Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection and Forensics of Encryption Behavior of Storage File and Network Transmission Data

In recent years, with the widespread application of encryption technology, criminals can hide malicious data without being discovered by security regulatory authorities, which has brought serious challenges to computer forensic investigation. Therefore, it is urgent to study the technology of detection and forensics of encrypted data. This paper proposes a method for encryption detection based on a deep convolutional neural network. The method first converts the raw data into two-dimensional matrixes as the input of the convolutional neural network. Then, the multiscale feature extraction mechanism with multiple activation functions is utilized to provide representative features as the input of subsequent layers. Next, the residual learning operation can further enhance the discrimination of features. By this mean, a network which can automatically extract and learn global contextual information of encrypted data is constructed. The experiment results show that the proposed method achieves high accuracy in the detection of storage file and network transmission data compare to the competitive methods and the detection accuracy on different types of mixed data is higher than 99%. Moreover, the proposed method can accurately detect data encrypted with different algorithms. The average detection rate of DES-encrypted data is higher than that of competitors by more than 5%.
The CNS Myelin Proteome: Deep Profile and Persistence After Post-mortem Delay

Myelin membranes are dominated by lipids while the complexity of their protein composition has long been considered to be low. However, numerous additional myelin proteins have been identified since. Here we revisit the proteome of myelin biochemically purified from the brains of healthy c56Bl/6N-mice utilizing complementary proteomic approaches for deep qualitative and quantitative coverage. By gel-free, label-free mass spectrometry, the most abundant myelin proteins PLP, MBP, CNP, and MOG constitute 38, 30, 5, and 1% of the total myelin protein, respectively. The relative abundance of myelin proteins displays a dynamic range of over four orders of magnitude, implying that PLP and MBP have overshadowed less abundant myelin constituents in initial gel-based approaches. By comparisons with published datasets we evaluate to which degree the CNS myelin proteome correlates with the mRNA and protein abundance profiles of myelin and oligodendrocytes. Notably, the myelin proteome displays only minor changes if assessed after a post-mortem delay of 6 h. These data provide the most comprehensive proteome resource of CNS myelin so far and a basis for addressing proteomic heterogeneity of myelin in mouse models and human patients with white matter disorders.
Further Investigations into the Physiological Relationship Between an Epiphyllous Liverwort and its Host Leaves

Experiments were carried out to investigate the physiological relationship between an epiphyllous liverwort, Radula flaccida Lbg. et G. , and its host leaves. The osmotic potential of the epiphylla cell sap (-30 to -35 bar) was found to be much lower than that of the host cells (-10 to -12 bar). There is therefore a good physiological basis for the movement of water from the host leaves to the epiphylla. The amount of light incident on the host leaf which is intercepted by even the heaviest epiphylla colony investigated was less than 2 per cent and this was found to produce no measurable difference between the chlorophyll contents of epiphylla-colonized and uncolonized parts of the host leaf. 14C02 light fixation products were found not to move between the host leaf and the epiphylla in either direction. It is concluded that the dependence (parasitism) of R. flaccida on its host leaves is partial and does not include the derivation of organic food substances.
SafeCity: Toward Safe and Secured Data Management Design for IoT-Enabled Smart City Planning

The interaction among different Internet of Things (IoT) sensors and devices become massive and insecure over the Internet as we probe to smart cities. These heterogeneous devices produce an enormous amount of data that is vulnerable to various malicious threats. The generated data need to be processed and analyzed in a secure fashion to make smart decisions. The smart urban planning is becoming a reality through the mass information generated by the Internet of Things (IoT). This paper exhibits a novel architecture, SafeCity, that limelight the ecosystem of smart cities consists of cameras, sensors, and other real-world physical devices. SafeCity is a three-layer architecture, i.e., a data security layer, a data computational layer, and a decision-making layer. At the first layer, payload-based symmetric encryption is used to secure the data from intruders by exchanging only the authentic data among the physical devices. The second layer is used for the computation of secured data. Finally, the third layer extracts visions from data. The secured exchange of data is ensured by using Raspberry Pi boards while the computation of data is tested on trustworthy datasets, using the Hadoop platform. The assessments disclose that SafeCity presents precious insights into a secured smart city in the context of sensors based IoT environment.
Path-Loss Optimized Indoor Laser-Based Visible Light Communication System for Variable Link Length Gigabit-Class Communication

