Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Task Offloading Strategies in IoV

In this paper, we study task offloading for mobile edge computing in the Internet of Vehicles (IoV). We use response time as a system performance metric and use a closed queuing network to analyze the optimal task offloading probability, which enables task offloading to have latency fairness.When a vehicle offloads a task, there is an optimal offload probability allocation ratio so that the task has the same response time regardless of whether the task is offloaded to the cloud center or the edge layers to achieve task offload fairness. The distribution ratio to achieve fairness in task offloading is affected by the number of vehicles. Through experiments, we learn that the more vehicles there are, the more computational tasks need to be offloaded to the cloud server to achieve fairness. Also, the average response time of the edge server grows rapidly as the number of vehicles increases.
Security Strategies for Unmanned Aircraft Systems Networks

The rapid growth of Unmanned Aircraft Systems (UASs) usage in both commercial and defense areas has increased the requirement for advanced security schemes for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications for UASs. We define a UAS as consisting of a Control Station (CS) on the ground and at least one UAV. The pilot in the CS may have Visual Line-of-Sight (VLOS) or may not have VLOS to the airborne UAVs. The integration of UASs into the National Airspace (NAS) will rely on Unmanned Traffic Management (UTM) systems which can support Detect-And-Avoid (DAA) and de-confliction of flight paths. In case of non-cooperative traffic, tactical (in-flight) deconfliction becomes a critical element. In Beyond Visual Line of Sight (BVLoS) operations, where a remote pilot is unable to visually monitor the airspace beyond LoS, Sense-and-Avoid strategies become necessary. Further, the communication devices and messages must be protected against hacking and cyber-attacks. This paper investigates several use cases for UAS-to-UAS or V2V operations for UASs and the cyber-security strategies for protecting V2V communications addressed by IEEE P1920.2, a new standard for V2V communications for UASs, currently under development. This discussion included authentication of V2V parties, protection of Remote ID, cryptographic key management, and authorization policies based on a zero-trust architecture.
Sliding Mode Interval Observer-Based Controller Design for Multi-Agent Systems Under DoS Attack

The consensus problem of the multi-agent systems with uncertainty and denial-of-service attack is studied. The interval observer control method is utilized to reduced the influence of unknown external disturbances. By introducing sliding mode term, the observation error of the system is released and the robustness is improved. Using the coordination theory, the control algorithm based on the sliding mode interval observer drive the uncertain system to achieve the coordinated consensus. In order to make the multi-agent systems achieve asymptotically consensus under denial-of-service attack, an attack time security protocol based on persistent dwell time scheme is proposed. Finally, the simulation results are given to demonstrate the validity and superiority of the proposed method.
Does the Quality of Real Economic Growth Occur in Indonesia? Pro-Poor Based on Human Capital Perspectives

This study aims to investigate whether the quality of real economic growth occurs in Indonesia which is pro-poor based on human capital. The data used in this study is panel data sourced from the Indonesian Central Bureau of Statistics. The panel data covers 505 regencies/cities spread across 33 provinces in Indonesia during 2014-2020. The total number of panel data is 3535 in 7 years. The panel data model is used to attain the purpose of this study. The results of this study show that the economic growth of the 505 regencies/cities in Indonesia during 2014-2020 has not been pro-poor economic growth yet or the economic growth has not improved the economic conditions of the poor. This highlights that the quality of economic growth in Indonesia is still low. Therefore, the Indonesian government has to formulate a new economic framework orientation that is more focused on labor-intensive which is based on human capital through reforming pro-poor education and health programs and public infrastructures (Aggregate Supply Side). In addition, the quality of fiscal and monetary policy management (Aggregate Demand Side) which is pro-poor has to be strengthened. This comprehensive policy will shape the long-term self-sufficient quality of economic growth.
A New Intrusion Detection System Based on Convolutional Neural Network

In 2020 only, 36 billion records have been leaked, 95% of those attacks have been caused by human error. Therefore organizations have been looking for multiple technologies to secure there system. One of the modern techniques is using machine learning for traffic classification which help us detect attacks based on monitoring data flow in our network or our workstation. In this paper we presents an implementation of new proposed model based on convolutional neural network (CNN) and long short term memory (LSTM). It is evident from the investigations that different machine learning methods can be used for intrusion detection. Further, the results demonstrated that the usage of machine learning techniques produce positive impact on improving the overall performance of the intrusion detection system in terms of accuracy and lowering false negatives. We using two of most known Deep Learning algorithms CNN and GRU (Convolutional Neural Network, Gated Recurrent Unit) to have a base ground regarding the performance of our proposed model. we are getting encouraging results with implementation of two of famous data-set (NSL-KDD and CIC-IDS2018) We tested our models on binary and multi-class classification for further observation.
The Benefits of Combining Bobath and Vojta Therapies in Infants with Motor Development Impairment—A Pilot Study

