Miracle-dz/UniFAR-FTUs huggingface dataset:
Life time improvement of Wireless Body Area Networks using Clustered Voronoi Tessellation

Research in the field of Wireless Body Area Networks has been increasing remarkably due to the advancement of technology in the field of wireless communication and micro-electronics. These miniaturized sensors are being widely used in various fields of health monitoring due to their capability of providing real-time data to the patients. One of the main challenges associated with 3-D WBANs is to do with the lifetime of a WBAN network. The stringent requirements of the sensors used in WBANs such as battery operated devices opens up a challenge to hoard as much energy as possible. A lot of the energy is lost while transmitting the data to the base station. Therefore, we have attempted to reduce the energy dissipation of sensors by grouping them into clusters. We have introduced a clustering algorithm based on the computational geometry concept of Voronoi Tessellation. We adopt the two phase structure of Low Energy Adaptive Cluster Hierarchy (LEACH) protocol and design an enhanced version of LEACH by incorporating Voronoi based clustering. This algorithm is defined by weighing the significance of prioritized nodes and 3-D positioning of the sensors on the body as well. The simulation results demonstrate that our algorithm does indeed increase the network lifetime as compared to its conventional LEACH protocol approach.
Time series forecasting : advances on Theta method

Accurate and robust forecasting methods for univariate time series are critical as the historical data can be used in the strategic planning of such future operations as buying and selling to ensure product inventory and meet market demands. In this context, several competitions for time series forecasting have been organized, with the M3-Competition as the largest. As the winner of M3-Competition, the Theta method has attracted attention from researchers for its predictive performance and simplicity. The Theta method is a combination of other methods, which proposes the decomposition of the deseasonalized time series into two other time series called "theta lines". The first completely removes the curvatures of the data, thus accurately estimating the long-term trend. The second doubles the curvatures to better approximate short-term behavior. Several issues have been raised about the Theta method, even by its originators. They include the number of theta lines, their parameters, weights to combine them, and construction of prediction intervals, among others. This doctorate thesis resolves part of these issues. We derive optimal weights for combine the theta lines, this result is used to derive statistical models which generalizes /approximate the standard Theta method. The statistical methodology is considering for parameter estimation and for compute the prediction intervals. The optimal weights are also used to propose new methods that hold two or more theta lines. Part of proposed methodology is implemented in a package for R-programming language. In an empirical investigation using the M3-Competition data set with more than 3000 time series, the proposed methods/models demonstrated significant accuracy. The study’s primary approach, the Dynamic Optimised Theta Model, outperformed all benchmarks methods, constituting, in all likelihood, the highest-performing method for this data set available in the literature.
Detection Algorithm of Social Community Structure based on Bluetooth Contact Data

In this paper, we consider social network analysis that focuses on community detection. Social networks embed community structure characteristics, i.e., a society can be partitioned into many social groups of individuals, with dense intra-group connections and much sparser inter-group connections. Exploring the community structure allows predicting as well as understanding individual's behaviors and interactions between people. In this paper, based on the interaction information extracted from a real-life Bluetooth contacts, we aim to reveal the social groups in a society of mobile carriers. Focusing on estimating the closeness of relationships between network entities through different similarity measurement methods, we introduce the clustering scheme to determine the underlying social structure. To evaluate our community detection method, we present the evaluation mechanism based on the basic properties of friendship.
SPATIOTEMPORAL DATA MINING : ISSUES , TASKS AND APPLICATIONS

Spatiotemporal data usually contain the states of an object, an event or a position in space over a period of time. Vast amount of spatiotemporal data can be found in several application fields such as traffic management, environment monitoring, and weather forecast. These datasets might be collected at different locations at various points of time in different formats. It poses many challenges in representing, processing, analysis and mining of such datasets due to complex structure of spatiotemporal objects and the relationships among them in both spatial and temporal dimensions. In this paper, the issues and challenges related to spatiotemporal data representation, analysis, mining and visualization of knowledge are presented. Various kinds of data mining tasks such as association rules, classification clustering for discovering knowledge from spatiotemporal datasets are examined and reviewed. System functional requirements for such kind of knowledge discovery and database structure are discussed. Finally applications of spatiotemporal data mining are presented.
Articles in Natural Languages and Artificial Languages

