Miracle-dz/UniFAR-FTUs huggingface dataset:
Linear Active Disturbance Rejection Control With Partially Canceling Estimated Total Disturbance

Active disturbance rejection control (ADRC) is a quickly developing practical control technology while its tuning is an important research topic. The current mainstream tuning method entirely cancels the estimated total disturbance and results an integrator in linear ADRC's feedback compensator, thus limits the ability to meet design specification of system's type number. This note proposes to partially cancel the estimated total disturbance in linear ADRC. Theoretical analysis and numerical examples show that partially canceling can remove the integrator of the feedback compensator and improve the transient performance of the linear ADRC system.
RBFNN-based Bilateral Control Design for Delayed Nonlinear Teleoperation System With General Environments

In this paper, an RBFNN-based bilateral control design is developed for delayed nonlinear teleoperation system with uncertainties to achieve both stability and good transparency performance. The environment force is modeled in the general form via RBFNN algorithm with the identification of its parameter and transmitted to rebuild the environment force in the master side, which prevents the power signals such as environment force transmitting in the communication channel and avoids the passivity problem in the traditional teleoperation control. The desired master/slave trajectory planners are designed to produce the desired trajectories for the master/slave tracking, and the master/slave controllers are designed afterwards for the good tracking of master/slave with nonlinearities and uncertainties. Thus, the global stability of the delayed teleoperation system is theoretically guaranteed, and the good transparency performance with both position tracking and force feedback is achieved. The simulation is conducted in MATLAB/Simulink environment, and the results demonstrate the effectiveness of the developed bilateral control design.
Temperature Distribution Analyses with Wireless Sensor Networks and Machine Learning

This paper introduces a new way to analyze temperature distribution from an air conditioning unit inside an office using Wireless Sensor Networks and Machine Learning algorithms. The system is able to retrieve real time temperature data and use it to analyze if the air conditioning unit is influencing the temperature inside an office using the temperature difference from various points inside the office. Besides the system architecture, this paper includes a real case scenario implementation and a comparison and discussion of the result for the multiple Machine Learning algorithms applied to categorize if the air conditioner is affecting the office temperature.
Building an Automatic Image Tagger with DenseNet and Transfer Learning

Automatic tagging for digital images, a.k. a. multi-label image classification has long been a prevalent topic in computer vision, as it can be applied to a variety of other fields. An automatic tagging model has the ability to automatically put one or more appropriate tags which realistically reflect the content of an image. Successfully building a good tagging model would, in turn, aid a lot in the development of software and automated systems that implements image searching, image retrieving, and visual storytelling. Based on DenseNet - an advanced deep learning model used for solving the classification problem, we build models to tackle the tagging problem. Training on the ILSVRC 2016 dataset and experimenting on the test dataset we have made, our BW_CE model achieves an 0.5975 on the micro-average F_1 score metric which surpassed 0.4259 of the Microsoft Computer Vision, and reaches an 0.8565 on the micro-average recall compared to 0.8139 of the Google Cloud Vision.
The Usual Suspects: Machine Learning Based Predictive Policing for Criminal Identification

Crime is a critical problem faced by almost every state. Particularly, the crime rates in the under developed countries are alarmingly serious. Generally, in under developed countries, the criminal record is maintained manually and linkage between different criminal records is near to impossible. Investigation officers have to manually look for clues and leads to predict potential suspects who may be involved in a recent crime. Mostly the serial offenders follow peculiar methodologies to execute unlawful activities, referred to as the modus operandi. In this paper, we propose a machine learning based predicting policing algorithm exploiting the modus operandi features of a recent crime and the existing prior criminal records. The proposed model predicts and probabilistically shortlist the potential suspects who may be involved in a recent crime. Thereby, meaningful leads are produced to aid the investigation process. In this paper, we take the Punjab province of Pakistan as a case study and apply the proposed model on the real data collected from various police stations. The results show that the identification of potential suspects proved vital for the cases which involves criminals who had previous criminal record.
A Hybrid Neural Network for Graph-Based Human Pose Estimation From 2D Images

This paper investigates the problem of human pose estimation (HPE) from single 2-dimensional (2D) still images using a convolutional neural network (CNN). The aim was to train the CNN to analyze a 2D input image of a person to determine the person’s pose. The CNN output was given in the form of a tree-structured graph of interconnected nodes representing 2D image coordinates of the person’s body joints. A new data-driven tree-based model for HPE was validated and compared to the traditional anatomy-based tree-based structures. The effect of the number of nodes in anatomy-based tree-based structures on the accuracy of HPE was examined. The tree-based techniques were compared with non-tree-based methods using a common HPE framework and a benchmark dataset. As a result of this investigation, a new hybrid two-stage approach to the HPE estimation was proposed. In the first stage, a non-tree-based network was used to generate approximate results that were then passed for further refinement to the second, tree-based stage. Experimental results showed that both of the proposed methods, the data-driven tree-based model (TD_26) and the hybrid model (H_26_2B) lead to very similar results, obtaining 1% higher HPE accuracy compared to the benchmark anatomy-based model (TA_26) and 3% higher accuracy compared to the non-tree-based benchmark (NT_26_A). The best overall HPE results were obtained using the anatomy-based benchmark with the number of nodes increased from 26 to 50, which also significantly increased the computational cost.
Human Motion Prediction Based on Visual Tracking

