Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Bootstrapping Pedestrian Detection in Downward-Viewing Fisheye Cameras Using Pseudo-Labeling

Downward-viewing fisheye cameras have attracted much attention in surveillance systems due to the wide coverage and less occlusion. However, pedestrian detection in downward-viewing fisheye cameras remains an open problem due to a lack of large-scale labeled dataset. Furthermore, it’s time-consuming and labor-intensive to label a downward-viewing fisheye dataset manually. To address this, we propose a self-bootstrapping pedestrian detection method, which automatically pseudo-labels downward-viewing fisheye images by making full use of spatial and temporal consistency of pedestrians in the cameras to improve the accuracy of pedestrian detection. We segment the downward-viewing fisheye images into two regions and propose the pseudo-labeling methods for them progressively: a cyclic fine-tuned detector for the oblique region and a visual tracking method for the vertical region. Combining the pseudo-labels from two regions, we fine-tune the network for better accuracy. Experimental results show that the proposed approach reduces time consumption by about 95% compared with the labor-intensive manual labeling while it still reaches competitive and comparable Average Precision (AP).
Automated Traffic Engineering in SDWAN: Beyond Reinforcement Learning

Traffic engineering (TE) is a critical and difficult problem that involves assigning traffic with various requirements to paths with different constraints. Recently, machine learning algorithms, especially deep neural networks (DNN), are applied to TE, yet they all assume that the network is a black box, limiting them to only model-free reinforcement learning (RL) algorithms. In this paper, we introduce differentiable programming to TE, and show that the network environment can be sufficiently modeled for TE optimization. Specifically, we design a fully-differentiable network environment, $\partial \mathrm{NE}$, that can be directly integrated into any DNN models. With $\partial \mathrm{NE}$, we can differentiate with respect to control parameters, and directly evaluate gradients between actions and states to facilitate gradient descent based training of DNN models. We show with a proof-of-concept prototype that $\partial \mathrm{NE}$ accelerates DNN training for TE by 228× and achieves higher scalability compared to existing network simulators. Most importantly, dNE opens up the possibility to apply arbitrary deep learning models to TE beyond RL.
Hardware Implementation of Blind Source Separation Algorithm Using ZYBO Z7& Xilinx System Generator

Blind Source Separation (BSS) is termed as the extraction of source signals from mixed data without or with little knowledge about the source signals. It is used in many fields of research such as military, medical and industry. Among the many challenges that must be overcome to apply BSS to a real system, implementation restrictions are the most difficult. Software implementations require a significant amount of overhead to make them applicable to real-time systems and have a lower maximum speed of operation than hardware implementations. When speed is desired, the algorithm should be implemented on specialized hardware such as a Field Programmable Gate Array(FPGA) which allows the user to take advantage of parallel computational abilities. This paper describes the methodology for implementing real-time DSP applications on FPGA and the concept of hardware software cosimulation of the Second Order Blind Identification (SOBI) algorithm using MATLAB Simulink and Xilinx System Generator (XSG). Performances of efficient architectures are implemented on FPGA ZYBO Z7 Zynq-7020.
The roles of cofactors protein S and factor V in the TFPI anticoagulant pathway

3 DECLARATION OF ORIGINALITY . 4 ACKNOWLEDGEMENTS . 5 TABLE OF CONTENTS . 6 FIGURE LEGEND . 11 TABLE LEGEND . 13 ABBREVIATIONS . 14
Autonomous Rendezvous Guidance via Deep Reinforcement Learning

This paper aims developing a new guidance algorithm for autonomous rendezvous with small continuous thrust. In particular, the goal is to create an algorithm that can meet the requirements of real-time and trajectory optimization at the same time. To Achieve the goal, this paper designed a new guidance controller based on deep reinforcement learning (DRL). The DRL guidance controller can map the motion state to the acceleration command directly by learning from experience, so that it can meet the real-time requirement. At the same time, by setting an appropriate reward function during learning, the terminal constraints and fuel optimization can be realized. Simulation results show that the algorithm is feasible and robust even though there is measurement error or model error. Compared with traditional PD algorithm, time and fuel consumption are reduced by more than 30%.
Machine Learning Based Automatic Classification of Respiratory Signals using Wavelet Transform

Respiratory signals emanating from human lungs give vital and indicative information regarding the health status of a patient’s lungs. Conventional clinical methods require professional pulmonologists to diagnose such signals properly and are also time consuming. In this proposed work, an efficient and automated method is proposed for the diagnosis and classification of respiratory signals into normal and abnormal respiratory sound. Respiratory signal is cleaned using a band pass filter, followed by features extraction in wavelet domain. Discriminatory features from the filtered signals are fed to SVM for purpose of classification of signals. Proposed methodology has achieved an accuracy of 92.30% in correctly classifying the pathological lung sounds. Outcomes of the proposed algorithm are promising and indicates its usability for some real time application.
Using NLP and Machine Learning to Detect Data Privacy Violations

