Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal power allocation for relayed transmissions over Rayleigh fading channels

Relayed transmission is a way to attain broader coverage by splitting the communication link from the source to the destination into several links/hops. One of the main advantages of this communication technique is that it distributes the use of power throughout the hops which means longer battery lives and lower interference introduced to the rest of the network. In this context, this paper investigates the optimal allocation of power over these links/hops for a given power budget. All hops are assumed to be subject to independent Rayleigh fading. The optimization criterion used is outage probability which is the probability that the link quality from source to destination falls below a certain threshold. Numerical results show that optimizing the allocation of power enhances the system performance, specially if the links are highly unbalanced in terms of their average fading power. They also show that non-regenerative systems with optimum power allocation can outperform regenerative systems with no power optimization.
Application of Topic Models to Judgments from Public Procurement Domain

In this work, automatic analysis of themes contained in a large corpora of judgments from public procurement domain is performed. The employed technique is unsupervised latent Dirichlet allocation (LDA). In addition, it is proposed, to use LDA in conjunction with recently developed method of unsupervised keyword extraction. Such an approach improves the interpretability of the automatically obtained topics and allows for better computational performance. The described analysis illustrates a potential of the method in detecting recurring themes and discovering temporal trends in lodged contract appeals. These results may be in future applied to improve information retrieval from repositories of legal texts or as auxiliary material for legal analyses carried out by human experts.
Analysing the quality of collaboration in task-oriented computer-mediated interactions: introduction to the workshop proceedings

We have developed an explanatory, descriptive model broad enough to reflect the different elements of collaborative work which are part of commercial and public organisations. Our model aims to establish a structured representation of the attributes which influence and form part of collaborative work. It is predominantly based on the existing literature on computer supported cooperative work, distributed cognition, education, social and organisational psychology, management science, and collaboration within healthcare teams; and is additionally supported by our experience of working with a range of industrial organisations. We applied the model in a practical way to underpin the concepts of a new methodology called CoScope, which was designed to assess the collaboration capability of organisations. The methodology focuses on the extent to which
Encrypted Fuzzy Vault Based on Fingerprint

Biometrics is the only factor that is directly linked with the distinguishing characteristic of an individual and has been widely applied to identity authentication, biometrics-based key management system may be expected to be a powerful competitor in the future. A popular technique of such a system is the fuzzy vault scheme. In this paper, a new scheme is proposed, which referred to as encrypted fuzzy vault scheme. The fingerprint-based implementation also is presented. In locking phase, a secret key is encrypted by the key derived from a set of random data. Then these data are encoded on the fingerprint minutiae. At last, the minutiae are encrypted by the private list. By these ways, the construction of the encoding polynomial is decoupled from the size of the secret key and less storage is required, but the higher level of security is achieved.
A computational treatment of V-V compounds in Japanese

We examine how a large-scale computational grammar can account for the complex nature of Japanese verbal compounds. Previous computational Japanese grammars have tried to avoid the problem by simple solutions such as enumerating as many verbal compounds in the lexicon as possible. In contrast, we develop the analysis that is linguistically adequate and computationally tractable and thus meets the requirement of a syntactically and semantically precise natural language processing of Japanese like Bond et al. (2005). Our analysis distinguishes between two kinds of verbal compounds: syntactic compounds, which are fully productive; and lexical compounds, which are of varying productivity.
Evolving Artificial Neural Networks using Cartesian Genetic Programming

NeuroEvolution is the application of Evolutionary Algorithms to the training of Artificial Neural Networks. NeuroEvolution is thought to possess many benefits over traditional training methods including: the ability to train recurrent network structures, the capability to adapt network topology, being able to create heterogeneous networks of arbitrary transfer functions, and allowing application to reinforcement as well as supervised learning tasks. This thesis presents a series of rigorous empirical investigations into many of these perceived advantages of NeuroEvolution. In this work it is demonstrated that the ability to simultaneously adapt network topology along with connection weights represents a significant advantage of many NeuroEvolutionary methods. It is also demonstrated that the ability to create heterogeneous networks comprising a range of transfer functions represents a further significant advantage. This thesis also investigates many potential benefits and drawbacks of NeuroEvolution which have been largely overlooked in the literature. This includes the presence and role of genetic redundancy in NeuroEvolution's search and whether program bloat is a limitation. The investigations presented focus on the use of a recently developed NeuroEvolution method based on Cartesian Genetic Programming. This thesis extends Cartesian Genetic Programming such that it can represent recurrent program structures allowing for the creation of recurrent Artificial Neural Networks. Using this newly developed extension, Recurrent Cartesian Genetic Programming, and its application to Artificial Neural Networks, are demonstrated to be extremely competitive in the domain of series forecasting.
ARGUMENT STRUCTURE IN LEARNER WRITING: A CORPUS- BASED ANALYSIS USING ARGUMENT MAPPING