Following and moving according to human motion is an important task for mobile robots. To ensure more compliant motion planning and execution of mobile robots, they can not be controlled by real-time visual information. In the tracking process, robots need to recognize and track the target then, plan the motion and execute the motion. In practical applications, the environments are very complex, such as illumination, shadows and occlusion, which the traditional visual tracking algorithms often deviate or lose the target. Therefore, to achieve fast human motion tracking, it is necessary to predict human motions by video prediction. In this paper, we propose a human motion tracking algorithm of mobile robot based on video prediciton. (1) The multi-layer generation adversarial loop network, which trained by off-line video dataset and learn how to predict human motion. (2) We used the pre-trained model to predict the state of the tracking target in the video. (3) This video prediction model was integrated into human-to-robot tracking algorithms of mobile robot following human system. Experiments show that the proposed algorithm can track human motion at a certain speed and precision.
Using Machine Learning techniques to improve Intrusion Detection Accuracy

In recent years, network intrusions have emerged in an endless stream and have generated a lot of financial losses. An intrusion detection system (IDS) is used to detect network attackers. Most current intrusion detection systems are based on a specific predefined pattern (signature) that matches known attack functions. The main limitation of the signature-based approach is that it does not recognize new attacks, even small changes in known vulnerabilities. In addition, in order to keep the extensive signature database up-to-date, an important administrative expense is required. In this paper, based on machine learning techniques, k-means clustering algorithm and an alternative method of support vector machine classification algorithm, it can automatically construct the distribution of normal packet payload and detect its deviation. Our method shows that the proposed hybrid algorithm provides significant detection accuracy than the most used open-source Snort system.
Feature extraction and calibration of EEG signals in sitting and walking meditation

Meditation has been proved beneficial to people's physical and mental health in a large number of scientific reports. However, there are few studies on walking meditation. Walking meditation is a common meditation which involves walking with meditation. In order to investigate the differences in effects between sitting and walking meditation, the EEG signals of 7-Day Zen participants in the following four states are recorded: sitting around, strolling, sitting meditation, and walking meditation. Rhythm-based EEG features are extracted to identify the above four states, and features are calibrated to improve the performance of classification. The experimental results show that the personalized calibration methods effectively solve the individual differences in the EEG signals. The proposed system can identify the four states with high accuracy, which provides a further interpretation of neural actions introduced by meditation.
Effect of Virtual Reality at Exhibitions on Visitor Experiences Underpinned by Museum Studies: “Seeing The Past Through The Present - The Virtual Reality Of The Taipei West District Beimen VR Tour”

As technology never ceases to move forward, modes of visiting exhibitions and exploring art are also turning more and more diversified. In recent years, the rise of virtual reality (VR) has even broken the boundaries of “space”, allowing exhibition designs and displays to no longer be limited in inner spaces. The theory in this case study is built up by visitor experiences. Using the project of “Seeing The Past Through The Present-The Virtual Reality Of The Taipei West District Beimen VR Tour” as an example, the questionnaire survey approach is taken to discuss the influence that the combination of virtual reality, culture, and history had on users, as a reference for future display designs.
Temporal pattern of Klebsiella pneumoniae carbapenemase (KPC) on surfaces of an intensive care unit of a large hospital

