Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer-based feedback in formative assessment

Formative assessment concerns any assessment that provides feedback that is intended to support learning and can be used by teachers and/or students. Computers could offer a solution to overcoming obstacles encountered in implementing formative assessment. For example, computer-based assessments could be scored automatically whereupon feedback can be directly provided to students. As well, reports that provide information on student learning for use by teachers and others could be generated automatically. The extent to which computers could support formative assessment practices was the central focus of this dissertation, which covered three main areas: 1. Item-based feedback provided to students through a computer (Chapters 2, 3, and 4) 2. Feedback provided through a computer to educators based on students’ assessment results (Chapters 5 and 6) 3) A comparison of three approaches to formative assessment: Data-based decision making (DBDM), assessment for learning (AfL), and diagnostic testing (DT) (Chapter 7) In Chapter 2, the effects of written feedback in a computer-based assessment for learning on students’ learning outcomes were investigated in an experiment at an institute of higher education in the Netherlands. Chapter 3 presented a review of the relevant literature regarding the effectiveness of different methods in providing written feedback through a computer-based assessment for learning. Chapter 4 presented a meta-analysis of the effects of methods used to provide item-based feedback on students’ learning outcomes in a computer-based environment. Chapter 5 focused on the interpretation of score reports generated by the pupil-monitoring Computer Program LOVS by teachers, internal support teachers, and principals. Chapter 6 investigated how these reports could be redesigned to support users in interpreting pupils’ test results. Chapter 7 was not directly concerned with computer-based feedback in formative assessment but related to a broader approach in which computer-based applications may be used to support learning. The chapter provided a theoretical comparison of three approaches to formative assessment, which are currently the most frequently discussed in the literature on educational research: data-based decision making (DBDM); assessment for learning (AfL); and diagnostic testing (DT).
Security & Scalability of Content-Centric Networking

By suggesting radical changes to the current Internet, approaches to clean-slate architectures run the risk of introducing new opportunities for attacks. These attacks can range from new forms of denial-of-service to attacks against other users’ privacy. In this thesis, we analyse the architecture proposed by Content-Centric Networking from a security perspective. One security-critical feature of Content-Centric Networking is the introduction of general-purpose caches that are shared by a small number of users. We show how attackers can leverage these caches to monitor what content its users are retrieving. More generally, we argue that there is a tradeoff between network efficiency and user privacy. Countermeasures against cache-based privacy attacks need to carefully explore this tradeoff.
Information and Computation

Cybersemiotics and the Question of Knowledge (S Brier) Information Dynamics in a Categorical Setting (M Burgin) Mathematics as Biological Process (G Chaitin) Information, Computation, Measurement and Irreversibility (J Collier) From Descartes to Turing: The Computational Content of Supervenience (B Cooper) On the Algorithmic Nature of the World (J-P Delahaye & H Zenil) A Dialogue Concerning Two Possible World Systems (G Dodig-Crnkovic & V Mueller) Does Computing Embrace Self-Organization? (W Hofkirchner) Analysis of Information and Computation in Physics Explains Cognitive Paradigms: From Full Cognition to Laplace Determinism to Statistical Determinism to Modern Approach (V Kreinovich & R Araiza) Bodies - Both Informed and Transformed (B J MacLennan) Computation on Information, Meaning and Representations, an Evolutionary Approach (C Menant) Interior Grounding, Reflection, and Self-Consciousness (M Minsky) Insights into the Biological Computing (W Riofrio) Super-Recursive Features of Natural Evolvability Processes and the Models for Computational Evolution (D Roglic) A Sketch of a Modeling View of Computing (O Shagrir) What's Information, for an Organism or Intelligent Machine? How Can a Machine or Organism Mean? (A Sloman) Inconsistent Information as a Natural Phenomenon (C N J de Vey Mestdagh & J H Hoepman)
Multi-brain fusion and applications to intelligence analysis

In a rapid serial visual presentation (RSVP) images are shown at an extremely rapid pace. Yet, the images can still be parsed by the visual system to some extent. In fact, the detection of specific targets in a stream of pictures triggers a characteristic electroencephalography (EEG) response that can be recognized by a brain-computer interface (BCI) and exploited for automatic target detection. Research funded by DARPA's Neurotechnology for Intelligence Analysts program has achieved speed-ups in sifting through satellite images when adopting this approach. This paper extends the use of BCI technology from individual analysts to collaborative BCIs. We show that the integration of information in EEGs collected from multiple operators results in performance improvements compared to the single-operator case.
Optimisation of permeable reactive barrier systems for the remediation of contaminated groundwater

