Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey on Real Time Traffic Analysis on Big Data

With the rapid growth of industrialization, there have been several demerits along with the benefits they bring in to our lives. Traffic congestion is one of the major by-products of this growth. It is an on-going phenomenon where people tend to move towards urban areas where the businesses are concentrated. Traffic if not managed properly, results in loss of valuable time and irrecoverable impact on the environment. Big data analysis would help to effectively manage and implement measures to reduce the burden on roads as well as the lives by collection and analysis of the traffic data. This paper presents the survey on traffic avoidance techniques using mapreduce, intelligent transportation system, ABC algorithm through big data analysis to provide safety and congestion free traffic.
Spinal Afferent Innervation of the Colon and Rectum

Despite their seemingly elementary roles, the colon and rectum undertake a variety of key processes to ensure our overall wellbeing. Such processes are coordinated by the transmission of sensory signals from the periphery to the central nervous system, allowing communication from the gut to the brain via the “gut-brain axis”. These signals are transmitted from the peripheral terminals of extrinsic sensory nerve fibers, located within the wall of the colon or rectum, and via their axons within the spinal splanchnic and pelvic nerves to the spinal cord. Recent studies utilizing electrophysiological, anatomical and gene expression techniques indicate a surprisingly diverse set of distinct afferent subclasses, which innervate all layers of the colon and rectum. Combined these afferent sub-types allow the detection of luminal contents, low- and high-intensity stretch or contraction, in addition to the detection of inflammatory, immune, and microbial mediators. To add further complexity, the proportions of these afferents vary within splanchnic and pelvic pathways, whilst the density of the splanchnic and pelvic innervation also varies along the colon and rectum. In this review we traverse this complicated landscape to elucidate afferent function, structure, and nomenclature to provide insights into how the extrinsic sensory afferent innervation of the colon and rectum gives rise to physiological defecatory reflexes and sensations of discomfort, bloating, urgency, and pain.
An API for Development of User Defined Scheduling Algorithms in Aneka PaaS Cloud Software

Cloud computing provides resources using multitenant architecture where infrastructure is created from one or more distributed datacenters. Scheduling of applications in cloud infrastructures is one of the main research area in cloud computing. Researchers have developed many scheduling algorithms and evaluated them using simulators such as CloudSim. Their performance needs to be validated in real-time cloud environments to improve their usefulness. Aneka is one of the prominent PaaS software which allows users to develop cloud application using various programming models and underline infrastructure. This chapter presents a scheduling API developed for the Aneka software platform. Users can develop their own scheduling algorithms using this API and integrate it with Aneka to test their scheduling algorithms in real cloud environments. The proposed API provides all the required functionalities to integrate and schedule private, public, or hybrid cloud with the Aneka software.
Supply chain improvement in LARG (Lean, Agile, Resilient, Green) context: A Risk Management Approach

Implementation of LARG (Lean, Agile, Resilient, Green) practices in supply chain management is a complex task mainly because ecological, economical and operational goals are usually in conflict. To implement these LARG practices successfully, companies need relevant decision making tools allowing processes performance control and improvement strategies visibility. To contribute to this issue, this work tries to answer the following research question: How to master performance and anticipate problems in supply chain LARG practices implementation? To answer this question, a risk management approach (RMA) is adopted. Indeed, the proposed RMA aims basically to assess the ability of a supply chain, guided by “Lean, Green and Achievement” performance goals, to face “agility and resilience risk” factors. To proof its relevance, a logistics academic case study based on simulation is used to illustrate all its stages. It shows particularly how to build the “LARG risk map” which is the main output of this approach.
A New Retrieval System Based on Low Dynamic Range Expansion and SIFT Descriptor

When comparing the intelligibility of Low Dynamic Range (LDR) content with that of the human visual system (HSV), we find that the former is quite limited. This is straightforward, as LDR technologies could only handle 8 or 12-bit per color channel. This being said, LDR images are still used in a wide range of multimedia applications. This paper presents a solution for efficiently indexing LDR content by introducing a novel descriptor based on LDR content expansion. To increase the richness of features that are strongly dependent on the illumination of the scene, the LDR image is converted to High Dynamic Range (HDR) one using reverse Tone Mapping Operator (rTMO). The result HDR image is in turn tone mapped and the relevant features are determined according the Scale Invariant Feature Transform (SIFT) descriptor. After that, the obtained features are gathered into a vector using Bag-of-Visual-Word (BoVW) strategy. A set of routine benchmarking experiments utilizing the Wang and Pascal Voc databases indicates that our system performs well for image retrieval. These experiments also demonstrate that features extracted from reverse tone mapped and tone mapped image are more descriptive than those extracted from LDR and HDR contents.
Deep Conditional Color Harmony Model for Image Aesthetic Assessment

