Reviewerly/exHarmony huggingface dataset:
Deep k-Means: Jointly clustering with k-Means and learning representations

We study in this paper the problem of jointly clustering and learning representations. As several previous studies have shown, learning representations that are both faithful to the data to be clustered and adapted to the clustering algorithm can lead to better clustering performance, all the more so that the two tasks are performed jointly. We propose here such an approach for k-Means clustering based on a continuous reparametrization of the objective function that leads to a truly joint solution. The behavior of our approach is illustrated on various datasets showing its efficacy in learning representations for objects while clustering them.
The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets

This paper analyses herding in cryptocurrency markets in the time of the COVID-19 pandemic. We employ a combination of quantitative methods to hourly prices of the four most traded cryptocurrency markets - USD, EUR, JPY and KRW - for the period from 1st January 2019 to 13th March 2020. While there are several strong theoretical reasons to observe the "black swan" effect on cryptocurrency herding, our results suggest that COVID-19 does not amplify herding in cryptocurrency markets. In all markets studied, herding remains contingent on up or down markets days, but does not get stronger during the COVID-19. These results are important for cryptocurrency investors and regulators to enhance their understanding of cryptocurrency markets and the financial effects of the COVID-19 pandemic.
Human action recognition using attention based LSTM network with dilated CNN features

Human action recognition in videos is an active area of research in computer vision and pattern recognition. Nowadays, artificial intelligence (AI) based systems are needed for human-behavior assessment and security purposes. The existing action recognition techniques are mainly using pre-trained weights of different AI architectures for the visual representation of video frames in the training stage, which affect the features’ discrepancy determination, such as the distinction between the visual and temporal signs. To address this issue, we propose a bi-directional long short-term memory (BiLSTM) based attention mechanism with a dilated convolutional neural network (DCNN) that selectively focuses on effective features in the input frame to recognize the different human actions in the videos. In this diverse network, we use the DCNN layers to extract the salient discriminative features by using the residual blocks to upgrade the features that keep more information than a shallow layer. Furthermore, we feed these features into a BiLSTM to learn the long-term dependencies, which is followed by the attention mechanism to boost the performance and extract the additional high-level selective action related patterns and cues. We further use the center loss with Softmax to improve the loss function that achieves a higher performance in the video-based action classification. The proposed system is evaluated on three benchmarks, i.e., UCF11, UCF sports, and J-HMDB datasets for which it achieved a recognition rate of 98.3%, 99.1%, and 80.2%, respectively, showing 1%–3% improvement compared to the state-of-the-art (SOTA) methods.
Predictive analytics with gradient boosting in clinical medicine

Predictive analytics play an important role in clinical research. An accurate predictive model can help clinicians stratify risk thereby allowing the identification of a target population which might benefit from a certain intervention. Conventionally, predictive analytics is performed using parametric modeling which comes with a number of assumptions. For example, generalized linear regression models require linearity and additivity to hold for the underlying data. However, these assumptions may not hold in practice. Especially in the era of big data, a large number of covariates or features can be extracted from an electronic database which might have complex interactions and higher-order terms among the covariates. Conventional modeling methods have trouble capturing such high-dimensional relationships. However, some sophisticated machine learning techniques have been invented to handle this situation. Gradient boosting is one of these techniques which is able to recursively fit a weak learner to the residual so as to improve model performance with a gradually increasing number of iterations. It can automatically discover complex data structure, including nonlinearity and high-order interactions, even in the context of hundreds, thousands, or tens-of-thousands of potential predictors. This paper aims to introduce how gradient boosting works. The principles behind this learning machine are explained with a small example in a step-by-step manner. The formal implementation of gradient tree boosting is then illustrated with the caret package. In the simulated example complexity of data structure is created by generating certain interactions between the covariates. This example shows that gradient boosting can better capture these complex relationships than a generalized linear model-based approach.
The influence of virtual reality in e-commerce

