Miracle-dz/UniFAR-FTUs huggingface dataset:
Building a Post-Search Academic Search Engine Based on a Serial of Clustering Methods

Academic search engines, such as Google Scholar and Scirus, provide a Web-based interface to effectively find relevant scientific articles to researchers. However, current academic search engines are lacking the ability to cluster the search results into a hierarchical tree structure. In this paper, we develop a post-search academic search engine by using a mixed clustering method. In this method, we first adopt a suffix tree clustering and a two-way hash mechanism to generate all meaningful labels. We then develop a divisive hierarchical clustering algorithm to organize the labels into a hierarchical tree. According to the results of experiments, we conclude that using our mixed clustering method to cluster the search results can give significant performance gains than current academic search engines. In this paper, we make two contributions. First, we present a high performance academic search engine based on our mixed clustering method. Second, we develop a divisive hierarchical clustering algorithm to organize all returned search results into a hierarchical tree structure.
Data Mining in Electronic Commerce

Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transac tion costs and better customer relations. Success depends on skilled informa tion technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges.
An adaptive spatial error concealment (SEC) with more accurate MB type decision in H.264/AVC

Transmission of compressed video over error prone channels may result in packet losses or errors, which can significantly degrade the image quality. Therefore an error concealment scheme is applied at the video receiver side to mask the damaged video. Considering there are 3 types of MBs (macro blocks) in natural video frame, i.e., Textural MB, Edged MB, and Smooth MB, this paper proposes an adaptive spatial error concealment which can choose 3 different methods for these 3 different MBs. For choice of the appropriate method, 2 factors are taken into consideration. Firstly, standard deviation of our proposed edge statistical model is exploited. Secondly, some new features of latest video compression standard H.264/AVC, i.e., intra prediction mode is also considered for criterion formulation. Compared with previous approaches, which are only based on deterministic measurement, proposed method achieves more accurate MB type decision which leads to better image recovery. Subjective and objective image quality evaluations in experiments confirmed this.
A century of t‐tests

One hundred years ago, an author under the pseudonym of “Student” published a paper which was to become famous. It was entitled “The probable error of a mean”. But what we now know as Student's t‐test attracted little attention. It took another statistician of genius, R. A. Fisher, to amend, publicise and make it ubiquitous. But both Student's and Fisher's published versions were based upon faulty data. Stephen Senn reminds us of the third dedicated researcher and the quarter of a century delay before the story behind Student's t‐test emerged.
Solid-state control of electric drives

A tutorial review of the dc and ac electric-drive field is presented. The goal is to present fundamental concepts, principle concerns, and key developments in electric-drive technology. Principles of ac and dc power converters and ac and dc motors are presented. Then the combination of the converter and motor to provide a complete drive system is discussed along with drive-system characteristics and methods for analyzing performance. Finally, some application guidelines for both ac and dc systems are given.
Object detection in clutter with learning maps

The nonparametric classification scheme called learning vector quantization, LVQ, is applied to the detection of an object in clutter with SAR imagery. The LVQ structure self-organizes into a decision mapping via unsupervised training which can require a relatively large data set. A physical model can generate a training set for the LVQ prior to further training with an actual data set. The approach is especially attractive when the physical model is too complex to yield an optimal, or near optimal, decision rule. Here a two-scale electromagnetic scattering model is used derive a SAR image model that includes obscuration, shadowing and range inversion. The LVQ performance achieved is good and comparable to the optimum in cases permitting analysis.
Design and implementation of visual feedback for an active tracking

Active visual tracking is used to direct the attention of the camera to an object and maintain it in the camera's field of view. A steered camera is used to decrease relative motion of the target in the image plane. This leads to better performance of the mean-shift based tracking algorithm, which requires the object tracked in the current and the previous frame to overlap. A classical PID controller and a nonlinear fuzzy controller have been tested in steering the camera head.
A 23-ns 1-Mb BiCMOS DRAM

A 1-Mb BiCMOS DRAM having a 23-ns access time is described. The DRAM uses a direct sensing technique and a nonaddress-multiplexing configuration. This technique combines the NMOS differential circuit on each pair of data lines with a common highly sensitive bipolar circuit. The resulting chip has been verified to have high-speed characteristics while maintaining a wide operating margin and a relatively small chip size of 62.2 mm/sup 2/, in spite of a 1.3- mu m lithography level. >
Handbook Of Networked And Embedded Control Systems

