Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotion recognition using EEG signal and deep learning approach

Emotion is a mental state, which originates in the brain and is closely related to the nervous system. Emotion can be defined as a feeling expressed through, or detectable by voice intonation, facial expression body language, as response from one’s mood relationship with others and most importantly the circumstance they are in. Although, Brain Computer Interface (BCI) are being developed to find a better human-machine interaction system using brain activity and it is frequently implemented by Electroencephalogram (EEG) signals. EEG is a well established approach to measure the brain activities which can be analyzed and processed to distinguish different emotions. In this thesis, we present an approach to classify human emotions using EEG signal by Convolutional Neural Network(CNN). In our model, we use the Dataset for Emotion Analysis using Physiological signals (DEAP) dataset, a benchmark for emotion classification research, to transform the EEG signal from time domain to frequency domain and extract the features to classify the emotions. Emotion can be classified based on the two dimensions of valence and arousal. Previous researches have used fewer channels and participants. Our approach which was carried out on 32 participants, has achieved an accuracy of 94.75% for the valence and 95.75% on the arousal detection, which is quite competitive with other methods of emotion recognition.
Terapia de resincronización cardíaca con o sin cardiodesfibrilador versus terapia con desfibrilador automático, Pereira (Colombia): un estudio de cohorte

Objective: To evaluate survival in patients with cardiac resynchronization with or without cardioverterdefibrillator versus patients with implantable cardioverter- defibrillator alone. Materials and methods: Retrospective cohort, the primary end point was death from cardiac causes, the exposure was electrophysiological therapies, and the information sources were medical files and other records. Results: 70 elderly patients with functional devices; 82 % of them received optimized medical therapy. No significant association was found between survival of patients with cardiac resynchronization therapy with or without defibrillator and cardioverter-defibrillator therapy alone (log rank test, p = 0.54), but the former had a longer survival time (ANOVA p = 0.0012). The hazard ratio was 0.017 for day 371 and 0.15 for day 2169. Fourteen deaths occurred during the observation period, three of them from non-cardiac causes. Conclusion: Cardiac resynchronization therapy was associated with significantly longer survival time.
Wavelets : Introduction and Applications for Economic Time Series

Wavelets are local functions that enable us to cut up data into different layers of frequency. A wavelet basis is formed by translating and dilating a small wave, making it possible to analyze data at different scales. Although wavelet analysis is promising, it has not entered mainstream study of economic phenomena. The aim of this thesis is to give an intuitive theoretical understanding of wavelets, and describe how they can be used in time series analysis. Applications for economic time series are presented, as well as some thoughts of how the field of economics will progress due to wavelet analysis.
Novel Methods for Efficient Realization of Logic Functions Using Switching Lattices

Two-dimensional switching lattices including four-terminal switches are introduced as alternative structures to realize logic functions, aiming to outperform the designs consisting of one-dimensional two-terminal switches. Exact and approximate algorithms have been proposed for the problem of finding a switching lattice which implements a given logic function and has the minimum size, i.e., a minimum number of switches. In this article, we present an approximate algorithm, called janus, that explores the search space in a dichotomic search manner. It iteratively checks if the target function can be realized using a given lattice candidate, which is formalized as a satisfiability (SAT) problem. As the lattice size and the number of literals and products in the given target function increase, the size of a SAT problem grows dramatically, increasing the run-time of a SAT solver. To handle the instances that janus cannot cope with, we introduce a divide and conquer method called medea. It partitions the target function into smaller sub-functions, finds the realizations of these sub-functions on switching lattices using janus, and explores alternative realizations of these sub-functions which may reduce the size of the final lattice. Moreover, we describe the realization of multiple functions in a single lattice. Experimental results show that janus can find better solutions than the existing approximate algorithms, even than the exact algorithm which cannot determine a minimum solution in a given time limit. On the other hand, medea can find better solutions on relatively large size instances using a little computational effort when compared to the previously proposed algorithms. Moreover, on instances that the existing methods cannot handle, medea can easily find a solution which is significantly better than the available solutions.
An empirical investigation of challenges and recommendations for welcoming sexual and gender minority veterans into VA care

