Miracle-dz/UniFAR-FTUs huggingface dataset:
Authenticating Drone-Assisted Internet of Vehicles Using Elliptic Curve Cryptography and Blockchain

The inclusion of drones in Internet of Vehicles (IoV) is a current trend that presents significant trade-offs. On the one hand, Unmanned Aerial Vehicles (UAVs) provide advantages such as enabling ground communications also when physical obstacles limit the connectivity. On the other hand, they increase the attack surface. For instance, physical attacks on drones provide the attacker with credentials that can be used to inject bogus information into the IoV network, thus jeopardizing not only security but also users’ safety. In this scenario, authentication plays a fundamental role to guarantee security. It is however fundamental to develop authentication protocols that can, at the same time, protect ground users’ data and prevent attacks to drones. However, currently available authentication schemes cannot guarantee security in case of attacks to drones. In this paper, we propose a Blockchain-supported authentication protocol for Drone-assisted IoV using Elliptic curve cryptography (BDIVE). Compared to existing authentication protocols, we extend the threat model from an honest-but-curious drone to active attacks against drones. BDIVE provides both energy-efficiency, traceability, and accountability thanks to the use of blockchain at the Trusted Authority (TA). Using Burrow-Abadi–Needham (BAN) logic, we analyze and prove the security of mutual authentication in BDIVE. We also prove the security of BDIVE against several attacks by implementing it in AVISPA. To assess its scalability and energy efficiency, we implement BDIVE using Omnetpp with its Castalia simulator. The comparison of BDIVE with currently existing authentication protocols, shows that it reduces the energy consumption up to 70% and the computational cost up to 68%, while providing resistance to previously unconsidered attack vectors.
Coupling Crustal‐Scale Rift Architecture With Passive Margin Salt Tectonics: A Geodynamic Modeling Approach

Continental rifted margins are often associated with widespread, thick evaporite (i.e., salt) deposits and pronounced salt tectonics. The majority of salt basins formed during the latest stages of rifting, prior to continental breakup. We use 2D thermo‐mechanical finite element modeling of lithospheric extension to investigate the interplay between rifted margin architecture, late syn‐rift salt deposition, and post‐rift salt tectonics. We focus on four different types of continental margins: (a) narrow, (b) intermediate, (c) wide, and (d) ultra‐wide margins. We evaluate the: (a) interplay between laterally variable syn‐rift extension, salt deposition and salt tectonics, (b) influence of syn‐rift basin architecture on post‐rift salt flow, (c) spatial and temporal distribution of salt‐related structural domains, and (d) contrasting styles of salt tectonics for different margin types. Narrow and intermediate margins form partially isolated salt basins associated with prominent base‐salt relief, limited translation but significant diapirism, and minibasin development. Wide and ultra‐wide margins form wide salt basins with subtle base‐salt relief that results in significant seaward salt expulsion and overburden translation. These wide margins demonstrate significant updip extension with the development of post‐rift normal faults and rollovers, mid‐margin translation associated with complex diapirism and downdip diapir shortening. All margins contain a distal salt nappe that varies in width and complexity. We also test the effect of different salt viscosities, relative post‐salt progradation rates, and pre‐salt sediment thicknesses. The results are comparable to several examples of salt‐bearing rifted margins and improve our understanding of their dynamics and on the controls on their salt tectonics variability.
Improved Pixel and Bit Confusion-Diffusion Based on Mixed Chaos and Hash Operation for Image Encryption

Confusion and diffusion are two main concepts in cryptography that affect the quality of encryption and resistance to various attacks. Both of these concepts are influenced by the quality of the keystream used. Keystream generation needs to involve plain images as one way to bind sensitivity to plain images. But when it involves plain images directly, encryption security has negative side effects. So this research proposes to perform a diffusion operation with a logistic map on a plain image before generating the keystream. Not only using one keystream, the proposed method also uses three other keystreams and Arnold transform whose input parameters are processed by hash operations. Two keystreams are generated using the four-dimensional Lorenz system for the permutation of column and row image pixels. Next, sixteen Arnold transforms with different parameters are used for bit permutation. The last keystream is generated with a Henon map to carry out the diffusion stage two so that a cipher image is produced. Based on the testing results, the proposed method has satisfactory encryption results that are resistant to various attacks, especially brute force, statistical, and differential attacks.
Contraction Analysis of Geodesically Convex Optimization

