Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Parts-Based and Global Representation for Image Classification

Nonnegative matrix factorization (NMF), known as a famous matrix factorization technique, has been widely used in pattern recognition and computer vision. NMF represents the input data matrix as a product of two nonnegative factors. As NMF is based on the Euclidean distance, which is sensitive to noise or errors in the data, some robust NMF methods are proposed. Mainly focusing on parts-based representation, these robust NMF methods often neglect global representation of data. In fact, the global geometry information of data is more robust than the local information about the noisy data in terms of image classification. In order to effectively improve the robustness of NMF and learn part-based and global representation of the data, a novel method low-rank nonnegative factorization (LRNF) is proposed in this paper. First, we assume that the data are grossly corrupted, and the $L_{1} $ norm is used as a sparse constraint on the assumed noise matrix. Then, LRNF learns a low-rank matrix with the global representation ability. Finally, we make a nonnegative factorization of the learned low-rank matrix. We can obtain a base matrix, which preserves locality and globality properties of the data in the meantime. Extensive experiments have been conducted on nine real-world image databases to verify the performance of the proposed LRNF method by comparing with the state-of-the-art algorithms on robust dimensionality reduction.
Optimization and Performance Improvement of Multi-excitation Double-stator Flux-switching Machine

By integrating the special structure of partitioned stator with the less-rare-earth PM machine, a new multi-excitation double-stator flux-switching machine (MEDS-FSM) is proposed, which has advantages of low rare-earth PM usage, high torque output and wide speed range. Considering the optimization accuracy and efficiency, a system optimal design method based on parameters sensitivity analysis (PSA) and genetic algorithm optimization (GAO) is presented for the proposed motor. The performance comparison between the initial and the optimized machine verifies the proposed method. The anti-demagnetization property and flux-weakening capability for wide-speed range operation are also analyzed, which further confirms the MEDS-FSM an interesting candidate for EV propulsion.
Power Control in D2D-Based Vehicle Communication with Delayed CSI Feedback

We maximize vehicle throughput in device-to-device (D2D)-enabled vehicle communication links guaranteeing the reliability communication of cellular user equipment (CUE). The channel state information (CSI) of vehicle to vehicle (V2V) is reported to base station (BS) periodically. which means that every V2V link has latency CSI feedback from BS. Based on this, V2V link is described by dynamic channel gain. The objective function can be converted as a convex expression through successive convex approximation. And the constraints can be transformed into determine form through Bernstein approximation. Then the optimal solution of problem can be obtained. Computer simulation verifies the feasibility of the proposed algorithm.
A Prototype Application to Generate Synthetic Datasets for Information Visualization Evaluations

The evaluation is an essential step of works that propose new information visualization techniques or tools. A common type is the controlled experiment in which the researchers measure the user performance to execute specific tasks using the proposed method. Furthermore, the dataset used for these tests must contain known desired features to be evaluated (e.g., level of noise, the percentage of missing values) in a controlled way. Thus, this article proposes an application to generate synthetic databases for evaluating information visualization techniques and tools. The system aims to create a dataset generator model that allows the construction of datasets with a diversity of profiles in a controlled manner. The creator of the model can save it for future experiments or updates and can export it enabling other groups to replicate the experiments easily. For a better understanding of application features and how to use it, this work also shows two use scenarios explaining the created model for each situation.
Open Set Radar HRRP Recognition Based on Random Forest and Extreme Value Theory

Most of the progresses achieved in radar high range resolution profile (HRRP) recognition rely on the closed set condition, where the test sample is from a known class. In realistic scenario, however, the test sample may be drawn from unknown classes, which is regarded as an open set recognition task. In such cases, conventional recognition algorithms will inevitably make a wrong prediction. In this paper, the open set problem is addressed by incorporating the extreme value theory (EVT) into the random forest (RF) classifier. The outputs of RF are analyzed to determine whether the test sample should be rejected as an unknown class. At the training phase, a Weibull-based extreme-value meta-recognition is introduced to describe the statistical characteristics of the known classes. At the testing phase, a probability estimation method is introduced to compute the probabilities of the test sample belonging to known and unknown classes based on trained Weibull distributions. The test sample is assigned to the class of highest probability. Experimental results demonstrate that the proposed method outperforms the state-of-art NN, 1-vs-set machine and W-SVM in rejecting unknown classes.
A Sensorless Rotor Position Estimation Method Based on the Field Current Harmonic for an LCI-Fed EESM

