Reviewerly/exHarmony huggingface dataset:
Robust textual watermarking for high resolution videos based on Code-128 barcoding and DWT

Embedding of hidden textual information is often met in many applications, and traditionally a textual watermark is represented as an image. The attempts to hide textual data as a set of codes in spatial domain of the host image lead to high instability of the extracted textual watermark caused by possible intentional and accidental attacks during the transmission and storing of the watermarked videos. We propose an alternative method based on the low cost additional transform of textual watermark using barcoding that allows us to restore the distorted watermark with a high probability achieving its full restoration under some typical Internet attacks. Also we promote the idea of video watermarking when the used codec is unknown that extends three conventional strategies of video watermarking technique.
Artificial intelligence system based on the layout effect of both sides in volleyball matches

The traditional sports match analysis mostly adopts the method of manual observation and recording, which is not only time-consuming and laborious but also has the defects of subjectivity and inaccuracy in the judgment results, resulting in the deviation of the match data analysis and statistical results. The purpose of this paper is to study an artificial intelligence system that can automatically analyze and evaluate the effect of both sides in volleyball matches. In this paper, the system is divided into two steps: detection and tracking of moving objects, recognition, and classification of players’ behaviors and movements. About moving target detection and tracking, this paper proposes a moving target fast detection framework based on a mixture of mainstream technologies and a MeanShift target tracking method based on Kalman filtering and adaptive target region size. For behavior and action recognition and classification, this paper proposes a classifier combining BP neural network and support vector machine. Experimental results show that the proposed algorithm and classifier are effective. By analyzing the performance of the proposed classifier, the classification accuracy is 98%.
A Non-local Mean Temporal Filter for Video Compression

Modern video codecs exploit the temporal and spatial correlations of video signal to achieve compression. The noise in video signal corrupts such correlations and impairs the coding efficiency. Prior works in VP8, VP9, and HEVC exploit the use of temporal filtering to remove certain noise from the source signal. They typically compare a pair of pixels along a motion trajectory and decide the filter coefficients based on the pixel value difference. It is observed that such noise removal allows better rate-distortion performance trade off and hence improves the objective compression efficiency. Note that the compression distortion is evaluated against the original video signal in all cases. This work proposes a non-local mean temporal filter for noise removal. Instead of comparing a pair of pixels along the motion trajectory, it compares two pixel blocks surrounding the pixels of interest. Their distance in L2 norm is then normalized by the frame noise level, which is used to determine the temporal filter coefficients in a non-parametric model. It is experimentally shown that the proposed non-local mean filter approach achieves improved compression efficiency over other contenders.
Informative Path Planning for Active Field Mapping under Localization Uncertainty

Information gathering algorithms play a key role in unlocking the potential of robots for efficient data collection in a wide range of applications. However, most existing strategies neglect the fundamental problem of the robot pose uncertainty, which is an implicit requirement for creating robust, high-quality maps. To address this issue, we introduce an informative planning framework for active mapping that explicitly accounts for the pose uncertainty in both the mapping and planning tasks. Our strategy exploits a Gaussian Process (GP) model to capture a target environmental field given the uncertainty on its inputs. For planning, we formulate a new utility function that couples the localization and field mapping objectives in GP-based mapping scenarios in a principled way, without relying on manually-tuned parameters. Extensive simulations show that our approach outperforms existing strategies, reducing mean pose uncertainty and map error. We present a proof of concept in an indoor temperature mapping scenario.
DFVS: Deep Flow Guided Scene Agnostic Image Based Visual Servoing

Existing deep learning based visual servoing approaches regress the relative camera pose between a pair of images. Therefore, they require a huge amount of training data and sometimes fine-tuning for adaptation to a novel scene. Furthermore, current approaches do not consider underlying geometry of the scene and rely on direct estimation of camera pose. Thus, inaccuracies in prediction of the camera pose, especially for distant goals, lead to a degradation in the servoing performance. In this paper, we propose a two-fold solution: (i) We consider optical flow as our visual features, which are predicted using a deep neural network. (ii) These flow features are then systematically integrated with depth estimates provided by another neural network using interaction matrix. We further present an extensive benchmark in a photo-realistic 3D simulation across diverse scenes to study the convergence and generalisation of visual servoing approaches. We show convergence for over 3m and 40 degrees while maintaining precise positioning of under 2cm and 1 degree on our challenging benchmark where the existing approaches that are unable to converge for majority of scenarios for over 1.5m and 20 degrees. Furthermore, we also evaluate our approach for a real scenario on an aerial robot. Our approach generalizes to novel scenarios producing precise and robust servoing performance for 6 degrees of freedom positioning tasks with even large camera transformations without any retraining or fine-tuning.
Disclosure Avoidance in the Census Bureau’s 2010 Demonstration Data Product

