Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning within the Web Application Security Scope – Literature Review

Over the last few years, several breakthroughs in deep learning have contributed to the development of new models. One of many areas they are applied to is the web application security scope. Web applications are still one of the biggest information and business security threats. Requests sent to the Web application are divided into normal and malicious. Malicious requests contain a payload that exploits a discovered vulnerability. Detection of Web attacks can be reduced to natural language processing classification problem. Lately, pre-trained models on Transformer neural networks showed promising results in the detection of Web attacks. In development of models, the preprocessing step of data preparation is crucial. After preparation of good datasets and application of powerful models it is very important to evaluate and compare performance of algorithms. The goal of this paper is to conduct an overview of the deep learning methods used for Web attack detection. The research is conducted by querying scientific databases, analyzing relevant articles within the security scope, and summarizing the proposed state-of-the-art approaches. Findings of reviewed papers were summarized based on implementation details and used performance metrics. Also, open problems will be emphasized, as well as challenges and possibly new opportunities for the future research.
Physics-Based Coherent Modeling of Long-Range Millimeter-Wave Propagation and Scattering in Rain

Precipitation and in particular rain present a random medium with constituent particles having dimensions comparable to the wavelength at millimeter-wave frequencies. Due to considerable scattering of waves from such particles, significant signal attenuation and phase front aberration can take place which are important factors that must be considered in modern communication system or radar design. This paper presents a fast and accurate numerical method for phase coherent modeling of wave propagation and scattering in random media, like rain. In this approach, the random medium is divided into sufficiently large finite slabs and each slab is modeled as a network with multiple input/output ports where ports represent rays with different polarizations propagating in different directions entering and leaving the slabs. The cascaded networks of slabs can be used to find the response of the random medium to any arbitrary source. The proposed method considers multiple scattering among all scatterers and thus the entire physics of wave-particle interactions is accounted for. In addition, the method can model both the mean and the fluctuating parts of waves in the random medium. To demonstrate the applicability of the proposed method, it is used for the calculation of the specific attenuation in rain media as a function of rain rates and frequency within the millimeter-wave band. The results show a good match with the available rain attenuation data in the literature.
Analyzing Sentiment of Reddit Posts for the Russia-Ukraine War

Currently, we are witnessing a major global crisis caused by the ongoing war in Ukraine. In this paper, we deepen the insights into people’s opinions about the Russia-Ukraine war by analyzing the posts on the Reddit subreddit r/UkrainianConflict. The extracted posts are preprocessed and the sentiment polarity (neutral, positive or negative) is determined using the Valence Aware Dictionary and sEntiment Reasoner (VADER). We analyze the frequency of the most representative keywords in two longer periods in May and October 2022. The keyword Zelensky appeared more in positive contexts than in negative ones. Keywords Ukraine, Russia, and especially Putin appeared more frequently in negative contexts. Finally, the trend of Google searches on the war is compared with the number of daily subreddit posts.
Incentive Mechanism for Spatial Crowdsourcing With Unknown Social-Aware Workers: A Three-Stage Stackelberg Game Approach

In this paper, we investigate the incentive problem in Spatial Crowdsourcing (SC), where mobile social-aware workers have unknown qualities and can share their answers to tasks via social networks. The objectives are to recruit high-quality workers and maximize all parties’ utilities simultaneously. However, most existing works assume that the qualities of workers are known in advance or cannot take all parties’ utilities into account together, especially having not considered the impact of social networks. Thus, we propose an incentive mechanism based on the multi-armed bandit and three-stage Stackelberg game, called TACT. We first design a greedy arm-pulling scheme to recruit workers, which not only can solve the exploration-exploitation dilemma but also takes workers’ social relations into account. Based on the recruitment results, we further design the utility functions incorporating with social benefits for workers, and model the payment computation problem as a three-stage Stackelberg game among all participants. Next, we derive the optimal strategy group so that each party can maximize its own utility to form a multi-win situation. Moreover, we theoretically prove the unique existence of Stackelberg equilibrium and the worst regret bound. Finally, we conduct extensive simulations on a real trace to corroborate the performance of TACT.
Digital One-Shot Charge-balancing Method for Implantable Current-Mode Electrical Stimulation

A low-power digital charge balancing system, which ensures the safe operation of constant-current biphasic stimulation is presented. The concept of the proposed charge-balancing technique is to utilize a hybrid method consisting of anodic pulse modulation and short-term offset current injection. Furthermore, a dual thresholding strategy is employed to guarantee precise and low-power imbalance compensation. The charge-balancing system is capable of canceling large persistent imbalances by adjusting the input code of an 8-bit current-steering digital-to-analog converter (DAC) as well as injecting an offset current in a power-efficient way. The performance of the designed charge balancer is evaluated by modeling a 1 mA biphasic constant-current stimulator with 8-bit DAC resolution. The charge-balancing system is implemented on a Cyclone IV FPGA, and measurement results evidence the safe, accurate and low-power charge-balancing performance in which the balance offset current injection is performed in less than 5% of the stimulation time while the dynamic power consumption is at 0.76 mW.
Reconstruction of short genomic sequences with graph convolutional networks

