Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel deep learning framework for improving the quality of services using block chain technology

Electronic Health Record (EHR) holds immensely sensitive information and consisting of ‎crucial data related to the patients. Storing and organizing such data is highly arduous task. ‎Researches still going on to improve the Quality of Service (QOS) of such data. Existing ‎study focused only on improving the system throughput, privacy and latency issues. But they ‎did not tend to scrutinize the scalability and privacy of such data records. In this paper, we ‎propose a novel deep learning framework to improve the Quality of Service using block chain ‎technology. Initially the data is classified into high priority and low priority based on its ‎nature by using Recurrent Neural Network (RNN). Then, the classified high priority data is ‎further allowed to each block of a block chain and the low priority data is stored and ‎maintained as log file. Finally, the results are compared based on the evaluation metrics ‎which demonstrates our proposed novel deep learning framework achieves better accuracy.
Lenalidomide Treatment in Myelodysplastic/ Myeloproliferative Neoplasms: A Case Report and Review of the Literature

Case Report Myelodysplastic/ myeloproliferative neoplasm with ring sideroblasts and thrombocytosis is a rare overlap syndrome, no formal guidelines for the management of this disease exist. We report the case of a 64 years old male patient who was admitted for myelodysplastic/myeloproliferative neoplasm with ring sideroblasts and thrombocytosis with mutation in the Janus Kinase 2 gene and hyperdiploid clone. Lenalidomide given for a period of 6 months performed to maintain a normal blood platelet count with, for the first time, sustained platelet response 24 months after discontinuation of treatment.
Teaching Computer Organization Using Field Programmable Gate Array: An Incremental Approach

Hiraki, 2006) use active tool for teaching computer organization and architecture by taking benefit of simulation and Field Programmable Gate Array (FPGA) technology. FPGA technology helps designing high performance systems at low cost(Sugawara & Hiraki, 2006). This paper presents novel approach to teach computer organization using an incremental approach of working models on FPGA. Our methodology of teaching digital computer organization course differs from the previous (traditional) one in two aspects. The first aspect is the use of the incremental approaches of working models. This teaching tool helps computer engineering students to be familiarized practically with computer organization through development, design, and implement of a functional and synthesizable processor. The second aspect is the use of Field programmable gate arrays to implement a real processor. Hence, the students feel the joy of designing and running a functional processor in real hardware. The students also have the chance of adding more instructions and improve the performance. The previous teaching method for the course was based on theory of computer construction. This method teaches students how computers work by studying their inner details (e.g.,
SARS-CoV-2 serology across scales: a framework for unbiased seroprevalence estimation incorporating antibody kinetics and epidemic recency

Serosurveys are a key resource for measuring SARS-CoV-2 cumulative incidence. A growing body of evidence suggests that asymptomatic and mild infections (together making up over 95% of all infections) are associated with lower antibody titers than severe infections. Antibody levels also peak a few weeks after infection and decay gradually. We developed a statistical approach to produce adjusted estimates of seroprevalence from raw serosurvey results that account for these sources of spectrum bias. We incorporate data on antibody responses on multiple assays from a post-infection longitudinal cohort, along with epidemic time series to account for the timing of a serosurvey relative to how recently individuals may have been infected. We applied this method to produce adjusted seroprevalence estimates from five large-scale SARS-CoV-2 serosurveys across different settings and study designs. We identify substantial differences between reported and adjusted estimates of over two-fold in the results of some surveys, and provide a tool for practitioners to generate adjusted estimates with pre-set or custom parameter values. While unprecedented efforts have been launched to generate SARS-CoV-2 seroprevalence estimates over this past year, interpretation of results from these studies requires properly accounting for both population-level epidemiologic context and individual-level immune dynamics.
Хімія харчових продуктів і матеріалів / Chemistry of food products and materials

The article presents the results of studying how impedance analysis can be used for determination of monosodium glutamate in order to identify food fraud. We have suggested that the parameters of complex conductivity (admittance) of a two-terminal circuit could allow detecting monosodium glutamate (E 621), an additive used in the food industry to enrich the taste. The method involves passing current of different frequencies through solid foodstuffs and a cell with liquid foodstuffs, measuring the electrical conductivity, and determining and analysing the frequency dependence of admittance. The active G component and the reactive B component of the admittance have been measured at different frequencies, from 100 Hz to 100 kHz. For the experiment, food samples were prepared in accordance with the Codex Alimentarius recommendations for the dosage of the food additive E 621: orange juice with monosodium glutamate added in the amount of 0.3%, and mashed potatoes with glutamate added in the amount of 1%, of the total weight of the products. The temperature of the tested products was 22 ± 0.2°С. The results of the studies have shown the dependences of the admittance components on the frequency for the control samples of juice and mashed potatoes and for the samples with monosodium glutamate added. The dependence of the active component and the reactive component of the foodstuff admittance have been established, with monosodium glutamate (added in the above-specified proportion) and without it. The difference is in how the dependences change in their nature. The monosodium glutamate curves both in juice and in mashed potatoes are similar. The samples containing monosodium glutamate have far higher values of the active and reactive admittance component than the control samples do, with a distinct peak of the reactive component characteristic. Therefore, impedance analysis is a possible method to detect quickly the flavour enhancer monosodium glutamate in foods of different consistency and thus identify food fraud.
European Stroke Organisation and European Society for Swallowing Disorders guideline for the diagnosis and treatment of post-stroke dysphagia

