Miracle-dz/UniFAR-FTUs huggingface dataset:
Android Source Code Vulnerability Detection: A Systematic Literature Review

The use of mobile devices is rising daily in this technological era. A continuous and increasing number of mobile applications are constantly offered on mobile marketplaces to fulfil the needs of smartphone users. Many Android applications do not address the security aspects appropriately. This is often due to a lack of automated mechanisms to identify, test, and fix source code vulnerabilities at the early stages of design and development. Therefore, the need to fix such issues at the initial stages rather than providing updates and patches to the published applications is widely recognized. Researchers have proposed several methods to improve the security of applications by detecting source code vulnerabilities and malicious codes. This Systematic Literature Review (SLR) focuses on Android application analysis and source code vulnerability detection methods and tools by critically evaluating 118 carefully selected technical studies published between 2016 and 2022. It highlights the advantages, disadvantages, applicability of the proposed techniques, and potential improvements of those studies. Both Machine Learning (ML)-based methods and conventional methods related to vulnerability detection are discussed while focusing more on ML-based methods, since many recent studies conducted experiments with ML. Therefore, this article aims to enable researchers to acquire in-depth knowledge in secure mobile application development while minimizing the vulnerabilities by applying ML methods. Furthermore, researchers can use the discussions and findings of this SLR to identify potential future research and development directions.
Web Cryptography API: Prevalence and Possible Developer Mistakes

In this paper, we analyze mistakes that web developers can make when using the Web Cryptography API. We evaluate the impact of the uncovered mistakes and discuss how they can be prevented. Furthermore, we derive best practices from these mistakes to provide guidance to developers. To assess the relevance of the Web Cryptography API, we empirically evaluate how prevalently it is used by popular web applications on the Internet and in GitHub repositories, finding that only a small proportion of web applications use it. The most widely used operation by far is the generation of cryptographically secure random values, which was not possible in browser-based JavaScript prior to the Web Cryptography API.
Preliminary Analysis of Privacy Implications Observed in Social-Media Posts Across Shopping Platforms

The widespread activity of hash-tagging, especially among the Gen-Z population, and the impact of social commerce on average consumers raise questions about privacy implications and dangers of anonymous cyberstalking. In this work, we examined the privacy implications observed in hash-tag-based social-media posts (of average users and influencers) by following the trails of online shopping platform(s) product listings, consumer reviews, social-commerce policies, and influencer posts. We have conducted a preliminary analysis considering cyberstalking as one of the avenues that an anonymous stalker may use to impact the social-media user negatively. Further, we have conceptualized the trails behind hash-tagging activities in terms of a privacy threat model, the need for practical data analysis tools, and the lack of mitigation strategies at various layers. Mainly, this paper throws light on the need for more robust user privacy policies and the impact on socio-economic-privacy aspects. This paper also demonstrates the need for expanding the scope of digital investigations and DFIR tools beyond just the devices of individuals (including victims, suspects, perpetrators, and cyber-criminals) and to thoroughly prepare the forensic professionals to consider the online presence of individuals in its entirety including anonymous cyberstalking avenues and to raise awareness about the abuse of social networks.
A Novel Channel Estimation Scheme for MIMO-OFDM Systems based on CDD

In recent wireless communication systems, OFDM-MIMO systems have been used widely because of its high robustness and high transmission capability. By using forward error correction on a large number of antennas in CDD, it is possible to increase frequency selectivity and achieve OFDM frequency diversity (FEC). Frequency selective fading requires a precise estimate of the CDD-OFDM channel. An easy-to-use approach to estimating time-varying channels is presented in this paper. To estimate the channel coefficients, distributed pilot structures are first created to test the frequency correlation of CDD-OFDM channels. The proposed channel estimator has a substantially better performance measure over time-varying channels compared to previous techniques.
Simulation Model of a 2-kW IPT Charger with Phase-Shift Control: Validation through the Tuning of the Coupling Factor

When applied to road vehicle electrification, inductive power transfer (IPT) technology has the potential to boost the transition from combustion engines to electric motors powered by a battery pack. This work focuses on the validation of a PSpice circuit model developed as a replica of a 2-kW IPT prototype with series-series compensation operating at 18.65 kHz. The laboratory prototype has the three stages commonly found in an IPT system: an inverter, controlled by the phase-shift technique, a coil coupling and a load. Simulations were run with the circuit model for three different distances between the two coils of the inductive coupling, all of which are of interest for practical chargers: 125, 150 and 175 mm. The validation approach was based on tuning the magnetic coupling factor for each distance and a set of ten load resistances, until the best match between the simulated and the experimental peak currents supplied by the inverter was found in each case. The coupling factors obtained from the simulation work are in good agreement with their experimental counterparts for the three distances, provided the duty cycle of the inverter output voltage is not too small. The circuit model developed is, therefore, able to reproduce the behavior of the laboratory prototype with sufficient accuracy over a wide range of distances between coils and loading conditions.
Automatic Music Generation: Comparing LSTM and GRU