In this paper, corpus-based linguistics (CBL) is applied to the analysis of argument structure in Malaysian written learner English. Botley and Metom (2011) have demonstrated the value of using a corpus of student essays, a "learner corpus", to explore the interlanguage (Selinker, 1972) of learners in the Malaysian university setting. This paper goes further by focusing on the critical thinking and argumentation abilities of students writing in a foreign language. The data consists of a sample of argumentative essays from the CALES learner corpus (Corpus Archive of Leaner English in Sabah/Sarawak). The essays were all written by Malay L1 students at a major Malaysian public university between 2005 and 2011. Because the essays were argumentative in terms of task type, it is reasonable to expect that the data should yield rich information on the argumentation strategies used by the students in writing their essays. In order to study these argumentation strategies, this paper makes use of a graphical representation scheme for arguments, known as "argument mapping" (Horn, 1999; ter Berg and van Gelder, 2007). Argument maps are diagrams consisting of square boxes linked with arrows, which can be used to represent various aspects of argumentation such as conclusions, premises, claims, contentions, reasons and objections. Using the argument mapping technique, this study seeks empirical data on how Malaysian undergraduates may structure their written arguments. This is currently a bottom-up exploratory study with few pre-set hypotheses. Despite this, it is hoped that the findings will lead to useful insights into how the writers of the essays manage, or do not manage, to construct clear, well-reasoned arguments. The findings should also be very helpful to educators and researchers in areas such as English for Academic Purposes (EAP) and critical thinking.
Data archiving and processing method using wavelet decomposition for structural health monitoring

This paper presents a method, which can be implemented under limited resources such as wireless sensor network devices, for damage detection of civil structures. The detection method is applied to transient waves from vibration responses, and it consists of Haar wavelet decomposition, thresholding, quantization and differentiating metric. Before the differentiating metric, a target signal is compressed into the wavelet coefficients “signature”. The results indicate that a single measurement channel without time synchronization of other measurement points is effective for instant damage detection through the case study using the ASCE SHM benchmark program. It is also shown that we can reduce the data to a small fraction of the original data volume using this method, while retaining enough information to detect structural damage and its severity. The approach illustrated in this paper implies a promise of wavelet-based data compression and analysis for the increasing volume of Structural Health Monitoring (SHM) data.
What Corpus Linguistics can oer Contact Linguistics: the c-oral-brasil corpus experience O que a Linguística de Corpus pode oferecer à Linguística de Contato: a experiência do corpus c-oral-brasil

Contact Linguistics, throughout its history, has been mostly a data-oriented subdiscipline. From the gathering of word lists in colonial settings by pioneer scholars to the current compilation of narratives, interviews and databanks, Contact Linguistics, dierently from other Linguistics subdisciplines, has strived to base its findings on the analysis of actual language produced by speakers of a given language, and not on solely introspective methodologies. On the other hand, Corpus Linguistics has brought innovative methodological approaches to mostly every subfield in linguistic research. Corpora compilation parameters have taken representativeness and balance seriously and this, in turn, has aided the finding of generalizations about linguistic systems. In this paper, the c-oral-brasil corpus, a spontaneous speech corpus of informal Brazilian Portuguese is presented, and some of its characteristics that may be explored very profitably by a Contact Linguistics perspective are highlighted.
Reusable QoS specifications for systematic component-based design

For successful and effective software development the ability to predict impact of design decisions in early development stages is crucial. Typically, to provide accurate predictions the models have to include low-level details such as used design patterns (e.g., concurrency design patterns) and underlying middleware platform. These details influence Quality of Service (QoS) metrics, thus are essential for accurate prediction of extra-functional properties such as performance and reliability. Existing approaches do not consider the relation of actual implementations and performance models used for prediction. Furthermore, they neglect the broad variety of implementations and middleware platforms, possible configurations, and varying usage scenarios. To allow more accurate performance predictions, we extend classical performance engineering by automated model refinements based on a library of reusable performance completions.
Greek Interjections: Syntax, Semantics and Pragmatics

Interjections in Ancient Greek have long lacked a comprehensive account, despite their frequent occurrence in major texts. The present study of their semantics and pragmatics, encompassing all items encountered in Greek drama from the 5th century BC, applies a moderate minimalism, theory-driven method. Readers are offered a thorough and detailed study of this elusive, and in several respects deviant, class of linguistic items.
Segmentation‐based skinning

