric property matching with human tissue model. Finally, Specific Absorption Rate (SAR) is evaluated to check its feasibility and workability in wireless medical applications.
Secrecy Performance of Decode-and-Forward Based Hybrid RF/VLC Relaying Systems

In this paper, we consider the secrecy of a hybrid radio frequency (RF)/visible light communication (VLC) system equipped with decode-and-forward relaying. We develop physical layer security algorithms that mitigate eavesdropping on both RF and VLC networks based on zero-forcing beamforming techniques. We evaluate the system performance in terms of secrecy capacity (SC) and outage probability (OP) for two network scenarios, namely non-cooperative (NCPS) and cooperative power saving (CPS) models. The NCPS case assumes fixed power at both source and relay while the CPS case assumes total average power shared between the source and relay. Our objective is to find the minimum power that satisfies a specific SC for both cases. Our simulation results show that CPS policy achieves higher SC and maintain lower OP compared with the NCPS one for the same amount of power. We also show that, for both cases, the hybrid RF/VLC network can improve SC compared with standalone RF or VLC networks.
Improved Feature Vector of Median Filtering Residual for Image Forensics

For the detection of median filtering (MF) forensics, this paper proposes an improved feature vector that consists of the refined feature vector of the MFR AR (Median Filter Residual Autoregressive Model) and an additional feature vector about the gradient and edge lines of an image. The improved nine dimensionality feature vector is trained in a SVM (Support Vector Machine) classifier for the median filtering detection (MFD) of the forged images. The performance of the proposed MFD scheme is measured with several types of the operated images: median filtered, unaltered and JPEG compressed, respectively.Subsequently, the AUC (Area Under Curve) results of the proposed MFD scheme have 0.99 over on the trained SVM (Support Vector Machine) classifier. Thus, it confirmed that the grade evaluation of the proposed scheme is ‘Excellent (A)’ in terms of the AUC evaluation.
Ant Brood Clustering on Intel Xeon Multi-core: Challenges and Strategies

Swarm intelligence algorithms such as ant colony optimization (ACO) and particle swarm optimization (PSO) are computationally intensive. Ant Brood Clustering (ABC) algorithm is one of the techniques in the family of ant algorithms. It is based on how ants cluster their brood or corpse into different piles. ABC has been efficiently used in solving the clustering problem in data analytics. However, they are computationally intensive. In this paper, we explore and evaluate eight parallel strategies of the ant brood clustering algorithm on Intel Xeon multi-core shared memory machine exploiting algorithm level and program level parallelism. A speedup of 25.3x is achieved with 56 hardware threads on dual 14 core Intel Xeon shared memory machine with coarse-grained, data level protection strategy. We show that contrary to other swarm intelligence techniques, fine grained parallelism is not suitable for this algorithm.
Optimal Control for Self-Steering of a Truck System

In the next few years, vehicles with self-steering features and autonomy will go into marketplaces as a result of a lot of research on autonomous vehicles in recent years. Even the World Economic Forum has estimated that the digital alteration in the automotive business sector will create a huge amount of money in that area. The problem of trajectory path and path following for autonomous vehicles is now arising due to the complexity of the vehicle dynamic model. This paper considers a trajectory path that leads to minimal time and energy consumption. An optimal trajectory path is generated by means of Pentraxin Maximum Principle (PMP) with fixed final state and fixed final time as the constraints. In addition, it is considered an optimal non-linear control approach to control the system. Full state feedback control is selected to design the Linear Quadratic Regulator (LQR) controller. The proposed system is considered for a non-linear truck system by solving the control problem of the linearization of the truck system iterated for every time instant along with the trajectory conditions.
Emotional Bots: Content-based Spammer Detection on Social Media

Recent research indicates that a considerable amount of content on social media is generated by automated accounts. The automata present sophisticated behavior —mimicking humans—aiming at evading traditional detection methods. In this paper, we present a supervised approach to detect automated accounts on Twitter using mainly content-based features. We performed our experiments using four datasets that contain tweets from almost 20K malicious and benign accounts. Our methodology is lightweight and employs users—metadata, content and sentiment features. It performs well on unseen data (0.95 F1-score) reaching 95% precision and recall. This work also demonstrates that sentiment characteristics can add value to social spambot detection algorithms when combined with known features.
A 3.77TOPS/W Convolutional Neural Network Processor With Priority-Driven Kernel Optimization

