Miracle-dz/UniFAR-FTUs huggingface dataset:
Fusion of Convolutional Neural Networks for P300 Based Character Recognition

One of the most popular brain-computer interface (BCI) system is P300 speller, which is used for character recognition. For real-time application, the improved character recognition is required for P300 speller. In this work, a deep learning technique based on convolutional neural network (CNN) has been proposed for character recognition. Deep learning techniques based feature set can represent the electroencephalogram (EEG) signal better compared to hand-crafted feature set. In this work, two parallel CNN model with different kernel size is proposed to extract multi-resolution feature from the dataset. The proposed CNN model extracts spatial and temporal feature from the dataset. To mitigate the over-fitting problem, dropout is used before the fully-connected layer of the CNN architecture, which improves the network performance. Rectified linear units (ReLU) is used to accelerate the training process. The scores of these two CNN architectures are fused together for P300 detection. Also, ensemble of CNN (ECNN) architecture is proposed to reduce the variation between the classifiers and enhance the character recognition performance. The proposed method is tested on BCI Competition III dataset and the results are fairly comparable and better with the earlier methods.
Predictive Supervisory Control for Timed Discrete Event Systems under Communication Delays

In many cyber-physical systems, controllers and plants are located at different sites. Communications between a controller and a plant are carried out over a wired or wireless communication network. Communication delays in such systems must be addressed when designing controllers. In this paper, we model a cyber-physical system as a timed discrete event system. We investigate predictive supervisory control of timed discrete event systems with communication delays in both observation channel and control channel. We derive a necessary and sufficient condition for the existence of an admissible networked supervisor that ensures the language generated by the closed-loop system equals a given specification language. An example is given to illustrate the results.
Ellipsoid-based interval estimation for Takagi-Sugeno fuzzy systems

This paper proposes a two-step interval estimation approach for discrete-time Takagi-Sugeno fuzzy systems with unknown but peak bounded uncertainties. A Takagi-Sugeno fuzzy observer is designed by using the L∞ technique to attenuate the influence of the uncertainties and then improve the estimation accuracy. Based on the designed observer, the interval estimation results are determined via ellipsoidal analysis. By integrating the L∞ observer design with the ellipsoidal analysis, the proposed method can obtain accurate interval estimation results with high computational efficiency. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed approach.
Stochastic Subgradient Methods for Dynamic Programming in Continuous State and Action Spaces

In this paper, we propose a numerical method for dynamic programming in continuous state and action spaces. We first approximate the Bellman operator by using a convex optimization problem, which has many constraints. This convex program is then solved using stochastic subgradient descent. To avoid the full projection onto the high-dimensional feasible set, we develop a novel algorithm that samples, in a coordinated fashion, a mini-batch for a subgradient and another for projection. We show several salient properties of this algorithm, including convergence, and a reduction in the feasibility error and in the variance of the stochastic subgradient.
Traumatic tympanic membrane perforations: an overview in tertiary care hospital

This hospital-based prospective study was conducted in the Department of Ear Nose Throat, Head and Neck Surgery, Government Medical College Srinagar, Jammu and Kashmir, for a period of 1 year from June 2013 to June 2014 with the aim of studying the aetiological factors, clinical presentation and management options for traumatic tympanic membrane perforations. A total of 50 patients with traumatic perforations of the tympanic membrane were enrolled in the study, comprising of 34 males and 16 females patients. Age of the patients ranged from 12 to 56 years, with a mean age of 32 years. The results showed that the most common mode of trauma was slaps (56%); sudden hearing loss and tinnitus were the two most common presentations (92%). Audiometry shows that the larger the tympanic membrane perforation, the larger the air–bone gap. Hearing loss was highest at the lowest frequencies and generally decreased as the frequency increased. The differences in air–bone gaps between small and large perforations were significant at all frequencies (P<0.05, Student’s t-test). Overall, 72% of patients responded to conservative management, whereas 28% needed fat myringoplasty and/or chemical cauterization.
Graph classiﬁcation with kernels, embeddings and convolutional neural networks

—In the graph classiﬁcation problem, given is a family of graphs and a group of different categories, and we aim to classify all the graphs (of the family) into the given categories. Earlier approaches, such as graph kernels and graph embedding techniques have focused on extracting certain features by processing the entire graph. However, real world graphs are complex and noisy and these traditional approaches are computationally intensive. With the introduction of the deep learning framework, there have been numerous attempts to create more efﬁcient classiﬁcation approaches. We modify a kernel graph convolutional neural network approach, that extracts subgraphs (patches) from the graph using various community detection algorithms. These patches are provided as input to a graph kernel and max pooling is applied. We use different community detection algorithms and a shortest path graph kernel and compare their efﬁciency and performance. In this paper we compare three methods: a graph kernel, an embedding technique and one that uses convolutional neural networks by using eight real world datasets, ranging from biological to social networks.
Stenosis Detection From Time-of-Flight Magnetic Resonance Angiography via Deep Learning 3D Squeeze and Excitation Residual Networks

