Miracle-dz/UniFAR-FTUs huggingface dataset:
SignCol: Open-Source Software for Collecting Sign Language Gestures

Sign(ed) languages use gestures, such as hand or head movements, for communication. Sign language recognition is an assistive technology for individuals with hearing disability and its goal is to improve such individuals' life quality by facilitating their social involvement. Since sign languages are vastly varied in alphabets, as known as signs, a sign recognition software should be capable of handling eight different types of sign combinations, e.g. numbers, letters, words and sentences. Due to the intrinsic complexity and diversity of symbolic gestures, recognition algorithms need a comprehensive visual dataset to learn by. In this paper, we describe the design and implementation of a Microsoft Kinect-based open source software, called SignCol, for capturing and saving the gestures used in sign languages. Our work supports a multi-language database and reports the recorded items statistics. SignCol can capture and store colored(RGB)frames, depth frames, infrared frames, body index frames, coordinate mapped color-body frames, skeleton information of each frame and camera parameters simultaneously.
Tri-Iron Tetra-Oxide and Silicone Composite Beam Actuator

Soft actuators should have flexible body and be controlled by external inputs. For soft actuator development, it is very important which materials are utilized. Silicone is a typical material for soft actuators and has high flexibility and versatility. We developed a new material actuator by adding the electrical conductivity of tri-iron tetra-oxide to the silicone material properties. We show the whole fabrication process for the actuator. And, for the detailed characteristic investigation, we conduct actuation test of the developed beam type actuator. First, the electrostatic attraction force is applied to the actuator. We show that the actuator is controlled as the amplitude and frequency of the input voltage potential. Also, we searched its resonant frequency for larger motion. In addition, we tested the actuator in influence of magnet and confirm that it works well. It is expected that the actuator can be adapted to various robot researches.
Nonlinear Robust Compensation Method for Trajectory Tracking Control of Quadrotors

In this paper, a nonlinear robust control method is developed for trajectory tracking of a quadrotor aircraft. The proposed approach combines the robust signal compensation method and the backstepping technique. First, the quadrotor dynamic system with multiple disturbances and uncertainties is divided into four subsystems. Then, the nominal controllers are constructed for the subsystems without disturbance terms, while the robust signal compensators are introduced to compensate for the effect of nonlinearities and uncertainties. Furthermore, the Lyapunov analysis has shown that the proposed controller can achieve stable tracking in the presence of violent discontinuous disturbances and uncertainties. The tracking error converges to an arbitrarily small neighborhood of zero. Finally, the proposed control design is validated with real flight experiments. The results show superior trajectory tracking performance of the quadrotor system affected by external disturbances.
Adaptive CPG-Based Impedance Control for Assistive Lower Limb Exoskeleton

Adaptation to user's impedance properties has been viewed imperative in developing assistive exoskeletons. However, most exoskeletons are still rather controlled with predefined joint trajectories. Inspired by the success in locomotion control for humanoid robots using central pattern generator (CPG) and impedance control for human-robot interaction, an impedance control based on CPG that can adapt to human's impedance properties is presented in this work. The proposed method is validated by a simulation in MATLAB/Simulink, integrating a 6-DOF lower limb exoskeleton model with a musculoskeletal human model. Simulation results indicate that the lower limb exoskeleton, incorporating the proposed adaptive CPG-based impedance controller, can effectively assist human walking.
Assured Delegation on Data Storage and Computation via Blockchain System

With the widespread of cloud computing, the delegation of storage and computing is becoming a popular trend. Concerns on data integrity, security, user privacy as well as the correctness of execution are highlighted due to the untrusted remote data manipulation. Most of existing proposals solve the integrity checking and verifiable computation problems by challenge-response model, but are lack of scalability and reusability. Via blockchain, we achieve efficient and transparent public verifiable delegation for both storage and computing. Meanwhile, the smart contract provides API for request handling and secure data query. The security and privacy issues of data opening are settled by applying cryptographic algorithms all through the delegations. Additionally, any access to the outsourced data requires the owner’s authentication, so that the dat transference and utilization are under control.
Effects of Performing Endurance and Strength or Plyometric Training Concurrently on Running Economy and Performance

ABSTRACT BOTH STRENGTH AND PLYOMETRIC TRAINING HAVE BEEN SHOWN TO BENEFIT ENDURANCE RUNNING PERFORMANCE. ONE OF THE BENEFITS IS THAT SUCH TRAINING METHODS HELP TO IMPROVE RUNNING ECONOMY THROUGH IMPROVED NEUROMUSCULAR ADAPTATIONS. HOWEVER, THERE ARE SOME STUDIES THAT SHOW AN INTERFERENCE EFFECT WITH CONCURRENT STRENGTH AND ENDURANCE TRAINING. THE PURPOSE OF THIS ARTICLE IS TO ELUCIDATE THE BENEFITS OF STRENGTH AND PLYOMETRIC TRAINING FOR ENDURANCE RUNNERS AND PROVIDE COACHES AND RUNNERS WITH INFORMATION FOR CONCURRENT ENDURANCE AND STRENGTH OR PLYOMETRIC TRAINING.
Predict the Best Graph Partitioning Strategy by Using Machine Learning Technology