Privacy concerns are constantly increasing in different sectors. Regulations such as the EU's General Data Protection Regulation (GDPR) are pressuring organizations to handle the individual's data with reinforced caution. As information systems deal with increasingly large amounts of personal data in essential services, there is a lack of mechanisms to help organizations in protecting the involved data subjects. In this paper, we propose and evaluate the use of Named Entity Recognition as a way to identify, monitor and validate Personally Identifiable Information. In our experiments, we used three of the most well-known Natural Language Processing tools (NLTK, Stanford CoreNLP, and spaCy). First, we assess the effectiveness of the tools with a generic dataset. Then, machine learning models are trained and evaluated with datasets built on data that contain personally identifiable information. The results show that models' performance was highly positive in accurately classifying both generic and more context-specific data. We observe the relationship between the datasets' training size and respective performance and estimate the appropriate size for model training within this context. Furthermore, we discuss how our proposal can effectively act as a Privacy Enhancing Technology as well as the potential risks and associated impacts.
When Raft Meets SDN: How to Elect a Leader over a Network

This paper discusses the benefits in the operation of a Raft based SDN controller cluster, when the election of the cluster leader becomes more or less “fair. Raft is a leader based consensus algorithm, which is used by the most popular open-source SDN controllers for replicating the network state. It requires all state changes to be confirmed by the leader, thus the leader election is very crucial for the Raft performance. In case that the inter-controller communication delay is the same for all controller pairs, the election process is absolute fair, meaning that the leadership is shared equally among the controllers. In all other cases, some controllers become leaders more frequently in benefit or at a cost of the average time required for a network state update. In this paper, we model this time as a function of the leadership probabilities of the cluster controllers. We also model these probabilities as a function of the time that each controller is waiting after detecting the current leader failure and before starting its campaign. We configure different ranges for the controller waiting times, adjusting the leadership probabilities and decreasing the average response time. Our model is confirmed by testbed experimentation.
Poster Abstract: LocTag: Passive WiFi Tag for Robust Indoor Localization via Smartphones

Indoor localization via smartphones has attracted many researchers' attention over the past few years. However, it still lacks a mature solution robust to complex indoor environments. In this paper we design LocTag, the first passive WiFi tag for localizing commercial off-the-shelf smartphones in arbitrary indoor environments with or even without APs (Access Points) deployment. Unlike conventional passive WiFi tags, LocTag backscatters ambient WiFi signals from APs or smartphones for localization instead of communications. To do so, we propose several techniques including triggering source selection, WiFi compatible modulation, and random multiple access. We prototype LocTag using a FPGA (Field Programmable Gate Array) and apply it in a typical indoor localization scenario. The experiment results show that sub-meter level accuracy is achieved via LocTag. Although our work is still at its early stage, it sheds new light on the robust indoor localization via passive WiFi tags.
Metallographic Image Segmentation Method Based on Superpixels Algorithm and Transfer Learning

In the production process of aluminium alloy, we can evaluate the microstructure and characterization of alloy metals by using optical microscope and transmission electron microscope. Specifically, we could access the second phase distribution and grain of alloy surface by optical microscope, which are significant indications of alloy qualities and mechanical properties. In the actual industrial production process, the majority of meaningful information are extracted by manual counting. This kind of complicated statistics greatly increase the work of quality inspectors and researchers, which make them hard to concentrate on research of properties of alloy metal. Based on this, we proposed a deep learning method that utilizing U-Net to realize the accurate segmentation of the alloy matrix and the second phase in the metallographic image based on superpixels algorithm and transfer learning. Researchers can easily get some important information about distribution and proportion of the second phase to improve work efficiency. The final experiments indicate that the method proposed in this paper can effectively distinguish alloy matrix and second phase in metallographic image and achieve good results on accuracy, recall, precision and generalization.
Research on Adaptive Robust Control of Electro-hydraulic Composite Cylinder Under Complex Load Conditions

