Miracle-dz/UniFAR-FTUs huggingface dataset:
FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks

Parallel training of an ensemble of Deep Neural Networks (DNN) on a cluster of nodes is an effective approach to shorten the process of neural network architecture search and hyper-parameter tuning for a given learning task. Prior efforts have shown that data sharing, where the common preprocessing operation is shared across the DNN training pipelines, saves computational resources and improves pipeline efﬁciency. Data sharing strategy, however, performs poorly for a heterogeneous set of DNNs where each DNN has varying computational needs and thus different training rate and convergence speed. This paper proposes FLEET, a ﬂexible ensemble DNN training framework for efﬁciently training a heterogeneous set of DNNs. We build FLEET via several technical innovations. We theoretically prove that an optimal resource allocation is NP-hard and propose a greedy algorithm to efﬁciently allocate resources for training each DNN with data sharing. We integrate data-parallel DNN training into ensemble training to mitigate the differences in training rates and introduce checkpointing into this context to address the issue of different convergence speeds. Experiments show that FLEET signiﬁcantly improves the training efﬁciency of DNN ensembles without compromising the quality of the result.
An ensemble-based feature selection methodology for case-based learning

Case-based learning (CBL) approach has been receiving a lot of attention in medical education, as an alternative to the traditional learning environment. This student-centric teaching methodology, exposes the medical students to practice the real-world scenarios. In order to support the learning outcomes of students, existing systems do not provide computer-based as well as experiential knowledge-based support for CBL practice. Medical literature contains textual knowledge, which can be used as a very beneficial source for the computer-based CBL practice. Therefore, designing and developing of an automated CBL approach is a challenging problem. In order, to solve this problem, the text mining domain provides the basic framework for constructing domain knowledge, where the feature selection is considered to be one of the most critical requirement to select the appropriate features. Keeping in view these facts, this research, provides contribution, in the following areas: (1) Feature Ranking; where we propose, an innovative unified features scoring algorithm to generate a final ranked list of features, (2) Feature Selection; where we propose, an innovative threshold value selection algorithm to define a cut-off point for removing irrelevant features for the domain knowledge construction, and (3) CBL Platform; where we designed and developed, an interactive CBL system consisting of experiential as well as domain knowledge to nurture medical students for their professional learning and development. We perform both quantitative and qualitative evaluation of our proposed (1) methodology on benchmark datasets, and (2) CBL approach. The extensive experimental results show that our approach provides competitive accuracy and achieved (1) on average, more than 5% increase in f-measure and predictive accuracy as compared to state-of-the-art methods, and (2) a success rate of more than 70% for students’ interaction, group learning, solo learning, and improving clinical skills.
A mesh-free approach for the simulation of incompressible flows

In this work, we propose to investigate numerically the incompressible flows by the Asymptotic Numerical Method (ANM) with the Moving Least Square (MLS). The mathematical formulation is based on theNavier-Stokes equations written in a strongly formulation to avoid all difficulties of the numerical integration. The used algorithm is developed to investigate the effective of the ANM with the MLS in the strongly formulation.
Improving Mispronunciation Detection of Mandarin Tones for Non-Native Learners With Soft-Target Tone Labels and BLSTM-Based Deep Tone Models

We investigate the effectiveness of soft-target tone labels and sequential context information for mispronunciation detection of Mandarin lexical tones pronounced by second language (L2) learners whose first language (L1) is of European origin. In conventional approaches, prosodic information (e.g., F0 and tone posteriors extracted from trained tone models) is used to calculate goodness of pronunciation (GOP) scores or train binary classifiers to verify pronunciation correctness. We propose three techniques to improve detection of mispronunciation of Mandarin tones for non-native learners. First, we extend our tone model from a deep neural network (DNN) to a bidirectional long short-term memory (BLSTM) network in order to more accurately model the high variability of non-native tone productions and the contextual information expressed in tone-level co-articulation. Second, we characterize ambiguous pronunciations where L2 learners’ tone realizations are between two canonical tone categories by relaxing hard target labels to soft targets with probabilistic transcriptions. Third, segmental tone features fed into verifiers are extracted by a BLSTM to exploit sequential context information to improve mispronunciation detection. Compared to DNN-GOP trained with hard targets, the proposed BLSTM-GOP framework trained with soft targets reduces the tones’ averaged equal error rate (ERR) from 7.58% to 5.83% and the averaged area under ROC curve (AUC) is increased from 97.85% to 98.31%. By utilizing BLSTM-based verifiers the EER further decreases to 5.16%, and the AUC is increased to 98.47%.
Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields

In healthcare, the analysis of patients' activities is one of the important factors that offer adequate information to provide better services for managing their illnesses well. Most of the human activity recognition (HAR) systems are completely reliant on recognition module/stage. The inspiration behind the recognition stage is the lack of enhancement in the learning method. In this study, we have proposed the usage of the hidden conditional random fields (HCRFs) for the human activity recognition problem. Moreover, we contend that the existing HCRF model is inadequate by independence assumptions, which may reduce classification accuracy. Therefore, we utilized a new algorithm to relax the assumption, allowing our model to use full-covariance distribution. Also, in this work, we proved that computation wise our method has very much lower complexity against the existing methods. For the experiments, we used four publicly available standard datasets to show the performance. We utilized a 10-fold cross-validation scheme to train, assess, and compare the proposed model with the conditional learning method, hidden Markov model (HMM), and existing HCRF model which can only use diagonal-covariance Gaussian distributions. From the experiments, it is obvious that the proposed model showed a substantial improvement with p value ≤0.2 regarding the classification accuracy.
Development of a Standalone Application to Measure Crosstalk in MMG Signals from Forearm Muscles during Wrist Postures

Mechanomyography (MMG) signals can be used to study and analyze skeletal muscles. It retains its potential application in various fields including athletics, sports, medicine and prosthetic control. MMG signals do exhibit crosstalk from adjacent muscles. The measurement of crosstalk in MMG signals could be beneficial for the study of muscle mechanics. Hence, this research contributes to the development of a standalone application (APP) to measure crosstalk in MMG signals coming from human forearm muscles during various wrist postures. The application has been developed on National Instruments LabVIEW software version 14.0. Peak cross correlations have been used as a measure of crosstalk between neighboring muscles. The results produced by APP while measuring crosstalk in MMG signals are very close to literature. Hence the results for APP have been validated by previous studies. The APP can be used for both forms of MMG data either stored in the form of tdms files or real-time signals. MMG signals are acquired, displayed, processed and finally used for measurement of crosstalk. All the steps are done automatically in the APP. Hence APP cannot only save time to measure crosstalk through other tedious methods but it also provides a source of MMG data validation in a real-time environment.
Study on the method of SLAM initialization for monocular vision

Visual SLAM is considered as a key technology to realize the autonomous positioning and navigation of mobile robots, and also a hot research technology in the fields of unmanned driving, augmented reality and smart home. Among them, monocular camera based visual SLAM is one of the hotspots in the field of visual SLAM. In today’s mainstream visual SLAM, PTAM needs to manually select two images to complete the initial keyframe trajectory and map point estimation process, which limits the practical application of the system and reduces the success rate of initialization. In addition, ORB_SLAM system adopts the statistical model selection method to realize the automatic initialization process. This paper first introduces the definition of the homography matrix and the fundamental matrix and the implementation algorithm, and then elaborates on the use of a certain scoring strategy to select the model method for initialization.
IoT: Internet of Threats? A Survey of Practical Security Vulnerabilities in Real IoT Devices

The Internet of Things (IoT) is rapidly spreading, reaching a multitude of different domains, including personal health care, environmental monitoring, home automation, smart mobility, and Industry 4.0. As a consequence, more and more IoT devices are being deployed in a variety of public and private environments, progressively becoming common objects of everyday life. It is hence apparent that, in such a scenario, cybersecurity becomes critical to avoid threats like leakage of sensible information, denial of service (DoS) attacks, unauthorized network access, and so on. Unfortunately, many low-end IoT commercial products do not usually support strong security mechanisms, and can hence be target of—or even means for—a number of security attacks. The aim of this article is to provide a broad overview of the security risks in the IoT sector and to discuss some possible counteractions. To this end, after a general introduction to security in the IoT domain, we discuss the specific security mechanisms adopted by the most popular IoT communication protocols. Then, we report and analyze some of the attacks against real IoT devices reported in the literature, in order to point out the current security weaknesses of commercial IoT solutions and remark the importance of considering security as an integral part in the design of IoT systems. We conclude this article with a reasoned comparison of the considered IoT technologies with respect to a set of qualifying security attributes, namely integrity, anonymity, confidentiality, privacy, access control, authentication, authorization, resilience, self organization.
Clustering-Based Feature Selection