Background: In infants presenting with motor development impairment, early kinesiotherapeutic interventions aim to normalise the pattern of movements and improve recovery. By applying Bobath and Vojta methods, we aimed to identify a combined approach regarding motor deficit in infants with neurological disabilities. Methods: We designed a prospective interventional study on 108 infants with motor developmental delay and applied Bobath, Vojta, or combined Bobath and Vojta therapy in three equal groups. Results: In the combined Bobath and Vojta group, complete motor recovery was achieved for 50% of the participants, with full recovery after six months, whereas in Bobath- or Vojta-only therapy groups, the total recovery for all participants was achieved at seven months. Regarding infants with muscular hypertonia, Bobath therapy initiation demonstrated complete recovery in 5 months in more than 50% of the cases, while for Vojta this was achieved in only 33.57% of the cases. Conclusions: The comparative evaluation conducted by analysing the data regarding the application of the Bobath and Vojta methods showed that combining these two therapies results in a shorter motor deficit recovery time than if a single therapy is applied. These findings have important implications for the selection of rehabilitation therapies in infants with neurological motor development issues.
Short Versus Long Antibiotic Therapy and Risk of Recurrence of Acute Cholangitis Due to Malignant Biliary Strictures

Malignancies can cause severe stenosis of the biliary tract and therefore predispose a patient to bacterial cholangitis. Upon endoscopic drainage, antibiotic therapy (AT) is performed according to individual clinical judgement, as the optimal duration of AT is unclear to date, especially in the case of multidrug-resistant organisms (MDROs). In a case-based retrospective study, patients with malignant biliary strictures and acute cholangitis were included upon endoscopic retrograde cholangiography (ERC). The outcome of cases treated with short AT (≤6 days) was compared to that of long AT (≥7 days). Recurrent cholangitis (RC) before scheduled stent exchange was the primary end point. In total, 124 patients were included, with 183 cases of proven cholangitis in total. The overall median duration of AT was 7 days (range 1–20), with 74 cases (40%) receiving short AT and 109 (60%) receiving long AT. Short AT was not an independent risk factor for RC (HR = 0.66, p > 0.2), while colonization with MDROs was associated with a higher risk of RC (HR = 2.21, p = 0.005). Placement of a metal stent was associated with minor risk of RC (HR = 0.4, p = 0.038). In conclusion, short AT is possible in selected patients with non-severe cholangitis and malignant biliary strictures. Scheduled screening for MDROs is recommended and placement of a metal stent should be performed if possible.
Intelligent Processing of Data Streams on the Edge Using Reinforcement Learning

A key challenge in many IoT applications is to en-sure energy efficiency while processing large amounts of streaming data at the edge. Nodes often need to process time-sensitive data using limited computing and communication resources. To that end, we design a novel R - Learning based Offloading framework, RLO, that allows edge nodes to learn energy optimal decisions from experience regarding processing incoming data streams. In particular, when should the node process data locally? When should it transmit data to be processed by a fog node? And when should it store data for later processing? We validate our results on both real and simulated data streams. Simulation results show that RLO learns with time to achieve better overall-rewards with respect to three existing baseline schemes. Moreover, the proposed algorithm excels the existing baseline schemes when different priorities were set on the two objectives. We also illustrate how to adjust the priorities of the two objectives based on the application requirements and network constraints.
Rapid identification of pathogens in blood serum via Raman tweezers in combination with advanced processing methods

Pathogenic microbes contribute to several major global diseases that kill millions of people every year. Bloodstream infections caused by these microbes are associated with high morbidity and mortality rates, which are among the most common causes of hospitalizations. The search for the “Holy Grail” in clinical diagnostic microbiology, a reliable, accurate, low cost, real-time, and easy-to-use diagnostic method, is one of the essential issues in clinical practice. These very critical conditions can be met by Raman tweezers in combination with advanced analysis methods. Here, we present a proof-of-concept study based on Raman tweezers combined with spectral mixture analysis that allows for the identification of microbial strains directly from human blood serum without user intervention, thus eliminating the influence of a data analyst.
Aerobic exercise 3 times per week in adult rats did not influence the progression of periodontal disease

