Reviewerly/exHarmony huggingface dataset:
Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature

Face recognition across aging emerges as a significant area among researchers due to its applications such as law enforcement, security. However, matching human faces with different age gaps is still bottleneck due to face appearance variations caused by aging process. In regard to mitigate such inconsistency, this chapter offers five sequential processes that are Image Quality Evaluation (IQE), Preprocessing, Pose Normalization, Feature Extraction and Fusion, and Feature Recognition and Retrieval. Primarily, our method performs IQE process in order to evaluate the quality of image and thus increases the performance of our Age Invariant Face Recognition (AIFR). In preprocessing, we carried out two processes that are Illumination Normalization and Noise Removal that have resulted in high accuracy in face recognition. Feature extraction adopts two descriptors such as Convolutional Neural Network (CNN) and Scale Invariant Heat Kernel Signature (SIHKS). CNN extracts texture feature, and SIHKS extracts shape and demographic features. These features plays vital role in improving accuracy of AIFR and retrieval. Feature fusion is established using Canonical Correlation Analysis (CCA) algorithm. Our work utilizes Support Vector Machine (SVM) to recognize and retrieve images. We implement these processes in FG-NET database using MATLAB2017b tool. At last, we validate performance of our work using seven performance metrics that are Accuracy, Recall, Rank-1 Score, Precision, F-Score, Recognition rate and computation time.
An Improved Algorithm for Moving Object Detection in YOLO UAV Videos

&lt;p&gt;Recently, moving object detection (MOD) in UAV (Unmanned Aerial. Vehicle) videos has been widely used in many fields. However, different objects and different algorithms often result in different detection accuracy. SSD (Single Shot MultiBox Detector) series and YOLO (You Only Look Once) version 5 are two popular object detection model, and their performance are always evaluated and compared with other improved method for optimizing detection accuracy. In this paper, an improved YOLO_v5 detection algorithm was proposed to further improve the detection accuracy. It adopted a cascaded inter-frame verification mechanism which is based on the neural network and uses spatial information and integrates object speed and direction as well to improve the detection accuracy of moving objects. To evaluate its performance, the open UAV video data from Stanford University was used to test the algorithms, and three types of moving objects were analyzed. The experimental results demonstrate that the proposed MOD method can improve the detection accuracy of small moving objects, which have a good application value, and can lay a foundation for subsequent related studies.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer

Imitation Learning uses the demonstrations of an expert to uncover the optimal policy and it is suitable for real-world robotics tasks as well. In this case, however, the training of the agent is carried out in a simulation environment due to safety, economic and time constraints. Later, the agent is applied in the real-life domain using sim-to-real methods. In this paper, we apply Imitation Learning methods that solve a robotics task in a simulated environment and use transfer learning to apply these solutions in the real-world environment. Our task is set in the Duckietown environment, where the robotic agent has to follow the right lane based on the input images of a single forward-facing camera. We present three Imitation Learning and two sim-to-real methods capable of achieving this task. A detailed comparison is provided on these techniques to highlight their advantages and disadvantages.
TRADITIONAL INVESTING VS NEW TRENDS: A REVIEW OF THE INVESTMENT LANDSCAPE IN TRANSITION TOWARDS INDUSTRY 4.0

The aim for the current paper is to provide an overview analysis on the main financial markets. As a result, the study will investigate the topic of mutual funds, stock market or cryptocurrency investments. Consequently, this article provides evidence that in the European Union, one out of five individuals has at least one investment source. In effect, the research shows that if there is demand, it will also be a supply, both factors ensuring the existence of the stock market. Unlike most of the previous studies it has a double approach. First of all, this study critically examines the framework of the main investment sources starting from the traditional ones – like mutual funds, to the new ones – cryptocurrency market. Additionally, this paper develops investigations in the field of quantitative analysis presenting the respondents’ opinions regarding the future of cryptocurrencies as well as their reliability compared to mutual funds. These findings can serve as the foundation of a potential investment strategy for any consumer with medium- and long-term financial objectives.
Classification of Web Pages Using the Machine Learning Algorithms with Web Page Recommendations

