Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of LDPC Codes Based on Deep Reinforcement Learning

This paper proposes a novel deep reinforcement learning method for constructing low-density parity-check (LDPC) codes. In this method, both deep neural network trained by reinforcement learning and Monte Carlo tree search (MCTS) are combined to discover edge growth routes without any human-defined knowledge. Due to the great ability of prediction shown by deep reinforcement learning, the proposed method has a long-term vision for constructing codes with a potential of better performance compared with the traditional computer-search-based methods. In addition, the proposed method is flexible for constructing codes with different parameters.
Optimizing Online Control of Constrained Systems with Switched Dynamics

This paper studies the online control of switched systems over a finite time horizon subject to time-varying constraints on the continuous states and bounded input set. The formulation leads to optimization problems of the class mixedinteger nonlinear programming (MINLP), which is known to be computationally hard. This paper proposes a method to approximate the optimal solution while keeping the computational effort low enough, to enable real-time applicability for many systems. The main idea is to use heuristics based on the value function for relaxed sub-problems to prune the tree encoding the possible sequences of discrete choices (i.e. the selected continuous dynamics) over a prediction horizon. Numeric tests show that the times for computation are drastically below those for standard MINLP solution in the vast majority of cases, while good approximations of the optimal solutions are obtained.
Using Raspberry Pi to Create a Solution for Accessing Educative Questionnaires From Mobile Devices

Raspberry Pi has the capability of being configured as an access point and as a web server, so it can be used for creating applications reachable from mobile devices without needing an Internet connection. The proposal is based on the possibility for students to answer questionnaires from their mobile devices. The teacher can observe answers in real time. This solution adds dynamism integrating the existing mobile devices in the classroom. This paper explains the proposal features in terms of used hardware and software, as well as the functional tests performed and their implementation allowing replication.
Link Delay Estimation Using Sparse Recovery for Dynamic Network Tomography

When the scale of communication networks has been growing rapidly in the past decades, it becomes a critical challenge to extract fast and accurate estimation of key state parameters of network links, e.g., transmission delays and dropped packet rates, because such monitoring operations are usually time-consuming. Based on the sparse recovery technique reported in [Wang et al. (2015) IEEE Trans. Information Theory, 61(2):1028--1044], which can infer link delays from a limited number of measurements using compressed sensing, we particularly extend to networks with dynamic changes including link insertion and deletion. Moreover, we propose a more efficient algorithm with a better theoretical upper bound. The experimental result also demonstrates that our algorithm outperforms the previous work in running time while maintaining a similar recovery performance, which shows its capability to cope with large-scale networks.
Optimal Sizing of Distributed Generations in DC Microgrids with Lifespan Estimated Model of Batteries

This paper studies optimal sizing method of distributed generations in DC microgrids (MGs). According to the models of photovoltaic (PV) units, wind turbine (WT) units and grid-connected converter (GCC) integrated in DC MGs, the economic objective function can be established with essential system constraints. By employing particle swarm optimization (PSO) algorithm, optimal sizing problem can be solved as a nonlinear optimization process targeting at the lowest annualized capital cost. Moreover, in order to make the optimal sizing results more accurate, lifespan estimated model of batteries is considered in this paper to evaluate the replace period and cost of the batteries in DC MGs. Corresponding simulation with different state of charge (SoC) of batteries and different operation modes of DC MGs are conducted and analyzed.
Data Augmentation with Improved Generative Adversarial Networks

Data augmentation is always a routinely trick in neural network training to improve generalization of a model. However, traditional transformation based methods are domain-specific, the transformations are required to be carefully designed. Recently, Generative Adversarial Networks (GAN) has been proposed to generate new samples which match the real data distribution. But directly using GAN generated samples in data augmentation faces the problems of label absence and uncertain data quality. In this paper, we propose an efficient and robust data augmentation method using GAN generated samples. This method proposes a modified GAN to generate more diverse samples and label them with a soft distribution labeling method. With an improved stochastic gradient descent, all the data are used to train the final classifier. The experiments are conducted on the widely used datasets: MNIST, SVHN and CIFAR-10. Our method empirically obtains promising results, even with few original data.
Chrominance based Splicing Image Forgery Localization with Relevance Vector Machine

