Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Supervised Online Elastic Extreme Learning Machine for Data Classification

The Extreme Learning Machine (ELM) is a fast and efficient learning algorithm for single-hidden layer feedforward neural networks (SLFN). Recently,with the increase in data volume in real-world applications, and the need to process data from streaming, two problems have become recurrent in data classification: it is not possible to gather all the necessary data before training the algorithms, and it is difficult to manually label the data for the classification tasks. To address these problems, many variations of ELM have been proposed to allow semi-supervised learning, online sequential learning, or both. In this paper, we propose a variation of ELM called Semi-Supervised Online Elastic Extreme Learning Machine (SSOE-ELM), an algorithm that uses both labeled and unlabeled data to learn in an online sequential way (chunk-by-chunk). We compare our approach to the SOS-ELM in several benchmarks. Our experimental results show that SSOE-ELM outperforms SOS-ELM in accuracy, generalization ability and in training speed.
Calibration Time Reduction for Motor Imagery-Based BCI Using Batch Mode Active Learning

Brain-computer interface (BCI) has enormous potential applications from rehabilitation of neural disease patient to driver’s drowsiness prediction. However, the feasibility of BCI in the practical application other than laboratory prototype is dependent on the usability of a human subject on the go. Most of the time, it needs long and in-depth calibration of the system for training purpose. Different novel methods have been proposed to shorten the calibration time maintaining the robust performance. One of them is active learning (AL) which asks for labeling the training samples and it has the potential to reach robust performance using reduced informative training set. In this work, one of the AL methods, query by committee (QBC) is applied by three state-of-the-art feature extraction methods coupled with linear discriminant analysis classifier for motor imagery-based BCI. The joint accuracy by the members of QBC has obtained the baselines using maximum 33% of the whole training set. It also shows a significant difference at the 5% significance level from contemporary AL methods and random sampling method. Thus, QBC has reduced the labeling effort as well as the training data collection effort significantly more than that of random labeling process. It infers that QBC is a potential candidate for abridging overall calibration time of BCI systems.
Few-shot Classifier GAN

Fine-grained image classification with a few-shot classifier is a highly challenging open problem at the core of a numerous data labeling applications. In this paper, we present Few-shot Classifier Generative Adversarial Network as an approach for few-shot classification. We address the problem of few-shot classification by designing a GAN model in which the discriminator and the generator compete to output labeled data in any case. In contrast to previous methods, our techniques generate then classify images into multiple fake or real classes. A key innovation of our adversarial approach is to allow fine- grained classification using multiple fake classes with semi- supervised deep learning. A major strength of our techniques lies in its label-agnostic characteristic, in the sense that the system handles both labeled and unlabeled data during training. We validate quantitatively our few-shot classifier on the MNIST and SVHN datasets by varying the ratio of labeled data over unlabeled data in the training set. Our quantitative analysis demonstrates that our techniques produce better classification performance when using multiple fake classes and larger amount of unlabelled data.
Quality of Life After Epilepsy Surgery in Children: A Systematic Review and Meta-Analysis.

BACKGROUND Pediatric epilepsy surgery is a treatment modality appropriate for select patients with debilitating medication-resistant seizures. Previous publications have studied seizure freedom as the main outcome of epilepsy surgery. However, there has been no systematic assessment of the postoperative life quality for these children. OBJECTIVE To estimate the quality of life (QOL) long-term outcomes after surgery for intractable epilepsy in pediatric patients. METHODS A systematic search of the PubMed and Cochrane databases was performed. Studies reporting questionnaire-assessed QOL at least 12 months postoperatively were included. QOL means and standard deviations were compared between surgically and medically managed patients, between the preoperative and postoperative state of each patient, and were further stratified into patients achieving seizure freedom, and those who did not. Meta-analysis was performed using fixed effects models for weighted mean differences (WMD), 95% confidence intervals (CI) and sensitivity analyses. Funnel plots and Begg's tests were utilized to detect publication bias. RESULTS The search yielded 18 retrospective studies, reporting 890 surgical patients. Following epilepsy surgery, children had significant QOL improvement compared to their preoperative state (WMD: 16.71, 95% CI: 12.19-21.22, P < .001) and better QOL than matched medically treated controls (WMD: 12.42, 95% CI: 6.25-18.58, P < .001). Patients achieving total seizure freedom after surgery had significant postoperative QOL improvement (WMD: 16.12, 95% CI: 7.98-24.25, P < .001), but patients not achieving seizure freedom did not achieve statistical significance (P = .79). CONCLUSION Epilepsy surgery can effectively improve QOL in children with medication-resistant seizures, through seizure freedom, which was associated with the greatest improvement in life quality.
ACJIS: A Novel Attentive Cross Approach For Joint Intent Detection And Slot Filling