Approximately 1 million gay and lesbian Americans are veterans. With the repeal of the “Don’t Ask, Don’t Tell,” the Veterans Affairs (VA) health care system is focusing on the unique needs of lesbian, gay, bisexual, and transgender (LGBT) veterans. As a result of research in the private sector documenting stigmatizing attitudes and discrimination toward LGBT individuals in health care settings, the Institute of Medicine and The Joint Commission published recommendations for responding to the needs of LGBT individuals. However, minimal research has examined the unique needs of LGBT veterans and their experiences in VA. This 2-site (Oklahoma City, OK; Houston, TX), mixed-methods study included 202 VA providers and 58 LGBT veterans. Experiences at VA, comfort in providing/receiving care, barriers LGBT veterans face in coming to VA, and recommendations for making VA more welcoming were assessed. Six focus groups and 6 individual interviews were conducted with veterans, and providers completed anonymous surveys. Less than one third of LGBT veterans and providers viewed VA as welcoming to LGBT veterans. Half of providers indicated they do not assess sexual orientation with any of their patients. Furthermore, half of providers reported that they do not alter their treatment plans even if they know the veteran is lesbian, gay, or bisexual. Many constructive suggestions regarding how VA
On-Demand Transmission for Edge-Assisted Remote Control in Industrial Network Systems

Sensing data and control commands are frequently exchanged over communication networks for remote data acquisition and distributed control in industrial network control systems. The system performance relies on the design of sensing, transmission, and control. Due to the harsh environment in industrial field and the limited network resources, it is very challenging to meet the high requirement on transmission reliability for remote feedback control. In order to enhance the transmission ability for this kind of systems, an edge-assisted system architecture is proposed for the sensing and control processes. The parameter estimation for the sensing process is executed in the so-called edge estimator, and the controller is designed in a remote control center. Under this architecture, in this article an on-demand transmission scheme is designed by characterizing the overall effects of transmission reliability on the estimation and control performance. The overall system is optimized by formulating a revenue–cost maximization problem subject to the constraints of system stability, estimation convergence, spectrum utilization, and energy budget. The formulated mixed-integer nonlinear programming problem can be effectively solved with the block coordinate descent method. It is decomposed into two subproblems over disjoint variable sets. Simulation results demonstrate the advantages on both network-wide revenue and control-transmission cost.
Design of a Fuzzy Sliding Mode Controller for the Autonomous Path-Following of a Quadrotor

This paper presents the design and the performance analysis of nonlinear controllers for the positioning and path-following of a quadrotor. The design of the fuzzy sliding mode controller (FSMC) has been carried out based on sliding mode control (SMC) integrated into a fuzzy logic approach in order to eliminate the chattering. For the performance analysis, another nonlinear controller based on backstepping technique has been designed. A hierarchical two-loop architecture is proposed in order to solve the regulation and trajectory tracking control problem. The effectiveness of the nonlinear controllers is verified by simulation in a virtual environment, where the quadrotor must attain the desired position and follow the desired trajectory even in the presence of external disturbances. The performance analysis of the nonlinear controllers taken as a case study in this research is done using suitable performance indices such as ISE, IAE, among others. The comparative results show that the FSMC controller overcomes the backstepping controller in terms of performance and robustness.
Design Research and Object-Oriented Ontology

Abstract In this paper we recount several research projects conducted at ImaginationLancaster (http://imagination.lancs.ac.uk) a Design-led research laboratory, all of which consider Object-Oriented Ontology (OOO). The role OOO plays in these projects is varied: as a generative mechanism contributing to ideation; as a framework for analysis; and as a constituent in developing new design theory. Each project’s focus is quite unique—an app, a board game, a set of Tarot cards, a kettle and a living room—however they are all concerned with developing new understandings relating to the ubiquitous, networked, ‘smart’ technologies which are often referred to as the Internet of Things (IoT). Through our reflexive account of these research projects the aim of this paper is to provide insights into, and promote new discussion about, the relevance and scope for OOO in socio-technical Design Research, and beyond.
Visualizing Monte Carlo Error and Terminating Markov Chain Monte Carlo Simulation

Author(s): Robertson, Nathan Lane | Advisor(s): Flegal, James M | Abstract: Markov chain Monte Carlo (MCMC) is a sampling technique that allows for estimating features of intractable probability distributions. Output analysis of MCMC samples aims to assess the quality of the sampler and the resulting estimates. We provide an example based overview of current best practices using visualizations and the estimation of features of interest using Markov chain central limit theorems. Most features of interest are functionals of expectations or quantiles. The estimation of quantiles has thus-far been limited to univariate theory and marginal limiting distributions, while the estimation of expectations enjoys multivariate approaches through a joint limiting distribution. In this work, we provide an extension to jointly estimate combinations of functionals of expectations and quantiles through a joint limiting distribution for the Monte Carlo error. We use this limiting distribution to establish a procedure for finding sets of simultaneous intervals forming confidence regions of approximately correct coverage probabilities. These simultaneous intervals motivate a class of visualizations for Monte Carlo errors for a broad class of estimation procedures for which a multivariate normal limiting distribution holds. Finally, we consider MCMC sample size through sequential stopping rules which terminate simulation once the Monte Carlo errors become suitably small. We develop a general sequential stopping rule for combinations of expectations and quantiles from Markov chain output and provide a simulation study to illustrate the validity.
A general estimation framework for nonlinear singularly perturbed systems