Permeable reactive barriers (PRBs) are one of the leading technologies being developed in the search for alternatives to the pump-and-treat method for the remediation of contaminated groundwater. A new optimising design methodology is proposed to aid decision-makers in finding minimum cost PRB designs for remediation problems in the presence of input uncertainty. The unique aspects of the proposed methodology are considered to be: design enhancements to improve the hydraulic performance of PRB systems; elimination of a time-consuming simulation model by determination of approximating functions relating design variables and performance measures for fully penetrating PRB systems; a versatile, spreadsheet-based optimisation model that locates minimum cost PRB designs using Excel's standard non-linear solver; and the incorporation of realistic input variability and uncertainty into the optimisation process via sensitivity analysis, scenario analysis and factorial analysis. The design methodology is developed in the context of the remediation of nitrate contamination due to current concerns with nitrate in New Zealand. Three-dimensional computer modelling identified significant variation in capture and residence time, caused by up-gradient funnels and/or a gate hydraulic conductivity that is significantly different from the surrounding aquifer. The unique design enhancements to control this variation are considered to be the customised down-gradient gate face and emplacement of funnels and side walls deeper than the gate. The use of velocity equalisation walls and manipulation of a PRB's hydraulic conductivity within certain bounds were also found to provide some control over variation in capture and residence time. Accurate functional relationships between PRB design variables and PRB performance measures were shown to be achievable for fully penetrating systems. The chosen design variables were gate length, gate width, funnel width and the reactive material proportion. The chosen performance measures were edge residence, centreline residence and capture width. A method for laboratory characterisation of reactive and non-reactive material combinations was shown to produce data points that could realistically be part of smooth polynomial interpolation functions. The use of smooth approximating functions to characterise PRB inputs and determine PRB performance enabled the creation of an efficient spreadsheet model that ran more quickly and accurately with Excel's standard non-linear solver than with the LGO global solver or Evolver genetic-algorithm based solver. The PRB optimisation model will run on a standard computer and only takes a couple of minutes per optimisation run. Significant variation is expected in inputs to PRB design, particularly in aquifer and plume characteristics. Not all of this variation is quantifiable without significant expenditure. Stochastic models that include parameter variability have historically been difficult to apply to realistic remediation design due to their size and complexity. …
Attempting to reduce the vanishing gradient effect through a novel recurrent multiscale architecture

This paper proposes a possible solution to the vanishing gradient problem in recurrent neural networks, occurring when such networks are applied to solving tasks where detection of long term dependencies is required. The main idea consists of pre-processing the signal (a time series typically) through a discrete wavelet decomposition, in order to separate the short term information from the long term ones, and treating each scale by different recurrent neural networks. The partial results concerning all the sequences at diverse time/frequency resolutions are combined through an adaptive nonlinear structure in order to achieve the final goal. This new preprocessing based approach is distinct from the other one reported in literature to-date, as it tends to mitigate the effects of the problem under study avoiding relevant changing in network's architecture and learning techniques. The overall system (called recurrent multiscale network, RMN) is described and its performances tested through typical tasks namely the latching problem and time series prediction.
Computer Use Ethics among University Students and Staffs: The Influence of Gender, Religious Work Value and Organizational Level.

Purpose – The purpose of this paper is to investigate the extent to which individual characteristics, which are gender, religious (Islamic) work value, and organization level (students and staff), are related to attitudes toward computer use ethics. This investigation is conducted in an academic setting in Malaysia, among those subscribing to the same religious value.Design/methodology/approach – The research used a cross‐sectional survey approach in an attempt to achieve the objective. A total of 550 responses were collected from academic staff and students in an institution of higher learning in Malaysia. Respondents were given several scenarios of ethical behavior to rate their attitudinal directions in computer work ethic. Their perceptions would indicate whether they are inclined towards ethical or unethical types of computer use behaviors based on the action in the scenarios.Findings – The findings indicate that individual characteristics consisting of individuals’ value for Islamic work ethics and .
Addressing Data Sparseness in Contextual Population Research

The use of multilevel modeling with data from population-based surveys is often limited by the small number of cases per Level 2 unit, prompting a recent trend in the neighborhood literature to apply cluster techniques to address the problem of data sparseness. In this study, the authors use Monte Carlo simulations to investigate the effects of marginal group sizes on multilevel model performance, bias, and efficiency. They then employ cluster analysis techniques to minimize data sparseness and examine the consequences in the simulations. They find that estimates of the fixed effects are robust at the extremes of data sparseness, while cluster analysis is an effective strategy to increase group size and prevent the overestimation of variance components. However, researchers should be cautious about the degree to which they use such clustering techniques due to the introduction of artificial within-group heterogeneity.
A study with multi-word feature with text classification

