Miracle-dz/UniFAR-FTUs huggingface dataset:
Key Issues for the Construction of Salient Object Datasets with Large-Scale Annotation

Salient object detection (SOD) has been extensively studied in recent decades, especially after the boom of convolutional neural networks (CNNs). To direct supervised CNN-based methods to its highest function for SOD, more challenging datasets with reasonable large-scale annotations have been proposed. However, due to a lack of verdict of defining multiple salient objects on images or sequences with complex natural scenes and objects, there are certain degrees of bias in current SOD datasets. Therefore, we survey the methods for salient object annotation and further conclude several key issues for the future SOD dataset construction. To the best of our knowledge, this is the first work that synthesizes all the existing salient object annotation methods.
Using IPv6 protocol in V2X networks based on IEEE 802.11-OCB mode of operation

The following article is focused on the characteristics of the V2X networks based on the IEEE 802.11 standard, its OCB mode of operation, and their relation to the use of IPv6 protocol. The article lists many specifics of V2X networks, resulting mainly from the mobility of network nodes - moving vehicles or pedestrians, and standing roadside units. The different nature mobility of network nodes in relation to the wireless networks and the IPv6 protocol determines specific technical requirements for modification of current technologies. The following review based on ongoing research and development is important to highlight the possibilities of IPv6 communication across the V2X network. The article aims to clarify discrepancies in the terms used in V2X networks and to specify the related IPv6 protocol properties in more detail.
Sleep Apnea Event Detection from Sub-frame Based Feature Variation in EEG Signal Using Deep Convolutional Neural Network

The topic of automatic detection of sleep apnea which is a respiratory sleep disorder, affecting millions of patients worldwide, is continuously being explored by researchers. Electroencephalogram signal (EEG) represents a promising tool due to its direct correlation to neural activity and ease of extraction. Here, an innovative approach is proposed to automatically detect apnea by incorporating local variations of temporal features for identifying the global feature variations over a broader window. An EEG data frame is divided into smaller sub-frames to effectively extract local feature variation within one larger frame. A fully convolutional neural network (FCNN) is proposed that will take each sub-frame of a single frame individually to extract local features. Following that, a dense classifier consisting of a series of fully connected layers is trained to analyze all the local features extracted from subframes for classifying the entire frame as apnea/non-apnea. Finally, a unique post-processing technique is applied which significantly improves accuracy. Both the EEG frame length and post-processing parameters are varied to find optimal detection conditions. Large-scale experimentation is executed on publicly available data of patients with varying apnea-hypopnea indices for performance evaluation of the suggested method.
Gradual Changes in Genetic Based Developments for Phylogenetic Trends in Picornaviruses

On the basis of the several techniques, the new groups and the strains within the Picornaviruses were classified. New groups like human pathogen EV22 (Echovirus 22) was discovered that was found to be highly different from other Picornaviruses to date. The biological properties of Picornaviruses were studied and on the basis of biological properties the viruses were classified into similar groups. The protein composition is rare because most of the copies out of 3 would not be processed to VP2 and VP4. EV23 was said to be similar to EV22 in various sections of genome. The Picornavirus classification is done hierarchically of a family using the quantitative approach with the help of PEDs (pairwise evolutionary distances). Comparison of the GENETIC classification with expert-based Picornavirus taxonomy and the differences in the frameworks were demonstrated, related to the virus groups and genetic diversity that show the classification content and structure. In the GENETIC classification, human Rhinovirus A, human Rhinovirus C and genus Aphthovirus were separated.
Overlapping Community Detection via Minimum Spanning Tree Computations

Contemporary social networks deal with Big Data in which a large amount of useful information is hidden. Detecting communities in such networks constitutes a particularly challenging computational task. In this paper, we propose an algorithm for detecting overlapping communities, which builds on an hierarchical divisive method called ST (AlgoCloud2018), originally designed to detect disjoint communities efficiently and without significant loss of information. The method is based on first computing a minimum spanning tree of the original graph and then calculating the edge and vertex betweenness centrality measures on the tree, considerably speeding up calculations.
A Captcha-Based Graphical Password With Strong Password Space and Usability Study

Security for authentication is required to give a superlative secure users' personal information. This paper presents a model of the Graphical password scheme under the impact of security and ease of use for user authentication. We integrate the concept of recognition with re-called and cued-recall based schemes to offer superior security compared to existing schemes. Click Symbols (CS) Alphabet combine into one entity: Alphanumeric (A) and Visual (V) symbols (CS-AV) is Captcha-based password scheme, we integrate it with recall-based $n \times n$ grid points, where a user can draw the shape or pattern by the intersection of the grid points as a way to enter a graphical password. Next scheme, the combination of CS-AV with grid cells allows very large password space ($2.4 \times 10^{4}$ bits of entropy) and provides reasonable usability results by determining an empirical study of memorable password space. Proposed schemes support most applicable platform for input devices and promising strong resistance to shoulder surfing attacks on a mobile device which can be occurred during unlocking (pattern) the smartphone.
Integrated Optimal Design of Speed Profile and Fuzzy PID Controller for Train With Multifactor Consideration

