Miracle-dz/UniFAR-FTUs huggingface dataset:
Irreversible n to p Transition and Corresponding Performance Improvement of RGO/TiO2 Nanotubes Hybrid Vapor Sensor Devices by Varying Electrophoretic Deposition Time

In this paper, an irreversible n-type to p-type conductivity transition is correlated with the corresponding vapor sensing performance improvement of reduced graphene oxide (RGO) and TiO2 nanotubes-based hybrid vapor sensor devices. The RGO was deposited on top of electrochemically grown TiO2 nanotube (NT) array by electrophoretic deposition technique. The deposition time was varied from 0.5 to 3 h and it was established through XPS and electrochemical impedance spectroscopy (EIS) measurement that the conductivity of the RGO changed from n- to p-type, in an irreversible manner, when the deposition time exceeded 2.6 h. Characterizations such as an FESEM and an RAMAN spectroscopy study revealed that with increase in electrodeposition time, the percentage coverage of the RGO on TiO2NT matrix (n-type in nature) was increased monotonically reaching almost 90% coverage in 2.6 h, causing the overall device conductivity to change from n- to p-type (i.e., the overall conductivity is governed by the conductivity of p-type RGO, as it covered 90% of the nanotube matrix). The sensing performance of the hybrid structure was also investigated and the device with higher electrodeposition time (p-type) was found to offer better vapor sensing performance towards acetone (as the test species) compared to its n-type counterpart (lower electro-deposition time). Such improvement in sensing performance was possibly attributed to the additional adsorption sites and higher electron mobility of p-RGO over n-RGO.
Quantifying and Optimizing Data Access Parallelism on Manycores

Data access parallelism (DAP) indicates how well available hardware resources are utilized by data accesses. This paper investigates four complementary components of data access parallelism in detail: cache-level parallelism (CLP), bank-level parallelism (BLP), network-level parallelism (NLP), and memory controller-level parallelism (MLP). Specifically, we first quantify these four components for a set of 20 multi-threaded benchmark programs, and show that, when executed on a state-of-the-art manycore platform, their original values are quite low compared to the maximum possible values they could take. We next perform a limit study, which indicates that significant performance improvements are possible if the values of these four components of DAP could be maximized. Building upon our observations from this limit study, we then present two practical computation and network access scheduling schemes. Both these schemes make use of profile data, but, while the compiler-based strategy uses fixed priorities of CLP, BLP, NLP, and MLP, the machine learning-based one employs a predictive machine learning model. Our experiments indicate 30.8% and 36.9% performance improvements with the compiler-based and learning-based schemes, respectively. Our results also show that the proposed schemes consistently achieve significant improvements under different values of the major experimental parameters.
Synchronous Buck-Boost Converter for Energy Harvesting Application

High-temperature thermoelectric generators (TEGs) can be exploited for electrical energy harvesting in a variety of industrial processes that generate excess heat. In order to harvest the maximum amount of energy, a power converter in combination with a maximum power point tracking (MPPT) algorithm is required. This work presents a different approach of using a synchronous buck-boost converter in combination with the perturb and observe (P&O) MPPT algorithm, that uses a variable step size control for improved dynamics. The buck-boost converter is connected to a battery, that is being charged using the TEGs. Experimental results of the implemented MPPT algorithm are shown to prove that the converter reaches the maximum power point in a reasonable time interval. The measured efficiency of the converter is between 96-and 97 %.
Pareto-Optimal Multi-Controller Placement in Software Defined Network

In recent days, the software defined network (SDN) is growing rapidly because of its simple network management. Despite all those advantages, controller placement problem is a key concern in SDN. Consequently, in this paper a solution for multi-controller placement problem (MPP) is proposed, considering the load capacity and the latency of the controller. Further, an optimization algorithm called multiobjective ant lion optimization (MO-ALO) is used to select the multi-controller based on the load capacity factor and the propagation latency. Hence, the chose controllers particularly POX, NOX, BEACON and FLOODLIGHT are put and reproduced in Pareto-based Optimal Controller placement (POCO) apparatus for the Internet 2 topology. The simulation results demonstrate the viability of the MO-ALO algorithm on the controller selection process. Further, from the assessment and representation of POCO tool unmistakably the proposed work gets great solution for the SDN multi-controller placement problem.
Privacy Preservation of Location Information Based on MinHash Algorithm in Online Ride-Hailing Services

