Miracle-dz/UniFAR-FTUs huggingface dataset:
Tratamentos farmacológicos para o transtorno de ansiedade social: existem novos parâmetros na atualidade?

BACKGROUND: Social anxiety disorder (SAD), despite its low detection rates and high level of associated comorbidities, is considered a treatable condition. Although the condition's response to several drug classes is well established, the algorithms for the treatment of SAD require regular updating. OBJECTIVE: To perform a systematic literature review on the efficacy of pharmacological treatments for SAD based on controlled trials published between 2005 and 2010. METHOD: Searches were performed in the electronic databases PsycInfo, Lilacs, and Medline using the search terms "social phobia or social anxiety and treatment". RESULTS: In accordance with the inclusion criteria adopted, 29 articles were included and analyzed. The following drugs, grouped according to class, proved efficient to treat SAD: a) SSRIs: escitalopram, fluvoxamine, citalopram, GR205171, and sertraline; b) SNRI: venlafaxine; c) MAOIs: phenelzine, moclobemide; d) amino acids: f-cycloserine; and (e) anticonvulsants: tiagabine. DISCUSSION: The use of SSRIs and SNRIs to treat SAD is well established and these are still considered the first-line treatment for the condition; however, evidence suggests the future potential of D-cycloserine and anticonvulsants, whose efficacy must be confirmed by further controlled trials. The action profiles of the different medications used to treat SAD at the neurobiological level, as well as that of associated treatments, need to be explored in greater depth.
Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates

Understanding the structures of political debates (which actors make what claims) is essential for understanding democratic political decision making. The vision of computational construction of such discourse networks from newspaper reports brings together political science and natural language processing. This paper presents three contributions towards this goal: (a) a requirements analysis, linking the task to knowledge base population; (b) an annotated pilot corpus of migration claims based on German newspaper reports; (c) initial modeling results.
Towards systematic black-box testing for exploitable race conditions in web apps

In this research, we focus on a stealthy issue in web apps called a race condition. This is often omitted in black-box security tests because no real guidelines exist of how to perform these tests and no dedicated tools exist to support this. That is why, in this research, we developed the first systematic method to test for race conditions in web apps from a black-box perspective. We also built a tool to support the exploitation and evaluated both in comparison with related tools. The toolset performed better than or equal to existing tools. Also, using the method yielded clear results, and we found critical issues with financial impact in two popular e-commerce web apps. Based on these results, we conclude that we have successfully created a method and toolset that are sufficient for security testing. We are also aware that much more research is required to expand upon these findings. Still, we hereby achieved the first step towards systematic testing for race conditions in web apps, and by that, we hope that this will have a positive effect on software quality in the future.
Building the Rice Blast Disease Prediction Model based on Machine Learning and Neural Networks

Rice blast disease (RBD) is one of the most damaging crop disease for the rice in Taiwan. RBD may be widespread and cause severe losses if it is not controlled in the early stage. The goal of this research is to build an early warning mechanism for the RBD using the machine learning model under current climatic condition. Five years of climatic data (ranging from 2014 to 2018) are used as candidate features in our model, which are collected by the Taiwan government. The RBD conditions are labeled via the field observation during these years. With the climate data, we conduct the recursive feature elimination algorithm to select the key features that have impacts on the RBD. To derive the RBD prediction model, we applied the Auto-Sklearn and neural network algorithms to train the classification model. The experiment results show that the proposed model can classify the RBD conditions (whether exacerbated or relieved) with an accuracy of 72% in average. In particular, our model can achieve an accuracy of 89% in the exacerbation case, which demonstrates the effectiveness of the proposed classification model. KeywordsRice Blast Disease, Recursive Feature Elimination, Neural Network, Auto-Sklearn
Speech Recognition system for mobile devices

