Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing generalized additive neural networks with multilayer perceptrons

In this dissertation, generalized additive neural networks (GANNs) and multilayer perceptrons (MLPs) are studied and compared as prediction techniques. MLPs are the most widely used type of artificial neural network (ANN), but are considered black boxes with regard to interpretability. There is currently no simple a priori method to determine the number of hidden neurons in each of the hidden layers of ANNs. Guidelines exist that are either heuristic or based on simulations that are derived from limited experiments. A modified version of the neural network construction with cross-validation samples (N2C2S) algorithm is therefore implemented and utilized to construct good MLP models. This algorithm enables the comparison with GANN models. GANNs are a relatively new type of ANN, based on the generalized additive model. The architecture of a GANN is less complex compared to MLPs and results can be interpreted with a graphical method, called the partial residual plot. A GANN consists of an input layer where each of the input nodes has its own MLP with one hidden layer. Originally, GANNs were constructed by interpreting partial residual plots. This method is time consuming and subjective, which may lead to the creation of suboptimal models. Consequently, an automated construction algorithm for GANNs was created and implemented in the SAS R © statistical language. This system was called AutoGANN and is used to create good GANN models. A number of experiments are conducted on five publicly available data sets to gain insight into the similarities and differences between GANN and MLP models. The data sets include regression and classification tasks. In-sample model selection with the SBC model selection criterion and out-of-sample model selection with the average validation error as model selection criterion are performed. The models created are compared in terms of predictive accuracy, model complexity, comprehensibility, ease of construction and utility. The results show that the choice of model is highly dependent on the problem, as no single model always outperforms the other in terms of predictive accuracy. GANNs may be suggested for problems where interpretability of the results is important. The time taken to construct good MLP models by the modified N2C2S algorithm may be shorter than the time to build good GANN models by the automated construction algorithm.
On partial neural-net decoding with retransmissions for data communications

The authors discuss basic issues on partial decoding of linear block codes with retransmissions by recently developed neural network technology. In particular, the basic structure of a neural-net decoder is defined based on syndrome decoding methods. Partial decoding by the pocket algorithm and the random sampling algorithm is proposed, and its throughput performance is examined. The complexity issues of using feedforward neural networks for decoding programs are also addressed. Neural-net decoding could be a good alternative for providing an error control mechanism for data communications. <<ETX>>
Survey on Cooperative Clustering Models

Clustering is the process of finding similar groups of data entities. The important application of clustering is to modularize a software system by grouping together related software entities, thereby providing a high-level view of the system. A number of clustering algorithms and measures have been proposed and applied to obtain a better software modularization. But a single clustering method cannot adequately handle all sorts of cluster structures and properties such as overlapping, shape, size and density. Cooperative clustering (CC) model involves cooperation among multiple clustering techniques for the goal of increasing the homogeneity of objects within the clusters. The cooperative clustering models are capable of showing significant improvement over individual clustering algorithms for software modularization.
TRANSFORMASI BENTUK DAN POLA RUANG KOMUNAL DI KOTA LAMA SEMARANG

Pembentukan citra sebuah kawasan kota tua tidak hanya sekedar dilihat dari kualitas fisik bangunannya saja, akan tetapi juga dilihat dari kualitas fisik lingkungan yang diikuti dengan fungsi sosial lingkungan. Fungsi sosial lingkungan ini dapat terwujud dalam bentuk ruang komunal.Ruang komunal di kota lama Semarang adalah taman srigunting (yang sebelumnya merupakan permakaman, halaman gereja, tempat pedagang kaki lima) dan polder tawang (yang sebelumnya halaman stasiun, terminal, lapangan sepakbola. Fenomena tersebut menunjukkan bahwa telah terjadi perubahan/transformasi ruang komunal di kawasan Kota Lama Semarang. Penelitian ini bertujuan untuk untuk memperkaya teori transformasi spasial yang berkaitan dengan bentuk dan pola ruang komunal. Penelitian ini menggunakan pendekatan penelitian kualitatif. Selain itu, metode pengolaan data dilakukan dengan metode studi kasus dan analisa deskriptif kualitatif.Berdasarkan hasil analisis penelitian, ditemukan bahwa terjadi transformasi bentuk dan pola ruang komunal di Kota Lama Semarang. Kemudian, transformasi tersebut terjadi pada beberapa hal, yaitu: fungsi, bentuk, sirkulasi,aktivitas, dan identitas ruang komunal.
A survey of genetic feature selection in mining issues

