Miracle-dz/UniFAR-FTUs huggingface dataset:
PPEM: Privacy‐preserving EM learning for mixture models

Privacy is becoming increasingly important in collaborative data analysis, especially those involving personal or sensitive information commonly arising from health and commercial settings. The aim of privacy preserving statistical algorithms is to allow inference to be drawn on the joint data without disclosing private data held by each party. This paper presents a privacy‐preserving expectation–maximization (PPEM) algorithm for carrying out maximum likelihood estimation of the parameters of mixture models. We address the scenario of horizontally partitioned data distributed among three or more parties. The PPEM algorithm is a two‐cycle iterative distributed algorithm for fitting mixture models under privacy‐preserving requirements. A distinct advantage of PPEM is that it does not require a trusted third party for cooperative learning, unlike most existing schemes that implement a master/slave hierarchy. By adopting a ring topology and adding random noises to messages before encryption, PPEM helps prevent information leakage in the case of corrupted parties. Furthermore, in contrast to existing works, which typically assume a Honest‐but‐Curious adversary, we consider the much stronger case of a Malicious adversary. For illustration, PPEM is applied to two of the most popular mixture models, namely, the normal mixture model (NMM) and t‐mixture model (tMM), and their effectiveness is analyzed through a security analysis. A real data example is also presented to evaluate the computational complexity and accuracy of PPEM relative to its non‐privacy‐preserving version.
Two-Dimensional Boundary Recovery Procedure for Non-Manifold and Intersecting Boundaries

To ensure all boundary edges of each region are retained in the final mesh during mesh generation, a procedure suitable for non-manifold with intersecting boundaries is proposed to recover the non-manifold and intersecting boundaries. In the procedure, Steiner points are inserted directly on the interFigure of missing boundaries and mesh edges from a triangulation. Based on the boundary recovery procedure, Delaunay triangulation algorithms for the domain with interior constraints and for the non-manifold are proposed, and the latter can be used as a new effective polygon Boolean operations algorithm.
The development of the computer-based instructional media with the interactive tutorial model

This research aims to develop the computer-based instructional media with the interactive tutorial model. This research used the development model design composed by Borg and Gall modified by Sugiyono. The development was conducted through the following steps: (1) potentials and problems; (2) data collection; (3) media design; (4) validation; (5) revision; (6) small group trial; (7) revision; (8) big group trial; and (9) revision. At the stage of potentials and problems, researchers created a need analysis of the results of interviews with mathematics teachers. The media was developed for four meetings, validated by three validators and furthermore revised based on the input from validators. The revised media was tested on small and big groups by students at the Junior High School 8 Pekanbaru. The results of the data analysis showed that the computer-based instructional media with the interactive tutorial model on the coordinate system is valid with the average value of 3.4 and is practical to use by students.
PENERAPAN MIXTURE MODEL KEPADA APLIKASI HELPDESK BERBASIS WEB

The use of mixture models in the application implementation helpdesk aims to find the spread of data from various data hordes in order to find data near the desired results. It aims to facilitate the search for information derived from the company's technical documents. The use of helpdesk applications is to manage complaint data or any problems that are owned by customers, thus requiring service providers. Because companies engaged in services have a number of customers who are not small and have different problems. As well as having customers in different locations - requiring customer data management that can store location data with ease to use, it is preferred to store customer's address data. For that helpdesk application also uses map mapping online by using google map.Purpose in order to present the data online and can be accessed wherever located.
Extending the Limits of the Random Exploration Graph for Efficient Autonomous Exploration in Unknown Environments

The autonomous construction of environment maps using mobile robots is a fun-damental problem of robotics; this is because virtually all tasks performed by robots need a representation of the working environment to operate. Although many works have addressed this problem known as SLAM, it still remains open; since most of the solutions do not consider a planner that allows the robot to explore autonomously the working environment or the works that consider it, they have developed slow algorithms that do not guarantee a total coverage of the environment or an optimal development of the exploration, which may result in maps of poor quality or definitely not usable given this lack of information. Thus, this work presents a new exploration method based on the random exploration graph (REG), which, unlike its predecessor, defines a systematic analysis of the next positions to be explored eliminating randomness in decision-making and thus minimizing the amount of movements that the robot must make to reach them and the time required to achieve total coverage of the environment. Additionally, a series of tests carried out on the proposed method are presented, and the results obtained in classical variables such as time and distance allow to validate the efficiency of our approach.
A 14-nA, Highly Efficient Triple-Output Thermoelectric Energy Harvesting System Based on a Reconfigurable TEG Array