In the era of big data, the development of location-aware technologies such as mobile communications, sensing devices, and mobile positioning has created big data for location. It has brought great convenience to people's production and daily life. But, users' location information used in LBS (Location-Based Services) can easily lead to the personal privacy leakage. Therefore, the privacy protection of location based big data is increasingly concerned by all sectors of society. The privacy preservation issues of the user location information in the popular online ride-hailing field will be discussed in this paper, and the ride-hailing model discussed here refers to the dispatch model. It is considered that users would rather use the landmark entities around them to describe their approximate location than their exact location when they are using a car navigation platform. Then, a complete location privacy preservation scheme based on the MinHash algorithm (LPPM in short) is proposed in this paper. LPPM can not only hide the location information effectively but also improve the speed of distance calculation between users and drivers. The core idea of LPPM is to select a certain range of landmarks near the user's exact location as his location features, and to use the MinHash algorithm to optimize the similarity assessment method between feature sets. The security analyses indicate that LPPM is of high security, and the final experimental results confirm that LPPM is effective.
Impact of Path Metrics on RPL's Performance in Low Power and Lossy Networks

Recent years witness immense growth in the Internet of Things (IoT) based applications ranging from healthcare sector to agriculture sector due to 6LoWPAN enabled sensor radios. Routing of data packets in the sensor (or) low power and lossy network (LLN) environment always remains a challenge due to sensor node's inherent limitations such as tiny memory, limited processing capability, and limited battery energy. To meet the specific routing requirement in harsh environments, IETF standardized IPV6 based routing protocol for low power and lossy networks (RPL) intended to support 6LoWPAN enabled sensor radios for large-scale deployment. RPL selects the best path based on objective function (OF) or path metrics. The strength of the RPL depends on choosing of best OF, which creates an optimum path between a source and sink nodes. In this paper, simulation and analysis of RPL protocol carried out with two different internal objective functions (hop length and link quality) using Cooja Simulator. The performance of the OF is measured based on following metrics such as throughput, end-to-end latency, packet drop, energy and loss ratio. This analysis paves the way for developing an improved version of RPL with apt OFs that withstand both in static and mobile wireless conditions.
Aberrances of Cortex Excitability and Connectivity Underlying Motor Deficit in Acute Stroke

Purpose This study was aimed at evaluating the motor cortical excitability and connectivity underlying the neural mechanism of motor deficit in acute stroke by the combination of functional magnetic resonance imaging (fMRI) and electrophysiological measures. Methods Twenty-five patients with motor deficit after acute ischemic stroke were involved. General linear model and dynamic causal model analyses were applied to fMRI data for detecting motor-related activation and effective connectivity of the motor cortices. Motor cortical excitability was determined as a resting motor threshold (RMT) of motor evoked potential detected by transcranial magnetic stimulation (TMS). fMRI results were correlated with cortical excitability and upper extremity Fugl-Meyer assessment scores, respectively. Results Greater fMRI activation likelihood and motor cortical excitability in the ipsilesional primary motor area (M1) region were associated with better motor performance. During hand movements, the inhibitory connectivity from the contralesional to the ipsilesional M1 was correlated with the degree of motor impairment. Furthermore, ipsilesional motor cortex excitability was correlated with an enhancement of promoting connectivity in ipsilesional M1 or a reduction of interhemispheric inhibition in contralesional M1. Conclusions The study suggested that a dysfunction of the ipsilesional M1 and abnormal interhemispheric interactions might underlie the motor disability in acute ischemic stroke. Modifying the excitability of the motor cortex and correcting the abnormal motor network connectivity associated with the motor deficit might be the therapeutic target in early neurorehabilitation for stroke patients.
An Efficient DTW Algorithm for Online Signature Verification

Handwritten signatures are thought as unremarkably accepted biometric for validation and recognition of an individual since every individual includes a distinguishing signature with its precise behavioral property, thus it's important to prove the genuineness of signature itself. So, the main theme of this research paper is concentrate on online signature verification system based on DTW-Dynamic Time Warping features extraction. That means in this paper represented system online signature verification, is simple and proficient method with the help of Dynamic Time Warping algorithm. The different steps constitute for verifying on-line handwritten signature system initially extracting pen position data from input signature, secondly the data is pre-processed and normalized. By applying DTW algorithm calculate warping distance to improve the distinguish between a genuine and its forgery signature. Finally the output of DTW algorithm is used for acquiring decision about verification result.
A Learning-Based Semantic Approximate Query over RDF Knowledge Graph

Large scale RDF knowledge graphs (KG) usually contain billions of labeled entities, and how to obtain the desired search results efficiently over such RDF KG for given SPARQL queries have attracted increasing attentions recently. However, it is difficult for users to write a complex SPARQL query without full knowledge of the underlying KG schema due to the "schema-free" feature of RDF KG (e.g. there are several ways to represent the similar knowledge). Furthermore, it is also a challenge to get all possible search results for a simple SQPRQL expression. In this paper, we study the problem of relaxing SPARQL queries over RDF KG in order to acquire semantic approximate results for an "incomplete query". The basic idea behind this paper is to conduct a text-based learning for achieving the semantics of entities and relations and then take use of these semantic information to generate the relaxed SPARQL query avoid user intervention. We first propose a corpus translating algorithm to transform the discrete RDF triples into the trainable corpus with high quality. And then we use the CBOW model to train the semantic vector for each entities and relations. Thirdly, we relax the SPARQL query by taking use of the semantic vectors and obtain the semantic approximate results over RDF KG. Experiments on DBpedia datasets confirm the superiority of our solution.
A Higher-Order Latent Space Network Model