In this thesis, an Automatic Speech Recognition system for large vocabulary (more than 10000 words) tasks is presented. This system is intended to run on resource-constrained mobile devices such as PDAs and is based on Audimus, a hybrid Hidden Markov Model / Multi Layer Perceptron based speech recognizer, tailored for the European Portuguese language. The system’s main goal is to ease interaction with these devices by adding a speech interface. The present thesis describes the progressive porting of the speech recognition system from the desktop computer to the mobile device. The main components of the system (feature extraction, acoustic model, and decoder) were progressively ported to the mobile device, producing intermediate Distributed Speech Recognition systems. To overcome the limitations of the devices, it was necessary to choose appropriate algorithms and to balance the speed and memory use of the system with its performance in terms of word error rates (WER). The final system had the same characteristics as the baseline system. The obtained system was tested in a 520 MHz Pocket PC with 64 MB of RAM running Windows Mobile 6.0. The tests used a continuous, 13,161-word radiology task with existing language models. The baseline system, tested with audio recorded with the PDA’s internal microphone and with a speaker and microphone adapted acoustic model, achieved an average WER of 3.75%. The two intermediate distributed systems had WER of 3.80% and 4.71%, respectively, while the final embedded system presented an average WER of 7.77%, running at 0.71x RT.
ALGORITHM AND FRAMEWORK FOR TESTING AND IMPLEMENTATION TECHNIQUE IN AUTOMATION OF UNIVERSITY

- Implementation of testing techniques to achieve the goal of error free automation software for the automation of university, is very lengthy, time consuming and costly. It is another fact that testers cannot detect all the errors and bugs through one testing method amongst all. In this paper a resultant algorithm is presented which shows the integration of different testing methods in a single platform. A framework describe as the methodology which shows the implementation of testing life cycle process and flow of testing phase in order to use of testing method in a order to cover up all the errors of the automation software. The resultant algorithm shows the implementation of the testing methods and their strategies to develop an agile environment for the testing and to cover all the possible error of the automation software.
Enhancing Service Discovery Performance Over Home Networks

Service discovery protocols are used to facilitate dynamic cooperation among devices / services with minimal user intervention. These protocols work by exchanging messages to identify and locate the relevant services. When new devices and services are added the flow of messages often appears as a burst. These bursts act as a critical factor that may cause the loss messages which dramatically influences the performance of the service discovery process. Thus, the loss of messages results in uncompleted discovery process which will directly affect the sharing of resources in home networks. This paper proposes an algorithm that computes the minimum period of time required between a consecutive burst of messages and determines the minimum required queue sizes for the routers to manage the traffic and avoid dropped messages. The algorithm has been applied to the Universal Plug and Play (UPnP) protocol and considered the used of Active Queue Management (AQM). It was tested when the routers were connected in two configurations; decentralised and centralised. The message length and bandwidth of the links among the routers were taken into consideration. The result shows better improvement in number of dropped messages `among the routers.
Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks

Claim verification is generally a task of verifying the veracity of a given claim, which is critical to many downstream applications. It is cumbersome and inefficient for human fact-checkers to find consistent pieces of evidence, from which solid verdict could be inferred against the claim. In this paper, we propose a novel end-to-end hierarchical attention network focusing on learning to represent coherent evidence as well as their semantic relatedness with the claim. Our model consists of three main components: 1) A coherence-based attention layer embeds coherent evidence considering the claim and sentences from relevant articles; 2) An entailment-based attention layer attends on sentences that can semantically infer the claim on top of the first attention; and 3) An output layer predicts the verdict based on the embedded evidence. Experimental results on three public benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines.
FPGA realisation of the genetic algorithm for the design of grey-scale soft morphological filters

Although soft morphological filters give excellent performance in restoring corrupted images, they are hard to design. One successful approach is to use iterative search methods such as genetic algorithms. However, they can be computationally exhaustive taking weeks to converge. Parallel genetic algorithms give massive reductions in processing time, but their performance when implemented on multiprocessor systems or cluster of workstations has a heavy dependence on the number of servers. Also, they require some sort of synchronisation between the working processors. Because of their parallel capabilities, field programmable gate arrays (FPGAs) provide a cheap and efficient way to implement parallel algorithms. In this paper, a realisation of the genetic algorithm on an FPGA is introduced. The genetic algorithm is given the task of optimising a grey-scale soft morphological filter. The soft morphological filter is implemented using a parallel quick sorting algorithm. The results of synthesizing and simulating the design on SPARTAN II FPGA resulted in a good utilisation of the device resources. Also, the optimisation process was performed in a very short time. In contrast with the standard parallel genetic algorithms, the design does not require any synchronisation or management schemes.
De-Mixing Sentiment from Code-Mixed Text

Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. It is an increasingly common occurrence in today’s multilingual society and poses a big challenge when encountered in different downstream tasks. In this paper, we present a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data. Our method consists of three components, each seeking to alleviate different issues. We first generate subword level representations for the sentences using a CNN architecture. The generated representations are used as inputs to a Dual Encoder Network which consists of two different BiLSTMs - the Collective and Specific Encoder. The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words. This, combined with a Feature Network consisting of orthographic features and specially trained word embeddings, achieves state-of-the-art results - 83.54% accuracy and 0.827 F1 score - on a benchmark dataset.
Squeak by Example

