Reviewerly/exHarmony huggingface dataset:
Speech Based Interaction System Using DNN and i-vector

In this paper, a speech based interaction system using Deep Neural Network (DNN) and i-vector based DNN approaches are proposed. In DNN based approach, Mel-frequency cepstral coefficients (MFCC) features are extracted from the speech signal and it is directly given to DNN. In i-vector based DNN approach, DNN is trained using i-vector which is formed from Gaussian Mixture Model-Universal Background Model (GMM-UBM). For both approaches, the performance of the system is obtained in the form of confusion matrix and compared. In addition to that, GMM-UBM based approach is also compared with the proposed work. MFCC is used for representing the characteristics of the speech and auto encoder is used for classification purpose. It uses stacked two auto encoder layers and one soft max layer. The proposed system achieves improvement in performance when increasing the number of hidden units and the input dimension of MFCC features. The proposed work is to develop ASR system for isolated words in Tamil language and the experiments are conducted for speaker independent case. The results demonstrated that i-vector based DNN approach provides 100% recognition rate for 17 classes with 20 hidden units in each of the 2 layers. The dimension of i-vector is 100.
The Use of Digital Devices in Marketing Library Products in an Inclusively Engaged Academic Library

This chapter identifies critical issues in the use of digital devices, opportunities, and challenges in marketing library products and services in academic libraries. It examines empirical literature as a review method on the use of digital devices in engaged academic libraries in marketing library products and services. From the findings, it was revealed that library products and services are underutilized because of lack of marketing strategies and programs to create awareness of available resources and services in academic libraries. This resulted in negative attitudes and behaviors towards reading culture among library users. Digital devices transform and widen the reach, management, storage, and delivery of information. They make library products and services more interactive and maximize utilization of library products and services. The study shows that digital devices influenced attitudes, behaviors, and perceptions of library users positively, which shows that there is a great need to employ digital devices in marketing library products and services in academic libraries.
TabularVis: An Interactive Relationship Visualization Tool Supported by Optimization and Search Algorithms

Visualizing relationships is becoming more and more essential nowadays, so we need efficient visualization solutions to meet the requirements of the users. One of its forms is when we use tables to describe the relationships and their associated values. In this work, we present TabularVis, an interactive, web client based application for visualizing relationships based on tabular data. Our visualization technique was inspired by the Table Viewer web application of the popular genome visualization software called Circos. For our solution, we only kept its central idea, and we built our new extensions and features around it. We also propose an automatic sorting solution to reduce the number of intersections in the diagram along with an edge bundling technique to improve the clarity of it. In the paper, we present the features of our visualization, which mainly focuses on improving the readability of the diagram in those cases when the difference between the values of the table is extremely small or large. Also, we compared our solution with Circos in a survey to prove the effectiveness of our proposed features. Beside this, we compared the sorting method with the edge bundling technique as well to confirm their performance. The result of the surveys is also presented in the paper. Furthermore, we provide running time measurements as well to show the effectiveness of the presented methods.
A genetic algorithm for discriminative graph pattern mining

In this paper, we introduce a new algorithm EWALDIS (Evolution- and random WALk-based algorihm for DIScriminative patterns) for mining discriminative patterns on the local level of dynamic attributed multigraphs. It uses a random walk-based approach [1] and a genetic algorithm to mine inexact patterns from the perspective of attributes and also times-tamps. This also means that it does not require the discretization of the timestamps to be able to find some patterns. Moreover, by utilizing sampling techniques, the algorithm does not have to traverse the whole search space. EWALDIS is an improved version of WALDIS [7].
DFRWS IoT Forensic Challenge Report 5

In this chapter, we report on the findings of our group’s analysis of the Digital Forensic Research Workshop (DFRWS) IoT Forensic Challenge (2018–2019).
Exploring color variations for vector graphics

We propose a novel and intuitive method for exploring recoloring variations of vector graphics. Compared with existing methods, ours is specifically tailored for vector graphics, where color distributions are sparser and are explicitly stored using constructs like solid colors or gradients, independent from other semantical and spatial relationships. Our method tries to infer some of them before formulating color transfer as a transport problem between the weighted color distributions of the reference and the target vector graphics. We enable creative exploration by providing fine-grain control over the resulting transfer, allowing users to modify relative color distributions in real-time.
Analysis of the (μ/μI,λ)-σ-Self-Adaptation Evolution Strategy with Repair by Projection Applied to a Conically Constrained Problem