This paper presents an inductorless triple-output thermoelectric energy harvesting (EH) system based on a reconfigurable thermoelectric generator (TEG) array. The TEG array in the proposed system can be dynamically reconfigured to achieve maximum power point tracking (MPPT) and voltage conversion (VC) for three outputs. Two regulated outputs and an energy recycling mechanism that can store excessive energy in the battery and supplement insufficient energy to the load are provided. An optimization method based on Lagrange multipliers is proposed to size the switches in the switch array and the areal cost can be reduced by 43%. To optimize the control overhead, the building blocks in the control stage are designed to operate in four different frequency domains. An adaptive frequency control scheme is applied to the regulation control loop, making the system able to maintain regulation over a wide power range while achieving 14-nA quiescent current. In addition, a fast wake-up response scheme is designed to cater for the requirement of some applications with duty-cycled operation. The experimental results show that a higher than 86% efficiency can be achieved over an input voltage range of 0.15–10.8 V and an output power range of $38~\mu \text{W}$ –200 mW with a 99% peak efficiency.
Shape-based three-dimensional body composition extrapolation using multimodality registration

The ubiquity of commodity-level optical scan devices and reconstruction technologies has enabled the general public to monitor their body shape related health status anywhere, anytime, without assistance from professionals. Commercial optical body scan systems extract anthropometries from the virtual body shapes, from which body compositions are estimated. However, in most cases, these estimations are limited to the quantity of fat in the whole body instead of a fine-granularity voxel-level fat distribution estimation. To bridge the gap between the 3D body shape and fine-granularity voxel-level fat distribution, we present an innovative shape-based voxel-level body composition extrapolation method using multimodality registration. First, we optimize shape compliance between a generic body composition template and the 3D body shape. Then, we optimize data compliance between the shape-optimized body composition template and a body composition reference from the DXA pixel-level body composition assessment. We evaluate the performance of our method with different subjects. On average, the Root Mean Square Error (RMSE) of our body composition extrapolation is 1.19%, and the R-squared value between our estimation and the ground truth is 0.985. The experimental result shows that our algorithm can robustly estimate voxel-level body composition for 3D body shapes with a high degree of accuracy.
A Discriminant Analysis and Goal Programming Approach to solve the Multiple Criteria Data Envelopment Analysis Model

Data Envelopment Analysis (DEA) is a method measuring the relative performance of a group of decision making units (DMUs) which consume a number of inputs and produce several outputs at different quantities. In spite of its popularity, DEA still endures some kinds of shortcomings. For instance, DEA lacks the discriminating power among efficient DMUs. In this paper, we introduce a method which utilizes goal programming and discriminant analysis to solve the multiple criteria DEA model. The proposed method develops a classification function which separates efficient and inefficient DMUs and generates an efficiency ranking for all DMUs. Furthermore, it allows decision-makers to incorporate a priori information about the factor weights via proportional virtual weights restrictions or other forms of weights restrictions. Performance of the proposed method is illustrated by two real applications, which have been studied in the literature.
CT-realistic data augmentation using generative adversarial network for robust lymph node segmentation

As an important task in medical imaging analysis, automatic lymph node segmentation from computed tomography (CT) scans has been studied well in recent years, but it is still very challenging due to the lack of adequately-labeled training data. Manually annotating a large number of lymph node segmentations is expensive and time-consuming. For this reason, data augmentation can be considered as a surrogate of enriching the data. However, most of the traditional augmentation methods use a combination of affine transformations to manipulate the data, which cannot increase the diversity of the data’s contextual information. To mitigate this problem, this paper proposes a data augmentation approach based on generative adversarial network (GAN) to synthesize a large number of CT-realistic images from customized lymph node masks. In this work, the pix2pix GAN model is used due to its strength for image generation, which can learn the structural and contextual information of lymph nodes and their surrounding tissues from CT scans. With these additional augmented images, a robust U-Net model is learned for lymph node segmentation. Experimental results on NIH lymph node dataset demonstrate that the proposed data augmentation approach can produce realistic CT images and the lymph node segmentation performance is improved effectively using the additional augmented data, e.g. the Dice score increased about 2.2% (from 80.3% to 82.5%).
Health Evaluation of MVB Based on SVDD and Sample Reduction

