Reviewerly/exHarmony huggingface dataset:
Multilevel Video Access Control Mechanism Using Low Power Based Audio Watermarking

As the number of violent crimes and incidents worldwide has soared, market demand for intelligent video surveillance solutions is gradually increasing as a solution for social safety, and technology development for next-generation video solutions is underway (SME Technology Roadmap, [1]). However, existing security video transmission solutions can be consistently generated without rating authority during creation of video, resulting in problems that are shared without any control by each agency, department, or person. At this time, leakage of security video can cause serious damage to the public institutions concerned. Therefore, it is necessary to set access authority according to the intention of an original video sender and it should be transmitted inserting user’s authority. The watermark information should be extracted and played in real time or in VOD according to their authority. At this time, used watermark was redesigned to improve energy efficiency. As such this paper we propose a multilevel video access control mechanism to prevent leakage of personal information in security video.
Machete: Dissecting the Operations of a Cyber Espionage Group in Latin America

Reports on cyber espionage operations have been on the rise in the last decade. However, operations in Latin America are heavily under researched and potentially underestimated. In this paper we analyze and dissect a cyber espionage tool known as Machete. Our research shows that Machete is operated by a highly coordinated and organized group who focuses on Latin American targets. We describe the five phases of the APT operations from delivery to exfiltration of information and we show why Machete is considered a cyber espionage tool. Furthermore, our analysis indicates that the targeted victims belong to military, political, or diplomatic sectors. The review of almost six years of Machete operations show that it is likely operated by a single group, and their activities are possibly state-sponsored. Machete is still active and operational to this day.
Analysis of Native and Non-native Speakers' English Compositions based on Word-frequency Distribution and Text Statistics

In this paper, word-frequency distribution of JACET 8000 basic words and text statistics were researched to compare and analyze differentials of English compositions (essays) written by native speakers and non-native speakers. As for the native speakers' essays, the Guiraud Index in each Level 2-8 to Average sentence length and Automated Readability Index had higher correlation coefficients. Meanwhile, on the non-native speakers' essays, the index values to Sentence count showed moderate correlation coefficients. It was observed that the productivity and readability of the compositions seem to depend on ranges of basic content words which native or non-native writers have acquired and can use in English. To verify the word-frequency distribution as proficiency rating measurement for non-native speakers, the estimation experiment was carried out based on a multiple-regression model using word-frequency distribution of 68 English compositions written by the non-native writers. The estimated scores of the learners showed a correlation score 0.475 to their actual TOEIC scores. These results confirmed the possibility of the word usage statistics for the objective evaluation of L2 (second language) learners' language proficiency.
Knowledge-Aware and Retrieval-Based Models for Distantly Supervised Relation Extraction

Distantly supervised relation extraction (RE) has been an effective way to find novel relational facts from text without a large amount of well-labeled training data. However, distant supervision always suffers from wrong labelling problem. Many neural approaches have been proposed to alleviate this problem recently, but none of them can make use of the rich semantic knowledge in the knowledge bases (KBs). In this paper, we propose a knowledge-aware attention model, which can leverage the semantic knowledge in the KB to select the valid sentences. Furthermore, based on knowledge representation learning (KRL), we formalize distantly supervised RE as relation retrieval instead of relation classification to leverage the semantic knowledge further. Experimental results on widely used datasets show that our approaches significantly outperform the popular benchmark methods.
pml – a language for authoring and editing physics text

Abstract Expressing physics on a web page with the fruitful precision required in order to best support learning tends to be a demanding technical task for authors and content curators. That is as a result of the need to adhere to various conventions, such as the spacing between numbers and units, and ensure that unhelpful distributions of words do not detract from meaning, such as splitting equations over lines, or placing number and unit on different lines, whatever the display device. In short, there is a lot to get right. Current standards in markup languages such as html do not lend much support to avoiding of these kinds of mis-steps. Some current techniques suggest a mixture of technologies, such as embedding LATeX via MathJax to represent equations, but this is inelegant, introduces dissipative friction into the authoring and editing systems, and results in extra difficulties in separating content from display technologies whilst ensuring representational consistency across the different technologies deployed. A system using this physics markup language can be implemented using standard web technologies (generating clean, standards-compliant and mobile-friendly html, with hooks for CSS, and relying only on JavaScript) and authoring can be in any text editor.
Nominal coreference resolution for Polish

