Miracle-dz/UniFAR-FTUs huggingface dataset:
Novice-Centric Visualizations for Machine Learning

This thesis focuses on visualizations for machine learning tasks. More specifically, we create a taxonomy for existing machine learning visualizations, and design a system to help machine learning novices perform labelling tasks. There are many mature visualizations to help people understand the performance of current classifiers, including scatterplots, confusion matrices and ROC curves. However, most machine learning researchers are unaware of the visualization possibilities that exist, and many published visualizations are too task-oriented or dataset-oriented to be easily applied to other tasks. This thesis defines a taxonomy for machine learning visualizations in three dimensions: the data displayed, the advanced features to add for a specific task, and the goal of the visualizations. This taxonomy seeks to help machine learning researchers select a better visualization method to analyze their data. Previous machine learning tools focus on presenting comprehensive information to experts, treating machine learning as a black-box for end-users, or explaining the reason behind the prediction in a simple and clear way. However, to build a machine learning system, one needs to label data first, and a lot of machine learning novices want to build a classifier themselves simply by labelling data. This inspired our idea to design and implement the Label-and-Learn system, which includes five visualizations to help users better understand their data, the likelihood of the classifier’s success, and to improve their user experience. To evaluate the utility of our Label-and-Learn system, we ran user studies to compare the visualization system and traditional system in the quality of the labels, the user’s mental model about the task, and the user experience. The results from the experiment show that visualizations have no negative effect on the quality of the labels, but do improve the user’s mental model and the user experience. The success of the Label-and-Learn system should inspire further research in using visualizations to improve the user experience of data labelling in machine learning tasks.
Statistical Quantification of Individual Differences (SQuID): an educational and statistical tool for understanding multilevel phenotypic data in linear mixed models

Phenotypic variation exists in and at all levels of biological organization: variation exists among species, among‐individuals within‐populations, and in the case of l within‐populations abile traits, within‐individuals. Mixed‐effects models represent ideal tools to quantify multilevel measurements of traits and are being increasingly used in evolutionary ecology. Mixed‐effects models are relatively complex, and two main issues may be hampering their proper usage: (i) the relatively few educational resources available to teach new users how to implement and interpret them and (ii) the lack of tools to ensure that the statistical parameters of interest are correctly estimated. In this paper, we introduce Statistical Quantification of Individual Differences (SQuID), a simulation‐based tool that can be used for research and educational purposes. SQuID creates a virtual world inhabited by subjects whose phenotypes are generated by a user‐defined phenotypic equation, which allows easy translation of biological hypotheses into quantifiable parameters. Statistical Quantification of Individual Differences currently models normally distributed traits with linear predictors, but SQuID is subject to further development and will adapt to handle more complex scenarios in the future. The current framework is suitable for performing simulation studies, determining optimal sampling designs for user‐specific biological problems and making simulation‐based inferences to aid in the interpretation of empirical studies. Statistical Quantification of Individual Differences is also a teaching tool for biologists interested in learning, or teaching others, how to implement and interpret linear mixed‐effects models when studying the processes causing phenotypic variation. Interface‐based modules allow users to learn about these issues. As research on effects of sampling designs continues, new issues will be implemented in new modules, including nonlinear and non‐Gaussian data.
A Comparison Study on Statistical Modeling Methods

Abstract The statistical modeling of input random variables is necessary in reliability analysis, reliability-based design optimization, and statistical validation and calibration of analysis models of mechanical systems. In statistical modeling methods, there are the Akaike Information Criterion (AIC), AIC correction (AICc), Bayesian InformationCriterion, Maximum Likelihood Estimation (MLE), and Bayesian method. Those methods basically select the best fitted distribution among candidate models by calculating their likelihood function values from a given data set. Thenumber of data or parameters in some methods are considered to identify the distribution types. On the other hand,the engineers in a real field have difficulties in selecting the statistical modeling method to obtain a statistical modelof the experimental data because of a lack of knowledge of those methods. In this study, commonly used statisticalmodeling methods were compared using statistical simulation tests. Their advantages and disadvantages were then analyzed. In the simulation tests, various types of distribution were assumed as populations and the samples were generated randomly from them with different sample sizes. Real engineering data were used to verify each statisticalmodeling method.
Evaluation Criteria for BCI Research