Skeleton‐driven animation is popular by its simplicity and intuitive control of the limbs of a character. Linear blend skinning (LBS) is up to date the most efficient and simple deformation method; however, painting influence skinning weights is not intuitive, and it suffers the candy‐wrapper artifact. In this paper, we propose an approach based on mesh segmentation for skinning and skeleton‐driven computer animation. We propose a novel and fast method, based in watershed segmentation to deal with characters in T‐Pose and arbitrary poses, a simple weight assign algorithm based in the rigid skinning obtained with the segmentation algorithm for the LBS deformation method, and finally, a modified version of the LBS that avoids the loss of volume in twist rotations using the segmentation stage output values. Copyright © 2015 John Wiley & Sons, Ltd.
A Keyword-based Similarity Content Search in Named Data Networking

Named Data Networking is a future Internet architecture which enables content retrieve and access by its name. Different from the current network, Named Data Networking is more like a distributed network. Nevertheless, users do not know name of the content. In this paper, we present a Keyword-based NDN protocol which is an extension to the current Named Data Networking instead of centralized search mechanism. By using inverted tables as an index for content both in cache and repository, we manage to act the same like modern search engine, but completely different inside and faster.
Reconsidering the Knowledge Audit Process: Methodological Revisions in Practice

Over the last 20 years, the knowledge audit and map have been acknowledged as important tools in helping to identify and visually represent the knowledge present in organizational contexts. Despite this recognition and the availability of theoretical material, there has been a dearth of literature relating to the application of these tools. Consequently, a number of methodologies based on KM practice and research emerged in the early 2000s; several of which still inform the knowledge audit and map processes today. This paper revisits and extends research carried out by the authors in 2004 to present how this work has been expanded further through the incorporation of ensuing developments in the field with a view of continuing the development of knowledge auditing and mapping approaches. As such, a detailed methodology based on a recent case study is presented, including comparisons between the two methodological approaches, a rationale for the consequent modifications to the original case methodology and concomitant benefits for the amended approach. Copyright © 2013 John Wiley & Sons, Ltd.
S-parameter Reciprocity Relations, Normalization, and TLR Error Box Completion

Personal use of this material is permitted. However, permission to reprint/replublish this material for ad-vertising or promotional purposes or for creating new collective works for resale or redistribution to serversor lists or to reuse any copyrighted component of this work in other works must be obtained.The following preprint di ers from the nal publication:S. Vandenberghe, D. Schreurs, G. Carchon, B. Nauwelaers, W. De Raedt, \S-parameter Reciprocity Relations,Normalization, and TLR Error Box Completion," Int J RF and mm-wave CAE, John Wiley & Sons, Inc.,vol. 12, no. 5, 2002, pp. 418{427.
Analysis of time fluctuating characteristics of linear predictive coefficients

In order to obtain a high quality in speech synthesis, it is necessary to increase the frame rate in speech analysis. It has not been thoroughly investigated how frequent the optimal frame rate should be. First, we examined time fluctuating characteristics of the LPC parameters, such as log area ratio (LAR) and line spectrum pair (LSP) by spectral analysis. Next, we discuss the spectral interpolation distortion of these parameters, and the optimum coding to minimize the distortion. In case of LAR, it is possible to decrease to decrease the frame rate by 10 % through a non-uniform allocation of frame rates among different parameters. In case of LSP, the non-uniform allocation of frame rates is not necessary because the fluctuation characteristics of spectral interpolation distortion do not heavily depend on the order of LSP.
A real-time sub-carrier allocation scheme for multiple access downlink OFDM transmission

In this paper, we propose sub-carrier allocation algorithms for a multiple access scheme in downlink OFDM transmission. Knowing the channel characteristics of all the users at the base station, the sub-carrier allocation algorithm assigns sub-carriers to the users in a way that the total transmit power is minimized. An optimal algorithm is presented to provide the best sub-carrier assignment. The complexity of the optimal solution renders it impractical for real-time application. To deal with the frequently changing channel characteristic of a fast time-varying multipath fading channel, a heuristic algorithm based on constructive assignment and iterative improvement is proposed which can give out a valid solution in real time. Experimental results show that the performance of this real-time algorithm is close to that of the optimal allocation.
2D Hand Tracking Based on Flocking with Obstacle Avoidance

Hand gesture-based interaction provides a natural and powerful means for human-computer interaction. It is also a good interface for human-robot interaction. However, most of the existing proposals are likely to fail when they meet some skin-coloured objects, especially the face region. In this paper, we present a novel hand tracking method which can track the features of the hand based on the obstacle avoidance flocking behaviour model to overcome skin-coloured distractions. It allows features to be split into two groups under severe distractions and merge later. The experiment results show that our method can track the hand in a cluttered background or when passing the face, while the Flocking of Features (FoF) and the Mean Shift Embedded Particle Filter (MSEPF) methods may fail. These results suggest that our method has better performance in comparison with the previous methods. It may therefore be helpful to promote the use of the hand gesture-based human-robot interaction method.
High-voltage-tolerant I/O buffers with low-voltage CMOS process

