Miracle-dz/UniFAR-FTUs huggingface dataset:
Building Reliable Distributed Systems With P

Fault-tolerant distributed systems are difficult to get right because they must deal with concurrency and failures. Despite decades of research, current approaches for a more rigorous way of building robust distributed systems are unsatisfactory. In this paper, we present P, a new approach that makes it easier to build, specify, and test distributed systems. Our programming framework provides two important features. First, we provide a high-level language for formally specifying implementations, abstractions, and specifications of protocols. The P compiler automatically generates efficient C code from the input program. Second, we provide a tool for compositional systematic testing of a P program. Together, these attributes have the power to generate and reproduce within minutes, executions that could take months or even years to manifest in a live distributed system.
The ML decoding performance of LDPC ensembles over Z/sub q/

We derive the asymptotic spectra of low-density parity-check (LDPC) ensembles over Z/sub q/. We consider two ensembles of LDPC matrices, one is binary and the other q-ary. We also show that for modulo-additive noise channels, both ensembles achieve the random coding error exponent, for graphs with sufficiently large connectivity.
Radio resource allocation for multiuser OFDMA distributed antenna systems

In this paper, we investigate the problem of resource allocation with fairness constraints for OFDMA distributed antenna system (DAS). We formulate this problem as a binary integer programming problem, and transform it into a generated directed graph with the linear optimal distributed method to solve. Some simulation results compare the proposed algorithm with a given heuristic algorithm for purpose of verifying its performance. Consequently, other simulation results are provided to show that DAS outperforms the conventional central multi-antenna systems.
Acceptance of Moodle by professors: a study in a Portuguese Higher Education Institution

This paper presents and discusses the results of a study carried out with professors of the University of Aveiro, Portugal (UA), which aim is to characterize their acceptance of Moodle. The data were collected through the application of a questionnaire based on the Technology Acceptance Model. In general, the professors expressed a positive attitude towards the items that measure the Perceived Ease of Use, the Perceived Usefulness, the Attitude Toward Using and the Social Influence. There were defined groups of professors based on gender, scientific area and age group, and statistical tests were performed. Although the majority of the items did not show any statistical significant differences between the groups, probably revealing that the use of Moodle is already widespread in the teaching and learning process at UA, it was possible to identify some differences, mainly with respect to gender and scientific area.
Combination of multiple classifiers for handwritten word recognition

Due to large shape variations in human handwriting, recognition accuracy of cursive handwritten word is hardly satisfying using a single classifier. In this paper we introduce a framework to combine results of multiple classifiers and present an intuitive run-time weighted opinion pool combination approach for recognizing cursive handwritten words with a large size vocabulary. The individual classifiers are evaluated run-time dynamically. The final combination is weighted according to their local performance. For an open vocabulary recognition task, we use the ROVER algorithm to combine the different strings of characters provided by each classifier. Experimental results for recognizing cursive handwritten words demonstrate that our new approach achieves better recognition performance and reduces the relative error rate significantly.
Making Sense of Suppressions and Failures in Sensor Data: A Bayesian Approach

Sensor networks allow continuous data collection on unprecedented scales. The primary limiting factor of such networks is energy, of which communication is the dominant consumer. The default strategy of nodes continually reporting their data to the root results in too much messaging. Suppression stands to greatly alleviate this problem. The simplest such scheme is temporal suppression, in which a node transmits its reading only when it has changed beyond some e since last transmitted. In the absence of a report, the root can infer that the value remains within ±e hence, it is still able to derive the history of readings produced at the node. The critical weakness of suppression is message failure, to which sensor networks are particularly vulnerable. Failure creates ambiguity: a non-report may either be a suppression or a failure. Inferring the correct values for missing data and learning the parameters of the underlying process model become quite challenging. We propose a novel solution, BaySail, that incorporates the knowledge of the suppression scheme and application-level redundancy in Bayesian inference. We investigate several redundancy schemes and evaluate them in terms of in-network transmission costs and out-of-network inference efficacy, and the trade-off between these. Our experimental evaluation shows application-level redundancy outperforms retransmissions and basic sampling in both cost and accuracy of inference. The BaySail framework shows suppression schemes are generally effective for data collection, despite the presence of failures.
ANIMATED TELECONFERENCING : VIDEO DRIVEN FACIAL ANIMATION

