Miracle-dz/UniFAR-FTUs huggingface dataset:
Training an FCN with Synthetic Images for Component Segmentation with Applications in Orientation Estimation and Image Inpainting

The detection and segmentation of real objects along with their components is still an advanced topic. More detailed segmentation is needed to solve tasks like position and orientation estimation or for doing inpainting of components. In this paper, we specifically focus on cars and present a segmentation of components, such as rims or lights, which requires detailed and accurate training data. A decomposed 3D model is used to render highly detailed images of the car that fit to corresponding ground truth images. The main challenge is to create high quality synthetic datasets that allow reliable and accurate segmentation of real-world footage. Different camera shots, filters, environment maps, shapes, and neural networks are used and their benefits as well as problems are discussed within this paper. The accuracy and reliability of the segmentation depends on the quality and variability of the rendered images. We started with simple 3D models and a real-time renderer and reached accurate and reliable segmentation with almost photorealistic images that are created with a global illumination renderer. These results are then used for replacing components of the car as well as for deriving the position of special points of interest, like the center of a wheel, which is also necessary for subsequent processing such as correctly aligning the 3D model with the real camera stream for Augmented Reality applications. Here, the quality of replacing a component of an object with its rendered 3D counterpart depends on the accuracy of segmentation. Therefore, segmented components are used to determine the position and orientation of the car along with the size of the inpainting area. Then, a matching rendered image of the component is inpainted only into the segmented area. In this regard, we also compare two different approaches for deriving the center points of the components of an object.
On the Effectiveness of Extracting Important Words from Proxy Logs

Modern http-based malware imitates benign traffic to evade detection. To detect unseen malicious traffic, many methods using machine learning techniques have been proposed. These methods took advantage of the characteristic of malicious traffic, and usually require additional parameters which are not obtained from essential security devices such as a proxy server or IDS (Intrusion Detection System). Thus, most previous methods are not applicable to actual information systems. To tackle a realistic threat, a linguistic-based detection method for proxy logs has been proposed. This method extracts words as feature vectors automatically with natural language techniques, and discriminates between benign traffic and malicious traffic. The previous method generates a corpus from the whole extracted words which contain trivial words. To generate discriminative feature representation, a corpus has to be effectively summarized. This paper extracts important words from proxy logs to summarize the corpus. To define the word importance score, this paper uses term frequency and document frequency. Our method summarizes the corpus and improves the detection rate. We conducted cross-validation and timeline analysis with captured pcap files from Exploit Kit (EK) between 2014 and 2016. The experimental result shows that our method improves the accuracy. The best F-measure achieves 1.00 in the cross-validation and timeline analysis.
Towards Setplays Learning in a Multiagent Robotic Soccer Team

This work proposes a novel machine learning strategy to build new predefined cooperative plans in multiagent systems. State machines describe these plans, named setplays. Each state represents the current behavior of each agent following a setplay. In turn, a setplay requires conditions to define the transitions to other states and at least one final state. A final state may represent success or failure as a result of a setplay execution. We present a proposal to learn, from a human specialist knowledge, a dataset to enable agents to learn new setplays. Preliminary results from dataset exploration experiments are presented and future work is described.
Analysis of Hacking Related Trade in the Darkweb

The non-referenced web is estimated at five hundred times the size of the surface web. Darkweb represents about 6% of the non-referenced web and includes all kinds of delinquency: drug trafficking, counterfeiting, hacking market etc. Several studies about Darkweb marketplaces have been carried out, focusing mainly on the analysis of drug trafficking or the extraction of products sold in different forums. To the best of our knowledge, by the time of this study, no work has yet been done to analyze hacking trade business.Most hackers rely on malwares and softwares offered in different cyber markets to commit their attacks. The main objective of our work is to present an exploratory analysis of the illegal trade that is developing in this marketplaces in order to have a clear idea of threats that may harm individuals, industries and organizations. Through this work, we have been able to give a clear insight on the hacking market. The main motivation of sellers in this market is profit making, in fact this market generated over 26 million USD during the period studied. Product accessibility is also an alarming factor: 85% of the products offered do not exceed 150 USD, making cyber crime accessible to all. Finally, one cell controls almost the entire market, indicating the presence of a well-organized infrastructure. The results of this analysis are discussed below.
Control of High Gain Modified SEPIC Converter: A Constant Switching Frequency Modulation Sliding Mode Controlling Technique