The World Wide Web holds a huge source of a variety of information.Web users are provided with a lot of information with numerous options and choices because of which decision-making will be difficult for the users.The users must be provided with a recommendation system that makes it easy for making their decisions, one such system is the web page recommendation system.The problem faced by web users is they can't get the required web pages when they search the web.To provide the solution to this problem web page recommendation systems are proposed.Recommendation system provides the users with interesting Webpages or websites where they can be reduced their searching time or surfing time.The web user needs effective and some suggestions for accessing the website efficiently.Therefore, the web recommendation systems are very useful for the user by efficiently handling the website.In the proposed work Web page recommendation system is implemented based on web page classification and by determining the page rank, where the classification of the web pages is done based on the machine learning algorithms.The classification techniques are used with the various machine learning algorithms in our work which are k-nearest neighbor, support vector machine, ADABOOSTER, and entropy-based ensemble Random Forest.The proposed algorithm in our research work is an entropy-based ensemble random forest.The novelty of this research work is the use of the entropy-based ensemble random forest.The web scraping technique has been used to fetch images and text from various websites.The data collected of 3000 are pre-processed and applied to the various classifiers to classify the web pages.The web pages were classified with the highest accuracy of 99.55% using the entropy-based ensemble random forest.The comparison of the entropy based random forest and the Gini-based random forest is shown to achieve the novelty.We have taken the data from the web pages in the form of images of around 17034 are classified them using the convolution neural network and pre-trained convolution neural network known as Resnet50.Compared its results with our proposed ensemble entropy-based random forest.Based on the web classification the web page recommendation is done.The page ranks for the collected web pages are calculated and the web page which will be similar to the given web page will be given as output.The page rank algorithm is used for the calculation of page rank.For every source website, a similar target web page is calculated.This is going to form the recommendation system.
Improving Students’ Narrative Text Reading by Using Electronic Interactive Novel at SMAN 1 Bluto Sumenep

Abstract: Reading is an important skill for students to have. Despite its importance, students in SMAN 1 Bluto still struggle in reading instruction. To solve this problem, Electronic Interactive Novel (EIN) has the potential to be used in teaching as a media to help students improve their reading skill. This research aims to investigate the use of EIN to improve the 10th grade students’ reading skill. The design of this study is classroom action research with 35 students of class 10-4 from SMAN 1 Bluto as the participants. The research instruments used are in the form of tests conducted at the end of each cycle, observation sheet with field notes, and questionnaire. The finding of the research shows that the implementation of EIN improves the students’ reading skill of narrative text in the teaching process and the test result. The result of evaluation carried out in end of cycle 1 was 46 percent students achieve the minimum score of 70. With that reasoning, the first cycle was considered unsuccessful. In cycle 2.89 percent students managed to achieve the minimum score of 70. Thus, the second cycle was deemed successful. Therefore, it can be concluded that Electronic Interactive Novels can be implemented to improve the narrative reading skill of tenth grade students. However, teachers need to pay great attention to the pace with which they conduct the instruction using EIN to make sure their students can follow well. Keywords: reading; electronic interactive novel; classroom action research Abstrak: Membaca adalah kemampuan penting untuk dimiliki oleh siswa. Tapi, siswa SMAN 1 Bluto masi mengalami kesulitan dalam pembelajaran membaca. Untuk menyelesaikan masalah ini, EIN memiliki potensi untuk digunakan sebagai media pembelajaran demi meningkatkan kemampuan membaca siswa. Penelitian ini bertujuan untuk memeriksa pengunaan Novel Interaktif Elektronik (EIN) dalam meningkatkan kemampuan membaca siswa kelas 10. Penilitan ini adalah penelitian tindakan kelas dengan 35 siswa kelas 10-4 SMAN 1 Bluto sebagai partisipan. Instrumen penelitian yang digunakan yaitu tes yang diadakan di akhir siklus, lembar observasi dengan catatan lapangan, dan kuesioner. Hasil temuan menunjukkan bahwa penggunaan EIN meningkatkan kemampuan membaca siswa. Hasil evaluasi siklus pertama menunjukkan 46 persen siswa dapat mencapai nilai minimal yaitu 70. Dengan pertimbangan tersebut, siklus pertama dianggap tidak berhasil. Pada siklus kedua, 89 persen siswa mampu mencapai nilai minimal, sehingga siklus kedua dinilai berhasil. Maka, dapat disimpulkan bahwa Novel Interaktif Elektronik dapat digunakan untuk meningkatkan kemampuan siswa membaca teks naratif. Dengan catatan, guru harus memperhatikan laju proses pembelajaran agar siswa dapat mengikuti dengan baik. Kata kunci: membaca; novel interaktif elektronik; penelitian tindakan kelas
Online Land Registration Using Blockchain

