Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Lift as a Practical Measure of Surprise in a Document Stream

We describe how the concept of Lift can be generalized to order small documents in a corpus by their degree of similarity. This surprisal norm can be used in conjunction with other features to search over the corpus. From an information theoretic point of view surprisal is the combination of the Mutual-information of all word pairs in a documents. We show how the calculation of surprisals can be performed efficiently on a document stream using sketching techniques.
E-LOTOS Core Language

This paper presents an integrated core data and behaviour language for the new LOTOS standard. It is not intended to be directly usable for specifications, but some additional syntax sugar can be defined to make it more usable and compatible with existing specifications. The language is first-order, monomorphic, strongly typed and allows subtyping. It supports concurrency, real-time, exception handling, pattern-matching and some imperative features.
Harmonizing critical success factors in agile ISD projects

Over time, various information systems development (ISD) approaches have emerged, principal of which are plan-driven and agile approaches. While agile ISD approaches have been promoted as replacements for plan-driven development to enhance responsiveness to changing customer needs, in practice organizations tend to use practices from both of them. This combination represents a challenge for decision-makers that utilize critical success factors (CSFs) to manage the development project because, so far, prior research has identified CSFs for either traditional or agile ISD projects, but not when an agile ISD project has elements from plan-driven development added. This paper theorizes about how CSFs change for agile ISD projects which have plan-driven practices added and empirically investigates the content of these CSFs. Through a qualitative study eight CSFs, allocated into four method dimensions, are identified to give a parsimonious number of areas that contribute to the success of an agile ISD projects.
Models for innovation diffusion

The diffusion of innovation has been defined traditionally as the process by which an innovation is communicated through a social system to its members. The 4 parts of the diffusion process are: 1) the innovation 2) channels of communication 3) time and 4) the social system. The diffusion process is one of the most widely studied of all social processes with research in all of the social sciences education geography and business. Such widespread interest results in frequent new findings but also has the disadvantage that researchers interested in the subject must keep up with and know the language of several disciplines. This manual provides an introduction that brings together findings from several disciplines and sets them out in a clear and consistent manner. Chapter 1 introduces basic concepts such as the S-shaped curve. Chapter 2 presents a fundamental deterministic diffusion model that allows for the review and integration of several widely cited models: the external-influence model the internal-influence model and the mixed-influence model. This chapter also emphasizes both the explicit and the implicit assumptions underlying the fundamental model. Chapter 3 discusses flexible diffusion models which are pattern-sensitive and can accommodate a variety of diffusion patterns. Chapter 4 includes selected advanced diffusion models such as dynamic models multi-innovation models space and time models and models that incorporate change agents. Chapter 5 discusses the applications of diffusion models in different contexts and disciplines. Chapter 6 includes recommendations for the technical development and use of diffusion models.
A correlogram algorithm for image indexing and retrieval using wavelet and Rotated Wavelet Filters

In this paper, a new image-indexing algorithm with the combination of wavelet and Rotated Wavelet Correlogram (RWC) is proposed in contrast to the Gabor Wavelet Correlogram (GWC). The 0° and 90° information (sub-band) of image collected from Standard Wavelet Filters (SWFs) and +45° and −45° are collected from Rotated Wavelet Filters (RWFs) and further used for correlogram feature calculation. The data handling with proposed method is very less compared with GWC therefore this method is six times faster than the GWC, which make it suitable for online application. The retrieval results of the proposed method are tested on three different databases, i.e., Corel 1000 (DB1) natural image database, MIT VisTex (DB2) texture image database and Corel 2450 (DB3) natural image database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared with most of existing techniques on respective databases.
Mise en oeuvre de nouvelles techniques pour la sécurité informatique basées sur les algorithmes évolutionnistes et les fonctions de Hachage

The subject of this thesis focuses on computer security in particular symmetric encryption algorithms and cryptographic hash functions. First, we implemented two encryption systems presenting an improvement of the encryption system SEC "Balancing Process" and "Binary Fusion Process". These are systems that generate their own symmetric key (secret) session. They allow to change the appearance frequencies of characters through random characterized operations. By combining each of these two techniques with "SEC", the resulting encryption system becomes more resistant to cryptanalysis study by appearance frequency and the exhaustive search. Then we designed a new encryption system whose internal structure is essentially based on Tabu search. Finally, we are interested in the application of memetic algorithm, which is a hybrid metaheuristic combining an evolutionary algorithm and a local search method for solving a scheduling problem in the domain of symmetric cryptography. In the second part of this thesis, we addressed the field of cryptographic hash functions in which we came to the design and construction of a new hash function that combines the techniques of encryption standard DES (Data Encryption Standard) and SHA (Secure Hash Algorithm) family. This hybridization ensures a strong confusion in the compression function and therefore the resulting hash function becomes more efficient.
A hybrid speech recognition system using HMMs with an LVQ-trained codebook

