Reviewerly/exHarmony huggingface dataset:
A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems

Recommender systems (RSs) commonly adopt a user-item rating matrix to describe users' preferences on items. With users and items exploding, such a matrix is usually high-dimensional and sparse (HiDS). Recently, the idea of deep learning has been applied to RSs. However, current deep-structured RSs suffer from high computational complexity. Enlightened by the idea of deep forest, this paper proposes a deep latent factor model (DLFM) for building a deep-structured RS on an HiDS matrix efficiently. Its main idea is to construct a deep-structured model by sequentially connecting multiple latent factor (LF) models instead of multilayered neural networks through a nonlinear activation function. Thus, the computational complexity grows linearly with its layer count, which is easy to resolve in practice. The experimental results on four HiDS matrices from industrial RSs demonstrate that when compared with state-of-the-art LF models and deep-structured RSs, DLFM can well balance the prediction accuracy and computational efficiency, which well fits the desire of industrial RSs for fast and right recommendations.
LCF: A Local Context Focus Mechanism for Aspect-Based Sentiment Classification

Aspect-based sentiment classification (ABSC) aims to predict sentiment polarities of different aspects within sentences or documents. Many previous studies have been conducted to solve this problem, but previous works fail to notice the correlation between the aspect’s sentiment polarity and the local context. In this paper, a Local Context Focus (LCF) mechanism is proposed for aspect-based sentiment classification based on Multi-head Self-Attention (MHSA). This mechanism is called LCF design, and utilizes the Context features Dynamic Mask (CDM) and Context Features Dynamic Weighted (CDW) layers to pay more attention to the local context words. Moreover, a BERT-shared layer is adopted to LCF design to capture internal long-term dependencies of local context and global context. Experiments are conducted on three common ABSC datasets: the laptop and restaurant datasets of SemEval-2014 and the ACL twitter dataset. Experimental results demonstrate that the LCF baseline model achieves considerable performance. In addition, we conduct ablation experiments to prove the significance and effectiveness of LCF design. Especially, by incorporating with BERT-shared layer, the LCF-BERT model refreshes state-of-the-art performance on all three benchmark datasets.
Wide neural networks of any depth evolve as linear models under gradient descent <sup>*</sup>

A longstanding goal in deep learning research has been to precisely characterize training and generalization. However, the often complex loss landscapes of neural networks have made a theory of learning dynamics elusive. In this work, we show that for wide neural networks the learning dynamics simplify considerably and that, in the infinite width limit, they are governed by a linear model obtained from the first-order Taylor expansion of the network around its initial parameters. Furthermore, mirroring the correspondence between wide Bayesian neural networks and Gaussian processes, gradient-based training of wide neural networks with a squared loss produces test set predictions drawn from a Gaussian process with a particular compositional kernel. While these theoretical results are only exact in the infinite width limit, we nevertheless find excellent empirical agreement between the predictions of the original network and those of the linearized version even for finite practically-sized networks. This agreement is robust across different architectures, optimization methods, and loss functions.
Focus Your Attention

Learning semantic correspondence between image and text is significant as it bridges the semantic gap between vision and language. The key challenge is to accurately find and correlate shared semantics in image and text. Most existing methods achieve this goal by representing the shared semantic as a weighted combination of all the fragments (image regions or text words), where fragments relevant to the shared semantic obtain more attention, otherwise less. However, despite relevant ones contribute more to the shared semantic, irrelevant ones will more or less disturb it, and thus will lead to semantic misalignment in the correlation phase. To address this issue, we present a novel Bidirectional Focal Attention Network (BFAN), which not only allows to attend to relevant fragments but also diverts all the attention into these relevant fragments to concentrate on them. The main difference with existing works is they mostly focus on learning attention weight while our BFAN focus on eliminating irrelevant fragments from the shared semantic. The focal attention is achieved by preassigning attention based on inter-modality relation, identifying relevant fragments based on intra-modality relation and reassigning attention. Furthermore, the focal attention is jointly applied in both image-to-text and text-to-image directions, which enables to avoid preference to long text or complex image. Experiments show our simple but effective framework significantly outperforms state-of-the-art, with relative [email protected] gains of 2.2% on both Flicr30K and MSCOCO benchmarks.
Convolutional Character Networks

