Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing, Using and Evaluating Educational Games: Challenges, Some Solutions and Future Research

Educational games can use storytelling as the underlying model for designing their content and narrative to enhance learning outcomes. We need to evaluate their efficacy to maximise the user benefits. However, the field of user experience (UX) evaluation is full of challenges in itself; and evaluating the UX for digital educational games adds many other complexities. This paper presents various aspects involved in developing digital educational games and in evaluating the user experience derived from these. Felder Silverman learning style mode is presented to incorporate it into educational games and cater for a range of learners. A story design model called Movement Oriented Design (MOD) is presented to guide the process of creating effective educational narratives. A User Experience Research (UXeR) meta-model is presented as the pathway to advance research in this multidisciplinary field.
Practical solutions for resilience in SlapOS

SlapOS is an open source operating system for distributed cloud computing based on the motto “everything is a process”. SlapOS combines grid computing and Enterprise Resource Planning (ERP) to provide Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) through a simple, unified API which one can learn in a matter of minutes. SlapOS opens new perspectives for research in the area of resilience and security on the Cloud. In this paper we address the question of the lack of resiliency at current IaaS providers and we show how the combination of a simple leader election algorithm and a resource and monitoring system may help in the SlapOS cloud system for solving this issue. The approach exhibits technical and research directions for more elaborated studies.
Q+Tree: An Efficient Quad Tree based Data Indexing for Parallelizing Dynamic and Reverse Skylines

Skyline queries play an important role in multi-criteria decision making applications of many areas. Given a dataset of objects, a skyline query retrieves data objects that are not dominated by any other data object in the dataset. Unlike standard skyline queries where the different aspects of data objects are compared directly, dynamic and reverse skyline queries adhere to the around-by semantics, which is realized by comparing the relative distances of the data objects w.r. t. a given query. Though, there are a number of works on parallelizing the standard skyline queries, only a few works are devoted to the parallel computation of dynamic and reverse skyline queries. This paper presents an efficient quad-tree based data indexing scheme, called Q+Tree, for parallelizing the computations of the dynamic and reverse skyline queries. We compare the performance of Q+Tree with an existing quad-tree based indexing scheme. We also present several optimization heuristics to improve the performance of both of the indexing schemes further. Experimentation with both real and synthetic datasets verifies the efficiency of the proposed indexing scheme and optimization heuristics.
Decision-theoretic Image Retrieval with Embedded Multi-resolution Mixtures

The design of an effective architecture for image retrieval requires careful consideration of the interplay between the three major components of a retrieval system: feature transformation, feature representation, and similarity function. We introduce a decision theoretic formulation of the retrieval problem that enables the design of systems where all components are optimized with respect to the same end-to-end performance criteria: the minimization of the probability of retrieval error. The new formulation is shown to have two appealing properties. First, it leads to an optimal similarity function (the posterior probability of the query under the database image class) that generalizes many of its previously proposed counterparts. Second, it enables a theoretical characterization of the impact of the feature transformation and representation in the probability of error. In addition to exposing the major limitations of a large body of previous retrieval approaches, this characterization allows the derivation of a series of conditions for the optimal design of the feature transformation and representation. The search for a practical solution that can satisfy these conditions leads to the adoption of an embedded multi-resolution mixture representation and originates an efficient algorithm for optimal feature selection. The resulting retrieval architecture achieves a good compromise between retrieval accuracy, invariance, perceptual relevance of similarity judgments, and complexity. Extensive experimental results show that decision-theoretic retrieval performs well on color, texture, and generic image databases in terms of both retrieval accuracy and perceptual relevance of similarity judgments. Author email: nuno.vasconcelos@compaq.com c Compaq Computer Corporation, 2002 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of the Cambridge Research Laboratory of Compaq Computer Corporation in Cambridge, Massachusetts; an acknowledgment of the authors and individual contributors to the work; and all applicable portions of the copyright notice. Copying, reproducing, or republishing for any other purpose shall require a license with payment of fee to the Cambridge Research Laboratory. All rights reserved. CRL Technical reports are available on the CRL’s web page at http://crl.research. compaq.com. Compaq Computer Corporation Cambridge Research Laboratory One Cambridge Center Cambridge, Massachusetts 02142 USA
Production Planning for Perishable Products with Partial Postponement Strategy

