Deep Learning-Based Attack Detection and Classification in Android Devices.

The increasing proliferation of Androidbased devices, which currently dominate the market with a staggering 72% global market share, has made them a prime target for attackers. Consequently, the detection of Android malware has emerged as a critical research area. Both academia and industry have explored various approaches to develop robust and efficient solutions for Android malware detection and classification, yet it remains an ongoing challenge. In this study, we present a supervised learning technique that demonstrates promising results in Android malware detection. The key to our approach lies in the creation of a comprehensive labeled dataset, comprising over 18,000 samples classified into five distinct categories: Adware, Banking, SMS, Riskware, and Benign applications. The effectiveness of our proposed model is validated using well-established datasets such as CICMalDroid2020, CICMalDroid2017, and CICAndMal2017. Comparing our results with state-of-the-art techniques in terms of precision, recall, efficiency, and other relevant factors, our approach outperforms other semi-supervised methods in specific parameters. However, we acknowledge that our model does not exhibit significant deviations when compared to alternative approaches concerning certain aspects. Overall, our research contributes to the ongoing efforts in the development of advanced techniques for Android malware detection and classification. We believe that our findings will inspire further investigations, leading to enhanced security measures and protection for Android devices in the face of evolving threats.Partial funding for open access charge: Universidad de Málag

A multifaceted exploration of ontogenetic variation in vertebral neural canal size across contemporary populations

DATA AVAILABILITY STATEMENT : The data that support the findings of this study are openly available in the SVAD Zenodo Community repository at https://zenodo.org/communities/svad/ (DOI: 10.5281/zenodo.6342097).OBJECTIVES : Vertebral neural canal (VNC) dimensions are considered a reliable indicator of childhood stress. However, no study has characterized variation in VNC size or shape or the impact of extrinsic or intrinsic factors on their range of variation. The present study explores VNC dimensions of subadult samples varying in chronology, population of origin, geography, and socioeconomic backgrounds. MATERIALS AND METHODS : Antero-posterior (AP) and transverse (TR) diameters were measured on the tenth thoracic to the fifth lumbar vertebrae of 1404 contemporary individuals aged between birth and 22 years from Colombia (N = 28), France (N = 484), the Netherlands (N = 23), Taiwan (N = 31), and the United States (N = 838), and compared to lumbar diameters of subadults from the Spitalfields collection (N = 84) and the East Smithfield cemetery (N = 65). VNC variation was evaluated with skeletal growth profiles, principal component analyses (PCA), MANOVAs and ANOVAs. RESULTS : AP diameter growth ends during childhood, while TR diameter growth progressively slows before ending in adolescence. The Colombian sample presented the smallest VNC diameters compared to the other contemporary and historic samples. VNC shape (AP/TR ratio) was similar in contemporary samples. MANOVAs and ANOVAs revealed significant differences in VNC size according to country of origin and socio-economic status, primarily differentiating the Colombian sample. DISCUSSION : The overall consistency in size and shape among groups is remarkable. While physiological stress may contribute to variability in VNC size, intrinsic ontogenetic processes and other individual and environmental factors also influence variability in VNC size.National Institute of Justice and National Science Foundation.http://wileyonlinelibrary.com/journal/ajpa2023-12-09hj2023AnatomySDG-03:Good heatlh and well-bein

IoT-Based Smart Management of Healthcare Services in Hospital Buildings during COVID-19 and Future Pandemics

The paper aims to design and develop an innovative solution in the Smart Building context that increases guests' hospitality level during the COVID-19 and future pandemics in locations like hotels, conference locations, campuses, and hospitals. The solution supports features intending to control the number of occupants by online appointments, smart navigation, and queue management in the building through mobile phones and navigation to the desired location by highlighting interests and facilities. Moreover, checking the space occupancy, and automatic adjustment of the environmental features are the abilities that can be added to the proposed design in the future development. The proposed solution can address all mentioned issues regarding the smart building by integrating and utilizing various data sources collected by the internet of things (IoT) sensors. Then, storing and processing collected data in servers and finally sending the desired information to the end-users. Consequently, through the integration of multiple IoT technologies, a unique platform with minimal hardware usage and maximum adaptability for smart management of general and healthcare services in hospital buildings will be created

Where to Decide? Centralized vs. Distributed Vehicle Assignment for Platoon Formation

