Miracle-dz/UniFAR-FTUs huggingface dataset:
Extraction of a smoke region using fractal coding

Damage due to fire can be minimized by discovering fires early. Fires produce smoke as well as flames, so detecting smoke from data like surveillance camera images data can make it easier to discover fires early. However, in attempting to discover fires at locations which are hard to see with the human eye, as in cases like forest fires, we are faced with a problem in that smoke does not have a determinate form in camera images. It has a pattern similar to features like clouds, sky and forests, and thus is difficult to detect with processing approaches like simple background subtraction or pattern matching. So This work proposes a technique for extracting smoke regions from an image using fractal encoding concepts. We were led to this idea by noticing that smoke shapes have the property of self-similarity, and here we attempt to extract smoke regions by discovering the distinguishing features of smoke regions in the code produced by fractal encoding of an image.
Maskless Lithographic Pattern Generation System upon Micromirrors

In this study, we focus our attention on the generation of a complex lithographic pattern on a huge substrate with no manipulation of the light beam shape and with a uniform reflection angle. To increase robustness and the flexibility of the lithographic pattern generation method, the process is conceptualized as dual pattern recognition in two contrary views, which are the substrate’s view and the micromirror’s view. The lithographic pattern generation method through dual recognition is devised. Then, a prototype maskless lithography system using the devised method is implemented. To ensure the system works, maskless lithography is performed to fabricate actual wafers. The results verify that the method is robust and flexible enough to generate lithographic patterns in any possible lithographic configuration. Moreover, the system is precise enough to attain the lithographic quality required by display panel manufacturers.
Extensible Pre-Authentication in Kerberos

EXTENSIBLE PRE-AUTHENTICATION IN KERBEROS Phillip Hellewell Department of Computer Science Master of Science Organizations need to provide services to a wide range of people, including strangers outside their local security domain. As the number of users grows larger, it becomes increasingly tedious to maintain and provision user accounts. It remains an open problem to create a system for provisioning outsiders that is secure, flexible, efficient, scalable, and easy to manage. Kerberos is a secure, industry-standard protocol. Currently, Kerberos operates as a closed system; all users must be specified upfront and managed on an individual basis. This paper presents EPAK (Extensible Pre-Authentication in Kerberos), a framework that enables Kerberos to operate as an open system. Implemented as a Kerberos extension, EPAK enables many authentication schemes to be loosely coupled with Kerberos, without further modification to Kerberos. EPAK provides the mutual benefits of enhancing the flexibility of Kerberos and increasing the viability of alternate authentication systems as they move to the enterprise.
A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation

This paper presents a connected coherence tree algorithm (CCTA) for image segmentation with no prior knowledge. It aims to find regions of semantic coherence based on the proposed epsiv-neighbor coherence segmentation criterion. More specifically, with an adaptive spatial scale and an appropriate intensity-difference scale, CCTA often achieves several sets of coherent neighboring pixels which maximize the probability of being a single image content (including kinds of complex backgrounds). In practice, each set of coherent neighboring pixels corresponds to a coherence class (CC). The fact that each CC just contains a single equivalence class (EC) ensures the separability of an arbitrary image theoretically. In addition, the resultant CCs are represented by tree-based data structures, named connected coherence tree (CCT)s. In this sense, CCTA is a graph-based image analysis algorithm, which expresses three advantages: (1) its fundamental idea, epsiv-neighbor coherence segmentation criterion, is easy to interpret and comprehend; (2) it is efficient due to a linear computational complexity in the number of image pixels; (3) both subjective comparisons and objective evaluation have shown that it is effective for the tasks of semantic object segmentation and figure-ground separation in a wide variety of images. Those images either contain tiny, long and thin objects or are severely degraded by noise, uneven lighting, occlusion, poor illumination, and shadow.
Vesicomyidae (Bivalvia): Current Taxonomy and Distribution

