Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Representation for Data: Sparse and Low-Rank

Recently, the sparse coding method has been successfully applied in the low-rank subspace clustering algorithm. In these methods, the image is represented as a low-rank linear combination of the atoms of the sparse dictionary, and the coefficient matrix is used to compute the similarity matrix for spectral clustering. However, the low-rank representation obtained by the subspace clustering may not be enough to describe the data structure. In this paper, we propose a new model by combining sparse coding and low-rank subspace clustering in the objective function simultaneously. The new model seeks for solutions which have sparse representation and low-rank representation as close as possible. In this case, the new representation obtained by our method is both sparse and low-rank. An efficient iteratively algorithm is also proposed to solve this model. Extensive experiments on several databases demonstrate that our method performs better than state-of-the-art methods.
Optimization Framework of Humanoid Walking Pattern for Human Motion Retargeting

In this paper, we propose a novel method for retargeting human movements including the steps taken by a humanoid robot. For applications, such as wearable device evaluation by a humanoid, it is necessary to reproduce the original human motions as closely as possible and to generate motions that can be realized on a real robot without the robot losing its balance. The proposed method features the optimization framework that integrates the walking pattern generation based on the linear inverted pendulum model whose parameters are optimized to ensure both human likeness and feasibility of the generated motions. The effectiveness of the retargeting method is validated by experiments that reproducing the measured human working motion on the humanoid HRP-4.
Early experiences in 4D quantitative analysis of insulin granules in living beta-cells

Pancreatic beta cells biosynthesize and package insulin in insulin granules, whose secretion is regulated to maintain blood glucose homeostasis. The detailed knowledge of the dynamics of insulin granules could reveal defects in the intracellular handling and secretion of these granules, leading to impaired insulin secretion and consequently to the development of several metabolic diseases, including type-2 diabetes and the metabolic syndrome. The use of spinning disk confocal and light sheet microscopy with fast sequential scanning that enable rapid volumetric imaging, allows to monitor at high spatial and temporal resolution the dynamics of insulin granules. However, obtaining all the information for accurate 3D imaging and particle tracking within a single cell is complex and challenging, and extracting information from the particle tracking data requires to analyse the segments of motion trajectories. To this aim, we present in this study a quantitative analysis of the 4D motion of insulin granules in glucose-stimulated INS- 1E beta cells. First, we tracked each granule inside the cells via a computer-based automatic approach relying on a two-step iterative process. Next, we removed the artifacts and introduced a set of quantitative cinematic features describing granule dynamics. Finally, we implemented an unsupervised machine learning based exploratory data analysis, which allows to distinguish two sets of granules marked by distinct dynamics: a first pool is characterized by a diffusive dynamic behavior, and a second pool that is characterized by a more directed and targeted movement. These pools may have distinct functional roles and/or interactions with other structures and organelles in beta cells that could be selectively impaired in pathological settings.
A Complete Data Science Work-flow For Insurance Field

In recent years, "Big Data" has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, health-care, finance, business, and ultimately our society itself. Learning from Big Data has become a significant challenge and requires development of new types of algorithms. Most machine learning algorithms can not easily scale up to Big Data. MapReduce is a simplified programming model for processing large datasets in a distributed and parallel manner. In this paper, we present our work carried in a big data project1 which is dedicated to the insurance sector. This allows us to validate our method on real-world data for insurance. We present the complete pipeline or work-flow going from data collection to visualization, passing by data fusion, data analysis, clustering, and prediction tasks. The insurance dataset is enriched with data collected from heterogeneous sources. A predictive and analysis system is proposed by combining the clustering result with decision trees. We use the topological approach, especially the SOM method, for its interest in being able to cluster and visualize the data at the same time. We make the source code of our SOM-MapReduce algorithm, written with Spark using the MapReduce paradigm, publicly available2.
Enhanced Deep Learning with Improved Feature Subspace Separation

This research proposes a new deep convolutional network architecture that improves the feature subspace separation. In training, the system considers M classes of input sets $\{\mathcal{C}_i \}_{i = 1}^M$ and M deep convolutional networks $\{DN_i \}_{i = 1}^M$ whose filter and other parameters are randomly initialized. For each input class $\mathcal{C}_i$, Convolutional Neural Network generates a set of features ℱi. Then, a local subspace Si is matched for each set ℱi. This is followed with a full training of the deep convolutional network DNi based on a decision criteria developed with computation of rejections of all features in $\{\mathcal{F}_i \}_{i = 1}^M$ to Si. Five different deep convolutional network topologies are used to show that the proposed technique works better for small network topologies and has comparable performance to more complex networks.
Evaluation of Classification Algorithms, Linear Discriminant Analysis and a New Hybrid Feature Selection Methodology for the Diagnosis of Coronary Artery Disease

