Reviewerly/exHarmony huggingface dataset:
Social media video summarization using multi-Visual features and Kohnen's Self Organizing Map

Social networking tools such as Facebook, YouTube, Twitter, and Instagram, are becoming major platforms for communication. YouTube as one of the primary video sharing platform serves over 100 million distinct videos, 300 hours of videos are uploaded on YouTube every minute along with textual data. This massive amount of multimedia data needs to be managed with high efficiency, the irrelevant and redundant data needs to be removed. Video summarization ideals with the problem of redundant data in a video. A summarized video contains the most distinct frames which are termed as key frames. Most of the research work on key frames extraction considers only a single visual feature which is not sufficient for capturing the full pictorial details and hence affecting the quality of video summary generated. So there is a need to explore multiple visual features for key frames extraction. In this research work a key frame extraction technique based upon fusion of four visual features namely: correlation of RGB color channels, color histogram, mutual information and moments of inertia is proposed. Kohonen Self Organizing map as a clustering approach is used to find the most representative frames from the list of frames coming after fusion. Useless frames are discarded and frames having maximum Euclidean distance within a cluster are selected as final key frames. The results of the proposed technique are compared with the existing video summarization techniques: User generated summary, Video SUMMarization (VSUMM), and Video Key Frame Extraction through Dynamic Delaunay Clustering (VKEDDCSC) which shows a considerable improvement in terms of fidelity and Shot Reconstruction Degree (SRD) score.
Endorsement in Hyperledger Fabric

Hyperledger Fabric, the popular open-source permissioned blockchain platform, contains a mechanism called endorsement for establishing the validity of a transaction. In this paper we take a comprehensive look at endorsements in Fabric by reviewing their definition, security model and role in Fabric. We then review motivation, architecture and design for the recently introduced state-based endorsement. This feature permits developers of smart contracts to set endorsement policies at the level of individual state variables, enabling more fine-grained security policies. We further introduce cryptographic schemes for privacy-preserving endorsements that hide the identity of an endorser and ensure unlinkability across multiple endorsements. We present security model and analysis for both approaches, as well as benchmarks to evaluate the practicality of the proposed schemes.
Digital Augmentation of Historical Objects Through Tangible Interaction

The technological advances brought about by the Internet of Things enable new opportunities for a more direct interaction among users, objects, and places. This is an extremely valuable innovation for the cultural heritage sector, as it allows a more transparent use of technology in the digital augmentation of museums and cultural heritage sites. The possibility to augment physical objects with sensors detecting when they are moved and manipulated enables scenarios where descriptive information about objects is presented to users at the very exact time they are looking at them, stimulating engagement. This article describes a collaborative research effort among cultural heritage professionals, human--computer interaction experts, and developers that was aimed at investigating the goals and constraints curators consider for a physical encounter between visitors and historic relics. In a case study, we co-designed an interactive plinth centred on tangible interaction and evaluated the impact on the user experience of combining digital information with a hands-on experience of relics of World War I. Our findings show that visitors value this type of tangible interaction with collection objects positively, as it allows the discovery of details and the learning of aspects that normally go unnoticed. The synergy between physical and digital aspects stimulates empathy with the original users of the object and fosters social interaction.
ANiTW: A Novel Intelligent Text Watermarking technique for forensic identification of spurious information on social media

Digital Watermarking is required in multimedia applications where access to sensitive information has to be protected against malicious attacks. Since the digital text is one of the most widely used digital media on the Internet, the significant part of Web sites, social media, articles, eBooks, and so on is only plain text. Thus, copyrights protection of plain-texts is still a remaining issue that must be improved to provide proof of ownership and verify content integrity of vulnerable digital texts. In this research, we propose a novel intelligent text watermarking technique called ANiTW which utilizes an instance-based learning algorithm to hide an invisible watermark into Latin text-based information such that the hidden watermark can be extracted, even if a malicious user manipulates a portion of the watermarked information. Extensive experiments demonstrate the superior efficiency of the ANiTW with a significant improvement especially in the short text domain. To the best of our knowledge, this is the first intelligent text watermarking technique that provides an invisible signature for forensic identification of spurious information on social media by evaluating the manipulation rate of watermarked information, while the other existing approaches only consider the robust/fragile marking of signature into cover text. Copyright © Elsevier Ltd. All rights reserved.
A Novel Framework for Pharmaceutical Supply Chain Management using Distributed Ledger and Smart Contracts

