Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-dimensional dosimetry of hadron beams

A novel 3-D dosimeter system comprising a leuco-dye doped plastic 3-D dosimeter (PRESAGE(TM) ) and an in-house CCD-based optical computed tomography (CT) scanner has recently been introduced. PRESAGE(TM) is based on a clear polyurethane matrix doped with radiochromic components, leuco dyes, which generate a colour change, and hence optical density change, on exposure to ionizing radiation. A prototype CCD-based scanning system that has been constructed (with a similar configuration of a "first generation" X-ray CT scanner) provides optical attenuation profiles of the irradiated dosimeter following the Beer-Lambert law. A complete plane of data is acquired at each step, obtaining a 2D projection planar image of an object in a relatively short time depending on the hardware and averaging options selected. This source-detector combination measures parallel beam projection geometry, then the sample is rotated, and the acquisition is repeated until the desired number of 2-D projections has been obtained, and thus, data for a complete 3-D region are acquired. This thesis focuses on dosimetry measurements of a 60 MeV proton beam and investigates possible LET dependence and proton energy in PRESAGE(TM). Experimentally, we observed that in the plateau region the dose-response is independent of linear energy transfer (LET) until we get the expected lower response of the dosimeter to the dose deposited at the Bragg maxima caused by the high-LET at this point. We used the track structure theory (TST) to give possible explanations for the LET dependence of PRESAGE(TM). From these calculations, we found that PRESAGE(TM), in general, is not LET independent which makes it unsuitable for proton dosimetry. Then we investigated the behaviour of both an optically absorbing and an optically scattering dosimeter using our optical-CT apparatus with the aim of eliminating optical artefacts due to the optical-CT scanner from that radiation induced effects in our measurements. A calibration protocol/ jig system in which a standard distribution is irradiated in the same dosimeter thus establishing a dose conversion for each batch with specific dose sensitivity was carried out, yielding three-dimensional dose maps that are of sufficient accuracy, resolution and precision to allow verification of complex treatment deliveries.
Efficient Authenticated ID-Based Group Key Agreement Protocol

Group key exchange protocols play main role in group applications such as computer networks, telecommunication and military systems. Given the wide range of usage of group applications, research and design of these protocols can play an important role in securing international exchanges, trade and military issues. Most existing group key exchange protocols establish multi-party or group key exchange protocols based on extending the two party well known Diffie- Hellman key exchanges. The security of these protocols is based on this assumption that difficulty of solving group Dif- fie-Hellman problem can be reduced to the difficulty of two party Diffie-Hellman problems. In spite of this assumption, Group key exchanges are not simply extension of two party protocols. In fact there are many differences and challenges that must be considered in design and implementation of these protocols. This difference is due to essential difference between peer to peer communication and group communication. In group communication in addition to considering the main goal e.g. obtaining group session key, consideration of group member changing is important too. In this paper, a Dynamic ID-based Group Key Agreement protocol based on Weil pairing is proposed. This complete ternary tree struc- ture is used and dynamic changes in group are considered too. Finally we show that the proposed protocol is more effi- cient than other protocols with similar structure and also satisfies all known security requirements .
Antinoise Hyperspectral Image Fusion by Mining Tensor Low-Multilinear-Rank and Variational Properties

Enhancing the spatial resolution of hyperspectral (HS) images by fusing with higher spatial resolution multispectral (MS) data is of significance for applications. However, due to the narrow bandwidth, HS images (HSIs) are vulnerable to various types of noise, such as Gaussian noise and stripes, which can severely affect the fusion performance. This paper focuses on antinoise HS and MS image fusion to enhance the spatial details and suppress the noise. By analysis of the intrinsic structure and noise properties, we formulate this problem as the minimization of an objective function. Under the optimization framework, small multilinear ranks in tensor are first used to identify the intrinsic structures of the clean HSI part. Then, considering the high spectral correlation, it is assumed that any bands can be represented by the combination of certain adjacent bands. The difference between one band and its corresponding combination can be used to preserve the spatio-spectral consistency and characterize the distribution of sparse noise (such as stripe noise), based on the variational properties along two directions. The alternating direction method of multipliers (ADMM) is applied to solve and accelerate the model optimization. Experiments with both simulated- and real-data demonstrate the effectiveness of the proposed model and its robustness to the noise, in terms of both qualitative and quantitative perspectives.
Incremental Dialogue Processing