According to the World Health Organization (WHO), 31% of the world’s total deaths in 2016 (17.9 million) was due to cardiovascular diseases (CVD). With the development of information technologies, it has become possible to predict whether people have heart diseases or not by checking certain physical and biochemical values at a lower cost. In this study, we have evalated a set of different classification algorithms, linear discriminant analysis and proposed a new hybrid feature selection methodology for the diagnosis of coronary heart diseases (CHD). Throughout this research effort, using three publicly available Heart Disease diagnosis datasets (UCI Machine Learning Repository), we have conducted comparative performance evaluations in terms of accuracy, sensitivity, specificity, F-measure, AUC and running time.
GrantExtractor: A Winning System for Extracting Grant Support Information from Biomedical Literature

As the important information in MEDLINE database, grant support (GS) refers to funding agencies and contract numbers. For funding organizations, GS plays a crucial role in tracking their funding outcomes. In this paper, we present a pipeline system called GrantExtractor that is able to automatically extract funding information from biomedical literature. GrantExtractor is a novel solution to the practical problem of GS information extraction, which is related to both name entity recognition and relation extraction. Our approaches rely on an integration of several modern machine learning techniques. In particular, funding sentences in articles are first identified by a sentence classifier. Entities of grant numbers and agencies are then extracted from these funding sentences by a bi-directional LSTM and the CRF layer (BiLSTM-CRF), as well as pattern matching. After removing noisy numbers by a multi-class model, we finally match each grant number with its corresponding agency. Experimental results on benchmark datasets show that GrantExtractor clearly outperformed all baseline methods. In addition, GrantExtractor won the first place in Task 5C of 2017 BioASQ challenge, achieving the Micro-recall of 0.9526 for 22,610 articles. This number is 33% higher than 0.7174, which is the highest score as the baseline of“BioASQ Filtering” provided by National Library of Medicine (NLM). Moreover, GrantExtractor has achieved the Micro F-measure score as high as 0.90 in the task of extracting grant pairs.
Deep Convolution Neural Networks for Drug-Drug Interaction Extraction

When a patient takes two or more drugs within a certain time, the efficacy of one drug may be influenced by the other. This phenomenon is called drug-drug interactions (DDIs). DDIs are important and helpful information for both medical staff and patients to make sure that the drugs co-administrated at the same time have a positive effect on therapy of patients. Many approaches have been applied into drug-drug interaction extraction tasks such as support vector machine (SVM), recurrent neural network (RNN) and long short-term memory (LSTM) in particular. However, the structures of these models are relatively shallow for DDI extraction research compared with the deep neural networks employed in the field of computer vision. However much better results can be obtained with a deep problem-specific architecture which develops hierarchical representations. Hierarchical and deep neural networks may improve DDI extraction. To address this problem, we present a hierarchical and deep neural network to enrich the feature extraction process to enhance the performance of DDI extraction. In this article, we present a deep convolutional neural network (DCNN) based on DDI extraction method. In this method, we firstly apply embedding mechanism to get the semantic and syntactic of the original biomedical literature. Then a novel architecture using small convolutions is proposed, which takes raw biomedical literature as input and operates directly at the word level to get the embedding-based convolutional features. Finally, these features are fed to softmax classifier to extract DDIs from biomedical literature. Our experimental results on the DDIExtraction 2013 corpus show that the performance of network increases as the network gets deeper and hits its peak at depth 16, which obtains a better result (an F1 score $\mathrm {o}\mathrm {f}0.845$) than other state-of-the-art methods.
Extracting Multiple Planar Surfaces Effectively and Efficiently Based on 3D Depth Sensors

Reliable and efficient planar surface extraction based on the 3D depth sensors is a crucial component in mobile robotics. However, extracting planes efficiently remains challenging, especially when small planar structures are urged to be perceived. This paper proposes a new method that can extract the multi-scale planar surfaces efficiently. The depth image is dynamically divided into rectangle regions, where points in each region lie on a common plane. Then, the planar primitives are generated by clustering these regions into some distinct groups according to their plane parameters. Finally, the pixel-wise segmentation results are achieved by growing each distinct group. The first novelty is the dynamic region size adjusting algorithm that can reduce the number of regions to be clustered and improve the plane fitting accuracy. The second one is the region clustering algorithm, whose worst-case time complexity is guaranteed to be log-linear. Comprehensive experiments show that our algorithm outperforms the state-of-the-art methods in quality and runs an order of magnitude faster.
Optimization of Sign-Preserving Noise-Aided Min-Sum Decoders with Density Evolution

