Miracle-dz/UniFAR-FTUs huggingface dataset:
Motor imagery recognition with automatic EEG channel selection and deep learning

Objective. Modern motor imagery (MI)-based brain computer interface systems often entail a large number of electroencephalogram (EEG) recording channels. However, irrelevant or highly correlated channels would diminish the discriminatory ability, thus reducing the control capability of external devices. How to optimally select channels and extract associated features remains a big challenge. This study aims to propose and validate a deep learning-based approach to automatically recognize two different MI states by selecting the relevant EEG channels. Approach. In this work, we make use of a sparse squeeze-and-excitation module to extract the weights of EEG channels based on their contribution to MI classification, by which an automatic channel selection (ACS) strategy is developed. Further, we propose a convolutional neural network to fully exploit the time-frequency features, thus outperforming traditional classification methods both in terms of accuracy and robustness. Main results. We execute the experiments using EEG signals recorded at MI where 25 healthy subjects performed MI movements of the right hand and feet to generate motor commands. An average accuracy of 87.2±5.0% (mean±std) is obtained, providing a 37.3% improvement with respect to a state-of-the-art channel selection approach. Significance. The proposed ACS method has been found to be significantly advantageous compared to the typical approach of using a fixed channel configuration. This work shows that fewer EEG channels not only reduces computational complexity but also improves the MI classification performance. The proposed method selects EEG channels related to hand and feet movement, which paves the way to real-time and more natural interfaces between the patient and the robotic device.
Standards for Cloud Risk Assessments—What's Missing?

Conducting a risk assessment (RA) for cloud computing platforms presents new challenges in the space of Information Security Management Systems (ISMS). ISO 27001 RA methods have been used for Alcohol Monitoring Systems (AMS) across a portfolio of products and services. Scaling these localized techniques to national and international cloud security standards shows that a ‘one size fits all’ RA approach does not exist in the industry today. The context and methods for conducting cloud RA are examined across representative national and international standards and guidelines.
Smart Healthcare Ecosystem for Elderly Patient Care

The healthcare sector is one of the rapidly growing service-based sectors in the world. Constant technological adoptions have been implemented to provide enhanced patient care and other healthcare-related services. Recently, the rapidly growing Internet and sensing technologies, cloud platforms, and remote healthcare monitoring have paved a way to build smart healthcare ecosystems. However, special attention needs to be given for delivering high-quality clinical services for the growing elderly population and critically ill patients who are finding it difficult to reach out for professional medical help either due to terminal illness or because of their remote geographical location. In this paper, we propose a service model for a smart healthcare ecosystem where the patient data is collected via medical IoT sensors connected to the patient, sensor’s data is stored in cloud infrastructure, and is analyzed by an expert from a remote telemedicine center. Moreover, an authorized telemedicine infrastructure’s person can regularly monitor the activities of the caregiver and interact with the patient without having the patient to visit the hospital. This proposed healthcare service model aims to improve trust, reliability, and cost-effectiveness of the overall healthcare service delivery.
Multi-View Spectral Clustering via ELM-AE Ensemble Features Representations Learning

Spectral cluster based on multi-view data has proven effective for clustering multi-source real-world data because consensus and complementary information of multi-view data ensure the result of clustering. Feature learning is the vital step in spectral clustering, and excellent feature representations can effectively improve spectral clustering performance. In this article, we propose Multi-View Spectral Clustering via ELM-Autoencoder Ensemble Features Representations Learning (MvSC-EF-ELM). First, Extreme Learning Machine as an Autoencoder(ELM-AE) learns feature representations by adopting a singular value decomposition, which reconstructs the inputs using the embedding feature space. Second, the single view and multi-view spectral clustering algorithm are applied embedding feature representations space into the eigenspace and cluster’s them, respectively. Experiments on benchmark datasets demonstrate that our approach empirically validates the power of ELM-AE for feature learning from raw data and may effectively facilitate multi-view spectral clustering and induce superior clustering results.
Attention as a Unitary Concept

In this paper, I discuss attention in terms of selecting visual information and acting on it. Selection has been taken as a bedrock concept in attention research since James (1890). Selective attention guides action by privileging some things at the expense of others. I formalize this notion with models which capture the relationship between input and output under the control of spatial and temporal attention, by attenuating or discarding certain inputs and by weighing energetic costs, speed, and accuracy in meeting pre-chosen goals. Examples are given from everyday visually guided actions, and from modeling data obtained from visual searches through temporal and spatial arrays and related research. The relation between selection, as defined here, and other forms of attention is discussed at the end.
Efficient Calculation of the Radiation by an Electrically Large Slot in a Rectangular Cavity

