Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical Layer Security in Two-tier Heterogeneous Cellular Networks over Nakagami Channel during Uplink Phase

In this paper, we study the connection and secrecy performance under two-tier cellular network during uplink phase, where both large-scale factor (path propagation loss) and small-scale factor (Nakagami fading) are considered, which are beyond the scope of the existing works. Analytical expressions of network connection and secrecy probabilities are derived, which are validated through Monte Carlo simulations. Numerical results reveal that the theoretical expressions of connection and secrecy probabilities are in good agreement with simulation results. Moreover, simulation results demonstrate that larger m value, which means stronger LOS component, leads to a worse connection performance, but a better secrecy secrecy performance.
Deep User Modeling for Content-based Event Recommendation in Event-based Social Networks

Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in event recommendation. However, the content-based approaches in existing event recommender systems cannot fully represent the preference of each user on events since most of them focus on exploiting the content information from events' perspective, and the bag-of-words model, commonly used by them, can only capture word frequency but ignore word orders and sentence structure. In this paper, we shift the focus from events' perspective to users' perspective, and propose a Deep User Modeling framework for Event Recommendation (DUMER) to characterize the preference of users by exploiting the contextual information of events that users have attended. Specifically, we utilize convolutional neural network (CNN) with word embedding to deeply capture the contextual information of a user's interested events and build up a user latent model for each user. We then incorporate the user latent model into probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct experiments on the real-world dataset crawled from a typical EBSN, Meetup.com, and the experimental results show that DUMER outperforms the compared benchmarks.
An Improved Guided Filtering Algorithm for Image Enhancement

Guided image filter (GIF) is popular in image processing and computer vision for the properties of edge-preserving and low computational complexity, but GIF may suffer from over-smoothing (halo artifacts) near sharp edges and under-smoothing at flat regions. There is a tradeoff between them in the original cost function of GIF. In this paper, an improved guided filter (IGIF) is proposed by incorporating an adaptive structure aware constraint. The adaptive structure aware constraint can well preserve edges and smooth details through assigning different weights to different local structure. Simultaneously, thanks to the L1 penalty, the proposed IGIF can exactly remove small details at the flat regions. To illustrate the effectiveness of the proposed IGIF, we apply it to image enhancement. Experimental results show that the proposed filter can produce enhanced images with better visual quality as well as quantitative performance.
Evolving Face Of Consumer Ethics : An Empirical Study Of Indian Millennial

The widespread acceptance of digital technologies and internet has transformed the consumption of information and in turn has led to new opportunities for ethical breaches like digital piracy. Easy access of information such as internet, digital libraries and lax privacy laws have put young millennial through new ethical dilemmas. Millennial - the ever swelling market segment have been studied regarding their insight on business ethics, but research has been minimal on their ethical beliefs and ideologies. This paper examines the effect of materialism, cultural values and role of religiousness on ethical orientation among young professionals. The finding of the study reveals that materialism and cultural values have a strong impact on consumer ethics. Illegally downloading of information and software is rampant among this segment. The paper categorizes the young professionals as textbfLouds and textbfCompliants, on the basis of ethical orientation. Acceptance of unethical practices significantly varies between these categories. The paper contributes to the body of knowledge of ethics and is unique, as studies in the Asian context and particularly Indian context are very limited.
Web Analytics Tools Usage among Indian Library Professionals

As the world is moving towards digital content, new tools and techniques have evolved to create a digital world where information can be retrieved easily with the help of these tools and techniques. Libraries have benefited more through this revolution and have already been using many ICT tools to provide better digital services to the patrons. A library website is an important organ of the library to disseminate and communicate the information among clientele. Web analytics or web metrics tool is essential to provide the website statistics and its performance. The present study investigates and explores the web analytic tools used by Indian library professionals to measure the statistics of their library websites. The investigators used an online social networking portal ‘LIS Links’ to conduct an online survey with the help of Google forms and the final analysis were done with the valid responses. The responses were received from library professionals in India from all categories of institutions. The study also discusses about free and paid web analytics tools and the popular ones among the Indian libraries. The results may help the budding librarians to understand the tools and to use these in the effective way to improve library resources, services and products on the basis of metrics.
A Correntropy Based Control Algorithm for a Single-Phase Double Stage Grid Integrated Solar PV System

This paper presents the implementation of a double stage solar photovoltaic (PV) single phase grid connected system using correntropy based control algorithm. This algorithm helps to obtain sinusoidal grid currents even in the presence of nonlinear loads connected at PCC (Point Of Common Coupling), thus improving the power quality of the system. It has almost no steady state error as well as strong filtering ability. The proposed system is configured using a solar PV array, a single phase grid, a voltage source converter (VSC), a DC link capacitor, a boost converter, a ripple filter, an interfacing inductor and the nonlinear loads. The boost converter extracts maximum power from the solar PV array using the drift free MPPT (Maximum Power Point Tracking) method. A prototype is developed to affirm the system performance by testing it under various conditions. The grid conditions of the system obtained after the application of this control strategy conforms well to the harmonic standard IEEE 519.
Improved Automatic Feature Selection Approach for Health Risk Prediction

