Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid RBFN Based Encrypted SSH Traffic Classification

This paper presents a hybrid Radial Basis Function Network (RBFN) based machine learning approach for encrypted data traffic classification. We concentrate on classifying the SSH-based encrypted traffic from the non SSH traffic. The hybrid RBFN network is employed with k-means clustering and gradient descent learning technique for the binary classification. The suggested technique is experimented on standard datasets including AMP, MAWI, DARPAA99, and NIMS with k-fold cross-validation. Simulation results show that RBFN network is able to provide flow accuracy of more than 99% for all the datasets. Comparative analysis is made to show the efficacy of the proposed scheme with other competent techniques.
Challenges of using MIMO channel technology in 5G wireless communication systems

Multiple input multiple output (MIMO) systems, which are suitable for mmWave communications, facilitate building large arrays of communication channel with Reasonable form factors indoor and outdoor communications. In both, indoors and outdoors communications a reasonable coverage can be achieved by the high gains of array. Deploying MIMO systems in frequency-division duplex mode faces the issue of high feedback overhead of channel state information (CSI) to the transmitter. This issue is attributed to the used multiplexing algorithm. From other hand, it is expected that by 2020, all new 5G technologies will be in operation. Hence, it is crucial to investigate and analyze using the MIMO system with orthogonal frequency-division multiplexing OFDM modulation in 5G technology. This research sheds light on the potential challenges of implementing MIMOOFDM in 5G technology. It proposed four models with different number of transmitter and receiver antenna elements in the array. All proposed model used 2 elements per row in transmitter and receiver antenna. The four models are implemented in two cases Line of sight LOS and Non Line of Sight NLOS. The channel rank and condition number of channel matrix are regarded as significant and accurate mathematical indicators which can be used to evaluation MIMO channel technology. The condition number is calculated from the obtained results of four models with rank of channel metrics.
The standardized extract of Nigella sativa and its major ingredient, thymoquinone, ameliorates angiotensin II-induced hypertension in rats

Abstract Background This study investigated the effect of hydroalcoholic extract of Nigella sativa (N. sativa) and its active component, thymoquinone (TQ) on hypertension induced by angiotensin II (AngII), the main product of renin–angiotensin system (RAS). Methods Seven animal groups (n=7 for each group) were used as follows: (1) control, (2) AngII (300 ng/kg), (3) AngII+losartan (Los; 10 mg/kg), (4) TQ (40 mg/kg)+AngII, and (5–7) three doses of N. sativa (200, 400, and 600 mg/kg)+AngII. Los and AngII were injected intravenously; TQ and extracts were injected intraperitoneally. In TQ and N. sativa-treated groups, 30 min after injection of the extract and TQ, AngII was injected. Cardiovascular parameters were recorded by power lab system after cannulation of femoral artery. The maximum changes (∆) of systolic blood pressure (SBP), mean arterial pressure (MAP), and heart rate (HR) were calculated and used for statistical analysis. Results AngII significantly increased maximal ∆SBP, ∆MAP, and ∆HR compared with the control (p<0.001), and these effects significantly were blunted by Los. TQ and two higher doses (400 and 600 mg/kg) of N. sativa significantly could antagonize effect of AngII on ∆SBP, ∆MAP (p<0.05 to p<0.001). AngII-induced changes of HR are also significantly decreased by TQ and dose 600 mg/kg of extract (p<0.01 and p<0.05, respectively). Conclusions The N. sativa and its component TQ have the beneficial effect on hypertension probably due to attenuation cardiovascular effects of AngII.
Motion planning for mobile robots using the safety velocity obstacles method

