Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving AODV Route Recovery Mechanisms with Connectivity and Particle Swarm Optimization

This paper proposes a new function for the Ad Hoc On-Demand Distance Vector (AODV) algorithm using the connectivity concept to decide between source and local repair when a link break occurs. Besides, we used a Computational Intelligence algorithm, called Particle Swarm Optimization (PSO), which enables to find candidate solutions in all considered search space in order to reduce average data packet delivery delay sent over the network. The performance of the original AODV protocol, according to its Request for Comments (RFC), is compared with this new implemented approach, called here as AODVPSO. Three metrics were used to evaluate the performance of the algorithm: throughput (as data packet delivery fraction), routing overhead and average data packet delivery delay. We observed that the AODV-PSO outperformed the original AODV protocol in the scenarios studied in this paper.
The preference for approximation

A curious fact about the communication of numerical information is that speakers often choose to use approximate or rounded expressions, even when more precise information is available (for instance, reporting the time as ‘three thirty’ when one’s watch reads 3:27). It has been proposed that this tendency towards rounding is driven by a desire to reduce hearers’ processing costs, a specific claim being that rounded values produce the same cognitive effect at less cognitive effort than non-round values (Van der Henst, Carles and Sperber, 2002). To date, however, the posited processing advantage for roundness has not been experimentally substantiated. Focusing on the domain of temporal expressions, we report on two experiments that demonstrate that rounded clock times are easier to remember and manipulate than their non-round counterparts, a finding that provides evidence for the influence of processing considerations on numerical expression choice. We further find a role for domain-specific granularity of measurement.
Effects of salt-inducible kinase 1 (SIK1) ablation on intestinal ion transport modulation

Essential hypertension is a multifactorial and polygenetic condition strongly associated to high salt intake and sodium absorption dysregulation. Salt-inducible kinase 1 (SIK1), a sucrose nonfermenting-1-related serine/threonine kinase, is part of a cell sodium-sensing network that controls active sodium transport by regulating Na+-K+-ATPase activity and has been established as a key determinant in blood pressure regulation. Recently, loss of SIK1 has been shown to trigger a blood pressure rise in mice under chronic high salt intake. In the present work, we further explored the role of salt-inducible kinase 1 in sodium-sensible hypertension development. In a particular set of experiments, we took a first step in investigating the influence of SIK1 over transcellular sodium transport in the gastrointestinal tract, focusing on the characterization of intestinal ion transport mechanisms that are potentially under the regulation of SIK1 such as the pump-leak coupling between Na+-K+-ATPase and basolateral K+ currents. Changes in membrane potential were examined after exposure of cells to drugs known to block or open ATP-sensitive K+ channels and the effects of dexamethasone treatment on basolateral K+ conductance and Na+-K+-ATPase protein expression and activity were assessed. Our results demonstrate that dexamethasone causes a concentration-dependent modulation of basolateral K+ currents that is independent of Na+-K+ATPase protein overexpression and most likely secondary to increased NKA activity, as a result of the functional coupling between Na+-K+-ATPase and KATP channels. Furthermore, we also report for the first time that dexamethasone induces Na+-K+-ATPase upregulation in intestinal epithelial cells at higher treatment concentrations.
ICGenealogy: Mapping the Function of Neuronal Ion Channels in Model and Experiment

Ion channel models are the building blocks of computational neuron models. Their biological fidelity is therefore crucial for the interpretability of simulations. However, the number of published models, and the lack of standardization, make the comparison of models with one another and with experimental data difficult. Here, we present a framework for the automated large-scale classification of ion channel models. Using annotated metadata and model responses to a set of voltage-clamp protocols, we assigned 2378 models of voltage- and calcium-gated ion channels coded in NEURON to 211 clusters. The IonChannelGenealogy web interface provides an interactive resource for the categorization of new and existing models and experimental recordings. It enables quantitative comparisons of simulated and/or measured ion channel kinetics, and facilitates field-wide standardization of experimentally-constrained modeling.
Inhibitory, and Gastroprotective Principles and New Oleanane-Type Triterpene Oligoglycosides, Calendasaponins A, B, C, and D, from Egyptian Calendula officinalis

