Miracle-dz/UniFAR-FTUs huggingface dataset:
“There and Back Again?” On the Influence of Software Community Dispersion Over Productivity

Estimating and understanding productivity still represents a crucial task for researchers and practitioners. Researchers spent significant effort identifying the factors that influence software developers’ productivity, providing several approaches for analyzing and predicting such a metric. Although different works focused on evaluating the impact of human factors on productivity, little is known about the influence of cultural/geographical diversity in software development communities. Indeed, in previous studies, researchers treated cultural aspects like an abstract concept without providing a quantitative representation. This work provides an empirical assessment of the relationship between cultural and geographical dispersion of a development community—namely, how diverse a community is in terms of cultural attitudes and geographical collocation of the members who belong to it—and its productivity. To reach our aim, we built a statistical model that contained product and socio-technical factors as independent variables to assess the correlation with productivity, i.e., the number of commits performed in a given time. Then, we ran our model considering data of 25 open-source communities on GitHub. Results of our study indicate that cultural and geographical dispersion impact productivity, thus encouraging managers and practitioners to consider such aspects during all the phases of the software development lifecycle.
A Novel Two-Stream Model for Human Motor Characteristics Learning

Electroencephalogram (EEG) is one of the most pop-ular sources for learning human motor characteristics. However, most current methods only consider its temporal features and overlook spatial information. This paper proposes a novel two-stream model based on spatial and temporal features to learn human motor characteristics from EEG signals. The spatial stream is analyzed by graph neural networks (GNN) to extract the relationship among different channels. Adjacent matrices can be learned through the training process, unlike pre-defined ones used in other GNN-based EEG analysis methods. The temporal stream is processed with mixed convolution structures, which consist ID convolution, depthwise convolution, and separable convolution. The features of two streams are fused with fully-connected neural networks for human motor characteristics learning. Experimental results show the proposed model can achieve the accuracy of 0.684/0.709 (four classes motor imagery) in subject-independent/dependent schemes, demonstrating the promising for it used in brain computer interface (BCI) scenarios.
Heterodyne AC Kelvin Probe Force Microscopy for Nanoscale Surface Potential Imaging in Liquids

Knowledge of electric charge and potential distributions at the nanoscale is of great interest in the fields of material science and biology. The required high measurement accuracy, spatial resolution, and applicability to aqueous environments are not always provided by conventional techniques such as Kelvin probe force microscopy (KPFM) due to averaging artifacts and the use of a dc bias. This article presents the development of an atomic force microscopy measurement mode, enabling quantitative surface potential measurements of nanoscale structures with high measurement accuracy in air and liquid (aqueous) environments. Averaging artifacts caused by the influence of the cantilever cone, cantilever beam and tip-sample distance in dc-bias-free KPFM (AC-KPFM) are eliminated by the implemented heterodyne detection and single-pass operation. The accuracy of the potential measurement as compared to amplitude-modulated KPFM (AM-KPFM) modes is greatly improved while keeping the advantages of closed-loop and dc-bias-free operation. Experiments on a gold-aluminum test sample and collagen fibrils show quantitative surface potential measurements on nanoscale structures and operability in an aqueous environment.
Distribution-aware Low-bit Quantization for 3D Point Cloud Networks

Various low-bit quantized methods have been widely exploited and shown decent performance on 2D vision tasks in recent years. Complemented with 2D images, 3D point clouds provide an opportunity to understand the surrounding environ-ment better. However, low-bit quantization methods designed for 2D vision tasks are not readily transferable to 3D point clouds due to the higher dimension of 3D data and the increased proportion of activations. In this work, we propose a novel quantization framework, DASCQ, for 3D point cloud processing. First, a new distribution-aware strategy (DA) is presented to decrease the deviation caused by extremely low-bit quantization through activation and weight distribution analysis. Second, a soft constraint manner (SC) is designed to smooth the training of quantized networks which suffer from backward propagation errors. We evaluate our approach on two 3D point cloud datasets, ModelNet40 and S3DIS. Results indicate that the performance of the proposed approach is superior to other state-of-the-art quantization methods on both shape classification and scene semantic segmentation tasks.
Decentralized adaptive fuzzy sliding mode control for reconfigurable modular manipulators

A stable decentralized adaptive fuzzy sliding mode control scheme is proposed for reconfigurable modular manipulators to satisfy the concept of modular software. For the development of the decentralized control, the dynamics of reconfigurable modular manipulators is represented as a set of interconnected subsystems. A first‐order Takagi–Sugeno fuzzy logic system is introduced to approximate the unknown dynamics of subsystem by using adaptive algorithm. The effect of interconnection term and fuzzy approximation error is removed by employing an adaptive sliding mode controller. All adaptive algorithms in the subsystem controller are derived from the sense of Lyapunov stability analysis, so that resulting closed‐loop system is stable and the trajectory tracking performance is guaranteed. The simulation results are presented to show the effectiveness of the proposed decentralized control scheme. Copyright © 2009 John Wiley & Sons, Ltd.
The Five-Day Wave on a Sphere with Realistic Zonal Winds.