The main purpose of our work was to develop and implement a motion planning algorithm that can ensure the avoidance of static and moving obstacles in the workspace of the robot. We used the Velocity Obstacles method, which is suitable for motion planning of mobile robots in a dynamic environment. With this method, we planned evasive maneuvers by using information about actual velocity and actual position of the robot and obstacles which information are supposed to be known or well estimated. To choose the velocity of the robot, in the most former publications, the authors always tried to find the velocity, that is the fastest solution to reach the target position. While following such a path, the robot usually goes very close to the obstacles which can result a collision-risk, if only inaccurate size, position and velocity information are available about the obstacles. In our work, next to the fastest target reaching method, we introduced a new method, that can ensure the safest motion planning. We named this method Safety Velocity Obstacles (SVO) method. Simulations were performed to test the motion of the robot determined by SVO.
Towards an Inherently Secure Run-Time Environment for Medical Devices

Connected medical devices are promising in medical area. However, these devices are known to be poor in security and strongly vulnerable to code injection attacks. These attacks aim at modifying the run-time behavior of an application by violating its control flow graph. Control flow hijacking can lead hackers to remotely control medical application and endanger patient's life. This article focuses on devices and provides an overview of the different countermeasures that mitigate run-time attacks. Finally, from this state of the art, we will expose the perspectives and the ongoing work to make medical devices inherently more secure.
Parallels and promising directions in the study of genetic, cultural, and moral evolution

Experimental evolution has yielded surprising insights into human history and evolution by shedding light on the roles of chance and contingency in history and evolution, and on the deep evolutionary roots of cooperation, conflict and kin discrimination. We argue that an interesting research direction would be to develop computational and experimental systems for studying evolutionary processes that involve multiple layers of inheritance (such as genes, epigenetic inheritance, language, and culture) and feedbacks (such as gene-culture coevolution and mate choice) as well as open-ended niche construction---all of which are important in human history and evolution. Such systems would also be a clear way to motivate evolution and computation to scholars and students across diverse cultural and socioeconomic backgrounds, as well as to scholars and students in the social sciences and humanities. In principle, computational models of cultural evolution could be compared to data, given that large-scale datasets already exist for tracking cultural change in real-time. Thus, experimental evolution, as a laboratory and computational science, is poised to grow as an educational tool for people to question and study where we come from, why we believe what we believe, and where we as a species may be headed.
Analysis and Classification of Service Interactions for the Scalability of the Internet of Things

Scalability is an important concern for Internet of Things (IoT) applications since the amount of service interactions may become overwhelming, due to the huge number of interconnected nodes. In this paper, we present an IoT scenario for real-time Electrocardiogram (ECG) monitoring, in order to analyze how well different kinds of service interactions can fulfill the scalability requirements of IoT applications.
Chromium and Nickel Concentrations in Subjects with a Stainless Steel Metal-on-Metal Cervical Disc Arthroplasty: Results from a Prospective Longitudinal Study with 7 Years Follow-Up

ABSTRACT Background: Cervical disc arthroplasty (CDA) has emerged as an alternative to anterior cervical discectomy and fusion for degenerative cervical disc disease. The artificial discs provide intervertebral motion using multicomponent articulation and thus tend to generate particulate debris and soluble metal ions. Limited information is available on the long-term metal concentrations and associated systemic adverse events observed in metal-on-metal CDA. Serum chromium (Cr) and nickel (Ni) concentrations were assessed in patients implanted with ball-in-trough stainless steel–based cervical disc through 7 years. Methods: A prospective, nonrandomized longitudinal study was conducted that included 25 patients following rigorous exclusion criteria that included no previous permanent metal implants and no professional exposure to metal particles. Blood serum Cr and Ni concentrations were assayed preoperatively and at 3, 6, 12, 24, 36, 60, and 84 months postoperatively using high-resolution inductively coupled plasma–mass spectrometry. Longitudinal statistical comparisons were made using the Friedman test with statistical significance at P < . 05. Results: Median serum concentrations determined preoperatively and at 3, 6, 12, 24, 36, 60, and 84 months postoperatively were 0.074, 0.106, 0.132, 0.170, 0.172, 0.274, 0.192, and 0.203 ng/mL for Cr and 0.085, 0.178, 0.222, 0.175, 0.205, 0.284, 0.181, and 0.194 ng/mL for Ni. The serum Cr concentrations were statistically higher for all postoperative time periods compared to preoperative concentration (Friedman P <.01), whereas serum Ni concentration was statistically higher at the 84-month postoperative time period than the preoperative concentration (Friedman P <.01) and then the concentration at 3, 12, 24, and 60 months postoperatively (Friedman P < .03). Conclusions: The Cr concentrations detected at all postoperative times were statistically higher than preoperative concentrations, whereas Ni concentration was statistically higher than the preoperative concentration only at 84 months.
Functional tractography of white matter by high angular resolution functional‐correlation imaging (HARFI)

