Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-related quality dimensions in linked data

Over the last few years, there has been an increasing di↵usion of Linked Data as a standard way to publish interlinked structured data on the Web, which allows users, and public and private organizations to fully exploit a large amount of data from several domains that were not available in the past. Although gathering and publishing such massive amount of structured data is certainly a step in the right direction, quality still poses a significant obstacle to the uptake of data consumption applications at large-scale. A crucial aspect of quality regards the dynamic nature of Linked Data where information can change rapidly and fail to reflect changes in the real world, thus becoming out-date. Quality is characterised by di↵erent dimensions that capture several aspects of quality such as accuracy, currency, consistency or completeness. In particular, the aspects of Linked Data dynamicity are captured by Time-Related Quality Dimensions such as data currency. The assessment of Time-Related Quality Dimensions, which is the task of measuring the quality, is based on temporal information whose collection poses several challenges regarding their availability, representation and diversity in Linked Data. The assessment of Time-Related Quality Dimensions supports data consumers in their decisions whether information are valid or not. The main goal of this thesis is to develop techniques for assessing Time-Related Quality Dimensions in Linked Data, which must overcome several challenges posed by Linked Data such as third-party applications, variety of data, high volume of data or velocity of data. The major contributions of this thesis can be summarized as follows: it presents a general settings of definitions for quality dimensions and measures adopted in Linked Data; it provides a large-scale analysis of approaches for representing temporal information in Linked Data; it provides a sharable and interoperable conceptual model which integrates vocabularies used to represent temporal information required for the assessment of Time-Related Quality Dimensions; it proposes two domain-independent techniques to assess data currency that work with incomplete or inaccurate temporal information and finally it provides an approach that enrich information with time intervals representing their temporal validity.
Efficient Load Balancing Schemes for Large-Scale Real-Time HLA/RTI Based Distributed Simulations

The Real-time extension of High Level Architecture (HLA) is very essential and useful for many large-scale distributed simulation systems. Most previous attempts to design the real-time Run time Infrastructure (RT-RTI) have enabled the usage of supported scheduling and prioritization services from underlying Real-time operating systems (RTOSs) augmented by communication QoS mechanisms. In this paper, we wish to build on this functionality by proposing an algorithm that differentiates services process- ing within the RTI itself by incorporating resources load balancing mechanisms with several scheduling and allocation policies. We focus our efforts on making the RTI's internal operations organized and well suited to the tasks and services it will be providing throughout the lifetime of HLA-compliant federations. We discuss our load balancing strategies and demonstrate through our analytical performance evaluation and simulation experiments that our proposed Real-time RTI framework exhibits a better performance in terms of the number of tasks served within deadlines compared with existing real-time RTI frameworks.
Estimation of capacity and required transmission power of WCDMA downlink based on a downlink pole equation

A simple method is derived for estimating the downlink capacity and required base station transmission power in a WCDMA system. This is based on a downlink pole equation which is mostly similar with the well known CDMA uplink pole equation. It is shown that the total downlink transmission power is composed of two terms. The first term is equal to what would be needed in the absence of interference and the second one shows the increase due to multiple access interference which is inversely proportional to 1-/spl eta//sub DL/ where /spl eta//sub DL/ is called the downlink loading. The method can be used in radio network dimensioning where extensive simulations can not be run. Usage of the method is demonstrated by some examples.
A 250-622 MHz deskew and jitter-suppressed clock buffer using two-loop architecture

A 250-622 MHz clock buffer has been developed, using a two-loop architecture: a delay-locked loop (DLL) for deskew, and a frequency-locked loop (FLL) for reference frequency supply to the DLL. The DLL incorporates a current-mode phase detector which utilizes a flip-flop metastability to detect a phase difference in the order of 20 ps. A measured jitter is suppressed to less than 40 ps RMS over the operating frequency range. A DLL acquisition time of 150 ns typical is simulated at 400 MHz. A 0.4-/spl mu/m CMOS technology is used to fabricate the chip.
Mendeley's open data for science and learning: a reply to the DataTEL challenge

In this paper two open access data sources, provided by Mendeley Ltd., are described in detail. The first data source is a snapshot of 50,000 user libraries, enabling researchers to test collaborative filtering algorithms for scientific paper recommendation. The second data source is Mendeley's API that provides access to 50 million research articles, crowdsourced from over 1.5 million users. This API allows researchers to build third party applications on top of real-time and large scale data. By providing data sources for the study of collaborative filtering, metadata extraction, deduplication and related research, Mendeley hopes to enable researchers in these domains to better collaborate and share knowledge, ultimately encouraging good research practices and advancing the state of scientific knowledge.
Sharing of exploring information using belief measure for multi robot exploration