To analyze the performance of BCI systems, some evaluation criteria must be applied. The most popular is accuracy or error rate. Because of some strict prerequisites, accuracy is not always a suitable criterion, and other evaluation criteria have been proposed. This chapter provides an overview of evaluation criteria used in BCI research. An example from the BCI Competition 2005 is used to display results using different criteria. Within this chapter, evaluation criteria for BCI systems with more than two classes are presented, criteria for evaluating discrete and continuous output are included, and the problem of evaluating self-paced BCI operation is addressed. Special emphasis is put on discussing different methods for calculating the information transfer rate. Finally, a criterion for taking into account the response time is suggested.
Progress Report of SLP Noisy Speech Recognition Evaluation WG : Individual evaluation framework for each factor affecting recognition performance (2)

Abstract Performance improvement of noisy speech recognition is urgent for practical use of speech recognition. Many researchers have been try in to overcome this problem. We organized a working group under Special Interest Group of Spoken Language Processing in Information Processing Society of Japan, to develop evaluation frame works of noisy speech recognition to compare many methods for processing of noisy speech. In this paper, we first review the series of CENSREC (Corppus and Environment of Noisy Speech RECognition) and then introduce the CENSREC1-C, the newest CENSREC. Finally we describe the road-maps of future CENSRECs.
SDN-based Implementation of P2P Streaming Networks with Dynamic Reconfiguration

—The Software Deﬁned Networking (SDN) technology is one of the major technical infrastructures for the digital society. This paper focuses on a dynamic streaming network and its efﬁcient implementation. A Peer to Peer (P2P) streaming network tends to be unbalanced because of the dynamic nature of its nodes to join and leave, and often suffers from delivery delay. Therefore, it is required to reconﬁgure the network dynamically to keep it balanced. This paper proposes an SDN-based scheme for adaptive reconﬁguration of the structure and routing in a P2P streaming network. This scheme enables fully decentralized delivery under a centralized control. Some simulation-based experiments conﬁrmed that this scheme worked effectively.
Image Fire Detection Algorithm Based on Amplitude Spectrum of Boundary Chain-code

Most traditional image fire detection technologies detect fire based on flame space feature such as flame color,boundary and so on,studies lack in image frequency domain.So an image fire detection algorithm based on Amplitude spectrum of boundary chain-code is presented.It converts the chain-code to a sequence array and obtains the amplitude distribution with FFT,analyzes the frequency domain of the image and obtains the relationship of spatial domain features and frequency domain distribution.Amplitude spectrum of boundary chain-code analysis is applied in the research of the image fire detection.Simulation results show the effectiveness of the algorithm.
Teaching Theory Anthropologically, Close to the Ground

Abstract: teaching anthropological theory is complex because there are so many potential approaches one could take. This essay explores how to teach theory to undergraduates by employing an empirical perspective. Doing so helps students learn a mode that is new to them and fits their intellectual development, while staying true to anthropology's disciplinary style.
Study on performance optimization for Chinese speech retrieval

A new unit, named word fragment of language model was proposed to take full advantage of the Chinese linguistic information among adjacent syllables, and an algorithm for word fragment selection was studied.The experimental results show, with the language model based on word fragment, syllable accuracy for recognizer is improved and the speech retrieval system gives better performance than the one with only syllable based model.
Authentic Design of Online Mathematics Assessment on Senior High Math Course

Rapid technological developments of this decade requires lecturers to pack an interesting learning for students with ICT based, one concrete manifestation of creativity by lecturer is made the form of online math assessment. From the results of the survey in the Kopert is VI Central Java region, there are still some colleges still use conventional ratings in high school math courses. The main reason is the high school math courses is a subject which is very important for students to be understood in theory and practice in preparation to become teachers in secondary schools. The purpose of this study is to produce products such as online math assessment for high school math courses that is valid or feasible to be used in all of mathematics education program in Central Java, which can produce human resources that is intelligent, creative, innovative and able to compete in the 21st century. Research method used in this study is Borg and Gall development model that is in practice only to the stage of Develop preliminary form of product Phase to produce valid product by experts. This stage is expert validation by four lecturers namely two lecturers of subject matter experts and two media expert lecturers from University PGRI of Semarang and Semarang State University. From the research results, it can be concluded that the online math authentic assessment with wondershare- Based has been valid or feasible, validated by experts, namely: (1) validation of the first subject matter expert with the percentage of the general aspects of 87.5%, the aspect of the material substance of 91.7%, and feasibility aspects of language by 94.4% (2) validation of the second subject matter experts at 88%, the aspect of the material substance of 92%, and feasibility aspects of the language of 88.9% (3) validation of media with the percentage of the general aspects of 88%, presenting aspects of learning as much as 85%, the feasibility aspects of language by 75%, and feasibility aspects of the graph 82.14% (4) validation of media with the percentage of the general aspects of 81.25%, presenting aspects of learning as much as 75%, the feasibility aspects of language by 81.25%, and feasibility aspects of the graph 75%. Of the results percentage of the expert validation, the resulting product is feasible or valid. The validity of these products are expected to facilitate lecturers and students in learning in implementing the learning and assessment process, then conducted a limited test on the assessment authentic product and it is obtained an average of student learning outcomes is more than 85. This shows that the website media makes the assessment and mastery of the material process easier.
Behavioral Mechanisms In Ecology