A theoretical performance analysis of the (μ/μI, λ)σ-self-adaptation evolution strategy (σSA-ES) is presented considering a conically constrained problem. Infeasible offspring are repaired using projection onto the boundary of the feasibility region. Closed-form approximations are used for the one-generation progress of the evolution strategy. Approximate deterministic evolution equations are formulated for analyzing the strategy's dynamics. By iterating the evolution equations with the approximate one-generation expressions, the evolution strategy's dynamics can be predicted. The derived theoretical results are compared to experiments for assessing the approximation quality. It is shown that in the steady state the (μ/μI, λ)σSA-ES exhibits a performance as if the ES were optimizing a sphere model. Unlike the nonrecombinative (1, λ)-ES, the parental steady state behavior does not evolve on the cone boundary but stays away from the boundary to a certain extent.
On Exactly Learning Disjunctions and DNFs Without Equivalence Queries

In this paper we address the issue of exactly learning boolean functions. The notion of exact learning introduced by [2] endows a learner with access to oracles that can answer two types of queries: membership queries and equivalence queries, in which however, equivalence queries are unrealistically strong and cannot be really carried out. Thus we investigate exact learning without equivalence queries and provide some positive results of exactly learning disjunctions and DNFs as follows (without equivalence queries). We present a general result for exactly properly learning disjunctions if probability mass of negative inputs and probabilities that all bits are assigned to 0 and 1 are all positive. Moreover, with at most n membership queries, we can reduce sample and time complexity. We present a general result for exactly properly learning the class of s-DNFs with random examples, and obtain two concrete results under uniform distributions. First, the class of l-term s-DNFs with $$l_1$$ $$\log 2l$$ -terms can be exactly learned using $$O(2^{s+l_1} s\ln n)$$ examples in time linear in $$((\frac{2en}{s})^s,2^{s+l_1}s\ln n)$$ . Second, if assume each literal appears in at most d terms, the class of l-term s-DNFs with $$l_1$$ $$\log 4sd$$ -terms can be exactly learned using $$O(2^{s+l_1}\cdot e^{\frac{l}{sd}} s\ln n)$$ examples in time linear in $$((\frac{2en}{s})^s,2^{s+l_1}\cdot e^{\frac{l}{sd}}s\ln n)$$ .
How We Harnessed the Potential of UAV Imagery in the Classroom through VR and Other Technologies

Moving from a project workflow of 3d modelling to print, 3d print, milling, and model building, the Knowlton School has found emerging technologies that can be integrated into the curriculum and student experience.UAV scans can bring accurate site plans and models into the creative process while virtual and augmented reality can provide a more immersive way to interact with the project.This paper provides the steps to identify what technologies fit your environment and how to move towards adoption among faculty and students.Augmented reality.
ELECTRICAL ENGINEER KAZUO MORITA AND HYDROPOWER—HISTORY BEFORE THE DEVELOPMENT OF COLONIAL KOREA—

The Nippon Chisso Hiryo K.K. company constructed the Pujon River Hydropower Plant in colonial Korea during the 1920s.The plant was very large for its time, even when compared to those built in the Japanese Empire.It later became an important trigger for Korean industrialization in the 1930s.The construction plan was proposed by electrical engineer Kazuo Morita (1872 -1966).Although Morita conducted several large and similarly important hydropower projects in Japan, the scope of his work has not received a great deal of attention.This study therefore follows Morita's work to clarify three characteristics that he acquired during that time of fierce electric power development boom in Japan (between World War I and the 1920s).This research determined the kind of experience Morita gained and what development concepts he maintained in the years before planning the giant Pujon River plant.First, he expanded his work assignments through the personal contacts he gained while developing giant hydropower plants for the Anglo-Japanese Hydro-Electric Co. during the late Meiji Period.Second, he was earnestly oriented toward and held a positive attitude about new technology.Third, he developed a technological system centered on electrical engineers and
Feature selection method for image steganalysis based on weighted inner-inter class distance and dispersion criterion

In order to improve the detection of hidden information in signals, additional features are considered as inputs for steganalysers. This research study proposes a feature selection method based on Weighted Inner-Inter class Distance and Dispersion (W2ID) criterion in order to reduce the steganalytic feature dimensionality. The definition of W2ID criterion and an algorithm determining the weight for the W2ID criterion based on the frequency statistical weighting method are proposed. Then, the W2ID criterion is applied in the decision rough set α-positive domain reduction, producing the W2ID-based feature selection method. Experimental results show that the proposed method can reduce the dimension of the feature space and memory requirements of Gabor Filter Residuals (GFR) feature while maintaining or improving the detection accuracy.
Designation of Candidate Solutions in Differential Evolution Based on Bandit Algorithm and its Evaluation

