Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymptotic Consensus Tracking of Uncertain Multi-Agent Systems with a High-Dimensional Leader: A Neuro-Adaptive Approach

In this note, the asymptotic consensus tracking problem is addressed for uncertain multi-agent systems (MASs) with undirected communication topologies and a high-dimensional leader, where the uncertainties may contain unmodeled dynamics and external disturbance which are prior unknown. To remove the effect of high-dimensional leader, an observer based compensation controller is firstly designed. A neural-adaptive based feedback controller is then designed. Note that the feedback term contains a discontinuous controller which is used to eliminate the effect of imprecise approximation of neural network. Furthermore, if the leader is assumed to be globally reachable, it is shown that asymptotic consensus tracking is achieved in MAS by choosing appropriate control parameters. The obtained theoretical result is finally validated by simulation.
Phantasy: Low-Latency Virtualization-Based Fault Tolerance via Asynchronous Prefetching

Fault tolerance has become increasingly critical for virtualized systems as growing amount of mission-critical applications are now deployed on virtual machines rather than directly on physical machines. However, prior hardware-based fault-tolerant systems require extensive modification to existing hardware, which makes them infeasible for industry practitioners. Although software-based techniques realize fault tolerance without any hardware modification, they suffer from significant latency overhead that is often orders of magnitude higher than acceptable. To realize practical low-latency fault tolerance in the virtualized environment, we first identify two bottlenecks in prior approaches, namely the overhead for tracking dirty pages in software and the long sequential dependency in checkpointing system states. To address these bottlenecks, we design a novel mechanism to asynchronously prefetch the dirty pages without disrupting the primary VM execution to shorten the sequential dependency. We then develop Phantasy, a system that leverages page-modification logging (PML) technology available on commodity processors to reduce the dirty page tracking overhead and asynchronously prefetches dirty pages through direct remote memory access via RDMA. Evaluated on 25 real-world applications, we demonstrate Phantasy can significantly reduce the performance overhead by 38 percent on average, and further reduce the latency by 85 percent compared to a state-of-the-art virtualization-based fault-tolerant system.
Real-Time Foot Clearance and Environment Estimation Based on Foot-Mounted Wearable Sensors

The ambulatory gait monitoring systems (AGMS) using wearable devices have a great attention for health monitoring of individuals. Foot clearance is one of gait parameters and an indicator of gait quality and safety. Conventionally, foot clearance is calculated by post-processing using several methods such as extended Kalman filter (EKF), a weighted Fourier linear combiner (WFLC), a simple biomechanical foot model, zero velocity update (ZVU), and optimally filtered direct and reverse integration (OFDRI). However, real-time foot clearance (FC) estimation is required to apply to control of neural prostheses and assistive devices to prevent falling down. In addition, since humans adapt to several environments during gait motion, walking patterns are different among leveled walk, ramp walk, and stair walk. Therefore, environmental recognition also has an important role for the devices. Aim of this study is an implication of a system which performs real-time foot clearance and environment estimation in several situations. Different configurations of infrared (IR) distance sensors are utilized with a foot-mounted inertia measurement unit (IMU: an acceleration sensor, a gyro sensor, a magnetometer, and an air pressure sensor) sensor. Two IR sensors are attached to both sides of a shoe with different orientation. Developed system is tested in case of leveled walk, ramp walk, and stair walk. As the result, the developed system can estimate foot clearance and find characteristics of walking in different gait motion.
Social Recommendation with Missing Not at Random Data

With the explosive growth of online social networks, many social recommendation methods have been proposed and demonstrated that social information has potential to improve the recommendation performance. However, existing social recommendation methods always assume that the data is missing at random (MAR) but this is rarely the case. In fact, by analysing two real-world social recommendation datasets, we observed the following interesting phenomena: (1) users tend to consume and rate the items that they like and the items that have been consumed by their friends. (2) When the items have been consumed by more friends, the average values of the observed ratings will become smaller, not larger as assumed by the existing models. To model these phenomena, we integrate the missing not at random (MNAR) assumption in social recommendation and propose a new social recommendation method SPMF-MNAR, which models the observation process of rating data based on user's preference and social influence. Extensive experiments conducted on large real-world datasets validate that SPMF-MNAR achieves better performance than existing social recommendation methods and the non-social methods based on MNAR assumption.
Word RNN as a Baseline for Sentence Completion