Intracranial artery stenosis is an important public health concern internationally, due to it being one of the major causes of ischemic stroke. In this study, we aim to provide a computer-aided diagnosis algorithm capable of automatically distinguishing between Internal Carotid Artery (ICA) stenosis and normal to minimize the labor-intensiveness of stenosis detection. Using Time-of-Flight Magnetic Resonance Angiography (TOF-MRA), a novel deep learning detection model via 3D Squeeze and Excitation Residual Networks (SE-ResNet) is proposed. Pre-processing of TOF-MRA, data augmentation, training of 3D SE-ResNet, and testing using patch-based and patient-based methods with cross-validation is described. The proposed network using a database consisting of 50 normal cases (ICA-N) and 41 stenosis cases (ICA-S) with grade level of above 30% was evaluated. All 41 ICA-S cases were categorized according to the diameter (D_stenosis) of the artery at the site of the most severe stenosis by expert radiologists, whereas percent stenosis was measured by Warfarin-Aspirin Symptomatic Intracranial Disease (WASID) method. The proposed 3D SE-ResNet was further compared with more conventional networks including 3D ResNet and 3D VGG. The results showed the capability to detect stenosis achieving overall Area Under the Curve (AUC) and accuracies of 0.947 and 91.0% for patch-based and 0.884 and 81.0% for patient-based testing, respectively. In addition, the proposed 3D SE-ResNet achieved better performance against conventional 3D ResNet and 3D VGG with improvement rates of 0.053 and 0.095 for patch-based and 0.053 and 0.065 for patient-based testing in terms of AUC, respectively.
Price Control for Heterogeneous Thermostatically Controlled Loads in Communication and Computation Delay Environments

In this paper, we examine the potential of thermostatically controlled loads (TCLs) to provide demand response services in real-time energy markets (15 minutes) to optimize the tradeoff between the electricity bills and occupants’ comfort requirements. A distributed optimization scheme is presented based on the derived second-order thermal dynamics and heterogeneous TCL models, in which a central controller is allowed to collect information from and broadcast control signals to TCLs. However, due to the real-time consideration, traditional synchronized computation does not scale well with the problem size, as the speed of the algorithm is limited by the slowest agent. This is particularly true in a heterogeneous network where the computing nodes experience different computation and communication delays. To perform distributed optimization in the presence of delay, we apply an asynchronous distributed alternating direction method of multipliers (ADMM) where the central controller makes decisions when it receives information from a predefined number of TCL in the population. Through rigorous theoretical results we show that the proposed algorithm converges to the optimal solution. We also provide numerical results in which different populations of TCLs with varying levels heterogeneity are optimized.
Impact of Channel Correlation on Different Performance Metrics of OSSK-Based FSO Systems

In this paper, we study the impact of correlation on the bit error rate (BER) and the channel capacity of a free-space optical (FSO) multiple-input-multiple-output (MIMO) system employing optical space shift keying (OSSK) over a fading channel. In order to study a practical correlated channel, we consider the effect of channel correlation due to both small-and large-scale eddies and show that the use of OSSK over correlated FSO channel can lead to an improved system performance with increasing correlation level of upto 0.9. In this work, we first develop an analytical framework for different performance metrics of the OSSK multiple-input single-output system with correlation and then extend our investigation by proposing an asymptotically accurate mathematical framework for MIMO. We also validate all the analytical results using MATLAB simulations. Finally, we develop an experimental setup of FSO with two correlated links to study the throughput and latency of the links at different turbulence levels.
Long-term Behavior of Mean-field Noisy Bounded Confidence Models with Distributed Radicals

In this paper, we consider the mean-field model of noisy bounded confidence opinion dynamics under exogenous influence of static radical opinions. The long-term behavior of the model is analyzed by providing a sufficient condition for exponential convergence of the dynamics to stationary state. The stationary state is also characterized by a global estimate for a sufficiently large noise. Furthermore, we consider the order-disorder transition in the model in order to identify the effect of the (relative) mass of the radicals on the critical noise level at which this transition occurs. A numerical scheme for approximating the critical noise level is provided and validated through numerical simulations of the mean-field model and the corresponding agent-based model for a particular distribution of radical opinions.
Constrained Online Learning in Networks with Sublinear Regret and Fit

