Miracle-dz/UniFAR-FTUs huggingface dataset:
EEG Fingerprints: Phase Synchronization of EEG Signals as Biomarker for Subject Identification

The goal of biometrics is to recognize humans based on their physical and behavioral characteristics. Preliminary studies have demonstrated that the electroencephalogram(EEG) is potentially more secure and private than traditional biometric identifiers. At present, the EEG identification method targets specific tasks and cannot be generalized. In this study, a novel EEG-based biometric identification method that extracts the phase synchronization (PS) features for subject identification is proposed under a variety of tasks. We quantified the PS features by the phase locking value (PLV) in different frequency bands. Subsequently, we employed the principal component analysis (PCA) to reduce the dimension. Then, we used the linear discriminant analysis (LDA) to construct a projection space and projected the features onto the projection space. Finally, a feature vector was assigned to the class label. The experimental results of the proposed method used on 3 datasets with different cognitive tasks showed high classification accuracies and relatively good stabilities. From the results, we found that particularly in the beta and gamma bands, the average accuracies are more than 97% with the standard deviation equal to or less than the magnitude 10e-2 for both Dataset 1 and Dataset 2. For Dataset 3, the PS feature vectors in all off the bands have high classification accuracies, which are more than 97% with the standard deviation of the same magnitude. Our work demonstrated that the phase synchronization of EEG signals has task-free biometric properties, which can be used for subject identification.
Multiagent-Based Transactive Energy Management Systems for Residential Buildings With Distributed Energy Resources

Proper management of building loads and distributed energy resources (DER) can offer grid assistance services in transactive energy (TE) frameworks besides providing cost savings for the consumer. However, most TE models require building loads and DER units to be managed by external entities (e.g., aggregators), and in some cases, consumers need to provide critical information related to their electricity demand and usage, which hampers their privacy. This article introduces a transactive energy management framework for the buildings in a residential neighborhood to address grid overloading and cost optimization of the buildings. The decentralized coordination for the energy management system is realized by using a multiagent system architecture, which provides the consumers with full decision-making authority and preserves their privacy. A new event-triggered transactive market algorithm is developed, where the buildings trade energy to maximize profits, while the regional grid operator procures energy-supply flexibility of active consumers to prevent transformer overloading. A two-stage energy management system is developed for the residential buildings that schedules building loads and DER units in day-ahead stage to minimize cost and inconveniences for the consumer while participating in the real-time transactive market to maximize profits. An optimal bidding model is developed for the buildings that incorporates the degradation of residential storage devices for energy trading. Case studies and analyses with actual Australian building data and electricity tariff structures indicate the efficacy of the proposed methodology for effective mitigation of transformer overloading at a negligible cost compared to transformer replacement cost. Results also indicate that the proposed system can provide 15–20% cost savings for the consumers while minimizing their inconveniences and degradation of storage devices.
ATLaS: A Framework for Traceability Links Recovery Combining Information Retrieval and Semi-Supervised Techniques

Current Model-Based Systems Engineering (MBSE) practices to design and implement complex systems require modeling and analysis based on many representations: structure, dynamics, safety, security, etc. This induces a large volume of overlapping heterogeneous artefacts which are subject to frequent changes during the project life cycle. In order to verify and validate systems requirements and ensure that models meet user's needs, MBSE techniques shall rely on consistent traceability management. In this paper, we investigate the benefits of Information Retrieval (IR) techniques and the latest advances in Natural Language Processing (NLP) approaches to suggest stakeholders with candidate semantic links generated from the processing of structured and unstructured contents. We illustrate our approach called ATLaS (Aggregation Trace Links Support) through an application on the design and analysis of a mobility service gathering several industrial partners. We provide an empirical evaluation regarding its limitations as part of an industrial MBSE process. Most importantly, we highlight how our method drastically reduces the false positive links generated compared to current IR techniques. The results obtained suggest a good synergy between the presented approach and MBSE techniques.
Relational Recurrent Neural Networks For Vehicle Trajectory Prediction

Scene understanding and future motion prediction of surrounding vehicles are crucial to achieve safe and reliable decision-making and motion planning for autonomous driving in a highway environment. This is a challenging task considering the correlation between the drivers behaviors. Knowing the performance of Long Short Term Memories (LSTMs) in sequence modeling and the power of attention mechanism to capture long range dependencies, we bring relational recurrent neural networks (RRNNs) to tackle the vehicle motion prediction problem. We propose an RRNNs based encoder-decoder architecture where the encoder analyzes the patterns underlying in the past trajectories and the decoder generates the future trajectory sequence. The originality of this network is that it combines the advantages of the LSTM blocks in representing the temporal evolution of trajectories and the attention mechanism to model the relative interactions between vehicles. This paper compares the proposed approach with the LSTM encoder decoder using the new large scaled naturalistic driving highD dataset. The proposed method outperforms LSTM encoder decoder in terms of RMSE values of the predicted trajectories. It outputs an estimate of future trajectories over 5s time horizon for longitudinal and lateral prediction RMSE of about 3.34m and 0.48m, respectively.
Classifying Red and Healthy Eyes using Deep Learning

