Miracle-dz/UniFAR-FTUs huggingface dataset:
The divider set: a new concept in morphology

A new concept in the area of morphology is introduced. It is a generalization of the thinning and skeletonization concepts. It can be used in most applications where skeletons or Voronoi sets are utilized, having specific advantages over the traditional results due to its rigorous mathematical definition. A set of rules for the construction of the innovative form of skeleton are presented, based on a mathematical description of the construction process. Certain examples are given, with an eye to specific applications, such as robotic navigation or OCR, among others.
Foundations of Decision Support Systems

Foundations of Decision Support Systems focuses on the frameworks, strategies, and techniques involved in decision support systems (DSS). The publication first takes a look at information processing, decision making, and decision support; frameworks for organizational information processing and decision making; and representative decision support systems. Discussions focus on classification scheme for DSS, abilities required for decision making, division of information-processing labor within an organization, and decision support. The text then elaborates on ideas in decision support, formalizations of purposive systems, and conceptual and operational constructs for building a data base knowledge system. The book takes a look at building a data base knowledge system, language systems for data base knowledge systems, and problem-processing systems for data base knowledge systems. Topics include problem processors for computationally oriented DSS, major varieties of logical data structures, and indirect associations among concepts. The manuscript also examines operationalizing modeling knowledge in terms of predicate calculus; combining the data base and formal logic approaches; and the language and knowledge systems of a DSS based on formal logic. The publication is a valuable reference for researchers interested in decision support systems.
Empirical Research on the Evolution of Consumers' Trust in B2C E-business in China

Extant studies have not yet combined trust and satisfaction with loyalty theoretically over the longitudinal stages. Little empirical studies have addressed how trust evolves over time, which results in the sparse insight about how trust beliefs changes at multiple stages. This paper conceptualizes the dynamics of trust building by identifying the constructs that contribute to changing trust-related beliefs from pre-purchase preparation stage, to post-purchase interaction, and then to relationship commitment. The dynamic model is tested through three-phase lab and online experiments. The empirical research results show that, online consumers’ trusting intention at pre-purchase preparation stage significantly positively affects their satisfaction at post-purchase interaction stage, which indirectly positively affects their e-loyalty at last, almost completely mediated by their relationship commitment to the B2C e-vendor. During the process by which consumers’ trust evolves over time, integrity, ability and benevolence beliefs are revised gradually and the new information (such as the level of satisfaction and e-loyalty) is integrated with the former beliefs to produce the new ones. Compared with former beliefs, satisfaction and e-loyalty have a higher effect on the new beliefs.
Teaching through adaptive hypertext on the WWW

Since early 1994 the introductory course 2L670, "Hypermedia Structures and Systems" has been available on World Wide Web, and is an optional part of the curriculum in computing science at the Eindhoven University of Technology. The course has since been completed by more than 200 students from six different universities, five in the Netherlands and one in Belgium. In order to participate in this course the student only needs a World Wide Web browser. There is no need for separate e-mail, netnews, bulletin boards or ftp software (although these additional tools could be useful for communicating with the teacher and with other students, and for handing in the final assignment). In this paper we present the evolution of this course, from a static hypertext document (used in 1994) to a fully adaptive hypertext courseware (used since January 1997). The current edition of the course features automatic evaluation of small tests, a repository for assignment work, a discussion system, complete monitoring of each student's progress, and adaptive content and link structure. We also reflect on the problems students have experienced with each version, and the solutions that have lead to the current courseware and that will lead to future developments.
A Generic Approach For Predicting Functional Sites in Proteins

INTRODUCTION Protein sequences stored in the UniProt database (Universal Protein Resource) are characterized by manually transferring experimental findings to reviewed Swiss-Prot entries, or in the case of TrEMBL proteins by applying computer driven methods. We present a new component of the TrEMBL automatic annotation pipeline employed for functional site prediction. The method uses a combination of a decision tree algorithm (1) and a weighted finite state machine (WFSM) approach(2), which produces predictive models for functional sites. The approach is highly efficient, does not require human interference and can be used to assign large numbers of site specific annotations to uncharacterized proteins. Taxonomic and sequence specific data are used to build the predictive models. The approach can be extended to include structural data as well.
Practical Analysis of the Dynamic Characteristics of JavaScript