The development of virtual reality (VR), together with other technological innovations will shape the future of e-retailing. This research studies the effectiveness of different VR formats and devices in a virtual store environment, namely V-commerce. This study proposes and test a conceptual model that analyses the relations between sense of presence, brand recall and purchase intention, while also going deeper into their antecedents. Our findings suggest differences in purchase intention depending on VR format and device used. No differences are shown in sense of presence and affect by VR format and device. Our tested model suggests a dual route of influence of VR on consumers' purchase intention in virtual stores: one through emotions and sense of presence and the other through the affect evoked by the virtual environment and brand recall. Lastly, some managerial implications and methodological issues are considered.
Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning

In this paper, we perform a systematic study about the on-body sensor positioning and data acquisition details for Human Activity Recognition (HAR) systems. We build a testbed that consists of eight body-worn Inertial Measurement Units (IMU) sensors and an Android mobile device for activity data collection. We develop a Long Short-Term Memory (LSTM) network framework to support training of a deep learning model on human activity data, which is acquired in both real-world and controlled environments. From the experiment results, we identify that activity data with sampling rate as low as 10 Hz from four sensors at both sides of wrists, right ankle, and waist is sufficient in recognizing Activities of Daily Living (ADLs) including eating and driving activity. We adopt a two-level ensemble model to combine class-probabilities of multiple sensor modalities, and demonstrate that a classifier-level sensor fusion technique can improve the classification performance. By analyzing the accuracy of each sensor on different types of activity, we elaborate custom weights for multimodal sensor fusion that reflect the characteristic of individual activities.
Learning to Select Knowledge for Response Generation in Dialog Systems

End-to-end neural models for intelligent dialogue systems suffer from the problem of generating uninformative responses. Various methods were proposed to generate more informative responses by leveraging external knowledge. However, few previous work has focused on selecting appropriate knowledge in the learning process. The inappropriate selection of knowledge could prohibit the model from learning to make full use of the knowledge. Motivated by this, we propose an end-to-end neural model which employs a novel knowledge selection mechanism where both prior and posterior distributions over knowledge are used to facilitate knowledge selection. Specifically, a posterior distribution over knowledge is inferred from both utterances and responses, and it ensures the appropriate selection of knowledge during the training process. Meanwhile, a prior distribution, which is inferred from utterances only, is used to approximate the posterior distribution so that appropriate knowledge can be selected even without responses during the inference process. Compared with the previous work, our model can better incorporate appropriate knowledge in response generation. Experiments on both automatic and human evaluation verify the superiority of our model over previous baselines.
Scalable Deep Hashing for Large-Scale Social Image Retrieval

Recent years have witnessed the wide application of hashing for large-scale image retrieval, because of its high computation efficiency and low storage cost. Particularly, benefiting from current advances in deep learning, supervised deep hashing methods have greatly boosted the retrieval performance, under the strong supervision of large amounts of manually annotated semantic labels. However, their performance is highly dependent upon the supervised labels, which significantly limits the scalability. In contrast, unsupervised deep hashing without label dependence enjoys the advantages of well scalability. Nevertheless, due to the relaxed hash optimization, and more importantly, the lack of semantic guidance, existing methods suffer from limited retrieval performance. In this paper, we propose a SCAlable Deep Hashing (SCADH) to learn enhanced hash codes for social image retrieval. We formulate a unified scalable deep hash learning framework which explores the weak but free supervision of discriminative user tags that are commonly accompanied with social images. It jointly learns image representations and hash functions with deep neural networks, and simultaneously enhances the discriminative capability of image hash codes with the refined semantics from the accompanied social tags. Further, instead of simple relaxed hash optimization, we propose a discrete hash optimization method based on Augmented Lagrangian Multiplier to directly solve the hash codes and avoid the binary quantization information loss. Experiments on two standard social image datasets demonstrate the superiority of the proposed approach compared with stateof- the-art shallow and deep hashing techniques.
Self-Guided Network for Fast Image Denoising

During the past years, tremendous advances in image restoration tasks have been achieved using highly complex neural networks. Despite their good restoration performance, the heavy computational burden hinders the deployment of these networks on constrained devices, \eg smart phones and consumer electronic products. To tackle this problem, we propose a self-guided network (SGN), which adopts a top-down self-guidance architecture to better exploit image multi-scale information. SGN directly generates multi-resolution inputs with the shuffling operation. Large-scale contextual information extracted at low resolution is gradually propagated into the higher resolution sub-networks to guide the feature extraction processes at these scales. Such a self-guidance strategy enables SGN to efficiently incorporate multi-scale information and extract good local features to recover noisy images. We validate the effectiveness of SGN through extensive experiments. The experimental results demonstrate that SGN greatly improves the memory and runtime efficiency over state-of-the-art efficient methods, without trading off PSNR accuracy.
Pruning by explaining: A novel criterion for deep neural network pruning

