Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Privacy Preserving Scheduling in VANET Using NS-2

The potential growth in Mobile Ad-hoc Network triggered the Vehicular Ad-hoc network (VANET) for traffic security and efficiency on roads, because once deployed they could bring a new environment to drivers. Vehicular communication in real time environment makes privacy a real challenge, which might affect the large scale deployment of these problems. In many of the existing systems the master secrete is not properly secured and in case of large deployment of vane privacy become crucial since the number of nodes get increases. Thevanet entities are not always adaptable to hold the huge certificate burden faced in real time applicatios. However it is challenging to authenticate and schedule messages in vanet. So an ID based two layer TA clustered authentication scheme is proposed. Moreover the real time challenges in vanet scenario is simulated successfully, so that it reduces the certificate burden and enhance the safe keeping of master secrete.
EEG-Based Emotion Classification Using Wavelet Decomposition and K-Nearest Neighbor

Research on the correlation of EEG signals to emotions based on high/low arousal and valence, has been done before. Some research using the Eigen-Emotion Pattern Kernel method and the Support Vector Machine. The others using the Higuchi Fractal Dimension (FD) Spectrum, the Multifractal Detrended Fluctuation Analysis (MDFA) and the Hidden Markov Model (HMM), but the accuracy is not too good. This research uses Wavelet Decomposition and k-Nearest Neighbor to improve accuracy. The results show that the optimum k values of the k-Nearest Neighbor parameters for this research are 21. Valence's classification accuracy results using Wavelet and k-NN, compared with previous research has the same relative accuracy, ie 57.5%. While the result of arousal classification accuracy using wavelet and k-NN is 64.0%, better than previous research.
Effect of Linagliptin vs Placebo on Major Cardiovascular Events in Adults With Type 2 Diabetes and High Cardiovascular and Renal Risk: The CARMELINA Randomized Clinical Trial

Importance Type 2 diabetes is associated with increased cardiovascular (CV) risk. Prior trials have demonstrated CV safety of 3 dipeptidyl peptidase 4 (DPP-4) inhibitors but have included limited numbers of patients with high CV risk and chronic kidney disease. Objective To evaluate the effect of linagliptin, a selective DPP-4 inhibitor, on CV outcomes and kidney outcomes in patients with type 2 diabetes at high risk of CV and kidney events. Design, Setting, and Participants Randomized, placebo-controlled, multicenter noninferiority trial conducted from August 2013 to August 2016 at 605 clinic sites in 27 countries among adults with type 2 diabetes, hemoglobin A1c of 6.5% to 10.0%, high CV risk (history of vascular disease and urine-albumin creatinine ratio [UACR] >200 mg/g), and high renal risk (reduced eGFR and micro- or macroalbuminuria). Participants with end-stage renal disease (ESRD) were excluded. Final follow-up occurred on January 18, 2018. Interventions Patients were randomized to receive linagliptin, 5 mg once daily (n = 3494), or placebo once daily (n = 3485) added to usual care. Other glucose-lowering medications or insulin could be added based on clinical need and local clinical guidelines. Main Outcomes and Measures Primary outcome was time to first occurrence of the composite of CV death, nonfatal myocardial infarction, or nonfatal stroke. Criteria for noninferiority of linagliptin vs placebo was defined by the upper limit of the 2-sided 95% CI for the hazard ratio (HR) of linagliptin relative to placebo being less than 1.3. Secondary outcome was time to first occurrence of adjudicated death due to renal failure, ESRD, or sustained 40% or higher decrease in eGFR from baseline. Results Of 6991 enrollees, 6979 (mean age, 65.9 years; eGFR, 54.6 mL/min/1.73 m2; 80.1% with UACR >30 mg/g) received at least 1 dose of study medication and 98.7% completed the study. During a median follow-up of 2.2 years, the primary outcome occurred in 434 of 3494 (12.4%) and 420 of 3485 (12.1%) in the linagliptin and placebo groups, respectively, (absolute incidence rate difference, 0.13 [95% CI, −0.63 to 0.90] per 100 person-years) (HR, 1.02; 95% CI, 0.89-1.17; P < .001 for noninferiority). The kidney outcome occurred in 327 of 3494 (9.4%) and 306 of 3485 (8.8%), respectively (absolute incidence rate difference, 0.22 [95% CI, −0.52 to 0.97] per 100 person-years) (HR, 1.04; 95% CI, 0.89-1.22; P = .62). Adverse events occurred in 2697 (77.2%) and 2723 (78.1%) patients in the linagliptin and placebo groups; 1036 (29.7%) and 1024 (29.4%) had 1 or more episodes of hypoglycemia; and there were 9 (0.3%) vs 5 (0.1%) events of adjudication-confirmed acute pancreatitis. Conclusions and Relevance Among adults with type 2 diabetes and high CV and renal risk, linagliptin added to usual care compared with placebo added to usual care resulted in a noninferior risk of a composite CV outcome over a median 2.2 years. Trial Registration ClinicalTrials.gov Identifier: NCT01897532
Requirements Engineering in Global Software Development: A Survey Study from the Perspectives of Stakeholders

