Miracle-dz/UniFAR-FTUs huggingface dataset:
PersistOM: An Objects-to-Multivalued Database Mapping Mechanism

Most modern business applications today are developed by using object technology such as Java to build application software and using a relational or multivalued database to store data. Nevertheless, impedance mismatch exists between objects and data store mechanisms. Hence, object persistence has become a necessary practice to map database records into objects for further in-memory processing. However, most research works focus on objects to relational database mapping and very few works focus on objects to multivalued database (O/M) mapping. Nonetheless, these few existing O/M mapping mechanisms are either hard to be extended or difficult to be managed. This paper presents the design of an O/M mapping mechanism called PersistOM with design patterns. A set of design patterns has been applied to make the PersistOM easy to be extended and reused. Layers architectural pattern was applied to structure the whole mapping mechanism to ensure each mapping sub-layer is at a particular level of abstraction. Simulation results show that PersistOM not only shortens the overall development period, but also is comparatively easy to be modified and extended.
Floatcascade learning for fast imbalanced web mining

This paper is concerned with the problem of Imbalanced Classification (IC) in web mining, which often arises on the web due to the "Matthew Effect". As web IC applications usually need to provide online service for user and deal with large volume of data, classification speed emerges as an important issue to be addressed. In face detection, Asymmetric Cascade is used to speed up imbalanced classification by building a cascade structure of simple classifiers, but it often causes a loss of classification accuracy due to the iterative feature addition in its learning procedure. In this paper, we adopt the idea of cascade classifier in imbalanced web mining for fast classification and propose a novel asymmetric cascade learning method called FloatCascade to improve the accuracy. To the end, FloatCascade selects fewer yet more effective features at each stage of the cascade classifier. In addition, a decision-tree scheme is adopted to enhance feature diversity and discrimination capability for FloatCascade learning. We evaluate FloatCascade through two typical IC applications in web mining: web page categorization and citation matching. Experimental results demonstrate the effectiveness and efficiency of FloatCascade comparing to the state-of-the-art IC methods like Asymmetric Cascade, Asymmetric AdaBoost and Weighted SVM.
Econophysics and Economic Complexity

This paper discusses the debate between those advocating a computational and those advocating a dynamic definition of complexity, and how this relates to issues in econophysics. It then reviews the criticisms that have been raised about ways in which econophysics has been done, noting that many of these are now being dealt with. Finally, it argues that while an obvious way to resolve many of these matters is to have economists and physicists work together, the physicists should be sure to work with economists who understand the complexity critique of conventional economic theory and are thus not led astray into building models that have some of the problems of standard economics models that most econophysicists have striven to overcome.
Transcription Factors as Targets for Cancer Therapy: AP-1 a Potential Therapeutic Target

The field of cancer therapy is rapidly moving forward with the development of numerous prospective new agents designed to inhibit cellular factors involved in signal transduction, cell proliferation, and the onset of apoptosis. At the core of these biological processes are transcription factors that are the functional mediators of these effects. Transcrip- tion factors are the downstream targets of numerous signal transduction pathways that are central to the process of car- cinogenesis. The Activator Protein-1 (AP-1) complex is one such factor that has a central role in multiple processes in- volved in tumorigenesis including proliferation, migration, invasion and metastasis. The focus of this review is, using AP- 1 as a model, to discuss transcription factors as targets for cancer therapy. The feasibility of targeted disruption of AP-1 by various agents such as dominant-negative mutants, small molecule inhibitors, transcription factor decoys (TFD), che- motherapeutic drugs, chemoprevention agents, siRNA and natural products will be explored.
Cloning, characterization and expression of the gene coding for a cytosine-5-DNA methyltransferase recognizing GpC.

A novel gene encoding a cytosine-5-DNA methyltransferase recognizing the dinucleotide GpC was cloned from Chlorella virus NYs-1 and expressed in both Escherichia coli and Saccharomyces cerevisiae . The gene was sequenced and a predicted polypeptide of 362 amino acids with a molecular weight of 41.903 kDa was identified. The protein contains several amino acid motifs with high similarity to those of other known 5-methylcytosine-forming methyltransferases. In addition, this enzyme, named M. Cvi PI, shares 66% identity and 76% similarity with M. Cvi JI, the only other cytosine-5-DNA methyltransferase cloned from a Chlorella virus. The short, frequently occurring recognition sequence of the new methyltransferase will be very useful for in vivo chromatin structure studies in both yeast and higher organisms.
CASCADED CONDENSATION TRACKING FOR MULTIPLE OBJECTS