With the rapid development of industrial automation, high-precision modeling and motion control of linear motion actuators have become very important in practical engineering applications. However, the conventional linear motion actuators such as electric cylinders are difficult to achieve good control effects under complex load conditions. In order to solve this control problem, this paper adopts the Electro-hydraulic composite cylinder, a new type of integrated actuator as the controlled object and designs a control scheme for electro-hydraulic hybrid drive loads. The electric part of the composite cylinder is actively controlled, and an adaptive robust controller with continuous friction compensation is designed. At the same time, the robust integral of the sign of the error(RISE) is introduced in the controller to compensate for the approximate error and the bounded disturbance. The hydraulic part assists and produces a load balancing force through the combination of the hydraulic cylinder and the accumulator to attenuate the effects of complex load conditions on the entire controlled system. The Simulation results demonstrate the effectiveness of the proposed control method.
Normal-To-Lombard Speech Conversion by LSTM Network and BGMM for Intelligibility Enhancement of Telephone Speech

Noise in the environment significantly decreases the speech intelligibility of telephone conversations. Despite clean speech output from the device, the listener is still hard to get information. This study focuses on intelligibility enhancement (IENH) of telephone speech in near-end background noise based on normal-to-Lombard speech conversion. The proposed approach uses long short-term memory (LSTM) and Bayesian Gaussian mixture model (BGMM) to build the speech mapping model. Compared with previous studies, we fully consider the short-term correlations of speech and implement feature mappings with higher dimensional features and more types of features. Evaluations indicate that the proposed approach has achieved better results in both objective and subjective evaluation.
Foods used by male mallards wintering in southeastern Missouri

Although winter foods of mallards (Anas platyrhynchos) have been documented in several studies, the importance of ecological or biological factors on the consumption of specific food groups often was ignored. Consequently, we evaluated whether age, pair status, molt status, habitat, year, or season influenced foods consumed by male mallards in southeastern Missouri during winters 1983-86. Seeds of moist-soil plants composed 61.4 and 46.0% of the aggregate dry mass diet of ducks collected in 1983-84 and 1984-85. Agricultural grain made up 33.8% of the aggregate dry mass diet in 1984-85, and acorns accounted for 64.5% of the diet in 1985-86. Our analysis revealed that habitat where birds were collected (P < 0.01) and annual variation (P < 0.01) were predominate factors influencing male mallard diet during winter. We attribute annual differences in food consumption primarily to annual variation in mast production. Invertebrates were present in 82% of 156 food samples, but composed only 7.3% dry mass of all ducks collected. Invertebrate consumption was greater during mid-winter than during other portions of winter (P < 0.01), probably a result of population growth and life history strategies of invertebrate species. Consumption of food groups did not differ among adult and immature males (P = 0.75), paired and unpaired males (P = 0.15), or males of different molt status (P = 0.22). These results suggest that age and physiological factors are less important than environmental factors in determining food use by male mallards during winter. Providing a diversity of habitats and suitable foods may be the best management approach to compensate for annual variation in availability of individual food resources.
Ergodic Capacity Performance of D2DIoT Relay NOMA-SWIPT Systems with Direct Links

We investigate the Ergodic capacity (EC) performance of device-to-device (D2D) Internet of Things (IoT) relay non-orthogonal multiple access (NOMA)- simultaneous wireless information and power transfer (SWIPT) systems where the relayed communication is supported with direct link. A two-user case is considered in which a base station transmits symbols to two NOMA users, and the energy harvesting (EH) based relay node via a direct link. The EH based relay node harvests the energy from the BS’s signal and again transmits a superimposed composite NOMA signal intended for the user with poor channel condition and for its D2D user to offload its data traffic. A D2D user offloading is considered to further enhance the spectral efficiency of the system. We derive the analytical expressions for the EC of each of the user and the ergodic sum capacity (ESC) of the system and validate them with simulation results. In such settings, our results demonstrate that the EC of a node and the ESC of the system can be improved through the maximal ratio combining (MRC) scheme compared to a system with single signal decoding scheme. Our results also indicate that the overall ESC of the NOMA-SWIPT system can be improved by having a direct link for a user in the system.
Keratoconus Detection Using the Fusion Features of Anterior and Lateral Segment Photographed Images

Keratoconus (KC) is a type of eye disease that involves the thinning of the corneal layer and a change in the semispherical shape of the cornea to a bulging cone shape when viewed laterally. KC is difficult to detect in the early stages of the disease, as the patient does not feel any pain. Hence, the development of a KC detection (KD) method using a digital image processing approach is needed for the early detection of KC so that physicians can provide patients with the subsequent treatment sooner. The objective of this study was to develop a method of KD using a camera from a smart device to capture anterior and lateral segment photographed eye images (A&LSPIs). A total of 280 images comprising 140 KC and 140 normal A&LSPIs were used in this study, and all images were validated by a qualified expert. First, the corneal area of both image views was segmented so the geometric features could be extracted using the automated modified active contour model and the semiautomated spline function for the anterior and lateral images, respectively. Then, the features were selected using infinite latent feature selection (ILFS) by identifying the feature rankings based on the graph weighting that was automatically learned by the probabilistic latent semantic analysis (PLSA). The results showed that the all-combined features, where the proposed and improved features were successfully top ranked, had 96.05% accuracy, 98.41% sensitivity and 93.65% specificity with the RFn=50 classifier, outperforming the 7-NNMaha and SVMRBF classifiers. In conclusion, this study successfully developed a keratoconus detection system based on fusion features using a digital image processing approach for A&LSPIs captured with a smartphone camera.
Neuro-Control for Continuous-Time Stochastic Nonlinear Systems via Online Policy Iteration Algorithm