: Global software development (GSD), where software teams are located in different parts of the world, has become increasingly popular. However, GSD involves issues such as geographical distance, cultural diversities, differences in time zones, and language barriers amongst stakeholders, making it different from collocated software development, and requirements engineering (RE) is very much affected by these issues. To examine what stakeholders think about the impact of these factors on the different activities of RE in a GSD environment, we have conducted a survey among organizations located in different parts of the world which were involved in GSD. In this paper, we present the findings of the survey. The survey pointed to the facts that stakeholders are not very aware of the risks and challenges associated with GSD, that stakeholders are not clear about how the geographical dispersion of development teams could affect the RE process, and that the lack of RE methods for GSD makes stakeholders use the conventional techniques and approaches of RE that do not consider the factors involved in GSD. The findings of our study will enable readers to gain a better understanding of what different groups of stakeholders think about RE in GSD.
Hierarchy Training Strategy in Image Classification

With the deepening of convolutional neural networks, training a delicate network may be more difficult. This article explores which training strategies on a limited set of training data will make the network perform better, and demonstrated a terse hierarchy training strategy. We only need to do a small-scale data processing, add a priori class label for each piece based on the original dataset, the network will learn functions to classify the priori class and final class simultaneously and end to end. The knowledge learned by hierarchy A network part could guide the classification task at the hierarchy B network part. Experiments on CIFAR-10 show that network trained by this strategy will perform better and robust. Finally, the classification accuracy on the test set also has a certain improvement compared with the ordinary training strategy.
High Efficient Interleaved Boost Converter for Photovoltaic Applications

This paper aims to identify the efficient interleaved boost converter for photovoltaic applications. The soft switching of boost converter has been done to reducing the switching losses thereby enhancing the efficiency. The soft switching circuit consists of coupled inductor and capacitor, connected across the switch legs of interleaved boost modules and output stages. The volume and cost of the converter circuit can be reduced by coupling two input inductors. The operation of MOSFET is based on Zero Voltage Switching (ZVS) technique. The proposed converter has been simulated with MATLAB. The duty cycle of three level interleaved boost converters is controlled by PI Controller. The proposed three level interleaved boost converter simulation results are checked with hardware results.
Adaptive Face Blending for Face Replacement System