We propose UCT-Grid Area Search (UCT-GAS), which is an efficient optimization method that roughly estimates specific values in areas, and consider exploration and exploitation in optimization problems. This approach divides the search space and imagines it to be a multi-armed bandit, which enables us to use bandit algorithms to solve mathematical programming problems. Although the search speed is fast than other search algorithm like differential evolution, it might converge to a local solution. In this study, we improve this algorithm by replacing its random search part with differential evolution after several searches. Comparative experiments confirmed the search ability of the optimal solution, and our method benefits by showing that it avoids falling into a local solution and that its search speed is fast.
Technology Procurement in Election Systems

Election administrators have a variety of responsibilities that extend beyond the management of election day. They are also responsible for the procurement of the voting systems that will be used by the public. There are a variety of approaches that can be utilized to make this process more efficient. Focusing on Florida, the author discusses the process that was utilized to innovate technology procurement and provides recommendations for the profession to close the information gap that exists between procurement experts, election officials, and vendors.
Genetically-Modified K-Medoid Clustering Algorithm for Heterogeneous Data Set

In recent days, researchers are doing research studies for clustering of data which are heterogeneous in nature. The data generated in many real-world applications like data form IoT environments and big data domains are heterogeneous in nature. Most of the available clustering algorithms deal with data in homogeneous nature, and there are few algorithms discussed in the literature to deal the data with numeric and categorical nature. Applying the clustering algorithm used by homogenous data to the heterogeneous data leads to information loss. This chapter proposes a new genetically-modified k-medoid clustering algorithm (GMODKMD) which takes fused distance matrix as input that adopts from applying individual distance measures for each attribute based on its characteristics. The GMODKMD is a modified algorithm where Davies Boudlin index is applied in the iteration phase. The proposed algorithm is compared with existing techniques based on accuracy. The experimental result shows that the modified algorithm with fused distance matrix outperforms the existing clustering technique.
University of the Arts London London College of Communication

User Experience Design (UXD) globally remains in the grip of a human-centred view of the world. One in which human actions are predictable and are reliably guided towards consistent outcomes through careful manipulation of signs and signifiers. Human needs and requirements are seen as of overriding importance in the creation of situations where people interact with computers. On MA User Experience Design at London College of Communication we are challenging many of these assumptions through collaborative practices with a wide range of industry partners.
What Are You Reading: A Big Data Analysis of Online Literary Content

The combination of Internet and literature gives rise to the online literature, which is sharply distinct from traditional literature. Online literary content has become an important and active Internet resource and has now been formalized into a large and rapidly growing industry. Despite the great success of online literature platforms, little is known about the structure of literary content and patterns and characteristics in the creation and consumption of online literary content. In this paper, we conducted a deep analysis of online literary content on leading online literature platforms based on the real big data. From the crawled data, we obtained a set of interesting results, which describes the content-related and writing-related characteristics of online literature content. Our analysis results will shed light on the current status of online literary content and reveal some interesting research topics.
Pedestrian Detection and Trajectory Estimation in the Compressed Domain Using Thermal Images

Since a few decades, the Unmanned Aerial Vehicles (UAVs) are considered precious tools for different military applications such as the automatic surveillance in outdoor environments. Nevertheless, the onboard implementation of image and video processing techniques poses many challenges like the high computational cost and the high bandwidth requirements, especially on low-performance processing platforms like small or medium UAVs. A fast and efficient framework for pedestrian detection and trajectory estimation for outdoor surveillance using thermal images is presented in this paper. First, the detection process is based on a conjunction between contrast enhancement techniques and saliency maps as a hotspot detector, on Discrete Chebychev Moments (DCM) as a global image content descriptor and on a linear Support Vector Machine (SVM) as a classifier. Second, raw H.264/AVC compressed video streams with limited computational overhead are exploited to estimate the trajectories of the detected pedestrians. In order to simulate suspicious events, six different scenarios were carried out and filmed using a thermal camera. The obtained results show the effectiveness and the low computational requirements of the proposed framework which make it suitable for real-time applications and onboard implementation.
Supporting Abstraction Skills Using Augmented Reality?