This work addressed the tactical production planning for perishable products with partial postponement strategy in which the cost is minimized subject to a set of constraints such as labor working time and machine time. For a specific final configuration some portion of demand has less demand fluctuation and we can use make-to-stock strategy to respond demand. But for other portion of demand which has high degree of uncertainty, we use concept of postponement and it will be beneficial. We compute the level of postponement capacity and finished goods inventory by developing a mixed integer nonlinear programming model. We have analyzed the behavior of the model to make managerial insight under different scenarios of fill rates and finished-goods inventory costs. Our finding shows the finished goods inventory and fill rates can decrease the expected total cost and increases postponement capacity.
Parents, quality, and school choice: why parents in Nairobi choose low-cost private schools over public schools in Kenya’s free primary education era

Abstract Low-cost private schools (LCPS) are widespread in Kenya, particularly in urban areas. This study examines the reasons that parents send children to fee-charging schools in a context of free public primary education. Drawing on parent survey and interview data, as well as interviews with national policy makers, we found that parents who chose LCPS for their children were more driven by quality concerns than were public school parents. We also present data on the costs of the school types, compared to household income. Despite being termed ‘low cost’, the fees charged by schools primarily serving the poor were often a heavy burden on families. We conclude with recommendations for maximising the impact of LCPS on educational access and quality.
The Cytoplasmic Domain of Varicella-Zoster Virus Glycoprotein H Regulates Syncytia Formation and Skin Pathogenesis

The conserved herpesvirus fusion complex consists of glycoproteins gB, gH, and gL which is critical for virion envelope fusion with the cell membrane during entry. For Varicella Zoster Virus (VZV), the complex is necessary for cell-cell fusion and presumed to mediate entry. VZV causes syncytia formation via cell-cell fusion in skin and in sensory ganglia during VZV reactivation, leading to neuronal damage, a potential contributory factor for the debilitating condition of postherpetic neuralgia. The gH cytoplasmic domain (gHcyt) is linked to the regulation of gB/gH-gL-mediated cell fusion as demonstrated by increased cell fusion in vitro by an eight amino acid (aa834-841) truncation of the gHcyt. The gHcyt regulation was identified to be dependent on the physical presence of the domain, and not of specific motifs or biochemical properties as substitution of aa834-841 with V5, cMyc, and hydrophobic or hydrophilic sequences did not affect fusion. The importance of the gHcyt length was corroborated by stepwise deletions of aa834-841 causing incremental increases in cell fusion, independent of gH surface expression and endocytosis. Consistent with the fusion assay, truncating the gHcyt in the viral genome caused exaggerated syncytia formation and significant reduction in viral titers. Importantly, infection of human skin xenografts in SCID mice was severely impaired by the truncation while maintaining the gHcyt length with the V5 substitution preserved typical replication in vitro and in skin. A role for the gHcyt in modulating the functions of the gB cytoplasmic domain (gBcyt) is proposed as the gHcyt truncation substantially enhanced cell fusion in the presence of the gB[Y881F] mutation. The significant reduction in skin infection caused by hyperfusogenic mutations in either the gHcyt or gBcyt demonstrates that both domains are critical for regulating syncytia formation and failure to control cell fusion, rather than enhancing viral spread, is severely detrimental to VZV pathogenesis.
Cosimulation Environment for Event-Driven Distributed Controls of Smart Grid

This paper proposes a cosimulation environment for “hardware in the loop” or “software in the loop” validation of distributed controls in a Smart Grid. The controls are designed using model-driven engineering with the IEC 61499 Function Block architecture. These are connected with plant models, for example, in Matlab/Simulink, through communication channels such as UDP or TCP sockets. This solution enables multi-closed-loop plant-controller simulation. The communication between plant and controller is event-driven. In order to perform a realistic simulation, the proposed solution takes into account computation and communication delays on the controller side in Function Blocks and compensates model time on the plant side in Matlab model accordingly. Causality and accuracy of the method have been formally addressed. This approach has been tested and demonstrated with several Smart Grid-related examples.
Co-design for community capacity building

This doctoral research will examine the use of co-design---a "democratic approach that is focused on the processes and procedures of design. [that] collaboratively engages, consults and develops solutions to problems" (Cook, 2011, p. 50)---as a mechanism to build the capacity of lay people and communities to develop or influence socially sustainable solutions responsive to their needs and aspirations. The engagement of lay people and communities and their empowerment are complex phenomena through which individuals formulate meanings and actions that reflect their desired degree of participation in individual and collective decision-making processes (Tritter & McCallum, 2005). Therefore, this research also seeks to identify co-design processes and procedures that recognize different relevant forms of knowledge and experience of both professionals and lay people, while allowing for varying levels of participation in different stages of the design process.
Security Prospects through Cloud Computing by Adopting Multiple Clouds

