Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding TCP over TCP: effects of TCP tunneling on end-to-end throughput and latency

TCP tunnel is a technology that aggregates and transfers packets sent between end hosts as a single TCP connection. By using a TCP tunnel, the fairness among aggregated flows can be improved and several protocols can be transparently transmitted through a firewall. Currently, many applications such as SSH, VTun, and HTun use a TCP tunnel. However, since most applications running on end hosts generally use TCP, two TCP congestion controls (i.e., end-to-end TCP and tunnel TCP) operate simultaneously and interfere each other. Under certain conditions, it has been known that using a TCP tunnel severely degrades the end-to-end TCP performance. Namely, it has known that using a TCP tunnel drastically degrades the end-to-end TCP throughput for some time, which is called TCP meltdown problem. On the contrary, under other conditions, it has been known that using a TCP tunnel significantly improves the end-to-end TCP performance. However, it is still an open issue --- how, when, and why is a TCP tunnel malicious for end-to-end TCP performance? In this paper, we therefore investigate effect of TCP tunnel on end-to-end TCP performance using simulation experiments. Specifically, we quantitatively reveal effects of several factors (e.g., the propagation delay, usage of SACK option, TCP socket buffer size, and sender buffer size of TCP tunnel) on performance of end-to-end TCP and tunnel TCP.
HybridSeqNet: A Multimodal Approach Incorporating Convolutional and Long Short-Term Memory Networks for Comprehensive Structural Protein Classification

In the field of bioinformatics and molecular biology, the accurate classification of proteins based on their sequences plays a crucial role in understanding their functions and interactions. This research paper presents a novel deep learning architecture, named HybridSeqNet, that leverages the strengths of Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) to enhance the accuracy of protein structure classification from sequence data. The proposed HybridSeqNet architecture capitalizes on the hierarchical features captured by CNNs in local regions of protein structure sequences and the temporal dependencies captured by LSTMs in longer-range contexts. This joint architecture synergistically integrates these two neural network paradigms, enabling the model to effectively learn intricate patterns within protein sequences. The dataset used contained more than 4,00,000 protein structure sequences. Various types of protein structures like DNA, RNA, hybrid DNA-RNA, etc. are considered for classification. We also compare our model with various existing models. HybridSeqNet achieves remarkable classification accuracy of 92.86% when compared to traditional models. The architecture’s effectiveness is demonstrated through comprehensive experiments on a huge dataset of protein structure sequences.
Introduction to the xps Package: Comparison to Affymetrix Power Tools (APT)

The aim of this document is to compare the results obtained with package xps to the results obtained with Affymetrix Power Tools (APT version apt-1.10.2). For this purpose the Exon Array Data Set ”Tissue Mixture” will be used, which can be downloaded from the Affymetrix web-site for expression arrays ”Human Genome U133 Plus 2.0”, ”Human Gene 1.0 ST” and ”Human Exon 1.0 ST”, respectively. First, APT and xps will be compared for each array type seperately, then the results obtained with the different arrays will be compared to each other for both APT and xps. To speed up analysis only two human tissues will be used, namely breast and prostate.
Improved BER analysis of OFDM communication system on correlated Nakagami-m fading channel

In this paper, performance of OFDM digital communication systems have been analyzed over correlated Nakagami-m fading channel using maximal ratio combining (MRC) diversity at the receiver. We have studied statistical characteristics of inter-carrier-interference over correlated Nakagami-m fading channel and also presented signal to interference noise ratio (SINR) over correlated Nakagami-m fading channels. We also derived a novel mathematical expression of bit error rate (BER) for QPSK/QFSK OFDM systems.
Prediction of Brain Tumor Severity with Magnetic Resonance Imaging using Ridge Regression Over Recurrent Neural Network

