Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Examination of Nonequilibrium Thermodynamic Models of Thermodiffusion in Liquids

We analyze existing models for material transport in non-isothermal non-electrolyte liquid mixtures that utilize non-equilibrium thermodynamics. Many different sets of equations for material have been derived that, while based on the same fundamental expression of entropy production, utilize different terms of the temperature- and concentration-induced gradients in the chemical potential to express the material flux. We reason that only by establishing a system of transport equations that satisfies the following three requirements can we obtain a valid thermodynamic model of thermodiffusion based on entropy production, and understand the underlying physical mechanism: (1) Maintenance of mechanical equilibrium in a closed steady-state system, expressed by a form of the Gibbs–Duhem equation that accounts for all the relevant gradients in concentration, temperature, and pressure and respective thermodynamic forces; (2) thermodiffusion (thermophoresis) is zero in pure unbounded liquids (i.e., in the absence of wall effects); (3) invariance in the derived concentrations of components in a mixture, regardless of which concentration or material flux is considered to be the dependent versus independent variable in an overdetermined system of material transport equations. The analysis shows that thermodiffusion in liquids is based on the entropic mechanism.
Performance Evaluation of CoAP and MQTT_SN in an IoT Environment

The fast growth of the Internet of Things (IoT) has made this technology one of the most promising paradigms of recent years. Wireless Sensor Networks (WSNs) are one of the most important challenges of the Internet of things. These networks are made up of devices with limited processing power, memory, and energy. The constrained nature of WSNs makes it necessary to have specific restricted protocols to work in these environments. In this paper, we present an energy consumption and network traffic study of the main IoT application layer protocols, the Constrained Application Protocol (CoAP), and the version of Message Queue Telemetry Transport (MQTT) for sensor networks (MQTT_SN). The simulations presented evaluate the performance of these protocols with different network configurations.
Exploratory Case studies of Principal leadership for teachers’ professional development in Central Vietnam

Teacher professional development (TPD), which plays a significant role in better school improvement and student achievements, has been regarded as a long process from the pre-service teacher training for to-be teachers at higher education institutions and continues in the form of continuous job-embedded and collaborative learning activities. Aspects of TPD activities to favourable workplace conditions to enhance teachers’ teaching knowledge, teaching skills and attitudes have been studied. This research was conducted to find out what the principals’ roles and support in promoting TPD in four schools in Hong Lam province, Vietnam. The study utilises multiple-site case study design relating to principal leadership and TPD in those schools. The four key workplace conditions were identified in this study including collaboration, teacher empowerment, supervision and evaluation, and teachers’ motivational strategies.
Single‐stage retrograde endoscopic common bile duct stone removal might be sufficient in moderate acute cholangitis with a stone size ≤12 mm: A retrospective cohort study with propensity score matching

Studies elucidating the efficacy and safety of single‐stage endoscopic treatment for acute moderate cholangitis with choledocholithiasis are limited. Therefore, we performed a retrospective analysis of single‐stage endoscopic treatment for common bile duct (CBD) stone removal in patients with moderate cholangitis to determine the rates of successful stone extraction and complications. A retrospective chart review was conducted for all consecutive patients (n = 632) undergoing endoscopic retrograde cholangiopancreatography (ERCP) for CBD stone removal between January 2010 and September 2013 at the study institution. A total of 346 patients excluded based on the following criteria: procedural failure requiring an anatomy‐modifying operation, stenosis of the pyloric ring, tumor‐related obstruction, treatment requiring only biliary drainage, failure to locate the papilla, intolerance due to inadequate sedation, CBD stone > 12 mm, CBD sludge, and non‐naïve papilla in ERCP. The remaining 286 patients were randomly matched at a 1:2 ratio using propensity score together with age and sex with the Number Cruncher Statistical System statistical software. After propensity score matching, there were 90 and 134 patients in the moderate and no/mild cholangitis groups, respectively. The baseline characteristics and endoscopic findings were comparable between the two groups. The success rate of complete stone extraction was similar between the moderate and no/mild cholangitis groups (90.0% vs 91.0%, P = .793). Additionally, the complication rates were comparable between the moderate and no/mild cholangitis groups (post‐ERCP pancreatitis, 13.3% vs 10.4%, P = .542; pneumonia, 4.4% vs 1.5%, P = .183; perforation, 0% vs 1.5%, P = .243; and mortality, 2.2% vs 0.7%, P = .350). However, there was a trend for higher bleeding rate in the moderate cholangitis group compared with the no/mild cholangitis group (2.2% vs 0%, P = .084). Single‐stage retrograde endoscopic CBD stone removal in patients with moderate cholangitis and choledocholithiasis sized ≤12 mm might be adequate based on the high success rate (90.0%) for stone removal and low complication rates.
Successful and Less Successful Interventions with Youth and Senior Athletes Insights from Expert Sport Psychology Practitioners

