Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Variability Aware Path Selection for Self-Referencing Based Hardware Trojan Detection

Hardware Trojans can be inserted by an adversary at untrusted third-party fabrication houses. Many Side-channel analysis based detection techniques have been proposed in past to detect such Trojans. However, their efficiency is highly affected by process variations. Hardware Trojan (HT) inserted in an IC affects the path delays within the IC. In this work, we exploit the fact that the path delays of topologically symmetric paths in an IC will be affected similarly by process variations. We tend to choose paths that are minimally affected by process variations. In this paper, we propose a path selection technique for delay based HT detection technique. We further use the concept of self-referencing to improve detection accuracy as well as to eliminate the requirement of golden ICs. Simulations performed using ISCAS-85 benchmarks establish that the proposed method is achieving a true positive rate of 100% with a false positive rate less than 5%. We have considered maximum of 10% intra-die and 15% inter-die variations in threshold voltage (Vth).
One Size Does Not Fit All: In-Test Workload Adaptation for Performance Testing of Enterprise Applications

The identification of workload-dependent performance issues, as well as their root causes, is a time-consuming and complex process which typically requires several iterations of tests (as this type of issues can depend on the input workloads), and heavily relies on human expert knowledge. To improve this process, this paper presents an automated approach to dynamically adapt the workload (used by a performance testing tool) during the test runs. As a result, the performance issues of the tested application can be revealed more quickly; hence, identifying them with less effort and expertise. Our experimental evaluation has assessed the accuracy of the proposed approach and the time savings that it brings to testers. The results have demonstrated the benefits of the approach by achieving a significant decrease in the time invested in performance testing (without compromising the accuracy of the test results), while introducing a low overhead in the testing environment.
Fault Detection Method Based on Moving Window Principal Component Analysis for Three-Tank System

In this paper, a fault detection method based on moving window principal component analysis (MWPCA) is introduced. This method is applied to the three-tank system, and three typical faults of three water tank system are detected. The results show that the MWPCA has better erformance compared with the traditional principal component nalysis(PCA) method.
Attack Resilience and Recovery using Physical Challenge Response Authentication for Active Sensors Under Integrity Attacks

Author(s): Shoukry, Yasser; Martin, Paul; Yona, Yair; Diggavi, Suhas; Srivastava, Mani | Abstract: Embedded sensing systems are pervasively used in life- and security-critical systems such as those found in airplanes, automobiles, and healthcare. Traditional security mechanisms for these sensors focus on data encryption and other post-processing techniques, but the sensors themselves often remain vulnerable to attacks in the physical/analog domain. If an adversary manipulates a physical/analog signal prior to digitization, no amount of digital security mechanisms after the fact can help. Fortunately, nature imposes fundamental constraints on how these analog signals can behave. This work presents PyCRA, a physical challenge-response authentication scheme designed to protect active sensing systems against physical attacks occurring in the analog domain. PyCRA provides security for active sensors by continually challenging the surrounding environment via random but deliberate physical probes. By analyzing the responses to these probes, and by using the fact that the adversary cannot change the underlying laws of physics, we provide an authentication mechanism that not only detects malicious attacks but provides resilience against them. We demonstrate the effectiveness of PyCRA through several case studies using two sensing systems: (1) magnetic sensors like those found wheel speed sensors in robotics and automotive, and (2) commercial RFID tags used in many security-critical applications. Finally, we outline methods and theoretical proofs for further enhancing the resilience of PyCRA to active attacks by means of a confusion phase---a period of low signal to noise ratio that makes it more difficult for an attacker to correctly identify and respond to PyCRA's physical challenges. In doing so, we evaluate both the robustness and the limitations of PyCRA, concluding by outlining practical considerations as well as further applications for the proposed authentication mechanism.
Key Frame Extraction from Video Based on Determinant-Type of Sparse Measure and DC Programming

Video is human’s favorite multimedia data type due to its abundant amount of information and intuitive experience compared with text, audio, and image. With rapid progress of computer and network technologies, the amount of video data increases fast, massive storage and frequent retrieval inevitably lead to huge spatio-temporal cost, and how to manage the massive video data efficiently becomes a challenging issue. Key frame extraction is considered as one of the most critical issues in video processing. In this paper, we introduce a novel key frame extraction method based on sparse modeling. Assume that each video frame signal can be expressed as a linear combination of the representative key frames and formulate the problem of finding the representatives as a sparse vector problem. We consider a sparsity measure that is based on the determinant of the Gram matrix of the signals. Based on this measure, we propose a novel key frame extraction formulation based on sparse modeling with the determinant measure of sparsity. The formulation can be expressed as the difference of two convex functions, making the objective function neither convex nor concave. Thus the formulation cannot be easily solved by standard convex optimization methods. Difference of convex (DC) programming is introduced to solve the optimization problem.
Antifungal Activity of Commercial Essential Oils and Biocides against Candida Albicans

