Miracle-dz/UniFAR-FTUs huggingface dataset:
Adopting Good-Learners' Paths in an Intelligent Tutoring System

This paper proposes an approach to following good-learners' paths in Intelligent Tutoring Systems (ITS). Based on a theory which states that learning can be acquired through imitation of competent models, we observed and analyzed the exercises completed by good learners to predict an appropriate sequence of questions for the students to follow. We applied the Hidden Markov Model to represent students' skills and the generative Markov Decision Process to model the exercise experience of good learners. The resulting model represents recommendations of dynamic sequences of questions. A preliminary study has been conducted to elicit a real-world dataset of a programming course. It aims to measure the reliability of the proposed approach, in comparison with the conventional approach where teachers determined the questions. The approach performance is indicated by increased percentages of good learners. The experiment results show that the proposed approach has a better reliability than the conventional approach.
The Study of Using Eye Movements to Control the Laparoscope Under a Haptically-Enabled Laparoscopic Surgery Simulation Environment

The purpose of this study is to investigate the possibility to use eye movements to control the laparoscope during a laparoscopic surgery. Laparoscopic surgery usually needs at least two doctors, a surgeon and a laparoscope assistant. The view of the operating surgeon is provided by the laparoscope assistant. As misunderstandings or conflicts of cooperation may happen, an ideal way is that the surgeon has a full control of all the instruments including the surgical tools and laparoscope. To achieve it, an eye based interaction method is introduced in this paper that allows surgeons to control the view by themselves. With recent developments in the eye tracker platforms and associated eye tracking technologies, many non-contact eye tracking systems are available. It can record where a person is looking at any time and a sequence of eye movements. This information can be used to know where is the attention and interest of the person on a display. As such, surgeon's attention can be captured and then be followed by moving the laparoscope to the region of interest. To have a safe and efficient evaluation on the usability, a virtual reality based laparoscopic surgery simulation is built. It is based on Unity with two haptic devices simulating the surgical tools, a 3D mouse providing 6 degrees-of-freedom control of the camera and an eye tracker capturing eyes' positions on a display. Experiments on moving a camera left, right, up, down, in, out and to specified locations using eyes are conducted, and moreover the performances of the proposed eye based self-control and the 3D mouse based other-control are compared. The results are promising where the proposed pointing method leads to 43.6% faster completion of the tasks against the traditional other-control method using the 3D mouse.
Age-Related Effects of Multi-screen Setup on Task Performance and Eye Movement Characteristics

Multi-screens or wide screen setup is becoming increasingly popular in many work places and training environments. However, there has been limited studies of their effect on human health and performance. In this study, we investigate individuals performance and eye movement characteristics while performing a visual task on multi-screen setup consisting of three monitors. During the task, subjects had to share their attention among three screens to identify the location of the visual stimulus and respond accordingly. Subjects' score was calculated based on validity of input to stimulus and response time, while fixation characteristics were investigated with respect to eye movements. The results show that the use of 3-screens added extra demand on the individual causing a decrease on the score and decrease in reaction time. In a further investigation, we found statistically significant negative correlation between the task score and the participant's age while a statistically significant positive correlation between the response time and the participant's age. In addition, the use of multi-screens to perform the tasks caused both fixation occurrences and duration to decrease, denoting an increased alertness since respond was given with less fixation duration and occurrences and less response time.
A Finite- Time Small-Gain Theorem for Infinite Networks and its Applications

We prove a small-gain theorem which addresses the problem of global uniform finite-time stability of infinite networks. The network is composed of a countable set of finite-dimensional subsystems of ordinary differential equations, each of which is interconnected with a finite number of its “neigh-bors” only and is assumed to be finite-time input-to-state stable with respect to its finite-dimensional inputs produced by this finite set of the neighbors. As a corollary we obtain a new result on decentralized finite-time stabilization of infinite networks composed of a countable set of strict-feedback form systems of ordinary differential equations. For this, we combine our new small-gain theorem with the method proposed by S. Pavlichkov and C.K. Pang (NOLCOS-2016) for the gain assignment of the strict-feedback form systems in the case of finite networks. The current results address the finite-time stability and finite-time stabilization and redesign the technique proposed in recent work by S. Dashkovskiy and S. Pavlichkov (IFAC World Congress-2017) for asymptotic stabilization of infinite networks.
Vezetéknélküli és mobil szélessávú hozzáférési technológiák alkalmazása