In this paper, we explore applying machine learning techniques to find a best partitioner for a given graph. We use some metrics to describe the graph, and use these metrics as the input and the partitioner ranking of a graph execution algorithm as the label to train a model. Our experiment shows KNN and decision tree are good models for this problem.
Comparative Analysis of Voice Codecs Over Different Environment Scenarios in VoIP

Codecs is an essence of Voice over Internet Protocol (VoIP) which stands for coder-decoder. Voice over Internet Protocol applications can choose from a variety of different speech codecs which differs in bandwidth, bit rate, algorithm complexity, delay and many other factors. Capacity and Quality of Service (QoS) are the key challenges in the VoIP that needs to be resolved before the deployment of VoIP network. Capacity of VoIP depends on the chosen voice codecs. The use of appropriate voice codec is very important in order to generate maximum QoS and capacity for the implementation of VoIP. In this paper, we have proposed an industrialist solution for the selection of voice codec in different environments. The objective of this paper is to provide the basic understanding of the codecs, an appropriate selection and a comparison of codecs such as G.711, G.726, G.729A, G.723. 1, GSM-FR and iLBC in different environments such as: WLAN/WAN (802.11g), 802.11n, WLAN_11Mb, WiMax, MANETS, LAN, VPN and UMTS.
Wind Farm Grid Integration Architecture Using Unified Expandable Power Converter

This paper proposes a novel unified expandable low switch power electronic converter architecture for grid integration of direct drive variable speed wind turbine (VSWT) system using permanent magnet synchronous generator (PMSG). The proposed unified expandable power converter can interface two or more bidirectional output ports, such as wind generators, energy storages, and grid. The size of the power converter is compact because of fewer number of power electronic switches and protection devices, and its architecture is easily expandable to accommodate more outputs, i.e., in this case, the wind turbines. A generalized sequential space-vector modulation technique is developed based on the operational principle of the proposed converter to control the outputs autonomously in order to track the maximum power point for individual VSWTs-driven PMSGs. It is expected that the proposed approach will reduce the cost of power electronic converters in a wind farm, compared to both ac- and dc-link-based topologies, which are available for the moment.
Dark-Bright Optical Soliton and Conserved Vectors to the Biswas-Arshed Equation With Third-Order Dispersions in the Absence of Self-Phase Modulation

The form-I version of the new celebrated Biswas-Arshed equation is studied in this work with the aid of complex envelope ansatz method. The equation is considered when self-phase is absent and velocity dispersion is negligibly small. New Dark-bright optical soliton solution of the equation emerge from the integration. The acquired solution combines the features of dark and bright solitons in one expression. The solution obtained are not yet reported in the literature. Moreover, we showed that the equation possess conservation laws (Cls).
Feature Extraction for Fault Diagnosis Utilizing Blind Parameter Identification of MAR Model

This paper proposes a novel feature extraction method with the combination of variational mode decomposition (VMD) and multiple autoregressive model (MAR) for the fault diagnosis of rolling element bearings. With the collected fault signals, VMD is firstly applied to decompose the fault signal into some components, after which MAR model is built with all the components and its order is determined by Schwartz Bayes Criterion. Then, all parameters within the MAR model are identified blindly through QR decomposition. Finally, principal component analysis is applied to extract the key features, with which support vector machine is trained and used to recognize the fault mode. In order to assertain the effectiveness of the proposed method, engineering experiment and comparative analysis are conducted. The experimental result shows the superiority of the proposed method.
Effect of Diesel – Biodiesel – Ethanol Fuel Blends on Low Temperature NOX Reduction Activity over a Lean NOX Catalyst

The lean NOX catalyst in reducing Nitrogen Oxide (NOX) emission is necessary for a modern compression ignition (CI) engine. However, the high operating temperature is the main problem for the catalyst. Thus, the aim of this research is to investigate the hydrocarbon selective catalytic reduction (HC-SCR) in order to reduce nitrogen oxide containing in exhaust gas of a CI engine. The research carries out by using diesel, biodiesel and ethanol at different blending ratio as the fuel. The engine speed is kept constant at 1,500 rpm and the engine loads are varied at 25, 50 and 75 % of maximum engine load. The result shows that when increase of ratio of ethanol affect the increase of HC:NOX. In diesel, biodiesel and ethanol fuel blends at a ratio of 30:20:50 shows the maximum nitrogen reduction with products from HC-SCR can reduced nitrogen oxide up to 22 %. Then increase the amount of hydrogen (approximate 3,000 and 6,000 ppm) in exhaust gas to promote the HC-SCR reaction of nitrogen oxide reduction in low temperature. The effect of hydrogen addition can reduced nitrogen oxide up to 55.26 %.
A Tracking Model for Bulk Material Properties Based on the Monte Carlo Method

