Reviewerly/exHarmony huggingface dataset:
Robust video data security using hybrid cryptography-steganography technique

The interest in the digital videos confidentiality in the current electronic and interrelated world has increased. Thus, this paper aimed at making a video steganography scheme for an acceptable security with high speed of calculation by embedding data (video frames) in other video frames. The techniques of embedding and encrypting video frames in a cover video file were done through two ways. Firstly, two keys and XOR bit operation were employed to create a large range of different keys for encryption. Secondly, a modified method of Least Significant Bit (LSB) technique was used for hiding high resolution video frames (bitmap color) in selective cover video frames, offering two security layers. The procedures of encrypting and hiding video data were successfully tested on many secret videos such ad Eye Video, Secret Medicine Video and Traffic Video. All experiments were conducted using MATLAB-GUI software, representing an efficient and easy tool for video management supported by powerful testing tools as histograms and mathematics for video quality. Experimental result demonstrated a good performance with low correlation and very high PSNR of the Stego video frames.
Efficient Pixel-Value Differencing Based Hybrid Steganographic Method Using Modulus Function

In the era of cloud computing and Big Data, steganographic methods are playing a pivotal role to provide security to sensitive contents. In the steganographic domain, pixel-value differencing (PVD) proposed by Wu and Tsai has been one of the most researched and popular methods as the PVD technique provides good quality stego-image along with high embedding capacity. This article extends the Wu and Tsai's work by proposing a new hybrid steganography scheme which works in two phases to increase the embedding capacity along with stego-image quality. In the first phase, the cover image is preprocessed using a segmentation table to make the image more robust for PVD method. In the second phase, the resultant image is partitioned into 2×1 pixels size blocks in a non-overlapping fashion and then modulus function based scheme is applied in reversible manner. Thus, a significant amount of secret data is embedded into the image. The experimental results prove that the proposed scheme has significantly improved in embedding capacity and quality as compared to the other related PVD-based methods.
Video Footage Highlight Detection in Formula 1 Through Vehicle Recognition with Faster R-CNN Trained on Game Footage

Formula One, and its accompanying e-sports series, provides viewers with a large selection of camera angles, with the onboard cameras oftentimes providing the most exciting view of events. Through the implementation of three object detection pipelines, namely Haar cascades, Histogram of Oriented Gradient features with a Support Vector Machine, and a Faster Region-based Convolutional Neural Network (Faster R-CNN), we analyse their ability to detect the cars in real-life and virtual onboard footage using training images taken from the official F1 2019 video game. The results of this research concluded that Faster R-CNNs would be best suited for accurate detection of vehicles to identify events such as crashes occurring in real-time. This finding is evident through the precision and recall scores of 97% and 99%, respectively. The speed of detection when using a Haar cascade also makes it an attractive choice in scenarios where precise detection is not important. The Haar cascade achieved the lowest detection time of only 0.14 s per image at the cost of precision (71%). The implementation of HOG features classifier using an SVM was unsuccessful with regards to detection and speed, which took up to 17 s to classify an image. Both the Haar cascade and HOG feature models improved their performance when tested on real-life images (76% and 67% respectively), while the Faster R-CNN showed a slight drop in terms of precision (93%).
Collaborative Adversarial Learning for Relational Learning on Multiple Bipartite Graphs

Relational learning aims to make relation inference by exploiting the correlations among different types of entities. Exploring relational learning on multiple bipartite graphs has been receiving attention because of its popular applications such as recommendations. How to make efficient relation inference with few observed links is the main problem on multiple bipartite graphs. Most existing approaches attempt to solve the sparsity problem via learning shared representations to integrate knowledge from multi-source data for shared entities. However, they merely model the correlations from one aspect (e.g. distribution, representation), and cannot impose sufficient constraints on different relations of the shared entities. One effective way of modeling the multi-domain data is to learn the joint distribution of the shared entities across domains. In this paper, we propose Collaborative Adversarial Learning (CAL) that explicitly models the joint distribution of the shared entities across multiple bipartite graphs. The objective of CAL is formulated from a variational lower bound that maximizes the joint log-likelihoods of the observations. In particular, CAL consists of distribution-level and feature-level alignments for knowledge from multiple bipartite graphs. The two-level alignment acts as two different constraints on different relations of the shared entities and facilitates better knowledge transfer for relational learning on multiple bipartite graphs. Extensive experiments on two real-world datasets have shown that the proposed model outperforms the existing methods.
Intelligent Contract for Power Market Transaction Based on the BlockChain