Speed profile design and tracking control are two important issues to ensure the performance of the automatic train operation system. Aiming to realize the maximum optimization probability of train running, this paper constructs an integrated optimal method through speed profile design and fuzzy PID controller design. In the recommended speed profile design, three typical running strategies are included in the proposed comprehensive scheme to extend solution space, and the constructed train performance simulation is combined with NSGA-II algorithm to solve such multiobjective problem. As for recommended speed profile tracking, in order to improve the tracking performance of the train, which is a nonlinear system, a fuzzy PID controller is designed to adaptively adjust PID gains. Note that multiple indicators of punctually, energy consumption, stopping accuracy and comfort are all considered in the integrated design. Taking Beijing Subway Line 8 as the numerical example, the results show that the design of speed profile and fuzzy PID controller are both effective, and energy consumption saves about 10.40% with other better indicators compared with the original data.
EEG-based Depression Detection Using Convolutional Neural Network with Demographic Attention Mechanism

Electroencephalography (EEG)-based depression detection has become a hot topic in the development of biomedical engineering. However, the complexity and nonstationarity of EEG signals are two biggest obstacles to this application. In addition, the generalization of detection algorithms may be degraded owing to the influences brought by individual differences. In view of the correlation between EEG signals and individual demographics, such as gender, age, etc., and influences of these demographic factors on the incidence of depression, it would be better to incorporate demographic factors during EEG modeling and depression detection. In this work, we constructed an one-dimensional Convolutional Neural Network (1-D CNN) to obtain more effective features of EEG signals, then integrated gender and age factors into the 1-D CNN via an attention mechanism, which could prompt our 1-D CNN to explore complex correlations between EEG signals and demographic factors, and generate more effective high-level representations ultimately for the detection of depression. Experimental results on 170 (81 depressed patients and 89 normal controls) subjects showed that the proposed method is superior to the unitary 1-D CNN without gender and age factors and two other ways of incorporating demographics. This work also indicates that organic mixture of EEG signals and demographic factors is promising for the detection of depression.Clinical relevance-This work indicates that organically mixture of EEG signals and demographic factors is promising for the detection of depression
Data Mining and Fusion of Unobtrusive Sensing Solutions for Indoor Activity Recognition

This paper proposes the fusion of data from unobtrusive sensing solutions for the recognition and classification of activities in home environments. The ability to recognize and classify activities can help in the objective monitoring of health and wellness trends in ageing adults. While the use of video and stereo cameras for monitoring activities provides an adequate insight, the privacy of users is not fully protected (i.e., users can easily be recognized from the images). Another concern is that widely used wearable sensors, such as accelerometers, have some disadvantages, such as limited battery life, adoption issues and wearability. This study investigates the use of low-cost thermal sensing solutions capable of generating distinct thermal blobs with timestamps to recognize the activities of study participants. More than 11,000 thermal blobs were recorded from 10 healthy participants with two thermal sensors placed in a laboratory kitchen: (i) one mounted on the ceiling, and (ii) the other positioned on a mini tripod stand in the corner of the room. Furthermore, data from the ceiling thermal sensor were fused with data gleaned from the lateral thermal sensor. Contact sensors were used at each stage as the gold standard for timestamp approximation during data acquisition, which allowed the attainment of: (i) the time at which each activity took place, (ii) the type of activity performed, and (iii) the location of each participant. Experimental results demonstrated successful cluster-based activity recognition and classification with an average regression co-efficient of 0.95 for tested clusters and features. Also, an average accuracy of 95% was obtained for data mining models such as k-nearest neighbor, logistic regression, neural network and random forest on Evaluation Test.Clinical Relevance—This study presents an unobtrusive (i.e., privacy-friendly) solution for activity recognition and classification, for the purposes of profiling trends in health and wellbeing.
HERCULES: A Three Degree-of-Freedom Pneumatic Upper Limb Exoskeleton for Stroke Rehabilitation*

This paper outlines the construction, current state, and future goals of HERCULES, a three degree-of-freedom (DoF) pneumatically actuated exoskeleton for stroke rehabilitation. The exoskeleton arm is capable of joint-angle control at the elbow in flexion and extension, at the shoulder in flexion and extension, and at the shoulder in abduction and adduction. In the near future we plan to embed kinematic synergies into the control system architecture of this arm to gain dexterous and near-natural movements.Clinical Relevance— This device can be used as an upper limb rehabilitation testbed for individuals with complete or partial upper limb paralysis. In the future, this system can be used to train individuals on synergy-based rehabilitation protocols.
An Evaluation of Known and Potential Sampling Techniques for Hellbender, Cryptobranchus alleganiensis