A radios hozzaferesi technologiak jelentos fejlodesi lepcsohoz erkeztek. Az uzletileg is sikeres beszedatvitel mellett megjelentek a szelesebb illetve szelessavu hozzaferesi modok, melyek mar versenytarsaiva valhatnak az elterjedt vezetekes xDSL technologiaknak. A kettos konvergenciahatas kovetkezteben reszben a mobil technologiak altal nyujtott felhasznaloszintu savszelessegek nottek meg, reszben az IP alapu mar eleve szelessavu hozzaferesi modok valtak vezeteknelkulive. Ennek eredmenyekeppen szamos technologiai megoldas szuletett, melyek viszonyai es alkalmazasi lehetosegei kerulnek osszefoglalasra. A figyelembe vett aspektusok a kovetkezok: – felhasznaloi igenyek, – a technologia kepessegei, alkalmazasi lehetosegei, – rentabilitas, megvalosithatosag.
A Clock Boosted Charge Pump with Reduced Rise Time

This paper introduces a Dickson charge pump topology using a clock boosting technique. Theoretical analysis of the rise time reveals that the proposed solution allows a reduction of the rise time for equal area occupation as compared to the conventional Dickson charge pump. Simulation results confirm the effectiveness of the proposed topology and the accuracy of the developed analytical model.
Robust Stabilization of Uncertain Systems with Measurement Sensitivity Using Output Feedback

We prove that global robust stabilization is possible by output feedback for a class of nonlinear systems with measurement sensitivity characterized by an unknown time-varying parameter. The nonlinear systems under consideration are assumed to satisfy a linear growth condition with a polynomial output-dependent rate, and thus cover a larger class of uncertain systems than the previous ones studied in [15], [2]. To deal with effectively the polynomial nonlinearity and the measurement sensitivity, we propose an estimator with a high-gain that needs to be updated dynamically. Then, an estimator-based controller is constructed step-by-step, achieving global state regulation of the resulting closed-loop system.
Modular Two-Level Voltage Source Converter for Direct Current Transmission Systems

This paper presents a modular two-level voltage source converter (M2L-VSC) suitable for short distance dc transmission systems with relatively low dc operating voltage. The proposed M2L-VSC consists of two sets of three-phase cells, with each cell has its own capacitor, and these capacitors do not discharge when converter is blocked during dc short circuit fault. The main attributes of the proposed M2L-VSC are absence of the 2nd order harmonic from the arm currents, and reduced energy storage requirement as the cell capacitor only experience high-order harmonic currents associated with the switching frequency as in conventional two-level converter (C2L). The technical feasibility of the M2L-VSC for dc transmission systems has been assessed using simulations and corroborated experimentally. It has been shown that the transient responses of M2L-VSC to ac and dc faults are similar to that of the modular multilevel converter (MMC).
Empirical Study on the Financial Reporting of Intangible Assets by Romanian Companies

The purpose of this paper is to identify to what extent Romanian companies quoted in the Bucharest Stock Exchange present information concerning intangible assets (IA), what the structure of the assets of the analyzed companies is, and what the difference between the accounting value of companies, computed through the net accounting asset, and their market value is, determined as the product between the number of shares and the average quotation price. We have analyzed the annual financial statements corresponding to the fiscal year closed on 31.12. 2010 and the annual reports drawn according to the regulation of the National Commission for Mobile Values no. 1/2006 concerning the issuers and mobile operations for 23 companies quoted in the Bucharest Stock Exchange. In the data collection stage, we have resorted to mediated data collection techniques from the annual financial statements and from the management reports, and in the processing and analysis stage we used the empirical comparative analysis in order to identify the resemblances and differences between the information published by companies in various activity fields and the quantitative analysis. The data have been processed using the SPSS software.
Design of a Position Controller for PMSM with Compensation of Nonideal Back-EMF Effect

This paper focuses on the position control of permanent magnet synchronous motor (PMSM) and proposes a control framework based on adaptive robust control (ARC) as a solution. Slightly different from the conventional one, a novel PMSM model considering nonideal back-EMF effect is first developed, which is subject to parametric uncertainties and uncertain nonlinearities. An adaptive robust controller with guaranteed tracking performance is then described. The proposed controller can compensate the effect of nonideal back-EMF and other parametric uncertainties adaptively, while the uncompensated disturbances and the estimation errors also can be handled. The tracking performance of the proposed control algorithm for position control of PMSM is evaluated by simulation results.
More Value, Less Privacy, How to Evaluate the Privacy Based on Trajectories Visualization & Analyzation

The trade-off among individual privacy, data utility and data feature of service has been a great concern when designing and evaluating privacy preserving schemes in trajectories publishing. The trajectories data is spatial and temporal correlated strongly. So, Privacy-preserving over them should take the human behaviors and their status into account. In this paper, we develop a novel method to investigate and analyze users' behaviors as well as the crowd density after abstracting users' ROIs. Finally, we evaluate the privacy stress via a well-designed indictor based on the trajectories visualization and analyzation. Experiments show that the method is capable of effectively finding both crowd living patterns and distribution, and the proposed indictor can quantize the mobility data utility precisely in grid.
Efficient Acquisition of Map Information Using Local Data Sharing Over Hierarchical Wireless Network for Service Robots