Abstract A 5-day zonal wavenumber 1 oscillation has been well-documented from analysis of surface pressure data, and it has been suggested that it corresponds to the gravest symmetric low-frequency external normal mode of the atmosphere. Previous discussions of such global normal modes have assumed a basic state atmosphere at rest. In this investigation we solve linearized equations governing this mode in an atmosphere with a realistic distribution of zonal winds and including the surface temperature gradient in the lower boundary condition. Time-dependent solutions are obtained for zonal wavenumber 1 on a sphere using finite differences in the latitude-altitude plane. The frequency of symmetric forcing at the lower boundary is varied to find the resonant frequency of the gravest mode. In the presence of solstice zonal winds there is a large latitudinal asymmetry in the response in the upper stratosphere and in the mesosphere. An important feature of this asymmetry is relatively large amplitudes in the su.
Cluster observations of fast shocks in the magnetosheath launched as a tangential discontinuity with a pressure increase crossed the bow shock

[1] The interaction of a tangential discontinuity (TD) and accompanying dynamic pressure increase with the Earth's bow shock launches a fast shock that travels ahead of the TD in the magnetosheath and carries a significant portion of the pressure change. In this event study, we use observations from the Cluster spacecraft and magnetohydrodynamic simulations to identify the fast shock and its properties and to track the TD in the magnetosheath. Velocities of the fast shock and the TD were determined by triangulation using the four distant Cluster spacecraft. The fast shock is a planar structure, traveling nearly perpendicular to B at the magnetosonic speed in the plasma rest frame. Changes in density and ∣B∣ are correlated, with about a 20% increase in each. A current was observed tangential to the plane of the fast shock, and the positive E •J there provided an electromagnetic energy source for the observed heating of the ions. The fast shock is generated by the pressure change and determines the timing of the initial response of the magnetopause to that change. The TD was moving nearly in the −XGSE direction and was being compressed as it moved inward. The passage of the TD ushered in large-scale compressive structure in the magnetosheath magnetic field, which satisfied the mirror mode instability criterion. Velocities of a fast rarefaction wave, reflected from the magnetopause, and an additional slow-mode structure, which was not a product of the initial interaction with the bow shock, were determined by triangulation.
Towards Automatic Exploit Generation for Identifying Re-Entrancy Attacks on Cross-Contract

The core of Ethereum is a smart contract, which enables developers to create blockchain-based applications in a secure and inexpensive manner. Everyone has access to the source code of Ethereum-based smart contracts. The smart contract has become a target for numerous attackers due to its transparency. Since 2015, when the first Ethereum block was discovered, many security incidents have occurred. Several code analysis methods have been developed for detecting the re-entrancy vulnerability of smart contracts. However, Existing strategies for detecting cross-contract vulnerability tend to experience both false negative and false positive results. To enhance the re-entrancy detection technique of existing works, we propose a challenge to enhance re-entrancy detection technique for Ethereum blockchain smart cross-contract. We proposed multi-agent deep reinforcement learning fuzzing to provide an exploit generator on cross-contract. We will discover a novel solution for enhancing re-entrancy detection techniques for cross-contract by integrating all these systems.
Review of Power Generation Optimization Algorithms: Challenges and its Applications

Optimizing the power system is challenging to address because power systems are enormous and complicated and can be impacted by several unanticipated occurrences. As a result, approaches to addressing these challenges should be an active main focus of this study. Energy users can optimize the power system to help the system run efficiently, cut down on peak load use, and minimize control adjustments. The new approaches and management techniques to ensure flexibility have emerged from the increasing trend of renewable energy integration with power generation uncertainty and availability. This paper contains a research study of different optimization algorithms. The pros and cons of recently used optimization techniques in power systems are briefly discussed in this paper. The algorithms that are discussed in this article are Power optimization algorithm (PSO), Genetic Algorithm (GA), Tabo Search Algorithm (TSA), Artificial Neural Network (ANN), Fuzzy Logic, Level Shifting Phase Disposition (LSPD), Simulated Annealing (SA) and Random Search Algorithm (RS). A thorough analysis of power optimization techniques, the difficulties with traditional approaches, and finally, the most modern optimization algorithms are covered. Overall, this review will strengthen the initiatives toward developing reliable and sustainable power systems using practical power optimization algorithms in future applications.
On Emulating and Controlling Rician Propagation in Wireless Laboratory

