Miracle-dz/UniFAR-FTUs huggingface dataset:
Developed Model to Control Congestion onConverge Network

Congestion control techniques like Active Queue Management (AQM), Carrier Sense Multiple Access/Carrier Detection (CSMA/CD) have not proven to be very efficient in the presence of overwhelming complex converge network. Thus, vast packets in a complex converged network leads to collisions, network degradation and high degree of packet loss. Bandwidth utilization factor has a high effect on the network such that controlling the level of utilization via the management of the number of users and the amount of packets on the network rendered the latency very insignificant. As a consequence of this, high throughput and very minimal packet loss was achieved in the experiment. This was confirmed analytically by varying the utilization factor between 40% and 90% while keeping other parameters in the experiment constant
State of the art of content-based image classification

Explosive growth of digital technologies has spawned a plethora of images available online. Therefore, content-based image classification has been the subject of many research works in recent years. This paper reviewed some of the most commonly used image classification approaches. Most of the existing approaches used low-level features and intermediate semantic modelling. Image classification using low-level features used colour, texture and shape features directly from the image in combination with learning methods to classify images into several semantic classes (i.e. indoor, outdoor, city, landscape, sunset, forest, etc.). Alternatively, intermediate semantic modelling assigned semantic concepts (i.e. sky, people, grass, etc.) to the image content and the image was classified based on these semantic concepts. Low-level strategies were often used when a small number of categories have to be recognized, as well as when the categories were easily separated. Nevertheless, as the number and ambiguity of the categories increase it was clear that approaches using intermediate semantic concepts were more appropriate.
Realistic simulation and experimental validation of adjacent-channel interference in planning of industrial wireless networks

Interference is among of the most critical issues in wireless communication, especially in industrial application fields. Even though interference is rather well understood today, there is a lack of validated simulation models integrated with common network simulators. We present an integrated simulation and planning toolkit that allows us to estimate adjacent channel interference among multiple wireless networks such as IEEE 802.11 WLANs. We performed an extensive set of lab experiments not only to validate the presented simulation model but also to come up with quick lookup tables to be used for industrial network planning. The developed simulation framework provides very accurate physical layer modeling that is of utmost importance for WiFi planning in automation facilities. Not limited to the IEEE 802.11 protocols, the proposed interference model can be easily adapted for coexistence planning of WLANs and the IEEE 802.15. 4 based WPANs in the 2.4 GHz band.
Analysis and Identification of Malicious JavaScript Code

ABSTRACT Malicious JavaScript code has been actively and recently utilized as a vehicle for Web-based security attacks. By exploiting vulnerabilities such as cross-site scripting (XSS), attackers are able to spread worms, conduct Phishing attacks, and do Web page redirection to “typically” porn Web sites. These attacks can be preemptively prevented if the malicious code is detected before executing. Based on the fact that a malignant code will exhibit certain features, we propose a novel classification-based detection approach that will identify Web pages containing infected code. Using datasets of trusted and malicious Web sites, we analyze the behavior and properties of JavaScript code to point out its key features. These features form the basis of our identification system and are used to properly train the various classifiers on malicious and benign data. Performance evaluation results show that our approach achieves a 95% or higher detection accuracy, with very small (less than 3%) false positive and false negative ratios. Our solution surpasses the performance of the comparable literature.
Flexible Human Behavior Analysis Framework for Video Surveillance Applications

We study a flexible framework for semantic analysis of human motion from surveillance video. Successful trajectory estimation and human-body modeling facilitate the semantic analysis of human activities in video sequences. Although human motion is widely investigated, we have extended such research in three aspects. By adding a second camera, not only more reliable behavior analysis is possible, but it also enables to map the ongoing scene events onto a 3D setting to facilitate further semantic analysis. The second contribution is the introduction of a 3D reconstruction scheme for scene understanding. Thirdly, we perform a fast scheme to detect different body parts and generate a fitting skeleton model, without using the explicit assumption of upright body posture. The extension of multiple-view fusion improves the event-based semantic analysis by 15%–30%. Our proposed framework proves its effectiveness as it achieves a near real-time performance (13–15 frames/second and 6–8 frames/second) for monocular and two-view video sequences.
Going for gold as a leisure tourism continuum: clustering motivations for gold panning revisit intention