As one of the important features, color provides plenty useful information to represent images. Thus, color harmony, which is defined as “two or more colors are sensed together as a single, pleasing, collective impression” [1], can also be served as a fundamental feature and plays a key role to determine the aesthetics quality of images. To reveal the inherent color harmony attribute within patch and the harmonious relations between image patches which construct the pleasing colorful images, we designed a conditional random field (CRF) based color harmony model in this paper to accomplish the image aesthetic assessment tasks. Unlike the previous learning based color harmony models, we used deep neural networks to obtain the coherence properties between original image patch pairs, and embedded these relations along with each patch's own color harmony characteristic into a CRF to measure the harmony scores of the entire image. The experimental results on a public dataset show that the proposed deep conditional color harmony model is superior to the existing color harmony models in respect of the image aesthetic assessment.
A Deep Recurrent Neural Network with BiLSTM model for Sentiment Classification

In the field of sentiment classification, opinions or sentiments of the people are analyzed. Sentiment analysis systems are being applied in social platforms and in almost every business because the opinions or sentiments are the reflection of the beliefs, choices and activities of the people. With these systems it is possible to make decisions for businesses to political agendas. In recent times a huge number of people share their opinions across the Internet using Bengali. In this paper a new way of sentiment classification of Bengali text using Recurrent Neural Network(RNN) is presented. Using deep recurrent neural network with BiLSTM, the accuracy 85.67% is achieved.
Pharmacokinetics and Pharmacodynamics of an Oral Formulation of Apixaban in Horses After Oral and Intravenous Administration

Horses with inflammatory and infectious disorders are often treated with injectable heparin anticoagulants to prevent thrombotic complications. In humans, a new class of direct oral acting anticoagulants (DOAC) appear as effective as heparin, while eliminating the need for daily injections. Our study in horses evaluated apixaban, a newly approved DOAC for human thromboprophylaxis targeting activated factor X (Xa). Our goals were to: (1) Determine pharmacokinetics and pharmacodynamics of apixaban after oral (PO) and intravenous (IV) administration in horses; (2) Detect any inhibitory effects of apixaban on ex vivo Equid herpesvirus type 1 (EHV-1)-induced platelet activation, and (3) Compare an anti-Xa bioactivity assay with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) for measuring apixaban concentrations. In a blinded placebo-controlled cross-over study, five horses received a single dose (0.2 mg/kg) of apixaban or placebo PO or IV. Blood was collected before and at 3 (IV) or 15 (PO) min, 30 and 45 min, and 1, 2, 3, 4, 6, 8, and 24 h after dosing for measuring apixaban UPLC-MS concentrations and anti-Xa activity. Pharmacodynamic response was measured in a dilute prothrombin time (dPT) assay. Flow cytometric EHV-1-induced platelet P-selectin expression and clinical pathologic safety testing were performed at baseline, 2 and 24 h and baseline and 24 h, respectively. We found no detectable apixaban in plasma PO administration. After IV administration, plasma apixaban levels followed a two-compartment model, with concentrations peaking at 3 min and decreasing to undetectable levels by 8 h. The elimination half-life was 1.3 ± 0.2 h, with high protein binding (92–99%). The dPT showed no relationship to apixaban UPLC-MS concentration and apixaban did not inhibit EHV-1-induced platelet activation after IV dosing. Apixaban anti-Xa activity showed excellent correlation to UPLC-MS (r2 = 0.9997). Our results demonstrate that apixaban has no apparent clinical utility as an anticoagulant for horses due to poor oral availability.
Online Prediction of Robot to Human Handover Events Using Vibrations

One of the main issues for a robotic passer is to detect the onset of a handover, in order to avoid the object from being released when the human partner is not ready or if some impact occurs. This paper presents the methodology for a robotic passer, that is potentially able to estimate the interaction forces by the receiver on the object, thus to achieve fluent and safe handovers. The proposed system uses a vibrator that energizes the object and an accelerometer that monitors vibration propagation through the object during the handover. We focused on the machine-learning technique to classify between four states during object handover. A neural network was trained for these four states and tested online. In experimental trials an accuracy of 85.2 % and 93.9% were obtained respectively for four classes and two classes of actions by a neural network classifier.
NFV Orchestration and Rapid Migration Based on Elastic Virtual Network and Container Technology

Telcos and cloud service providers are keen to exploit virtual network functions (VNF) because it has the potential to reduce reliance on expensive proprietary networking gear and increase network elasticity. In this paper, we propose a NFV Orchestration and Rapid Migration (NORM) architecture and system to realize a complete virtual network layout through the SDN and NFV. Through the port and MAC address mapping, NORM can complete the physical nodes accessing the Virtual Networks (VNs), while deploying related VNFs through Docker. At the same time, we set up a white list function to reserve a place for the hosts to be accessed in advance to facilitate quick access from the host. Through a combination of multiple methods, NORM can support resource customization, automated deployment, and migration flexibility. We designed a complete SDN network through experiments and evaluated the relevant performance. The experimental results verified that the use of Docker compared to the VM improved three to four times in performance.
Deep Emotion Transfer Network for Cross-database Facial Expression Recognition