Preface Part I. Fundamentals Fundamentals of Dynamical Systems Control of Single-Input Single-Output Systems Basics of Sampling and Quantization Discrete-Event Systems Introduction to Hybrid Systems Finite Automata Basics of Computer Architecture Real-Time Scheduling for Embedded Systems Network Fundamentals Part II. Hardware Basics of Data Acquisition and Control Programmable Logic Controllers Digital Signal Processors Microcontrollers SOPCs: Systems on Programmable Chips Part III. Software Fundamentals of RTOS-Based Digital Controller Implementation-Aware Embedded Control Systems From Control Loops to Real-Time Programs Embedded Real-Time Control via MATLAB, Simulink, and xPC Target LabVIEW Real-Time for Networked/Embedded Control Control Loops in RTLinux Part IV. Theory An Introduction to Hybrid Automata An Overview of Hybrid Systems Control Temporal Logic Model Checking Switched Systems Feedback Control with Communication Constraints Networked Control Systems: A Model-Based Approach Control Issues in Systems with Loop Delays Part V. Networking Network Protocols for Networked Control Systems Control Using Feedback over Wireless Ethernet and Bluetooth Bluetooth in Control Embedded Sensor Networks Part VI. Applications Vehicle Applications of Controller Area Network Control of Autonomous Mobile Robots Wireless Control with Bluetooth The Cornell RoboCup Robot Soccer Team: 1999-2003 Index
New models and algorithms for semi-naive Bayesian classification focused on the AODE paradigm.

Classification is one of the most popular tasks in machine learning, partly motivated by the high demand in real life applications, i.e.: spam filtering, cancer diagnosis, gene identification, credit assessment, etc. In this context, Bayesian networks entail an ideal framework to directly deal with the uncertainty encountered. In this dissertation, we work with the family of semi-naive Bayesian network classifiers (BNCs) that either do not perform structural learning or it is very simple. Particularly we focus on the Averaged One-Dependence Classifier (AODE), one of the most efficient and effective approaches to alleviate naive Bayes' independence assumption. This thesis comprises three main parts: ? The first one presents four new classifiers based on AODE. One of them is proposed to alleviate AODE's computational needs in terms of memory storage and classification time, which is called HODE (Hidden One-Dependence Estimators) . The other three are different alternatives to handle numeric attributes in BNCs: GAODE (Gaussian AODE) and HAODE (Hybrid AODE), based on (conditional) Gaussian networks; and MTE-AODE, based on the use of mixtures of truncated exponentials. The second one shows how the application of different disjoint (traditional) discretization techniques has a low impact on the performance of the semi-naive BNCs. Hence it studies and recommends the use of non-disjoint the discretization techniques as an attractive alternative. Experimental results indicate that the different alternatives proposed are beneficial for a particular group of datasets, whose common characteristics are unknown. That motivates the study of the domain of competence for these classifiers according to different complexity measures. Furthermore, an automatic procedure to advise on the best semi-naive BNC to use for a particular dataset is proposed.
Twice-Modeling Approach for Concurrent Process Planning without Forced Decoupling

A strategy other than forced decoupling or tearing to solve the problem in concurrent design process planning is produced. Twice-modeling concept is proposed by building an Information Flow Matrix (IFM) based on the influence of relationships between information flows during task execution. After re-constructing the IFM to determine the order levels of information flows, the provider-receiver relationship between tasks and information flows are used to instruct the planning of the key tasks. A design task is then decomposed according to the usage or supply of information flow. Adaptive subdivision of design tasks is realized based on synchronous mapping of Coded Design Structure Matrix (C-DSM) and IFM. Finally the whole design process planning is completed by the priority definition of the subtasks and a DSM based level generate algorithm. The proposed method is illustrated by an application to the vehicle overall design process planning. The “pseudo coupled” or “pseudo dependent” relationships are eliminated effectively without forced decoupling and the concurrency of the whole design process is improved obviously.
Analyzing common algebra-related misconceptions and errors of middle school students

ANAL YZING COMMON ALGEBRA-RELATED MISCONCEPTIONS AND ERRORS OF MIDDLE SCHOOL STUDENTS Sarah B. Bush October 6, 2011 The purpose of this study was to examine common algebra-related misconceptions and errors of middle school students. In recent years, success in Algebra I is often considered the mathematics gateway to graduation from high school and success beyond. Therefore, preparation for algebra in the middle grades is essential to student success in Algebra I and high school. This study examines the following research question: What common algebra-related misconceptions and errors exist among students in grades six and eight as identified on student responses on an annual statewide standardized assessment? In this study, qualitative document analysis of existing data was used in order to analyze sixthand eighth-grade student responses on a statewide standardized assessment. Secondary data sources consisted of Algebra I student responses which were also analyzed qualitatively using document analysis and follow-up interviews with key informants. The primary analysis indicated that (l) numerous misconceptions and errors identified in the review of literature were present on both the sixthand eighth-grade
Unified accurate CAD models for RF, microwave and millimeter-wave integrated circuits