Intent detection and slot filling are two important tasks in Spoken Language Understanding. The Condition Random Fields (CRF) was introduced for the tasks pretty much the same fashion to deep neural networks. Recently, attention based encoder-decoder models have shown promising results for joint intent detection and slot filling tasks in spoken language understanding and dialog systems. However, the two tasks are often trained separately. In this paper, we propose ACJIS, a novel Attentive Cross approach for Joint Intent detection and Slot filling. We introduce a cross attention approach to enhance the modeling power on capturing the meaning of word at both tagging level and word level. In order to utilize the information from the two tasks, we leverage multi-task learning to train the model. Our model generates state-of-the-art results on the bench-mark ATIS task. The proposed model also achieves significant gains over the attention based RNN modeling approach for intent detection and slot filling respectively.
Constant Velocity 3D Convolution

We propose a novel three-dimensional (3D)-convolution method, cv3dconv, for detecting spatiotemporal features from videos. It reduces the number of sum-of-products of 3D convolution by thousands of times by assuming the constant moving velocity of the camera. We observed that a specific class of video sequences, such as those captured by an in-vehicle camera, can be well approximated with piece-wise linear movements of 2D features in the temporal dimension. Our principal finding is that the 3D kernel, represented by the constant-velocity, can be decomposed into a convolution of a 2D kernel representing the shapes and a 3D kernel representing the velocity. We derived the efficient recursive algorithm for this class of 3D convolution which is exceptionally suited for sparse data, and this parameterized decomposed representation imposes a structured regularization along the temporal direction. We experimentally verified the validity of our approximation using a controlled dataset, and we also showed the effectiveness of cv3dconv for the visual odometry estimation task using real event camera data captured in urban road scene.
Once-Daily Cyclosporine-A-MiDROPS for Treatment of Dry Eye Disease

Purpose To determine if a Microemulsion Drug Ocular Penetration System (MiDROPS) formulation of cyclosporine A (CsA) delivers more drug and is more efficacious for treatment of dry eye disease (DED) than the current clinical formulation. Methods Tissue distribution of CsA was quantified by liquid chromatography with tandem mass spectrometry (LC-MS/MS). To assess tolerability, CsA-MiDROPS (0.1%) was applied to the eyes of rabbits twice per day for 14 days and assessed using ophthalmoscopic examinations. Mice were exposed to desiccating stress for 10 days and received daily topical instillation of the vehicle or test agent. Cornea staining was done to quantify corneal permeability. Histologic quantification of goblet cell (GC) density and CD4+ T-cell infiltration in the conjunctiva was performed. Results Ophthalmic distribution studies indicate significantly increased drug concentration with CsA-MiDROPS compared with Restasis. CsA-MiDROPS is well tolerated with little toxicity in a 2-week tolerability study. In the DED model, both 0.05% and 0.1% CsA-MiDROPS conferred a significant effect and were more effective than Restasis for treating experimental DED when dosed twice per day. As compared with Restasis dosed twice per day, 0.1% CsA-MiDROPS dosed once per day demonstrated superiority. Conclusions CsA-MiDROPS showed superior drug delivery and efficacy compared with other clinical formulations. As this product is simple to produce and needs to be only applied once daily, the clinical development of CsA-MiDROPS will help to reduce societal and patient burdens by lowering drug costs and accelerating/improving the activity of CsA. Translational Relevance MiDROPS has broad application concerning the ophthalmic development of lipophilic small molecule therapeutics.
Improving the accuracy of intelligent forecasting models using the Perturbation Theory

In time series analysis and forecasting, machine learning (ML) models have been widely used due to their flexibility and accuracy. However, the tuning process of their parameters is a hard task, mainly when complex time series are addressed. So, it is difficult to guarantee the optimal adjustment of the ML model parameters. This paper proposes a recursive approach based on the Perturbation theory to correct the forecasting of ML models. From the initial forecasting given by an ML model, a new ML model is trained using the error series (the difference between the actual series and forecasting) of the first model to decrease the overall error of the system. This process can be recursively repeated until convergence or some stop criterion. The response of the perturbative approach is composed of the sum of the predictions (perturbations) of the ML models trained in each recursion. The proposed approach is investigated with four ML models: Support Vector Regression, Multilayer Perceptron, Long Short-Term Memory, and Radial Basis Function network. The evaluation is performed with an experimental investigation conducted on four time series: Canadian Lynx, Sunspot, Star Brightness, and S&P500 index. The results show that the perturbative approach improves significantly the accuracy of all evaluated ML models.
Biomedical Serious Game System for Balance Rehabilitation of Hemiparetic Stroke Patients

