Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Efficient Algorithm for Weighted Vertex Cover in Bipartite Graphs Based on a Dual Problem

The (un-weighted) vertex cover problem in general graphs is a classical NP-hard problem, but it is polynomial time solvable in bipartite graphs. This paper considers two combinatorial optimization problems. One is the weighted vertex cover problem and the other is the so-called maximum edge packing problem. We proved that in bipartite graphs, maximum edge packing problem can be viewed as the dual of the weighted vertex cover problem, and hence these two problems are polynomial time solvable. We explored the relationships between these two problems in bipartite graphs and some structural results are obtained accordingly. Furthermore, a new efficient algorithm for the weighted vertex cover problem in bipartite graphs is also derived. Our method generalized some previous algorithms for un-weighted vertex cover in bipartite graphs.
Adaptive Consistency Strategy of Multiple Controllers in SDN

In large-scale software-defined networking (SDN), the logically centralized controller usually uses multiple distributed controllers to manage and operate the network in a cooperative manner from the perspective of global network view. One of the major challenges with respect to deploying multiple controllers in SDN is how to synchronize the state among controllers to maintain information consistency. Aiming at solving this problem, we propose a periodically adaptive synchronization strategy of controllers. At first, the consistency level of controllers in the network is quantified based on the characteristics of SDN. Then, an adaptive synchronization strategy is proposed. By the use of this strategy, controllers are divided into three kinds of roles, and the synchronization period is adjusted dynamically by an adaptive function according to the current network state to attain a certain consistency level. The simulation results show that the proposed strategy has merits of reducing communication overhead of controllers as well as improving network availability compared with the other non-adaptive strategies. However, it lacks consideration of load balance among controllers, which will be our future research direction.
Annotation Assistance Using Japanese Sign Language Words Recognition

This paper describes one of the assistant methods for annotation tasks using Japanese sign language words recognition. The proposed words recognition is composed of body parts tracking, feature extraction, and words recognition. The recognition segments a video into meaningful units and annotates word labels to them automatically. This paper reports evaluation results using a video database for the correctness of words recognition and the work-reduction ratio of annotation tasks.
A Guided Approach to Prioritize Object-Oriented Test Suites Using MC/DC Testing

Concolic testing is used to prioritize test suites generated from object oriented programs. Various techniques proposed and validated to prioritize test cases at the maintenance phase of software system. But to prioritize test suites generated from object-oriented safety critical programs is a new challenge. MC/DC ("Modified Condition Decision Coverage") testing criteria is a well-known technique for testing safety critical systems. We propose a method to prioritize the given test suites using MC/DC criteria. In this paper, concolic testing is applied on several standard Java programs to generate test suites and recorded their execution time. The generated test suites are used by a coverage analyzer to measure MC/DC%. MC/DC score and time taken for each test suite are formulated to produce weight value for each test suite, which in turn helps to achieve prioritized test suites.
Joint Caching and Resource Allocation in D2D-Assisted Heterogeneous Networks

Device-to-device (D2D) communications combined with Heterogeneous networks (Hetnets) has attracted growing interest. Indeed, Hetnets deploy small-cells within macro-cells in order to offload traffic and improve the overall network coverage and capacity. Whereas, D2D promotes the use of communications between users for content delivery without going through the small or macro bases stations. Hence, it reduces communication delays and improves the spectral efficiency. In this context, we aim in this paper at reducing the average transmission delay, defined as the average sum delays of contents transmission to satisfy users’ requests in a macro-cell, by jointly optimizing caching placement and channel resource allocation, in cache-enabled Hetnet with D2D assistance. At first, a lower-bound expression of the average transmission delay is derived. Then, the optimization problem is formulated. Afterwards, we propose a sub-optimal random search algorithm and a low-complexity greedy algorithm that solve the problem. Finally, numerical results illustrate the performances of the proposed algorithms.
The Impact of Occupational Burnout on the Turnover Intention of Traditional Chinese Medicine Doctors in Xianyang

Objective To understand the status of the occupational burnout and turnover intention of traditional Chinese medicine (TCM) doctors in Xianyang. We also explore the influence of occupational burnout on turnover intention. Methods We selected 490 cases of TCM doctors from four Chinese medicine hospitals at the second-class level and above as research objects. Using stratified sampling and cluster sampling, a questionnaire was compiled including the basic situation, occupational burnout and turnover intention of TCM doctors in Xianyang. Results The scores for occupational burnout and turnover intention were 59.21±13.64 and 15.44±3.58, respectively. In addition, the turnover intention among certain ages and technical titles all had statistical differences (P<0.05). Emotional exhaustion and depersonalization dimension of occupational burnout are evidently related to turnover intention (P<0.05). The multiple linear regression analysis showed that the proportion of turnover intention interpreted by sex, age, technical title and educational background was 5.68%, but this proportion increased to 18.17% after adding further components of occupational burnout. Age and emotional exhaustion respectively had the greatest effects on turnover intention (Beta=0.211, －0.357). Conclusion Turnover intention is closely related to the occupational burnout of TCM doctors in Xianyang. We also find that age and emotional exhaustion exert a significant influence on turnover intention.
Predictive Model to Determine Customer Desertion in Peruvian Banking Entities