Clouds impose new security challenges, which are amongst the biggest obstacles when considering the usage of cloud services. This triggered a lot of research activities in this direction, resulting in a quantity of proposals targeting the various security threats. Besides the security issues coming with the cloud paradigm, it can also provide a new set of unique features which open the path towards novel security approaches, techniques and architectures. This paper initiates this discussion by contributing a concept which achieves security merits by making use of multiple distinct clouds at the same time.
Resilience in the U.S. Air Force: A Factor Analysis of Two Resilience Scales

ABSTRACT Resilience among military populations is critical to symptom prevention and research. The authors examine the factor structure of the Connor-Davidson Resilience Scale and the Dispositional Resilience Scale used to measure resilience and hardiness among U.S. Airmen. Data for this study comes from the 2013 Community Assessment Survey (N = 45,634). The sample was randomly divided into exploratory (n = 22,693) and confirmatory (n = 22,941) factor analyses subsets, subsequently used to test the factor structure of each scale. Neither instrument fully performed as anticipated according to the literature, suggesting the need for additional research into the unique challenges of accurately measuring resilience and hardiness among U.S. Airmen.
Political Ideology and Precautionary Reasoning: Testing the Palliative Function of Right-Wing Ideology on Obsessive-Compulsive Symptoms

Precautionary models of political ideology suggest that people on the right are motivated by needs to neutralize threats in everyday life more than those on the left. This article examines whether this ideological difference extends to needs to engage in everyday precautionary actions as indexed by obsessive-compulsive symptoms. Across four samples (including a nationally representative sample), we find that stronger obsessive-compulsive symptoms are associated with more right-wing ideological preferences, particularly for social issues. Using a communication experiment, we find that exposure to real-world policy assurances from the Republican Party (i.e., the U.S. conservative party) provides a small, most likely short-lived reduction in obsessive-compulsive symptoms among Americans. We discuss how these findings shed light on the key paradox about the psychology of right-wing ideology: how right-wing ideology can function to reduce insecurity while people on the right still have higher levels of insecurity.
Evaluating Reliability of Motion Features in Surveillance Videos

Although a tremendous effort has been made to perform a reliable analysis of images and videos in the past fifty years, the reality is that one cannot rely 100% on the analysis results. The only exception is applications in controlled environments as dealt in machine vision, where closed world assumptions apply. However, in general, one has to deal with an open world, which means that content of images may significantly change, and it seems impossible to predict all possible changes. For example, in the context of surveillance videos, the light conditions may suddenly fluctuate in parts of images only, video compression or transmission artifacts may occur, a wind may cause a stationary camera to tremble, and so on. The problem is that video analysis has to be performed in order to detect content changes, but such analysis may be unreliable due to the changes, and thus fail to detect the changes and lead to “vicious cycle”. The solution pursuit in this paper is to monitor the reliability of the computed features by analyzing their general properties. We consider statistical properties of feature value distributions as well as temporal properties. Our main strategy is to estimate the feature properties when the features are reliable computed, so that any set of features that does not have these properties is detected as being unreliable. This way we do not perform any direct content analysis, but instead perform analysis of feature properties related to their reliability. The main effort in video analysis nowadays is still in making the feature computation more reliable. Our claim is that we need to accept the fact that the computed features will never be 100% reliable, and focus our attention on computing reliability measures. This way system decisions will only be made when features are sufficiently reliable. This means for an intelligent system for video analysis that in addition to feature computation, it should perform instantaneous evaluation of their reliability, and adapt its behavior in accordance to the reliability. For example, if the goal of a system is to monitor motion activity, and to signal an alarm if the activity is high, the system is allowed to make reliable decisions only if there exist a subset of the computed motion activity features that is sufficiently reliable. The monitoring of features reliability and adjusting the system behavior accordingly, seems to be the only way to deploy autonomous video surveillance systems.
Using EEG to investigate premature aging and cognitive decline in adults with Down's Syndrome

Medical Research Council Studentship (3.5 years support: £57078) The Health Foundation (University fees: £18944) Alzheimer’s Research UK Scholarship (pilot phase funding: £4570) Addenbrooke’s Charitable Trust (cross-sectional phase funding: £10000) Marmaduke Shield Fund (equipment fund: £1786) Fearnsides Fund (conference support: £670)
Designing Correlation Indices with Bucketing and Composition