Eye is one of the most vital organs of human body. Despite being small in size, humans cannot see the life around them without it. Human eye is protected by a thin covering termed as conjunctiva which protects the eye from dust particles. It plays the role of lubricant in the eye which prevents any sort of friction in opening and closing of eye. Broadly there are two kinds of conjunctiva: bulbar and palpebral. The membrane covering the inner portion of eyelids is termed as palpebral conjunctiva and the one covering the outside portion of the eye is called as bulbar conjunctiva (white portion of eye). Due to the dilation of blood vessels the white portion of the eye also termed as sclera becomes red in color. This condition is also termed as hyperemia. The study of this development is vital in diagnosis of various pathologies. It could be result of some trauma, injury or other eye related diseases which needs to be identified for timely treatment. Enormous amount of studies have been done to study the structure and functionality of human eye. This paper highlights the work done so far for measuring the level of redness in the eye using various methodologies ranging from statistical ways to machine learning techniques and proposes a methodology using Matlab and Convolutional neural network to automate this evaluation process.
Mitigation of Leakage-Ground Currents in Transformerless Grid-Tied Inverters via Virtual-Ground Connection

This article studies the benefits of the virtual-ground (VG) connection for single-phase transformerless grid-tied inverters, which is used to mitigate the leakage-ground currents (LGCs) issue. This article also proposes common-mode and differential-mode models of single-phase inverters connected to the grid through an $LCL$ output filter, which are used as a theoretical support. It is shown that LGCs are simply common-mode currents (CMCs) circulating through the ground path, which represents a contribution in the development of an approach to find solutions in general scenarios. In most of the cases, these currents are produced by the existence of common-mode voltages (CMVs), which find a way through the ground path established after the stray capacitances in the terminals of, for instance, photovoltaic (PV) panels. To prove the advantages of the proposed interconnection and for analysis, the single-phase full-bridge version of a two-level conventional inverter, as well as a neutral-point-clamped (NPC) inverter, is considered for model derivation and experimental validation.
Perbandingan Efektifitas Proses Pembelajaran Menggunakan Metode E-Learning dan Konvensional

This study focuses on the comparison between e-learning methods and conventional method (face-to-face) to find out the weaknesses and strengths of e-learning applied at Muhammadiyah University in Sidoarjo. E-Learning used in this study is Edmodo. The research method in this study used Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The sampling technique used is cluster random sampling which is taken proportionally for each cluster. Based on the results of the study it was found that the conventional method (face to face) is still considered better by students than e-learning because it is easier to understand the material and easier to interact with the teacher. However, e-learning itself also has advantages compared to conventional, namely in terms of flexibility in lecture time and ease of collecting assignments
The Respective Roles of Intellectual Creativity and Automation in Representing Diversity: Human and Machine Generated Bias

The paper traces the development of the discussion around ethical issues in artificial intelligence, and considers the way in which humans have affected the knowledge bases used in machine learning. The phenomenon of bias or discrimination in machine ethics is seen as inherited from humans, either through the use of biased data or through the semantics inherent in intellectually-built tools sourced by intelligent agents. The kind of biases observed in AI are compared with those identified in the field of knowledge organization, using religious adherents as an example of a community potentially marginalized by bias. A practical demonstration is given of apparent religious prejudice inherited from source material in a large database deployed widely in computational linguistics and automatic indexing. Methods to address the problem of bias are discussed, including the modelling of the moral process on neuroscientific understanding of brain function. The question is posed whether it is possible to model religious belief in a similar way, so that robots of the future may have both an ethical and a religious sense and themselves address the problem of prejudice.
3D reconstruction from coded plenoptic sampling