With the recent advances in machines & technology, there is revolution in healthcare industry also. Thus, it gave rise to the concept of Electronic Health Record (EHR) which stores patients demographics, lab tests & results, medical history, habits etc. in electronic form. EHR is voluminous data which is difficult to store, maintain or alter. To provide extended life for people, health risk prediction model using this EHR is formulated in this work. Feature Selection is used to select only associated or relevant data from the dataset. Logistic Regression is used with improved loss functionality parameter which increases the accuracy, response time and performance of the system. Representation Learning enables formation of feature vector of the selected features thus calculating their scores. Further, risk prediction is performed by the neural network model. Deep Neural Network (DNN) is used with many hidden layers containing activation functions. Transfer learning is used to avoid re-training of the whole system every time new data enters the model. Dataset used here is of hypertension. EHR dataset is also synthetically created for analysis.
Enhanced Attack Aware Security Provisioning Scheme in SDN/NFV Enabled over 5G Network

Software Defined Network (SDN) and Network Function Virtualization (NFV) are essential technologies that support next generation 5G networks. Security provisioning in 5G network is major issue due to involvement of numerous users. To provide security against major attacks in SDN and NFV enabled 5G network, in this paper an enhanced attack aware security provisioning scheme is proposed. In this work, security is provided by following process: (i) Initial Authentication process, (ii) Classification of packets, and (iii) Switch migration process. Initial authentication is performed at Access Point (AP) for each user by Secure ID based Authentication (SIA) scheme. The suspected packets are detected in controller and classified at Virtual Network Function (VNF). For packet classification, the optimal packet features are selected using Genetic Algorithm with Correlation (GAC) based feature selection algorithm. We have proposed a Radial Basis Function with Extreme Learning Machine (RBF-ELM) classifier. Then, the malicious packets are dropped at VNF and normal packets are redirected to destination address through controller. To mitigate flow table overloading attack, we have presented an Enhanced Artificial Bee Colony (EABC) algorithm in controller. Experimental result shows that our proposed security provisioning scheme shows better performance in terms of delay, amount of redirected packets, detection accuracy, packet transmission rate and packet loss ratio.
A polymorphic residue that attenuates the antiviral potential of interferon lambda 4 in hominid lineages

As antimicrobial signalling molecules, type III or lambda interferons (IFNλs) are critical for defence against infection by diverse pathogens. Counter-intuitively, expression of one member of the family, IFNλ4, is associated with decreased clearance of hepatitis C virus (HCV) in the human population; by contrast, a natural in-frame nucleotide insertion that abrogates IFNλ4 production improves viral clearance. To further understand how genetic variation between and within species affects IFNλ4 function, we screened a panel of extant coding variants of human IFNλ4 and identified three variants that substantially affect antiviral activity (P70S, L79F and K154E). The most notable variant was K154E, which enhanced in vitro activity in a range of antiviral and interferon stimulated gene (ISG) assays. This more active E154 variant of IFNλ4 was found only in African Congo rainforest ‘Pygmy’ hunter-gatherers. Remarkably, E154 was highly conserved as the ancestral residue in mammalian IFNλ4s yet K154 is the dominant variant throughout evolution of the hominid genus Homo. Compared to chimpanzee IFNλ4, the human orthologue had reduced activity due to amino acid substitution of glutamic acid with lysine at position 154. Meta-analysis of published gene expression data from humans and chimpanzees showed that this difference in activity between K154 and E154 in IFNλ4 is consistent with differences in antiviral gene expression in vivo during HCV infection. Mechanistically, our data suggest that human-specific K154 likely affects IFNλ4 activity by reducing secretion and potency. We postulate that evolution of an IFNλ4 with attenuated activity in humans (K154) likely contributes to distinct host-specific responses to and outcomes of infection, such as HCV.
Improved Performance of Pentacene OTFT by using Hybrid Oxide of Nd and Hf as Gate Dielectric

Pentacene organic thin-film transistors (OTFTs) adopting high-k NdON, HfON and NdHfON as gate dielectrics are fabricated. Their electrical and physical characteristics are compared. With appropriate Nd/Hf ratio in the dielectric, the OTFT with NdHfON gate dielectric can achieve improved performance such as a carrier mobility of $1.1~{\mathrm {cm}}^{2}/{\mathrm {V}}\cdot {\mathrm {s}}$ and a small threshold voltage of-1.20 V. The AFM results of the pentacene layer and the dielectric layer reveal that both improved dielectric and pentacene morphologies lead to the higher carrier mobility ofthe device.
Transaction-Based Flow Rule Conflict Detection and Resolution in SDN