Due to the large domain discrepancy between the training and testing data and the inaccessibility of annotating sufficient training samples, cross-database facial expression recognition which has more application value remains to be challenging in the literature. Previous researches on this problem are based on shallow features with limited discrimination ability. In this paper, we propose to address this problem with a Deep Emo-transfer Network (DETN). Specifically, maximum mean discrepancy was embedded in the deep architecture to reduce dataset bias. Furthermore, a very common but widely ignored bottleneck in facial expression, imbalanced class distribution, has been taken into account. A learnable class-wise weighting parameter was introduced to our network by exploring class prior distribution on unlabeled data so that the training and testing domains can share similar class distribution. Extensive empirical evidences involving both lab-controlled vs. real-world and small-scale vs. large-scale facial expression databases show that our DETN can yield competitive performances across various facial expression transfer tasks.
An Enhanced Deep Extreme Learning Machine for Integrated Modular Avionics Health State Estimation

Integrated modular avionics (IMA) is one of the most advanced systems whose performance deeply impact on the security of civil aircraft. In order to enhance the safety and reliability of aircraft, the health state of the IMA must be estimated accurately. Since IMA is a real-time system, the estimation algorithm should have fast learning speed to satisfy the real-time requirement. In this paper, an enhanced deep extreme learning machine is developed to estimate the health states of IMA. First, the enhanced deep extreme learning machine is built in a novel fashion by using a dropout technique and extreme learning machine autoencoder. Second, multiple-enhanced deep extreme learning machines with different activation functions are employed to estimate the health states, simultaneously. Finally, a synthesis strategy is designed to combine all the results of different enhanced deep extreme learning machines. In such a manner, the robust and accurate estimation results can be obtained. In order to collect the data under different health states, a performance degradation model of IMA is built by the intermittent faults. The proposed method is applied to health state estimation, and the results confirm that the proposed method can present a superior estimation to the conventional methods.
Fully convolutional network and graph-based method for co-segmentation of retinal layer on macular OCT images

Retinal layer segmentation in optical coherence tomography (OCT) images is crucial for the diagnosis and study of retinal diseases. Graph-based methods are commonly used in layer segmentation. However, most of these methods require a lot of human efforts for determining an appropriate model to compute good edge weights. In this paper, we propose a novel automatic method for segmenting retinal layers in macular OCT images. Specially, we propose a new fully convolutional deep learning architecture with a side output layer to directly learn optimal graph-edge weights from raw pixels. The architecture can automatically learn multi-scale and multi-level features to generate accurate boundary probabilities as good edge weights without hand-crafted appropriate models. The boundaries are finalized by using graph segmentation method. The proposed method is evaluated on a dataset with 130 OCT B-scans. The experimental results show the mean absolute boundary positioning differences are $1.48\pm 0.34$ pixel.
A 5-kW Isolated High Voltage Conversion Ratio Bidirectional CLTC Resonant DC–DC Converter With Wide Gain Range and High Efficiency

This paper presents a novel high voltage conversion ratio (HVCR) bidirectional CLTC resonant dc–dc converter. Based on inductor-inductor-capacitor (LLC), series resonant converter, and capacitor-inductor-inductor-capacitor (CLLC), the novel CLTC structure is proposed. With the use of auxiliary transformer and extra resonant capacitor, a CLTC resonant dc–dc converter can achieve zero voltage switching in all load ranges. Also, CLTC has a good gain characteristic with high power load. In this study, a review of the statuses for the isolated HVCR bidirectional converters is made. The operating principle of a bidirectional power flow in two directions is analyzed in details. In addition, by changing ac output resistance, a modified fundamental harmonic analysis (FHA) method makes the calculated gain curve more accurately than traditional FHA. Also, voltage and current stresses of power switches and resonant capacitors are given. A parameter optimization method is proposed to minimize the impedance angle of CLTC, which corresponds to less reactive power and turn-off current. The optimal parameters show advantages in voltage and current stresses. Moreover, power losses are analyzed in this study, showing advantage of CLTC in efficiency. At last, the experimental results based on a 5-kW prototype converter are presented to validate the theoretical analyses.
Robust Clustering-based Segmentation Methods for Fingerprint Recognition