The novelty of this paper is to propose a new LDPC decoder called Sign-Preserving Noise-Aided Min-Sum (SP-NA-MS) decoder that improves the decoding performance compared to classical Offset Min-Sum (OMS) decoder when messages are quantized, with only 3 or 4 bits. The particularity of the SP-NA-MS decoder is that the variable-to-check messages are never set to 0, and always carry the sign information. Moreover, the decoder incorporates random perturbation due to deliberate noise injection. The parameters of the SP-NA-MS decoders are optimized in the asymptotic limit of the code length thanks to the Density Evolution (DE) method. The finite-length simulations confirm the conclusions of the DE analysis and gain of up to 0.3 dB in SNR can be obtained compared to regular OMS with same quantization level.
Adaptive Unscented Kalman Filter Based Line of Sight Rate for Strapdown Seeker

Line of sight (LOS) rate is required in accurate guidance way, whereas the strapdown seeker cannot measure the LOS rate directly. The unscented Kalman filter (UKF) is used in strapdown seeker since there is a strong nonlinearity in the state and measurement equations which is established by attitude and kinematic relationship between missile and target. In order to improve the robustness and precision of LOS rate estimation, an adaptive filtering algorithm is proposed in this paper to reasonably adjust the contribution of the dynamic model information. Simulation results indicate that compared to UKF, the adaptive UKF (AUKF) has a significant improvement in filtering accuracy.
Cooperative control feedback: On backoff misbehavior of CSMA/CA MAC in channel-hopping cognitive radio networks

Due to dynamic channel availability in cognitive radio networks (CRNs), rendezvous problem is known as the most challenging issue in the design of media access control (MAC) protocol, which is a key step for secondary users (SUs) to start communication. With the concept of blind rendezvous, numerous channel-hopping sequence (CHS) based rendezvous schemes have been proposed to solve this problem in these years. Currently, little attention is paid to the design of a carrier sensing multiple access/collision avoidance (CSMA/CA) MAC based on these rendezvous schemes and also the rendezvous de-synchronization problem brought by multiple rendezvous networking paradigm into MAC design. To this end, we propose a cooperative channel-hopping based CSMA/CA MAC (named CoCH-CSMA/CA MAC) which works on the top of existing CHS based rendezvous schemes. Resulting from the rendezvous de-synchronization problem, a new type of collision, named false collision, is identified. As a SU cannot discern the false collision by itself, we design a cooperative control feedback scheme which employs correlation-based signal detection to reduce the coordination overhead of cooperation and helps SUs to avoid backoff misbehavior. Moreover, we analyze the behavior of our MAC protocol and its advantage. Extensive simulations prove that the cooperative control feedback scheme can effectively alleviate the impact of rendezvous de-synchronization problem on backoff misbehavior and improve network performance.
Robustness of Automotive SOTA: State-of-the-Art in Uncertainty Modelling

This paper identifies the need for thorough experimental based study for Software-over-the-air (SOTA) in an automotive context. The paper outlines the challenges and context for automotive SOTA with an extensive literature review. It then details the early stages of the experimental studies, which aim to identify the key control and noise factors that affect performance of the SOTA in an automotive environment. This contribution establishes a framework for uncertainty modelling of SOTA as a system which highlights the needs to develop solutions requiring big data gathering and analysis as next research opportunities to the scientific community.
ANN-Based Observer for Controlling a SynRM

The rotor position for electric motors, especially synchronous motor type, is an important information in speed / motion control applications. The position information of the rotating machines can be measured via incremental/absolute encoder, resolver or hall-sensors. However, these measuring devices have many disadvantages such as increased hardware complexity, high cost, increased volume, cable addition, decreased noise immunity, decreased reliability and increased maintenance. All these drawbacks can be overcome by using a sensorless algorithm. In this study, a Synchronous Reluctance Motor (SynRM) is controlled by using sensorless algorithm based on Artificial Neural Network (ANN). The algorithm presents not only position/speed estimation but also torque and flux observation which are critical to obtain maximum torque per ampere from SynRM. In this study, the motor is powered via solar photovoltaic panels. The environmental conditions such as irradiation and temperature affect the motor control performance. Therefore, the estimation performance of the ANN-based observation is analyzed under various environmental conditions. The results show that the sensorless system has satisfactory performance in all conditions.
PACAS: Privacy-Aware, Data Cleaning-as-a-Service