We consider the problem of sharing knowledge in multi-robot exploration. It is difficult for each robot to explore accurately because of sensor errors and dead reckoning errors. We use the belief measure as the expression of sensor values in each robot for exploring an unknown environment. Then, multiple robots share the knowledge about some targets or some obstacles of the environment considering the degree of trust for other robots. The key point of this method is that robots have not a common map, but each robot has his map for sharing exploring information. The effectiveness of our approach is demonstrated by a real experiment for the case of two mobile robots.
Particle Modeling of Plasmas On Supercomputers

Particle simulation of plasmas (ionized gases) has become a powerful tool for in vestigating the physics of this complex state of matter, such as normal modes of nonuniform plasmas, and nonlinear be havior. Whereas much of this complex behavior is difficult to measure experi mentally, computer modeling can provide detailed information to augment our un derstanding of physical phenomena. We describe the basic methods of particle simulation and present a number of ex amples that highlight applications of this approach to plasma physics.
CONSENSUS-BASED CROWDSOURCING: TECHNIQUES AND APPLICATIONS

Crowdsourcing solutions are receiving more and more attention in the recent literature about social computing and distributed problem solving. In general terms, crowdsourcing can be considered as a social-computing model aimed at fostering the autonomous formation and emergence of the so-called wisdom of the crowd. Quality assessment is a crucial issue for the effectiveness of crowdsourcing systems, both for what concerns task and worker management. Another aspect to be considered in crowdsourcing systems is about the kind of contributions workers can make. Usually, crowdsourcing approaches rely only on tasks where workers have to decide among a predefined set of possible solutions. On the other hand, tasks leaving the workers a higher level of freedom in producing their answer (e.g., free-hand drawing) are more difficult to be managed and verified. In the Thesis, we present the LiquidCrowd approach based on consensus and trustworthiness techniques for managing the execution of collaborative tasks. By collaborative task, we refer to a task for which a factual answer is not possible/appropriate, or a task whose result depends on the personal perception/point-of-view of the worker. We introduce the notion of worker trustworthiness to denote the worker “reliability”, namely her/his capability to foster the successful completion of tasks. Furthermore, we improve the conventional score-based mechanism by introducing the notion of award that is a bonus provided to those workers that contribute to reach the consensus within groups. This way, groups with certain trustworthiness requirements can be composed on-demand, to deal with complex tasks, like for example tasks where consensus has not been reached during the first execution. In LiquidCrowd, we define a democratic mechanism based on the notion of supermajority to enable the flexible specification of the expected degree of agreement required for obtaining the consensus within a worker group. In LiquidCrowd, three task typologies are provided: choice, where the worker is asked to choose the answer among a list of predefined options; range, where the worker is asked to provide a free-numeric answer; proposition, where the worker is asked to provide a free text answer. To evaluate the quality of the produced results obtained through LiquidCrowd consensus techniques, we perform a testing against the SQUARE crowdsourcing benchmark. Furthermore, to evaluate the capability of LiquidCrowd to effectively support a real problem, real case studies about web data classification have been selected.
Unknown word sequences in HPSG

This work consists of an investigation into the properties of unknown words in HPSG, and in particular into the phenomenon of multi-word unknown expressions consisting of multiple unknown words in a sequence. The work presented consists ﬁrst of a study determining the relative frequency of multiword unknown expressions, and then a survey of the eﬃcacy of a variety of techniques for handling these expressions. The techniques presented consist of modiﬁed versions of techniques from the existing unknown-word prediction literature as well as novel techniques, and they are evaluated with a speciﬁc concern for how they fare in the context of sentences with many unknown words and long unknown sequences.
Automated Textbook Indexing with Naive Bayes Classifier Trained on Wikipedia Articles

Creating the index for a book is either arduous or expensive for authors. If an automated process can be introduced which effectively generates an index with approximately the same accuracy as a human, it would reduce the time and money authors need to spend on their work, allowing them to allocate those resources in more useful ways. This research looks to establish the efficacy of using a Naïve Bayes’ classifier trained with data extracted from Wikipedia articles to create an index for Biology, a Biology textbook. Wikipedia article titles are used to label each class of training data. This approach relies on the assumption that the subset of all desired index entries is contained in the set of all possibleWikipedia article titles. Paragraph-level data from Wikipedia and Biology are processed for features such as word existence, first word of paragraph, word in first sentence, and a number of other binary traits.
The Space Communications Systems Simulator and its use to predict space station communications in the presence of pulsed radio frequency interference

