Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometric Multimedia Time Series

This thesis provides a new take on problems in multimedia times series analysis by using a shape-based perspective to quantify patterns in time, which is complementary to more traditional analysis-based time series techniques. Inspired by the dynamical systems community, we turn time series into shapes via sliding window embeddings, which we refer to as “time-ordered point clouds” (TOPCs). This framework has traditionally been used on a single 1D observation function for deterministic systems, but we generalize the sliding window technique so that it not only applies to multivariate data (e.g. videos), but that it also applies to data which is not stationary (e.g. music). The geometry of our time-ordered point clouds can be quite informative. For periodic signals, the point clouds fill out topological loops, which, depending on harmonic content, reside on various high dimensional tori. For quasiperiodic signals, the point clouds are dense on a torus. We use modern tools from topological data analysis (TDA) to quantify degrees of periodicity and quasiperiodicity by looking at these shapes, and we show that this can be used to detect anomalies in videos of vibrating vocal folds. In the case of videos, this has the advantage of substantially reducing the amount of preprocessing, as no motion tracking is needed, and the technique operates on raw pixels. This is also one of the first known uses of persistent H2 in a high dimensional setting. Periodic processes represent only a sliver of possible dynamics, and we also show that sequences of arbitrary normalized sliding window point clouds are approximately isometric between “cover songs,” or different versions of the same song, possibly with radically different spectral content. Surprisingly, in this application, an incredibly simple geometric descriptor based on self-similarity matrices performs the best, and it also enables us to use MFCC features for this task, which was previously thought not to be possible due to significant timbral differences that can exist between versions. When combined with traditional pitch-based features using similarity metric fusion, we obtain state of the art results on automatic cover song identification. In addition to being used as a geometric descriptor, self-similarity matrices provide a unifying description of phenomena in time-ordered point clouds throughout our work, and we use them to illustrate properties such as recurrence, mirror symmetry in time, and harmonics in periodic processes. They also provide the base representation for designing isometry blind time warping algorithms, which we use
A Study on Advantages of Data Mining Classification Techniques

The data mining has a basic principle for analyzing the data from different angles and categorize it and finally to condense it. In today’s world data mining have increasingly become very interesting and popular in terms of all application. Data and information have become major virtue for most of the organizations or institutions. The success of any organization confide in imposingly on the orbit to which the data acquired from business operations is utilized. Classification is an important task in knowledge discovery in databases (KDD) process. This paper provides different classification techniques analogous as Decision tree Induction, Bayesian Classification, Neural networks, Support Vector Machines. Keywords—Data Mining, Classification, Decision tree induction,Neural networks.
Is this my language

It used to be taken for granted that language documenters would develop anorthography for the language which they document in cases where no writingsystem exists already. Such systems facilitate the production of materialsfor revitalization of the languages. Lately however questions have been raisedas to whether the time and money expended in such endeavors are worth it.Two main reasons are that the orthographies are not often used anyway and,where they are successful, since they are often standard orthographies, theydestroy variation in languages. In this paper, I argue that standardization goeswith literacy development, and is desirable in situations where it is clear thatsuch languages would be used in school situations. However, most languagesof endangered communities do not have any prospect of being used in school.Because of this rather than focus on the development of a standard orthographysystem, documenters should rather develop systems that enable communitiesto write in the vernacular. Such systems use “orthographic transcription” whichminimally ensures the association of sounds with letters. Beyond that, speakersare allowed to write as they speak. This means that colloquial expressionsand dialectal differences would be incorporated into the system of writing. Theadvantage of this system is that adults particularly do not have to spend a longtime learning to represent their languages in ways that may not necessarily bethe same as the way they speak. I discuss the experience I had with Nyagbowhere my development of a vernacular writing system proved more successfulwith the community than an attempt to develop a standard orthography.
Parallelization of the K-means fast learning artificial neural network

