Miracle-dz/UniFAR-FTUs huggingface dataset:
Games without Frontiers: Theories and Methods for Game Studies and Design

Games without Frontiers is a cornerstone in multi-disciplinary game studies, drawing from study of art, psychology, systems theory, aesthetics, and communication studies. The author introduces a set of concepts, categorizations, and analysis methods for study and design of games. The book also offers a comprehensive perspective to psychological nature of play with discussions on goals, human abilities, and emotions. Games without Frontiers covers games of all kinds: it is based on an empirical sample of over 100 games, which includes computer and video games, board games, card games, and television game shows. Games without Frontiers is essential reading for game scholars, teachers, and students interested in analyzing games from psychological, aesthetic, and design perspectives. -- "Remarkable and ambitious piece of work. ” - Professor James Paul Gee, University of Wisconsin, Author of What Video Games Have to Teach Us About Learning and Literacy -- "Hugely ambitious work, which puts a stake in the ground for game studies. " - Jesper Juul, Ph.D., Singapore-MIT Gambit Game Lab, Author of Half-Real: Video Games between Real Rules and Fictional Worlds.
A method for capturing and translating qualitative user experience into design specifications: the haptic feedback of appliance interfaces

The paper describes a methodological approach specifically developed to capture and transform the qualitative User Experience (UX) of a consumer product into quantitative technical specifications. Merging the potentialities of Virtual Prototypes (VPs) and Digital Mock-Ups (DMU), a flexible design scenario is built to interpret usersâ desires. Visual, sound and haptic stimuli are reproduced in order to let users live a realistic multisensory experience interacting with the virtual replica of the product. Parametric models are defined to acquire usersâ preferences while optimization algorithms are used to transform them into technical specifications. The aim of the approach is to propose a robust technique to objectify usersâ desires and enable their direct and active participation within the product development process. The methodology is derived merging insights coming from four case studies as well as indications available in literature. Specifically the paper describes how to design the multisensory UX with household appliance doors and drawers with a specific focus on the haptic/force feedback objectification.
Fuzzy layer selection method in hierarchical cellular systems

Hierarchical cellular systems are proposed to accommodate mobile users of different speeds. While the micro layer serves the slow users, the macro layer serves the fast users. After a handoff is initiated, we must make a decision determining whether it should go to the micro or macro cell. In this paper, this problem is tackled by making use of past cell dwell times and occupancies in the target micro and macro cell. Heuristically, we want those mobile users with small cell dwell times, i.e. fast velocity, to join the macro layer. It is also desirable to select the target cell which has lower occupancy, in order to prevent call blocking. These heuristics are incorporated in a fuzzy layer selection algorithm, which is devised in terms of fuzzy set theory and fuzzy logic. The performance is compared to the popular threshold method in the literature by simulation.
Asynchronous Pseudolite Navigation Using C/N0 Measurements

The problem of indoor navigation is investigated using a Commercial Off-the-Shelf (COTS) pseudolite system. The system is operated in synchronous and asynchronous mode. It is shown that, when the system is operated in synchronous mode, it is unsuitable for deep indoor operations: in complex propagation environments, the synchronisation required for metre level navigation is difficult to achieve and different solutions have to be adopted. Two asynchronous approaches are thus considered and indoor navigation with metre level accuracy is demonstrated using C/N0 measurements. The approaches use a modified Receiver Signal Strength (RSS) navigation algorithm and a weighted centroid technique, respectively. In both cases, a pre-filtering stage has been adopted to enhance the quality of C/N0 measurements. The two methods have been compared under different operating conditions and the advantages and drawbacks of the two techniques have been analysed. The experiments demonstrate that metre level accuracy can be achieved using asynchronous pseudolites. These results are particularly encouraging since they were obtained without exploiting map constraints and prior knowledge of the user position.
A comparison of the effect of implicit/explicit and immediate/delayed corrective feedback on learners' performance in tailor-made test