JavaScript is a dynamic object-oriented programming language, which is designed with flexible programming mechanisms. JavaScript is widely used in developing sophisticated software systems, especially web applications. Despite of its popularity, there is a lack of software tools that support JavaScript for software engineering clients. Dataflow analysis approximates software behavior by analyzing the program code; it is the foundation for many software tools. However, several unique features of JavaScript render existing dataflow analysis techniques ineffective. Reflective constructs, generating code at runtime, make it difficult to acquire the complete program at compile time. Dynamic typing, resulting in changes in object behavior, poses a challenge for building accurate models of objects. Different functionalities can be observed when a function is variadic; the variance of the function behavior may be caused by the arguments whose values can only be known at runtime. Object constructors may be polymorphic such that objects created by the same constructor may contain different properties. In addition to object-oriented programming, JavaScript supports paradigms of functional and procedural programming; this feature renders dataflow analysis techniques ineffective when a JavaScript application uses multiple paradigms. Dataflow analysis needs to handle these challenges. In this work, we present an analysis framework and several dataflow analyses that can handle dynamic features in JavaScript. The first contribution of our work is the design and instantiation of the JavaScript Blended Analysis Framework (JSBAF). This generalpurpose and flexible framework judiciously combines dynamic and static analyses. We have implemented an instance of JSBAF, blended taint analysis, to demonstrate the practicality of the framework. Our second contribution is an novel context-sensitive points-to analysis for JavaScript that accurately models object property changes. This algorithm uses a new program representation that enables partial flow-sensitive analysis, a more accurate object representation, and an expanded points-to graph. We have defined parameterized state sensitivity (i.e., k-state sensitivity) and evaluated the effectiveness of 1-state-sensitive analysis as the static phase of JSBAF. The third contribution of our work is an adaptive context-sensitive analysis that selectively applies context-sensitive analysis on the function level. This two-staged adaptive analysis extracts function characteristics from an inexpensive points-to analysis and uses learningbased heuristics to decide on an appropriate context-sensitive analysis per function. The experimental results show that the adaptive analysis is more precise than any single contextsensitive analysis for several programs in the benchmarks, especially for those multi-paradigm programs. The research in this thesis was supported by National Science Foundation CCF-0811518 and IBM Open Collaborative Research Program.
Autonomous Navigation Through Crowds

This research topic of this paper addresses the goal of enabling a mobile robot to navigate autonomously in densely populated environments. The main contribution of this report is the presentation of a framework for feeding real world data to a maximum entropy inverse reinforcement learning algorithm, for modelling socially acceptable navigation behavior, and the presentation of some preliminary experimental results. The data collection is done using a low cost Kinect RGB-D camera.
A 1.2-V low-power BiCMOS class AB log-domain filter

A third-order class AB current-mode Chebyshev filter based on the log-domain instantaneous companding principle and integrated in a 1-/spl mu/m BiCMOS process is presented. It has a nominal cutoff frequency of 320 kHz corresponding to a bias current of 1 /spl mu/A and can be frequency tuned over almost three decades up to about 10 MHz. It operates with a nominal supply voltage of 1.2 V maintaining a dynamic range at 1% total harmonic distortion (THD) of 65 dB. For cutoff frequencies in the range of 10 kHz, the supply voltage can be reduced down to 0.9 V. Typical to companding systems, the class AB filter shows a measured SNR versus input signal that saturates to a maximum value which for this particular realization is 52.5 dB. It has an active area of 0.55 mm/sup 2/ and dissipates 65 /spl mu/W, leading to a power consumption per pole and edge frequency of 67.7 pJ. These results demonstrate the potential of instantaneous companding filters for very low-voltage and low-power applications with moderate linearity and SNR requirements.
Computational Cameras: Approaches, Benefits and Limits

A computational camera uses a combination of optics and software to produce images that cannot be taken with traditional cameras. In the last decade, computational imaging has emerged as a vibrant field of research. A wide variety of computational cameras have been demonstrated-some designed to achieve new imaging functionalities, and others to reduce the complexity of traditional imaging. In this article, we describe how computational cameras have evolved and present a taxonomy for the technical approaches they use. We explore the benefits and limits of computational imaging, and discuss how it is related to the adjacent and overlapping fields of digital imaging, computational photography and computational image sensors. Over the last century, the evolution of the camera has been truly remarkable. However, through this evolution the basic model underlying the camera has remained essentially the same, namely, the camera obscura (Figure 1(a)). The traditional camera has a detector and a standard lens which captures only those principal rays that pass through its center of projection, or effective pinhole, to produce the familiar linear perspective image. In other words, the traditional camera performs a very simple and restrictive sampling of the complete set of rays, or the light field, that resides in any real scene. A computational camera (Figure 1(b)) uses a combination of novel optics and computations to produce the final image [Nayar 2006a]. The novel optics is used to map rays in the light field of the scene to pixels on the detector in some unconventional fashion. For instance, the ray shown in Figure 1(b) has been geometrically redirected by the optics to a different pixel from the one it would have arrived at in the case of a traditional camera. As illustrated by the change in color from yellow to red, the ray could also be photometrically altered by the optics. In all cases, the captured image is optically coded and may not be meaningful in its raw form. The computational module has a model of the optics, which it uses to decode the captured image to produce a new type of image that could benefit a vision system. The vision system could either be a human observing the image or a computer vision system that uses the image to interpret the scene it represents. 3 Figure 1: (a) The traditional camera model, which is based on the camera obscura. (b) A computational camera uses optical coding followed …
Lesion classification in mammograms using convolutional neural networks and transfer learning