Multifunction vehicle bus (MVB) is the most widely used train communication network whose performance degradation and anomaly will heavily affect the train’s safe and stable operation. However, current scheduled maintenance and post-failure maintenance of MVB cannot detect the early anomaly and evaluate the health condition of the network in time. This paper provides a method to detect the anomaly and evaluate the health condition of MVB based on a one-class classification (OCC) algorithm called density-based sample reduction for support vector data description (DBSRSVDD). First, network features are extracted from physical layer waveform parameters. In order to reduce the computational complexity of SVDD, a sample reduction operation is conducted to screen out the edge samples as support vector candidates. Then, the SVDD models representing the normal patterns of a single MVB node are trained based on the support vector candidates. Performance degradation of the node is quantified by the distance between the tested sample and the trained hyper sphere. The whole network’s health condition is the linear weighted sum of the nodes’ scores based on their bandwidth occupancy. The experimental results show that the proposed method can detect the anomaly and degradation of MVB successfully, improve accuracy, and reduce training time compared with the existing methods.
ScOSA - Scalable On-Board Computing for Space Avionics

Computational demands on spacecraft have continuously increased for years whereby available space qualified hardware is not capable of satisfying these requirements completely. This paper introduces a way to overcome this problem by combining highly reliable space qualified hardware with highly performant commercial off-the-shelf (COTS) components. This combination of hardware enabled us to develop a new type of on-board computing (OBC) system, called ScOSA (Scalable On-Board Computing for Space Avionics). A ScOSA system uses a distributed approach whereas it consists of multiple nodes. They are classified as reliable or high performance nodes. The reliable nodes are based on radiation-hardened Leon3 processors, the high-performance nodes on COTS Xilinx Zynq (CPU and FPGA). All nodes are connected by a SpaceWire network with a meshed topology that provides redundant data paths to establish fault tolerance. A large number of existing systems can be connected to our system given that SpaceWire is widely used within the space domain. ScOSA has additional capabilities during operation that set it apart from traditional on-board computing systems. During runtime a dynamic reconfiguration of the whole system can be performed. By this, faulty nodes can be removed or recovered nodes can be reintegrated into the system. Additionally, computation tasks can be started, stopped or shifted between all active nodes. Also, connected FPGAs can be reprogrammed. As a consequence, these reconfiguration capabilities can be used to fulfill changing requirements without exchanging the underlying hardware. This can also be used to handle different spacecraft modes or mission phases. The contribution of this paper is to explain the details of the ScOSA architecture, implementation and its functionality. Additionally, we will show the results of running representative applications from the space robotics, earth-observation and satellite avionics domains. These applications were selected for evaluating the system capabilities and include, among others, autonomous navigation and capture operations, stereo image processing and optical ship detection. Testing and demonstration will be done in Hardware-in-the-Loop simulators or on robotic testbeds (namely DLR’s EPOS and OOS-Sim).
LPANNI: Overlapping Community Detection Using Label Propagation in Large-Scale Complex Networks

Overlapping community structure is a significant feature of large-scale complex networks. Some existing community detection algorithms cannot be applied to large-scale complex networks due to their high time or space complexity. Label propagation algorithms were proposed for detecting communities in large-scale networks because of their linear time complexity, however most of which can only detect non-overlapping communities, or the results are inaccurate and unstable. Aimed at the defects, we proposed an improved overlapping community detection algorithm, LPANNI (Label Propagation Algorithm with Neighbor Node Influence), which detects overlapping community structures by adopting fixed label propagation sequence based on the ascending order of node importance and label update strategy based on neighbor node influence and historical label preferred strategy. Extensive experimental results in both real networks and synthetic networks show that, LPANNI can significantly improve the accuracy and stability of community detection algorithms based on label propagation in large-scale complex networks. Meanwhile, LPANNI can detect overlapping community structures in large-scale complex networks under linear time complexity.
Contextual Action with Multiple Policies Inverse Reinforcement Learning for Behavior Simulation

Machine learning is a discipline with many simulator-driven applications oriented to learn behavior. However, behavior simulation it comes with a number of associated difficulties, like the lack of a clear reward function, actions that depend of the state of the actor and the alternation of different policies. We present a method for behavior learning called Contextual Action Multiple Policy Inverse Reinforcement Learning (CAMP-IRL) that tackles those factors. Our method allows to extract multiple reward functions and generates different behavior profiles from them. We applied our method to a large scale crowd simulator using intelligent agents to imitate pedestrian behavior, making the virtual pedestrians able to switch between behaviors depending of the goal they have and navigating efficiently across unknown environments.
A Multi-Task Learning Model for Better Representation of Clothing Images

