Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Laser-Based 3D SLAM for Coal Mine Rescue Robots

An accurate description of laneway space with self-localization is a key issue when coal mine rescue robots (CMRRs) perform post-disaster exploration and rescue missions. The 3D simultaneous localization and mapping (SLAM) is an effective but time-critical and highly challenging task in complex laneway scenarios, especially after disasters. In this paper, we propose a novel real-time 3D SLAM based on normally distributed transform (NDT) that employs pose graph optimization and loop closure to further improve mapping consistency. We innovatively extract floors and walls in the laneway as plane nodes to construct landmark constraints, in addition to applying pose nodes from the lidar odometry via NDT. A lightweight and effective loop detection method is conducted using odometry with an appearance-based approach to building a globally consistent map. The proposed method was evaluated on a public dataset, and field tests in an underground coal mine were performed. Results indicate that our algorithm can achieve lower computational complexity and drift, which can provide pose estimation and environment description for CMRRs to realize remote control assistance and automatic navigation in coal mine rescue missions.
Implementation of Modulated Wideband Converter compressed sensing scheme based on COTS lowpass filter with amplitude and phase compensation for spectrum monitoring

This paper presents an implementation of the Modulated Wideband Converter (MWC) compressed sensing scheme using Commercial Off-The-Shelf (COTS) low-pass filter (SXLP-36+ from Mini-Circuits®) for spectrum surveillance in order to reconstruct a wideband signal at sub-Nyquist rate. From a theoretical point of view, the MWC scheme achieves a correct reconstruction only if all analog components are assumed ideal. Indeed, to deploy this scheme into practice, the impacts of non-ideal components need to be considered, especially lowpass filters. In this paper, the SXLP-36+ lowpass filter is characterized in order to implement amplitude and phase compensation method without any optimization algorithms. The goal of this digital compensation is to achieve an output as close as possible to the theoretical MWC output. The results of spectrum reconstruction are evaluated with respect to probability of correct reconstruction and false alarm criteria.
Gamified Online Active Learning Theory

There exists a relationship between learning and behavior/attitude which reflects the substantial differences in acquisition of knowledge. These behaviors account for degree on which learning benefit is attained among learners. In an effort to explore this affiliation, the current study examines the theory behind active learning through an online gamified platform. An indepth scholarly analysis of theories and literatures were carried out, which governed the development of the learning theory. The theory is accompanied by attribution of gamification as a technique onto which those behaviors and attributes must fit. In conjunction to that, a conceptual learning model is proposed as an underlying framework to support the active learning process. The model follows the Input-Process-Outcome design to demonstrate a novel solution in bridging the difference between learning and learner satisfaction.
How Mobile Contributors Will Interact With Each Other in Mobile Crowdsourcing With Word of Mouth Mode

Mobile crowdsourcing is a promising paradigm for collecting sensing data by leveraging contributions of numerous mobile smart phones. It works efficiently with Word of Mouth Mode (WoM), especially for sensing tasks with time and location constraints, since the sensing task can be spread quickly among mobile contributors in the WoM mode. In this paper, we first investigate the behaviors of contributors, categorize all contributors into four types according to their different behaviors, and propose an inviting algorithm for contributors to recruit cooperators through social closeness. Then, we design a reward mechanism for crowdsourcing platform to evaluate the budget and pay the reward to contributors, meanwhile stimulate contributors to make the maximum contribution. Furthermore, considering two different scenarios, we model the interactions among contributors as two Stackelberg games, and backward induction approach is used to analyze each game. We propose an algorithm to compute the best response of every contributor, and we theoretically prove that this proposed algorithm may converge a unique Stackelberg equilibrium. The proposed approach can be applied to task formulation and task budget evaluations for crowdsourcing platforms.
Resistive Coupled VO2 Oscillators for Image Recognition

Oscillator networks are known for their interesting collective behavior such as frequency locking, phase locking, and synchronization. Compared to other artificial neural network implementations, timing rather than amplitude information is used for computation. We have fabricated and simulated small networks of coupled V02 oscillators and investigated the electrical behavior. It is demonstrated experimentally and through simulations that the coupled oscillators lock in frequency and the phase relation can be adjusted by the coupling resistance. Pattern recognition was simulated in resistor-coupled networks with up to nine oscillators (pixels), demonstrating the possibility of implementation of this task with compact VO2 circuits.
Protective Effects of Plathymenia reticulata and Connarus favosus Aqueous Extracts against Cadmium- and Mercury-Induced Toxicities