Convolutional neural network (CNN) has become very popular in image classification tasks. With the increasing demand on intelligent classification on battery-powered devices, energy-efficient ASICs for CNN are badly needed. While previous CNN ASIC processors support operations of different kernel sizes, they sacrifice efficiency to support flexible convolution operations. In fact, convolution operations with a certain kernel size are dominating in many real-case CNNs. This brief proposes a kernel-optimized architecture for <inline-formula> <tex-math notation="LaTeX">$3\,{\times }\,3$ </tex-math></inline-formula> kernels (KOP3), which are dominating operations in mainstream image classification CNNs. Although KOP3 aims at <inline-formula> <tex-math notation="LaTeX">$3\,{\times }\,3$ </tex-math></inline-formula> kernel operations, it also provides programmability to support arbitrary kernel sizes. KOP3 achieves average energy efficiency of 3.77TOPS/W, which is <inline-formula> <tex-math notation="LaTeX">$4.01{ \times }$ </tex-math></inline-formula> better than the best state-of-the-art CNN ASIC processor.
Classification of pathological and non-pathological Cardiodynamicsgram (CDG) using nonlinear dynamics indexes

Cardiodynamicsgram (CDG) has emerged recently as a new noninvasive recording method of cardiac dynamics information during heart-beating process, which can be used for the early detection of myocardial ischemia. Automatic classification between pathological and non-pathological CDG can assist doctors in identification of the latent myocardial ischemia for their planning of minimizing ischemic time. In this work, we propose a classification methodology to classify pathological and non-pathological CDG. As the cardiac characteristics extracted by conventional morphology analysis methods is relatively limited and not comprehensive enough to reflect the dynamical characteristics of CDG, we extract three nonlinear dynamics indexes which include $C_{0}$ complexity (${C}0$), Approximate entropy (ApEn) and Lyapunov-exponent spectrum (LypE) as a measure of the complexity and nonlinearity of CDG in this work. These indexes are inputted into five different classifiers, including Naive bayes classifier (NBC), Support vector mechines (SVM), K-Nearest neighbour classifier (KNN), Multilayer perceptron (MLP), and Decision tree classifier (DTC). Experimental results at Chinese National Center for Cardiovascular Diseases show that encouraging classification accuracy can be achieved. Additionally, the computation time of these indexes extraction and classfication is very short, which is helpful for building up a real-time software tool towards assisting the physician in cardiology departments.
Sentence Level Indonesian Sign Language Recognition Using 3D Convolutional Neural Network and Bidirectional Recurrent Neural Network

Sign Language Recognition (SLR) is a relatively challenging research field which allows opportunity for improvements. In this research, we propose sentence-level SLR using deep learning method by combining Convolutional Neural Network (CNN) and Bidirectional Recurrent Neural Network (Bi-RNN). Specifically, 3D CNN is implemented to extract features from each video frame and bidirectional-RNN is implemented to extract the unique features from the video frame's sequential behavior, which later generate a possible sentence. There are two key takeaways from this paper. The first is our proposed dataset of Indonesian Sign Language (SIBI) which is comprised of 30 sentences in SIBI. The second is our novel approach of using deep learning and Connectionist Temporal Classification (CTC) loss function in sentence-level SLR. The result shows that the hyperparameter used, in this case Hyperparameter 1, achieves the best result. Also, this research found that deeper network does not necessarily guarantee good results. A bigger number of dataset also affects the performance of the system.
Revealing the processing history of pitch-shifted voice using CNNs

With the emergence of varieties of audio editing software, disguised voices can be generated easily and are able to spoof the automatic speaker verification system. In this work, we focus on the spoofed voice disguised by pitch shifting which is commonly used by popular audio editing software and design a forensic chain to reveal the processing history of pitch-shifted voice signals blindly based on convolutional neural networks (CNNs). Because tracing the disguising tool before recognizing the disguising factor from the spoofed speech helps much to recognize the disguising factor, the proposed framework enables the detection of the disguised voice and the utilized tools. To our knowledge, it is the first attempt, focusing on voice transformation without any prior knowledge of the original speech, to recognize the disguising tool and the disguising factor from the spoofed speech, which is more suitable in real-world applications. Experimental results show that the disguising factors can be recognized with accuracy higher than 90%, indicating a high possibility of restoring the original voice from its disguised counterpart.
A Two-stage Prediction Method of News Popularity only using Content Features

News popularity prediction benefits election, investment decisions, news recommendation, etc. Existing prediction methods using news dynamic trends or propagation characteristics need an observation period after news publication, thus failing to meet the requirements of timely prediction. Aiming at predicting news popularity before its publication, we use features extracted only from news content. Considering differences existing between news columns (financial, sports, entertainment news, etc we propose a two-stage method to predict news popularity. Firstly, we select global features related to column information from all news content and classify the news to different columns. Secondly, we select local features related to news popularity separately from each column of news content, reconstruct news VSM with the selected features, and predict the popularity of each column of news. We collect S687 pieces of news including four columns over 6 months from Sohu News. The average popularity prediction F-value of the four columns is 91.4%. Compared with traditional method which predicts news popularity without considering column differences, our method improves by 2.2%. Furthermore, we find that popularity of social and financial news is more predictable than sports and entertainment news.
Activity Recognition System for Dementia in Smart Homes based on Wearable Sensor Data