Due to the modern technology, image forgery is very much prevalent nowadays. Image forgery may result to misleading facts if used without proper verification. Thus, image forgery detection plays a very vital role. The image forgery detection techniques confirm the credibility of the digital images with no prior information about the original image. Since, the luminance component of the image is perceived by humans, tampering may result in some unnatural clues in the chrominance component. In this paper, the goal is to detect and localize the digital images tampered using image splicing techniques by analyzing the chrominance component. The inconsistencies in the noise level in a image due to tampering is evaluated and used as the feature to classify using Relevance Vector Machine (RVM). The proposed method obtains an accuracy of 98.75% for Cb channel while 99.02% accuracy is obtained for Cr channel which is the highest accuracy among all existing methods.
Quick Best Action Identification in Linear Bandit Problems

In this paper, we consider a best action identification problem in the stochastic linear bandit setup with a fixed confident constraint. In the considered best action identification problem, instead of minimizing the accumulative regret as done in existing works, the learner aims to obtain an accurate estimate of the underlying parameter based on his action and reward sequences. To improve the estimation efficiency, the learner is allowed to select his action based his historical information; hence the whole procedure is designed in a sequential adaptive manner. We first show that the existing algorithms designed to minimize the accumulative regret is not a consistent estimator and hence is not a good policy for our problem. We then characterize a lower bound on the estimation error for any policy. We further design a simple policy and show that the estimation error of the designed policy achieves the same scaling order as that of the derived lower bound.
Multi-stereo Matching for Light Field Camera Arrays

Light field cameras capture information about the incoming light from multiple directions, going beyond classical capturing of light intensity performed by regular RGB cameras. This enables the computation of more accurate depth maps compared to stereo methods based on conventional cameras. However, the very small angular resolution of light field cameras limits their practical use in 3D applications. In this paper, we introduce for the first time in the literature the use of light field camera arrays, with the aim of improving the depth maps while providing a wide field of view. In this context, a novel algorithm for multi-stereo matching based on light field camera arrays is proposed. The disparity maps for the sub-aperture images are computed based on light field camera pairs using a novel multi-scale and multi-window stereo-matching algorithm. A global energy minimization based on belief propagation is proposed to regularize the results. The resulting depth maps are efficiently fused by means of k-means clustering. The proposed approach demonstrates very promising results for accurate 3D scene reconstruction and free navigation applications.
Control of Novel PV Inverter with ANFIS in Grid Tied PV System Under Unbalanced Grid Condition

This paper discusses the comparison of fuzzy and adaptive neuro-fuzzy controllers to control PV inverter interfacing with grid. The PV interfacing with a novel seven level converter is operating under grid unbala nce. The proposed converter has the capability to reduce number of power electronic devices and power consumption. The time delays take place with filters can be easily extracted by using both conventional and proposed controllers. The main problem with fuzzy controller is that rules are not consistent; there is a possibility of mismatch between the rules. In order to ext- ract these problems the methods used in neural networks are applied to learn the rules in fuzzy, which results an adaptive control techni- que and is named as adaptive neuro-Juzzy controller (ANFIS). The multilevel inverter is actively controlled by ANFIS to compensate time delays, harmonics. The proposed system is developed and simu- lated to evaluate the performance under different operating conditions.
Topic based Summarization of Multiple Documents using Semantic Analysis and Clustering

