Reviewerly/exHarmony huggingface dataset:
Light fields: from shape recovery to sparse reconstruction (Conference Presentation)

The availability of commercial light field cameras has spurred significant research into the use of light fields and multi-view imagery in computer vision. In this talk, we discuss our results over the past few years, focusing on a few themes. First, we describe our work on a unified formulation of shape from light field cameras, combining cues such as defocus, correspondence, and shading. Then, we go beyond photo-consistency, addressing non-Lambertian objects, occlusions, and material recognition. We also discuss applications for light field cameras such as motion deblurring and descattering. Finally, we show that advances in machine learning can be used to interpolate light fields from very sparse angular samples, in the limit a single 2D image, and create light field videos from sparse temporal samples.
LibGuides: Topics in Business, Industry, and Marketing Research: Industry Research

Recommended resources for business, industry, and marketing research by RCAD faculty and librarians.
Semantic Labeling of natural Scene Images Using Color Features

Scene image classification systems firstly need to locate the objects, and then classify the whole image. The color feature is importance to describe the properties of an image surface. The paper presents a framework for scene images to label local regions using color features. The paper uses maker-controlled watershed algorithm to segment the input image into regions. This paper uses the segmented regions as a basic input unit, and then extract Color Histogram (CH) and Color Moment (CM) features in HSV space. This system performs labeling using 3-layer Feed Forward Neural Network (FFNN) classifier. The system tests accuracy on public Microsoft Research Cambridge (MSRC) 9-class dataset.
Strategies to architect AI Safety Defense to guard AI from Adversaries

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Visual Summarization of Scholarly Videos Using Word Embeddings and Keyphrase Extraction

Effective learning with audiovisual content depends on many factors. Besides the quality of the learning resource's content, it is essential to discover the most relevant and suitable video in order to support the learning process most effectively. Video summarization techniques facilitate this goal by providing a quick overview over the content. It is especially useful for longer recordings such as conference presentations or lectures. In this paper, we present an approach that generates a visual summary of video content based on semantic word embeddings and keyphrase extraction. For this purpose, we exploit video annotations that are automatically generated by speech recognition and video OCR (optical character recognition).
Regression with Conditional GAN.

In recent years, impressive progress has been made in the design of implicit probabilistic models via Generative Adversarial Networks (GAN) and its extension, the Conditional GAN (CGAN). Excellent solutions have been demonstrated mostly in image processing applications which involve large, continuous output spaces. There is almost no application of these powerful tools to problems having small dimensional output spaces. Regression problems involving the inductive learning of a map, $y=f(x,z)$, $z$ denoting noise, $f:\mathbb{R}^n\times \mathbb{R}^k \rightarrow \mathbb{R}^m$, with $m$ small (e.g., $m=1$ or just a few) is one good case in point. The standard approach to solve regression problems is to probabilistically model the output $y$ as the sum of a mean function $m(x)$ and a noise term $z$; it is also usual to take the noise to be a Gaussian. These are done for convenience sake so that the likelihood of observed data is expressible in closed form. In the real world, on the other hand, stochasticity of the output is usually caused by missing or noisy input variables. Such a real world situation is best represented using an implicit model in which an extra noise vector, $z$ is included with $x$ as input. CGAN is naturally suited to design such implicit models. This paper makes the first step in this direction and compares the existing regression methods with CGAN. We notice however, that the existing methods like mixture density networks (MDN) and XGBoost do quite well compared to CGAN in terms of likelihood and mean absolute error, respectively. Both these methods are comparatively easier to train than CGANs. CGANs need more innovation to have a comparable modeling and ease-of-training with respect to the existing regression solvers. In summary, for modeling uncertainty MDNs are better while XGBoost is better for the cases where accurate prediction is more important.
KLASIFIKASI GIGITAN ULAR MENGGUNAKAN LOCAL BINARY PATTERN DAN NAÏVE BAYES

Cases of poisonous snake bites around the world are estimated to occur around 421,000 cases and 20,000 of them die every year. Identifying snake bite marks on victims will greatly help the medical team in handling victims of snake bites and will avoid fatal errors such as the death of the victim. This research will try to create a system that can classify snake bites images. The system has been built using the extraction method Local Binary Pattern (LBP) and Naive Bayes. Parameter r is a radius, while paramter P is the number of neighbor . The best result of this system has accuracy 83.33%, precision 1.00, recall 0.75, and F1 Score 0.86,parameter that used are r = 1 with P = 8 and r = 3 with P = 16. The dataset used has 20 data, the data divided into 14 training data and 6 testing data.
EXTRACT TRANSFORM AND LOADING TOOL FOR EMAIL