Functional magnetic resonance imaging with BOLD contrast is widely used for detecting brain activity in the cortex. Recently, several studies have described anisotropic correlations of resting‐state BOLD signals between voxels in white matter (WM). These local WM correlations have been modeled as functional‐correlation tensors, are largely consistent with underlying WM fiber orientations derived from diffusion MRI, and appear to change during functional activity. However, functional‐correlation tensors have several limitations. The use of only nearest‐neighbor voxels makes functional‐correlation tensors sensitive to noise. Furthermore, adjacent voxels tend to have higher correlations than diagonal voxels, resulting in orientation‐related biases. Finally, the tensor model restricts functional correlations to an ellipsoidal bipolar‐symmetric shape, and precludes the ability to detect complex functional orientation distributions (FODs).
Classification from Positive, Unlabeled and Biased Negative Data

In binary classification, there are situations where negative (N) data are too diverse to be fully labeled and we often resort to positive-unlabeled (PU) learning in these scenarios. However, collecting a non-representative N set that contains only a small portion of all possible N data can often be much easier in practice. This paper studies a novel classification framework which incorporates such biased N (bN) data in PU learning. We provide a method based on empirical risk minimization to address this PUbN classification problem. Our approach can be regarded as a novel example-weighting algorithm, with the weight of each example computed through a preliminary step that draws inspiration from PU learning. We also derive an estimation error bound for the proposed method. Experimental results demonstrate the effectiveness of our algorithm in not only PUbN learning scenarios but also ordinary PU learning scenarios on several benchmark datasets.
A Web Resource for Designing Subunit Vaccine Against Major Pathogenic Species of Bacteria

Evolution has led to the expansion of survival strategies in pathogens including bacteria and emergence of drug resistant strains proved to be a major global threat. Vaccination is a promising strategy to protect human population. Reverse vaccinology is a more robust vaccine development approach especially with the availability of large-scale sequencing data and rapidly dropping cost of the techniques for acquiring such data from various organisms. The present study implements an immunoinformatic approach for screening the possible antigenic proteins among various pathogenic bacteria to systemically arrive at epitope-based vaccine candidates against 14 pathogenic bacteria. Thousand four hundred and fifty nine virulence factors and Five hundred and forty six products of essential genes were appraised as target proteins to predict potential epitopes with potential to stimulate different arms of the immune system. To address the self-tolerance, self-epitopes were identified by mapping on 1000 human proteome and were removed. Our analysis revealed that 21proteins from 5 bacterial species were found as virulent as well as essential to their survival, proved to be most suitable vaccine target against these species. In addition to the prediction of MHC-II binders, B cell and T cell epitopes as well as adjuvants individually from proteins of all 14 bacterial species, a stringent criteria lead us to identify 252 unique epitopes, which are predicted to be T-cell epitopes, B-cell epitopes, MHC II binders and Vaccine Adjuvants. In order to provide service to scientific community, we developed a web server VacTarBac for designing of vaccines against above species of bacteria. This platform integrates a number of tools that includes visualization tools to present antigenicity/epitopes density on an antigenic sequence. These tools will help users to identify most promiscuous vaccine candidates in a pathogenic antigen. This server VacTarBac is available from URL (http://webs.iiitd.edu.in/raghava/vactarbac/).
Secondary Electron Energy Contrast of Localized Buried Charge in Metal–Insulator–Silicon Structures