Spoken language unfolds in time, and is understood and generated in a continuous process: when I speak spontaneously, I don’t plan full sentences which I then merely ‘read out’, and you don’t have to wait for me to finish my utterance before you can start to think about and react to it. This may seem obvious, and yet many branches of linguistics, for different reasons, abstract away from these continuous processes and focus on the sentence as their unit of analysis. In this talk, I will briefly review the evidence for incremental processing, and will then describe a model of such processing that we have recently developed (Schlangen & Skantze, EACL 2009 / Dialogue & Discourse 2011), and two implementations of the model in example dialogue systems (Skantze & Schlangen, EACL 2009; Bus, Baumann & Schlangen, SIGdial 2010), and discuss what we have learned from these implementations.
Survey of Factors Influencing Job Satisfaction among Nurses in Ahmadu Bello University Teaching Hospital Zaria, Nigeria

Job satisfaction has been a recurring problem amongst nurses in Nigeria. As such, unfavourable working conditions are compounding problems of shortage and anticipated turnover among the Nigerian nursing workforce. This study assessed the factors influencing job satisfaction among nurses in Ahmadu Bello University Teaching Hospital (ABUTH), Zaria, Kaduna State, Nigeria. A quantitative descriptive cross-sectional design was adopted for the study, and stratified random sampling technique was used to select a sample of 360 participants from a population of 695 nurses. Revised Nursing Work Index (NWIR) questionnaire was adopted as the data collection tool, and data was analysed using Statistical Package for Social Science (SPSS) Version 21.0. The major causes of job dissatisfaction among the participants were poor salary, working conditions and staffing. The study recommends that ABUTH management should improve upon nurses' remuneration, create a healthy practising environment and improve their staffing of nurses to retain the practising nurses and attract the younger generation into the profession. Short Research Article Kever et al.; AJRNH, 1(1): 1-14, 2018; Article no.AJRNH. 44613 2
Intersection of Distributed Meshes for Multi-Tracer Transport Schemes

•One of the important goals of ACES4BGC project is to implement and optimize newcomputationally efﬁcient advection algorithms for large number of tracer species•The resulting upgrades to the Community Earth System Model (CESM) will deliver newscientiﬁc capabilities, offer unprecedented accuracy in representing biogeochemical in-teractions, and yield improved predictive skill and computational performance. •Signiﬁcant computational challenges must be overcome to meet the scientiﬁc require-ments for supporting large numbers of reactive tracers in CESM. The most signiﬁcantchallenge is presented by the advection of many tracers in the atmosphere and oceancomponent models.
Millimeter-Wave SIW Filter Based on the Stepped-Impedance Face-to-Face E-Shaped DGSs

In this paper, a novel defected ground structure (DGS) is proposed and used to integrate into the metallic layer of millimeter-wave substrate integrated waveguide (SIW) to form bandpass filters. The lower stopband and upper stopband of the designed bandpass filter are achieved by, respectively, the nature cut-off frequency of SIW and the band notching property of the new proposed DGS. Numerical optimizations and experimental demonstrations are carried out to validate two of the proposed filters operating at different frequencies within Ka-band and the good agreements are obtained.
AN ANALYSIS OF ENERGY-EFFICIENT VOICE OVER IP COMMUNICATION IN WIRELESS NETWORKS