This work presents a method of using hand-drawn art for an animated teleconferencing system. This low bandwidth solution to teleconferencing uses a computer generated animated character to represent the expression of a human user as seen by a desktop camera. Using hand-drawn art provides many advantages over traditional teleconferencing, including privacy, bandwidth, and entertainment value. The framework of the system is divided into three components: the video tracker which is able to locate, in real-time, features on a human face from live video; an expression mapper which is able to intelligently select which artworks should be used to represent the current expression; and a rendering engine which is able to morph in real-time the artworks to produce a nal image.
Explaining Classification by Finding Response-Related Subgroups in Data

A method for explaining results of a regression based classifier is proposed. The data is clustered using a metric extracted from the classifier. This way, clusters found are related to classifier predictions, and each cluster can be considered a possible explanation for classification result. The clusters are described by simple rules, meant to be easy for a human to understand. The key points of the work are presenting a modular framework for explaining the classification, and studying and comparing two different approaches for extracting a metric from a classifier model.
Research on Virtual Endoscopy path planning

Some key techniques and realizations of Virtual Endoscopy are systematically investigated in this thesis. It consists of the reading of CT data, the pre-processing of medical images, the techniques of segment tissues, 3D Reconstruction and the techniques of path planning. Great stress is laid on 3D Reconstruction and the techniques of path planning in this thesis. The primary work of studies is as follows: First of all, Study the loading of the chest CT data and the pre-process of filtering and interpolation. Secondly, the semi-automatic thresholding algorithm based on thresholding segmentation is proposed. The CT images are segmented using the methods. In addition, Marching Cubes algorithms is improved in the process of VE rendering algorithm to improve the efficiency of rendering. Finally, based on the technique of distance transform method, this thesis puts forward a distance transform method on the basis of maximum-cost spanning tree, which is able to create central path quickly and to solve some branch questions.
Diversity of Butyrivibrio Group Bacteria in the Rumen of Goats and Its Response to the Supplementation of Garlic Oil

This study aimed to investigate the diversity of the Butyrivibrio group bacteria in goat rumen and its response to garlic oil (GO) supplementation as revealed by molecular analysis of cloned 16S rRNA genes. Six wethers fitted with ruminal fistulas were assigned to two groups for a cross-over design with 28-d experimental period and 14-d interval. Goats were fed a basal diet without (control) or with GO ruminal infusion (0.8 g/d). Ruminal contents were used for DNA extraction collected before morning feeding on d 28. A total bacterial clone library was firstly constructed by nearly full-length 16S rRNA gene cloned sequences using universal primers. The resulting plasmids selected by Butyrivibrio-specific primers were used to construct a Butyrivibrio group-specific bacterial clone library. Butyrivibrio group represented 12.98% and 10.95% of total bacteria in control and GO group, respectively. In libraries, clones were classified to the genus Pseudobutyrivibrio, Butyrivibrio and others within the family Lachnospiraceae. Additionally, some specific clones were observed in GO group, being classified to the genus Ruminococcus and others within the family Ruminococcaceae. Based on the criterion that the similarity was 97% or greater with database sequences, there were 29.73% and 18.42% of clones identified as known isolates (i.e. B. proteoclasticus and Ps. ruminis) in control and GO groups, respectively. Further clones identified as B. fibrisolvens (5.41%) and R. flavefaciens (7.89%) were specifically found in control and GO groups, respectively. The majority of clones resembled Ps. ruminis (98% to 99% similarity), except for Lachnospiraceae bacteria (87% to 92% similarity) in the two libraries. The two clone libraries also appeared different in Shannon diversity index (control 2.47 and GO group 2.91). Our results indicated that the Butyrivibrio group bacteria had a complex community with considerable unknown species in the goat rumen.
A Small Improvement in the Walking Algorithm for Point Location in a Triangulation

The paper shows a simple technique which saves some edge tests in the walking algorithm for point location. The walking technique does not achieve the logarithmic per point complexity of the location-datastructure-based methods but does not need any auxiliary data structure and is very simple to implement, therefore, it is very popular in practice. The suggested idea did not bring a substantial improvement in our tests but it is very simple and there is an open door to further more substantial improvement in the future research.
Extrastriate form and motion processing in cone dysfunction and normal vision