Introduction Physical exercise has proven efficacy in the prevention and treatment of chronic diseases, and its anti-inflammatory effect has been evaluated as a potential preventive factor in the progression of periodontal disease, in addition to improving physiological parameters. Methods To test this hypothesis regarding its preventive factor, we evaluated the effects of aerobic training on the progression of periodontal disease in 8-month-old Wistar rats (n = 44). The animals underwent a swimming protocol lasting six weeks, with periodontal disease induced by ligature in the fifth week, totaling fourteen days of ligature placement. Anthropometric parameters were measured for subsequent calculations of BMI and Lee's Index. Interleukin-1β testing was performed to measure serum inflammatory parameters, and alveolar bone loss was measured using images to calculate the area of loss. Results The trained animals showed no significant differences compared to the non-trained animals in terms of anthropometric measures. Regarding the area of bone loss, although there were significant differences between the groups with and without periodontal disease, exercise did not demonstrate an impact on rats with the disease. IL-1β analysis did not detect any measurable values in the samples in either group. Discussion These findings indicate that the applied exercise protocol was not sufficient to attenuate the progression of periodontal disease. This study did not find an effective impact of physical exercise on the analyzed parameters; however, the results are important in highlighting that the experimental animal model for inducing periodontal disease is efficient, which may encourage further investigations to determine factors that can attenuate its progression. Similarly, the application or development of new exercise protocols that can benefit and enrich the discussion on its positive effects in this disease is important, as there is already evidence suggesting an effective relationship between exercise and disease progression.
Intraplate earthquake swarm in Belo Jardim, NE Brazil: reactivation of a major Neoproterozoic shear zone (Pernambuco Lineament)

SUMMARY Intraplate earthquakes in stable continental areas have been explained basically by reactivation of pre-existing zones of weakness, stress concentration, or both. Zones of weakness are usually identified as sites of the last major orogeny, provinces of recent alkaline intrusions, or stretched crust in ancient rifts. However, it is difficult to identify specific zones of weakness and intraplate fault zones are not always easily correlated with known geological features. Although Northeastern Brazil is one of the most seismically active areas in the country (magnitudes 5 roughly every 5 yr), with hypocentral depths shallower than ∼10 km and seismic zones as long as 30–40 km, no clear relationship with the known surface geology can be usually established with confidence, and a clear identification of zones of weakness has not yet been possible. Here we present the first clear case of seismic activity occurring as reactivation of an old structure in Brazil: the Pernambuco Lineament, a major Neoproterozoic shear zone. The 2004 earthquake swarm of Belo Jardim (magnitudes up to 3.1) and the recurrent activities in the nearby towns of Sao Caetano and Caruaru (magnitudes up to 4.0 and 3.8), show that the Pernambuco Lineament is a weak zone. A local seismic network showed that the Belo Jardim swarm of 2004 November occurred by normal faulting on a North dipping, E–W oriented fault plane in close agreement with the E–W trending structures within the Pernambuco Lineament. The Belo Jardim activity was concentrated in a 1.5 km (E–W) by 2 km (downdip) fault area, and average depth of 4.5 km. The nearby Caruaru activity occurs as both strike-slip and normal faulting, also consistent with local structures of the Pernambuco Lineament. The focal mechanisms of Belo Jardim, Caruaru and S. Caetano, indicate E–W compressional and N–S extensional principal stresses. The NS extension of this stress field is larger than that predicted by numerical models such as those of Coblentz & Richardson and we propose that additional factors such as flexural stresses from the nearby Sergipe-Alagoas marginal basin could also affect the current stress field in the Pernambuco Lineament.
Load Prediction in Edge Computing Using Deep Auto-Regressive Recurrent Networks

Load prediction is an essential technique to improve edge system performance by proactively configuring and allocating system resources. Traditional load prediction methods obtain high prediction when handling loads exhibiting cyclical trend behavior, but they are unable to capturing highly-variable loads in edge computing environments. Existing studies fit prediction models via independent time series and output single-point real-value predictions. However, in practical edge scenarios, it is more valuable to obtain application value by utilizing the probability distribution of future loads rather than directly predicting specific values. To solve these problems, we propose an Edge Load Prediction method empowered by Deep Auto-regressive Recurrent networks (ELP-DAR). The ELP-DAR uses the time-series data of edge loads to train deep auto-regressive recurrent networks, which integrate Long Short-Term Memory (LSTM) into the S2S framework to calculate the parameters of the probability distribution at the next time-point. Therefore, the ELP-DAR can efficiently extract the essential representations of edge loads and learn their complex patterns, and the probability distribution for highly-variable edge loads can be accurately predicted. Extensive simulation experiments are conducted to validate the effectiveness of the proposed ELP-DAR method based on real-world edge load datasets. The results show that the ELP-DAR achieves higher prediction accuracy than other benchmark methods with different prediction lengths.
A Sub-THz Micro-Doppler Radar for Counter-Surveillance Applications

