Miracle-dz/UniFAR-FTUs huggingface dataset:
Link Stability Based Optimized Routing Framework for Software Defined Vehicular Networks

The dynamic nature of vehicular networks imposes a lot of challenges in multihop data transmission as links are vulnerable in their existence due to associated mobility of vehicles. Thus, packets frequently find it difficult to get through to the destination due to the limited lifetimes of links. The conventional broadcasting based vehicular ad-hoc network (VANET) routing protocols struggle to accurately analyze the link dynamicity due to the unavailability of global information and inefficiencies in their route discovering schemes. However, with the recently emerged software defined vehicular network (SDVN) paradigm, link stability can be better scrutinized pertaining to the availability of global network information. Thus, in this paper, we introduce an optimization based novel packet routing scheme with a source routing based flow instantiation (FI) operation for SDVN. The routing framework closely analyzes the stability of links in selecting the routes and the problem is formulated as a minimum cost capacitated flow problem. Furthermore, an incremental packet allocation scheme is proposed to solve the routing problem in a less time complexity. The objective is to find multiple shortest paths which are collectively stable enough to deliver a given number of packets. The FI scheme efficiently delivers and caches flow information in the required nodes with a reduced extent of communication with the control plane. With the help of realistic simulation, we show that the proposed routing framework excels in terms of the performance over the existing routing schemes of both SDVN and conventional VANET.
Optimizing Tensile Strength and Yield Point of Steel Bar with Artificial Neural Network with Enhanced Parallel Cat Swarm Optimizer

In this paper, the process of optimization of chemical concentration of steel bar has been proposed which affects the tensile strength and yield point of steel bar. An Enhanced Parallel Cat Swarm Optimization (EPCSO) was used along with three layer feed forward ANN to optimize the result. The Enhanced Parallel Cat Optimization (EPCSO) is the combination of a Parallel Cat Swarm Optimization (PCSO) and a Taguchi Method which gives better performance and accuracy as compare to the other Particle Swarm Optimization (PSO)-based algorithms for solving the optimization problems. For optimizing the tensile strength and yield point of steel bar EPCSO-based ANN can give better accurate results than the TPSO-based ANN.
Prevalence of Skin diseases among Hepatitis C virus infected patients and their association with HCV infection in Islamabad, Pakistan

Background: Hepatitis C is a serious disease caused by infection of HCV virus. This virus mainly infects hepatocytes but it also replicates in the other tissues of human body, causing different types of extrahepatic manifestations.Methods: We conducted a case-control study from March 2014 to June 2014, to investigate frequency of skin diseases among 75 HCV infected patients and 75 controls identified in NOORI hospital, Islamabad Pakistan. We also investigated the association of skin diseases with HCV infection. Case patients were confirmed symptomatic cases of hepatitis C infection with laboratory and clinical criteria meeting standards provided by CDC. Controls were selected as the people without HCV, HBV, HIV and other chronic diseases. Data regarding sociodemographic characteristics, clinical data and skin diseases was collected using structured performa. Chi square test was used to see association.Results: The prevalence of skin diseases in cases were 33.3% (25/75). Most common skin diseases among HCV patients was Pruritus (32%) followed by Psoriasis (12%). Case-patients were more likely than controls to have skin diseases (OR=4.85, CI 1.95-12.12). Conclusion: Our study indicates high prevalence of skin diseases in HCV infected patients. It also showed clear association of skin diseases with HCV infection. We recommend studies on large scale to have a better look on these skin complications and use of these Skin diseases as prognostic indication of HCV infection.
Efficient Topology Reconstruction via Machine Learning Based Traffic Patterns Recognition in Optically Interconnected Computing System

The traffic flows in parallel computing systems show clustered, correlative nature and the flows are always latency-sensitive. These flows have been abstracted as “Coflow” to pursue overall optimization. Concurrent Coflows on the network show very novel traffic patterns. On the other hand, multiple optical interconnection network architectures have been proposed to enable the traffic adaption topology reconstructions. Nevertheless, topology reconstruction strategies are application-agnostic, and their optimization objective of network performance cannot meet the Coflow demand. In order to exert the flexibility of optical topology to promote the performance of parallel computing application by Coflow acceleration, the traffic patterns are preferred to be well recognized and then an adaptive topology is generated accordingly. To avoid further complex, such recognition is expected to finish without prior knowledge from the application layer. Then, the topology should be reconstructed to minimize the Coflow completion time. To implement these procedures, we proposed a traffic pattern-aware topology reconstruction strategy. Our strategy first combines CNN and spectral clustering to realize the traffic patterns awareness. And then, the genetic searching algorithm is used to mind the proper topology. Based on real traffic trace from Facebook computing application, large-scale simulations have verified the efficiency of such a strategy by lowering the completion time of computing jobs. In addition, the experimental demonstration has confirmed the conclusions.
A 1.8V 9bit 10MS/s SAR ADC in 0.18µm CMOS for bioimpedance analysis