Management of oral candidosis, most frequently caused by Candida albicans, is limited due to the relatively low number of antifungal drugs and the emergence of antifungal tolerance. In this study, the antifungal activity of a range of commercial essential oils, two terpenes, chlorhexidine and triclosan was evaluated against C. albicans in planktonic and biofilm form. In addition, cytotoxicity of the most promising compounds was assessed using murine fibroblasts and expressed as half maximal inhibitory concentrations (IC50). Antifungal activity was determined using a broth microdilution assay. The minimum inhibitory concentration (MIC) was established against planktonic cells cultured in a range of concentrations of the test agents. The minimal biofilm eradication concentration (MBEC) was determined by measuring re-growth of cells after pre-formed biofilm was treated for 24 h with the test agents. All tested commercial essential oils demonstrated anticandidal activity (MICs from 0.06% (v/v) to 0.4% (v/v)) against planktonic cultures, with a noticeable increase in resistance exhibited by biofilms (MBECs > 1.5% (v/v)). The IC50s of the commercial essential oils were lower than the MICs, while a one hour application of chlorhexidine was not cytotoxic at concentrations lower than the MIC. In conclusion, the tested commercial essential oils exhibit potential as therapeutic agents against C. albicans, although host cell cytotoxicity is a consideration when developing these new treatments.
Research on Harmonic Compensation of Distribution Network with High Permeability Access of New Energy Power Generation

The depletion of traditional fossil fuels has brought about a constant increase in pressure on the global environment. Owing to fast penetration of power electronics devices, several loads within distributed generation systems are nonlinear. As a result, renewable energy generation such as wind turbine (WT) and Solar Photovoltaic (PV) introduce new challenges to the distribution system, such as harmonic instability that needs attention. Harmonic current is among the most vital sources of disturbance, causing deterioration of the effectiveness of electronic equipment. However, the inherent harmonic caused by these power electronic devices can have an effect on the power system voltage and subsequently customer equipment. For this reason, a research on harmonic current compensation is important. Shunt active power filters based LLCL to analyze the harmonics currents created by the power electronic devices in new energy generation is presented here. Specific harmonics compensation in the power system is performed under synchronous coordinate transformation. The distinctive features of this design are motivated by the provision of effective DC utilization, high performance in relations to mitigation of harmonic current and offer precisely controlled output current. The effective compensation and suppression of reactive power and harmonics in the power grid enhance the quality of power supply in the network. The research proposed is verified by MATLAB/Simulink.
Design of microstrip patch reflectarray antenna at W-band

Printed Reflectarrays have a wide range of advantages of Reflector antennas and Phased array antennas. In this paper, the phase compensation techniques for Microstrip Reflectarray antennas have been investigated for W-Band frequency and reviews of various techniques for phase compensation, like variable sizes of the patch, adding stubs of variable lengths which form the essential part in the design of a Reflectarray antenna. A waveguide simulator concept is implemented in the characterization of reflected phase. An array of 9×9 patch elements with and without patch compensation is designed at 94GHz with a waveguide feed using ANSYS HFSS. A good agreement of antenna performance has been observed in terms of SLL and HPBW.
Reduced visual function associated with infantile spasms in children on vigabatrin therapy.

PURPOSE To use visual evoked potential (VEP) testing to determine whether visual deficits are present in children with a history of vigabatrin use. METHODS Contrast sensitivity and visual acuity were assessed by visual evoked potential testing and compared between 28 children (mean age, 4.90 +/- 4.92 years) with seizure disorders who had taken vigabatrin and 14 typically developing children (mean age, 3.14 +/- 1.70 years). Exclusion criteria were heritable eye disease, suspected cortical visual impairment, nystagmus, and prematurity >2 weeks. The effects of the following factors on contrast sensitivity and visual acuity were examined: type of seizure (infantile spasms versus other), ERG result, duration of vigabatrin therapy, cumulative dosage of vigabatrin, and other seizure medications (other versus no other medication). RESULTS Contrast sensitivity and visual acuity were reduced in vigabatrin-treated children with infantile spasms compared with vigabatrin-treated children with other seizure disorders and typically developing control subjects. The other factors examined had no significant effect on contrast sensitivity or visual acuity, with adjustment for seizure type. CONCLUSIONS Children with infantile spasms on vigabatrin may have compromised visual function, even in the absence of suspected cortical visual impairment. The children tested in the present study have reduced vision, probably associated with infantile spasms rather than vigabatrin.
Density-functional based tight-binding: an approximate DFT method

