Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Live and Dead Cell Classification via Hyperspectral Imaging

Classification of live and dead human ovarian cancer cells (SKOV3) is an important task in biomedicine research. Fluorescence is the most common technique to distinguish live and dead cells. However, it requires physical contact to the cells, which affects the appearance of the cells and their features to a certain degree. There is an urgent need to develop a novel non-invasive technology for this application. This paper proposes the first hyperspectral image (HSI) based system to address this issue. A microscopic hyperspectral imaging system is built to capture cell images. Then morphological methods are employed to extract spectral-spatial features for an SVM classifier that is capable of automatically distinguishing live and dead SKOV3 cells. We show that tensor morphology profile (TMP) leads to the best discriminative capability when compared to other features. At the same time, our work also shows that HSI works better than RGB images for this novel application thanks to its fine spectral information.
Photo Filter Recommendation Through Analyzing Objects, Scenes and Aesthetics

Photo filters are widespread—they give photos a stylized look without requiring the user to have professional knowledge of image processing. However, with the increasing number of photo filters and limited display size of mobile devices, selecting a proper filter for a given photo can be a tedious task for camera phone users. In this paper, we present a photo filter recommendation approach to address this problem. In particular, we rely on state-of-the-art deep learning approaches to extract objects, scenes, and image aesthetics reliably and represent them using deep features as the observations of our recommendation model. Furthermore, we collect 68,400 filtered photos from Instagram to learn the relationships among objects, scenes, aesthetics, and filter types. Experimental results using the FACD benchmark dataset demonstrate the state-of-the-art recommendation performance of the proposed approach; these results show that objects, scenes, and aesthetic attributes influence filter preference.
Thermal Simulation and Optimization Study for Magnetic Coupler of Static Electric Vehicle Wireless Power Transfer Systems

When the static wireless charging system works for a long time, the heating of the magnetic coupler will bring adverse effects on the stability, safety and other aspects of the system. In this paper, the heating of magnetic coupler under SAE standard is studied by simulation software. The temperature variation and distribution of coil, magnetic core and aluminum plate are simulated. On this basis, the optimization scheme of the magnetic coupler is presented from the perspective of thermal design.
Learning Transition Statistics in Networks of Interacting Agents

Studying the decision-making of agents can reveal group behavior and internal lines of influence. We work with systems of interacting agents, where the decision-making of each agent is affected by their neighbors within some graph structure. As agents make choices, the stochastic transitions between chosen group actions can be learned, and thus the group behavior can be characterized and predicted. We express each element of the transition matrix P as a product of factors that depends on the agent neighborhood structure and leading to a separable estimator for the unknown pij of interest. This enables us to find a maximum likelihood estimator (MLE) for each factor and thus effectively estimate each pij with reduced complexity. We derive analytical concentration bounds for the error rates of this approach and demonstrate it on data sets.
A Novel DCT-Based Just Noticeable Difference Model via Block Energy

Just noticeable difference (JND), which reflects the minimum visibility threshold of human visual system, is usually utilized to remove the perceptual redundancies for image/video coding. However, most of the existing DCT-based JND models failed to effectively estimate contrast masking (CM) of image blocks with different complexity. In this paper, we propose an improved CM model via frequency energy of DCT block, which is calculated based on DCT coefficient distribution. Then, by integrating luminance adaptation and contrast sensitivity function, an effective JND model in the DCT domain is realized. Experimental results have confirmed that the proposed method can produce more accurate JND threshold and remove more visual redundancies than the state-of-the-art JND models.
Non-linear System Identification and State Estimation in a Pneumatic Based Soft Continuum Robot

Sensory data play a significant role in the control of robots. While soft robots are promising for operation in unstructured environments, it may be difficult to sensorize them due to their inherent softness. One way to overcome this challenge is to use an observer/filter to estimate the variables (states) that would have been measured by those sensors. Nevertheless, applying an observer/filter to a soft robot introduces the challenge of requiring an analytical model of these highly nonlinear systems. In this paper, we develop a framework based on nonlinear system identification and state estimation to estimate the curvature angle of a pneumatic-based tentacle soft robot. We model the tentacle using the wavelet/sigmoid network, and use an Extended Kalman Filter (EKF) to estimate the curvature and verify the estimate using camera vision. The results show that EKF can estimate the curvature angle at a low error, even when the identified system model is not accurate and the sensor measurement is noisy.
A Simple but Effective Way to Improve the Performance of RNN-Based Encoder in Neural Machine Translation Task