Optimization is central to problems across machine learning, estimation, automatic control, and many other areas. Convex optimization problems are an important subclass, admitting global solutions and mature solution methods. Recent attention has focused on geodesic convexity, or g-convexity, which allows non-convex problems to be transformed into convex ones posed over Riemannian manifolds. The main contribution of this paper is to provide a bridge between g-convexity and contraction analysis of nonlinear dynamical systems. Specifically, we show the equivalence between strong geodesic convexity and contraction of natural gradient flows. Given this result, existing tools for the analysis and synthesis of nonlinear contracting systems can be considered to both discover and efficiently optimize g-convex functions. In addition, the contraction perspective allows one to easily apply results to time-varying optimization settings, and to recursively build large optimization structures out of simpler elements.
Realization of a 2-V Programmable Josephson Junction Array Using Balanced Segmentation Scheme

This article proposes a balanced segmentation scheme to improve the microwave transmission and develops a 2-V programmable Josephson junction array using double-stacked superconductor-normal-metal-superconductor Josephson junctions. The junction numbers of the sub-Josephson arrays are divided into a ternary sequence. The least significant bit is one stack or two junctions. In order to characterize the chip with many subarrays, an automatic measurement system with self-made multichannel current sources was established. It can analyze the critical current, the margin of the voltage steps, and the number of junctions in each subarray. The measurement found that the mentioned junction arrays can work at several microwave frequency ranges with an operating margin higher than 1.5 mA. Moreover, the circuit can produce intrinsically stable dc voltages from –2 to +2 V and generate stepwise-approximated ac voltages.
Dense holomorphic curves in spaces of holomorphic maps and applications to universal maps

We study when there exists a dense holomorphic curve in a space of holomorphic maps from a Stein space. We first show that for any bounded convex domain $\Omega\Subset\mathbb{C}^n$ and any connected complex manifold $Y$, the space $\mathcal{O}(\Omega,Y)$ contains a dense holomorphic disc. Our second result states that $Y$ is an Oka manifold if and only if for any Stein space $X$ there exists a dense entire curve in every path component of $\mathcal{O}(X,Y)$. In the second half of this paper, we apply the above results to the theory of universal functions. It is proved that for any bounded convex domain $\Omega\Subset\mathbb{C}^n$, any fixed-point-free automorphism of $\Omega$ and any connected complex manifold $Y$, there exists a universal map $\Omega\to Y$. We also characterize Oka manifolds by the existence of universal maps.
The “fruit and whole‐grain” pattern is associated with a low prevalence of hypertriglyceridemia among middle and older‐aged Korean adults: Using Korea National Health and Nutrition Examination Survey 2013–2018 data

Abstract Hypertriglyceridemia is a well‐known risk factor of various chronic diseases including diabetes mellitus, metabolic syndrome, obesity, and cardiovascular diseases. This study aimed to determine dietary patterns and explore the relationship between dietary patterns and hypertriglyceridemia in the Korean adult population. We utilized a cross‐sectional and nationally representative survey, the Korea National Health and Nutrition Examination Survey 2013–2018 database. From 47,217 subjects who participated in the survey between 2013 and 2018, only subjects over 40 years old were included. Subjects lacking 24‐h recall data and data on hypertriglyceridemia and body mass index, and who had implausible energy intake were excluded. A total of 19,806 participants' data were analyzed. Dietary data were based on 24‐h recall data, and dietary patterns were derived using factor analysis. Triglyceride levels greater than 200 mg/dl were considered hypertriglyceridemia, according to the Korean Society of Lipid and Atherosclerosis. Three dietary patterns— “oil and fats & seasoning”, “soybean paste and vegetable”, and “fruit and whole‐grain”— explained 7.9%, 6.3%, and 5.8% of variation in food intake, respectively. Comparing the lowest and highest dietary pattern score groups after adjusting for potential confounders revealed an inverse relationship between “fruit and whole‐grain” dietary pattern and hypertriglyceridemia in men (odds ratio [OR]: 0.61, 95% confidence interval [CI]: 0.45–0.82, p for trend <.0001); which was only marginal in women (OR: 0.78, 95% CI: 0.58–1.07, p for trend: .628). A diet containing high proportions of fruit and whole‐grain may have preventive effects on hypertriglyceridemia in middle and older aged Korean adults.
GENERALIZED HARDY–CESÀRO OPERATORS BETWEEN WEIGHTED SPACES