ESTIMATION of unmeasured variables is a central objective in a broad range of applications. However, the estimation process turns into a challenging task when the underlying model is nonlinear and even more so when additionally it exhibits multiple time-scales. The current results on estimation for systems with two time-scales apply to linear systems, and limited classes of nonlinear plants and specific observers. Therefore, a new and robust estimation framework for nonlinear systems with variables evolving in different time-scales is needed. This work focuses on developing a rigorous theoretical body for the state estimation of a general class of nonlinear singularly perturbed systems by assuming that the input and output are measured. In the first part of the thesis, we consider the estimation of the slow state of globally Lipschitz nonlinear singularly perturbed systems by using a full-order observer synthesised for the reduced order (slow) model. We deal with the case when the measured output is disturbed by bounded measurement noise. We prove a global exponential inputto-state (ISS) practical stability property for the estimation error with ISS gain from the measurement noise. Moreover, we show that our assumptions are such that they also imply practical L2 stability of the error dynamics. Our findings apply to a general class of nonlinear globally Lipschitz singularly perturbed systems, and to a number of fullorder observers. In order to prove the robustness results to singular perturbations and to measurement noise of the observer, we first show that the plant has bounded solutions under an appropriate set of assumptions on the corresponding boundary layer and reduced order models. We demonstrate the applicability of our findings by showing that the stated assumptions hold for at least four classes of plants and nonlinear observers. Moreover, we present simulation results for numerical examples. In the second part of the thesis, we generalise current results in the literature and the results of the first part by considering broader classes of plants and estimators of general dimension to cover reduced-order, full-order and higher-order observers. Similarly
Evaluation of DFIG placement on small signal stability in multi-machine power systems

Doubly fed induction generator (DFIG) behavior is different from synchronous generators. So, integration this generators with conventional synchronous generators, can make so many challenges for power systems. The most important challenge, can be doubly fed induction generator effect on dynamic and transient stability of power system. In this paper, small signal and transient stability analysis of a two-area power system is investigated that contains conventional synchronous generators and DFIG. Then the effect of DFIG placement in different buses is discussed. At first, small signal stability analysis without power system stabilizer (PSS) is carried, then synchronous generator equipped with PSS. Also, the best place in order to installation of DFIG in power system is determined and transient stability analysis has been done on it. To implementation of simulations, MATLAB software is used.
A Semantic Path Based Approach to Match Subgraphs from Large Financial Knowledge Graph

— In the past, people studied the stock market based on the assumption that the stock entity is known to be affected by the news. However, due to this assumption, these methods inevitably ignore the news without stock entities, and many news without stock entities will also have a significant impact on financial markets. In order to solve this problem, this paper proposes a subgraph matching algorithm based on semantic paths. Matching subgraphs on a knowledge graph that collects a large amount of stock market information and matching the affected stock entities from the semantic level can make a comprehensive analysis on Various news with or without entities. The main research work and achievements of this paper are as follows: First, starting with structured data, the paper complements semi-structured data and unstructured data to build a knowledge graph of the stock market and covering most of the stock market entities. Secondly, based on the analysis of LDA topic model, this dissertation extracts useful topics from financial news and constructs a news graph. A subgraph matching algorithm based on semantic path is proposed. From the knowledge graph, subgraphs matching with news graph are searched for mining the associated entities in the financial news. Finally, according to the result of subgraph matching, experiments and simulated investment are designed. The strategy achieved 15.96% excess return relative to the benchmark. The effectiveness of the subgraph matching algorithm based on semantic path is verified, and the feasibility of the algorithm in actual investment is proved.
Overlapping issues and solutions in data visualization techniques

The tremendous growth of big data has caused the data visualization process becomes more complex and challenging, and yet, data is expected to be increased from time to time. With these massive and complex data, it is getting harder for the data analyst to interpret or read the data in order to gain new knowledge or information. Therefore, it is important to visualize these data using different techniques. However, there are many remaining issues in data visualization techniques. These issues make the data visualization a big challenge to the data analyst. The most common issue in data visualization techniques is the overlapping issue. This paper reviews the overlapping issues in multidimensional and network data visualization techniques. The existing solutions are also reviewed and discussed in term of advantages and disadvantages. This paper concludes the advantages of the overlapping issues and solutions, before discussing their drawbacks. This paper suggests the color-based approach, relocation, and reduction of data sets to solve the overlapping issues.
Efficient Rare Failure Analysis Over Multiple Corners via Correlated Bayesian Inference