Platooning is a promising cooperative driving application for future intelligent transportation systems. In order to assign vehicles to platoons, some algorithm for platoon formation is required. Such vehicle-to-platoon assignments have to be computed on-demand, e.g., when vehicles join or leave the freeways. In order to get best results from platooning, individual properties of involved vehicles have to be considered during the assignment computation. In this paper, we explore the computation of vehicle-to-platoon assignments as an optimization problem based on similarity between vehicles. We define the similarity and, vice versa, the deviation among vehicles based on the desired driving speed of vehicles and their position on the road. We create three approaches to solve this assignment problem: centralized solver, centralized greedy, and distributed greedy, using a Mixed Integer Programming solver and greedy heuristics, respectively. Conceptually, the approaches differ in both knowledge about vehicles as well as methodology. We perform a large-scale simulation study using PlaFoSim to compare all approaches. While the distributed greedy approach seems to have disadvantages due to the limited local knowledge, it performs as good as the centralized solver approach across most metrics. Both outperform the centralized greedy approach, which suffers from synchronization and greedy selection effects.Since the centralized solver approach assumes global knowledge and requires a complex Mixed Integer Programming solver to compute vehicle-to-platoon assignments, we consider the distributed greedy approach to have the best performance among all presented approaches

60-GHz Millimeter-Wave Propagation Inside Bus: Measurement, Modeling, Simulation, and Performance Analysis

Millimeter-wave (mmWave) transmission over the unlicensed 60-GHz spectrum is a potential solution to realize high-speed internet access, even inside mass transit vehicles. The solution involves communication between users and a mmWave-band on-board unit that aggregates/disseminates data streams from/to commuters and maintains the connection with the nearest terrestrial network infrastructure node. In this paper, we provide a measurement-based channel model for the 60-GHz mmWave propagation inside a typical inter-city bus. The model characterizes power delay profile (PDP) of the wireless intra-vehicular channel, and it is derived from about 1000 data sets measured within the bus. The proposed analytical model is further translated into a simple simulation algorithm that generates in-vehicle channel PDPs. Different goodness-of-fit tests confirm that the simulated PDPs are in good agreement with the measured data. Finally, a tapped-delay-line (TDL) channel model is formulated from the proposed PDP model, and the TDL model is used to study the bit error rate (BER) performance of the mmWave link inside bus under varying data rates and link lengths

Survey of smart parking systems

The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.Fil: Diaz Ogás, Mathias Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Fabregat Gesa, Ramon. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

Probing with Noise: Unpicking the Warp and Weft of Taxonomic and Thematic Meaning Representations in Static and Contextual Embeddings

The semantic relatedness of words has two key dimensions: it can be based on taxonomic information or thematic, co-occurrence-based information. These are captured by different language resources—taxonomies and natural corpora—from which we can build different computational meaning representations that are able to reflect these relationships. Vector representations are arguably the most popular meaning representations in NLP, encoding information in a shared multidimensional semantic space and allowing for distances between points to reflect relatedness between items that populate the space. Improving our understanding of how different types of linguistic information are encoded in vector space can provide valuable insights to the field of model interpretability and can further our understanding of different encoder architectures. Alongside vector dimensions, we argue that information can be encoded in more implicit ways and hypothesise that it is possible for the vector magnitude—the norm—to also carry linguistic information. We develop a method to test this hypothesis and provide a systematic exploration of the role of the vector norm in encoding the different axes of semantic relatedness across a variety of vector representations, including taxonomic, thematic, static and contextual embeddings. The method is an extension of the standard probing framework and allows for relative intrinsic interpretations of probing results. It relies on introducing targeted noise that ablates information encoded in embeddings and is grounded by solid baselines and confidence intervals. We call the method probing with noise and test the method at both the word and sentence level, on a host of established linguistic probing tasks, as well as two new semantic probing tasks: hypernymy and idiomatic usage detection. Our experiments show that the method is able to provide geometric insights into embeddings and can demonstrate whether the norm encodes the linguistic information being probed for. This confirms the existence of separate information containers in English word2vec, GloVe and BERT embeddings. The experiments and complementary analyses show that different encoders encode different kinds of linguistic information in the norm: taxonomic vectors store hypernym-hyponym information in the norm, while non-taxonomic vectors do not. Meanwhile, non-taxonomic GloVe embeddings encode syntactic and sentence length information in the vector norm, while the contextual BERT encodes contextual incongruity. Our method can thus reveal where in the embeddings certain information is contained. Furthermore, it can be supplemented by an array of post-hoc analyses that reveal how information is encoded as well, thus offering valuable structural and geometric insights into the different types of embeddings

