Reviewerly/exHarmony huggingface dataset:
Hypernetwork Knowledge Graph Embeddings

Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness. Inferring missing relations (links) between entities (nodes) is the task of link prediction. A recent state-of-the-art approach to link prediction, ConvE, implements a convolutional neural network to extract features from concatenated subject and relation vectors. Whilst results are impressive, the method is unintuitive and poorly understood. We propose a hypernetwork architecture that generates simplified relation-specific convolutional filters that (i) outperforms ConvE and all previous approaches across standard datasets; and (ii) can be framed as tensor factorization and thus set within a well established family of factorization models for link prediction. We thus demonstrate that convolution simply offers a convenient computational means of introducing sparsity and parameter tying to find an effective trade-off between non-linear expressiveness and the number of parameters to learn.
How pre-service English teachers enact TPACK in the context of web-conferencing teaching: A design thinking approach

Technological Pedagogical Content Knowledge (TPACK) is a teacher knowledge construct in relation to integrating technology into teaching. However, whether this theoretical concept exists in practice remains contentious. To ascertain the nature of TPACK, the present study aimed to understand how six pre-service English teachers enacted various forms of TPACK through design thinking while considering contextual problems that impacted on their web-conferencing teaching over the course of 14 weeks. This study examined the design conversations in which six distance teachers discussed ways to design online teaching materials and activities as well as to solve any contextual problems encountered. Through quantitative content analysis of coded post-teaching discussions and qualitative analysis of interviews, it was found that while the teachers' discussions conspicuously displayed an orientation towards Pedagogical Content Knowledge, as opposed to technology-based knowledge, their discussions were particularly not associated with Technological Pedagogical Knowledge. In addition, two contextual factors were found to influence their web-conferencing teaching: technical problems related to sound quality were identified as a micro-level contextual factor while the teachers' concerns about their students’ prior knowledge and short attention spans were viewed as a student-centric contextual factor. These findings provide insights into how individual TPACK subdomains were enacted through design thinking that enabled the teachers to overcome contextual problems. The present study contributes to our understanding of how TPACK enactment is mediated by the context, empirically validating TPACK construct in practice through design thinking.
Detection and Classification of Continuous Volcano-Seismic Signals With Recurrent Neural Networks

This paper introduces recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) to detect and classify continuous sequences of volcano-seismic events at the Deception Island Volcano, Antarctica. A representative data set containing volcano-tectonic earthquakes, long-period events, volcanic tremors, and hybrid events was used to train these models. Experimental results show that RNN, LSTM, and GRU can exploit temporal and frequency information from continuous seismic data, attaining close to 90%, 94%, and 92% events correctly detected and classified. A second experiment is presented in this paper. The architectures described above, trained with data from campaigns of seismic records obtained in 1995-2002, have been tested with data from the recent seismic survey performed at the Deception Island Volcano in 2016-2017 by the Spanish Antarctic scientific campaign. Despite the variations in the geophysical properties of the seismic events within the volcano across eruptive periods, results provide good generalization accuracy. This result expands the possibilities of RNNs for real-time monitoring of volcanic activity, even if seismic sources change over time.
Distribution-Oriented Aesthetics Assessment With Semantic-Aware Hybrid Network

Image aesthetics assessment has emerged as a hot topic in recent years due to its potential in numerous high-level vision applications. In this paper, distinguished from existing studies relying on a single label, we propose quantifying image aesthetics by a distribution over multiple quality levels. The distribution-based representation characterizes the disagreement among users' aesthetic preferences regarding the same image, and is also compatible with the traditional task of aesthetic label prediction. Our framework is developed based on fully convolutional networks and enables inputs of varying sizes. In this way, we circumvent the fixed-size constraint of prevalent convolutional neural networks, and avoid the risk of impairing the intrinsic aesthetic appeal of images. Moreover, given the fact that aesthetic perceiving is coupled with semantic understanding, we present a novel semantic-aware hybrid NEtwork (SANE), which harvests the information from object categorization and scene recognition to enhance image aesthetics assessment. Experiments on two benchmark datasets have well verified the effectiveness of our approach in both scenarios of aesthetic distribution prediction and aesthetic label prediction, and highlighted the benefits of input preserving as well as semantic understanding for images.
A reversible and secure patient information hiding system for IoT driven e-health