This work applies a new efficient method to calculate the scattering of electrical large structures on the subject of the radiation of an electrically large slot in a rectangular cavity, excited by an electrically large antenna in the internal region. Due to the method only the antenna and the slot need to be discretized into small elements, which are then represented as electrically small dipole moments, which radiate in the respective region. Once the equivalent dipole moments are known, the generated fields can easily and efficiently be calculated everywhere. The method and the results are validated with the method of moments for the internal and external region and very good agreement can be noted.
Istanbul as a Global Financial Center Istanbul as a Global Financial Center

The GARCH model is one of the most widely-applied econometric models in finance, but the results gleaned are rarely tested for structural breaks. This study’s main aim is to explain the monetary policy strategies held by CBRT and the historical change of monetary policy tools used within these strategies, as well as to analyze liquidity management after the 2008 crisis under MS-GARCH, MS-PGARCH and MS-APGARCH methods. In order to test the effects of CBRT’s new wave of policies we created an O/N interbank rate and a two-sided transaction volume series by taking natural logarithms. We distinguish three different regimes in our study. According to our results, the volatility shocks caused by the liquidity arrangements of CBRT show persistence. Additionally, according to the power coefficient (which is close to 2) shocks caused by monetary authorities prevail for a longer period in financial markets.
Comparison of Tuplet of Techniques for Facial Emotion Detection

Intelligent systems use real-time emotion recognition for interacting with humans in a better way to dynamically adapt their responses based on user emotions. Emotion detection is a combination of facial expression, gestures and speech features. With proper emotion detection using facial features, the mental health of subjects could be monitored and improved, thus preventing unruly actions. Machine learning techniques applied for feature extraction and classification would perform emotion detection based only on facial expression. In this work accuracy of up to 0.9042 was achieved using a combination of Latent Dirichlet Allocation (LDA) + Support Vector Machines (SVM).
An Energy-aware Spiking Neural Network Hardware Mapping based on Particle Swarm Optimization and Genetic Algorithm

Spiking Neuron Network (SNN) is a biological neural network model which shows great capability in the time series data processing and pattern recognition etc. according to the recent research. It has been implemented in hardware system with a good scalability, where the Networks-on-Chip (NoC) interconnection strategy is widely used for the data communications between the neurons. The mapping between a SNN and a NoC hardware system is one of the challenge for the development of the hardware SNNs. In this paper, a hybrid Particle Swarm Optimization (PSO) algorithm for hardware SNN mapping is proposed with the object of minimizing the energy consumption. Compared to the conventional PSO, it can search the mapping solutions through three directions which can speed up the finding. In the meantime, the Genetic Algorithm (GA) is combined to provide the mutation operation to avoid converging to the local optimum. A typical hardware SNN is used as the testbench and results show that an effective hardware SNN mapping is obtained with a low energy consumption, and local optimum is avoided compared to other approaches.
Paradoxical modulation of influenza by intranasal administration of non-replicating adenovirus particles

Respiratory mucosal infection by airborne microbes is a common event that occurs every day. We report here that intranasal administration of non-replicating adenovirus (Ad) particles to mice could either confer rapid protection against influenza virus (IFV) challenge independent of adaptive immunity, or exacerbate influenza by triggering rapid death. The life-or-death outcome hinges on the time interval between Ad administration and IFV challenge in conjunction with specific mouse/IFV strains. Intranasal instillation of Ad particles 1–47 days prior to IFV challenge conferred rapid protection against influenza in Balb/c mice whereas exposure to Ad 39 days prior to challenge with a specific IFV strain or 1 day post-challenge with that IFV strain induced rapid death in C57BL/6 mice. Notably, consecutive administrations of Ad prior to IFV challenge conferred a synergy in triggering a potent anti-influenza state; even a detrimental Ad exposure 39 days before challenge with the deadly IFV strain was reversed to a beneficial one by subsequent Ad boosts. Results revealed an intricate relationship between infection and innate immunity that is a linchpin around which effects revolve from protective immunity to collateral damage. It is urgent to repeat the experiments with an expanded scope for characterizing the status that defines susceptibility or resistance to IFV infection and subsequently reveal the underlying mechanisms. Whether broad heterologous protective effects induced by AdE and adaptive immunity elicited by vaccination could confer synergy during mitigation of a pandemic remains to be seen.
Bivariate Empirical Mode Decomposition Descriptors for Shape Based Image Retrieval