We discuss an optical ray-tracing approach to minimizing path-loss in a variable link length indoor blue laser down-converted white light visible light communication (VLC) system. For a given link length, minimum path-loss is achieved by finding optimum positions of transmitter and receiver lenses relative to phosphor and detector respectively such that collection efficiency is maximized. The designed VLC system is experimentally implemented for two different optimized link lengths of 25 and 300 cm. The down-converted white light for the optimized link is found to exhibits narrow beam spread, spot-lighting type illumination profile. The white light is measured to quantify the beam profile, color rendering, illuminance and percentage of blue-content. The illumination beam profile and propagation characteristics are found to be in good agreement with optical simulations. Communication experiments with on-off modulated at 1.5 Gbps achieved BER of ∼3 × 10−3 for the optimized link, which is below the forward-error correction threshold. The above communication performance is achieved at illumination levels at the receiver as low as ∼45 and 16 lx. Comparison with previous laser-based VLC implementations shows that the path-loss optimization helps achieve gigabit-class communication at practically relevant link lengths and the lowest illuminance levels thus far reported. Such low illuminance level, narrow beam spread VLC luminaires can potentially coexist with existing lighting infrastructure for eye-safe indoor applications.
Autonomous Railway Traffic Object Detection Using Feature-Enhanced Single-Shot Detector

With the high growth rates of railway transportation, it is extremely important to detect railway obstacles ahead of the train to ensure safety. Manual and traditional feature-extraction methods have been utilized in this scenario. There are also deep learning-based railway object detection approaches. However, in the case of a complex railway scene, these object detection approaches are either inefficient or have insufficient accuracy, particularly for small objects. To address this issue, we propose a feature-enhanced single-shot detector (FE-SSD). The proposed method inherits a prior detection module of RON and a feature transfer block of FB-Net. It also employs a novel receptive field-enhancement module. Through the integration of these three modules, the feature discrimination and robustness are significantly enhanced. Experimental results for a railway traffic dataset built by our team indicated that the proposed approach is superior to other SSD-derived models, particularly for small-object detection, while achieving real-time performance close to that of the SSD. The proposed method achieved a mean average precision of 0.895 and a frame rate of 38 frames per second on a railway traffic dataset with an input size of $320\times320$ pixels. The experimental results indicate that the proposed method can be used for real-world railway object detection.
A lightweight anonymous two‐factor authentication protocol for wireless sensor networks in Internet of Vehicles

Internet of Vehicles (IoV), as the next generation of transportation systems, tries to make highway and public transportation more secure than used to be. In this system, users use public channels for their communication so they can be the victims of passive or active attacks. Therefore, a secure authentication protocol is essential for IoV; consequently, many protocols are presented to provide secure authentication for IoV. In 2018, Yu et al proposed a secure authentication protocol for WSNs in vehicular communications and claimed that their protocol could satisfy all crucial security features of a secure authentication protocol. Unfortunately, we found that their protocol is susceptible to sensor capture attack, user traceability attack, user impersonation attack, and offline sink node's secret key guessing attack. In this paper, we propose a new authentication protocol for IoV which can solve the weaknesses of Yu et al's protocol. Our protocol not only provides anonymous user registration phase and revocation smart card phase but also uses the biometric template in place of the password. We use both Burrow‐Abadi‐Needham (BAN) logic and real‐or‐random (ROR) model to present the formal analysis of our protocol. Finally, we compare our protocol with other existing related protocols in terms of security features and computation overhead. The results prove that our protocol can provide more security features and it is usable for IoV system.
The Anyplace 4.0 IoT Localization Architecture

The Internet of Things (IoT) revolution has massively introduced sensor-rich tracking devices to an ever growing landscape of smart spaces (e.g., factories, hospitals, and ships). One problem that remains unsolved over the years is the localization problem for IoT, given that Satellite-based solutions are inaccurate in indoor spaces where human activity takes place 80-90% of the time. In this paper, we introduce a novel open-source architecture for IoT localization, coined Anyplace 4.0 IoT (A4IoT), which exploits signal fingerprinting to organize under the same roof a wide range of different localization technologies (e.g., Wi-Fi, BLE, Cellular, UWB, Computer Vision). We present the technical requirements of A4IoT inspired by the Alstom SA smart factory, operating worldwide in rail transport markets. A4IoT comprises a crowdsourcing architecture where deployers can collect and organize fingerprint signals inside smart spaces in a designated localization service running on the Edge (from Raspberry to Datacenter). The service incorporates timeseries databases for tracking targets and deployers can provide accurate room-level localization accuracy $(\approx 2\mathrm{m})$ on a variety of platforms (e.g., Android, Linux, Mac, Windows, Robot OS) but also integrate A4IoT through Web 2.0 endpoints to their software ecosystems.
DFT investigations on arylsulphonyl pyrazole derivatives as potential ligands of selected kinases