Internet of things (IoT) coupled with mobile cloud computing has made a paradigm shift in the service sector. IoT-assisted mobile cloud based e-healthcare services are making giant strides and are likely to change the conventional ways of healthcare service delivery. Though numerous approaches for preventing unauthorized access to information exchanged between a mobile phone and cloud platform do exist, but there is no security mechanism to prevent unauthorized access by the cloud administrators. With an aim to ensure security of client data such as Electronic Patient Records (EPR), we propose a novel high-capacity and reversible data hiding approach for securely embedding EPR within the medical images using Optimal Pixel Repetition (OPR). OPR converts every pixel of the input image to a 2 × 2 block to facilitate reversibility by ensuring all the pixels in a 2 × 2 block to have different values. Since a 2 × 2 block is comprised of 4-pixel elements, which could be arranged in sixteen possible ways; we generate a lookup table corresponding to sixteen possible positions of pixels. EPR hiding in each block is achieved by permuting the pixels of a block according to the four-bit word of secret data, resulting in a histogram invariant stego image. The histogram invariance improves the robustness of the proposed scheme to statistical attacks. A stego image is said to hide embedded data securely, when it provides better imperceptivity for an appreciably high payload. Thus, while using information embedding approach for securing client data on a mobile-cloud platform, high imperceptivity is a desirable feature. Experimental results show that average PSNR obtained is 42 dB for payload 1.25 bpp by our scheme, showing its effectiveness for preventing unauthorized access to client’s sensitive data.
Toward Construction-Based Data Hiding: From Secrets to Fingerprint Images

Data hiding usually involves the alteration of a cover signal for embedding a secret message. In this paper, we propose a construction-based data hiding technique which transforms a secret message into a fingerprint image directly. Unlike the conventional data hiding techniques, this scheme does not need any cover signals to participate. Instead, it generates the fingerprint image based on a piece of hologram phase constructed from the secret message. The hologram phase consists of the spiral phase and the continuous phase. First, we propose to map the secret message to a polynomial and encode it into a set of points with different polarities, from which the spiral phase is computed and constructed. Then, we construct the continuous phase by decomposing a fingerprint image synthetically generated. The spiral phase and the continuous phase are combined to form the hologram phase. This is eventually used to construct a fingerprint image in a common form such as the grayscale fingerprint image, binary fingerprint image, or thinned fingerprint image. The secret message can be extracted by detecting the encoded points in the constructed fingerprint. We conduct the experiments by constructing fingerprint images with ordinary sizes, the results show that the secret message can be extracted accurately. It is also difficult to detect the existence of secret message from the constructed fingerprint images.
Collaborative Neural Social Recommendation

Collaborative filtering (CF) is one of the most popular techniques for building recommender systems. To overcome the data sparsity in CF, social recommender systems have emerged to boost recommendation performance by utilizing social correlation among users' interests. Recently, inspired by the immense success of deep learning for embedding learning, neural network-based recommender systems have shown promising recommendation performance. Nevertheless, few researchers have attempted to tackle the social recommendation problem with neural models. To this end, in this paper, we design a neural architecture that organically combines the intrinsic relationship between social network structure and user-item interaction behavior for social recommendation. Two key challenges arise in this process: first, how to incorporate the social correlation of users' interests in this neural model, and second, how to design a neural architecture to capture the unique characteristics of user-item interaction behavior for recommendation. To tackle these two challenges, we develop a model named collaborative neural social recommendation (CNSR) with two parts: 1) a social embedding part and 2) a collaborative neural recommendation (CNR) part. In CNSR, the user embedding leverages each user's social embedding learned from an unsupervised deep learning technique with social correlation regularization. The user and item embeddings are then fed into a unique neural network with a newly designed collaboration layer to model both the shallow collaborative and deep complex interaction relationships between users and items. We further propose a joint learning framework to allow the social embedding part and the CNR part to mutually enhance each other. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of our proposed model.
Mask-Pose Cascaded CNN for 2D Hand Pose Estimation From Single Color Image

We present a cascaded convolutional neural network for 2D hand pose estimation from single in-the-wild RGB images. Inspired by the commonly used silhouette information in the generative pose estimation approaches, we build the cascaded network with two stages, including mask prediction stage as well as pose estimation stage. We find that the two stages network architecture for end-to-end training could benefit from each other for detecting the hand mask and 2D pose. To further improve the hand pose detection accuracy, we contribute a new RGB hand dataset named OneHand10K, which contains 10K RGB images. Each image contains one single hand. We manually obtain the segmented mask and labeled keypoints for guided learning. We hope that this dataset will be a benchmark and encourage more people to conduct research on this challenging topic. Experiments on the validation dataset have demonstrated the superior performance of the proposed cascaded convolutional neural network.
Knowledge graph embedding with concepts