aponins A (1, 0.0012%), B (2, 0.0024%), C (3, 0.0011%), and D (4, 0.0056%), officinosides A (0.0013%), B (0.0016%), C (0.0028%), and D (0.0028%), glycosides A (5, 2f) 0.090%), B (6, 2f) 0.080%), C (7, 2f) 0.085%), D (8, 2f) 0.060%), D2 (9, 2f) 0.030%), F (10, 2f) 0.0091%), calenduloside D (11, 4) 0.0009%), arvensoside A (12, 5) 0.0014%), rutin (13, 6) 0.0025%), quercetin 3-O-neohesperidoside (14, 7) 0.0036%), quercetin 3-O-2 G -rhamnosylrutinoside (15, 8) 0.018%), isorhamnetin 3O-glucoside (16, 9) 0.0015%), isorhamnetin 3-O-rutinoside (17, 7) 0.060%), isorhamnetin 3-O-neohesperidoside (18, 7) 0.0099%), isorhamnetin 3-O-2 G -rhamnosylrutinoside (19, 8) 0.024%), and icariside C3 (20, 10) 0.0006%).
THE USE OF COHESIVE DEVICES IN STUDENTS’ WRITING

ABSTRACT This research aimed to find out types of grammatical and lexical cohesive devices and the correctness of cohesive devices in students’ writing. This study was designed as qualitative study. The samples of this research were 3 groups of students in Pre-Intermediate, Advanced, and Academic Writing course. The data were collected through the document of students’ recount writings analyzed by using the theory of cohesion by Halliday and Hasan (1976). The analysis was concerned with: grammatical cohesion (reference, substitution, ellipsis, conjunction), and lexical cohesion (reiteration and collocation). The results of this study show that the three groups of students produced the five types of cohesive devices i.e; the third-semester students in Pre-Intermediate writing course utilized conjunction 49.64%, reference 45.84%, lexical 3.93%, ellipsis 0.39%, and substitution 0.19%. The fifth-semester students in Advance writing course utilized reference 49.44%, conjunction 44.51, lexical 5.73%, substitutuion 0.19, and ellipsis 0.12%. The seventh-semester students in Academic Writing course utilized reference 62.59%, conjunction 35.49%, lexical 1.82%, substitution 0.10%, and ellipsis 0%. Therefore, it is concluded that the trend of the three levels of students shows that the higher proficiency level of students, the more reference they acquired. In substitution, the proficiency levels of students do not influence to the achievement of substitution. In other words, the use of substitution in three groups of students are quite balance. In ellipsis, conjunction, and lexical cohesion, the higher proficiency of students, the less ellipsis, conjunction, and lexical cohesion emerged. It is concluded that Indonesian major students could produce cohesive devices in their writing. However, they are quite ignore some cohesive devices i.e., substitution and ellipsis. In term of their incorrect use, the researcher concludes that there is small percentage of incorrect cohesive devices used by the students, they missuse certain cohesive devices.e. g; reference and conjunction. They do not only create disorganized texts but also make the content incomprehensible. Based on the analysis of those three groups of students, the researchers come to final conclusion that this study known as cross sectional study which is type of study that involves a representative subjects at one specific point in time in. Therefore, this is also the new phenomena in language research that has not been done by the previous researcher that makes it different with another research.
CTCP-TUBE: Improving TCP-Friendliness Over Low-Buffered Network Links

