Miracle-dz/UniFAR-FTUs huggingface dataset:
A Critical Evaluation on Low-Cost Consumer-Grade Electroencephalographic Devices

Electroencephalography (EEG) has been recognized as one the finest cost-effective techniques to measure the electrical activity of the human brain. Since the electrical activity measured at the scalp is an abstract representation of the regional brain activity of the human brain, the EEG readings (EEG data) are often useful in many aspects. The value of an EEG data is proportionate to an electrical potential measured at a specific position on the scalp using an electrode. One of the major uses of EEG data from the medical domain is detecting the brain disorders such as Epileptic Seizure. Brain Computer Interfacing (BCI) is another use of EEG data. The paper discusses the analytical procedures of EEG data acquired using consumer-grade low-cost EEG devices in order to achieve the objective of implementation of a cost-effective BCI system. Furthermore, the paper presents a critical evaluation of some selected consumer-grade EEG devices from the point of view of cost-effectiveness against the expected accuracy. We have obtained a maximum of 76.6% for user intention detection with a low-cost EEG device Emotiv's “Insight”. This is a remarkable achievement in a context that, the maximum accuracy reported for the EEG devices in the class was only 60.57%. We emphasize the key impact behind this achievement is the employment of the technique Multi-Agent Systems (MAS), which is an Artificial Intelligence (AI) approach, for EEG data analysis and intention classification.
Ship detection in foggy remote sensing image via scene classification R-CNN

The object detection networks via Faster R-CNN for ship detection have demonstrated impressive performance. However, the complexity of weather conditions in high resolution satellite images exposes the limited capacity of these networks. Images interfered by fog are common in optical remote sensing images. In this paper, we embrace this observation and introduce our research. Unlike SAR images, optical sensor images are very susceptible to the effects of the weather, especially clouds and fog. So, accurate target information cannot be obtained from these image, which reduces the accuracy of ship detection. To solve this problem, we attempts to introduce the image defogging methods into object detection networks to suppress the interference of clouds. Secondly, the SC-R-CNN structure is proposed, which uses the scene classification network (SCN) to realize the classification of fog-containing images and cascaded with the object detection network to form a dual-stream object detection framework. In addition, the combination of defogging methods and the SC-R-CNN network also produces more optimized results. We use the remote sensing image data set containing various types of weather conditions to confirm the validity and accuracy of the proposed method.
Treatment modalities for hip and knee osteoarthritis: A systematic review of safety

Current guidelines on the management of hip and knee osteoarthritis (OA) do not compare safety of treatment modalities. We therefore systematically reviewed 20 studies investigating mortality and serious complications of both medical and surgical treatments for hip and knee OA using PubMed, Scopus, Web of Knowledge and Google Scholar. Mortality was the highest for naproxen (hazard ratio (HR) = 3 (1.9, 4.6)) and lowest for total hip replacement (relative risk (RR) = 0.7 (0.7, 0.7)). Highest gastrointestinal complications were reported for diclofenac (odds ratio (OR) = 4.77 (3.94, 5.76)) and lowest for total knee replacement (HR = 0.6 (0.49, 0.75)). Ibuprofen had the highest renal complications (OR = 2.32 (1.45, 3.71)), whereas celecoxib had the highest cardiovascular risk (OR = 2.26 (1, 5.1)) and lowest was for tramadol (RR = 1.1 (0.87, 1.4)). Results show that medical management of hip and knee OA, particularly with non-steroidal anti-inflammatory drugs, may carry higher mortality compared to surgery. Careful consideration should be given to medical management taking into account known co-morbidities.
Review of Algorithms for Tag Detection in Video Sequences

Detecting moving objects in video sequences may be particularly challenging because of the characteristics of the objects, such as their size, colour, contrast, velocity and trajectory. Industrial video tagging systems should generate tags based on information inferred from video frames and learn relations between given concepts. In opposite to traditional methods such systems should effectively segment semantic objects in tagged videos, even when the image-based object detectors provide inaccurate proposals. Such systems usually base on the knowledge which is constantly updated to acquire the dynamics of the indexed concepts. In this paper we present a short review of the most importants algorithms for detection of markers in video sequences and problems related with practical applications.
Predicting Tourists’ Behavior Of Virtual Museum Using Support Vector Machine With Feature Selection Technique

