Reviewerly/exHarmony huggingface dataset:
Application of machine learning techniques for the estimation of seismic vulnerability in the city of Port-au-Prince (Haiti)

High resolution, city-level exposure databases are important tools for risk planning and damage assessment. However, this kind of information is not always available, especially in developing countries where cities have grown rapidly without excessive planning or oversight, and sending a team of engineers to manually generate such a database by traditional means can be prohibitively costly. In recent years, we have seen tremendous growth in the availability of Earth Observation (EO) data, such as multispectral imagery or LiDAR point clouds, and also the processing capabilities of said data, not just in raw computing power but also in novel statistical modeling and analytical techniques known as Machine Learning (ML) and Computer Vision (CV). The aim of this work is to apply Machine Learning methods to aid in the generation of such databases as a case study in Port-au-Prince, Haiti, and to expose these methods in a comprehensible, reproducible fashion, as a series of open-source programming scripts.
Probabilistic Deterministic Finite Automata and Recurrent Networks, Revisited

Reservoir computers (RCs) and recurrent neural networks (RNNs) can mimic any finite-state automaton in theory, and some workers demonstrated that this can hold in practice. We test the capability of generalized linear models, RCs, and Long Short-Term Memory (LSTM) RNN architectures to predict the stochastic processes generated by a large suite of probabilistic deterministic finite-state automata (PDFA) in the small-data limit according to two metrics: predictive accuracy and distance to a predictive rate-distortion curve. The latter provides a sense of whether or not the RNN is a lossy predictive feature extractor in the information-theoretic sense. PDFAs provide an excellent performance benchmark in that they can be systematically enumerated, the randomness and correlation structure of their generated processes are exactly known, and their optimal memory-limited predictors are easily computed. With less data than is needed to make a good prediction, LSTMs surprisingly lose at predictive accuracy, but win at lossy predictive feature extraction. These results highlight the utility of causal states in understanding the capabilities of RNNs to predict.
Do people want to message Chatbots?: developing and comparing the usability of a conversational vs. menu-based Chatbot in context of new hire onboarding

How should people interact with chatbots? This question has become relevant as chatbots grow in recognition within the field of human-computer interaction. Should chatbots strive to have intelligent and realistic conversations with their users? Or, does a simplified, menu-based approach provide the better experience? To answer these questions, a human-centred design process was used to design, develop, and evaluate the usability of two chatbots in context of new hire onboarding. A conversational chatbot with natural language processing was built using Google Dialogflow, while a technology-limited, menu-based chatbot was built with Landbot. 17 participants were split into three groups to perform a qualitative user test, where group 1 tested both bots, group 2 tested only the conversational bot, and group 3 tested only the menu-based bot. Afterwards, all participants were given a quantitative, Likert-scale survey to measure the usability, intelligence, and satisfaction of the chatbots. The results indicate that users preferred a menu-based over a conversational chatbot experience due to its greater ease of use, less likelihood for errors, convenience of graphical user interface elements, and suitability for scenarios where information needs to be provided rather than requested. Conversational chatbot experiences were found to be more convenient when users had direct questions, although they are more complex to implement when compared to menu-based chatbots.
Sound design through large audience interaction

In collaboration with Volvo Cars, we presented a novel design tool to a large public of approximately three million people at the three leading motor shows in 2017 in Geneva, Shanghai and New York. ...
Optimization Problems in Directed Graph Visualization

Author(s): Besa Vial, Juan Jose | Advisor(s): Goodrich, Michael T | Abstract: Drawing digraphs presents unique challenges that do not occur when drawing undirected graphs. Many digraphs tend to represent transitive relationships; that is they have a flow, and show a progression. When drawing digraphs the drawing style used must attempt to transmit this structural characteristic. In this dissertation, we study several optimization problems that are unique to drawing digraphs. First, we study the complexity the k-Modality problem of planar digraphs. Second, we turn to the practical task of drawing minimum width phylogenetic trees, which are used in the study of evolutionary relationships. Finally, we study a classical graph drawing problem, the One-Sided Crossing Minimization problem, in the novel evolving data setting.An embedding is k-modal if every vertex is incident to at most k pairs of consecutive edges with opposite orientations. We study the k-Modality problem, which asks for the existence of a k-modal embedding of a planar digraph. This combinatorial problem is at the very core of a variety of constrained embedding questions for planar digraphs and flat clustered networks. We characterize the complexity of finding minimum modality embeddings, relate it to other graph drawing problems, both directed and undirected, and present fixed-parameter tractable algorithms for some important families.We then study the problem of drawing small width phylogenetic trees. Phylogenetic trees are xivrooted trees that describe the evolutionary relationships derived from a common ancestor. In these the vertical distance represents the amount of time that passes; thus, the length of the edges is fixed. The Phylogenetic Tree Drawing problem asks for a minimum-width orthogonal upward drawing of a phylogenetic tree. We show that finding such a drawing is NP-hard for binary trees with unconstrained combinatorial order and provide a linear-time algorithm for ordered trees. We also study several heuristic algorithms for the unconstrained case and show their effectiveness through experimentation.Finally, we study a restricted version of the One-Sided Crossing Minimization problem in the evolving data setting. Algorithms that solve this problem attempt to minimize the number of crossings between two adjacent layers when drawing layered graphs. We reduce the problem to that of sorting a list in the evolving data setting and study it from this viewpoint. In this model, a sorting algorithm maintains an approximation to the sorted order of a list of data items while simultaneously, with each comparison made by the algorithm, an adversary randomly swaps the order of adjacent items in the true sorted order. The experiments we perform in this dissertation provide empirical evidence that some quadratic-time algorithms such as insertion sort and bubble sort are asymptotically optimal for any constant rate of random swaps. In fact, these algorithms perform as well as or better than algorithms such as quicksort that are more efficient in the traditional algorithm analysis model.
Conclusions – Entanglements: A New Interpretation and Perspectives in Science and Technology