The goal of the research work is to predict Brain tumors using the Ridge Regression algorithm and compare it with the Recurrent Neural Network. The research study involves two groups, Ridge Regression and Recurrent Neural Network, which were both examined using a sample size of 10 calculated with an alpha value of 0.05 and a confidence interval of 95% using the ClinCalc software. The image analysis dataset used in this study contains 82,347 tuples and was obtained from Kaggle.com. The Ridge Regression algorithm obtained an accuracy of 96.47%, while the Recurrent Neural Network classifier achieved an accuracy of 90.11%. An independent sample t-test conducted in SPSS statistical analysis indicated a statistically significant difference between the two algorithms with a p-value of 0.002 (p<0.05). The results suggest that the Ridge Regression algorithm significantly outperformed the Recurrent Neural Network algorithm in terms of accuracy in predicting Brain tumors.
PENGGUNAAN MODEL PEMBELAJARAN SMALL GROUP DISCUSSION UNTUK MENINGKATKAN HASIL BELAJAR IPS TERPADU DI MTS

Absrak: Penelitian ini bertujuan untuk mengetahui apakah ada perbedaan yang signifinkan antara hasil belajar siswa melalui pengajaran dengan model pembelajaran small group discussion dan model pembelajaran konvensional pada mata pelajaran IPS Terpadu kelas VIII MTs Darul Hidayah Kubu Raya. Penelitian ini berbentuk penelitian eksperimen dengan jenis eksperimen yang dilakukan adalah eksperimen semu (Quasy Experiment Reasearch). Berdasarkan hasil perhitungan statistik dari rata-rata hasil post-test kelas eksperimen 77,38 dan rata-rata hasil post-test kelas kontrol 64,84. Dari post-test yang dihitung dengan menggunakan program SPSS diperolehsenilai 4,239 dansenilai 2,010atau (4,293< 2,010) dengan signifikansi sebesar 0,000 atau < 0,05 maka Ha diterima. Hal ini menyatakanterdapat perbedaan yang signifinkan antara hasil belajar siswa melalui pengajaran dengan model pembelajaran small group discussion dan model pembelajaran konvensional pada mata pelajaran IPS Terpadu kelas VIII MTs Darul Hidayah Kubu Raya. Kata Kunci: Small Group Discussion , Hasil Belajar. Abstract : The purpose of this research is to know whether there was any significant difference between the result or outcome students’ learning through teaching by using of small group discussion learning model and conventional learning model on IPS Terpadu subject to the VIII class students of MTs Darul Hidayah Kubu Raya. The form of this research is experimental-study and the researcher used variety of semi-experimental that conducted was quasi-experiment. Furthermore, based on the result of statistical calculation, the average of post test of experiment class was 77.38 and the average of post-test on control class was 64.84. The counting of post-test by using SPSS which calculate was 4.239 and table was 2.010 (4.293<2.010) thus the significant was 0.000 or < 0.05 it meant that Ha was accepted. It showed that there was any significant of difference between the result of students’ learning through small group discussion learning model and conventional learning model on IPS Terpadu subject to the VIII class students of MTs Darul Hidayah Kubu Raya. Keywords: SmallGroup Discussion , ResultsLearning .
Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a RNN Based Question Generation Model

In recent years, knowledge graphs such as Freebase that capture facts about entities and relationships between them have been used actively for answering factoid questions. In this paper, we explore the problem of automatically generating question answer pairs from a given knowledge graph. The generated question answer (QA) pairs can be used in several downstream applications. For example, they could be used for training better QA systems. To generate such QA pairs, we first extract a set of keywords from entities and relationships expressed in a triple stored in the knowledge graph. From each such set, we use a subset of keywords to generate a natural language question that has a unique answer. We treat this subset of keywords as a sequence and propose a sequence to sequence model using RNN to generate a natural language question from it. Our RNN based model generates QA pairs with an accuracy of 33.61 percent and performs 110.47 percent (relative) better than a state-of-the-art template based method for generating natural language question from keywords. We also do an extrinsic evaluation by using the generated QA pairs to train a QA system and observe that the F1-score of the QA system improves by 5.5 percent (relative) when using automatically generated QA pairs in addition to manually generated QA pairs available for training.
Treatment-seeking young adults from families with alcohol problems. What have they been through? What state are they in?