This study is focused on reflections of expert sport psychology practitioners about their interventions with competitive youth and senior elite athletes. Two objectives include: (1) to identify key structural components used by practitioners to describe sport psychology interventions and integrate them into an empirical framework, and (2) to analyze the practitioners’ experiences in regard of their successful and less successful interventions in competitive youth and elite senior sport contexts using the empirical framework. We conducted semi-structured interviews with twelve internationally recognized sport psychology practitioners (SPPs) and analyzed the data thematically. The empirical framework derived from the SPPs’ accounts contains eight structural components integrated into two categories: (1) the content and focus (with three components, e.g., adaptation of content), and (2) the organization and delivery of interventions (with five components, e.g., initiation and assessment of athletes’ needs). Using the empirical framework we found differences between successful and less successful interventions and between youth and senior contexts in terms of needs assessment, adaptation and breadth of content, athlete-practitioner relationship, and intervention settings. The empirical framework might inform SPPs in their efforts to design, implement, and evaluate their services in these two contexts.
Actuation and stiffening in fluid-driven soft robots using low-melting-point material

Soft material robots offer a number of advantages over traditional rigid robots in applications including human-robot interaction, rehabilitation and surgery. These robots can navigate around obstacles, elongate, squeeze through narrow openings or be squeezed - and they are considered to be inherently safe. The ability to stiffen compliant soft actuators has been achieved by embedding various mechanisms that are generally decoupled from the actuation principle. Miniaturisation becomes challenging due to space limitations which can in turn result in diminution of stiffening effects. Here, we propose to hydraulically actuate soft manipulators with low-melting-point material and, at the same time, be able to switch between a soft and stiff state. Instead of allocating an additional stiffening chamber within the soft robot, one chamber only is used for actuation and stiffening. Low Melting Point Alloy is integrated into the actuation chamber of a single-compartment soft robotic manipulator and the interfaced robotic syringe pump. Temperature change is enabled through embedded nichrome wires. Our experimental results show higher stiffness factors, from 9–12 opposing the motion of curvature, than those previously found for jamming mechanisms incorporated in separate additional chambers, in the range of 2–8 for the same motion.
Evolutionary V2X Technologies Toward the Internet of Vehicles: Challenges and Opportunities

To enable large-scale and ubiquitous automotive network access, traditional vehicle-to-everything (V2X) technologies are evolving to the Internet of Vehicles (IoV) for increasing demands on emerging advanced vehicular applications, such as intelligent transportation systems (ITS) and autonomous vehicles. In recent years, IoV technologies have been developed and achieved significant progress. However, it is still unclear what is the evolution path and what are the challenges and opportunities brought by IoV. For the aforementioned considerations, this article provides a thorough survey on the historical process and status quo of V2X technologies, as well as demonstration of emerging technology developing directions toward IoV. We first review the early stage when the dedicated short-range communications (DSRC) was issued as an important initial beginning and compared the cellular V2X with IEEE 802.11 V2X communications in terms of both the pros and cons. In addition, considering the advent of big data and cloud-edge regime, we highlight the key technical challenges and pinpoint the opportunities toward the big data-driven IoV and cloud-based IoV, respectively. We believe our comprehensive survey on evolutionary V2X technologies toward IoV can provide beneficial insights and inspirations for both academia and the IoV industry.
Novel One Time Signatures (NOTS): A Compact Post-Quantum Digital Signature Scheme

The future of the hash based digital signature schemes appears to be very bright in the upcoming quantum era because of the quantum threats to the number theory based digital signature schemes. The Shor’s algorithm is available to allow a sufficiently powerful quantum computer to break the building blocks of the number theory based signature schemes in a polynomial time. The hash based signature schemes being quite efficient and provably secure can fill in the gap effectively. However, a draw back of the hash based signature schemes is the larger key and signature sizes which can prove a barrier in their adoption by the space critical applications, like the blockchain. A hash based signature scheme is constructed using a one time signature (OTS) scheme. The underlying OTS scheme plays an important role in determining key and signature sizes of a hash based signature scheme. In this article, we have proposed a novel OTS scheme with minimized key and signature sizes as compared to all of the existing OTS schemes. Our proposed OTS scheme offers an 88% reduction in both key and signature sizes as compared to the popular Winternitz OTS scheme. Furthermore, our proposed OTS scheme offers an 84% and an 86% reductions in the signature and the key sizes respectively as compared to an existing compact variant of the WOTS scheme, i.e. WOTS+.
Low-Profile Beamforming-Network-Avoiding Multi-Beam Antenna Based on Parasitic Patch and Shorting-Pin