Genome reconstruction, without prior knowledge about the sequence we are reconstructing, is performed with tools called de novo genome assemblers. These tools rely on numerous heuristics and usually provide a fragmented reconstruction, even for sequences shorter than the entire genomes or chromosomes. One of the most common approaches to de novo assembly, called Overlap-Layout-Consensus, constructs a graph from short overlapping fragments, which heuristics then simplify and find a path through. In this work, we explore how graph neural networks (GNNs) can assist with this task, and show that the GNN-based Layout phase can reconstruct longer sequences than naive search algorithms or heuristics deployed in de novo assemblers, with no significant difference in compute time on sequences up to 10 Mbp in length.
Ultrafast and Low-Power Graphene Wheatstone Bridge Respiratory Sensor

This letter presents a novel respiratory sensor that is both low-power and ultrafast and was manufactured using a standard graphene foundry service. The sensor is based on four microribbon patterned graphene resistors arranged in a Wheatstone bridge configuration, with two encapsulated resistors acting as a reference and the other two nonencapsulated resistors serving as sense resistors exposed to human breath samples. The sensor has demonstrated the ability to detect various patterns of human breath, with an average fall time of approximately 500 ms ± 23 ms and a rise time of around 560 ms ± 32 ms. Furthermore, the sensor also has been tested on six volunteers and has been used to measure breath variation during chewing a mouth freshener containing menthol, indicating its potential for respiratory sensing and breath analysis. The results indicate that the sensor can be utilized as a miniature wearable device that is lightweight and has low power consumption, with a power usage of only 0.3 mW.
Fault Classification in Distribution Systems Using Deep Learning With Data Preprocessing Methods Based on Fast Dynamic Time Warping and Short-Time Fourier Transform

Traditional fault classification methods typically rely on manual feature extraction and the application of machine-learning algorithms. However, these approaches encounter difficulties when extracting features and handling large-scale datasets. This study proposes a data preprocessing method for accurately detecting various types of short-circuit faults in power systems, which can lead to more effective power repair and maintenance processes. The proposed method involves converting the measured voltage and current signals into time and frequency domains using the short-time Fourier transform (STFT) to produce a time-frequency energy map. A convolutional neural network (CNN) is subsequently trained and tested to classify the short-circuit faults. However, overfitting may occur during the CNN training process owing to the large volume of data with similar features. To address this issue, this study proposes a data reduction method based on the fast dynamic time warping (Fast-DTW) algorithm, which compares waveform features and eliminates highly similar data regarded as redundant data from the dataset. The simulation results show that the proposed method can improve the model training performance and its adaptability to different power system topologies, as tested in two simulation environments: power systems computer-aided design (PSCAD)/electromagnetic transients, including DC (EMTDC), and the real-time digital simulator (RTDS). The STFT transformation is implemented in MATLAB. The simulation results demonstrate that the proposed method reduces redundant data by 40.2%, while decreasing the model training time. Consequently, the overall accuracy, precision, recall and F1 score of the fault classification reaches 99.37%, 99.36%, 99.35% and 99.35%, confirming the effectiveness of the proposed method for fault classification.
A Fast-Transient mitigation technique for a dual-loop, linear search, self-timed, Asynchronous Digital LDO

In this paper is presented a fast-transient mitigation technique which applies to a self-timed, dual-loop Asynchronous Digital LDO. The number of conducting transistors is controlled by an Asynchronous Finite State Machine (AFSM) employing a linear-search algorithm that is able to operate without a clock oscillator. It relies on a simple Request-Acknowledge (REQ-ACK) protocol to manage the power stage. The proposed mitigation technique has two components. One relies on using a secondary loop that has bigger transistors. The LDO reaches steady state faster than the main loop due to the higher adjustment steps. The other component controls the operating frequency by altering the REQ-ACK protocol in order to sample and update the output faster. The circuit was simulated and fabricated using Infineon’s proprietary 130nm BCD technology and was assembled in SSOP-14 plastic package. Also, the proposed circuit was measured and the results show more than 50 percent improvement and a settle time reduction with a factor of 4 on sudden load changes while a light increase in output voltage ripple for steady-state operation must be considered.
A Transient Response Improved Output Capacitorless LDO

