Miracle-dz/UniFAR-FTUs huggingface dataset:
On the L0-Norm Based Sparse Time-Frequency Distribution Reconstruction

Joint time-frequency (TF) domain often provides an additional insight into the observed phenomenon, rather than observing the phenomenon in the time and frequency domain separately. Recently, a compressive sensing (CS) approach has been proposed to solve one of the main problems of the signal TF representation: the removal of artefacts. These artefacts, also called the cross-terms, are a mathematical byproduct of the time-frequency distribution (TFD) calculation, and are usually suppressed with a low-pass filtering, the procedure which also reduces the TF resolution. The resolution loss, in the CS-based approach, is avoided by an optimization procedure, which enforces the sparsity constraints to the resulting sparse TFD. In this paper, we investigate the application of the $\ell_{0}$ -norm based reconstruction algorithms for the sparse TFD calculation. Furthermore, we employ the adaptive CS scheme in order to improve the quality of the resulting sparse TFDs, while reducing the reconstruction algorithm execution time.
Preliminary Results for Secure Traffic Regulation

In this paper, we propose a solution for traffic regulation in Intelligent Transportation Systems (ITS). Due to the heterogeneity and dynamic nature of transportation networks, the big challenge is to be able to take the correct decisions to smooth the traffic flow. This decision is at the same time centralized within a Central Communication Coordinator (CCC) which is in charge of controlling the traffic lights and also distributed among the vehicles that are in the nearby of cross section. The decision should be made upon a permanent exchange of information between the vehicles and the CCC’s. Thus, an important issue is to ensure a robust and secure communication between the different components able to resist to hypothetic hacker’s attacks. The aim of this paper is to setup an intelligent system to regulate traffic flow taking into account different types of attacks. We show in this paper our simulated results using the client MATLAB together with the server SUMO (Simulation of Urban Mobility) so that client can access and modify the simulation environment. We simulated one intersection using an algorithm to regulate the traffic and explain our obtained results.
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?

Character-level features are currently used in different neural network-based natural language processing algorithms. However, little is known about the character-level patterns those models learn. Moreover, models are often compared only quantitatively while a qualitative analysis is missing. In this paper, we investigate which character-level patterns neural networks learn and if those patterns coincide with manually-defined word segmentations and annotations. To that end, we extend the contextual decomposition technique (Murdoch et al. 2018) to convolutional neural networks which allows us to compare convolutional neural networks and bidirectional long short-term memory networks. We evaluate and compare these models for the task of morphological tagging on three morphologically different languages and show that these models implicitly discover understandable linguistic rules.
In-Motion Iterative Fine Alignment Algorithm for On-Board Vehicular Odometer-Aided SINS

This research proposes a novel in-motion fine alignment algorithm for vehicular dead reckoning (DR) with odometer-aided strapdown inertial navigation system (SINS) while the map matching result is used for a group of landmark points to estimate misalignment angles. The proposed algorithm is designed based on principle of similarity, that is, trajectory of DR is similar to the true trajectory that the main difference between these two trajectories is rotation and scale. Further, the results from map matching are introduced as a group of landmark points to estimate the residual of azimuth error angle after coarse alignment and the scale factor error of the odometer. It is theoretically proved that the alignment effectiveness based on the results from map matching is equivalent to that on single zero error landmark point. Finally, digital simulations are conducted to verify the presented algorithm and test the performance.
A New Web Deception System Framework

Web applications have many vulnerabilities that allow attackers to compromise sensitive data and gain unauthorized access to the production web servers. Compromised web-sessions represent a major threat. Current random attacks draw attention to the need for new protection and detection tools. In this paper, we propose a web deception scheme to mitigate web attacks in the production web site. The solution is more like a call for arms, using game theory, honeyweb, and honeytokens with ransomware and intrusion detection. The proposed scheme is explained in details as well as simulation results.
A Technique to Resolve Data Integrity and Confidentiality Issues in a Wireless Sensor Network

