Reviewerly/exHarmony huggingface dataset:
Designing Android-Based Augmented Reality Application on Three Dimension Space Geometry

Abstract Augmented reality (AR) technology is a visual technology that combines virtual world objects into real world views in real time. By utilizing augmented reality technology on an android smartphone, a geometry object of space can be visualized concretely through three-dimension virtual modeling that is similar to the original object. This research incorporates AR technology into 3dimension space geometry objects and then it is implemented in three dimension objects lectures for cadets of Ship Machinary Study Program. The purpose of this research is to find out: how the cadets response in using the AR application. This application is an application that runs on the android mobile platform, where the AR application requires video streaming taken from a smartphone camera as an input source, then this application will detect markers using a tracking system, after the markers have been detected, the object model in the geometry of the space will appear above the marker as if the object model is real. The methods or stages in designing this application use the Prototype Model, whereas the implementation of the application uses a one-group pretest-posttest design pattern. The results showed that the use of the AR application on the Android platform was able to get a positive response from cadets in lecturing the Concept of Geometry Course, especially three-dimension objects.
Quadratic Interpolation Based Simultaneous Heat Transfer Search Algorithm and Its Application to Chemical Dynamic System Optimization

Dynamic optimization problems (DOPs) are widely encountered in complex chemical engineering processes. However, due to the existence of highly constrained, nonlinear, and nonsmooth environment in chemical processes, which usually causes nonconvexity, multimodality and discontinuity, handling DOPs is not a straightforward task. Heat transfer search (HTS) algorithm is a relative novel metaheuristic approach inspired by the natural law of thermodynamics and heat transfer. In order to solve DOPs efficiently, a new variant of HTS algorithm named quadratic interpolation based simultaneous heat transfer search (QISHTS) algorithm is proposed in this paper. The QISHTS algorithm introduces three modifications into the original HTS algorithm, namely the effect of simultaneous heat transfer search, quadratic interpolation method, and population regeneration mechanism. These three modifications are employed to provide lower computational complexity, as well as to enhance the exploration and exploitation capabilities. Therefore, the ensemble of these modifications can provide a more efficient optimization algorithm with well-balanced exploration and exploitation capabilities. The proposed variant is firstly investigated by well-defined benchmark problems and then applied to solve four chemical DOPs. Moreover, it is compared with different well-established methods existing in the literature. The results demonstrate that QISHTS algorithm has the greatest robustness and precision than other competitors.
E-Voting System using Biometrics

India is a democratic country, which is highly populated and comprised with different religions, languages and culture. Being a democratic country, every citizen has the right to elect their leader through voting. In the present system conducting election all over the country is a very huge and complicated task which needs high expenditure and man power. The proposed method uses less work force and the elections can be conducted in a fair way. New voting methodology helps to identify Voter with their person information. It takes biometric thumb impression of the all voters and store in the database. During the election time voter thumb impression is verified with the database information and if found right he will be given the right to vote, this ensures no rigging in polling. Proposed method is very accurate and leads to overcome the problems in present polling system.
Recognition of static hand gestures of Indian sign language using CNN

Sign languages are natural languages used by hearing impaired people which use several means of expression for communication in day to day life. It relates letters, words, and sentences of a spoken language to gesticulations, enabling them to communicate among themselves. The deaf community can interact with normal people with an automation system that can associate signs to the words of speech. This will support them to enhance their abilities and make them aware of doing better for the mankind. A vision based system that provides a feasible solution to Indian Sign Language (ISL) recognition of static gestures is presented in this paper. The proposed method doesn’t require that signers wear gloves or any other marker devices to simplify the process of hand segmenting. After modeling and analysis of the input hand image, classification method is used to recognize the sign. The classification is done using Computational Neural networks(CNN). Detection using CNN is rugged to distortions such as change in shape due to camera lens, different lighting conditions, various poses, presence of occlusions, horizontal and vertical shifts, etc. We are able to recognize 5 ISL gestures with a recognition accuracy of 90.55 %.
Co-occurrence graph-based context adaptation: a new unsupervised approach to word sense disambiguation