A speech recognition system using the neurally inspired learning vector quantization (LVQ) to train hidden Markov model (HMM) codebooks is described. Both LVQ and HMMs are stochastic algorithms holding considerable promise for speech recognition. In particular, LVQ is a vector quantizer with very powerful classification ability. HMMs, on the other hand, have the advantage that phone models can easily be concatenated to produce long utterance models, such as word or sentence models. The algorithm described combines the advantages inherent in each of these two algorithms. As the result of phoneme recognition experiments using a large vocabulary database of 5240 common Japanese words uttered in isolation by a male speaker, it is confirmed that the high discriminant ability of LVQ could be integrated into an HMM architecture easily extendible to longer utterance models. <<ETX>>
Multi-Agent Approach for facing challenges in Ultra-Large Scale systems

The primary characteristics of ULS systems are ultra-large size, number of lines of code (LOC); number of people employing the systems; amount of data stored, accessed, manipulated, and refined; number of connections and interdependencies among software components; number of hardware elements. These characteristics of ULS systems make it impossible to rely on our current knowledge and techniques of software development. We will face fundamental challenges in the design and evolution, orchestration and control, and monitoring and assessment of ULS systems. In this paper we try to make a combination between ULS systems and Multi Agent Systems and to use Multi Agent System strengths to help tackling some challenges found in ultra-large scale systems. Banking system is an instance of ULS systems. As a case study, in a real project we have designed Automated Teller Machine (ATM) combination with biometric sensors based on multi-agent architecture. DOI: http://dx.doi. org/10.11591/ijece.v4i2. 4102
Back propagation in realistic parallel environment

Various ways of parallelizing the back propagation algorithm (BP) are studied on the bulk-synchronous parallel machine (BSP) like parallel model. Optimal number of processors and criteria for choosing the right task decomposition are presented. Theoretical results are verified in the parallel virtual machine (PVM) environment on a cluster of workstations.
High-quality computational tools for linear-algebra problems in FEM electromagnetic simulation

A key ingredient of finite-element analysis programs is the linear-algebra solver, typically either a linear-system solver or an eigensolver. The first part of This work tries to justify why it is important to have recourse to publicly available software for addressing this part of the computation. A number of libraries are mentioned as successful examples that exhibit a series of desirable qualities. Although some of these libraries force the programmer to somewhat change the programming style and may be difficult to learn, the benefits usually pay off the extra effort. The second part of the paper describes one of these libraries in some detail, namely SLEPc, the Scalable Library for Eigenvalue Problem Computations, which is used to illustrate the benefits of modem software paradigms for scientific and engineering computing.
Optimal adaptive location area design and inactive location areas

In this paper the location area design (LOAD) problem is addressed by using a constrained maximum spanning tree algorithm (CMST). The inactive location area concept is introduced in order to dynamically redesign the location areas (LA). The proposed LOAD-CMST scheme is evaluated in a Manhattan grid, for different user distribution, mobility and incoming call rate pattern representing the changing conditions of the network. A fixed LA design for a busy hour is conducted, and results are compared with those of an adaptive LA design using the inactive location areas (ILA) concept. Results show a gain in location registration procedures and a better performance of the network in terms of signaling load when the LOAD-CMST is used to redesign the LA, and the ILAs are introduced.
Structural learning and its applications to rule extraction

The article presents the concept of structural learning with forgetting. To evaluate its effectiveness, various examples are tried, such as the discovery of a Boolean function and the classification of iris. Its extension to recurrent networks is also described. A database on mushrooms is used to demonstrate the effectiveness of rule extraction from training data. A comparative study on the performance of various structural learning methods is also reported. <<ETX>>
Content based Message Filtering from Online Social Walls

Online Social Networks (OSNs) are very common in today’s world. OSNs typically have an area where users post messages or comment on posts written on public / private areas commonly known as walls. Users have the ability to view the public posts made by other users. However OSN users have no direct control on the content of messages posted on their walls, apart from blocking another user entirely from writing on his wall. Users may not be interested in viewing all the messages that are posted on their walls, and might not wish to block another user entirely from writing any message. In this work, a flexible rule-based system is proposed which gives the users the ability to control the messages posted on their walls through customizable filtering rules applied to user walls. Also recommendation of filtering rules is given to users with similar interests. Messages can be classified into different classes based on their content which is achieved through Machine Learning Techniques based on soft classification of OSN messages.
A Profile of Kindergarten Readiness in White Center: Fall 2007. Princeton, NJ: Mathematica Policy Research