The paper presents a blending method for the face replacement system based on a weight map that is adaptive to pose. First, the geometric landmarks of the face are detected from the image containing the target face in order to proceed with the face replacement. Next, triangulation is performed on the face region according to the distribution of the detected landmark points. If the same process is applied to the reference image, and the affine warping transformation is performed between the corresponding triangular mesh obtained from the reference face and target face, a reference face with the same pose as the target face can be newly generated. Finally, the skin tone of the reference face is matched as the skin tone of the target face, and the face replacement is performed. However, an unnatural boundary is created between the replaced region and the background target face. This boundary can usually be removed with the blending technique. In this paper, we generate a standard weight map from the standard front face model in preprocessing step and generate the pose adaptive weight map corresponding to the pose using the transformation relation between the standard face and the target face. The obtained weight map can adaptively cope with the pose change as compared with the consistently set weight map. A more natural face replacement results, therefore, can be obtained.
A Study on the Integration of Machine Learning in Wireless Communication

Presently, we are observing a paradigm shift in communication technology as every enterprise is trying to shift their focus to smart communication networks to take advantage of network traffic data. Modern communication networks, in particular, mobile networks, create a gigantic measure of information at the system framework level and at the end-user level. This information is an important supply of potentially valuable data such as the area, mobility pattern and the call inclinations of the user. The vision of network operators is to make use of this massive amount of traffic data for in-house administration purposes that involves network management and optimization. Keeping in mind the end goal to make this vision a reality, there is a solid requirement for the advancement and usage of new machine learning algorithms for big data analytics in communication networks. These must have the capacity to separate helpful data from this system activity while considering constrained communication resources and then try to exploit this information for external or in-house services.
Novel Multi-Stage Photonic Crystal Mach-Zehnder Optical Filters

In this letter, a general approach is proposed and conceptually designed for a single-stage and a lattice type multi-stage photonic crystal Mach–Zehnder interferometer (PC-MZI) optical filters. Accordingly, new symmetric and asymmetric PC-MZI structures based on hexagonal photonic crystal have been utilized and demonstrated to manage wavelength specifications, such as central wavelength, passband width, and bandwidth. The simulation results show that 1- and 3-dB bandwidths can obtain about 29.6 and 51.2 nm for the single-stage, 8.1 and 14 nm for the two-stage, and 3.3 and 5.76 nm for three-stage PC-MZI, respectively. Furthermore, it is observed that by combining one additional stage to single-stage PC-MZI, the passband and skirt become flattened and sharp, respectively, and in addition, a 3-dB bandwidth is reduced by 365% and 888% for two- and three-stage PC-MZI, respectively. Finally, it is shown that the single-stage PC-MZI is a wideband filter, whereas the multi-stage is a narrowband filter.
NeuTu: Software for Collaborative, Large-Scale, Segmentation-Based Connectome Reconstruction

Reconstructing a connectome from an EM dataset often requires a large effort of proofreading automatically generated segmentations. While many tools exist to enable tracing or proofreading, recent advances in EM imaging and segmentation quality suggest new strategies and pose unique challenges for tool design to accelerate proofreading. Namely, we now have access to very large multi-TB EM datasets where (1) many segments are largely correct, (2) segments can be very large (several GigaVoxels), and where (3) several proofreaders and scientists are expected to collaborate simultaneously. In this paper, we introduce NeuTu as a solution to efficiently proofread large, high-quality segmentation in a collaborative setting. NeuTu is a client program of our high-performance, scalable image database called DVID so that it can easily be scaled up. Besides common features of typical proofreading software, NeuTu tames unprecedentedly large data with its distinguishing functions, including: (1) low-latency 3D visualization of large mutable segmentations; (2) interactive splitting of very large false merges with highly optimized semi-automatic segmentation; (3) intuitive user operations for investigating or marking interesting points in 3D visualization; (4) visualizing proofreading history of a segmentation; and (5) real-time collaborative proofreading with lock-based concurrency control. These unique features have allowed us to manage the workflow of proofreading a large dataset smoothly without dividing them into subsets as in other segmentation-based tools. Most importantly, NeuTu has enabled some of the largest connectome reconstructions as well as interesting discoveries in the fly brain.
The Knowledge Management System Development for Smart Education

