Reviewerly/exHarmony huggingface dataset:
Indonesian Sign Language Letter Interpreter Application Using Leap Motion Control based on Naïve Bayes Classifier

Abstract Most deaf children are born into hearing families and the majority do not begin their Sign Language learning before age 3-6 in school. As children learning Sign Language from infancy show higher language proficiency than those who do not, it underlines the importance of hearing parents learning how to sign. The recent innovation in the technology and introduction of new gesture control based devices, like leap motion controller. Technology that can track finger and hand gestures accurately at very low cost. The research method in Naïve Bayes algorithm, in order to classify 26 alphabet letter of Indonesian sign language including letter J and Z which using gesture for describe it. The Leap Motion controller is a consumer gesture sensor aimed to augment a user’s interactive experience with their computer and it was utilized as an interface for hand motion tracking without the need of wearing any external instruments. It use for derive feature data from the deaf children gesture recording to set data in application. The accuracy level of the test result reached 96%.
Automatic Geohazard Detection Using Neural Networks

Abstract In this paper, we demonstrate the potential of neural networks in the automation of shallow geohazard detection and identification on seismic images. We discuss technical considerations and method limitations. The method used in this paper trains a neural network prediction model to automatically detect features in seismic images by estimating model parameters from a large set of input training images that have been manually interpreted. In this case, the independent variable is the seismic image and the dependent variable is the human interpretation. We used a separate test data set that was not used in training the model to validate our results. The novel approach and workflow presented in this paper is a significant advancement in geohazard detection and identification projects. The time taken to complete such a project using a conventional approach is significantly reduced – our model interprets entire seismic volumes in seconds with consistency, minimal human input and comparable accuracy.
Report of the SIGCSE committee on computing education in liberal arts colleges

research-article Share on Report of the SIGCSE committee on computing education in liberal arts colleges Authors: Douglas Baldwin SUNY Geneseo SUNY GeneseoView Profile , Amanda Holland-Minkley Washington & Jefferson College Washington & Jefferson CollegeView Profile , Grant Braught Dickinson College Dickinson CollegeView Profile Authors Info & Claims ACM InroadsVolume 10Issue 2June 2019 pp 22–29https://doi.org/10.1145/3314027Published:25 April 2019Publication History 3citation181DownloadsMetricsTotal Citations3Total Downloads181Last 12 Months25Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Pipelined Parallel Rotational Visual Cryptography (PPRVC)

Visual Cryptography (VC) has emerged as a separate discipline to meet the security concerns of vital image resources. Traditional (k, m) VC creates m encrypted shares out of given secret images. Decryption can be done by stacking specified number of shares, say k, to reveal the secret images. Rotational VC is a technique in which the shares stacked with the specific rotated shares reveals secret images. Parallel Rotational VC (PRVC) is a General Purpose Graphic Processing Unit (GPGPU) implementation of rotational (2, 3) VC scheme in which GPGPU generates three shares for three secret images. Reconstruction of secret images is possible with the intersection of a given pair of share and a rotated share. We propose Pipelined PRVC (PPRVC) which combines task parallelism capability of a GPGPU with the data parallelism. The result showed a significant computational acceleration over PRVC.
Statistical Test Methodology for Evaluating Electromagnetic Information Leakage From Mobile Touchscreen Devices

This paper presents a statistical-test-based methodology for evaluating the electromagnetic (EM) emanations from mobile touchscreen devices. These EM emanations potentially contain information that can be exploited to reconstruct screen images of the device remotely. The proposed method evaluates the threat of such EM-based information leakage by performing a statistical test of measured EM waves and environmental noise without performing ad-hoc screen restoration or image processing. In particular, we employ the Kolmogorov-Smirnov test, a nonparametric statistical test method, to compare two distributions of radiated EM waves: one that potentially contains screen information and another with environmental noise. In addition, this paper demonstrates the validity of our methodology via experiments involving authentic tablet devices in real-world settings and discusses a possible method to determine the confidence interval that can be adopted as a criterion for security evaluation depending on the environment.
Participatory Video, Giving Voice and Respect to the Epistemic Sovereignty of Communities in Rural Zimbabwe