Service robots in a robotic wireless network (RWN) download map information from map servers for safe movement. A RWN needs to support a number of service robots. However, this functionality is constrained by the limited radio resources of a RWN. This paper addresses the issue of improving wireless-resource efficiency for a number of robots to download map information. We propose a data sharing scheme for robots to save consumption of wireless resources in map information acquisition. In the proposed scheme, robots are clustered, and a majority of client robots locally obtain the map information from a few cluster-head robots that acquire the map information directly from the server. To reduce wireless resource consumption, local data sharing among robots employs a lower transmission power than that is used in directly downloading from the server. The proposed scheme carries out efficient cluster formation so as to reduce the number of robots that download the map from the server, and to increase the number of robots that obtain the map information from the local data sharing.
Iterative Reweighted Quantile Regression Using Augmented Lagrangian Optimization for Baseline Correction

Based on baseline is a smooth curve and under the collected spectrum, a robust penalized quantile regression with B-spline basis has been proposed to baseline estimation. Then an iterative reweighted method has been adopted for quantile regression optimization. Instead of man tuning the hyperparameter in penalized quantile regression, augmented Lagrangian method is applied to hyperparameter optimization. Experiments on simulated and real data sets show that our method is more effective in baseline correction than other baseline estimation methods in simulated data set. For real data set, the calibration results after the baseline correction step are better than other preprocessing and baseline correction methods.
Analysis of Robust Transient Stability of Power Systems by using Sum of Squares programming

This paper analyzes the transient stability of power systems with uncertain parameters. More precisely, we suppose that the inertia constants and damping coefficients of generators are uncertain, and consider the problem of checking the stability of an equilibrium state for all possible values of the uncertain parameters, and estimating the intersection of the region of attraction for those parameter values. By using a polytope representation for the uncertain parameters, we present a method to solve the problem. In the presented method, we solve a sum of squares (SOS) programming problem based on the polytope representation. It is proven that if this SOS programming problem is feasible, then our method provides a solution to the analysis problem of the transient stability.
Diagnosis and compensation of line Current Measurements for Grid Side Converter in PV Application

This paper proposes a fast and easy fault detection and isolation (FDI) technique for line currents measurement of Grid side converter used in PV system. The considered measurement error is offset and scaling errors. Their consequences cause undesirable periodic undulations and DC components, which lead to system performance degradation. The proposed compensation method based on analytical analysis of line currents in (d,q) synchronous reference frame. It is investigated to compute the average value of the undesired periodic undulations in grid frequency (ω)) and (2ωg) through a simple band-pass filter. This method does not need an additional hardware. Simulation results are presented to demonstrate the effectiveness of the proposed FDI and error compensation algorithm.
Emotion Recognition on Selected Facial Landmarks Using Supervised Learning Algorithms

Facial landmarks may be used to localize the movement of facial muscles that help identify an emotion. It is important that these points are appropriately represented to achieve a successful emotion recognition rate. In this paper, the extraction of 68 facial landmarks, normalization methods and classification of 7 basic emotions are presented. The Cohn-Kanade Database is used as a test bed for the different emotion recognition tasks. The images are normalized by transforming the inputs based on similarity (CKCT) and the mean shape (CKMS). Forward Search and Principal Component Analysis are used to identify the most important features among the 68 facial points. Decision Tree, Logistic Regression, K-Nearest Neighbor and Multilayer Perceptron algorithms are used in building classifiers on reduced and complete feature set. It is interesting to note that facial points in the mouth area are found to be significant in the classification of emotions.
Robust Distributed Control Beyond Quadratic Invariance

The problem of robust distributed control arises in several large-scale systems, such as transportation networks and power grid systems. In many practical scenarios controllers might not have enough information to make globally optimal decisions in a tractable way. We propose a novel class of tractable optimization problems whose solution is a controller complying with any specified information structure. The approach we suggest is based on decomposing intractable information constraints into two subspace constraints in the disturbance feedback domain. We discuss how to perform the decomposition in an optimized way. The resulting control policy is globally optimal when a condition known as Quadratic Invariance (QI) holds, whereas it is feasible and it provides a provable upper bound on the minimum cost when QI does not hold. Finally, we show that our method can lead to improved performance guarantees with respect to previous approaches, by applying the developed techniques to the platooning of autonomous vehicles.
A Precise and Accurate Indoor Localization System based on Sub-Harmonic FMCW Radar

This paper presents an accurate indoor localization system based on sub-harmonic frequency modulated continuous wave (FMCW) radar where a 24 GHz frequency divider-by-10 is used as an active reflector tag. Indoor positioning measurements are performed using least-mean-squares (LMS) algorithm. Measurement results show that the system has a positioning accuracy of 26.8 cm and a positioning precision of 2.97 cm, which outperforms other state-of-the-art FMCW indoor localization systems. It also further proves the system ability to mitigate multi-path interferences in complex indoor environments.
Arikui - A Dubious User Detection System for Online Dating in Japan

