Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear electrostatics: the Poisson–Boltzmann equation

The description of a conducting medium in thermal equilibrium, such as an electrolyte solution or a plasma, involves nonlinear electrostatics, a subject rarely discussed in the standard electricity and magnetism textbooks. We consider in detail the case of the electrostatic double layer formed by an electrolyte solution near a uniformly charged wall, and we use mean-field or Poisson–Boltzmann (PB) theory to calculate the mean electrostatic potential and the mean ion concentrations, as functions of distance from the wall. PB theory is developed from the Gibbs variational principle for thermal equilibrium of minimising the system free energy. We clarify the key issue of which free energy (Helmholtz, Gibbs, grand, …) should be used in the Gibbs principle; this turns out to depend not only on the specified conditions in the bulk electrolyte solution (e.g. fixed volume or fixed pressure), but also on the specified surface conditions, such as fixed surface charge or fixed surface potential. Despite its nonlinearity the PB equation for the mean electrostatic potential can be solved analytically for planar or wall geometry, and we present analytic solutions for both a full electrolyte and for an ionic solution containing only counterions, i.e. ions of sign opposite to that of the wall charge. This latter case has some novel features. We also use the free energy to discuss the inter-wall forces which arise when the two parallel charged walls are sufficiently close to permit their double layers to overlap. We consider situations where the two walls carry equal charges, and where they carry equal and opposite charges.
Schema inference for multi-model data

The knowledge of a structural schema of data is a crucial aspect of most data management tasks. Unfortunately, in many real-world scenarios, the data is not accompanied by it, and schema-inference approaches need to be utilised. In this paper, we focus on a specific and complex use case of multi-model data where several often contradictory features of the combined models must be considered. Hence, single-model approaches cannot be applied straightforwardly. In addition, the data often reach the scale of Big Data, and thus a scalable solution is inevitable. In our approach, we reflect all these challenges. In addition, we can also infer local integrity constraints as well as intra- and inter-model references. Last but not least, we can cope with cross-model data redundancy. Using a set of experiments, we prove the advantages of the proposed approach and we compare it with related work.
DepthGrillCam: A Mobile Application for Real-time Eating Action Recording Using RGB-D Images

An automatic meal recording is one of typical applications of image recognition technology. In fact, some mobile apps on meal recording have been released so far. Most of the apps assume that a user takes a meal photo before start eating. However, this approach is not appropriate for the meals in which foods are served while taking meals such as food buffets, shared large plates and hot pots. In this study, we propose a mobile meal recording system that estimates food calories during eating in the real-time way by eating action recognition with RGB-D images obtained by a front-mounted depth sensor on a smartphone. % which is used for face recognition. In the experiments with the mobile app implemented for an iPhone, in the situation of eating grilled meat, the proposed system improved the accuracy of calorie estimation by up to 28% and recognized the correct meal category with 6.67 times higher accuracy in eating action recognition compared to the baseline system.
A Literature Survey of Drunk Driving Detection Approaches

Drunk and distracted driving have been prime reasons for road accidents. An increase in population in urban cities leads to the risk of increasing deceased cases due to road accidents. Metropolitan cities needed a preventive & scalable system to prevent the loss of life due to accidents. Nevertheless, each method has limitations, such as usability, complexity, scalability, and burdensome implementation. This work describes the various approaches used to date in drunk driving systems with their pros and cons. Techniques are also grouped based on the methodology adopted by the researcher as follows; Alcohol sensor-based, IOT or Videos Based, Ignition Control using Hardware-based, Touch-based technology, Using Machine Learning or Neural Networks, and Hybrid approaches. This work also lists the accuracy of machine learning algorithms like Linear Discriminant Analysis, Support Vector Machine, Ada Boost, and Random Forest acclimated to the drunk-driving system.
Model-checking legal contracts with SymboleoPC