Abstract Word sense disambiguation (WSD) is the task of selecting correct sense for an ambiguous word in its context. Since WSD is one of the most challenging tasks in various text processing systems, improving its accuracy can be very beneficial. In this article, we propose a new unsupervised method based on co-occurrence graph created by monolingual corpus without any dependency on the structure and properties of the language itself. In the proposed method, the context of an ambiguous word is represented as a sub-graph extracted from a large word co-occurrence graph built based on a corpus. Most of the words are connected in this graph. To clarify the exact sense of an ambiguous word, its senses and relations are added to the context graph, and various similarity functions are employed based on the senses and context graph. In the disambiguation process, we select senses with highest similarity to the context graph. As opposite to other WSD methods, the proposed method does not use any language-dependent resources (e.g. WordNet) and it just uses a monolingual corpus. Therefore, the proposed method can be employed for other languages. Moreover, by increasing the size of corpus, it is possible to enhance the accuracy of WSD. Experimental results on English and Persian datasets show that the proposed method is competitive with existing supervised and unsupervised WSD approaches.
A (2, 2) Visual Cryptography Technique with Improved Contrast

Visual cryptography is a technique used to share a secret among many participants. The problem with visual cryptography technique is that the secret is not perfectly recovered, i.e. a black secret pixel is perfectly recovered but a white secret pixel is recovered with less contrast. Because of this, the overall quality of recovered secret is poor. So to improve the quality of recovered secret image, we need to improve the contrast. In this paper, we have proposed a (2, 2) visual cryptography technique with improved contrast as compared to the traditional (2, 2) visual cryptography scheme (VCS). In the proposed technique, we have used additional basis matrices for encrypting the secret pixels. The quality of the recovered secret image is compared with traditional (2, 2) VCS using various image quality metrics.
Path Planning of Manipulator Based on RRT-Connect and Bezier Curve

This paper presents a path planning approach for a manipulator based on RRT-Connect and Bezier curve. RRT-Connect is first used to search for a collision-free path in the joint space. Then, a dichotomy merge is adopted to remove redundant path nodes. Finally, the Bezier curve is utilized to smooth the transition points in the path. The simulation and experimental results on the Kinova manipulator show that this method can effectively plan a smooth and accessible path in the obstacle environment.
Unsupervised Novelty Detection in Video with Adversarial Autoencoder Based on Non-Euclidean Space

Novelty is the quality of being different, new and unusual. Identifying it is an important issue in various fields such as anomaly detection in video. To detect the novelty, there are supervised learning methods that define and classify inliers and outliers, and unsupervised learning methods that define the distribution of inliers and identify whether objects are normal or abnormal. The former has limitations that the labeled data is required and the novelty which cannot be defined is not detected. To cope with the problems, the latter has recently been explored, but it is difficult to define an appropriate distribution for normal data and learn in an end-to-end manner due to unavailability of outliers. In this paper, we propose a novel one-class novelty detection method with constant curvature adversarial autoencoder. It consists of three components: an encoder, a decoder, and a discriminator. The encoder and discriminator interact with each other in adversarial and learn the distribution of normal data only. The decoder reconstructs the data to verify that the feature of the data is well extracted to the latent variable that is the output of the encoder. We also train the model to define a distribution for normal data as a constant curvature manifold, a non-Euclidean space, for the diversity of data distribution. The proposed method is verified with the well-known benchmark datasets: MNIST, CALTECH-256, and UCSD Pedestrian 1. For the area under curve as a measure of the performance, the proposed method shows the state-of-the-art performance with 0.87, 0.94, and 0.89 on average for the datasets, respectively.
An ensemble clustering approach for topic discovery using implicit text segmentation

Text segmentation (TS) is the process of dividing multi-topic text collections into cohesive segments using topic boundaries. Similarly, text clustering has been renowned as a major concern when it comes to multi-topic text collections, as they are distinguished by sub-topic structure and their contents are not associated with each other. Existing clustering approaches follow the TS method which relies on word frequencies and may not be suitable to cluster multi-topic text collections. In this work, we propose a new ensemble clustering approach (ECA) is a novel topic-modelling-based clustering approach, which induces the combination of TS and text clustering. We improvised a LDA-onto (LDA-ontology) is a TS-based model, which presents a deterioration of a document into segments (i.e. sub-documents), wherein each sub-document is associated with exactly one sub-topic. We deal with the problem of clustering when it comes to a document that is intrinsically related to various topics and its topical structure is missing. ECA is tested through well-known datasets in order to provide a comprehensive presentation and validation of clustering algorithms using LDA-onto. ECA exhibits the semantic relations of keywords in sub-documents and resultant clusters belong to original documents that they contain. Moreover, present research sheds the light on clustering performances and it indicates that there is no difference over performances (in terms of F-measure) when the number of topics changes. Our findings give above par results in order to analyse the problem of text clustering in a broader spectrum without applying dimension reduction techniques over high sparse data. Specifically, ECA provides an efficient and significant framework than the traditional and segment-based approach, such that achieved results are statistically significant with an average improvement of over 10.2%. For the most part, proposed framework can be evaluated in applications where meaningful data retrieval is useful, such as document summarization, text retrieval, novelty and topic detection.
MRGAN: a generative adversarial networks model for global mosaic removal