Abstract The article presents current research on coreference resolution for Polish, from development of a sufficiently general model of reference relations to implementation of tools using this model to automatically detect coreference in written texts. The task is accomplished using corpus approach, with manual annotation of reference structures, verification of the proposed theory on the corpus data, implementation of automatic tools in different technical architectures and finally assessing their quality using standard evaluation methods.
Reliable Blockchain Based Stochastic Game System

Recently, due to the development of network and the development of hardware, users can enjoy games anywhere, and as a result various games have been developed so that items in the game using cash and game cache have skyrocketed. The user (participant) uses his cash and game cache to estimate the probability and make the investment. However, there is a problem that the item does not come out even though the number of times of approaching the probability is reached. The reason is that developers can manipulate the probability information stored in the database, and the data of the probability game can be recorded uncertainly so that users can suffer damage. In this paper, we propose a reliable system based on a blockchain for a random item in a game. The developer marks the probability in the blockchain. The user can verify the information stored in the blockchain. Since the event that the user participates in is also stored in the blockchain, the user verifies/checks the information stored in the blockchain, thereby enhancing the reliability of the corresponding game.
Using the Properties of Primate Motion Sensitive Neurons to Extract Camera Motion and Depth from Brief 2-D Monocular Image Sequences

Humans and most animals can run/fly and navigate efficiently through cluttered environments while avoiding obstacles in their way. Replicating this advanced skill in autonomous robotic vehicles currently requires a vast array of sensors coupled with computers that are bulky, heavy and power hungry. The human eye and brain have had millions of years to develop an efficient solution to the problem of visual navigation and we believe that it is the best system to reverse engineer. Our brain and visual system appear to use a very different solution to the visual odometry problem compared to most computer vision approaches. We show how a neural-based architecture is able to extract self-motion information and depth from monocular 2-D video sequences and highlight how this approach differs from standard CV techniques. We previously demonstrated how our system works during pure translation of a camera. Here, we extend this approach to the case of combined translation and rotation.
Joint Extraction of Opinion Targets and Opinion Expressions Based on Cascaded Model

Fine-grained opinion analysis is a very important task, especially identifying opinion target and opinion expression. In this paper, a new neural architecture is proposed for the sentence-level joint extraction of opinion target and opinion expression. The neural architecture namely cascaded model includes pre-trained model BERT Base, linguistic features, bi-directional LSTM, soft attention network and CRF layer from bottom to top. The cascaded model provides the best joint extraction results in the SemEval-2014/2016 Task 4/5 data sets compared with the state-of-the-art. There are three main contributions in our work, (1) attention network is introduced into the task of sentence-level joint extraction of opinion target and opinion expression, which enhances the dependence between opinion target and opinion expression. (2) pre-trained model BERT-Base and linguistic features are introduced into our work, which greatly improve the convergence speed and the performance of the cascaded model. (3) opinion target and opinion expression are synchronously extracted, and achieved better results compared with the most of the existing pipelined methods.
Robot Navigation with PolyMap, a Polygon-Based Map Format

Vector-based map formats such as PolyMap are more compact in terms of memory footprint compared with regular occupancy grid map formats. The sparse nature of a PolyMap along with the explicit representation of traversable space and frontiers makes it suitable for mono and multi-robot autonomous exploration. In this paper, we propose a PolyMap-based navigation stack suitable for autonomous exploration. This navigation stack internally relies on a topological graph that is automatically computed from a PolyMap. This graph is critical to significantly reduce the search space when computing a global path compared with traditional occupancy grid maps. We also discuss two optimizations to speed up path planning computation on this graph. Our experiments demonstrate the efficiency of our PolyMap-based navigation stack.
The Social Net of Sentiment: Improving the Base Sentiment Analysis of High Impact Events with Lexical Category Exploration

Social networking has created a large source of peoples’ opinions and statements available on the internet. This is particularly true in the case of high impact events, such as the Parkland school shooting in Florida. This paper approaches the analysis of high impact events in two ways. First, tweet sentiment analysis using the NLTK machine learning standard with TextBlob is applied. This approach is then augmented with lexical categorical analysis using the Python tool Empath as an added analysis step. The TextBlob standards are compared to Empath’s sentiment analysis results to compare the accuracy of the two methods. The paper presents this combined approach to improve sentiment analysis by using Empath as an added analysis step and briefly discuss future further refinements.
Design and Application of Integrated Meteorological Service System in the Yangtze River Basin