The content based image retrievals include the color based image retrieval, the shape based image retrieval and the texture based image retrieval. Among them, the shape based image retrieval is the most common one because of the simplicity reason. The shape of an image is a plane closed curve comprised of many two dimensional data points. Therefore, it can be viewed as a complex valued sequence. On the other hand, the bivariate empirical mode decomposition represents the complex valued sequence as several components called the intrinsic mode functions (IMFs). The bivariate empirical mode decomposition descriptors are formed by applying the bivariate empirical mode decomposition to the complex valued sequence generated by the plane closed curve. This paper employs the total numbers of the local maxima of both the real parts and the imaginary parts as well as the peak frequencies and the bandwidths of the IMFs as the features for performing the shape based image retrieval. The computer numerical simulation results show that the accuracy rate and the recall rate of the image retrieval are improved compared to those based on the Fourier descriptor and the shape context based descriptor used in the MPEG7 image databases.
A Systematic Review of Machine Learning in Substance Addiction

Substance addiction affects millions of people worldwide and there is no cure for addiction. With the emergence of machine learning, it has open doors for healthcare industry to incorporate technology to help healthcare workforce to make better decision in treating patients. By applying machine learning in understanding patients with substance addiction, it can help in determining their treatment. This paper aims to provide a summary of how effective machine learning method is applied in addiction studies in which 11 studies are included in this paper by using PRISMA methodology to find sources.
Escitalopram improves neural functional prognosis and endothelial dysfunction in patients with acute cerebral infarction.

BACKGROUND Escitalopram is one of the most commonly used SSRIs at present, which has the characteristics of quick onset, less interactions with other drugs, and relative safety. OBJECTIVE This study aims to investigate the effects of escitalopram on neural functional prognoses and endothelial dysfunction after acute ischemic stroke. METHODS One hundred eligible patients afflicted with acute ischemic stroke were randomized into two groups: control and treatment groups. Patients in the treatment group received escitalopram in addition to the basic therapies in the control group over a period of 90 days. Neurological deficits were quantified using the National Institutes of Health Stroke Scale (NIHSS) score and Barthel index (BI) score, cognitive impairment was determined using the Mini-Mental State Examination (MMSE) score, depressive symptoms were measured using the 17-item Hamilton Depression Rating Scale (HAMD). Furthermore, post-stroke depression (PSD) was defined based on the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition), with a HAMD score ≥17. Flow-mediated vascular dilatation (FMD) of the brachial artery was use as a surrogate indicator for endothelial dysfunction assessment with ultrasound. RESULTS The mean NIHSS and HAMD scores on day 90 after treatment were significantly lower in the treatment group than in the control group (2.17±0.36 vs. 4.24±0.85; 5.81±1.35 vs. 10.43±4.91; P < 0.01), while the mean BI score and FMD were significantly higher in the treatment group (93.08±6.23 vs. 79.64±7.56, P < 0.01; 8.71±2.35 vs. 5.83±1.21, P < 0.05) than in the control group. The improvement in MMSE score was not significantly different between the two groups. CONCLUSIONS Treatment with escitalopram early after ischemic stroke can improve neural functional prognoses and endothelial dysfunction. Escitalopram had less side effects, which is worthy of clinical prophylactic application.
Tumor Segmentation in Brain MRI using Fully Convolutional Network

In biomedical image processing and cancer studies tumor segmentation is one of the most indispensable tasks. Early diagnosis of tumorous cells aids substantially in early treatment planning and enhances the chance of survival of the patient. Manual segmentation of tumor cells in Brain MRI is difficult and time-consuming and also requires expertise in this field. In this paper, we have presented a deep Fully Convolutional Network designed using the TensorFlow library, which can successfully perform segmentation tasks in medical images. Brain MRI images of 225 patients are used as a training data set and a separate set from 20 patients is used to test the performance of the network. Two different loss functions: dice loss and weighted cross-entropy are used in the learning algorithm. We obtained a 0.76 Dice Score coefficient (i.e., 76% of the overlapping between the predicted image and ground truth) for a training of 100 epochs.
Day-ahead Peak Regulation of Building Energy System with Multi-loads Rebound Effect

Buildings have the characteristics of similar energy supply demand and utilization scenarios, which is a high-quality resource to carry out peak regulation. It is determined that the effect of market price, user behavior and other factors should be further taken into account during the regulation process, as located at the distribution level. From the view of energy service providers, the mode of building energy system participating in peak regulation is proposed. Considering the influence of price incentives and load rebound on the time and quantity characteristics of typical building loads, a load response model with load response rate and linear attenuation rebound is built. Then, a peak regulation model of building energy service provider participating in day-ahead market is constructed, to maximize the comprehensive benefits. The effectiveness of the proposed model is verified by comparing the effects of different load rebound effects on building peak regulation in three scenarios.
Investigation of Core Compositions for Efficient 976 nm Lasing From Step Index Large-Mode-Area Fiber