Multimedia technology has greatly influenced tourism industry in Thailand. It can increasingly promote Thai cultural tourism byapplying the virtual reality technology to create Chaiya national virtual museum. Significantly, the virtual reality method is considered as a modern and popular method integrating with a game design. The paper employs the method that selects a variety of information features and the appropriate characteristics from support vector machine classification technique in order to create a model to predict tourists’ behavior. The researchers adopt the feature selection methods consisting of the genetic search, the best-first search, and the greedy stepwise search algorithms integrated with the wrapper method. These methods are applied to evaluate the performance of Chaiya national virtual museum. In sum, the results of this work reveal the feature selection textbf{{method can select the best data set features and improve the efficiency of predictive model.
Impact of Approximate Multipliers on VGG Deep Learning Network

This paper presents a study on the applicability of using approximate multipliers to enhance the performance of the VGGNet deep learning network. Approximate multipliers are known to have reduced power, area, and delay with the cost of an inaccuracy in output. Improving the performance of the VGGNet in terms of power, area, and speed can be achieved by replacing exact multipliers with approximate multipliers as demonstrated in this paper. The simulation results show that approximate multiplication has a very little impact on the accuracy of VGGNet. However, using approximate multipliers can achieve significant performance gains. The simulation was completed using different generated error matrices that mimic the inaccuracy that approximate multipliers introduce to the data. The impact of various ranges of the mean relative error and the standard deviation was tested. The well-known data sets CIFAR-10 and CIFAR-100 were used for testing the network’s classification accuracy. The impact on the accuracy was assessed by simulating approximate multiplication in all the layers in the first set of tests, and in selective layers in the second set of tests. Using approximate multipliers in all the layers leads to very little impact on the network’s accuracy. In addition, an alternative approach is to use a hybrid of exact and approximate multipliers. In the hybrid approach, 39.14% of the deeper layer’s multiplications can be approximate while having a reduced negligible impact on the network’s accuracy.
Improved Source End Power Factor Enhancement of BLDC Motor Using Bridgeless-Landsman Converter

A power factor correction (PFC) at the AC mains for brushless DC motor (BLDCM) can be integrally achieved by devising a Landsman converter in bridgeless (BL) configuration. The speed control of BLDCM is controlled with the use of the fuzzy logic controller. The PFC based BL Landsman converter works in discontinuous inductor current mode (DICM) and the DC link voltage at the front-end of voltage source inverter is regulated to control the speed. Accordingly, the PFC at the input side is also achieved near to unity. During electronic commutation of BLDCM, the voltage source inverter is operated by switching signals of low frequency. Consequently, the losses pertaining to switching of solid-state switches are abated. Also, the conduction losses associated with Diode Bridge rectifier is eradicated. An improved performance of the proposed arrangement is attained due to the reduced number of power conversion stages along with torque ripple minimization.
IoT based home automation

Abstract A major problem we still face today in this connected world, where more than 100 Quadrillion bytes of data are created every day, accelerating with the growth of the Internet of Things (IoT), needs to be properly harvested. There is not one complete solution to monitor one’s homes securely and remotely with less human intervention. Since, each device comes with its own way to connect, it makes the system clustered and difficult to control and automate. Thus, lack of intercommunication between various appliances makes the complete system obsolete which ultimately affects the security of one’s home or offices. This paper proposes an efficient home automation system using Node Microcontroller Unit (Node MCU) which focuses on sensing and maintaining suitable indoor climate conditions like temperature and humidity. Additionally, paying attention to secure homes from any theft or hazard. Also, providing flexibility to control electronic devices from any part of the world through smartphones or PC’s via internet.
Heterogeneous Hidden Markov Models for Context Modeling Through Eye Gaze Observations

Eye gaze patterns are known to have correlation with cognitive context, such as cognitive understanding, difﬁculty, fatigue and inattention. Traditional eye-gaze metrics that are developed for such analyses, such as ﬁxation duration, saccade length, the nearest neighbor index (NNI), fail to accommodate the dynamic nature of mental states. In a recent work, a hidden Markov model-based observation model was suggested, where the gaze-patterns on the Cartesian plane, which correspond to each cognitive state, are modeled as Gaussians. However, we recognized that a single observation model is not sufﬁcient to represent diverse gaze patterns that correspond to different cognitive states of the brain. In this paper, we assume a heterogeneous hidden Markov model to represent such observations and demonstrate a modiﬁed Baum-Welch approach to train such a model. The effectiveness of our approach is demonstrated using eyetracking data collected from volunteers engaged in a simulated task that required varying levels of cognitive inputs.
Vigilance demand and the effects of stimulant drugs in a five-choice reaction-time procedure in mice

