Miracle-dz/UniFAR-FTUs huggingface dataset:
Deployable and Scalable Information-Centric Networking

The current Internet architecture has remained relatively unchanged since its invention for end-to-end data communication. However, it is often posited today that the information objects must be addressed by the information objects themselves, rather than by where they are located in the network as it is done in the current Internet architecture. That is to say, it is information itself that should be central in the network architecture, because information consumers do not care where the information is located, but which information they need. Such a research position has led the research community to redesigning the Internet into so called information-centric networking (ICN), where the network allows users to discover the information directly and not addressed indirectly by which hosts possess it. However, the existing ICN studies leave the following significant problems open, which hinders the deployment of ICN. 1. Functionality problem: The existing ICN proposals are most single-strategy, which focuses on only the content retrieval. Hence, they fail to address the critical importance of providing efficient service access, such as publishing user-generated content. 2. Deployability problem: Most of the existing ICN proposals are clean-slate approaches hardly deployable in the Internet, as they require substituting the global IP networks. Moreover, some of them are partial solutions still with major drawbacks unresolved, regarding the capability requirements to individual network devices. 3. Scalability problem: ICN’s key component, route-by-name scheme, brings a challenge to scalability because of a large number of information objects. Feasible and efficient name resolution service is required, but none of the current literatures provide sufficient solutions.
Adaptive noise cancelling applied to image restoration

The one-dimensional adaptive noise cancelling principle is applied to two-dimensional imagery to remove line-interferences and random noise. For destriping LANDSAT or TM images, the preliminary results indicate that application of the adaptive cancelling principle is a promising and effective approach. Two different implementations are discussed. One models the stripings as periodic interferences and includes such a model in the adaptive cancelling scheme. The other one adaptively 'zero-means' the striped image on a line-by-line basis. For 'cancelling' wide-band noise, a novel bootstrap scheme is used to overcome the unavailability of the secondary noise source required by the adaptive cancelling scheme. The result is satisfactory and this novel scheme is applicable to time-signals as well.
A pivoting approach to the solution of inference problems

It is noted that inference problems in sentence logic may easily be transformed into equivalent numerical problems which can often be solved efficiently using techniques from integer programming. The author reviews this transformation and describes a heuristic solution method which has given good performance in practice. Some techniques from the integer programming literature that are useful for preconditioning numerically framed inference problems are also described. It is suggested that the elementary heuristic presented here, if properly implemented in the context of an efficient simplex routine, might prove useful for fast inference in propositional logic. <<ETX>>
Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data

Click through rate (CTR) and conversation rate estimation are two core prediction tasks in online advertising. However, four major challenges emerged as data scientists trying to analyze the advertising data - sheer volume, the amount of data available for mining is massive; complex structure, there is no easy way to tell what factors drive a user to click an ad or make a conversion and how the factors interacted with one another; high cardinality for categorical variables, features like device id usually have tons of possible values which will lead to very sparse data; severe skewness in response variable with the majority of the users not clicking the ad. In this paper, I will make a comprehensive summary of the state-of-art machine learning models (decision tree based, regularized logistic regression, online learning, and factorization machine) that are often used in the industry to solve the problem. Insights and practical tricks are then provided based on a wide range of experiments conducted on multiple data sets with different characteristics.
Electroencephalography (EEG)‐Based Brain–Computer Interfaces

Brain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). In this article, we aim at providing an accessible and up-to-date overview of EEG-based BCI, with a main focus on its engineering aspects. We notably introduce some basic neuroscience background, and explain how to design an EEG-based BCI, in particular reviewing which signal processing, machine learning, software and hardware tools to use. We present Brain Computer Interface applications, highlight some limitations of current systems and suggest some perspectives for the field.
Adaptive Resource Management Framework for Mobile Terminals---The ACTORS Approach

A resource management framework for media applications modelled as dataflow streams is described. The framework is aimed at multi-core applications and the main resource managed is the CPU time. The management is implemented using reservation-based scheduling in combination with optimization and feedback. Experiences of applying the framework to media applications are reported. A simulation model of the framework is also presented.
New adaptive frequency estimation methods using variable sampling processing