No AccessThe Physics of Polymer InteractionsOct 2019Conclusions – Entanglements: A New Interpretation and Perspectives in Science and TechnologyJean Pierre IbarJean Pierre IbarSearch for more papers by this authorhttps://doi.org/10.3139/9781569907115.008SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2019Pages: 417-426Print ISBN: 978-1-56990-710-8eISBN: 978-1-56990-711-5 Copyright & PermissionsCopyright by Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
An Experimental Study on the Training Method of Athletes' Physical Fitness in College Physical Education

In view of the problems existing in the physical fitness training of men's basketball team members in colleges and universities, this paper explores the quality level and difference between the game training method and the conventional training way in three aspects of strength, speed and endurance of basketball team members by means of comparative experimental research and reasonable grouping. The results show that the game training method can improve the strength, speed and endurance of the test subjects, and the effect of speed, endurance and explosive force is obvious. It can also effectively improve the enthusiasm and interest of the test subjects to participate in the training. The study also found that this training method is inferior to the traditional one in terms of maximum strength. The results of the experiment can provide some reference for the design of the training scheme of men's basketball team in colleges and universities.
Fractional Anisotropic Diffusion Model for Image Smoothing

Image smoothing is one of the most useful application of image processing. The methods available for image smoothing can be divided into linear based (isotropic) and nonlinear based methods (anisotropic). Gaussian smoothing is one of the most linear model of image smoothing. It is unable to preserve edge details in the image. Partial differential equation(PDE) based methods are non-linear in nature and. They are able to preserve image boundaries. These methods have better trade-off between noise suppression and edge preserving. However, it is found that these methods creates side effects such as blocky effect, speckle effect after processing. The present manuscript presents a novel fractional calculus based approach for image smoothing. The model not only reduces the effect of noise but also preserves edges. The performance of proposed method has been validated by introducing various kinds of noises such as Gaussian, speckle and salt and pepper into image. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Matrix (SSIM) have been used for quantitative evaluations. Higher values of PSNR and SSIM has been achieved by proposed method.
Topic Classification of Islamic Questionand Answer Using Naive Bayes Classifier

Topic classification is one of the most important components in an automatic Islamic question-answering system, which is capable of automatically providing the most relevant answers given a question about the Islamic issue. In our research, the Islamic question-answering system to be built collects existing Islamic questions and answers from trusted online Islamic consultation websites. To speed up the search for finding the appropriate answers, each Q & A entry should be classified into a topic. However, the question-answering system cannot directly adopt the topic classes provided by the online Islamic consultation websites, because different websites use different classifications. Since the number of Q & A entries could reach tenth thousands, an automatic topic classification method is required. In this paper, a naive Bayes classifier is implemented to classify Q & A entries. The classifier gives a satisfying result with 0.88 precision.
A Slippery Slope, or Who Is to Draw the Line?

Is there reason to fear that any regulation of speech, which our laws have always recognized, will lead to the suppression of voices we currently like? Is the cost of having free speech the toleration of hate speech? This chapter relies on leading legal scholars to show that the slippery slope is not an argument, and that invoking it serves to shut down productive debate rather than identify a genuine risk. It is not only possible but necessary to draw a line regarding hate speech, and it is possible to define such speech.
Been using Known and @microdotblog as web ...