Supply chain in industry is a flow of information and goods traversal through multiple suppliers such as raw material suppliers, manufacturers, distributors, retailers and end users. Supply chain management (SCM) helps in smooth monitoring and accounting of information and goods traversal among multiple users. However, the existing supply chain management processes lack in offering transparency of information, user's privacy, timely updates on demand peaks, improper tracing of information, quality management, deal repudiation and trust among users. To address these problems, we propose a novel framework for pharmaceutical supply chain management which uses an ethereum blockchain, a distributed ledger technology. We propose different smart contracts to manage pharmaceutical supply chain processes over an ethereum blockchain. This framework helps in achieving user privacy, transparency of the supplied drug, tracking of drugs, quality management, non-repudiation and demand supply management with the improved trust among the users. At the end, we analyze the proposed framework in terms of fulfilling the pharmaceutical supply chain management requirements.
The optimum daily direction of solar panels in the highlands, derived by an analytical method

It is well known, that for the efficient usage of solar energy the choosing the optimal location and direction of solar panels is one of most important issues. Finding the correct solution for this problem is especially significant in mountain regions that have a complex topography. A new method for the determination of the optimal daily panel direction is presented. The method is based on a simple mathematical model of solar radiation, which determines the amount of solar energy for any instant of time and any point taking into account shadowing of the Sun caused by surrounding relief. The optimal direction of the solar panel is determined analytically, as a solution of the derived system of equations. An algorithm for the determination of daily, monthly, seasonal and annual optimal directions is realised. The results of calculations and measurements for the chosen site are presented and the adequacy of the method is tested. The method can be especially useful for the smart determination of optimal direction and installation of panels in mountain regions which have high touristic potential and require clean energy usage.
Is there a risk-return trade-off in cryptocurrency markets? The case of Bitcoin

This study examines the link between returns and volatility of Bitcoin, at both contemporaneous and intertemporal levels, employing high-frequency data. The intraday price variability is proxied by four different measures, namely realized variance, jump variation, downside realized semivariance, and negative signed jump variation. The empirical analysis suggests that all realized volatility proxies have a significant and negative contemporaneous relation with Bitcoin returns. On the other hand, there is weak evidence of a negative intertemporal relation between returns and realized variance, jump variation, and downside realized semivariance. Accordingly, the existence of a positive risk-return trade-off in Bitcoin markets seems to be unsubstantiated. The findings are robust, even after controlling for a number of relevant determinants of the price formation process of Bitcoin.
A Survey of Question Answering over Knowledge Base

Question Answering over Knowledge Base (KBQA) is a problem that a natural language question can be answered in knowledge bases accurately and concisely. The core task of KBQA is to understand the real semantics of a natural language question and extract it to match in the whole semantics of a knowledge base. However, it is exactly a big challenge due to variable semantics of natural language questions in a real world. Recently, there are more and more out-of-shelf approaches of KBQA in many applications. It becomes interesting to compare and analyze them so that users could choose well. In this paper, we give a survey of KBQA approaches by classifying them in two categories. Following the two categories, we introduce current mainstream techniques in KBQA, and discuss similarities and differences among them. Finally, based on this discussion, we outlook some interesting open problems.
An efficient high-capacity reversible data hiding scheme for encrypted images