The highly developed medical technology has increased the average human life span, but also speeds up the process of the world’s population aging. In the next few decades, Dementia will affect a considerably increasing number of the elderly. Taking care of the person with dementia is an overwhelming task and can cause physical, emotional and economic pressures to families and caregivers. This works aims to alleviate the caregiver’s burden, which can constantly monitor the elderly in smart homes, and in turn, send alert to the caregiver whenever abnormal activities of the elderly occur. This system adopts the unsupervised clustering algorithm, e.g. Dirichlet process mixture model (DPMM), to get the potential clusters about the hands movements because the number of clusters is unknown in priori. In order to store spatial information of hands movements within a special activity, N-gram model is used to extract the feature basing on the result of DPMM, and in turn integrate position and hands movement features. The activity recognition (AR) model is used to recognize the living activities with promising performance, in which the precision and recall of this proposed system is up to 96.8% and 96.7%, respectively, even for the similar activities, such as “eating mear’ and “having medicine
One Size Does Not Fit All: Querying Web Polystores

Data retrieval systems are facing a paradigm shift due to the proliferation of specialized data storage engines (SQL, NoSQL, Column Stores, MapReduce, Data Stream, and Graph) supported by varied data models (CSV, JSON, RDB, RDF, and XML). One immediate consequence of this paradigm shift results into data bottleneck over the web; which means, web applications are unable to retrieve data with the intensity at which data are being generated from different facilities. Especially in the genomics and healthcare verticals, data are growing from petascale to exascale, and biomedical stakeholders are expecting seamless retrieval of these data over the web. In this paper, we argue that the bottleneck over the web can be reduced by minimizing the costly data conversion process and delegating query performance and processing loads to the specialized data storage engines over their native data models. We propose a web-based query federation mechanism—called PolyWeb—that unifies query answering over multiple native data models (CSV, RDB, and RDF). We emphasize two main challenges of query federation over native data models: 1) devise a method to select prospective data sources—with different underlying data models—that can satisfy a given query and 2) query optimization, join, and execution over different data models. We demonstrate PolyWeb on a cancer genomics use case, where it is often the case that a description of biological and chemical entities (e.g., gene, disease, drug, and pathways) spans across multiple data models and respective storage engines. In order to assess the benefits and limitations of evaluating queries over native data models, we evaluate PolyWeb with the state-of-the-art query federation engines in terms of result completeness, source selection, and overall query execution time.
Poster: Performance Evaluation of an Open-Source Audio-Video Bridging/Time-Sensitive Networking Testbed for Automotive Ethernet

Automotive Ethernet (AE) is becoming more and more relevant to the automotive industry due to its support of emerging in-car applications, which have high bandwidth demands and stringent requirements in terms of latency and time synchronization. One of the standards under consideration for AE is IEEE 802.1 Audio Video Bridging (AVB)/Time Sensitive Networking (TSN) that provides deterministic data link layer and bounded latency to real-time traffic classes. So far, this protocol stack has only been evaluated using either simulations or proprietary and expensive platforms. In this paper, we design a real testbed system for AE using general-purpose single-board computers and conduct experiments to assess the real-time performance of an open-source AVB/TSN implementation. Our preliminary results show that even under heavy load, AVB/TSN can fulfil the latency requirements of AE while keeping a constant latency variation.
Number Type Identification of Chinese Personal Noun Phrases via Piece-wise Linear Support Vector Machine

Number information plays an important role in many Chinese natural language processing tasks. Different from English, Chinese does not explicitly show grammatical number information by inflection. Mostly existing number type identification methods for Chinese personal noun phrases are based on rules, some machine learning-based methods explore diverse features, including both flat and structured to train the classification models. However, the unbalanced data between different classes in the problem of personal noun phrases number type identification leads to the low classification accuracy in the small sample class. In this paper, we propose a Piece-wise Linear Support Vector Machine(PL-SVM) method to divide the large number class into N parts, and train the local linear SVM to construct classification boundary and fit the train data, to improve the classification accuracy in small sample class. Experiments on ACE2005 show that our method can not only achieves better performance on large number class, but also improves the classification accuracy on small number class, which makes up the flaw of traditional methods in some extent. Furthermore, the experiment results show that our method can improve the resolution accuracy when it is implemented in Chinese coreference resolution.