The Condensation and the Wavelet Approximated Reduced Vector Machine (W-RVM) approach are joined by the core idea to spend only as much as necessary effort for easy to discriminate regions (Condensation) and measurement locations (W-RVM) of the feature space, but most for regions and locations with high statistical likelihood to contain the object of interest. We unify both approaches by adapting the W-RVM classifier to tracking and refine the Condensation approach. Additionally, we utilize Condensation for abstract multi-dimensional feature vectors and provide a template based tracking of the three-dimensional camera scene. Moreover, we introduce a robust multi-object tracking by extensions to the Condensation approach. The new 3D Cascaded Condensation Tracking (CCT) for multiple objects yields a more than 10 times faster tracking than state-of-art detection methods. In our experiments we compare different tracking approaches using an active dual camera system for face tracking.
Regularization Approaches to Demosaicking

Demosaicking is the process of reconstructing a full resolution color image from the sampled data acquired by a digital camera that apply a color filter array to a single sensor. In this paper, we propose a regularization approach to demosaicking, making use of some prior knowledge about natural color images, such as smoothness of each single color component and correlation between the different color channels. Initially, a quadratic strategy is considered and a general approach is reported. Then, an adaptive technique is analyzed, in order to improve the reconstruction near the edges and the discontinuities of the image. This is performed using a novel strategy that avoids computational demanding iterations. The proposed approach provides good performances and candidates itself for many applications. Moreover, since the response of the pixel sensors can be taken into account, it can handle nonideal acquisition devices.
Cartooning Using Reference Images

This paper proposes the method that makes an input target image into exaggerated cartoon-like images by using reference images. To deform a target image, we extract feature points from a target image and define the feature point model on reference images. And then, we apply feature based warping method to this deformation. For our result be felt more cartoonish, we additionally apply the luminance quantization method and the edge enhancement method to the deformed target image. At this time, we control intensities of the target image deformation, the luminance quantization and the edge enhancement for the capability that is able to create various results.
Graph Rigidity for Near-Coplanar Structure from Motion

Recent algorithms for monocular motion capture (MoCap) estimate weak-perspective camera matrices between images using a small subset of approximately-rigid points on the human body (i.e.\ the torso and hip). A problem with this approach, however, is that these points are often close to coplanar, causing canonical linear factorisation algorithms for rigid structure from motion (SFM) to become extremely sensitive to noise. In this paper, we propose an alternative solution to weak-perspective SFM based on a convex relaxation of graph rigidity. We demonstrate the success of our algorithm on both synthetic and real world data, allowing for much improved solutions to marker less MoCap problems on human bodies. Finally, we propose an approach to solve the two-fold ambiguity over bone direction using a k-nearest neighbour kernel density estimator.
GC 1 - Quadratic Trigonometric Spline Preserving the Shape of Monotonic Data

This paper describes the use of GC 1 quadratic trigonometric spline interpolant for preserving the shape of monotonic data. Simple data dependent constraints are derived for shape parameters to preserve the monotonicity through monotonic data. The necessary and sufficient conditions for the monotonicity of the trigonometric quadratic interpolant have been derived. The scheme is computationally economic and time saving as compared to existing schemes. The degree of smoothness of the under discussion scheme is GC 1 .
Anything to Keep You Happy

Whilst there is an abundant supply of theoretical and empirical contributions in cooperative bargaining models on the transfer of material resources within couples and the labour supply patterns of individuals in couples, this literature has so far not been interested in measuring empirically the utilities of partners. Conversely the literature on hedonic wellbeing has recently moved on to assess couple’s happiness and the extent to which individuals in couples influence each other’s happiness without making reference to the problem of allocation of resources. The latter is however important to both the individual and couple happiness, and in the limit determine whether couples stay together. Here we address the role of the allocation of resources, particularly in the form of time use time and the effect it has on the joint and individual utilities in the couple.
Replacement of Graphic Translations with Two-Dimensional Cellular Automata, Twenty Five Neighborhood Model

Images are being processed and manipulated at every step with the help of modern multimedia tools. In most gaming devices and cartoon series, movement of images are confined or restricted to right, left, up & down directions only. Cellular automata can be successfully applied in image processing. Cellular Automata is a methodology that uses discrete space to represent the state of each element of a domain and this state can be changed according to a transition rule. With our scheme, we are not only able to translate the image into x and y-axis, but also diagonal translations can be achieved. Uniform cellular automata rules are constructed to transform the images in all the directions.
E-business Model Ontologies 1.1 Introduction