Abstract The way in which people use their spare time differs. Leisure activities and tourism are among the most common of these. Gold panning is a rather unusual niche leisure and tourism activity. Despite some differences, leisure and tourism are generally interrelated in many ways, thus gold panning, is a good example of the leisure tourism continuum. In this study, gold panning is considered as a set of leisure activities within the context of tourism. The aim of the paper is to determine the profile of a gold panner based on their motivations. Therefore, respondents are clustered by means of motivation into two homogenous groups defined as ‘nature seekers’ and ‘true gold panners’. The results suggest that the clusters differ significantly with regards to age, education and marital status, but also in terms of the degree of interest in gold and revisit intention. The originality of the research lies in the fact that gold panning as a leisure activity has seldom been explored under the guise of a leisure and tourist context within existing literature, especially through the means of quantitative research.
A game theoretic approach to energy efficient cooperative cache maintenance in MANETs

There have been an increasingly large number of mobile handsets equipped with dual or multiple network interfaces. The server interface (e.g., GPRS, EDGE, UMTS) is responsible for communicating with the network operator, while the peer interfaces (e.g., Bluetooth, IEEE 802.11) are used to connect with other computing devices. However, they are usually used separately. In this paper, we investigate the use of both network interfaces to support energy efficient data applications among mobile clients. Specifically, we proposed a fully distributed protocol for mobile handsets to form cooperative groups to maintain cache consistency with minimal communication with the network operator. Our proposed protocol takes advantage of the low power consumption and high data rate of the peer interface. The aim is to reduce the use of the server interface, which is typically slower and involves higher power consumption. Furthermore, we also consider the presence of selfish clients. It is shown that groups formed by the proposed protocol constitutes a pure Nash equilibrium. This suggests that our protocol is robust even in the presence of selfish clients. Simulation results confirm that, given the same energy resource, mobile clients running the proposed protocol complete more queries, experience longer lifetime and achieve smaller query latency
Image enhancement using the mean dynamic range maximization with logarithmic operations

In this paper we use a logarithmic model for gray level image enhancement. We begin with a short presentation of the model and then, we propose a new formula for the mean dynamic range. After that we present two image transforms: one performs an optimal enhancement of the mean dynamic range using the logarithmic addition, and the other does the same for positive and negative values using the logarithmic scalar multiplication. We present the comparison of the results obtained by dynamic ranges optimization with the results obtained using classical image enhancement methods like gamma correction and histogram equalization.
A media-based social interactions analysis procedure

There is a growing number of opportunities for users to share media using Web content providers. Moreover, in the context of Web-based Social Networks, there is also the possibility that shared media will be used in subsequent social interactions. Considering the importance of understanding the underlying collaborations, we have been investigating a human-readable technique for representing media-based social interactions. In this paper we propose a procedure for analyzing interactions among users in Social Networks, and analyze social interactions based on video and link sharing from Web providers to a Social Network. The results can be used by social application developers, e.g. in the evaluation of the user interfaces they design, or by social scientists interested in analyzing the social interactions.
15 TOWARDS A THREE-FACTOR MOTIVATION-LEARNING FRAMEWORK IN NORMAL AGING

Most behavior stems from motivation. As we maneuver through the environment we choose actions from a large repertoire of behaviors. These behaviors are strongly affected by our learning history, but also by our current motivational state to approach positive outcomes or avoid negative outcomes. For example, one could be motivated to be on time for a meeting or to avoid being late for a meeting. Similarly, one could be motivated to achieve a particular score on an exam or avoid falling below a particular score. The goal is the same, but the motivational frame through which one views the goal is different. The approach-avoidance dichotomy is well established in the traditional psychology of motivation (Aarts, Gollwitzer, & Hassin, 2004; Ferguson & Bargh, 2004; Fishbach, Friedman, & Kruglanski, 2003; Gray, 1970, 1985; Higgins, 2000; Hull, 1943; Lewin, 1935; Mowrer, 1960; Murty, LaBar, Hamilton, & Adcock, 2011). Perhaps surprisingly, most cognitive research focuses on information processing and its effects on learning and behavior, with little attention paid to the factors that drive or motivate one to act. Interestingly, this artificial separation of motivation research from learning research was not present in the 1950s and 1960s (Miller, 1957, 1959; Young, 1959). However, as psychology became more divided and area-driven, learning research became the domain of cognitive and animal psychologists, whereas motivation was primarily studied by social and educational psychologists. In many ways, the cognitive neuroscience revolution that began in the 1980s and 1990s provided the necessary spark for bringing research on learning and research on motivation back together. Cognitive neuroscience research makes clear that the brain does not distinguish between “motivational” brain systems and “learning” brain systems. In fact, some of the most important brain regions for learning, such as the prefrontal cortex, the anterior cingulate, and the caudate nucleus, are known to be involved in motivation, affect, and 15
Antimicrobial susceptibility of Haemophilus influenzae, Neisseria meningitidis and Streptococcus pneumoniae isolates causing meningitis in the Netherlands 1993-1994 Enting,

Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva. nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.
Heteronormative communication with lesbian families in antenatal care, childbirth and postnatal care.