Reversible data hiding in encrypted images is an effective technique to embed information in encrypted domain, without knowing the original content of the image or the encryption key. In this paper, a high-capacity reversible data hiding scheme for encrypted images based on MSB (most significant bit) prediction is proposed. Since the prediction is not always accurate, it is necessary to identify the prediction error and store this information in the location map. The stream cipher is then used to encrypt the original image directly. During the data hiding phase, up to three MSBs of each available pixel in the encrypted image are substituted by the bits of the secret message. At the receiving end, the embedded data can be extracted without any errors and the original image can be perfectly reconstructed by utilizing MSB prediction. Experimental results show that the scheme can achieve higher embedding capacity than most related methods.
Intraoperative Correction of Liver Deformation Using Sparse Surface and Vascular Features via Linearized Iterative Boundary Reconstruction

During image guided liver surgery, soft tissue deformation can cause considerable error when attempting to achieve accurate localization of the surgical anatomy through image-to-physical registration. In this paper, a linearized iterative boundary reconstruction technique is proposed to account for these deformations. The approach leverages a superposed formulation of boundary conditions to rapidly and accurately estimate the deformation applied to a preoperative model of the organ given sparse intraoperative data of surface and subsurface features. With this method, tracked intraoperative ultrasound (iUS) is investigated as a potential data source for augmenting registration accuracy beyond the capacity of conventional organ surface registration. In an expansive simulated dataset, features including vessel contours, vessel centerlines, and the posterior liver surface are extracted from iUS planes. Registration accuracy is compared across increasing data density to establish how iUS can be best employed to improve target registration error (TRE). From a baseline average TRE of 11.4 ± 2.2 mm using sparse surface data only, incorporating additional sparse features from three iUS planes improved average TRE to 6.4 ± 1.0 mm. Furthermore, increasing the sparse coverage to 16 tracked iUS planes improved average TRE to 3.9 ± 0.7 mm, exceeding the accuracy of registration based on complete surface data available with more cumbersome intraoperative CT without contrast. Additionally, the approach was applied to three clinical cases where on average error improved 67% over rigid registration and 56% over deformable surface registration when incorporating additional features from one independent tracked iUS plane.
Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model

The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.
Gaussian Mixture Model Based Probabilistic Modeling of Images for Medical Image Segmentation

In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background.It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images.The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc.Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background.During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background.The maximization of this distance using differential calculus potentially leads to the desired segmentation results.The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy.Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art.
Extended Reality Framework for Remote Collaborative Interactions in Virtual Environments

This paper proposes the implementation of a framework for the development of collaborative extended reality (XR) applications. Using the framework, developers can focus on understanding which collaborative mechanisms they need to implement for the respective reality model application. In this paper we specifically study collaborative mechanisms around object manipulation in Virtual Reality (VR). As such, we planned a VR prototype using the proposed framework, which was used to validate the various interaction and collaboration features in VR. The gathered data from the user tests revealed that they enjoyed the experience and the collaborative mechanisms helped them work together. Furthermore, to understand whether the framework allowed for the development of XR applications, we decided to implement an augmented reality prototype as well. Afterwards, we ran an experiment with 4 VR and 3 AR users sharing the same virtual environment. The experiment was successful at allowing them to interact in real-time in the same shared environment. Therefore, the framework enables the development of XR applications that support different mixed-reality technologies.
Decentralized authorization in constrained IoT environments exploiting interledger mechanisms

We present models that utilize smart contracts and interledger mechanisms to provide decentralized authorization for constrained IoT devices. The models involve different tradeoffs in terms of cost, delay, complexity, and privacy, while exploiting key advantages of smart contracts and multiple blockchains that communicate with interledger mechanisms. These include immutably recording hashes of authorization information and policies in smart contracts, resilience through the execution of smart contract code on all blockchain nodes, and cryptographically linking transactions and IoT events recorded on different blockchains using hash-lock and time-lock mechanisms. In the case of two ledgers, an authorization and a payment ledger, the authorization ledger can be a private Ethereum network or a permissioned ledger such as Hyperledger Fabric. For decentralized authorization where a subset of m-out-of-n authorization servers are required, we present two policies for selecting the m servers. The first policy can utilize statistics of the authorization servers such as transaction cost and response time. The second policy selects the first m servers that respond. The proposed models are evaluated on the public Ethereum testnets Rinkeby and Ropsten, and for different implementations on the Hyperledger Fabric permissioned ledger, in terms of execution cost (gas), delay, and reduction of data that needs to be sent to the constrained IoT devices.
The Influence of Self-Efficacy and Work Input on Physical Education Teachers’ Creative Teaching