Unified CAD-oriented circuit model is proposed and developed for accurate representation of a variety of planar integrated circuits (ICs). It is realized by implementing a so-called "short-open calibration" (SOC) procedure that uniquely calibrates (de-embeds) the results obtained from a full-wave method of moments (MoM) with two calibration standards: short and open elements. Stemming from the concept of TRL calibration in microwave measurements, this SOC is used to evaluate and to remove unwanted numerical error terms. These error terms mainly come from the approximation of excitation mechanisms in the deterministic 3-D MoM and inconsistently between the 2-D and 3-D MoM-based simulations of uniform line sections. Following a description of our modeling technique related to the SOC and 3-D MoM, different classes of planar structures are characterized in terms of their CAD circuit models that involve all physical effects, such as frequency dispersion, high-order mode effects and unbounded radiation losses. The circuit models developed are very useful not only in gaining physical insight into the electrical behavior of planar structures and also in implementing efficient circuit network-oriented design/optimization approaches. The SOC-extracted circuit parameters are presented for a microstrip interdigital capacitor (IDC) and finite-ground CPW gap structures, which are also well verified by our measurements.
A stable neural network-based adaptive controller for robot manipulators

A stable neural network-based adaptive controller design for integrating a neural network (NN) approach with an adaptive implementation of the sliding mode control with the sector is presented in this paper for the trajectory tracking control of a robot with unknown nonlinear dynamics. The sliding mode control with the sector serves two purposes, one is to provide the global stability of the closed loop system when the system goes out of the control, the other is to improve the tracking performance within the NN approximation region. The system stability and tracking error convergence are proved using Lyapunov techniques that yield a NN weight tuning algorithm. Finally, the effectiveness of the proposed control approach is illustrated through simulation studies.
Deconstructing Engagement

Background. The discourse of learner engagement has become prevalent in formal education research and practice in recent years. However, the term has multiple meanings and various constructions, particularly in the context of games and learning, where the coming together of two different disciplinary constructs creates ambiguity and tensions. Aim. The notions of engagement with education and engagement with games are not necessarily mutually compatible, and this article aims to explore this. Method. This article starts by exploring the use of the term by deconstructing the idea of engagement, and highlighting issues with the way in which people use it in different contexts. Next, it discusses practical and theoretical limitations of the concept, such as the assumed link between engagement and learning and measurement of engagement. Results. The core contribution of the article is a synthesis of the complex literature in this field presented as a model of engagement with learning based on six ways of constructing engagement: participation, attention, captivation, passion, affiliation, and incorporation. Conclusion. The article concludes by discussing the benefits and possible uses of this model, and highlighting necessary further research to forward the field of games and learning in this area.
A distributed hierarchial p-tree link protection scheme for mesh networks

This paper presents a distributed scheme for link failure recovery in mesh optical networks, based on the use of network hierarchical spanning trees. The scheme intends to maximize restorability in a network with known working and spare capacities. The hierarchical protection tree (p-tree) provides the hierarchical layering of the network. The straddling links that are not located on the tree are protected through tree branches to the higher layer Parent nodes. The links on the tree are protected by links to backup parent nodes. We study the problem of finding the most optimized network tree to achieve maximum restorability, and present heuristics for finding the best tree. Our algorithm includes two steps: Selection of the best root node for the tree, and construction of the tree based on distribution of tree ID labels among the nodes. Each node selects a primary parent node and a backup parent node, and constructs pre-determined protection paths accordingly. In case of failure, all connections on a link are switched quickly to the protection path as a bundle. We perform restorability analysis for several real and arbitrary long-haul networks and show that our scheme provides excellent network restorability along with exceptional scalability and maintainability.
A review of some preliminary results on the auditory representation of speech

Speech perception studies have traditionally focused on the identification of the acoustic cues that are needed to distinguish the sounds of speech. Recently, however, techniques have been developed that allow investigation of the auditory speech cues. Direct physiological measurements have been made of the output of the auditory system at the levels of the auditory nerve and cochlear nucleus during stimulation by speech signals (e.g. Sachs & Young, 1979). In the present paper, some studies that have used psychophysical techniques to estimate the output of the human auditory periphery during speech stimulation and comparison with perceptual measures of the same stimuli are reviewed
Towards efficient collaboration for e-service transaction management : semantics, security and reliability