TITLE Heteronormative communication with lesbian families in antenatal care, childbirth and postnatal care. AIM This paper is a report of a study of lesbian parents' experience of antenatal care, childbirth and postnatal care. BACKGROUND In a strictly heteronormative-based care system, 'parents' naturally implies a man and a woman, and all communication and routines are based on heterosexual couples. METHOD A qualitative interview study was carried out in 2008. Ten mothers, eight of whom were in a relationship with another woman, participated. The participants had experience from several care facilities from three different cities in central Sweden. FINDINGS Most participants had positive experiences but also complained that the focus was not always on the pregnancy and that no parenthood education had been offered. Heteronormativity was communicated by midwives and nursing staff throughout the process - from antenatal care to postnatal care, via forms, journals, verbal communication and orientation visits. This was experienced as embarrassing for the participant parents, and they also described encountering what they interpreted as embarrassment on the part of care providers. Participants called for increased knowledge about lesbian parenting, since they believed this would influence and facilitate communication, not least with midwives in antenatal care. CONCLUSION Midwives educated in lesbian issues could ask questions and communicate in a more neutral way at the first meeting, and thereby make prospective parents feel less insecure. Special parenthood education groups for lesbians are recommended so that lesbian couple can meet others with similar experiences and so that the focus will be on prospective parenthood and not on their sexual orientation.
Visualization Analytics for Second Language Vocabulary Learning in Virtual Worlds

Language learning occurring in authentic contexts has been shown to be more effective. Virtual worlds provide simulated contexts that have the necessary elements of authentic contexts for language learning, and as a result, many studies have adopted virtual worlds as a useful platform for language learning. However, few studies so far have examined the relationship between learning outcomes and learning paths and strategies inside a virtual world. This study was designed to fill this research gap. In order to understand the impact of different learning strategies on learning outcomes in a virtual world, a visualization analytic method was developed to examine the recorded learner paths within a virtual world while learning occurred. In particular, the visualization analytic method adopted in this study was based on social network analysis. This study included 14 participants who were learners of Mandarin Chinese as a foreign language. The learning outcomes were based on their test scores from 7 learning sessions and the post-test. Through the visualization analysis, the current study revealed a link between the learning paths and strategies and learners’ outcomes. The strategies include the “nearest strategy,” the “focus strategy,” and the “cluster strategy. ” The findings show that high-achieving and low-achieving students tend to use different strategies in learning new words. The visualization analytics thus effectively displays the learning strategies of vocabulary acquisition. Our methods could be applied to other second language learning studies, and the results can also provide insights into the construction of future virtual worlds for learning second languages.
Tick-borne encephalitis virus subtypes emerged through rapid vector switches rather than gradual evolution

Tick-borne encephalitis is the most important human arthropod-borne virus disease in Europe and Russia, with an annual incidence of about 13 thousand people. Tick-borne encephalitis virus (TBEV) is distributed in the natural foci of forest and taiga zones of Eurasia, from the Pacific to the Atlantic coast. Currently, there are three mutually exclusive hypotheses about the origin and distribution of TBEV subtypes, although they are based on the same assumption of gradual evolution. Recently, we have described the structure of TBEV populations in terms of a clusteron approach, a clusteron being a structural unit of viral population [Kovalev and Mukhacheva (2013) Infect. Genet. Evol. , 14, 22–28]. This approach allowed us to investigate questions of TBEV evolution in a new way and to propose a hypothesis of quantum evolution due to a vector switch. We also consider a possible mechanism for this switch occurring in interspecific hybrids of ticks. It is necessarily accompanied by a rapid accumulation of mutations in the virus genome, which is contrary to the generally accepted view of gradual evolution in assessing the ages of TBEV populations. The proposed hypothesis could explain and predict not only the formation of new subtypes, but also the emergence of new vector-borne viruses.
Ruminative Reinforcement Learning: Improve Intelligent Inventory Control by Ruminating on the Past

Reinforcement Learning (RL) can solve practical sequential decision problems, even when structures of the problems are less understood. However, some sequential decision problems intrinsically have structural parts that are easily to formulate and distinguish from less understood parts. Exploiting this knowledge may help improve performance of RL. This study proposed and investigated an approach to exploit the knowledge of structural parts of inventory management problems in the context of RL. The proposed method is motivated by human behavior of ruminating on what has happened and what would happen if alternative choices would have been taken. Our investigation provides an insight into RL mechanism and our experimental results show viability of the approach.
Sentiment Clustering with Topic and Temporal Information from Large Email Dataset

