Reviewerly/exHarmony huggingface dataset:
Reversible Data Hiding in Encrypted 2D Vector Graphics Based on Reversible Mapping Model for Real Numbers

Currently, much attention has been paid to reversible data hiding (RDH) in an encrypted domain due to the popular deployment of cloud storage. However, nearly all existing RDH schemes in the encrypted domain are proposed for raster images, and very little work has been done to 2D vector graphics, which are represented in real numbers. In this paper, a reversible mapping model for real numbers is first built. It maps the points in R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> non-intersecting subsets in R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , which guarantees that s bits can be embedded into each real number. Based on the model, an RDH scheme in encrypted 2D vector graphics is put forward. In the scheme, a user encrypts 2D engineering graphics and stores them in the cloud, and then the cloud service provider can perform information hiding, extraction, and even recover the encrypted 2D vector graphics. For the authorized user, it can acquire the recovered 2D vector graphics from the cloud and obtain their original versions after decryption. For an unauthorized user, he can only acquire the encrypted 2D vector graphics with a hidden message, and only approximate 2D vector graphics can be obtained even if he knows the decryption key but does not know the hiding key. The experimental results and analysis show that it can strike a good balance between security, distortion, and capacity. It provides a new paradigm for RDH in the encrypted domain for the data represented in real numbers.
Multimodal Remote Sensing Image Registration Based on Image Transfer and Local Features

Automatic registration is still a challenging problem for multimodal remote sensing images including optical, light detection and ranging, synthetic aperture radar images, and so on. Due to the differences in imaging principles, the gray value, texture, and landscape characteristic of these images are different in the local area. This also makes it difficult to obtain satisfactory results for the conventional image registration methods. In order to achieve registration of multimodal images to obtain complementary information, we apply the transfer algorithm based on a deep image analogy to the preprocessing of image registration. It eliminates the differences in multimodal remote sensing images by blending the original image structure and texture. The conventional local feature-based method is applied to match the original and generated images. Correspondences are increased and the registration error is reduced. The experiments demonstrate that our method can effectively deal with multimodal data and produce more accurate results. The algorithm is based on the joint of image deep semantic features and indirectly achieves matching of the original image pair. It provides a new solution to the problem of multimodal images registration.
From Robot to Virtual Doppelganger: Impact of Visual Fidelity of Avatars Controlled in Third-Person Perspective on Embodiment and Behavior in Immersive Virtual Environments

This study presents the second phase of a series of experiments investigating the impact of avatar visual fidelity on the sense of embodiment and users' behavior in immersive virtual environments. Our main focus concerns the similarity between users and avatars, a factor known as truthfulness. Our experiment requires the participants to control three avatars using a third-person perspective: a robot, a suit and their virtual doppelganger (virtual representation of the self). In order to analyze users' reactions and strategies, each task of the scenario of the virtual reality application can potentially affect the integrity of their characters. Our results revealed that ownership, one of the three factors of the sense of embodiment, is higher for the participants controlling their self-representation than with abstract representations. Furthermore, avatar visual fidelity seems to affect users' subjective experience, half of the panel reported having different behavior depending on the controlled character. Abstract representations allow the users to adopt more risky behaviors, while self-representations maintain a connection with the real world and encourage users to preserve the integrity of their avatar.
Price clustering and sentiment in bitcoin

While prior research provides evidence of price clustering in bitcoin, this study seeks to explain the unusual level of bitcoin price clustering using various measures of bitcoin-level and market-wide sentiment. Our results suggest that sentiment has a strong positive association with price clustering. In economic terms, a one standard deviation increase in sentiment – measured by Google Trends – explains about 2.5% to 5% of the unusual level of price clustering in Bitcoin. We also find that our results are robust when we use other measures of investor sentiment.
KaaS: Key as a Service over Quantum Key Distribution Integrated Optical Networks

In the Internet Age, optical networks are vulnerable to numerous cyberattacks, and conventional key distribution methods suffer from the increased computational power. QKD can distribute information-theoretically secure secret keys between two parties based on the principles of quantum mechanics. Integrating QKD into optical networks can leverage existing fiber infrastructures with wavelength division multiplexing for the practical deployment of secret keys, and accordingly employ the secret keys for optical-layer security enhancement. Then, how to efficiently deploy and employ secret keys over QKD-integrated optical networks are emerging as two challenges. This article proposes a framework of key as a service (KaaS, i.e., providing secret keys as a service in a timely and accurate manner to satisfy the security requirements) to jointly overcome these two challenges. To enable the typical functions (i.e., secret-key deployment and employment) in KaaS, two secret-key virtualization steps, that is, key pool (KP) assembly and virtual key pool (VKP) assembly, are introduced. Also, we illustrate a new QKD-integrated optical network architecture from a holistic view, where the control layer is implemented by software defined networking for efficient network management. A time-shared KP assembly strategy and an on-demand VKP assembly strategy are presented for KaaS implementation. The success probabilities of KP assembly and VKP assembly are defined to evaluate the benefits of KaaS for efficiently deploying and employing secret keys as well as for security enhancement over QKD-integrated optical networks.
Development of a virtual reality platform for effective communication of structural data in drug discovery