The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs. Recent efforts to reduce these overheads involve pruning and compressing the weights of various layers while at the same time aiming to not sacrifice performance. In this paper, we propose a novel criterion for CNN pruning inspired by neural network interpretability: The most relevant units, i.e. weights or filters, are automatically found using their relevance scores obtained from concepts of explainable AI (XAI). By exploring this idea, we connect the lines of interpretability and model compression research. We show that our proposed method can efficiently prune CNN models in transfer-learning setups in which networks pre-trained on large corpora are adapted to specialized tasks. The method is evaluated on a broad range of computer vision datasets. Notably, our novel criterion is not only competitive or better compared to state-of-the-art pruning criteria when successive retraining is performed, but clearly outperforms these previous criteria in the resource-constrained application scenario in which the data of the task to be transferred to is very scarce and one chooses to refrain from fine-tuning. Our method is able to compress the model iteratively while maintaining or even improving accuracy. At the same time, it has a computational cost in the order of gradient computation and is comparatively simple to apply without the need for tuning hyperparameters for pruning.
CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation with only image-level labels saves large human effort to annotate pixel-level labels. Cutting-edge approaches rely on various innovative constraints and heuristic rules to generate the masks for every single image. Although great progress has been achieved by these methods, they treat each image independently and do not take account of the relationships across different images. In this paper, however, we argue that the cross-image relationship is vital for weakly supervised segmentation. Because it connects related regions across images, where supplementary representations can be propagated to obtain more consistent and integral regions. To leverage this information, we propose an end-to-end cross-image affinity module, which exploits pixel-level cross-image relationships with only image-level labels. By means of this, our approach achieves 64.3% and 65.3% mIoU on Pascal VOC 2012 validation and test set respectively, which is a new state-of-the-art result by only using image-level labels for weakly supervised semantic segmentation, demonstrating the superiority of our approach.
Automated Colorization of a Grayscale Image With Seed Points Propagation

In this paper, we propose a fully automatic image colorization method for grayscale images using neural network and optimization. For a determined training set including the gray images and its corresponding color images, our method segments grayscale images into superpixels and then extracts features of particular points of interest in each superpixel. The obtained features and their RGB values are given as input for, the training colorization neural network of each pixel. To achieve a better image colorization effect in shorter running time, our method further propagates the resulting color points to neighboring pixels for improved colorization results. In the propagation of color, we present a cost function to formalize the premise that neighboring pixels should have the maximum positive similarity of intensities and colors; we then propose our solution to solving the optimization problem. At last, a guided image filter is employed to refine the colorized image. Experiments on a wide variety of images show that the proposed algorithms can achieve superior performance over the state-of-the-art algorithms.
Loyalty program effectiveness: Theoretical reviews and practical proofs

Loyalty programs are widely used by organizations as a structured customer relationship management (CRM) tool to build and extend customer-supplier relationship.Although a large number of benefits are offered through them to both companies and consumers, loyalty programs face a set of planning and implementation pitfalls.Scholars or practitioners rarely discuss such pitfalls.Thus, this paper intends to add further values to the current literature by exploring/investigating the main loyalty scheme pitfalls, both theoretically and practically.The study explores a set of loyalty program planning problems and some of the execution's drawbacks, including clear aim need, loyalty program design, budgeting and experienced employee involvement.In addition to loyalty programs benefits and rewards offered, loyalty programs target customer selection problems using loyalty program promotional tools.For the study, 161 managers and employees who were involved in loyalty program planning and execution were surveyed.Smart-PLS was used to test the developed model and hypotheses.The study found that all studied loyalty program elements identified affected their planning and implementation.However, some of these elements where seen important to be considered when planning loyalty schemes, such schemes' benefits and rewards offered were still not planned properly and did not meet customer needs or even expectations and, in most cases, the loyalty programs' aims were not clear to all their stakeholders.The paper also provides additional discussion about additional issues of loyalty schemes planning and execution problems and proposes a set of solutions and recommendations, which might highlight some of the future venues with this regard..
Language-agnostic BERT Sentence Embedding