This paper presents high-voltage-tolerant I/O buffer designs for a 1.9-V external cache interface and a 3.3-V system interface using 1.9-V MOS transistors in a 0.21-/spl mu/m process with 40-/spl Aring/ gate-oxide thickness. Various circuit techniques are used for 1.9- and 3.3-V I/O buffers to ensure that the voltage across the gate oxide of every MOS element is below specified limits of 2.2 V for transient (short duty cycle) and 1.9 V for steady state. Only one PMOS pullup driver transistor between the bond pad and the power supply, and one NMOS pulldown driver transistor between the bond pad and ground, are used for the 1.9-V I/O buffer design, while cascoded MOS transistors between the bond pad and power supply or ground terminals are used for the 3.3-V I/O buffer design. The primary design goal is to ensure the reliability of MOS elements by avoiding excessive gate oxide stress due to high electric fields. However, due to differences in requirements for speed, power-supply voltage, and tristate leakage current, completely different circuit techniques have been used for the two designs. Both of the designs have been successfully implemented in a 400-MHz UltraSPARC microprocessor.
Incorporation of CFD Generated Aerodynamic Data in Store Separation Predictions

The United States Air Force SEEK EAGLE Office (AFSEO) is transitioning to a more flexible process for simulating store separations. Currently, wind tunnel testing provides the primary source of data used to predict the dynamics of a store released from an aircraft. A six-degree-of-freedom (6-dof) program reads the wind tunnel provided aerodynamic database to estimate the store loads at each position in the aircraft flow field calculated as the integration marches forward in time. When wind tunnel data is unavailable or when engineers desire an additional source of predicted separation trajectories, time-accurate Computational Fluid Dynamics (CFD) is often used to predict the store separation event. In cases where the store of interest is sufficiently similar to previously flight tested stores, the store release trajectories are simply assumed to mimic those of the store’s near twin. The current process, which can use all or some of the previously mentioned techniques—wind tunnel testing, time-accurate CFD, and analogies to similar stores—could be made faster and more accurate if several new elements were added. One addition is the use of a semiempirical aerodynamic model that can use a very small aerodynamic database to estimate the store loads that are fed to a 6-dof. The original wind tunnel based approach and time accurate-CFD, as well as the aforementioned semi-empirical approach, were applied to a store separation from the F-15E Strike Eagle prior to verification of the techniques through flight testing.
Synergy: A Workbench for Collaborative Filtering Algorithms on User Interaction Data

Collaborative Filtering (CF) is a process used by m any recommender systems for recommending content based on similarities between users. CF traditionally used i tem purchase history or rating information only, for fi nding out similar users. Nowadays users interact with the con tent in many different ways. Users can rate, tag, like, dis like or subscribe to the content and some information about relationship between users is also available usuall y in social network form. This additional interaction informati on can be used to filter out similar users more effectivel y. Various CF algorithms have been proposed to take into account this additional information and suggest better content. But it is very hard to figure out which algorithm will work b est on a particular dataset. In this paper, we propose a gen eric data model, for storing user interaction data, which can easily support many collaborative filtering algorithms. We have also implemented a tool which assists in creating, running and comparing different collaborative filtering alg orithms. The tool is based on the proposed data model and ca n easily be extended by using plugins. It is helpful in figu ring out which algorithm works best on a particular domain.
Belief network induction

This dissertation describes BNI (Belief Network Inductor), a tool that automatically induces a belief network from a database. The fundamental thrust of this research program has been to provide a theoretically sound method of inducing a model from data, and performing inference over that model. Along with a solid grounding in probability theory, BNI has proven to be a quick, practical method of inducing data models that are highly accurate. The results include a belief network that stores beta distributions in the conditional probability tables, coupled with theorems demonstrating how to maintain these distributions through inference; techniques for applying neural network and other learning techniques to the task of conditional probability table learning; and a decision theoretic sampling theory which addresses scalability issues by characterizing the size of the sample needed to produce high quality inferences. The setting for this work is in database mining. Database mining is one of the fastest growing topics in Artificial Intelligence today, with industry providing at least as much impetus as research labs and universities. The general goal is to extract interesting quantities or relationships that are ``hidden'''' in large corporate or scientific databases, with the potential benefits of a successful technology being enormous. For example, models can be built that characterize what types of customers will respond to what types of marketing schemes, retailers will be able to predict sales to help determine correct inventory levels and distribution schedules, and insurance companies will be able to predict expected claim costs and better classify who will buy what type of coverage.