The power market is changing with the development of distributed energy. The development of the micro-grid introduces a small power generation system for distributed energy. It has become a trend for the micro-grid to participate in the power market. Blockchain is a database technology with transparent distribution through a decentralization mechanism. In the blockchain, the transaction data are maintained by entire nodes in the power market. It solves the trust problem between the transaction agents. Intelligent contract technology can make transaction contracts computerized and executed automatically, which has dramatically improved transaction efficiency. In this paper, an intelligent contract for the power transaction is analyzed based on the concepts of the blockchain. The roles of the micro-grid and the related intelligent contract technologies in the power market are also investigated in the electricity reform. Finally, a blockchain scheme for point-to-point trading is designed for future purposes.
Active Imaginative Reading . . . and Listening

December 01 2020 Active Imaginative Reading . . . and Listening David Rosenboom David Rosenboom Roy E. Disney Family Chair in Musical Composition, The Herb Alpert School of Music, California Institute of the Arts, Website: www.davidrosenboom.com Search for other works by this author on: This Site Google Scholar Author and Article Information David Rosenboom Roy E. Disney Family Chair in Musical Composition, The Herb Alpert School of Music, California Institute of the Arts, Website: www.davidrosenboom.com Online Issn: 1531-4812 Print Issn: 0961-1215 ©2020 ISAST2020ISAST Leonardo Music Journal (2020) 30: 1–2. https://doi.org/10.1162/lmj_a_01085 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation David Rosenboom; Active Imaginative Reading . . . and Listening. Leonardo Music Journal 2020; 30 1–2. doi: https://doi.org/10.1162/lmj_a_01085 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsLeonardo Music Journal Search Advanced Search This content is only available as a PDF. ©2020 ISAST2020ISAST Article PDF first page preview Close Modal Supplementary data Ringing Minds (2014) listening as performanceShort excerpt of Ringing Minds, collaboration with Tim Mullen and Alexander Khalil; part of Propositional Music, David Rosenboom 50-Year Retrospective, Whitney Museum of American Art, New York, (2015); for listening-as-performance brain quartet, hyper-brain analysis, violin, electronics, Yamaha Disklavier, lithoharp; Matt Wachter and Glenn Snyder, realization of hyper-brain visualizing concept. (© ℗ David Rosenboom and the David C. Rosenboom 2013 Trust 2015)- mp4 file You do not currently have access to this content.
Vocabulary Learning Scheduling by Solving an Assignment Problem

This paper proposes a vocabulary learning strategy based on the forgetting curve theory and assignment problem. The proposed strategy implements some quantitatively analytic methods to make full use of both user and word information to produce a more efficient learning schedule than traditional vocabulary learning strategies. Based on the forgetting curve theory and some word learning facts, this paper formulates the concerned vocabulary learning into a quantitative optimization problem. Then the learning optimization problem is transformed into an assignment problem based on the features of complement series. To improve student with step-by-step learning, the multi-stage strategy is proposed. Simulations are done to demonstrate the effectiveness of the proposed strategy.
Decentralized Moving Horizon Estimation for Networked Navigation System with packet dropouts

This paper is concerned with state estimation for networked navigation system with packet dropouts. The packet dropout phenomenon is modeled by a stochastic variable satisfying the Bernoulli binary distribution, the predictor of the lost measurement is used as a compensator when packets dropout occur. At each local estimator, moving horizon strategy is applied to update the state, in which only cooperative packets and relative measurements over the most recent time horizon are used. Then the fusion estimator is obtained by applying the covariance intersection criterion. Finally, an illustrative example is given to demonstrate the efficiency of the proposed method.
EntityLDA: A Topic Model for Entity Retrieval on Knowledge Graph

Encoding great aof information, knowledge graph (KG) has become a popular data source for information retrieval, especially the entity retrieval task. However, many online KGs include both structured triples and unstructured texts, which makes it difficult to represent entities in a unified form. Moreover, there is also a vocabulary gap between queries given by users and triples contained in KG. To solve these problems, we propose EntityLDA, a topic model which jointly models structured and unstructured parts of KG in order to get complete descriptions of entities. It also bridges the vocabulary gap between users and KG by connecting related words with shared topics. We further propose a retrieval solution based on EntityLDA to retrieve entities under different circumstances. Experimental results show that EntityLDA outperforms baselines in both quantity and quality.
Stress monitoring of long-span steel truss during construction stage in Hangzhou International Expo Center