The extracts of Plathymenia reticulata and Connarus favosus are widely used in the folk medicine. The potential protective effects of these extracts have been evaluated against cadmium in the yeast Saccharomyces cerevisiae , and against mercurial contamination in zebrafish Danio rerio . In yeast, both extracts efficiently protected the Δ ycf1 mutant strain exposed to cadmium chloride restoring the growth, the expression of stress-response genes and decreasing the level of oxidative stress. In zebrafish, the supplementation of methylmercury-contaminated diet with both plant extracts similarly protected fish through the suppression of the methylmercury-induced lipid peroxidation, decrease of acetylcholinesterase activity, and restoring the expression levels of stress-response genes. This study particularly demonstrates the protective potential of both aqueous extracts against methylmercury, and could represent an interesting alternative for the Amazonian fish-eating communities to cope with the impact of chronic exposure to contaminated diets.
Examplar-Based Speechwaveform Generation for Text-To-Speech

This paper presents a hybrid text-to-speech framework that uses a waveform generation method based on examplars of natural speech waveform. These examplars are selected at synthesis time given a sequence of acoustic features generated from text by a statistical parametric speech synthesis model. In order to match the expected degradation of these target synthesis features, the database of units is constructed such that the units’ target representations are generated from the same parametric model. We evaluate two variants of this framework by modifying the size of the examplar: a small unit variant (where unit boundaries are determined by pitch mark location) and a halfphone variant (where unit boundaries are determined by subphone state forced alignment). We found that for a larger dataset (around four hours of training data) the examplar-based waveform generation variants are rated higher than the vocoder-based system.
Joint Time and Energy Allocation for QoS-Aware Throughput Maximization in MIMO-Based Wireless Powered Underground Sensor Networks

We study the optimal resource allocation in the MIMO-based wireless powered underground sensor network (WPUSN) for throughput maximization. Compared to existing WPUSNs that rely on single-antenna wireless power transfer techniques to transmit geological data in real time, the MIMO-based WPUSN can be adaptively replenished by beamforming. Note that, WPUSN has two remarkable features: the severe wireless path loss and diverse data traffic demands from different underground sensors. Further, by considering the quality of service (QoS) with respect to diverse data traffic demands and communication reliability, the throughput of the network is considered as a crucial measure, which suffers from a significant loss since it has to encounter a seriously unreasonable energy scarcity under the strong-heterogeneity underground environment. To this end, the MIMO is used to eliminate the unreasonable distribution of harvested energy resources and improve effective throughput for WPUSNs. In this paper, we formulate a non-convex optimization problem to maximize the system throughput in MIMO-based WPUSNs with QoS assurance. Specifically, we find there exists no mutual forbearance between the time allocation and the beamforming weight, and transform this non-convex problem to a solvable convex-constrained problem with convex sub-problems. Finally, we give a closed-form solution and show its advantages by simulations.
Deep Ensemble Learning for Skin Lesion Classification from Dermoscopic Images

Skin cancer is one of the leading causes of death globally. Early diagnosis of skin lesion significantly increases the prevalence of recovery. Automatic classification of the skin lesion is a challenging task to provide clinicians with the ability to differentiate between different kind of lesion categories and recommend the suitable treatment. Recently, Deep Convolutional Neural Networks have achieved tremendous success in many machine learning applications and have shown an outstanding performance in various computer-assisted diagnosis applications. Our goal is to develop an automated framework that efficiently performs a reliable automatic lesion classification to seven skin lesion types. In this work, we propose a deep neural network-based framework that follows an ensemble approach by combining ResNet-50 and Inception V3 architectures to classify the seven different skin lesion types. Experimental validation results have achieved accurate classification with an assuring validation accuracy up to 0.899.
Event2vec: Heterogeneous Hypergraph Embedding for Event Data

Network embedding learns low-dimensional representations of nodes with the goal of preserving the original network structure. However, most existing embedding methods lack the ability to handle event data, which are ubiquitous in the real world, due to the following three challenges: (1) participating objects in an event are often of different types, which limit the feasibility of using homogeneous network embedding methods; (2) relations among nodes in each event are much more complicated, i.e., more than two objects are involved in one event, thus it is far from enough to only preserve pairwise proximity; (3) there may exist relevance among different events, which has effects on the representations. In this paper, we model event data as a heterogeneous hypergraph, where participating objects in one event are represented as a hyperedge, and propose a novel embedding framework, namely event2vec, for learning effective representations of objects by preserving both the intra-event proximity and inter-event proximity. Extensive experiments on large-scale real-world datasets demonstrate that the representations learned by event2vec can outperform state-of-the-art methods.
On Algorithms Selection for Unsupervised Anomaly Detection