In this thesis, we address the challenges of a collaborative Web services-based transaction context by presenting an access control framework and models for supporting semantic, secure and reliable collaboration. The proposed approaches depend on workflow technologies by integrating control flows and data flows into a workflow model, and they employ a relaxed transaction concept for fault tolerance, in order to maintain the processes consistencies, as well as, data consistencies even in presence of failures. To achieve these objectives, we first locate relevant Web services by a semantic approach. The key idea of our approach is to indirectly associate the intention of users to the advertisements in Web services by applying Probabilistic Latent Semantics Analysis (PLSA). Then, we model the internal activities within an institution by employing workflow technology whilst the individual workflow models of participating institutions can be mapped to process views, which are further described by Web services. Based on the process view model, a two layered access control architecture is proposed to protect computing resources. Finally, the atomicity concept is relaxed by integrating transactions and exception handling models in order to ensure the reliable collaborations on the Web. The main contributions of this thesis are threefold. First, we proposed a novel approach to find relevant services through the combination of keyword technique and the semantics extracted from the services’ descriptions by using a probabilistic semantic approach in order to handle poor scalability and lack of semantics. Second, a novel two layered access control model based on the general principles of the order of security priority in an organization is proposed. With the model, various roles are associated with views, so that right users can only see necessary parts of workflows exposed to them. Third, the atomicity sphere model is relaxed by considering two levels of atomicity abstraction for supporting reliable collaboration at the level of process, as well as, at the level of data to maintain the process consistency and data consistency in case of failures.
Efficient algorithms for data mining

This thesis focuses on the computational aspects and the development of scalable and efficient algorithms for some important data-mining techniques. The emerging field of data mining requires the processing of very large datasets. This makes the development of efficient and scalable algorithms an essential step towards the success of data mining. Data mining combines many techniques and methods from other areas such as statistics, machine learning, and database. Although some data-mining techniques have been identified in other fields for more than a decade, most of the proposed algorithms in the literature were designed under the assumption of small datasets, either in terms of the number of examples/points or in terms of the number of dimensions/attributes of the examples. These limitations and other inherent properties of these techniques generally make them inappropriate for data-mining applications. Thus, there is a great need for the development of efficient and scalable algorithms that will allow for the application of these techniques to large datasets. In this thesis we investigate the development of scalable and efficient algorithms for classification, clustering, and similarity join. We mainly follow two approaches to achieve this goal: (1) developing exact algorithms while reducing the computational and I/O requirements, and (2) developing approximation algorithms while reducing the computational and I/O requirements and maintaining a good quality. The proposed algorithms are shown to be efficient, scalable, and robust across different datasets.
Notice of Retraction Single neuron adaptive control based on ant colony algorithm

This paper proposes a method of designing a single neuron adaptive controller, which combines single neuron structure with ant system algorithm. Single neuron structure can implement functions of adaptive and self-learning by adjusting weighting parameters. However, the value of neuron proportional coefficient and the learning rates parameters will affect the performance if they are selected inappropriately. Thus we introduce ant colony algorithm to find optimal solutions for this combinatorial optimization problem. With optimizing neuron proportional coefficient and weighting parameters, the performance makes a further improvement in process control system. The simulation result indicates the validity of the method.
Weight limiting, weight quantisation and generalisation in multi-layer perceptrons

If a multilayer perceptron (MLP) is to be implemented on fixed point hardware then the robustness of the structure to weight quantisation is important. Most work on MLP performance totally neglects this issue and it is only addressed after a network has been trained. It is shown that both generalisation performance and robustness to weight quantisation can be improved by including explicit weight-range limiting into the MLP training procedure. This is illustrated by results of simulations on a speech recognition problem. >
Text Filtering by Boosting Linear Perceptrons

in information retrieval, lack of positive examples is a main cause of poor performance. In this case most learning algorithms may not characteristics in the data to low recall. To solve the problem of unbalanced data, we propose a boosting method that uses linear perceptrons as weak learnrs. The perceptrons are trained on local data sets. The proposed algorithm is applied to text filtering problem for which only a small portion of positive examples is available. In the experiment on category crude of the Reuters-21578 document set, the boosting method achieved the recall of 80.8%, which is 37.2% improvement over multilayer with comparable precision.