Stimulant drugs used for treating attention-deficit hyperactivity disorder (ADHD) increase signal-detection accuracy in five-choice serial reaction-time procedures. These increases may result from drug-induced increases in control exerted by the stimuli that prompt responses, which was assessed in the present study. Mice were trained with food reinforcement to nose poke into one of five holes after its illumination (signal), and effects of methylphenidate, d-amphetamine, and pentobarbital were assessed. Subsequently, the time from trial onset to signal was changed from fixed to variable for one group of subjects. A ‘warning’ stimulus (change in ambient lighting) preceding the signal was added for a second group. Effects of the drugs were reassessed. Dose-related increases in accuracy of signal detection (nose pokes in hole where a signal was displayed) were obtained with methylphenidate and d-amphetamine, but not with pentobarbital. When the presignal time was variable, increases in signal detection were not obtained with either stimulant. When a warning stimulus preceded the signal, the increases in accuracy were similar to those obtained without the warning stimulus. Hence, a procedure that increased vigilance demand (using a variable prestimulus period) eliminated the effects of drugs useful in treating ADHD, whereas a procedure that decreased vigilance demand (adding the warning light) had no appreciable effects on the response to stimulant drugs. Taken together, the present results suggest that the five-choice serial reaction-time has predictive validity for selecting drugs effective for treating ADHD, although effects can depend critically on the stimulus conditions used and the vigilance required by the procedure.
Magnitude Variation of Arterial Blood Pressure Measured Using Holo-Hilbert Spectral Analysis

Arterial blood pressure (ABP) is one of the most crucial cardiovascular indicators in clinical practice. Hilbert–Huang transform (HHT) has been performed on ABP signals and resulted in ABP variability. The instantaneous P-wave interval variation had been further examined with baroreflex sensitivity. However, the instantaneous magnitude variation of ABP signal is still unclear with the pulse pressure (PP) variability. In 2016, Holo–Hilbert spectral analysis (HHSA) extended the HHT method for identifying the amplitude-modulated (AM) characteristics of signals. This method was applied to investigate the magnitude variation of ABP signal during different respiratory manipulations in this study. The results indicated that the AM parts were moderately correlated with PP series and corresponding respiratory patterns. The [Formula: see text]-values on PP series are [Formula: see text], and [Formula: see text] for spontaneous breathing, six-cycle breathing, and hyperventilation, respectively. The values on respiratory patterns are [Formula: see text], and [Formula: see text] for spontaneous breathing, six-cycle breathing, and hyperventilation, respectively. This study concludes that ABP signal with HHSA presents the corresponding PP series, the respiratory-related activities, and the respiratory effect on PP variability. This is the first demonstration of the magnitude variation of ABP signal and further research in this area is warranted.
Reproducibility of Task-Oriented Bimanual and Unimanual Strength Measurement in Children with Unilateral Cerebral Palsy

Abstract Aim: To examine reproducibility of the arm-hand strength measured while performing the bimanual crate task and the unimanual pitcher task. Methods: 105 children diagnosed with unilateral Cerebral Palsy, aged between 6 and 18 years, participated in this study. The test–retest reliability of the force generated during bimanual crate task and unimanual pitcher task of the Task-oriented Arm-hAnd Capacity instrument was investigated using intraclass correlation two-way random model with absolute agreement. The intraclass correlations were calculated for two age groups (6–12 and 13–18 years old). Results: The results showed good test–retest reliability for the crate and pitcher task with the non-affected hand for both age groups. The results of the pitcher task for the affected hand showed moderate test–retest reliability for both age groups. Conclusion: The Task-oriented Arm-hAnd Capacity instrument has moderate to good test–retest reliability. It is a simple and objective instrument to assess task-oriented strength in children with unilateral cerebral palsy.
Cognitive impairment and homelessness: A scoping review

Abstract This paper reports the findings of a scoping review designed to identify research which has explored the relationship between cognitive impairment and homelessness. A systematic search of databases for articles published between 2007 and 2017 was conducted using keywords relating to cognitive impairments and homelessness. Sources were expanded using manual searches of citations and grey literature. Forty studies represented in 45 papers were selected for review using predefined inclusion criteria. Sources were subject to quality appraisal and data were extracted in line with review questions. Prevalence studies were over‐represented in the review, while qualitative data were lacking. Aetiology of impairments was delineated by acquired and developmental causes. A variety of measures were employed by studies which were not validated in homeless populations. Studies did not give sufficient consideration to co‐occurring disorders and overlapping symptoms between aetiologies. Because of these factors, it was difficult to conclude that all studies had accurately measured what they set out to; however, the evidence suggested that cognitive impairment was disproportionately over‐represented in homeless populations. Cognitive impairment was found to be both a risk factor to and perpetuator of homelessness. Risk factors for homelessness were similar to those of the general population, though exaggerated by sequelae of certain cognitive impairments. The results of this review suggest that more attention needs to be paid to the underlying socioeconomic disadvantages, persons with cognitive impairments face which may lead to homelessness. Further research should prioritise the voice of homeless persons with cognitive impairments, to better understand both causes of homelessness and effective methods of rehabilitation.
Hallucinating Point Cloud into 3D Sculptural Object