Channel emulators are usually used to evaluate preliminary performances of the newly developed wireless algorithms in the laboratory without resorting to the on-site measurements which are generally complex and costly. In this study, a simple but effective channel emulator that features a benchtop-sized (small-sized) reverberation chamber (RVC), radio frequency (RF) cable, and power controller is proposed for emulating a Rician propagation environment with a flexibly controllable k-factor. The developed emulator has the ability to precisely introduce the Rician propagation effect (with the desired k-factor) to the input RF signal. The performance of the proposed emulator was evaluated through measurement and its accuracy was validated by conducting a Kolmogorov–Smirnov (KS) goodness-of-fit (GoF) test. The emulator is suitable for both algorithm/prototype testing and educational purpose in wireless laboratories.
Explosive Marine Cyclogenesis in a Three-Layer Model with a Representation of Slantwise Convection: A Sensitivity Study

Abstract A three-layer primitive equation model with a representation of slantwise convection is used to study explosive marine cyclogenesis. A simple representation of boundary layer and shallow and deep cloud types is considered. Apart from the control simulation. experiments are conducted to test the sensitivity of the bent-back warm front and the explosive deepening to various model parameters. The major results are as follows: 1) The control simulation produces a realistic explosive cyclone. 2) The cloud mass fraction at the top of the boundary layer, which is equal to the fractional area of slantwise ascent in a grid box, essentially determines the cloud mass flux transported into the large-scale atmosphere. The thermal gradient in the bent-back warm frontal region and the final deepening strongly depend on this parameter. 3) Shallow clouds are relatively insignificant in affecting the dynamics of explosive cyclones. 4) The surface drag force weakens the development of strong horizontal wind shear a.
Results of the ESO-SEST Key Programme: CO in the Magellanic Clouds. V. Further CO observations of the Small Magellanic Cloud

We present further results of fully-sampled observations in the J =1 ! 0a nd J =2 ! 1 rotational transitions of 12 CO and 13 CO, obtained with the SEST telescope at 43 00 or 22 00 resolution, toward the Small Magellanic Cloud. These observations concern six molecular clouds with a variety of physical conditions. Maps in the 12 CO(1! 0), 12 CO(2! 1), and in some cases of 13 CO(1! 0) lines are presented, as well as proles of the four lines at the peak positions convolved to the same angular resolution of 43 00 . The line ratio results are interpreted in Paper IV of this
On the Relation of Zakai’s and Mortensen’s Equations

The problem of optimal control for partially observable diffusion processes is studied by the dynamic programming in function space approach first proposed by R. E. Mortensen. The density of the conditional distribution of the (unobservable) signal given past and present observations, which satisfies the Zakai equation of nonlinear filtering, is viewed as the new state of the system. A verification result is established for the corresponding Bellman–Hamilton–Jacobi equation, known as Mortensen’s equation.
QoE-Oriented Resource Allocation Design Coping with Time-Varying Demands in Wireless Communication Networks

Efficiently utilizing the network resources to minimize the operation costs while satisfying customer’s Quality-of-Experience (QoE) related requirement as well as dynamic demands is a challenging task of all network operators. This paper aims to develop a stationary capacity allocation method that anticipates time-varying demand and keeps the network operating under constraints on a stochastic blocking probability. Queuing delay requirement is also regarded as an QoE-oriented practical design. Employing an approximation of time-varying queuing model and continuous time Markov chain (CTMC) for queue length, the technical designs are stated as a convex stochastic optimization based on which a dynamic capacity allocation is proposed by using Lagrangian and gradient descent searching method. Numerical studies confirm that our proposed framework can efficiently and dynamically allocate optimal capacity for a blocking probability of less than 1% and the probability of violating the queuing-delay requirement is less than 5%.
Detecting More Objects Indoors: a Depth-guided Detector for Mobile Robots

In robotic target object manipulation tasks, object detection plays a crucial role on ensuring types and general positions of target objects. However, directly applying existing detectors in real-life scenarios is hard to achieve required performance. This is mainly because long distance and complicated environment cut down visual information quality of the target objects. To increase valid detection distance and accuracy for the robots, this paper proposes a depth-guided detector. In this detector, depth information is utilized to select a subregion of the current frame to obtain better visual information, and a motion tracker is exploited to relate adjacent frames. Our experiment shows the proposed method enhances the valid detection distance in real-life scenarios in an acceptable efficiency cost.
Presentation Attack Detection in Iris Recognition through Convolution Block Attention Module

Presentation Attacks (PAs) are a common spoofing mechanism in biometric authentications, especially iris-based models. The detection of these attacks is useful for distinguishing whether a sensor is presented with a live biometric or impersonated biometric through a recording, printout or spoof In recent studies, Convolutional Neural Networks have shown exceptional performance in detecting these attacks. In this paper, we propose an attention-based iris PA detection (PAD) termed d-CBAM that uses a convolution block attention mechanism introduced between the dense blocks of DenseNet. The core of this work is inspired by the use of DenseNet as a feature extractor i.e, feature maps from the last dense block are taken and passed to the attention maps. We have tested d-CBAM on the benchmark Clarkson, Notre Dame and NDCLD15 datasets, over which d-CBAM has shown better results in comparison to some traditional PAD solutions such as DenseNet, Spoof Net and Meta-Fusion. The error metrics (APCER and BPCER) were also noted to be competitive with the state-of-the-art.
A Liquid Sensor Based on Frequency Selective Surfaces

