Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Based Region of Interest Enhanced Video Compression

Artificial Intelligence, especially deep learning based workloads for the analysis of video data are on the rise. Examples include residential and commercial security systems where the camera data are analyzed for potential intruders, in-door retail cameras that track people movement for behavior analysis etc. In an End-to-End intelligent video solution, video analytics will be carried out at multiple places from edge IP camera to high performance cloud servers. Typically, the results of the video analytics at one stage will be sent to the next stage along with the video data for efficient processing. However, a traditional video encoder does not have an understanding of the scene and/or the priority of the objects in the frame. It tries to compress the frames with a goal to produce visually pleasing video for the human viewers by maximizing the rate-distortion performance. As a result, in the compressed video frame, the details of the regions of interest to the inferencing engine may be lost due to the artifacts introduced by the lossy compression process. We propose a novel AI based low-power architecture that utilizes the results of the machine learning at the given stage to improve the performance of the overall pipeline. The proposed method will identify the regions of interest in the frame and preserve the quality of those regions without significantly reducing the quality of the remaining regions. Simulation results show that the machine vision accuracy when inferred on the compressed video streams using the proposed method are significantly better compared to the inferencing results on the compressed video streams produced by the traditional video compression methods.
THE PRODUCTIVITY OF LITERARY LITERACY ARENA IN CENTRAL JAVA

Literature is not merely a complementary activity, but it must be able to bring to a better nation civilization. Literature needs to be read and written as a medium to describe humans with various problems. Literature is created to have recreational function, didactic function, morality and even religious function. So complex is the function of literature, but reading and writing the literary works especially novel often becomes a heavy burden. For this reason, a solution is needed to increase the interest in reading and writing literature among the society. One alternative solution is by producing the literary arena. Investigating the literary arena in Central Java is an important thing to see as well as develop the literary literacy. The literary community can be used as an arena for literacy movements to make its members literate throughout life through community involvement in appreciating and expressing literature. The literary arena in the form of community and organizing a competition in Central Java is interesting to be examined in order to see literary literacy in it. The literary arena in Central Java can be a free and open market for literary quality competition to be used and disseminated. This research used qualitative method to increasing the number of literary works and to explore the productivity of the literacy arena in Central Java. Literary literacy movement certainly cannot be separated from cultural capital, social capital, symbolic capital, and aesthetic capital. For this reason, the role of government is needed to encourage the growth of literary literacy in Central Java.
SISTEM INFORMASI PENDAFTARAN PEMBUATAN KTP ELEKTRONIK DI DINAS KEPENDUDUKAN DAN PENCATATAN SIPIL KABUPATEN KUANTAN SINGINGI

Electronic Resident Identity Card  is a national identity card made through electronics, based on physical aspects or its use which functions on a computerized basis. During this time the process of obtaining an Electronic Resident Identity Card is still manual which results in less efficiency. Community services are limited in the management of Electronic Resident Identity Cards, which is 50-100 in one day which aggravates the many queues and the loss and damage of applications for making Electronic Resident Identity Cards. The results of this study are building a web-based system in the process of registering an Electronic Resident Card. This web-based system makes it easy for applicants to register an Electronic Identity Card to carry out the registration process. The advantage of this web-based system can prevent the queue of people who want to make an Electronic Identity Card  at the Population and Civil Registration Office of Kuantan Singingi Regency.
Deep Semantic Segmentation in Autonomous Driving

The first autonomous cars developed by Carnegie Mellon University and ALV appeared in 1984. There are many datasets built specifically for testing autonomous driving (AD) applications. They are divided into two main categories: real and synthetic. In addition to geometric information, there is a very important information prior that can be used in AD applications. The chapter focuses on computer vision tasks for AD applications. It explores the AD field and the role of recent computer vision algorithms in self-driving car development, such as deep learning convolutional neural network (CNN), which showed significant improvement in terms of accuracy for various tasks in different applications. CNNs and in particular deep learning have played a great role in computer vision, and they are exploited in various applications for AD. The chapter presents a brief introduction about deep CNN, i.e., how neural networks are used in machine learning, and the idea behind it.
Re-Pair in Small Space

Re-Pair is a grammar compression scheme with favorably good compression rates. The computation of Re-Pair comes with the cost of maintaining large frequency tables, which makes it hard to compute Re-Pair on large scale data sets. As a solution for this problem we present, given a text of length n whose characters are drawn from an integer alphabet, an O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) time algorithm computing Re-Pair in n lg max(n, τ) bits of working space including the text space, where τ is the number of terminals and non-terminals.
A Brazilian Portuguese Real-Time Voice Recognition to deal with sensitive data