The paper presents a parallelization study of an improved K-means fast learning artificial neural network (K-FLANN) within the parallel virtual machine (PVM) environment. The study discusses the improvements made on the K-FLANN II algorithm, which eventually lead to a consistent set of cluster centroids, regardless of the data presentation sequence DPS. To further improve clustering efficiency, a form of hierarchical clustering is explored, leading to the parallel-distributed implementation of the K-FLANN. Results of the investigation are presented along with a discussion of the fundamental behavior of the parallel network.
Digital Image Steganography: A Survey

Today Information and communication technology is growing rapidly because Internet is creating a borderless world. Therefore, digital communication has become essential part so security is a prime concern to communicate secretly. As a result, the security of information against unauthorized access has become a prime objective. The paper focuses on survey of various steganography techniques
Memory and Motor Processes of Password Entry Error

Passwords are tightly interwoven with the digital fabric of our current society. Unfortunately, passwords that provide better security generally tend to be more complex, both in length and composition. Complex passwords are problematic both cognitively and motorically, leading to both memory and motor errors during recall and entry. It is important that we better understand and disentangle the two error sources, as password entry errors can have significant negative consequences, such as being locked out of a critical information system. We present a computational cognitive model of password recall and typing, with memory and motor errors each contributing to password entry error. With this synthesis we can study human-computer interaction issues involving the usability of computer access control systems, specifically the password as an authentication mechanism. Ultimately we hope to make science-based recommendations for password policies that promote the use of passwords that are more usable.
Exploring the maximum capability of adaptive feedback

The main purpose of adaptive feedback is to deal with dynamical systems with internal and/or external uncertainties, by using the on‐line observed information. Thus, a fundamental problem in adaptive control is to understand the maximum capability (and limits) of adaptive feedback. This paper gives a survey of some basic ideas and results developed recently in this direction, for several typical classes of uncertain dynamical systems including parametric and non‐parametric non‐linear systems, sampled‐data systems and time‐varying systems. Copyright © 2002 John Wiley & Sons, Ltd.
Diachronic syntax based on constituency and dependency annotated corpora

This contribution presents two syntactically annotated corpora of Old French, Modéliser le changement: les voies du français (MCVF) and the Syntactic Reference Corpus of Medieval French (SRCMF). The focus is on how the underlying syntactic theory (constituency vs. dependency) influences the grammar model and how this choice is reflected in the syntactic annotations of the corpora. The comparison relates to the most relevant general properties of the corpora as well as to two phenomena, null subjects and cleft constructions. Null subjects highlight possible conflicts between syntactic annotation models and syntactic theory, and the information-structural properties of cleft constructions pose a particular problem for the interpretation and annotation of historical corpora. Both phenomena are major instances of diachronic variation in French. The study is relevant for corpus users working on diachronic syntax, as well for corpus builders wishing to design a grammar model for annotation.
Design method of an intelligent oil-hydraulic system (load sensing oil-hydraulic system)

Load sensing oil-hydraulic system is an intelligent oil-hydraulic system and effective in energy saving. This system has been successfully used in construction machinery and injection molding machines. This system, however, has an inherent drawback that its dynamic behavior becomes oscillatory and sometimes unstable, caused by the interaction of the dynamics between the load sensing mechanism and the load. Therefore, it is important to investigate a systematic design method for this system. In this paper, the design method for a load sensing oil-hydraulic system is proposed, which is based on the evaluation of its dynamic characteristics and overall efficiency taking into account its dynamic characteristics. Furthermore, it is shown that the design method can be realized with ease on the OHC-Sim simulation package.
Enhanced Security Approach for Detecting Intrusions in Multitier Web Applications