This paper investigates, analyses and presents a control scheme for a modified SEPIC converter. High voltage conversion ratio is accomplished by using fewer passive components. The modified SEPIC converter is a combination of two different circuits. The first part is a standard SEPIC converter circuit, and the second part is a switched capacitor circuit. A high voltage conversion ratio is achieved up to 29 times by the input voltage, if the duty ratio is kept 0.9. However, high performance control of DC-DC converters is a difficult task due to nonlinearities and higher order behaviour. Control methods, such as PI controller or linear state space approaches cannot achieve the goal in a wide operating range. Instead, Sliding Mode Control (SMC) method is a suitable solution in such circumtances. Since it is insensitive to parameter deviations and free from system dynamics. This study accomplishes a SMC to the modified SEPIC converter and also maintains the constant switching frequency. The complete DC-DC converter along with the control algorithm using Matlab/Simulink simulation software is demonstrated. Simulations show impressive results with the expectations for different perturbation scenarios and matches the developed theoretical background.
Spectral Unmixing With Perturbed Endmembers

We consider the problem of supervised spectral unmixing with a fully-perturbed linear mixture model where the given endmembers, as well as the observations of the spectral image, are subject to perturbation due to noise, error, or model mismatch. We calculate the Fisher information matrix and the Cramer–Rao lower bound associated with the estimation of the abundance matrix in the considered fully-perturbed linear spectral unmixing problem. We develop an algorithm for estimating the abundance matrix by minimizing a constrained and regularized maximum-log-likelihood objective function using the block coordinate-descend iterations and the alternating direction method of multipliers. We analyze the convergence of the proposed algorithm theoretically and perform simulations with real hyperspectral image data sets to evaluate its performance. The simulation results corroborate the efficacy of the proposed algorithm in mitigating the adverse effects of perturbation in the endmembers.
Coordinated Planning of Multi-Energy System with District Heating Network

This paper proposes a coordinated planning method for the multi-energy system, which includes multi combined heat and power systems and a district heating network connected with them. To improve the flexibility of the operating system, the district heating network is under quantity regulation. The model of district heating network includes operation constraint equations and temperature correction equations. Based on which, the optimal objective is to minimize the capital cost of the equipment as well as the annual operation cost of the multi-energy system. Moreover, the permeation of the renewable energy for the multi-energy system is discussed based on the three cases, i.e., (i) no renewable energy, (ii) a certain wind power, (iii) a certain wind power and an uncertain solar power. The results show that the coordinated planning of MES with multi heat sources can improve the flexibility of the operating system and the ability to absorb renewable energy.
A Review of the Prediction Method for Intelligent Transport System

The management for Intelligent Transport System (ITS) is an important control because it reduces traffic time, the safety of fuel energy, the accident decreased and so on. A key of ITS control is the transport system prediction method that helps to manage the travel time, traffic flow and traffic congestion. This paper proposes the review of prediction method that researchers evaluate the many algorithms for the experiment. The information and the method to predict are the key factors for achieving the goal. The traditional algorithm is a parametric method such as autoregressive moving average model (ARMA), autoregressive integrated moving average models (ARIMA) and vector auto regression (VAR). Non-parametric is a method that helps to support for process development of ITS such as neural network, machine learning and deep learning, etc. Both methods utilize the data but apply the different method to process.
Deep Reinforcement Learning in Robotics Logistic Task Coordination

Reinforcement Learning is a conventional ap-proach in the robotics field for solving control problems asan alternative to classic control algorithms. By learning tosolve logistics tasks through simulated experience (explorationand exploitation paradigm) and making use of artificial neu-ral networks, Deep RL algorithms showed the capability ofgeneralizing solutions even for situations in which it hadnever been trained. In this paper, we present our resultsof adopting DRL as a high-level control algorithm in twodifferent logistic tasks proposed by the Logistics League at 2017Latin America Robotics Competition. We also show the roboticand computational architectures built over Festo's RobotinoPlatform to accommodate our algorithm and the implicationsof training and fine-tuning it in a computational environmentbefore deploying it to the real robot.
Outliers Removal of Highly Dense and Unorganized Point Clouds Acquired by Laser Scanners in Urban Environments

Recently, there is a tremendous interest in the processing of unorganized point clouds, generated using a variety of 3D scanning technologies such as structured light and LIDAR systems. Without a doubt, the most compelling problem in this domain is the removal of outliers. To effectively address the aforementioned issue, we present a novel method, that detects accurately and efficiently the outliers by exploiting the spatial coherence in the object geometry and the sparsity of the outliers in the spatial domain. This is achieved by solving a convenient convex method called Robust PCA (RPCA). To demonstrate the effectiveness of the proposed technique, we evaluate it by using real scanned point clouds which are extremely dense consisting of millions of points.
Abnormal Bus Data Detection of Intelligent and Connected Vehicle Based on Neural Network