Thank you for reading behavioral mechanisms in ecology. Maybe you have knowledge that, people have look numerous times for their chosen books like this behavioral mechanisms in ecology, but end up in malicious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some malicious virus inside their desktop computer. behavioral mechanisms in ecology is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the behavioral mechanisms in ecology is universally compatible with any devices to read.
Dialog State Tracking Challenge Handbook

Background, motivation, and challenge overview In dialog systems, "state tracking" – sometimes also called "belief tracking" – refers to accurately estimating the user’s goal as a dialog progresses. Accurate state tracking is desirable because it provides robustness to errors in speech recognition, and helps reduce ambiguity inherent in language within a temporal process like dialog. Dialog state tracking is an important problem for both traditional unimodal dialog systems, as well as speech-enabled multi-modal dialog systems on mobile devices, on tablet computers, and in automobiles.
A Bottom-up Algorithm for XML Twig Queries

Twig query is a core operation in processing and optimizing XML structural queries. Recently, various algorithms have been proposed for finding twig patterns efficiently. Most of them are based on region labeling, pay little attention on nodes level information, and require additional caches such as stacks or lists to maintain the intermediate matching results, which cause the performance bottleneck of these algorithms. In contrast to previous work, we present a bottom-up algorithm for twig queries, which does not require additional caches and introduces idea of string searching to determine binary relationship between two nodes. Be- sides, this paper presents a node filtering mechanism–PathLevel, which can also be used in other algorithms for speeding up the query. Comprehensive experiments on several datasets demonstrate our method is an effective way for twig query.
Multi-objective optimization of process parameters in an area-forming rapid prototyping system using the Taguchi method and a grey relational analysis

The rapid prototyping technique has been developed over several decades. As the application becomes more widespread, the qualities of the parts produced by a rapid prototyping system must be considered. The area-forming rapid prototyping system is an emerging technology. This system has the advantage of a short build time but displays a weakness in the dimensional accuracy and surface roughness. To improve these weaknesses and simultaneously maintain a short build time, this study optimizes the process parameters using the grey relational Taguchi method. From the results, the best combinations of process parameters both for each quality and for multiple qualities are discovered. The optimization of process parameters is verified using the extra samples.
Intercomparison of the Global Ocean Reanalysis Data

This study summarized the results of the international ocean reanalysis intercomparison project. We introduced the characteristics of various ocean reanalysis systems and analyzed the assimilated performance on the typical eight oceanic variables (heat content, steric height, sea level, surface heat fluxes, mixed layer depth, subsurface salinity, depth of 20 °C isotherm, sea ice). In general, ensemble means show better estimations than those of any individual ocean reanalysis, but it depends on analyzed regions and variables. Among the eight oceanic variables, salinity and sea ice variabilities have large spreads among models. The deep sea, Southern Ocean, and coastal regions including western boundary current commonly appear as the areas with largest uncertainty between different objective analyses and assimilation models. We expect that intercomparison project for the ocean assimilation models independently operated in Korea should be processed, which allows us to join relevant international pro-grams in the near future.
Pose-varied Face Recognition Based on Neural Network and Hierarchical Support Vector Machines

The paper presents a method of pose-varied face recognition based on neural network and hierarchical support vector machines.At the stage of training,it transforms the feature vector of pose-varied image to the feature vector of standard image using neural network,then clusters the standard image feature vector,and trains the hierarchical support vector machines using the result of clustering.At the stage of the recognition,it transforms the feature vector of pose-varied image to the feature vector of standard image using the neural network,estimates which person the image mostly belongs to using hierarchical support vector machines,and confirms estimation using negative algorithm.The experiment shows that the effect is better.
Method for multi-lanes detection in traffic surveillance video