In this paper, a sensorless rotor position estimation method based on the field current harmonic for a load-commutated inverter (LCI)-fed electrically excited synchronous motor (EESM) operating at zero or low speed is proposed. A detailed theoretical analysis and mathematical deduction are presented. The field current harmonic is used instead of the extra high-frequency signal injection. A reasonable harmonic frequency is selected, and a realizable field current control method is given. To simplify the complex signal processing and improve the measurement accuracy, a specific bandpass filter and a normalized quadrature phase-locked loop are designed. The phase change caused by the filters is compensated properly. The proposed approach is demonstrated through the simulation and experiment with a 15-kW LCI-fed EESM drive platform, confirming its feasibility and accuracy.
Rao-Blackwellized Particle Filter with Grid-Mapping for AUV SLAM Using Forward-Looking Sonar

This paper adresses underwater localization for an AUV using SLAM and Forward Looking Sonar (FLS) data. The proposed method is Rao-Blackwellized Particle Filter (RBPF) with grid mapping. The original RBPF uses multiple suggested trajectories, where each of which has a occupancy grid map. Analogously, range measurements from the FLS are computed for each trajectory. A scan matching procedure is then performed for the measurements of each trajectories and the best matches are then chosen. The performance is confirmed through simulations and experiments. The suggested method enables SLAM in enclosed underwater environments using noisy FLS measurements without any type of artificial landmarks or assumption on the type of environment, and without any increment in runtime complexity for long operation times.
An Academic Social Network Friend Recommendation Algorithm Based on Decision Tree

With the rapid development of smart city services, social network plays a greater role in extensive areas with smart technology. How to use the smart technology to recommend friends from many users accurately and availably has been a research focus in the field of social recommendation. A friend recommendation algorithm based on Decision Tree with the background of the SCHOLAT, a large online academic social network site. The proposed algorithm translates the problem of friend recommendation into a prediction of binary-class. Firstly, the feature selection method, which the Relief and K-means algorithm are mainly applied, is used to remove the irrelevant and redundant features in the data preprocessing. After obtaining the effective features, we use a learning model to train the selected data. The Decision Tree method is introduced as a base classifier to predict the class of candidate user(recommendation or not recommendation). Secondly, combined with the AdaBoost enhanced algorithm, the results of Decision Tree classifier are weighted to adjusted for a better accurate result, which ultimately forms recommendation lists for the target users. Finally, the experimental results on the SCHOLAT for friend recommendation show that the effectiveness and practicability of the proposed algorithm.
Applying CNN Classifier to Road Interchange Classification

The identification and classification of interchange structures in OSM (Open Street Map) data can provide important information for the construction of multi-scale model, navigation and location services, congestion analysis, etc. The traditional method of interchange identification relies on the low-level characteristics of artificial design, and cannot distinguish the complex interchange structure with interferences effectively. In this paper, a new method based on convolutional neural network for identification of the interchange is proposed. The method combines vector data with raster image, and uses neural network to learn the fuzzy characteristics of the interchange, and classifies the complex interchange structure in OSM. Experiments show that this method has strong anti-interference, and has achieved good results in the classification of complex interchanges, and there is room for further improvement with the expansion of the sample base and the optimization of neural network model.
A Novel Path Planning for AUV Based on Objects’ Motion Parameters Predication

The path planning for autonomous underwater vehicle in unknown complicated oceanic environment is still a task with challenges up to now. A novel path planning algorithm is proposed to deal with the above problem based on moving objects’ motion parameters predication (OMPP). Rapid random search tree algorithm is presented to divide sonar data into segments to obtain separate objects, and virtual centroid is introduced as the replacement of the object in motion parameters’ predication. To predict the motion parameters of moving object including velocity and heading angles, an adaptive neuro-fuzzy inference system is adopted. Then, the OMPP algorithm is executed to plan an optimal path, where particle swarm optimization is used to produce temporary waypoints for obstacle avoidance, and a smooth path to destination is automatically produced under the guiding of temporary waypoints and destination. Finally, simulations are conducted in MATLAB soft environment, the results show that the OMPP algorithm is feasible, and the path planned by this algorithm is optimal comparing with other two algorithms.
Visual Search in Digital Libraries and the Usage of External Terms

This paper focuses on the application of visual search interfaces in the context of digital libraries. The main objective is to represent a simplified and intuitive interactive approach for retrieving similar publications based on a preselected one. This would enable the scholar to perform more detailed research with the reduced mental workload, in comparison to traditional keyword-based search. The proposed approach, in an innate and conceptual manner, makes possible the application of suggested terms from other external resources. Accordingly, the set of terms can be extended with synonyms, narrowed, broadened or closely related terms. Such suggestions may result from a simple language thesaurus, any SKOS modelling scheme, and the deployment of word embedding approaches, such as word2vec. To provide a better picture of why a particular publication is presented in the results list, the matched terms are colored.
Molecular palaeontology illuminates the evolution of ecdysozoan vision

