Miracle-dz/UniFAR-FTUs huggingface dataset:
Interest as a Proxy of Engagement in News Reading: Spectral and Entropy Analyses of EEG Activity Patterns

Objective measurements of engagement are increasingly sought after by both the media industry and scholar communities to explain what drives people to consume audiovisual contents. However, engagement is a complex construct that, at the psychological level, has been mainly operationalised through indicators of attentional and emotional processes, often overlooking motivational factors. We claim that in the context of news consumption, motivation, operationalised as intrinsic interest for consuming a given content, needs to be factored in together with attentional and emotional processes. The present work provides an objective metric for motivation based on electroencephalographic (EEG) registration of users’ neural activity, while they read sets of news pre-classified in terms of their potential interest. We focus on a metric that has been used as an indicator of the degree to which an item or event induces the motivation to approach or escape, the so called frontal alpha asymmetry (FAA). Moreover, in addition to the traditional approach to the analysis of EEG signals, we also introduce a more novel technique based on estimating the entropy of the signals. Results confirm that FAA is indeed a good proxy for objective monitoring of interest in media contents and that entropy analysis, although its interpretation in terms of information processing warrants further investigation, is also sensitive to the manipulation of interest, providing results that complement traditional power spectrum analysis.
Interleaved-Input Series-Output Ultra-High Voltage Gain DC–DC Converter

The ultra-high voltage gain dc–dc converters are extremely required because of the low voltage level of many nonpolluting power resources. This paper proposes an ultra-high voltage gain converter based on an interleave technique and using three-winding coupled inductor and voltage multiplier cell. The interleave technique is useful for reducing the size of the filter components. In the proposed converter, high voltage gain is achieved without extreme duty cycle of solid-state switches. The voltage stress across the semiconductor devices can be reduced by increasing the turn ratio of the secondary of the coupled inductors. In addition, because of the implementation of the interleave structure, peak-to-peak variation of the main input current and switching losses are substantially reduced. The steady-state principles of the proposed converter in continuous conduction mode operation are given. Also, design equations are presented. Validity and performance of the proposed converter are surveyed by simulation and experimental results of a 120 W prototype.
Learn, Assign, and Search: Real-Time Estimation of Dynamic Origin-Destination Flows Using Machine Learning Algorithms

A common way to estimate dynamic origin–destination (O-D) flows is to establish and solve a bilevel optimization model. Though numerous efforts have been devoted to effectively and efficiently solving the model, challenges still exist because of the interdependence of jointly solving the upper level O-D estimation and lower level traffic assignment problems and the nonconvexity of the model. This paper presents an alternative framework for estimating dynamic O-D flows using machine learning algorithms. The framework consists of three major modules: a learner that learns the dynamic mapping patterns describing the relationship between prior O-D flows and observed link flows, an assigner that assigns a given O-D matrix to different links based on the learner, and a searcher that iteratively searches the optimal O-D solution using the assigner. A convolutional neural network is designed as the learner and trained as the assigner. Next, the algorithms to estimate a regular O-D matrix and real-time O-D flows are separately developed by using the assigner and two designed genetic algorithms built as the searcher. The framework was evaluated with a realistic network in the downtown area of Kunshan, China. The experimental studies show that the framework can achieve satisfactory estimation performances in real time. Meanwhile, it takes raw flow ranges as the prior inputs, making it robust in the case of lacking an accurate target O-D matrix.
Phylogenetic Distribution and Diversity of Bacterial Pseudo-Orthocaspases Underline Their Putative Role in Photosynthesis

Orthocaspases are prokaryotic caspase homologs – proteases, which cleave their substrates after positively charged residues using a conserved histidine – cysteine (HC) dyad situated in a catalytic p20 domain. However, in orthocaspases pseudo-variants have been identified, which instead of the catalytic HC residues contain tyrosine and serine, respectively. The presence and distribution of these presumably proteolytically inactive p20-containing enzymes has until now escaped attention. We have performed a detailed analysis of orthocaspases in all available prokaryotic genomes, focusing on pseudo-orthocaspases. Surprisingly we identified type I metacaspase homologs in filamentous cyanobacteria. While genes encoding pseudo-orthocaspases seem to be absent in Archaea, our results show conservation of these genes in organisms performing either anoxygenic photosynthesis (orders Rhizobiales, Rhodobacterales, and Rhodospirillales in Alphaproteobacteria) or oxygenic photosynthesis (all sequenced cyanobacteria, except Gloeobacter, Prochlorococcus, and Cyanobium). Contrary to earlier reports, we were able to detect pseudo-orthocaspases in all sequenced strains of the unicellular cyanobacteria Synechococcus and Synechocystis. In silico comparisons of the primary as well as tertiary structures of pseudo-p20 domains with their presumably proteolytically active homologs suggest that differences in their amino acid sequences have no influence on the overall structures. Mutations therefore affect most likely only the proteolytic activity. Our data provide an insight into diversification of pseudo-orthocaspases in Prokaryotes, their taxa-specific distribution, and allow suggestions on their taxa-specific function.
A Modified Imputation Method to Missing Data as a Preprocessing Technique

