Miracle-dz/UniFAR-FTUs huggingface dataset:
Antimicrobial resistance and virulence genes of Enterococcus faecalis isolated from subclinical bovine mastitis cases in China

This study aimed to investigate the antimicrobial resistance and virulence genes of Enterococcus faecalis isolated from subclinical bovine mastitis cases in China. Enterococcus faecalis isolates were identified by 16S rRNA amplification and sequencing. Antimicrobial susceptibility was determined by the disc diffusion method. Antimicrobial resistance and virulence genes were tested by PCR. Overall, E. faecalis was recovered from 81 of 1,787 (4.5%) mastitic milk samples. The isolates showed high resistance against tetracycline (87.7%) and erythromycin (79.0%). The most prevalent resistance genes found in the E. faecalis were tetK (96.3%), tetL (79.0%), and tetM (87.7%) for tetracycline and ermC (97.5%) for erythromycin. Moreover, gelE (70.4%), esp (85.2%), efaA (91.4%) were the most common virulence genes. This is the first report to characterize E. faecalis recovered from subclinical bovine mastitis cases in China.
Designing Data Interchange Services for Learning Components

The paper proposes design principles for data representation in cloud data interchange services among various information systems, with applications on e-learning systems. These principles contribute to detailing and improving standards in the process of retaining learning objects in repositories and facilitate discovering digital content used for teaching, learning, or training among various information systems. Equivalence algorithms and canonical representation are used in order to ensure the uniform representation in the cloud database. The solution we describe, proposed to be provided within cloud architectures, brings important advantages in e-learning systems communication and educational institutions cooperation, since different institutions use different e-learning systems and do not have automatic means of exchanging information. The advances in representing learning objects contribute to facilitating the exchange of these resources among different stakeholders.
Dependency Parser for Malayalam: A Machine Learning Approach

A Dependency parser is important for applications like sentence structure identification, machine translation, clause boundary identification, question answering system etc. No work has been done so far in the field of dependency parser for Malayalam using machine learning approaches. Like any other Indian languages, Malayalam is a morphologically rich- less word order language (MoR-LWO). This property has the negative effect on the correctness of ordinary parsing. The proposed system is a dependency parser model for Malayalam using machine learning. Malt Parser with LIBSVM algorithm is used for modeling the proposed system.
A Study on Communication System in VANET

VANET (Vehicle Ad-Hoc Network) is one of the new technologies in the communication system. It supports inter-vehicle communication through the 802. 11p.Many studies have looked at improving the quality of the communication system in VANET. Optimization and effectiveness issues in VANET are still open to exploration. Some researchers have used the natural-inspired algorithm to accomplish the issues in the VANET environment. Many tools can be used as a VANET simulator. While they are helpful, only some of these tools can be used for sophisticated analysis. It can support in-depth analysis or good graphical user interface features. But they cannot be thoroughly combined now. Each simulator has a different characteristic to produce a result. It helps to make a realistic traffic model in VANET. In this paper, we provide a brief information about the VANET communication systems, simulators and models.
A Low Cost Automated RFID Tag Antenna Measurement Set-up Based on UHF-RFID Reader

This paper describes the development of a low cost automated measurement set-up that allows characterizing UHF-RFID tag antennas in real conditions. The system includes Thing Magic Mercury 6e (M6e) UHF RFID development kit, stepper motor (Brother model KE58KM2-032), a driving board and a single circularly polarized patch antenna having a gain of 6 dBi at the frequency range of 800-1000 MHz.The obtained results (power activation, read range, and radiation pattern) using this system have been compared with the classical platform measurement system in term of both sets of data, shows a good agreement.
Recognition of Real and Imaginary Fist Movements Based on Dynamical Mode Decomposition Spectrum of EEG

Brain is a high dimensional complex dynamical system whose governing equations are unknown. Its functions are inferred through analysis of EEG signals which is a very difficult and complex task. The power spectrum analysis remained as one of the foremost method for feature extraction for BCI applications. In the context of BCI, spatial information is inevitable. Moreover while considering the complicated circuitry involved in generation of EEG signals the usual power spectrum analysis becomes insufficient. Recently developed data driven method called dynamic mode decomposition (DMD) is a good candidate for analysis of such complex signals. From the time-resolved spatial data, DMD algorithm develops a linear spatiotemporal model which can be used both for signal classification and prediction. For some dynamical systems, the model can also be used to control the behavior of the system. In this work, the EEG signals are modeled using the DMD method. Real and Imaginary motor movements of fist have been classified from EEG data by extracting the power spectrum of the various DMD components. The DMD spectrum provided 16% more accuracy compared to Fourier power spectrum.
Real Time Face Recognition Comparison Using Fisherfaces and Local Binary Pattern

