Miracle-dz/UniFAR-FTUs huggingface dataset:
Lexical encoding formats for multi-word expressions: The challenge of "irregular" regularities

This chapter contributes a general overview and discussion of lexical encoding formats for multi-word expressions (MWEs) that can be used in NLP systems, in particular with large-scale grammars. The presentation is kept general in the sense that we will try to elicit basic aspects of lexical encoding and then elaborate on the specific sorts of challenges encountered when dealing with MWEs, especially the “irregular” regularities mentioned in the title. These insights will eventually be used to classify and evaluate different approaches to encoding. Even though this kind of evaluation cannot be conclusive given the diversity of languages and tastes, we will nevertheless argue in favor of fully flexible encoding formats exemplified with PATR-II and XMG, as opposed to the fixed encoding formats of DuELME and Walenty.
Distributed/concurrent garbage collection in distributed shared memory systems

We present a distributed and concurrent garbage collection algorithm that is well suited for distributed shared memory systems. Traversal of objects during garbage collection happens "in place" in the sense that the objects are not moved to any special node for traversal. Thus garbage collection activity does not interfere with the caching and locality inherent to the application. Cyclic garbage is reclaimed without moving objects, physically or logically. Furthermore, cycles are handled naturally by the algorithm and it is not necessary that a special mechanism be introduced for this purpose. Local garbage collection is asynchronous. Global garbage collection is realized by pair-of-nodes communication; no global synchronization is required. In this sense, the algorithm is scalable. Non-ideal communication channel are allowed and no bound on message delivery time is required. <<ETX>>
Spade: A Modular Framework for Analytical Exploration of RDF Graphs

RDF data is complex; exploring it is hard, and can be done through many different metaphors. We have developed and propose to demonstrate Spade, a tool helping users discover meaningful content of an RDF graph by showing them the results of aggregation (OLAP-style) queries automatically identified from the data. Spade chooses aggregates that are visually interesting, a property formally based on statistic properties of the aggregation query results. While well understood for relational data, such exploration raises multiple challenges for RDF: facts, dimensions and measures have to be identified (as opposed to known beforehand); as there are more candidate aggregates, assessing their interestingness can be very costly; finally, ontologies bring novel specific challenges but also novel opportunities, enabling ontology-driven exploration from an aggregate initially proposed by the system. Spade is a generic, extensible framework, which we instantiated with: (i) novel methods for enumerating candidate measures and dimensions in the vast space of possibilities provided by an RDF graph; (ii) a set of aggregate interestingness functions; (iii) ontology-based interactive exploration , and (iv) efficient early-stop techniques for estimating the interestingness of an aggregate query. The demonstration will comprise interactive scenarios on a variety of large, interesting RDF graphs.
An embeded tree data model for web content adaptation

This thesis presents a novel data model named Content Container Tree (CCT) to enable dynamic web content adaptation for heterogeneous terminal devices. The rationale for the CCT data model is that a web page can be visually divided into several segments and each segment can be iteratively divided into several smaller sub-segments until certain granularity level has been reached; the hierarchy of the web page segments can be expressed with a tree structure; thus, by traversing this segmentation tree, the user can find and locate a particular segment of interest which is at the dimension comparable to the screen size of the terminal device. The CCT data model organizes the semantics of the hierarchical content segments with a tree structure and embeds this semantic tree into the content DOM tree to enable the user to navigate through the content segments at different levels and retrieve the target content segment according to their semantics. Based on the CCT data model, the high level structural adaptation can be explicitly separated from the low level presentational adaptation, resulting in a highly modular and extensible framework for web content adaptation which is proposed in this thesis too. The CCT web content adaptation approach is distinguished from other approaches in the following aspects: first, the CCT approach is data oriented rather than process oriented; second, the CCT approach supports multilevel granularity of the contents; finally, CCT approach is dynamic; result pages can be generated on the fly by request. As an empirical evaluation, a content adaptation application is built according to the CCT adaptation framework to show the feasibility and effectiveness of our approach.
A Review of Hierarchical Networks Routing Protocols In Wireless Sensor Networks

Wireless Sensor Networks (WSNs) is a collection of sensor nodes with capability of sensing various types of environmental and physical conditions, data processing and provide wireless communication . The most important feature of routing protocol, in order to be efficient for WSNs, is the energy consumption and extension of the network’s lifetime. Wireless network are highly dependent on specific application and have a limited transmission range, and their processing and storage capabilities as well as their energy resources are also limited. In this paper, we give a survey and analyze different hierarchical based routing protocols for wireless sensor networks and compare them based on various characteristics.
Survey Designs and Spatio-Temporal Methods for Disease Surveillance