Previous work in network analysis has focused on modeling node roles in the graph. In this work, we introduce edge role discovery and develop a general framework for modeling edge roles in large networks. In addition, a general class of higher-order role discovery methods are proposed that leverage features based on induced subgraphs (graphlets, motifs) for learning better and more useful roles. All methods are fast with a runtime that is linear in the number of edges and able to scale to large real-world networks via an effective parallel implementation. The experimental results demonstrate the utility of edge roles for network analysis tasks on a variety of graphs from various problem domains.
Design and Optimization of Scheduling and Non-Orthogonal Multiple Access Algorithms With Imperfect Channel State Information

Non-orthogonal multiple access (NOMA) is a promising candidate technology for 5G cellular systems. In this paper, design and optimization of scheduling and NOMA algorithms is investigated. The impact of power allocation for NOMA systems with round-robin scheduling is analyzed. A statistic model is developed for network performance analysis of joint scheduling of spectrum resource and power for NOMA algorithms. Proportional fairness scheduling for NOMA is proposed with a two-step approach, with objective of achieving high throughput and user fairness with low computational complexity. In the first step, an optimal power allocation strategy is developed with an objective of maximizing weighted sum rate. In the second step, three fast and scalable scheduling and user pairing algorithms with QoS guarantee are proposed, in which only a few user pairs are checked for NOMA multiplex. The algorithms are extended to the cases with imperfect channel state estimation and more than two users being multiplexed over one resource block. Numerical results show that the proposed algorithms are faster and more scalable than the existing algorithms, and maintain a higher throughput gain than orthogonal multiple access.
ELI: Empowering LTE with Interference Awareness in Unlicensed Spectrum

The advent of LTE into the unlicensed spectrum has necessitated the understanding of its operational efficiency when sharing spectrum with different radio access technologies. Our study reveals that LTE, owing to its inherent transmission characteristics, suffers significant performance degradation in the presence of interference caused by hidden terminals. This motivates the need for interference-awareness in LTE's channel access in unlicensed spectrum. To address this problem, we propose ELI. ELI's three-pronged solution equips the LTE base station with novel techniques to: (a) accurately detect and measure interference caused by hidden terminals, (b) collect interference statistics from clients across different channels with affordable overhead, and (c) leverage interference-awareness to improve its channel access performance. Our evaluations show that ELI can achieve 1.5-2x throughput gains over baseline schemes. Finally, ELI is LTE-LAA/MulteFire-standard compliant and can be deployed over the existing LTE-LAA implementation without any modifications.
Non Verbal Behaviour Analysis for Distress Detection Using Texture Analysis

Facial expressions and gestures, which forms a part of inter personal communication, can reflect a person's true psychological state and his intent towards the whole communication scenario. During the long journey of technology development, lot of methods have been introduced for detecting the mental state of a person from such nonverbal communication aspects exhibited by the subject. The analysis of such non verbal behaviours can be used for many applications in psychiatry, in clinical, educational and organizational psychology some of which includes social skills training, military psychiatric evaluation, understanding interactions between a patient and therapist, children and parents, couples, civil servants and public etc. In this paper a novel method is presented to determine the distress level of a person by interpreting his facial expressions from salient patches using Segmentation based Fractal Texture Analysis (SFTA). To acquire texture patterns, fractal dimensions of the regions are found. Naïve Bayes classifier is used to classify the detected emotion into a predefined class. Viola Jones algorithm is used for the purpose of detection of face. The changes in the selected facial patches due to a change in emotion are extracted and analyzed for the detection of the affective state of the person. Successful testing of the proposed system is done with Extended Cohn-Kanade (CK+), Japanese female facial expression (JAFFE) database and Affectiva-MIT Facial Expression Dataset (AMFED).
Hallucinating very low-resolution and obscured face images

Most of the face hallucination methods are designed for complete inputs. They will not work well if the inputs are very tiny or contaminated by large occlusion. Inspired by this fact, we propose an obscured face hallucination network(OFHNet). The OFHNet consists of four parts: an inpainting network, an upsampling network, a discriminative network, and a fixed facial landmark detection network. The inpainting network restores the low-resolution(LR) obscured face images. The following upsampling network is to upsample the output of inpainting network. In order to ensure the generated high-resolution(HR) face images more photo-realistic, we utilize the discriminative network and the facial landmark detection network to better the result of upsampling network. In addition, we present a semantic structure loss, which makes the generated HR face images more pleasing. Extensive experiments show that our framework can restore the appealing HR face images from 1/4 missing area LR face images with a challenging scaling factor of 8x.
Analyzing Deep CNN-Based Utterance Embeddings for Acoustic Model Adaptation