Abstract We characterize those non-negative, measurable functions ψ on [0, 1] and positive, continuous functions ω1 and ω2 on ℝ+ for which the generalized Hardy–Cesàro operator $$\begin{equation*}(U_{\psi}f)(x)=\int_0^1 f(tx)\psi(t)\,dt\end{equation*}$$ defines a bounded operator Uψ: L1(ω1) → L1(ω2) This generalizes a result of Xiao [7] to weighted spaces. Furthermore, we extend Uψ to a bounded operator on M(ω1) with range in L1(ω2) ⊕ ℂδ0, where M(ω1) is the weighted space of locally finite, complex Borel measures on ℝ+. Finally, we show that the zero operator is the only weakly compact generalized Hardy–Cesàro operator from L1(ω1) to L1(ω2).
l-Arginine as Bio-Based Curing Agent for Epoxy Resins: Temperature-Dependence of Mechanical Properties

The precise characterization of new bio-based thermosets is imperative for the correct assessment of their potential as matrix material in fiber-reinforced polymer composites. Therefore, the mechanical properties of diglycidyl ether of bisphenol a (DGEBA) cured with l-arginine were investigated to determine whether the bio-based thermoset possesses the required mechanical properties for application as a matrix material. The cured thermoset is called Argopox. The mixture of amino acid and epoxy resin was prepared via three-roll milling and cured in the presence of an urea-based accelerator. The tensile, compression, flexural and toughness properties of Argopox were characterized at T=−40 ∘C, 22 ∘C and 80 ∘C to determine the temperature-dependence of the thermoset’s mechanical properties in its service temperature range. The glass transition temperature Tg was analyzed via dynamic mechanical analysis (DMA) and is approximately 119 ∘C. The tensile, compression and flexural strength at 22 ∘C are about 56 MPa, 98 MPa and 85 MPa, respectively. The critical stress intensity factor KIC and fracture energy GIC at 22 ∘C are roughly 1.1 MPam0.5 and 510 Jm−, respectively. Consequently, Argopox possesses mechanical properties that reach performance levels similar to that of materials which are already used as matrix for fiber reinforced composites.
Six-Degree-of-Freedom Posture Measurement Technologies Using Position Sensitive Detectors (PSDs): State of the Art

Six degree-of-freedom (6-DOF) posture measurement is an important academic research topic which has been broadly applied in many fields. As a high-speed photoelectronic sensor with ultra-high resolution and precision, position sensitive detector (PSD) has shown to be one of the most competitive candidates in 6-DOF measurement. This review presents the research progress of PSD-based 6-DOF posture measurement systems in the field of large-scale equipment assembly, ultra-precision manufacturing and other emerging areas. A total of six methods for implementing 6-DOF measurement are summarized and their advantages and limitations are discussed. Meanwhile, the paper illustrates challenges, potential solutions and future development trends.
Area-Efficient 1T-2D-2MTJ SOT-MRAM Cell for High Read Performance