To inform program development in health in the rural areas of Masvingo Province in Zimbabwe, Ministry of Health and Child Care authorised cooperating partners to execute participatory video projects in rural districts in Masvingo Province. The Participatory Video process was a result of the desire to go beyond prescribed, non-participative or cultural insensitive data gathering methods. Two videos were produced that significantly shaped thinking of stakeholders being informed on health-seeking behaviours and utilisation of services. This use of Information and Communication Technologies proved a fruitful way to engage, interact, and develop public conversations, giving a voice to formerly disenfranchised groups (e.g. ostracised religious groups or 'closed' communities'). Participatory Video is recognised as a culturally aligned method appropriate for a setting other than the West.
Beta Chaotic Map Based Image Steganography

Steganography is one of the growing security fields. It is the science of embedding sensitive data in images using different techniques. In this paper, we proposed a steganographic scheme based on Beta chaotic map in order to embed secret images bits into cover images. Thus a secure transmission of data could be established. We applied the PSNR, MSE and histogram tests on the obtained images and comparisons were made with different existing methods; prove that our proposed scheme has a good steganographic quality.
Combining Voronoi Graph and Spline-Based Approaches for a Mobile Robot Path Planning

Potential function based methods play significant role in both global and local path planning. While these methods are characterized with good reactive behaviour and implementation simplicity, they suffer from a well-known problem of getting stuck in local minima of a navigation function. In this paper we propose a modification of our original spline-based path planning algorithm for a mobile robot navigation, which succeeds to solve local minima problem and considers additional criteria of start and target points visibility to help optimizing the path selection. We apply a Voronoi graph based path as an input for iterative multi criteria optimization algorithm and present a path finding strategy within different homotopies that uses the new method. The algorithm was implemented in Matlab environment and demonstrated significantly better results than the original approach. The comparison was based on success rate, number of iterations and running time of the algorithms. In total, several thousands tests were performed in 18 different simulated environments.
A Novel Remote Eye Gaze Tracking System Using Line Illumination Sources

This paper proposes a novel system to estimate the 3D point of gaze using observations of the pupil center and corneal reflections (glints). A mathematical model is developed with two solutions to estimate the corneal center efficiently using lines of LED lights. Differing from existing 3D approaches requiring associating light sources with glints, the model automatically associates glints and LED lines and can handle missing glints well. The new model also enables a user-friendly calibration process, allowing natural head movement. Experiments demonstrate that the proposed system can achieve accurate gaze estimation with natural head movement. The performance is impressive when using the natural calibration, requiring less user cooperation.
Net2Text: An Edge Labelling Language Model for Personalized Review Generation

Writing an item review for online shopping or sharing the dining experience of a restaurant has become major Internet activities of young people. This kind of review system could not only help users express and exchange experience but also prompt business to improve service quality. Instead of taking time to type in the review, we would like to make the review process more automated. In this work, we study an edge labelling language model for personalized review generation, e.g., the problem of generating text (e.g., a review) on the edges of the network (e.g., online shopping). It is related to both network structure and rich text semantic information. Previously, link prediction models have been applied to recommender system and event prediction. However, they could not migrate to text generation on the edges of networks since most of them are numerical prediction or tag labelling tasks. To bridge the gap between link prediction and natural language generation, in this paper, we propose a model called Net2Text, which can simultaneously learn the structural information in the network and build a language model over text on the edges. The performance of Net2Text is demonstrated in our experiments, showing that our model performs better than other baselines, and is able to produce reasonable reviews between users and items.
AdaCML: Adaptive Collaborative Metric Learning for Recommendation

User preferences are dynamic and diverse in real world, while historical preference of a user may not be equally important as current preference when predicting future interests. As a result, learning the evolving user representation effectively becomes a critical problem in personalized recommendation. However, existing recommendation solutions often use a fixed user representation, which is not capable of modeling the complex interests of users. To this end, we propose a novel metric learning approach named Adaptive Collaborative Metric Learning (AdaCML) for recommendation. AdaCML employs a memory component and an attention mechanism to learn an adaptive user representation, which dynamically adapts to locally activated items. In this way, implicit relationships of user-item pairs can be better determined in the metric space and users' interests can be modeled more accurately. Comprehensive experimental results demonstrate the effectiveness of AdaCML on two datasets, and show that AdaCML outperforms competitive baselines in terms of Precision, Recall, and Normalized Discounted Cumulative Gain (NDCG).
ON A MULTIVARIATE GENERALIZED POLYA PROCESS WITHOUT REGULARITY PROPERTY

