Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Dense Light Field Reconstruction with Occlusion Awareness

Light field (LF) reconstruction is a fundamental technique in light field imaging and has applications in both software and hardware aspects. This paper presents an unsupervised learning method for LF‐oriented view synthesis, which provides a simple solution for generating quality light fields from a sparse set of views. The method is built on disparity estimation and image warping. Specifically, we first use per‐view disparity as a geometry proxy to warp input views to novel views. Then we compensate the occlusion with a network by a forward‐backward warping process. Cycle‐consistency between different views are explored to enable unsupervised learning and accurate synthesis. The method overcomes the drawbacks of fully supervised learning methods that require large labeled training dataset and epipolar plane image based interpolation methods that do not make full use of geometry consistency in LFs. Experimental results demonstrate that the proposed method can generate high quality views for LF, which outperforms unsupervised approaches and is comparable to fully‐supervised approaches.
Document Layout Analysis and Recognition Systems

Automatic extraction of relevant knowledge to domain-specific questions from Optical Character Recognition (OCR) documents is critical for developing intelligent systems, such as document search engines, sentiment analysis, and information retrieval, since hands-on knowledge extraction by a domain expert with a large volume of documents is intensive, unscalable, and time-consuming. There have been a number of studies that have automatically extracted relevant knowledge from OCR documents, such as ABBY and Sandford Natural Language Processing (NLP). Despite the progress, there are still limitations yet-to-be solved. For instance, NLP often fails to analyze a large document. In this thesis, we propose a knowledge extraction framework, which takes domainspecific questions as input and provides the most relevant sentence/paragraph to the given questions in the document. Overall, our proposed framework has two phases. First, an OCR document is reconstructed into a semi-structured document (a document with hierarchical structure of (sub)sections and paragraphs). Then, relevant sentence/paragraph for a given question is identified from the reconstructed semi structured document. Specifically, we proposed (1) a method that converts an OCR document into a semi structured document using text attributes such as font size, font height, and boldface (in Chapter 2), (2) an image-based machine learning method that extracts Table of
The implementation of the Kruskal algorithm for the search for the shortest path to the location of a building store in the city of Bogor

The shortest path is a very important thing in a business sector, especially in sending goods by land. One of the obstacles faced in the shipment of goods is the determination of the trajectory. Determining the best trajectory can minimize shipping costs and time is more efficient A designated algorithm or system is required to search for the shortest and the most optimal traffic path from various numbers of existing alternative pathways. The Kruskal algorithm is one of the algorithms used in solving minimum spanning tree problems by choosing the side that has the smallest weight of each node with the notes that the selected sides cannot form a closed circular. For Case on study, we used data activities is controlled by land transportation from PT. T A. The results showed that with the application of the Kruskal algorithm in the case of finding the closest route to the building store for shipping goods, the optimal path was obtained. The distances of the 12 data that was tried there were 83% data which were smaller than the google map direction API.
Effects of Residual Hardware Impairments on Secure NOMA-Based Cooperative Systems

Non-orthogonal multiple access (NOMA) has been proposed as a promising technology that is capable of improving the spectral efficiency of fifth-generation wireless networks and beyond. However, in practical communication scenarios, transceiver architectures inevitably suffer from radio frequency (RF) front-end related impairments that cause non-negligible performance degradation. This issue can be addressed by analog and digital signal processing algorithms, however, inevitable aspects of this approach such as time-varying hardware characteristics and imperfect compensation schemes result to detrimental residual distortions. In the present contribution we investigate the physical layer security of NOMA-based amplify-and-forward relay systems under such realistically incurred residual hardware impairment (RHI) effects. Exact and asymptotic analytic expressions for the corresponding outage probability (OP) and intercept probability (IP) of the considered setup over multipath fading channels are derived and corroborated by respective simulation results. Based on this, it is shown that RHI affects both the legitimate users and eavesdroppers by increasing the OP and decreasing the IP. For a fixed OP, RHI generally increases the corresponding IP, thereby reducing the secure performance of the system. Further interesting insights are provided, verifying the importance of the offered results for the effective design and deployment of secure cooperative communication systems.
Integrating Vision and Language for Automatic Face Descriptions