This brief proposes a compact spin-orbit torque magnetic random-access memory (SOT-MRAM) cell structure with high read performance for embedded nonvolatile memory (eNVM) applications. The memory cell is composed of one transistor, two Schottky diodes, two magnetic tunnel junctions (MTJs) and one shared heavy metal (HM) layer, which can be abbreviated as 1T-2D-2MTJ cell. The two MTJs are stacked on the same HM layer for smaller area overhead and configured to be complementary with the magnetization of their pinned layers in the opposite direction to double the read sense margin. The write operation is also simplified since a current through the HM layer changes the resistances of both MTJs simultaneously. The evaluations include the micromagnetic simulation of the SOT device, the analysis of PVT variations for the read operation on bit-cell level, the estimation of bit-cell area and the simulations of performance and power consumption on array level. The results show that the proposed SOT-MRAM improves the performance and reliability of the read operation and achieves 75% (50%) reduction in bit-cell area compared to 4T-2MTJ SOT-MRAM (2T-2MTJ STT-MRAM), 83.0% reduction in write power consumption and 91.1% reduction in write latency compared to 2T-2MTJ STT-MRAM.
CSI-DeepNet: A Lightweight Deep Convolutional Neural Network Based Hand Gesture Recognition System Using Wi-Fi CSI Signal

Hand gesture is a visual input of human-computer interaction for providing different applications in smart homes, healthcare, and eldercare. Most deep learning-based techniques adopt standard convolution neural networks (CNNs) which require a large number of model parameters with high computational complexity; thus, it is not suitable for application in devices with limited computational resources. However, fewer model parameters can reduce the system accuracy. To address this challenge, we propose a lightweight heterogeneous deep learning-based gesture recognition system, coined CSI-DeepNet. The CSI-DeepNet comprises four steps: i) data collection, ii) data processing, iii) feature extraction, and iv) classification. We utilize a low-power system-on-chip (SoC), ESP-32, for the first time to collect alphanumeric hand gesture datasets using channel state information (CSI) with 1,800 trials of 20 gestures, including the steady-state data of ten people. A Butterworth low-pass filter with Gaussian smoothing is applied to remove noise; subsequently, the data is split into windows with sufficient dimensions in the data processing step before feeding to the model. The feature extraction section utilizes a depthwise separable convolutional neural network (DS-Conv) with a feature attention (FA) block and residual block (RB) to extract fine-grained features while reducing the complexity using fewer model parameters. Finally, the extracted refined features are classified in the classification section. The proposed system achieves an average accuracy of 96.31% with much less computational complexity, which is better than the results obtained using state-of-the-art pre-trained CNNs and two deep learning models using CSI data.
The Value of Local Content in ELT Materials in Indonesia

The new paradigm on the position of English in the world has shifted as now English was no longer the property of native speakers. This shifting paradigm challenge the traditional assumption of English teaching world on whether English teachers should incorporate local or target culture in their teaching materials. This is a qualitative research using critical literature review method. This article provides a discussion about the issue of representation of local content in English teaching materials in Indonesia context. The issue is analysed from various perspectives, they are: world Englishes, English as International Language, culture and identity, materials development and how they shape the idea of how English teaching materials in Indonesia should be developed. Conclusion and a few recommendations about the issue of local content in teaching materials in Indonesia are presented and discussed in the end of the paper.
Robust Generation of Ready-to-Use Cryopreserved Motor Neurons from Human Pluripotent Stem Cells for Disease Modeling

Human pluripotent stem cell (hPSC)-derived motor neurons (MNs) act as models for motor neuron diseases (MNDs), such as amyotrophic lateral sclerosis (ALS) or spinal muscular atrophy. However, the MN differentiation efficiency and viability following cryopreservation require further development for application in large-scale studies and drug screening. Here, we developed a robust protocol to convert hPSCs into MN cryopreservation stocks (hPSCs were converted into >92% motor neural progenitors and >91% MNs). Near-mature MNs were cryopreserved at a high thawing survival rate and 89% MN marker expression on day 32. Moreover, these MNs exhibited classical electrophysiological properties and neuromuscular junction (NMJ) formation ability within only 4–6 days after thawing. To apply this platform as an MND model, MN stocks were generated from SOD1G85R, SOD1G85G isogenic control, and sporadic ALS hPSC lines. The thawed ALS MNs expressed ALS-specific cytopathies, including SOD1 protein aggregation and TDP-43 redistribution. Thus, a stable and robust protocol was developed to generate ready-to-use cryopreserved MNs without further neuronal maturation processes for application in MND mechanistic studies, NMJ model establishment, and large-scale drug screening.
Regularity for convex variational problems on BD