Compound TCP (CTCP) is a sender-side improvement for TCP in high-speed and long distance networks. CTCP is a synergy of delay and loss-based congestion control, effectively combining them to achieve good efficiency, RTT fairness and TCP-friendliness. However, CTCP requires detecting incipient congestion effectively by estimating the backlogged packets and comparing it to a pre-defined parameter gamma. Choosing the appropriate value for gamma could be a problem because this parameter depends on both network configuration and the number of concurrent flows, which are generally unknown to the endsystems. As a consequence, when operating over under-buffered links, CTCP may demonstrate poor fairness to regular TCP flows that may be comparable to HSTCP. In this paper, we present a novel technique that automatically tunes CTCP parameters so that it greatly improves the TCPfriendliness of CTCP over under-buffered links. This new technique, called Tuning-By-Emulation (TUBE), dynamically estimates the average queue size for a regular TCP flow, and based on which sets the parameter gamma. This way CTCP can effectively lower gamma on under-buffered links to keep good TCPfriendliness, and alternatively increases gamma if the link buffer is sufficient to ensure high throughput. Our extensive packet-level simulations and test-bed experiments on a Windows implementation confirm the effectiveness of CTCP-TUBE.
Boosted Object Detection Based on Local Features

Object detection is to find and localize objects of a specific class in images or videos. This task is the foundation of image and video understanding, thus it becomes one of the most popular topics in the area of computer vision and pattern recognition. Object detection is not only essential for the study of computer vision, pattern recognition and image processing, but also valuable in the applications of public safety, entertainment and business. In this research, we aim to solve this problem in two focused areas: the local feature design, and the boosting learning. Our research on local features could be summarized into a hierarchical structure with 3 levels. The features in different levels capture different object characteristic information. In the lower level, we investigate how to design effective binary features, which perform quite well for the object categories with small intra-class variations. In the middle level, we consider integrating the gradient information and structural information together. This results in more discriminative gradient features. In the higher level, we discuss how to construct the co-occurrence features. Using such features, we may get a classifier with high accuracy for general object detection. After the feature extraction, boosted classifiers are learned for the final decision. We work on two aspects to improve the effectiveness of boosting learning. Firstly, we improve the discriminative ability of the weak classifiers by the proposed basis mapping. We show that learning in the mapped space is more effective compared to learning in the original space. In addition, we explore the efficiency-accuracy trade-off problem in boosting learning. The Generalization and Efficiency Balance (GEB) framework, and the hierarchical weak classifier are designed for this target. As a result, the resulting boosted classifiers not only achieve high accuracy, but also have good generalization and efficiency. The performance of the proposed local features and boosting algorithms are evaluated using the benchmark datasets of faces, pedestrians, and general objects. The experimental results show that our work achieves better accuracy compared to the methods using traditional features and machine learning algorithms.
Survey on end to end congestion control techniques in different network scenarios

Most of the traffic on the Internet is depend upon the Transmission Control Protocol (TCP), so the performance of TCP is directly related to Internet. Many TCP variants are developed and modified according to the environment and communication needs. Most of current TCP variants have set of algorithms which control the congestion in critical situations and maintain the throughput and efficiency of network. Now a day’s TCP is facing fast growth of Internet with the demands of faster data communication techniques on high speed links. In last 15 years many computer systems and cellular networks become linked together with protocol stack used in TCP. TCP variants with different congestion control techniques are working in different operating systems but a very small number of techniques are able to minimize the congestion in the network. This paper presents a survey on end-to-end congestion control techniques used in different TCP versions. The main purpose of this study is to review the characteristics and behavior of TCP variants with different techniques to control the congestion in the different network scenarios.
An aircraft recognition method based on principal component analysis and image model-matching

A method using principal component analysis and template matching to recognize planes accurately in the high-resolution CCD image of airport is proposed in this paper.Firstly,we process the image by a mean filter and histogram equalization.According to analysis of the histogram,an airport in the scene is identified.The plane areas in the image are obtained based on segmentation of the airport.By principal component analysis,the main direction of each plane area is obtained and used to rotate the area to a horizontal direction.Afterwards,target areas are matched to the template library to identify whether the target area is a plane.The experimental results show that our method is effective for plane target recognition.
Tourism demand modelling and forecasting using data mining techniques in multivariate time series: a case study in Turkey