In this paper, a predictive model to determine customer desertion in Peruvian banking entities is proposed. The purpose of the model is the early identification of customers that reflect a behavior tending towards desertion based on financial movements, transactions, product acquisition, etc. The model is based on the analysis of a customer dataset to identify common traits through the use of SAP Predictive Analytics, and then comparing these traits to a different customer dataset, identifying those that are more likely to leave the entity. The commercial use of this model is the immediate application of loyalty initiatives that would enable the entity to retain the customer. The model was tested in order to identify the most efficient and precise one, being the R-K Means algorithm the best performing one, with a 93.20% accuracy and a better false positive/negative relation (8 and 3 respectively).
Disease Classification Model Based on Qubit Neural Tree Networks

This paper introduces a quantum-inspired Qubit neural tree networks with improved Qubit neuron, overlayer connections and different phase operation functions for different neurons to construct disease classification model. A hybrid evolutionary algorithm that combines the modified PIPE algorithm with beetle antennae search is also proposed to optimize the structure and parameters of the Qubit neural tree network. Simulation results on two disease classification problems show that this model has more advantages in classification precision, feature selection and structure simplification, especially for classification with multi-class attributes.
A Murdoch-Based ROS Package for Multi-robot Task Allocation

Due to the rapid development of robotics application in a wide range of industries and the rapid growing complexity of tasks coordination, researches in Multi-Robot Task Allocation (MRTA) systems have increased swiftly in recent years. This work implemented an auction-based MRTA system using the MURDOCH architecture on ROS. Murdoch is an architecture which allows robots to place bids on tasks that they are capable of executing, and it selects the most fitted robot based on bids values calculated by a fitness function. The aim of this work is to present an adapted MURDOCH architecture that focused on proving a more generic MRTA solution, which may be applied to wider range of problems. Using ROS and a few other tools, our architecture was successfully validated, and it implemented this MURDOCH architecture in a readily scalable way. A few possible approaches for future improvements were also suggested.
A Backstepping Torque Control of a Five Phase Permanent Magnet Synchronous Machine

This paper is concerned with a backstepping torque control of five Phase Permanent Magnet Synchronous Machine (5P-PMSM). A non-sinusoidal EMF is considered. The design procedure of the control scheme is discussed. Indeed, the objective of the proposed backstepping controller is the minimization of electromagnetic torque ripples due to the non sinusoidal EMF, and also the minimization of the copper losses. Simulation results show the effectiveness of our design, with good transient performances and an excellent torque ripples reduction.
Incentive Driving Multipath Inter-Domain Routing

To deploy multipath inter-domain routing, this paper tries to integrate our designed incentive mechanism into multipath inter-domain routing and propose an Incentive Driving Multi-path Inter-domain Routing scheme (IDMIR). Under the incentive mechanism, it can collect the path-let information from the selected nodes from the feasible BGP paths and build a scale-limited topology to compute the special routes. Meanwhile, it can adjust the protocol performance by configuring the path-let visibility degree (R) and topological nodes size (N). The simulation results demonstrate that IDMIR has strong path discovery capability with reasonable overhead.
A Novel Online Error Correction Scheme for Sensors in Aerospace Applications

Sensors that form an integral part of any measurement system are prone to various types of errors, which are contributed by fabrication processes, calibration schemes, operating environment etc. Different types of sensors, which are used in aerospace applications, cannot afford to have such errors as the measurements are very crucial for a successful mission. Many of the error correction schemes are based on modeling of sensor characteristics and are highly complex and non-flexible. Moreover, these schemes are not ideal for online error corrections, which are essential for realtime systems. Artificial Neural networks (ANN) with proper learning strategies prove to be good candidates in such cases. The proposed Meta-cognitive Extreme Learning Machine (McELM) based ANN framework, employed for error correction, automatically captures the non-linear behaviour of sensors to external environmental disturbances like temperature, structural vibration etc. A clustering based thresholding scheme has been used to improve the adaptiveness and stability of the ANN network. The performance of the algorithm has been evaluated on experimental data obtained from pressure sensors used for absolute pressure measurement onboard Indian launch vehicles. The proposed cognition based scheme builds a computationally less complex network with lesser training samples and a simpler hidden neuron layer for sensor error correction.
Isolated Odia Digit Recognition Using HTK: An Implementation View