In relational query processing, there are generally two choices for access paths when performing a predicate lookup for which no clustered index is available. One option is to use an unclustered index. Another is to perform a complete sequential scan of the table. Online analytical processing (OLAP) workloads often do not benefit from the availability of unclustered indices; the cost of random disk I/O becomes prohibitive for all but the most selective queries. Unfortunately, this means that data warehouses and other OLAP systems will frequently perform sequential scans, unless they can satisfy nearly all of the queries posed to them by a single clustered index [6], or unless specialized data structures – like bitmap indices, materialized views, or cubes – can be used to answer queries directly. We present a new index data structure called a correlation index (CI) that enables OLAP databases to answer a wider range of queries from a single clustered index or sorted file. The CI exploits correlations between the key attribute of a clustered index and other unclustered attributes in the table. We show that CIs can be implemented as an “add-on” to an existing database via a simple query rewriting scheme. In order to predict when CIs will exhibit wins over alternative access methods, we develop an analytical cost model that is suitable for integration with existing query optimizers. We also develop algorithms that search for strong candidate CIs and that recommend ways to “bucket” CIs to reduce their space utilization. We compare CI performance against sequential scans and unclustered B+Tree indices in PostgreSQL. Our results on several different data sets validate the accuracy of our cost model and establish numerous cases where CIs accelerate lookup times dramatically over other access methods. We also show that standard B+Trees can benefit from correlations just as CIs do, and that CIs are typically much smaller than B+Trees (by up to three orders of magnitude), making it possible to maintain many of them in memory over a single table.
Algorithms for Locating Constrained Optimal Intervals

In this work, we obtain the following new results. 1. Given a sequence $D=((h_1,s_1), (h_2,s_2) . , (h_n,s_n))$ of number pairs, where $s_i>0$ for all $i$, and a number $L_h$, we propose an O(n)-time algorithm for finding an index interval $[i,j]$ that maximizes $\frac{\sum_{k=i}^{j} h_k}{\sum_{k=i}^{j} s_k}$ subject to $\sum_{k=i}^{j} h_k \geq L_h$. 2. Given a sequence $D=((h_1,s_1), (h_2,s_2) . , (h_n,s_n))$ of number pairs, where $s_i=1$ for all $i$, and an integer $L_s$ with $1\leq L_s\leq n$, we propose an $O(n\frac{T(L_s^{1/2})}{L_s^{1/2}})$-time algorithm for finding an index interval $[i,j]$ that maximizes $\frac{\sum_{k=i}^{j} h_k}{\sqrt{\sum_{k=i}^{j} s_k}}$ subject to $\sum_{k=i}^{j} s_k \geq L_s$, where $T(n')$ is the time required to solve the all-pairs shortest paths problem on a graph of $n'$ nodes. By the latest result of Chan \cite{Chan}, $T(n')=O(n'^3 \frac{(\log\log n')^3}{(\log n')^2})$, so our algorithm runs in subquadratic time $O(nL_s\frac{(\log\log L_s)^3}{(\log L_s)^2})$.
An Individual Positive Emotion Exercise: Its Influence on Self-Efficacy and Procrastination of Nursing Students

Two of indicators to be succeed in learning are high self-efficacy and low procrastination behavior. In order to create these conditions, teachers have to contribute actively not only by giving order relating to the teaching materials, but also to conduct certain strategy in order to help their students for improving their psychological ability which will support accomplishment academic goal eventually. One of the skills advantages learning process is an emotion regulation. In achieving emotion regulation, three aspects have to be considered: awareness, execution, and evaluation. Through emotional positive state, students are ready to engage with their tasks. Improving emotion can be implemented through individual or group exercise. The aim of this study measured the difference of self-efficacy and procrastination between experiment group and control group. The experiment group was given individual emotion training and the control group experienced regular class without specific treatment. This research used an experimental method with nonrandomized two groups posttest only design. A group of 91 students from a nursing school was involved in this research. Two adaptation scales, namely Students’ self-efficacy and procrastination were used for measurement. The result revealed only one hypothesis was accepted, procrastination difference level between two groups. However, the effect size showed a small contribution from emotion regulation on it. It indicated that individual emotion exercise for five weeks decreasing procrastination. For the more effective strategy, this exercise should be accompanied by other treatments such as short training and specific emotion target.
Diagnosis using fault trees induced from simulated incipient fault case data

Fault tree analysis is widely used in industry for fault diagnosis. The diagnosis of incipient or 'soft' faults is considerably more difficult than that of 'hard' faults, which is the case considered normally. A detailed fault tree model reflecting signal variations over a wide range is required in the case of soft faults. This paper presents comprehensive results describing the diagnosis of incipient faults based on fault trees derived using the IFT induction algorithm. The test system is a robot arm controlled by a pneumatic servomechanism. Detailed simulations using a nonlinear dynamic model were used to provide a training set of examples. The effectiveness of the diagnosis is demonstrated using comparative results based on a neural network approach.