The study investigated the effects of correction of learners' grammatical errors on acquisition. Specifically, it compared the effects of timing of correction (immediate versus delayed correction) and manner of correction (explicit versus implicit correction). It also investigated the relative effects of correction of morphological versus syntactic features and correction of developmental early versus developmental late features. Data for the study were collected from 56 intermediate level students of English as a Foreign Language in Iranian university and private language school settings. Each participant was required to read and then retell a written text in their own words during an oral interview with the researcher. During or following the interview the researcher corrected the participants on their grammatical errors implicitly (using recasts) or explicitly (providing metalinguistic information). Individualised tests focusing on the errors that had been corrected were constructed for each participant and administered. Statistical analyses were conducted on the scores the participants received on their individualised tests. Results showed no significant differences for timing of correction. However, significant differences were found for manner of correction. Participants who received explicit correction gained significantly higher scores than those who received implicit correction. This finding lends support to the argument of Schmidt (1994) concerning the role of metalinguistic awareness in language acquisition. Correction of morphological features was found to be more effective than that of syntactic features. It is argued that morphological features are generally learnt as items whereas syntactic features involve system learning. Correction of developmental early features was found to be more effective than correction of developmental late features. This finding lends support to suggestions that corrective feedback (like other types of form-focused instruction) needs to take into account learners' cognitive readiness to acquire features (Pienemann 1984; Mackey 1999). iii Analyses of the interactions between independent variables were also conducted. It was found that explicit correction was more effective for the acquisition of developmental early features and implicit correction was more effective for the acquisition of developmental late features The implications of these results for both second language acquisition and language pedagogy are considered.
Scene Oriented Classification of Blurry and Noisy Images Using SVM with Fuzzy C Mean Clustering

Image classification is a challenging task in image processing especially in the case of blurry and noisy images. In this work, we present an extension of scene oriented hierarchical classification of blurry and noisy images using Support Vector Machine (SVM) and Fuzzy C-Mean. Generally, a system for scene-oriented classification of blurry and noisy images attempts to simulate major features of the human visual observation. These approaches are based on three strategies such as Global pathway for extracting essential signature of image, local pathway for extracting local features, and then outcome of both global and local phase are combined and define feature vector and clustered using Monte Carlo approach. Afterwards, these clustered results are fed to a SOTA Algorithm (combination of self organizing map and hierarchical clustering) for final classification. But in these approaches, combination of self organizing map and hierarchical clustering has the problem in terms of accuracy and computation time of classification, especially when used large dataset for classification. To overcome this problem, we propose a combination of Support Vector Machine (SVM) and Fuzzy C-mean. Our proposed approach provides better result in terms of accuracy, especially when used with large dataset. The proposed method is computationally efficient because fuzzy c-mean clustering is faster and less time consuming as compared to hierarchical clustering.
Genetic Hyper-Heuristics for Graph Layout Problems

Graph layout problems are a special class of combinatorial optimisation problems, aiming at discovering a linear layout of an input graph such that a certain objective function is optimised. A linear layout is a labeling of the nodes of a graph with unique integers. Graph layout problems are mainly NP-complete, meaning that a guaranteed optimal solution cannot be reached in polynomial time. Because a large number of problems in science and engineering can be formulated as graph layout problems, a variety of methods have been proposed for addressing them. These methods are mainly heuristic in nature and based on graph-theoretic concepts. The best graph-theoretic heuristic algorithms can produce good-quality solutions in a short time, but, of course, they do not guarantee the optimality of the solutions obtained, and the solutions may be far from ideal. Meta-heuristic and Hyper-heuristic approaches are popular alternatives to classical optimisation techniques in a variety of domains. However, in the case of graph layout problems, there has been a limited exploration of such methods. Although meta-heuristics applied to these problems have shown to typically produce better results than graphtheoretic heuristics, in practice, graph-theoretic heuristics are much more preferred due to being substantially faster and more reliable. This thesis presents Genetic Hyper-Heuristics, which are heuristic search algorithms, exploring the space of problem solvers. They are designed to automatically evolve heuristic algorithms for graph layout problems. The evolved heuristics are reusable, meaning that they are intended for use on unseen problems. The central organising element of the genetic hyper-heuristics is a specialised Genetic Programming system. In addition to its application as a hyper-heuristic, our proposed genetic programming system can be utilised as a meta-heuristic in order to solve this class of problems. This is also presented in the thesis. In this study, we particularly focus on two of the most important graph layout problems, namely bandwidth and envelope reduction problems, and use them as testbeds for our algorithms. In order to assess the performance of the heuristics generated, we evaluate them on a large set of standard benchmark matrices from the Harwell-Boeing sparse matrix collection against the best bandwidth and envelope reduction algorithms from the literature. The results obtained show that the evolved heuristic algorithms are able to outperform state-of-the-art human-designed algorithms. In addition to being highly effective, the heuristics generated by our hyperheuristic methods are very fast, and therefore suitable for practical use.
Linear electronic analog/digital conversion architectures, their origins, parameters, limitations, and applications