A novel, simple and easy-to-fabricate liquid sensor using frequency-selective surfaces (FSSs) is proposed. The new sensor concept is based on modifying the capacitance between adjacent FSS elements when materials of different electrical characteristics are inserted. The change in capacitance produces a change in resonant frequency. The FSS design consists of a 9 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 9 array of square loops on <inline-formula> <tex-math notation="LaTeX">$0.31\lambda \times 0.31\lambda $ </tex-math></inline-formula> square unit cells with trenches between the loops. The trenches are filled with liquids under test (LTUs). The structure operates at 4.6 GHz without any liquid. When liquids are inserted in the trenches, the resonance frequency varies in relation to the dielectric constant of the liquid. This is observed by measuring the transmission coefficient (<inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula>). Butan-1-ol, ethanol, methanol, propan-2-ol, and xylene are used to demonstrate the sensing function. A maximum sensitivity of 8.65% for xylene was achieved. Furthermore, very low differences were observed between the measured and expected dielectric constant and loss tangent, thus validating the design. The device is inexpensive, compact, and easy to make and scalable for large-area operations in liquid detection for microwave sensing applications. This technique has potential applications in reconfigurable FSS.
Spatial correlation analysis revisited: Theory, and application to “radar backscatter model” data

Three novel correlation techniques for the estimation of atmospheric wind velocities using spaced antenna (SA) radars are introduced. These techniques apply the concepts of spatial correlation analysis, providing an alternative to the conventional full correlation analysis (FCA). The techniques are implemented using simulated data. The first technique is applicable to three or more noncolinear antennas. This technique is shown to produce velocities in excellent agreement with the FCA “true” velocity and is also subject to the “triangle size effect” (TSE), whereby the velocity decreases below the actual wind velocity with decreasing antenna spacing. The remaining two techniques are applicable to four antennas arranged in either a square or a “Y” configuration. These two techniques are shown to be independent of the TSE and may therefore provide a valuable alternative to the FCA in situations where the FCA may underestimate the actual wind velocity.
The relationship between carbon monoxide abundance and visual extinction in interstellar clouds.

Carbon monoxide column densities are compared to visual extinctions toward field stars in the rho Oph and Taurus molecular cloud complexes. The relationship of C/sup 18/O column density to A/sub v/ is established, and new determinations for /sup 13/CO column densities are given for a range of visual extinctions extended to beyond 20 mag. A prescription for determining hydrogen column densities and masses of molecular clouds from observations of CO isotopes is presented and discussed critically. These measurements agree well with the predictions of gas phase chemistry models which include chemical fractionation and selective isotopic photodestruction. The functional dependence of the C/sup 18/O column density on A/sub v/ is characterized by two different regimes separated by a distinct transition observed to occur at 4 mag in both molecular cloud complexes, whereas the functional dependence of /sup 13/CO is quite different in the two regions. We find that some saturation occurs for C/sup 18/O emission at high visual extinction and use the rarer isotopic species C/sup 17/O and /sup 13/C/sup 18/O to correct for it.
Finite‐volume schemes for Friedrichs systems in multiple space dimensions: A priori and a posteriori error estimates

We consider a class of finite‐volume schemes on unstructured meshes for symmetric hyperbolic linear systems of balance laws in two and three space dimensions. This class of schemes has been introduced and analyzed by Vila and Villedieu ( 1998 ). They have proven an a priori error estimate for approximations of smooth solutions. We extend the results to weak solutions. This is the base to derive an a posteriori error estimate for finite‐volume approximations of weak solutions. © 2004 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2005
Control of magnetic transition of ZnO:Co grown by RF-sputter using post-annealing

Ferromagnetic semiconductor has attracted much attention for the application of spintronic devices, which will bring next generation of the information technology. Cobalt-doped Zinc Oxides (ZnO: Co) is strong candidate of this material group. The ZnO: Co films were grown on a silicon substrate (100) at room temperature by radio-frequency (rf) sputtering deposition and followed by post-annealing treatment for 3 hours at 400°C and 800°C in the air. The transition from paramagnetic to ferromagnetic occurs after annealing at 400°C, and the properties return to paramagnetic like as-deposition when the temperature rises to 800°C. The XRD measurement of ZnO: Co films exhibited a wurtzite structure in the (002) plane and was free from secondary phases. Then, post-annealing at 400°C due to shift peak and decrease oxygen element, meanwhile the crystallinity significantly up to 800°C.