In the paper, our research of abnormal bus data analysis of intelligent and connected vehicle aims to detect the abnormal data rapidly and accurately generated by the hackers who send malicious commands to attack vehicles through three patterns, including remote non-contact, short-range non-contact and contact. The research routine is as follows: Take the bus data of 10 different brands of intelligent and connected vehicles through the real vehicle experiments as the research foundation, set up the optimized neural network, collect 1000 sets of the normal bus data of 15 kinds of driving scenarios and the other 300 groups covering the abnormal bus data generated by attacking the three systems which are most common in the intelligent and connected vehicles as the training set. In the end after repeated amendments, with 0.5 seconds per detection, the intrusion detection system has been attained in which for the controlling system the abnormal bus data is detected at the accuracy rate of 96% and the normal data is detected at the accuracy rate of 90%, for the body system the abnormal one is 87% and the normal one is 80%, for the entertainment system the abnormal one is 80% and the normal one is 65%.
Combine Trust and Interest Similarity for Enhanced-Quality Recommendations

Collaborative filtering based methods have a low performance in the context of social recommendation due to the data sparsity issue and not considering the social network information that can be exploited to improve the performance. Trust-based methods attempt to reduce the data sparsity by utilizing the social network information. In this paper, we propose a hybrid personal trust model which adaptively combines the rating-based trust model and explicit trust metric to resolve the drawback caused by insufficient past rating records. The proposed method takes user-item matrix and user interaction information as inputs, and calculates the degree of direct trust to produce an initiative trust matrix．Then，following the predefined trust propagation rules，the algorithm infers the degree of indirect trust between users and transforms the initiative trust matrix to a denser trust matrix．The denser trust matrix and rating matrix are collectively used to find the k nearest neighbors for the target user. We present our empirical experiments in a real sparse data set MovieLens, the experimental results shows our algorithm can increase the data density and achieve lower MAE, that is to say, the proposed approach can efficiently improve recommendation quality.
Automatic Extract Handwriting Marked Regions in Business Document Images

One of the difficulties in the automation of market trading, data crawling in commercial activities, such as banking operation is the identification of handwriting marked regions. This is the first and important step in mixed-document image understanding. In this paper, we propose a method to extract the handwriting marked regions in business forms. Our method is a combination of printed form analysis and colored handwritten symbols detection. This program is primarily designed to support commercial activities and assist users in document layout analysis. Evaluated on the real commercial dataset, the method achieved high performance.
Control of energy transfer between a storage battery and the electricity grid

This paper studies the transfer of energy between the low-voltage grid and a battery through an inverter. The inverter is used to control the transfer of energy from the battery to the grid. The concept of multilevel voltage source inverter is described. A Takagi Sugeno fuzzy controller is employed to control the voltage inverter to ensure a constant DC link voltage. The gains of the controller are calculated using linear matrix inequalities (LMI). The stability analysis of the system is performed using the Lyapunov and H-infinity theorem performance. The results of the simulation are made using MATLAB / SIMULINK to test the effectiveness of the proposed control algorithm.The validity of the model and its control strategies are greatly confirmed by the extensive simulation results.
Tapered Thin Core Fiber Based Multi-Mode Interferometer for Refractive Index Sensing

A thin core fiber (TCF) taper based hybrid modal interferometer was fabricated by arc discharge technique. Due to mode mismatch and the tapering of the thin core fiber, high order cladding modes are excited for enhanced refractive index sensing, obtaining a total sensitivity up to 442.59dB/RIU ranging from 1.333 to 1.38 by using the differential intensity demodulation method. With the advantages of high sensitivity, linear response, small size, ease of fabrication and low cost, the proposed sensor may have various applications in biochemical sensing field.
Machine Learning-Based Techniques for Fall Detection in Geriatric Healthcare Systems

Intelligent IoT-based ambient assisted living systems (AALS) have been a major research focus area in recent times. According to the studies conducted by National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%-10% requiring utmost care. Application of machine learning in areas of AALS such as fall detection, therefore, has the potential to have huge public impact. In this paper, we propose a fall detection system that takes into account not only various wearable sensor node parameter readings for a subject, but also his biological and physiological profile. The profile is used to determine a fall risk category for the subject. We performed machine learning experiments using public datasets for fall detection which included wearable sensor node readings. The algorithms were then retrained by feeding in the risk categorization of the subject, and results from this analyses are presented. The objective of the experiments was to find out the impact of a subject’s risk categorization on the accuracy of fall detection. The algorithms presented here form part of a comprehensive geriatric healthcare system under development, which comprises wearable sensor nodes, co-ordinator nodes, an indoor localization framework and cloud-hosted application servers. A brief overview of the system capabilities is also presented.
Evaluation of Smartphone-based Interfaces for Navigation Tasks in Unstructured Environments for Ground Robots