Various A/D and D/A conversions architectures, as evolved for communication oriented and computer related instrumentation, are described, and their relative advantages and disadvantages reviewed. Important parameters of performance are defined, and a number of critical factors involved in the design of A/D converters are considered. Emphasis is placed on the need for realistic "error budgets" and a recognition of the uncertainties and harsh realities of the environment in place. A number of A/D and D/A converter applications are reviewed to illustrate the diversity of requirements to be met by conversion system designers.
Variables Selection for Multiclass SVM Using the Multiclass Radius Margin Bound

Support vector machines (SVM) are considered as a powerful tool for classification which demonstrate great performances in various fields. Presented for the first time for binary problems, SVMs have been extended in several ways to multiclass case with good results in practice. However, the existence of noise or redundant variables can reduce their performances, where the need for a selection of variables. In this work, we are interested in determining the relevant explanatory variables for an SVM model in the case of multiclass discrimination (MSVM). The criterion proposed here consist in determining such variables using one of the upper bounds of generalization error specific to MSVM models known as radius margin bound [1]. A score derived from this bound will establish the order of relevance of variables, then, the selection of optimal subset will be done using forward method. The experiments are conducted on simulated and real data, and some results are compared with those of other methods of variable selection by MSVM.
EFL teachers' perceptions and perspectives on internet-assisted language teaching

[Abstract]: Although the rapid increase of Internet availability has generated great interest in Internet-assisted language teaching (IALT) among English as a foreign language (EFL) teachers, little is known at present about EFL teachers’use of the Internet. This article addresses this issue and reports the results of a study that examined Korean secondary school EFL teachers’ perceptions and perspectives on the use of the Internet for teaching purposes. A total of 101 teachers participated in a survey and responded to the questions of how they think about IALT, how they use the Internet, and what types of resources they use on the Internet. The findings of the study suggest that there are three key factors affecting the use of the Internet in the classroom: teachers’personal interest in Internet use; teachers’ abilities to integrate Internet resources into classroom activities; and computer facilities and technical support in schools. In-service teacher training courses for IALT are strongly recommended to further develop and implement IALT.
The use and abuse of PowerPoint in Teaching and Learning in the Life Sciences: A Personal Overview

Abstract The use of PowerPoint for teaching presentations has considerable potential for encouraging more professional presentations. This paper reviews the advantages and disadvantages associated with its use in a teaching and learning context and suggests some guidelines and pedagogical strategies that need to be considered where it is to be used. It summarises some of the key principles of presentation that are frequently ignored and suggests some of the approaches that need to be incorporated into good practice in Life Science teaching and learning. Its use is often limited to an information transmission mode and this paper emphasises that this is a very restricted pedagogical use of a very powerful and flexible teaching and learning support tool.
Co-design of very flexible actuated structures

A numerical investigation on open-loop control and combined structural and control design (co-design) of very flexible beams is presented. The objective is to allow for an efficient design of these systems by identifying design strategies that provide significant performance advantages with respect to conventional sequential design methods. The control vector parametrisation method, implemented for both a B-splines (local) and discrete sines (global) set of basis functions, is used in conjunction with a gradient based optimiser to solve first the open-loop control and then the co-design problems. Numerical results show the impact of the time-frequency resolution of the parametrisation on the outcome of the optimisation. Overall, B-splines can achieve higher performance as they better exploit the flexible, high frequency driven, behaviour of the structure, particularly as large deformations lead to changes in the natural frequencies of the system. The discrete sines based parametrisation, on the other hand, is found to be a more robust choice. The mutual influence between control and structural dynamics during the design process is showed and used to explain the ability of the optimiser to approach a global optimum. In particular, it was found that control and structural disciplines can freeze the design around specific characteristic frequencies (locking), limiting the advantages of a co-design approach based on gradient methods.
Distributed balanced edge-cut partitioning of large graphs having weighted vertices