This paper is concerned with the neuro-control for continuous-time nonlinear systems subject to stochastic disturbance. Due to the stochastic disturbance, the traditional value function in existing literature cannot meet the stochastic control problems, since mixed second partial derivatives are employed to construct modified value function of conditional expectation. To solve the Hamilton-Jacobi-Bellman equation, a novel online policy iteration algorithm with an Ito correction term is developed with establishing a critic neural network to approximate the optimal value function. ˆ Thus, the online optimal control can be obtained in a closed-loop form. The closed-loop system is guaranteed to be stable in probability via Lyapunov’s direct method. Finally, numerical example is provided to illustrate the effectiveness of the developed control method.
Automatic Detection Of Pathological Myopia And High Myopia On Fundus Images

Automatic detection of myopia plays a significant role in clinical practice. Few studies have been done on the detection of pathological myopia, and no attention has been paid to the distinguishment between it and high myopia. Additionally, they are hard to differentiate because of the high similarity between them. In this paper, we design a network with two branches for different classification tasks, where the first one is to distinguish the normal and abnormal while the other is to classify pathological myopia and high myopia. We manage to improve the classification accuracy by combining Binary Cross-Entropy loss and Triplet loss. Extensive experiments are conducted for comparison between our method and other universal classification models using a private retinal fundus dataset. The results demonstrate that our method achieves the best performance with 81.82%, 83.61% and 83.52% on the accuracy, precision and sensitivity, respectively.
Finite-time time-varying group formation tracking for second-order multiagent systems by continuous integral sliding mode

This paper investigates finite-time time-varying group formation (FT-TVGF) tracking control problems for second-order multiagent systems (MASs) with disturbances and directed topologies. A new method to solve FT-TVGF tracking control problems is provided. The agents in MASs are divided into three types including the virtual leader, the group leader and the follower. The overall movement of MASs can be controlled by the virtual leader. Collaborations among subgroups can be realized by communications among group leaders. Followers achieve the desired time-varying group formation. Moreover, the directed topologies are considered to save communication bandwidth and a continuous integral sliding mode control protocol is presented to suppress disturbances and eliminate chattering caused by disturbances. The stability of MASs is proven based on Lyapunov theory. Finally, numerical simulation examples are designed to validated the effectiveness of theoretical results.
Improved Template Matching Based Stereo Vision Sparse 3D Reconstruction Algorithm

Stereo vision 3D reconstruction using keypoints matching methods can bring up problems, such as uneven distribution of points, or even mismatches. To solve the problems, we proposed a novel stereo vision 3D reconstruction algorithm based on template matching. First, considering that the similarity between left and right image of binocular camera can be high, template matching can be brought in to minify the searching range of every keypoint, thus enhance the accuracy of matches and make the reconstruction result more complete. The specific process of the proposed method includes the following steps: templates extraction, keypoint density index calculation, template matching, keypoint matching within template matched areas, point cloud generation, and point cloud stitching. Furthermore, a simulation environment was built up in Gazebo, and 3D reconstruction was carried out using the proposed method. Comparison experiment was carried out using brute force keypoint matching method and FLANN. Simulation results showed that the proposed method performed better and improved in both utilization of keypoints and reconstruction result.
A Comparative Performance Analysis of Blind Equalization Algorithms in Single Carrier Visible Light Communications

It is very important to estimate and equalize the multipath optical channel blindly in visible light communication (VLC) systems. It is recommended that it is used Constant Modulus Algorithm (CMA), modified CMA (M-CMA) and normalized CMA (N-CMA) algorithms, which are among the most commonly used blind equalization algorithms to eliminate inter-symbol interference (ISI) caused by a multipath optical channel in a closed environment in this paper. Computer simulations and comparative performance analysis of the algorithms mentioned above are performed over the Mean Square Error (MSE) and ISI performance metrics in stationary and non-stationary environment. It is observed that the best performances are obtained with M-CMA algorithm in MSE comparisons and the best performances are obtained with NCMA algorithm in ISI comparisons from the computer simulation results.