Abstract Using the density functional theory (DFT) formalism, we have investigated the properties of some arylsulphonyl indazole derivatives that we studied previously for their biological activity and susceptibility to interactions of azoles. This study includes the following physicochemical properties of these derivatives: electronegativity and polarisability (Mulliken charges, adjusted charge partitioning, and iterative-adjusted charge partitioning approaches); free energy of solvation (solvation model based on density model and M062X functional); highest occupied molecular orbital (HOMO)–lowest occupied molecular orbital (LUMO) gap together with the corresponding condensed Fukui functions, time-dependent DFT along with the UV spectra simulations using B3LYP, CAM-B3LYP, MPW1PW91, and WB97XD functionals, as well as linear response polarisable continuum model; and estimation of global chemical reactivity descriptors, particularly the chemical hardness factor. The charges on pyrrolic and pyridinic nitrogen (the latter one in the quinolone ring of compound 8, as well as condensed Fukui functions) reveal a significant role of these atoms in potential interactions of azole ligand–protein binding pocket. The lowest negative value of free energy of solvation can be attributed to carbazole 6, whereas pyrazole 7 has the least negative value of this energy. Moreover, the HOMO–LUMO gap and chemical hardness show that carbazole 6 and indole 5 exist as soft molecules, while fused pyrazole 7 has hard character.
Optimization of Smooth Support Vector Machine Algorithm Using Backpropagation Nguyen Widrow Algorithm in Classification of Mellitus Diabetes Disease

In this study, a combination of Smooth Vector Support Machine (SSVM) and the Nguyen Widrow Back propagation algorithm is performed. For many data one of the weaknesses of the SSVM algorithm is the low level of accuracy, so to optimize the level of accuracy SSVM is done by combining the SSVM algorithm and Back propagation Neural Network algorithm. In this algorithm the initial weight value is determined by the formula proposed by Nguyen and Widrow, so the initial weight value is more proportionate to the network architecture used. In this combination algorithm, SSVM algorithm functions as a training function for the dataset by the network and test data classification algorithm functions. Back propagation for the results of the experiment SSVM algorithm the average value of MSE is 0.7569 whereas for the Combination algorithm the average value of MSE is 0.6672 and the Accuracy value is 96.6% as a whole it can be seen that the increase in the value of MSE with the Combination algorithm is 10.84% with an Accuracy value of 15.80%.
Weverify: Wider and Enhanced Verification for You Project Overview and Tools

This paper presents an overview of the WeVerify H2020 EU project, which develops intelligent human-in-the-loop content verification and disinformation analysis methods, tools and services. Social media and web content are analysed and contextualised within the broader online ecosystem, in order to expose fabricated content, through cross-modal content verification, social network analysis, micro-targeted debunking, and a blockchain-based public database of known fakes.
Matching Color Aerial Images and Underwater Sonar Images Using Deep Learning for Underwater Localization

Underwater localization is a challenging task due to the lack of a Global Positioning System (GPS). However, the capability to match georeferenced aerial images and acoustic data can help with this task. Autonomous hybrid aerial and underwater vehicles also demand a new localization method capable of combining the perception from both environments. This study proposes a cross-domain and cross-view image matching, using a color aerial image and an underwater acoustic image to identify if these images are captured in the same place. The method is designed to match images acquired in partially structured environments with shared features, such as harbors and marinas. Our pipeline combines traditional image processing methods and deep neural network techniques. Real-world datasets from multiple regions are used to validate our work, obtaining a matching precision of up to 80%.
Public Key Certificate Privacy in VoNDN: Voice Over Named Data Networks

Named Data Network (NDN) is a network paradigm that attempts to answer today’s needs for distribution. One of the NDN key features is in-network caching to increase content distribution and network efficiency. However, this feature may increase the privacy concerns, as the adversary may identify the call history, and the callee/caller location through side-channel timing responses from the cache of trusted Voice over NDN (VoNDN) application routers. The side-channel timing attack can be mitigated by countermeasures, such as additional unpredictable delay, random caching, group signatures, and no-caching configurations. However, the content distribution may be affected by pre-configured countermeasures, which may be against the original purpose of NDN. In this work, the detection and defense (DaD) approach is proposed to mitigate the attack efficiently and effectively. With the DaD usage, an attack can be detected by a multi-level detection mechanism, in order to apply the countermeasures against the adversarial faces. Also, the detections can be used to determine the severity of the attack. In order to detect the behavior of an adversary, a brute-force timing attack was implemented and simulated of the VoNDN application on NDN-testbed. A trusted application that mimics the VoNDN and identifies the cached certificate on a worldwide NDN-testbed. In simulation primary results showed that the multi-level detection based on DaD mitigated the attack about 39.1% in best-route, and 36.5% in multicast communications. Additionally, the results showed that DaD preserves privacy without compromising the efficiency benefits of in-network caching in the VoNDN application.
Real-Time LQG Control Strategies for Electrohydraulic Steering of Mobile Machines