The development of robots that autonomously assist humans is still a challenge in unstructured domestic environments. A practical approach is through teleoperation. Conventional teleoperation uses specialized mechanical devices that provide user-friendly interfaces to operate robots, but these devices might be complex and expensive. In this paper, we compare the performance of three smartphone-based interfaces for robot navigation tasks. First, we show our system setup and our simulated domestic environments, then, we present our three smartphone-based interfaces that leverage the capabilities of ubiquitous smartphones to allow users to operate a domestic robot -PR2- in the simulated environments. Subsequently, we explain the procedure to complete our usability study. In this study, we establish completion time and the path smoothness as objective measurements, and we combine these measurements with a Nasa-TLX evaluation. Finally, we show our usability study results, and we present our conclusions.
DBkWik: A Consolidated Knowledge Graph from Thousands of Wikis

Popular knowledge graphs such as DBpedia and YAGO are built from Wikipedia, and therefore similar in coverage. In contrast, Wikifarms like Fandom contain Wikis for specific topics, which are often complementary to the information contained in Wikipedia, and thus DBpedia and YAGO. Extracting these Wikis with the DBpedia extraction framework is possible, but results in many isolated knowledge graphs. In this paper, we show how to create one consolidated knowledge graph, called DBkWik, from thousands of Wikis. We perform entity resolution and schema matching, and show that the resulting large-scale knowledge graph is complementary to DBpedia.
User-Side Updating of Third-Party Libraries for Android Applications

A Third-Party Library(TPL) is often used in developing Android applications, however older TPLs may have vulnerabilities. Hence developers need to keep them in their applications the latest version. Nevertheless, there is a lot of applications using older TPLs. In this paper, we propose a new method which users enable to update TPLs in Android applications. An Android application and TPLs can be converted to smali file which is more of an assembly based language. A smali file can be replaced with another smali file on the same class. Our method takes advantage of its properties and exchanges a vulnerable TPL for an security fixed one. Moreover, we apply it to real applications and evaluate feasibility of it.
Performance and Long-Term Reliability of Polarization-Maintaining Fiber Connectors for Fiber-Optic Current Sensors

The performance and long-term reliability of polarization-maintaining (PM) fiber-optic connectors is studied experimentally for use in interferometric fiber-optic current sensors. In particular, we investigate the effect of accelerated ageing and repeated open/close operations on the connectors' polarization extinction ratio (PER) and insertion loss between -40 and 80°C and determine the influence on sensor accuracy. The results show that PM connectors (FC/PC type with reduced mechanical tolerances for high PER) exhibit good repeatability and largely preserve their performance over extended periods of ageing. Nevertheless, the observed long-term changes in the temperature dependence of the PER can restrict the usability of these components in high-end sensor applications and call for alternative solutions.
Lyapunov Controlled Boost PFC Converter Using D-Q Coordinate Transformation

In this paper, a Lyapunov control scheme using D-Q coordinate transformation is proposed for the boost PFC converter. A virtual steady operating point is constructed for this AC system through single-phase D-Q coordinate transformation. On this basis, the Lyapunov control equation is derived. In the outer-voltage loop, the load current is fed forward to improve the load dynamic response. Finally, the validity and superiority of the proposed control scheme are verified by the experimental results.
The N∗Fisher-Snedecor F Cascaded Fading Model

The Fisher-Snedecor F distribution was recently proposed as an accurate and tractable composite fading model in the context of device-to-device communications. The present work derives the product of the Fisher-Snedecor ${\mathcal{F}}$ composite fading model, which is useful in characterizing fading effects in numerous realistic communication scenarios. To this end, novel analytic expressions are first derived for the probability density function, the cumulative distribution function and the moment of the product of N statistically independent, but not necessarily identically distributed, Fisher-Snedecor ${\mathcal{F}}$ random variables. Capitalizing on these expressions, we derive tractable closed-form expressions for channel quality estimation of the proposed model as well as the corresponding outage probability and average bit error probability for binary modulations. The offered results are corroborated by extensive Monte-Carlo simulation results, which verify the validity of the derived expressions. It is shown that the number of cascaded channels affects considerably the corresponding performance, as a variation of over an order of magnitude is observed across all signal-to-noise ratio regimes.
Meta-Analysis of Chromium and Pig Reproduction

Chromium is ubiquitous; it is found in humans, animals, and the environment as it circulates through crops, animal feed, animals, manure, and soil. Chromium has also been used in pig feed. Chromium is reported to improve pig reproduction, but studies are conflicting, so we conducted a meta-analysis of 312 records. We found that chromium did not affect pig litter size and piglet weight. Chromium is useful in animal husbandry, but excessive chromium can significantly inhibit plant growth. This meta-analysis suggests that pig farms should supplement chromium to pigs as needed based on feed chromium content.