Quality of Service in Vehicular Ad Hoc Networks: Methodical Evaluation and Enhancements for ITS-G5

After many formative years, the ad hoc wireless communication between vehicles has become a vehicular technology available in mass production cars in 2020. Vehicles form spontaneous Vehicular Ad Hoc Networks (VANETs), which enable communication whenever vehicles are nearby without need for supportive infrastructure. In Europe, this communication is standardised comprehensively as Intelligent Transport Systems in the 5.9 GHz band (ITS-G5). This thesis centres around Quality of Service (QoS) in these VANETs based on ITS-G5 technology. Whilst only a few vehicles communicate, radio resources are plenty, and channel congestion is a minor issue. With progressing deployment, congestion control becomes crucial to preserve QoS by preventing high latencies or foiled information dissemination. The developed VANET simulation model, featuring an elaborated ITS-G5 protocol stack, allows investigation of QoS methodically. It also considers the characteristics of ITS-G5 radios such as the signal attenuation in vehicular environments and the capture effect by receivers. Backed by this simulation model, several enhancements for ITS-G5 are proposed to control congestion reliably and thus ensure QoS for its applications. Modifications at the GeoNetworking (GN) protocol prevent massive packet occurrences in a short time and hence congestion. Glow Forwarding is introduced as GN extension to distribute delay-tolerant information. The revised Decentralized Congestion Control (DCC) cross-layer supports low-latency transmission of event-triggered, periodic and relayed packets. DCC triggers periodic services and manages a shared duty cycle budget dedicated to packet forwarding for this purpose. Evaluation in large-scale networks reveals that this enhanced ITS-G5 system can reliably reduce the information age of periodically sent messages. The forwarding budget virtually eliminates the starvation of multi-hop packets and still avoids congestion caused by excessive forwarding. The presented enhancements thus pave the way to scale up VANETs for wide-spread deployment and future applications

LiNEV: Visible Light Networking for Connected Vehicles

DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) has been introduced to visible light networking framework for connected vehicles (LiNEV) systems as a modulation and multiplexing scheme. This is to overcome the light-emitting diode (LED) bandwidth limitation, as well as to reduce the inter-symbol interference caused by the multipath road fading. Due to the implementation of the inverse fast Fourier transform, DC-OFDM suffers from its large peak-to-average power ratio (PAPR), which degrades the performance in LiNEV systems, as the LEDs used in the vehicles’ headlights have a limited optical power-current linear range. To tackle this issue, discrete Fourier transform spread-optical pulse amplitude modulation (DFTS-OPAM) has been proposed as an alternative modulation scheme for LiNEV systems instead of DCO-OFDM. In this paper, we investigate the system performance of both schemes considering the light-emitting diode linear dynamic range and LED 3 dB modulation bandwidth limitations. The simulation results indicate that DCO-OFDM has a 9 dB higher PAPR value compared with DFTS-OPAM. Additionally, it is demonstrated that DCO-OFDM requires an LED with a linear range that is twice the one required by DFTS-OPAM for the same high quadrature amplitude modulation (QAM) order. Furthermore, the findings illustrate that when the signal bandwidth of both schemes significantly exceeds the LED modulation bandwidth, DCO-OFDM outperforms DFTS-OPAM, as it requires a lower signal-to-noise ratio at a high QAM order

Electrical equivalent circuit models for brushless doubly-fed induction machines: A review

A Brushless Doubly-Fed Induction Machine (BDFIM) has wide application potential in wind turbines, industrial drives, small hydro power plants, power supply of ships, aircraft starters, and electric vehicles. In electric vehicles, a BDFIM offers a robust structure without needing permanent magnet materials, slip-rings, and brushes, which makes it particularly suitable for heavy vehicles, such as buses, agriculture and construction vehicles, where the low power density of a BDFIM is of less significance compared to that in passenger cars. To effectively analyze and control a BDFIM in various applications, it is essential to develop its accurate electrical equivalent circuit models. This paper presents a comprehensive review on various electrical equivalent circuit models of BDFIMs in the literature with different level of complexity. A full order model is complex, but can represent the complete electrical behavior of a BDFIM. By simplifying the full order model and neglecting certain parameters, a reduced order model requires fewer state variables due to practical approximations. Dynamic behavior of BDFIMs under various operating conditions are demonstrated in the paper to showcase the effectiveness of these existing models. This review emphasizes the significance of electrical equivalent circuit models in the design, analysis and control of BDFIMs. Future research directions for the model refinement are recommended