Since sentence completion task requires diverse abilities including linguistic proficiency, common knowledge, and logical reasoning, the task has been used to measure the reading comprehension level of not only humans but also machines. This work presents a word Recurrent Neural Network (RNN), a popular neural language model, as a competitive baseline for sentence completion by improving the performance with an appropriate network structure and hyper-parameters. We also propose a bidirectional version of word RNN, which has been shown to give further improvement. In addition, we formulate and compare blank loss and sentence loss criteria for selecting the best option to complete the sentence with a trained word RNN. Despite its simple architecture, the accuracy results of our word RNNs exceed the previous best results on Microsoft Research Sentence Completion Challenge and the Scholastic Aptitude Test (SAT) sentence comnletion Questions.
Quantified Insights into LED Variability

There is a variation in the characteristics of an LED partly due to variation in the manufacturing process and the sourcing of materials from differing vendors. From a thermal perspective these variations result in differing thermal characteristics such as RthJC (the thermal resistance between chip junction and case) and Zth (the thermal impedance curve). This study seeks to quantify the measured variation of a number of LED part samples and provide insight into the root cause of those variations. Such information would provide additional confidence to a lighting engineer when selecting LEDs, when modelling their thermal behaviour and thus modelling their thermally dependent optical performance.
A Harmonic Motif Modularity Approach for Multi-layer Network Community Detection

During the past several years, multi-layer network community detection has drawn an increasing amount of attention and many approaches have been developed from different perspectives. Despite the success, they mainly rely on the lower-order connectivity structure at the level of individual nodes and edges. However, the higher-order connectivity structure plays the essential role as the building block for multiplex networks, which may contain better signature of community than edge. The main challenge in utilizing higher-order structure for multi-layer network community detection is that the most representative higher-order structure may vary from one layer to another. In this paper, we propose a higher-order structural approach for multi-layer network community detection, termed harmonic motif modularity (HM-Modularity). The key idea is to design a novel higher-order structure, termed harmonic motif, which is able to integrate higher-order structural information from multiple layers to construct a primary layer. The higher-order structural information of each individual layer is also extracted, which is taken as the auxiliary information for discovering the multi-layer community structure. A coupling is established between the primary layer and each auxiliary layer. Finally, a harmonic motif modularity is designed to generate the community structure. By solving the optimization problem of the harmonic motif modularity, the community labels of the primary layer can be obtained to reveal the community structure of the original multi-layer network. Experiments have been conducted to show the effectiveness of the proposed method.
Implementation and Evaluation of Peripheral Light Feedback for Mid-Air Gesture Interaction in the Car

Mid-air gesture interaction has been identified as a promising input modality in smart environments such as modern cars. Unlike touch input, users usually have little experience with gesture interaction. In combination with a lack of haptic feedback they often struggle to execute gestures correctly. To compensate this, users require adequate feedback that supports them already during the execution of a gesture. We conducted a user study with 21 participants in a driving simulator that examined the benefit of additional light feedback for gestures with different complexity while driving. Although we found no benefit of peripheral light feedback for simple confirmation gestures, there was a significant increase of accuracy for more complex pointing gestures. Our results imply that the value of peripheral light feedback increases along with the complexity of the gesture at the cost of increased attention drawn by the feedback.
Dense People Counting Using IR-UWB Radar With a Hybrid Feature Extraction Method

People counting is one of the hottest issues in sensing applications. Impulse radio ultrawideband radar has been extensively adopted to count people because it provides a device-free solution without illumination and privacy concerns. However, current solutions have limited performances in congested environments due to signal superpositions and obstructions. In this letter, a hybrid feature extraction method based on the curvelet transform and the distance bin is proposed. First, 2-D radar matrix features are extracted at multiple scales and multiple angles by applying the curvelet transform. Then, the distance bin concept is introduced by dividing each row of the matrix into several bins along the propagating distance to select features. A radar signal data set is constructed for three density scenarios, including people randomly walking in a constrained area at densities of three and four persons per square meter and people in a queue with an average between-person distance of 10 cm. The number of people in the data set scenarios varies from 0 to 20. Four classifiers—a decision tree, an AdaBoost classifier, a random forest, and a neural network—are compared to validate the hybrid features. The random forest achieves the highest accuracy of above 97% in the three density scenarios. To further investigate the reliability of the hybrid features, they are compared with three other features: cluster features, activity features, and features extracted by a convolutional neural network. The comparison results reveal that the proposed hybrid features are stable, and their performance is substantially more effective than that of the others.
Experimental Verification on Thermal Modeling of Medium Frequency Transformers