Music has been a universal and essential component of ancient human civilizations throughout the world. Its origin is unclear and transcends our oldest records, but its importance remains preserved to date. The abstract and enchanting nature of music has attracted many brilliant minds throughout history and continues to do so even today. The idea of creating music without human intervention is not new, and time has witnessed many gifted individuals partaking in the fulfilment of this goal. The contemporary seeker has something the others lacked, technical advancements and knowledge. With the advent of AI, it was only natural that man used this tool to try and achieve this long-sought desire. With the growing advancements in deep learning and its success in tackling the most complex problems, it is proving itself as the closest solution to the realization of this goal. This paper explores the rich history and the implications of automatic music generation in the present-day world. It also conducts a comparison between the music generated by the implementation of two different deep learning techniques, the LSTM and GRU architectures.
F-theory and M-theory perspectives on N=2 supersymmetric gauge theories in four dimensions

Deformations of the original F-theory background are proposed. These lead to multiple new dualities and physical phenomena. We concentrate on one model where we let seven-branes wrap a multi-centered Taub-NUT space instead of R4. This configuration provides a successful F-theory embedding of a class of recently proposed four-dimensional N = 2 superconformal (SCFT) a la Gaiotto. Aspects of Argyres- Seiberg duality, of the new Gaiotto duality, as well as of the branes network of Benini- Benvenuti and Tachikawa are captured by our construction. The supergravity theory for the conformal case is also briefly discussed. Extending our construction to the non-conformal case, we find interesting cascading behavior in four-dimensional gauge theories with N = 2 supersymmetry. Since the analysis of this unexpected phenomenon is quite difficult in the language of type IIB/F-theory, we turn to the type IIA/M-theory description where the origin of the N = 2 cascade is clarified. Using the T-dual type IIA brane language, we first start by studying the N = 1 supersymmetric cascading gauge theory found in type IIB string theory on p regular and M fractional D3-branes at the tip of the conifold. We reproduce the supersymmetric vacuum struc- ture of this theory. We also show that the IIA analog of the non-supersymmetric state found by Kachru, Pearson and Verlinde in the IIB description is metastable in string theory, but the barrier for tunneling to the supersymmetric vacuum goes to infinity in the field theory limit. We then use the techniques we have developed to analyze the N = 2 supersymmetric gauge theory corresponding to regular and fractional D3-branes on a near-singular K3, and clarify the origin of the cascade in this theory. %%%%Differentes deformations de la geometrie originale de la theorie F sont proposees. Ces dernieres generent une multitude de nouvelles dualites ainsi que de nouveaux phenomenes physiques. Nous nous concentrons sur un seul modele ou les membranes en sept dimensions spatiales s'enveloppent autour d'un espace Taub-NUT avec multi-centres au lieux de l'espace R4 original. Cette configuration genere avec succes la realisation, en theorie F, d'une famille de theories de jauges superconformes en quatres dimensions avec N = 2 supersymetries nouvellement proposees par Gaiotto. Deplus, plusieurs aspects de la dualite d'Argyres-Seiberg, de la nouvelle dualite de Gaiotto ainsi que du reseaux de membranes de Benini-Benvenuti et Tachikawa sont realises par notre construction. La theorie de supergravite pour le cas conforme est brievement discutee. La generalisation de notre construction au cas non-conforme mene a l'observation surprenante de cascade chez les theories de jauges avec N = 2 supersymetries en quatres dimensions. Puisque l'analyse de ce phenomene est difficile dans le language de type IIB/ theorie F, nous nous tournons vers le type IIA/theorie M ou l'origine de ce phenomene est elucidee. En utilisant le langage des membranes en type IIA sous la dualite-T, nous debutons par l'etude de cascade chez les…
Improved Graph Convolutional Neural Networks based on Granger Causality Analysis for EEG Emotion Recognition

How to learn the adjacency matrix of graph convolution neural network (GCNN) is one of the key issues in EEG emotion recognition. Most current GCNN models adopt the spatial position between EEG channels to define the adjacency matrix, which have ignored the effective connection characteristics among the EEG signals. Granger causality (GC) analysis can reveal the causal connectivity between EEG channels. In this paper, an improved GCNN method based on the GC analysis is proposed for EEG emotion recognition. Firstly, the causal association matrix is calculated by the GC analysis. Then, a reasonable threshold is used to adaptively converts the causal association matrix into the adjacency matrix, which makes the adjacency matrix more consistent with the cognitive law of the human brain. Finally, the DEAP emotion dataset is used to test the performance of our method. The experimental results demonstrate that the proposed GC-GCNN method achieves better recognition performance than the state-of-the-art methods, in which the average accuracies of 90.11%, 89.48% and 86.35% are respectively obtained for arousal, valence, and arousal-valence classifications.
RF Energy Harvesting System: Design of Antenna, Rectenna, and Improving Rectenna Conversion Efficiency