In this work a 9-bits low power 10MS/s asynchronous SAR ADC in 180nm CMOS tecnology is presented. The ADC core occupies an active area of 0.124mm2. The ADC main parameters were extracted from post-layout simulations, which resulted in SNR of 55.29dB and ENOB of 8.59 bit at 1.8Vsupply and a sampling frequency of 10MS/s while consuming 0.692mW. The Figure of Merit (FoM) obtained was 145.93 fJ/conversion-step
Comprehensive Modeling and Simulation of an Anthropomorphic Robotic Exoskeleton for Rehabilitation

This paper presents a novel approach for dynamic modeling of the lower limb rehabilitation exoskeletons in view of requirements of repetitive movements with accuracy, and precise tasks in rehabilitation processes. It is necessary to achieve optimal control performance to realize well rehabilitation results in this kind of exoskeletons. There have been numerous exoskeletons designed in the past which have been modelled using Euler- Lagrange method. This method requires complex computations with large number of variables and is less flexible in addition and subtraction of variables. Therefore, the methodology of bond graph modeling is employed to build a dynamic model with very less computations and much flexibility. Modeling and simulation of an active three d.o. f single leg exoskeleton has been carried out and the dynamic responses have been analyzed to verify the effectiveness and performance advantages of the proposed method. Future research work will focus on designing an intelligent and optimized control strategy for lower limb rehabilitation exoskeletons to achieve desired performance.
III-Nitride Deep UV LED Without Electron Blocking Layer

AlGaN-based deep UV (DUV) LEDs generally employ a p-type electron blocking layer (EBL) to suppress electron overflow. However, Al-rich III-nitride EBL can result in challenging p-doping and large valence band barrier for hole injection as well as epitaxial complexity. As a result, wall plug efficiency (WPE) can be compromised. Our systematic studies of band diagram and carrier concentration reveal that carrier concentrations in the quantum well and electron overflow can be significantly impacted because of the slope variation of the quantum barrier (QB) conduction and valence bands, which in turn influence radiative recombination and optical output power. Remarkably, grading the Al composition from 0.60 to 0.70 for the 12-nm-thick AlGaN QB of the DUV LED without the EBL can lead to 13.5% higher output power and similar level of overflown electron concentration (∼1 × 1015/cm3) as opposed to the conventional DUV LED with the p-type EBL. This paradigm is significant for the pursuit of higher WPE or shorter emission wavelength for DUV LEDs and lasers, as it provides a new direction for addressing electron overflow and hole injection issues.
Worst-Case Robust Beamforming Design for Wireless Powered Multirelay Multiuser Network With a Nonlinear EH Model

This paper studies joint source and relay beamforming for a wireless powered downlink multirelay multiuser network. Considering nonlinear energy harvesting and imperfect channel state information, we aim to minimize the total transmit power at the base station by jointly optimizing the source beamforming and the relay beamforming weights under the energy causality constraints at the relays and the signal-to-noise ratio constraints at the users. The formulated problem is highly nonconvex, and thus, it is difficult to solve. To solve the problem, we first transform it into a worst-case optimization, and then, an iterative algorithm is developed to solve this worst-case optimization. Numerical results show the advantage of the proposed robust scheme.
Neural Check-Worthiness Ranking with Weak Supervision: Finding Sentences for Fact-Checking

Automatic fact-checking systems detect misinformation, such as fake news, by (i) selecting check-worthy sentences for fact-checking, (ii) gathering related information to the sentences, and (iii) inferring the factuality of the sentences. Most prior research on (i) uses hand-crafted features to select check-worthy sentences, and does not explicitly account for the recent finding that the top weighted terms in both check-worthy and non-check-worthy sentences are actually overlapping [15]. Motivated by this, we present a neural check-worthiness sentence ranking model that represents each word in a sentence by both its embedding (aiming to capture its semantics) and its syntactic dependencies (aiming to capture its role in modifying the semantics of other terms in the sentence). Our model is an end-to-end trainable neural network for check-worthiness ranking, which is trained on large amounts of unlabelled data through weak supervision. Thorough experimental evaluation against state of the art baselines, with and without weak supervision, shows our model to be superior at all times (+13% in MAP and +28% at various Precision cut-offs from the best baseline with statistical significance). Empirical analysis of the use of weak supervision, word embedding pretraining on domain-specific data, and the use of syntactic dependencies of our model reveals that check-worthy sentences contain notably more identical syntactic dependencies than non-check-worthy sentences.
Artificial Noise Aided Hybrid Analog-Digital Beamforming for Secure Transmission in MIMO Millimeter Wave Relay Systems