Data cleaning consumes up to 80% of the data analysis pipeline. This is a significant overhead for organizations where data cleaning is still a manually driven process requiring domain expertise. Recent advances have fueled a new computing paradigm called Database-as-a-Service, where data management tasks are outsourced to large service providers. We propose a new Data Cleaning-as-a-Service model that allows a client to interact with a data cleaning provider who hosts curated, and sensitive data. We present PACAS: a Privacy-Aware data Cleaning-As-a-Service framework that facilitates communication between the client and the service provider via a data pricing scheme where clients issue queries, and the service provider returns clean answers for a price while protecting her data. We propose a practical privacy model in such interactive settings called (X,Y,L)-anonymity that extends existing data publishing techniques to consider the data semantics while protecting sensitive values. Our evaluation over real data shows that PACAS effectively safeguards semantically related sensitive values, and provides improved accuracy over existing privacy-aware cleaning techniques.
An Advanced TCAM-SRAM Architecture for Ranges Towards Minimizing Packet Classifiers

Packet classification (PC) plays an important role in many networking services on the Internet including security, quality of service provisioning, virtual private networking, monitoring and multimedia communications. It is often a performance bottleneck for routers for they need to classify every packet. To meet the demand for high-speed packet classification, Ternary Content Addressable Memories (TCAMs) could be a good solution. However, storing ranges in TCAMs suffers from the well-known range-to-prefix blowout problem. In this paper, we first introduce an advanced TCAM-SRAM architecture to reduce the number of prefixes needed to represent all ranges. And then we provide new upper bounds on TCAM worst-case rule expansion. With the suggested scheme, a W-bit range can be encoded in 2[w_b+1]/2 TCAM entries. Lastly, we do some simulations based on random and real-world rule sets to show that the proposed solution performs better than the existing algorithms in terms of the number of TCAM entries.
Fault-Tolerant and Self-Adaptive Market-Based Coordination Using Hoplites Framework for Multi-Robot Patrolling Tasks

An autonomous robot team can be employed for continuous coverage of a dynamic environment. In this paper, we propose a novel approach for creating multi-robot patrolling policies, which is fault-tolerant and self-adaptive. A dynamic priority queue and time-out replanning mechanism are maintained by each robot to schedule the tasks fault-tolerantly in the context of the market-based method. Hoplites framework is adapted by introducing a self-adaptive threshold adjustment and sharing mechanism to provide a high-level coordination. This work is demonstrated by a multi-robot patrolling task implemented on Robot Operating System (ROS). A flexible tool, Stage, is leveraged to provide the simulated environment. The experimental results validate the effectiveness and availability.
Long-term Localization of Mobile Robots in Dynamic Changing Environments

Long-term localization in dynamic changing environments is still a challenge in robotics. Traditional localization algorithms typically assume that the environment is static. However, in many real-world applications, such as parking lots and industrial plants, there are always dynamic objects (e.g. moving people) and semi-dynamic objects (e.g. parked cars and placed goods). In this paper we address this challenge by introducing a long-term localization algorithm in the environments which combine dynamic objects and semi-dynamic objects. Localizability-based-updating particle filter (LU-P F) algorithm is proposed here. Not only we use localizability matric to build an updating mechanism, but also it is used for localization system. Besides, we propose the dynamic factor as long-memory information to serve as prior knowledge, which improves the robustness of updating process. Experiments in parking lots demonstrate that our approach has better localization results with a more accurate up-to-date map compared to other methods.
Generalized Iterative Closet Point Algorithm Based on Lie Algebra Parameterization

The Generalized Iterative Closest Point(G-ICP) algorithm is a high-precision point cloud registration algorithm, which uses Euler angles to parameterize a rotation matrix. However, Euler angle parameterization has singularity at a specific angle, which leads to application limitations. In this paper, a new G-ICP algorithm has been proposed, in which the Lie algebra on so(3) is used to parameterize a rotation matrix and the Gauss-Newton method is employed to optimize the objective function of the G-ICP algorithm. The experimental results have demonstrated that our method performs well compared with the state-of-the-art G-ICP alorithm [1] and simultaneously holds better estimation accuracy of pose with fast convergence speed in the open RGB-D TUM datasets.
Vision Based Autonomous Runway Identification and Position Estimation for UAV Landing

Vision based autonomous landing has turned out to be a deep-seated technology of UAV (Un-manned Aerial Vehicle) navigation, guidance and control system in the present scenario of image processing techniques. This study focuses on the two main UAV landing problems reliant on the airborne front-looking cam: how the runway can be recognized in image frames taken by the nose camera (a novel algorithm has been presented which includes Otsu’s method, Sobel operator for edge detection, spectral residual saliency map (SRS), binary gradient mask and dilation); Second problem is how to align the UAV according to the runway position for critical landing phase (pixel approximation technique has been employed to estimate the position of the runway left, right, sharp left, sharp right, top or bottom for the support of landing UAV). The results obtained in present experimental method rigorously validated the effectiveness and strength of the proposed method. Moreover, accuracy of the implied runway recognition model is found to be 94% and the processing time is 0.32s which outperforms the other reported models. It is strongly believed that these results would open new vistas for the upcoming studies in the field.