Security in WSN infrastructure is the ability to transmit electronic data from source to destination so that only intended recipients can receive the actual data without compromising its integrity and losing its confidentiality throughout the entire transmission process. The use of standard public or private key cryptographic techniques can solve various confidentiality issues such as they can prevent various security attacks. The establishment of key in WSN can also be done with the help of protocols where after the deployment, sensor nodes set up a common secret key either through agreement of key or through key transport. In this paper, a hybrid technique has been proposed which can resolve data integrity and confidentiality issues with higher time and space efficiencies along with time-efficient key management and source as well as data validation system. This research is using simulation of all these blueprint technique to check their performances. The implementation is done on the java platform and all the data transmissions are performed in the wireless sensor environment. The performances of various techniques have been analyzed in terms of produced results. The proposed technique is further compared with different standard and existing techniques to show their efficiency rather than the rest.
Evaluating the Performance of Deep Learning Techniques on Classification Using Tensor Flow Application

In Deep Learning, Artificial intelligence is the overall bigger domain, in which machines given the capability to learn new instances of data and then adapt to the basic domain of Machine Learning. Deep Learning is a subset of it, which goes into further accuracy that uses neural networking technology to go in and enable more complex situational data to come in and make more precise decisions. The objective of this research is to find out the details like Ratio of training data, Noise, Batch Size, Properties of features, learning rate, Type of Activation function, Level of Regularization, Rate of Regularization in constructing Neural Network on four different Classification Datasets after directly manipulating design providing in Tensor flow playground application. The Evaluation parameters consider in our experiments are Test loss and Training lose. The findings in our research is to specify that, how many hidden layers and number of neurons in each hidden layer are needed, for each type of classification problem. These findings help the researchers to fix the Maximum number of neurons and hidden layers needed in solving the four different types of classification problems by achieving test loss less than 0.005.
The headspace Brief Interventions Clinic: Increasing timely access to effective treatments for young people with early signs of mental health problems

Timely access to treatment in the early stages of mental illness is pivotal to recovery and prevention of longer‐term disablement. Yet, this can be challenging at times of growing service demands. The headspace Brief Interventions Clinic (BIC) is an innovative treatment model aiming to promote quick access to evidence‐based interventions for young people presenting with early signs of mental disorders.
Solder Joint Analysis on Coated Aluminum for Silicon Solar Cell Interconnection

Foil metallized solar cells combine the high-efficiency passivated emitter and rear cell technology with a cost-efficient rear side metallization based on a thin aluminum foil. The cell interconnection with the soldering processes usually used in photovoltaics, is hampered by the native oxide film Al2O3. This work evaluates solder joints on Al substrates, modified with either physical vapor deposition (PVD), roll cladding or zincate treatment. Our analysis reveals low contact resistivities of ~0. 02 m Ωcm2, temperature-stable for 1000 hours aged at 85°C in ambient air. Additional tests under damp heat conditions (85°C, 85 % relative humidity) with encapsulated samples yield as well no electrical degradation for the PVD coated samples within 1000 hours aging. The mechanical characterization by a 90° peel test shows initial adhesion of 6 N/mm (PVD), 4 N/mm (roll cladding) and 0.8 N/mm (zincate). Degradation of the roll cladded samples is observed and analyzed further by EDX spectroscopy.
Comparison of Visual Cortex Functional Connectivity Patterns Based on Steady-state Monochromatic Flicker and Oscillating Checkerboard Visual Stimulus