Squeak is a modern open-source development environment for the classic Smalltalk-80 programming language. This book, intended for both students and developers, will guide you gently through the language and tools by means of a series of examples and exercises. We are making this book available to you under the Creative Commons Attribution-ShareAlike 3.0 license. You can either download the PDF for free from SqueakByExample.org, or you can buy a softcover copy from lulu.com. (You can also pay for the PDF download from lulu.com, if you would like to make a contribution to this effort.) Additional material is available from the book's web page at SqueakByExample.org.
A performance analysis of the reliable multipeer protocol (ReMP) for dynamic multimedia communications

Reliable Multipeer Protocol (ReMP) is a reliable multipeer data transfer protocol, which has been designed to support multimedia group communications. In this paper, we describe ReMP focusing on a reliable data transmission and analyze the performance on the host processing requirements. In addition, ReMP is compared to other reliable multicast protocols. Our numerical results show that ReMP can be more generally used for multimedia group applications, and are acceptable in terms of the performance and the memory requirements.
On Retrieval of Nearly Identical Video Clips with Query Frame

Video clips retrieval from a video database is a challenging problem for researchers because of voluminous data. Video Clips retrieval with query frame finds the video clips that have similar frames to query frame. Applying matching to all frames of any video clips is time consuming and computationally complex for database containing of several large size video clips. In the present article, we present a three level near identical video clips retrieval algorithm. In the first step, we identify keyframes of video clips in database through establishment of shot transition boundaries and then on keyframes of video clips we apply Integration of Curvelet transform and Simple Linear Iterative Clustering algorithm (SLIC) to get segmented query frames. In the second step, query frame will undergo Integration of Curvelet transform and Simple Linear Iterative Clustering algorithm to get segmented query frames. Now in the third step, the segmentation results of query frame are searched to obtain matched result in database. The method is experimented on 25 different video clips belonging to the categories- animations, serials, personal interviews, news and movies. Performance of the method is assessed with the parameters Precision, Recall, F-measure, Accuracy, Detection Percentage, Jaccard Criteria, and Missing Factor. The furnished performance results have shown sound performance of the proposed method over the other state-of-art methods.
A Quality‐of‐Things model for assessing the Internet‐of‐Things' nonfunctional properties

The Internet of Things (IoT) is in a “desperate” need for a practical model that would help in differentiating things according to their nonfunctional properties. Unfortunately, despite IoT growth, such properties either lack or ill‐defined resulting into ad hoc ways of selecting similar functional things. This paper discusses how things' nonfunctional properties are combined into a Quality‐of‐Things (QoT) model. This model includes properties that define the performance of things' duties related to sensing, actuating, and communicating. Since the values of QoT properties might not always be available or confirmed, providers of things can tentatively define these values and submit them to an Independent Regulatory Authority (IRA) whose role is to ensure fair competition among all providers. The IRA assesses the values of nonfunctional properties of things prior to recommending those that could satisfy users' needs. To evaluate the technical doability of the QoT model, a set of comprehensive experiments are conducted using real data sets. The results depict an acceptable level of the QoT estimation accuracy.
Real-time Control Design with Resource Constraints

Nowadays, most control systems are manipulated by control algorithms that are implemented as software components. In many practical applications, such software components execute on platforms characterized with constrained resources, such as in embedded systems. To ensure the correct timing behavior of the control software, real-time kernel is employed to schedule several such components running on the same platform and sharing the same limited resources. However, the integration of real-time systems and control systems needs careful analysis and deliberate design with new techniques. In this dissertation, the design problem for real-time control systems in resourceconstrained platforms is investigated. The background knowledge of real-time systems and control systems is introduced, and the state of the art is reviewed. Several new approaches are proposed to contribute to the existing technologies. The limited-preemption scheduling is utilized to improve the responsiveness of control tasks and hence improve the control performance, without jeopardizing the schedulability of the whole system. A general framework for real-time control design is presented where the delay and jitter effect are incorporated into the performance optimization. The resource constraints are characterized using the convex approximation of the EDF deadline space, and a two-step procedure for period and deadline selection is proposed. The utilization of multiprocessor platform is investigated. A search algorithm is presented to exploit the internal parallelism of an application and partition it into several flows that are then implemented using resource reservation to guarantee the timely behavior and provide temporal isolation. The benefits of using this method is shown by a control application involved with a ball-and-plate system.
Data distribution system: clustering based on neural network technologies