 Abstract— In our daily life, Internet services and applications are playing an important role, such as enabling the personal information management and communication from anywhere. Web services have moved to a multitier design wherein the web server runs the application front-end logic and data are outsourced to a database or file server, to accommodate this increase in application and data complexity. Replica the network performance of user sessions across both the front-end webserver and the back-end database, an IDS system Double Guard is approached in this paper. We are able to search out attacks that self-governing IDS would not be able to discern, by checking both web and subsequent database requests. Likewise, in stipulations of functionality, reporting and training sessions, we enumerate the limitations of any multitier. With the help of lightweight virtualization and an Apache web server with MySQL, we implemented DoubleGuard.We then framed and processed real-world traffic over a 15-day period of system deployment in both stable and dynamic network applications. Lastly, we were able to depict a broad range of attacks with 100 percent accuracy while maintaining 0 percent false positives for static web services and 0.6 percent false positives for dynamic web services with the help of DoubleGuard.
A New Semantic-Based Hierarchical Clustering For Textual Data Algorithm

The suffix tree clustering (STC) is a fast incremental clustering and it uses a tree form as a structure to represent data. However, hierarchy of clusters that are generated by taking the hierarchy of the constructed suffix tree directly is not reasonable. That reason is meaning of the words are ignored and some large-sized with poor quality of clusters are returned by STC. In this paper, a new semantic-based hierarchical clustering for textual data is proposed. The propose uses semantic suffix net as a structure to represent data and net pruning techniques as a logic to combine the related suffixes through their suffix links. Consequently, the hierarchy of clusters is generated per se when the resulting of tree pruning techniques is a tree form. While textual information can be group by using both string matching and meaning of the words as well as the hierarchy of their clusters can be return directly. That means the quality of clusters is increasing. Also, the merge or split suffered of a hierarchical clustering is reduced. Therefore, the propose algorithm can be used to reduce the suffered of a hierarchical clustering and enhance the quality of clusters.
Green Computing: Modeling the Correlation between Incoming Requests and Power Consumption

Enterprise data centers require integrated methodologies to manage the rapidly increasing demand for power. Previous approaches, however, have been largely ineffective because they offer no coordinated or comprehensive method to manage power in the data center (DC). In particular, they lack a method to model power utilization or trace its use in the data center, and also lack a comprehensive architecture for power management. This research specifically addresses those deficiencies. We describe (1) an effective method to model power utilization, which is also easy to implement; (2) a method for tracing the use of power in the data center; and (3) a comprehensive data center power management architecture based on these power modeling and tracing capabilities.
Scheduling Groups of Tasks in Distributed Hard Real-Time Systems

IN THE NEXT GENERATION HARD REAL-TIME SYSTEMS, IT IS ANTICIPATED THAT MANY APPLICATIONS WILL CONTAIN COLLECTIONS OF TASKS RUNNING ON A DISTRI- BUTED SYSTEM, AND THAT MANY OF THESE TASKS MUST BE EXECUTED ACCORDING TO CERTAIN PRECEDENCE AND REAL-TIME CONSTRAINTS. THE SPACE STATION, NEW PROCESS CONTROL APPLICATIONS INTEGRATED WITH EXPERT SYSTEMS SOFTWARE, OTHER REAL-TIME AI APPLICATIONS SUCH AS AN AUTONOMOUS LAND ROVER, AND SDI ARE SOME EXAMPLES OF SUCH FUTURE APPLICATIONS. IN THESE NEXT GENERATION, LARGE- SCALE, HARD REAL-TIME SYSTEMS, ONE WILL FIND TASKS WITH DIFFERING FUNCTION- AL COMPLEXITY AND MULTIPLE LEVELS OF TIMING CONSTRAINTS. FOR EXAMPLE, PROCESSING DATA FROM A SENSOR IS SOMETIMES FUNCTIONALLY SIMPLE, AND MIGHT NEED TO OCCUR IN THE MICROSECOND RANGE, WHILE CONSISTENT UPDATES OF REPLI- CATED FILES IS MORE COMPLEX AND MIGHT NEED TO OCCUR IN THE SECONDS RANGE. AT EVEN HIGHER LEVELS, CONTROL AND COORDINATION TASKS MIGHT BE QUITE INTRI- CATE AND NEED TO OCCUR IN THE SECONDS OR MINUTES RANGE, AND MOVEMENT OF MATERIAL IN AN AUTOMATED FACTORY MIGHT BE BASED ON COMPLICATED COORDINATION ALGORITHMS, BUT HAVE DEADLINES IN THE RANGE OF MINUTES OR EVEN HOURS. THE WORK PRESENTED IN THIS PAPER IS APPLICABLE TO THE GROUPS OF TASKS WHERE THEIR DEADLINES ARE IN THE ONE HALF SECOND RANGE OR GREATER, AND, THUS, DOES NOT ADDRESS TIGHT TIMING CONSTRAINTS SUCH AS THOSE THAT OCCUR IN SENSOR PROCESSING. WE ASSUME THAT THE SYSTEM UNDER CONSIDERATION HAS LOWER
A pragmatic tutorial dialogue system: design, implementation and evaluation in a health sciences domain