Anomaly detection, which aims at identifying unexpected trends and data patterns, has widely been used to build error detectors, failure predictors or intrusion detectors. Internal faults or malicious attacks have a different impact on the behavior of the system. They usually manifest as different observable deviations from the expected behavior, which may be identified by anomaly detection algorithms. Our study aims at investigating the suitability of unsupervised algorithms and their families in detecting either point, contextual or collective anomalies. To provide a complete picture, we consider both sliding and non-sliding window algorithms which operate in unsupervised mode. Along with qualitative analyses of each algorithm and family, we conduct an experimental campaign in which we run each algorithm on three state-of-the-art datasets in which we inject either point, contextual or collective anomalies. Results show that non-sliding algorithms are capable to detect point and collective anomalies, while they cannot effectively deal with contextual ones. Instead, sliding window algorithms require shorter periods of training and naturally build a local context, which allow them to effectively deal with contextual anomalies. Such observations are summarized to support the choice of the correct algorithm depending on the investigated class(es) of anomaly.
Xth Person View Video for Observation from Diverse Perspectives

This paper proposes "Xth Person View Video" that enables observation from the viewpoints of diverse people and objects using multiple-view video technique. Observation from various perspectives is achieved by properly switching "Outside-In images" captured from the outside to the inside of the target scene and "Inside-Out images" captured from the inside to the outside. To generate Outside-In video, we use the Bullet-Time video technique, which presents images by switching multiple-viewpoint videos based on the capturing camera alignment. "Enhanced Bullet-Time" is used to make appropriate observations on target objects moving in the space. Inside-Out video is generated by extracting the view of an appropriate direction from a panoramic video. To make observers intuitively understand the viewpoint movement between Outside-In video and Inside-Out video, which normally looks different, we employ "Pseudo Viewpoint Transition", which expresses viewpoint movement between two viewpoints via simple 3D CG models. The effectiveness of the proposed method is evaluated through both objective and subjective evaluations.
Software-Defined Dependable Computing for Spacecraft

In this contribution, we provide insights on the practical feasibility, effectiveness, and validation of a software-based fault-tolerance architecture we developed for use aboard small satellites. We exploit thread-level coarse-grain lockstep to facilitate forward-error-correction and assures computational correctness on an FPGA-based MPSoC. It can be implemented using standard open-source and FPGA design tools, requires only standard COTS components, and is processor architecture and operating system agnostic.
Weighted Quantization and Hamming Search for Fast Image Super-Resolution

Image super-resolution (SR) is a problem estimating high resolution image according to low resolution image. A number of practical approaches have been proposed ranging from interpolation-based to neural networks. In this paper, we focus on the patch-based neighbor embedding approach and propose a fast weighted quantization and Hamming search (WQHS) algorithm. At the offline stage, the WQHS method jointly pursue the linear projections for binary coding and the corresponding weight coefficients, which together can largely reduce the binary quantization loss. Based on the learnt hash functions, the database patches of low resolution can be indexed using the multiple hash tables. At the online stage, we devise a fast nearest neighbor search strategy for each query patch of low resolution that can work well with the code weights over the indexing tables. We evaluate our method on standard image datasets and demonstrate competitive or even better performance, compared to the state-of-the-art methods.
Multiscale Quantum Harmonic Oscillator Algorithm With Strict Metastability Constraints for Multi-Modal Optimization

Multi-modal optimization is a troublesome problem faced by optimization algorithms. The multiscale quantum harmonic oscillator algorithm (MQHOA) utilizes group statistics strategy to evaluate the state of the population and neglects the individual state. It will lead the particles to be trapped in local optima when addressing multi-modal optimization problems. This paper proposes a modified MQHOA by introducing strict metastability constraints strategy (MQHOA-SMC). The new strategy adopts a joint constraint mechanism to make the particle states mutual constraint with each other. The modified algorithm enhances the ability to find a better quality solution in local areas. To demonstrate the efficiency and effectiveness of the proposed algorithm, simulations are carried out with SPSO2011, ABC, and QPSO on classical benchmark functions and with the newly CEC2013 test suite, respectively. The computational results demonstrate that MQHOA-SMC is a competitive algorithm for multi-modal problems.
Vehicle Accident Risk Prediction Based on AdaBoost-SO in VANETs

With the rapid expansion of road traffic and vehicle scale, citizens have to face the serious life safety risks caused by vehicle accidents while enjoying an increasingly convenient life. Fortunately, vehicular ad hoc networks have provided us with important information about vehicle big data, which makes us have new methods to analyze vehicle traffic accidents. In this paper, we tackle the problem of predicting the risk of vehicle accidents and propose the trichotomy Adaboost with SMOTE and One-Hot encoding (AdaBoost-SO) algorithm to attain a vehicle accident risk prediction model. In this paper, predicting accident risk is mainly based on the use of big data mining and analysis of real-life accidents data. First, the experimental dataset is reconstructed by a synthetic minority oversampling technique. We complement the missing data and encode each sample feature to one-hot code. Second, the trichotomy AdaBoost algorithm is, respectively, used to train a series of weak classifiers from the experimental dataset and then combine them into a strong classifier to get the prediction model. Finally, the extensive simulation results illustrate that using the prediction model by trichotomy AdaBoost-SO algorithm can take the area under the curve of 0.77 and real-time into account. Through risk prediction and early warning, it provides the intelligent transportation system and driving safety assistance with a theoretical foundation.
Data Inspection in SDN Network