Abstract This paper presents a new method for creating and monitoring controlled localized negatively charged regions inside insulators with a scanning electron microscope (SEM). A localized buried charged region is created and observed close to the point where a high voltage primary beam (10 kV) strikes a metal–insulator–silicon specimen. The amount of buried charge within the insulator at any given moment can be dynamically monitored by detecting the appearance of a second peak in the secondary electron (SE) energy spectrum. SE energy spectral signals were obtained through the use of a compact high signal-to-noise energy analyzer attachment that was fitted on to the SEM specimen stage. An electrostatic model, together with Monte Carlo simulations, is presented to explain how the SE charge contrast effect functions. This model is then experimentally confirmed by using the SE energy spectral signal induced by a gallium ion beam inside a dual focused ion beam-SEM instrument.
Programmable Transfer Function Optical-DAC Using an InP Segmented Mach-Zehnder Modulator

A programmable InP-based Tbit optical-DAC transmitter is discussed. Through electro-optic co-design and simulation of the transmitter’s components, the performance is evaluated. The comparison with a traditional DAC plus linear driver approach is presented, with a significant advantage of the optical DAC for complex modulation schemes. Low power (1.3 pJ/bit) transmission of a 56 Gbaud dual-polarization probabilistically-shaped 256-QAM signal is shown.
Intra-Data Center Network Optimization by Means of Application-Based Traffic Modeling and Optical Circuit Switching

In this paper, we investigate a hybrid electro-optical Intra-Data Center Network based on a service-customized Long-Range Dependence traffic model specific to machine-generated data. We show that different application classes, typically hosted by data centers, possess distinct and highly varying behaviors, which need to be considered for an efficient architecture design and optimization. In this regard, using a statistics-based simulative demonstration, we show the benefits brought by optical circuit switching, namely due to its potential in handling bulky and highly intensive data traffic peculiar to some applications. Owing to the fact that cost savings represent another essential target of network optimization, we also quantify the gains related to capital and operational expenditures specific to the new hybrid scheme.
High-productivity Programming and Optimization Framework for Stream Processing on FPGA

Because of the recent slowdown in silicon technology and increasing power consumption of hardware, several dedicated architectures have been proposed in High Performance Computing (HPC) to exploit the limited number of transistors in a chip with low power consumption. Although Field-Programmable Gate Array (FPGA) is considered as one of the promising technologies to realize dedicated hardware, it is difficult for non-domain experts to program FPGAs due to the gap between thier applications and hardware-level programming models for FPGAs. In this paper, we propose a C/C++ based programming framework, C2SPD, to describe stream processing on FPGA. It uses SPGen, a dataflow High Level Synthesis (HSL) tool, as the FPGA backend. C2SPD can program FPGAs by adding directives to serial code, and the compiler translates it into optimized SPGen code. The range of application is limited in C2SPD due to its domain-specific approach. However it can generate efficient hardware on FPGAs when the programming model matches the target application. 2D-stencil computation is written in C2SPD to evaluate the performance of our C2SPD compiler implementation. 2 lines of the C2SPD directives are added into the serial C code to generate FPGA hardware. The Optimized FPGA hardware achieves 175.41 GFLOPS by using 256 stream cores, which is 220 times faster than a single stream core.
Why I like it: multi-task learning for recommendation and explanation

We describe a novel, multi-task recommendation model, which jointly learns to perform rating prediction and recommendation explanation by combining matrix factorization, for rating prediction, and adversarial sequence to sequence learning for explanation generation. The result is evaluated using real-world datasets to demonstrate improved rating prediction performance, compared to state-of-the-art alternatives, while producing effective, personalized explanations.
Epitopes of Immunoreactive Proteins of Streptococcus Agalactiae: Enolase, Inosine 5′-Monophosphate Dehydrogenase and Molecular Chaperone GroEL