In drug discovery, structural knowledge of a target enables structure-based design approaches and thereby reduces the time and labor required to develop a therapy. Whilst molecular graphics frameworks coupled with computational analysis are now ubiquitous tools for the structural and computational biologist, sharing the detailed visualization and derived structural information with non-expert users still presents a challenge. Here we describe an intuitive virtual world for viewing, manipulating, and modifying chemical and macromolecular structures in a fully immersive and collaborative 3D environment. By reducing the barriers to viewing and interacting with structural data, structural analysis can be democratized to a general scientist, which in turn fosters novel collaboration, ideas, and findings in structural biology and structure-based drug discovery.
An assessment of geometry teaching supported with augmented reality teaching materials to enhance students’ 3D geometry thinking skills

The aim of this research was to examine the effect of Augmented Reality (AR) supported geometry teaching on students' 3D thinking skills. This research consisted of 3 steps: (1) developing a 3D thinking ability scale, (ii) design and development of an AR Geometry Tutorial System (ARGTS) and (iii) implementation and assessment of geometry teaching supported with ARGTS. A 3D thinking ability scale was developed and tested with experimental and control groups as a pre- and post-test evaluation. An AR Geometry Tutorial System (ARGTS) and AR teaching materials and environments were developed to enhance 3D thinking skills. A user study with these materials found that geometry teaching supported by ARGTS significantly increased the students' 3D thinking skills. The increase in average scores of Structuring 3D arrays of cubes and Calculation of the volume and the area of solids thinking skills was not statistically significant (p > 0.05). In terms of other 3D geometric thinking skills' subfactors of the scale a statistically significant difference was found in favour of the experimental group in pre-test and post-test scores (p < 0.05). The biggest difference was found on ability to recognize and create 3D shapes (p < 0.01).The results of this research are particularly important for identifying individual differences in 3D thinking skills of secondary school students and creating personalized dynamic intelligent learning environments.
Predicting employee information security policy compliance on a daily basis: The interplay of security-related stress, emotions, and neutralization

We conceptualized security-related stress (SRS) and proposed a theoretical model linking SRS, discrete emotions, coping response, and information security policy (ISP) compliance. We used an experience sampling design, wherein 138 professionals completed surveys. We observed that SRS had a positive association with frustration and fatigue, and these negative emotions were associated with neutralization of ISP violations. Additionally, frustration and fatigue make employees more likely to follow through on their rationalizations of ISP violations by decreased ISP compliance. Our findings provide evidence that neutralization is not a completely stable phenomenon but can vary within individuals from one time point to another.
Efficient algorithms to identify periodic patterns in multiple sequences

Periodic pattern mining is a popular data mining task, which consists of identifying patterns that periodically appear in data. Traditional periodic pattern mining algorithms are designed to find patterns in a single sequence. However, in several domains, it is desirable to discover patterns that are periodic in many sequences. An example of such application is market basket analysis. Given a database of sequences of transactions made by customers, discovering sets of items that are periodically bought by customers can help understand customer behavior. To discover periodic patterns common to multiple sequences, this paper extends the traditional problem of mining periodic patterns in a sequence. Two novel measures are defined called the standard deviation of periods and the sequence periodic ratio. Two algorithms are proposed to mine these patterns efficiently called MPFPSBFS and MPFPSDFS, which perform a breadth-first search and depth-first search, respectively. Because the sequence periodic ratio is neither monotone nor anti-monotone, these algorithms rely on a novel upper-bound called boundRa and two novel search space pruning properties to find periodic patterns efficiently. The algorithms have been evaluated on multiple datasets. Results show that they are efficient and can filter numerous non periodic itemsets to identify periodic patterns.
ARDIS: a Swedish historical handwritten digit dataset