The authors come up with a definition of measuring differentiations between features,and then put forward a method of clustering-based feature selection(Below referred to as CBFS). The time complexity of the method is nearly linear with both the size of dataset and the number of features.Besides,the method is applicable to the selection of features in large dataset.It can particularly handle data with both Nominal and Continuous Features.The results of the experiment on UCI datasets show that the method is effective and practicable.
Research on improving the authenticity of simulated infrared image using adversarial networks

When the real infrared image is insufficient, the simulation infrared image is an important data supplement to the real infrared image. However, the authenticity of simulated infrared image often does not meet the requirements of real images. So improving the authenticity of simulated infrared image plays an important role in related fields. In order to achieve this goal, a method based on deep learning is proposed in this paper. Unlike traditional methods of using manual modification by experience, the proposed method can convert non-realistic simulation infrared image input into a realistic one with similar scene structure. First, we generate a large number of simulation infrared images through the simulation system. Then, we propose an optimization method to improve the authenticity of simulated infrared images. Finally, we designed a comparison experiment between the original simulation infrared image and the optimized simulation infrared image, and finally verify the effectiveness.
Computing the Intersection of a Plane and Geometric Primitives in VRML Model for Rapid Prototyping Software

VRML(Virtual Reality Modeling Language)format as an international standard for virtual reality,has al- ready been widely adopted for graphical representation of 3D objects over the Web.Adopting VRML model in RP(Rapid Prototyp- ing)can reduce the precision loss which is caused by triangulation in generating STL file.Hence exploring a slicing method and de- veloping a slicing software for VRML model is important and significant to improve the accuracy of RP products.Finding inter- sections of a plane and VRML model is the key operation in slicing algorithm.This paper presents a method for calculating the in- tersections between a set of parallel planes and VRML geometric primitives.Based on the analysis of the relative position between a plane and a geometric primitive,intersection conditions in all cases were obtained,and the geometric parameters and corresponding equations of intersections were derived.The algorithm had been tested,and applications show that it is robust and effective.
CRF Based Research on a Unified Approach to Word Segmentation and POS Tagging for Pre-Qin Chinese

This paper explores the cross field between NLP and ancient Chinese,particularly the pre-Qin documents.The text of "Zuo Zhuan" is firstly analyzed after manual segmentationand POS tagging.Then the Conditional Random Fields model(CRF) is adopted for the word segmentation(WS),POS tagging(PT) and a unified process of WS and PT,respectively.The precision and recall of the unified approach are much higher than the independent WS and PT in the open test,with a F-score of 94.60% in WS and 89.65% in PT.This method is suitable for the study of ancient Chinese vocabulary and corpus construction,and can be applied to compensatethe manual tagging.
Path planning for autonomous buses based on optimal control

This thesis presents an algorithm to generate trajectories for an autonomous bus approaching a bus stop. The path planning algorithm is formulated as an Optimal Control Problem (OCP) which is solved by means of nonlinear programming (NLP) using the direct multiple shooting method. This method has shown to be a good choice for solving nonlinear Boundary Value Problems (BVP) like this one -where there are constraints such as the limits of the road, the model dynamics or passengers comfort- due to its highly accurate solution and faster convergence and stability than other methods like direct single shooting methods. It uses a kinematic bicycle model with a coordinate transformation which uses the vehicle position along the path as independent variable instead of using time which permits the definition of the constraints independently of the vehicle’s speed. The OCP is solved in MATLAB using CasADi, a symbolic tool for solving nonlinear optimization problems that provides high level interfaces to make the problem writing easier, in addition of having better performance than similar tools. The proposed algorithm is evaluated in multiple scenarios like different kinds of bus stops and paths inside confined areas, giving as a result a trajectory that meets with the imposed constraints successfully. Experimental tests on a real autonomous bus are carried out, resulting in a smooth bus stop manoeuvre that the passengers evaluated as fully acceptable.
A Survey on Electronic Image Stabilization

Electronic image stabilization(EIS)technique has been widely utilized for removing the unwanted motion fluctuations from video sequence.It is one of the primary development directions of the future image stabilization technique.This paper firstly introduces the basic principles of EIS and its architecture.After giving an overview of the state-of-the-art of key techniques including motion estimation,motion correction and image compensation in EIS,we list a number of general means to assess the performance of video stabilization process.Finally,the status about the application of EIS is summarized and its development trend is analyzed.Survey results show that EIS technique possesses promising application prospect and great market demand,as research on EIS technique has turn into engineering application.
Industrial wireless sensor and actuator networks in industry 4.0: Exploring requirements, protocols, and challenges—A MAC survey