Three known techniques, variations of visual search and capture by hand or nets, and two potential techniques utilizing electroshocking were evaluated for their effectiveness and efficiency in capturing hellbenders (Cryptobranchus alleganiensis) in the Allegheny River drainage, northwestern Pennsylvania. Under conditions of controlled sampling effort, the two electroshocking techniques accounted for 78.2% of the 87 individuals captured. Hellbenders appeared to exhibit no ill effects or differential displacement from electroshocking. Unlike the other techniques, the success of electroshocking was independent of the study site and time of day.
Discrimination of Two-Class Motor Imagery in a fNIRS Based Brain Computer Interface

The purpose of this study was to discriminate between left- and right-hand motor imagery tasks. We recorded the brain signals from two participants using a fNIRS system and compared different feature extraction (mean, peak, minimum, skewness and kurtosis) and classification techniques (linear (LDA) and quadratic discriminant analysis (QDA), support vector machine (SVM), logistic regression, K-nearest-neighbor (KNN), and neural networks with Levenberg-Marquardt (LMA), Bayesian Regularization (BRANN) and Scaled Conjugate Gradient (SCGA) training algorithms). The results showed poor classification accuracies (<58%) when skewness and kurtosis were used. When mean, peak, and minimum were used as features, QDA, SVM and KNN produced higher classification accuracies relative to LDA and logistic regression. Overall, BRANN led to the highest accuracies (>98%) when mean, peak and minimum were used as features.
Data Augmentation for Automatic Identification of Spatiotemporal Dispersion Electrograms in Persistent Atrial Fibrillation Ablation Using Machine Learning

Catheter ablation is increasingly used to treat atrial fibrillation (AF), the most common sustained cardiac arrhythmia encountered in clinical practice. A recent breakthrough finding in AF ablation consists in identifying ablation sites based on their spatiotemporal dispersion (STD). STD stands for a delay of the cardiac activation observed in intracardiac electrograms (EGMs) across contiguous leads. In practice, interventional cardiologists localize STD sites visually using the PentaRay multipolar mapping catheter. This work aims at automatically characterizing STD by classifying EGM data into STD vs. non STD groups using machine learning (ML) techniques. A dataset of 23082 multichannel EGM recordings acquired by the PentaRay coming from 16 persistent AF patients is included in this study. A major problem hampering the classification performance lies in the highly imbalanced dataset ratio. We suggest to tackle data imbalance using adapted data augmentation techniques including 1) undersampling 2) oversampling 3) lead shift 4) time reversing and 5) time shift. These tools are designed to preserve the integrity of the cardiac data and are validated by a partner cardiologist. They provide enhancement in classification performance in terms of sensitivity, which increases from 50% to 80% while maintaining accuracy and AUC around 90% with oversampling. Bootstrapping is applied to check the variability of the trained classifiers.Clinical relevance-The machine learning techniques developed in this contribution are expected to aid cardiologists in performing patient-tailored catheter ablation procedures for treating persistent AF.
Self-Regulated and Co-ordinated Smart Tumor Homing for Complex Vascular Networks

Inspired by the collaborative movements of the living cells in different natural biological processes, this paper highlights a self-regulated and co-ordinated targeting strategy to detect tumor in complex human vasculature. We show by computational experiments that simple and non-centralized nanoparticles with limited locomotive capability, when they collaborate and move in the form of groups, can be very robust in the tumor homing process. Once reaching tumor location, such nanoparticles (potential contrast agents) can deposit themselves on tumor through the enhanced permeability and retention (EPR) effect thereby making the malignant cancerous area more prominent. Hence, existing medical imaging techniques with limited resolution can be used to detect small malignant tumors. We believe that our work will help in early cancer detection through an independent, non-centralized and self-controlled system.Clinical Relevance:— With the help of target amplification through contrast agents such as self-regulated nanoparticles contrast enhanced medical imaging can detect the tumor at its initial stages when 90% of the cancers are curable
Estimation of Wideband Dynamic mmWave and THz Channels for 5G Systems and Beyond