This article is about data distribution of high load systems. We proposed a way of distribution by means of data clustering based on neural network technologies. Clustering of the test sample of data from server stations performed. This sample of data received from cloud computing server stations distributed across different data centers. Results of experimental research proved the effectivity of neural networks in case of high load data distribution.
Similarity Rules! Exploring Methods for Ad-Hoc Rule Detection

We examine the role of similarity in ad hoc rule detection and show how previous methods can be made more corpus independent and more generally applicable. Specifically, we show that the similarity of a rule to others in the grammar is a crucial factor in determining the reliability of a rule, providing information unavailable in frequency. We also include a way to score rules which are not in the training data, thereby providing a platform for grammar generalization.
Deriving Gene Associations for Metabolic Models

We define a formal procedure for inferring gene-protein-reaction (GPR) relations from complete metabolic models, using a logical representation of knowledge and a small set of inference rules. We show that different use cases of metabolic models requires difference GPR relations. Three examples from the yeast Saccharomyces cerevisae illustrate the procedure and demonstrate its usefulness.
Optimizing Woven Fabric Defect Detection For Inspection Using Image Processing And Fuzzy Logic At Cv. Maemunah Majalaya

West Java has the largest number of textile industry in Indonesia. One of the companies that engaged in textiles is CV. Maemunah which located in the district of Ibun, Majalaya Bandung. The product of CV. Maemunah has been exported to Japan. To require fabric export best quality is needed to compete with other country. For to get the best quality of product needs to consider their quality control. In The inspection process still manually used four inspection stations with two workers in each station and average 23 second processing time needed inspection per screen. Therefore, unbalance of production volume with inspection process. The effect is bottle neck in inspection process. Inspection Process in CV. In this research proposed designing automated fabric inspection using image processing and Fuzzy Logic Model. Undertake extraction using GLCM to get value of autocorrelation, cluster shade and number of object. The proposed fabric inspection using Fuzzy Logic implemented with MATLAB provides better result in identifying fabric defect and optimizing process time. Using 35 data test produces an overall accuracy 82.86% and average process time 2.528 second. Therefore, using automated fabric inspection can decrease process time 17 second. Keywords: Automated Fabric Inspection, Fabric Defect Detection, Image Processing, Fuzzy Logic Model
SAR Calculations of Novel Dual-band PIFA for Mobile Phone Applications

Aims: The objective of this paper is to design a new PIFA for cell phone applications with low SAR. Study Design: The designed PIFA antenna involves a patch with slots on the upper side of first substance layer FR-4, shorting wall, and a ground plane in the bottom side of second substance layer FR-4 and exciting (feeding port). Place and Duration of Study: Department of Electronics and Communication Engineering, Faculty of Engineering, University of Zagazig. Between December 2017 to March 2019. Methodology: it will be utilized SAM head model and a hand phantom as a user’s head and hand. Time-domain solver considers hexahedral mesh with adaptive meshing scheme used in this simulation. Results: The simulation result has been obtained using MWS CST tools which constructed on the finite integration technique. And the measurement results are accomplished with vector analyzer ZVA 67. The simulated and the investigational results are the same approximately. Conclusion: The antenna provides high gain for both frequency bands (900MHZ and 1800MHZ) with approximately 90% radiation efficiency. Moreover, The SAR analysis of the suggested antenna clearly demonstrates little SAR characteristics suitable for human usage competed with the standards of the safe SAR values.
Overview of Side Channel Cipher Analysis Based on Deep Learning

According to the widespread use of embedded cryptographic devices, one of the major security threats, side channel attack and protection research, has grown rapidly in the last decade. Along with the popularity of machine learning and deep learning, artificial intelligence technology combined with side channel technology has gradually emerged, which has achieved quite good processing results. Due to the popularity of deep learning in recent years, it is indeed to complete the work with making an overview of side channel analysis based on deep learning. So in order to perfect the theory, this paper begins with the basic theory of the side channel attack. Combined with common models, the methodological analysis of deep learning side channel analysis is summarized and the existing research results are introduced next. Finally, the paper looks forward to the future deep learning side channel technology.