Post-stroke dysphagia (PSD) is present in more than 50% of acute stroke patients, increases the risk of complications, in particular aspiration pneumonia, malnutrition and dehydration, and is linked to poor outcome and mortality. The aim of this guideline is to assist all members of the multidisciplinary team in their management of patients with PSD. These guidelines were developed based on the European Stroke Organisation (ESO) standard operating procedure and followed the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. An interdisciplinary working group identified 20 relevant questions, performed systematic reviews and meta-analyses of the literature, assessed the quality of the available evidence and wrote evidence-based recommendations. Expert opinion was provided if not enough evidence was available to provide recommendations based on the GRADE approach. We found moderate quality of evidence to recommend dysphagia screening in all stroke patients to prevent post-stroke pneumonia and to early mortality and low quality of evidence to suggest dysphagia assessment in stroke patients having been identified at being at risk of PSD. We found low to moderate quality of evidence for a variety of treatment options to improve swallowing physiology and swallowing safety. These options include dietary interventions, behavioural swallowing treatment including acupuncture, nutritional interventions, oral health care, different pharmacological agents and different types of neurostimulation treatment. Some of the studied interventions also had an impact on other clinical endpoints such as feedings status or pneumonia. Overall, further randomized trials are needed to improve the quality of evidence for the treatment of PSD.
OPTH_A_246905 1689..1695

Purit Petpiroon Thanyathorn Vijittrakarnrung 1 Wasu Supakontanasan Apichat Tantraworasin 2,3 Yanin Suwan 1 1Department of Ophthalmology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; 2Department of Surgery and Clinical Epidemiology and Clinical Statistic Center, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; 3Pharmacoepidemiology and Statistics Research Center (PESRC), Faculty of Pharmacy, Chiang Mai University, Chiang Mai, Thailand Importance: To compare treatment outcomes among a stent group, ligature group and combined group in eyes with refractory glaucoma. Background: Various techniques have been used intra-operatively to restrict the aqueous flow in Baerveldt glaucoma implantation. Design: Retrospective chart review. Participants: All glaucoma patients aged over 18-years old who had Baerveldt implantation in Ramathibodi Hospital, Bangkok, Thailand, between October 2011 and February 2018 were included for analysis. Methods: Retrospective interventional research was conducted. All glaucoma patients who underwent drainage device implantations were retrospectively reviewed from medical records and divided into three groups: stent group, ligature group, and combined group. The primary outcome was post-operative intraocular pressure (IOP) changes and BCVA (logMAR) change. The secondary outcome is treatment failure. Repeated measurements with mixed models and multi-level parametric survival model stratified by propensity score and eye side were used to compare the primary and secondary outcomes between stent group and ligature group. Main Outcome Measures: Treatment failure between two groups. Results: A total of 163 patients with a mean age of 57.11 ± 19.04 years, implanted with Baerveldt tube, were eligible. There were no significant differences between stent and ligature groups in terms of post-operative IOP changes [mean difference with 95% confidence interval = 0.53 (−0.49, +1.55) vs −0.02 (−0.84, +0.81); P = 0.411] and post-operative BCVA (logMAR) change (0.02 (−0.13, +0.18) vs –0.05 (−0.18, +0.07); P = 0.465). The hazard ratio and 95% confidence interval of treatment failure in ligature group were 2.57 (0.72–9.19) compared with the stent group. Conclusion: This study suggests a trend toward a better result in the stent group compared with the ligature group. Further research with a larger sample size and randomized control trial is warranted.
Global explanations with decision rules: a co-learning approach