The aim of this paper is to investigate the usage of articles (i.e., definiteness and specificity) in natural (i.e., English, Samoan, and Lillooet Salish) and artificial (i.e., Esperanto, Unish, and Sambahsa) languages and then to suggest an optimal pattern of articles in artificial languages. The observation in this paper is supported by the typology of articles in natural languages, language acquisition, markedness of articles, and historical developments of articles. Then, it proposes that articles are a recent and uncommon grammatical realization and show great variation across languages. Finally, this paper proposes the ‘Minimal Realization Principle’ for article uses in artificial languages. Sunyoung Park (First Author) Department of English Language and Literature, Sejong University 98 Gunja-dong, Gwangjin-gu, Seoul, Korea 143-747 Email: sunpark@sejong.ac. kr Jin-young Tak (Corresponding Author) Department of English Language and Literature, Sejong University 98 Gunja-dong, Gwangjin-gu, Seoul, Korea 143-747 Email: jytak@sejong.ac. kr Received 20 January, 2017; Revised 19 February, 2017; Accepted 2 March, 2017 106 Articles in Natural Languages and Artificial Languages
Semi-supervised classification based on clustering adjusted similarity

Abstract Graph plays crucially important roles in graph-based semi-supervised learning (SSL). Most SSL methods construct a single graph over all instances to explore the manifold structure of instances, and then enforce the smoothness constraint over such graph. However, instances in the real world are not always evenly distributed. Some instances from different classes but close to decision boundary may be close to each other, and thus they are easy to be misclassified. To mitigate this issue, we propose an approach called semi-supervised classification based on clustering adjusted similarity (SSC-CAS). SSC-CAS firstly takes advantage of clustering on both labeled and unlabeled instances to explore the global structure and discrimination of instances, and then quantifies the similarity between pairwise cluster centers. Second, it adjusts the similarity between pairwise instances by multiplying the similarity between centers of clusters they belong to. In this way, if two instances are from different clusters, the similarity between them is reduced; otherwise, unchanged. After that, SSC-CAS performs graph-based semi-supervised classification on the graph constructed by the adjusted similarity. Empirical study on both synthetic and UCI data-sets demonstrates that SSC-CAS not only has better performance than other related comparing methods, but also is robust to the input parameters.
An efficient simulation model of the Hopfield neural networks

A novel simulation model of Hopfield neural networks is formulated. It shows the same behavior of a Hopfield neural network but reduces the simulation time at the expense of increased network complexity. The new bound of the proposed model is derived. It was shown to be much better than that of the traditional model of Hopfield neural networks. <<ETX>>
A Relay System for Supporting the Execution of Context-Aware Robot Services on ROS

Recent robot software platform research focuses on providing intelligent service via abstraction of robot devices. Context-aware techniques are necessary for intelligent robot services, which are based on the perception of environmental information obtained from heterogeneous sensors in IoT environment. Robot Operating System (ROS) provides protocols to operate robot devices. ROS includes functions for abstracting heterogeneous sensors themselves in order to control the robot, however, it lacks the ability to provide context information that the robot can perceive based on environmental information through consistent collection methods. In this paper, we propose a relay system for ROS to provide context-aware robot service. The proposed system makes it possible for ROS to control and provide context-aware robot services with relay of an external context-aware system and ROS. In experiments, we demonstrate procedures that robot services abstracted from ROS and an external context-aware system works together based on the proposed system.
Can Initial Narrative VisualizationTechniques and Storytelling help Engage Online-Users with Exploratory Information Visualizations?

We present the results of three web-based field experiments, in which we evaluate the impact of using initial narrative visualization techniques and storytelling on user-engagement with exploratory information visualizations. We conducted these experiments on a popular news and opinion outlet, and on a popular visualization gallery website. While datajournalism exposes visualizations to a large public, we do not know how effectively this public makes sense of interactive graphics, and in particular if people explore them to gain additional insight to that provided by the journalists. In contrast to our hypotheses, our results indicate that augmenting exploratory visualizations with introductory ‘stories’ does not seem to increase user-engagement in exploration.
An interior water is essential for maintaining the structure of FKBP12