Our team of artists and machine learning researchers designed a creative algorithm that can generate authentic sculptural artworks. These artworks do not mimic any given forms and cannot be easily categorized into the dataset categories. Our approach extends DeepDream from images to 3D point clouds. The proposed algorithm, Amalgamated DeepDream (ADD), leverages the properties of point clouds to create objects with better quality than the naive extension. ADD presents promise for the creativity of machines, the kind of creativity that pushes artists to explore novel methods or materials and to create new genres instead of creating variations of existing forms or styles within one genre. For example, from Realism to Abstract Expressionism, or to Minimalism. Lastly, we present the sculptures that are 3D printed based on the point clouds created by ADD.
Development of Criteria for Epilepsy Genetic Testing in Ontario, Canada

Abstract Multiple genes/variants have been implicated in various epileptic conditions. However, there is little general guidance available on the circumstances in which genetic testing is indicated and test selection in order to guide optimal test appropriateness and benefit. This is an account of the development of guidelines for genetic testing in epilepsy, which have been developed in Ontario, Canada. The Genetic Testing Advisory Committee was established in Ontario to review the clinical utility and validity of genetic tests and the provision of genetic testing in Ontario. As part of their mandate, the committee also developed recommendations and guidelines for genetic testing in epilepsy. The recommendations include mandatory prerequisites for an epileptology/geneticist/clinical biochemical geneticist consultation, prerequisite diagnostic procedures, circumstances in which genetic testing is indicated and not indicated and guidance for selection of genetic tests, including their general limitations and considerations. These guidelines represent a step toward the development of evidence-based gene panels for epilepsy in Ontario, the repatriation of genetic testing for epilepsy into Ontario molecular genetic laboratories and public funding of genetic tests for epilepsy in Ontario. RÉSUMÉ Élaborer des critères en vue du dépistage génétique de l’épilepsie en Ontario (Canada). De multiples gènes et variations génétiques sont responsables de la variété des conditions épileptiques existantes. Cependant, très peu de lignes directrices permettent de déterminer les situations en vertu desquelles le dépistage génétique est indiqué et de choisir des tests qui soient appropriés et bénéfiques. Dans le cas de l’Ontario (Canada), nous voulons nous pencher sur l’élaboration de lignes directrices en matière de dépistage génétique de l’épilepsie. Ainsi, un Comité consultatif de dépistage génétique a été établi dans cette province afin d’examiner la pertinence clinique et la validité de tests génétiques de même que leur prestation. Dans le cadre de son mandat, le Comité a également formulé des recommandations se rapportant au dépistage génétique de l’épilepsie. Parmi ces recommandations, il a inclus le fait de consulter obligatoirement, avant tout test, un épileptologue, un généticien ou un généticien biochimique clinique. Il a aussi recommandé aux professionnels de la santé d’établir des procédures diagnostiques préalables et de déterminer les circonstances en fonction desquelles le dépistage génétique est indiqué ou non. Enfin, il a fourni des indications en ce qui regarde la sélection des tests génétiques, notamment leurs restrictions et d’autres considérations générales. En ce qui concerne l’Ontario, l’ensemble de ces lignes directrices représente un pas vers la constitution de panels de séquençage génétique basés sur des données probantes mais aussi vers le rapatriement du dépistage de l’épilepsie dans des laboratoires ontariens de génétique moléculaire et le financement public de tests génétiques pour cette maladie.
Reinforcement Learning Based Speech Enhancement for Robust Speech Recognition

Conventional deep neural network (DNN)-based speech enhancement (SE) approaches aim to minimize the mean square error (MSE) between enhanced speech and clean reference. The MSE-optimized model may not directly improve the performance of an automatic speech recognition (ASR) system. If the target is to minimize the recognition error, the recognition results should be used to design the objective function for optimizing the SE model. However, the structure of an ASR system, which consists of multiple units, such as acoustic and language models, is usually complex and not differentiable. In this study, we propose to adopt the reinforcement learning (RL) algorithm to optimize the SE model based on the recognition results. We evaluated the proposed RL-based SE system on the Mandarin Chinese broadcast news corpus (MATBN). Experimental results demonstrate that the proposed SE system can effectively improve the ASR results with a notable 12:40% and 19:23% error rate reductions for signal to noise ratio (SNR) at 0 dB and 5 dB conditions, respectively.