In large undergraduate classes, it is time-consuming, costly and seldom practical for the teacher to provide individualised feedback to students on their written responses to questions. A request for advice in relation to these issues, from the coordinator of a large first-year health sciences course at the University of Otago, motivated the research described in this thesis. An intelligent tutoring system (ITS) engages students in a dialogue; students enter their contributions to the dialogue as free text and the system gives a response. Such systems, which employ natural language as their interface, are called dialogue-based ITS or tutorial dialogue systems; they offer some promise for supporting and enhancing student understanding of key concepts through the provision of individualised feedback. The appeal of tutorial dialogue is that questions are embedded in a tutorial plan: the questions arise in a meaningful context and concepts and ideas are linked together in a coherent form. This allows each student contribution to be individually assessed. However, ITS are not currently in widespread use in higher education settings and there has been considerable controversy around their application in this context. Practical issues relating to the time and cost for development, the difficulty of adapting to specific teaching contexts, and pedagogical objections which relate to the idea of student modelling are among some of the barriers to their widespread adoption. In this thesis, a rationale is presented for revisiting tutorial dialogue systems in the context of large-class teaching. Two broad goals for this research are delineated. The first goal was to design, build and evaluate a new tutorial dialogue system for the cardiovas-
Intrinsic fault tolerance of multi level Monte Carlo methods

Monte Carlo (MC) and Multilevel Monte Carlo (MLMC) methods applied to solvers for Partial Differential Equations with random input data are shown to exhibit intrinsic failure resilience. Sufficient conditions are provided for non-recoverable loss of a random fraction of samples not to fatally damage the asymptotic accuracy vs. work of an MC simulation. Specifically, the convergence behavior of MLMC methods on massively parallel hardware is analyzed mathematically and computationally, under general assumptions on the node failures and on the sample failure statistics on the different MC levels, in the absence of checkpointing, i.e. we assume irrecoverable sample failures with complete loss of data. Modifications of the MLMC with enhanced resilience are proposed. The theoretical results are obtained under general statistical models of CPU failure at runtime. Specifically, node failures with the so-called Weibull failure models on massively parallel stochastic Finite Volume computational fluid dynamics simulations are discussed.
Enriched forms for Dynamic Queries

Current scientific directories and internet databases sustain large and also heterogeneous files. These real- world directories contain hundreds or perhaps thousands involving relations and also attributes. Traditional predefined problem forms can't satisfy different ad-hoc concerns from consumers on people databases. This specific paper offers DQF, some sort of novel databases query kind interface, which will be able to dynamically generate query kinds. The essence of DQF should be to capture some sort of user's desire and position query kind components, assisting him/her making decisions. The generation of a query form is an iterative process and it is guided from the user. In each technology, the technique automatically produces ranking provides of kind components along with the user next adds the desired form components in the query kind. The standing of kind components is dependant on the grabbed user desire. A user could also fill the actual query kind and distribute queries to see the problem result at each technology.
Importance of Data Preprocessing For Improving Classification Performance on CAC Data Set