Producing accurate, usable data while protecting respondent privacy are dual mandates of the US Census Bureau. In 2019, the Census Bureau announced it would use a new disclosure avoidance technique, based on differential privacy, for the 2020 Decennial Census of Population and Housing []. Instead of suppressing data or swapping sensitive records, differentially private methods inject noise into counts to protect privacy. Unfortunately, noise injection may also make the data less useful and accurate. This paper describes the differentially private Disclosure Avoidance System (DAS) used to prepare the 2010 Demonstration Data Product (DDP). It describes the policy decisions that underlie the DAS and how the DAS uses those policy decisions to produce differentially private data. Finally, it discusses usability and accuracy issues in the DDP, with a focus on occupied housing unit counts. Occupied housing unit counts in the DDP differed greatly from 2010 Summary File 1 differed greatly, and the paper explains possible sources of the differences.
Bilinear Semi-Tensor Product Attention (BSTPA) model for visual question answering

We propose a semi-tensor product attention network model as a visual question answering tool for complex interaction over image features. Proposed model performs matrix multiplication of two arbitrary dimensions, which is used to overcome possible dimensional limitations and improve recognition flexibility. In used block-wise operation we preserve spatial and temporal information but reduce the number of parameters by using low-rank pooling scheme. Applied BERT pre-train model is tuned to recognize question features. The proposed model is evaluated on the VQA2.0 dataset. Research results show that our model has good accuracy and easy reconfiguration for future research.
Visual Neural Decomposition to Explain Multivariate Data Sets

Investigating relationships between variables in multi-dimensional data sets is a common task for data analysts and engineers. More specifically, it is often valuable to understand which ranges of which input variables lead to particular values of a given target variable. Unfortunately, with an increasing number of independent variables, this process may become cumbersome and time-consuming due to the many possible combinations that have to be explored. In this paper, we propose a novel approach to visualize correlations between input variables and a target output variable that scales to hundreds of variables. We developed a visual model based on neural networks that can be explored in a guided way to help analysts find and understand such correlations. First, we train a neural network to predict the target from the input variables. Then, we visualize the inner workings of the resulting model to help understand relations within the data set. We further introduce a new regularization term for the backpropagation algorithm that encourages the neural network to learn representations that are easier to interpret visually. We apply our method to artificial and real-world data sets to show its utility.
Simulation-based optimization for modeling and mitigating tunnel-induced damages

This research develops a simulation-based optimization approach that is capable of modeling and mitigating tunnel-induced damages. Two fuzzy cognitive maps (FCMs) (i.e., one with self-feedback and the other without self-feedback) are learned from historical datasets by using the real-coded genetic algorithm (RCGA) on a data-driven modeling manner. Then, the optimal variable value set is searched in the input space. Two new measures, namely “maximum response” and “average response”, are proposed to search the optimal variable value set in the input space in the FCM dynamic simulation process. A realistic tunnel case in the Wuhan metro system in China is extensively investigated to demonstrate the applicability and effectiveness of the developed approach. Results indicate that (1) The FCM with self-feedback is more stable than the FCM without self-feedback considering its higher coefficient of determination in the testing samples, where less modification of input variables realizes comparable improvement in the objective in the FCM with self-feedback. (2) The measure “maximum response” shows a larger change in the objective than the measure “average response”, where modifications in input space are similar. (3) It is revealed that the ground settlement is more sensitive to TBM operational parameters than tunnel geometry and geological conditions in the two learned FCMs. The developed approach provides insights into a better understanding of causal relationships among factors in tunnel-induced damages, enabling the planning of proactive control strategies for mitigating tunnel-induced damages.
Monitoring Financial Stability Based on Prediction of Cryptocurrencies Price Using Intelligent Algorithm