Software-defined Networking (SDN) brings new vitality to traditional network technology as its nice property of network programmability makes our network more open and flexible. By using interfaces of SDN controllers, different applications with diverse network functions can deploy their needed flow rules into SDN switches. However, some of these flow rules would probably produce conflicts that result in invalidation of network functions and cause security issues. To address this issue, we design a novel approach, Transaction-based flow rule Conflict Detection and Resolution (TCDR), which can isolate the flow rules of different network functions to avoid interference between different network functions. Meanwhile, our proposed method introduces a transaction-based authentication to guarantee the legality of flow rules. Finally, we implement a prototype of our solution, and evaluate its effectiveness and efficiency. The performance evaluation shows that TCDR can reject illegal flow rules and avoid many flow rule conflicts with a small overhead.
An Elegant End-to-End Fully Convolutional Network (E3FCN) for Green Tide Detection Using MODIS Data

Using remote sensing (RS) data to monitor the onset, proliferation and decline of green tide (GT) has great significance for disaster warning, trend prediction and decision-making support. However, remote sensing images vary under different observing conditions, which bring big challenges to detection missions. This paper proposes an accurate green tide detection method based on an Elegant End-to-End Fully Convolutional Network (E3FCN) using Moderate Resolution Imaging Spectroradiometer (MODIS) data. In preprocessing, RS images are firstly separated into subimages by a sliding window. To detect GT pixels more efficiently, the original Fully Convolutional Neural Network (FCN) architecture is modified into E3FCN, which can be trained end-to-end. The E3FCN model can be divided into two parts, contracting path and expanding path. The contracting path aims to extract high-level features and the expanding path aims to provide a pixel-level prediction by using a skip technique. The prediction result of whole image is generated by merging the prediction results of subimages, which can also improve the final performance. Experiment results show that the average precision of E3FCN on the whole data sets is 98.06%, compared to 73.27% of Support Vector Regression (SVR), 71.75% of Normalized Difference Vegetation Index (NDVI), and 64.41% of Enhanced Vegetation Index (EVI).
Energy-Efficient Algorithm for Load Balancing and VMs Reassignment in Data Centers

Nowadays, cloud computing is an effective solution for providing computing services to consumers. However, data centers that host computing resources are still faced with a misuse of resources and a workload imbalance of servers, where some servers become overloaded while others are underloaded or even idle. This results in performance degradation and resource wastage. The load balancing is a key aspect and has an important role in the management of cloud data centers. It allows an optimal use of the resources and improves the desired Quality of Service (QoS) using optimal methods for allocating resources and distributing workload. In this paper, we propose a load-balancing algorithm that is based on a new parameter called the balance factor of the data center, introduced here, to determine if a data center is imbalanced or not, in order to redistribute the workload equally over all the hosts. To minimize the energy consumption of the data center, our strategy relies on the live migration of virtual machines (VMs) while using a mathematical model. The simulation results, using the CloudSim toolkit, have shown that the energy efficiency can be managed by reassigning VMs to the data-center hosts.
Gene expression and single nucleotide polymorphism of ATP7B are associated with platinum-based chemotherapy response in non-small cell lung cancer patients

Objectives: Platinum-based chemotherapy is first-line treatment for non-small cell lung cancer (NSCLC) patients. The efficacy is limited by drug resistance. Recent studies suggest that ATP7B, a copper efflux transporter, may be involved in platinum resistance. However, the clinical significance of ATP7B expression in NSCLC is controversial. Moreover, the effects of single nucleotide polymorphisms (SNPs) in ATP7B gene on the response to platinum-based chemotherapy are scarcely understood. The aim of our study is to evaluate the clinical value of ATP7B in NSCLC patients and explore the interrelationships between ATP7B SNPs and protein expression, and their association with chemotherapy response. Materials and Methods: A total of 247 NSCLC patients were recruited in this study. Among them, 158 patients who received platinum-based chemotherapy were used to explore the interrelationships between ATP7B SNPs, protein expression and chemotherapy response, while 89 patients who underwent surgical resection were used to further investigate the association between ATP7B SNPs and expression level. We genotyped 15 SNPs of ATP7B by Sequenom MassARRAY and determined ATP7B protein levels by immunohistochemistry. Results: Patients with ATP7B-negative tumors had improved chemotherapeutic response (p=0.025) and better overall survival (p=0.044) compared with the patients with ATP7B-positive tumors. The multivariate Cox regression analysis revealed that ATP7B expression was an independent prognostic factor (HR=0.639, 95%CI=0.424-0.962, p=0.032). Moreover, we found that the rs9526814 GG genotype was significantly associated with favorable response to platinum-based chemotherapy when compared with TT+TG genotypes (OR=0.362, 95CI%=0.140-0.935, p=0.036). Mechanistically, rs9526814 GG genotype showed a strong trend towards reduced expression level of ATP7B compared with the TT+TG genotypes (p= 0.048). Conclusion: Our findings indicate that ATP7B rs9526814 may contribute to platinum resistance by influencing ATP7B gene expression and can be used as a potential biomarker to predict the sensitivity of platinum-based chemotherapy in NSCLC patients.
Low‐intensity exercise therapy with high frequency improves physical function and mental and physical symptoms in patients with haematological malignancies undergoing chemotherapy