Database in the real world inevitably contains inconsistent data that are highly susceptible to noise, and incomplete or missing data. The quality of data affects the results of data analysis, and data of low-quality leads to poor mining results. Hence, this study attempts to address the problems of missing values by devising a modified imputation method, which will be part of the preprocessing step in a data mining task. The modified imputation method was examined using open data sets, and was investigated vis-à-vis well-known imputation schemes, namely mean imputation, regression, expectation maximization, and Markov Chain Monte Carlo; with root mean square error, bias, mean square error and execution time as performance metrics; using Statistical Analysis System (SAS) tool. The modified imputation method shows promising results and performs better among well-known imputation schemes.
Influence of Nickel Powder on Microstructure of Carbon Steel Weld using GMAW

The purpose of this research was studied overlay weld of ASTM A36 carbon steel using gas metal arc welding (GMAW) without and with nickel powder adding. ER 70S-6 filler metal consumable electrode was used in diameter 2 mm. Metallurgical structure was examined using an optical microscope (OM). In addition, the mechanical property of the weldment evaluated by micro vickers hardness was measured by means of the variation in the percentage of nickel powder adding. The weldment without and with adding nickel powder 4.25, 7.25 and 15.75 g/min were produced and carried out to the metallography analysis, The experimental result showed that defect-free of all welded-specimens. The highest value of microhardness was 527.4 Hv with the nickel powder 15.75 g/min. Moreover, the hardness of weld metal tends to decrease with decreasing of the weight fraction of nickel adding.
Derive Respiratory Signal Form ECG Using KPCA for Application of Sleep Apnea Detection

The respiratory system rate is an essential sign used to keep an eye on the progression of health issues and an abnormal breathing rate is an important marker of severe health issues, as it's far modulated by the fluctuations of the autonomic nervous system (ANS). It performs the important part in the detection of Sleep-associated respiration disorders like sleep apnea, stress level testing, and lots of different applications. Here, respiratory signal is extracted from ECG called as EDR using Kernel PCA algorithm. KPCA with a combination of different kernels give good quality surrogate respiratory signals. Cross-Correlation coefficient (c) and Magnitude squared coherence coefficient (msc) are utilized as evaluation parameters. RBF kernel gives better EDR signals than other kernels with $\text{c}=0.82$ and $\text{msc}=0.98$. Support Vector Machine (SVM) classifier is employed for detection of normal and diseased sample. SVM results Performance measures as the $accuracy=40.8\%,\quad sensitivity =80.6\%$, and $specificity=79.8\%$.
Long Short Term Memory and Rolling Window Technique for Modeling Power Demand Prediction

Forecasting monthly electric energy consumption is significant for electric power engineering and its production planning. This paper has implemented Long Short Term Memory (LSTM) technique to forecast the energy consumption of a University and further it proposes a model to automate the forecast. The paper also highlights the improvement in performance with the implementation of rolling window technique over LSTM.
Model Predictive Control for Unmanned Tracked Vehicle Path Following

Traditional path following algorithms seldom analyze vehicle dynamics so that unintended error would occur when the vehicle steers in high speed. To solve these problems, this paper established a linear path following model, which includes a 3 degree of freedom (DOF) dynamic model based on model predictive control (MPC). In this model, speed of left and right track are used as control variables, and the MPC algorithm is designed to control an electric tracked vehicle follow a desired trajectory and fulfilling its physical constraints. Quadratic programming is used to solve the linear MPC by linearization of the error model. The control system is testified in MATLAB/Simulink and the performance is satisfied in different situations.
An Overview of Various DC-DC Converter Techniques used for Fuel Cell based Applications

Fuel cells are becoming attractive energy sources for numerous applications like electric vehicle and hybrid energy systems because they do not discharge any pollutants and their output is independent of the environmental conditions unlike PV cell and wind energy plants. Typical output voltage of a fuel cell is 0.6V - 0.7V, hence dc to dc boost converter is used for stepping-up and for controlling the output voltage of fuel cell, while interleaving is done to reduce input current ripple. Bidirectional dc to dc converter is applied for the interfacing of various storage devices like batteries which provide power during peak demand. Thus, interleaved dc to dc converters and bidirectional dc to dc converters are getting more and more attention in academic research and in industrial applications. Therefore, this paper discusses 2-ø, 3-ø, 4-ø and 6-ø interleaved dc to dc converter and numerous topologies of bidirectional dc to dc converters researched in recent years.
Research on Compound Faults Diagnosis of Rolling Bearings Based on Parallel Dual Q-Factors and AMCKD