Colour vision is known to have arisen only twice—once in Vertebrata and once within the Ecdysozoa, in Arthropoda. However, the evolutionary history of ecdysozoan vision is unclear. At the molecular level, visual pigments, composed of a chromophore and a protein belonging to the opsin family, have different spectral sensitivities and these mediate colour vision. At the morphological level, ecdysozoan vision is conveyed by eyes of variable levels of complexity; from the simple ocelli observed in the velvet worms (phylum Onychophora) to the marvellously complex eyes of insects, spiders, and crustaceans. Here, we explore the evolution of ecdysozoan vision at both the molecular and morphological level; combining analysis of a large-scale opsin dataset that includes previously unknown ecdysozoan opsins with morphological analyses of key Cambrian fossils with preserved eye structures. We found that while several non-arthropod ecdysozoan lineages have multiple opsins, arthropod multi-opsin vision evolved through a series of gene duplications that were fixed in a period of 35–71 million years (Ma) along the stem arthropod lineage. Our integrative study of the fossil and molecular record of vision indicates that fossils with more complex eyes were likely to have possessed a larger complement of opsin genes.
Real-Time Underwater Positioning and Navigation of an AUV in Deep Waters

Due to the absence of GPS, navigation of autonomous vehicles underwater requires the integration of various measurements to provide the best location estimate. Usually in littoral waters, adequate navigational accuracy may be obtained by integrating odometry measurements provided by a Doppler Velocity Log (DVL) into an Inertial Navigation System (Aided INS). However, due to the bulk attenuation of seawater at the acoustic centre frequency at which DVLs typically operate, odometry estimates become increasingly unreliable when the vehicle flies more than 200 m above the bottom (depending on the DVL central frequency). Such a case occurs during experiments in deep waters. This work addresses a theoretical and experimental study on the feasibility of navigating the AUVs using a multi-input Extended Kalman Filter (EKF) integrating proprioceptive measurements (i.e., INS data and speed-over-water observations from DVL) with a set of exteroceptive sensor data, when available. The filter was integrated on-board the CMRE Ocean Explorer Class Version C (OEX-C) AUVs, and tested at sea for the first time in deep water during the NATO exercise Dynamic Mongoose'17 off the South coast of Iceland (June-July 2017).
Wireless Power Supply for Small Household Appliances Using Energy Model

In order to resolve the safety and stability of the power supply for household appliances, a system of wireless power supply for household appliances with Bluetooth communication based on electromagnetic resonance is proposed. In this paper, the energy model of wireless power transfer system is first proposed, which is based on the circuit equations and variable substitution, more accurate than the coupled-mode model and simpler than the circuit model. Then, the characteristics of output power and transfer efficiency of the wireless power transfer system are analyzed, and a system of wireless power supply for small household appliances has been designed and implemented. Furthermore, Bluetooth communication is applied to achieve various protection functions of the system, therefore enhancing the safety and stability of the system. Experimental results show that this power supply system achieves a stable output dc voltage of 48 V and a large output power of 100 W, the total efficiency is more than 80% within a distance of 30 cm.
Underwater Depth Map Estimation from Video Sequence with Graph Cuts

Low-cost and small-scale underwater vehicles (UVs) are receiving increasing attention due to their small size and superior maneuverability. However, performing autonomous navigation remains a major challenge for small-scale UVs. Conventional sensor suites are unsuitable for small-scale UVs due to size limitations. Thus, cameras are good alternative. Among all the information cameras can offer, depth is one of the valuable sources which can be used for low level obstacle detection and collision avoidance. Though there is intensive research on depth estimation in general, limited literature about depth map estimation for the underwater scenes exist. In this paper, we first use unsupervised color correction method to enhance the underwater image sequences. Then, we estimate camera positions from motion based on feature extraction and matching. Similar to stereo depth estimation, we tackle video sequence depth map estimation as an energy-minimization problem. Lastly, multi-label graph cuts is applied to solve it. The proposed method was tested on a sequence of video and the effectiveness is demonstrated.
Computer Aided Design Using a Rational Quadratic Trigonometric Spline with Interval Shape Control