In this paper, we review the feature selection problem in mining issues. The application of soft computing techniques to data mining and knowledge discovery is now emerging in order to enhance the effectiveness of the traditional classification methods coming from machine learning. A survey of the approaches presented in the literature to select relevant features by using genetic algorithms is given. The different values of the genetic parameters utilized as well as the fitness functions are compared. A more detailed review of the proposals in the mining fields of databases, text and the Web is also given.
Measuring perceived adaptiveness in a robotic eldercare companion

Based on observations in previous experiments with a robotic companion in eldercare and on findings in related literature, we developed the concept of Perceived Adaptiveness. We integrated this in our technology acceptance methodology for robotic eldercare companions and found in a small experiment that adaptiveness of the system as perceived by elderly users is indeed a relevant item, being a direct influence on Perceived Usefulness.
Load Shedding for Window Joins over Streams

我们在滑动窗口上流技术的地址几负担加入。 我们首先包括 aux 窗户和连接, 并集,' 或 ' 操作 ', 汇合指令窗户构造一个双窗户体系结构模型，并且在 aux 窗户上造统计。 与统计，我们开发流生产最大的子集 join 产量的策略的有效负担。 以便加速流过程的负担，二进制索引的树被利用了在流评估上减少费用。 当溪流有高到达率时，我们建议流前面、后面流的合并的一条途径，并且发现在他们之间的一宗最佳的交易。 至于可变速度比率的情形，我们开发重新分配中央处理器资源并且动态地缩放窗口的一个计划。 另外，我们证明流的那负担没在重新分配的过程期间被影响。 合成、真实的数据在我们的实验被使用，并且结果显示出我们的策略的诺言。 电子增补材料电子增补材料为在 http://dx.doi. org/10.1007/s11390-007-9024-8 的这篇文章是可得到的并且为授权的用户可存取。
Multiple agent team theoretic decision-making for searching unknown environments

This paper explores the usage of team theory results to multiple agent search problems. We present a new formulation of a multiple agent search problem that can be solved as a nonlinear optimization problem in a centralized perfect information case and also has features that allows the problem to be reformulated in the framework of a linear-quadratic-Gaussian problem that admits a decentralized team-theoretic solution using Radner's result that equates person-by-person optimality with global optimality. Both the centralized strategy and the team theoretic strategies are derived and some numerical results are presented for illustration. This is the first contribution in the literature that combines fundamental results from search theory and team theory to solve practical problems.
A Coupled User Clustering Algorithm Based on Mixed Data for Web-Based Learning Systems

In traditional Web-based learning systems, due to insufficient learning behaviors analysis and personalized study guides, a few user clustering algorithms are introduced. While analyzing the behaviors with these algorithms, researchers generally focus on continuous data but easily neglect discrete data, each of which is generated from online learning actions. Moreover, there are implicit coupled interactions among the data but are frequently ignored in the introduced algorithms. Therefore, a mass of significant information which can positively affect clustering accuracy is neglected. To solve the above issues, we proposed a coupled user clustering algorithm for Wed-based learning systems by taking into account both discrete and continuous data, as well as intracoupled and intercoupled interactions of the data. The experiment result in this paper demonstrates the outperformance of the proposed algorithm.
Highly successful learning based on modified Catmull-Rom spline activation function