We present an efficient 976 nm fiber laser achieved in a step index large-mode-area (LMA) fiber by 915 nm diode pumping. The 976 nm lasing was enabled using spectroscopic engineering in a fiber design that helps suppress the competing 1030 nm gain. Our study shows, alumino-phospho-silicate (Al:P) is advantageous for shaping a gain spectrum in favor of 976 nm compared to other widely used compositions, hence facilitating otherwise the challenging 976 nm lasing. In addition, a germanium (Ge)-cladding was introduced to achieve LMA and low numerical aperture step-index fiber, permitting a wavelength selective bending technique. By applying the bending technique, an efficient 976 nm lasing was achieved with up to 15 W output power (pump power limited) with 28% slope efficiency in the step-index LMA fiber.
Identification of Emerging Hazards in Mussels by the Galician Emerging Food Safety Risks Network (RISEGAL). A First Approach

Emerging risk identification is a priority for the European Food Safety Authority (EFSA). The goal of the Galician Emerging Food Safety Risks Network (RISEGAL) is the identification of emerging risks in foods produced and commercialized in Galicia (northwest Spain) in order to propose prevention plans and mitigation strategies. In this work, RISEGAL applied a systematic approach for the identification of emerging food safety risks potentially affecting bivalve shellfish. First, a comprehensive review of scientific databases was carried out to identify hazards most quoted as emerging in bivalves in the period 2016–2018. Then, identified hazards were semiquantitatively assessed by a panel of food safety experts, who scored them accordingly with the five evaluation criteria proposed by EFSA: novelty, soundness, imminence, scale, and severity. Scores determined that perfluorinated compounds, antimicrobial resistance, Vibrio parahaemolyticus, hepatitis E virus (HEV), and antimicrobial residues are the emerging hazards that are considered most imminent and severe and that could cause safety problems of the highest scale in the bivalve value chain by the majority of the experts consulted (75%). Finally, in a preliminary way, an exploratory study carried out in the Galician Rías highlighted the presence of HEV in mussels cultivated in class B production areas.
The Design of Parametric Acoustic Antenna for Horizontal Radio-Acoustic Sounding System for the Wind Turbine

Radio sounding systems are perspective candidates for use as remote wind sensors for wind turbine. An obstacle to their use is the need, on the one hand, to provide direct contact of the acoustic emitter with the external environment, and on the other hand, the requirement to protect it from the negative influence of the incoming air flows. The report discusses the possibility of creating a sharply directed low-frequency acoustic emitter with a small aperture due to the use of the known effect of diffraction of sound by sound in the air. The possibility of the operation of such a radiator in a pulsed mode is experimentally shown.
Community Detection for Information Propagation Relying on Particle Competition

In the process of information propagation, different communities may be formed due to different opinions, interests or hobbies. However, for the application for information propagation, targeted dynamic community detection methods have not been proposed previously. In this paper, we propose a particle competition aided community detection scheme for the sake of solving the dynamic community detection for information propagation. In comparison to traditional particle competition models, the particles in our proposed model are capable of performing the operations of walking, splitting and jumping and the domination matrix of the network changes continuously. Moreover, with the aid of combining the previous particle competition experiences as well as the defined particle's walking rules, our proposed community detection scheme can automatically select and update the core nodes based on the results of previous evolution. Finally, simulation results show both the effectiveness and superiority of our proposed particle competition aided community detection model for information propagation, which may have compelling applications in the context of the spread of opinions and computer viruses, etc.
Robust Demand Response for Device Scheduling under False Data Injection Attacks in Smart Grids

Utilities are required to exchange data with home energy management systems (HEMS) for the realization of efficient demand response (DR) systems. Dependability of DR schemes on HEMSs opens new attack vectors for adversaries, as HEMSs are outside the control of utilities. Adversaries, as well as authentic users, can execute false data injection attacks (FDIA) to manipulate these systems without causing significant security breaches, unlike in traditional FDIAs. Despite the research on FDIAs and attack detection on smart grids, comparatively less amount of work looked into the robustness of distributed DR schemes against FDIAs. To address that gap, we propose a robust abstracted device scheduling architecture. The proposed architecture utilizes both attack detection and impacts mitigation strategies, as it can be leveraged to improve the robustness of distributed DR schemes in the future smart grids.
The Biological chemistry of magnesium

Fundamentals of inorganic chemistry experimental methods transport and storage metalloproteins and metalloenzymes - oxygen carriers and hydrolases, redox chemistry alkali and alkaline earth metals metals in the regulation of biochemical events cell toxicity and chemotherapeutics metal complexes as probes of structure and reactivity case studies. (Part contents).