Black-box machine learning models can be extremely accurate. Yet, in critical applications such as in healthcare or justice, if models cannot be explained, domain experts will be reluctant to use them. A common way to explain a black-box model is to approximate it by a simpler model such as a decision tree. In this paper, we propose a co-learning framework to learn decision rules as explanations of black-box models through knowledge distillation and simultaneously constrain the black-box model by these explanations; all of this in a differentiable manner. To do so, we introduce the soft truncated Gaussian mixture analysis (STruGMA), a probabilistic model which encapsulates hyper-rectangle decision rules. With STruGMA, global explanations can be extracted by any rule learner such as decision lists, sets or trees. We provide evidences through experiments that our framework can globally explain differentiable black-box models such as neural networks. In particular, the explanation ﬁdelity is increased, while the accuracy of the models is marginally impacted.
Learning by Matching

Summary We consider an experimental setting in which a matching of resources to participants has to be chosen repeatedly and returns from the individual chosen matches are unknown but can be learned. Our setting covers two-sided and one-sided matching with (potentially complex) capacity constraints, such as refugee resettlement, social housing allocation, and foster care. We propose a variant of the Thompson sampling algorithm to solve such adaptive combinatorial allocation problems. We give a tight, prior-independent, ﬁnite-sample bound on the expected regret for this algorithm. Although the number of allocations grows exponentially in the number of matches, our bound does not. In simulations based on refugee resettlement data using a Bayesian hierarchical model, we ﬁnd that the algorithm achieves half of the employment gains (relative to the status quo) that could be obtained in an optimal matching based on perfect knowledge of employment probabilities.
Research on Image Classification Algorithm Based on Pytorch

With the development of artificial intelligence and deep learning, image classification technology has ushered in new opportunities and challenges. The so-called image classification problem is the problem that the user passes in the image, and then the computer sends out the classification and description of the content of the image. Feature description and detection as a traditional image classification method have many drawbacks, such as low accuracy and long time-consuming problems. Among the existing deep learning frameworks, Pytorch is relatively effective. Researchers can use the Pytorch framework to construct convolutional neural network models quickly and easily, and train them on massive data sets. This paper briefly introduces the image classification algorithm, neural network and Pytorch. The classification experiment is carried out on the Fashion MNIST data set, and the accuracy of the model reaches 88.39%. The future research direction is discussed.
An estimation method of underwater target spectrum line parameters based on convolutional neural network

The spectrum line parameters of underwater target radiated noise are important basis for underwater target recognition. In this paper, the convolution neural network (CNN) is applied to estimate the spectrum line parameters. The low frequency analysis record (LOFAR) spectrum of radiated noise is used as input data, and a suitable convolution neural network is constructed to estimate the spectrum line parameters. The processing results of simulation data show that the estimation sensitivity of convolution neural network reaches 91.6% under the condition of low SNR. It proves the effectiveness of CNN in estimating spectrum line parameters of underwater targets.
Intention prediction of UAVs based on improved DDQN

Intention prediction plays an indispensable role in future informationized air combat, which will help the command and control center to make a more correct decision. In this paper, an intelligent intention prediction approach based on an improved double deep Q network (DDQN) is developed to generate real-time intention flight paths for unmanned aerial vehicles (UAVs) in complex air combat environment. Initially, by introducing an actual topographic map, the different threats of UAVs in different terrains on the map are analyzed, based on which, a terrain environment reward function is constructed. Secondly, by splitting a complete maneuver action into six basic maneuver units and determining the probability value of each unit, a maneuver reward function is obtained. Further, to improve the real-time performance and accuracy of the standard DDQN algorithm, an improved DDQN algorithm is proposed by using temporal-difference (TD) method and binary tree data structure. Finally, the simulation results verify show that the proposed algorithm has achieved better results under complex terrain conditions.
PAC Learnability of Approximate Nash Equilibrium in Bimatrix Games

Computing Nash equilibrium in bimatrix games is PPAD-hard, and many works have focused on the approximate solutions. When games are generated from a ﬁxed unknown distribution, learning a Nash predictor via data-driven approaches can be preferable. In this paper, we study the learnability of approximate Nash equilibrium in bimatrix games. We prove that Lipschitz function class is agnostic Probably Approximately Correct (PAC) learnable with respect to Nash approximation loss. Additionally, to demonstrate the advantages of learning a Nash predictor, we develop a model that can eﬃciently approximate solutions for games under the same distribution. We show by experiments that the solutions from our Nash predictor can serve as eﬀective initializing points for other Nash solvers.
An unsupervised prediction method for radar transmitter degradation fault based on isolation forest

The transmitter is prone to premature degradation and failure for the frequent work and the harsh working environment of the marine radars. The existing method of degradation fault based on data-driven require a large number of negative samples or need to set failure threshold. To solve the above problems, an unsupervised prediction method for radar transmitter degradation fault based on isolation forest is proposed. The proposed unsupervised prediction method can predict the degradation fault of radar transmitter when there are no fault samples and the fault threshold is not reached.
A review on elicit potentials in the brain to recognize Brain-Machine interface