Speech recognition is generally performed by a few cloud providers centrally. Such an approach is not suitable for financial and medical institutions because sensitive data cannot be provided openly to third parties. This work proposes a simple chatbot for Brazilian Portuguese real-time speech recognition, well-defined purpose, specific vocabulary, and a secure way without exposing sensitive data for use on mobile devices. The proposed system got a word error rate of 2.38% for a speech recognition task.
Intention as commitment toward time

In this paper we address the interplay among intention, time, and belief in dynamic environments. The first contribution is a logic for reasoning about intention, time and belief, in which assumptions of intentions are represented by preconditions of intended actions. Intentions and beliefs are coherent as long as these assumptions are not violated, i.e. as long as intended actions can be performed such that their preconditions hold as well. The second contribution is the formalization of what-if scenarios: what happens with intentions and beliefs if a new (possibly conflicting) intention is adopted, or a new fact is learned? An agent is committed to its intended actions as long as its belief-intention database is coherent. We conceptualize intention as commitment toward time and we develop AGM-based postulates for the iterated revision of belief-intention databases, and we prove a Katsuno-Mendelzon-style representation theorem.
Aligned attention for common multimodal embeddings

Deep learning has been attributed to incredible advances in computer vision, natural language processing, and general pattern understanding. Recent discoveries have enabled efficient vector representations of both visual and written stimuli. Robustly transferring between the two modalities remains a challenge that could yield benefits for search, retrieval, and storage applications. We introduce a simple, yet highly effective approach for building a connection space where natural language sentences are tightly coupled with visual data. In this connection space, similar concepts lie close, whereas dissimilar concepts lie far apart, irrespective of their modality. We introduce an attention mechanism to align multimodal embeddings that are learned through a multimodal metric loss function. We evaluate the learned common vector space on multiple image–text datasets—Pascal Sentences, NUS-WIDE-10k, XMediaNet, Flowers, and Caltech-UCSD Birds. We extend our method to five different modalities of image, sentence, audio, video, and 3D models to demonstrate cross-modal retrieval on the XMedia dataset. We obtain state-of-the-art retrieval and zero-shot retrieval across all datasets.
Private Colocation Discovery: Taming the Coronavirus While Preserving Privacy

Successful containment of the Coronavirus pandemic rests on the ability to quickly and reliably identify those who have been in close proximity to a contagious individual. Existing tools for doing so rely on the collection of exact location information of individuals over lengthy time periods, and combining this information with other personal information. This unprecedented encroachment on individual privacy at national scales has created an outcry and risks rejection of these tools. We propose an alternative: an extremely simple scheme for providing fine-grained and timely alerts to users who have been in the close vicinity of an infected individual. Crucially, this is done while preserving the anonymity of all individuals, and without collecting or storing any personal information or location history. Our approach is based on using short-range communication mechanisms, like Bluetooth, that are available in all modern cell phones. It can be deployed with very little infrastructure, and incurs a relatively low false-positive rate compared to other collocation methods. We also describe a number of extensions and tradeoffs. We believe that the privacy guarantees provided by the scheme will encourage quick and broad voluntary adoption. When combined with sufficient testing capacity and existing best practices from healthcare professionals, we hope that this may significantly reduce the infection rate.
TwoFold Frisky Algorithm (TFFA): A Fast Frequent Itemset Algorithm

Finding frequent itemsets and generation of association rules by using frequent items plays an important role in the field of data mining. Many algorithms were proposed to get frequent itemsets, but the most popular algorithm is Apriori which is implemented on the horizontal database. In which the method frequently scans the database and returns the flood of candidates which are the significant disadvantages. A novel algorithm based on the vertical database was introduced, which overcomes the disadvantages of Apriori. The proposed algorithm discards the calculation of a few frequent itemsets by taking the next maximum itemset in the process of generating maximal frequent itemset. That means when the length of a frequent itemset with the higher value in powers of two appears then it neglects the lower valued itemsets though it is in powers of two. The simulation results were compared with Apriori and FP-Growth algorithms. It was shown that the novel implementation performed better than Apriori and FP-Growth.
Image Visually Meaningful Cryptography Based on Julia Set Generating and Information Hiding