Large scale graphs are sometimes too big to store and process on a single machine. Instead, these graphs have to be divided into smaller parts and distributed over several machines, while minimizing the dependency between the different parts. This is known as the graph partitioning problem, which has been shown to be NP-complete. The problem is well studied, however most solutions are either not suitable for a distributed environment or unable to do balanced partitioning of graphs having weighted vertices. This thesis presents an extension to the distributed balanced graph partitioning algorithm JA-BE-JA, that solves the balanced partitioning problem for graph having weighted vertices. The extension, called wJA-BE-JA, is implemented in both the Spark framework and in Scala. The two main contributions of this report are the algorithm and a comprehensive evaluation of its performance, including a comparison with the recognized METIS graph partitioner. The evaluation shows that a random sampling policy in combination with the simulated annealing technique gives good results. It further shows that the algorithm is competitive to METIS, as the extension outperforms METIS in 17 of 20 tests.
Learning of robust principal component subspace

We study various neural algorithms for learning so-called robust principal component subspace. Standard principal components and the corresponding subspace are defined in terms of quadratic optimization criteria, leading to algorithms having linear learning term. The robust algorithms are derived by optimizing a similar criterion that grows less that quadratically. This introduces a nonlinearity into the gradient algorithms, but makes the results more robust against strong noise and outliers.
The Effectiveness of Risk Management Committee and Hedge Accounting Practices in Malaysia

This study examines the effectiveness of Risk Management Committee (RMC) in influencing hedge accounting practices among non-financial companies listed on the Bursa Malaysia. Our regression results reveal that that there is no significant relationship between the application of hedge accounting and the effectiveness of RMCs. However, there are positive and significant relationships between the choice of hedge accounting and each of company size and leverage. The implications of the findings were discussed.
Symbolic design and optimization techniques for analogintegrated circuits

An analog circuit design problem typically has many acceptable solutions. However, within the very broad design space, there will usually exist one optimal design that minimizes (or maximizes) one of the objectives, given a constraint on the other metrics. The rising complexity of the circuits and the absence of closed-form expressions for certain metrics (like total integrated noise) have led to a SPICE-simulation-based numerical approach to analog circuit design and optimization, which is very slow for circuits comprising more than a handful of transistors. The research presented in this dissertation focuses on symbolic design and optimization techniques for analog integrated circuits. These techniques are based on computer optimization programs that use closed-form symbolic expressions for all relevant performance metrics of the analog circuit, bypassing the need to interface with a circuit simulator. In the first part of this work, we deal with the problem of computing total integrated noise in an analog circuit. We demonstrate a technique to compute the total integrated noise by visual inspection in linear, passive networks, and then extend the technique to show how one can symbolically integrate a general noise transfer function of any order to get closed-form expressions for total integrated noise. Such expressions were not readily available and had prevented the adoption of symbolic analysis in the design and optimization of noise limited analog circuits. Compared to previously known methods, this technique is efficient in terms of computation cycles and memory requirement, and provides the answer in a single step. We next present three proof-of-concept examples that illustrate how symbolic analysis can be applied to the design and optimization of representative analog blocks. The presented techniques are general, and taken together, can help provide a circuit designer with the best design, find sensitivities to circuit parameters, and enable rapid design portability to different sets of specification or process corners. In the first example, we present a nested-Miller-compensated three-stage operational transconductance amplifier for use in high-speed switched-capacitor circuits. Simulation results show that the 90-nm prototype amplifier achieves a 0.1 % dynamic error settling time of 2.53 ns with a total integrated noise of 240 μVrms, while consuming 5.2 mW from a 1-V power supply. In the second example, we present the design and optimization of continuous-time active-RC and gm-C low-pass filters. Starting from a given LC ladder-filter realization, we develop a systematic method of choosing the right optimization variables and using signal-flow-graph manipulations to convert a given LC ladder-filter realization into the final analog circuit. This is done in such a way that the symbolic expressions for noise, power and area turn out to be posynomial functions, enabling the formulation of the design and optimization problem as a geometric program (GP) that can be quickly solved to get the globally-optimal…
Joint sequence detection and phase estimation using the EM algorithm

In burst communications, each burst of data arrives with a different, unknown carrier phase. Traditional methods of data detection in the presence of unknown phase are inappropriate here. Recently, several authors have proposed a variety of methods to detect data bursts with an unknown phase. One very successful method is based on the EM algorithm, an iterative statistical method. In our paper, we show that data recovery with the EM algorithm corresponds to carrying out joint maximum likelihood (ML) data detection and phase estimation using an iterative method. At a time when the EM algorithm is growing in both popularity and applicability, this paper provides an important engineering perspective on the EM algorithm, putting it in a traditional detection and estimation framework. Additionally, this paper offers novel, alternative methods to tackle the limitations of the EM algorithm, and presents new, detailed simulation results demonstrating its quality performance. <<ETX>>