In this work we study the regularity properties of minima of convex variational problems on BD, the space of functions of bounded deformation. This space is larger than BV by Ornstein's Non-Inequality. In essence, we prove that from a Sobolev regularity and partial regularity perspective, such problems lead to the same regularity of minima as the corresponding full gradient functionals on BV. As to the former, our focus is on existence of (possibly only one) $W_{\text{loc}}^{1,1}$-minimiser under similar conditions known for the BV-theory, covering the limiting case of the area-integrand; hence the paper complements the joint work arXiv:1610.08565 with Kristensen, where the focus was on establishing regularity for all generalised minima. Second, we establish a novel Poincar\'e convolution-type inequality that yields the claimed partial regularity by adapting an approach due to Anzellotti & Giaquinta.
On the validity of the Girsanov transformation method for sensitivity analysis of stochastic chemical reaction networks

We investigate the validity of the Girsanov Transformation (GT) method for parametric sensitivity analysis of stochastic models of chemical reaction networks. The validity depends on the likelihood ratio process being a martingale and the commutation of a certain derivative with expectation. We derive some exponential integrability conditions which imply both these requirements. We provide further conditions in terms of a reaction network that imply these exponential integrability conditions.
Joint DNN Partition and Resource Allocation Optimization for Energy-Constrained Hierarchical Edge-Cloud Systems

Hierarchical edge-cloud systems collaboratively utilize the resources of both the edge server and central cloud, enabling deep neural network (DNN) partition between the edge and cloud to accelerate the inference. However, the limited energy budgets of both the edge server and central cloud restrict them from providing optimal DNN inference services. Moreover, considering the high dynamics in stochastic environments, the long-term system performance should be optimized under long-term energy constraints. How to improve the long-term DNN inference performance in such energy-constrained hierarchical edge-cloud systems is less studied by existing related works. In this paper, we aim to jointly optimize DNN partition and computing resource allocation to minimize the long-term average end-to-end delay of multiple types of deep learning (DL) tasks while guaranteeing the energy consumption of the edge server and central cloud within their energy budgets. Based on the Lyapunov optimization technique and reinforcement learning, we design a novel deep deterministic policy gradient based DNN partition and resource allocation (DDPRA) algorithm to train policy to decide DNN partition dynamically by observing the environment. Moreover, the DDPRA algorithm is embedded with a heuristic computing resource allocation (HCRA) algorithm, which effectively reduces the complexity of policy training by decoupling and optimizing the computing resource allocation separately. We analyze the complexity of our algorithms and conduct extensive simulations. The numerical results demonstrate the superiority of our algorithm in comparison with 5 other schemes in multiple scenarios.
Ecossistemas de Software “mortos”: uma visão a partir de DevRel (Developer Relations)

Ecossistemas de Software (ECOS) dependem de plataformas que servem como ambientes para a interação entre desenvolvedores. Quando a organização proprietária da plataforma não suporta a sinergia entre os objetivos organizacionais e as expectativas dos desenvolvedores, o ECOS pode morrer. Neste artigo, relata-se um estudo realizado no StackOverflow (SO) e no GitHub (GH) com foco em Web ECOS: AngularJS, PhantomJS e Moment.JS. Foram analisadas métricas com base em taxas de resposta e issues fechadas, para entender o que acontece nesses ECOS antes, durante e após a morte da plataforma. Identificou-se comportamentos nas métricas que podem indicar sinal de risco.