In this study, the authors introduce a novel deep generative adversarial networks (GANs) model for global mosaic removal. The methods used in the proposed study consist of GANs model and a novel algorithm for maintaining and repairing (MR) images. The conventional mosaic removal algorithms all employ the correlation between the inserted pixel and its neighbouring pixels, which have a limited effect on the local mosaic removal but do not work well for the global mosaic removal. To respond to this difficulty, the authors introduce an MRGAN model with two novel parsing networks. Unlike previous GANs, the MR algorithm is used to calculate the pixel loss and content loss. The experimental comparison results show that the proposed MRGAN model has achieved leading results for the global mosaic removal task.
Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

Wearable sensors play a significant role for monitoring the functional ability of the elderly and in general, promoting active ageing. One of the relevant variables to be tracked is the number of stair steps (single stair steps) performed daily, which is more challenging than counting flight of stairs and detecting stair climbing. In this study, we proposed a minimal complexity algorithm composed of a hierarchical classifier and a linear model to estimate the number of stair steps performed during everyday activities. The algorithm was calibrated on accelerometer and barometer recordings measured using a sensor platform worn at the wrist from 20 healthy subjects. It was then tested on 10 older people, specifically enrolled for the study. The algorithm was then compared with other three state-of-the-art methods, which used the accelerometer, the barometer or both. The experiments showed the good performance of our algorithm (stair step counting error: 13.8%), comparable with the best state-of-the-art (p &gt; 0.05), but using a lower computational load and model complexity. Finally, the algorithm was successfully implemented in a low-power smartwatch prototype with a memory footprint of about 4 kB.
Prediction of soil humidity based on random weight Particle Swarm Optimized Extreme Learning Machine

Abstract The prediction of high quality soil moisture is of great significance to agricultural production and scientific research. In order to solve the problem that the prediction results of (ELM) regression model of limit learning machine are affected by input parameters, the stochastic weight particle swarm optimization algorithm (RandWPSO) is applied to ELM regression model. In this paper, a soil moisture prediction method of particle swarm optimization limit learning machine based on random inertia weight is proposed. In this method, the data of soil temperature and light intensity measured by sensor are used to preprocess the data, the training sample set is constructed, and the ELM regression model is established. The input weight and threshold in ELM are optimized by using random weight particle swarm optimization algorithm to avoid falling into local optimization, thus the prediction model of soil moisture based on RandWPSO-ELM is established. The soil moisture of sugar beet in Hulan area was studied. The experimental results show that the method has high accuracy and stability, and can provide an effective reference for the growth of sugar beet in greenhouse.
The Decentralized Rights Locker

This article explains how the distributed ledger technology that has successfully decentralized currencies can be used to enable a decentralized marketplace for digital content rights. Today, multiple centralized rights lockers from companies and consortiums offer similar but incompatible systems to register a consumer’s video purchases and manage content rights. Each is representing a monopoly that centrally governs the content rights and is motivated to do so only as long as it remains profitable. Blockchain technology can be used to establish a permanent, secure, decentralized, and distributed registry for content registration, assignment, and archival of rights. The benefits of leveraging blockchain for video content distribution and content rights management include longevity that is independent of a single standard, company, or governing body as well as security of transactions with cryptographic guarantee of entitlements and grants of content licenses that cannot be changed but can be verified permanently. Lastly, the distributed and replicated nature of the peer-to-peer (P2P) network provides resiliency, eliminating a single point of failure like a company or governing body. This article will provide a novel system design that explains how a blockchain can be applied to not only manage content rights but to also secure content by enabling digital rights management (DRM) and playback control as an integral part of the decentralized network. It also details how to improve common ledger technology to overcome concerns of performance and abuse that are relevant to this application.
Application of Long-Short Memory Neural Networks in Semantic Search Engines Development