The paper presents the discussion and design of a rectenna at 2.45GHz wireless frequency. Rectenna consists of an integration of an antenna and a rectifier circuit. A patch rectenna with FR-4 substrate is designed to map a dip at 2.45GHz to capture Wi-Fi signals and consume power -11.92db. The rectifier circuit includes a bridge rectifier, and instead of a simple diode, Schottky diodes HSMS8101 are used, having a threshold voltage between 0.15-0.45 Volts. The antenna is designed in CST. However, the rectifier circuit and matching circuitry are designed in ADS Keysight. An impedance matching circuitry is used to integrate both designs, which is designed using RL circuit to avoid an imaginary impedance of 3j, and it integrates the designs at 50 Ohm. The proposed rectenna’s RF to DC conversion efficiency is 67.9%, which is much improved for low input power density over a bandwidth of 150 MHz and attained an output voltage of 306mv and output power of 30mW. This research has produced some critical designs and results for wireless energy harvesting, and it is a vital step to the possible widespread application of rectennas soon.
Stage Wise Prediction of Covid-19 Pneumonia from CT images using VGG-16 and SVM

In COVID-19 time, finding medication was the tedious process. Proposed work explains about the segregation of covid-19 CT scan images into categories like mild, moderate and severe on the basis of pneumonia. The dataset uses 227 CT scan images which have been collected manually from hospitals. At first, the CT scan input images are preprocessed using K-means clustering algorithm. Then Watershed algorithm is used for the segmentation of the pre-processed images to get the affected region. After getting the affected region, VGG-16 model is used for feature extraction process, for model training 53 CT scan images are used as the testing dataset from 185 CT images. Using extracted feature, SVM model will classify the Covid19 pneumonia as mild, moderate, or severe. Finally the classifier has given an accuracy of 96.15% for the prediction of Covid-19 pneumonia stages.
Particle size distribution: A key factor in estimating powder dustiness

ABSTRACT A wide variety of raw materials, involving more than 20 samples of quartzes, feldspars, nephelines, carbonates, dolomites, sands, zircons, and alumina, were selected and characterised. Dustiness, i.e., a materials' tendency to generate dust on handling, was determined using the continuous drop method. These raw materials were selected to encompass a wide range of particle sizes (1.6–294 µm) and true densities (2650–4680 kg/m3). The dustiness of the raw materials, i.e., their tendency to generate dust on handling, was determined using the continuous drop method. The influence of some key material parameters (particle size distribution, flowability, and specific surface area) on dustiness was assessed. In this regard, dustiness was found to be significantly affected by particle size distribution. Data analysis enabled development of a model for predicting the dustiness of the studied materials, assuming that dustiness depended on the particle fraction susceptible to emission and on the bulk material's susceptibility to release these particles. On the one hand, the developed model allows the dustiness mechanisms to be better understood. In this regard, it may be noted that relative emission increased with mean particle size. However, this did not necessarily imply that dustiness did, because dustiness also depended on the fraction of particles susceptible to be emitted. On the other hand, the developed model enables dustiness to be estimated using just the particle size distribution data. The quality of the fits was quite good and the fact that only particle size distribution data are needed facilitates industrial application, since these data are usually known by raw materials managers, thus making additional tests unnecessary. This model may therefore be deemed a key tool in drawing up efficient preventive and/or corrective measures to reduce dust emissions during bulk powder processing, both inside and outside industrial facilities. It is recommended, however, to use the developed model only if particle size, true density, moisture content, and shape lie within the studied ranges.
A Converse Result on Convergence Time for Opportunistic Wireless Scheduling