Over the past few years, many innovative e-business ideas have been considered. Such ideas reveal new value propositions, which are enabled by new technological possibilities, such as the widespread use of the Internet and technologies on top of it. During 1998-1999, the e-business hype reached its top. Recently, it became clear that many e-business ideas are not successful (Shama 2001). Many enterprises doing e-business have not been able to create profit with their e-business ideas. Some of these companies who relied entirely on future e-business profits have gone bankrupt. An important reason for the failure of e-business ideas is the lack of a sound value proposition to customers. Moreover, many ideas did not contribute sufficiently to profitability of enterprises. Rather, many enterprises focused on maximizing market share and establishing a trusted brand name. However, we still believe that many potential successful e-business ideas exist, which utilize enabling Internet related technical innovations in a profitable way. Moreover, some industries are forced to find new value propositions. For instance, the digital content industry is facing challenges with respect to new value propositions utilizing Internet technology, e.g. how to earn money by streaming music to an end-consumer's device. One of the key-problems with innovative e-business ideas is that they tend to be formulated vaguely initially, and often lack a precise description. As a result, many innovative e-business ideas are somewhat unfocused and inaccurate. This makes it different to put the idea into operation, and to develop a supporting information system. What is needed is an in-depth exploration process of an e-business idea, to understand the idea better as well as to formulate it more precisely, and to focus the idea into a direction that is feasible from an economical and technical perspective. This chapter provides an ontological perspective on the exploration of such innovative e-business ideas. Our e 3 value ontology (see also (Gordijn 2002)) to do so is on the one hand based on the analysis of economic value creation, distribution, and consumption in a multi-actor network. On the other hand, the e 3 value ontology is founded on requirements engineering and underlying conceptual modeling techniques, borrowed from the information systems community. Requirements engineering is the process of developing requirements through an iterative cooperative process of analyzing the problem, documenting the resulting observations in a variety of representation formats, and checking the accuracy of the understanding gained (Loucopoulos 1995). This document is …
Identification of Clinically Relevant Protein Targets in Prostate Cancer with 2D-DIGE Coupled Mass Spectrometry and Systems Biology Network Platform

Prostate cancer (PCa) is the most common type of cancer found in men and among the leading causes of cancer death in the western world. In the present study, we compared the individual protein expression patterns from histologically characterized PCa and the surrounding benign tissue obtained by manual micro dissection using highly sensitive two-dimensional differential gel electrophoresis (2D-DIGE) coupled with mass spectrometry. Proteomic data revealed 118 protein spots to be differentially expressed in cancer (n = 24) compared to benign (n = 21) prostate tissue. These spots were analysed by MALDI-TOF-MS/MS and 79 different proteins were identified. Using principal component analysis we could clearly separate tumor and normal tissue and two distinct tumor groups based on the protein expression pattern. By using a systems biology approach, we could map many of these proteins both into major pathways involved in PCa progression as well as into a group of potential diagnostic and/or prognostic markers. Due to complexity of the highly interconnected shortest pathway network, the functional sub networks revealed some of the potential candidate biomarker proteins for further validation. By using a systems biology approach, our study revealed novel proteins and molecular networks with altered expression in PCa. Further functional validation of individual proteins is ongoing and might provide new insights in PCa progression potentially leading to the design of novel diagnostic and therapeutic strategies.
Low Complexity Multiuser MIMO Scheduling with Channel Decomposition

We propose a practical SVD-based scheduling technique for MIMO broadcast channel. Multiuser diversity is exploited by scheduling transmissions simultaneously to a group of users who have the best instantaneous channel quality. In order to avoid the inter-user interference, we use a transmit preprocessing technique which decomposes the multiuser MIMO downlink channel into multiple parallel independent single-user MIMO channels. Simulation results show that the proposed suboptimal multiuser scheduling algorithm achieve almost the same bit error rate performance as the optimal scheduling algorithm.
On signifying the complexity of inter-agent relations in agentsheets games and simulations

