Reviewerly/exHarmony huggingface dataset:
Information Security Risk Management Planning of Digital Certificate Management Case Study: Balai Sertifikasi Elektronik

Otoritas Sertifikat Digital (OSD) is a certificate authority held by Balai Sertifikasi Elektronik (BSrE) of the Badan Siber dan Sandi Negara (BSSN). Digital certificate management is the primary business process of Otoritas Sertifikat Digital Layanan Universal (OSD LU), which, if it is interrupted, the OSD business process cannot run smoothly and has an impact on the digital certification process. With the problem of maintaining the reliability and security of the system it there is a need to classify, analyzed, and carried these risks correctly to diminish the negative repercussions that may arise at an agreeable level. The main goal is to address the issues about risk management planning, particularly in the absence of an information security risk management plans within BSrE. We carried the process of risk assessment utilizing the ISO 27005 framework combined with NIST SP 800-30 revision 1 in assessing the security risks. The results of the risk assessment of the 27 assets identified and 26 risk scenarios that must be mitigated and 38 risk scenarios acceptable to the organization. We hope with this study it can help the BSrE in analyzing and to maintaining the security management plan within the OSD LU system and to provides control recommendations as an effort to minimize information security risks in data communication application within the business processes and produce a strategic plan for handling and accepting risk accompanied by a person in charge of the risk scenario.
A Crowd Density Detection Algorithm for Tourist Attractions Based on Monitoring Video Dynamic Information Analysis

In this paper, we analyze and calculate the crowd density in a tourist area utilizing video surveillance dynamic information analysis and divide the crowd counting and density estimation task into three stages. In this paper, novel scale perception module and inverse scale perception module are designed to further facilitate the mining of multiscale information by the counting model; the main function of the third stage is to generate the population distribution density map, which mainly consists of three columns of void convolution with different void rates and generates the final population distribution density map using the feature maps of different branch regressions. Also, the algorithm uses jump connections between the top convolution and the bottom void convolution layers to reduce the risk of network gradient disappearance and gradient explosion and optimizes the network parameters using an intermediate supervision strategy. The hierarchical density estimator uses a hierarchical strategy to mine semantic features and multiscale information in a coarse-to-fine manner, and this is used to solve the problem of scale variation and perspective distortion. Also, considering that the background noise affects the quality of the generated density map, the soft attention mechanism is integrated into the model to stretch the distance between the foreground and background to further improve the quality of the density map. Also, inspired by multitask learning, this paper embeds an auxiliary count classifier in the count model to perform the count classification auxiliary task and to increase the model’s ability to express semantic information. Numerous experimental results demonstrate the effectiveness and feasibility of the proposed algorithm in solving the problems of scale variation and perspective distortion.
Contextual Lexicon Based Sentiment Analysis in Myanmar Text Reviews

A lot of information related to several commercial application available online which can be used to provide the guidance and suggestions to possible new customers. People desire to distribute the opinions and state the sentiments in their own language. Sentiment analyzers developed for English language, are not workable for Myanmar language. Mining sentiments in Myanmar text come with a lot of issues and challenges. The direction of the sentiment is highly depend on the context of sentiment text. Thus, it is significant challenge to consider contextual lexical information in order to correctly classify the polarity. This paper aims to improve the existing challenges problem of language and analyze the sentiment classification of food and restaurants domain by using contextual analysis with lexicon based approach in Myanmar text reviews. The effect of intensifier, negations and objective words are important role in the context of sentiment orientation. This paper addresses sentiment classification for Myanmar Language and overcome one of the problems of language specific challenges. The accuracy of the proposed system is higher than the classification without using context information (negation, intensifier and objective words). Overall accuracy of the proposed system is 92% and weighted average F-measure for imbalance class of 1200 reviews is 0.93.
Z2-ZNCC: ZigZag Scanning based Zero-means Normalized Cross Correlation for Fast and Accurate Stereo Matching on Embedded GPU