Face recognition has been gaining popularity by computer vision researchers over last two decades. Face recognition concerns to identify person from an image set. In general there are three face recognition classes, i.e., holistic based, feature-based, and hybrid methods. Fisherfaces extends Eigenfaces approach using Fisher's linear discriminant to improve classification rate by maximising the ratio of between-class to within-class scatters. In the other hand, local binary pattern employs shape and texture in local pixel neighbourhoods to build a global representation of a face image. Both methods are implemented and analysed using three distinct face image dataset and a real time video application. The accuracy, training and testing time for both algorithms are measured in some experiments using k-fold cross validation scheme. From experiment result, it is concluded that Fisherfaces has a good prediction time that makes it a good choice for real time face recognition applications. In contrast, local binary pattern can handle classifier addition, so it can be used in dynamic face recognition scenarios.
Practical Approach of Fast-Data Architecture Applied to Alert Generation in Emergency Evacuation Systems

This paper describes a proof of concept of a Fast-Data architecture to generate early response alerts on unforeseen events. For achieving that, in this work is presented the implementation of a fully integrated system capable to handle and process streaming data in order to generate an alert response for each generated event. The deployment stated are composed by a simulated wireless sensor network for generating environmental values, a centralized Kafka server for data segmentation and a machine learning model deployed in a Spark cluster for generating the emergency alerts. Also, a simulation was conducted assuming that a fire had affected the simulated scenario in order to determine and evaluate the system's behavior. Finally, the classification model is presented as an early system alternative based on real-time processing and can be used in different areas of occupational safety.
The Platelet Response to Tissue Injury

In recent years, various studies have increasingly explained platelet functions not only in their central role as a regulator in cellular hemostasis and coagulation. In fact, there is growing evidence that under specific conditions, platelets act as a mediator between the vascular system, hemostasis, and the immune system. Therefore, they are essential in many processes involved in tissue remodeling and tissue reorganization after injury or inflammatory responses. These processes include the promotion of inflammatory processes, the contribution to innate and adaptive immune responses during bacterial and viral infections, the modulation of angiogenesis, and the regulation of cell apoptosis in steady-state tissue homeostasis or after tissue breakdown. All in all platelets may contribute to the control of tissue homeostasis much more than generally assumed. This review summarizes the current knowledge of platelets as part of the tissue remodeling network and seeks to provide possible translational implications for clinical therapy.
Maternal Diabetes and Fetal Programming Toward Neurological Diseases: Beyond Neural Tube Defects

The purpose of this review was to search for experimental or clinical evidence on the effect of hyperglycemia in fetal programming to neurological diseases, excluding evident neural tube defects. The lack of timely diagnosis and the inadequate control of diabetes during pregnancy have been related with postnatal obesity, low intellectual and verbal coefficients, language and motor deficits, attention deficit with hyperactivity, problems in psychosocial development, and an increased predisposition to autism and schizophrenia. It has been proposed that several childhood or adulthood diseases have their origin during fetal development through a phenomenon called fetal programming. However, not all the relationships between the outcomes mentioned above and diabetes during gestation are clear, well-studied, or have been related to fetal programming. To understand this relationship, it is imperative to understand how developmental processes take place in health, in order to understand how the functional cytoarchitecture of the central nervous system takes place; to identify changes prompted by hyperglycemia, and to correlate them with the above postnatal impaired functions. Although changes in the establishment of patterns during central nervous system fetal development are related to a wide variety of neurological pathologies, the mechanism by which several maternal conditions promote fetal alterations that contribute to impaired neural development with postnatal consequences are not clear. Animal models have been extremely useful in studying the effect of maternal pathologies on embryo and fetal development, since obtaining central nervous system tissue in humans with normal appearance during fetal development is an important limitation. This review explores the state of the art on this topic, to help establish the way forward in the study of fetal programming under hyperglycemia and its impact on neurological and psychiatric disorders.
Bias Detection of Palestinian/Israeli Conflict in Western Media: A Sentiment Analysis Experimental Study

The online mass media plays a critical role in influencing the public opinion about controversial political events. Bias in press reports and articles to some ideological or political sides is common and opposites the neutrality nature of press and media. Bias can take different aspects and ways. One of the main aspects of press bias is using mislead terms and vocabularies. In summer 2014, Western media, news and press agencies covered Israeli war on Gaza. In general, Palestinian people complain that there is a notable bias in western media with the Israeli story and opinion and vice versa. In this research paper we report a text mining experimental study, that’s have conducted on western media analysis to identify patterns in the press orientation and further in the media bias towards side to another. We have followed the text mining techniques and machine learning in an effort to detect the bias in news agencies. We have crawled news articles form seven major outlets in the western media. Then we have made preprocessing to convert them into useful structured form, building sentiment classifiers that be able to predict articles bias. In addition, we have compared three of supervised machine learning algorithms used in sentiment classification associated with different number of grams, where we have found that SVM with bio-gram gave the better outperformed outputs, with performance metrics are 91.76% accuracy, 88.33% recall and f- measure 91.46%.
FlinkCL: An OpenCL-Based In-Memory Computing Architecture on Heterogeneous CPU-GPU Clusters for Big Data