In the process of mine production, the properties of the ore is an important reference for the operation of each working procedure. In this article, a mathematical model is established to track bulk materials in the fine ore bin. For the uncertainty of the movement of materials within the fine ore bin, monte carlo method random sampling simulation is used to produces a large amount of enter and discharge sequence. According to calculation result, the properties of the discharged ore over a period of time can be calculated. Experiments show that the model has good precision, and can provide good support for the decision of mine processes.
Accurately Motion Control of a Liquid Metal Droplet in One-Dimensional Fluidic Channel

As an emerging multifunctional material, Gallium based room temperature liquid metal attract particular attentions in a variety of applications due to its mobility and deformability. However, accurately control the motion of a liquid metal droplet still remains unrevealed, which restrict the applications of liquid metal in many fields, especially in micro fluidic applications. In this paper, we propose a hybrid control framework to accurately control the motion of a liquid metal droplet in one-dimensional channel filled with aqueous solution. The electrical field is provided by a simple programmable DC power supply. The dynamic model of a liquid metal droplet immersed in fluidic channels is firstly discussed when the electrical field is applied. Then, a region reaching controller is designed to calculate the desired current input to drive the liquid metal droplet to the destination with vision feedback. The effectiveness of the proposed approach is verified by experiments of accurately control a liquid metal Galinstan droplet to destination inside a PMMA fluidics channels. The proposed control approach may generate profound impacts on developing tiny soft robots or micro-fluidic systems that accurately driven by liquid metal droplets.
Multi-Targets Influence Maximization Algorithm Based on Multi-Cascade Model

The problem of maximizing personalization influence is to use the online social network as the background to target the specific network users and mine the initial influence communication user set that maximizes the impact of the network users. But existing algorithms still rely on traditional traditional models and cannot imitate real information propagation. On the other hand, the algorithm research on multi-cascade model needs to be expanded. In order to better conform to the information propagation in real life and improve the performance of the algorithm, this paper adopts a multi-cascade model to set the user state to an integer value reflecting the influence quantity, instead of the active or inactive two states in the traditional independent cascade model. Users with the same hobbies tend to get together, so you can use this feature to classify the entire target set using clustering. Based on the above, this paper proposes a multi-targets influence maximization algorithm based on multi-cascade model, clustering candidate users, using clustering center as the seed node to spread information, to maximize the impact on specific users. The intensity of the impact on the target user is measured by the frequency of the individual. The comparison experiments on real social networks show that the personalized impact maximization algorithm based on multi-cascade model has better time performance and propagation effect than the algorithm based on traditional independent cascade model.
Bionic Visual-based Data Conversion for SLAM

Simultaneous localization and mapping (SLAM) is the key function for most mobile robots to achieve autonomous navigation. The traditional visual SLAM uses the camera to acquire data and constructs a sparse or dense 3D map, which is convenient for robot localization but difficult for obstacle avoidance and autonomous navigation. Thus, we propose an innovative data conversion algorithm based on bionic visual characteristics which can construct a two-dimensional accurate map for indoor navigation in this paper. The algorithm has two main parallel threads: Ground Detection and Data Conversion. The ground detection thread detects the ground in real time, and gets the transformation matrix from the camera to the ground based on the geometrical invariability. The data conversion thread first filters the depth data, and then proposes a variable-resolution model based on human visual characteristics, which can keep the conversion time consumption at a low level without affecting the accuracy. Each group of experiments shows that the data converted by our algorithm have high-precision, and can be used to construct the map for navigation accurately.
Targeted Solicitation of Product Reviews

Customer reviews have become an essential resource when people search for goods or services on the Internet. Previous work has shown that reducing a product's uncertainty is critical to its purchase decision. Thus, reviews are more effective when they reduce a product's uncertainty. Existing e-commerce platforms typically ask users to write free-form text reviews, which are sometimes augmented by a small set of predefined questions, e.g., “rate the product description's accuracy from 1 to 5. ” In this paper, we argue that this “passive” style of review solicitation is suboptimal in achieving low-uncertainty “review profiles” for products. Its key drawback is that some product aspects receive a very large number of reviews while other aspects do not have enough reviews to draw confident conclusions. Therefore, we hypothesize that we can achieve lower-uncertainty review profiles by carefully selecting which aspects users are asked to rate. To test this hypothesis, we propose various techniques to dynamically select which aspects to ask users to rate given the current review profile of a product. We use Bayesian principles to define reasonable review profile uncertainty measures; specifically, we apply Bayesian inference to measure an aspect's rating variance. We compare our proposed aspect selection techniques to several baselines on several review profile uncertainty measures. Experimental results on two real-world datasets show that our methods lead to better review profile uncertainty compared to aspect selection baselines and traditional passive review solicitations.
A General Approach of Calculating Semantic Object Distances from Complex Point Clouds