Data Mining usage in Health Sector increased much in this decade because of the need for efficient treatment. From cost-cutting in medical expenses to acting as a Decision Support System for patient diagnosis, Data Mining nowadays is a strong companion in Health Sector. The dataset used in this thesis belongs to Dr. Nurhan Seyahi. Dr. Nurhan Seyahi and his colleagues made a research about Coronary Artery Calcification in 178 patients having renal transplantation recently. They used conventional statistical methods in their research. By using the power of data mining, this thesis shows the importance of feature selection and discretization used with classification methods for acting as a decision support system in patient diagnosis for CAC Dataset. Just by looking at seven important attributes, which are; age, time of transplantation, diabetes mellitus, phosphor, rose angina test, donor type and patient history, doctors can decide whether the patient has coronary artery calcification or not with approximately 70% accuracy. After the discretization process this accuracy approximately increases to 75% in some algorithms. Thus becoming a strong decision support system for doctors working in this area.
ML-based Approaches for Joint SAR Imaging and Phase Error Correction

This paper addresses a series of iterative sparse recovery approaches with application to the synthetic aperture radar (SAR) imaging which suffers from motion-induced model errors. These types of errors result in phase errors in SAR data, which cause defocusing of the reconstructed images. The proposed phase-error correction approaches combine the maximum a posterior (MAP) algorithm and the iterative sparse maximum likelihood-based (SMLA) approaches (referred to as the PE-MAP-SMLA approaches) to solve a joint optimization problem to achieve phase errors estimation and SAR image formation simultaneously. A new PESLIM approach is also proposed that extends the idea of the classical sparse and learning via iterative minimization (SLIM) approach. A closed-form expression for the recursive estimate of the phase errors parameters is derived. A general form of each of these iterative approaches consists of three steps, the first of which is for image formation, the second is for phase errors estimation and the last is for nuisance parameters estimation. The proposed approaches can correct the phase errors accurately, and the reconstruction quality of the SAR images can be improved significantly. Finally, simulation results of 1-D spectral estimation and 2-D SAR imaging examples are generated to show the effectiveness of the proposed approaches.
Low-power technique for on-chip memory using biased partitioning and access concentration

In this paper, we propose a low-power technique for on-chip memory using biased partitioning and access concentration (BPAC) technique. Memory array is partitioned into different sizes of two sub-arrays by inserting transfer-gate into a bit-line. When a smaller array is accessed, the larger array is electrically separated to reduce power. In addition, we perform code motion so that the code with higher access frequency be made to concentrate in the smaller sub-array. We applied BPAC technique to instruction memory of MPEG4 codec LSI. Power consumption was reduced by 40%.
Image Segmentation using MSNCut Algorithm

In this paper a hybrid technique for the segmentation of image has been proposed and it is named as MSNCut technique. Here image segmentation is the process of dividing the given image into number of regions that possess similar properties such as color, texture and intensity which are useful for the image analysis. The image is generic here, in other words image may be tree, river, building, medical or any general image. In this proposed method first the input image is pre-processed by mean shift algorithm to divide it into its constituent regions. Then the resultant image is represented as a bi-partite graph and finally the resultant graph (image) is processed under normalized cut to classify the image into meaningful classes.
The Mechanics of Representing Knowledge

This paper describes mechanical knowledge representation schemes found in several popular expert system building tools (ESBTs). In order to realize the full potential of ESBTs, it must be possible to develop a knowledge base in one ESBT and transfer the knowledge into another. Porting a knowledge base across ESBTs requires a clear understanding of the mechanical knowledge representation properties supported by each tool. In the following discussion, properties considered include: canonicity; truth value; plausibility, certainty, and possibility (PCP); temporality; and procedural knowledge. The nature of each property is described along with comments on related knowledge base characteristics. A summary table appears below relating these properties to several popular ESBTs. Overlap found in many of the mechanical knowledge representation properties suggests that automatic knowledge base translation is feasible.