In this paper, a new method to design multi-beam antenna based on parasitic patch and shorting-pin is proposed and demonstrated through two cases of single- and multi-source. For the single-source multi-beam antenna, by arranging the parasitic patch shorted with pins around a square patch, the beam is divided into four parts and a multi-beam radiation is realized. Also, arc slots in the parasitic patch are added to improve the bandwidth and shorting-pins located along the edges of square patch are introduced to adjust the operating frequency, gain and pitch angle. The multi-source multi-beam antenna has a similar construction with the single-source case except it has four ports. In addition, an asymmetric gap is added in the square patch to improve the isolation between the four ports. Finally, both the simulated and measured results verify the proposed methodology. The realized single-source antenna operates in the range of 5.22 – 5.42 GHz. Four beams point at <inline-formula> <tex-math notation="LaTeX">$(\varphi $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\theta) =$ </tex-math></inline-formula> (0°, 41°), (90°, 35°), (180°, 41°) and (270°, 38°). For the multi-source case, the corresponding bandwidth is 5.25 – 5.48 GHz. The radiation beams are steered to <inline-formula> <tex-math notation="LaTeX">$(\varphi $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\theta) =$ </tex-math></inline-formula> (0°, 30°), (270°, 37°), (90°, 30°) and (180°, 37°).
Attributed Community Search with Forbidden Nodes

The study of community search problems in large graphs has attracted more and more attention: given a query node q, it is required to return communities containing the query node. In recent years, some researchers have considered keyword information to make the attributed community search is gradually emerging, which is widely used in social network marketing and setting up of social events. However, some problems require that the query communities cannot contain certain forbidden nodes, which has not received any attention in previous work. Based on this, this paper proposes undirected attributed community search with forbidden nodes problem (FATQ), which not only avoids forbidden nodes from existing in the query communities, but also guarantees the structure cohesiveness of the query communities and the keyword cohesiveness of the communities. Finally, this paper proposes two different search algorithms for FATQ problem and evaluates our algorithms on multiple datasets. The results show that correctness and effectiveness of the proposed methods.
Robust and Efficient Vehicles Motion Estimation with Low-Cost Multi-Camera and Odometer-Gyroscope

In this paper, we present a robust and efficient estimation approach with multi-camera, odometer and gyroscope. Robust initialization, tightly-coupled optimization estimator and multi-camera loop-closure detection are utilized in the proposed approach. In initialization, the measurements of odometer and gyroscope are used to compute scale, and then estimate the bias of sensors. In estimator, the pre-integration of odometer and gyroscope is derived and combined with the measurements of multi-camera to estimate the motion in a tightly-coupled optimization framework. In loop-closure detection, a connection between different cameras of the vehicle can be built, which significantly improve the success rate of loop-closure detection. The proposed algorithm is validated in multiple real-world datasets collected in different places, time, weather and illumination. Experimental results show that the proposed approach can estimate the motion of vehicles robustly and efficiently.
Relaxing the Conservatism of Passivity Condition for Impedance Controlled Series Elastic Actuators

This paper proposes a practical and less conservative passivity analysis for series elastic actuators (SEAs) by introducing load port definition and shows that the achievable stiffness by the impedance control of SEA can be set higher than the inherent stiffness of SEA depending on the condition of the load dynamics. Since SEA can inherently measure or estimate a transmitted force thanks to its embedded spring element, impedance control is often exploited to render compliant behaviors related between the motion and the force. Although the stability of the SEA control system is of great importance, the conventional passivity analysis gives conservative criteria, and indeed limits the actual actuator performance. To tackle the conservatism of the conventional passivity in SEAs, we first explore the dynamic characteristics of SEA including load dynamics, which has been ignored for the sake of simplicity of the passivity analysis by excluding uncertain load dynamics. The inclusion of the load dynamics into the passivity analysis allows us to properly derive the less conservative limit of achievable stiffness by impedance control and the factors that determine the limit. The proposed analysis is verified by numerical simulations and applied to a passivity observer design for experimental validation on an actual SEA setup.
Explaining Static Analysis - A Perspective

Static code analysis is widely used to support the development of high-quality software. It helps developers detect potential bugs and security vulnerabilities in a program's source code without executing it. While the potential benefits of static analysis tools are beyond question, their usability is often criticised and prevents software developers from using static analysis to its full potential. In the past decade, researchers have studied developer needs and contrasted them to available static analysis tool functionalities. In this paper, we summarize the main design challenges for building usable static analysis tools, and show that they revolve around the notion of explainability, which is a subarea of usability. We present existing analysis tools and current research in static analysis usability, and detail how they approach those challenges. This leads us to proposing potential lines of future work in explainability for static analysis, namely turning static analysis tools into assistants and teachers.
Prototype Development of an Assistive Lower Limb Exoskeleton