By improving the precision and accuracy of public health surveillance tools, we can improve cost-efficacy and obtain meaningful information to act upon. In this dissertation, we propose statistical methods for improving public health surveillance research. In Chapter 1, we introduce a pooled testing option for HIV prevalence estimation surveys to increase testing consent rates and subsequently decrease non-response bias. Pooled testing is less certain than individual testing, but, if more people to submit to testing, then it should reduce the potential for non-response bias. In Chapter 2, we illustrate technical issues in the design of neonatal tetanus elimination surveys. We address identifying the target population; using binary classification via lot quality assurance sampling (LQAS); and adjusting the design for the sensitivity of the survey instrument . In Chapter 3, we extend LQAS survey designs for monitoring malnutrition for longitudinal surveillance programs. By combining historical information with data from previous surveys, we detect spikes in malnutrition rates. Using this framework, we detect rises in malnutrition prevalence in longitudinal programs in Kenya and the Sudan. In Chapter 4, we develop a computationally efficient geostatistical disease mapping model that naturally handles model fitting issues due to temporal boundary misalignment by assuming that an underlying continuous risk surface induces spatial correlation between areas. We apply our method to assess socioeconomic trends in breast cancer incidence in Los Angeles between 1990 and 2000. In Chapter 5, we develop a statistical framework for addressing statistical uncertainty associated with denominator interpolation and with temporal misalignment in disease mapping studies. We propose methods for assessing the impact of the un-
ICT-mediated practice and constructivist practices: is this still the best plan for teachers’ uses of ICT?

Information and Communication Technologies (ICT) have been introduced into schools internationally along with the expectation that teachers will use constructivist practices in their use. Despite ICT now being in schools for many years, research shows teachers are not changing in the expected constructivist direction. This article reports on a qualitative, five-year longitudinal study of the ICT practices of a small number of teachers to understand changes that developed in their practices. While ICT became more integrated into their teaching, school and systemic factors, as well as teachers’ core beliefs about learning, contributed to their maintaining of teacher-centred practices. While constructivist practices were not adopted, this article illustrates the changes the teachers made to their practices were educationally valuable as they supported the development of knowledge valued by contemporary society.
Locating Latent Design Information in Developer Discussions: A Study on Pull Requests

A software system’s design determines many of its properties, such as maintainability and performance. An understanding of design is needed to maintain system properties as changes to the system occur. Unfortunately, many systems do not have up-to-date design documentation and approaches that have been developed to recover design often focus on how a system works by extracting structural and behaviour information rather than information about the desired design properties, such as robustness or performance. In this paper, we explore whether it is possible to automatically locate where design is discussed in on-line developer discussions. We investigate and introduce a classifier that can locate paragraphs in pull request discussions that pertain to design with an average AUC score of 0.87. We show that this classifier, when applied to projects on which it was not trained, agrees with the identification of design points by humans with an average AUC score of 0.79. We describe how this classifier could be used as the basis of tools to improve such tasks as reviewing code and implementing new features.
Clustering student behavior based on quiz activities on moodle LMS to discover the relation with a final exam score

One strategy in using the Learning Management System (LMS) is to provide activities that can be accessed by students as learning material. The problem is that the teacher cannot see the students’ online activity directly. In this study, the students’ behavior was discovered from the e-learning log file on the quiz activity. Quis activities are performed six times and end with a final exam. Four attributes were proposed as the student behavior model, they are the frequency of repeating quiz work (freq), the duration on each quiz work (our), increased score when repeating the quiz (perf), and the best score of all attempts (best). The preferable clustering result using K-means algorithm was obtained with 2 (two) clusters. Each cluster was paired with the final exam score which shows that the percentage of failures in cluster_0 is 85%. This result corresponds to the centroid value in cluster_0 that is lower in all attributes compare to the cluster_1. It concludes to evidence that the activity of working on online quizzes has a relation with the final exam score. This information can be used for early intervention by the teacher to prevent the student from failing.
Efficient Maximum Clique Computation over Large Sparse Graphs