A sigmoidal-like activation function, a modified Catmull-Rom spline activation function, is proposed. The advantages of the proposed activation are low complexity of the hardware implementation and arithmetic operation. Since it gives a good error signal for updating the weight values, the usual gradient based training algorithm can be used to train the network with the proposed activation function. Our experiments based on the well established benchmarks, such as LT, SONAR, IRIS, OCTANE, and 5-BIT COUNT data sets, show that the convergent speed and the successful rate based on the proposed model is more than those of the existing models using the sigmoidal function. The network obtained by using the proposed activation function is more compacted than the network obtained under the sigmoidal function in term of the arithmetic operations and the network components.
Introduction to multigrid using matlab

Multigrid is a powerful numerical tool for solving a variety of engineering problems. We motivate the use of the technique, introduce its theoretical basis, provide a step‐by‐step procedure for its use, and present examples. We include a tutorial MATLAB program, which allows the user to experiment with the technique for a typical problem.
Live Poetry: An Integrated Approach to Poetry in Performance.

1. Key Challenges for the Scholar of Live Poetry . 15 1.1 Neglected History / a History of Neglect . 15 1.2 Like So Much Hot Air? Live Poetry and the Discourse of Orality . 18 1.2. 1 Orality versus Literacy: The Great Divide . 19 1.2. 2 From Orality to Literacy: The Evolutionist Model . 22 1.2. 3 Performance Poetry: A Controversial Form of Live Poetry . 29 1.3 Literature Review . 33
Online profiling and feedback-directed optimization of java

The dynamic nature of the Java TM programming language presents a number of challenges for Java Virtual Machine (JVM) implementations. Constructs such as dynamic class loading and reflection make traditional whole program analysis and optimization difficult, or even impossible; however, Java's dynamic execution environment also presents a potential performance advantage over the traditional static compilation model: the ability to perform feedback-directed optimizations, where profiling information is collected at runtime and used to tailor the optimization decisions that are made. Although previous work has shown that feedback-directed optimizations can substantially improve program performance, most of these systems used offline profiles collected using a separate training run. Performing profiling and optimization online during the same run is an attractive approach because it avoids the need for a separate training run. Unfortunately, the overhead of collecting online profiles is often a problem, and is one of the main reasons why today's JVM's perform only limited forms of feedback-directed optimizations. The first contribution of this thesis is a new technique called an instrumentation sampling framework, a mechanism that allows previously expensive instrumentation to be executed with low overhead. The instrumentation sampling framework is designed as an automatic code transformation that takes instrumented code as input, and produces a ii modified version of the code that collects a similar profile, but executes with low overhead. We implemented and evaluated our framework in the Jikes Research Virtual Machine; our results demonstrate that the sampling framework effectively reduces the overhead of several types of instrumentation while having only a minimal effect on the accuracy of the profiles collected. The second contribution of this thesis is the design and implementation of an on-line system that uses instrumentation sampling to drive feedback-directed optimizations and improve the performance of Java programs. Our implementation is built on top of the general adaptive optimization architecture of the Jikes RVM. Our system collects intra-procedural edge profiles using the instrumentation sampling framework and uses the resulting profiles to drive four feedback-directed optimizations. Our empirical evaluation demonstrates that our online approach can improve the performance of long-running programs without degrading the performance of short-running programs. iii Acknowledgements This thesis would not have been possible without the help of many people. I would like to start by thanking my advisor, Professor Barbara Ryder, for the guidance and support that she gave me throughout my entire graduate school experience. She taught me many of the fundamental skills …
A Method to Convert Sana’ani Accent to Modern Standard Arabic