Steady-state visual evoked potential (SSVEP) and steady-state motion visual evoked potential (SSMVEP) are commonly implemented in brain computer interfaces (BCIs). The primary visual system is believed to consist of two pathways: a ventral pathway involved with conscious perception and a dorsal pathway for moving visual information processing. In this paper, as monochromatic flicker and oscillating checkerboard served as distinct stimuli for the elicit of SSVEP and SSMVEP respectively, we investigated functional connectivity patterns of SSVEP and SSMVEP by applying directed transfer function (DTF) to electroencephalography (EEG) signals. The value of flow gain, which is defined as the ratio of outflow to inflow of information flows in one channel, was used to measure the activating level of specific brain region in the information transmission process. We found that the occipital region was strongly activated by flicker stimulation whereas the strongest response of oscillating checkerboard was in the middle temporal visual region. Owing to the difference of activating area, fourteen-channel canonical correlation analysis (CCA) was also applied to compare the recognition accuracy. Results indicated that, due to the wider area activated by oscillating checkerboard, it achieved a higher recognition performance with short response time when compared to monochromatic flicker.
Effective Drive Current in Scaled FinFET and NSFET CMOS Inverters

This work investigates the impact of CMOS scaling on the DC effective drive current, $I_{eff}$ [1], $I_{eff}=1/2\times(I_{high}+I_{low})=1/2\times [d_{s}(V_{gs}=0.5V_{dd},V_{ds}=V_{dd})+(V_{gs}=V_{dd},V_{ds}=V_{dd}/2)$] of aggressively scaled MOSFETs down to 7nm node technology. FinFET and Nanosheet (NSFET) architectures [2], [3] behave similarly, and $l_{eff)}$ continues to predict the intrinsic delay for aggressively scaled MOSFETs for both device architectures, even for supply-voltages $(V_{dd})$ as low as ~0.2V, where these devices operate in the subthreshold regime. The ratio of $l_{eff}/I_{on}, I_{on}=I_{ds}(V_{ds}=V_{gs}=V_{dd})$, as a function of $V_{dd}$, and threshold voltage $(V_{th})$) show that this ratio can be significantly modulated about a typical value of ~0.5, in a manner that can be easily related to classical MOSFET behavior.
Maturation of Breathing‐Related Inhibitory Neurotransmission in the Medulla Oblongata of the Embryonic and Perinatal Zebra Finch (Taeniopygia guttata)

The medullary portion of the embryonic zebra finch hindbrain was isolated and superfused with physiologically relevant artificial cerebral spinal fluid. This in vitro preparation produced uninterrupted rhythmic episodes of neural activity via cranial nerve IX (glossopharyngeal) from embryonic day 4 (E4) through hatching on E14. Cranial nerve IX carries motor activity to the glottis during the inspiratory phase of breathing, and we focused on the role of synaptic inhibition during the embryonic and perinatal maturation of this branchiomotor outflow. We show that spontaneous neural activity (SNA) is first observed on E4 and temporally transforms as the embryo ages. To start, SNA is dependent on the excitatory actions of GABAA and glycine. As the embryo continues to develop, GABAergic and glycinergic neurotransmission take on a modulatory role, albeit an excitatory one, through E10. After that, data show that GABAergic and glycinergic neurotransmission switches to a phenotype consistent with inhibition, coincident with the onset of functional breathing. We also report that the inhibitory action of GABAergic and glycinergic receptor gating is not necessary for the spontaneous generation of branchiomotor motor rhythms in these birds near hatching. This is the first report focusing on the development of central breathing‐related inhibitory neurotransmission in birds during the entire period of embryogenesis.
Parametric Sensitivity Analysis of a Multiple Model Adaptive Predictive Control for Regulation of Mean Arterial Blood Pressure

Postsurgical complication of hypertension may occur in cardiac patients. To decrease the chances of complication it is necessary to lower high blood pressure as soon as possible. Continuous infusion of vasodilator drugs, such as sodium nitroprusside (Nipride), would quickly lower the blood pressure in most patients. However, each patient has a different sensitivity to infusion of Nipride. The parameters and the time delays of the blood pressure control system are initially unknown. Moreover, the parameters of the transfer function associated with a particular patient change over time. The objective of the study is to develop a procedure for blood pressure control in the presence of uncertainty of parameters and considerable time delays. In this paper, a sensitivity analysis was performed, changing the parameter that controls the convergence rate of weight factors (V). The simulation results showed significant changes in settling time (Ts), stressing the importance of this parameter on the control model definition. Considering a V = 0.05 was obtained Ts = 195s and, for same patient, Ts = 510s by increasing the value to V = 0.4, with the Root Mean Square Error (RMSE) varying but always lower than 1%.
Insulin-Like Growth Factor Binding Proteins in Autoimmune Diseases