Legal contracts specify requirements for business transactions. As any other requirements specification, contracts may contain errors and violate properties expected by contracting parties. Symboleo was recently proposed as a formal specification language for legal contracts. This paper presents SymboleoPC, a tool for analyzing Symboleo contracts using model checking. It highlights the architecture, implementation and testing of the tool, as well as a scalability evaluation with respect to the size of contracts and properties to be checked through a series of experiments. The results suggest that SymboleoPC can be usefully applied to the analysis of formal specifications of contracts with real-life sizes and structures.
Advanced malware and their impact on virtualization: A case study on hybrid feature extraction using deep memory introspection

As pandemic has hit the world, virtualization has become the hot topic of today’s era. Almost every organization has shifted to the virtual environment (specially cloud computing). However, the security concerns in virtualization are the central issue for the researchers as well as organizations. Attackers use different tactics to exploit the vulnerabilities present in virtual components. In this paper, we provided a detailed study on the malware families & along with their impact on virtualization. In addition, malware log extraction using deep memory introspection has been explored. Various plugins have been explained, along with the variety of features that are essential for malware analysis purposes. A case study has also been provided using the testbed set up in our lab to provide the practical insight on deep memory introspection using open source tools such as LibVMI, DRAKVUF, etc., along with their usage to extract different features outside the VM at the hypervisor. We hope that our work will help readers to understand the malware logs and extract important features for malware analysis in a virtual environment.
Shifted Delta Cepstral Coefficients with RNN to Improve the Detection of Parkinson’s Disease from the Speech

Parkinson’s disease (PD) is a progressive neurodegenerative disorder of the central nervous system identified by motor and non-motor activities abnormalities. PD affects respiration, laryngeal, articulation, resonance, and prosodic aspects of speech production. Detection of PD from speech is a non-invasive approach useful for automatic screening. Perceptual attributes of speech due to PD are manifested as temporal variations in speech. In this regard, current work investigated the use of LSTM and BiLSTM networks with shifted delta cepstral (SDC) features to detect PD from speech. Further in BiLSTM networks, a multi-head attention mechanism is introduced, assuming that each head captures distinct information to detect PD. SDC features obtained from MFCCs, and SFFCCs are used for developing the PD detection system. The performance of the experiments is validated using the PC-GITA database. The experimental results revealed that BiLSTM networks give a relative improvement of 4-5% over the LSTM networks. The use of a multi-head attention mechanism further improved the detection accuracy of the PD detection system, showing that it can capture various discriminative features.
Adaptive visualization of tourists' preferred spots and streets using trajectory articulation

Walking tourism, in which regional resources are organized with interesting themes, can provide visitors with original local walking experiences. Our project aims to collect user data through a mobile application and explore potential geographic resources such as appealing spots and streets for improving city-scale tourism. A density map with GPS trajectory data is one of the easiest ways of visualizing them without any modeling costs. However, both user and technical factors make it difficult to interpret the heatmap in a detailed and concise way. Specifically, analysts have difficulty in deciphering the areas of real interest based on the heat map using the data as areas associated with high density of GPS locations may not be solely due to their attractiveness, e.g., rest areas. In addition, the heat map that does not retain the topography of the streets cannot achieve hot street visualization. In our research, built-in smartphone sensors are employed to distinguish multiple user contexts (e.g., stopping / walking and indoors / outdoors) during their walking tours, which equalize the degree of inherent density biases in each GPS trajectory and add attributes to each location point. Our analysis software accumulates the processed trajectories and generates a density map by applying different weight rules (e.g., a street-oriented rule and an indoor-oriented rule) based on semantic attributes and analytical requests. Our mobile cooperative approach realizes adaptive heatmap generation to the analyzer's expectations, that is, concise hot spots visualization and hot streets visualization.
UAV based 5G indoor localization for emergency services