Abstract Aims: This study surveys the childhood experiences of treatment-seeking young adult offspring of problem drinkers (AOPDs) and their psychological state at treatment baseline. Methods: Clients (N=502) entering a Danish nationwide treatment facility for young AOPDS completed the survey. Clients completed the Adult Children of Alcoholics Trauma Inventory, The Family Tree Questionnaire, the CORE–OM 34, Major Depression Inventory, and the Work and Social Adjustment Scale at treatment start. Results: A total of 48% of the clients' mothers and 75% of the clients' fathers were problem drinkers. Both parents were problem drinkers in 25% of cases, and 27% had at least one problem drinking stepparent. Mothers had on average drunk during 11.4 years of the clients' childhood (0–18 years). Fathers had on average drunk during 13.4 years; 46% knew or believed that at least one of their parents suffered from a psychiatric illness; 44% reported physical violence; 63% reported psychological abuse; and 38% had not s.
A Robust and Safe Artificial Intelligence Based Robot Navigation for Smart Buildings

In recent days Chabot's are used by examining their key characteristics, applications, and implications and its importance. The fundamental features of Chabot, which include natural language processing, machine learning, and artificial intelligence. These technologies enable Chabot's to understand and respond to human input, simulating meaningful conversations. Proposed system present future directions and challenges in the field of Chabot and robots. In conclusion, Chabot robots represent a transformative technology that enables human-like conversations, with wide-ranging applications and implications. While the field is rapidly evolving, further research, innovation, and ethical considerations are essential to fully harness the potential of Chabot robots and ensure their responsible and beneficial integration into society.
Evidências de validade do Instrumento de Avaliação Neuropsicológica Breve Neupsilin

Objetivou-se fornecer evidencias de validade do Instrumento de Avaliacao Neuropsicologica Breve NEUPSILIN, desenvolvido no Brasil. Participaram 102 homens e mulheres, de 18 a 40 anos de idade, divididos em 3 grupos: baixa escolaridade (2 a 4 anos), escolaridade intermediaria (5 a 8 anos) e alta escolaridade (9 anos ou mais de estudo formal). Foram aplicados o NEUPSILIN, o Raven e o Inventario Beck de Depressao e avaliados os habitos de leitura e escrita. Os dados foram analisados com os testes ANOVA e de correlacao de Pearson. Encontrou-se diferenca significativa entre os 3 grupos nas tarefas: Ordenamento Ascendente de Digitos, Processamento de Inferencias e Fluencia Verbal. O efeito da educacao foi significativo em 19 tarefas do NEUPSILIN. Memoria, Linguagem e Praxias apresentaram correlacao alta com o Raven. O NEUPSILIN correlacionou-se moderadamente com a frequencia de habitos de leitura e escrita. Os resultados indicaram evidencias de validade a partir da diferenciacao nos escores devido a escolaridade, da correlacao entre NEUPSILIN e Raven (validade convergente) e entre NEUPSILIN e frequencia de habitos de leitura e escrita (validade concorrente).
Why do we need evidence-based methods in Cochrane?

If systematic reviews are to provide the information that people need when making decisions about health and social care, we need to be confident that the methods used to plan, conduct, and report these reviews result in valid and reliable evidence that fairly represents the sum of knowledge. This year's Annual Cochrane Methods Symposium at the Cochrane Colloquium (colloquium.cochrane.org) focuses on why the methods used to produce reviews for evidence-based care should themselves be evidence-based.
Penicillin tolerance in nutritionally variant streptococci

Eleven strains of nutritionally variant streptococci were tested for their susceptibility to penicillin by a broth dilution method. All minimal inhibitory concentrations were low. It was found that plates containing only vitamin B6 (pyridoxal HCl) and cysteine did not reveal true minimal bactericidal concentrations. When penicillinase was added to the subculture medium and a staphylococcal streak was made across the plates, all 11 strains were found to have high minimal bactericidal concentrations and were shown to be tolerant to penicillin.
A Qualitative Analysis for Predicting the Future of Auto Text Summarization in Natural Language Processing