Vesicomyid bivalves are a consistent component of communities of sulphide-rich reducing environments distributed worldwide from 77° N to 70°S at depths from 100 to 9050 m. Up-to-now the taxonomy of the family has been uncertain. In this paper, the current state of vesicomyid taxonomy and distribution at the generic rank are considered. This survey is founded on a database including information both from literature sources and also unpublished data of the authors on all recent species of vesicomyids. We suggest that the Vesicomyidae is not a synonym of Kelliellidae, and is therefore a valid family name. We propose to divide the family Vesicomyidae into two subfamilies: Vesicomyinae and Pliocardiinae. The Vesicomyinae includes one genus, Vesicomya, which comprises small-sized bivalves characterized by non-reduced gut and the absence of subfilamental tissue in gills. Symbiosis with chemoautotrophic bacteria has, so far, not been proved for Vesicomya and the genus is not restricted to sulphide-rich reducing habitats. The subfamily Pliocardiinae currently contains about 15 genera with mostly medium or large body size, characterized by the presence of subfilamental tissue in the gills. The Pliocardiinae are highly specialized for sulphide-rich reducing environments, harbouring chemoautrophic bacteria in their gills. This is the first summary of the generic structure of the family Vesicomyidae that allow us to analyze the distribution of vesicomyids at the generic level. We recognize here five different distribution patterns that are related to the specific environmental demands. The general trends in the distribution patterns of the vesicomyids are an occurrence of the majority of genera in broad geographical ranges and the prevalence of near continental type of distribution.
Privacy Loss in Multiagent Planning A Classical Definition with Illustration

Privacy is often cited as the main reason to adopt a multiagent approach for a certain problem. This also holds true for multiagent planning. Still, papers on multiagent planning hardly ever make explicit in what ways their systems protect their users' privacy, nor do they give a quantitative analysis. The reason for this is that a theory of privacy loss in multiagent planning is virtually non-existent so far. This paper proposes a measure for privacy loss based on Shannon's theory of information. To illustrate our approach, we apply this metric to an existing multiagent planning system to assess its merits when it comes to privacy on two domains. For this, we compare its plans to centrally generated solutions (by a trusted third party) for the same problems. The results clearly establish the need for such an analysis: even though the multiagent planner seemingly exchanges little information, its overall performance with respect to privacy is less than that of the centralised system (not taking into account the privacy loss with respect to the central planner, of course).
The anesthetic propofol shifts the frequency of maximum spectral power in EEG during general anesthesia: analytical insights from a linear model

The work introduces a linear neural population model that allows to derive analytically the power spectrum subjected to the concentration of the anesthetic propofol. The analytical study of the power spectrum of the systems activity gives conditions on how the frequency of maximum power in experimental electroencephalographic (EEG) changes dependent on the propofol concentration. In this context, we explain the anesthetic-induced power increase in neural activity by an oscillatory instability and derive conditions under which the power peak shifts to larger frequencies as observed experimentally in EEG. Moreover the work predicts that the power increase only occurs while the frequency of maximum power increases. Numerically simulations of the systems activity complement the analytical results.
GPU-Based Implementation of JPEG2000 Encoder

JPEG2000 has become one of the most rewarding image coding standards. It provides a practical set of features which weren’t necessarily available in the previous standards. The features were realized as a result of two new techniques, namely the Discrete Wavelet Transform (DWT), and Embedded Block Coding with Optimized Truncation (EBCOT). The complexity of EBCOT Tier-1 makes its implementations very difficult and time consuming. In this paper, we focus on accelerating JPEG2000 encoder by using general-purpose processing on Graphical Processing Unit (GPU). We use CUDA platform to implement DWT and EBCOT Tier-1 as the most important sections of JPEG2000. Resulting implementation of proposed architecture performs very well compared to other available implementations.
Letter-to-Phoneme Conversion Based on Two-Stage Neural Network Focusing on Letter and Phoneme Contexts