A multi-lanes detection method is proposed by extracting the edge of the background image automatical- ly for intelligent applications of traffic surveillance video processing, which requires stationary scene and lanes in- formation in advance. It focuses on static image processing: after background extraction from traffic video, filters and mathematical morphology are used for pretreatment. Hough transformation locates the lanes by gaining its skull from the edge of the background. With geometrical restriction, improper lines are excluded from lanes, and road ar- ea is confirmed. Several scence tests have been done to insure the method is effective. The proposed method turns out to be practically valuable, and has the robustness to detect multi-lanes from different scenes of traffic surveil- lance video under variant illumination conditions.
Learning and Exploiting Configuration Knowledge for a Portfolio-based Planner

In the recent years the field of automated plan generation has significantly advanced and several powerful domain-independent planners have been developed. However, no one of these systems clearly outperforms all the others in every known benchmark domain. It would then be useful to have a multi-planner system capable of automatically selecting and combining the most efficient planning technique(s) for each given domain. In this paper we propose a planner, called PbP (Portfolio-based Planner), which automatically configures a portfolio of existing planners, possibly using a useful set of macro-actions for each of them. The configuration relies on some knowledge about the performance of the planners in the portfolio and the observed usefulness of sets of macro-actions, which is automatically generated by a statistical analysis considering a set of training problems for the domain under consideration. The configuration knowledge for the given domain consists of a promising combination of planners in the portfolio, each one with a (possibly empty) set of macro-actions, and additional information specializing their round-robin scheduling at planning time. PbP has two variants, one focusing on speed (PbP.s) and one on plan quality (PbP.q). A preliminary version of PbP.s entered the learning track of the sixth IPC, and was the overall winner of this competition track. An experimental analysis presented in the paper confirms the effectiveness of PbP.s, indicates that PbP.q performs better than the IPC6 planners, shows that the learned configuration knowledge can be very useful for PbP.s/q, and demonstrates that PbP.s/q can perform much better than the basic planners forming the portfolio.
Vicariously Sharing Captured Web Experiences through an Automated Recommendation System

Our daily experiences are rich in content that we want to recall in the future. These previous experiences are often where we begin in our search for information needed when we work. In a community, we can rely on others to suggest materials when our own expertise fails to provide us with what we need. Likewise, others will make referrals only from things that they have previously experienced. In this paper, we present WebMemex, a system that recommends related Web pages to what the user is currently viewing. This system acts as an instantiation of an architecture to automatically capture and access infor mation in a manner similar to when a person is searching for information related to her current work context—where the related information being retrieved is something she has previously seen or that her friends have seen before and could ultimately suggest to her.
Link Management Security in Bluetooth

Bluetooth is a very useful and low cost wireless technology which has been developed for transferring data over short distances.Bluetooth is nowadays widely used for many purposes like file transfer, communicating with mouse and keyboard, listening to audio and many other applications. Bluetooth has many advantages over the other wireless networks such as Wi-Fi and Infrared. Security is a major concern in bluetooth as all the security options have not been explored in this wireless technology. Diffie Hellman protocol is a widely used protocol in the field of network security. But this protocol is basically a key exchange protocol and is applied during the exchange of keys In this thesis the diffie hellman protocol is applied on the random number, which is directly sent from the verifier to claimant, rather than the key itself. Then this random number is used for generating the keys.Sending the random number directly can cause security issues as the random number, bluetooth address and PIN can be known by the eavesdropper who can be a threat to the network. So this step improves the security of the bluetooth network.
Software defined network as solution to overcome security challenges in IoT

IOT has proven to be the most emerging technology where millions of devices can connect to the internet which makes the life feasible without any human intervention. This paper has analysed the challenges associated with IOT technology. Two major issues security and privacy of data and user information has discussed in detail. In this research paper solution of major issue of security is also proposed. One of the important requirements of IOT is to have a quality of service where the data among the devices should be as high as possible without degrading the performance. In this paper, we have proposed a software model based on Software Defined Network (DTN). Software Defined Network is a technology that increases the performance of the network and reduces the hardware usage and also provides a better security and privacy compare to the traditional networks. Lastly, the paper has discussed about the architectural design of SDN and suitable for IoT and Ad-hoc networks.