According to the requirement of building function, the large-span steel structures are constantly appearing in recent years. It is necessary to establish a set of feasible methods for health monitoring of long-span steel structures. This paper introduces the structural health monitoring of large-span steel structure in Construction Stage, including the method of Structural simulation, the wireless monitoring system of large-span steel structure, the installation method of sensors, the judgment method of structural safety and the maintenance of monitoring system. Taking Hangzhou International Expo Center as an example, all construction steps of the main large-span steel structure were simulated by the finite element analysis software. Through the structure of simulation calculation, the key members of the structure were found out, and the monitoring scheme was determined and carried out. In order to get the accurate stress data during the implementation of the monitoring scheme, this paper introduced some attentive details of sensor installation. The limitation of the key steel elements was obtained according to the related norms, and it provided the judgment for the safe of structure. After the smooth construction of the project, we could find that the stress monitoring system meted the monitoring requirement during the construction stage, and the results were an important evaluation index for the structure. The research methods used in this paper could be seen as useful reference for construction of similar projects. Stress monitoring of long-span steel struss during construction stage in Hangzhou International Expo Center.
TLS-ART-MC, A New Algorithm for Thai Word Segmentation

For the Thai language, NLP has begun since 1981, focused on Lexical analysis, which is the most important process that will severely affect the following process. The first phase of these studies is related to syllable segmentation to serve the word processing application, which is a popular program during that time.
A Word-Oriented Modular Division Algorithm and Its Efficient Implementation Based on CSA

Modular Division is an essential operation in RSA and ECC cryptosystem. Compared with other essential operations, modular division is most complicated and time-consuming operation. Its implementation performance has a great effect on the performance of relevant cryptosystem. Especially, when operand is large number (1024-bit or larger), the optimization of modular division performance is vital to improve the performance of whole cryptosystem. In this paper, we first propose a word-oriented modular division algorithm and then develop an efficient CSA implementation architecture. Experiment shows that the proposed architecture can get better performance than other architectures and the longer size of operand is, the better performance is. It is significant for modular division application.
Development of a Remote-Controlled Drone System by Using Only Eye Movements for Bedridden Patients

In recent years, the number of bedridden patients with limb disabilities such as amyotrophic lateral sclerosis (ALS), has increased due to an extended life span and an aging society. Human eye movements are not relatively attacked even if ALS develops. Focusing on this point in this paper, we proposed a new remote-controlled drone system by using only the patient’s eye movements in order to maintain and improve the quality of life of bedridden patients, including ALS patients. Furthermore, we presented experimental results to verify the effectiveness of the drone system. The experiment was conducted for three medical staff members who can evaluate this system considering bedridden patients. From the experimental results, we clarified what should be improved in order for bedridden patients to use this drone system.
Analysis of MHT and GBT Approaches to Disparate-Sensor Fusion

Multi-sensor multi-target tracking requires the solution to a challenging data association problem. The problem simplifies when a portion of the target state vector and the corresponding sensor data satisfy a particular Markovian assumption. This leads to quantifiable benefits in performance vs. complexity of the tracking solution. This paper summarizes recently-obtained technical advances in graph-based tracking and applies this to a benchmark study with respect to an advanced track-oriented multiple-hypothesis tracking solution.
Balanced Tree Partitioning with Succinct Logic

As a widely used data structure, graphs are good at characterizing data with internal associations, such as social and biological data. Tree structured data are special and are widely used in many real-world applications, such as organizational structure analysis and genealogical knowledge graph reasoning. For example, in kinship knowledge graph analysis, when a genealogical tree is particularly large (more than 25 levels and 45,000 nodes), it is a great challenge to partition this large tree into a specified number of subtrees with succinct logic and a balanced number of nodes. Therefore, in this paper, we propose the TPA (tree partitioning algorithm) algorithm to achieve a balanced and succinct logic partition of large-scale tree structured data. TPA first extracts all related nodes from a massive graph database and then constructs the convergent subgraph into a complete tree with a specified root node. Specifically, several virtual nodes are supplemented for generation-skipping connected nodes to achieve correct node numbering and partitioning. Finally, a graph partitioning algorithm is executed on the complete tree to obtain a specified number of subtrees with succinct logic and balanced node scales. Experiments conducted on four real-world datasets verify the effectiveness of our TPA algorithm.
The Linear Geometry Structure of Label Matrix for Multi-label Learning