We present a technique for detecting drones by exploiting their unique micro-Doppler signatures. While most commercially available drones are usually employed for recreational activities, their advantageous features, such as excellent camera resolution, load bearing capabilities, etc., could be misused to carry out illegal surveillances, drug trafficking etc. Because of their low-radar cross section (RCS) profile and low speed, it is difficult to detect drones using conventional radar systems. Moreover, additional information is needed to further distinguish them from surrounding clutter, such as birds. To address these issues, we have designed a sub-THz continuous wave (CW) micro-Doppler radar that can capture the micromotions unique to drones. The radar operates at 270 GHz and can successfully discriminate the rotational and translational motions of the targets. A real-world experimental setup is built to demonstrate the operation of the proof-of-concept radar designed in this work. A simple classification technique based on Short-Time Fourier Transform (STFT) is used to extract the micro-Doppler features of the drones.
Numerical Study of the Effect of the Trailing-Edge Devices (Gurney Flap and Divergent Trailing-edge Flap) on the Aerodynamic Characteristics of an Airfoil in Transonic Flow for Drone Applications

The primary objective of this research was to examine how the inclusion of mini Trailing-edge devices (referred to as mini TEDs) impacts the aerodynamic properties of the RAE-2822 airfoil. Utilizing ANSYS Fluent software, we conducted numerical simulations to analyze the behavior of compressible flow around the airfoil, specifically at a Mach number (Ma) of 0.73, while varying the angles of attack. The mini TEDs employed in our simulations com - prised a Gurney flap and a divergent trailing-edge. Our findings demonstrate that incorporating these mini TEDs at the trailing-edge leads to a noteworthy increase in the lift coefficient (CL) and drag coefficient (CD). However, it is worth noting that this enhancement resulted in an improved lift-to-drag ratio for the airfoil for specific angles of attack. These outcomes underscore the potential advantages of employing mini TEDs to enhance aerodynamic performance, especially in aerospace drone applications.
RIS-Assisted Grant-Free NOMA: User Pairing, RIS Assignment, and Phase Shift Alignment

This paper introduces a reconfigurable intelligent surface (RIS)-assisted grant-free non-orthogonal multiple access (GF-NOMA) scheme. We propose a joint user equipment (UE) clustering and RIS assignment/alignment approach that jointly ensures the power reception disparity required by the power domain NOMA (PD-NOMA). The proposed approach maximizes the network sum rate by judiciously pairing UE with distinct channel gains and assigning RISs to proper clusters. To alleviate the computational complexity of the joint approach, we decouple UE clustering and RIS assignment/alignment subproblems, which reduces run times 80 times while attaining almost the same performance. Once the proposed approaches acknowledge UEs with the cluster index, UEs are allowed to access corresponding resource blocks (RBs) at any time requiring neither further grant acquisitions from the base station (BS) nor power control as all UEs are requested to transmit at the same power. In addition to passive RISs containing only passive elements and giving an 18% better performance, an active RIS structure that enhances the performance by 37% is also used to overcome the double path loss problem. The numerical results also investigate the impact of UE density, RIS deployment, RIS hardware specifications, and the fairness among the UEs in terms of bit-per-joule energy efficiency.
Sistemas conversacionais: aspetos culturais influenciam a avaliação de UX de um protótipo conceptual de assistente de voz

Este artigo analisa a experiência de 100 indivíduos (50 brasileiros e 50 portugueses) no contexto de um sistema conversacional com assistente de voz automatizado. A avaliação da UX foi realizada em diferentes etapas: cálculo da estatística descritiva, análise das frases ditas pelos utilizadores e aplicação da escala SUS. Esta abordagem metodológica permitiu verificar que o protótipo conceptual simula um produto com grande potencial para facilitar o dia a dia dos profissionais que atuam em salões de estética e beleza. No entanto, há funcionalidades que devem ser redesenhadas, como a escolha dos nomes dos botões e a acessibilidade da ferramenta. Outra contribuição do estudo é a confirmação de que há opiniões divergentes entre as populações da amostra, brasileira e portuguesa, mostrando que a solução teve melhor aceitação entre os portugueses. Esta última questão é um dos aspetos mais relevantes dos achados deste trabalho, pois pode comprometer o sucesso de tal assistente.
Adaptive User-centric Virtual Cell Handover Decision-making in 5G Vehicular Networks

Connected vehicles enable massive data sharing and support intelligent transportation services. Consequently, a stable connection is compulsory to transmit across the network successfully, where 5G technology introduces more bandwidth, stability, and reliability. However, 5G communication is susceptible to frequent handovers and connection drops. A user-centric perspective helps cope with the smaller communication range in ultra-dense 5G networks. We thus introduce a Connectivity-oriented SARSA Reinforcement Learning (CO-SRL) algorithm for user-centric to efficiently handle virtual cell (VC) management and reduce the number of handovers (HO). The adaptability of the algorithm copes with high vehicular mobility and dynamic traffic and communication, deciding on in-rage cellular towers and VC size. Realistic simulated analyses showed CO-SRL reduced the number of handovers and the cumulative handover time.