The rolling bearings often suffer from compound faults in practice, moreover, several faults add together and interfere with each other, which make it difficult to separate week fault signals from them through conventional ways. In order to improve the compound faults diagnosis of rolling bearings via signals' separation, the paper proposes a new method to identify compound faults from mixed-signals, which is based on parallel dual Q-factors method and the adaptive maximum correlation kurtosis deconvolution (AMCKD) method. With the approach, the vibration signal is firstly decomposed into high and low resonance components by the sparse decomposition based on dual Q-factors method, which can sparsely represent the signal and extract the fault impact signal. Then, the low-resonance component is processed by AMCKD, and the AMCKD can adaptively optimize the selection of parameters M and L. Finally, the compound faults can be separated effectively by the method, which makes the fault features more easily extracted and more clearly identified. Simulated analysis result validates the effectiveness of the proposed method in compound faults separating.
A Compare-Aggregate Model with Embedding Selector for Answer Selection

Answer selection is a challenging task in natural language processing that requires both natural language understanding and word knowledge. At present, most of recent methods draw on insights from attention mechanism to learn the complex semantic relations between questions and answers. Previous remarkable approaches mainly apply general Compare-Aggregate framework. In this paper, we propose a novel Compare-Aggregate framework with embedding selector to solve answer selection task. Unlike previous Compare-Aggregate methods which just use one type of Attention mechanism and lack the use of word vectors at different level, we employ two types of Attention mechanism in a model and add a selector layer to choose a best input for aggregation layer. We evaluate the model on the two answer selection tasks: WikiQA and TrecQA. On the two different datasets, our approach outperforms several strong baselines and achieves state-of-the-art performance.
Research on Humanoid Pneumatic Soft Robot Hand

Inspired by the physiological structure of octopus brachiopods, two kinds of bionic soft fingers with adjustable stiffness were designed and manufactured. Then the humanoid pneumatic soft robot hand was developed based on the two soft fingers. The static models of the proposed soft fingers were established. According to the simplified model and the bending geometry of the spring, the mapping relationship between the spring model elongation and the soft finger bending geometry parameters (bending direction, bending angle, bending radius) was established. The single-cavity bending, multi-cavity coupling bending and grasping experiments were carried out. Also, the pneumatic soft robot hand was manufactured and tested for grasping objects with different geometrical shape and size, which indicates its flexibility and dexterity.
Towards a better formalisation of the side-channel threat. (Vers une meilleure formalisation des attaques par canaux cachés)

Dans le cadre de la securite des systemes embarques, il est necessaire de connaitre les attaques logicielles et physiques pouvant briser la securite de composants cryptographiques garantissant l’integrite, la fiabilite et la confidentialite des donnees. Etant donne que les algorithmes utilises aujourd’hui comme Advanced Encryption Standard (AES) sont consideres comme resistants contre la cryptanalyse lineaire et differentielle, d’autres methodes plus insidieuses sont utilisees pour recuperer les secrets de ces composants. En effet, la cle secrete utilisee pour le chiffrement de donnees peut fuiter pendant l’algorithme. Il est ainsi possible de mesurer cette fuite et de l’exploiter. Cette technique est appelee attaque par canal auxiliaire.Le principal objectif de ce manuscrit de these est de consolider les connaissances theoriques sur ce type de menace. Pour cela, nous appliquons des resultats de theorie de l’information a l’ etude par canal auxiliaire. Nous montrons ainsi comment il est possible de comparer un modele de fuite par canal auxiliaire a un modele de transmission de l’information. Dans un premier temps, nous montrons que la securite d’un composant est fortement dependante du rapport signal a bruit de la fuite. Ce resultat a un impact fort car il ne depend pas de l’attaque choisie. Lorsqu’un designer equipe son produit, il ne connait pas encore la maniere dont son systeme embarque pourra etre attaque plusieurs annees plus tard. Les outils mathematiques proposes dans ce manuscrit pourront aider les concepteurs a estimer le niveau de fiabilite de leurs puces electroniques.
Location-Based Seeds Selection for Influence Blocking Maximization in Social Networks