Armed with attention mechanism, the recurrent neural network-based encoder-decoder model (or sequence to sequence model) has become the standard architecture to tackle many sequence Nature Language Processing (NLP) tasks. With regard to the neural machine translation (NMT) tasks, our paper proposed a new architecture to proficiently mines the ability of attention mechanism and stacked recurrent neural networks. As a lot of work has given proved that each layer of the stacked recurrent neural networks learns different aspects of a sequence. That means the information represented by each layer is important in terms of the translation task. However, usually, most work just simply adopt stacked recurrent neural networks as a whole part as the encoder or decoder and then combined them with the attention mechanism. While our work creatively usestheattentionmechanismtoexploreeachlayeroftheencoder. In this way, many aspects of the sequence could be learned. For example, linguistic features and semantics information of a word in a sentence could be clearly captured and finally influences the generation of the current translation word. Experiments have shown the effectiveness of our model and an average of 5.67 points BLEU scores were promoted.
Deep Deterministic Policy Gradient-based Parameter Selection Method of Notch Filters for Suppressing Mechanical Resonance in Industrial Servo Systems

This paper presents a parameter selection method of notch filters for suppressing mechanical resonances in industrial servo systems using the deep deterministic policy gradient (DDPG) algorithm. Several methods for tuning the notch filter parameters were studied such as the fast-Fourier-transform-based methods, extended-Kalman-filter-based methods and adaptive notch filter methods. However, these methods do not find the Q parameters of notch filters which play an important role in determining the system stability, and do not consider the cases in which multiple notch filters are required. Deep-Q-network-based method was developed to solve these problems, but the notch filter parameter tuning is limited to discrete action spaces. This paper develops a new parameter selection method of notch filters, using the DDPG algorithm. DDPG algorithm, which is a model-free and actor-critic algorithm using deep neural networks, is utilized for its capability to operate over continuous action spaces. Experiments are performed using an actual industrial servo system to demonstrate that the developed parameter selection method successfully finds the notch filter parameters to suppress the resonances of the system.
Weighted-Ring Similarity Measurement for Community Detection in Social Network

Community discovery using topological structure of social network graph is a key issue in community mining algorithms. In the social network, the rings are formed between vertices and vertices. The closer relationship between two vertices, the more rings are formed. Since the vertices contribute differently to the ring, the same type of rings contributes differently to the similarity between the vertices. Therefore, how to assign a reasonable weighting coefficient to each ring so that it can correctly represent the similarity between the vertices is the key issue. In this paper, according to using the theory of set pair analysis, the social network is regarded as a combination of a certain and an uncertain system, considering the topology's contribution to the similarity between vertices, a new algorithm for measuring the similarity between vertices based on weighted rings is proposed, and then the algorithm is applied to community discovery. The experimental results show that the proposed methods provide us with a useful way for measuring the similarity between the vertices.
Child Face Recognition with Deep Learning

Face recognition is a kind of identifying people in image. Face recognition technology play a role for children life. It is used for finding the missing children, school safety and school social network activities. Generally, older ages can recognize easily from each other but children are very hard to recognize. Convolutional Neural Networks (CNNs) is a top performer on face recognition. In this paper, the accuracy and performance of three Convolutional Neural Networks (CNNs) such as VGG Face based on two architectures (VGG16 and ResNet50), and MobileFaceNet on child face dataset is tested. The experiments results are shown and evaluated. According to experiments results, MobileFaceNet on child face dataset provide better accuracy than others. Among three proposed methods, the best recognition accuracy is 99.75% from MobileFaceNet.
Analysis and Performance Evaluation of a 3-DOF Wearable Fingertip Device for Haptic Applications

Wearable fingertip device is a promising form to display haptic stimuli since it provides a lightweight and natural way for operators to grasp or manipulate the objects in the virtual environment. This paper focuses on the analysis and performance evaluation of a wearable fingertip device for haptic applications. The device is equipped with three small servo motors and can provide 3-DOF (degree of freedom) force feedback at fingertip with contact/non-contact capability. It combines a five-bar linkage and a slider-crank linkage, and these two linkages are decoupled, leading to simpler kinematics than some devices with coupled structures. In order to present the device, its mechanical analysis, kinematics analysis, and static force analysis were carried out at first. Then, four experiments were designed and conducted to evaluate the device performance quantitatively. The first experiment aimed to verify the effectiveness in rendering variable stiffness. The second experiment investigated its capability in providing different skin stretch directions for operators. The third experiment evaluated its performance improvement during virtual manipulation. The last experiment aimed to verify the effectiveness in displaying mass information during remote manipulation. The experimental results indicated that this device was capable of rendering various stiffness. It could generate eight clear skin stretch directions. The subjects had better performance during virtual manipulation with cutaneous feedback provided by the device than without cutaneous feedback. The device was also capable of displaying mass information during remote manipulation.
The Application Of Subspace Clustering Algorithms In Drill-Core Hyperspectral Domaining