Document summarization addresses the problem of presenting the information in a compact form to the readers. Different approaches to summarize documents have been proposed and evaluated in literature. Common research problems in multi-document summarization are Redundancy and Extraction of sentences; that are important and semantically linked with other sentences. With the combination of agglomerative hierarchical clustering and Latent Semantic Analysis (LSA); which measures semantic similarity between sentences and reduces dimensions by preserving only highly weighted vectors, we propose a novel multi document summarization approach. Latent Dirichlet Allocation Model is used to identify important topic terms in the resultant summary. We have used Recall Oriented Understudy for Gisting Evaluation (ROUGE) metric to evaluate our system against other state-of-the art techniques using Document Understanding Conference (DUC) dataset 2004. Experimental results show that there is substantial performance improvement using our system and it makes better summary as compared to the other state-of-art techniques.
Compressed Sensing for Wideband HF Channel Estimation

Compressive sensing theory is suitable for sparse channel estimation, since the acquired measurement can be reduced in comparison with linear estimation methods. In this paper, we analyze the wideband HF channel estimation. Experimental results demonstrate that this channel is sparse in the delay spread domain. Moreover, the use of sparse recovery algorithms achieves better results in terms of Mean-Square Deviation than the Least Square algorithm.
High Efficient Ultrathin GaAs Optical Absorption Devices with Ring-shaped Nanohole Arrays

We have proposed ultrathin GaAs film with ring-shaped nanohole arrays (RNHs). By tailoring the inner and outer radii of RNHs properly and combining antireflection coating (ITO) and back-reflector (Ag), the photocurrent $(26.40 mA/c{m}^{2})$ generated in the GaAs based dielectric-semiconductor-metal (DSM) configuration can be increased dramatically by 133.2% with respect to the planar counterpart of equivalent thickness (46 nm). This work provides a promising way to harvest broadband/wide-angle incident light toward high efficient ultrathin GaAs photovoltaic and other optoelectronic devices.
Vehicle Dynamics and Road Slope Estimation based on Cascade Extended Kalman Filter

Vehicle dynamic states used in various advanced driving safety system are influenced by road geometry. Among the road geometry information, the vehicle pitch angle affected by longitudinal road slope and acceleration-deceleration is essential parameter used in pose estimation with navigation system, advanced adaptive cruise control and others on sag road. Although the road slope data is essential parameter, the sensor measuring the road slope is not wide used. The digital map including the road geometry data and high-precise positioning system such as DGPS-RTK(Differential Global Positioning System – Real Time Kinematics) are used unusually. In this paper, low cost cascade extended Kalman filter(CEKF) based road slope estimation method is proposed. Two sequential EKFs measure several vehicle states such as yaw rate, steering angle and velocity, and use nonlinear vehicle dynamics model. The performance of proposed estimation algorithm is evaluated by simulation and T-car experiment
Non-line of Sight Error Mitigation in Bayesian Differential Localization Systems

This paper evaluates and compares the accuracies of the three commonly used Bayesian filters namely extended Kalman Filter (EKF), H-infinity filter and unscented Kalman filter (UKF) in differential time of arrival (TDoA) localization topology in ultra-wide band (UWB) systems. Furthermore, two non-line of sight (NLOS) error suppression mechanisms are introduced and the accuracies of these methods when applied on the mentioned filters are evaluated. The results are evaluated in several trials both in LOS and NLOS conditions using a mobile node carried by a robot. In practical trials, it was witnessed that the H-infinity filter is more robust against non-ideal conditions with excessive noise or inaccurate model of the system whereas the UKF filter has the highest localization accuracy in nearly ideal cases such as LOS. In NLOS conditions, the proposed methods are able to suppress the error up to 50% with the prerequisite that the NLOS condition is correctly determined.
HUMBI 1.0: HUman Multiview Behavioral Imaging Dataset