I.1 White Center Response Rates to Fall 2007 ELI Kindergarten Readiness Data Sources . 5 II.1 Family and Household Characteristics in White Center: Fall 2007 ELI Kindergarten Readiness Baseline 9 II.2 Mother and Father Characteristics in White Center: Fall 2007 ELI Kindergarten Readiness Baseline 10 II.3 Child Characteristics in White Center: Fall 2007 ELI Kindergarten Readiness Baseline 12 II.4 Parent Use of Social Services and Satisfaction Level in White Center: Fall 2007 ELI Kindergarten Readiness Baseline 13 II.5 Health Service Use in White Center: Fall 2007 ELI Kindergarten Readiness Baseline 15 II.6 Early Childhood Program and Kindergarten Transition Experiences in White Center: Fall 2007 ELI Kindergarten Readiness Baseline 16 II.7 Kindergarten and Current Child Care Experiences in White Center: Fall 2007 ELI Kindergarten Readiness Baseline 18 III.1 Child Language and Literacy Skills in White Center: Fall 2007 ELI Kindergarten Readiness Baseline 26 III.2 Child Mathematics Skills in White Center: Fall 2007 ELI Kindergarten Readiness Baseline 27
Development of in vivo high resolution individual based neuroanatomical atlases using magnetic resonance imaging

The study of individual variation in brain anatomy has been neglected by most researchers in the functional brain imaging field. Almost all results of functional imaging experiments are referenced to standard atlases or "average" anatomical features. We believe that individual variation will inform us of crucial structural, developmental and functional aspects of the human brain, particularly with the rapidly expanding knowledge of the human genome. As a first step towards this we describe a non-invasive method of generating high resolution unique anatomical atlases for each subject in an experimental group using magnetic resonance imaging (MRI). Essentially, the method enables images of partial brain regions from multiple imaging sessions to be progressively combined into a high resolution neuroanatomical atlas. In this way, a single high resolution brain volume can be built up like "patchwork" from separate partial brain volumes. This will allow precise localisation and correlation of functional results and we believe will be hypothesis-generating in the areas of developmental, neurobiology and clinical neuroscience.
A process framework and typology for software product updaters.

textabstractProduct software is constantly evolving through extensions, maintenance, changing requirements, changes in configuration settings, and changing licensing information. Managing evolution of released and deployed product software is a complex and often underestimated problem that has been the cause of many difficulties for both software vendors and customers. This paper presents a framework and typology to characterize techniques that support product software update methods. The framework is based on a detailed process model of software updating. Finally, this paper assesses and surveys a variety of existing techniques against the characterisation framework and lists unsolved problems related to software product updaters
Hierarchical Poisson disk sampling distributions

A relaxation method is presented that generates a series of spatially distributed stochast ic samples f ollowing the Poisson disk distribution over a range of scales . The series is structured so that a prefix subsequence f orms a lower frequency sampling pattern that is also Poisson disk distributed. Un like strict dart-throwing, the relaxation procedure is guaranteed to terminate. The hierarchical structure allows straightforward adaptive sampling, using techniques analogous to ordered dithering. Also presented is an efficient method to optimize the loca tion of samples under a minimum root mean square quantization error criterion.
Neural network based signal prediction and parameter estimation for underwater layered media systems identification

A method for underwater layered media (UWLM) modeling accounting for attenuation effects is proposed. A simple nonlinear structure and an algorithm for implementing and simulating the model based on the behavior of the media is designed. The assumption of suitable parameters in the modeling permits us to design a more accurate model. A model that responds to the acoustical input is employed to test the artificial neural network's ability in signal prediction and parameter estimation. Experimental results from the implemented programs are presented. A feed-forward multi-layered neural network trained by the back-propagation algorithm using the min-max amplitude ranges of an output signal of the UWLM is designed and evaluated. This is based on a computer simulation of synthetic data, to estimate the parameters of the media including attenuation factors, reflection coefficients, travel times and decay parameters. Based on experiments estimating the parameters of the media and predicting its output signal, the network produces the same computational results as those of the original assumed media structure. The results of computer simulation of the underwater layered media modeling in conjunction with the proposed neural network training process are given. The results suggest that the proposed network can supplement, or replace conventional techniques for parameter estimation and output prediction in system identification. The method presented also offers advantages in speed and efficiency over existing estimates techniques. <<ETX>>
Artificial Life IX: Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems

Artificial Life is an interdisciplinary effort to investigate the fundamental properties of living systems through the simulation and synthesis of life-like processes. The young field brings a powerful set of tools to the study of how high-level behavior can arise in systems governed by simple rules of interaction. Some of the fundamental questions include: What are the principles of evolution, learning, and growth that can be understood well enough to simulate as an information process? Can robots be built faster and more cheaply by mimicking biology than by the product design process used for automobiles and airplanes? How can we unify theories from dynamical systems, game theory, evolution, computing, geophysics, and cognition? The field has contributed fundamentally to our understanding of life itself through computer models, and has led to novel solutions to complex real-world problems across high technology and human society. This elite biennial meeting has grown from a small workshop in Santa Fe to a major international conference. This ninth volume of the proceedings of the international A-life conference reflects the growing quality and impact of this interdisciplinary scientific community.
Sparse DNN-based speaker segmentation using side information

Sparse deep neural networks (SDNNs) for speaker segmentation are proposed. First, the SDNNs are trained using the side information that is the class label of the input. Then, speaker-specific features are extracted from the super-vector feature of the speech signal by the SDNNs. Lastly, the label of each speech frame is obtained by K-means clustering, which is used to segment different speakers of a continuous speech stream. The performance evaluation using the multi-speaker speech stream corpus generated from the TIMIT database shows that the proposed speaker segmentation algorithm outperforms the Bayesian information criterion method and the deep auto-encoder networks method.
A Statistical Approach to Automatic Phonetic Transcription of Japanese Orthographic Words

We address the problem of automatically transcribing Japanese orthographic words into symbols representing their pronunciations. Such a function is necessary for commercial continuous speech recognition systems since there are constant needs to create new recognition lexica for new applications or purposes. Simple look-up schemes are not adequate to deal with Japanese, while methods based on morphological analysis require in-depth linguistic knowledge and development effort. In this paper, we propose a statistical approach which is based on an N-gram language model. It is assumed that the pronunciation of a character only depends on the previous one to two characters and their pronunciations. Given an orthographic word, our method outputs the most likely phonetic transcription. It is shown that our approach provides superior performance to the public-domain conversion tool KAKASI on ten out of twelve test sets.
Combined watermarking and fingerprinting technologies for digital image copyright protection

Abstract A combined scheme of watermarking and fingerprinting technologies for image copyright protection is proposed. Most previous watermarking algorithms embedded either a pseudorandom sequence or a visually recognizable pattern as a watermark into the original media. The present paper advocates that a practical watermarking system should combine these two types of watermarks for advanced copyright protection. First, the original image is decomposed by a three-level discrete wavelet transform. Then, a visually recognizable pattern is permuted by a key, key0. This is then expanded and embedded into one of the middle-frequency sub-bands. Next, key0 is also used to choose the random locations from the counterpart middle-frequency sub-band for constructing the fingerprint of this image. Under these circumstances, this scheme is suitable for large-scale use of watermarking aimed at simultaneous identification and transaction tracking for owners. Extensive simulation results show that the watermarked image preserves high fidelity, and the embedded watermark, together with the constructed fingerprint, could be perfectly extracted under attack-free conditions. In addition, the combined scheme is robust to common image processing operations, geometric distortion, collusion attack and composite attack.
Linear-median hybrid edge detectors

New types of edge detectors optimized for noisy conditions are introduced and analyzed. The detectors contain linear masks followed by the median operation. The linear masks are designed for detecting actual edges and not for detecting false edges. Extensive numerical simulations show that these edge detectors give better edge signals, attenuate noise better, and yet give as sharp edges as commonly used 3 {\times} 3 Sobel and Prewitt detectors. The detectors can be easily implemented with common image-processing equipment. Examples on actual images confirm the analytical and simulated results.
Evolving SoccerBots : A Retrospective

In the RoboCup97 robot soccer tournament, we entered a team of softbot programs whose player strategies had been entirely learned by computer. Our team beat other human-coded competitors and received the RoboCup97 Scientific Challenge award. This paper discusses our approach, and details various ways that, in retrospect, it could have been improved.
Economic states on neuronic maps

We test the idea of visualizing economic statistics data on self-organization related maps, which are the LLE, ISOMAP and GTM maps. We report initial results of this work. These three maps all have distinguished theoretical foundations. The statistic data usually span high-dimensional space, sometimes more than 10 dimensions. To perceive these data as a whole and to foresee future trends, perspective visualization assistance is an important issue. We use economic statistics for the United States over the past 25 years (1977 to 2001) and apply them on the maps. The results from these three maps display historic events along with their trends and significance.