This paper outlines the frame work for an isolated Odia digit recognition. The HTK toolkit which consider the Hidden Markov Model (HMM), as the base for developing the system. The recording environment is simple and created using the HTK tool. Initially the system is trained for 900 signals of 10 Odia digits spoken by 10 different speakers. The work done on this paper also signifies the role of HTK tool commands, applied in various levels of the system advancement. 10 samples, (each sample consists of 10 untrained signal) from the untrained 100 signals are considered for testing at each time. The trial results show that the average performance of our system is encouraging.
DataJoin: An Energy-Efficient Joining Scheme for 802.15.4e TSCH Networks

This paper considers the problem of a node joining an existing 802.15. 4e TSCH network. We propose DataJoin, an energy-efficient scheme based on two ideas: i) it schedules the transmission of enhanced beacon frames in a smart way by taking advantage of the information on the common TSCH schedule and ii) it piggybacks the next beacon transmission times onto some acknowledgment frames to allow a joining node to overhear this information.We compare DataJoin with the Baseline scheme that corresponds to the state-of-the-art protocol for joining TSCH networks. Simulation comparisons show lower energy consumption by the joining node under DataJoin along with similar joining delays and average network energy consumption for both schemes.
Smart Data Scouting in Professional Soccer: Evaluating Passing Performance Based on Position Tracking Data

Sports analytics in general and soccer analytics, in particular, have evolved in recent years due to the increased availability of large data amounts of (tracking) data. Especially in terms of evaluating tactical behavior, data science could change the way we think about soccer. In this study, we evaluate passing performance in soccer to prove the hypothesis that tactical behavior in team sports can be analyzed based exclusively on tracking data. To prove this point, we explore the relationship between changes in spatiotemporal variables in relation to passing and key performance indicators. Based on our results that demonstrate the ability of spatiotemporal variables to predict pass accuracy and key performances indicators on an individual level, we confirmed our hypothesis. Furthermore, we calculated a simple composite performance indicator to evaluate passes and players based on tracking data. In conclusion, our results can be used as an approach for real-time evaluation of tactical behavior and as a new method to scout and evaluate players in soccer and team sports in general.
H_/H∞ fault detection Proportional Integral observer for uncertain T-S fuzzy systems

In this work, we investigate the detection and estimation problems of Sensor and Actuator Faults (SAF) for uncertain nonlinear systems represented by Takagi-Sugeno (TS) fuzzy models subject to an Unknown Bounded Disturbance (UBD). For this, we design a Proportional Integral (PI) observer to estimate state and both sensor and actuator fault vectors, simultaneously. To maximize the fault detection and to minimize the disturbance effects, we consider the H_/H∞ multiobjective performances. By solving a set of Linear Matrix Inequalities (LMIs) and using Lyapunov theory, we prove the existence of this observer and we guarantee the convergence of both the state estimation errors and fault estimation errors. The descriptor formulation is used to get relaxed LMIs. Finally, a numerical example and simulation results are illustrated to prove the mentioned process efficiency.
KADetector: Automatic Identification of Key Actors in Online Hack Forums Based on Structured Heterogeneous Information Network

Underground forums have been widely used by cybercriminals to exchange knowledge and trade in illicit products or services, which have played a central role in the cybercriminal ecosystem. In order to facilitate the deployment of effective countermeasures, in this paper, we propose and develop an intelligent system named KADetector to automate the analysis of Hack Forums for the identification of its key actors who play the vital role in the value chain. In KADetector, to identify whether the given users are key actors, we not only analyze their posted threads, but also utilize various kinds of relations among users, threads, replies, comments, sections and topics. To model the rich semantic relationships, we first introduce a structured heterogeneous information network (HIN) for representation and then use a meta-path based approach to incorporate higher-level semantics to build up relatedness over users in Hack Forums. To reduce the high computation and space cost, given different meta-paths built from the HIN, we propose a new HIN embedding model named ActorHin2Vec to learn the low-dimensional representations for the nodes in HIN. After that, a classifier is built for key actor identification. To the best of our knowledge, this is the first work to use structured HIN for underground participant analysis. Comprehensive experiments on the data collections from Hack Forums are conducted to validate the effectiveness of our developed system KADetector in key actor identification by comparisons with alternative methods.
Game-Theoretic Incentive Model for Improving Mobile Code Offloading Adaptability

Due to the limited computational capabilities of the mobile device they usually need to delegate resource intensive tasks to the cloud. Code offloading is one of the techniques used for such purposes. In Code offloading, the mobile application is partitioned to identify resource intensive tasks which are then transferred to the server for remote processing. Various techniques have been in use for performing code offloading but none of them is economically viable due to which this model is not frequently used in the industry. In this paper, we tried to address the issues which make code offloading expensive and came up with code offloading model that can make the process economically viable. We developed a game theoretic model that provides incentive to mobile users to open their devices for offloading. Simulations have been done to validate the mathematical model and a prototype has also been developed to see how the framework behaves in the real world scenario.
Distributed Resource Allocation Approach For Device-to-Device Multicast Communications