The DFTB method, as well as its self-consistent charge corrected variant SCC-DFTB, has widened the range of applications of fundamentally well established theoretical tools. As an approximate density-functional method, DFTB holds nearly the same accuracy, but at much lower computational costs, allowing investigation of the electronic structure of large systems which can not be exploited with conventional ab initio methods. In the present paper the fundaments of DFTB and SCC-DFTB and inclusion of London dispersion forces are reviewed. In order to show an example of the DFTB applicability, the zwitterionic equilibrium of glycine in aqueous solution is investigated by molecular-dynamics simulation using a dispersion-corrected SCC-DFTB Hamiltonian and a periodic box containing 129 water molecules, in a purely quantum-mechanical approach.
The impact of renal function on the prognostic value of N-terminal pro–B-type natriuretic peptide in patients with coronary artery disease

Background The impact of renal function on the prognostic value of N-terminal pro–B-type natriuretic peptide (NT-proBNP) remains unclear in coronary artery disease (CAD). This study sought to investigate the value of using NT-proBNP level to predict prognoses of CAD patients with different estimated glomerular filtration rates (eGFRs). Methods A retrospective analysis was conducted from a single registered database. 2087 consecutive patients with CAD confirmed by coronary angiography were enrolled. The primary endpoint was allcause mortality. Results The mean follow-up time was 26.4 ± 11.9 months and death events occurred in 197 cases. The NT-proBNP levels increased with the deterioration of renal function, as well as the optimal cutoff values based on eGFR stratification to predict endpoint outcome (179.4 pg/mL, 1443.0 pg/mL, 3478.0 pg/mL, for eGFR ≥ 90, 60–90 and < 60 mL/min/1.73 m2, respectively). Compared with the routine cut-off value or overall optimal one, stratified optimal ones had superior predictive ability for endpoint in each eGFR group (all with the highest Youden’s J statistics). And the prognostic value became weaker as eGFR level decreased (eGFR ≥ 90 vs. 60–90 vs. < 60 mL/min/1.73 m2, odds ratio [OR] 7.7; 95% confidence interval [CI] 1.7–33.9 vs. OR 4.8; 95% CI 2.7–8.5 vs. OR 3.0; 95% CI 1.5–6.2). Conclusions This study demonstrated that NT-proBNP exhibits different predictive values for prognosis for CAD patients with different levels of renal function. Among the assessed values, the NT-proBNP cut-off value determined using renal function improve the accuracy of the prognosis prediction of CAD. Moreover, lower eGFR is associated with a higher NT-proBNP cut-off value for prognostic prediction. (Cardiol J 2019; 26, 6: 696–703)
Intelligent wireless network deployment for search and rescue situations

Under search and rescue situations the time to find and to rescue victims is very important. The use of autonomous mobile robots can increase the possibility to detect victims while avoiding to expose the life of human teams. In this paper is presented a methodology for the deployment of wireless nodes to provide communication ways on emergency situations. A mobile robot is used to release stand-alone wireless nodes while exploring the environment. Due to the stochastic nature of signals and the unknown factors on the environment, the a priori assumptions of the expansion of the Radio Frequency can not be used. Is then that, analyzing signals of released nodes that the mobile robot decides where to add another node, in order to assure environment coverage. The proposed methodology has proved to provide total coverage with non exhaustive exploration. Exploration is directed by an a priori knowledge of the environment outside of scope of this paper.
The evolving role of tiotropium in asthma

Tiotropium is a long-acting muscarinic antagonist (LAMA) that exerts its bronchodilatory effect by blocking endogenous acetylcholine receptors in the airways. Its safety and efficacy are well established for the treatment of COPD, and it is now being recognized for its role in improving lung function and control in asthma. This review discusses the evolving role of tiotropium delivered by the Respimat® in patients across the range of asthma severities and ages, and provides an overview of safety and efficacy data. Tiotropium is the only LAMA currently approved for the treatment of asthma, and evidence from a large-scale clinical trial program, including several Phase III studies in adults, has demonstrated that tiotropium improves lung function and asthma control, with a safety profile comparable with that of placebo. Clinical trials in adolescent patients (aged 12–17 years) have also shown improvements in lung function and trends toward improved asthma control. Of note, the efficacy and safety profiles are consistent regardless of baseline characteristics and phenotype. Given the large and growing body of evidence, it is likely that as clinical experience with tiotropium increases, this treatment may possibly emerge as the key choice for add-on therapy to inhaled corticosteroids/long-acting β2-agonists, and in patients who do not tolerate long-acting bronchodilators or other medications, in the future.
WI-LO: Wireless indoor localization through multi-source radio fingerprinting