This paper is devoted to solving a problem of improving the content of education and instructional materials used. The digital technologies are developing so fast that the engineering aspects of application and scientific investigations start blurring the aspects associated with the methodology and content of smart learning. The need for development of digital economy places severe requirements not only upon know-how, but also upon the content of smart learning, i.e., the content of educational courses shall be a reflection of professional activity. The techniques and methods of knowledge management can provide for a desired rate and quality of development of educational materials. This paper describes a knowledge management system which employs intelligent database, network and co-authorship instruments to insure the compliance of the content being developed with the requirements of digital economy and fills out the smart learning with a qualitative content.
A Situation Emergency Building Navigation Disaster System Using Wireless Sensor Networks

In an emergency, Wireless Sensor Networks (WSN) have major application in the navigation services. It also provides a natural choice of the infrastructure to support emergency navigation services, given their relatively easy deployment and having the ability of sensing and communication. We propose a congestion adaptive navigation algorithm for emergency situations to navigate the people from hazardous area. The navigation algorithm is interacting between sensors and users. It could help the people to safely exit the building from hazardous area. When the sensors detect any emergency situation, at the time server find out the users location by using Global Positioning system (GPS) of their mobile phones and then the server send the page layout that shows a path from hazardous area to the nearest user exit. In case the people in lift of that building, the proposed system using Passive Infrared Sensor (PIR) to detect the people in the lift and then server stops the lift in secure floor. After the server provides page layout for the user to reach the secure area. Using this proposal, it helps to navigate the people from hazardous area to secure area in any kind of emergency.
Will People Morally Crack Under the Authority of a Famous Wicked Robot?

Authority and obedience are key regulatory elements in a society. Robots are becoming important part of our world, and are starting to interact in domains in which authority is an important aspect, as healthcare, teaching or law enforcement. Yet, there is little research on how people behave when robots show authority. In particular, although extensive investigations have been carried out on how authority can circumvent people's morality with experiments such as Milgram's or Stanford Prison, almost no research evaluated the effect of robots pushing the limits of people's own morality. This experiment tries to study this aspect by using a robot (geminoid) with the appearance, and thus authority of a famous person, and by pushing the boundaries asking morally controversial requests. The results show that, even though most people hesitate and recognize the requests as socially inappropriate, they obey to robots with authority. This suggests that the authority of the robot can push people to perform tasks usually considered as inappropriate.
Nonlinear Dynamic Analysis of a Skyhook-Based Semi-Active Suspension System With Magneto-Rheological Damper

Response characteristics of a magneto-rheological suspension with a modified skyhook control scheme, focusing directly on the command current, were analyzed considering hysteresis nonlinearities. Suspension performance and its dynamic stability were investigated under harmonic excitation at frequencies up to 50 Hz to illustrate highly nonlinear influences of the excitation frequency and magnitude. The nonlinear response characteristics of the system, attributed to damper hysteresis and on–off control, were thoroughly analyzed considering the bifurcation diagrams, Lyapunov exponent spectra, phase portraits, and the Poincare maps. These, invariably, showed the presence of quasi-periodic, multiperiodic, and chaotic responses of the system under mid- as well as high-frequency excitations. The conducted electromagnetic interference (EMI) due to high frequency variations in the drive current, caused by the nonlinear system responses, was further evaluated using the method described in EN 55022 for the first time. The assessments using the GB9254 guidelines showed that the EMI levels exceed the recommended limits. The chaos-induced EMI may thus be detrimental to the electronic control systems of the automobile and cannot be ignored. This work is considered to serve as a theoretical foundation for future studies on EMI reduction and nonlinear control of intelligent semi-active suspensions.
FAST CONVOLUTIONAL SPARSE CODING WITH ℓ0 PENALTY

