Miracle-dz/UniFAR-FTUs huggingface dataset:
A survey of peer-to-peer content distribution technologies

Distributed computer architectures labeled "peer-to-peer" are designed for the sharing of computer resources (content, storage, CPU cycles) by direct exchange, rather than requiring the intermediation or support of a centralized server or authority. Peer-to-peer architectures are characterized by their ability to adapt to failures and accommodate transient populations of nodes while maintaining acceptable connectivity and performance.Content distribution is an important peer-to-peer application on the Internet that has received considerable research attention. Content distribution applications typically allow personal computers to function in a coordinated manner as a distributed storage medium by contributing, searching, and obtaining digital content.In this survey, we propose a framework for analyzing peer-to-peer content distribution technologies. Our approach focuses on nonfunctional characteristics such as security, scalability, performance, fairness, and resource management potential, and examines the way in which these characteristics are reflected in---and affected by---the architectural design decisions adopted by current peer-to-peer systems.We study current peer-to-peer systems and infrastructure technologies in terms of their distributed object location and routing mechanisms, their approach to content replication, caching and migration, their support for encryption, access control, authentication and identity, anonymity, deniability, accountability and reputation, and their use of resource trading and management schemes.
ADAPTIVE BACKSTEPPING AND PID OPTIMIZED BY GENETIC ALGORITHM IN CONTROL OF CHAOTIC

In this paper, the robust adaptive control scheme based on adaptive back- stepping design is used to control the autonomous second order strict feedback form such as the Lorenz Chaotic system. The design procedure is recursive; at the i th step, the i th subsystem is stabilized with respect to a Lyapunov function Vi by the design of a stabi- lizing function i, tuning function i, and the update law _ ^ i for the unknowns' adaptive parameters estimates ^ i, the feedback control u is designed at the nal step. This proce- dure possesses strong properties of global stability and tracking which are built into the nonlinear system in a number of steps, which is never higher than the system order, without any growth restrictions on nonlinearities. The results show the guarantee of all the parameter estimates to converge to their true values. Some simulation results for the chaotic system cited above are shown to illustrate the parameter convergence with adaptive backstepping design. Keywords: Adaptive backstepping design, Adaptive parameters estimates, Lyapunov, Stabilizing and tuning functions, Up date and control laws
Unicast-friendly multicast in IEEE 802.11 wireless LANs

The IEEE 802.11 protocol has become the de facto standard in wireless LANs. However, it reveals the unfairness problem between unicast flows and multicast flows since multicast packets are not subject to binary exponential backoff. To prevent a multicast flow from overwhelming the wireless link bandwidth is a crucial issue. This paper seeks to achieve fairness between unicast and multicast flows by introducing Unicast-Friendly Multicast (UFM). The central idea behind the UFM algorithm is to dynamically change the contention window size for multicast packets to limit the bandwidth share of a multicast flow equal to that of a unicast flow. The proposed UFM algorithm adjusts the multicast contention window size depending on the number of competing stations. We present two versions of UFM: the first one calculates the multicast window size by inferring the average contention window size of unicast flows, while the second one maintains the mapping table between the number of competing stations and the corresponding multicast window size given by related analysis. Simulation reveals that both versions of UFM achieve the fairness by providing almost the fair share of bandwidth to each flow regardless of unicast or multicast under the saturated network conditions.
Trading for COTS components in open environments

Trading is a well-known mechanism for searching and locating services in object-oriented systems. However, current traders present some limitations when used in open component-based system environments. In this paper we analyze the required features that COTS components traders should have, and describe the design and implementation of COTStrader; an Internet-based trader for COTS components that handles the heterogeneity, scalability and evolution of COTS traders.
Pre-service Science Teachers' Images and Misconceptions of Atomic Orbital and Self-ionization Concepts

The purpose of this study was to uncover pre-service science teachers' images and misconceptions regarding atomic orbital and self-ionization concepts. This study involved a total of 68 pre-service science teachers working at a public university during the 2015-2016 academic year. In this qualitative study, data were collated with the use of worksheets that participating teachers completed by drawing and explain the concepts regarding atomic orbital and self-ionization concepts. Collated data were then subject to content analysis. The results of this study showed that participants' images about atomic orbital and self-ionization concepts were insufficient and that pre-service science teachers hold various misconceptions about orbital and self-ionization concepts. The reasons of the misconceptions could be backed by the pre-service science teachers' inadequacy regarding the mental construction of proper images of scientific concepts and as a recommendation of the study applications are required for improving pre-service science teachers' scientific concepts images being aware of their inadequacy.
Adaptive real-time monitoring for large-scale networked systems