The main aim of the article is to compare two real-time control strategies for electrohydraulic power steering system of modern mobile machines with Linear-quadratic Gaussian (LQG) controllers. The both modification of LQG controller are designed on the basis of two multivariable uncertain plant models. The plant models are obtained by identification procedure via "black box" approach and takes into account the deviations of the parameters that characterize the way that the control signal acts on the state of the models. The difference between the two modifications of the embedded controllers lies in the different types of output signals, which are used as feedback in control system. The comparative analysis of time and frequency domain properties of controllers are performed. The results obtained show the better performance of one system. The control algorithms are implemented in 32-bit microcontroller. A number of experiments have been executed, which confirm the quality of the electrohydraulic power steering control system.
Classification-based Content Sensitivity Analysis

With the availability of user-generated content in the Web, malicious users have access to huge repositories of private (and often sensitive) information regarding a large part of the world’s population. In this paper, we propose a way to evaluate the harmfulness of text content by deﬁning a new data mining task called content sensitivity analysis . According to our deﬁnition, a score can be assigned to any text sample according to its degree of sensitivity. Even though the task is similar to sentiment analysis, we show that it has its own peculiarities and may lead to a new branch of research. Thanks to some preliminary experiments, we show that content sensitivity analysis can not be addressed as a simple binary classiﬁcation task.
El plan estratégico de ciudadanía e integración 2007-2010

En torno al ano 2000 se ha entrado en una tercera etapa en el proceso de formacion de la “Espana inmigrante”, porque la cuestion migratoria se ha “institucionalizado” (Cachon,2002). Tras una segunda etapa que vino marcada por el desajuste en el mercado de trabajo que actuo como el motor que puso a Espana en el mapa migratorio internacional como un posible pais de destino, desde 2000 los flujos aumentaron notablemente y se diversificaron; pero lo mas significativo son los cambios en la opinion publica, los conflictos ligados a la inmigracion que van surgiendodesde ese ano, los debates politicos sobre la cuestion y los intentos de construir un sistema de gestion de los flujos migratorios y de afrontar los retos de la ciudadaniay la integracion con los inmigrantes. En esta etapa la inmigracionse ha consolidado “como cuestion politica, incluso como una de las cuestiones politicas clave” (Lucas, 2002). Esta tercera etapa tiene dos periodos muy diferenciados que estan relacionados con el cambio de gobierno que se produjo en 2004: el nuevo Reglamento de extranjeria de diciembre de 2004 (y la“normalizacion” de 2005) y el Plan Estrategico de Ciudadania e Integracion 2007-2010 (cuyos preparativos comenzaron en diciembre de 2004, aunque no se haya aprobado hasta febrero de 2007).
X-Ray Baggage Inspection With Computer Vision: A Survey

In the last decades, baggage inspection based on X-ray imaging has been established to protect environments in which access control is of vital significance. In several public entrances, like airports, government buildings, stadiums and large event venues, security checks are carried out on all baggage to detect suspicious objects (e.g., handguns and explosives). Although improvements in X-ray technology and computer vision have made many X-ray detection tasks that were previously unfeasible a reality, the progress that has been made in automated baggage inspection is very limited compared to what is needed. For this reason, X-ray screening systems are usually being manipulated by human inspectors. Research and development experts who focus on X-ray testing are moving towards new approaches that can be used to aid human operators. This paper reports the state of the art in baggage inspection identifying three research fields that have been used to deal with this problem: i) X-ray energies, because there is enough research evidence to show that multi-energy X-ray testing must be used when the material characterization is required; ii) X-ray multi-views, because they can be an effective option for examining complex objects where the uncertainty of only one view can lead to misinterpretation; and iii) X-ray computer vision algorithms, because there are a plethora of computer vision approaches that can address many 3D object recognition problems. Besides, this paper presents useful public datasets that can be used for training and testing, and also summarizes the reported experimental results in this field. Finally, this paper addresses the general limitations and show new avenues for future research.
Point Cloud Registration Algorithm Based on the Grey Wolf Optimizer

In view of the long computation time and low registration accuracy of the current point cloud registration algorithm, a point cloud registration algorithm based on the grey wolf optimizer (GWO) is proposed, denoted PCR-GW. The algorithm uses the centralization method to solve the translation matrix and then simplifies the points of the initial point cloud models by using the intrinsic shape signatures (ISS) feature. Next, various parameters of the rotation matrix are obtained via the GWO algorithm by employing the quadratic sum of the distances between corresponding points in the simplified point cloud as the objective function. Finally, the point cloud registration process is completed by using the obtained transformation matrix. By conducting a registration experiment on the point cloud library model and comparing PCR-GW with the traditional algorithms, the algorithm proposed in this article is shown to be promising for improving the computation speed and registration accuracy.