In this dissertation, computer vision and Natural Language Processing (NLP) are integrated to create a unique example of a face-to-text and text-to-face system. Its intention is to provide a solution that can help humans to perform their jobs with better quality and with a quick response. The aim is to create a system that can be used, for example, to describe faces for visually impaired people or to generate faces from descriptions for criminal investigations. However, this is a preliminary version as it is an ambitious goal to be achieved during the time available for its realization. To accomplish this motivation, a system was created with the capability of describing, textually, facial images, along with the ability to automatically generate face images from text descriptions. The system is divided into two sub-systems. The first part predicts attributes from the face images through a Convolutional Neural Network (CNN) method that are used, further, as a base to the Natural Language Generation (NLG) model. The descriptions are generated on a rule-based methodology. The second part of the system uses a simple keyword extraction technique to analyze the text and identify the attributes on that description. After that, it uses a conditional Generative Adversarial Network (GAN) to generate a facial image with a specific set of desired attributes. The reason why attributes are used as a base on the method is because they are a dominant identifier that can efficiently transmit characteristic about a face. The results demonstrate, once again, that either CNN and GAN methods are presently the best options for recognition and generation tasks, respectively. This conclusion is due to their convincing results. On the other hand, the NLP methods worked well for their purposes. However, its results are less remarkable, especially the NLG model. This work proposes a reliable and functional solution for solving this complex system. Nevertheless, this area needs an extensive investigation and development.
The Selection and Optimization of a Negative comment Identification Method

This paper introduces the research background and significance of negative comments, and analyzes the present situation and development trend of negative comments identification at home and abroad. Several popular classification algorithms of naive Bayes, support vector machines and neural network are introduced. The word bag model, TF-IDF model and Word2Vec/Doc2Vec model feature model are also introduced. The data of feature extraction and classification algorithm in IMDB movie review website are simulated, and the results are compared and analyzed, and an optimal combination of feature extraction and classification algorithm is obtained.
Variable Selection to Determine Majors of Student using K-Nearest Neighbor and Naïve Bayes Classifier Algorithm

Appropriate student’s major placement in high school can help students to better improve their academic achievement. There are many variables which must be considered to determine the student’s majors, such as: Gender, Interests, Intelligence Quotient (IQ); Four subjects in Junior High School (JHS), average junior high school grades, matriculation score of four subjects, and average rate of matriculation. The number of variables used in the selection, causes some weaknesses among them, such as the complexity of variable, the inefficiency of variable and the existence of some variables which is only as an addition without having a significant contribution. This study aims to reduce the number of these variables so it will become easier to analyze and to be applied. The Process of reduction was done by combining experiments with Predefined attributes. A total of ten combinations were attempted using K-Nearest Neighbor (K-NN) and Naïve Bayes Classifier (NBC) which then was measured by Confusion Matrix accuracy. The experimental result showed that the combination of variables which produce the best accuracy were the 9th and 10th experiment with variable matriculation, interest, and IQ, and an accuracy of 96.77% from K-NN also 98.38% from NBC. By combining both algorithms, 99.87% of maximum accuracy was obtained from those three variables. New information which can be extracted from this research is that there are only three important variables to determine major placement in Senior High School, Average Scores of Matriculation, Interests and IQ followed by four supporting variables such as the scores of Mathematic, Physics, English and Economics in Matriculation.
State-of-the-Art Javascript Language for Internet of Things

Currently, two technologies stand out in the field of research, the Internet of Things (IoT) and Javascript (JS). On one hand, IoT enables everyday objects to connect to the network, analyze, capture, and interact with the environment. On the other hand, we have JS, a programming language that was initially inside browsers but now it is being used in several backgrounds. This paper correlates IoT and JS technologies, showing how Javascript can be applied in the context of the Internet of Things. The survey has considered the work of the last ten years and presents state of the art from Javascript applied to the IoT-side.
Adapting Data Modeling Techniques for Data Warehouse Design

Since a data warehouse's primary function is to facilitate end-user analysis and decision making, the method in which the data warehouse is modeled is critical to its success. Two modeling methods are explored, the entity relationship (ER) model and the dimensional model. A review of the literature reveals ways of trying to work around the limitations of each model, including the use of hybrid models. Since hybrids themselves create limitations in terms of available tools and data warehouse robustness, however, this study concludes by recommending a combination of the ER and dimensional models, not as a hybrid but as a process. The dimensional model is recommended for the front-end of the data warehouse, providing ease of use and access to the end user. The ER model is recommended as a back-end process to support the extraction of data from the production system and the loading of the data warehouse.
Comparing MT Approaches for Text Normalization