This paper studies the problem of MCC-Sparse, Maximum Clique Computation over large real-world graphs that are usually Sparse. In the literature, MCC-Sparse has been studied separately and less extensively than its dense counterpart MCC-Dense, and advanced algorithmic techniques that are developed for MCC-Dense have not been utilized in the existing MCC-Sparse solvers. In this paper, we design an algorithm MC-BRB which transforms an instance of MCC-Sparse to instances of k-clique finding over dense subgraphs (KCF-Dense) that can be computed by the existing MCC-Dense solvers. To further improve the efficiency, we then develop a new branch-reduce-&-bound framework for KCF-Dense by proposing light-weight reducing techniques and leveraging the existing advanced branching and bounding techniques of MCC-Dense solvers. In addition, we also design an ego-centric algorithm MC-EGO for heuristically computing a near-maximum clique in near-linear time. We conduct extensive empirical studies on large real graphs and demonstrate the efficiency and effectiveness of our techniques.
Diagnosing quality of service faults in distributed applications

QoS management refers to the allocation and scheduling of computing resources. Static QoS management techniques provide a guarantee that resources will be available when needed. These techniques allocate resources based on worst-case needs. This is especially important for applications with hard QoS requirements. However, this approach can waste resources. In contrast, a dynamic approach allocates and deallocates resources during the lifetime of an application. In the dynamic approach the application is started with an initial resource allocation. If the application does not meet its QoS requirements, a resource manager attempts to allocate more resources to the application until the application's QoS requirement is met. While this approach offers the opportunity to better manage resources and meet application QoS requirements, it also introduces a new set of problems. In particular, a key problem is detecting why a QoS requirement is not being satisfied and determining the cause and, consequently, which resource needs to be adjusted. This paper investigates a policy-based approach for addressing these problems. An architecture is presented and a prototype described. This is followed by a case study in which the prototype is used to diagnose QoS problems for a web application based on Apache.
A Novel Algorithm For Speech Based Authentication Of Digital Images Using DWT .

A B S T R A C T Security of important data has always been an important necessity of human being. Traditionally important used to be stored in form of papers. Development of digital computers and computing technologies has enabled data to be secured digitally. Many algorithms have been developed for securing text data. Many secure data transmission applications has been developed. Authentication is used for securing content for authentication. Authentication can be done in special domain, spectral domain and in hybrid domain. In this paper we have proposed a novel algorithm to embed an audio signal into an image using discrete wavelet transform and a fragmenting scheme. This algorithm provides a better and faster encoding and decoding process as compared to traditional methods.
Precombining adaptive-order equalizer for downlink MAI suppression in CDMA systems

A new MAI suppression method - precombining adaptive-order equalizer - is proposed for a cellular CDMA system downlink. By means of increasing and smoothing the SNR variation of the input signal and optimizing the equalizer order, it greatly improves the performance of the common equalizer-based MAI suppression receivers in low SNR and fading cases. The proposed approach is especially suitable for 3G high-speed downlink packet access (HSDPA) systems. The receiver structure is described and represented mathematically. The receiver performance is evaluated through simulation and compared with common equalizer-based and RAKE receivers.
Real-Time Fault Detection and Reconfiguration of a Three-Phase Electric Motor Drive

Variable-frequency drives (VFDs) are widely used for control of electrical machines such as induction motors (IMs) or permanent-magnet synchronous motors (PMSMs). Similar to other electrical devices, these drives are subject to failure. Several types of faults are associated with VFDs. For instance, faults such as open-switch faults, and short-circuit switch faults are the two common faults in VFDs. These faults can yield catastrophic consequences if proper remedial action is not taken. A unique remedial topology for the post-fault period and a new pulse width modulation (PWM) strategy are proposed so that not only the motor drive can continue the operation, but also this new method reduces the common-mode voltage of the three-phase inverter. In this work, switch failures are investigated, and a new solution to detect openswitch faults are proposed using an algorithm based on current analyses. Extensive simulations are done to simulate a real system response during the post-fault phase on different operating points and at various torque loads. These simulation results were used to develop a detection algorithm. These results were also verified using an experimental setup. The proposed method offers an efficient solution for fault detection in VFDs. First, a fast response detection of a failure in an inverter is possible. Second, it provides an alternative back-up method for fault detection in electric drive systems.
An Application of Integrated 3D Technologies for Replicas in Cultural Heritage