by JOHN KOTWICKI Mobile wireless devices that place a heavy demand on their wireless interface cards are notorious for quickly depleting energy supplies. Software strategies for minimizing the amount of time network interfaces remain active help reduce energy waste, however, introduce a tradeoff, usually in the form of increased latency or loss in network traffic. Real-time, interactive network applications, such as Voice over IP (VoIP), are particularly sensitive to an increase in these parameters, and therefore require special consideration. This work uses two models to illustrate the balance between energy savings and predicted user satisfaction with a VoIP implementation developed for analysis. After examining the specific implementation, this work extends these results in simulation to enumerate the possibilities that might exist in a productionquality system. The results of these simulations demonstrate VoIP applications in wireless networks exhibit acceptable quality while achieving significant energy savings from software techniques that control the network interface.
The dark side of analogy-making

Analogy-making is the most beautiful manifestation of the fundamental property of human’s thought. It shows how the incoming information maps to the already memorized old one, and they both change until fit consistently. What we see depends on what we already know and vice versa, what we know depends on what we have already seen. It is broadly accepted that ability to make deep analogies is one of the strongest predictors of the intelligence, the creativity, the ability to understand causal relationships. However, analogy-making, being one of the strongest instrument of human though, may have a dark side too. There are examples in the literature how wrong analogies may produce various fallacies of human thought. Widespread beliefs in world conspiracy, superstitions, etc. could be a product of wrong analogies. Different effects of the known phenomena of confirmation bias are also modeled by the sub-processes of analogy-making. This paper systematizes different types of fallacies and possibilities how those effects could be modeled within a cognitive architecture are discussed.
Empirical Lessons for Philosophical Theories of Mental Content

This thesis concerns the content of mental representations. It draws lessons for philosophical theories of content from some empirical findings about brains and behaviour drawn from experimental psychology (cognitive, developmental, comparative), cognitive neuroscience and cognitive science (computational modelling). Chapter 1 motivates a naturalist and realist approach to mental representation. Chapter 2 sets out and defends a theory of content for static feedforward connectionist networks, and explains how the theory can be extended to other supervised networks. The theory takes forward Churchland’s state space semantics by making a new and clearer proposal about the syntax of connectionist networks − one which nicely accounts for representational development. Chapter 3 argues that the same theoretical approach can be extended to unsupervised connectionist networks, and to some of the representational systems found in real brains. The approach can also show why connectionist systems sometimes show typicality effects, explaining them without relying upon prototype structure. That is discussed in chapter 4, which also argues that prototype structure, where it does exist, does not determine content. The thesis goes on to defend some unorthodox features of the foregoing theory: that a role is assigned to external samples in specifying syntax, that both inputs to and outputs from the system have a role in determining content, and that the content of a representation is partly determined by the circumstances in which it developed. Each, it is argued, may also be a fruitful way of thinking about mental content more generally. Reliance on developmental factors prompts a swampman-type objection. This is rebutted by reference to three possible reasons why content is attributed at all. Two of these motivations support the idea that content is partly determined by historical factors, and the third is consistent with it. The result: some empirical lessons for philosophical theories of mental content.Philosophy of Mind
Behavioral Experiments With Social Algorithms: An Information Theoretic Approach to Input–Output Conversions

ABSTRACT While traditional computer-mediated communication happened through transparent, passive, and neutral channels, today’s communication channels are obscure, proactive, and distorted. Social algorithms, guided by a socio-technological codependency, often bias communication, usually in pursuit of some third-party goal of commercial or political nature. We propose a method to derive several summary measures to tests for transformational accuracy when transforming input into an output. Since dynamical flexibility of social algorithms prevents anticipating their behavior, we study these black boxes as if we study human behavior, through controlled experiments. We conceptualize them as noisy communication channels and evaluate their throughput with the same information theoretic measures engineers had originally used to minimize communicative distortion (i.e., mutual information). We use repeated experiments to reverse-engineer algorithmic behavior and test for its statistical significance. We apply the method to three artificial intelligence algorithms: a neural net from IBM’s Watson, and to the recommender engines of YouTube and Twitter.
A P300-Based Brain-Computer Interface for Chinese text Input