ABSTRACT Convolutional neural networks (CNNs) have recently been successfully used in the medical field to detect and classify pathologies in different imaging modalities, including in mammography. One disadvantage of CNNs is the need for large training datasets, which are particularly difficult to obtain in the medical domain. One way to solve this problem is using a transfer learning approach, in which a CNN, previously pre-trained with a large amount of labelled non-medical data, is subsequently fine-tuned using a smaller dataset of medical data. In this paper, we use such a transfer learning approach, which is applied to three different networks that were pre-trained using the Imagenet dataset. We investigate how the performance of these pre-trained CNNs to classify lesions in mammograms is affected by the use, or not, of normalised images during the fine-tuning stage. We also assess the performance of a support vector machine fed with features extracted from the CNN and the combined use of handcrafted features to complement the CNN-extracted features. The obtained results are encouraging.
Accurate indoor positioning methods for smart devices using improved pedestrian dead-reckoning and collaborative positioning techniques

In view of arising demand for indoor Location-Based Services (LBS), many indoor positioning systems (IPS) have been developed by utilizing various technologies and yet none of them are gaining mainstream adoption. Perhaps the most promising IPS are those which are based on smart-devices, because the smart-devices are increasingly widespread and originally equipped with several useful sensors. This thesis aims at proposing accurate indoor positioning methods that leverage on ready infrastructure like smart-devices and Wi-Fi access points. Pedestrian Dead-Reckoning (PDR) is employed for its simplicity. In order to retain the relatively high positioning accuracy of PDR for long-term positioning, the Received Signal Strength Indicator values obtained from the site’s Wi-Fi access points are used in two unique ways to mitigate the accumulative error of PDR. Besides, the estimated position of individual pedestrian can also be refined via an iterative process based on information derived from a Directed Graph. The Directed Graph is used to represent the relations among the pedestrians and the site’s Wi-Fi access points. The proposed indoor positioning methods had been tested at real sites within Swinburne University of Technology Sarawak Campus, and were benchmarked against some existing indoor positioning methods. The test results imply that the proposed methods outperform some existing methods in terms of positioning accuracy. Publications arising from this Work Journal papers • Liew, L. S. and Wong, W. S. H. (2016). Improved pedestrian dead-reckoning based indoor positioning by rssi-based heading correction. IEEE Sensors Journal, 16(21):7762–7773 • Liew, L. S. and Wong, W. S. H. Directed graph based collaborative indoor positioning method. Elsevier Measurement (In Review) Conference paper • Liew, L. S. and Wong, W. S. H. (2014). Indoor positioning method based on inertial data, rssi and compass from handheld smart-device. 2014 International Conference on Computer, Communications, and Control Technology (I4CT), pp. 48-52. doi: 10.1109/I4CT.2014. 6914143 Book chapter • Wong, W. S. H. , Liew, L. S. , Lai, C. H. and Liu, L. Accurate Indoor Positioning Technique Using RSSI Assisted Inertial Measurement. In: Jung, H. K. , Kim, J. T. , editors. Future Information Communication Technology and Applications: ICFICE 2013. Springer Netherlands; 2013. p. 121-129.
Gaussian Two-Way Channel with Constellation-based Input