Diamond drilling is used in the mining industry to extract drill-cores for characterising mineral deposits. Traditionally, drill-cores are visually analysed by an on-site geologist, subjected to geochemical analyses, and then, few representative samples subjected to additional high-resolution mineralogical studies. However, the choice in samples is frequently subjective and the mineralogical analyses are highly time-consuming. In order to optimize the choice of samples and accelerate the analyses, drill-cores can be partitioned into domains, and then, laboratory analyses can be carried out on selected domains. Nevertheless, in the mining industry, automatic drill-core domaining still remains a challenge. Recently, hyperspectral imaging has become an important technique for the analysis of drill-cores in a non-invasive and non-destructive manner. Several clustering algorithms of hyper-spectral data are proposed for automatic drill-core domaining. In this paper, we suggest using advanced subspace clustering algorithms (i.e., sparse subspace clustering algorithm, spectral-spatial sparse subspace clustering algorithm). These algorithms work based on the self-representation property of the hyperspectral data. The clustering methods are tested on two drill-core samples which present different mineralogical and structural features. The subspace clustering algorithms are compared with the result of the K-means clustering algorithm. Our experimental results show that subspace clustering algorithms provide accurate drill-core domains and it is shown that including spatial information significantly improves the clustering results.
Distributed collaborative control strategy for integrated energy microgrid based on multi-agent system

Economic dispatch problem (EDP) in integrated energy microgrid is a basic optimization problem of power system. With the abundance of various forms of energy in the micro grid, this paper proposes a distributed cooperative algorithm based on the consistency framework to solve the EDP of the comprehensive energy economy model. This method makes use of the principle that under the optimal output power value, all micro sources can achieve the economic optimum with the same rate of cost increment. Therefore, the cost slight increase rate of each micro source is selected as the consistency variable, and the local power supply and demand mismatch value is taken as the feedback variable to meet the constraint conditions. Using multi-parameter perturbation theory and graph theory, the convergence of the algorithm is proved. The algorithm is a completely distributed algorithm with no leader or virtual command node. Simulation results show the effectiveness of this algorithm. At the same time, the simulation results directly illustrate the variation law of convergence speed.
Anomaly Detection for Power Grid Based on Time Series Model

In the process of informationization and networking of smart grids, the original physical isolation was broken, potential risks increased, and the increasingly serious cyber security situation was faced. Therefore, it is critical to develop accuracy and efficient anomaly detection methods to disclose various threats. However, in the industry, mainstream security devices such as firewalls are not able to detect and resist some advanced behavior attacks. In this paper, we propose a time series anomaly detection model, which is based on the periodic extraction method of discrete Fourier transform, and determines the sequence position of each element in the period by periodic overlapping mapping, thereby accurately describe the timing relationship between each network message. The experiments demonstrate that our model can detect cyber attacks such as man-in-the-middle, malicious injection, and Dos in a highly periodic network.
SimObturation: Virtual Reality Application for Simulation of Dental Obturation Procedure Training with Phantom Omni Device Integration

The constant evolution of virtual reality applications in areas of dentistry has been gaining proeminence and contributing in several areas of knowledge associated with the training of professionals to perform the dental procedures. In this work, we present the modeling of a virtual simulator prototype for training an obturation process, using the Phantom device a represent of the clinical instrument with interaction with the tooth represent by an OBJ file, allowing interaction of the professional in surgical procedure. In addition, the device simulates the generation of strength, tactile sensation, kinesthetic and proprioceptive sensation through its indicator when collision occurs with the object. The prototype is under development, preliminary testing will be supported by experts in the field, who will validate the visual and tactile feedback of the prototype.
Temperature Field Analysis of Stator Transposition Strand Based on Three-Dimensional Coupled Thermal-fluid Network Model

This paper presents a three-dimensional(3-D) coupled thermal-fluid network model to calculate the motor temperature, which solves the problem that the temperature field of the stator transposed strands is hard to calculate. Taking a 250MW hydro-generator as an example, this model couples the fluid field and temperature field, and takes the transposed structure of stator winding into account to calculate the temperature of the stator. Otherwise, this paper also discusses the influence of different number of ventilation ducts on temperature distribution. Finally, using finite element method(FEM) to verify the correctness of the proposed model.
A Genetic-Taguchi Global Design Optimization Strategy for Interior PM Machine