In recent decades, 3D acquisition by laser scanning or digital photogrammetry has become one of the standard methods of documenting cultural heritage, because it permits one to analyze the shape, geometry, and location of any artefact without necessarily coming into contact with it. The recording of three-dimensional metrical data of an asset allows one to preserve and monitor, but also to understand and explain the history and cultural heritage shared. In essence, it constitutes a digital archive of the state of an artefact, which can be used for various purposes, be remodeled, or kept safely stored. With the introduction of 3D printing, digital data can once again take on material form and become physical objects from the corresponding mathematical models in a relatively short time and often at low cost. This possibility has led to a different consideration of the concept of virtual data, no longer necessarily linked to simple visual fruition. The importance of creating high-resolution physical copies has been reassessed in light of different types of events that increasingly threaten the protection of cultural heritage. The aim of this research is to analyze the critical issues in the production process of the replicas, focusing on potential problems in data acquisition and processing and on the accuracy of the resulting 3D printing. The metric precision of the printed model with 3D technology are fundamental for everything concerning geomatics and must be related to the same characteristics of the digital model obtained through the survey analysis.
Biotea-2-Bioschemas, facilitating structured markup for semantically annotated scholarly publications

The total number of scholarly publications grows day by day, making it necessary to explore and use simple yet effective ways to expose their metadata. Schema.org supports adding structured metadata to web pages via markup, making it easier for data providers but also for search engines to provide the right search results. Bioschemas is based on the standards of schema.org, providing new types, properties and guidelines for metadata, i.e., providing metadata profiles tailored to the Life Sciences domain. Here we present our proposed contribution to Bioschemas (from the project “Biotea”), which supports metadata contributions for scholarly publications via profiles and web components. Biotea comprises a semantic model to represent publications together with annotated elements recognized from the scientific text; our Biotea model has been mapped to schema.org following Bioschemas standards.
A New Genetic Algorithm for Multiple-Choice Multidimensional Knapsack Problem

A New Genetic Algorithm (NGA) for solving the Multiple Choice Multidimensional Knapsack Problem (MMKP) is presented in this paper. The MMKP can be applied to solve a wide variety of real life problems i.e., in any area where tasks must be scheduled or budgeted. This paper introduces NGA algorithm that hybridize the solution construction mechanism of GA operators (hybrid selection operator, hybrid cross-over operator and new hybrid mutation operator) for permutation encoding genetic algorithm. In addition we present a strong initial population is created by the Maximizing Value per Resources Consumption (MVRC) heuristic algorithm. The experimental results show that the method is very efficient and competitive to solve the MMKP compared with the better existing methods
Structural biology of the C-terminal domain of eukaryotic replication factor Mcm10

Mcm10 plays a key role in initiation and elongation of eukaryotic chromosomal DNA replication. As a first step to better understand the structure and function of vertebrate Mcm10, we have determined the structural architecture of Xenopus laevis Mcm10 (XMcm10) and characterized each domain biochemically. Limited proteolytic digestion of the full-length protein revealed N-terminal (NTD), internal (ID), and Cterminal (CTD) structured domains. Analytical ultracentrifugation revealed that XMcm10 self-associates and that the N-terminal domain forms homodimeric assemblies. DNA binding activity of XMcm10 was mapped to the ID and CTD, each of which binds to singleand double-stranded DNA with low micromolar affinity. The structural integrity of XMcm10-ID and CTD is dependent on the presence of bound zinc, which was experimentally verified by atomic absorption spectroscopy and proteolysis protection assays. The ID and CTD also bind independently to the N-terminal 323 residues of the p180 subunit of DNA polymerase -primase. We propose that the modularity of the protein architecture, with discrete domains for dimerization and for binding to DNA and DNA polymerase -primase, provides an effective means for coordinating the biochemical activities of Mcm10 within the replisome.
RTTSMCE: a response time aware task scheduling in multi-cloudlet environment

ABSTRACT Mobile cloud computing can address power consumption problems of the mobile device by offloading application from the mobile device to Remote cloud. But Latency issues are raised due to the long physical distance between a mobile user and Remote cloud. The emerging cloud concept can solve latency issues in mobile cloud computing. Some existing works are focusing on cloudlets section in the multi-cloudlet environment. However, few works considering task allocation in the multi-cloudlet environment to improve user satisfaction. In this paper, we have proposed Response time aware task scheduling in the multi-cloudlet environment (RTTSMCE) to deal with two problems. First, a cloudlet server selection based on response time and second a task scheduling among cloudlets using load balancing algorithms to optimize response time of the cloud server. From the stimulation, the proposed algorithm shows a better performance compared to traditional load balancing algorithms.