Sentiment analysis with features addition to opinion words has been an appealing area in recent studies. Some research has been conducted for finding relationship between sentiments, topics and temporal sentiment analysis. Nevertheless, Email sentiment analysis received relatively less attention due to the complexity of its structure and indirectness of its language. This paper introduces a systematic framework for sentiment clustering using topic and temporal features for large Email datasets. Interesting Email and sentiment distribution patterns are summarized and discussed with empirical results.
IMM estimator for maneuvering target tracking with Improved Current Statistical Model

Target tracking is one of the most important applications of Wireless Sensor Networks (WSN). It is the state estimation of a moving object based on sensor information. Interacting Multiple Model (IMM) estimator is a self adaptable, multiple model filters with less computational complexity where estimation is done by probabilistically integrating different models. This paper proposes IMM estimator with Improved Current Statistical Model (ICSM). The main feature of this estimator is ability to track the maneuvering target according to its current acceleration so that it can track accurately as its original motion. IMM is implemented with two models namely Constant velocity (CV) and ICSM to track the uniform and maneuvering targets respectively. The estimator is examined for different motions and simulation result is compared with the traditional IMM estimator. The accuracy of the proposed tracking has been validated for missing event cases and different scenarios.
MIMO Equalization With State-Space Channel Models

State-space models are proposed to represent multiple-input multiple-output (MIMO) frequency-selective wireless channels with the motivation of better model approximation and more robust channel equalization performance when the order of the channel model is lower that of the true channel. We develop a simple framework under which the equalizers for state-space channel models can be designed using the existing methods for designing equalizers for finite impulse response (FIR) models. In particular, a MIMO minimum mean-squared error decision feedback equalizer is developed for state-space models. When only estimates of the channel are available to the receiver, the equalization performance is affected by the channel estimation accuracy. Because reduced-order state-space models can provide lower H 2 channel estimation error than reduced-length FIR models, state-space based equalizers typically exhibit significantly smaller symbol error rate than FIR-based ones. Thus, state-space channel models can be a more robust choice than FIR models in the presence of model order selection error. Numerical simulation also shows that adaptive state-space based receivers have slower but comparable convergence rates compared to FIR-based adaptive equalizers.
Prediction of cerebral hyperperfusion after carotid endarterectomy using cerebral blood volume measured by perfusion-weighted MR imaging compared with single-photon emission CT.

BACKGROUND AND PURPOSE Cerebral hyperperfusion syndrome is a rare but serious complication of carotid revascularization, including carotid endarterectomy (CEA) and carotid stent placement, which can occur in patients with preoperative impairments in cerebral hemodynamics. The purpose of this study was to determine whether preoperative cerebral blood volume (CBV) measured by perfusion-weighted MR imaging (PWI) could identify patients at risk for cerebral hyperperfusion after CEA. MATERIALS AND METHODS CBV was measured by using PWI before CEA in 70 patients with unilateral internal carotid artery (ICA) stenosis (>or=70%) and without contralateral ICA steno-occlusive disease. Cerebral blood flow (CBF) was also measured by using single-photon emission CT before and immediately after CEA and on the 3rd postoperative day. RESULTS A significant correlation was observed between preoperative CBV and increases in CBF immediately after CEA (r = 0.785, P < .0001). Whereas hyperperfusion immediately after CEA (CBF increase of >or=100% compared with preoperative values) was observed in 7 of 15 patients (47%) with elevated preoperative CBV, no patients with normal preoperative CBV exhibited post-CEA hyperperfusion. Furthermore, elevated preoperative CBV was the only significant independent predictor of post-CEA hyperperfusion. Finally, hyperperfusion syndrome developed on the 5th postoperative day in 2 of the 7 patients who displayed hyperperfusion immediately after CEA. CONCLUSION Measurements of preoperative CBV by PWI might help to identify patients at risk for cerebral hyperperfusion after CEA in the absence of contralateral ICA steno-occlusive disease.
Elliptic Metric K-NN Method with Asymptotic MDL Measure

We describe an adaptive metric learning model combining the generative and the discriminative models for the face recognition. The asymptotic model based on the MDL measure is formulated for each class to estimate the variance by using small training examples. The feature fusion method is introduced to assume the missing patterns between the classes and to deal with the k-th nearest neighbor classification. The metric parameters obtained from the asymptotic MDL estimation are refined by using the synthesized feature patterns. We demonstrate an improved recognition performance on the ORL and UMIST face databases.