Millimeter wave (mm-wave) communications, especially mm-wave relay systems, have been considered as a key technology for next-generation wireless networks due to the rich spectrum resources. However, the problem of information leakage in mm-wave relay systems has not been well investigated. In this paper, artificial noise (AN) aided two-stage secure hybrid beamforming algorithm is proposed in MIMO mm-wave relay system from the perspective of physical layer security. In each time slot of the relay communications, the RF analog beamforming matrices are designed in the first stage, and baseband digital beamforming matrices are designed in the second stage. Through the proposed algorithm, the joint optimization is avoided and the feedback is reduced, which brings low complexity. Moreover, the AN is applied to fight against the eavesdropper by deteriorating the eavesdropping channel. The simulation results show that the proposed hybrid beamforming algorithm considerably improves the performance and achieves a balance between complexity and performance.
Feasibility Study of a No-Insulation REBCO Magnet for Persistent Mode Operation

A no-insulation solenoid magnet for persistent mode operation has been designed and constructed. The closed loop assembly used a 172–m-long REBCO superconducting tape, including 130 m for the magnet, 6 m for the switch, and 36 m for the soldered joints. The solenoid coil is of 30 mm ID, 68 mm OD, 40 mm length, 840 turns in 105 layers, and inductance of 20 mH. The assembly of magnet, switch, and joint has been operating in a persistent mode with a field decay rate of 18.8 ppm/h at the temperature of 30 K. Its operating current is 88.35 A, to achieve the designed central magnetic field of 1.5 T. The circuit resistance is 1.04 × 10–10 Ω, calculated from the inductance and field decay rate.
High GMR Effect and Perfect Microstructure in CoFe/Cu Multilayers

CoFe-/Cu-based multilayers with two different types of a buffer layer, NiFeCr, and Ta/NiFeCr have been produced by magnetron sputtering. The crystal structure of the superlattice layers has been studied. A correlation between the features of the microstructure and the magnitude of the magnetoresistance has been found. Superlattices with giant magnetoresistive effect reaching up to 83% have been fabricated.
Time-Scale Separation in Networks: State-Dependent Graphs and Consensus Tracking

This paper studies the coupled dynamics spanning multiple time-scales that arise in networked systems. Two particular cases are examined. In the first case, agents evolve according to the consensus dynamics over state-dependent graphs whose weight dynamics are slow varying. In the second, the consensus dynamics are coupled to rapidly evolving nonlinear node dynamics. In both instances, graph-based guarantees are provided that certify the existence of a separation principle across time scales. Further, the effect of the network's structure on the composite multiple time-scale system's stability and basin of attraction is quantified in each case. As illustrated by specific numeric examples, these results provide designers with a network-centric approach to improve the performance and stability of such coupled systems.
Real time ECG on internet using Raspberry Pi

The current status of heart disease in India is alarming, with projections suggesting that by the year 2020, the population of heart patients in India will surpass all the countries. Thus, monitoring the electrical activity of heart i.e. electrocardiogram is a necessity rather than luxury. ECG machines available in hospital are rather expensive. This paper aims at charting the ECG signal using the Raspberry Pi Arm processor. The ECG signals are recorded from the patient via the AD8232 ECG module and then this data is digitized using serial ADC MCP3008. The processor maps the signal on an online graphics tool plotl.ly using python programming. The main advantage of this system is that the result can be viewed at any place at any time. In this way, Internet of Things (IOT) concept is used to diagnose the heart defect and abnormalities in ECG without involving expensive ECG machines.
Data Analytics Layer For high-interaction Honeypots

Security of VMs is now becoming a hot topic due to their outsourcing in cloud computing paradigm. All VMs present on the network are connected to each other, making exploited VMs danger to other VMs. and threats to organization. Rejuvenation of virtualization brought the emergence of hyper-visor based security services like VMI (Virtual machine introspection). As there is a greater chance for any intrusion detection system running on the same system, of being dis-abled by the malware or attacker. Monitoring of VMs using VMI, is one of the most researched and accepted technique, that is used to ensure computer systems security mostly in the paradigm of cloud computing. This thesis presents a work that is to integrate LibVMI with Volatility on a KVM, a Linux based hypervisor, to introspect memory of VMs. Both of these tools are used to monitor the state of live VMs. VMI capability of monitoring VMs is combined with the malware analysis and virtual honeypots to achieve the objective of this project. A testing environment is deployed, where a network of VMs is used to be introspected using Volatility plug-ins. Time execution of each plug-in executed on live VMs is calculated to observe the performance of Volatility plug-ins. All these VMs are deployed as Virtual Honeypots having honey-pots configured on them, which is used as a detection mechanism to trigger alerts when some malware attack the VMs. Using STIX (Structure Threat Information Expression), extracted IOCs are converted into the understandable, flexible, structured and shareable format.
A high displacement ultrasonic horn with a cuboid hole in the front end