One of the main characteristics of social media data is the use of non-standard language. Since NLP tools have been trained on traditional text material their performance drops when applied to social media data. One way to overcome this is to first perform text normalization. In this work, we apply text normalization to noisy English and Dutch text coming from different social media genres: text messages, message board posts and tweets. We consider the normalization task as a Machine Translation problem and test the two leading paradigms: statistical and neural machine translation. For SMT we explore the added value of varying background corpora for training the language model. For NMT we have a look at data augmentation since the parallel datasets we are working with are limited in size. Our results reveal that when relying on SMT to perform the normalization it is beneficial to use a background corpus that is close to the genre you are normalizing. Regarding NMT, we find that the translations - or normalizations - coming out of this model are far from perfect and that for a low-resource language like Dutch adding additional training data works better than artificially augmenting the data.
Edge Computing Simulators for IoT System Design: An Analysis of Qualities and Metrics

The deployment of Internet of Things (IoT) applications is complex since many quality characteristics should be taken into account, for example, performance, reliability, and security. In this study, we investigate to what extent the current edge computing simulators support the analysis of qualities that are relevant to IoT architects who are designing an IoT system. We first identify the quality characteristics and metrics that can be evaluated through simulation. Then, we study the available simulators in order to assess which of the identified qualities they support. The results show that while several simulation tools for edge computing have been proposed, they focus on a few qualities, such as time behavior and resource utilization. Most of the identified qualities are not considered and we suggest future directions for further investigation to provide appropriate support for IoT architects.
Traceability approach for conflict dissolution in handling requirements crosscutting

Requirements crosscutting in software development and maintenance has gradually become an important issue in software engineering. There are growing needs of traceability support to achieve some possible understanding in requirements crosscutting throughout phases in software lifecycle. It is aimed to manage practical process in addressing requirements crosscutting at various phases in order to comply with industrial standard. However, due to its distinct nature, many recent works are focusing on identification, modularization, composition and conflict dissolution of requirements crosscutting which are mostly saturated at requirements level. These works fail to practically specify crosscutting properties for functional and nonfunctional requirements at requirements, analysis and design phases. Therefore, this situation leads to inability to provide sufficient support for software engineers to manage requirements crosscutting across the remaining development phases. This thesis proposes a new approach called the Identification, Modularization, Design Composition Rules and Conflict Dissolutions (IM-DeCRuD) that provides a special traceability to facilitate better understanding and reasoning for engineering tasks towards requirements crosscutting during software development and evolution. This study also promotes a simple but significant way to support pragmatic changes of crosscutting properties at requirements, analysis and design phases for medium sizes of software development and maintenance projects. A tool was developed based on the proposed approach to support four main perspectives namely requirements specification definition, requirements specification modification, requirements prioritization setting and graphics visualizing representation. Software design components are generated using Generic Modeling Environment (GME) with Java language interpreter to incorporate all these features. The proposed IM-DeCRuD was applied to an industrial strength case study of medium-scaled system called myPolicy. The tool was evaluated and the results were verified by some experts for validation and opinion. The feedbacks were then gathered and analyzed using DESMET qualitative method. The outcomes show that the IM-DeCRuD is applicable to address some tedious job of engineering process in handling crosscutting properties at requirements, analysis and design phases for system development and evolution.
Overcoming the barriers to the use of journal articles within the geosciences

Abstract Journal articles offer students an invaluable insight into current research and active debates. However, students are reluctant to engage with journals, and citation analyses suggest that their use is declining in favour of more accessible sources such as the internet. This project examined barriers to student engagement with journal literature and the extent to which these can be overcome by additional training. A questionnaire survey revealed various barriers including the complex content, the difficulty of finding relevant articles, the associated time commitment, and access problems. To address these barriers, two interventions were implemented: a detailed guide on how to find relevant literature; and tutorial sessions at which set reading was discussed. Citation analysis of pre- and post-intervention coursework indicated a significant increase in the use of journal articles. A focus group confirmed the value of the interventions and provided feedback that has informed a series of recommendations.
The Impact of Newark's Operation Ceasefire on Trauma Center Gunshot Wound Admissions

We examined the impact of Newark's Operation Ceasefire on gunshot wound admissions (GSW) to the Level 1 Trauma Center at University Hospital for a three-year period. Interrupted time series analyses revealed that there were no statistically significant reductions in GSW rates in the Ceasefire Zone after the introduction of Ceasefire in Newark. Similarly, there were no statistically significant changes in GSW rates in the matched comparison zone or the Trauma Center's wider catchment area. A dual kernel density hot spot map confirmed these findings by identifying changes in hot spots that were non-systematic, with no consistent pattern of change over time. Future research should focus on conducting both process and outcome evaluations of the various adaptations of the Ceasefire model in order to help inform policy makers' decisions regarding the selection of violence reduction programs.
An improved saliency detection method based on hypercomplex Fourier transform