In this study multiple linear regression, multilayer perceptron (MLP) regression, and support vector regression (SVR) are used to make multivariate tourism forecasting for Turkey. This paper is a comparative study of data mining techniques based on multivariate regression modelling with monthly data points to forecast tourism demand; it focuses on Turkey. Both MLP and SVR methods are widely employed in the variety forecasting problems. Most of the previous research on tourism forecasting used univariate time series or a limited number of variables with mostly yearly or quarterly, and rarely monthly frequencies. However, the application of data mining techniques for multivariate forecasting in the context of tourism demand has not been widely explored. This paper differs from earlier research in two ways: 1) it proposes multivariate regression modelling with monthly data points to forecast tourism demand; and 2) it focuses on Turkey by using a dataset with the most recently accumulated (between January 1996 and Dec 2013) 67 time series with respect to Turkey and its top 26 major tourism clients. Comparison of forecasting performances in terms of relative absolute error (RAE) and root relative squared error (RRSE) measurements shows that the SVR model, with RAE = 12.34% and RRSE = 14.02%, gives a better performance. The results obtained in this study provide information for researchers interested in applying data mining techniques to tourism demand forecasting and help policy makers, government bodies, investors, and managers for their regularization, planning, and investments by way of accurate tourism demand forecasting.
How and why born global firms differ in their speed of internationalisation - a multiple case study approach

This article analyses the research problem of how and why born global firms (BGF) differ in their speed of internationalisation. It is based on the conceptual framework of the BGF theory and the Uppsala internationalisation process model. The research questions will be answered using a multiple case study research design with qualitative, in-depth, face-to-face, semi structured interviews of twenty subject matter experts (SME). The research results show that factors like the skills of the entrepreneur and the management team, their business networks, the business model of the BGF, the market entry mode, the successful implementation of a structured market development process, the uniqueness of the technology and the product portfolio, the availability of market opportunities, and the size of their home market influence the speed of internationalisation.
Mobile Phones for Teaching and Learning

Mobile phones have become so ubiquitous that they turned into an important part of our life. According to Parsons, mobile subscriptions exceed 6 billion subscriptions globally. Similarly, Ipsos and Verizon (as cited in Tan & El-Bendary) found out that adopting mobile phones with smart technologies has increased fast which also coincided with a more utilization of their Internet capabilities. With the abundance of knowledge the Internet provides, mobile phones become an invaluable pathway for that knowledge. The fact that these gadgets are well-liked by students make them one of the best tools to be adopted by educational institutions. This study will investigate the actual academic use of mobile phones among students and teachers, their attitudes toward using them as learning or teaching tools, and if there is a significant difference in attitudes of the participants toward using mobile as learning or teaching tools based on the job criteria (Student vs. Faculty Member).
The effect of music on body sway when standing in a moving virtual environment

Movement of the visual environment presented through virtual reality (VR) has been shown to invoke postural adjustments measured by increased body sway. The effect of auditory information on body sway seems to be dependent on context with sounds such as white noise, tones, and music being used to amplify or suppress sway. This study aims to show that music manipulated to match VR motion further increases body sway. Twenty-eight subjects stood on a force plate and experienced combinations of 3 visual conditions (VR translation in the AP direction at 0.1 Hz, no translation, and eyes closed) and 4 music conditions (Mozart’s Jupiter Symphony modified to scale volume at 0.1 Hz and 0.25 Hz, unmodified music, and no music) Body sway was assessed by measuring center of pressure (COP) velocities and RMS. Cross-coherence between the body sway and the 0.1 Hz and 0.25 Hz stimuli was also determined. VR translations at 0.1 Hz matched with 0.1Hz shifts in music volume did not lead to more body sway than observed in the no music and unmodified music conditions. Researchers and clinicians may consider manipulating sound to enhance VR induced body sway, but findings from this study would not suggest using volume to do so.
Improving Statistical Machine Translation with Target-Side Dependency Syntax