Abstract This paper introduces a new image-based handwritten historical digit dataset named Arkiv Digital Sweden (ARDIS). The images in ARDIS dataset are extracted from 15,000 Swedish church records which were written by different priests with various handwriting styles in the nineteenth and twentieth centuries. The constructed dataset consists of three single-digit datasets and one-digit string dataset. The digit string dataset includes 10,000 samples in red–green–blue color space, whereas the other datasets contain 7600 single-digit images in different color spaces. An extensive analysis of machine learning methods on several digit datasets is carried out. Additionally, correlation between ARDIS and existing digit datasets Modified National Institute of Standards and Technology (MNIST) and US Postal Service (USPS) is investigated. Experimental results show that machine learning algorithms, including deep learning methods, provide low recognition accuracy as they face difficulties when trained on existing datasets and tested on ARDIS dataset. Accordingly, convolutional neural network trained on MNIST and USPS and tested on ARDIS provide the highest accuracies $$58.80\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>58.80</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and $$35.44\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>35.44</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , respectively. Consequently, the results reveal that machine learning methods trained on existing datasets can have difficulties to recognize digits effectively on our dataset which proves that ARDIS dataset has unique characteristics. This dataset is publicly available for the research community to further advance handwritten digit recognition algorithms.
Sentiment Analysis with NLP on Twitter Data

Every social networking sites like facebook, twitter, instagram etc become one of the key sources of information. It is found that by extracting and analyzing data from social networking sites, a business entity can be benefited in their product marketing. Twitter is one of the most popular sites where people used to express their feelings and reviews for a particular product. In our work, we use twitter data to analyze public views towards a product. Firstly, we have developed a natural language processing (NLP) based pre-processed data framework to filter tweets. Secondly, we incorporate Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) model concept to analyze sentiment. This is an initiative to use BoW and TFIDF are used together to precisely classify positive and negative tweets. We have found that by exploiting TF-IDF vectorizer, the accuracy of sentiment analysis can be substantially improved and simulation results show the efficiency of our proposed system. We achieved 85.25% accuracy in sentiment analysis using NLP technique.
BPTM: Blockchain-Based Privacy-Preserving Task Matching in Crowdsourcing

Task matching in crowdsourcing is designed to provide convenient task information retrieval and has been extensively explored.In general, the task matching process is required to be reliable and to meet privacy requirements.However, most existing privacy-preserving task matching solutions for crowdsourcing focus on privacy issues but ignore the reliability of the process.In this paper, we propose a blockchainbased task matching scheme for crowdsourcing with a secure and reliable matching.Instead of utilizing a centralized cloud server, we employ smart contracts, an emerging blockchain technology, to provide reliable and transparent matching.In this way, data confidentiality and identity anonymity are achieved effectively and efficiently.The extensive privacy analysis and performance evaluation show that our solution is secure and feasible.
Ranking products with online reviews: A novel method based on hesitant fuzzy set and sentiment word framework

Recently, sentiment analysis (SA) and multi-attribute decision making (MADA) have been extensively studied respectively, which aims to help decision makers make informed decisions. However, rather less attention has been paid to the field of combining SA and MADA. Therefore, in this paper, we propose a novel method to rank products through online reviews. To begin with, it is a novel idea to view different sentiment scores of one feature as the different membership degrees. Further, we propose the fuzzy sentiment word framework and corresponding computation rules to calculate the sentiment score of each feature in each review, which later can be used to obtain the overall performance of each feature concerning different products based on hesitant fuzzy set (HFS). Next, the attention degree of each feature is considered in the process of calculating weight of different features. In addition, based on 2-addiitive fuzzy measure and Choquet integral, we extend TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) method, which concerns decision make’s psychological behavior, to deal with criteria interactions (positive, mutual independent and negative) in the process of MADM. Furthermore, we use a case study to demonstrate the efficiency and applicability of the proposed method.
Anomaly Detection and Repair for Accurate Predictions in Geo-distributed Big Data

The increasing presence of geo-distributed sensor networks implies the generation of huge volumes of data from multiple geographical locations at an increasing rate. This raises important issues which become more challenging when the final goal is that of the analysis of the data for forecasting purposes or, more generally, for predictive tasks. This paper proposes a framework which supports predictive modeling tasks from streaming data coming from multiple geo-referenced sensors. In particular, we propose a distance-based anomaly detection strategy which considers objects described by embedding features learned via a stacked auto-encoder. We then devise a repair strategy which repairs the data detected as anomalous exploiting non-anomalous data measured by sensors in nearby spatial locations. Subsequently, we adopt Gradient Boosted Trees (GBTs) to predict/forecast values assumed by a target variable of interest for the repaired newly arriving (unlabeled) data, using the original feature representation or the embedding feature representation learned via the stacked auto-encoder. The workflow is implemented with distributed Apache Spark programming primitives and tested on a cluster environment. We perform experiments to assess the performance of each module, separately and in a combined manner, considering the predictive modeling of one-day-ahead energy production, for multiple renewable energy sites. Accuracy results show that the proposed framework allows reducing the error up to 13.56%. Moreover, scalability results demonstrate the efficiency of the proposed framework in terms of speedup, scaleup and execution time under a stress test.
Revisiting Hidden Markov Models for Speech Emotion Recognition