Mobile stereo matching systems are becoming more important in many applications such as auto-driving and autonomous robots. However, to maintain its low power consumption, mobile platforms have only limited hardware resources. Accurate stereo matching methods require a high computational complexity, and it is difficult to maintain both acceptable accuracy and processing speed on the mobile platforms. To solve this trade-off, in this paper, we propose a novel acceleration approach for a well-known matching algorithm Zero-means Normalized Cross Correlation (ZNCC), and show its effectiveness on a Jetson TX2 embedded GPU. By combining our new approach, Z <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> - ZNCC, with the Semi-Global Matching (SGM) algorithm, our system achieves a low error rate of 7.76% while keeping 28 fps for 1242×375 pixels images with the maximum disparity of 128 on the KITTI 2015 dataset. This performance is higher than previous state-of-the-art system on the same hardware platform.
Externalizing Transformations of Historical Documents: Opportunities for Provenance-Driven Visualization

Transcription, annotation, digitization and/or visualization are common transformations that historical documents such as national records, birth/death registers, university records, letters or books undergo. Reasons for those transformations span from the (physical) protection of the original materials to disclosure of "hidden" information or patterns within the documents. Even though such transformations bring new insights and perspectives on the documents, they also modify the documents' content, structure, and/or artifactual form and thus, occlude prior knowledge and interpretation. When it comes to visualization as a means to transform historical documents from written to abstract visual form, there is typically little acknowledgment or even understanding of the previous transformation steps these documents have gone through. The "tremendous rhetorical force" [3] of visualization, we argue, should not be at the expense of the multiple pasts, contexts, and curators that are inherent in historical record collections. Rather, the urgent question for the fields of visualization and the (digital) humanities is how to better support awareness of these multiple layers of interpretation and the people behind them when representing historical documents. We begin to address this question based on a collection of historical university records by (a) investigating common transformation processes of historical documents, and (b) discussing opportunities and challenges for making such transformations transparent through what we call "provenance-driven visualization"; the idea for a visualization that makes visible the layers of transformation (including interpretation, re-structuring, and curation) inherent in historical documents.
Huawei Ban in the US: Projected Consequences for International Trade

On May 19, 2019, the US President Donald Trump made an executive decision of blacklisting the Chinese tech giant Huawei in US market. The paper aims to discuss the tentative alternative consequences of banning Huawei in US market. As a very recent incident, there is no literature available on this topic. The paper has used the published sources and attempt to predict the consequences of this decision. The author expects that this paper will be helpful in conducting further study in the areas of international trade and political economy.
Context Aware Feature Interaction based Recommendation System

Additional context information has strong support for all kinds of recommendation systems, so context aware recommendation systems have been widely concerned in recent years. The existing mainstream context aware recommendation model adopts the neural network method, which combines the user's long-term and short-term preferences with the input query to carry out personalized product recommendation.Based on the matrix factorization model, this paper proposes a new interaction mode network model, which consists of three modules: context user / item interaction module, attention mechanism module and context environment overall role module. In this model, we use bilinear function to establish the interaction between context and user / item, and add attention mechanism to distinguish the importance of different context information. Finally, we add user score bias and item score bias which are changed by context environment to the traditional matrix factorization method. Combined with the above methods, we set up a matrix factorization recommendation model based on context aware feature interaction, named “feature interactive network model” (FINM). Through experiments on data sets, it is shown that the algorithm proposed in this paper is superior to the general recommendation algorithm.
An attraction-based approach for instance selection

Due to the increasing size of the datasets, instance selection techniques have been applied for reducing the computational resources involved in data mining and machine learning tasks. In this paper, we propose an attraction-based approach for selecting instances. The algorithm adopts the notion of attraction for selecting the most representative instances of each class. The resulting approach allows the user to define how many representative instances should be selected. Our method was evaluated in a classification task considering 14 well-known datasets. The performance of the proposed algorithm was compared to the performances of 8 prototype selection algorithms in terms of accuracy and reduction rate. The experimental results show that, in general, the proposed algorithm provides a good trade-off between reduction rate and accuracy with a low time complexity.
Design of an efficient image protection method based on QR code

This paper presents the design and the verification of an efficient image protection method based on the QR code, which is a type of two-dimensional barcode widely used in various fields. For this purpose, we design a new image protection system consisting of a secure image generator and a secure image recognizer. One adds a new pre-processing block to the typical QR code generator and the other combines the existing QR code reader with a new post-processing block. The new architecture provides image de-identification. It is also flexible, allowing the use of text-based compression and encryption. We have implemented prototype applications for verifying the functions of the secure image generator and those of the secure image recognizer. As a result, it is shown that the proposed architecture can be used as a good solution for image privacy protection, especially in offline environments.
Integrating Structured and Unstructured Patient Data for ICD9 Disease Code Group Prediction