Multi-label learning annotates a data point with the relevant labels. Under the low-rank assumption, many approaches embed the label space into the low-dimension space to capture the label correlation. However these approaches usually have weak prediction performance because the low-rank assumption is usually violated in real-world applications. In this paper, we observe the fact that the linear representation of row and column vectors of label matrix does not depend on the rank structure and it can capture the linear geometry structure of label matrix (LGSLM). Inspired by the fact, we propose the LGSLM classifier to improve the prediction performance. More specifically, after rearranging the columns of a label matrix in decreasing order according to the number of positive labels, we capture the linear representation of the row vectors of the compact region in the label matrix. Moreover, we also capture the linear and sparse representation of column vectors using the $$L_1$$ -norm. The experimental results for five real-world datasets show the superior performance of our approach compared with state-of-the-art methods.
Semantic-based Architecture Smell Analysis

Software smells have negative impacts on the reliability and modifiability of software systems. The smells in architecture design can be cascaded down to the implementation level and cause issues that require much effort to fix. Therefore, early detection of the architecture smells can benefit the overall quality of the software system. This paper presents an integration of methods that formally define the software architecture design towards architecture smell detection. Our approach serves as a framework that allows the architectural structures and behaviours to be formally analysed based on a coherent technique. We evaluated the accuracy and performance of our approach with the models generated from open source projects. The results show that our approach is effective and functions well.
TFBO: A Trusted Framework based on Blockchain for Outsourcing Data Entry

In China, the manual approach is still widely used in outsourcing data entry because of the poor recognition of OCR for Chinese. However, little consideration has been given to enhancing the credibility of manual data entry. In this paper, we propose TFBO (Trusted Framework Based on Blockchain for Outsourcing Data Entry). Firstly, we propose the system architecture and transaction flow of TFBO. Secondly, we propose Assign-on-chain to ensure the allocation process is traceable, which applies GODSS to generate a globally optimal of services selection and broadcasts the selection on the blockchain. Thirdly, we propose Result-on-chain uses searchable encryption to ensure the final entry result is irreversible and can be used for the deposit. Finally, we propose Check-on-chain to avoid the malicious behavior of clients or subcontractors through the smart contract about decentralized checking. The extensive credibility analysis and performance evaluation show that our solution is trusted and feasible.
Investigating Feasibility of Active Learning with Image Content on Mobile Devices Using ELM

This work investigates the feasibility of using the computational resources of a mobile device in active learning usage scenarios. It addresses the two main concerns, namely a way of fast model training or updating when more labels become available without re-training the whole Deep Learning model in case of image analysis, and the feasibility of running active learning workloads directly on a mobile device to improve the responsiveness and avoid using Cloud computing resources that become expensive at a large scale. The results found that a mobile phone (Apple iPhone Xs in particular) is superior to CPU-bound workloads on a modern laptop. Two special discoveries relate to the latency of the first prection that turns out to be 20x faster on a phone, and some kind of short-lived acceleration after a user touches a phone’s screen that let small batches of up to 20 images to be processed twice faster than usual, in only 0.1 s for a classification of 20 images.
An Overview of Semantic Search Engines

Semantic Search is a search technique that improves searching precision by understanding the purpose of the search and the contextual significance of words as they appear in the searchable data space, whether on the web to generate more relevant result. We highlight here about Semantic Search, Semantic Web and discuss about different type of Semantic search engine and differences between keyword base search and Semantic Search and the advantage of Semantic Search. We also give a brief overview of the history of semantic search and its feature scope in the world.
A New Convolutional Neural Network for Super-Resolution by Global and Local Residual

With deep learning applied in image super-resolution (SR), more CNN-based method was applied to image super-resolution problems in recent researches. A lot of training time and memory space were used to make network convergence by tradition SR methods, these defects limit the application of SR. To address this issue, a new convolution neural network structure for single image super-resolution (SISR) was proposed. In this work, network structure designed mainly in three aspects: First, global residual and residual block was combined to make the network convergence faster without batch normalization. Second, residual block was redesigned to make the network easier to train. Last point is that data augmentation was applied to solve the problem of poor network generality when the quantity of train images is insufficient. Experiments proved that our method can restore more high frequency details of images, get better in visual effect, improved the PSNR and SSIM of result.