In this paper, we consider a network of agents that select actions in order to minimize an objective function of which they only have local observations and subject to a set of constraints. The objective function and the constraints can vary arbitrarily over time. The selection of actions, also called a strategy, is causal and decentralized, i.e., the dynamical system that determines the actions of a given agent depends only on the constraints at the current time and on its own actions and those of its neighbors. We propose a decentralized saddle point algorithm to select the actions and we show that the network fit and regret are sublinear with respect to the time horizon of the problem. Specifically, we define the global fit of a strategy as a vector that integrates over time the global constraint violations as seen by a given node. The fit is a performance loss associated with online operation as opposed to offline clairvoyant operation which can always select an action, if one exists, that satisfies the constraints at all times. If this fit grows sublinearly with the time horizon it suggests that the strategy approaches the feasible set of actions. Likewise, we define the regret of a strategy as the difference between its accumulated cost and that of the best fixed action that one could select knowing beforehand the time evolution of the objective function. Numerical examples support the theoretical conclusions.
Global Frequency Synchronization over Networks of Uncertain Second-Order Kuramoto Oscillators via Distributed Adaptive Tracking

In this work, we consider the problem of global frequency synchronization of a network of second-order Kuramoto oscillators, cast as a distributed tracking problem, in the sense that the reference synchronization frequency for the network is generated by an autonomous leader. The main contribution of this paper is to develop a novel control strategy for the problem of leader-follower frequency synchronization, by exploiting the adaptive control framework to cope with parametric uncertainties in the oscillators. These adaptive controllers (one for each system) are interconnected with a distributed observer, used to reconstruct the reference signal for the systems not directly connected to the leader. Adopting the Lie Groups formalism for the unit circle to globally characterize the phase dynamics, we show that synchronization is not hindered if the physical couplings are in part preserved. Stability of the closed-loop interconnection is analyzed with Lyapunov-like arguments and verified in a numerical simulation.
NTTU: An Area-Efficient Low-Power NTT-Uncoupled Architecture for NTT-Based Multiplication

Large integer multiplication, or large degree polynomial multiplication, is the most time-consuming operation in fully homomorphic encryption (FHE). Low area and power consumption are difficult to maintain while achieving high performance for a large size multiplier. To address this issue, an area-efficient low-power architecture for multiplication, named NTTU, is proposed in this article. First, a combined number theoretic transform (NTT) method consisting of decimation-in-time (DIT) NTT for input in natural order and bit-reversed order is proposed to eliminate the steps of zero padding, scramble, and the first stage in NTT, thereby achieving a reduction of <inline-formula><tex-math notation="LaTeX">$7N/2$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>7</mml:mn><mml:mi>N</mml:mi><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq1-2958334.gif"/></alternatives></inline-formula> clock cycles compared with the single-type NTT method. Second, the NTT-uncoupled architecture is proposed to uncouple the multiplication components, decreasing the storage space for coefficients by 1/2 compared with state-of-the-art designs. Third, a parallel computing architecture based on a crossed memory access scheme is proposed, therein reducing the corresponding execution time by one-half compared with serial execution. Synthesized using 65 nm technology, the proposed architecture can multiply two 1024k/768k integers in 1.7 ms at 500 MHz at a cost of 13.66/7.67 million gates and 726.7/550.2 mW, and a 71.17 percent/30.37 percent area time product (ATP) reduction is achieved compared with the state-of-the-art ASIC designs.
Exact Delay Consensus Margin of First-Order Agents under PID Protocol

In this paper we study robust consensus problems for continuous-time first-order multi-agent systems (MAS) connected by an undirected network. We derive analytical expressions of the delay consensus margin achievable by the PID feedback protocols; the delay consensus margin is a robustness measure that defines the maximal range of delay within which robust consensus can be achieved despite the uncertainty in the delay. The results show how the agent dynamics and graph connectivity may fundamentally limit the range of delay tolerable, so that consensus can and cannot be maintained in the presence of uncertainty in the delay. Somewhat surprisingly but yet in an intuitively consistent manner, we find that the delay consensus margin achieved by PID feedback coincides with that by PD protocols, which can be determined by solving efficiently a convex optimization problem in one variable, or alternatively, a unimodal optimization problem.
Joint Mobile Data Collection and Energy Supply Scheme for Rechargeable Wireless Sensor Networks

To alleviate energy hole problem inherent in static Base station (BS) scheme and extend network work of battery-restricted wireless rechargable sensor networks, a joint mobile data col-lection and adaptive charging scheduling (MDC-ACS) scheme based on virtual grid is proposed in this paper, aiming to achieve whole network energy balance and high charging effi-ciency. In MDC-ACS scheme, network is firstly divided into several grids and several rendezvous are determined using geo-graphic information. Then, mobile data collector (MDC) moves in grids for data collection within the application delay and moving trajectory is guided by rendezvous. Due to the limited battery capacity of mobile wirelsee charging vehicle (WCV) and its energy consumption while cruising, an adaptive charging schedule scheme is proposed for maximizing recharging benefit. With extensive simulation, we demonstrate that MDC-ACS scheme can achieve better charging benefit and reduce average charging delay.
The Divergence of all Sampling-based Methods for Calculating the Spectral Factorization