Abstract: If a person wishes to make purchases properties, an intermediary known as a dealer (as in India's old Land Administration system) would help facilitate the transaction by creating and compiling all the necessary physical papers to support a settlement as proof of ownership. A recognized federal department will document the property, and all its qualities will be recorded in a book by the broker before the contract is made. Files may be lost or tampered with in this situation since anybody who has access can access or edit the papers, and that in turn compromises this physical evidence of land ownership. Our suggested smart contract-based solution, on the other hand, is more efficient in terms of time and security, as well as synchronized and less susceptible to distortion or fraud over the course of completing a purchase or asset management process. As a result of a thorough examination of something like the traditional land application procedure, as well as an evaluation of Blockchain's leadership and integrity repair work, as well as the virtualization factor, we have proposed a solution using Blockchain technology.
Understanding the effect of sparsity on neural networks robustness

This paper examines the impact of static sparsity on the robustness of a trained network to weight perturbations, data corruption, and adversarial examples. We show that, up to a certain sparsity achieved by increasing network width and depth while keeping the network capacity fixed, sparsified networks consistently match and often outperform their initially dense versions. Robustness and accuracy decline simultaneously for very high sparsity due to loose connectivity between network layers. Our findings show that a rapid robustness drop caused by network compression observed in the literature is due to a reduced network capacity rather than sparsity.
SAViR-T: Spatially Attentive Visual Reasoning with Transformers

We present a novel computational model, "SAViR-T", for the family of visual reasoning problems embodied in the Raven's Progressive Matrices (RPM). Our model considers explicit spatial semantics of visual elements within each image in the puzzle, encoded as spatio-visual tokens, and learns the intra-image as well as the inter-image token dependencies, highly relevant for the visual reasoning task. Token-wise relationship, modeled through a transformer-based SAViR-T architecture, extract group (row or column) driven representations by leveraging the group-rule coherence and use this as the inductive bias to extract the underlying rule representations in the top two row (or column) per token in the RPM. We use this relation representations to locate the correct choice image that completes the last row or column for the RPM. Extensive experiments across both synthetic RPM benchmarks, including RAVEN, I-RAVEN, RAVEN-FAIR, and PGM, and the natural image-based "V-PROM" demonstrate that SAViR-T sets a new state-of-the-art for visual reasoning, exceeding prior models' performance by a considerable margin.
Encoding large information structures in linear algebra and statistical models

Large information sizes in samples and features can be encoded to speed up the learning of statistical models based on linear algebra and remove unwanted signals. Encoding information can reduce both sample and feature dimension to a smaller representational set. Here two examples are shown on linear mixed models and mixture models speeding up the run time for parameter estimation by a factor defined by the user's choice on dimension reduction (can be linear, quadratic or beyond based on dimension specification).
An Enhanced AdaBoost Classifier for Smart City Big Data Analytics

The targeted goal regarding the smart cities is improving the goodness of their people and to raise the economic improvement in maintaining certain rate or level. Smart cities would increase all set of utilities, which involves healthcare, education, transportation and agriculture among other utilities. Smart cities are depended on the ICT framework, which includes the Internet of Things methodology. These methodologies make bulk of diverse in data, which referred to as big data. Moreover, these data have no purpose by themselves. Modules needed to improve as new to explain the large amount of data collected and one of the good methods to solve is to use the methods of big data analytics. It shall be maintained and designed through the methods of analytics to get good understanding and in order to increase the utilities of smart city.
Time and Error Analysis of Neural Network Model of Rectangular Vibrating Membrane

Abstract Series solution of rectangular vibrating membrane (SSRVM) of the motion has been represented by Neural Network (NN) model. Experiments have been performed to find a combination of NN structure and size of sample data such that error and time cost of SSRVM are the minimum. Error and time cost of NN models have been studied w.r.t structure, size of sample, random choice of initial weights and selection of data. Peak signal to noise ratio (PSNR) between series solution and solution by NN model is approximately 50 dB. This ensures that solution obtained by NN model is acceptable at human visual system. The average training time of constructed NN models is better than state-of-the-art related to solution of differential equation by NN. NN models are 26 times faster than SSRVM for solution prediction.
Panoramic Panoptic Segmentation: Insights Into Surrounding Parsing for Mobile Agents via Unsupervised Contrastive Learning

In this work, we introduce panoramic panoptic segmentation, as the most holistic scene understanding, both in terms of Field of View (FoV) and image-level understanding for standard camera-based input. A complete surrounding understanding provides a maximum of information to a mobile agent. This is essential information for any intelligent vehicle to make informed decisions in a safety-critical dynamic environment such as real-world traffic. In order to overcome the lack of annotated panoramic images, we propose a framework which allows model training on standard pinhole images and transfers the learned features to the panoramic domain in a cost-minimizing way. The domain shift from pinhole to panoramic images is non-trivial as large objects and surfaces are heavily distorted close to the image border regions and look different across the two domains. Using our proposed method with dense contrastive learning, we manage to achieve significant improvements over a non-adapted approach. Depending on the efficient panoptic segmentation architecture, we can improve 3.5-6.5% measured in Panoptic Quality (PQ) over non-adapted models on our established Wild Panoramic Panoptic Segmentation (WildPPS) dataset. Furthermore, our efficient framework does not need access to the images of the target domain, making it a feasible domain generalization approach suitable for a limited hardware setting. As additional contributions, we publish WildPPS: The first panoramic panoptic image dataset to foster progress in surrounding perception and explore a novel training procedure combining supervised and contrastive training.
Analisis Sistem Informasi Akuntansi Persediaan pada Minimarket New Sarana