This paper presents a new dataset called HUMBI - a large corpus of high fidelity models of behavioral signals in 3D from a diverse population measured by a massive multi-camera system. With our novel design of a portable imaging system (consists of 107 HD cameras), we collect human behaviors from 164 subjects across gender, ethnicity, age, and physical condition at a public venue. Using the multiview image streams, we reconstruct high fidelity models of five elementary parts: gaze, face, hands, body, and cloth. As a byproduct, the 3D model provides geometrically consistent image annotation via 2D projection, e.g., body part segmentation. This dataset is a significant departure from the existing human datasets that suffers from subject diversity. We hope the HUMBI opens up a new opportunity for the development for behavioral imaging.
Wideband Radiation From an Offset-Fed Split Ring Resonator With Multi-Order Resonances

In this letter, we exhibit the design of an ultrawideband antenna, covering WLAN, WIMAX, and the forthcoming 5G communications band, i.e., from 2.3 to 6.1 GHz, by using the natural oscillating modes of a split ring resonator (SRR). The antenna is composed of a radiating SRR and a coplanar waveguide feeding structure. The excitation of multiple resonances on a single SRR is achieved through an offset feeding technique. In order to improve the impedance bandwidth of the proposed antenna, the width of the SRR's branch is tuned, and several slits are carefully designed on the ground region. Detailed analysis reveals that the proposed antenna can not only radiate in an ultrawideband but also resonate at its lowest mode of a high quality. This peculiar property indicates that the antenna would be a good candidate for incorporating sensing and communication applications.
Secure Probabilistic Caching for Stochastic Multi-User Multi-Relay Networks

In this paper, we study a stochastic multi-user multi-relay network, where the data transmission can be overheard by multiple eavesdroppers. The users, relays and eavesdroppers are assumed to arrive at the network subject to homogeneous poisson point process (PPP). Cache equipped at the relays and users can pre-store part of file contents, which can help enhance the transmission security. Hence, it is of vital importance to design an effective secure probabilistic caching strategy to ensure the security of content transmission. To this end, we first consider several cache-aided transmission modes of self-fetch, D2D-transmission, relay-transmission, and source-transmission. We then study the network secrecy performance by analyzing the expression of secure cache throughput and an analytical lower bound. In order to maximize the secure cache throughput, we further optimize the probabilistic caching strategy by using a heuristic algorithm. Numerical results are provided to demonstrate that the proposed strategy outperforms the conventional caching strategies.
A Data Driven Empirical Iterative Algorithm for GSR Signal Pre-Processing

In this paper, we introduce a data driven iterative low pass filtering technique, the Empirical Iterative Algorithm (EIA) for Galvanic Skin Response (GSR) signal preprocessing. This algorithm is inspired on Empirical Mode Decomposition (EMD), with performance enhancements provided by applying Midpoint-based Empirical Decomposition (MED), and removing the sifting process in order to make it computational inexpensive while maintaining effectiveness towards removal of high frequency artefacts. Based on GSR signals recorded at the wrist we present an algorithm benchmark, with results from EIA being compared with a smoothing technique based on moving average filter - commonly used to pre-process GSR signals. The comparison is established on data from 20 subjects, collected while performing 33 different randomized activities with right hand, left hand and both hands, respectively. In average, the proposed algorithm enhances the signal quality by 51%, while the traditional moving average filter reaches 16% enhancement. Also, it performs 136 times faster than the EMD in terms of average computational time. As a show case, using the GSR signal from one subject, we inspect the impact of applying our algorithm on GSR features with psychophysiological relevance. Comparison with no preprocessing and moving average filtering shows the ability of our algorithm to retain relevant low frequency information.
Updated Meta-analysis on the Closure of Patent Foramen Ovale in Reduction of Stroke Rates: the DEFENSE-PFO Trial Does not Change the Scenario