The Space Communications System Simulator (SCSS) is a tool which has played an essential role in the development of complex communication systems for NASA's space programs including the Space Station Freedom and the Space Shuttle. The paper describes the capabilities of the SCSS and one particular example of how the SCSS was applied to characterize the performance of the Space Station's primary communications subsystem in a hostile interference environment. <<ETX>>
Fusion in Computer Vision: Understanding Complex Visual Content

This book presents a thorough overview of fusion in computer vision, from an interdisciplinary and multi-application viewpoint, describing successful approaches, evaluated in the context of international benchmarks that model realistic use cases. Features: examines late fusion approaches for concept recognition in images and videos; describes the interpretation of visual content by incorporating models of the human visual system with content understanding methods; investigates the fusion of multi-modal features of different semantic levels, as well as results of semantic concept detections, for example-based event recognition in video; proposes rotation-based ensemble classifiers for high-dimensional data, which encourage both individual accuracy and diversity within the ensemble; reviews application-focused strategies of fusion in video surveillance, biomedical information retrieval, and content detection in movies; discusses the modeling of mechanisms of human interpretation of complex visual content.
A Review and Comparison of Well-Known Methods for Object Detection and Tracking in Videos

Moving object detection and tracking are main initial steps in object recognition, context analysis and index processes for visual surveillance system. IT is a big challenge for researchers to make a decision on which detection & tracking algorithm is more suitable. There is a variety of object D&T algorithms (i.e. methods) and publications on their performance comparison. This paper provides a systematic review of these algorithms and performance measures.
FUZZY BASED RECOGNITION OF HANDWRITTEN ARABIC NUMERALS

Human generated patterns like handwritten characters are found to be fuzzy in nature upto certain extent. This presented work proposes a fuzzy conceptual approach to classify Handwritten Arabic Numerals based on invariant moments features and the divisions of numeral image into several parts. The Moment invariants features are well known for independence of size, slant, orientation, translation and other variations of handwritten characters. A database, created by American University in Cairo, of 7000 samples of each number from 700 different writers is used. Each image is normalized to 40X40 pixel size. Seven central invariant moments are evaluated for each image and its parts by dividing it by three different ways, i.e. three feature groups. The algorithm is experimented for 500 samples of each numeral image and 161 features were evaluated corresponding to each image. The performance rate of the method is found to be 95.14%.
ON THE DOUBLE OBJECT CONSTRUCTION

This article concerns the structure of a double object construction. It is argued that Larson's arguments for the structure of double objects raise some empirical problems concerning anaphor binding and a semantic relation between the two objects. Next, it is pointed out that the structure corresponding to the present-day double object construction was extremely common in Old English and the structure similar to the dative construction of today began to appear at the beginning of the Middle English period. To accommodate the facts and at the same time to solve the problems with Larson's analysis, it is proposed that the structure of a double object construction is a basic form and the structure of a dative construction is derived from it by passivization within VP. *
Automated Support for Framework-Based Software Evolution

In this paper, we show how elaborate support forframework-based software evolution can be provided basedon explicit documentation of the hot spots of object-orientedapplication frameworks. Such support includes high-leveltransformations that guide a developer when instantiatingapplications from a framework by propagating the necessarychanges, as well as application upgrading facilitiesbased on these transformations. The approach relies on activedeclarative documentation of the design and evolutionof the framework's hot spots, by means of metapatterns andtheir associated transformations.
The Effect of Cognitive Style and Report Format on Task Performance: The MIS Design Consequences