We explore why deep convolutional neural networks (CNNs) with small two-dimensional kernels, primarily used for modeling spatial relations in images, are also effective in speech recognition. We analyze the representations learned by deep CNNs and compare them with deep neural network (DNN) representations and i-vectors, in the context of acoustic model adaptation. To explore whether interpretable information can be decoded from the learned representations we evaluate their ability to discriminate between speakers, acoustic conditions, noise type, and gender using the Aurora-4 dataset. We extract both whole model embeddings (to capture the information learned across the whole network) and layer-specific embeddings which enable understanding of the flow of information across the network. We also use learned representations as the additional input for a time-delay neural network (TDNN) for the Aurora-4 and MGB-3 English datasets. We find that deep CNN embeddings outperform DNN embeddings for acoustic model adaptation and auxiliary features based on deep CNN embeddings result in similar word error rates to i-vectors.
Robust Pupil Localization Algorithm Based on Circular Hough Transform for Extreme Pupil Occlusion

Pupil localization has been the core of eye tracking research. Accuracy of an eye tracking application relies on how center of the pupil is localized under various occlusions-such as eyelids and eyebrows. Previous works have preserved the accuracy of pupil localization when the eyelid covers the pupil up to 70% occlusion. However, the existing algorithm fails to maintain accuracy of pupil localization when the coverage of the occlusion is 70% or more. To deal with this problem, we propose a robust pupil localization algorithm based on circular Hough transform. The proposed algorithm was tested on 37 images taken from CASIA Iris dataset with various simulated eyelid occlusions. Our method achieved significant accuracy compared with previous state-of-the-art method-yielding 90.71% accuracy on 90% eyelid occlusion. The experimental results demonstrate that the proposed algorithm is promising to handle extreme eyelid occlusion in various eye tracking applications.
Power HIL Emulation of AC Machines with Parallel Connected ANPC Bridge Arms

The paper deals with the development of a Power Hardware-In-the-Loop (PHIL) simulator using parallel connected Active Neutral Point Clamped (ANPC) bridge arms. The parallel connection offers better emulation precision due to the increased number of available voltage levels and the higher apparent switching frequency. The concept to emulate the transient inductance and the inner voltage of an AC machine is introduced and analyzed in a single phase model using interleaved modulation in the parallel arms, and level shifted carriers for the upper and lower side of the arms. The static and dynamic precision of the emulation is influenced by the current balancing applied for distributing the power evenly on the parallel arms. A compromise is required to keep both the emulation and balancing error at low level.
A Novel Approach for Detecting Malware in Android Applications Using Deep Learning

Detection of android malware depends on the feature vector extraction of android applications statically and dynamically. Static analysis has advantage over dynamic analysis as it covers all source code from byte code and manifest files which contains the permission for applications whereas dynamic analysis of the APK files includes the features like the no. of system calls an application makes, network url it access etc. Feature vector updation in the dataset due to version updates of application creates a challenge for the existing tool to classify the application as malicious or benign. This work creates an combination of neural network and automated tool which collects and updates the feature vectors in the training dataset. This dataset is used by the neural network for its reinforcement training for better classification and classify the application in three classes malicious, benign and can't say. Tensor flow is used for making a neural network which learn from the data extracted in the form of feature vector and classify the applications in the category of malicious, benign or can't say. The work has been able to achieve accuracy of over 80% for a dataset of 1000 sample applications with over 15 different feature vector extracted using designed automated feature vector collection modules.
Mining Customer Opinion for Topic Modeling Purpose: Case Study of Ride-Hailing Service Provider

The popularity of ride-hailing services in the form of smartphone application as a transportation solution has become center of attention. The convenience offered has made many people use it in daily life and discuss it on social media. As a result, ride-hailing service providers utilize social media for capturing customers' opinions and marketing their services. If customers' statements about ride-hailing services are analyzed further, service providers can get insight for evaluating their services to meet customers' satisfaction. Text mining approach can be useful to analyze large number of posts and various writing styles to extract hidden information. Furthermore, by applying topic modeling, service providers can identify the important points that were spoken by customers without previously giving label or category to the text. Latent Dirichlet Allocation was used in this study to extract topics based on the posts from ride-hailing customers published on Twitter. This study used 40 parameter combinations for LDA to get the best one to obtain the topics. Based on the perplexity value, there were 9 topics discussed by customers in their posts including the top words in each topic. The output of this study can be used for the service providers to evaluate and improve the services.