Influence blocking maximization (IBM) is a key problem for viral marketing in competitive social networks. Although the IBM problem has been extensively studied, existing works neglect the fact that the location information can play an important role in influence propagation. In this paper, we study the location-based seeds selection for IBM problem, which aims to find a positive seed set in a given query region to block the negative influence propagation in a given block region as much as possible. In order to overcome the low efficiency of the simulation-based greedy algorithm, we propose a heuristic algorithm IS-LSS and its improved version IS-LSS+, both of which are based on the maximum influence arborescence structure and Quadtree index, while IS-LSS+ further improves the efficiency of IS-LSS by using an upper bound method and Quadtree cell lists. The experimental results on real-world datasets demonstrate that our proposed algorithms are able to achieve matching blocking effect to the greedy algorithm as the increase in the number of positive seeds and often better than other heuristic algorithms, whereas they are four orders of magnitude faster than the greedy algorithm.
Rotation Invariant Feature Extraction and Matching Methodology for IRIS Recognition

In this multimedia era, the iris data are collected and recorded by the use of hardware devices like camera for biometric authentication system. This produces a huge amount of iris image data which requires an efficient authentication system. A significant work has been done for authentication systems, although the performance of them is far behind to fulfill the stated requirement. In this paper, we propose an effective and efficient iris recognition system for individual verification and identification. The proposed system utilizes a novel rotation invariant feature extraction technique with a sophisticated feature-matching methodology to solve the problem in robust environment. The experimental results of the proposed system shows its effectiveness and robustness in different standard iris-databases. Three widely well known databases (IIT Delhi database, MMU1, MMU2) are used for the experimental purpose. The mean-precision, mean-recall, mean-F-measure, and accuracy are used as the performance measure in this work.
Tutorial: Dealing with rotation matrices and translation vectors in image-based applications: A tutorial

Rotation matrices are a convenient and intuitive way to describe algebraically the relative orientation of multiple cameras or of the same camera shooting from different points of view. However, the definition of a rotation matrix is prone to intrinsic ambiguity, which often leads to a mismatch with the physical rotation one wants to describe, even if the definition is mathematically correct. This is a common source of errors whenever it is required to compute a rotation matrix from camera orientation data, or vice versa, to recover such data from a given rotation matrix. This tutorial aims to describe and solve the main factors that generate the ambiguity in using rotation matrices and to permit dealing with them properly both in theory and in practice. Through a detailed analysis of these factors, which ranges from basic mathematical aspects to the notation used to refer to them, it is shown how to avoid errors in the algebraic description of the relative orientation of different cameras by means of rotation matrices. This work is followed by another contribution, in which the interaction between rotation matrices and translation vectors (used to describe the shifts between pairs of cameras) is also analyzed, and a recommendation on how to define a common reference system coherent with a camera (a crucial aspect to model the camera acquisition geometry) is given. The two contributions jointly embrace the entire description of the relative acquisition geometry of images taken from different points of view and provide a complete and error-free methodology to recover it or to extract useful data from it. This topic is particularly important in a wide variety of aerospace applications, which often rely on multiple imaging sensors whose information should be merged, or on imaging devices carried by manned or unmanned vehicles. Such applications range from flying object detection to tridimensional reconstruction by using aerial or satellite images to drone automatic navigation, to change detection for area monitoring to georegistration by ground-to-aerial image matching.
Real-Time Tracking IDs and Joints of Users

This paper proposes a method of using multiple depth sensors vulnerable to joint occlusion and rotation to capture the dynamic motions of two users. The proposed method captures a series of multi-user motions by tracking user IDs, sorting out ID specific joints, and incorporating the joint movement data based on weighting. The ellipses accelerate the process of sorting out the depth data per user. Then, the sorted depth data add to the accuracy of joint positions for the adjustment of lower-limb joints. The proposed method enables an accurate restoration of 3D poses even in dynamic motions, and is applicable to experiential and training programs.
A Study on Classification of Traffic Accident Injury Grade Using CNN and NASS-CDS Data

In this paper, we propose a new occupant injury prediction algorithm using real car accident data base, NASS-CDS DB. Field crash data which are collected by IIHS is used as input of convolutional neural network to estimate occupant injury. And in order to applying CNN, we are encoding crash information data as two dimensional image. Our experiment results can be shown the validity of our proposed algorithm.
PRIMER: An Emotionally Aware Virtual Agent

PRIMER is a proof-of-concept system designed to show the potential of immersive dialogue agents and virtual environments that adapt and respond to both direct verbal input and indirect emotional input. The system has two novel interfaces: (1) for the user, an immersive VR environment and an animated virtual agent both of which adapt and react to the user’s direct input as well as the user’s perceived emotional state, and (2) for an observer, an interface that helps track the perceived emotional state of the user, with visualizations to provide insight into the system’s decision making process. While the basic system architecture can be adapted for many potential real world applications, the initial version of this system was designed to assist clinical social workers in helping children cope with bullying. The virtual agent produces verbal and non-verbal behaviors guided by a plan for the counseling session, based on in-depth discussions with experienced counselors, but is also reactive to both initiatives that the user takes, e.g. asking their own questions, and the user’s perceived emotional state.