Secret and private image security storage and fast transmission are a relatively concerned issue in the field of information security. In existed image encryption schemes, plain image is transformed to meaningless random noise signal which is vulnerable to attack with suspicion. Image visually meaningful cryptography (IVMC) based on information hiding is difficult to hide large size images because of limited hiding capacity. To solve this problem,a new IVMC based on Julia set images (JSI) generating and information hiding was proposed.In this solution, firstly, JSI are generated by utilizing Julia set generating parameters (JSGP) and specific pixel coloring scheme (SPCS), as keys, escape-time algorithm (ETA). Then the secret image or the encrypted secret image is embedded into JSI’s detail-rich small areas to form a beautiful stego-JSI, so as to achieve the purpose of IVMC. The receiver can use the same JSGP and SPCS keys to generate the same cover-JSI, which greatly facilitates the extraction and decryption of the encrypted image. Theoretical analysis and experimental results show that the proposed method has good artistic beauty, high hiding capacity, good imperceptibility, strong anti-compression performance and good anti-steganalysis ability, as well as large key space and good key sensitivity.
Chinese Landmark Recognition

Landmark recognition is a useful yet challenging task due to the lack of large annotated landmark datasets. Previous related work often neglects non-English speaking areas, such as Chinese landmarks. Tests using commercial landmark recognition APIs also indicate the incompleteness of such landmark data. Our project releases Chinese landmark and attraction dataset for recognition of human-made and natural landmarks in China. The dataset contains 42,548 images depicting 987 unique landmarks. Moreover, we build a landmark recognition model based on Convolution Neural Network and transfer learning techniques. Lastly, an iOS application is built based on our landmark recognition model. It allows user to do landmark prediction on images taken by camera and images from device gallery.
<i>Geocarto International’s</i> on-going long-term mission to publish research on Earth Observations and GIS: our notable contributions to Sustainability Development Goals of the United Nations 2030 Agenda for Sustainable Development

During our long history of 35 years of publication, Geocarto International’s primary mission has been to publish research focused on Earth observations and GIS with an emphasis on disseminating thi...
RETRACTED ARTICLE: Visualization and Integration of Images of Radioactive Substances as Point Cloud Data in 3-D Environment Models

We, the American Nuclear Society, the Editor and Publisher of Nuclear Technology, have retracted the following article: Yuki Sato & Tatsuo Torii: Visualization and Integration of Images of Radioactive Substances as Point Cloud Data in 3-D Environment Models. Nuclear Technology, 2020, DOI: 10.1080/00295450.2020.1722555 The authors have requested the retraction of their article due to an identified defect in the Compton camera software, which resulted in an incorrect output value for the interaction position of the gamma rays in the sensor of the camera. It is expected that the experimental results may not change significantly with the bug correction; however, many figures would need to be replaced. The authors may seek a separate publication of an updated manuscript in the future.We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as „Retracted„.
Image inpainting-based behavior image secret sharing

&lt;abstract&gt; &lt;p&gt;The polynomial-based image secret sharing (ISS) scheme encodes a secret image into &lt;italic&gt;n&lt;/italic&gt; shadows assigned to &lt;italic&gt;n&lt;/italic&gt; participants. The secret image with high resolution is decoded by Lagrange interpolation when collecting any &lt;italic&gt;k&lt;/italic&gt; or more shadows. Thus, ISS is used in applications such as distributive storage in the cloud, digital watermarking, block chain, and access control. Meaningful shadows are significant in ISS because meaningful shadows decrease the suspicion of image encryption and increase the efficiency of shadow management. Generally, previously meaningful ISS schemes were achieved through embedding the shadows into cover images using information hiding techniques and suffer from large pixel expansion and complex decoding procedure. Digital image processing, such as inpainting (texture synthesis), is a standard technique in multimedia applications. It will be highly significant if ISS can be performed in the processing of a normal digital image processing technique. Generally, the encoding method of an ISS scheme entails the use of a mathematical function that is sensitive to any slight change in the ISS output; therefore, the development of a method for performing the ISS procedure and simultaneously achieving image processing behavior is a key challenge. In this paper, we exploit the behavior ISS (BISS) and realize an image inpainting-based BISS scheme for the (&lt;italic&gt;k&lt;/italic&gt;, &lt;italic&gt;n&lt;/italic&gt;) threshold. Using screening operations, a secret image is encoded into the pixels of cover images by polynomial-based ISS in the processing of inpainting shadows to obtain meaningful shadows similar to the input cover images. In addition, the secret image can be losslessly decoded by Lagrange interpolation when collecting any &lt;italic&gt;k&lt;/italic&gt; or more shadows. Experiments are given to confirm the efficiency of the scheme.&lt;/p&gt; &lt;/abstract&gt;
Part of Speech Tagging and Named Entity Recognition

In this chapter we explore Part of Speech (POS) tagging using the openNLP library. openNLP is an interface to the Apache’s Natural Language Processing toolkit of the same name. Apache OpenNLP is a JAVA based machine learning toolkit for the processing of natural language text. Much in the way that the mallet package for R is an interface to MALLET, the openNLP package in R provides and R-based interface to the Apache library.
Efficient Evolutionary Neural Architecture Search (NAS) by Modular Inheritable Crossover

