There have been many studies on understanding data visualizations regarding general users. However, we have a limited understanding of how people with ADHD comprehend data visualizations and how it might be different from the general users. To understand accessible data visualization for people with ADHD, we conducted a crowd-sourced survey involving 70 participants with ADHD and 77 participants without ADHD. Specifically, we tested the chart components of color, text amount, and use of visual embellishments/pictographs, finding that some of these components and ADHD affected participants’ response times and accuracy. We outlined the neurological traits of ADHD and discussed specific findings on accessible data visualizations for people with ADHD. We found that various chart embellishment types affected accuracy and response times for those with ADHD differently depending on the types of questions. Based on these results, we suggest visual design recommendations to make accessible data visualizations for people with ADHD.

In this paper, we investigate the Antenna Orientation (AO) and Antenna Orientation and Power Assignment (AOPA) problems concerning symmetric connectivity in Directional Wireless Sensor Networks (DWSNs) where each sensor node is equipped with 2 ≤ k ≤ 5 directional antennas having beamwidth θ ≥ 0. The AO problem for DWSNs is closely related with the well-known Euclidean Degree-Bounded Minimum Bottleneck Spanning Tree (EBMBST) problem where different cases for the degree bound have been studied. While current works on DWSNs focus on solving each case of k(=2, 3, 4) separately, we propose a uniform approach for the AO problem that yields constant-factor approximation algorithms for the AO as well as the EBMBST problem where the degree bound is between 2 and 4. Our method achieves the same constant factors. For the AOPA problem, to the best of our knowledge, our paper provides the first results concerning this problem. We show that the problem is NP-hard when 2 ≤ k ≤ 4. We also establish the first constant-factor approximation algorithms for the problem. Finally, we perform some simulations to understand the practical performance of our algorithms.

Consider a set S of nodes in the plane such that the unit-disk graph G(S) spanning all nodes is connected. Each node in S is equipped with a directional antenna with beam-width θ = π/2. The objective of the directional antenna orientation (AO) problem concerning symmetric connectivity is to determine an orientation of the antennas with a minimum transmission power range r = O(1) such that the induced symmetric communication graph is connected. Another related problem is the AO and power assignment (AOPA) problem whose objective is to assign each node u ∈ S an orientation of its antenna as well as a range r(u) such that the induced symmetric communication graph is connected and the total power assigned Σu∈Sr(u)βis minimized, where β ≥ 1 is the distance-power gradient (typically 2 ≤ β ≤ 5). In this paper, we study both problems by first proving that they are NP-hard. We then propose two algorithms for the AO problem that orient the antennas to yield a symmetric connected communication graph where the transmission power ranges are bounded by 9 and 7, respectively, which are currently the best results for this problem. We also propose constant-factor approximation algorithms for the AOPA problem where our constants are smaller than Aschner et al's. Finally, we study the performance of our algorithms through simulations.

Consider a set S of nodes in the plane such that the unit-disk graph G(S) spanning all nodes is connected. Each node in S is equipped with a directional antenna with beam-width θ = π/2. The objective of the Directional Antenna Orientation (AO) problem concerning symmetric connectivity is to determine an orientation of the antennas with a minimum transmission power range r = O(1) such that the induced symmetric communication graph is connected. Another related problem is the Antenna Orientation and Power Assignment (AOPA) problem whose objective is to assign each node u ϵ S an orientation of its antenna as well as a range r(u) such that the induced symmetric communication graph is connected and the total power assigned ΣuϵSr(u)β is minimized, where β ≥ 1 is the distance-power gradient (typically 2 ≤ β ≤ 5). In this paper, we study both problems by first proving that they are NP-hard. To the best of our knowledge, these NP-hardness results have not been obtained before in the literature. We then propose an algorithm for the AO problem that orients the antennas to yield a symmetric connected graph where the transmission power range is bounded by 9 which is currently the best result for this problem. (Previous bound for this problem is 14√2 by Aschner et al). We also propose a constant-approximation algorithm for the AOPA problem where our constant is smaller than the one in Aschner et al's algorithm.

In this paper, we study the Antenna Orientation (AO) problem concerning symmetric connectivity in Directional Wireless Sensor Networks. We are given a set of nodes each of which is equipped with one directional antenna with beam-width θ = 2π/3 and is initially assigned a transmission range 1 that yields a connected unit disk graph spanning all nodes. The objective of the problem is to compute an orientation of the antennas and to find a minimum transmission power range r = O(1) such that the induced symmetric communication graph is connected. We propose an algorithm that orients the antennas to yield a symmetric connected graph where the transmission power range is bounded by 6 which is currently the best result for this problem. We also study the performance of our algorithm through simulation.

Support Vector Data Description (SVDD) is known as one of the best kernel-based methods for one-class classification problems. SVDD requires fully labelled data sets. However, in reality, an abundant amount of data can be easily collected, while the labelling process is often expensive, time-consuming, and error-prone. Therefore, partially labelled data sets are popular and easy to obtain. In this paper, we propose a semi-supervised learning method, Fuzzy Entropy Semi-supervised SVDD (FS3VDD), to extend SVDD to cope with partially labelled data sets. The learning model employs fuzzy membership and fuzzy entropy to help the labelling of the unlabeled data.

Since the emergence in the popularity of XML for data representation and exchange over the Web, the distribution of XML documents has rapidly increased. It has become a challenge for researchers to turn these documents into a more useful information utility. In this paper, we introduce a novel clustering algorithm PCXSS that keeps the heterogeneous XML documents into various groups according to their similar structural and semantic representations. We develop a global criterion function CPSim that progressively measures the similarity between a XML document and existing clusters, ignoring the need to compute the similarity between two individual documents. The experimental analysis shows the method to be fast and accurate.