1. Department of Biochemistry and Biophysics and Howar d Hughes Medical Institute, 242 Anatomy Chemistry, University of Pennsylvania, Phil adelphia, PA 19104. 2. Department of Chemistry, University of Pennsylvania , 231 South 34th Street, Philadelphia, PA 19104. 3. Department of Chemical and Biomolecular Engineering , University of Pennsylvania, 220 South 33rd Street, Philadelphia, PA 19104. 4. Current Address: 905 Furnas Hall, Department of Che mical and Biological Engineering, SUNY, Buffalo, NY 14260.
A New Segmentation Algorithm for Prostate Boundary Detection in 2D Ultrasound Images

ii I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. Abstract Prostate segmentation is a required step in determining the volume of a prostate, which is very important in the diagnosis and the treatment of prostate cancer. In the past, radiologists manually segment the two-dimensional cross-sectional ultrasound images. Typically, it is necessary for them to outline at least a hundred of cross-sectional images in order to get an accurate estimate of the prostate's volume. This approach is very time-consuming. To be more efficient in accomplishing this task, an automated procedure has to be developed. However, because of the quality of the ultrasound image, it is very difficult to develop a computerized method for defining boundary of an object in an ultrasound image. The goal of this thesis is to find an automated segmentation algorithm for detecting the boundary of the prostate in ultrasound images. As the first step in this endeavour, a semi-automatic segmentation method is designed. This method is only semi-automatic because it requires the user to enter four initialization points, which are the data required in defining the initial contour. The discrete dynamic contour (DDC) algorithm is then used to automatically update the contour. The DDC model is made up of a set of connected vertices. When provided with an energy field that describes the features of the ultrasound image, the model automatically adjusts the vertices of the contour to attain a maximum energy. In the proposed algorithm, Mallat's dyadic wavelet transform is used to determine the energy field. Using the dyadic wavelet transform, approximate coefficients and detailed coefficients at different scales can be generated. In particular, the two sets of detailed coefficients represent the gradient of the smoothed ultrasound image. Since the gradient modulus is high at the locations where edge features appear, it is assigned to be the energy field used to drive the DDC model. The ultimate goal of this work is to develop a fully-automatic segmentation algorithm. Since only the initialization stage requires human supervision in the proposed semi-automatic initialization algorithm, the task of developing a fully-automatic segmentation algorithm is reduced to designing a fully-automatic initialization process. Such a process is introduced in this thesis. In this work, the contours …
Towards Scalable Graph Analytics on Time Dependent Graphs

Edge parameters in time dependent graphs vary as a function of time. Graph analytics in such context takes into consideration the fact that the actual view of the graph changes with time. As an example, single source shortest path (SSSP) in time dependent graphs analyses optimal arrival time with varying start time of graph traversal and/or varying waiting time at each node during traversal. Analytics in time dependent graphs have been applied mostly sequentially in various applications[1, 2]. In this work, we evaluate the graph analytics in large distributed graphs on HPC systems.
Color Features and Color Spaces Applications to the Automatic Image Annotation

Nowadays images play a crucial role in different fields such as medicine, advertisement, education and entertainment. Describing images content and retrieving them are important fields in image processing. Automatic image annotation is a process which produces words from a digital image based on the content of this the image by using a computer. In this chapter, after an introduction to neighbor voting algorithm for image annotation, we discuss the applicability of color features and color spaces in automatic image annotation. We discuss the applicability of three color features (color histogram, color moment and color Autocorrelogram) and three color spaces (RGB, HSI and YCbCr) in image annotation. Experimental results, using Corel5k benchmark annotated images dataset, demonstrate that using different color spaces and color features helps to select the best color features and spaces in image annotation area.
Clustering, dimensionality reduction, and side information

Recent advances in sensing and storage technology have created many high-volume, high-dimensional data sets in pattern recognition, machine learning, and data mining. Unsupervised learning can provide generic tools for analyzing and summarizing these data sets when there is no well-defined notion of classes. The purpose of this thesis is to study some of the open problems in two main areas of unsupervised learning, namely clustering and (unsupervised) dimensionality reduction. Instance-level constraint on objects, an example of side-information, is also considered to improve the clustering results. Our first contribution is a modification to the isometric feature mapping (ISOMAP) algorithm when the input data, instead of being all available simultaneously, arrive sequentially from a data stream. ISOMAP is representative of a class of nonlinear dimensionality reduction algorithms that are based on the notion of a manifold. Both the standard ISOMAP and the landmark version of ISOMAP are considered. Experimental results on synthetic data as well as real world images demonstrate that the modified algorithm can maintain an accurate low-dimensional representation of the data in an efficient manner. We study the problem of feature selection in model-based clustering when the number of clusters is unknown. We propose the concept of feature saliency and introduce an expectation-maximization (EM) algorithm for its estimation. By using the minimum message length (MML) model selection criterion, the saliency of irrelevant features is driven towards zero, which corresponds to performing feature selection. The use of MML can also determine the number of clusters automatically by pruning away the weak clusters. The proposed algorithm is validated on both synthetic data and data sets from the UCI machine learning repository. We have also developed a new algorithm for incorporating instance-level constraints in model-based clustering. Its main idea is that we require the cluster label of an object to be determined only by its feature vector and the cluster parameters. In particular, the constraints should not have any direct influence. This consideration leads to a new objective function that considers both the fit to the data and the satisfaction of the constraints simultaneously. The line-search Newton algorithm is used to find the cluster parameter vector that optimizes this objective function. This approach is extended to simultaneously perform feature extraction and clustering under constraints. Comparison of the proposed algorithm with competitive algorithms over eighteen data sets from different domains, including text categorization, low-level image segmentation, appearance-based vision, and benchmark data sets from the UCI machine learning repository, shows the superiority of the proposed approach.
Analysis and control of diffusion processes in networks