The focus of this thesis is continuous real-time monitoring, which is essential for the realization of adaptive management systems in large-scale dynamic environments. Real-time monitoring provides the necessary input to the decision-making process of network management. We have developed, implemented, and evaluated a design for real-time continuous monitoring of global metrics with performance objectives, such as monitoring overhead and estimation accuracy. Global metrics describe the state of the system as a whole, in contrast to local metrics, such as device counters or local protocol states, which capture the state of a local entity. Global metrics are computed from local metrics using aggregation functions, such as SUM, AVERAGE and MAX. A key part in the design is a model for the distributed monitoring process that relates performance metrics to parameters that tune the behavior of a monitoring protocol. The model has been instrumental in designing a monitoring protocol that is controllable and achieves given performance objectives. Our design has proved to be effective in meeting performance objectives, efficient, adaptive to changes in the networking conditions, controllable along different performance dimensions, and scalable. We have implemented a prototype on a testbed of commercial routers, which proves the feasibility of the design, and, more generally, the feasibility of effective and efficient real-time monitoring in large network environments.
Evaluating UMLS strings for natural language processing

The National Library of Medicine's Unified Medical Language System (UMLS) is a rich source of knowledge in the biomedical domain. The UMLS is used for research and development in a range of different applications, including natural language processing (NLP). In this paper we investigate the nature of the strings found in the UMLS Metathesaurus and evaluate them for their usefulness in NLP. We begin by identifying a number of properties that might allow us to predict the likelihood of a given string being found or not found in a corpus. We use a statistical model to test these predictors against our corpus, which is derived from the MEDLINE database. For one set of properties the model correctly predicted 77% of the strings that do not belong to the corpus, and 85% of the strings that do belong to the corpus. For another set of properties the model correctly predicted 96% of the strings that do not belong to the corpus and 29% of the strings that do belong to the corpus.
Antibiotic supplements affect electrophysiological properties and excitability of rat hippocampal pyramidal neurons in primary culture.

INTRODUCTION Antibiotic supplements are regularly used in neuronal culture media to control contamination; however, they can interfere with the neuronal excitability and affect electrophysiological properties. Therefore, in this study, the effect of penicillin/streptomycin supplements on the spontaneous electrophysiological activity of hippocampal pyramidal neurons was examined. METHODS Electrophysiological whole-cell patch-clamp recordings from rat hippocampal pyramidal cells in primary culture were performed to investigate the effects of antibiotic supplements on the intrinsic excitability of cultured cells. RESULTS The present findings indicated that presence of antibiotic supplements (penicillin/streptomycin) in the culture medium altered the intrinsic electrical activity of hippocampal pyramidal neurons in primary culture. These alterations included: 1) depolarized resting membrane potential; 2) a significant enhancement in the after-hyperpolarization amplitude; 3) a significant increase in the area under the action potential and in the decay and rise time of the action potential; 4) a significant broadening of action potential and 5) a significant reduction in the firing frequency. CONCLUSION These findings suggest that addition of antibiotic supplements to culture media influences the neuronal excitability and alters the electrophysiological properties of cultured neurons, possibly through changing the ionic conductance underlying neuronal excitability.
Continuous Detection of Motor Imagery in a Four-Class Asynchronous BCI

Asynchronous brain computer interface (BCI) is an important class of BCI systems that has not received enough attention from the BCI community. In this work we introduce for the first time a system for classification of four different motor imageries in the context of an asynchronous BCI system which distinguishes between periods of movement imagination occurrence and idling or resting periods of ongoing EEG signal as well as classifying the 4 class motor imageries. We used two multi class extensions of the method of Common Spatial Patterns (CSP) for feature extraction and LDA, SVM, and MDA well known classifiers for combination purposes. We have applied our procedure to data set Ilia from BCI Competition III [2]. Offline evaluation of a prototype system demonstrated true positive rates in the range of 56%- 88% with corresponding false positive rates in the range of 18%-9%.
Adolescent Self-control Predicts Midlife Hallucinatory Experiences: 40-Year Follow-up of a National Birth Cohort