Been using Known and @microdotblog as web app on my phone using save to homepage both work well. Rarely find myself turning to micropub clients.
Stochastic Digital Measurement Method and Its Application in Signal Processing

This is the peer-reviewed version of the chapter: Radonjic, A., Sovilj, P., Đorđevic Kozarov, J., Vujicic, V., 2019. Stochastic Digital Measurement Method and Its Application in Signal Processing, in: Advances in Engineering Research. Volume 27. Nova Science Publishers, Hauppauge, pp. 169–190. ISBN 978-1-5361-4803-9
Online copyright enforcement

Arguably the biggest challenge for copyright owners today is how to enforce their rights in a world where the ability to copy and distribute worldwide is instantaneous and nearly costless. Specific topics include the felony streaming loophole, the future of the DMCA’s safe harbor provisions (i.e. the notice-and-takedown system), and site blocking, an enforcement procedure that has become commonplace in many foreign jurisdictions but remains an anathema in the United States.
Clark Family Library: PrezTech Challenge: Bayline and Smith, CHM 270L, 1st Place

The Committee on Libraries and Educational Technology (COLET) is pleased to announce PrezTech Challenge. PrezTech Challenge encourages W&J faculty to enhance student learning by using library and educational technology.
Esploro: One Development Partner and Two Early Adopters Share Details of Their Implementations

The University of Iowa is a development partner for Esploro. Washington State University and the University of Montana are early adopters.This session will provide an overview of the implementation process and talk about some of the rationale used when making decisions on how to use this new research platform. Topics will include: building a campus hierarchy in Alma, gathering data for migration, and development goals. The session will also describe the current state of Esploro, the challenges we have overcome, and those that still lie ahead. Finally, we will cover how we are working with our Office of Research and how the library sees this new role for us on campus.
Examination of 2D Cardiac MRI using Softcomputing Assisted Scheme

This work presents an image inspection procedure to extract and evaluate the heart section from a two-dimensional (2D) Magnetic-Resonance-Image (MRI) collected from HVSMR2016 database. This study implements Social-Group-Optimization (SGO) and Tsallis Entropy (TE) assisted thresholding to enhance the test picture and Active-Contour-Segmentation (ACS) to extract Region-Of-Interest (ROI) from enhanced cardiac MRI. The main merit of the proposed technique is, the MRI is available in the gray scale form, and hence the computation cost is less when it is employed with a suitable heuristic technique. The cardiac MRI considered from the – database consist a three-dimension (3D) picture along with a 3D ground-truth (GT). In this work, the 2D slices are initially extracted from the test image and the GT and the extracted 2D slices are examined with the proposed scheme. After extracting the ROI, the essential similarity-measure (SM) are then calculated with a study between the ROI and GT. The outcome of this technique confirms that, proposed technique helps to achieve an average result $\gt 90$%. In future, it can be considered to examine the other MRIs existing in the literature.
“Mandarin For Newbies” Development Of Mobile Game For Learning Mandarin Language

Creating an application named “Mandarin for Newbies” aims to bring in more exposure and accessibility towards the importance of learning Mandarin Language, through the use of technology (Android SDK, Unity, Adobe Photoshop). This is proven to be of importance through the interviewee’s responses regarding the importance and usage of Mandarin as a language. Mandarin for Newbies application aims to provide an alternate and easier way of learning Mandarin language through the use of smartphones, providing an accessible way to learn, in comparison, with the standards way of learning. The uniqueness of this application is taking the learning aspect through the usage of space repetition, which is, a learning method that takes advantage of increasing intervals of time in between subsequent review of previously learned material in order of taking advantage of a psychological spacing effect. All the while, combining it with entertainment, that is a game. This allows the user to learn as they play. Several tests, including compatibility and user acceptance tests were performed to evaluate the prototype. Although the prototype has not been able to cover the whole aspect of learning, the test results shows a satisfactory result in terms of accessibility and basic functionality.
Joint Viewpoint and Keypoint Estimation with Real and Synthetic Data

The estimation of viewpoints and keypoints effectively enhance object detection methods by extracting valuable traits of the object instances. While the output of both processes differ, i.e., angles vs. list of characteristic points, they indeed share the same focus on how the object is placed in the scene, inducing that there is a certain level of correlation between them. Therefore, we propose a convolutional neural network that jointly computes the viewpoint and keypoints for different object categories. By training both tasks together, each task improves the accuracy of the other. Since the labelling of object keypoints is very time consuming for human annotators, we also introduce a new synthetic dataset with automatically generated viewpoint and keypoints annotations. Our proposed network can also be trained on datasets that contain viewpoint and keypoints annotations or only one of them. The experiments show that the proposed approach successfully exploits this implicit correlation between the tasks and outperforms previous techniques that are trained independently.
Efficient and Transparent Method for Large-Scale TLS Traffic Analysis of Browsers and Analogous Programs