The improvement of Letter-To-Phoneme (L2P) conversion that can output the phoneme strings corresponding to Out-Of-Vocabulary (OOV) words, especially in English language, has become one of the most important issues in Text-To-Speech (TTS) research. In this paper, we propose a Two-Stage Neural Network (NN) based approach to solve the problem of conflicting output at a phonemic level. Both Letter and Phoneme Context-Dependent models are combined and implemented in the first-stage NN to convert several letters into several phonemes. Then, the second-stage NN can predict the final output phoneme by observing on a combination of several consecutive phoneme sequences that obtained from the first-stage NN. Therefore, our L2P conversion module takes a sequence of letters as input and outputs only one phoneme at each time. By focusing mainly on the result of word accuracy of OOV words, this new approach usually provides a higher performance.
Randomized greedy: new variants of some classic approximation algorithms

We consider the performance of two classic approximation algorithms which work by scanning the input and greedily constructing a solution. We investigate whether running these algorithms on a random permutation of the input can increase their performance ratio. We obtain the following results: 1. Johnson's approximation algorithm for MAX-SAT is one of the first approximation algorithms to be rigorously analyzed. It has been shown that the performance ratio of this algorithm is 2/3. We show that when executed on a random permutation of the variables, the performance ratio of this algorithm is improved to 2/3 + c for some c > 0 This resolves an open problem of Chen, Friesen and Zhang [JCSS 1999]. (See also the paper by Poloczek and Schnitger in these proceedings for related results on this algorithm and its variants). 2. Motivated by the above improvement, we consider the performance of the greedy algorithm for MAX-CUT whose performance ratio is 1/2. Our hope was that running the greedy algorithm on a random permutation of the vertices would result in a 1/2 + c approximation algorithm. However, it turns out that in this case the performance of the algorithm remains 1/2. This resolves an open problem of Mathieu and Schudy [SODA 2008].
Structured Block Basis Factorization for Scalable Kernel Matrix Evaluation

Kernel matrices are popular in machine learning and scientific computing, but they are limited by their quadratic complexity in both construction and storage. It is well-known that as one varies the kernel parameter, e.g., the width parameter in radial basis function kernels, the kernel matrix changes from a smooth low-rank kernel to a diagonally-dominant and then fully-diagonal kernel. Low-rank approximation methods have been widely-studied, mostly in the first case, to reduce the memory storage and the cost of computing matrix-vector products. Here, we use ideas from scientific computing to propose an extension of these methods to situations where the matrix is not well-approximated by a low-rank matrix. In particular, we construct an efficient block low-rank approximation method---which we call the Block Basis Factorization---and we show that it has $\mathcal{O}(n)$ complexity in both time and memory. Our method works for a wide range of kernel parameters, extending the domain of applicability of low-rank approximation methods, and our empirical results demonstrate the stability (small standard deviation in error) and superiority over current state-of-art kernel approximation algorithms.
E-band transformer-based Doherty power amplifier in 40 nm CMOS

This paper presents an 80 GHz Doherty power amplifier in 40 nm CMOS technology. The amplifier combines 4 push-pull amplifiers by using 2×2 parallel-series combiner. In addition, it employs additional LC impedance matching to enhance the back-off efficiency. The transformer-based Doherty amplifier demonstrates both high linearity and high back-off efficiency. Thus, it is tailored for high PAPR mm-wave applications such as E-band communication. The two stage Doherty power amplifier achieves 16.2 dBm output power at 0.9 V supply with a PAE of 12%. Thanks to the linearization feature of the Doherty topology the 1 dB compression point of the amplifier is as high as 15.2 dBm while the PAE at P1dB is 11.1%.
SOME EXPERIENCE FROM THE DEVELOPMENT OF A SIMULATOR FOR A TELECOM CLUSTER ( CPPemu )