In this paper, a transient response improved output capacitorless low dropout regulator is designed in standard 0.13 $\mu$m CMOS technology. A method based on adaptively sense network (ASN) to increase the slew rate and provide extra discharge loop is adopted, which suppresses the undershoot and overshoot of output voltage. Simulation results reveal that the output voltage undershoot and overshoot is 97.03 mV and 140.87 mV, when load current step from 10 $\mu$A to 30 mA with the edge time is 100 ns, respectively. And the output voltage recovery time is less than 0.35 $\mu$s, the fast settle time is also achieved. Besides, the linear regulation of load and supply voltage is 0.043 mV/mA and 1.467 mV/V.
Unlocking the Potential of Wind Energy With Machine Learning-Based Avian Detection: A Call to Action

This systematic literature review explores the potential of machine learning-based approaches to detect and prevent bird collisions with wind turbines. It provides a comprehensive review of the current approaches and identifies critical gaps in the literature, which may serve as the groundwork for future research and development in this area. As a result, this work highlights the importance of inter- and transdisciplinary cooperations.
Spectrophotometer Design Using Single-Grating, Single-Sensor, Double-Beam Spectroscope

The double-beam structure in existing spectrophotometers is traditionally realized using two spectroscopes. Owing to inconsistencies in gratings, optical path mechanical structures, and sensors, the two spectroscopes tend to mismatch as the temperature varies, which leads to poor measurement reproducibility. This article proposes a single-sensor, single-grating, double-beam spectrophotometer design that simultaneously samples both optical paths to reduce the effect of temperature on the measurement reproducibility. Wavelength and energy calibration were implemented to further improve the instrument’s reproducibility. Experiments were performed to evaluate the effectiveness of this design. The results suggest that the spectrophotometer, denoted DS-36D, can significantly improve measurement reproducibility when the temperature varies. At fixed temperature and humidity, the standard deviation of measurement repeatability is 0.001, and the maximum smallest color deviation is 0.005. When the temperature varies from 15 °C to 30 °C, the maximum Allan deviation of color difference $\delta _{\Delta E}^{2}$ = $1.509\times 10^{-5}$ , with a maximum smallest color deviation of 0.036. The novel spectrophotometer design shown in this study outperforms other existing technologies.
Robust Speed Controller Using Dual Adaptive Sliding Mode Control (DA-SMC) Method for PMSM Drives

A robust mechanical parameter estimation together with adaptive speed control algorithms for permanent magnet synchronous motor (PMSM) drive systems based on the dual adaptive sliding-mode method is proposed in this paper. First, a robust adaptive sliding mode mechanical observer (RASM) and mechanical parameter identification (MPI) are proposed to eliminate system parameter errors in the first rotation. Based on RASM-MPI, the correct mechanical parameters are estimated from the system disturbance value. Then, an adaptive sliding-mode speed control (ASMSC) is applied to reduce the chattering inherent in the control signal and improve the convergence time to reach the sliding-mode surface. From the second rotation, the RASM identifies unknown external load torque and provides feed-forward compensation for the ASMSC to improve the speed control of the PMSM. All the proposed methods are applied to an industrial motor drive. The experimental results verified that the proposed scheme can estimate system parameters accurately within a short time. In addition, the speed controller can run stably, and with less error.
Self-Adaptation of Line Driver Pre-Emphasis Parameters via Embedded Line Loss Sensing

The paper proposes a method for automated optimization of line driver pre-emphasis parameters by utilizing the measured transfer function (TF) of the channel. Pulseecho measurements are performed in which pulses with a given frequency and duration are transmitted into the channel and the amplitude of the resulting echo detected to evaluate frequency-dependent line loss. The echo amplitude is indirectly detected using transmit pulses with different amplitudes and a simple comparator-based circuit that detects when the echo amplitude exceeds a user-defined threshold. The result is used to estimate the cable loss, and the process repeated for different input frequencies to estimate the channel TF. The proposed estimation procedure is validated using a transistor-level implementation in 65 nm CMOS for data rates up to 1 Gb/s, channel losses up to 15 dB, and a receiver reflection coefficient of −20dB.
Using scylindric algebra to support local variables in rely/guarantee concurrency*

Local variable blocks are a simple but effective program structuring technique. In the context of concurrency, local variables have the added advantage of avoiding interference from threads running concurrently with the thread declaring the local variable. To provide mechanised support for verifying concurrent programs in Isabelle/HOL, we have found making use of algebraic properties of programs simplifies the development of refinement laws. The algebraic properties of local variable blocks are similar to those of existential quantifiers in predicate calculus, the algebra of which is Tarski’s cylindric algebra. Hence to support local variables, we make use of a variant of cylindric algebra that allows quantification of a variable over a command, rather than a predicate. The approach allows a local variable to become a shared variable of parallel threads that are local to the block that introduced the variable.
A Class of Bayesian Lower Bounds for Parameter Estimation Via Arbitrary Test-Point Transformation