An accounting information system is a computer-based system designed to convert accounting data into information. This information is useful to support the continuity of a business. Inventory is one of the most important components in accounting information systems because inventory is one of the assets that has a relatively large number of current assets or assets owned by the company. This research aims to determine the accounting information system for the inventory of merchandise at the “New Sarana” minimarket. This research is a descriptive qualitative research with research procedures in the form of observation, interviews, and documentation. Based on the results of the research, it is found that the service at the “New Sarana” minimarket is quite good. However, in the application of the accounting information system it still needed to be improved because many shortcomings could result in the different data of the amount of inventory physically and that listed on the computer. This is due to the existence of double jobs, incomplete documents, the lack of quality of human resources and the level of security, and the absence of Standard Operational Procedure (SOP) listed in the minimarket.
Introduction

In 2016, Google shifted its default machine translation (MT) from statistical MT (SMT) to neural MT (NMT) for many language pairs. The noticeable improvement of target language fluency triggered much speculation about the future of translation, including claims that NMT would achieve human parity. In such speculation, translation performance was frequently evaluated in terms of language proficiency. This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book focuses on the types of translation loosely called commercial, industrial, or practical translation which are carried out by translation service providers and which deal with patents, manuals, and technical, business, or legal documents, etc. It defines metalanguages and places them in the historical context of translation studies. The book also explains the general requirements and building procedures of metalanguages.
Predicting Human Performance in Vertical Hierarchical Menu Selection in Immersive AR Using Hand-gesture and Head-gaze

There are currently limited guidelines on designing user interfaces (UI) for immersive augmented reality (AR) applications. Designers must reflect on their experience designing UI for desktop and mobile applications and conjecture how a UI will influence AR users' performance. In this work, we introduce a predictive model for determining users' performance for a target UI without the subsequent involvement of participants in user studies. The model is trained on participants' responses to objective performance measures such as consumed endurance (CE) and pointing time (PT) using hierarchical drop-down menus. Large variability in the depth and context of the menus is ensured by randomly and dynamically creating the hierarchical drop-down menus and associated user tasks from words contained in the lexical database WordNet. Subjective performance bias is reduced by incorporating the users' non-verbal standard performance WAIS-IV during the model training. The semantic information of the menu is encoded using the Universal Sentence Encoder. We present the results of a user study that demonstrates that the proposed predictive model achieves high accuracy in predicting the CE on hierarchical menus of users with various cognitive abilities. To the best of our knowledge, this is the first work on predicting CE in designing UI for immersive AR applications.
Real Image Restoration Using VAEs

Abstract— Old photos are an integral part of everybody’s life; they remind us of how one person has spent their life. As people used hard copies of photos before, those photos suffered severe degradation. This degradation in real-time images is intricate, causing thetypical restoration that might be solved through supervised learning to fail to generalize due to the domain gap between synthetic and real images. Therefore, this method uses various autoencoders to restore and colourize old images. Furthermore, this model uses a unique triplet domain translation network on real images and synthetic photo pairs. Precisely, VAEs, which are variational autoencoders, are trained to transform old pictures and clean pictures into two latent spaces. Therefore, the translation between these two latent spaces is comprehended with simulated paired data. This translation generalizes well to authentic images because the domain gap is encompassed in the close-packed latent space. Moreover, to manoeuvre numerous degradations present in one old picture, this model designs aworld branchwith a partial non-local block targeting the structured faults, like scrapes and dirt marks, and an area branch targeting unstructured faults, like noisesand fuzziness. Two branches are blended within the latent space, resulting in an improved ability to renew old pictures from numerous defects. Additionally, it applies another face refinementnetwork to revive fine details of faces within the old pictures, thus generating photos with amplified quality. Another autoencoder is encoded with colour images, and then the decoder decodes the features extracted from the encoder. Once a model is trained, testing is performed to colourize the photographs. Keywords— Degraded pictures, Variational Auto Encoders, Domain gap, Triplet domaintranslation.