In this paper, we address the problem of joint resource and power allocation in underlay device-to-device multi-cast communication for short (D2MD) to maximize system global energy efficiency (GEE). For this, we propose a two-stage semi-distributed solution. First, we model the resource sharing sub-problem as a transferable overlapping coalition formation game. Here, a D2MD group can participate in s coalitions and it can decide to join or leave a coalition based on specific split and merge rules. Similarly, a resource block can be shared among r D2MD groups where s, r are reuse and split factors. After that, the transmission power is centrally controlled by the The base station (BS) via a fractional programming framework. Finally, GEE is analysed via extensive numerical simulations with a spatial Poisson process for the users’ locations and applying two different clustering algorithms: K-nearest neighbour, and distance-limited.
Control of a mobile robot with Actuator Dynamics for Parking and Trajectory Tracking Tasks

This paper presents a pose controller for a mobile robot that considers the full dynamic model of a differential-drive mobile robot, including the actuators effects. This model is partially linearized by a state feedback thus allowing for the use of linear control laws. Then, a cascade control law, composed of linear velocity controller and a non-linear pose controller is proposed. The parameters of the whole controller structure are tuned taking into account the parking and trajectory tracking tasks. Finally, experimental results are shown for both control objectives, which demonstrate proper performance.
Generating Pseudo-Code from Source Code Using Deep Learning

Pseudo-code written in natural language and mathematical expressions is a useful description of source code. Pseudocode aids programmers in understanding the code written in a programming language they are not familiar with. However, writing pseudo-code for each code statement is labour intensive. In this paper, we propose a novel approach to automatically generate pseudo-code from source code using Neural Machine Translation. Our model is built upon the deep learning encoderdecoder using the attention-based Long Short-Term Memory architecture to capture the long-term dependencies in both source code and pseudo-code. An empirical evaluation on a real Python dataset demonstrates the applicability of our approach in practice.
RDF Graph Visualization Tools: a Survey

Semantic Web technologies are increasingly being used for the development of Future Internet applications, mainly due to the impressive growth of the Internet of Things research area. This spread pushes for effective and efficient ways to visualize the content of RDF ontologies and knowledge bases. Several strategies can be adopted to visualize semantic data and one of this consists in exploiting the graph representation intrinsic in the RDF model. In this paper, we propose a survey of the main tools for the graphical visualization of triples (being them terminological or assertional statements) exploiting a graph representation.
Extraction of Factors Related to Transportations and Destinations by Decision Trees for Personal Whereabouts Modeling

It has become possible to record the data of a visited place automatically, using a smartphone's GPS. The smartphone can visualize where a user visits and present the traveling route. In this study, we propose a personal whereabouts model for living pattern analysis, utilizing the smartphone's GPS. The proposed model aims to extract personal living patterns effectively. To this propose, it is important to analyze those factors related to location and environment. A decision tree based method is used to extract these factors for the location-based personal data analysis. We finally discuss the experiment design and our observations based on personal data collected from mobile environments.
Super-Resolution Limit for Raman Spectroscopy and Optical Coherence Tomography

We study resolution limit for measurements affected by noise and limited by the impulse-response of the measurement system, and discuss its implications for Raman spectroscopy and optical coherence tomography. Regardless of the width of the impulse response, we show theoretically and by numerical example, that the resolution can be enhanced indefinitely by improving signal-to-noise ratio, the number of measurement samples and by relaxing the accuracy needed for amplitudes.
Constant Modulus and Kurtosis based Blind Adaptive Array Interference Suppression: Comparisons and New Approach

This paper presents performance comparison of interference suppression on blind adaptive array algorithms. Co-channel interference becomes more extensive due to frequency resource exhaustion and small cell deployment which had been triggered by mobile traffic explosion. Multi-antenna signal processing is an effective means for such co-channel interference suppression. Strict approach is to utilize a priori information of channel state by estimating it via known training sequences. It gratuitously consumes radio resource and thus overhead inflates according to the increase of interference source to be suppressed. In addition, training sequence cannot be shared between wire-less communication systems employing different transmission schemes. Here we focus on blind adaptive array where any channel estimation is not required. Algorithms of interest in this study are based on constant modulus and independent component analysis (ICA) criteria. We present fundamental observation of their interference suppression performance compared in terms of modulation order, number of interference sources, and degree of freedom, then derive the most stable approach using ICA incorporated with beamspace pre-processing.