In this article, a new class of global mean-squared-error (MSE) lower bound for Bayesian parameter estimation is derived. First, it is shown that under the non-Bayesian framework, the Hammersley-Chapman-Robbins (HCR) for the problem of single-source parameter estimation, is related to the corresponding ambiguity function. This result is achieved by judicious choice of signal test-point. This result implies that optimal shift test-points may be parameter-dependent, but this approach cannot be imitated in test-point based Bayesian bounds, where the test-points should be independent of the random parameters. Based on this observation, a new class Bayesian MSE lower bound is derived. In the proposed class, the shift test-points in the Cauchy-Schwarz based bounds are substituted with arbitrary transformations. The proposed class generalizes the Weiss-Weinstein bound (WWB) to arbitrary transformations. For the problem of single source parameter estimation, a scaling transformation for the signal test-point is proposed based on the structure of the optimal test-point in the HCR bound in the non-Bayesian framework. The test-point scale parameters are analytically optimized and a closed-form expression for the bound, which depends on the ambiguity function, is derived. For the problem of direction-of-arrival (DOA) estimation using a linear array, a nonlinear transformation for the parameter of interest is proposed. In the problems of frequency estimation and DOA estimation, the proposed bound accurately predicts the threshold phenomenon of the maximum a-posteriori probability estimator, and is much tighter than the WWB in both the threshold prediction and the asymptotic performance. In addition, an asymptotic version of the bound was derived and it is analytically shown to be tighter than the Bayesian Cramér-Rao bound.
Strongly driven quantum Josephson circuits

In this thesis, we investigate the behavior of Josephson circuits under the action of strong microwave drives. Josephson circuits in the quantum regime are a building block to emulate a variety of Hamiltonians, useful to process quantum information. We are here considering a transmon device, made of a Josephson junction and a capacitor in parallel. Through numerical simulations and comparison with experimental results, we show that these drives lead to an instability which results in the escape of the circuit state into states which are no longer confined by the Josephson cosine potential. When the transmon occupies such states, the circuit behaves as if the junction had been removed and all non-linearities are lost, which translates into limitations on the emulated Hamiltonian strengths. In a second part, we propose and study an alternative circuit consisting of a transmon device with an extra inductive shunt, providing a harmonic confinement. This circuit is found to be stable for all pump powers. The dynamics of this circuit is also well captured by a time-averaged model, providing a useful tool for analytical investigation and fast numerical simulations. We developed a novel numerical approach that avoids the built-in limitations of perturbative analysis to investigate the dynamical behavior of both of these circuits. This approach, based on the Floquet-Markov theory, resulted in a modular simulation framework which can be used to study other Josephson-based circuits. Last, we study the properties of an asymmetric version of the Josephson Ring Modulator, a circuit currently used for amplification and conversion, as a more robust source of non-linearity to engineer two-photon and four-photon interaction Hamiltonians required for the catstate encoding of quantum information.
High-Efficiency Resource Allocation Scheme Introducing the Concept of Resource Sharing Paths in Industrial IoT

Industrial IoT is a key field of the 4th Industrial Revolution and wireless network technology is essential to support strict industrial requirements. Industrial wireless networks have reliable and real-time data transmission as the main requirement, and to achieve this, the network exploits two schemes: routing graph generation which calculates the data forwarding paths of each node, and scheduling, which allocates the communication time and frequency resources of each node. However, since conventional scheduling approaches allocate resources for all the calculated graphs, specific paths including routes close to the sink are used more redundantly. This phenomenon leads to unnecessary resource wastage and poor scalability in the industrial environment where the number of equipment used increases. To mitigate these problems, we propose a shared path-based scheduling scheme for efficient resource management and scalability improvement by setting the frequently overlapping sections of data flows as resource-sharing paths. The proposed scheme designates the resource sharing path and allocates resources for the shared path in consideration of network statuses, such as congestion and reliability. Simulation results show the proposed scheme is effective in terms of scalability and resource efficiency compared to traditional scheduling.
Privacy in Popular Children’s Mobile Applications: A Network Traffic Analysis

Children increasingly download and use mobile applications from marketplaces such as Apple’s App Store or the Google Play Store. One would expect that applications intended for children are free of third-party analytics, or at least make sure parents give their consent for collecting personal data from children. In this study, we performed an in-depth technical analysis of a representative snapshot of 15 applications from Google Play Store aimed at children (age group classifications 0–5, 6–8 and 9–12). We recorded the network traffic of these applications and compared it to the privacy policies of the applications. Across the applications, a significant number (13/15) were delivering more information about the users to various third parties than what was admitted in the respective privacy policies. We elaborate on details regarding the observed network traffic, and discuss implications of these findings on strategies for preserving user privacy, particularly for sensitive audiences such as children.