The overall design and system functions of the meteorological service integrated service system of the Yangtze River Basin are introduced.The system is based on CIMISS (National Integrated Meteorological Information Sharing Platform) for expansion and construction, integrating key correction forecasts, weather model comparisons, flood predictions, etc.The forecasting technology, starting from the flood control and drought relief of the service basin and the comprehensive dispatch of the reservoir, has realized the monitoring of water and rain conditions, the production of key business products, the production and distribution of integrated service products, and the quality tracking assessment.The application shows that the system can make the forecaster effectively grasp the service focus, improve the timeliness and pertinence of the service, and have a good promotion and application prospect in the basin meteorological service.
A Screen-Based Method for Automated Camera Intrinsic Calibration on Production Lines

For the manufacture of visual system product, it is necessary to calibrate a massive number of cameras in a limited time and space with a high consistency quality. Traditional calibration method with chessboard pattern is not suitable in the manufacturing industry since its requirement of motions leads to the problem of consistency, cost of space and time. In this work, we present a screen-based solution for automated camera intrinsic calibration on production lines. With screens clearly and easily displaying pixel points, the whole calibration pattern is formed with the dense and uniform points captured by the camera. The calibration accuracy is comparable with the traditional method with chessboard pattern. Unlike a variety of existing methods, our method needs little human interaction, as well as only a limited amount of space, making it easy to be deployed and operated in the industrial environments. With some experiments, we show the comparable performance of the system for perspective cameras and its potential in fisheye cameras with the developments of screens.
An Efficient Verification Way for the Encrypted EMRs Based on Sub_MB Tree

In order to improve the verification efficiency of the Encrypted EMRs, an approach based on Sub_MB is proposed. Firstly, a Merkle B tree is constructed to store the verification information, and the root and the internal nodes are signed. Then the clients could verify the correctness and completeness of data through computing the least verification Sub_MB. Our approach could reduce the cost needed in the process of verification; experiments show that our approach improves 10%-20% in contrast with previous work such as MB, EMB, and so on.
Research on stereo matching methods for long distance sea surface image

Stereo matching is one of the most important task of stereo binocular vision system. This paper will discuss stereo matching method used in tsunami warning system which measures tsunami based on stereo binocular vision system. To solve the problem of lacking obvious feature points in sea surface image, the paper extracts sea waves out from sea surface image to form feature points and integrates sea waves' size, shape, brightness, location and lengths in four orientations to distinguish each point and finishes stereo matching of tsunami warning system by the feature points and their features.
Research and Design of New Basic Geographic Information System Platform

The new basic geographic information system platform software is a high-tech technology that integrates computer graphics and databases to store and process space information. It organically combines geographical location and related attributes, and outputs accurately and realistically according to actual needs. To users, meet the requirements of urban construction, business management, the resident life to the space information request.
DEVELOPMENT OF A PERSONAL INFORMATION SYSTEM FOR MANAGING AUDIOVISUAL TRAINING AIDS: MOTIVATIONS, BENEFITS FOR THE TEACHER'S WORK

The contemporary teacher uses a rich collection of diverse computer-based teaching aids in their daily experience. One of the main problems in his work is maintaining this collection so that in case of need a desired file can be found quickly and reliably. In this paper, an approach to systematizing, organizing, storing and using the audiovisual aids with the personal information system myAudiovisualAids is proposed. The purpose of the implemented system is to support the user to organize his/her media learning resources created during the preparation of the lessons or his/her library of digital didactic aids. The motivations are justified and the benefits of developing and using the proposed system are addressed.
Reversible Data Hiding in Image System by Histogram-Shift Strategy

Personal privacy concerns in healthcare applications are even more vital. This write-up defines the application of clinical images in shielding person information. A relatively easy to fix photo hiding plan based upon bar graph countered is primarily utilized to protect medical documents. On top of that, we used 2 specifications, a linear forecaster of non-uniform weights as well as limits, to improve efficiency in terms of medical photo high quality and ingrained record ratio. Weights are used to boost prediction precision and therefore improve the ability to embed confidential information. Limits are made use of to identify image qualities as well as stay clear of overflow or underflow issues. For picture attribute decision, we select a peak factor and after that relocate one system to the right for data hiding. Prior to the data is concealed, the method will additionally pre-process the underflow and also overflow problems of the images embedded in the clinical records in the photo, without salt as well as pepper results on the picture generation. Speculative outcomes show that this approach can provide excellent embedding capacity without substantial distortion. In our technique, using weights as well as limits does increase the capacity of information based upon bar graphs to hide information while keeping picture high quality.
Decision Support System for Choosing an Elective Course Using Naive Bayes Classifier