This paper proves an impossibility result for stochastic network utility maximization for multi-user wireless systems, including multiple access and broadcast systems. Every time slot an access point observes the current channel states for each user and opportunistically selects a vector of transmission rates. Channel state vectors are assumed to be independent and identically distributed with an unknown probability distribution. The goal is to learn to make decisions over time that maximize a concave utility function of the running time average transmission rate of each user. Recently it was shown that a stochastic Frank-Wolfe algorithm converges to utility-optimality with an error of <inline-formula> <tex-math notation="LaTeX">$O(\log (T)/T)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> is the time the algorithm has been running. An existing <inline-formula> <tex-math notation="LaTeX">$\Omega (1/T)$ </tex-math></inline-formula> converse is known. The current paper improves the converse to <inline-formula> <tex-math notation="LaTeX">$\Omega (\log (T)/T)$ </tex-math></inline-formula>, which matches the known achievability result. It does this by constructing a particular (simple) system for which no algorithm can achieve a better performance. The proof uses a novel reduction of the opportunistic scheduling problem to a problem of estimating a Bernoulli probability <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> from independent and identically distributed samples. Along the way we refine a regret bound for Bernoulli estimation to show that, for any sequence of estimators, the set of values <inline-formula> <tex-math notation="LaTeX">$p \in [{0,1}]$ </tex-math></inline-formula> under which the estimators perform poorly has measure at least 1/6.
CurvChip - chip platform for investigating cell responses to curved surface features

Abstract Surface topographies are often discussed as an important parameter influencing basic cell behavior. Whereas most in-vitro studies deal with microstructures with sharp edges, smooth, curved microscale topographies might be more relevant concerning in-vivo situations. Addressing the lack of highly defined surfaces with varying curvature, we present a topography chip system with 3D curved features of varying spacing, curvature radii as well as varying overall dimensions of curved surfaces. The CurvChip is produced by low-cost photolithography with thermal reflow, subsequent (repetitive) PDMS molding and hot embossing. The platform facilitates the systematic invitro investigation of the impact of substrate curvature on cell types like epithelial, endothelial, smooth muscle cells, or stem cells. Such investigations will not only help to further understand the mechanism of curvature sensation but may also contribute to optimize cellmaterial interactions in the field of regenerative medicine.
A Rectangular Double-Helix Receiver Coil for Wireless Charging of Drones

Inductive Power Transfer (IPT) for drones is an efficient form of charging to increase functionality of the drone. The design of the magnetic coupler, specifically the receiving coil is important. This coil is placed on the drone and is required to be lightweight, have a strong coupling, high efficiency, and a high misalignment tolerance. A ferrite-less rectangular double-helix receiver coil is proposed with a planar rectangular transmitter to meet these requirements. The proposed coupler achieves a 0.27 coupling coefficient and coil-to-coil efficiency of 97.88% while charging a drone with a battery capacity of 10000mAh at a nominal voltage of 22.2V. The coupler has excellent misalignment tolerance both in the direction of the x and y-axis.
ML-based Anomaly Detection for Intra-Vehicular CAN-bus Networks

With advancements in the automotive industry and self-driving cars, automotive cybersecurity has become a pivotal and prioritized issue for all automakers. The CAN-bus is widely used to exchange data between vehicular networks and components, such as safety-critical systems and infotainment. Despite its importance, CAN bus systems lack security safeguards, exposing them to various security threats. This work proposes a novel two-stage Anomaly Detection System that detects malicious attacks with high detection accuracy incurring low latency. The novelty of this work is to use machine learning techniques like Decision Tree, Random Forest, and XGBoost to get predictable results while balancing with rule-based systems and keeping computational effort low. The experimental results demonstrate that our proposed model can detect different attacks with high accuracy (above 90%) on the CAN-intrusion-dataset with very low execution time and high F1-score. Thus, proving the effectiveness and efficiency of our hybrid model.
Estimating Orchestration Load in CSCL Situations Using EDA

This study investigates the extent to which Electrodermal Activity (EDA) sensor data can be triangulated with self-perception measures to estimate facets of teachers’ orchestration load in the context of Computer-Supported Collaborative Learning (CSCL). It was expected to observe variances in the EDA signal as a result of stressful moments and incidents related to orchestration. Study findings indicated that EDA variations concurred with situations in which the teacher reported feeling stressed when orchestrating CSCL Pyramid scripts.
High Performance Analog MEMS for IoT Based Condition Monitoring, Characterization with a Bearing Failure Emulation Test Bench.

High performance vibration measurements are nowadays required to assess the operating performance of modern electrical vehicles, where the increase of motor angular speed leads to the need to measure vibrations at ever higher frequencies. From a perspective of integration in IoT systems, high performance MEMS accelerometers are the solution to perform condition monitoring of roller bearing in modern EV motors. In this work, the authors present the characterization and the possible use of high performances commercial analog MEMS accelerometers for bearing condition monitoring, exploiting a pre-developed test bench able to emulate different kinds of failure of roller bearings. Three uniaxial MEMS accelerometers were mounted on an ad hoc structure to obtain a full triaxial accelerometer and an ad hoc emulation system was designed and used to obtain its response characterization. The experimental results show the astonishing performance of the tested accelerometers, that with a reasonably low cost, show metrological characteristics which are comparable to the one of their expensive piezoelectric counterparts. Moreover, the small size and the low power consumption make the integration of these sensors in an IoT context very easy and promising.