The plenoptic function describes a scene in terms of light rays, it is a 7-dimensional function with spectral, directional, spatial, and temporal variation. Traditional plenoptic sampling is acquired either by employing a standard plenoptic camera or a camera array, and the spatial-angular sampling can be potentially used to model 3D surface. In this dissertation, I present three coded plenoptic sampling schemes, i.e., the rotational cross-slit (R-XSlit) plenoptic sampling, the wavelength coded plenoptic sampling, and the polarimetric plenoptic sampling. The additional coded sampling information, such as non-centric sampling, spectral sampling, and polarization sampling, are conducive to 3D reconstruction. Therefore, I also develop the corresponding 3D reconstruction framework for each of them. First, I introduce the R-XSlit plenoptic sampling scheme by exploiting a special noncentric camera called the crossed-slit or XSlit camera. An XSlit camera acquires rays that simultaneously pass through two oblique slits. I show that instead of translating the camera as in the pinhole case, we can effectively sample the 4D plenoptic sampling by rotating individual or both slits while keeping the camera fixed, which makes the plenoptic sampling coded in the spatial-angular domain. The theoretical analysis shows that it provides denser spatial-angular sampling, which is beneficial for scene reconstruction and rendering. I develop a volumetric reconstruction scheme for scene reconstruction. Second, I present two wavelength coded plenoptic sampling schemes in the visible and infrared spectrum respectively. I firstly design a compact system with lights and cameras arranged on concentric circles to acquire a concentric wavelength coded plenoptic sampling in the visible spectrum, the cameras on each ring capture images in a unique spectrum. I employ the Phong dichromatic model onto its plenoptic function for 3D reconstruction and spectral reflectance map estimation. Experiments show that our technique can achieve high
Model Inference and Automatic Testing of Mobile Applications

We consider, in this paper, the problem of automatically testing Mobile applications while inferring formal models expressing their functional behaviours. We propose a framework called MCrawlT, which performs automatic testing through application interfaces and collects interface changes to incrementally infer models expressing the navigational paths and states of the applications under test. These models could be later used for comprehension aid or to carry out some tasks automatically, e.g., the test case generation. The main contributions of this paper can be summarised as follows: we introduce a flexible Mobile application model that allows the definition of state abstraction with regard to the application content. This definition also helps define state equivalence classes that segment the state space domain. Our approach supports different exploration strategies by applying the Ant Colony Optimisation technique. This feature offers the advantage to change the exploration strategy by another one as desired. The performances of MCrawlT in terms of code coverage, execution time, and bug detection are evaluated on 30 Android applications and compared to other tools found in the literature. The results show that MCrawlT achieves significantly better code coverage in a given time budget.
An Efficient Grid-Connected Three-Phase Single-Stage Boost Current Source Inverter

The proposed three-phase boost Current Source Inverter (CSI) is equipped with Reverse-Blocking IGBTs (RB-IGBT) and the Phasor Pulse Width Modulation (PPWM) switching pattern to provide system efficiency greater than 92% and high boost ratios <inline-formula> <tex-math notation="LaTeX">$(V_{LL}/V_{dc})$ </tex-math></inline-formula> up to 3.5 in a single stage. The boost CSI results in elimination of dc-dc boost converter or a step-up transformer needed in dc-ac converters operating with a dc input voltage lower than the output line-to-line voltage. In this paper, the relationship between the fundamental component of the inverter output current and the PPWM modulation index is derived and then confirmed by simulation and experimentally obtained data in both stand-alone and grid-tied modes of operation. In this work, a <inline-formula> <tex-math notation="LaTeX">$2\,\,kW, 208\,\,V_{LLrms}, 60-120\,\,V_{dc}$ </tex-math></inline-formula> RB-IGBT-based boost CSI is prototyped to explore capabilities of the grid-tied boost CSI in terms of efficiency and THD for various input dc voltage and output power levels.
Optimal sensor placement based on multiattribute decision-making considering the common cause failure

A new optimal sensor placement is developed to improve the efficiency of fault diagnosis based on multiattribute decision-making considering the common cause failure. The optimal placement scheme is selected based on the reliability of the top event on condition that the number of sensors is preset. Specifically, a β-factor model is introduced to deal with the common cause failure, and dynamic fault tree is used to describe the dynamic failure behaviors. Besides, a dynamic fault tree is converted into a dynamic Bayesian network to calculate the reliability parameters, which construct the decision matrix. Furthermore, an efficient TOPSIS algorithm is adopted to determine the potential locations of sensors. In addition, a diagnostic sensor model is developed to take into account the failure sequence between a sensor and a component using a priority AND gate, and the failure probability of the top event for all sensor placement scenarios is calculated to determine the optimal sensor placement. Finally, a case is provided to prove that the common cause failure has made a considerable impact on the sensor placement.
LOGIC THRESHOLD FOR MICRORING RESONATOR-BASED BDD CIRCUITS: PHYSICAL AND OPERATIONAL ANALYSES