We investigated the potential of augmented reality (AR) to enable visualization of abstract concepts and present the first iteration of a teaching experiment that evaluates the use of AR as support for abstraction skills. Students were confronted with the task to present and explain information to different groups of stakeholders at the example of a coffee machine. Results show that students find it helpful to have a visual app-prototype and especially one that can be disassembled in different levels. The main goal was to sensitize students for the need to think about and to abstract information for certain roles and perspectives.
Adapting Covariate Shift for Legal AI

One of the fundamental assumptions with any machine learning (ML) system is that training data comes from the same distribution as the real world data. However, in m real-world applications, this important assumption is often violated including legal applications. A scenario where training and test samples follow different input feature distributions is known as covariate shift. This shift in data is often responsible for the deterioration in predictive performance of machine learning systems. The motivation of this research is to study the effect of covariate shift on deep learning systems used in legal research. In this work, we propose a unified framework to detect covariate shift on legal search queries impacting legal AI systems and formulate a strategy to adapt this shift on a periodic basis.
Estimation of marginal generalized linear model with subgroup auxiliary information

Marginal generalized linear model (MGLM) is a popular instrument for studying longitudinal and cluster data. This paper investigates an estimator for regression coefficients in MGLM, which incorporates subgroup auxiliary information. We propose to use the conditional expectation of the response in each subgroup as auxiliary information, and combine that information with the estimating equations of the quadratic inference function (QIF) method based on the framework of generalized method of moments (GMM). The asymptotic normality and test statistics of the proposed estimator are established, which indicate that the estimator of the proposed estimator is more efficient than the QIF one. Simulation studies are carried out to examine the performance of the proposed method under finite sample sizes, and an education data is used to illustrate our approach.
Large-Scale Nonlinear Variable Selection via Kernel Random Features

We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-based variable selection method applicable to large datasets. It sidesteps the typical poor scaling properties of kernel methods by mapping the inputs into a relatively low-dimensional space of random features. The algorithm discovers the variables relevant for the regression task together with learning the prediction model through learning the appropriate nonlinear random feature maps. We demonstrate the outstanding performance of our method on a set of large-scale synthetic and real datasets. Code related to this paper is available at: https://bitbucket.org/dmmlgeneva/srff_pytorch .
Quantum lock on dark states

We propose quantum protection circuit (quantum lock), based on dark states of ensembles of two-level atoms in optical cavity. The secret key is the splitting of atoms into pairs, and publicly accessible part of the lock is the tensor product of EPR singlets, corresponding to the given splitting. To open the lock one must move synchronously pairs of atoms from the correct splitting to the other cavity; the lock will open if atoms do not emit photons. This scheme has perfect secrecy: it is impossible to hack it, even with effective solutions of any classical computational problems, in contrast to the RSA scheme. The method of obtaining dark states through Stark shift of atomic excitation energy is also proposed. This scheme makes possible to create secret keys of a few tens of atoms that is sufficient for the most practical applications.
Life cycle analysis of study projects in “projects cloud” information system

The life cycles of projects implemented by students are studied. The data on the activities on projects are unloaded from the IT-service “Project Cloud”. To study the life cycle of projects, only completed projects were selected, where all tasks were marked as performed. For each project, its life cycle was plotted, then these graphs were compared to the standard life cycle schedule, and a generalized graph of the life cycle was constructed. Then, the clustering of projects with the help of the DidactoStudio program was carried out using the k-means algorithm and the Kohonen self-organizing map method. For clustering, we took the following project data: the duration of the project, the number of tasks in the project, the minimum duration of the project tasks, the maximum duration of the project tasks. The results of conducted life cycle analysis made it possible to evaluate the effectiveness of students work on the projects.
Authorship Attribution and <i>the New Oxford Shakespeare</i>: Some Facts and Misconceptions

In his mid-twentieth-century classic, Straight and Crooked Thinking, the eminent psychologist Robert H. Thouless exposed various kinds of faulty argument, but added that “there are routes to error ...
Utilisation of Linguistic and Paralinguistic Features for Academic Presentation Summarisation

We present a method for automatically summarising audio-visual recordings of academic presentations. For generation of presentation summaries, keywords are taken from automatically created transcripts of the spoken content. These are then augmented by incorporating classification output scores for speaker ratings, audience engagement, emphasised speech, and audience comprehension. Summaries are evaluated by performing eye-tracking of participants as they watch full presentations and automatically generated summaries of presentations. Additional questionnaire evaluation of eye-tracking participants is also reported. As part of these evaluations, we automatically generate heat maps and gaze plots from eye-tracking participants which provide further information of user interaction with the content. Automatically generated presentation summaries were found to hold the user’s attention and focus for longer than full presentations. Half of the evaluated summaries were found to be significantly more engaging than full presentations, while the other half were found to be somewhat more engaging.
A Study of the Information Behavior of Chinese Youth, Focusing on the Relevance between Media Access and Status Quo Evaluation

This study focuses on Chinese young people and examines their information behavior and its effects. This study is based on the results of a questionnaire survey conducted by several universities in seven regions of mainland China in 2012 . Following an examination of the status of media use and the results of “Lifestyle-value Test” and analyzing the correlation between the two, the study further explores the effects of daily media use on the international orientation, the evaluation of the government, and the life satisfaction of young Chinese.