Research on in-memory big data management and processing has been prompted by the increase in main memory capacity and the explosion in big data. By offering an efficient in-memory distributed execution model, existing in-memory cluster computing platforms such as Flink and Spark have been proven to be outstanding for processing big data. This paper proposes FlinkCL, an in-memory computing architecture on heterogeneous CPU-GPU clusters based on OpenCL that enables Flink to utilize GPU's massive parallel processing ability. Our proposed architecture utilizes four techniques: a heterogeneous distributed abstract model (HDST), a Just-In-Time (JIT) compiling schema, a hierarchical partial reduction (HPR) and a heterogeneous task management strategy. Using FlinkCL, programmers only need to write Java code with simple interfaces. The Java code can be compiled to OpenCL kernels and executed on CPUs and GPUs automatically. In the HDST, a novel memory mapping scheme is proposed to avoid serialization or deserialization between Java Virtual Machine (JVM) objects and OpenCL structs. We have comprehensively evaluated FlinkCL with a set of representative workloads to show its effectiveness. Our results show that FlinkCL improve the performance by up to <inline-formula><tex-math notation="LaTeX">$11 \times$</tex-math><alternatives> <inline-graphic xlink:href="chen-ieq1-2839719.gif"/></alternatives></inline-formula> for some computationally heavy algorithms and maintains minor performance improvements for a I/O bound algorithm.
Discourses Surrounding Feminism in Ukraine: A Sentiment Analysis of Twitter Data

This paper outlines the localization of the SentiStrength sentiment analysis program into Ukrainian and its application to tweets on feminism. The SentiStrength algorithm aims at detecting sentiment polarity and strength of a text. Thus, it helps to identify inappropriate affective utterances, associated with discrimination and aggression. The findings reveal a prevailing strong negative sentiment of the analyzed tweets. The direct expression of support for feminist goals by the Ukrainian Tweeter users couples with ambiguity about the idea of feminism.
Hemorrhage of a pancreatic metastasis from lung adenocarcinoma after osimertinib therapy.

Osimertinib is an epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI). It is used as anti-cancer therapy in patients with EGFR-TKI sensitizing mutations and EGFR T790M mutations and is recommended for treatment of patients with EGFR T790M-positive advanced non-small cell lung cancer (NSCLC) after progression on first-line EGFR-TKIs or frontline treatment in EGFR mutation-positive (exon 19 deletion or L858R) advanced NSCLC (1,2). Common adverse effects of osimertinib are similar to other EGFR-TKIs and are generally mild in severity and reversible (1). Previous reports have suggested an association between gefitinib or erlotinib treatment and organ bleeding or tumor hemorrhage (3-8). But to date there have been no reports of a similar association between osimertinib and tumor hemorrhage. Herein, we report an unusual case of hemorrhage of a pancreatic metastasis from lung adenocarcinoma after osimertinib therapy.
A Fast HEVC Inter CU Size Decision Algorithm Based on Multi-Class Learning

HEVC achieves much higher coding efficiency compared with previous coding standards at cost of significant computation complexity. This paper proposes an efficient machine-learning based inter CU size decision algorithm for HEVC. The proposed algorithm uses a wrapper method based on F-score to select features in several factors that may be related to the CU partition, and designs a three-output classifier to control the risk of false prediction by combining the machine learning classifier with the RDcost judgment of the original algorithm. Experimental results show that the proposed method provides a reduction of 45.64% on average in computational complexity with a small increase in the bit-rate and a negligible reduction in PSNR values.
Value in the Eyes of Users: A Case Study of Web-Based Applications Using Repertory Grid Technique

A few years ago, users were solely end-consumers with very limited knowledge and role. While today they are a common element in the development of any product or service with a fruitful knowledge and active role. Identifying the values and needs of the user constitutes a crucial activity in the development process. However, values are still implicit and inexpressive. Models and techniques which incorporate user value in the development process are still missing so far. Therefore, the objective of this paper is to understand the values and activities that lead to its creation from the point of view of users. The Repertory Grid Technique (RGT) has been employed where (22) users have been interviewed to derive their values and activities in web-based applications. The results show that the values related to everyday life activities are the most important to the user while values related to the design of the web application come second in terms of importance. The results also show that the most important activities to the user are those activities for learning, networking and sharing. The findings indicate that there is a strong linkage between user value and the concept of user experience which require to be explored further and considered in any new development projects.
Performance Analysis of the Microsoft. Net- and Java-Based Implementation of REST Web Services