Objective We aimed to analyze whether patent foramen ovale (PFO) closure reduces the risk of stroke, assessing also some safety outcomes after the publication of a new trial. Introduction The clinical benefit of closing a PFO has been an open question, so it is necessary to review the current state of published medical data in regards to this subject. Methods MEDLINE, EMBASE, CENTRAL/CCTR, SciELO, LILACS, Google Scholar and reference lists of relevant articles were used to search for randomized controlled trials (RCTs) that reported any of the following outcomes: stroke, death, major bleeding or atrial fibrillation. Six studies fulfilled our eligibility criteria and included 3560 patients (1889 for PFO closure and 1671 for medical therapy. Results The risk ration (RR) for stroke in the “closure” group compared with the “medical therapy” showed a statistically significant difference between the groups, favouring the “closure” group (RR 0.366; 95%CI 0.171–0.782, P=0.010). There was no statistically significant difference between the groups regarding the safety outcomes, death and major bleeding, but we observed an increase in the risk of atrial fibrillation in the “closure” group (RR 4.131; 95%CI 2.293–7.443, P<0.001). We also observed that the larger the proportion of effective closure, the lower the risk of stroke. Conclusion This meta-analysis found that stroke rates are lower with percutaneously implanted device closure than with medical therapy alone, being these rates modulated by the rates of hypertension, atrial septal aneurysm and effective closure. The publication of a new trial did not change the scenario in the medical literature.
An Ensemble Clusterer of Multiple Fuzzy $k$ -Means Clusterings to Recognize Arbitrarily Shaped Clusters

Fuzzy cluster ensemble is an important research component of ensemble learning, which is used to aggregate several fuzzy base clusterings to generate a single output clustering with improved robustness and quality. However, since clustering is unsupervised, where “accuracy” does not have a clear meaning, it is difficult for existing ensemble methods to integrate multiple fuzzy <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means clusterings to find arbitrarily shaped clusters. To overcome the deficiency, we propose a new ensemble clusterer (algorithm) of multiple fuzzy <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-means clusterings based on a local hypothesis. In the new algorithm, we study the extraction of local-credible memberships from a base clustering, the production of multiple base clusterings with different local-credible spaces, and the construction of cluster relation based on indirect overlap of local-credible spaces. The proposed ensemble clusterer not only inherits the scalability of fuzzy <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means but also overcomes the inability to find arbitrarily shaped clusters. We compare the proposed algorithm with other cluster ensemble algorithms on several synthetical and real datasets. The experimental results illustrate the effectiveness and efficiency of the proposed algorithm.
Time as a Service: White Rabbit at CERN

While pushing forward the boundaries of human understanding of the fundamental structure of the Universe, scientists and engineers at CERN also contributed to innovation above and beyond their immediate scientific mission by developing White Rabbit (WR): an open scientific hardware that mitigates the problem of time delays in CERN’s geographically distributed network. In a context of basic research and a deliberate decision to openly share the R&D process with no IP restrictions, WR is now commercialized in a diverse range of sectors beyond the scientific industry. We follow the emergence, development, and exploitation of WR to a) identify the antecedents in which capital-intensive open source hardware differ from open source software, b) to capture the mechanisms employed by CERN to stimulate firms’ R&D revelation and WR collaborative development, and c) to investigate the business model configurations that companies adopted in the commercial evolution of WR.
Investigating Users' IT Adaptation Behaviors: A Case of a Computerized Work System

IS research has shown that successful IT implementation projects rely on the extent to which IT users respond and adapt to the new technology and the changes the new technology comes with. Although the phenomenon of user IT adaptation has been studied previously in IS literature our understating of user IT adaptation is still limited. Drawing on the Coping Model of User Adaptation (CMUA) and the Approach vs. Avoidance model of coping, this study seeks to investigate how different user adaptation efforts evolve over time and subsequently influence IT use outcomes. Using a qualitative case study approach, 68 interviews were conducted at a medical center in Iran after the implementation of a Work System Computerization project. Our results indicate that emotions have a great influence on IT users’ adaptation efforts. Also, users’ IT appraisal might not necessarily happen in sequence as suggested by existing literature. Finally, our findings highlight the theoretical importance of preserving the distinction between approachand avoidance-oriented emotion-focused adaptation efforts in exploring how emotion-focused efforts may influence system usage.