This study conducts a qualitative analysis of the future prospects of auto summarization of text in Natural Language Processing. Auto-text summarization is one of the most discussed and well-known use cases of Natural Language Processing used to easily find the summary or conclusion of a lengthy text. Manually this process takes much more time but auto text-summarization reduces the time constraint by converting large text into a condensed form while maintaining its meaning and key information. This technique saves time and effort by focusing precisely on important points.In this paper, we also focused on the different types of text summarization and their uses, and a list of tools and methods available for text summarization. We are trying to predict the future of text summarization and the useful extensions needed in it to improvise the auto text-summarization process.
In‐Gel Direct Laser Writing for 3D‐Designed Hydrogel Composites That Undergo Complex Self‐Shaping

Self‐shaping and actuating materials inspired by biological system have enormous potential for biosensor, microrobotics, and optics. However, the control of 3D‐complex microactuation is still challenging due to the difficulty in design of nonuniform internal stress of micro/nanostructures. Here, we develop in‐gel direct laser writing (in‐gel DLW) procedure offering a high resolution inscription whereby the two materials, resin and hydrogel, are interpenetrated on a scale smaller than the wavelength of the light. The 3D position and mechanical properties of the inscribed structures could be tailored to a resolution better than 100 nm over a wide density range. These provide an unparalleled means of inscribing a freely suspended microstructures of a second material like a skeleton into the hydrogel body and also to direct isotropic volume changes to bending and distortion motions. In the combination with a thermosensitive hydrogel rather small temperature variations could actuate large amplitude motions. This generates complex modes of motion through the rational engineering of the stresses present in the multicomponent material. More sophisticated folding design would realize a multiple, programmable actuation of soft materials. This method inspired by biological system may offer the possibility for functional soft materials capable of biomimetic actuation and photonic crystal application.
Deep Reinforcement Learning for Channel State Information Prediction in Internet of Vehicles

In this paper, we address the issue of Channel State Information (CSI) prediction of the Internet of Vehicles (loV) system, which is a highly dynamic network environment. We propose a deep reinforcement learning-based approach to predict CSI with historical data and video footage captured by smart cameras. Specifically, we use a Conventional Neural Network (CNN) to extract unique environmental characteristics, which will be sent to a Recurrent Neural Network (RNN)-based learning model so that the future CSI can be predicted. Our approach also considers the heterogeneous nature of IoV communication environments by adopting transfer learning to reduce the training cost when applying our approach to different IoV scenarios. We assess the efficacy of our proposed approach using our designed IoV simulation platform. The experimental results confirm that our approach can accurately predict CSI by using historically generated data.
The use of constant modulus normalization for digital echo cancellation within RF repeaters

The use of constant modulus normalization for adaptive echo cancellation within a digital repeater is investigated. Echo cancellation coefficients are estimated iteratively from the phase information within the cross spectrum. This is an alternative to normalizing the cross spectrum by the power spectrum. Constant modulus and power spectrum normalizations show comparable echo cancellation results, both preventing frequency-dependent biases often associated with an input spectrum whose power varies over frequency. Both methods compensate for the bandwidth reduction associated with a digital sub-band filter. The constant modulus normalization has the advantage of fewer computations.
In vitro cellular response to oxidized collagen–PLLA hybrid scaffolds designed for the repair of muscular tissue defects and complex incisional hernias

Unique poly(l‐lactic acid) (PLLA)‐based scaffolds were constructed by embedding knitted PLLA yarns within a bioresorbable and differentially crosslinked three‐dimensional (3D) oxidized collagen scaffold. The scaffolds were designed specifically for the repair of complex incisional abdominal wall hernias and the repair of defects within planar muscular tissues, such as the bladder. The chemical composition of the collagen matrix and the percentage of scaffold infiltration were compared for the different scaffold compositions. The results demonstrate that the incorporation of the collagen sponge within the PLLA scaffold facilitated bladder smooth muscle cell (bSMC) adhesion and proliferation. The highest dose of oxidized collagen (Oxicol) demonstrated better cell adhesion, resulting in the largest cell densities and most uniform distribution throughout the 3D collagen sponge. This formulation promoted the greatest α‐smooth muscle actin (αSMA) expression detected through immunohistochemical staining and western blotting. For abdominal wall repair applications, the proliferation and differentiation of C2C12 myoblasts and myotube formation were studied. Following 7 days of myogenic induction, the greatest expression of mRNA of the myogenic markers myogenin and MRF4 was observed within the scaffolds with the highest dose of oxidized collagen, 1.5‐ and 3.85‐fold greater expressions, respectively, compared to PLLA with unmodified collagen. Furthermore, in vitro myotube formation and MyMC expression were enhanced in the Oxicol scaffolds. We conclude that the Oxicol scaffold formulation with a high‐dose oxidized collagen ratio provides enhanced myogenesis and αSMA, and the biological induction cues necessary to achieve better tissue integration, than standard PLLA scaffolds in the treatment of complex abdominal wall hernias. Copyright © 2013 John Wiley & Sons, Ltd.
Design of an E-Procurement Adoption Model (EPAM)