This paper proposes two adaptive frequency estimation methods using the variable sampling processing. Signal processing by controlling the sampling rate may introduce new possibilities for signal processing. One of them is a synchronous addition and subtraction method and other is a notch filter method. The processing of the proposed methods is the adaptation of time t in signal x:(t), but the conventional methods are the adaptation of amplitude of x:(t). Two methods are compared each other and with the conventional one in terms of the estimation accuracy, the convergence speed and the computation complexity. It is clear that the proposed methods have some better points than the conventional one.
CARACTERES AGRONÔMICOS DE B. brizantha cv. Xaraés (MG5), SOB DIFERENTES DOSES DE BIOFERTILIZANTE DE DEJETO DE BOVINO LEITEIRO / AGRONOMIC CHARACTERS OF B. brizantha cv. Xaraés (MG5), UNDER DIFFERENT DOSES OF BIOFERTILIZER OF DAIRY CATTLE

A producao de carne e leite no Brasil, e reconhecidamente produzida a pasto, e esse tipo de producao e um dos grandes desafios para a pecuaria, devido a implementacao de area, uso adequado de fertilizantes e manejo para manter essa produtividade. Atualmente a busca por sustentabilidade tem se tornado parte de nosso cotidiano, influenciando diversas atividades do dia-a-dia, incluindo a agricultura e pecuaria. Praticas de manejo ligadas a teoria de sustentabilidade tem ganhado um amplo espaco no Brasil e no mundo, nesse contexto, cada vez mais produtores rurais buscam formas de agregar valor a producao fazendo uso de biofertilizantes na producao de pasto. Desejando-se avaliar o desempenho agronomico da Brachiaria brizantha cv. Xaraes submetido a diferentes doses de biofertilizante, desenvolveu-se um experimento no Instituto de Biotecnologia – IBIOTEC, pertencente a UNIARA, Araraquara – SP, o trabalho foi conduzido em casa de vegetacao sob blocos inteiramente casualizados com 5 repeticoes. Os tratamentos consistiam de tres doses de biofertilizantes ( 5, 10 e 20 m 3 /ha -1 ), e um tratamento zero considerado o controle. O biofertilizante utilizado e de dejeto de vaca leiteira, submetido a tratamento em biodigestores anaerobios, coletado apos periodo conveniente de fermentacao. A brachiaria brizantha foi avaliada durante quatro cortes consecutivos para a avaliacao da Materia Verde (MV), Materia Seca (MS), altura, caracteres bromatologicos. A aplicacao do biofertilizante independente da dose em relacao ao controle, propiciou incremento na producao forrageira. Observou-se que os tratamentos com 10 e 20 m 3 /ha -1 dose de biofertilizante apresentara, melhores resultados para altura da planta (cm), producao de massa verde (MV) e materia seca (MS), para o parâmetro da bromatologia os teores de proteina foi maior para os tratamentos que receberam biofertilizante diferindo do tratamento controle. Os dados encontrados nesse trabalho indica que ha possibilidade de se produzir pasto de forma sustentavel e sem agredir o meio ambiente, e em substituicao a adubacao mineral.
An Exploratory Data Analysis of a Graduate Student (N=1)

undergraduate dissertation (protocol and interface for device control in the Home Environment) We developed a system composed of a hardware architecture for device control in the home, a server that mediates communication among devices via Microsoft’s Universal Plug’n’Play (UPnP) standard, and a graphical interface that consolidates device functions and presents them to the end user. We assessed user needs through a study of 15 volunteers and identified a target audience for our product characterized by a high level of comfort with current technologies and an eagerness to adopt new technologies. We also conducted a study of six volunteers to gauge the usability of our interface. We report on our system, its successes and shortcomings, alternatives to our system, and the methods and results of our studies. Abstract, Graduate dissertation (memory strategy use Among Graduate dissertation (memory strategy use Among older Adults: predictors and Health correlates in the Advanced cognitive training for independent and vital Elderly (ActivE) study) Memory encoding strategies for older adult participants in the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study are the subject of this dissertation. Strategies are cognitive mechanisms potentially available to conscious manipulation that process environmental stimuli to enhance
Analysis of frequency-smearing models simulating hearing loss

Various authors have created models of the psychoacoustic effects of sensorineural hearing loss that transform ordinary sounds into sounds that evoke the perception of hearing loss in normal-hearing listeners. In this thesis, models of the reduced frequency resolution, reduced temporal resolution, and loudness recruitment and absolute threshold loss were evaluating using a model of human perception. Confusion matrices of vowels were simulated using these models and compared to confusion matrices from five hearing-impaired subjects. It was found that the model of human perception used spectral cues in a different way than actually occurs in humans, making it difficult to evaluate the hearing loss models, and causing the simulated confusion matrices to be different than the real subjects' matrices. Even so, the frequency smearing models caused the total error rate to increase with increasing smearing bandwidth, and the results were generally consistent with the expected behavior. Thesis Supervisor: Louis D. Braida Title: Henry Ellis Warren Professor of Electrical Engineering
Novel Cryptographic Primitives and Protocols for Censorship Resistance