In this study, a P300 visual spelling system was presented using a novel method to input Chinese sinograms developed on a Hanyu Pinyin-based method. This method transcribes a Chinese pinyin into an initial consonant and vowel component according to its mandarin pronunciation. In this paradigm, each sinogram was input by selecting the initial consonant followed by the vowel components, and then by selection of the sinogram itself. Five healthy participants participated in the study and achieved an average offline accuracy of 90.2% and an average online input speed of one sinogram per 69 seconds. The experiment results make P300-BCIs accessible to individuals whose language has a logographic writing system.
RELATIONSHIP BETWEEN LOVE FOR KNOWLEDGE, EXCELLENT MUSLIM PERSONALITY AND HUMAN TRANSFORMATION CAPITAL: A MODEL BUILT WITH STRUCTURAL EQUATION MEASUREMENT (SEM) (Hubungan antara Cinta Ilmu, Muslim Cemerlang dan Transformasi Modal Insan: Pembinaan Model menggunakan Analisis Persamaan Struktural)

ABSTRACT The concept of love for knowledge is very relevant in building a great civilization. It is not only relevant, but should be given a serious emphasis because ilmu (knowledge) and amal (practice) are the core of human excellence. The purpose of the study was to model out the relationship between love for knowledge, excellent Muslim personality and Human transformation capital. The study was conducted on 767 students of the Institute of Higher Education around the Klang Valley, Malaysia. Structural Equation Modeling Analysis has been used for the purpose of identifying the role of human transformation capital as a mediator in determining the relationship between love for knowledge and excellent Muslim personality. The results of the analysis show that there was a significant positive relationship between love for knowledge and the excellent Muslims. While the transformation of human capital was found to function significantly as a partial mediator between love for knowledge and excellent Muslims. In other words, love for knowledge will affect the excellent Muslims and transform the human capital. It is recommended that these three important aspects become the fundamental values of success to develop a greater Islamic civilization. ABSTRAK Konsep cinta ilmu sangat relevan dalam membina sebuah tamadun yang hebat. Ia bukan sahaja relevan, tetapi cinta ilmu perlu diberikan penekanan yang serius kerana ilmu dan amal adalah teras kecemerlangan manusia.Tujuan kajian ini dilakukan adalah bagi menguji hubungan antara cinta ilmu, Muslim cemerlang dan Transformasi Modal Insan. Kajian dilakukan ke atas 767 orang mahasiswa Institusi Pengajian Tinggi sekitar Lembah Kelang. Analisis Persamaan Struktural (Structural Equation Modeling) digunakan bagi tujuan mengenal pasti peranan Transformasi Modal Insan sebagai mediator dalam menentukan hubungan antara cinta ilmu dan Muslim cemerlang. Hasil analisis menunjukkan terdapat hubungan signifikan positif antara cinta ilmu dengan Muslim cemerlang. Manakala transformasi modal insan didapati berfungsi secara signifikan sebagai partial mediator antara cinta ilmu dengan Muslim cemerlang. Dengan erti kata lain, cinta ilmu akan memberi kesan kepada Muslim cemerlang dengan kewujudan transformasi modal insan.
Aspect structure of compilers

Compilers are among the most widely-studied pieces of software; and, modularizing these valuable artifacts is a recurring theme in research. However, modularization of cross-cutting concerns in compilers is not yet well explored. Even today, implementation of one compiler concern scatters across and tangles with the implementation of several other concerns, thereby leading to a mismatch between different compiler modules and the operations they represent. Essentially, current compiler implementations fail to explicitly identify the control dependencies of different phases, and separately characterize the actions to execute during those phases. As a result, information about their program-execution path remains non-intuitive: it stays hidden within the program structure and cuts-across several phase implementations. Consequently, this makes compiler designs and artifacts difficult to comprehend, maintain and reuse. Such limitations occur primarily as a result of the inability of mainstream object-oriented languages, such as Java, to organize the cross-cutting concerns into clean modular units. This thesis demonstrates how such modularity-issues in compilers can be addressed with the help of a relatively new, yet powerful programming paradigm called aspect-oriented programming.
Constructing an Associative Memory System Using Spiking Neural Network