To explore the influence of different background factors on middle school PE teachers' self-efficacy, work input and creative teaching, and to reveal the relationship between teaching self-efficacy and work input on creative teaching.By means of self-efficacy, work engagement and creative teaching scale, a questionnaire survey was conducted among middle school PE teachers, and the data were processed and modeled by SPSS and AMOS statistical analysis software.Physical education (PE) teachers' self-teaching effectiveness was influenced by background factors such as gender, age, teaching age, full-time or part-time work and educational level. Work input was only affected by age, teaching experience and educational level, while creative teaching seemed to be only related to background factors such as educational background and full-time or part-time work; PE teachers' general teaching effectiveness and personal teaching effectiveness had significant positive effects on energy input, concentration input, dedication input, cognitive creativity, skill creativity and emotional creativity; Concentration input had a significant positive impact on the three-dimensional of creative teaching, while energy input and dedication input had no impact on the three-dimensional of creative teaching; Work input as an intermediary variable of self-efficacy's influence on creative teaching had been verified, but the real intermediary role was not the whole work input, but the concentration input in its structure.Both general and individual teaching effectiveness had positive effects on work input and creative teaching, but the energetic and dedicated input in work input cannot promote teachers' creative teaching effectively. Therefore, the professional ethics training of PE teachers in their enthusiasm and dedication to work should be strengthened.
A High Capacity Reversible Data Hiding in Encrypted AMBTC-Compressed Images

Recently, reversible data hiding in encrypted compressed images (RDHECI) has attracted more attention due to privacy information protection concerns. Meanwhile, AMBTC as one technique of lossy image compression that has lower storage costs and the simplicity in computation and is extensively used in many applications. Hence, this paper proposes an RDHECI scheme based on AMBTC, named O-AMBTC, to address privacy concerns. First, the original image is scrambled in a block-wise manner to generate the scrambled image which then is compressed by an AMBTC compression technique. Subsequently, the derived AMBTC compression codes are encrypted by using the methods of value modulation and stream cipher while the correlations between two quantization levels of AMBTC compression codes are retained and exploited to vacate redundant room to embed secret messages. Data hiding is then performed with the use of PBTL labeling strategy. In addition, another modified AMBTC compression code based RDHECI scheme, called M-AMBTC, is suggested to increase the ability to carry secret messages. In our dual approach, both the AMBTC-compressed image and the secret messages can be correctly recovered. Experimental results show that two proposed schemes are able to achieve average embedding rates as large as 0.6 bpp and 0.8 bpp when the block size is set to 2 × 2, respectively.
Understanding Users' Preferences for Augmented Reality Television

We examine users' preferences for Augmented Reality for television and report findings from an exploratory study with 172 participants we conducted to understand the perceived value of twenty distinct AR-TV scenarios. We connect our findings to participants' overall perceptions of and experience with AR technology as well as to their self-reported television watching behavior. Our results reveal high perceived value for AR-TV scenarios involving interactive content, wall-sized and room-sized video projections, multiple perspectives of the same movie scene, and for virtual objects coming out of the TV screen into the room. Other scenarios, such as live video of remote friends watching the same broadcast or multiple virtual channels displayed around the TV screen were rated less valuable.
Combining UML and ontology: An exploratory survey

UML models and ontologies are two knowledge representations with different strengths and weaknesses. Until recently, they were considered unrelated research domains. However, studies investigating their underlying paradigms and the approaches combining these two are increasingly emerging. Nevertheless, the state of the art research covering the relationship between the two is still under exploration. In this paper, we aim to provide a comprehensive overview of both domains by conducting a literature review of the relevant research work. In this survey, the relationship between UML and ontology is investigated from both the theoretical and practical perspectives. We present a detailed classification of the existing work based on the considered issues and their practical use cases. Finally, we provide an evaluation of the existing work according to the criteria we identified.