Internet users rely on the availability of websites and digital services to engage in political discussions, report on newsworthy events in real-time, watch videos, etc. However, sometimes those who control networks, such as governments, censor certain websites, block specific applications or throttle encrypted traffic. Understandably, when users are faced with egregious censorship, where certain websites or applications are banned, they seek reliable and efficient means to circumvent such blocks. This tension is evident in countries such as a Iran and China, where the Internet censorship infrastructure is pervasive and continues to increase in scope and effectiveness. An arms race is unfolding with two competing threads of research: (1) network operators’ ability to classify traffic and subsequently enforce policies and (2) network users’ ability to control how network operators classify their traffic. Our goal is to understand and progress the state-of-the-art for both sides. First, we present novel traffic analysis attacks against encrypted communications. We show that state-of-the-art cryptographic protocols leak private information about users’ communications, such as the websites they visit, applications they use, or languages used for communications. Then, we investigate means to mitigate these privacy-compromising attacks. Towards this, we present a toolkit of cryptographic primitives and protocols that simultaneously (1) achieve traditional notions of cryptographic security, and (2) enable users to conceal information about their communications, such as the protocols used or websites visited. We demonstrate the utility of these primitives and protocols in a variety of real-world settings. As a primary use case, we show that these new primitives and protocols protect network communications and bypass policies of state-of-the-art hardware-based and software-based network monitoring devices.
Interpreting training and digital pen technology

The first time I heard about digital pen technology was in 2009, at Monash University, when the representative of the US manufacturer Livescribe was testing the Australian market for distribution. I immediately realised that the features of their Smartpen (the first model was called Pulse), initially invented to facilitate the work of secretaries taking minutes in meetings by simultaneously filming notes and recording speech, had a great potential for the training of future interpreters, especially in the area of note-taking and consecutive interpreting.
Automatic Detection of Syntax-based Prosody Annotation Errors

Both unit-selection and HMM-based speech synthesis require large annotated speech corpora. To generate more natural speech, considering the prosodic nature of each phoneme of the corpus is crucial. Generally, phonemes are assigned labels which should reflect their suprasegmental characteristics. Labels often result from an automatic syntactic analysis, without checking the acoustic realization of the phoneme in the corpus. This leads to numerous errors because syntax and prosody do not always coincide. This paper proposes a method to reduce the amount of labeling errors, using acoustic information. It is applicable as a post-process to any syntax-driven prosody labeling. Acoustic features are considered, to check the syntax-based labels and suggest potential modifications. The proposed technique has the advantage of not requiring a manually prosody-labelled corpus. The evaluation on a corpus in French shows that more than 75% of the errors detected by the method are effective errors which must be corrected.
A robust multisource image automatic registration system based on the SIFT descriptor

Image registration is an essential step in many remote-sensing (RS) applications. This article presents a study of a multisource image automatic registration system (MIARS) based on the scale-invariant feature transform (SIFT), which has been demonstrated to be the most robust local invariant feature descriptor for automatically registering various RS images. The SIFT descriptor has two shortcomings: it is unsuitable for extremely large images and has an irregular distribution of feature points. Therefore, three steps are proposed for the MIARS: image division, histogram equalization and the elimination of false point matches by a subregion least squares iteration. Image division makes it possible to use the SIFT descriptor to extract control points from an extremely large RS image. Histogram equalization in prematching improves the contrast sensitivity of RS images. The subregion least squares iteration refines the registration accuracy. Images from multisensor systems, including Quickbird, IRS-P6, Landsat/TM, HJ-CCD, HJ-IRS, light detection and ranging (LiDAR) intensity images and aerial data, were selected to test the reliability of the MIARS. The results indicated that better registration accuracy was achieved, which will be very helpful in the future development of a registration model.
Stream-Based Extreme Learning Machine Approach for Big Data Problems

Big Data problems demand data models with abilities to handle time-varying, massive, and high dimensional data. In this context, Active Learning emerges as an attractive technique for the development of high performance models using few data. The importance of Active Learning for Big Data becomes more evident when labeling cost is high and data is presented to the learner via data streams. This paper presents a novel Active Learning method based on Extreme Learning Machines (ELMs) and Hebbian Learning. Linearization of input data by a large size ELM hidden layer turns our method little sensitive to parameter setting. Overfitting is inherently controlled via the Hebbian Learning crosstalk term. We also demonstrate that a simple convergence test can be used as an effective labeling criterion since it points out to the amount of labels necessary for learning. The proposed method has inherent properties that make it highly attractive to handle Big Data: incremental learning via data streams, elimination of redundant patterns, and learning from a reduced informative training set. Experimental results have shown that our method is competitive with some large-margin Active Learning strategies and also with a linear SVM.
Quality grading of impaired television pictures