Hidden Markov models (HMMs) have a long tradition in automatic speech recognition (ASR) due to their capability of capturing temporal dynamic characteristics of speech. For emotion recognition from speech, three HMM based architectures are investigated and compared throughout the current paper, namely, the Gaussian mixture model based HMMs (GMM-HMMs), the subspace based Gaussian mixture model based HMMs (SGMM-HMMs) and the hybrid deep neural network HMMs (DNN-HMMs). Extensive emotion recognition experiments are carried out on these three architectures on the CASIA corpus, the Emo-DB corpus and the IEMOCAP database, respectively, and results are compared with those of state-of-the-art approaches. These HMM based architectures prove capable of constituting an effective model for speech emotion recognition. Also, the modeling accuracy is further enhanced by incorporating various advanced techniques from the ASR area. In particular, among all of the architectures, the SGMM-HMMs achieve the best performance in most of the experiments.
SkeletonNet: A Hybrid Network With a Skeleton-Embedding Process for Multi-View Image Representation Learning

Multi-view representation learning plays a fundamental role in multimedia data analysis. Some specific inter-view alignment principles are adopted in conventional models, where there is an assumption that different views share a common latent subspace. However, when dealing views on diverse semantic levels, the view-specific characteristics are neglected, and the divergent inconsistency of similarity measurements hinders sufficient information sharing. This paper proposes a hybrid deep network by introducing tensor factorization into the multi-view deep auto-encoder. The network adopts skeleton-embedding process for unsupervised multi-view subspace learning. It takes full consideration of view-specific characteristics, and leverages the strength of both shallow and deep architectures for modeling low- and high-level views, respectively. We first formulate the high-level-view semantic distribution as the underlying skeleton structure of the learned subspace, and then infer the local tangent structures according to the affinity propagation of low-level-view geometric correlations. As a consequence, more discriminative subspace representation can be learned from global semantic pivots to local geometric details. Experimental comparisons on three benchmark image datasets show the promising performance and flexibility of our model.
Nonnegative Laplacian embedding guided subspace learning for unsupervised feature selection

Unsupervised feature selection plays an important role in machine learning and data mining, which is very challenging because of unavailable class labels. We propose an unsupervised feature selection framework by combining the discriminative information of class labels with the subspace learning in this paper. In the proposed framework, the nonnegative Laplacian embedding is first utilized to produce pseudo labels, so as to improve the classification accuracy. Then, an optimal feature subset is selected by the subspace learning guiding by the discriminative information of class labels, on the premise of maintaining the local structure of data. We develop an iterative strategy for updating similarity matrix and pseudo labels, which can bring about more accurate pseudo labels, and then we provide the convergence of the proposed strategy. Finally, experimental results on six real-world datasets prove the superiority of the proposed approach over seven state-of-the-art ones.
Ranking-based biased learning swarm optimizer for large-scale optimization

Large-scale optimization, solving real high-dimensional problems, has attracted many research interests. Large-scale optimization problems are far more difficult than traditional optimization problems due to their numerous local optimum. In this paper, a principle of maximizing the fitness difference between learners and exemplars is proposed to improve the performance of the optimization algorithm. Then based on the principle, a improved particle swarm optimization algorithm called the “ranking-based biased learning swarm optimizer for large-scale optimization” (RBLSO) is proposed. The proposed RBLSO contains two types of learning strategies, namely, ranking paired learning (RPL) and biased center learning (BCL). In RPL, the worse particles learn peer to peer from the better particles according to their ranks, so then the convergence speed will be accelerated. In BCL, each particle learns from the biased center that is defined as the fitness weighted center of the whole swarm. This operator is utilized to strengthen the explorative ability of the algorithm. To test the performances of the proposed algorithm, we conduct some experiments on the proposed learning mechanism. RBLSO is compared with several state-of-the-art large-scale optimization algorithms on two widely used benchmark function sets, CEC2010 and CEC2013. These two function sets were proposed for the special session and competition on large-scale global optimization held under the Congress on Evolutionary Computation (CEC) 2010 and 2013. Experimental results show that RBLSO is effective in solving large-scale optimization problems.