Unmmaned Aerial Vechicle (UAV) deployment as 5G Node Bs (gNBs) or relays is a promising technology for providing rapid 5th Generation (5G) connectivity. This can be useful for enabling accurate indoor localization within buildings but the impact of UAV jitter in windy conditions has to be considered and negated. In this paper, we propose a two-tiered UAV-based 5G system and show that this is an effective solution for precise positioning during emergency situations. Using positional jitter data from a real experiment, we evaluate the errors in localization estimates under wind and turbulence for the proposed system, and develop effective machine learning solutions to compensate for these errors. Thus, the paper presents an effective and practical solution for providing highly accurate localization with UAVs in critical communication scenarios.
On the performance of multi-tier space-air-ground integrated network exploiting mmWave and THz capabilities for 6G communication

Ubiquitous coverage and high data rate-based connectivity have become a necessity nowadays. Despite the technological advancements, reliable and efficient communication is yet dependent on infrastructure availability. In this regard, non-terrestrial networks (NTNs) have gained significant attention to complement the terrestrial communication. In addition, the millimeter wave (mmWave) and Terahertz (THz) communication are perceived as the potential solution to overcome the looming capacity crunch. Targeting the sixth generation (6G) horizon, in this paper, we propose a downlink multitier space-air-ground integrated network (SAGIN) comprising mmWave-enabled non-terrestrial entities, i.e., satellites (SATs) and high-altitude platforms (HAPs), for complementing the terrestrial THz-enabled small-cell base stations (SBSs). We analyze the performance of the proposed SAGIN architecture against association probability, network capacity, and outage probability. Our extensive simulation results validate the significance of NTNs to aid terrestrial networks to provide high data rates and uninterrupted coverage.
Cluster Formation and Cluster Head Selection to Increase the Lifetime of Heterogeneous Wireless Sensor Network

A WSN consists of a group of interconnected, compact sensor nodes that are capable of communication and data sharing. Although WSN applications are numerous, they are nevertheless severely constrained by the multiple drawbacks of a sensor network. The limited lifespan of WSN sensor nodes is caused by the limited power supply (i.e. battery). Controlling the energy that WSN uses is vital to increase its lifespan. The Major issue of reduction in energy consumption in energy limited sensor nodes and increase in network lifetime is considered in the paper. The authors of this manuscript have developed and implemented a method for cluster formation and cluster head election that is energy-efficient. In the proposed protocol, Base station (BS) is placed in the center of the network. The sensor node senses the data from the target area, sends sensory information to the cluster head, and then sends the aggregate message to the BS. In cluster-based WSN, each SN has a unique Id. Authors compare a propound protocol to LEACH and DDEEC protocols and the performance in terms of alive node per round, dead node per round, packet delivered to BS and the maximum number of cluster heads. The authors have presented a novel approach to increasing the network lifetime of heterogeneous WSN, taking numerous considerations into account.
A Survey on Surveillance using Drones

Drones are flying robots that can be remotely controlled. In this paper, we discussed different ways a drone can be used for surveillance. Drones are used to detect forest fires, manage traffic remotely, surveillance of crowds, etc. Drones make surveillance effective, accessible, and time-saving. Drones surveillance dearth the researcher’s conscientious, and therefore, we accumulate the associated literature and converse their realistic standpoint in general. This paper makes a specialty of video surveillance, the usage of drones in item detection and tracking, video summarization, continual tracking of the quarry, seek and rescue related operations in an adverse environment, traffic control in smart towns, and catastrophe control in a direful situation. This quick survey lock-up mild at the studies gaps.
A Deep Learning approach for detection and analysis of respiratory infections in covid-19 patients using RGB and infrared images.