This paper investigates the possibility of approximating the spectral factor of continuous spectral densities with finite Dirichlet energy based on finitely many samples of the spectral densities. It will be shown that there exists no sampling-based method which depends continuously on the samples and which is able to approximate the spectral factor arbitrarily well for all continuous densities of finite energy. Instead, to any sampling-based approximation method there exists a large set of spectral densities so that the approximation method does not converge to the spectral factor for every spectral density in this set as the number of available sampling points is increased. Finally, the paper discusses shortly some consequences of these results. Namely, it mentions implications on the inner-outer factorization, it discusses algorithms which are based on a rational approximation of the spectral density, and it considers the Turing computability of the spectral factor.
An Investigation of Factors Influencing Private Technology Organizations' Intention to Adopt Open Government Data in Saudi Arabia

Governments around the world are increasingly riding the wave of the open government data initiative, in which governments share their data freely to be used and reused by various stakeholders to stimulate innovation and economic growth, as well as promoting transparency and accountability. Private technology organizations are considered to be key players in the open government data ecosystem. Therefore, governments globally endeavor to encourage these organizations to adopt open government data to develop new social and economic benefits, products and services. However, adopting open government data is not an easy process because it relies on several factors. This research aims to investigate factors that may influence the decision of an organization to adopt open government data in Saudi Arabia. We have developed an integrated model to seek to understand why some organizations are willing to adopt open government data while others are not. This model was designed by combining aspects of two conceptual constructs, the technology-organization-environment framework and the information systems success model. These theories were extended by the addition of three components, political leadership, culture and open data intermediaries, to cover the open government data adoption context.
What the Literature and Instructors Say about the Analysis of Student Interaction Logs on Virtual Learning Environments

Online education has broadened the avenues of research on student behavior and performance. In this paper, we shed light on how student interaction logs on virtual learning environments have been analyzed. We conducted two studies: interviews with instructors and a systematic mapping of 136 papers on Education Data Mining and Learning Analytics, published between 2010 and Feb/2019. Then, we triangulate the results between the instructors’ answers and research results found in the mapping. The main contributions of this paper are: (i) an overview of Brazilian instructors’ experience with Virtual Learning Environments and requirements to improve student analysis; (ii) an overview and classification of the papers in this topic; (iii) a ranking of importance of the instructors’ main statements and paper results; and (iv) guidelines based on the requirements mentioned by instructors for designing tools that support their analyses. Finally, we discuss gaps in existing work that can ground and guide future research, such as tools to evaluate student interaction logs with video lectures and to analyze instructor interaction logs on Virtual Learning Environments.
An L2-Consistent Data Transmission Sequence for Linear Systems

In this paper, we tackle the problem of selecting a sparse data transmission sequence for a networked control system while guaranteeing a certain ${\mathcal{L}_2}$-induced norm bound with respect to an exogenous disturbance input. As the main contribution of this work, for every periodic transmission sequence, we provide a transmission sequence counterpart, aperiodic in general, that results in fewer or at most the same number of transmissions while still guaranteeing an ${\mathcal{L}_2}$ control policy with the same ${\mathcal{L}_2}$-induced norm bound as that of the periodic transmission policy. In this sense, the proposed transmission sequence is called ${\mathcal{L}_2}$-consistent. Moreover, we show that as the horizon approaches infinity, the proposed ${\mathcal{L}_2}$consistent transmission sequence counterpart of every periodic transmission sequence approaches a periodic transmission sequence with an equal or larger time period.
Generalized Framework for Gridding Approximation Approach to Yet Another H 2 Norm of Sampled-Data Systems

This paper considers treatment of yet another H2 norm in linear time-invariant (LTI) sampled-data systems, which is defined as the L2 norm of the response to the worst-timing impulse disturbance in those systems. This norm is introduced recently by the authors as an alternative to the two existing H 2 norms in LTI sampled-data systems, and it is called the third H 2 norm of those systems. Regarding the analysis and minimization problems of the third H 2 norm, our preceding studies introduce the idea of a gridding approximation approach to the sampling interval [0,h) at which the impulse disturbance occurs; the sampling interval is divided into N subintervals with an equal width and each of the beginning points of the N subintervals is regarded as the timing at which the impulse disturbance is considered. In this respect, this paper provides a generalized framework for the gridding approximation approach by taking advantage of the freedom in the point for each subinterval at which the impulse disturbance is dealt with. It is shown in this paper that the gridding approximation approach has the convergence rate of 1/N regardless of the point for each subinterval, provided that some nontrivial modification is applied to the gridding treatment. More importantly, this paper shows that taking the central point for each subinterval leads to quantitatively improved accuracy than that of our preceding studies for both the associated analysis and synthesis problems.