This paper presents lessons learned when developing and implementing a simulator for Ericsson’s Connectivity Packet Platform, the basis for most Ericsson 3G systems. CPP is a heterogeneous cluster with more than 20 types of processor boards with 1-5 MLOC of code running on each board, and several processors per board. The simulator, called the CPP Emulator (CPPemu), has been used mainly for embedded software development. CPPemu runs the unmodified software from the real CPP. Thus, CPPemu can replace a real CPP node. Designing and implementing CPPemu was a major undertaking, which required a simulation engine with high performance supporting roughly the same scalability as the platform itself. Multiple hosts are to simulate large configurations and several improvements to simulation technology were necessary to obtain a useable simulator. The project lead-time was approximately one year and CPPemu is now being deployed to testing and verification departments as well as application development projects. CPPemu has brought several benefits and spin-offs. Concurrent software behavior in the cluster is much easier to study and understand. The simulator has been used to support development of new boards, reducing time to market. There has also been a reduction in test system hardware costs.
NEDD9 and BCAR1 Negatively Regulate E-Cadherin Membrane Localization, and Promote E-Cadherin Degradation

The Cas scaffolding proteins (NEDD9/HEF1/CAS-L, BCAR1/p130Cas, EFSSIN, and HEPL/CASS4) regulate cell migration, division and survival, and are often deregulated in cancer. High BCAR1 expression is linked to poor prognosis in breast cancer patients, while upregulation of NEDD9 contributes to the metastatic behavior of melanoma and glioblastoma cells. Our recent work knocking out the single Drosophila Cas protein, Dcas, identified a genetic interaction with E-cadherin. As E-cadherin is often downregulated during epithelial-mesenchymal transition (EMT) prior to metastasis, if such an activity was conserved in mammals it might partially explain how Cas proteins promote aggressive tumor behavior. We here establish that Cas proteins negatively regulate E-cadherin expression in human mammary cells. Cas proteins do not affect E-cadherin transcription, but rather, BCAR1 and NEDD9 signal through SRC to promote E-cadherin removal from the cell membrane and lysosomal degradation. We also find mammary tumors arising in MMTV-polyoma virus T-antigen mice have enhanced junctional E-cadherin in a Nedd9−/− background. Cumulatively, these results suggest a new role for Cas proteins in cell-cell adhesion signaling in cancer.
3D reconstruction of non-rigid surfaces from realistic monocular video

A novel algorithm for recovering the 3D shape of deformable objects purely from realistic monocular video is presented in this paper. Unlike traditional non-rigid structure from motion (NRSfM) methods, which have been studied only on synthetic datasets and controlled lab environments that needs some prior constraints (such as manually segmented objects, limited rotations and occlusions, or full-length trajectories), the proposed method has been described and tested on realistic video sequences, which have been downloaded from some social networks (such as Facebook and Twitter). In order to apply NRSfM to the realistic video sequences, because of no-prior information about the scene and camera parameters, one should employ different methods that can handle a huge amount of unknown parameters (such as 3D shape and camera parameters) and deal with some other ambiguities such as incomplete segmentation and Tracking. In this paper, this goal is concerned by first proposing a novel method for completing the missing trajectories (as the most important challenge in realistic videos due to occlusions and lighting changes) and then applying a method that formulates the NRSfM as an energy minimization problem. The proposed method is evaluated on popular video segmentation datasets and its performance is compared to other available methods.
CMOS supporting circuitries for nano-oscillator-based associative memories

“Let physics do computing” is a promising approach to new-paradigm computing in the beyond CMOS era. Building associative memories based on the physics of nano oscillators, in particular, presents a lot of potential for intelligent information processing. In this paper, we discuss how CMOS supporting circuitries can interface the fabric of nano oscillators with digital computing world. Using CMOS ring oscillators to emulate the nano oscillator behavior, how to produce the associative memory function and to use it for image recognition is demonstrated by HSPICE simulation.
As neurociências ao serviço da linguagem