This project focuses on applying Extract, Transform and Load (ETL) operations on the relational data exchanged via emails. An Email is an important form of communication by both personal and corporate means as it enables reliable and quick exchange. Many useful files are shared as a form of attachments which contains transactional/ relational data. This tool allows a user to write the filter conditions and lookup conditions on attachments; define the attribute map for attachments to the database table. The Data Cleansing for each attribute can be performed writing rules and their matching state. A user can add custom functions for the data transformation. The aggregation of the data is done in the form of reports after the operation of data loading into the database is complete. The tool needs one-time setup per file template and its automated from that point.
Location-based Virtual Reality Experiences for Children:Japan-UK knowledge exchange network: London Workshop Programme

This provides the schedule, outlines and speaker bios for Location-based Virtual Reality Experiences for Children:Japan-UK knowledge exchange network: London Workshop
Modeling CDC in a fuzzy description logic A comparison of different approaches

Relative information about locations of places can be described using the cardinal direction calculus (CDC).Some extensions of the calculus allow to express vague information and others include nearness/closeness.The development of description logics for qualitative spatial calculi has also been done.In this paper, we propose several knowledge bases for fuzzy description logics to manage information on relative positions of points of interest (POI) with respect to cardinal directions and distance.The theory of vague description logics is used and applied on the cardinal direction calculus.The paper compares the different approaches and aims to objectify the comparison by generating artificial datasets that form the basis for the evaluation.The evaluation is performed in different scenarios with different parameters and the results are discussed.The advantages and disadvantages of each model are related to potential application scenarios.
Finding Documents Related to Taiwan in the Veritable Records of Qing Using Relevance Feedback

In this paper we describe a method for finding relevant texts in historical documents. Instead of using keyword search, it computes the level of relevance between texts. Using a subset of documents that are known to be relevant, our method computes the degree of relevance between the pre-selected texts and the remaining texts in the document set. The potential texts of interest obtained from the computation are listed by their ranks and given to historians to confirm their relevance. The marked results are then fed back to the system to start the next iteration. We applied our method to find Taiwan-related entries in the Veritable Records of Qing, and discovered an additional 988 related entries which were not included in the volume of Veritable Records of Qing – Taiwan Collection (which contains 2989 entries).
Application of Genetic Algorithms Task Scheduling in Cloud Computing

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Guides & Tutorials: Interlibrary Loan and Document Delivery Services (ILL&DDS): Pick-up Method

Provide support to the PolyU community by obtaining literature beyond collections in the PolyU Library.
A Bearing-Based Control Law for Trajectory Tracking

We consider the problem of tracking a desired trajectory using bearing-only information from stationary landmarks. We propose a control law that combines a gradient descent feedback with a velocity feedforward. These two terms are calculated solely by using the bearings, their time derivatives, and the knowledge of the dynamical model. Under some technical assumptions our controller shows global convergence. We analyze the limitations associated with this control law, and provide methods to bypass most of these limitations. Finally, we verify our controller through simulation.
Multipath Use Case and Requirement for Security

This document describes a use case of multipath to achieve full CIA+by using symmetric cryptography and point-to-point shared secrets.
Leveraging Out-of-Task Data for End-to-End Automatic Speech Translation.

For automatic speech translation (AST), end-to-end approaches areoutperformed by cascaded models that transcribe with automatic speechrecognition (ASR), then translate with machine translation (MT). A major causeof the performance gap is that, while existing AST corpora are small, massivedatasets exist for both the ASR and MT subsystems. In this work, we evaluateseveral data augmentation and pretraining approaches for AST, comparing all onthe same datasets. Simple data augmentation by translating ASR transcriptsproves most effective on the English--French augmented LibriSpeech dataset,closing the performance gap from 8.2 to 1.4 BLEU, compared to a very strongcascade that could directly utilize copious ASR and MT data. The sameend-to-end approach plus fine-tuning closes the gap on the English--RomanianMuST-C dataset from 6.7 to 3.7 BLEU. In addition to these results, we presentpractical recommendations for augmentation and pretraining approaches. Finally,we decrease the performance gap to 0.01 BLEU using a Transformer-basedarchitecture.
Face Recognition Process A Survey

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Neural Variational Collaborative Filtering for Top-K Recommendation

Collaborative Filtering (CF) is one of the most widely applied models for recommender systems. However, CF-based methods suffer from data sparsity and cold-start, more attention has been drawn to hybrid methods by using both the rating and content information. Variational Autoencoder (VAE) has been confirmed to be highly effective in CF task, due to its Bayesian nature and non-linearity. Nevertheless, most VAE models suffer from data sparsity, which leads to poor latent representations of users and items. Besides, most existing VAE-based methods model either user latent factors or item latent factors, which makes them unable to recommend items to a new user or recommend a new item to existing users. To address these problems, we propose a novel deep hybrid framework for top-K recommendation, named Neural Variational Collaborative Filtering (NVCF), where user and item side information is incorporated into the generative processes of user and item, to alleviate data sparsity and learn better latent representations of users and items. For inference purpose, we derived a Stochastic Gradient Variational Bayes (SGVB) algorithm to approximate the intractable distributions of latent factors of users and items. Experiments performed on two public datasets have showed our method significantly outperforms the state-of-the-art CF-based and VAE-based methods.
Guides & Tutorials: Interlibrary Loan and Document Delivery Services (ILL&DDS): Eligible Users