Achieving a higher transmission rate had always been a goal in the field of communications. Having a two-way channel in which two nodes transmit and receive data at the same time, is an important tool to achieve this goal. A two-way channel is the first step from pointto-point communication channel toward multi-user networks. In its ideal form, we can transmit data two times faster by using a perfect two-way channel. However, the area of two-way channels had not been of interest of researchers during the past years and number of articles on this area is considerably low comparing to other types of multiuser communication networks, such as multiple-access channel, broadcast channel and interference channel. On the other hand, use of analog-to-digital converters (ADC) is a must in modern systems to enable us to analyze data faster; nevertheless, presence of ADC add some other difficulties to the system. In this thesis, different scenarios about two-way channel are studied. The Shannon’s model of two-way channel and his inner and outer bounds on the capacity of this channel are presented. For the Gaussian Two-Way Channel with quantized output, in which the ambient noise has a Gaussian distribution, the expression of Shannon’s inner bound for both Gaussian and discrete inputs are derived. The best uniform quantizer to obtain the maximum achievable rate for Gaussian input is found numerically. Then we will evaluate the additive noise model for the quantizer from an information theoretic point of view. For the discrete input, the method of rotating one input with respect to other one is employed to enlarge the achievable rate region. At last, two scenarios will be studied in which, minimizing the power of interference, does not necessarily maximizes the transmission rate.
Filtering real data for IDS benchmarking

Current IDS evaluation approaches use simulated network traffic as base for the test data sets used in the evaluation. Simulated network traffic lacks the diversities characteristic to a real world network. These diversities may be caused by non-standard implementations of protocols or abnormal protocol behavior, like unfinished threeway TCP handshakes and teardowns. For realistic IDS evaluation, there are a need for test data sets based on real recorded network traffic. Such data sets must also be distributable since a valid test always should be possible to reproduce by other evaluators. Due to legal concerns test data sets based on real recorded traffic must be cleansed for sensitive information. For producing correct IDS metrics the test data sets should also be cleansed for known attacks. This MSc Thesis Project Plan presents a plan for devising a methodology and developing a protype for cleansing real data. Such a cleansed data set can be used in IDS benchmarking, providing more realistic evaluations. Other benefits are that the test may be reproduced by others and that an IDS’ performance may be measured more accurately than today.
MERGE: BIOLINGUISTIC CONSIDERATIONS

© 2013 by the English Linguistic Society of Japan * Portions of this material were presented at the 30th meeting of the English Linguistic Society of Japan hosted by Keio University, as part of the workshop on ‘Basic Operations of Syntax. ’ This paper also draws on my presentation “Not Chomskyan Enough” at the Symposium on ‘Language, Cognition, and Human Nature: Prospects of Linguistics’ that took place at the same meeting. I am grateful to the Society for inviting me to deliver these talks, and to the audience for useful comments and reactions. I’d like to dedicate this work to the memory of the late Jean-Roger Vergnaud (1945–2011), who was a good friend, and mentor, and who I think was on to something very important—‘much more’ indeed—when he wrote “On a certain notion of “occurrence”: The source of metrical structure and of much more” (Vergnaud (2003)). The present work was made possible through a Marie Curie International Reintegration Grant from the European Union (PIRG–GA–2009–256413), research funds from the Fundació Bosch i Gimpera, and a grant from the Spanish Ministry of Economy and Competitiveness (FFI–2010–20634).
New evidence against the modularity of grammar: Constructions, collocations, and speech perception

Abstract This paper combines quantitative corpus data and experimental evidence to address the question whether speech perception is influenced by knowledge of grammatical constructions and, more specifically, knowledge of preferred collocation patterns of these constructions. Lexical identification tasks are devised in which subjects are presented with synthesized, phonetically ambiguous stimuli. The results suggest that knowledge of constructions and collocations influences speech perception, thus providing evidence for a usage-based, non-modular view of grammar.
Optimizing the execution of many-task computing applications using in-memory distributed file systems

In many-task computing (MTC), applications such as scientiﬁc workﬂows or parameter sweeps communicate via intermediate ﬁles; application performance strongly depends on the ﬁle system in use. The state of the art uses runtime systems providing in-memory ﬁle storage that is designed for data locality: ﬁles are placed on those nodes that write or read them. With data locality, however, task distribution conﬂicts with data distribution, leading to application slowdown, and worse, to prohibitive storage imbalance. To overcome these limitations, we present MemFS, a fully symmetrical, in-memory runtime ﬁle system that stripes ﬁles across all compute nodes, based on a distributed hash function. Our cluster experiments with Montage and BLAST workﬂows, using up to 512 cores, show that MemFS has both better performance and better scalability than the state-of-the-art, locality-based ﬁle system, AMFS. Furthermore, our evaluation on a public commercial cloud validates our cluster results. On this platform MemFS shows excellent scalability up to 1024 cores and is able to saturate the 10G Ethernet bandwidth when running BLAST and Montage.
Android App Security- A Survey

Android is an open source mobile platform . Android applications use the advanced hardware and software, as well as local and served data uncover through the platform to bring values to the customer. To protect that value platform can provide an application environment that make sure the security of users, data, application, device and network. The main objective of this survey paper is to study and analyze various schemes that can enhance security management in android.