Moore’s Law has been the fuel of expansive innovation in computing. The chip industry kept the Moore’s law extant for almost four decades. However, the halt of the rapid progress of the silicon technology is incipient by reason of the physical limitations. Emerging computing proposals suggest several alternatives to current computing paradigms and technology-bases. The photonic circuitry is one of the most promising candidates with its high operation speed, energy efficient passive components, low crosstalk and appropriateness for parallel computation. Among various approaches to photonic logic, microring resonator-based Binary-Decision Diagram (BDD) architectures have a special place due to their small circuit footprint. In this paper, we study the physical structure and operational details of a microring resonator-based Half-Adder (HA) circuit and outline the conditions under which the performance and accuracy of information processing is compromised due to its physical limitations. Our analyses significantly contribute to determining key physical features and operations, which informs the future design and operational optimization of the microring resonator-based BDD logic circuits.
Relationship Prior and Adaptive Knowledge Mimic Based Compressed Deep Network for Aerial Scene Classification

The aerial scene classification is one of the major tasks in the remote sensing community that automatically labels the corresponding semantic categories of aerial images. Recently, a lot of methods based on deep neural networks have been proposed, in which hierarchical internal feature are extracted for representations. However, these presented methods often have complex structures and require large volume of memory, and a large number of labeled aerial scene images are difficult to obtain, hindering their implementation in practical applications. In this paper, we present the between-class similarity priori and adaptive knowledge mimic (BPKM) method for aerial scene classification. First, the method extracts the efficient prior relationship information of the scene images from large-scale network. Then, a compressed network is generated through learning the output and the intermediate representations of the large-scale network, and the compressed network achieves better feature description ability; in addition, an improved cross-entropy method with an adaptive threshold is applied to reduce the training time consumption. A large-scale data set (AID) and UC-Merced data set are considered for performance evaluation, and the experimental results indicate that the proposed method is about $24\times $ parameters saving compared to popular networks, e.g., AlexNet, and has outstanding classification performance in classes with similar features.
Calibration and Pre-Processing Techniques for a Smartphone-Based Driving Events Identification and Classification System

In recent years there has been a growing interest in monitoring and analyzing driving behaviors. This intense enthusiasm is due to the urgent needs for understanding and analyzing the relation of driving behavior with road safety and with the design of Advanced Driver Assistance Systems, Intelligent Transportation Systems and Autonomous Vehicles. It is well known that the backbone of any driving monitoring system is the ability to recognize driving actions or events and maneuvers. Recently, the prevalence of smartphones that have excellent computational power and equipped with a set of advanced sensors, have encouraged the development of enormous number of vehicular applications. Nevertheless, the accuracy of these smartphones-based applications critically depends on the calibration and pre-processing techniques that intended to mitigate problems associated smartphone's sensors. This study proposes and investigates a set of simple calibration and pre-processing techniques used to enhance the accuracy of a smartphone-based driving event detection and classification system. A number of experiments carried out to check the validity of the proposed techniques, and results obtained show that the accuracy of the system is of a high rate.
Western European Populations of the Ichneumonid Wasp Hyposoter didymator Belong to a Single Taxon

Hyposoter didymator (Hymenoptera, Ichneumonidae) is a generalist solitary endoparasitoid of noctuid larvae. In the present work, we tested whether populations of H. didymator were divided in several genetically distinct taxa as described for many other generalist parasitoid species, and whether differences in H. didymator parasitism rates were explained by the insect host species and/or by the plant on which these hosts were feeding on. The genetic analysis of natural populations collected in different regions in France and Spain on seven different insect hosts and seven different host plants (775 individuals) showed that H. didymator populations belong to a unique single taxon. However, H. didymator seems to be somewhat specialized. Indeed, in the fields it more often parasitized Helicoverpa armigera compared to the other host species collected in the present work. Also, H. didymator parasitism rates in field conditions and semi-field experimental studies were dependent on the host plants on which H. armigera larvae are feeding. Still, H. didymator can occur occasionally on non-preferred noctuid species. One hypothesis explaining the ability of H. didymator to switch hosts in natura could be related to fluctuating densities of the preferred host over the year; this strategy would allow the parasitoid to avoid seasonal population collapses.
The Effectiveness of the Automatic System of Fuzzy Logic-Based Technical Patterns Recognition: Evidence from Tehran Stock Exchange

The present research proposes an automatic system based on moving average (MA) and fuzzy logic to recognize technical analysis patterns including head and shoulder patterns, triangle patterns and broadening patterns in the Tehran Stock Exchange. The automatic system was used on 38 indicators of Tehran Stock Exchange within the period 2014-2017 in order to evaluate the effectiveness of technical patterns. Having compared the conditional distribution of daily returns under the condition of the discovered patterns and the unconditional distribution of returns at various levels of confidence driven from fuzzy logic with the mean returns of all normalized market indicators, we observed that in the desired period, after recognizing the pattern, all patterns investigated at the confidence level 0.95 with a fuzzy point 0.5 contained useful information, practically leading to abnormal returns.