This paper presents an efficient mechanism to convert Sana’ani dialect to modern standard Arabic. The mechanism is based on morphological rules related to Sana’ani dialect as well as Modern Standard Arabic. Such rules facilitate the dialect conversion to its corresponding MSA. The mechanism tokenizes the input dialect text and divides each token into stem and its affixes; such affixes can be categorized into two categories: dialect affixes and/or MSA affixes. At the same time, the stem could be dialect stem or MSA stem. Therefore, our mechanism, implemented by using a simple MSA stemmer, must pay attention to such situations. Then our dialect stemmer is applied to strip the resulting token and extract dialect affixes. At this point, the rules are applied to decide when to carry out the extraction of an affix. The experiment shows that Sana’ani dialect has three classes of distortions, which are prefixes, suffixes, and stems distortions. The algorithm normalizes such distortion based on the morphological rules. For each morphological rule the mechanism checks possibility of applying such a rule. That means if rule conditions be met, then the dialect affix will be replaced by its corresponding MSA. If there is no restriction on applying the rule related to the distorted stem, then the rule can be considered as a parallel corpus of the dialect and MSA. Finally, the experiment computes the distortion ratio of MSA in Sana’ani dialect. For a Sana’ani dialect sample of 9386 words, 16.29% of them have distorted suffixes, 0.70% have distorted prefixes and 2.17% contain distorted stems. These percentages are related only to the processed words.
Motion Synthesis and Coordinated Control in the Multi-Axle-Driving-Vehicle

Multi-axle-Driving-vehicle is a kind of the mobile robot and it also belongs to the construction machinery which generally has heavy load-capacity and huge size. As shown in Fig.1 and Fig.2, both hoisting-girder transporter and DCY900 transportation vehicle are multi-axle-driving vehicles. In order to decrease the ground-contacting pressure, the ground-clearance of the vehicle chassis and the size of the tyre, and to increase the loadingcapacity and the passing performance of the vehicle, the multi-axle driving and independently controlled steer axles are employed, and also distributed electro-hydraulic proportional control is applied to the heavy type of vehicles. Such a large-scale vehicle has to deal with the complex motion control problem. The controlled output motions of multiple axles should meet certain matching condition or corresponding relationship so as to make the whole vehicle to realize the expected contouring motion trace. For example, all the powered steer axles have to be coordinately controlled in real time in order to achieve smooth and accurate steering motion without slipping and sliding. Besides the steering function, the steer axles are also designed to automatically level the vehicle body when it moves in an uneven terrain. It follows that the motion synthesis and coordinated control methods should concurrently cope with the tasks and motions of multiple subsystems. Conventionally, coordinated control of a simple mechatronics system is realized through a centralized control scheme in which each of the actuators is directly linked to the controller through cable in a point-to-point manner. However, for a complex multi-tasking mechatronic system with a large number of subsystems and actuators, such a control scheme is impractical. This is especially true for a large-scale multi-axle vehicle because it is huge in size and has many distributed subsystems to be arranged anywhere in the vehicle. If a centralized control scheme is employed, it will result in a very messy wiring scheme. Thanks for the advanced network technologies, which provide us an effective way to realize coordinated control for the multi-axle driving vehicles. In a network environment, all the control devices such as sensors, actuators, and controllers are distributed and simply linked together through network interfaces (e.g., Field-bus, Industrial Ethernet, and mobile net) so as to achieve coordination and resources sharing efficiently. In convention, a network-based mechatronic control system is called an NCS (Networked Control System), which has many advantages over a centralized control system, e.g., low installation cost, ease of system maintenance, simplicity in failure diagnosis, and high flexibility in system management
Vermengde leer: Innovering in die onderrig van Praktiese Teologie aan voorgraadse studente

Blended learning: Innovation in the teaching of practical theology to undergraduate students. Blended learning is becoming increasingly prevalent in the academic environment. This approach to learning was developed for various reasons, including the problem of information overloading and the need for integration of theory and praxis. Recent research indicates that changes in the brain necessary for success in the learning process are related to numerous factors like practical exercises, emotions and background factors while learning. The purpose of this research was to evaluate through empirical research the innovative use of blended learning by first-year students in practical theology. The results of two empirical surveys indicate a positive experience of a variety of forms of learning by the students. The results are interpreted with the aid of theoretical insights from the fields of pedagogy and practical theology. Four pedagogical strategies are discussed, all of which individually contribute to the learning process. This includes pedagogies of contextualisation, interpretation, formation and performance. In conclusion, a number of recommendations are made about the use of blended learning in practical-theological teaching. It is done by making use of a case study within a theodramatic approach to practical theology. The use of the film Son of Man is examined as example in the light of the envisaged outcomes for practical-theological teaching.
Weakly supervised object recognition with convolutional neural networks