This paper reports the results of an empirical study about the semiotic engineering of signs of complexity for live documentation of games and simulations built with a visual programming learning environment. The study highlights the essence of the semiotic engineering process and shows how its outcome has been received by a group of users who can speak for a large portion of the live documentation system's user population. It also shows how the communication of complexity is, in and of itself, a major design challenge, especially when mastering complexity is one of the prime purposes of the documented object. Because the study was carried out in the context of a live documentation system, its conclusions can also illustrate how to conduct semiotically-inspired interaction design.
Learning Generic Invariances in Object Recognition: Translation and Scale

Invariance to various transformations is key to object recognition but existing definitions of invariance are somewhat confusing while discussions of invariance are often confused. In this report, we provide an operational definition of invariance by formally defining perceptual tasks as classification problems. The definition should be appropriate for physiology, psychophysics and computational modeling. For any specific object, invariance can be trivially “learned” by memorizing a sufficient number of example images of the transformed object. While our formal definition of invariance also covers such cases, this report focuses instead on invariance from very few images and mostly on invariances from one example. Image-plane invariances – such as translation, rotation and scaling – can be computed from a single image for any object. They are called generic since in principle they can be hardwired or learned (during development) for any object. In this perspective, we characterize the invariance range of a class of feedforward architectures for visual recognition that mimic the hierarchical organization of the ventral stream. We show that this class of models achieves essentially perfect translation and scaling invariance for novel images. In this architecture a new image is represented in terms of weights of ”templates” (e.g. “centers” or “basis functions”) at each level in the hierarchy. Such a representation inherits the invariance of each template, which is implemented through replication of the corresponding “simple” units across positions or scales and their “association” in a “complex” unit. We show simulations on real images that characterize the type and number of templates needed to support the invariant recognition of novel objects. We find that 1) the templates need not be visually similar to the target objects and that 2) a very small number of them is sufficient for good recognition. These somewhat surprising empirical results have intriguing implications for the learning of invariant recognition during the development of a biological organism, such as a human baby. In particular, we conjecture that invariance to translation and scale may be learned by the association – through temporal contiguity – of a small number of primal templates, that is patches extracted from the images of an object moving on the retina across positions and scales. The number of templates can later be augmented by bootstrapping mechanisms using the correspondence provided by the primal templates – without the need of temporal contiguity. This version replaces a preliminary CBCL paper which was cited as: Leibo et al. ”Invariant Recognition of Objects by Vision,” CBCL-291, November 2, 2010
A hybrid method for the efficient calculation of the band structure of 3-D metallic crystals

In this paper, we propose a new hybrid method to characterize the band structure of three-dimensional metallic crystals. We formulate an auxiliary problem that has the same spectrum as the metallic crystal. We prove that the spectrum of the auxiliary problem can be efficiently computed using the plane-wave method and the method of moments. Thus, the band structure of the metallic crystal can also be efficiently calculated. Our approach is much more effective than root-searching methods, because all of the eigenvalues are obtained at once and degenerate cases pose no difficulty. This is a generalization of a previously proposed method for two-dimensional structures.
An O(log N) deterministic packet-routing scheme

A deterministic O(log N)-time algorithm for the problem of routing an aribitrary permutation on an N-processor bounded-degree network with bounded buffers is presented. Unlike all previous deterministic solutions to this problem, our routing scheme does not reduce the routing problem to sorting and does not use the sorting network of Ajtai, et al. [1]. Consequently, the constant in the run time of our routing scheme is substantially smaller, and the network topology is significantly simpler.
Re-Visiting Backpropagation Network Optimization: Towards Maximally Pruned Networks

Backpropagation (BP) Neural Network (NN) error functions enable the mapping of data vectors to user-defined classifications by driving weight matrix modifications so as to reduce classification error over the training data set. Conventional BP error functions are usually only implicitly dependant on the weight matrix, however an explicit penalty term can be added so as to force numerically insignificant weights closer to zero. In our investigation, BP training is undertaken as a prelude to a pruning stage that selectively removes functionally unimportant weight matrix elements, thereby resulting in sparser network connectivity more suited for subsequent rule extraction. This paper investigates the usage of several error and activation functions in the effort to produce maximally clean network connections.
THE ROLE OF LINGUISTIC INFORMATION FOR SHALLOW LANGUAGE PROCESSING

Many methods in computational linguistics rely on shallow processing to achieve their goals. The advantage of these methods in comparison to deep processing is that they do not require the building of elaborate representations of the text to be processed or to perform reasoning on this data, and as a result they can be more easily implemented. This paper shows how shallow methods for automatic summarisation can improve their performance by adding different types of linguistic information.