This study aimed to investigate the effects of low-intensity exercise therapy (LIET) on the physical and mental symptoms and functions in patients with haematological malignancies undergoing chemotherapy. Forty-four patients hospitalised for chemotherapy performed LIET. The exercise intensity of LIET was defined as achieving <40% of the predicted maximum heart rate calculated using the Karvonen formula. LIET consisted of aerobic and resistance training, which was carried out on weekdays. The intervention was omitted in the case of poor general health status and strong patient refusal. Muscular and physical function, activities of daily living (ADLs), psychological distress and quality of life (QOL) were evaluated upon initiation of rehabilitation and at discharge. Participants were divided into high- and low-frequency groups according to their LIET frequency. Two-way repeated-measures analysis of variance was used for statistical analysis. In the high-frequency group, muscle function was maintained, while physical function, ADLs, psychological distress and QOL were significantly improved. However, in the low-frequency group, muscle function of the lower limb was significantly reduced and no other improvement was observed. Low-intensity exercise therapy could be a potential treatment strategy for patients with haematological malignancies undergoing chemotherapy who are unable to perform mid- or high-intensity exercise.
Toward a Better Robotic Hand Prosthesis Control: Using EMG and IMU Features for a Subject Independent Multi Joint Regression Model

Ahstract- The interest on wearable prosthetic devices has boost the research for a robust framework to help injured subjects to regain their lost functionality. A great number of solutions exploit physiological human signals, such as Electromyography (EMG), to naturally control the prosthesis, reproducing what happens in the human limbs. In this paper, we propose for the first time a way to integrate EMG signals with Inertial Measurement Unit (IMU) information, as a way to improve subject-independent models for controlling robotic hands. EMG data are very sensitive to both physical and physiological variations, and this is particularly true between different subjects. The introduction of IMUs aims at enriching the subject-independent model, making it more robust with information not strictly dependent from the physiological characteristics of the subject. We compare three different models: the first based on EMG solely, the second merging data from EMG and the 2 best IMUs available, and the third using EMG and IMUs information corresponding to the same 3 electrodes. The three techniques are tested on two different movements executed by 35 healthy subjects, by using a leave-one-out approach. The framework is able to estimate online the bending angles of the joints involved in the motion, obtaining an accuracy up to 0.8634. The resulting joint angles are used to actuate a robotic hand in a simulated environment.
No-Reference Quality Assessment for Stitched Panoramic Images Using Convolutional Sparse Coding and Compound Feature Selection

Image stitching-composition of different viewpoint images to form a 360-degree panoramic image-is an essential component towards immersive applications like VR and AR. A no-reference (NR) quality metric specifically designed to evaluate stitched panoramic images is highly desirable when ground-truth reference images are not available. In this paper, we use Convolutional Sparse Coding (CSC) with a set of convolutional filters to locate stitching-specific distortions in a target image, and design trained kernels to quantify the compound effects of multiple distortion types in a local region. Specifically, our contributions are: i) a training database labeled with location information of the distortion regions is released; ii) a NR metric is proposed to accurately assess stitching-specific artifacts like ghosting using convolutional sparse coding; and iii) a novel sequential feature selection algorithm is proposed to quantify the aforementioned compound distortion effects. In extensive experiments, we show that the performance of our proposed NR metric is comparable to the state-of-the-art full-reference metrics designed for stitched images.
Provably Efficient Algorithms for Placement of Service Function Chains with Ordering Constraints

A Service Function Chain (SFC) is an ordered sequence of network functions, such as load balancing, content filtering, and firewall. With the Network Function Virtualization (NFV) paradigm, network functions can be deployed as pieces of software on generic hardware, leading to a flexibility of network service composition. Along with its benefits, NFV brings several challenges to network operators, such as the placement of virtual network functions. In this paper, we study the problem of how to optimally place the network functions within the network in order to satisfy all the SFC requirements of the flows. Our optimization task is to minimize the total deployment cost. We show that the problem can be seen as an instance of the Set Cover Problem, even in the case of ordered sequences of network functions. It allows us to propose two logarithmic factor approximation algorithms which have the best possible asymptotic factor. Further, we devise an optimal algorithm for tree topologies. Finally, we evaluate the performances of our proposed algorithms through extensive simulations. We demonstrate that near-optimal solutions can be found with our approach.