This article provides an overview and description of the long-short memory approaches for the neural networks modelling and development. The authors show the possible application of these models and methods and consider its usage during the process of natural language understanding as the part of the semantic search system.
Certificate-less Public Key Encryption For Secure e-Healthcare Systems

The following topics are dealt with: learning (artificial intelligence); image classification; feature extraction; support vector machines; medical image processing; image segmentation; pattern classification; convolutional neural nets; diseases; health care.
Encoding Space to Count Multi-Targets with Multiplexed Binary Infrared Sensors

Recently, many researchers multiplex binary infrared sensors in object tracking, habitat monitoring, and atypical behavior detection. Due to the binary digit output of binary sensors, multiplex binary sensors may lead to count the wrong number of targets when three or more targets present in the field of interest (FOI). We call this as the invisible targets’ problem. To enhance the sensing results of sensors, a reference structure tomography technique is used to segment and code the FOI by modulating the sensing view of sensors. In this paper, we propose a subregion coding method to count targets moving in the FOI. Hexagon modulators are designed to make their projections segment the FOI into hexagon cells. We also propose a signature construct scheme to code cells and a encoder to count the number of targets in the FOI. Experiment results show that the accuracy to correctly count targets of our method is around 90% which is much better than 45% of a conventional method.
Multifidelity Reinforcement Learning With Gaussian Processes: Model-Based and Model-Free Algorithms

We study the problem of reinforcement learning (RL) using as few real-world samples as possible. A naive application of RL can be inefficient in large and continuous-state spaces. We present two versions of multifidelity RL (MFRL), model based and model free, that leverage Gaussian processes (GPs) to learn the optimal policy in a real-world environment. In the MFRL framework, an agent uses multiple simulators of the real environment to perform actions. With increasing fidelity in a simulator chain, the number of samples used in successively higher simulators can be reduced. By incorporating GPs in the MFRL framework, we empirically observe an up to 40% reduction in the number of samples for model-based RL and 60% reduction for the model-free version. We examine the performance of our algorithms through simulations and realworld experiments for navigation with a ground robot.
Automatic detection for students behaviors in a group presentation

This paper suggests a model for automatic detection of student behavior to be used in student presentations. The proposed approach is based on the combined use of computer vision libraries and machine learning algorithms to help and support in student assessment using video content. This paper is a part of a research study focusing on investigating and analysing, human behaviours and finding relations between human behaviours and their personal modalities using pattern recognition techniques. For the purpose of this study a group of specific behaviours expressed by students during group presentations in higher education level, are selected. The study proceeds with the detection of the occurrences of those behaviours and comparative analysis of the model's suggested behavioural patterns against those observed through the manual analysis of observations. Both approaches are based on the same set of video files.
Peeling off image layers on topographic architectures

Information patterns are ubiquitous and their automatic detection is fundamental in many computer vision tasks. Robust and fast detection of object candidates is essential as well as the ability to adapt the system to new situations. We propose a general, task independent method that i) locates interesting patterns on binary images by creating a hierarchical layered structure based on neighborhood topography of connected components, and ii) identifies object groups applying saliency principles. Saliency values are assigned to both object groups and individuals, that can be referred as a priority queue of image regions, or alternatively a proposal for blob processing order. Empirical analysis through several computer vision examples is provided including applications for blind and visually impaired people. The proposed algorithm can be efficiently implemented on processor arrays, since it mostly contains standard topographic instructions, and we also introduce a real-time implementation on the Eye-RIS/Toshiba SPS vision system.
GACAL: Conjecture-Based Verification

Abstract GACAL verifies C programs by searching over the space of possible invariants, using traces of the input program to identify potential invariants. GACAL uses the ACL2s theorem prover to verify these potential invariants, using an interface provided by ACL2s for connecting with external tools. GACAL iteratively searches for and proves invariants of increasing complexity until the program is verified.
Superspecial Trigonal Curves of Genus 5

This paper provides an algorithm enumerating superspecial trigonal curves of genus $5$ over finite fields. Executing the algorithm over a computer algebra system Magma, we enumerate them over finite fields $\mathbb{F}_{p^a}$ for any natural number $a$ if $p \leq 7$ and for odd $a$ if $p \leq 13$.
Worth-Focused Design: Book 1: Balance,Integration, and Generosity

Design now has many meanings. For some, it is the creation of value. For others, it is the conception and creation of artefacts. For still others it is fitting things to people. These differences refl