This paper presents a novel step-optimization strategy for interior permanent magnet synchronous machines (IPMSMs) design optimization, namely genetic-Taguchi global optimization (GTGO). The performances of IPMSM are accurately predicted by the magnetic-equivalent-circuit (MEC) model, and the Pareto solution of bi-objective functions of operational losses and material cost are iteratively searched by genetic algorithm (GA) based on the constraint conditions. Next, the Taguchi method further optimizes local variables which are difficult to model by MEC model to minimize torque ripple and total harmonic distortion (THD) of no-load back emf. Final, the validity of proposed GTGO is verified by finite-element analysis (FEA).
Fast-Forward Methods for Egocentric Videos: A Review

The emergence of low-cost, high-quality personal wearable cameras combined with a large and increasing storage capacity of video-sharing websites have evoked a growing interest in first-person videos. A First-Person Video is usually composed of monotonous long-running unedited streams captured by a device attached to the user body, which makes it visually unpleasant and tedious to watch. Thus, there is a rise in the need to provide quick access to the information therein. In the last few years, a popular approach to retrieve the information from videos is to produce a short version of the input video by creating a video summary; however, this approach disrupts the temporal context of the recording. Fast-Forward is another approach that creates a shorter version of the video preserving the video context by increasing its playback speed. Although Fast-Forward methods keep the recording story, they do not consider the semantic load of the input video. The Semantic Fast-Forward approach creates a shorter version of First-Person Videos dealing with both video context and emphasis of the relevant portions to keep the semantic load of the input video. In this paper, we present a review of the representative methods in both fast-forward and semantic fast-forward methods and discuss the future directions of the area.
Novel Approach towards Arabic Question Similarity Detection

In this paper we are addressing the automatic detection of Arabic question similarity, which is an essential issue in a variety of NLP/NLU applications such as question answering systems, virtual assistants, chatbots … etc. We are proposing and experimenting a rule-based approach that relies on lexical and semantic similarity between questions with the utilization of supervised learning algorithms. Our approach categorizes questions semantically according to their type and scope; this categorization is based on hypothetical rules that have been validated empirically, for example, a Timex Factoid question (a question asking about time) is less likely similar to an Enamex Factoid question (a question asking about a named entity). This article details the procedures of question pairs preprocessing, lexical analysis, feature extraction and selection and most importantly the similarity measures. According to the experiment we have conducted, our approach achieved promising precision and accuracy based on a test data of 1450 question pairs.
Efficient Randomized Hierarchy Construction for Interactive Visualization of Large Scale Point Clouds

Point cloud is widely used in various applications like 3D geographical information system (GIS), cultural heritage preservation, urban planning, etc. Most of these applications require interactive visualization of massive point cloud, which is challenging since their sizes are usually very large. This paper presents a method to construct a hierarchical data structure for point cloud data organization and real-time rendering, with an emphasis on speeding up the construction processing. The overall pipeline consists of the following three steps: first, the spatial extent of the whole dataset is divided into nested blocks; second, data in each block is reorganized using a octree based on random subsampling in a parallel fashion; finally, octree of all blocks are merged into a consistent hierarchy. The effectiveness and efficiency of the above approach was demonstrated by applying it to a set of point clouds of varying sizes reconstructed using photogrammetry pipeline.
Ensemble Regression Models Applied to Dropout in Higher Education

Context: School dropout is a significant challenge for the Brazilian education system. Several factors need to be corrected, and others eliminated so that students can to have access to higher education and guarantee the completion of their courses. Motivation: finding the best model to predict a specific problem is not a simple task. It's because the phenomena involved are not known, or are sophisticated modeling. Thus, combining models often produces better accuracy than individual models. Different models use this combination approach and have been applied in the context of Data Mining (MD), for prediction and classification. Objective: we propose in this study three different models to predict school dropout. These are based on Ensemble Regression. We apply the models in the context of the Brazilian Higher Education Institutions. Besides, it may help in the identification of the factors associated with dropout. For this, we used two techniques for the attribute selection: Stepwise and Pearson correlation. That techniques determine the factors related to dropout. Methodology: we used the data from the Census and Flow Indicators Higher Education. The methodology is based on CRISP-DM to understand, prepare, and model the data. We used predictive bagging methods to make a model to predict dropout. Results: the ensemble regression models proposed obtained better performance compared model literature. The ensemble model based on bagging of linear regression had a smaller prediction error. Besides, the models proposed in this study will help the educational administrators and policymakers working within the educational sector in the development of new policies that are relevant to student retention. But, the global implications of this research to practice is its ability to help in early identifying factories associated with students at risk of dropout of High Education.