In ultrasonic machining and processing or in underwater acoustics, the output end surface of a vibration system needs large amplitude to improve work efficiency. A uniform horn with a cuboid hole at front end is proposed for amplifying amplitude. The finite element method is used to calculate the displacement distribution, magnification factor of the output end surface and to optimize the geometrical size and the position of the cuboid hole at resonant frequency. The influence of position and geometrical size of the hole on resonant frequency of the horn is studied, and the maximum magnification factor can reach seven times greater than that of a uniform solid horn after optimization. The horn is connected to a longitudinal vibration transducer, the frequency, mode shape, displacement distribution of the output surface, and the amplitude magnification factor were measured by using a laser vibrometer. The experimental results were consistent with the analysis of the finite element calculation results. In addition, the test results also show that excitation voltage has a linear relationship with the output displacement of the horn.
Compensation Method for a Wideband Signal’s Squint Problem of an AESA SAR System Using a Cross-Range-Variant Antenna Gain Equalizer

In order to obtain a high-resolution image, a wideband signal is required in a synthetic aperture radar (SAR). However, in an active electronically steered array (AESA) SAR, consideration of the beam squint problem is indispensable especially when a forward directed AESA operates in a squint spotlight SAR mode. In this paper, we present an accurate compensation method for the squint problem without added hardware, such as true time delays. To clarify the beam squint effect, an antenna gain radiated to point targets located on the ground was derived and we confirmed that the antenna gain for the targets placed on the same cross-range axis has similar tendency within 1.5-dB difference. Based on the results, we proposed a cross-range-variant antenna gain equalizer. Through test simulations, we confirmed that the suggested method effectively compensated for the degraded resolution performance of about 14% for the AESA SAR.
An Investigation Into Patient Privacy Disclosure in Online Medical Platforms

Patients using online medical community platforms are a special group of social media users, unique in terms of their willingness and behavior in disclosing private information. However, patients’ willingness for information disclosure is crucial for efficient operation of the platforms. From the perspective of these platforms, this paper investigates the impact of fairness (outcome fairness and procedural fairness) on the patients’ willingness to disclose privacy in the platform, and the role of perceived platform interactivity in this effect. Survey analysis of 1,546 online medical platform users shows that outcome fairness and procedural fairness have a direct positive impact on both the patient’s perceived platform interactivity and their willingness for privacy disclosure, while the perceived interactivity is found to enhance the willingness for privacy disclosure. These findings provide theoretical and practical guidance for online medical community platforms in encouraging patients to disclose information.
A High Speed and Resource Efficient Approximation of Softmax Loss Function

This work focuses primarily on the successful design and implementation of a high speed and a resource efficient approximation of Softmax loss function. The implementation explores system generator tool together with Simulink as a viable alternative for rapid prototyping, evaluation, and deployment of Field Programmable Gate Array (FPGA) based solutions. Linear look-up table (LUT) based interpolation is used for approximation of non-linear functions such as exponential as well as the implementations of dividers. They are usually very costly when designed through conventional approaches. After rigorous analysis, a dynamic quantization scheme is developed which offers the best compromise between accuracy and speed of execution. These results are then further expanded to extract rules for the implementation of softmax approximators of varying sizes. The results show that for an architecture with the OEM claimed maximum frequency of 600 MHz the proposed system achieves a synthesis frequency of 400 MHz. These results show that design using Xilinx system generator provides designer access to a vast variety of data visualization and processing tools that come packed with Matlab Simulink without significant performance degradation.
An Efficient Authentication Scheme Based on Semi-Trusted Authority in VANETs

With the development of intelligent transportation systems, vehicular ad hoc networks (VANETs) are widely used in safety driving, and so on. However, existing signature schemes, such as pseudonym- and group-based schemes, have certain problems, such as the need for a certificate distribution and certificate revocation lists (CRLs). With such schemes, the vehicle needs to store a valid certificate generated by the management center. Simultaneously, the receiver needs to check the CRLs prior to message authentication. CRLs require large amounts of storage space and computational and communication resources. In addition, many such schemes are built on a trusted authority and do not meet real-world needs. Thus, we propose an efficient authentication scheme based on semitrusted authority in VANETs. In this scheme, we combine the self-healing key distribution method with a certificateless signature in a semitrusted authority environment, such that the receivers do not require to query the CRLs. Therefore, the vehicles do not have to store the CRLs, thereby saving storage space and communication resources. This also reduces the computational costs and improves the efficiency of the message authentication. Since the proposed scheme is built on a semitrusted authority, it is a more realistic approach.