Cone disorders result in poor visual acuity and colour blindness. While previous studies have investigated low-level vision, little is known about how cone loss impacts on extrastriate vision. This thesis used behavioural psychophysics and steady-state VEP (SSVEP) to examine effects of cone loss on coherent form, coherent motion and biological motion perception. Chapter one introduces the topic and background literature. Chapter two outlines the methods used within this thesis. Chapter three investigates the impact of simulated low-vision on form and motion perception. Normally sighted participants completed behavioural and SSVEP tests under blurred conditions. Blur led to reductions in perceptual sensitivity and coherence-related cortical signals in all three tasks, with coherent form perception faring the worst. Chapter four describes collection of control data for subsequent comparison with patient groups: behavioural and SSVEP measures under differing light levels chosen to stimulate rods and/or cones. The fifth and sixth chapters examine extrastriate vision in patients with stationary and progressive cone disorders. Comparisons of patients and controls on scotopic performance, mediated by rods reveal the extent to which cortical visual processing may have developed atypically in this group. Behavioural results in chapter 5 show that even at scotopic levels, cone disorder patients show some perceptual and SSVEP impairments compared to controls. Progressive patients show scotopic impairments on all three tests while stationary patients have impairments on coherent form and motion but not biological motion tests. Scotopic contrast sensitivity was also measured to check if extrastriate deficits could be explained by low-level deficits. Chapter six examines SSVEP results in the stationary patient group. Patients showed reduced VEP amplitudes relative to controls and there was some evidence of atypical motion topography. Results suggest that atypical photoreceptor function can affect the development and function of extrastriate vision. Potential advances in treatments for genetic visual disorders raise questions regarding neural plasticity, including the extent to which cortical visual processing can be reorganised following restoration of photoreceptor function.
An Integrated Framework for CALL Courseware Evaluation

The evaluation of courseware for CALL is one of the more challenging tasks a language teacher is faced with. Currently, most evaluation schemes consist of either a checklist or a list of questions to be answered. This paper offers an alternative approach to evaluation in the form of a flexible framework from which teachers can develop their own evaluation procedures. The components of the three major sections of the framework—operational description, teacher fit, and learner fit—and their interactions are described and some suggestions for use are offered. The evaluation framework described is one module of a more comprehensive framework incorporating courseware development and implementation as well.
Elevated levels of trace elements in cores of otoliths and their potential for use as natural tags

Variation in the chemical composition of fish otoliths has been used in recent years to address a range of ecological questions, including levels of stock mixing, variation in habitat use, and rates of larval exchange. While some of these questions have been answered with varying success, the degree to which discrete populations are connected via larval exchange remains unknown. To identify larval sources using natural variation in otolith chemistry, we must distinguish and measure the chemical composition of the otolith core, the portion of the otolith formed at the spawning site. Using laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS), we found that the core regions of otoliths from 6 different species of fishes were highly enriched in manganese (Mn), and elevated in magnesium (Mg) and barium (Ba), relative to adjacent regions of the otolith. These patterns were consistent for species drawn from different taxonomic groups, which inhabit temperate and tropical regions, are found in marine and freshwater, and utilize a variety of spawning modes. Variation among species in Mn concentration in the core also corresponds to maternal investment, measured by egg size. These data suggest that core enrichment may be a general characteristic of otoliths, and that the chemical composition of the otolith core is fundamentally different from other regions of the otolith. The localized elemental enrichment of the core underscores the importance of methods that analyze the core region in small, discrete samples if otolith chemistry is used to address questions of larval exchange among populations.
Leveraging Joint Interactions for Credibility Analysis in News Communities

Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations, reviews, quality ratings, and further insights on journalistic works. However, there is a complex interaction between different factors in such online communities: fairness and style of reporting, language clarity and objectivity, topical perspectives (like political viewpoint), expertise and bias of community members, and more. This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources. We develop a probabilistic graphical model that leverages this joint interaction to identify 1) highly credible news articles, 2) trustworthy news sources, and 3) expert users who perform the role of "citizen journalists" in the community. Our method extends CRF models to incorporate real-valued ratings, as some communities have very fine-grained scales that cannot be easily discretized without losing information. To the best of our knowledge, this paper is the first full-fledged analysis of credibility, trust, and expertise in news communities.
Enzymatic biosensor associated with Molecularly Imprinted Polymers for sensitive and selective detection of organophosphorus insecticides in olive oil