While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have yet to be explored.We systematically investigate methods for learning multilingual sentence embeddings by combining the best methods for learning monolingual and cross-lingual representations including: masked language modeling (MLM), translation language modeling (TLM) (Conneau and Lample, 2019), dual encoder translation ranking (Guo et al., 2018), and additive margin softmax (Yang et al., 2019a).We show that introducing a pre-trained multilingual language model dramatically reduces the amount of parallel training data required to achieve good performance by 80%.Composing the best of these methods produces a model that achieves 83.7% bi-text retrieval accuracy over 112 languages on Tatoeba, well above the 65.5% achieved by Artetxe and Schwenk (2019b), while still performing competitively on monolingual transfer learning benchmarks (Conneau and Kiela, 2018).Parallel data mined from CommonCrawl using our best model is shown to train competitive NMT models for en-zh and en-de.We publicly release our best multilingual sentence embedding model for 109+ languages at https://tfhub.dev/ google/LaBSE.
Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval

This paper proposes a deep hashing framework, namely, unsupervised deep video hashing (UDVH), for large-scale video similarity search with the aim to learn compact yet effective binary codes. Our UDVH produces the hash codes in a self-taught manner by jointly integrating discriminative video representation with optimal code learning, where an efficient alternating approach is adopted to optimize the objective function. The key differences from most existing video hashing methods lie in: 1) UDVH is an unsupervised hashing method that generates hash codes by cooperatively utilizing feature clustering and a specifically designed binarization with the original neighborhood structure preserved in the binary space and 2) a specific rotation is developed and applied onto video features such that the variance of each dimension can be balanced, thus facilitating the subsequent quantization step. Extensive experiments performed on three popular video datasets show that the UDVH is overwhelmingly better than the state of the arts in terms of various evaluation metrics, which makes it practical in real-world applications.
Privacy-Preserving Content Dissemination for Vehicular Social Networks: Challenges and Solutions

Vehicular social networks (VSNs), viewed as the integration of traditional vehicular networks and social networks, are promising communication platforms based on the development of intelligent vehicles and deployment of intelligent transportation systems. Passengers can obtain information by searching over Internet or querying vehicles in proximity through intra-vehicle equipment. Hence, the performance of content dissemination in VSNs heavily relies on inter-vehicle communication and human behaviors. However, privacy preservation always conflicts with the usability of individual information in VSNs. The highly dynamic topology and increasing kinds of participants lead to potential threats for communication security and individual privacy. Therefore, the privacy-preserving solutions for content dissemination in VSNs have become extremely challenging, and numerous researches have been conducted recently. Compared with related surveys, this article provides the unique characteristics of privacy-preserving requirements and solutions for content dissemination in VSNs. It focuses on: 1) a comprehensive overview of content dissemination in VSNs; 2) the privacy issues and potential attacks related to content dissemination; and 3) the corresponding solutions based on privacy consideration. First, the characteristics of VSNs, content dissemination and its solutions in VSNs are revealed. Second, the privacy issues for content dissemination in the current VSN architecture are analyzed and classified according to their features. Various privacy-preserving content dissemination schemes, attempting to resist distinct attacks, are also discussed. Finally, the research challenges and open issues are summarized.
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

Explainability and effectiveness are two key aspects for building recommender systems. Prior efforts mostly focus on incorporating side information to achieve better recommendation performance. However, these methods have some weaknesses: (1) prediction of neural network-based embedding methods are hard to explain and debug; (2) symbolic, graph-based approaches (e.g., meta path-based models) require manual efforts and domain knowledge to define patterns and rules, and ignore the item association types (e.g. substitutable and complementary). In this paper, we propose a novel joint learning framework to integrate induction of explainable rules from knowledge graph with construction of a rule-guided neural recommendation model. The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue. Extensive experiments1 show that our proposed method has achieved significant improvements in item recommendation over baselines on real-world datasets. Our model demonstrates robust performance over “noisy” item knowledge graphs, generated by linking item names to related entities.