Provide support to the PolyU community by obtaining literature beyond collections in the PolyU Library.
Linking Open Drug Data: Lessons Learned

Linked Open Data illustrates the concept that provides an optimum solution for information and dissemination of data, through the representation of the data in an open machine-readable format and to interlink it from diverse repositories to enable diverse usage scenarios for both humans and machines. The pharmaceutical/drug industry was among the first that validated the applicability of the approach for interlinking and publishing open linked data. This paper examines in detail the process of building Linked Data application taking into consideration the possibility of reusing recently published datasets and tools. Main conclusions derived from this study are that making drug datasets accessible and publish it in an open manner in linkable format adds great value by integration to other notable datasets. Yet, open issues arose clearly when trying to apply the approach to datasets coded in languages other than English, for instance, in Arabic languages.
PENERAPAN METODE DRILL DALAM PEMBELAJARAN MENULIS TEKS EDITORIAL

This research is based on the existence of students who experience difficulties in learning to write editorial texts, it is based on student evaluation data that is still not optimal in writing editorial texts, this is contrary to the innovation of learning methods used by teachers, to improve this research using application learning methods innovative, drill method. The method of drill or ready training is a method that can improve writing skills, so that it can be developed into a complete work. This study discusses improving students 'abilities in learning to write editorial texts, thus making the authors formulate the following problems: Is it necessary to improve students' ability to learn to write editorial texts using the drill method? This study uses the Pre-Experimental Design method with the design form of One-Group Pretest-Posttest Design. Based on the results of the study. Improving the ability of students to write editorial texts using the drill method. Based on the calculation of the hypothesis t test using the SPSS 21.0 program obtained the results of tcount (7,519) abel t table (1,734). This shows that Ha is accepted and H0 is rejected. Drill method proved effective in learning to write editorial texts.
Design of Taxation System Based on Blockchain Technology

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
A Hybrid System Using CNN and AE for Noisy Image Classification

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Implementasi User Centered Design Pada Sistem Pakar Diagnosis Gangguan Perkembangan Motorik Kasar Pada Anak Usia Dini

This study aims to build a knowledge-based psychology system for the diagnosis of gross motor development disorders in early childhood so that it can help educators in this case, PAUD teachers and parents/guardians in knowing information about gross motor development disorders in children. This study uses the development method of the UCD (User-Centered Design) system. The inference engine used is forward chaining that performs the search for progress in extracting information in this case, the symptoms that arise in children who are unusual with other children. The final results obtained from this study are an expert system for the diagnosis of gross motor development disorders in early childhood where the user chooses symptom data contained in the choice. Users will get the results of consultations in the form of conclusions about the diagnosis of the disorder and the implementation of the therapy.
The Implementation Of Schoology In Micro Teaching Class At Department Of English Education Universitas Muhammadiyah Surakarta 2019 Academic Year

This study aims to describe the implementation of Schoology and the problem on implementing Schoology in the Micro Teaching Class at Department of English Education Universitas Muhammdiyah Surakarta 2019 academic Year. This research is descriptive qualitative. The researcher uses interviews to obtain data. Interviews are the right technique for collecting data. Interviews are an appropriate method for exploring information. The research subjects are lecturers of Micro Teaching class and Micro Teaching students in 2019 Academic Year. The objects of the research are statements from students and lecturers of Micro Teaching class at Department of English Education Universitas Muhammadiyah Surakarta 2019 academic Year. To analyse the data, researcher uses a phenomenological approach. The results show that: 1) Schoology is applied as a class management tool. 2) The problem on implementing Schoology are Students tend to be unaware the deadline posted in schoology.
Development Method of Building a Modular Simulator of Quantum Computations and Algorithms

The article assumes a description of the fundamentals of the theory of quantum computing in the field of quantum algorithms. A universal concept of a quantum algorithm is given, and the time of operation of the algorithm with the determination of the probability of a particular result at the output is theoretically described. A method for constructing a modular simulator of quantum computations and algorithms, its architecture and the interactions of its various components is considered. The paper developed a method for constructing a quantum algorithm for graph interpretation, which is a study of the relationship between classical and quantum elements and concepts. An algorithm for graph interpretation and elimination (reduction) of graph vertices is built, and a method of paralleling an undirected graph model by fixing the values of graph vertices is implemented. The advantage of this strategy is that all these assessments can be carried out in parallel. In this paper, an assessment was made of the complexity of a particular algorithm based on the complexity function and a universal formula for calculating it was derived. The basics of developing quantum algorithms are described in accordance with specific software for implementing quantum algorithms and the stages of their development. Quantum algorithms involve the use of vector and matrix algebra. In accordance with this, “quantum” software is defined, including: a quantum intermediate representation of information, a quantum language of physical operations, and a quantum assembler.