REST APIs could be implemented using Java, Microsoft. Net-based technologies or a PHP framework. This paper compares the performance of REST APIs implemented with Java technologies and Microsoft. Net. Java-based RESTful APIs were implemented using Jersey and deployed on the Apache Tomcat Server. RESTful APIs with the same parameters were implemented also with MS. Net using the. Net Framework 6.0 and were deployed on the Microsoft IIS Server. Performance was evaluated and compared in several test scenarios. The results showed that the. Net API performs better than the Java-based API for the processing of PUT operations. Processing time was 11.6% lower in the case of the. Net API. Contrary the Java-based API gave a better performance for the GET operations by processing 80.36% more records in the same time.
DCQCN+: Taming Large-Scale Incast Congestion in RDMA over Ethernet Networks

Remote Direct Memory Access (RDMA) gains growing popularity in datacenter networks. The state-of-the-art congestion control scheme is DCQCN. However, DCQCN has performance problems when large-scale incast communication happens. DCQCN uses fixed period and steps for rate increase when probing for available bandwidth and this scheme is not scalable. Our key insight is that: senders should be aware of the scale of each incast, so that they can adjust their aggressiveness accordingly. The challenges come from different aspects. The scale of congestion is not easy to estimate while the control scheme should be cautiously designed. In this paper, we propose DCQCN+ to improve performance for large-scale incast congestion in RDMA networks. DCQCN+ adapts the rate control mechanisms to different scenarios. DCQCN+ can deal with incast congestion of at least 2,000 flows both in simulation and testbed. The scale is 10 times larger than that of DCQCN in simulation and 4 times larger in testbed. DCQCN+ also has 10 times smaller latency.
The Intersection of Diabetes and Cardiovascular Disease—A Focus on New Therapies

Diabetes is a leading cause of cardiovascular disease and its associated morbidity. While the medical community has had access to numerous glucose lowering therapies over the last decades, it was not until recently that newer agents demonstrated improvement in cardiovascular outcomes. In particular, diabetes care and management of its attendant cardiovascular risk is now being revolutionized with the development and provision of the SGLT-2 inhibitors and GLP1-receptor agonists. Given the exciting data with these new classes of diabetes therapeutics, there is a clear need to improve education and utilization of these evidence-based medications across a wide spectrum of clinicians, including cardiologists. The aim of this review is to familiarize the cardiovascular specialist with the benefits and harms of the most commonly used oral anti- hyperglycemic medications, with an emphasis on SGLT-2 inhibitors and GLP-1 receptor agonists.
Resource-Efficient State-Machine Replication with Multithreading and Vertical Scaling

State-machine replication (SMR) enables transparent and delayless masking of node faults. It can tolerate crash faults and malicious misbehavior, but usually comes with high resource costs, not only by requiring multiple active replicas, but also by providing the replicas with enough resources for the expected peak load. This paper presents a vertical resource-scaling solution for SMR systems in virtualized environments, which can dynamically adapt the number of available cores to current load. In similar approaches, benefits of CPU core scaling are usually small due to the inherent sequential execution of SMR systems in order to achieve determinism. In our approach, we utilize sophisticated deterministic multithreading to avoid this bottleneck and experimentally demonstrate that core scaling then allows SMR systems to effectively tailor resources to service load, dramatically reducing service provider costs.
A Recommendation Algorithm Based On Fuzzy Clustering

A recommendation algorithm based on fuzzy clustering is proposed in this paper. The idea of this system is as follows: Firstly, based on examples of hotel users' reviews and movie reviews collected by website crawler, the high-frequency valueless words and the low-frequency unimportant words are removed by means of term frequency-inverse document frequency (TF-IDF) algorithm of textual word segmentation. After then, term vector representation is used for preparation of subsequent related algorithms. Finally, fuzzy clustering is conducted to the review word segmentation dataset. Based on the clustering dataset mentioned above, the terms, sentences and users are separately clustered. The input of recommendation in each stage is used as the output of next recommendation to finally form an organic whole. Fuzzy clustering method adopted by the system greatly reduces the size of the dataset in the iterative process of the recommended algorithm and improves the precision of the recommendation.
Use of a FIR filter for fetal phonocardiography processing

Fetal phonocardiography (fPCG) is becoming a very useful tool for monitoring the foetus status. Measured signals in the abdominal area are often affected by many artifacts, so the Finite Impulse Response Filter (FIR filter) for noise suppression has been tested in this work. Testing is performed on 37 synthetic records and the evaluation of filter efficiency is performed on the basis of determining the signal-to noise-ratio (SNR) and using Bland-Atman statistics and the calculated value of the standard deviation (SD), sensitivity value (Se), Positive predictive value (PPV), accuracy (ACC) and F1, which is the overall probability of correct detection of beats. Testing is done for different FIR filter settings, and the results have shown that the FIR filter can be effectively used to reduce noise in the fPCG signal when the filter order is set to 1000.