Knowledge graph embedding aims to embed the entities and relationships of a knowledge graph in low-dimensional vector spaces, which can be widely applied to many tasks. Existing models for knowledge graph embedding primarily concentrate on entity–relation–entitytriplets, or interact with the text corpus. However, triplets are less informative, and the in-domain text corpus is not always available, making the embedding results deviate from the actual meaning. At the same time, our mental world contains many concepts about worldly facts. For human cognition, compared to knowledge that we learned, common-sense concepts are more basic and general, and they play important roles in human knowledge accumulation. In this paper, based on common-sense concepts information of entities from a concept graph, we propose a Knowledge Graph Embedding with Concepts (KEC) model that embeds entities and concepts of entities jointly into a semantic space. The fact triplets from a knowledge graph are adjusted by the common-sense concept information of entities from a concept graph. Our model not only focuses on the relevance between entities but also focuses on their concepts. Thus, this model offers precise semantic embedding. We evaluate our method on the tasks of knowledge graph completion and entity classification. Experimental results show that our model outperforms other baselines on the two tasks.
“Too true to be good?” when virtual reality decreases interest in actual reality

Virtual Reality (VR) technologies enable marketers to design websites that simulate experiential consumption (e.g., museum visits, leisure travel) closely. While the motivation for employing VR-style websites is to attract people to consume the experience in real life, we proposed and found that VR-style (vs. traditional) websites may dissuade them from future consumption (Study 1). We argued that perceived similarity between virtual and real experiences mediates this negative effect. Study 2 suggested that because people's enduring involvement with a product determines their ability to discriminate well between episodes of product experience, it causes variation in both perceived similarity and consumption intention, with the former mediating the latter. Study 3 showed that this negative effect reversed in experiences in which perceived similarity was low. Study 4 demonstrated that this negative effect disappeared when perceived similarity is irrelevant to the consumption decision, for example, when the decision is whether or not to recommend the experience to a friend.
Gender differences in participation and reward on Stack Overflow

Programming is a valuable skill in the labor market, making the underrepresentation of women in computing an increasingly important issue. Online question and answer platforms serve a dual purpose in this field: they form a body of knowledge useful as a reference and learning tool, and they provide opportunities for individuals to demonstrate credible, verifiable expertise. Issues, such as male-oriented site design or overrepresentation of men among the site's elite may therefore compound the issue of women's underrepresentation in IT. In this paper we audit the differences in behavior and outcomes between men and women on Stack Overflow, the most popular of these Q&A sites. We observe significant differences in how men and women participate in the platform and how successful they are. For example, the average woman has roughly half of the reputation points, the primary measure of success on the site, of the average man. Using an Oaxaca-Blinder decomposition, an econometric technique commonly applied to analyze differences in wages between groups, we find that most of the gap in success between men and women can be explained by differences in their activity on the site and differences in how these activities are rewarded. Specifically, 1) men give more answers than women and 2) are rewarded more for their answers on average, even when controlling for possible confounders such as tenure or buy-in to the site. Women ask more questions and gain more reward per question. We conclude with a hypothetical redesign of the site's scoring system based on these behavioral differences, cutting the reputation gap in half.
Prior Knowledge-Based Probabilistic Collaborative Representation for Visual Recognition

Collaborative representation is an effective way to design classifiers for many practical applications. In this paper, we propose a novel classifier, called the prior knowledge-based probabilistic collaborative representation-based classifier (PKPCRC), for visual recognition. Compared with existing classifiers which use the collaborative representation strategy, the proposed PKPCRC further includes characteristics of training samples of each class as prior knowledge. Four types of prior knowledge are developed from the perspectives of image distance and representation capacity. They adaptively accommodate the contribution of each class and result in an accurate representation to classify a query sample. Experiments and comparisons on four challenging databases demonstrate that PKPCRC outperforms several state-of-the-art classifiers.
Privacy-preserving public auditing for secure data storage in fog-to-cloud computing