Development of computer science has led to the blooming of artificial intelligence (AI), and neural networks are the core of AI research. Although mainstream neural networks have done well in the fields of image processing and speech recognition, they do not perform well in models aimed at understanding contextual information. In our opinion, the reason for this is that the essence of building a neural network through parameter training is to fit the data to the statistical law through parameter training. Since the neural network built using this approach does not possess memory ability, it cannot reflect the relationship between data with respect to the causality. Biological memory is fundamentally different from the current mainstream digital memory in terms of the storage method. The information stored in digital memory is converted to binary code and written in separate storage units. This physical isolation destroys the correlation of information. Therefore, the information stored in digital memory does not have the recall or association functions of biological memory which can present causality. In this paper, we present the results of our preliminary effort at constructing an associative memory system based on a spiking neural network. We broke the neural network building process into two phases: the Structure Formation Phase and the Parameter Training Phase. The Structure Formation Phase applies a learning method based on Hebb's rule to provoke neurons in the memory layer growing new synapses to connect to neighbor neurons as a response to the specific input spiking sequences fed to the neural network. The aim of this phase is to train the neural network to memorize the specific input spiking sequences. During the Parameter Training Phase, STDP and reinforcement learning are employed to optimize the weight of synapses and thus to find a way to let the neural network recall the memorized specific input spiking sequences. The results show that our memory neural network could memorize different targets and could recall the images it had memorized.
Performance Evaluation of XPath Form-Based Ontology Storage Model Regarding Query Processing and Ontology Update

With rapid growth of Internet, the amount of information in the Web is increasing exponentially. However, information on the current Web is understandable only for human, and thus it makes the exact information retrieval difficult. For solving this problem, the semantic Web is suggested and we must use ontology languages that can endow data to semantics for implementing it. For effective management of information in semantic Web environment, we already suggested the XPath form-based OWL storage (XPOS) model including hierarchy information between classes or properties as XPath form and enabling intuitive and effective information retrieval. In this paper, we focus on the quantitative experiment on the performance of XPOS model, Sesame, and the XML file system-based storage (XFSS) model regarding query processing and ontology update for comparative evaluation.
Delay-based Congestion Control for MPTCP

This document describes the mechanism of wVegas (weighted Vegas), which is a delay-based congestion control for MPTCP. The current congestion control algorithm of MPTCP, LIA, achieves only course- grained load balancing, since it is based on packet loss event. On the contrary, wVegas adopts packet queuing delay as congestion signals, thus achieving fine-grained load balancing. Compared with loss-based algorithms, wVegas is more sensitive to the changes of network congestion and thus achieves more timely traffic shifting and quicker convergence. WVegas has been implemented in the Linux Kernel and is part of the UCLouvain's MPTCP implementation now.
Evinrude: A Tool to Automatically Transform Program's Sources into Petri Nets

Model checking is a suitable formal technique to analyze parallel programs’ execution in an industrial context because automated tools can be designed and operated with very limited knowledge of the underlying techniques. However, the specification must be given using dedicated notations that are not always familiar to engineers (so far, model checking on UML raises complex problems that will not be solved immediately). This paper proposes an approach and its implementation as a tool to perform transformation of C source code into Petri nets, a suitable specification for model checking. To overcome the complexity of the resulting specification, we focus on specific aspects of the program. Hence, we never model the entire processed program, but only its relevant parts. In this paper, we will apply this approach on some examples using our tool: Evinrude.