Insulin-like growth factor binding proteins (IGFBPs) are a family of proteins binding to Insulin-like growth factors (IGFs), generally including IGFBP1, IGFBP2, IGFBP3, IGFBP4, IGFBP5, and IGFBP6. The biological functions of IGFBPs can be classified as IGFs-dependent actions and IGFs-independent effects. In this review, we will discuss the structure and function of various IGFBPs, particularly IGFBPs as potential emerging biomarkers and therapeutic targets in various autoimmune diseases, and the possible mechanisms by which IGFBPs act on the pathogenesis of autoimmune diseases.
Coordinated control strategy for a rotating flywheel pendulum

The rotating flywheel inverted pendulum (RFP) is an underactuated mechanical system requiring a nonlinear model to represent its full state behavior. To drive the pendulum from any initial condition to the unstable upper position, two controllers are usually required. Due to energy limitation, a nonlinear swing-up control is needed to bring the pendulum to the vicinity of the desired final position where a linear controller keeps the pendulum around the upper unstable equilibrium point. In a recent paper, a simple linear controller was proposed by the authors to stabilize the RFP assuring internal stability, for initial conditions near the unstable equilibrium point, using a linearized model for parameter design. In this paper, the full state space is considered for the initial conditions. A nonlinear model of a lab RFP is derived and, based on this model, a nonlinear energy based swing-up controller is combined with a local linear stabilizing controller allowing for transferring the pendulum from its stable rest position to the upward unstable angular position, stabilizing it and compensating a wide range of disturbances. A new lab prototype is described, developed to compare simulation with experimental results, validating the performance of the proposed coordinated control scheme.
The Bone-Protecting Efficiency of Chinese Medicines Compared With Western Medicines in Rheumatoid Arthritis: A Systematic Review and Meta-Analysis of Comparative Studies

Background: Rheumatoid Arthritis (RA) is a systemic autoimmune disease leading to joint destruction. The prevention of bone and cartilage destruction has received increased attention in recent years. Objective: To evaluate the current evidences regarding the bone-protecting efficacy of Chinese medicine or the combination of Chinese medicine and Western medicine for RA. Methods: We comprehensively searched PubMed, Embase, the Cochrane Library (www.thecochranelibrary.com), the China National Knowledge Infrastructure (CNKI), the Database for Chinese Technical Periodicals (VIP), and SinoMed. We then performed a systematic review and cumulative meta-analysis of all randomized controlled trials (RCTs) assessing the two therapy methods. Results: Sixteen studies including 1,171 patients were included in the final analysis. The results showed that Chinese medicine could significantly improve the bone mineral density (BMD) (mean difference [MD] = 0.05 /g·cm−2, 95% CI [0.03, 0.08], P < 0.00001), and decrease the serum matrix metalloproteinase 3 (MMP-3) ([SMD] = −2.84, 95% CI [−4.22, −1.47], P < 0.0001). Conclusions: Chinese medicine may provide an efficiently alternative choice for the treatment of RA in terms of the bone-protecting efficiency. Given the inherent limitations of the included studies, future well-designed RCTs are required to confirm and update the findings of this analysis.
A modified algorithm for accurate GPR wave velocity estimation with common offset setting antenna