Within a short period, the severe acute respiratory syndrome Coronavirus Disease 2019 (COVID-19) has become a devastating global pandemic, causing enormous losses to human civilization worldwide. A significant feature of COVID-19, according to recent investigations, is an altered respiratory state induced by viral infections. In this paper, we present a non-contact method for screening the respiratory health of COVID-19 patients using RGB-infrared sensors to analyze their breathing patterns. The block diagram the proposed method is shown in Fig. 1. First, we use facial recognition to obtain breathing data from the individuals. The respiratory data is applied to multiple neural networks, including LSTM, BiLSTM, GRU, and BiGRU. An attention mechanism is then used in the neural network to obtain a health screening result from the respiration dataset. With an accuracy of 70.83 percent, our BiGRU model accurately identifies the respiratory health condition whether it is normal or abnormal.
AI-driven Real-time System for Land Surveillance and Reconnaissance

Detection of threat elements during dangerous land missions such as rescue operations, bomb disposal, surveillance and reconnaissance using an unmanned ground vehicle (UGV) plays a significant role in technological warfare. Accurate and rapid detection of enemy arsenal and threats can help better plan and deploy military armaments while greatly reducing human casualty and economic losses. Unmanned ground systems with good maneuverability, superior wireless communication, and powerful multi-sensor and data processing capabilities can significantly advantage over the enemies. One of the key data processing tasks, i.e., land-warfare threat detection, is developed and tested in this paper. The goal is to identify and differentiate between threats in a warfare scenario. Four such threats have been defined and used to develop the detection algorithm for the study: soldiers, tanks, tents, and helicopters. Different object detection algorithms (ODAs) such as Single Shot Detector (SSD), CenterNet and Faster R-CNN based on pre-trained Convolutional Neural Networks were tested and compared. COCO evaluation metrics were used as performance parameters to evaluate each detection algorithm on the different threats selected. The results show that CenterNet ODA performs better in evaluation and inference when compared to other ODAs, obtaining the highest mean Average Precision of 85.89% and 93.75%, respectively, at 0.5 IoU with Resent101 V1 CNN architecture. The best trade-off between all performance parameters was obtained again using CenterNet Resent101 V1 FPN. The inference was tested on a Raspberry Pi-based UGV streamed data. It was concluded that such systems have a key role in future warfare with a strong communication system and a lighter version of ODA. Further, researchers and engineers can use the work achieved in this paper to develop robust detection and data processing models and incorporate it into various applications and domains.
Food Image Recognition by optimizing CNN with PSO and GA

This research work proposes a food image recognition model. This paper proposes a model constituting CNN with PSO and GA that aims to increase the accuracy. CNN as a classifier has been used for classification of food images. The purpose of optimizing CNN with PSO and GA is to do the classification part as efficiently as possible because this can change the whole picture as it is directly dependent on the overall dietary assessment results. The performance of the proposed model is evaluated using UEC FOOD-100, a dataset which contains pictures of 100 kinds of food. The proposed model yields an accuracy of 82.3%.
Bug localization in game software engineering: evolving simulations to locate bugs in software models of video games

Video games have characteristics that differentiate their development and maintenance from classic software development and maintenance. These differences have led to the coining of the term Game Software Engineering to name the emerging subfield that intersects Software Engineering and video games. One of these differences is that video game developers perceive more difficulties than other non-game developers when it comes to locating bugs. Our work proposes a novel way to locate bugs in video games by means of evolving simulations. As the baseline, we have chosen BLiMEA, which targets classic software engineering and uses bug reports and the defect localization principle to locate bugs. We also include Random Search as a sanity check in the evaluation. We evaluate the approaches in a commercial video game (Kromaia). The results for F-measure range from 46.80%. to 70.28% for five types of bugs. Our approach improved the results of the baseline by 20.29% in F-measure. To the best of our knowledge, this is the first approach that is designed specifically for bug localization in video games. A focus group with professional video game developers has confirmed the acceptance of our approach. Our approach opens a new research direction for bug localization for both game software engineering and possibly classic software engineering.
On a New Method to Estimate the Distance, Reddening, and Metallicity of RR Lyrae Stars Using Optical/Near-infrared (B, V, I, J, H, K) Mean Magnitudes: ω Centauri as a First Test Case