The convolution neural network is prominent in image processing, and a large number of excellent deep neural networks have been proposed in recent years. However, the hand-actuated design of a neural network is time-consuming, laborious, and challenging. Thus many neural architecture search (NAS) methods have been proposed, among which the evolutionary NAS methods have achieved encouraging results due to the global search capability of evolutionary algorithms. Nevertheless, most evolutionary NAS methods use only mutation operators for offspring generation, and the generated offspring networks could be quite different from their parent networks. To address this deficiency, we propose an efficient evolutionary NAS method using a tailored crossover operator. Different from existing mutation operators, the proposed crossover operator enables the offspring network to inherit promising modular from their parent networks. Experimental results indicate that our proposed evolutionary NAS method has achieved competitive results in comparison with some state-of-the-art NAS methods. Moreover, the effectiveness of our proposed modular inheritable crossover operator for offspring generation is validated.
Brain MR Multimodal Medical Image Registration Based on Image Segmentation and Symmetric Self-similarity

With the development of medical imaging technology, image registration has been widely used in the field of disease diagnosis.The registration between different modal images of brain magnetic resonance (MR) is particularly important for the diagnosis of brain diseases.However, previous registration methods don't take advantage of the prior knowledge of bilateral brain symmetry.Moreover, the difference in gray scale information of different modal images increases the difficulty of registration.In this paper, a multimodal medical image registration method based on image segmentation and symmetric self-similarity is proposed.This method uses modal independent self-similar information and modal consistency information to register images.More particularly, we propose two novel symmetric self-similarity constraint operators to constrain the segmented medical images and convert each modal medical image into a unified modal for multimodal image registration.The experimental results show that the proposed method can effectively reduce the error rate of brain MR multimodal medical image registration with rotation and translation transformations (average 0.43mm and 0.60mm) respectively, whose accuracy is better compared to state-of-the-art image registration methods.
A RFF Access Authentication Technology Based on K-Nearest Neighbor Method

Access security of wireless device is a great challenge to wireless communication networks, especially for radio frequency identification (RFID) and some other devices with limited computing power, which are faced with effective high-strength security challenges, and as an important non-cryptographic authentication technology, radio frequency fingerprinting (RFF) technology, for its full use of non-imitation radio frequency characteristics, attracts more and more attention. The generation mechanism of radio frequency fingerprint and the basic model of recognition system are described, and A RFF access authentication technology based on k-nearest neighbor (kNN) approach also is analyzed. In addition, the existing problems of radio frequency fingerprint recognition technology and the research direction of further improvement are pointed out.
Design of Cooling and Air Flow System Using NDLC Method Based on TIA-942 Standards in Data Center at CV Media Smart Semarang

CV Media Smart is a company that involved in the procurement of IT tools in schools and offices. With wide range coverage of schools and companies, CV Media Smart want to add more business process, therefore data center is needed to support existing and added later business process. The focus of this research is on cooling system and air flow. To support this research, NDLC (Network Development Life Cycle) is used as research method. NDLC is a method that depend on development process, like design of business process and infrastructure design. The reason why this research is using NDLC method is because NDLC is method that depend on development process. The standard that used in this research is TIA-942. Result of this research is a design of data center that already meet TIA-942 standard tier 1.
THE EFFECT OF READING STRATEGY ON COMPREHENSION ACHIEVEMENT IN CHINESE LANGUAGE

Reading skills play an important role in language learning. There are many reading strategies for effective reading. Yet, not many language teachers emphasized or integrated learning strategies into the learning process of reading skills, especially in Chinese language learning. This article aims to report the impact of reading strategies in teaching and learning on the achievement of Primary 5 students in Chinese national primary schools. The reading strategy of making inferences is the focus of this study. This study adopts a quasi-experimental design, with data collection through pre-test and post-test to identify the impact of reading strategy on student achievement. A descriptive analysis was used. The sample of this study consisted of 70 5th graders consisting of three groups based on three pre-test performance namely Category A (80 - 100), Category B (40-79) and Category C (0-39). The result showed that there was an increase in the number of pupils who received the Category A’s score for the treatment group, which increased from 9 students to 15 students (60%). On the contrary, for the control group, the findings show that pupil performance decreased, with the number of pupils in C category increased, from 4 students to 8 students. It can be concluded that the use of reading strategies can have a positive impact on reading comprehension in Chinese language. The findings implicated that the emphasis should be given to the learning of reading strategies in the teaching and learning reading skills. Language teachers should be aware of the importance of the reading strategies in developing reading skills among students. The learning and application of reading strategies should be giving more emphasis in mastering reading among students in language learning.