The vision to connect everyday physical objects to the Internet promises to create the Internet of Things (IoT), which is expected to integrate the diverse technologies such as sensors, actuators, radio frequency identification, communication technologies, and Internet protocols. Thus, IoT promises to transfer traditional industry to advance digital industry known as the Industry 4.0.
Business Platform Model for Smart Home for Technology Planning Task Force

In this study we used an architectural research method based on the Open Services Gateway Initiative and Mobile Agent Technology, which is a model for analyzing smart home business platforms for technology governance planning tasks that include the recommended decision flow process and as recommendations from established business models and expanded to include a series of additional parameters needed to produce a business platform so that it can analyze the business model offered by the developer and detailed analysis of proposed governance in the service sector. And finally this research produces a Framework that presents a technology governance model for updating strategic plans and governance models for technology and frameworks designed to meet the needs of the elderly population and as recommendations for smart home management that aim to measurably improve the welfare of people in Indonesia. The proposed model and strategy recognizes the diversity of the community with existing providers of household tools and developers to optimize resources to achieve the goals and objectives of elderly smart home residents
Data importance and feedback based adaptive level of authorization for the security of Internet of Things

With the rapid development of Internet of Things (IoT), it is widely used in various IoT applications. Generally, the IoT is used to interconnect each computing device assist by the unique identity of the Internet. Due to interconnections, the IoT constitutes intruder on the wireless communication channel, tampering with device, unauthorized access to the device and privacy risks. To enhance the IoT security, the data importance and feedback based adaptive level of authorization is proposed in this paper. The proposed method comprises of token initialization phase, request phase and authorization phase. The request phase is done between the IoT device and IoT server. Then, the authorization between the devices is performed by the proposed method. The log file and feedback table is the major concern which is stored in the authorization centre of the IoT system. These file consists of behaviour and performance among the IoT devices. Then, data importance is also included for the proposed authorization scheme based on the data size. Thus, the data importance and feedback based adaptive level of authorization is performed significantly. Finally, the simulation results of the proposed method are validated using DPWSim implementation. Then, the verification phase is analysed against the various security issues and attack analysis is compared with the existing systems. Thus, the proposed adaptive level of authorization enhances the security level for the Internet of Things.
An efficient approach for the detection of link failures in WBAN system for health care applications

Wireless body area network (WBAN) plays an important role in patient health care. The performance of this WBAN system is affected by link failures due to the presence of malicious sensor nodes. Hence, the detection and mitigation of this link failure is important for improving the efficiency of the WBAN system. This paper proposes a methodology for link failure detection using weight metric approach. The performance of the proposed methodology is analyzed in terms of packet delivery ratio (PDR), link failure detection latency, and link failure detection rate.
CTLBO: Converged teaching–learning–based optimization

Abstract Teaching–learning–based optimization (TLBO) is an algorithm based on the influence of a teacher on the output of learners in a class. This method has shown to be more effective and efficient than other optimizations in finding the maximum solutions. In this paper, a new improved version of TLBO algorithm, called the converged teaching-learning-based optimization (CTLBO), is presented. In fact, it combines a proposed convergence operator with the teacher phase to find better solutions with a higher convergence rate. The method is tested on some benchmark problems and the results are compared with the original TLBO and other popular evolutionary algorithms. Furthermore, the introduced algorithm is used for optimization of fuzzy tracking control of a walking humanoid robot. In elaboration, fuzzy tracking control, which has appropriate membership functions and error indices, is employed in this paper as a promising intelligent approach to control the nonlinear dynamics of a humanoid robot. Summation of integrals of absolute angle errors and absolute control efforts is regarded as the objective function addressed by both TLBO and CTLBO algorithms in the present investigation.
Accurate Detection of Road Network Anomaly by Understanding Crowd's Driving Strategies from Human Mobility

There are thousands of road closures and changed traffic rules that impact vehicle routing every day. Detecting the road closures and traffic rule changes is essential for dynamic route planning and navigation serving. In this article, we propose a driving-behavior modeling-based method for accurately and effectively detecting the road anomalies. In the first step, we detect the areas of anomalies by using the deviation between drivers’ actual and expected behaviors. To discover the cause of anomalies, we explore the drivers’ short-term destination and find the crucial link pairs in anomalous areas through a novel optimized link entanglement search algorithm, namely, the Select Link Entanglements (SELES) algorithm. Finally, we analyze the crowd's driving patterns to explain the road network anomalies further. Experiments on a very large GPS dataset demonstrate that the proposed approach outperforms the existing methods in terms of both accuracy and effectiveness.