Hemiparetic stroke patients can have several muscular and postural disorders which compromise their balance. Serious games (SG) emerged as a new approach to enhance conventional treatment by making it a motivating method to meet individual needs. This paper evaluated the therapeutic effects of a biomedical SG system developed for balance evaluation and training of hemiparetic stroke patients. The system consists of a balance board with inertial sensors and a computer system that runs the game. A novel scoring system for balance evaluation, which extracts metric information regarding patients’ performance while gaming, was proposed. A quasi-experimental study was performed with six hemiparetic patients in an exercise program using the SG twice per week for 10 weeks. Twelve healthy subjects were recruited for determining the baseline score for balance by using the proposed system. Significant effect sizes (ES) were observed for dynamic balance (ES = 0.9), functional mobility (ES = 0.4), and center of pressure displacement of the balance board (ES = 1.9). Significant correlations between game scores and clinical scales suggest that the use of the scoring system for balance evaluation is feasible. The results of this paper support the clinical potential of a biomedical SG for balance rehabilitation of hemiparetic stroke patients.
Formal Concept Analysis Reduction Method Based on Modified Hamming Distance

One of the approaches used in the field of data analysis is related to the theory of concept lattices, also known as Formal Concept Analysis (FCA). This family of methods is able to process and analyze object-attribute input data models. The output of these methods is represented as concept lattice, which is hierarchically organized structure of concepts. The concept represents groups of objects based on the presence of their shared attributes. One of the main problems of FCA methods is a large number of generated concepts. In this paper, we aim to reduce the number of concepts generated by a method called Generalized One-Sided Concept Lattice (GOSCL), which main advantage, compared to other FCA methods, is its capability to work with different types of attributes used in input data table. We propose reduction method, which is able to work with such heterogeneous input data table and is based on Modified Hamming Distance.
Incorporating Lexicons into LSTM for Sentiment Classification

Sentiment classification is a key task of natural language processing and a long-term goal of artificial intelligence. In this paper, we propose a new approach, which combines the Attention Mechanism, Bidirectional Long Short-Term Memory Network, and Lexicons for sentiment classification. Experiments on the Movie Review and Stanford Sentiment Treebank datasets demonstrate that the performance of our approach is better than other approaches.
Customer Life Time Value Model Framework Using Gradient Boost Trees with RANSAC Response Regularization

For many years, Customer lifetime value has been a mainstay concept in direct response marketing and has been increasingly considered. This paper presents a mathematical model framework for determination of customer lifetime value. The proposed multi-layer framework is based on a systematic theoretical taxonomy and on assumptions grounded in customer behavior. CLV, a recent marketing paradigm, pursues long-term relationship with profitable customers. It can be a starting point to understand relationship management and measure the true value of customers to be deployed toward the targeted customers and profitable customers, to foster customers’ full profit potential. Corporate success depends on an organization ’ ability to build and maintain loyal and valued customer relationships. In this paper, we propose a framework for analyzing customer value and segmenting customers based on their value. We also conducted an in-depth data analysis to find each member’s behavior and important attributes which plays a significant role in calculating CLV in multiple revenue bands. From the experimental validation, we concluded that our proposed framework works much better in predicting customer’s life time value in terms of revenue compared to other methods in past literature.
Driver Behavior Monitoring Using Tools of Deep Learning and Fuzzy Inferencing

Distracted driving is the main cause for car accidents. Driver inattention monitoring systems are promising solutions to mitigate this problem. In this paper, we propose a novel driver inattention monitoring system utilizing deep learning and fuzzy logic theory. A driver head pose estimation module is able to determine whether the driver is focusing on his frontal view, while a deep-learning-based distraction recognition module would detect whether the driver is performing a distraction activity. A danger level inference module based on fuzzy logic combines information from the head pose estimation and the distraction recognition modules to infer the danger level in a real-time manner. In the experimental work, a Convolutional Neural Network model is trained on data of high diversity allowing a more robust driver distraction detection compared to the model trained with only data collected by simulation experiments. In addition, we show that the proposed danger level inference strategy is an effective solution to detect dangerous driving situations by providing timely alerts depending on the vehicle speed.
A Partial Least Squares Aided Intelligent Model Predictive Control Approach

A data-driven model predictive control (MPC) that combines modified partial least squares (PLSs) and MPC is proposed in this paper. A theoretical comparison among traditional MPC, MPC in PLS framework and in modified PLS framework is presented, which demonstrates that the proposed MPC approach has high prediction precision and the ability in coping with dynamics in the process compared to MPC in traditional PLS framework. Furthermore, the proposed MPC requires no prior knowledge, and the simplicity in computation makes it possible to update the prediction model online. The model validity and intelligence of the control strategy are guaranteed by the model updating strategy to a certain degree. Steady-state performance and dynamic response of the proposed MPC is testified through a tracking control simulation of the benchmark of a continuous stirred tank heater system, which illustrates that the advantages of the proposed MPC.
A novel Bayesian approach to reliability modeling: The benefits of uncertainty evaluation in the model selection procedure