We carried out a series of experiments on text classification using multi-word features. A hand-crafted method was proposed to extract the multi-words from text data set and two different strategies were developed to normalize the multi-words into two different versions of multi-word features. After the texts were represented respectively using these two different multi-word features, text classification was conducted in contrast to examine the effectiveness of these two strategies. Also the linear and nonlinear polynomial kernel of support vector machine (SVM) was compared on the performance of text classification task.
Landsat-8 Satellite and Plan Position Indicator Lidar Observations for Retrieving Aerosol Optical Properties in the Lower Troposphere

Observation of optical properties of atmospheric aerosols, especially their behavior near the surface level, is indispensable for better understanding of atmospheric environmental conditions. Concurrent observations of ground-based instruments and satellite-borne sensors are useful for attaining improved accuracy in the observation of relatively wide area. In the present paper, aerosol parameters in the lower troposphere are monitored using a plan position indicator (PPI) lidar, ground-sampling instruments (a nephelometer, an aethalometer, and optical particle counters), as well as a sunphotometer. The purpose of these observations is to retrieve the aerosol extinction coefficient (AEC) and aerosol optical thickness (AOT) simultaneously at the overpass time of Landsat-8 satellite. The PPI lidar, operated at 349 nm, provides nearly horizontal distribution of AEC in the lower part of the atmospheric boundary layer. For solving the lidar equation, the boundary condition and lidar ratio are determined from the data of ground sampling instruments. The value of AOT, on the other hand, is derived from sunphotometer, and used to analyze the visible band imagery of Landsat-8 satellite. The radiative transfer calculation is conducted using the MODTRAN code with the original aerosol type that has been determined from the ground sampling data coupled with the Mie scattering calculation. Reasonable agreement is found between the spatial distribution of AEC from the PPI lidar and that of AOT from the blue band (band 2) of Landsat-8. The influence of AOT on the values of apparent surface reflectance is also discussed.
Weblogs for English language learning: students' perceptions

The digital explosion of information on the Internet has resulted in a need for a new and up-to-date way for Digital Natives to learn English. Educators have reported numerous benefits of using weblogs in English language learning. This article presents a small scale study on the use of weblogs for English language learning at tertiary level in Malaysia. Twenty six students kept weblogs for a duration of a semester. This study investigated how students perceived the use of weblogs for English language learning. A questionnaire which was made up of both close-ended and open-ended questions was administered at the end of the study. A mixture of quantitative and qualitative methods was used to analyse the students’ responses to the questionnaire. The study found that students were aware of their audience when they blogged and that they geared their writing towards their audience. In addition, they also interacted with others through the use of the comment feature on their weblogs. Furthermore, the majority of the students enjoyed blogging and found weblogs useful for English language learning. This study found that weblogs are promising interactive tools for English language learning.
Automatic construction of multi-document multimedia summaries

With the increase of video quantity on the Internet, multimedia information processing has been a focused topic in the recent years. Among the techniques for multimedia information processing, video summarization has become an important tool. Some successful approaches have been proposed by the researchers in multimedia community. In this thesis, we propose our novel video summarization algorithm, Video-MMR (Video Maximal Marginal Relevance) based on visual information by mimicking MMR (Maximal Marginal Relevance) in text summarization. Video-MMR is a generic algorithm regardless of the video genre and suitable for summarizing both a single video and a set of videos. Besides Video-MMR as our basis, we also develop our approaches as following: 1. Visual information is always the most important compared to acoustic and textual information. So we overcome limits of Video-MMR and propose a refinement, VideoMMR2, by only exploiting visual information. 2. Since in a video, visual information is only one of several cues, more variants of VideoMMR using multimedia cues are proposed by exploiting multimedia information such as text or audio. We extend Video-MMR to AV-MMR (Audio Video Maximal Marginal Relevance), Balanced AV-MMR, OB-MMR (Optimized Balanced Audio Video Maximal Marginal Relevance) and TV-MMR (Text Video Maximal Marginal Relevance). These multimedia MMR algorithms are generic algorithms which outperform Video-MMR if we take into account the text and audio information in the video. 3. In addition to the summarization algorithms, we also optimize the presentation of video summaries, otherwise a good summary can be corrupted by a bad presentation. So we try optimizing a static summary containing keyframes and keywords by suggesting the number of frames and text grams, and dynamic summary composed of video segments by optimizing average duration of segments. 4. In the domain of video summarization, we need an evaluation measure for new approaches. Many current measures are based on human assessment, and the automatic evaluation method for video summaries is still an open problem. In this thesis we propose an approach, VERT (Video Evaluation by Relevant Threshold) mimicking the evaluation measures BLEU and ROUGE in text community to facilitate the automatic evaluation procedure with the help of only a few human assessments. We describe the details of all the approaches and present experimental results. Therefore, a framework on video summarization is proposed, including an algorithm of video summarization using visual cue, its variants exploiting more multimedia cues, an optimization measure of summary presentation, and a new evaluation method of video summaries. It allows us to manage and browse multiple videos more efficiently.
User modelling in computer-based critics