Saliency detection is a challenging direction in the field of computer vision. It can help us quickly obtain important information of image and video, and plays an important role in image compression, image segmentation, target recognition and other applications. Based on the Hypercomplex Fourier Transform (HFT) model, median filter and threshold segmentation are employed to obtain the image salient region, which solves the problem that the boundary is not clear and multiple targets cannot be separated, and achieves more accurate and stable detection effect.
An Expulsion Fuse Model Including Melting and Arcing Times Representation Using ATP-EMTP

In many cases, simulations of electrical power systems (EPS) require more realistic representations of their components. As the behavior of the protection system plays an important role in several types of EPS studies, it is relevant for the improvement of the protection devices’ models. Thus, in this work, an expulsion fuse model to be used in ATP-EMTP simulations is proposed. The model considers the melting and arcing periods with mathematical approximations and yields well-estimated results for the fuse opening times in fault simulations performed in a study-case system. It is observed, that the proposed model describes the expulsion fuse characteristics in a satisfactory way.
Safety Assessment of Environment Perception in Automated Driving Vehicles

Important for the safety of automated driving is the perception provided by environment sensors such as radar, camera and lidar. Existing standards and test procedures do not directly allow to validate perception reliability, for instance because of the impracticably large number of required test kilometers. To address involved challenges, this thesis comprehensively studies how to demonstrate the perception reliability of environment sensing for a safety validation of automated driving systems.
Bronchoscopic Myths and Legends: The Utility of Rapid Onsite Cytologic Evaluation for Lymph Node Sampling by Transbronchial Needle Aspiration

When performing transbronchial needle aspirations, having a bedside cytopatholgist has become a common practice for many bronchscopists. It has been a common belief that rapid onsite evaluation is necessary to obtain adequate sampling and capture high diagnostic yields. We have evaluated the literature to determine the utility of bed side cytologic evaluation in transbronchial needle aspiration.
Robotic precision assembly system for microstructures

This article proposes a robotic precision assembly system for the typical parts of microstructures such as non-silicon microelectromechanical system parts. The assembly system contains three parallel assembly units and support system. In each unit, the images of the base part and target part can be obtained simultaneously from the coaxial alignment vision detection module. This article proposes a system calibration method to ensure accuracy of assembly system. Assembly experiments and accuracy validation tests are conducted. The experimental results show that the synthesizing assembly accuracy of the robotic precision assembly system can reach higher than 3 µm and the mean assembly cycle time of each part is less than 20 s, including the time for feeding and unloading parts. The robotic precision assembly system with nondestructive imaging and gripping can save on cost, achieve better quality results in less time, and greatly facilitate the precision assembly reliability and automation of microstructures.
Scientific Research Analysis Across Multiple Fields

This paper addresses the problem of scientific research analysis across multiple fields. We use Latent Dirichlet Allocation to model research topics in papers and show various insightful statistics and correlations within and across five research fields: Linguistics, Computational Linguistics, Psychology, Education, and Marketing. In particular, we introduce three new measures of coherence, importance, and influence for topics and pairs of topics and use them to show what relations exist between topics, how topics change over time within each field, and what temporal correlations and topic influences can be determined across fields. Finally, we talk about trend prediction and topic suggestion as future extensions of this research.
Scalable Knowledge Graph Construction from Text Collections

We present a scalable, open-source platform that “distills” a potentially large text collection into a knowledge graph. Our platform takes documents stored in Apache Solr and scales out the Stanford CoreNLP toolkit via Apache Spark integration to extract mentions and relations that are then ingested into the Neo4j graph database. The raw knowledge graph is then enriched with facts extracted from an external knowledge graph. The complete product can be manipulated by various applications using Neo4j’s native Cypher query language: We present a subgraph-matching approach to align extracted relations with external facts and show that fact verification, locating textual support for asserted facts, detecting inconsistent and missing facts, and extracting distantly-supervised training data can all be performed within the same framework.
Quantum finite automata using ancilla qubits

We introduce a new model of quantum finite automata. By using ancilla qubits, it becomes possible to recognize any regular language with certainty. Some nonregular languages can be recognized with one-sided unbounded error. We analyze a class of languages that can be recognized in this model in terms of a cascade composition of automata. This also allows to treat the case of an automaton with both classical and quantum states.