Natural Language Processing (NLP) is the field of Artificial Intelligence research that aims to teach computers to produce and understand human text and speech. Major applications include information retrieval, human-computer dialogue systems and natural language generation. Machine Translation (MT) is the application of NLP that focuses on the automatic translation between languages. Machine translation is a valuable tool for both personal and business applications. With the incredible diversity of languages accessible on the internet, automatic translation has become a necessity in this era of information. For example, the popular engine Google Translate is used to translate over 100 billion words1 per day (as of 2016). While other areas of NLP, for example speech recognition, have already reached a level enabling practical application, there remain many obstacles to producing high-quality automatic translation. In particular, translation is difficult for linguistically distant language pairs such as English–Japanese and for resource-poor languages such as Māori, Welsh and Zulu. In this thesis we focus on the challenges of translating between linguistically distant language pairs and a potential solution known as ‘syntax-based MT’. Syntax-based MT is an MT paradigm based on the principle of generalizing language with grammar. This additional layer of abstraction enables the design of more flexible translation rules. Source language input is first analyzed syntactically, for example in the form of dependency parses, and this grammatical skeleton is transformed to the target language using prelearned syntactic transfer rules. The syntactic frame is fleshed out by combining translated words and phrases to form a complete translation. https://googleblog.blogspot. jp/2016/04/ten-years-of-google-translate.html
Wireless resource allocation: auctions, games and optimization

This thesis addresses a problem at the nexus of communication theory, networking, and economics: how to allocate radio resources efficiently and fairly to support users' Quality of Service (QoS) requirements in wireless communication networks? Through a unified utility-based framework, we study resource allocation problems in both centrally controlled and distributed wireless networks, with an emphasis on users with elastic data traffic. In the first part of the thesis, we consider downlink scheduling in a centrally controlled cellular network. We explicitly take type II hybrid ARQ retransmission schemes into account, which have been proposed for next generation wireless communication standards. For some important cases, we characterize the optimal scheduling policies as fixed-priority policies, where the priorities can be explicitly calculated using an iterative algorithm. In some special cases, the priorities can even be calculated in closed form. We also show that a simple myopic scheduling policy, called the U'R rule, performs very close to the optimal scheduling policy in specific cases. In the second part of the thesis, we consider resource allocation in spectrum sharing models, which are motived by ongoing worldwide spectrum policy reform to promote distributed, efficient and in some cases market-based spectrum usage. We study two distinct models: the exclusive use model and the commons model. For the exclusive use model, we propose two simple auction mechanisms to allocate power among competing and selfish secondary users. We give sufficient conditions under which the auctions lead to socially optimal (efficient) resource allocations, and present an iterative algorithm which achieves those allocations in a distributed fashion. In the commons model, we design price-based mechanisms under which users cooperate with each other in a distributed manner. Both single carrier and multi-carrier networks are studied, and we use supermodular game theory and duality theory to show the convergence and study the properties of the proposed algorithms. Finally, we extend the discussion of the commons model to multi-hop wireless networks, where joint congestion control, scheduling, routing and power control is discussed.
Using the Fuzzy Spatial Relation “Between” to Segment the Heart in Computerized Tomography Images

Segmenting the heart in medical images is a challenging and important task for many applications. Most heart segmentation methods proposed in the literature deal with cardiac internal structures, but there is a real interest in segmenting the heart as a whole. In this paper, we address this problem and propose an automatic method, based on the modeling of spatial relations of ∗A. Moreno is currently at INSERM U.562 — Cognitive Neuroimaging Unit, CEA/SAC/DSV/I2BM/NeuroSpin, Bâtiment 145, Point Courrier 156, F-91191 Gif-surYvette CEDEX, France.