The paper answers a fundamental question: can the accuracy of Wi-Fi based localization be significantly increased by fusing information from alternate sources like LTE signals and magnetometers, collected through software defined radios and smart-devices? Further, it aims to eliminate dependency of well established localization techniques on only Wi-Fi access point (AP) positions, and instead, proposes a diversity-leveraging architecture called as the Wireless Locator (Wi-LO) framework. Wi-LO is a client-server paradigm for indoor localization that achieves precision by pattern-matching the collected signal samples with a priori references for each transmitter type, thus giving a stand-alone decision for these diverse sensing modes. The novelty of the paper is fusing these decision outcomes and resolving mis-matches (for e.g., Wi-Fi and LTE suggest different locations) in a seamless manner, and also identifying the best source to use at each location based on its spatial resolution. Wi-Lo is rigorously evaluated on a test-bed, with the proposed scheme of combining Wi-Fi, LTE and magnetometer performing better localization than the classical Wi-Fi-only approach in both urban (+8%) and rural (+25%) scenarios.
Robust and Distributed Computation of Aggregates in Wireless Sensor Networks

A wireless sensor network consists of a large number of small, resource-constrained devices and usually operates in hostile environments that are prone to link and node failures. Computing aggregates such as average, minimum, maximum and sum is fundamental to various primitive functions of a sensor network like system monitoring, data querying, and collaborative information processing. In this paper we present and analyze a suite of randomized distributed algorithms to efficiently and robustly compute aggregates. Our Distributed Random Grouping (DRG) algorithm is simple and natural and uses probabilistic grouping to progressively converge to the aggregate value. DRG is local and randomized and is naturally robust against dynamic topology changes from link/node failures. Although our algorithm is natural and simple, it is nontrivial to show that it converges to the correct aggregate value and to bound the time needed for convergence. Our analysis uses the eigen-structure of the underlying graph in a novel way to show convergence and to bound the running time of our algorithms. We also present simulation results of our algorithm and compare its performance to various other known distributed algorithms. Simulations show that DRG needs much less transmissions than other distributed localized schemes.
Nanotechnology Ab initio simulation of magnetic tunnel junctions

In this paper, we present the mathematical and implementation details of an ab initio method for calculating spin-polarized quantum transport properties of atomic scale spintronic devices under external bias potential. The method is based on carrying out density functional theory (DFT) within the Keldysh non-equilibrium Green’s function (NEGF) formalism to calculate the self-consistent spin densities. We apply this method to investigate nonlinear and non-equilibrium spin-polarized transport in a Fe/MgO/Fe trilayer structure as a function of external bias voltage.
Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network

Although traffic sign recognition has been studied for many years, most existing works are focused on the symbol-based traffic signs. This paper proposes a new data-driven system to recognize all categories of traffic signs, which include both symbol-based and text-based signs, in video sequences captured by a camera mounted on a car. The system consists of three stages, traffic sign regions of interest (ROIs) extraction, ROIs refinement and classification, and post-processing. Traffic sign ROIs from each frame are first extracted using maximally stable extremal regions on gray and normalized RGB channels. Then, they are refined and assigned to their detailed classes via the proposed multi-task convolutional neural network, which is trained with a large amount of data, including synthetic traffic signs and images labeled from street views. The post-processing finally combines the results in all frames to make a recognition decision. Experimental results have demonstrated the effectiveness of the proposed system.
Combining Discrete Lexicon Probabilities with NMT for Low-Resource Mongolian-Chinese Translation

Mongolian-Chinese neural machine translation (NMT) models often make mistakes in translating low-frequency words. We propose a method to alleviate this problem by improve NMT models with discrete translation lexicons that efficiently encode these low-frequency words. We describe a method to calcu-late the lexicon probability of generating the next word in the translation candi-date by using the attention vector of the NMT model to select which source word lexical probabilities the model should focus on. The method use this probabil-ity as a bias to combine with the stand-ard NMT probability. Experiments show an improvement of 4.02 BLEU score. We apply this method to large-scale corpus and improve the BLEU score. In addition, we also propose a novel approach to combine discrete probabilistic lexicons obtained from large-scale Mongolian - Chinese bilin-gual parallel corpus into NMT of small-scale corpus and enhance the perfor-mance of the system effectively.