Given a set of dictionary filters, the most widely used formulation of the convolutional sparse coding (CSC) problem is Convolutional BPDN (CBPDN), in which an image is represented as a sum over a set of convolutions of coefficient maps; usually, the coefficient maps are ℓ<inf>1</inf>-norm penalized in order to enforce a sparse solution. Recent theoretical results, have provided meaningful guarantees for the success of popular ℓ<inf>1</inf>-norm penalized CSC algorithms in the noiseless case. However, experimental results related to the ℓ<inf>0</inf>-norm penalized CSC case have not been addressed.In this paper we propose a two-step ℓ<inf>0</inf>-norm penalized CSC (ℓ<inf>0</inf>-CSC) algorithm, which outperforms (convergence rate, reconstruction performance and sparsity) known solutions to the ℓ<inf>0</inf>-CSC problem. Furthermore, our proposed algorithm, which is a convolutional extension of our previous work [1], originally develop for the ℓ<inf>0</inf> regularized optimization problem, includes an escape strategy to avoid being trapped in a saddle points or in inferior local solutions, which are common in nonconvex optimization problems, such those that use the ℓ<inf>0</inf>-norm as the penalty function.
Verification of Generated RTL from P4 Source Code

The P4 is a general and platform agnostic language for the description of packet processing functionality. So far it is being supported by a number of technology companies which provided a way for programming of their devices using the P4 language. One of possible platforms is a SmartNIC - a Field Programmable Gate Array (FPGA) device which connects flexibility with high performance into a compact package. FPGA circuits are typically programmed in a Hardware Description Language (HDL) like VHDL or Verilog. These languages are hard to learn for novices and the development of a network device is very time consuming. Therefore, researchers around the world are finding a way how to automate the translation process from P4 to HDL language because such approach allows easy and fast programming of FPGA SmartNICs to a big audience of network experts. There are currently available three main compilers for the translation of P4 source to HDL — SDNet P4FPGA and P4-to-VHDL. In our best knowledge, all mentioned compilers don't provide any automated test environment which can be used repeatedly for different P4 programs. In other words, the verification environment has to be written by hand for each P4 program. Our work demonstrates a possible solution for automated verification of generated Register Transfer Level (RTL) description of a packet processing device from provided P4 source code.
Identification of Human Dynamics in User-Led Physical Human Robot Environment Interaction

Human dynamic models are useful in design of physical human-robot and human-robot-environment interaction: informing choice of robot impedance, motivating relaxations to passivity-based safety constraints, and allowing online inference to user intent. Designing for performance objectives such as stable well-damped contact transitions also requires nominal models, but the use of human models in controller design is limited. Established approaches to identify human dynamics apply position or force perturbation and measure the corresponding response, mostly to validate neuromuscular hypotheses on motor control, which raises questions about their transferability to human-led collaboration. Here, human dynamics are identified in a task which closely resembles the final application, where the human leads the robot into contact with a (virtual) wall. This paper investigates the impact of human dynamics on coupled system behavior, and establishes a general framework for identification in human-led scenarios, making consideration of unmeasured human input. Experiments with different stiffness environments allow inference to human dynamics, and characterize the range of human dynamics which can be modulated by the user.
Improving Dependency Parsing of Verbal Arabic Sentences Using Semantic Features

Arabic is a Morphologically Rich Language with Flexible Word Order (MOR-FWO). Linguistic semantics in its words play a big role on understanding the meaning of the sentence in a certain context. This stresses the importance of using these semantics in improving sentence parsing. We propose a dependency parsing approach for Modern Standard Arabic (MSA) verbal sentences using a data-driven dependency parser namely, MaltParser, utilizing the semantic information available in lexical Arabic VerbNet (AVN) to complement the existing morpho-syntactic information already available in the data. This complementing information is encoded as an additional semantic feature for data driven parsing. We were able to build a dependency parser with accuracy of 71.5% Labeled Attachment Score (LAS), 77.5% Unlabeled Attachment Score (UAS), and 2% increasing in total accuracy compared to the case of not using semantic features.