Three Streptococcus agalactiae (group B streptococci, GBS) immunoreactive proteins: enolase (47.4 kDa), inosine 5′-monophosphate dehydrogenase (IMPDH) (53 kDa) and molecular chaperone GroEL (57 kDa) were subjected to investigation. Enolase protein was described in our previous paper, whereas IMPDH and GroEL were presented for the first time. The aim of our paper was to provide mapping of specific epitopes, highly reactive with umbilical cord blood serum. Bioinformatic analyses allowed to select 32 most likely epitopes for enolase, 36 peptides for IMPDH and 41 immunoreactive peptides for molecular chaperone GroEL, which were synthesized by PEPSCAN. Ten peptides: two in enolase, one in IMPDH and seven in molecular chaperone GroEL have been identified as potentially highly selective epitopes that can be used as markers in rapid immunological diagnostic tests or constitute a component of an innovative vaccine against GBS infections.
Mimic Encryption System for Network Security

With the rapid development of the Internet, increasingly more attention has been paid to network security problems. A network security defense technology has become a very important research field. Currently, most network equipment transmits data in plaintext at the data link layer, which exposes important information, such as IP addresses, port numbers, and application protocols, to an attacker and provides an opportunity for network attacks. To protect a network against attacks and ensure its security, this paper proposes a mimic encryption system for network security. Based on the concepts of moving target defense and mimic security defense, using the principles of randomization, dynamism, and diversification, a data link layer mimic encryption system is constructed from the underlying network of an information system. By transforming the frame format, a reconfigurable encryption algorithm, an hash algorithm, and a pseudo-random number generator are used to design different combination encryption modes. Then, the hash value of an encrypted frame is obtained by performing the hash operation, and feedback update is performed to generate new key parameters for the hash key pool. In addition, the pseudo-random selection of combinations of encryption algorithms and keys is performed to achieve “one frame-one key”. Finally, an FPGA is used as the network encryption card, and a CPU is used to realize two-party key agreement and the upper layer application. Using the FPGA + CPU hardware and software collaboration, the attack surface is expanded. Taking advantage of the high anti-interference property of an FPGA, part of the attack against the software system is filtered. The experimental results and analysis show that the encryption and decryption performance of this system in a 10 G network are approximately 500 MB/s. Thus, the system can effectively prevent the leakage of user data and resist network sniffing, vulnerability attacks, exhaustive key search attacks, and ciphertext-only attacks. Moreover, this system provides high security.
BER analysis using Pilot Contamination Mitigation Technique over Weibull Fading in Massive MIMO System

Each base station (BS) is equipped with M (unlimited) antennas serving K (large but finite) users. Further, we let τ (pilot length) be an arbitrary but small value. Pilot contamination results from the re-use of the same set of pilot sequences in the different cells of the system. To mitigate the underline problem in Massive MIMO system, pre-established algorithm (APA) is reviewed and bit error rate is estimated over Weibull fading scenario using APA algorithm. The numerical result shows significant improvement in BER and Spectral efficiency with increasing fading severity.
Universal Adaptive Stabilizer Based Optimization for Li-Ion Battery Model Parameters Estimation: An Experimental Study

This paper proposes a two-stage universal adaptive stabilizer (UAS)-based optimization technique for an accurate and efficient estimation of Li-ion battery model parameters. The first stage utilizes an UAS-based adaptive parameters estimation (APE) technique to acquire an initial estimate of battery model parameters. The second stage utilizes one of the three different optimization techniques, i.e., fmincon, particle swarm optimization (PSO), and hybrid PSO to improve the accuracy of battery model parameters obtained by the APE. The parameters estimated by the APE help in reducing the search space interval required by the optimization technique, thus reducing the computation time of the optimization process. Intensive computer simulation and experimentation are performed to estimate the battery terminal voltage using the estimated parameters. The accuracy of estimated battery parameters is evaluated by comparing the estimated and measured battery terminal voltage. The results show that the accuracy of the battery model parameters obtained by the optimization techniques alone is poor, and the required computation time is high. The accuracy of parameters obtained by UAS-based APE is good with very low computation time, while it is best when UAS-based APE is used in combination with the PSO, or hybrid PSO optimization techniques while requiring an intermediate amount of computation time.