Many famous attacks take web browsers as transmission channels to make the target computer infected by malwares, such as watering hole and domain name hijacking. In order to protect the data transmission, the SSL/TLS protocol has been widely used to defeat various hijacking attacks. However, the existence of such encryption protection makes the security software and devices confront with the difficulty of analyzing the encrypted malicious traffic at endpoints. In order to better solve this kind of situation, this paper proposes a new efficient and transparent method for large-scale automated TLS traffic analysis, named as hyper TLS traffic analysis (HTTA). It extracts multiple types of valuable data from the target system in the hyper mode and then correlates them to decrypt the network packets in real time, so that overall data correlation analysis can be performed on the target. Additionally, we propose an aided reverse engineering method to support the analysis, which can rapidly identify the target data in different versions of the program. The proposed method can be applied to the endpoints and cloud platforms; there are no trust risk of certificates and no influence on the target programs. Finally, the real experimental results show that the method is feasible and effective for the analysis, which leads to the lower runtime overhead compared with other methods. It covers all the popular browser programs with good adaptability and can be applied to the large-scale analysis.
Training corpus jos1M 1.2

The jos1M corpus contains 1 million words of sampled paragraphs from the Gigafida corpus. It is meant to serve as a training corpus for word-level tagging of Slovene. This silver-standard corpus is annotated for morphosyntactic descriptions and lemmas with about one fourth of the more problematic annotations hand-validated.The morphosyntactic descriptions are given in both the JOS/MULTEXT-East framework (http://nl.ijs.si/ME/V6/msd/), as well as in the framework of Universal Dependencies for Slovene (https://universaldependencies.org/treebanks/sl_ssj/index.html).The corpus is available in source TEI XML with the MSDs in English or Slovene and in the derived vertical format, used by CQP and (no)Sketch Engine concordancers and in CONLL-U, used by Universal Dependencies. Note that the corpus does not contain syntactic dependencies. The texts or paragraphs of the jos1M corpus overlap with this of the ssj500k annotated corpus (http://hdl.handle.net/11356/1210), but the latter has been fully manually annotated, as well as having its tokenisation and sentence segmentation corrected. The texts and paragraphs in the jos1M corpus are marked if they are also included in ssj500k, while the CONLL-U is also split into the part that is included in ssj500k and that which is not. The latter can serve as an additional training set for morphosyntactic tagging and lemmatisation to ssj500k.
Sensor-Based Human Activity Mining Using Dirichlet Process Mixtures of Directional Statistical Models

We have witnessed an increasing number of activity-aware applications being deployed in real-world environments, including smart home and mobile healthcare. The key enabler to these applications is sensor-based human activity recognition; that is, recognising and analysing human daily activities from wearable and ambient sensors. With the power of machine learning we can recognise complex correlations between various types of sensor data and the activities being observed. However the challenges still remain: (1) they often rely on a large amount of labelled training data to build the model, and (2) they cannot dynamically adapt the model with emerging or changing activity patterns over time. To directly address these challenges, we propose a Bayesian nonparametric model, i.e. Dirichlet process mixture of conditionally independent von Mises Fisher models, to enable both unsupervised and semi-supervised dynamic learning of human activities. The Bayesian nonparametric model can dynamically adapt itself to the evolving activity patterns without human intervention and the learning results can be used to alleviate the annotation effort. We evaluate our approach against real-world, third-party smart home datasets, and demonstrate significant improvements over the state-of-the-art techniques in both unsupervised and supervised settings.
Methodological Research on Testability Design for Microcontroller

Two methods about design for testability are given. Design for testability about calculator circuit SX1702 which based on 4-bit embeded-system are introduced include module division and ROM content testing, this technique provides both controllability and observability.
Centerline Extraction from 3D Airway Trees Using Anchored Shrinking

Centerline is a frequently applied 1D representation of 3D tubular and tree-like objects. This paper proposes a new curve skeletonization algorithm, which is computationally efficient, guarantees 1-point wide centerlines, and does not generate ‘spurious’ branches. The reported method is specifically targeting segmented intrathoracic airway trees but it is applicable to many other tasks. Our algorithm is based on iterative shrinking combined with branch-end detection and preservation.
A Novel PSO Methodology for Web Documents Retrieval

This paper focuses on retrieval of web documents with improved response time and similarity using particle swarm optimization (PSO) technique.Since the nature of the web data is distributed, volatile and uncertain, an accurate and speedy access is required.Hence a novel approach on evolutionary bio-inspired Swarm Intelligence techniques to optimize search process in Web Information Retrieval systems is proposed and developed.Here, we propose a novel algorithm using basic PSO technique which works on both small CACM and huge RCV1 collections.We apply this on the pre-processed documents to retrieve most similar documents with a very less response time.This paper also reveals a comparative study with the existing method.