Recent progress has been made on developing a unified framework for joint text detection and recognition in natural images, but existing joint models were mostly built on two-stage framework by involving ROI pooling, which can degrade the performance on recognition task. In this work, we propose convolutional character networks, referred as CharNet, which is an one-stage model that can process two tasks simultaneously in one pass. CharNet directly outputs bounding boxes of words and characters, with corresponding character labels. We utilize character as basic element, allowing us to overcome the main difficulty of existing approaches that attempted to optimize text detection jointly with a RNN-based recognition branch. In addition, we develop an iterative character detection approach able to transform the ability of character detection learned from synthetic data to real-world images. These technical improvements result in a simple, compact, yet powerful one-stage model that works reliably on multi-orientation and curved text. We evaluate CharNet on three standard benchmarks, where it consistently outperforms the state-of-the-art approaches [25, 24] by a large margin, e.g., with improvements of 65.33%→71.08% (with generic lexicon) on ICDAR 2015, and 54.0%→69.23% on Total-Text, on end-to-end text recognition. Code is available at: https://github.com/MalongTech/research-charnet.
Text Mining in Big Data Analytics

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.
Scale Match for Tiny Person Detection

Visual object detection has achieved unprecedented advance with the rise of deep convolutional neural networks. However, detecting tiny objects (for example tiny persons less than 20 pixels) in large-scale images remains not well investigated. The extremely small objects raise a grand challenge about feature representation while the massive and complex backgrounds aggregate the risk of false alarms. In this paper, we introduce a new benchmark, referred to as TinyPerson, opening up a promising direction for tiny object detection in a long distance and with massive backgrounds. We experimentally find that the scale mismatch between the dataset for network pre-training and the dataset for detector learning could deteriorate the feature representation and the detectors. Accordingly, we propose a simple yet effective Scale Match approach to align the object scales between the two datasets for favorable tiny-object representation. Experiments show the significant performance gain of our proposed approach over state-of-the-art detectors, and the challenging aspects of TinyPerson related to real-world scenarios. The TinyPerson benchmark and the code for our approach will be publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Worldwide validation of 8 satellite-derived and reanalysis solar radiation products: A preliminary evaluation and overall metrics for hourly data over 27 years

Gridded solar radiation products, namely satellite-derived irradiance and reanalysis irradiance, are key to the next-generation solar resource assessment and forecasting. Since their accuracies are generally lower than that of the ground-based measurements, providing validation of the gridded solar radiation products is necessary in order to understand their qualities and characteristics. This article delivers a worldwide validation of hourly global horizontal irradiance derived from satellite imagery and reanalysis. The accuracies of 6 latest satellite-derived irradiance products (CAMS-RAD, NSRDB, SARAH-2, SARAH-E, CERES-SYN1deg, and Solcast) and 2 latest global reanalysis irradiance products (ERA5 and MERRA-2) are verified against the complete records from 57 BSRN stations, over 27 years (1992–2018). This scope of validation is unprecedented in the field of solar energy. Moreover, the importance of using distribution-oriented verification approaches is emphasized. Such approaches go beyond the traditional measure-oriented verification approach, and thus can offer additional insights and flexibility to the verification problem.
Security of Cryptocurrencies in blockchain technology: State-of-art, challenges and future prospects

In contemporary era of technologies, blockchain has acquired tremendous attention from various domains. It has wide spectrum of applications ranging from finance to social services and has greatly influenced the emerging business world. Since, blockchain technology is getting embedded in the e-commerce services, the cryptocurrencies are gaining huge prevalence. Bitcoin and ethereum are few such crypto currencies, which have utilized decentralized nature of blockchain. Blockchain can be considered as a distributed database system containing immutable ledgers, which are prone to attack by malicious users. Although, from the initial digital currency to the present smart contract, the utilities of blockchain have been harnessed, the innovative technology has to rely on cryptography for its security. There are several reports, which emphases on the vulnerabilities and security of blockchain, however, there is a lack of a comprehensive and methodical survey in both application and technical views. In this survey article, the authors cover various aspects related to blockchain including its taxonomies and the situations in which a particular category of blockchain should be applied. The authors also focusses on the structure of blockchain and the working of the ongoing transactions in the cryptocurrency network. In addition, the authors also specify various categories of consensus protocols, smart contracts, forks, techniques for generating the consensus. A detailed taxonomy of blockchain along with their features and related real-world applications is also discussed. In addition, existing key platforms of blockchain related to the cryptocurrencies, hyperledger and multichain are also discussed. Existing emerging vulnerabilities of blockchain related to the recent attacks on bitcoin and etherum is also presented along with the defensive methodologies and future trends in blockchain.
Blockchain technology framework: Current and future perspectives for the tourism industry