The large-scale availability of healthcare data provides significant opportunities for development of advanced Clinical Decision Support Systems that can enhance patient care. One such essential application is automated ICD-9 diagnosis group prediction, useful for a variety of healthcare delivery related tasks including documenting, billing and insurance claims. Past attempts considered patients’ multivariate lab events data and clinical text notes independently. To the best of our knowledge, ours is the first attempt to investigate the efficacy of integration of both these aspects for this task. Experiments on MIMIC-III dataset showed promising results.
Visual Object Tracking in Spherical 360° Videos: A Bridging Approach

We present a novel approach for adapting existing visual object trackers (VOT) to work for equirectangular video, utilizing image reprojection. Our system can easily be integrated with existing VOT algorithms, significantly increasing the accuracy and robustness of tracking in spherical 360° environments without requiring retraining. Our adapted approach involves the orthographic projection of a subsection of the image centered around the tracked object each frame. Our projection reduces the distortion around the tracked object each frame, allowing the VOT algorithm to more easily track the object as it moves.
Evaluating Learned State Representations for Atari

Deep reinforcement learning, the combination of deep learning and reinforcement learning, has enabled the training of agents that can solve complex tasks from visual inputs. However, these methods often require prohibitive amounts of computation to obtain successful results. To improve learning efficiency, there has been a renewed focus on separating state representation and policy learning. In this paper, we investigate the quality of state representations learned by different types of autoencoders, a popular class of neural networks used for representation learning. We assess not only the quality of the representations learned by undercomplete, variational, and disentangled variational autoencoders, but also how the quality of the learned representations is affected by changes in representation size. To accomplish this, we also present a new method for evaluating learned state representations for Atari games using the Atari Annotated RAM Interface. Our findings highlight differences in the quality of state representations learned by different types of autoencoders and their robustness to reduction in representation size. Our results also demonstrate the advantage of using more sophisticated evaluation methods over assessing reconstruction quality.
Study on Improvement of Estimation Accuracy in Pose Estimation Model Using Time Series Correlation

Detecting human pose in a video is a difficult task. Although many high-performed human pose estimation models have been proposed in the last few years, the estimation accuracy has always been a major concern. In this study we present a method to improve the accuracy of human pose estimation for videos. Technically, predicted human pose is a set of time series data. Thus, by using time series correlation, human pose estimation can be performed in a better accuracy. We combine a CNN based human pose estimation model with a multiple object tracking framework to achieve this. Undetected/mis-detected body joints will be interpolated using the information from previous and following frames. As a result, our proposed method improved the accuracy of an existing CNN based human pose estimation model by reducing the number of undetected and mis-detected frames by 6.30% and 0.98% respectively.
Optimizing Video Quality Estimation Across Resolutions

Many algorithms have been developed to evaluate the perceptual quality of images and videos, based on models of picture statistics and visual perception. These algorithms attempt to capture user experience better than simple metrics like the peak signal-to-noise ratio (PSNR) and are widely utilized on streaming service platforms and in social networking applications to improve users' Quality of Experience. The growing demand for high-resolution streams and rapid increases in user-generated content (UGC) sharpens interest in the computation involved in carrying out perceptual quality measurements. In this direction, we propose a suite of methods to efficiently predict the structural similarity index (SSIM) of high-resolution videos distorted by scaling and compression, from computations performed at lower resolutions. We show the effectiveness of our algorithms by testing on a large corpus of videos and on subjective data.
Rubberband

Federated learning (FL) applications face the challenges of managing the temporary state at scale combined with the limited compute and storage capacity of the infrastructure and the heterogeneity/dynamism of the deployment environment. The paper proposes Rubberband - a middleware designed to manage the state in the FL deployments at scale.
Visualizing a Large Spatiotemporal Collection of Historic Photography with a Generous Interface

Museums, libraries, and other cultural institutions continue to prioritize and build web-based visualization systems that increase access and discovery to digitized archives. Prominent examples exist that illustrate impressive visualizations of a particular feature of a collection. For example, interactive maps showing geographic spread or timelines capturing the temporal aspects of collections. By way of a case study, this paper presents a new web-based visualization system that allows users to simultaneously explore a large collection of images along several different dimensions-spatial, temporal, visual, textual, and through additional metadata fields including the photographer name-guided by the concept of generous interfaces. The case study is a complete redesign of a previously released digital, public humanities project called Photogrammar (2014). The paper highlights the redesign's interactive visualizations that are now possible by the affordances of newly available software. All of the code is open-source in order to allow for reuse of the codebase to other collections with a similar structure.
Research on Approximate Fitting Method of Activation Function Based on Gradient Equalization in Deep Learning