An important scheme is constructed to model 2D objects using a rational quadratic trigonometric function. The proposed rational quadratic trigonometric spline (RQTS) curve schemes because of having weight in its formation, is beneficial for shape modeling. The curve accomplished by the developed method, possesses the finest suitable geometric properties such as convex hull, partition of unity, affine invariance and variation diminishing. The parameter in the construction of proposed method is beneficial for imperative shape properties like global and local tension properties which is responsible for modifying the shape according to one’s will. Moreover suggested scheme performance has smoothness of degree 2.
A Prophet-Based DTN Protocol for VANETs

We propose a vehicular delay tolerant network (VDTN) routing protocol which combines the message deliver strategy of Prophet protocol with the message copy control strategy of Spray and Wait protocol. The ONE simulator is used to perform simulations. From the simulation results, we conclude that the proposed protocol outperforms other well-known DTN routing protocols in terms of the average packet delivery ratio, average end-to-end delay and overhead.
A Novel Android Malware Detection Approach Using Operand Sequences

Android malware detection has become increasingly important over the past few years, due to the popularity of Android devices and the explosive growth of Android applications. This asks for more effective techniques to detect the Android malware. Some works in the literature show that the opcode sequences have a remarkable effect on Android malware detection. However, they omitted the information contained in operand sequences. In this paper, we do not analyse the opcode sequences but the API calls used in operand sequences, and abstract the API calls to their package names with the aim to be resilient to API changes in different Android API levels. In order to avoid to be computationally expensive, we only capitalize on the n-grams analysis. In addition, we apply the package level information extracted from API calls to build a Android malware prediction model. We perform experiments on malicious Android applications, composed of 5560 malware samples which are belong to Drebin dataset, 361 malware samples collected from Contagio Mobile Malware and 5900 benign Android applications retrieved from Google Play. Results show that the accuracy of our approach exceeds the opcode n-grams in some ways.
Heterogeneous Multi-Task Assignment in Mobile Crowdsensing Using Spatiotemporal Correlation

Mobile crowdsensing (MCS) is a new paradigm to collect sensing data and infer useful knowledge over a vast area for numerous monitoring applications. In urban environments, as more and more applications need to utilize multi-source sensing information, it is almost indispensable to develop a generic mechanism supporting multiple concurrent MCS task assignment. However, most existing multi-task assignment methods focus on homogeneous tasks. Due to the diverse spatiotemporal task requirements and sensing contexts, MCS tasks often differ from each other in many aspects (e.g., spatial coverage, temporal interval). To this end, in the paper, we present and formalize an important Heterogeneous Multi-Task Assignment (HMTA) problem in mobile crowdsensing systems, and try to maximize data quality and minimize total incentive budget. By leveraging the implicit spatiotemporal correlations among heterogeneous tasks, we propose a two-stage HMTA problem-solving approach to effectively handle multiple concurrent tasks in a shared resource pool. Finally, in order to improve the assignment search efficiency, a decomposition-and-combination framework is devised to accommodate large-scale problem scenario. We evaluate our approach extensively using two large-scale real-world data sets. The experimental results validate the effectiveness and efficiency of our proposed approach.
A Survey of Statistical Topic Model for Multi-Label Classification

Much of texts embedded in Web is annotated with human interpretable labels, such as tags on web pages and subject. Statistic topic model for multi-label classification is a power technology to handle the multi-labeled textual data at the word level. However, standard topic model is a completely unsupervised algorithm. Therefore, the key of incorporating supervised label set into its topic modeling procedure is to establish the relationship between topics and labels. In this paper, multi-label topic model is summarized by analysis of existing studies; especially, on the basis of relationship between topics and labels, we describe four categories of multi-label topic model, and their reprehensive models. To the best of our knowledge, this is the first effort to review the development of multi-label topic models.
Analysing Player Performance with Animated Maps

Over the last years, with the increasing use of telemetry techniques, the extraction of data from video games became a much easier and reliable task. Among many types of video games, MOBAs (Multiplayer Online Battle Arena) are one of the most widely played and, consequently, the analysis of game events has received attention of players, spectators, coaches and analysts to assess the performance of players. This data can range from players' position, to more specific events, such as, the position of a player's death. Our main goal is to get a better understanding of which techniques are more adequate to handle the visualization of this type of spatio-temporal information data, associated to player performance analysis in video games. This paper addresses this problem presenting a user study to evaluate the adequacy of animated maps and the analytical strategies followed by players when using spatio-temporal data to analyse player performance. To support the user study, we developed the VisuaLeague prototype for the visualization of in-game player trajectories and events during a match in the MOBA game League of Legends. The results support the adequacy of using the animated maps technique to convey information to users in this context. Moreover, they also point out towards a high degree of importance given to the spatio-temporal components of the data for player performance analysis.