Background: Carbapenem-resistant Enterobacteriaceae are an increasing cause of healthcare-associated infections worldwide.Patients with infections caused by Klebsiella pneumoniae resistant to carbapenems (KPC) have significant increases in both allcausemortality and 30-day mortality. The aims of this study was to investigate the prevalence of KPC on environmental samplescollected during and after an outbreak caused by KPC in an intensive care unit (ICU) of a teaching hospital.Methods: Between 2010 and 2014 we conducted a total of 132 environmental monitoring campaigns from different criticalsurface of ICU ward in a Teaching Hospital Policlinico Umberto I. Samples were collected on surfaces in patient rooms and healthcare area. All samples were cultured and K. pneumoniae isolates were identified by standard microbiological techniques. Thepresumptive colonies were confirmed and tested for antibiotic resistance by an automated system. K. pneumoniae resitant tocarbapenems were tested for carbapenemase production by modified Hodge test.Results: A total of 2526 environmental samples were collected from November 2010 to July 2014. Of those, 111 resulted positivefor K. pneumoniae while KPC were 95 (85.6% of all K. pneumoniae, 3.8% of total samples). KPC was recovered in all patientrooms with similar proportion (5.1-5.6%) with the exception of patient 6 bed room where it was lower (2.4%). The pathogen wasnot recovered in rooms dedicated to healthcare personnel and doctors. Among surfaces, the highest proportion of KPC resulted onbedrail (6.8%), more than double than other surfaces. Washbasins had nearly half of samples KPC positive (1.2%). Conclusions: Despite previous studies suggested that environment plays a minor role in the transmission of carbapenem-resistantenterobacteriaceae, our data highlighted that surfaces represents a significant reservoir for KPC possibly supporting transientlycontamination of hands of healthcare workers in our ICU. Our results confirm that KPC are more likely found on surfaces closer tothe patient than on those situated further away.
Combination of FFST and SR: A New Multi-Focus Image Fusion Strategy

A novel multi-focus image fusion strategy based on the combination of fast finite shearlet transform (FFST) and sparse representation (SR) is proposed. As a novel multi-scale geometry analysis tool, FFST has many good properties, such as lower computing complexity and multi-directional high-pass bands. To take advantage of these properties, the SR-based fusion rule for low-pass bands and the regional contrast fusion rule for high-pass bands are introduced. Experiments show that the proposed method has remarkable superiorities and can obtain better fusion results in both visual quality and objective assessments.
Automatic Recognition of Sound Direction in the Median Plane of Human Ears

The human ear orientates the sound source in the horizontal direction base on the binaural effect, while the orientation of human ear in median plane angle is based on auricle effect. This paper is based on a research device, which measures the sounds from sound sources at different angles in the median plane. Through acquisition of sound data at intervals of 5° between 0° and 180° in the median plane, the spectrum envelope of the sound is extracted to form data samples. The separability of data samples is studied by using three kinds of Minkowski distances. A convolutional neural network is constructed to carry out the research of automatic recognition the median plane angle of the sound source. The research proved that by using Convolution Neural Network (CNN), we can precisely orientate the direction in the median plane with a higher precision.
Twitter User Geolocation based on Heterogeneous Network and Label Propagation

Twitter user geolocation is applied into various applications, such as local messages recommendation and event location recognition. Existing methods do not usually utilize the potential positional correlation between heterogeneous information, resulting in low user geolocation accuracy. Therefore, this paper proposes a user geolocation method based on heterogeneous network and label propagation. First, a heterogeneous network is built based on heterogeneous information mined from user tweets. Then, by analyzing the factors that impact the efficiency and accuracy of label propagation, redundant nodes and edges in the initialized heterogeneous network are removed. Finally, the locations of unlabeled users are geolocated through label propagation in the simplified heterogeneous network. Experiments on the publicly available Twitter benchmark dataset show that proposed method accurately locate 57% of users within 161 km of their locations, a significant improvement over several existing methods.
HACO: A Framework for Developing Human-AI Teaming

We witnessed great advancement in artificial intelligence (AI) powered technologies over the past few decades. Wide use of AI technologies has led to the creation of an ecosystem where human and AI systems are partners, complementing each other with their strengths. To build a successful human-AI team, there are several considerations, including context awareness, effective communication, pro-activeness, etc. In this paper, we present a taxonomy of human-AI teaming concepts. We extend a multi-agent framework, Java Agent Development Framework (JADE), to support the proposed taxonomy. Our solution framework, Human-AI Collaboration (HACO), enables a model-driven development of human-AI teaming systems through graphical user interface. In this paper, we present the solution architecture for extending JADE with human-AI teaming taxonomy. A user study performed to assess the usefulness of HACO, shows that HACO is a promising framework. We evaluated the proposed framework by developing a set of use cases for a contact center and observed a signification reduction in the overall development effort. The framework video can be viewed at https://youtu.be/lNyrrk8dMqU.
A Multi-scale Network based on Attention Mechanism for Hyperspectral Image Classification

In recent years, with the theoretical progress of deep learning and the improvement of computational power, more and more deep learning based work has been proposed and achieved good results. However, due to its particularity, hyperspectral image (HSI) is difficult to directly apply the theory and experience of traditional deep learning. For HSI, we mainly face the following problems: effective use of spatial and spectral information, lack of annotated datasets and large spectral dimensions. These problems make it difficult to classify. Generally, in order to solve these problems, some pre-processing methods, such as PCA, and mutual information, will be taken, but this will lose some useful information. On the other hand, people usually consider spatial information on a joint-learning way (take a pixel-centric patch as input), but this approach may lead to some useless information being taken into account. Therefore, we apply the attention model to the HSI classification and reduce the dimension in a way that can be learned, we believe that this can also help the network to be more easily concentrated on similar pixels without being affected by abnormal pixels. In summary, we propose a new attention-based network called ABMN (attention based multiscale network), which is mainly composed of a dimension reduction block and a multi-scale spatial information extraction block. And excellent results are obtained on two popular hyperspectral image sets (Pavia University scene, Indian Pines), OA is 99.66% and 100% respectively.
Field Map Estimation in MRI using Compressed Sensing Algorithm