Nowadays, medium frequency transformers have gained growing attention in modern power system. Higher power density, which is realized by increasing the operating frequency, leads to the reduction of the magnetic components size. Along with it, cooling surface is consequently reduced which results in high thermal stress. Careful attention must be paid on the loss mechanisms and thermal analysis of a medium frequency transformer at the design stage. This paper develops an equivalent thermal circuit of an oil-immersed medium frequency transformer to predict its temperature profile. Thermal resistance, capacitance and the losses generated in the core and winding are carefully estimated. Two oil-immersed shell-type transformer prototypes with specifications as 5kW, 500/5000V, 5kHz, interleaved winding construction have been developed and built for verification purpose. Loss and temperature measurements have been performed to verify the presented framework. The accuracy of the proposed thermal model is benchmarked and corroborated through experimental measurements as well as FEM-CFD study with good agreement.
Contiguity Representation in Page Table for Memory Management Units

Conventional page-based memory management schemes have certain overheads related to system performance and memory utilization mainly due to page table walks. In addition, conventional translation look-aside buffers (TLBs) often suffer from a limited capacity. To reduce these problems, we propose an efficient scheme that accommodates a contiguity information in a page table. The presented memory management unit exploits the contiguity information. As a result, we can reduce the page table walks as certain physical memory is allocated in a contiguous way. Considering the conventional scheme as a reference, the comparative performance analysis and the simulation are conducted. Experiments with image processing applications indicate that the proposed scheme can improve the memory system performance, the memory utilization, and the utilization of a TLB with insignificant complexity overheads.
Factors Affecting the Application of Environmental Management Accounting in Construction Companies in Ho Chi Minh City

This research was aimed to identify and measure the factors affecting the adoption of environmental management accounting in the construction companies in Ho Chi Minh City. The model is tested using data collected from a sample of 200 Ho Chi Minh City construction companies, the survey period is 2 months from October to December 2017. The methodology includes qualitative and quantitative. Analytical techniques include descriptive statistics, scale reliability tests, EFA analysis, linear regression. There are 6 factors that positively influence environmental management accounting in the construction companies in Ho Chi Minh City, including the support of government, company size, manager perception, business strategy, corporate culture, the cost of organizing the environmental management accounting system, construction industry and qualified internal accountant. Research results show the positive role of applying environmental management accounting in corporate governance. The study suggests some policies for government agencies and managers involved in applying environmental management accounting in improving the efficiency of economic and environmental management in companies.
Torque-Sensorless Control for a Powered Exoskeleton Using Highly Back-Drivable Actuators

In recent years, most countries have experienced a shift toward aging population, leading to a decline in the working population; thus, an increased physical burden on workers results. The use of powered exoskeletons is one approach to reduce physical stresses on the bodies of workers and elderly people. However, conventional powered exoskeletons have many sensors and actuators mounted on them, resulting in problems associated with their high price, high weight, etc. In this paper, we introduce a new high-efficiency reduction gearbox, which is being researched and developed in our laboratory as a substitute for conventional actuators, and present the results of its attempted assist control without a torque sensor. It was found that the user's torque can be estimated with high accuracy without using a torque sensor by using a highly efficient gearbox with 1/102.14 reduction ratio. In addition, we performed motion identification using a nonlinear support vector machine with the aim of assist control with reduced discomfort on the body by suppressing vibrations.
Building Extraction From LiDAR Data Applying Deep Convolutional Neural Networks

Deep learning paradigm has been shown to be a very efficient classification framework for many application scenarios, including the analysis of Light Detection and Ranging (LiDAR) data for building detection. In fact, deep learning acts as a set of mathematical transformations, encoding the raw input data into appropriate forms of representations that maximize the classification performance. However, it is clear that mathematical computations alone, even highly nonlinear, are not adequate to model the physical properties of a problem, distinguishing, for example, the building structures from vegetation. In this letter, we address this difficulty by augmenting the raw LiDAR data with features coming from a physical interpretation of the information. Then, we exploit a deep learning paradigm based on a convolutional neural network model to find out the best input representations suitable for the classification. As test sites, three complex urban study areas with various kinds of building structures through the LiDAR data set of Vaihingen, Germany were selected. Our method has been evaluated in the context of “ISPRS Test Project on Urban Classification and 3-D Building Reconstruction. ” Comparisons with traditional methods, such as artificial neural networks and support vector machine-based classifiers, indicate the outperformance of the proposed approach in terms of robustness and efficiency.
Video Anomaly Detection and Localization using 3D SL-HOF Descriptor