Millimeter wave (mmWave) wideband channels in a multiple-input multiple-output (MIMO) transmission are described by a sparse set of impulse responses in the angle-delay, or space-time (ST), domain. These characteristics will be even more prominent in the THz band used in future systems. We consider two approaches for channel estimation: compressed-sensing (CS), exploiting the sparsity in the angular/delay domain, and low-rank (LR), exploiting the algebraic structure of channel matrix. Both approaches share several commonalities, and this paper provides for the first time i) a comparison of the two approaches, and ii) new versions of CS and LR methods that significantly improve performance in terms of mean squared error (MSE), computational complexity, and latency. We derive the asymptotic MSE bound for any estimator of the ST-MIMO multipath channels with invariant angles/delays and time-varying fading, with unknown angle/delay diversity order: the bound also accounts for the degradation introduced by sub-optimal separable channel models. We will show that in the considered scenarios both CS and LR approaches attain the bound. Our performance assessment over ideal and $3^{rd}$ generation partnership project (3GPP) channel models, suitable for the fifth-generation (5G) and beyond of cellular networks, shows the trade-off obtained by the methods over various metrics: i) CS methods are converging faster than the LR methods, both attaining the asymptotic MSE bound; ii) the CS methods depend on the array manifold, while LR methods are independent of the array calibration; iii) CS solutions are more complex than LR solutions.
Variable Tumor Microenvironment-on-a-chip with Temporal Angiogenic Switching System by Diffusion Control*

Organ-on-a-chip has the potential to replace preclinical trials which have been problematic for decades due to unaffordable cost and time. The performance of in vitro tumor-on-a-chip depends on how accurately the system represents analogous tumor-microenvironment (TME) and TME associated phenomena. In this study, we have focused on angiogenesis, one of the most significant features of TME for tumor growth and metastasis. Angiogenesis in TME is triggered through cascaded interactions among TME associated neighboring cells including immune cells, tumor cells, and fibroblast cells [1]. Therefore, temporally-controlled TME-on-a-chip is desired for an accurate representation of angiogenesis. However, conventional microfluidic devices cannot temporarily manipulate the condition of interacting cells and secreted signal molecules. Here, we proposed a hydrogel-based variable TME-on-a-chip with diffusion switch channels. The channels between hydrogel walls enable temporal diffusion control by controlling inflow. The diffusion control was observed in diffusion experiment with a fluorescent dye. Furthermore, experiment of HUVEC’s migration toward diffused VEGF also confirmed that TME-on-a-chip is capable of reproducing an angiogenic switch triggering through temporal diffusion control. Due to a simple fabrication procedure, the design of the microfluidic device can be easily modified to represent more complex variable TME models.
Subspace Clustering and Feature Typicality Degrees: a Prospective Study

Subspace clustering can offer, beside a decomposition of data into homogeneous and distinct clusters, a characterisation of the subspaces in which the clusters live. This paper explores the possibility of capturing the notion of characteristic features in the framework of typicality degrees, as typical features. To that aim, it discusses the notion of typicality degrees for features and proposes an Alternating Cluster Estimation algorithm, named TbSC, to exploit these degrees within subspace clustering. It illustrates their differences experimentally using simple data sets.
Flow-Aware QoS Engine for Ultra-Dense SDN Scenarios

Software-Defined Networking (SDN) is a promising technology for 5G thanks to the separation of data plane and control plane. However, especially in ultra-dense scenarios, as a result of the centrality of the SDN controller, the response time increases with the ultra high spiky demands of the in-coming heterogeneous flows. This sudden increase causes an uncontrollable rise in end-to-end (e2e) latency and drop rate in the controller. Moreover, this also leads to an unbalanced QoS provisioning in this heterogeneous flow management. To tackle these challenges, in this paper, we propose a Flow-Aware QoS Engine by considering both huge flow demands and QoS requirements of different 5G flows (URLLC, eMBB, mMTC). This novel QoS-based engine contains two steps in a single controller: The Admission Management and The Priority Management. In admission management, we modify the generic Loss Ratio-Based Random Early Detection Algorithm (LRED) by implementing an add-on containing the arrival rate of different flow types. In the proposed priority management, we design a tree-based priority management scheme where we dynamically assign priorities to each flow type regarding fairness. According to our thorough evaluation results, we get up to 53% lower response times, up to 58% lower e2e latencies, and up to 36% lower drop rates for three different flows in ultra-dense SDN scenarios.
Finite-Alphabet MMSE Equalization for All-Digital Massive MU-MIMO mmWave Communication

We propose finite-alphabet equalization, a new paradigm that restricts the entries of the spatial equalization matrix to low-resolution numbers, enabling high-throughput, low-power, and low-cost hardware equalizers. To minimize the performance loss of this paradigm, we introduce FAME, short for finite-alphabet minimum mean-square error (MMSE) equalization, which is able to significantly outperform a naïve quantization of the linear MMSE matrix. We develop efficient algorithms to approximately solve the NP-hard FAME problem and showcase that near-optimal performance can be achieved with equalization coefficients quantized to only 1–3 bits for massive multi-user multiple-input multiple-output (MU-MIMO) millimeter-wave (mmWave) systems. We provide very-large scale integration (VLSI) results that demonstrate a reduction in equalization power and area by at least a factor of <inline-formula> <tex-math notation="LaTeX">$ {3.9} {\times }$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$ {5.8} {\times }$ </tex-math></inline-formula>, respectively.