A user modelling approach for computer-based critics is described. The critiquing model approach to instantiating the cooperative problem-solving paradigm is considered. The theoretical background for cooperative problem-solving and the motivation for developing a user modelling approach in this domain are based on a need to provide systems that operate independently of explicit user direction. How to represent, acquire, and maintain consistency of the user model in a critiquing system for LISP programming called LISP-Critic is the fundamental issue addressed. A theoretical model of user domain knowledge, an analysis of the application domain, LISP, and research on the generation of explanations prescribe the contents of the user model. LISP-Critic has been extended to include a user modeling component which includes a database of information about the user and a modelling agent. The modelling agent encapsulates the access and update methods for th user model. <<ETX>>
Combination and optimization of classifiers in gender classification using genetic programming

In this paper, we have investigated the problem of gender classification using frontal facial images. Four different classifiers, namely K-means, k-nearest neighbors, Linear Discriminant Analysis and Mahalanobis Distance Based classifiers are compared. Receiver operating characteristics (ROC) curve along with the area under the convex hull (AUCH) have been utilized as the performance measures of the classifiers at different feature subsets. To measure the overall performance of a classifier with single scalar value, the new scheme of finding the area under the convex hull of AUCH of ROC curves (AUCH of AUCHS) is proposed. It has been observed that, when the number of macro features is increased beyond 5, the AUCH saturates and even decreases for some classifiers, illustrating the curse of dimensionality. We then used genetic programming to combine classifiers and thus evolved an optimum combined classifier (OCC), producing better performance than the individual classifiers. We found that using only two features, the OCC has comparable performance to that of original classifier using 20 macro features. It produces true positive rate values as high as 0.94 corresponding to false positive rate as low as 0.15 for 1: 3 train to testing ratio. We also observed that heterogeneous combination of classifiers is more promising than the homogenous combination.
Comparison of DSR, AODV, and DSDV Routing Protocols with Varying Pause Time &amp; Node Density over TCP &amp; CBR Connections in VANET

VANET (Vehicular Ad-hoc Network) is a new technology which has taken enormous attention. Because of some unique properties which makes it different from other ad hoc network. There are two types of topology based routing protocols in VANET proactive and reactive. The performance of routing protocols can vary with the various parameters such as speed, pause time, node density and traffic scenarios. Hence in this research we mainly focus on VANET topology based routing protocols. The performance of reactive (AODV, DSR) and proactive (DSDV) has been analyzed by means of packet delivery ratio, loss packet ratio & average end-to-end delay with varying pause time and node density under TCP & CBR connection.
Using Fuzzy Reasoning to Support a System of Diagnosis of Skin Disease

Faculty of Engineering, Kanto Gakuin University, 1-50-1, Mutsuurahigashi, Kanazawa-ku, Yokohama, 236-8501 Japan, Graduate School of Information, Production and Systems, Waseda University, 2-7, Hibikino, Wakamatsu-ku, Kitakyushu, 808-0135 Japan, Atom Giken Co., Ltd., 92-2, Katase, Fujisawa-city, Kanagawa, 251-0032 Japan, PROCOM Co., Ltd., 1-8-10, Kawaguchi, Kazo-city, Saitama, 347-0014 Japan, Department of Biocybernetics, Niigata University, 8050 Ikarashi-2, Niigata, 950-2181 Japan
The Impact of Name Ambiguity on Properties of Coauthorship Networks

Initial based disambiguation of author names is a common data pre-processing step in bibliometrics. It is widely accepted that this procedure can introduce errors into network data and any subsequent analytical results. What is not sufficiently understood is the precise impact of this step on the data and findings. We present an empirical answer to this question by comparing the impact of two commonly used initial based disambiguation methods against a reasonable proxy for ground truth data. We use DBLP, a database covering major journals and conferences in computer science and information science, as a source. We find that initial based disambiguation induces strong distortions in network metrics on the graph and node level: Authors become embedded in ties for which there is no empirical support, thus increasing their sphere of influence and diversity of involvement. Consequent ly, networks generated with initial-based disambiguation are more coherent and interconnected than the actual underlying networks, and individual authors appear to be more productive and more strongly embedded than they actually are.