This paper proposes a different likelihood formulation within the Bayesian paradigm for parameter estimation of reliability models. Moreover, the assessment of the uncertainties associated with parameters, the goodness of fit, and the model prediction of reliability are included in a systematic framework for better aiding the model selection procedure. Two case studies are appraised to highlight the contributions of the proposed method and demonstrate the differences between the proposed Bayesian formulation and an existing Bayesian formulation.
A Network Anomaly Detection Algorithm based on Natural Neighborhood Graph

As a kind of network security protection technology, intrusion detection technology has become one of the hot topics in the field of network security. In order to solve the problem that the methods of network anomaly detection have a high requirement on the purity of the normal data-set, and that the existing methods based on outlier detection need to set an anomaly threshold manually. Combining with the idea of Natural Neighborhood Graph, a network anomaly detection method (NAD-NNG) is proposed. In order to eliminate noise points or mislabel points and reduce the time complexity of anomalies detection, the algorithm uses the Natural Neighborhood Graph to cluster the normal data-set. Also, the algorithm can adaptively obtain a percentage value β for setting the anomaly threshold. Experiments on KDDCUP99 show that compared with the other two algorithms, the proposed method can achieve a higher detection rate based on a tolerable false alarm rate.
Adaptive One-Class Ensemble-based Anomaly Detection: An Application to Insider Threats

The malicious insider threat is getting increased concern by organisations, due to the continuously growing number of insider incidents. The absence of previously logged insider threats shapes the insider threat detection mechanism into a one-class anomaly detection approach. A common shortcoming in the existing data mining approaches to detect insider threats is the high number of False Positives (FP) (i.e. normal behaviour predicted as anomalous). To address this shortcoming, in this paper, we propose an anomaly detection framework with two components: one-class modelling component, and progressive update component. To allow the detection of anomalous instances that have a high resemblance with normal instances, the one-class modelling component applies class decomposition on normal class data to create k clusters, then trains an ensemble of k base anomaly detection algorithms (One-class Support Vector Machine or Isolation Forest), having the data in each cluster used to construct one of the k base models. The progressive update component updates each of the k models with sequentially acquired FP chunks; segments of a predetermined capacity of FPs. It includes an oversampling method to generate artificial samples for FPs per chunk, then retrains each model and adapts the decision boundary, with the aim to reduce the number of future FPs. A variety of experiments is carried out, on synthetic data sets generated at Carnegie Mellon University, to test the effectiveness of the proposed framework and its components. The results show that the proposed framework reports the highest F1 measure and less number of FPs compared to the base algorithms, as well as it attains to detect all the insider threats in the data sets.
Mutual Information Based K-Labelsets Ensemble for Multi-Label Classification

For solving multi-label classification problems, traditional random K-labelsets method has two main drawbacks: 1) the randomly selected label set may result in highly imbalanced data for single-label multi-class learning, and 2) the dependency relations among different labels in the same label set may cause serious information redundancy and overlap. Both of these two drawbacks can affect the generalization capability of the multi-label learner. In order to overcome these two problems, in this paper, we propose a K-labelsets ensemble method based on mutual information and joint entropy. First, the mutual information and joint entropy are adopted to evaluate the redundancy level and imbalance level of each K-labelset. Then, disjoint sampling is performed iteratively, where during each iteration, a number of K-labelsets with low mutual information are retained to be the candidates, and the one with the highest joint entropy is selected. Afterwards, for each selected K-labelset, the label powerset method is employed and a multi-class classification model is constructed. Finally, the multi-class models on different K-labelsets are integrated, and a voting based ensemble model is generated to perform the predictions for unseen samples. We conduct extensive experiments on real-world multi-label data sets. Experimental results demonstrate the effectiveness of the proposed method.
A fuzzy inference system for disturbance classification in power distribution networks using a low-cost PMU

Electrical distribution networks are evolving at a high pace, mainly due to the increase of distributed energy sources, the growing use of advanced metering infrastructures and new management systems. Phasor Measurement Units (PMUs) have the potential to play an essential role in the monitoring of power distribution systems. This paper proposes a fuzzy inference system to be embedded in a low-cost PMU for classifying the origin/cause of electrical disturbances in power distribution systems. Precise knowledge of the origin of electrical disturbances is fundamental to meet the requirements of load and network modeling tasks. Experimental results show the effective operation of the fuzzy inference system and its ability to address the measurement inaccuracies of the PMU. Furthermore, a remote server was used to correctly estimate electrical parameters of loads by using the output information of the fuzzy inference system.