Summary This paper describes an exploratory study to design an E-Procurement Adoption Model (EPAM). A taxonomy of issues influencing individual adoption is extracted from literature. Based on triangulatio n of expert interviews, case studies, and focus group di scussions, the EPAM is designed by identifying issu es confirmed in practice per role and change phase.
Performance on Autoencoder-Based MIMO Quantize-Forward Relay System for Various Learning Parameters

In multiple input multiple output (MIMO) quantize-forward (QF) relay systems, an autoencoder comprising an encoder, a decoder, and a channel component has been employed, demonstrating commendable performance. In the QF relaying, the relay quantizes the phases of received signals and forwards them to the destination. A neural network is subsequently integrated into the relay after quantization, introducing a non-linear beamforming effect. In assessing the efficacy of the autoencoder-based MIMO QF relay system applying phase quantization with neural network at the relay, we conduct a comprehensive analysis of bit error rates. This evaluation compares system performance related to diverse learning parameters, such as batch size, number of epochs, and neural network size at the relay. Simulation results clearly illustrate that these learning parameters significantly influence the overall performance of the system.
A Novel Approach of Customer Sentiment Analysis by CNN Based on PWWA

This article describes methods to ensure the accuracy of e-commerce products in addition to analyzing consumer reviews and supervised machine learning algorithms. People enjoy shopping online thanks to the expansion of the e-commerce sector, but ensuring product quality is a significant concern. People now rely on other customer reviews for quality products, such as comments, emotional responses, hashtags, etc. In this study, we have tried to explore the polarity of user reviews in Bangladesh as a positive, negative or neutral sentiment to drive online product quality. To evaluate the dataset, first, it has been categorized then the F1-Score of polarity detection of each category has been presented by applying both deep learning and supervised learning classifiers. The PWWA (Priority Word Weight Assignment) algorithm has been used to increase the model accuracy. It has been observed that on average the PWWA algorithm increased the accuracy (F1-score) of the learning models by 18% more than without applying PWWA.
ECORS: Energy consumption-oriented route selection for wireless sensor network

Automated metering infrastructure is employed widely in scientific fields as well as in industrial and commercial areas due to the development of wireless sensor network (WSN) technology. WSNs provide important features such as wireless multi-hop communication and they are easy to install everywhere; thus, extending the lifetime of WSNs is highly desirable. All WSN nodes consume a limited amount of energy from the battery during operations such as sensing, calculating, control, and communication and most of the power consumption is attributable to wireless communication. In this study, we propose energy consumption-oriented route selection (ECORS), which is a route selection algorithm that focuses on the remaining energy and energy consumption by WSN nodes. In ECORS, a sink node calculates all of the routes in the system by using the route lifetime (RL) as a metric according to the minimum residual energy (MRE) and the expected route cost (ERC). The route with the longest RL is selected by the proposed algorithm. By changing the route periodically using ECORS, the WSN system lasts 1.14 times longer as compared to that when the routes are fixed. We evaluated the performance of ECORS using an original WSN simulator. In realistic simulations, we measured the distance-packet error rate, current consumption, and discharge characteristic of a battery using actual sensor nodes assembled with an Arduino micro controller, XBee ZigBee wireless module, and lithium-polymer battery.