Accurate wave velocity estimation is an important part to determine the quality and reliability of GPR survey results, such as positioning of underground utility and characterization of materials. An algorithm was proposed to estimate the velocity accurately by modeling the GPR signal travel path. Both the effects of factors of antenna separation and radius of cylindrical target were taken into account. This model helped to establish a nonlinear relationship among the parameters of horizontal displacement of antenna, GPR signal travel time, wave propagation velocity. Levenberg-Marquardt iteration method was used to solve the non-linear least square fitting. Validation experiments were conducted in lab environment. The reflection data of an Y25 steel bar in air at depth 0.2m, 0.3m, 0.4m and 0.5m were extracted to test the algorithm. Errors of the estimated velocities is around 2% to 3% which is quite small and acceptable in reality. It is believed that this algorithm is qualified for future accurate GPR positioning survey and condition survey.
The Effect of the Training Set Size in a Skin Disorder Classification Application

An image processing technique for skin disorder classification is presented in this paper. A few photographs displaying the same skin disorder are statistically processed in order to define the range of the extracted features. These range limits form a Disease Signature. When a new photograph is analyzed a feature value is compared with the limits defined in each signature in order to select the most likely disease. The proposed method can be implemented on any low cost smart phone since the estimation of the employed features does not require complicated operations. The system is extendible since the Disease Signatures can be defined or customized by a user that is not necessarily qualified in computer science e.g., a dermatologist. Seven skin disorders are supported in the present version and the system is initially trained with a very small set of 3 to 5 photographs per disease. Then, an extended training set of 8 photographs is used, with an up to 2.66 times accuracy improvement.
Cost-efficient Data Acquisition on Online Data Marketplaces for Correlation Analysis

Incentivized by the enormous economic profits, the data marketplace platform has been proliferated recently. In this paper, we consider the data marketplace setting where a data shopper would like to buy data instances from the data marketplace for correlation analysis of certain attributes. We assume that the data in the marketplace is dirty and not free. The goal is to find the data instances from a large number of datasets in the marketplace whose join result not only is of high-quality and rich join informativeness, but also delivers the best correlation between the requested attributes. To achieve this goal, we design DANCE, a middleware that provides the desired data acquisition service. DANCE consists of two phases: (1) In the off-line phase, it constructs a two-layer join graph from samples. The join graph includes the information of the datasets in the marketplace at both schema and instance levels; (2) In the online phase, it searches for the data instances that satisfy the constraints of data quality, budget, and join informativeness, while maximizing the correlation of source and target attribute sets. We prove that the complexity of the search problem is NP-hard, and design a heuristic algorithm based on Markov chain Monte Carlo (MCMC). Experiment results on two benchmark and one real datasets demonstrate the efficiency and effectiveness of our heuristic data acquisition algorithm.
Methods of Calculation the Magnetic Forces Acting on Particles in Magnetic Fluids

In a nonuniform magnetic field a force acts on ferromagnetic particles, which results from the mutual reaction between the induced magnetic moment in a particle and applied external magnetic field. This phenomenon is named magneto-phoresis, and is used in a various commercial and industrial processes for, among others, for separation of solid magnetic particles suspended in fluids. There are two methods for calculating forces acting on a magnetic particle placed in a magnetic liquid: the first is an equivalent dipole method and the second method is based on Maxwell's stress tensor. Both of these forces can be used in hyperthermia based on magnetic fluids supplied to the patient's body via blood. These forces should cause the placement of ferromagnetic microscopic particles in an adequate place in the patient's body, and then they should be used to produce a heat that interacts with the tumor. These two methods are compared in terms of accuracy and parameters that affect their accuracy. This article will discuss two mentioned methods for calculating forces acting on a particle in a magnetic fluid.
Arg399Gln substitution in XRCC1 as a prognostic and predictive biomarker for prostate cancer: Evidence from 8662 subjects and a structural analysis

The Arg399Gln polymorphism in the X‐ray repair cross‐complementing group 1 gene (XRCC1) may alter the risk of prostate cancer (PCa). The present study aimed to investigate the association of the XRCC1‐Arg399Gln polymorphism with PCa risk in an Iranian population, as followed by a meta‐analysis and an in silico analysis.