Brain-machine interface is a technique with the help of which a user can generate a control signal to operate a machine by manipulating its state of cognition. In this technique, electrical signals generated in the central/peripheral nervous system are used to generate commands to interact with machines. In this paper, authors reviewed this technique in detail to find out the state of the art. This paper is written in such a way that a beginner can understand what a brain machine actually is. In this paper different types of methods to elicit signals for brain machine interface is explained along with different types of methods to record the signals. It is also described the state of the art in feature extraction and classification methods used in brain machine interface.
A non-integer step ratio parallel computing method for hybrid finite-element and discrete-element on multi-core computer

Commercial finite-element and discrete-element software usually adopts a unified computational time step and limit the number of our computer cores. In the face of large-scale with refined scale computing models, this will undoubtedly increase the time cost. The multi time step method of integer step ratio is a method to deal with this kind of problem. A non-integer ratio multi-time step computing method based on domain decomposition and subcycling method is proposed and applied to the finite-element and discrete-element engineering problems. The whole model is divided into finite-element method (FEM) subdomains and discrete-element method (DEM) subdomains according to the load balance of CPU. A coupling region between adjacent domains consists of multiple boundary nodes and particles based on the non-integer step ratio. Boundary data in coupling region are transferred through MPI communication protocol. This method can make full use of the advantages of multi-core computer and improve the calculation efficiency of engineering software.
Action recognition based on element-level fine-grained multi-modal fusion

Traditional action recognition algorithms often only pay attention to video RGB features or optical flow features. These methods do not make good use of the audio information in the video. Based on RGB and optical flow characteristics, this paper introduces the processing of audio information, and classifies videos based on element-level fine-grained multi-modal fusion. Through experimental comparison, the accuracy of the multi-modal fusion algorithm proposed in this paper is improved by 7.38% on the HMDB51 dataset and 3.18% on the UCF101 dataset compared to the simple modal splicing. At the same time, it is proved that the introduction of audio modes can effectively improve the performance of the model.
Recent development in operating system based on AUTOSAR standard used in ECUs for automotive vehicles

Development in automotive embedded systems has grown rapidly in recent years due to increased functionality in automotive vehicles. Nowadays, a lot of modern technologies have been implemented in automotive vehicles in order to improve the performance and reliability. Automotive Open System Architecture (AUTOSAR) is a software standard used for development of the new modules inside of the automotive Systems. The evolution in automotive vehicles impacted the usage and design of real time operating systems as automotive embedded systems are mainly dependent on real time operating systems used in ECUs inside automotive vehicle. AUTOSAR based operating system is also a real time operating system which is developed on the basis of OSEK (Open Systems and the Corresponding Interfaces for Automotive Electronics) standards. AUTOSAR is nothing but a standardized extension of OSEK and both play very important role in software development of automotive systems. In this paper, the recent trends in the development in automotive embedded system have been discussed. The standards required for designing a real time operating system have been briefly explained for better understanding of the developments in the automotive embedded systems.
Development of Pervasive IoT Based Healthcare Monitoring System for Alzheimer Patients

Alzheimer is a neurological degenerative disease that steadily and gradually progresses. This disease is incurable at present and those who suffer from it have the hesitation to travel beyond their homes in comparison to normal people. The main aim of this paper is to offer a continuous mechanism that enhances the Alzheimer patient’s quality of life as well as that of their caregivers. Proposed work is based on the Internet of things (IoT) based sensors data that figure out different parameters of the patient body like temperature, blood pressure, striding action, striding speed, and so on. All these sensory data will be obtained with the help of the Atmega Microcontroller. All the collected data are then sent to the cloud server with the help of parallel communication for data analysis. The desired parameter of the patient is retrieved which will help in providing real time support to the patients.
Inverse dynamics modeling of robots based on sparse spectral gaussian process regression

In robot control systems, inverse dynamics calculation of the robotic arm is essential. In this paper, we propose a different inverse dynamics modeling method—sparse spectral Gaussian process regression (SSGPR). Since using Gaussian process regression for robot inverse dynamics modeling is computationally intensive, to address this drawback, it is proposed to apply sparse spectral Gaussian process regression on robot inverse dynamics modeling, the core of which is the sparsification of the GP’s spectrum for the purpose of improving computational efficiency. Experiments show that sparse spectral Gaussian process regression can improve the computational efficiency and outperform other regression methods while ensuring the computational accuracy.