Fingerprint recognition has been widely studied for more than 45 years and yet it remains an intriguing pattern recognition problem. This paper focuses on the foreground mask estimation which is crucial for the accuracy of a fingerprint recognition system. The method consists of a robust cluster-based fingerprint segmentation framework incorporating an additional step to deal with pixels that were rejected as foreground in a decision considered not reliable enough. These rejected pixels are then further analysed for a more accurate classification. The procedure falls in the paradigm of classification with reject option- a viable option in several real world applications of machine learning and pattern recognition, where the cost of misclassifying observations is high. The present work expands a previous method based on the fuzzy C-means clustering with two variations regarding: i) the filters used; and ii) the clustering method for pixel classification as foreground/background. Experimental results demonstrate improved results on FVC datasets comparing with state-of-the-art methods even including methodologies based on deep learning architectures.
SnVera: A New Algorithm for Automation of Fact-Checking in Web Journalism Context

In this paper, we present a fact-checking algorithm named SnVera based on the confrontation of opinions. We mean by confronting opinions, using the identical point of view and controversial point of view of the sources of the corpus, as essential elements in the quest for truth. The main objective is to propose a new formalization of the problem of detection of the truth by introducing two new scores: the confirmation score and the challenge score.
Pulse Density Modulated ZVS Full-Bridge Converters for Wireless Power Transfer Systems

Pulse density modulation (PDM) is an advanced technique for maximum efficiency point tracking of wireless power transfer (WPT) systems. By using PDM, both voltage regulation and efficiency maximization can be achieved without dc/dc converters. PDM is also compatible with the dual-side soft switching technique that utilizes resonant tanks and synchronous rectification. However, this soft switching technique depends on coupling and load conditions. Hard switching may occur when the coupling of coils gets stronger or the equivalent load is not properly controlled. To eliminate the dependence and ensure the soft switching under various operating conditions, this paper proposes a PDM zero-voltage-switching (ZVS) full-bridge converter for WPT systems. The converter employs a ZVS branch between switching nodes to provide a ZVS current, and uses a specially designed modulator to obtain the valid ZVS current waveforms. Experimental results verified the proposed operating principles and showed that the additional power loss caused by the ZVS current is insignificant. The overall efficiency of the WPT prototype was 93 ∼ 73% when the power transfer distance was 0.1 ∼ 0.4 m, among which up to 85% efficiency was observed when the distance equaled the coil diameter.
Cybersecurity Risk Management Using Analytics: A Dynamic Capabilities Approach

The modern enterprise uses risk-driven and control-centered security management systems to protect information resources and sustain competitive advantage. Such systems have proven to be quite effective in the prevention of threats that exploit common vulnerabilities. However, they are less suited to dynamic response against threats such as Advanced Persistent Threats. The complex and dynamic nature of these threats demands a sophisticated and timely response capability to collect, process and analyze evidence to direct strategic and operational cybersecurity measures. To better understand how organizations are dealing with this new dynamic threat, we analyzed in-depth data from ten expert interviews to determine: How can organizations develop dynamic capabilities in cybersecurity risk management using business analytics? The results informed a framework that explains how organizations may develop higher-order 'analytics-enabled dynamic capabilities' such as enhanced cybersecurity awareness, dynamic cyber-risk assessment, and cybersecurity threat intelligence generation to better deal with both predictable and unpredictable threats.
False Data Injection Prevention in Wireless Sensor Networks using Node-level Trust Value Computation

Wireless Sensor Networks are extensively used in developing applications for surveillance, habitat monitoring, border security, intrusion detection etc. Most of these applications require secure data transmission among the nodes of the network. Out of the different types of attacks a data critical application faces, False Data Injection attacks are the most damaging one. So prevention of False Data Injection attacks is a crucial aspect while building data critical wireless sensor network applications. Researchers have suggested cryptographic schemes like RSA, ECC for the prevention of False Data Injection Attacks. Use of cryptographic techniques increases the computation complexity on all the nodes and the energy constraints on WSN demands an alternate solution for False Data Injection attacks prevention. The proposed work aims on using trust parameter of every nodes to distinguish malicious and non-malicious nodes and use only trusted nodes to forward the packet to destination thus by prevention FDI attacks. Simulation is carried out with the help of Network Simulator 2 (NS2). The results shows the energy consumption is less in the proposed scheme compared to the cryptographic techniques.
SpreadMax: A Scalable Cascading Model for Influence Maximization in Social Networks

The rumor spreading and influence maximization are two important forms of social network communication. Many social network applications utilize influence maximization for finding the seed node. The most common approach for influence maximization is greedy, which delivers an optimal solution. However, the greedy approach has high computational overhead and offers a limited spreading capability. We address this problem by proposing a novel framework, SpreadMax, for maximizing the spreading process with reduced computational overhead. Our model consists of two phases. In the first phase, we identify seed nodes using hierarchical reachability approach and these designated seed nodes spread infection during the second phase. The proposed model is validated through experiments using real-world data, in comparison with existing methods. We observed that the SpreadMax technique performs better in maximizing the influence.