Blockchain's ability to increase the level of disintermediation in tourism represents this technology's most effective influence on the industry. The advent of online travel agencies has changed tourism's market structure by transferring power from suppliers to consumers. This paper aims to develop a blockchain-based framework for the tourism industry by employing a qualitative method that uses the semi-structured interview to determine how domain experts conceive the future of intermediaries were the tourism industry to adopt blockchain technology. The results show that when taking into account blockchain's influence on businesses, blockchain is considered an appropriate technology for eliminating mediators from the tourism industry's supply chain and also for banning new mediators from gaining access to this industry, thereby removing intermediaries from the tourism market.
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey

Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems, and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns. However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a lot of attention in recent years for their ability to perform well on a range of network science tasks, such as link prediction and node classification. Despite the popularity of graph neural networks and the proven benefits of dynamic network models, there has been little focus on graph neural networks for dynamic networks. To address the challenges resulting from the fact that this research crosses diverse fields as well as to survey dynamic graph neural networks, this work is split into two main parts. First, to address the ambiguity of the dynamic network terminology we establish a foundation of dynamic networks with consistent, detailed terminology and notation. Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminology
Demystifying blockchain: A critical analysis of challenges, applications and opportunities

The blockchain is considered to be the potential driver of the digital economy. The Blockchain technology outweighs the challenges associated with the traditional transaction business governed and regulated by the third trusted party. There is a growth in the interest among the researchers, the industry, and the academia to study and leverage the potential of Blockchain. Blockchain provides a decentralized and distributed public ledger for all the participating parties. Though it seems that blockchain is a viable choice and solution for all the centralized governed and regulated transactions (in digital online space), it has potential challenges that need to be resolved; opportunities to be explored, and applications to be studied. This paper utilizes a systematic literature review to study several research endeavors made in the domain of blockchain. To further research on blockchain adoption, the paper theoretically constructs an integrated framework of the blockchain innovation adoption process in an organization considering organizational and user acceptance perspectives. This would facilitate its widespread adoption, thereby achieving sustained leadership solutions. The paper offers 23 propositions to information systems (IS)/information management (IM) scholars with respect to innovation characteristics, organizational characteristics, environmental characteristics, and user acceptance characteristics. Further, the paper explores several areas of future research and directions that can provide deep insights for overcoming challenges and for the adoption of blockchain technology.
Coarse-to-Fine CNN for Image Super-Resolution

Deep convolutional neural networks (CNNs) have been popularly adopted in image super-resolution (SR). However, deep CNNs for SR often suffer from the instability of training, resulting in poor image SR performance. Gathering complementary contextual information can effectively overcome the problem. Along this line, we propose a coarse-to-fine SR CNN (CFSRCNN) to recover a high-resolution (HR) image from its low-resolution version. The proposed CFSRCNN consists of a stack of feature extraction blocks (FEBs), an enhancement block (EB), a construction block (CB) and, a feature refinement block (FRB) to learn a robust SR model. Specifically, the stack of FEBs learns the long- and short-path features, and then fuses the learned features by expending the effect of the shallower layers to the deeper layers to improve the representing power of learned features. A compression unit is then used in each FEB to distill important information of features so as to reduce the number of parameters. Subsequently, the EB utilizes residual learning to integrate the extracted features to prevent from losing edge information due to repeated distillation operations. After that, the CB applies the global and local LR features to obtain coarse features, followed by the FRB to refine the features to reconstruct a high-resolution image. Extensive experiments demonstrate the high efficiency and good performance of our CFSRCNN model on benchmark datasets compared with state-of-the-art SR models. The code of CFSRCNN is accessible on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hellloxiaotian/CFSRCNN</uri> .
Deep Representation Learning on Long-Tailed Data: A Learnable Embedding Augmentation Perspective