Financial stability is the problem that correlated with many aspects. In this digital era, virtual currency has emerged as one of financial assets. Fluctuation from cryptocurrencies price made an impact into financial stability indirectly. It has been proved by the market capitalization of publicly traded cryptocurrencies in 2019 reach USD 240 billion. The percentages reach 66% for the market capitalization. Three highest cryptocurrencies uphold are Bitcoin, Ethereum and XRP. This condition made these three cryptocurrencies become the important investment products. However, cryptocurrency is the product with high volatility. Because of that, this study aims to monitor financial stability from cryptocurrencies prediction using artificial intelligent algorithm. This research copes the problem by predicting bitcoin, Ethereum and XRP using three different intelligent algorithms, which are K-Nearest Neighbours (KNN), Support Vector Machine (SVM) and Long Short-Term Memory (LSTM). By this prediction had been figured out about up and down value of cryptocurrencies using Root Mean Square Error (RMSE) to evaluate stabilization of finance. Accuration system with LSTM shown that the price of cryptocurrency will fit with the data actual using LSTM, in which the accuracy around 80%. This condition meant that LSTM had been succeeded to proposed as algorithm that could fit the cryptocurrencies value. It could strengthen usability to indicate financial stability refer into cryptocurrencies price.
On a Security-oriented Design Framework for Medical IoT Devices: The Hardware Security Perspective

As medical devices more and more use Internet of Things based technologies, serious concerns are raised about their security and the privacy of patient's personal health data. To address these concerns, while maintaining reasonable overheads, designers of medical devices need to take security into account from the beginning until the completion of their designs. In this work we identify the relevant security domains and focus to the Hardware Security perspective. Additionally, we present a secure design and evaluation framework which can assist designers towards more secure medical devices. The framework integrates a complete insulin pump architecture containing all the basic components used in such applications. To illustrate the advantages of the proposed framework we perform a Side Channel Analysis attack against the embedded encryption algorithm of the device to obtain the secret encryption key. Then, we make use of the framework to identify all the components of the system which are either directly or indirectly affected by the attack. This analysis leads us to determine more complex combined attacks which may complement the SCA attack into compromising the overall security of the system.
Japanese translation teaching corpus based on bilingual non parallel data model

In recent years, with the development of Internet and intelligent technology, Japanese translation teaching has gradually explored a new teaching mode. Under the guidance of natural language processing and intelligent machine translation, machine translation based on statistical model has gradually become one of the primary auxiliary tools in Japanese translation teaching. In order to solve the problems of small scale, slow speed and incomplete field in the traditional parallel corpus machine translation, this paper constructs a Japanese translation teaching corpus based on the bilingual non parallel data model, and uses this corpus to train Japanese translation teaching machine translation model Moses to get better auxiliary effect. In the process of construction, for non parallel corpus, we use the translation retrieval framework based on word graph representation to extract parallel sentence pairs from the corpus, and then build a translation retrieval model based on Bilingual non parallel data. The experimental results of training Moses translation model with Japanese translation corpus show that the bilingual nonparallel data model constructed in this paper has good translation retrieval performance. Compared with the existing algorithm, the Bleu value extracted in the parallel sentence pair is increased by 2.58. In addition, the retrieval method based on the structure of translation option words graph proposed in this paper is time efficient and has better performance and efficiency in assisting Japanese translation teaching.
Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

Recommender systems (RSs) are systems that produce individualized recommendations as output or drive the user in a personalized way to interesting or useful objects in a space of possible options. Recently, RSs emerged as an effective support for decision making. However, when people make decisions, they usually take into account different and often conflicting information such as preferences, long-term goals, context, and their current condition. This complexity is often ignored by RSs. In order to provide an effective decision-making support, a RS should be “holistic”, i.e., it should rely on a complete representation of the user, encoding heterogeneous user features (such as personal interests, psychological traits, health data, social connections) that may come from multiple data sources. However, to obtain such holistic recommendations some steps are necessary: first, we need to identify the goal of the decision-making process; then, we have to exploit common-sense and domain knowledge to provide the user with the most suitable suggestions that best fit the recommendation scenario. In this article, we present a methodological framework that can drive researchers and developers during the design process of this kind of “holistic” RS. We also provide evidence of the framework validity by presenting the design process and the evaluation of a food RS based on holistic principles.
An Efficient Machine Learning Approach for Accurate Short Term Solar Power Prediction