Software Defined Networks can be considered the most important development in Computer Networking in the last decade. Deep packet inspection (DPI) technology significantly enhances the security and management of current networks but combined with software-defined networking (SDN), DPI becomes an even more powerful tool can centralize network strategy control and quicken automation. The conversion from the traditional networks to the SDN network has a significant challenge that need a careful examination. We focus on Improved DPI can give the exhaustive data to notify the SDN controller about the situation of the network and its activity streams of traffic flows. This enables SDN to regard the network as a comprehensive asset as opposed to a different collection of devices. (e.g. switches, security and other Layer 4-7 elements). Ultimately, connecting SDN and DPI will let network pros apply policy control and automation to the whole network as opposed to individual components or elements. Leveraging a central DPI capability will give knowledge and intelligence to every important function (security, controller, policy, and so on.) -- instead of the current system of each functional box performing its own DPI. So, it became a must to inspect the data in SDN architecture that fit the benefits of central control.
Butterfly-Shaped Transmitting Coil for Wireless Power Transfer System in Millimeter-Sized Biomedical Implants

This paper presents a new butterfly-shaped transmitting (BS-Tx) coil of a magnetic resonance wireless power transfer (WPT) system for miniaturized biomedical implants. The range and misalignment tolerances are the main factors, which result in transfer efficiency degradation. The proposed BS-Tx coil improves the efficiency despite variations in the distance, angle, and axial misalignment with a 9-mm diameter receiver coil. A wireless power transfer efficiency exhibits a significant improvement as compared to a conventional WPT system under the same conditions.
CLF: An Online Coflow-Aware Packet Scheduling Algorithm

Literature on coflow-aware packet scheduling for input-queued switches is limited. Yet most of them are offline algorithms, requiring (unrealistic) a priori knowledge of all coflows and solving (time-consuming) linear programming (LP) problems for determining their expected coflow completion times (CCTs). In this paper, we propose an efficient online packet scheduling algorithm called Critical Line First (CLF). In CLF, coflows are ordered based on their easy-to-find ideal CCTs, or would-be-CCTs. In scheduling, coflows with the smallest would-be-CCTs are considered first; for each coflow chosen, packets on most heavily loaded rows/columns, i.e., critical lines, of the coflow traffic matrix are scheduled first. To avoid starvation, we propose to limit the number of times a coflow can be preempted by other coflows. Extensive simulation results show that our CLF outperforms all existing algorithms.
Performance Evaluation of Polymeric Insulators from a Dynamic Variations Analysis of Voltage Distribution along the String

This article presents a study of the dynamic voltage distribution along an electric insulator string, with the aim of investigating the possibility of implementing new technology to monitor the state of this string. It proposes to analyze the behavior of the potential distribution along the chain of a polymeric insulator under partial discharges conditions in different sectors, through the adequacy of the electrical model of the chain of ceramic insulators, already consolidated. These influences and perturbations are studied through simulations, where the model is applied using the platform MATLAB/Simulink®. To obtain the fundamental parameter of the model, the self-capacitance of each sheds of the polymer chain, the software is used COMSOL Multiphysics®, which allows to create and solve models using advanced methods of numerical analysis, based on the finite element method. The results obtained through simulations show the behavior of the potential distribution along the polymeric isolator for partial discharges in three different sectors. Finally, it is concluded that the results obtained from the electric model in the chain of polymeric insulators are consonant with the model already applied to ceramic insulators. With this validation, it was also possible to apply the model for the measurement of the dynamic voltage distribution, when short-circuiting the sheds specifically analyzed.
Abnormal Group-Based Joint Medical Fraud Detection

Joint fraud is one of the most common fraud types existing in medical fraud. However, joint fraud detection is a difficult problem because fraudsters take only a very small part of the population and fraudsters do everything to bypass fraud detection constraints. Most existing fraud detection studies focus on finding normal behavior patterns and treat those who possess behaviors that violate behavior patterns as fraudsters. However, these methods generally have high false positives because normal people may also sometime behave contrary to normal behavior patterns. To address this issue, we propose an abnormal group-based joint fraud detection method named abnormal group-based joint fraud detection method. This method can distinguish suspicious fraudsters from normal persons who have unusual behaviors by abnormal group mining in person similarity adjacency graph so that the occurrence of false positives caused by non-fraudulent abnormal behavior can be reduced. The extensive experiments using medical insurance data show that our approach has improved the precision of joint fraud detection by more than 10% compared with the conventional methods.