In this article, we propose an efficient correlated Bayesian inference (CBI) method to estimate the system-level failure rates for large-scale circuit systems over multiple process corners. The key idea is to encode the correlations of circuit performances among the different corners into the prior distributions of several carefully defined failure events. The hyper-parameters of these distributions can be learned from a few simulation samples via Bayesian inference and, next, the system-level failure rates over different corners can be simultaneously estimated by taking into account these prior distributions. An iteratively constrained inference method is further developed to guarantee the numerical stability of the proposed method and legalize all estimated failure rates. The numerical experiments demonstrate that compared to the state-of-the-art algorithm, the proposed method can achieve around $10\times $ runtime reduction without surrendering any accuracy.
A Statistical Causal Model for the Assessment of Dysarthric Speech and the Utility of Computer-Based Speech Recognition

The evaluation of the degree of speech impairment and the utility of computer recognition of impaired speech are separately and independently performed. Particular attention is paid to the question concerning whether or nut there is a relationship between naive listeners' subjective judgments of impaired speech and the performance of a laboratory version of a speech recognition system. It is a difficult task to relate a speech impairment rating with speech recognition accuracy. Towards this end, a statistical causal model is proposed. This model is very appealing in its structure to support inference, and thus can be applied to perform various assessments such as the success of automatic recognition of dysarthric speech. The application of this model is illustrated with a case study of a dysarthric speaker compared against a normal speaker serving as a control.
Attention Based GRU Network for Domain Adaptation in Sentiment Classification

 Domain adaptation in sentiment classification, as a worthwhile research area, aims to use the available labeled data of a source domain to classify the unlabeled data in the target domain. This approach saves costs and efforts and also improves the efficiency of sentiment classification. This paper proposes the attention based GRU network for domain adaptation in sentiment classification (AGN). This algorithm learns the semantic meanings of the text based on GRU or Bi-GRU, and makes hierarchical mapping from words to sentences and then to the document. The Attention layer is added to capture the core emotional words and sentences. Then, the adversarial idea is brought in, that is, auxiliary domain classification task and sentiment classification task are performed to jointly learn feature representations. Review datasets of five domains on the Amazon online platform are used, and an experiment is performed with 20 pairs of data. The result indicates that the proposed algorithms are able to improve the accuracy of sentiment classification. Finally, the AGN algorithms are interpreted by visualizing the Attention layer.
Source Code Plagiarism Detection in Academia with Information Retrieval: Dataset and the Observation

Source code plagiarism is an emerging issue in computer science education. As a result, a number of techniques have been proposed to handle this issue. However, comparing these techniques may be challenging, since they are evaluated with their own private dataset(s). This paper contributes in providing a public dataset for comparing these techniques. Specifically, the dataset is designed for evaluation with an Information Retrieval (IR) perspective. The dataset consists of 467 source code files, covering seven introductory programming assessment tasks. Unique to this dataset, both intention to plagiarise and advanced plagiarism attacks are considered in its construction. The dataset’s characteristics were observed by comparing three IR-based detection techniques, and it is clear that most IR-based techniques are less effective than a baseline technique which relies on Running-Karp-Rabin Greedy-String-Tiling, even though some of them are far more time-efficient.
Influence of contrast-reversing frequency on the amplitude and spatial distribution of visual cortex hemodynamic responses.

Visual stimulation is one of the most commonly used paradigms for cerebral cortex function investigation. Experiments typically involve presenting to a volunteer a black-and-white checkerboard with contrast-reversing at a frequency of 4 to 16 Hz. The aim of the present study was to investigate the influence of the flickering frequency on the amplitude of changes in the concentration of oxygenated and deoxygenated hemoglobin. The hemoglobin concentrations were assessed with the use of a high resolution diffuse optical tomography method. Spatial distributions of changes in hemoglobin concentrations overlaying the visual cortex are shown for various stimuli frequencies. Moreover, the hemoglobin concentration changes obtained for different source-detector separations (from 1.5 to 5.4 cm) are presented. Our results demonstrate that the flickering frequency had a statistically significant effect on the induced oxyhemoglobin changes (p < 0,001). The amplitude of oxy hemoglobin concentration changes at a frequency of 8 Hz was higher in comparison with that measured at 4 Hz :[median(25th-75thpercentiles) 1.24 (0.94-1.71) vs. 0.92(0.73-1.28)µM, p < 0.001]; 12 Hz:[1.24 (0.94-1.71) vs. 1.04 (0.78-1.32) µM, p < 0.001]; and 16 Hz:[1.24 (0.94-1.71) vs. 1.15(0.87-1.48) µM, p < 0.001]. No significant differences were observed between the size of an area of activation for various frequencies. The demonstrated superiority of 8 Hz over other frequencies can advance understanding of visual stimulations and help guide future fNIRS protocols.