With increasing popularity of fog-to-cloud based Internet of Things (IoT), how to ensure the integrity of IoT data outsourced in clouds has become one of the biggest security challenges. However, little effort has been put into addressing the problem. To fill this gap, this paper presents a tailor-made public auditing scheme for data storage in fog-to-cloud based IoT scenarios, which can achieve all indispensable performance and security requirements. Particularly, we design a tag-transforming strategy based on the bilinear mapping technique to convert the tags generated by mobile sinks to the ones created by the fog nodes in the phase of proof generation, which cannot only effectively protect the identity privacy, but also reduce the communication and computational costs in the verification phase; moreover, we present a zero-knowledge proof mechanism to verify the integrity of IoT data from various generators (e.g., mobile sinks and fog nodes) while achieving perfect data-privacy preserving. We formally prove the security of our scheme and evaluate its performance by theoretical analysis and comprehensive experiments. The results demonstrate that our scheme can efficiently achieve secure auditing for data storage in fog-to-cloud based IoT scenarios, and outperforms the straight-forward solution in communication and computational costs as well as energy consumption.
A Gated Peripheral-Foveal Convolutional Neural Network for Unified Image Aesthetic Prediction

Learning fine-grained details is a key issue in image aesthetic assessment. Most of the previous methods extract the fine-grained details via random cropping strategy, which may undermine the integrity of semantic information. Extensive studies show that humans perceive fine-grained details with a mixture of foveal vision and peripheral vision. Fovea has the highest possible visual acuity and is responsible for seeing the details. The peripheral vision is used for perceiving the broad spatial scene and selecting the attended regions for the fovea. Inspired by these observations, we propose a gated peripheral-foveal convolutional neural network. It is a dedicated double-subnet neural network (i.e., a peripheral subnet and a foveal subnet). The former aims to mimic the functions of peripheral vision to encode the holistic information and provide the attended regions. The latter aims to extract fine-grained features on these key regions. Considering that the peripheral vision and foveal vision play different roles in processing different visual stimuli, we further employ a gated information fusion network to weigh their contributions. The weights are determined through the fully connected layers followed by a sigmoid function. We conduct comprehensive experiments on the standard Aesthetic Visual Analysis (AVA) dataset and Photo.net dataset for unified aesthetic prediction tasks: 1) aesthetic quality classification; 2) aesthetic score regression; and 3) aesthetic score distribution prediction. The experimental results demonstrate the effectiveness of the proposed method.
Progressive Color Transfer With Dense Semantic Correspondences

We propose a new algorithm for color transfer between images that have perceptually similar semantic structures. We aim to achieve a more accurate color transfer that leverages semantically meaningful dense correspondence between images. To accomplish this, our algorithm uses neural representations for matching. Additionally, the color transfer should be spatially variant and globally coherent. Therefore, our algorithm optimizes a local linear model for color transfer satisfying both local and global constraints. Our proposed approach jointly optimizes matching and color transfer, adopting a coarse-to-fine strategy. The proposed method can be successfully extended from one-to-one to one-to-many color transfer. The latter further addresses the problem of mismatching elements of the input image. We validate our proposed method by testing it on a large variety of image content.
Differential mutation and novel social learning particle swarm optimization algorithm

Social Learning Particle Swarm Optimization (SLPSO) is an improved Particle Swarm Optimization (PSO) algorithm, which greatly improves the optimization performance of PSO. However, SLPSO still has some deficiency, such as poor balance between exploration and exploitation and low search efficiency, so that it cannot yet do well in solving many complex optimization problems. Thus, this paper proposes an improved SLPSO algorithm, that is, Differential mutation and novel Social learning PSO (DSPSO). Firstly, in order to balance exploration and exploitation better, a dynamic inertia weight is introduced to replace the random inertia weight of SLPSO, and a single-example learning approach and an example-mean learning one are proposed to replace the imitation component and the social influence component of SLPSO respectively. Secondly, the dimension-based velocity updating equation of SLPSO is divided into two particle-based updating equations with the two approaches, and the two are executed alternately to form a novel social learning PSO (NSLPSO), which enhance the exploitation of SLPSO. Finally, a dynamic differential mutation strategy is used in NSLPSO to update the three best particles to enhance the exploration to obtain DSPSO. Experimental results on the complex functions from CEC2013 reveal that DSPSO outperforms SLPSO and quite a few state-of-the-art and classic PSO variants.