Abstract Activation function plays an important role in neural network model. Neural networks can express more complex situations by introducing nonlinearity due to activation functions. In this paper, an approximate fitting method of activation function based on gradient bisection is proposed. The algorithm can fit the activation function curve well. You can use less space for storage on your computer. Through experiments, the storage capacity can be reduced by one time compared with the traditional approximate fitting method, but the accuracy of the model is not affected.
Evaluation issues of query result ranking for semantic search

Abstract Application of semantic in information retrieval is a dynamically developing area. Nowadays, elements of semantic search are used in popular systems such as Microsoft Azure, Abbyy Intelligent Search, Google Search with BERT. Using sematic search, it is possible to obtain documents that contain exact meaning instead of set of words. But Lucene is still one of the most popular libraries for search purpose and it has its own syntax for fuzzy, wildcard, proximity and other modifiers for queries. To evaluate precision and recall of search the authors have created a list of queries and divided it into groups according to a query type. The article contains results of this investigation for semantic search with metagraph knowledge base in comparison to Lucene with the same morphological analyzer. The quantity of documents for two types of search may be the same but ranking should be different. Ranking of queries is another issue and its evaluation is not a trivial task. In this article the authors applied Levenstein distance but then proposed a new method for comparison of ranking given by different search engines. All results were obtained on Open Corpora text corpus.
GMDH-Based Discovering Dynamic Regularities of the Ukraine Covid-19 Pandemic Process

This article presents the modeling results of the Ukraine Covid-19 pandemic process using official statistical data on the confirmed cases. Main goal is discovering dynamic regularities of the process given as daily data of the time series. That is why we use four different methods to build predictive difference models of the autoregression type: ordinary autoregression; autoregression of optimal structure obtained using the combinatorial-genetic GMDH algorithm COMBI-GA; another variant of the optimal structure built by the well-known method Lasso; and we compare prediction results of these methods with independent predictions published by the World Data Center. In our study, the baseline prediction is the one produced by the ordinary autoregression which includes all lags from 1 to a given their number. Unlike it, algorithms COMBI-GA as well as Lasso construct autoregressions with in some respect optimal compositions of lags. The WDC independent predictions are made using the Backpropagation ANN as nonlinear transformation of lag variables. This comparative study we carried out in two stages: first, for the period of strong quarantine in Ukraine and second, for the period of stepwise quarantine weakening. For both stages, the optimal models built by the COMBI-GA are most interpretable and demonstrate better predictive accuracy on validation datasets. This research is useful for defining tendency of coronavirus evolvement in time and predicting its future activity in order to take some protective measures.
A Comparative Analysis of Searchable Encryption Schemes using SGX

Searchable encryption (SE) schemes enable users to search over encrypted data that are outsourced to untrusted cloud servers. Although many software-based SE schemes have been developed to improve security and functionality, most of them have limitations in terms of performance and security assumptions. In order to increase the efficiency, and guarantee query and data privacy, several SE schemes based on trusted execution environment (TEE) such as Intel SGX, ARM TrustZone have been proposed recently. In this paper, we investigate the existing SGX-based searchable encryption schemes, and conduct a comparative analysis of them.
Technology and digitalization: organization of scientific research in the magistracy

Abstract The problem of effective search, explication, and analysis of contextual knowledge is associated with an ever-increasing volume of available information, its non-formalization and poor structuredness, a high rate of updating of scientific knowledge, the polysemy of the terminological base used, the transfer of terms from one subject area to another without the necessary interpretation and adaptation. The existing tools for searching, explicating and analysing contextual knowledge are of little demand due to the lack of meaningful information both about the tools themselves and about the methods and algorithms for their application in science and practice, as well as the limited set of services provided by analytical software. The educational-methodical complex “Technologies of data extraction and mining in scientific research” is aimed at the formation of research and analytical competencies of undergraduates. The results of mastering the educational product will allow undergraduates to consciously apply in research practice an integrated approach (synthetic method) to the search and analysis of contextual knowledge, to use analytical software and environments with built-in services for explication, clustering, and statistical processing of scientific texts on the basis of this approach.