5 different bio-tools have been developed for sensitive detection of three organophosphorus insecticides mainly used for the treatment of olive trees: Malaoxon, Omethoate and Methidathion-oxon. The systems are based on the immobilization of electric eel acetylcholinesterase on screen-printed electrode (SPE) and microtiter plates by 3 different methods of immobilization. These developed systems were optimized with laboratory samples and then tested on natural samples of olive oil after a simple liquid-liquid extraction. Among these five systems designed, the amperometric biosensor based on acetylcholinesterase immobilized by bioencapsulation in a sol-gel matrix presented the best performance in terms of operational stability, storage stability, reproducibility, and it proved to be best suited for the determination of insecticides in real samples of olive oil. This biosensor has been associated with a highly selective extraction method based on the use of Molecularly Imprinted Polymers (MIPs) to improve selectivity. The results showed that the association MIPs-biosensor can selectively detect and quantify the three organophosphorus insecticides in such a complex matrix as olive oil, with limits of detection much lower than the maximum residue limit tolerated by international regulations.
Co-saliency detection via similarity-based saliency propagation

In this paper, we present a method for discovering the common salient objects from a set of images. We treat co-saliency detection as a pairwise saliency propagation problem, which utilizes the similarity between each pair of images to measure the common property with the guidance of a single saliency map image. Given the pairwise co-salient foreground maps, pairwise saliency is optimized by combining the initial background cues. Pairwise co-salient maps are then fused according to a novel fusion strategy based on the focus of human attention. Finally we adopt an integrated multi-scale scheme to obtain the pixel-level saliency map. Our proposed model makes the existing single saliency model perform well in co-saliency detection and is not overly sensitive to the initial saliency model selected. Extensive experiments on two benchmark databases show the superiority of our co-saliency model against the state-of-the-art methods both subjectively and objectively.
Properties of chiral Wilson loops

We study a class of Wilson Loops in N = 4,D = 4 Yang-Mills theory belonging to the chiral ring of a N = 2,d = 1 subalgebra. We show that the expectation value of these loops is independent of their shape. Using properties of the chiral ring, we also show that the expectation value is identically 1. We find the same result for chiral loops in maximally supersymmetric Yang-Mills theory in three, five and six dimensions. In seven dimensions, a generalized Konishi anomaly gives an equation for chiral loops which closely resembles the loop equations of the three dimensional Chern-Simons theory.
Securing critical infrastructure: A semantically enhanced sensor based approach

Being center of both public and hazardous material transport system, railway is one of the most important critical infrastructures. This research is aiming to support secure access to information, dependable and reliable operation, efficient management and maintenance of the railway infrastructure. In this regard, this paper proposes a framework where sensors retrieve the contextual data from the infrastructure and a middleware processes these data and the semantics of data to derive decisions. These decisions allow monitoring of information and generate alarms for faults. The decisions may recommend actions to deal with the faults and provide secure access to information only to authorized personnel. The semantic technologies such as ontology language, rule language and reasoning process are used to design the middleware. The paper includes detailed description of the middleware and an overview of its implementation steps.
Extracting Actionable Knowledge from Decision Trees

Most data mining algorithms and tools stop at discovered customer models, producing distribution information on customer profiles. Such techniques, when applied to industrial problems such as customer relationship management (CRM), are useful in pointing out customers who are likely attritors and customers who are loyal, but they require human experts to postprocess the discovered knowledge manually. Most of the postprocessing techniques have been limited to producing visualization results and interestingness ranking, but they do not directly suggest actions that would lead to an increase in the objective function such as profit. In this paper, we present novel algorithms that suggest actions to change customers from an undesired status (such as attritors) to a desired one (such as loyal) while maximizing an objective function: the expected net profit. These algorithms can discover cost-effective actions to transform customers from undesirable classes to desirable ones. The approach we take integrates data mining and decision making tightly by formulating the decision making problems directly on top of the data mining results in a postprocessing step. To improve the effectiveness of the approach, we also present an ensemble of decision trees which is shown to be more robust when the training data changes. Empirical tests are conducted on both a realistic insurance application domain and UCI benchmark data
Stateless Adaptive Reliable Broadcast Protocol for Heterogeneous Wireless Networks

We address the problem of reliable broadcasting in spite of collisions and transmission losses. We do not aim to achieve reliable broadcast to all nodes in the network, rather, we seek to ensure reliable broadcast to a desired percentage of nodes. We first present a geometric based, probabilistic model to predict the optimal transmission range for maximizing 1-hop broadcast coverage in wireless networks as a function of range, sending rate and density. Though this adaptation technique can be incorporated into any broadcasting protocol, we extend Broadcast Protocol for Sensor Networks (BPS) [1] because of its simplicity, scalability and adaptability. The protocol can be applied under node heterogeneity in terms of transmission/reception ranges and packet delivery rate. Through extensive analytical and experimental results, we show multi fold advantages of adapting the protocol to network conditions.