Most of the multivariate counting processes studied in the literature are regular processes, which implies, ignoring the types of the events, the non-occurrence of multiple events. However, in practice, several different types of events may occur simultaneously. In this paper, a new class of multivariate counting processes which allow simultaneous occurrences of multiple types of events is suggested and its stochastic properties are studied. For the modeling of such kind of process, we rely on the tool of superposition of seed counting processes. It will be shown that the stochastic properties of the proposed class of multivariate counting processes are explicitly expressed. Furthermore, the marginal processes are also explicitly obtained. We analyze the multivariate dependence structure of the proposed class of counting processes.
GparMiner: A System to Mine Graph Pattern Association Rules

With the rapid development, social network analysis has been receiving significant attention. One popular direction in the filed is to mine graph-pattern association rules ( $$\mathsf {GPARs}$$ ). In the demo, we present $$\mathsf {GparMiner}$$ , a system for mining $$\mathsf {GPARs}$$ , on big and distributed social networks. The system has following characteristics: (1) it supports parallel mining computation, to handle sheer size of real-life social networks; (2) it provides graphical interface to help users monitor the mining progress and have a better understanding of $$\mathsf {GPARs}$$ .
Irradiance Field Reconstruction From Partial Observability of Solar Radiation

Photovoltaic (PV) panels have become a significant source of electric power generation. These panels are considered to be one of the cleanest energy production systems available, so their spread is expected to increase in the following years, especially because recent technologies have reduced the cost of these panels. Unlike classic energy production methodologies that are connected to the high-voltage transmission power lines, many PV panels are connected directly to the lower voltage distribution networks of the electric power grid, making the management of the grid an ongoing challenge. In this letter, we address this challenge and show that the irradiance field that is required to calculate the expected power output of the PV panels can be estimated in a simplistic methodology, using partial observability of the solar radiation. We validate our proposed methodology by conducting an empirical study that uses real data of the solar radiation taken from satellites, and we show that even when the observability of the solar radiation is as low as 10% (meaning that only one in ten points of interest in a regular grid is observable), the irradiance field can be accurately estimated.
Target Advertising Classification using Combination of Deep Learning and Text model

In recent years, there has been a great interest in online advertising not only to promote products and services but to build a brand of the company as well. To satisfy customer needs, some businesses apply intelligent technology to advertise their products and services based on customer interests. Other advertisers allow customers or members to upload their promotions using image and/or message to advertise their businesses and services. However, filtering of promotional advertising is an essential part to detect improper information before posting on the websites and social media. As a result, a model to classify promotional advertising is proposed to identify whether relevant promotion content for a specific business or service in order to meet precise customers' attention. The proposed algorithm in this study based on deep learning is designed to handle promotional image and message in competition with the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> KU Data Science Boot Camp 2018. Its performance is evaluated on the promotional advertising data provided by Wongnai. Finally, the accuracy of the proposed method can achieve satisfactory performance of 82.95% in testing data.
Spatial calibration method for master-slave camera based on panoramic image mosaic

The master-slave camera is composed of fish-eye panoramic camera and pan/tilt/zoom (PTZ) camera. The fish-eye panoramic camera is capable of monitoring a wide field of view, whereas the PTZ dome camera has high mobility and zoom ability. In order to achieve precise interaction, a calibration method based on panoramic image mosaic is proposed, which first composes a mosaic image by several PTZ images and then gets the mapping relationships recorded in the look-up table by template matching. So the mapping relationship between the panoramic image and the motion parameter of PTZ camera can be obtained. Experimental results have demonstrated that it can provide an efficient, fast, and automatic method for the calibration of the master-slave camera.
Piecewise Gamma Corrected Weighted Framework for Fuzzified Dynamic Intensity Equalization for Optimal Image Enhancement

In this paper, a new fusion-based image enhancement framework is proposed by harvesting the swarm intelligence of Levy Firefly optimizer. Piece-wise gamma corrected interim intensity channels are fused along with dynamic fuzzy equalization based channel. Thus, quality enhancement of the poorly acquired and/or dark image is achieved in this paper by an optimally weighted framework. Over-ranged intensity levels are reduced by the penalty term inclusion in the entropy-dependent fitness function. Thus, the constructive amalgamation of gamma-compressed, gamma-expanded and fuzzy-equalized channel is attained through an evolutionary approach. Due to the presence of these three interim channels, the desired image quality enhancement (contrast as well as entropy enhancement along with sharpness and colorfulness enhancement of the images) is achieved. The simulation results and their comparative analysis (with various state-of-the-art methodologies) authenticate the outperforming excellence of the proposed approach.
On machine test of Inverse Projected-Fringe system for measurement of form deviation and surface profile of axisymmetric objects