Subjective tests of pictures impaired by distortion and noise provide basic data for the design of television broadcasting systems but their complex nature is a source of difficulty. The results are affected by the many arbitrary factors involved in choosing such features as the type of psychometric test, the class of observers, and the test pictures and viewing conditions. Because these tests are inevitably costly and time consuming, it is the more regrettable that different workers have generally chosen different methods, yielding results that cannot be compared satisfactorily. As a possible basis for some degree of standardisation, a general-purpose quality-grading method is proposed. This is of the type, suitable for nonexpert observers, in which impaired pictures are presented in random sequence and the observers give their opinions in terms of a 5 grade scale of quality. Also described are statistical procedures for analysing the primary data and methods of presenting the results.
Yahoo! For Amazon: Sentiment Parsing from Small Talk on the Web

The internet has made it feasible to tap a continuous stream of public sentiment from the world wide web, quite literally permitting one to "feel the pulse" of any issue under consideration. We present a methodology for real time sentiment extraction in the domain of finance. With the advent of the web, there has been a sharp increase in the influence of individuals on the stock market via web-based trading and the posting of sentiment to stock message boards. While it is importantto capture this "sentiment" of small investors, as yet, no index of sentiment has been compiled. This paper comprises (a) a technology for extracting small investor sentiment from web sources to create an index, and (b) illustrative applications of the methodology. We make use of computerized natural language and statistical algorithms for the automated classification of messages posted on the web. We design a suite of classification algorithms, each of different theoretical content, with a view to characterizing the sentiment of any single posting to a message board. The use of multiple methods allows imposition of voting rules in the classification process. It also enables elimination of "fuzzy" messages which are better off uninterpreted. A majority rule across algorithms vastly improves classification accuracy, but also leads to a natural increase in the number of messages classified as "fuzzy". The classifier achieves an accuracy of 62% (versus a random classification accuracy of 33%), and compares favorably against human agreement on message classification, which was 72%. The technology is computationally efficient, allowing the access and interpretations of thousands of messages within minutes. Our illustrative applications show evidence of a strong link between market movements and sentiment. Based on approximately 25,000 messages for the last quarter of 2000, we found evidence that sentiment is based on stock movements.
Document Clustering Technique by K-means Algorithm and PCA

The amount of information is increasing rapidly with the development of the internet and the computer. Since these enormous information is managed by the document forms, it is necessary to search and process them efficiently. The document clustering technique which clusters the related documents through the similarity between the documents help to classify, search, and process the large amount of documents automatically. This paper proposes a method to find the initial seed points through principal component analysis when the documents represented by vectors in the feature vector space are clustered by K-means algorithm in order to increase clustering performance. The experiment shows that our method has a better performance than the traditional K-means algorithm. 키워드 : 문서 군집화, K 평균 알고리즘, 주성분 분석 Key word : Document Clustering, K-means algorithm, PCA Journal of the Korea Institute of Information and Communication Engineering 한국정보통신학회논문지(J. Korea Inst. Inf. Commun. Eng.) Vol. 18, No. 3 : 625~630 Mar. 2014
Improving Efficiency of Minimum Dominating Set Problem using Simulated Annealing Algorithms

The minimum dominating set problem of a graph G is to find a smallest possible dominating set. The minimum dominating set problem is a well-known NP-complete problem such that it cannot be solved in polynomial time. Heuristic or approximation algorithm, however, will perform well in certain area of application. In this paper, we suggest three different simulated annealing algorithms and experimentally show better efficiency improvement by applying these algorithms to the graph instances developed by DIMACS.
Buck-Boost Control of Four Quadrant Chopper using Symmetrical Impedance Network for Adjustable Speed Drive

This paper proposes buck-boost capabilities of four quadrant chopper for wide range speed control of DC motor drives using symmetrical impedance network called Z-source network. By controlling the shoot through duty ratio from 0 to 0.5 and non-shoot through techniques, the Z-source four quadrant choppers can produce any desired DC voltage across the DC motor. The switching patterns for both buck and boost operations are presented to achieve four modes of operation of DC motor. As a result, the proposed model will have ride through capability during voltage sags, manage during voltage swells, used in any types of DC voltage sources such as fuel cell and solar cell and improve the reliability by reducing EMI noise. Analysis and simulation results are presented to demonstrate these new findings.