The number of patients suffering from physical or neurological disorder with lower limb impairment is increasing rapidly; hence, not allowing them to perform activities of daily livings (ADLs). This paper presents a preliminary work towards the development of a low-cost active lower limb exoskeleton that will assist the wearer while performing ADLs. The exoskeleton is modeled in Solid Works, with a single actuator at the knee joint that is controlled by getting the information from foot sensitive resistors (FSRs) and encoders. The desired knee angle in the sagittal plane during the swing phase of the gait was fed to the controller and then modeling, and simulation technique was applied to achieve the desired output. The preliminary results were reasonable as the system has the ability to assist during slow walking. Future work includes the necessary improvements in the proposed system so that investigations will be carried out for walking assistance of disabled persons.
An In-pipe Inspection Module with an Omnidirectional Bent-pipe Self-adaptation Mechanism using a Joint Torque Control

This study presents an in-pipe inspection robot module, called AIRo-2.3s. This robot module can control its joint angle and torque and adapt to any directed bent pipe regardless of its orientation. Stretching the drive wheels against the inner wall of pipes is essential for adapting robots to be used in vertical pipes and slippery inner surfaces. To achieve this, a series elastic actuator (SEA) with a high reduction system and a polyurethane rubber is installed to sense the joint torque. More than 100 N constant traction force and a wide range of adaptive inner diameters (4 to 6 in.) are achieved despite the short body length, a minimum number of the drive wheels, and a simple joint of 1 degree of freedom. Experiments to verify the performance in bent pipes are conducted after the robot module configuration is described.
CrowdNet: Identifying Large-Scale Malicious Attacks Over Android Kernel Structures

While malicious attacks in Android devices are growing, machine learning-based malware prediction has become time-consuming and space-consuming. Open-source parallel frameworks for massive data processing can efficiently deal with iterative machine learning tasks based on their distributed computation and in-memory abstraction, but the performance of category validation actually degrades over Android kernel features in task_struct. In this paper, to thoroughly investigate Android kernel behaviors, we first present a kernel feature based framework, CrowdNet, for cloud computing platforms. CrowdNet includes an automatic data provider that collects footprints of kernel features and a parallel malware predictor that validates Android malicious behaviors. Then we calculate and select hidden centers by a heuristic approach for 12,750 Android applications to reduce the number of iterations and time complexity. Our experimental results show that CrowdNet protects large-scale data validation and speeds up the learning of kernel behaviors twofold. Further, identifying malicious attacks with CrowdNet improves the classification efficiency compared to traditional neural network and other machine learning techniques.
Multi-Sensor 6-DoF Localization For Aerial Robots In Complex GNSS-Denied Environments

The need for robots autonomously navigating in more and more complex environments has motivated intense R& D efforts in making robot pose estimation more accurate and reliable. This paper presents a multi-sensor multi-hypothesis method for robust 6-DoF localization in complex environments. Robustness and accuracy requirements are addressed as follows. First, camera and LIDAR features are seamlessly integrated in the same statistical framework, benefiting from their synergies and providing robustness in scenarios with low or varying densities of LIDAR and visual features. Second, a multi-hypothesis approach is adopted to cope with scenario symmetries. The method has been carefully designed to operate in real time using feature and hypothesis filtering and efficient hypothesis refinement, and has been coded in a multi-core implementation. The proposed method has been extensively validated for closed-loop aerial robot navigation in different urban and industrial scenarios and has shown advantages over well-known single-sensor techniques.
Fusing Lidar Data and Aerial Imagery with Perspective Correction for Precise Localization in Urban Canyons

This paper addresses a vehicle localization method that fuses aerial maps and lidar data in urban canyon environments where global positioning system (GPS) signals are inaccurate. The boundaries of buildings are extracted from the aerial map and they are matched to point cloud data provided by the lidar. However, most aerial maps contain perspective projection distortions which can be significant in urban canyons with tall buildings. In this study, a new method to correct such projection distortion is proposed and it is applied to precise localization by fusing the corrected map and lidar data. In order to achieve this, the semantic segmentation of an aerial image is performed using a convolutional neural network, and the mutual information between the lidar measurements and the building boundaries is obtained to measure their similarity. A particle filter framework is employed to localize the vehicle and match the map using the mutual information as the weight of a particle. An experimental dataset is then used to validate the feasibility of the proposed method.