Successful visual object recognition methods typically rely on training datasets containing lots of richly annotated images. Annotating object bounding boxes is both expensive and subjective. We describe a weakly supervised convolutional neural network (CNN) for object recognition that does not rely on detailed object annotation and yet returns 86.3% mAP on the Pascal VOC classification task, outperforming previous fully-supervised systems by a sizable margin. Despite the lack of bounding box supervision, the network produces maps that clearly localize the objects in cluttered scenes. We also show that adding fully supervised object examples to our weakly supervised setup does not increase the classification performance.
Neural network to reconstruct specular surface shape from its three shading images

This paper proposes a new method to reconstruct the shape of the specular surface by learning the mapping between three image irradiances observed under the illumination from three lighting directions and the corresponding surface gradient. The method uses Phong reflectance function (1975) which can describe the specular reflectance including Lambertian reflectance, and its neural network is constructed to determine the values of reflectance parameters and the objective surface gradient distribution under the condition that the values of reflectance parameters included in this function are unknown. The method reconstruct the surface gradient distribution after determining the values of reflectance parameters of a test object using two step neural network which consist of one to extract two gradient parameters from three image irradiances and its opposite one. The effectiveness of this proposed neural network was confirmed by computer simulations.
Multilevel Modeling of Categorical Outcomes Using IBM SPSS

1. Introduction to Multilevel Models with Categorical Outcomes. 2. Preparing and Examining the Data for Multilevel Anayses. 3. Specification of Generalized Linear Models. 4. Multilevel Models with Dichotomous Outcomes. 5. Multilevel Models with a Categorical Repeated Measures Outcome. 6. Two-Level Models with Multinomial and Ordinal Outcomes. 7. Two-Level Models with Count Data. 8. Concluding Thoughts.
Etude et modélisation du compartiment nitrifacteur de la boucle MELiSSA : coculture sur ammonium et triculture sur urée

Les procedes actuellement utilises a bord de la Station Spatiale Internationale permettent un traitement physico-chimique des dechets avec un recyclage partiel des elements en minimisant le reapprovisionnement en eau et oxygene. Voyager plus loin et sur de plus longues durees n’est possible que si un systeme de support de vie, permettant d’assurer une autonomie plus importante, est mis en place. Le projet MELiSSA est un systeme de support de vie bioregeneratif ayant pour objectifs de recycler les dechets produits par l’homme, de regenerer l’oxygene et l’eau et d’assurer une production de nourriture. L’utilisation de microorganismes pour assurer ces differentes fonctions, n’est realisable qu’en ayant la meilleure connaissance possible et surtout la maitrise des processus et des souches impliques. L’objectif de cette these est de developper un modele predictif pour le compartiment nitrificateur de la boucle MELiSSA. Dans un premier temps, les souches Nitrosomonas europaea ATCC® 19718 et Nitrobacter winogradskyi ATCC® 25391 ont ete etudiees et ont permis de definir un modele cinetique de ces deux bacteries en culture immergee. Dans un second temps, ce modele a ete combine a un modele N-bacs en serie pour composer un modele de nitrification dans une colonne a lit fixe a biomasse fixee : le modele NitriSim. Ce modele a ete teste sur une experience de long-terme menee dans une colonne a lit fixe dont le support est compose de billes Biostyr®. Le dernier chapitre de ce manuscrit decrit l’utilisation d’une souche degradant l’uree, Cupriavidus pinatubonensis, dans le reacteur a lit fixe. Les differents resultats ont permis de definir un modele de nitrification dans un reacteur a lit fixe et de presenter des perspectives possibles pour le traitement de l’urine au niveau du compartiment nitrifiant de MELiSSA.