RGBD sensors are widely used in current robot research as they have the capacities of measuring geometrical distances and providing dense point clouds. However, it is still a difficult problem to extract semantic object distances from these point clouds. To solve this problem, a novel approach based on BP neural network is proposed in this paper. As a start point, we first analyze the complexity of obtaining semantic object distances. Then, the principle of the proposed approach is introduced in detail. Besides, in this work, Tensorflow is used to implement the BP neural network based approach. To make the training/evaluation dataset, a special software tool is developed. Finally, an object with complex geometry has been selected as a test object, and the experimental results have verified the effectiveness of the approach.
Formalization of Insertion/Deletion Codes and the Levenshtein Metric in Lean

Formalization deals with expressing definitions or theorems and proofs at the level of fundamental logic, which allows for automatic verification by computer programs. We report on work done formalizing definitions and theorems in coding theory using the Lean theorem prover, released by Microsoft Research and Carnegie Mellon University in 2015. We formalize fundamental concepts regarding error-correcting codes capable of correcting insertions, deletions, or combinations of insertions and deletions. In particular, we formalize definitions and theorems about subsequences and supersequences, the Levenshtein distance, insertion/deletion spheres, and insertion/deletion codes.
Application of LDA-LR in Personalized News Recommendation System

With the arrival of the big data age, and users can't effectively find information that matches their interest in the vast quantities of data. The common personalized news recommendation system is based on the user - based collaborative filtering algorithm. However, the content coverage recommended by this keyword based recommendation is narrow and cannot meet users' requirements for the diversity of recommendation systems. For textual data like news, it contains semantic attributes. Therefore, this paper proposes a LDA-LR personalized news recommendation method, which uses the LDA theme model to train the theme distribution of each news, then calculates the user similarity based on the topic, and then combines the LR logistic regression model to filter, and obtains the final news recommendation list.
A Novel Content Popularity Prediction Algorithm Based on Auto Regressive Model in Information-Centric IoT

As information-centric networking (ICN) cache can effectively reduce the requests from customers to producers and improve the efficiency of content acquisition, there are many studies propose to improve system performance of the Internet of Things (IoT) by using the concept of the ICN. In the context of information-centric IoT, the addressing location based on content names and routing transport mechanisms, which presents a high demand for the statistics and prediction of the content popularity. To improve the accuracy of the content popularity prediction, in this paper, we demonstrate a particular analysis of the content popularity and propose a content popularity prediction algorithm based on auto-regressive (AR) model. The algorithm derives regression parameters based on least-squares estimates and predicts future trends of the content popularity through combining various known values in a certain period. The evaluation results show that the proposed algorithm can accurately predict the content popularity of the next time period in information-centric IoT. As a result, the algorithm can increase the cache hit rate in routers, and reduce the network traffic and service access delay effectively to improve the experience of users in various scenarios such as real-time streaming media services.
Summarizing Code Changes by Tracing an Operation History Graph

By replaying the edit history of source code, the changes in software can be precisely understood. Recently, several replay tools have been proposed to support developers’ tasks of understanding past code changes. However, replaying is still a time-consuming task due to a plethora of the edit operations. This paper proposes a novel method to generate a summary of edit histories. The method is supposed to be used before replaying with existing tools to easily find which part is important for the current understanding task. This paper also introduces OpG2, which is a graph format that represents change histories at code and syntax levels. We can easily summarize edit operations by tracing edges in the graph. A case study shows an example of the summarization process using an operation history derived from an actual software development.
Design and Control of a Variable Viscous Damping Actuator (VVDA) for Compliant Robotic Joints

Due to the increasing demand for safe human-robot or environment-robot interaction, the development of intrinsically compliant actuators becomes a key issue to the robotic design. However, the introduction of compliance in an actuation system will increase system oscillations and reduce the mechanical bandwidth of the system making the system difficult to control. In order to solve such problems, this paper proposes a novel Variable Viscous Damping Actuator (VVDA) which has a hydraulic transmission unit placed in parallel with a series elastic transmission element. The hydraulic transmission unit utilizes a servo motor and slider-crank linkage to change the area of internal orifices and therefore regulates its viscous damping coefficient. The dynamic model of the VVDA is then formulated and the orifice effect in the hydraulic transmission unit is analyzed. Experimental results show that the VVDA is able to reduce the vibration in a position control process by actively change the viscous damping of the system.