The propagation of a characteristic through a network is the subject of study of many scientific fields. Epidemics, viral marketing or information propagation through a social network are among the many examples of real phenomena modeled by the evolution of a characteristic propagating through the edges of a network. Thus, being capable of acting on these diffusion processes is of capital interest for many fields. Despite the large literature about the theoretical aspects of diffusion processes, and more specifically the discovery of an epidemic threshold under which the propagation is not sustainable, a number of practical limitations prevent the use of these studies in real-life scenarios. In this thesis, we work on reducing the distance separating theory from practice, following three distinct research directions: the generalization of theoretical results to a larger, and more realistic, class of diffusion models, the development of efficient dynamic control measures utilizing the structure of network to its advantage, and, finally, the definition of new mathematical tools bridging the gap between spatial and network approaches in epidemiology. More specifically, our work allows the rigorous analysis of the behavior of a network's characteristics when it converges, in a structural sense, to a given metric space, and could open the way to the application of control strategies on networks to spatial and macroscopic information (e.g. transportation or demographic data) about the contact network in a given population.
Reader characteristics, behavior, and success in fiction book search

We examined the search behaviors of diverse fiction readers in different search scenarios. The aim was to understand how fiction readers with varied reading preferences are selecting interesting novels in library catalogs. We conducted a controlled user study with 80 participants. Two reader groups were elicited according to similar reading preference patterns. The readers enjoyed the entertainment, escape, and comfort that reading as a pleasurable activity offered. The aesthetic readers valued the artistic and aesthetic pleasures, widening vocabulary, and ability to express oneself through fiction books. We compared the search queries and search actions between the 2 reader groups. Our results demonstrated that preference patterns were associated with readers' search behavior, that is, the number of viewed search result pages, opened book pages, dwell time on book pages, and the type of search queries. Based on the findings, we present 3 search tactics for fiction books in library catalogs: i) focused querying, ii) topical browsing, and iii) similarity‐based tactic. The most popular search tactic in each search scenario was “focused querying” with known author in both reader groups.
Voice similarity — a comparison between judgements by human listeners and automatic voice comparison

Comparison between the way human listeners judge voice similarity and how state-of-the art GMM-UBM systems for voice recognition compare voices is a little explored area of research. In this study groups of informants judged the similarity between voice samples taken from a set of fairly similar male voices that had previously been used in a voice line-up experiment. The result from the listening tests was then compared to the scores from a UBM-GMM automatic voice comparison system, built on the Mistral LIA_RAL open source platform. The results show a correlation between scores obtained from the automatic system and the judgements by the listeners. Listeners are, however, more sensitive to language dependent parameters or idiosyncratic phonetic features such as speaking tempo, while the system only bases its likelihood ratios on spectral similarities, i.e. timbre.
Synchronization induced by paced atrial subthreshold stimulation on the sinus node activity in animal experiment

INTRODUCTION Synchronization is the well-known phenomenon that indicates adjustment of frequencies of weakly interacting self-sustained periodic oscillators [1]. Besides the physics environment, synchronization is often encountered even in physiological systems exhibiting oscillatory behaviour. Above all others, the heart, the biological oscillator par excellence, has been object of a number of investigations. At the cellular level interesting experiments were carried out on spontaneously beating aggregates of cardiac cells from embryonic chicken heart stimulated with single impulse or impulses of different amplitudes and frequencies [2]. These experiments evidenced the interaction between stimuli and cardiac cells activity in terms of phase resetting (single stimulus) and synchronization (train of impulses). Aim of this study was to verify whether the electrical field generated inside the right atrium by subthreshold electrical impulses (impulses unable to induce cells activation) may condition the discharge rate of the sinus node cells [3].