Human language, with the emergence of the Cognitive Sciences, contributes so decisively to the opening of the interdisciplinary area which is already transversely considered, the Neurosciences. All that concerns the processing of language information occupies now its own space in the exploitation and the interpretation of several ongoing processes, namely the acquisition and the learning, as well as processes involving injuries of the language. The brain is the center of interpretation which allows any researcher, from now on, to deepen and to understand the origin of such complex systems like, for example, reading and discourse. The present discussion should contribute to the debate of the theoretical and methodological foundations of language neuro-functional organization in the brain as well to the study of language teaching.
BlinkFill: Semi-supervised Programming By Example for Syntactic String Transformations

The recent Programming By Example (PBE) techniques such as FlashFill have shown great promise for enabling end-users to perform data transformation tasks using input-output examples. Since examples are inherently an under-specification, there are typically a large number of hypotheses conforming to the examples, and the PBE techniques suffer from scalability issues for finding the intended program amongst the large space. We present a semi-supervised learning technique to significantly reduce this ambiguity by using the logical information present in the input data to guide the synthesis algorithm. We develop a data structure InputDataGraph to succinctly represent a large set of logical patterns that are shared across the input data, and use this graph to efficiently learn substring expressions in a new PBE system BlinkFill. We evaluate BlinkFill on 207 real-world benchmarks and show that BlinkFill is significantly faster (on average 41x) and requires fewer input-output examples (1.27 vs 1.53) to learn the desired transformations in comparison to FlashFill.
Fail High Reductions

Fail High Reductions (FHR) is a new method to guide the search in game trees in a very selective manner. The main idea is that the search algorithm should not spend too much eeort searching subtrees, which look too good to become part of the principal variation even at their roots. More precisely, a fail high node is a node v with a search window ; ] at which a static evaluation function e produces a cutoo. The FHR-algorithm reduces the search depths at these fail high nodes thus searching their subtrees with less eeort. FHR is domain independent in the sense that it only uses a static evaluation function, which always is assumed to exist, and the search windows to guide the search. In this paper we describe the incorporation of FHR in our chess program Zugzwang. Three diierent tests have been conducted to compare Zugzwang with and without FHR: First, we compare the results obtained from running the algorithm on the positions of the WinAtChess test suite. Second, we look at the results obtained from three matches of 50 games each. The games were played under tournament conditions between three diierent versions of Zugzwang each with and without FHR. Last but not least, we compare both versions by looking at the results of 100 games played against a chess program developed independently from Zugzwang. All three tests indicate that the FHR version is about 120-150 ELO points stronger than the version without FHR. Moreover, from the second test we obtain the result that with statistical signiicance the FHR version is stronger than the regular version.
Social Media engagement as an e-commerce driver, a consumer behavior perspective

This study explores the relationship between Social Media engagement and the activation of the consumer's purchase process. Following a consumer behavior perspective we study how the new communication paradigm of the Web 2.0 has change the first stage of the e-commerce purchase process, the problem recognition. We use the case of a smartphone purchase, to explore how differences in Social Media (Facebook) engagement would help describe existent differences between the consumer's triggers for problem recognition. The results suggest that there's a relationship between the level of engagement and the consumer's state that influences the activation of the problem recognition. Implications for e-commerce and Social Media managers are discussed.
Groupware and the Internet: charting a new world

We contrast Web-based groupware with other forms of communication, such as e-mail, listservs, newsgroups, and proprietary conferencing systems. We report the results of a study of more than 100 organizations that have used Web groupware to better understand how they are using it, and what advantages and disadvantages they have experienced. We then use this data to develop a framework for analyzing, organizing, and fitting groupware systems to organizational needs.
Special issues in the development of a large user based web application

The specification of Irisz Web application shows, that the software will suffer from a large user-originated load. While the usage-statistic measuring is necessary, this creates several needs towards the data and Web-service provider. The article discusses the specialities of the system, the constant pressure of timed data-processing, and providing maintenance abilities during active usage.