For non-cartesian magnetic resonance imaging, like spiral imaging, field inhomogeneity could cause image blurring, especially for long readout time. General correction method required field map estimation. However, when images are in low spin density, the estimated field map suffers from noise. A regularized method which utilizes the physical feature that field map is spatial smoothing, is proposed to estimate field map with little noise. The field map estimated by regularized method only have good performance while the images in low noise level. Once image suffers from severe noise, an accurate field map is still hard to obtain. In reality, to shorten scan time in spiral imaging, we would decrease the number of interleaves of sampling. As results of that, Signal-to-noise Ratio (SNR) of image gets lower, and effect of B0 inhomogeneity becomes serious problem. In such situation, a better way to calculate field map is required. In this paper, we propose optimized field map estimation method which employs compressed sensing algorithm. Actually, recovery expected signal of compressed sensing (CS) algorithm is noise reduction process, which could be used to estimate field map when images are in low SNR. The experiments show that using Wavelet transform as regularization term could perform better when images are in low Signal-to-Noise Ratio (SNR). To improve calculated field map further, both Total Variation (TV) term and Waveform term as regularization term are adapted. The method in this paper promises great field map estimation.
Exploiting Generalized Matched Filter for Efficient and Effective P300 Detection in Speller Paradigm

Brain computer interface (BCI) is aimed to produce a direct way for people to communicate with computer. P300 speller interprets brain signals so that people can spell a word without body movement. However, the conventional classification methods are time-consuming and not practical for application. In this paper, we propose a method that tackles the problem through a generalized matched filter. This work is distinguished by two key contributions. First, we investigate statistical signal processing and its feasibility in our task. A model based on hypothesis testing is built and we derive a generalized matched filter (GMF) which is a simple linear function of the signal due to the Gaussian noise assumption. Second, feature selection is introduced according to the estimated covariance matrix, which improves the performance and enhances the computing efficiency simultaneously. Experimental results have demonstrated the great effectiveness of our methods. We improve the performance significantly by 30.5%, 14% at 5, 15 epochs over the original GMF model on BCI competition III dataset II. Moreover, the results are also competitive and much more efficient in applications compared with state-of-the-art methods. Our algorithms need no time for training and cost only 0.016s per character for testing.
Numerical Study on Flow Behavior of Red Blood Cells through Symmetric Capillary Bifurcations

The red blood cell (RBC) partitioning properties at microvascular bifurcation are largely related to heterogeneous oxygen distributions in the microcirculatory networks. Here, three-dimensional, T-type symmetric capillary bifurcation models have been prepared and the flow behavior of RBCs through the capillary bifurcations has been investigated. Simulated blood flow was computed using the lattice Boltzmann method, in conjunction with the immersed boundary method, for incorporating fluid-membrane interactions between the flow field and deformable RBCs. To do this, first the straight vessel flow was simulated to determine the RBC flow at a parent vessel of the bifurcation model. The simulation results indicated two types of RBC arrangements according to the hematocrit: (i) zigzag-slipper and (ii) aligned-parachute shapes. Next, by adopting the RBC arrangements obtained from the straight vessel analysis, RBC partitioning in the capillary bifurcation was investigated. The simulation results were in agreement with the Pries' empirical model at high hematocrit. On the other hand, the bias of RBC flux for the parachute shape was larger than that of the empirical model at low hematocrit. These results suggest that the partitioning properties of RBCs in the microvascular bifurcation depend largely on the RBC arrangement in the parent vessel.
Cscore: A Novel No-Reference Evaluation Metric for Generated Images

The development of deep learning advances the field of image processing. In recent years, lots of methods have made out- standing achievements in the domain of text-to-image synthesis, like Generative Adversarial Networks (GANs). Until now, although some evaluation metrics has been proposed to measure the performance of GANs in text-to-image synthesis, the quality of these evaluation metrics has always been controversial. At present, there is no widely used evaluation metric to judge the quality of generated image. In this paper, a novel No-Reference image quality evaluation metric is proposed, which can be used to get a score for each generated image produced by deep learning without referring to the real image. This evaluation metric can provide a new way to verify the quality of complex networks by judging the quality of generated images retroactively.