An in-situ Inverse Projected-Fringe system for on-machine inspection of axisymmetric parts is developed. Parts considered for this study are specular with RMS surface roughness >1 μm and have large slopes with radii of about 50 mm. The in-situ system is developed based on the previously reported Inverse Projected-Fringe technique in order to determine the on-machine measurement repeatability and reproducibility for industrial applications. In order to measure the whole part, a stereo arrangement and a new data fusion technique are used to increase the field of view of the system. A measurement trace with the highest Signal to Noise Ratio (SNR) is selected to achieve the best accuracy. The repeatability and reproducibility tests of the system are reported. It is shown the in-situ Inverse Projected-Fringe system is capable of measuring the form deviation from a master-part with repeatability and reproducibility of ±1 to ±7 μm, equivalent to a dynamic range of one part in 25,000 to one part in 3,600, respectively for the areas with higher SNR to lower SNR. Simultaneously, the RMS surface roughness has been measured with an accuracy of ±1.2 μm or better.
Can Mobile Augmented Reality Stimulate a Honeypot Effect?

In HCI, the honeypot effect describes a form of audience engagement in which a person's interaction with a technology stimulates passers-by to observe, approach and engage in an interaction themselves. In this paper we explore the potential for honeypot effects to arise in the use of mobile augmented reality (AR) applications in urban spaces. We present an observational study of Santa's Lil Helper, a mobile AR game that created a Christmas-themed treasure hunt in a metropolitan area. Our study supports a consideration of three factors that may impede the honeypot effect: the presence of people in relation to the game and its interactive components; the visibility of gameplay in urban space; and the extent to which the game permits a shared experience. We consider how these factors can inform the design of future AR experiences that are capable of stimulating honeypot effects in public space.
What are the Stumbling Blocks to Making Product Lifecycle Management Routine in Organizations?

Product lifecycle management (PLM) systems are increasingly expansive enterprise systems manufacturing firms used to help build their capabilities and competitive advantages. Adoption and implementation of an enterprise system does not ensure its organizational acceptance and routinization. We identify the bottlenecks for PLM institutionalization; these systems are becoming the norm for organizational operations and routines. We surveyed 207 executives to determine some critical barriers, potential stumbling blocks, to PLM routinization in daily organizational practices. We found that lack of information technology (IT) infrastructure, integration, and interoperability of PLM systems to be major stumbling blocks for PLM postimplementation routinization.
Fast Asymptotic Square Root for Two Types of Special Pentanomials

Inspired by the Montgomery and generalized polynomial basis (GPB) squaring operation, we introduce and study the notion of asymptotic square root over binary extension fields GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ). This new notion is a natural generalization of Montgomery-like square root, a special form of square root that was recently introduced by Li et al. Given an arbitrary element A ∈ GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) and a fixed element w, the asymptotic square root is defined as A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> · w. We show that, by choosing a proper parameter w regarding different kind of irreducible polynomials that define GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ), such square root operation can achieve better space and time complexity. Meanwhile, we have proved that the complexity of asymptotic is linear with the generating polynomials. Specifically, for the field GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) is defined by two type of specific irreducible pentanomials, i.e., Type C.1 and Type C.2 pentanomials, we derived explicit formulae for space and time complexities associated with the asymptotic square root operator. On top of that, a practical application of the asymptotic square root in exponentiation computation is also presented.
Reverse Tone Mapping of High Dynamic Range Video Using Gaussian Process Regression

There is a rapid increase in the amount of High Dynamic Range (HDR) display devices that are able to offer significantly better viewing experience with higher contrast and richer colors. However, there is a considerable shortage of content that is able to offer the much-sought-after HDR experience. One approach is to capture new content using specialized HDR-cameras, which are typically fewer and costlier than the conventional cameras. Another approach is to convert existing Standard Dynamic Range (SDR) content into HDR artificially, such that, it can reasonably mimic the viewing experience that a "true" HDR content would have offered. With the latter approach, we propose a Gaussian Process Regression (GPR) based machine learning method for estimating HDR content from their SDR counterparts. GPR is known as a powerful technique for estimating continuous real-valued functions. Given a set of training SDR-HDR image pairs, our proposed method is able to estimate the reverse tone mapping function that is used to convert SDR signal into its HDR equivalent. Preliminary experimental results indicate that our approach produces visually pleasing HDR images.