Online dating constitutes one out of myriad popular services that can be accessed via the Internet nowadays. This paper introduces a novel detection system for identifying dubious users, i.e. users who utilize a Japanese online dating service for purposes besides dating. Examples of such purposes include sales and multi-level marketing, amongst others. More specifically, the proposed detection is characterized by simultaneously analyzing: (i) user profile data; (ii) user actions over their first few hours; and (iii) data retrieved from Facebook in order to find the likelihood that the user is a spammer. The resulting system successfully detects a number of spammers every day, thereby becoming a valuable tool for the customer service team in Eureka Inc, where it has been deployed.
A Convolutional Auto-Encoder Method for Anomaly Detection on System Logs

Anomaly detection on system logs is to report system failures with utilization of console logs collected from devices, which ensures the reliability of systems. Most previous researches split logs into sequential time windows and regarded each window as an independent instance for classification using popular machine learning methods like support vector machine(SVM), however, neglected the time patterns under logs. Those approaches also suffer from information loss due to the vector representation, and high dimensionality if there is a large number of log events. To make up these deficiencies, unlike most traditional methods that used a vector to represent a period behavior at the macro level, we construct a 2D matrix to reveal more detailed system behaviors in the time period by dividing each window into sequential subwindows. To provide a more efficient representation, we further use the ant colony optimization algorithm to find a highly-coupled event template as the horizontal index of the 2D window matrix to replace the disordered one. To capture time dependencies, a multi-module convolutional auto-encoder is configured as that different paralleled modules scan among different time intervals to extract information respectively. These features are then concatenated in latent space as the final input, which contains diversified time information, for classification by SVM. The experiments on Blue Gene/L log dataset showed that our proposed method outperforms the state-of-art SVM method.
An EEG signal denoising method based on ensemble empirical mode decomposition and independent component analysis

Event-related potential (ERP) signal is very weak. The actual EEG signal is accompanied by a large amount of neurogenic noises and non-neurogenic interferences. Aimed at the issue of reconstructing the valuable ERP signal from polluted EEG signal, an efficient approach that combining ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA) is proposed. Firstly, the noisy signal is decomposed by EEMD, and several interested intrinsic mode functions (IMFs) components are obtained based on correlation between the decomposed IMFs and the original signal. Then, these interested IMF components are de-noised with ICA. Finally, the desired ERP signal is reconstructed from these de-noised IMFs. Experimental results show the merit of the proposed algorithm on EEG signal de-noising.
ECRAM as Scalable Synaptic Cell for High-Speed, Low-Power Neuromorphic Computing

We demonstrate a nonvolatile Electro-Chemical Random-Access Memory (ECRAM) based on lithium (Li) ion intercalation in tungsten oxide (WO3) for high-speed, low-power neuromorphic computing. Symmetric and linear update on the channel conductance is achieved using gate current pulses, where up to 1000 discrete states with large dynamic range and good retention are demonstrated. MNIST simulation based on the experimental data shows an accuracy of 96%. For the first time, high-speed programming with pulse width down to 5 ns and device operation at scales down to $300\times 300\ \text{nm}^{2}$ are shown, confirming the technological relevance of ECRAM for neuromorphic array implementation. It is also verified that the conductance change scales linearly with pulse width, amplitude and charge, projecting an ultralow switching energy ∼1 fJ for $100\times 100\ \text{nm}^{2}$ devices.
Effect of contact velocity on the behaviour of decaying arcs in air

Hybrid direct current circuit breaker (HDCCB) consists of an ultra-fast electromechanical switch combined with power semiconductors to interrupt fault currents. When the ultra-fast electromechanical switch opens, arc plasma is generated between its contacts that commutate the fault current to the auxiliary circuit consisting of power semiconductors. Understanding of the arc behaviour due to the ultra-fast contact opening is necessary as the current commutation is driven by the arc voltage. This paper presents experimental results of a dynamic voltage-current (V-I) characteristics of a decaying arc plasma in air having contact opening velocities from 5 to 15 m/s. A pair of hemispherically capped copper contacts was used for the experiments. The contacts were covered by a glass tube, open from one end which makes the arc partially wall constricted. The contacts were opened with a dedicated Thomson coil based electromagnetic actuator. A computer controlled test system was used that allowed controlling the shape of the current pulse and the time instant of the contact opening on the current waveform. The conductance of the arc was calculated for different contact opening velocities. It was observed that the conductance decreased with an increase of the contact opening velocity. High speed imaging was performed to observe the physical behaviour of arcs having different contact opening speeds.