This paper considers learning deep features from long-tailed data. We observe that in the deep feature space, the head classes and the tail classes present different distribution patterns. The head classes have a relatively large spatial span, while the tail classes have a significantly small spatial span, due to the lack of intra-class diversity. This uneven distribution between head and tail classes distorts the overall feature space, which compromises the discriminative ability of the learned features. In response, we seek to expand the distribution of the tail classes during training, so as to alleviate the distortion of the feature space. To this end, we propose to augment each instance of the tail classes with certain disturbances in the deep feature space. With the augmentation, a specified feature vector becomes a set of probable features scattered around itself, which is analogical to an atomic nucleus surrounded by the electron cloud. Intuitively, we name it as ``feature cloud''. The intra-class distribution of the feature cloud is learned from the head classes, and thus provides higher intra-class variation to the tail classes. Consequentially, it alleviates the distortion of the learned feature space, and improves deep representation learning on long tailed data. Extensive experimental evaluations on person re-identification and face recognition tasks confirm the effectiveness of our method.
Hate Speech Epidemic. The Dynamic Effects of Derogatory Language on Intergroup Relations and Political Radicalization

Exposure to derogatory language about immigrants and minority groups leads to political radicalization and deteriorates intergroup relations. This article addresses the psychological processes responsible for these effects as well as those involved in hate‐speech proliferation in contemporary societies and discusses the factors that constrain its growth. We propose that frequent exposure to hate speech has severe effects on emotional, behavioral, and normative levels. Exposure to hate speech results in empathy being replaced by intergroup contempt as a dominant response to others—it is both a motivator and a consequence of derogatory language. The increased presence of hate speech in one's environment creates a sense of a descriptive norm that allows outgroup derogation. This leads to the erosion of existing antidiscriminatory norms. Finally, through a process of desensitization, hate speech reduces people's ability to recognize the offensive character of such language. Based on empirical evidence from the fields of social psychology and psychology of emotion and aggression, we propose a model that explains the described processes, and we trace the dynamics of this model using an agent‐based modeling approach. We show that the mechanisms potentially effective in constraining hate‐speech proliferation (empathy, norms) are eroded by hate speech itself. We argue that through basic psychological dynamics, societies become more accepting of derogatory language and less accepting of immigrants, as well as religious, ethnic, and sexual minorities.
Shopping in augmented reality: The effects of spatial presence, personalization and intrusiveness on app and brand responses

Augmented reality (AR) applications add a new dimension to the consumer experience by overlaying the consumer’s face or surroundings with virtual products. The aim of this study was to examine three underlying processes (spatial presence, perceived personalization, and perceived intrusiveness) that could explain the persuasiveness of AR apps. In two experiments, we compared an AR app to a non-AR app. Study 1 used an app that augmented the user’s face with virtual products (make-up), while Study 2 used an app that showed virtual products within one’s surroundings (furniture). The results showed that spatial presence and perceived personalization can explain positive persuasive responses towards AR apps. For the app that augmented the user’s face with virtual products, perceived personalization enhanced purchase intentions, while perceived intrusiveness had negative persuasive consequences. For the app that showed virtual products in one’s surroundings, spatial presence enhanced purchase intentions, and no negative persuasive consequences were found.
Stability Analysis of the Modified Levenberg–Marquardt Algorithm for the Artificial Neural Network Training

The Levenberg-Marquardt and Newton are two algorithms that use the Hessian for the artificial neural network learning. In this article, we propose a modified Levenberg-Marquardt algorithm for the artificial neural network learning containing the training and testing stages. The modified Levenberg-Marquardt algorithm is based on the Levenberg-Marquardt and Newton algorithms but with the following two differences to assure the error stability and weights boundedness: 1) there is a singularity point in the learning rates of the Levenberg-Marquardt and Newton algorithms, while there is not a singularity point in the learning rate of the modified Levenberg-Marquardt algorithm and 2) the Levenberg-Marquardt and Newton algorithms have three different learning rates, while the modified Levenberg-Marquardt algorithm only has one learning rate. The error stability and weights boundedness of the modified Levenberg-Marquardt algorithm are assured based on the Lyapunov technique. We compare the artificial neural network learning with the modified Levenberg-Marquardt, Levenberg-Marquardt, Newton, and stable gradient algorithms for the learning of the electric and brain signals data set.