Clothing images vary in style and everyone has a different understanding of style. Even with the current popular deep learning methods, it is difficult to accurately classify style labels. A style representation learning model based on the deep neural networks called StyleNet is proposed in this paper. We adopt a multi-task learning framework to build the model and make full use of various types of label information to represent the clothing images in a finer-grained manner. Due to the semantic abstraction of image labels in the current fashion field, using a simple migration learning method cannot fully meet the requirements of clothing image classification. An objective function optimization method is put forward by combining the distance confusion loss and the traditional cross entropy loss to improve the accuracy of StyleNet further. The experimental results show that by applying the multi-task representation learning framework, StyleNet can achieve a better classification accuracy, the optimized loss function can also bring performance improvement for deep learning models, and the classification effect of StyleNet becomes better as the size of the data set increases. In order to verify the robustness and effectiveness of the deep learning method in StyleNet, we also apply a Faster R-CNN module to pre-process the clothing images and use the result as the input of StyleNet. The classifier can only get a limited performance improvement, which is negligible compared with the methods proposed in this paper of increasing the depth of the neural network and optimizing the loss function.
Decentralized Gradient Tracking for Continuous DR-Submodular Maximization

In this paper, we focus on the continuous DR-submodular maximization over a network. By using the gradient tracking technique, two decentralized algorithms are proposed for deterministic and stochastic settings, respectively. The proposed methods attain the $\epsilon$-accuracy tight approximation ratio for monotone continuous DR-submodular functions in only $O(1/\epsilon)$ and $\tilde{O}(1/\epsilon)$ rounds of communication, respectively, which are superior to the state-of-the-art. Our numerical results show that the proposed methods outperform existing decentralized methods in terms of both computation and communication complexity.
A 3D Lung Nodule Candidate Detection by Grouping DCNN 2D Candidates

Lung cancer has attracted the attention of scientific communities as being the main causes of morbidity and mortality worldwide. Computed Tomography (CT) scan is highly indicated to detect patterns such as lung nodules, where their correct detection and accurate classification is paramount for clinical decision-making. In this paper, we propose a two-step method for lung nodule candidate detection using a Deep Convolutional Neural Network (DCNN), more specifically the Single Shot MultiBox Detector, for candidate detection in 2D images/slices, and then a fusion technique to group the inter-slice adjacent detected candidates. The DCNN system was trained and validated with data from Lung Image Database Consortium and Image Database Resource Initiative, we also use LUng Nodule Analysis 2016 challenge data and metrics to evaluate the system. We had as result sensitivity of 96.7% and an average of 77.4 False Positives (FPs) per scan (an entire set of CT images/slices for a patient). The sensitivity result is ranking two in the state of art (rank one is 97.1%) but with FPs/scan rate which is almost three times smaller than the first one (219.1).
Relative Pose Improvement of Sphere based RGB-D Calibration

RGB-Depth calibration refers to the estimation of both RGB and Depth camera parameters, as well as their relative pose. This step is critical to align streams correctly. However, in the literature there is still no general method for accurate RGB-D calibration. Recently, promising methods proposed to use spheres to perform the calibration, the centers of these objects being well distinguishable by both cameras. This paper proposes a new minimization function which constrains spheres centers positions by requiring the knowledge of sphere radius, and a previously calibrated RGB camera. We show the limits of previous approaches and their correction with the proposed method. Results demonstrate an improvement in relative pose estimation compared to the original method on the selected datasets.
Bifurcation Analysis and Chaos in Electronic Genetic Toggle Switch

In this paper, an electronic (Transistor MOSFET) genetic toggle switch is discretized by the Euler method. The proposed model is discussed by using ordinary differential equations that describe the system behaviour. The stability has been analysed using Jacobian matrix, theorems, and lemmas that illustrate its stability. Two units of MOSFET transistors compose the proposed circuit model, which acts as two genes repressing each other. Different behavior has been analysed such as bifurcation and chaos, based on the numerical simulations that illustrate the theoretical results.
A Wearable Real-time Human Activity Recognition System using Biosensors Integrated into a Knee Bandage

This work introduces an innovative wearable real-time Human Activity Recognition (HAR) system. The system processes and decodes various biosignals that are captured from biosensors integrated into a knee bandage. The presented work includes (1) the selection of an appropriate equipment in terms of devices and sensors to capture human activity-related biosignals in real time, (2) the experimental tuning of system parameters which balances recognition accuracy with real-time performance, (3) the intuitive visualization of biosignals as well as n-best recognition results in the graphical user interfaces, and (4) the on-the-air extensions for rapid prototyping of applications. The presented system recognizes seven daily activities: sit, stand, stand up, sit down, walk, turn left and turn right. The amount of activity classes to be recognized can be easily extended by the ”plug-and-play” function. To the best of our knowledge, this is the first work which demonstrates a real-time HAR system using biosensors integrated into a knee bandage.