Video anomaly detection plays a prominent and challenging role for automated video surveillance. To aim this, we propose a novel framework for local anomaly detection in videos based on 3D Spatially Localized Histogram of Optical Flow (3D SL-HOF) descriptor. The new 3D SL-HOF motion descriptor is capable of capturing global and local motion variations from spatially distributed optical flow map combined with 3D HOF descriptor which efficiently extracts motion velocity and orientation. Each video is described as a set of nonoverlapping spatio-temporal volumes (STVs) and are further partitioned spatially to form 3D local regions. The histogram of optical flow orientation and motion magnitude extracted from motion-rich STVs used as feature descriptor. To reduce computational burden, we compute features for foreground objects. Simple and cost-effective OCSVM classifier is employed to learn normal behaviour during training and detect anomaly from test data. We define Context location to detect abnormal behaviour in an unexpected region. We demonstrate the performance of the proposed method on the benchmarking UCSD Ped1 and Ped2 local anomaly datasets and UMN crowd activity global anomaly dataset. We achieve promising results and compare the performance with state-of-the-art methods.
Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals

Recognizing the very size of the brain’s circuits, hyperdimensional (HD) computing can model neural activity patterns with points in a HD space, that is, with HD vectors. Key examined properties of HD computing include: a versatile set of arithmetic operations on HD vectors, generality, scalability, analyzability, one-shot learning, and energy efficiency. These make it a prime candidate for efficient biosignal processing where signals are noisy and nonstationary, training data sets are not huge, individual variability is significant, and energy-efficiency constraints are tight. Purely based on native HD computing operators, we describe a combined method for multiclass learning and classification of various ExG biosignals such as electromyography (EMG), electroencephalography (EEG), and electrocorticography (ECoG). We develop a full set of HD network templates that comprehensively encode body potentials and brain neural activity recorded from different electrodes into a single HD vector without requiring domain expert knowledge or ad hoc electrode selection process. Such encoded HD vector is processed as a single unit for fast one-shot learning, and robust classification. It can be interpreted to identify the most useful features as well. Compared to state-of-the-art counterparts, HD computing enables online, incremental, and fast learning as it demands less than a third as much training data as well as less preprocessing.
Reducing Computation Effort by Parallel Optimization for Modular Multilevel Converters

The efficiency of a converter is important for automotive applications and influences a car's range. Modular Multilevel Series Parallel Converters offer increased efficiency compared to conventional converters. However, one drawback is the need of a complex and time-consuming control structure because online optimization methods are needed. This paper presents a control method which is based on dynamically updated look-up tables. Using parallel optimization enables the calculation of the optimal switching states asynchronously to the switching frequency. Thereby, the maximum switching frequency is increased. The simulative results clarify that active balancing and efficiency optimization shows a performance similar to online optimization methods. This finding should be considered when the control platform is chosen since a smaller and cheaper logic and processing unit can be used.
Fault Tolerance and Energy Sharing Analysis of a Single Phase Multilevel Inverter Topology

Multilevel Inverters (MLI) faces severe performance limitation owing to the low reliability of the power electronic devices and capacitor voltage balancing. Therefore, a novel fault tolerant MLI topology is proposed in this paper. The proposed topology can sustain single and multiple switch fault on all of its main inverter switches. The switching scheme opted results in inherent DC-link capacitor voltage balancing and equal (or best) voltage source utilization, under both healthy and faulty conditions. The validity of the proposed topology is verified by the obtained OPAL-RT results.
Exoskeleton : Kinematic Analysis and Dynamic Modeling

ORTE is a robotic platform developed to restore the mobility to the shoulder complex and elbow joint of upper limb impaired patients. ORTE pretends to be an effective and objective tool for the rehabilitation staff. This work presents the kinematic analysis and dynamic modeling of ORTE, a 6 Degrees-of-Freedom upper limb rehabilitation exoskeleton. The dynamic model of the mechanism is used to find the required torques to perform a set of different trajectories of daily living activities as eating or taking an object. This allows the optimal selection of actuators for this rehabilitation system. Keywords—Dynamics, Exoskeleton, Rehabilitation, Robotics, Upper Limb
Comparative Study of Color Image Segmentation by the Seeded Region Growing Algorithm

The choice of color representation can have distinguishable perceptual differences in the subject image which raises the following question: To what extent color representation can affect image processing results? In this paper, we study the effect of the RGB and HSV color representations on the segmentation result of the famous seeded region growing (SRG)algorithm. The implemented method involves three steps: 1) The automated seed selection, based on both color and space features,2) The region growing, based on the neighborhood similarity measured by the Euclidean distance, and finally,3) the region merging phase, introduced to overcome the over-segmentation issue and improve the results' accuracy. We used three metrics from the literature to evaluate the performances of our algorithm on both color spaces. The segmentation results were compared by combining the performance measures taken from a sample of images from the Berkeley dataset. The algorithm showcased more accurate results and consumed less execution time in the HSV color space compared to the RGB one.