Solar based electricity generations have experienced a strong and impactful growth in the recent years. The regulation, scheduling, dispatching and unit commitment of intermittent solar power is dependent on the accuracy of the forecasting methods. In this paper, a robust Expanded Extreme Learning Machine (EELM) is proposed to accurately predict the solar power for different time horizon and weather condition. The proposed EELM technique has no randomness due to the absence of random input layer weights and takes very less time to predict the solar power efficiently. The performance of the proposed EELM is validated through historical data collected from National Renewable Energy Laboratory (NREL) through various performance metrics. The efficacy of the proposed EELM method is evaluated against basic ELM and Functional Link Neural Network (FLNN) for 5 minute and 1 hour ahead time horizon.
Global Encoding for Long Chinese Text Summarization

Text summarization is one of the significant tasks of natural language processing, which automatically converts text into a summary. Some summarization systems, for short/long English, and short Chinese text, benefit from advances in the neural encoder-decoder model because of the availability of large datasets. However, the long Chinese text summarization research has been limited to datasets of a couple of hundred instances. This article aims to explore the long Chinese text summarization task. To begin with, we construct a first large-scale, long Chinese text summarization corpus, the Long Chinese Summarization of Police Inquiry Record Text (LCSPIRT). Based on this corpus, we propose a sequence-to-sequence (Seq2Seq) model that incorporates a global encoding process with an attention mechanism. Our model achieves a competitive result on the LCSPIRT corpus compared with several benchmark methods.
Assessment of technological resources for the production of composite products based on mathematical methods

Abstract This article is devoted to the selection and justification of the use of the corresponding mathematical methods in the formation of a composite product production program and the assessment of the importance of provided technological resources. The choice of marginal profit as criteria of the optimality of the production program is proved. The formalized mathematical formulations of integer and dual tasks of linear programming are presented. The tasks of analyzing the annual release plan for a range of six products is solved. It is concluded that the usage of mathematical methods of linear programming and the theory of dual is successful decision in the field of development of composite production programs.
Smart Contract-based Computing Resources Trading in Edge Computing

In recent years, there is an emerging trend that some computing services are moving from cloud to the edge of the networks. Compared to cloud computing, edge computing can provide services with faster response, lower expense, and more security. The massive idle computing resources closing to the edge also enhance the deployment of edge services. Instead of using cloud services from some primary providers, edge computing provides people a great chance to join the market of computing resources actively. However, edge computing also has some critical impediments that we have to overcome.In this paper, we design an edge computing service platform that can receive and distribute the computing resources from the end-users in a decentralized way. Without the centralized trade control, we propose a novel Blockchain-enabled decentralized technique to establish the trade trust among users and implement it with using embedded immutable intermediary smart contract. Our system also considers and resolves a variety of security and privacy challenges when utilizing the Blockchain technique. We implement our system and conduct extensive experiments to show the feasibility and effectiveness of our proposed system.
Emotion-Cause Joint Detection: A Unified Network with Dual Interaction for Emotion Cause Analysis

Emotion cause analysis has attracted much attention in the field of natural language processing. The existing works include emotion cause extraction (ECE) and emotion-cause pair extraction (ECPE), but the former requires emotion annotations, thereby restricting its application scenarios, and the latter consists of two steps in sequence, thereby making the second step depend on the results of first step. To tackle the limits, we implement emotion detection and cause detection as two sub-tasks in a unified framework. Based on this framework, we propose an emotion-cause joint detection (ECJD) method, which enhances the interaction of sub-tasks in a synchronous and joint way to improve performance. Specifically, we formalize ECE as a four-class classification problem, in which clause representation is evaluated from the dual perspective of both emotion and cause. We implement cause detection with consideration of relative position from emotion detection as prior knowledge so as to improve detection performance. The experimental evaluation based on an emotion cause corpus benchmark shows that our method achieves the best performance of cause detection without using emotion annotations and overcomes the limits of ECE and ECPE, and further demonstrates the effectiveness of our model.
A Comparison between KNN and SVM for Breast Cancer Diagnosis Using GLCM shape and LBP Features

This paper deals with Breast cancer diagnosis from given mammogram images. Initially, the input image is being pre-processed and then features are extracted from it for the further classification. Noise and other artifacts are removed using a 2D median filter, then the features are extracted using the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) feature extraction methods. Ten different features are collected from the given input image using which a Feature vector is framed. This feature vector is taken care of as a contribution to the classifiers. The classifiers used in this paper are Support Vector Machine (SVM) and K-Nearest Neighbour(KNN). To our knowledge there was no combination of features which we used were used in any of the works before. A correlation of these two classifiers are done and accuracy of 96% and 100% is acquired for SVM and KNN individually. The input data for this is taken from the CBIS-DDSM dataset.