The effective design and implementation of an information system is a complex undertaking which requires management to anticipate the possible effects of different psychological dispositions and various reports on task performance. To provide information that management may use in developing an information system, a group of subjects (n = 403) were (1) classified as high or low analytic depending upon their score on the Embedded Figures Test, (2) given five reports which they ranked according to their perception of the degree of difficulty they expected each report to present in performing a task, and (3) required to answer 20 questions with one of the previously ranked reports. The five reports used in the experiment were a raw data report (Report A) and four reports generated from the information presented in Report A. Report B was a tabular report of the percentage relationship of the raw data. Reports C, D, and E were a histogram of the raw data, a cumulative frequency graphic of the raw data and a cumulative frequency graphic of the percentages respectively. Therefore, there were two groups: High and Low Analytics, five reports and one task. The results of the experiment were (1) the perceived complexity rankings (lowest to highest) for both the high and low analytics were Report A, Report B, Report C, Report D, and Report E, (2) individuals classified as high analytic outperformed the individuals classified as low analytic on each of the five reports, and (3) for both the high and low analytics task performance decreased as perceived complexity increased. The implications of these results are (1) for tasks similar to the one tested that task performance may be enhanced by assigning such tasks to high analytic individuals, (2) individuals are able to perceive those reports which are likely to cause performance difficulties, and (3) using the report perceived as least complex is likely to be a valid report specification rule.
Bayesian inference for ion–channel gating mechanisms directly from single–channel recordings, using Markov chain Monte Carlo

The gating mechanism of a single–ion channel is usually modelled by a finite–state–space continuous–time Markov chain. The patch–clamp technique enables the experimenter to record the current flowing across a single–ion channel. In practice, the current is corrupted by noise and low–pass filtering, and is sampled with a typically very short sampling interval. We present a method for performing Bayesian inference about parameters governing the underlying single–channel gating mechanism and the recording process, directly from such single–channel recordings. Our procedure uses a technique known as Markov chain Monte Carlo, which involves constructing a Markov chain whose equilibrium distribution is given by the posterior distribution of the unknown parameters given the observed data. Simulation of that Markov chain then enables the investigator to estimate the required posterior distribution. As well as providing a method of estimating the transition rates of the underlying Markov chain used to model the single–channel gating mechanism and the means and variances of open and closed conductance levels, the output from our Markov chain Monte Carlo simulations can also be used to estimate single–channel properties, such as the mean lengths of open and closed sojourn times, and to reconstruct the unobserved quantal signal which indicates whether the channel is open or closed. The theory is illustrated by several numerical examples taken mainly from the ion–channel literature.
Effects of feature transformation and selection on classification of network traffic activities

As new technologies are emerging day by day, network, regardless of the Internet or Intranet within a corporation often plays a crucial role in connecting people from all around the world. From military use to achieving business goals and household need, data security often get attention from computer scientists. Traditional security measures that include the installation of firewall and antivirus software are commonly utilised to prevent intrusion. However, such types of defence are merely sufficient to secure a network and data travelling across it. Thus, second lines of defence like Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) are introduced to overcome the inadequacy of traditional security measures. Generally, IDS uses two approaches, the Anomaly Detection (A-IDS) and the Misuse Detection in order to identify patterns of intrusion. A-IDS often perform comparison of the model of normal and anomalous model. Depending on the ability to measure similarity or distance between a target and a known type, comparison is made to determine whether to establish a new target anomalous or not. This research aims to investigate the effects of feature transformation on the classification of network activities; the focus is to represent the data into point series form to permit the application of Time Series Classification (TSC). The TSC technique used is k-Nearest Neighbour (KNN) coupled with Dynamic Time Warping. Effects of using different similarity measures, Euclidean Distance (ED) and Cosine similarity algorithm are also investigated. Experiments conducted involve conversion of the categorical data by three different conversion techniques to generate point series data â€“ simple, probability and entropy conversion. Comparison between different classifiers is also conducted. The performance of the classifier is best using 1NN with Euclidean distance and entropy conversion for categorical data, where the recorded accuracy is 99.19%.
A Review Report on Searching Techniques on Encrypted Data

— Cloud computing has come as a storm in the streets of Information technology industry. Cloud computing provides a platform to store data on the virtual network. There are a number of cloud storage providers that are providing storage as a service to their users. But the privacy of data on this platform is the main hindrance in adopting cloud technology. To overcome this problem of data privacy, many encryption algorithms have been proposed to be implemented on the data before uploading it on the cloud. But if we talk about large database, then the problem of searching for a chunk of data from this large encrypted database is still there. A number of searching algorithm is there that are suitable for cloud platform, but they may fail in the case of progressive elliptic curve encryption. In this paper, we are comparing various searching techniques on encrypted data suitable for cloud platform .
A matrix product algorithm and its comparative performance on hypercubes

A matrix product algorithm is studied in which one matrix operand is transposed prior to the computation. This algorithm is compared with the Fox-Hey-Otto algorithm on hypercube architectures. The Transpose algorithm simplifies communication for nonsquare matrices and for computations where the number of processors is not a perfect square. The results indicate superior performance for the Transpose algorithm. <<ETX>>