We developed a new approach to provide accurate estimates of the metal content, reddening, and true distance modulus of RR Lyrae stars (RRLs). The method is based on homogeneous optical (BVI) and near-infrared (JHK) mean magnitudes and on predicted period–luminosity–metallicity relations (IJHK) and absolute mean magnitude–metallicity relations (BV). We obtained solutions for three different RRL samples in ω Cen: first overtone (RRc, 90), fundamental (RRab, 80), and global (RRc+RRab) in which the period of first overtones were fundamentalized. The metallicity distribution shows a well defined peak at [Fe/H]∼−1.98 and a standard deviation of σ = 0.54 dex. The spread is, as expected, metal-poor ([Fe/H] ≤ −2.3) objects. The current metallicity distribution is ∼0.3 dex more metal-poor than similar estimates for RRLs available in the literature. The difference vanishes if the true distance modulus we estimated is offset by −0.06/−0.07 mag in true distance modulus. We also found a cluster true distance modulus of μ = 13.720 ± 0.002 ± 0.030 mag, where the former error is the error on the mean and the latter is the standard deviation. Moreover, we found a cluster reddening of E(B − V) = 0.132 ± 0.002 ± 0.028 mag and spatial variations of the order of a few arcmin across the body of the cluster. Both the true distance modulus and the reddening are slightly larger than similar estimates available in the literature, but the difference is within 1σ. The metallicity dependence of distance diagnostics agrees with theory and observations, but firm constraints require accurate and homogeneous spectroscopic measurements.
A stereo vision geometric descriptor for place recognition and its GPU acceleration for autonomous vehicles applications

points issues d’une une cam´era st´er´eo pour des applications de v´ehicules autonomes. pr´esentons d’abord l’approche utilis´ee pour enregistrer la structure 3D de l’espace visible. Ensuite, nous proposons une optimisation param´etrique pour obtenir les meilleures performances en couplant les caract´erisitiques intrins`eques du capteur utilis´e (cam´era st´er´eo) et l’algorithme utilis´e. Enfin, nous proposons une impl´ementation optimis´ee GPU bas´ee sur CUDA. Par rapport `a un CPU, les temps de traitement sur GPU 7 fois fois une GeForce RTX 3080. Abstract – In this paper we present a geometric global descriptor dedicated for place recognition in autonomous vehicles applications that is based on stereo camera generated point clouds. We first present the approach used to record the 3D structure of the visible space. Then, we propose a parametric optimization to achieve the best performance by coupling the dedicated sensor (Stereo Camera) and the used algorithm. Finally, we propose a GPU implementation based on CUDA. Compared to a CPU, processing times on GPU are accelerated 7 times on a Jetson AGX Xavier and 30 times using a GeForce RTX 3080.
Isotropic non-Gaussian gNL-like toy models that reproduce cosmic microwave background anomalies

Context. Based on recent observations of the cosmic microwave background (CMB), claims of statistical anomalies in the properties of the CMB fluctuations have been made. Although the statistical significance of the anomalies remains only at the ∼2−3σ significance level, the fact that there are many different anomalies, several of which support a possible deviation from statistical isotropy, has motivated a search for models that provide a common mechanism to generate them. Aims. The goal of this paper is to investigate whether these anomalies could originate from non-Gaussian cosmological models, and to determine what properties these models should have. Methods. We present a simple isotropic, non-Gaussian class of toy models that can reproduce six of the most extensively studied anomalies. We compare the presence of anomalies found in simulated maps generated from the toy models and from a standard model with Gaussian fluctuations. Results. We show that the following anomalies, as found in the Planck data, commonly occur in the toy model maps: (1) large-scale hemispherical asymmetry (large-scale dipolar modulation), (2) small-scale hemispherical asymmetry (alignment of the spatial distribution of CMB power over all scales ℓ = [2, 1500]), (3) a strongly non-Gaussian hot or cold spot, (4) a low power spectrum amplitude for ℓ < 30, including specifically (5) a low quadrupole and an unusual alignment between the quadrupole and the octopole, and (6) parity asymmetry of the lowest multipoles. We note that this class of toy model resembles models of primordial non-Gaussianity characterised by strongly scale-dependent gNL-like trispectra.