Background: Associations between self-control in adolescence and adult mental health are unclear in the general population; to our knowledge, no study has investigated self-control in relation to psychotic-like symptoms. Aims: To investigate the relationship between adolescent self-control and the midlife mental health outcomes of anxiety and depression symptoms and psychotic-like experiences (PLEs), controlling for the effect of adolescent conduct and emotional problems and for parental occupational social class and childhood cognition. Methods: A population-based sample, the MRC National Survey of Health and Development (the British 1946 birth cohort) was contacted 23 times between ages 6 weeks and 53 years. Teachers completed rating scales to assess emotional adjustment and behaviors, from which factors measuring self-control, behavioral, and emotional problems were extracted. At age 53 years, PLEs were self-reported by 2918 participants using 4 items from the Psychosis Screening Questionnaire; symptoms of anxiety and depression were assessed using the scaled version of the General Health Questionnaire (GHQ-28). Results: After adjustment for the above covariates, poor adolescent self-control was associated with the presence of PLEs in adulthood, specifically hallucinatory experiences at age 53 years, even after adjustment for GHQ-28 scores. Conclusions: Lower self-control in adolescence is a risk factor for hallucinatory experiences in adulthood.
Usage Localization Algorithm for Mobile Robots Environment

The majority of localization algorithms start at a known position and add internal movement data and external environment data to this position each cycle. If the robot is replaced or the sensor data quality is too low, these algorithms are usually not able to recover to a useful position estimation Members of these so-called local approaches are the linear least squares estimator and the Kalman filter. Robots equipped with global localization algorithms like Markov localization and particle filter are able to localize themselves even under global uncertainty. This Article focuses on local and global localization, static environments, and passive approaches. Active approaches have to be discussed along with the decision making. To be able to cope with dynamic environments, map building is necessary. Both topics are not within the scope of this work.
Increased left ventricular torsion in uncomplicated type 1 diabetes : the role of coronary microvascular function

This is an uncopyedited electronic version of an article accepted for publication in Diabetes Care. The American Diabetes Association, publisher of Diabetes Care, is not responsible for any errors or omissions in this version of the manuscript or any version derived from it by third parties. The definitive publisherauthenticated version will be available in a future issue of Diabetes Care in print and online at http://care.diabetesjournals. org. Diabetes Care Publish Ahead of Print, published online June 9, 2009
Threshold concepts about online pedagogy for novice online teachers in higher education

ABSTRACT The use of threshold concepts to define key points of curricula is a relatively recent development in educational research. Threshold concepts represent crucial stages of learning, the acquisition of which enables learners to progress from one level of achievement to another. In this context, the learner is described as passing through an unsettling liminal space in which they may encounter troublesome knowledge and experience uncertainty or anxiety. When applied to online pedagogy in higher education contexts, academic staff become the learners as they extend their on-campus teaching knowledge into the online realm. In this setting, the identification of threshold concepts has the potential to inform the content of professional development (PD) programmes for novice online teachers. Because little research has yet been reported on threshold concepts associated with online teaching, this study identified these threshold concepts and investigated their specific nature. Funded by an Office for Learning and Teaching Australia Grant, the project employed a mixed-methods research approach. A mixture of qualitative and quantitative data was gathered from responses to questionnaires and reflective journal entries provided by university educators who were teaching in online contexts. Also, experts in the fields of PD, online teaching and threshold concepts were consulted using a modified Delphi technique that incorporated two rounds of surveys. Results of this study are discussed in association with potential applications to PD design for novice online educators, informed by the most fundamental learning experiences encountered by their more experienced colleagues.
Ubiquitous Data-Mining with Self-Organizing Maps

Advances in technology are turning our mobile phones or PDAs into powerful computing devices capable of executing data mining algorithms. This paper discusses how the Self Organizing Map (SOM) algorithm can be adapted to a cooperating network of these devices. This approach opens the door to several new applications of data mining, including active data-collecting devices and ondemand knowledge visualization. Our results show that the method proposed is viable to use in ubiquitous environments.
Reconstruction of Visual Sensory Space on the Hidden Layer in Layered Neural Networks

In layered neural networks, the input space is reconstructed on the hidden layer through the connection weights from the input layer to the hidden layer and the output function of each hidden neuron. The connection weights are modi ed by learning and realize the transformation to emphasize necessary information and to degenerate unnecessary one for calculating the output. In this paper, visual sensory signals are adopted as the input. In order to examine the reconstruction, (1)supervised or reinforcement learning is applied to a layered neural network at rst, (2)all the connection weights from the hidden layer to the output layer are reset to 0, (3)another supervised learning using some training data is applied, and nally (4)the output for the test data is compared to that when the rst learning was not applied. It is shown that the necessary information to generate the desired output in the rst learning was extracted on the hidden layer.
Computing optimal strategy against quantal response in security games