Department of InformaticsAbiyoga in Universitas Multimedia Nusantara offers five elective courses to the students. Choosing an elective course that is most suitable for the student interest and academic skills is dilemmatic. Thus, a decisionWicaksana, Arya support system is proposed to assist the students in choosing an elective course based on not only their interest but also academic skills. The system uses Naive Bayes Classifier and Laplace smoothing for theIswari, Ni Made Satvika classification process. The data used for this research is collected from 120 students. Learning from past students records, the system could predict the outcome of the student upon choosing an elective course. The evaluation of the system shows that the accuracy of the system is 0.30 and 0.33, recall is 0.318 and 0.378, precision is 0.215 and 0.407, and the F-score are 0.257 and 0.390. A test for two classes and three classes classification using 60 generated data shows improvement in the performance with the accuracy of 0.83 and 0.72 and the F-score of 0.843 and 0.728.
A Cross-Domain Theory of Mental Models

Cognitive models for human reasoning are often specialized and domain-specific. So the question whether human reasoning across domains shares the same (or at least a similar) mental representation and inference mechanism is still an unexplored territory, as is the endeavor to create cognitive computational models for multiple domains of human reasoning. In this paper, we consider the theory of mental models for conditionals as a test-case and aim to extend it towards syllogistic reasoning using a formal translation. The performance of this new cross-domain theory is comparable to the performance of state-of-the-art domain-specific theories. Potentials and limitations are discussed.
A Conflict Resolution Algorithm of Multi-Source Information Fusion for Internet of Things

It is of great importance to fuse multi-source information without conflict in Internet of things, therefore, in this paper, we aim to study on how to solve the conflict problem in multi-source information fusion. Three fundamental problems should be paid much attention, that is, 1) Fusion nodes selection, 2) Fusion time selection, and 3) Fusion algorithm design. Furthermore, the software architecture of IoT data monitoring system is made up of four modules, which are 1) data acquisition front end program, 2) data gateway program design, 3) system server program design, and 4) client software design. Afterwards, we propose a conflict resolution algorithm for multi-source information fusion to accurately monitor the actual situation of the data monitoring environment. Finally, experiments are conducted to make performance evaluation, and experimental results prove the effectiveness of the proposed solution.
Single image super-resolution reconstruction based on multi-directionality of the edge

Single image super-resolution(SR) reconstruction aims to recover the corresponding high resolution(HR) image through one low resolution(LR) image. SR reconstruction is an ill-posed problem, therefore, an effective image prior knowledge is meaningful to reconstruct the missing details in the LR image. In this paper, we propose a SR method by making use of the directional properties of image edges to construct local smoothing prior and non-local similarity prior. We utilize the directionlet that can effectively represent the image edge direction information to extract the directional feature information, after that, these directional information is used in the reconstruction framework based on TV and NLM to better protect the sharp edges of the image and improve the reliability of self-similar weight. The experimental results demonstrate that the proposed algorithm outperforms some of the current SR methods in terms of quantitatively and qualitatively.
Text to Image Synthesis Using Two-Stage Generation and Two-Stage Discrimination

In this paper, the method of two-stage generation and two-stage discrimination (2G2D) is proposed to generate high-resolution and more realistic images. It is a simple but effective way to synthesize images based on text descriptions. Our method generates the refined foreground image in the first stage, and then combines the text description to generate the final high-resolution image in second stage. We demonstrate the performance of the proposed method on the Caltech-UCSD Birds (CUB) dataset. Through the experimental results, our model can improve the resolution and the authenticity of content of the synthetic image better than the existing state-of-the-art methods.
Evaluations of the voice to text transfer in different conditions

The purpose of the research was evaluations of voice to text transfer in different conditons for the use in the verbal control of industrial robots. The research aims to find the best conditions to command an industrial robot using the human voice. A comprehensive study of existing problems and suggestions for simulation problems has been performed. Unlike some other works, it focused on the simply external problems to affect the voice to text transfer. The main problems of voice to text transfer (speech speed, speaker distance, ambient noise) has been established. The simulation using a personal computer equipped with a sound board and a headset microphone has been performed in all combinations of conditions. On the basis of the analyzes was establish the result of this research, the most suitable conditions and the worst conditions for voice to text transfer.