To step beyond the first-generation deployments of attacker-defender security games -- for LAX Police, US FAMS and others -- it is critical that we relax the assumption of perfect rationality of the human adversary. Indeed, this assumption is a well-accepted limitation of classical game theory and modeling human adversaries' bounded rationality is critical. To this end, quantal response (QR) has provided very promising results to model human bounded rationality. However, in computing optimal defender strategies in real-world security games against a QR model of attackers, we face difficulties including (1) solving a nonlinear non-convex optimization problem efficiently for massive real-world security games; and (2) addressing constraints on assigning security resources, which adds to the complexity of computing the optimal defender strategy. This paper presents two new algorithms to address these difficulties: Gosaq can compute the globally optimal defender strategy against a QR model of attackers when there are no resource constraints and gives an efficient heuristic otherwise; Pasaq in turn provides an efficient approximation of the optimal defender strategy with or without resource constraints. These two novel algorithms are based on three key ideas: (i) use of a binary search method to solve the fractional optimization problem efficiently, (ii) construction of a convex optimization problem through a non-linear transformation, (iii) building a piecewise linear approximation of the non-linear terms in the problem. Additional contributions of this paper include proofs of approximation bounds, detailed experimental results showing the advantages of Gosaq and Pasaq in solution quality over the benchmark algorithm (Brqr) and the efficiency of Pasaq. Given these results, Pasaq is at the heart of the PROTECT system, which is deployed for the US Coast Guard in the port of Boston, and is now headed to other ports.
Joint and Pipeline Probabilistic Models for Fine-Grained Sentiment Analysis: Extracting Aspects, Subjective Phrases and their Relations

Sentiment analysis and opinion mining are often addressed as a text classification or entity recognition problem, involving the detection or classification of aspects and subjective phrases. Many approaches do not model the relation between aspects and subjective phrases explicitly, implicitly assuming that a subjective phrase refers to a certain aspect if they co-occur together in the same sentence, thus potentially sacrificing accuracy. Instead, in the approach presented in this paper, we model the relation between aspects and subjective phrases explicitly, exploiting a flexible model based on imperatively defined factor graphs (IDF). The extraction of subjective phrases, aspects and the relation between them is modeled as a joint inference problem and compared to a pure pipeline architecture. Our goal is to analyse and quantify to what extent a joint model outperforms a pipeline model in terms of extraction of aspects, subjective phrases and the relation between them. Our results show that, while we have a substantial improvement on predicting targets using a joint inference model, the performance on subjective phrase detection and relation extraction actually decreases only slightly.
Data-driven text features for sponsored search click prediction

Much search engine revenue comes from sponsored search ads displayed with algorithmic search results. To maximize revenue, it is essential to choose a good slate of ads for each query, requiring accurate prediction of whether or not users will click on an ad. Click prediction is relatively easy for the ads that have been displayed many times, and have significant click history, but in the long tail with minimal or no click history, other features are needed to predict user response. In this work, we investigate the use of novel text features for this problem, within the context of a state-of-the-art sponsored search system. In particular, we propose the use of detailed word-pair indicator features between the query and ad. We compare the new features to the traditional vector-space and language modeling features extracted in a typical information-retrieval style. We evaluate these approaches in a maximum-entropy ranking model using the click-view data from a commercial search-engine traffic. We show that the word-pair features are highly helpful for sponsored search click prediction, not only improving over the sophisticated click-history feedback based systems, but also compensating for the lack of click history to some extent. In contrast, we find that the language and vector-space modeling approaches are significantly less effective.
Doubly-selective MMSE channel estimation and ICI mitigation for OFDM systems

In high mobility orthogonal frequency division multiplexing systems, subcarriers are no longer orthogonal, causing Inter-Carrier Interference (ICI). Equalization then becomes more challenging and requires an accurate estimate of the time-variant channel. In this paper, we propose a novel Minimum Mean Squared Error (MMSE) estimation of the sampled time-variant transfer function. Based on such channel estimation, we propose an iterative three step ICI mitigation technique whereby each step increases the channel estimation accuracy and, consequently, the performance of our MMSE equalization. In Step 1, we consider ICI as an additional noise term. In Step 2, we reduce the ICI at pilot positions and finally, in Step 3, we treat all estimated data symbols, obtained from Step 2, as if they were pilot symbols. We evaluate the Bit Error Ratio (BER) of our ICI mitigation technique by means of simulation and testbed measurements (up to 400 km/h). In both cases, we achieve a BER close to perfect channel knowledge and zero ICI.