Reviewerly/exHarmony huggingface dataset:
An Adaptive and Robust Edge Detection Method Based on Edge Proportion Statistics

Edge detection is one of the most fundamental operations in the field of image analysis and computer vision as a critical preprocessing step for high-level tasks. It is difficult to give a generic threshold that works well on all images as the image contents are totally different. This paper presents an adaptive, robust and effective edge detector for real-time applications. According to the two-dimensional entropy, the images can be clarified into three groups, each attached with a reference percentage value based on the edge proportion statistics. Compared with the attached points along the gradient direction, anchor points were extracted with high probability to be edge pixels. Taking the segment direction into account, these points were then jointed into different edge segments, each of which was a clean, contiguous, 1-pixel wide chain of pixels. Experimental results indicate that the proposed edge detector outperforms the traditional edge following methods in terms of detection accuracy. Besides, the detection results can be used as the input information for post-processing applications in real-time.
Robust Proof of Stake: A New Consensus Protocol for Sustainable Blockchain Systems

In the digital economy era, the development of a distributed robust economy system has become increasingly important. The blockchain technology can be used to build such a system, but current mainstream consensus protocols are vulnerable to attack, making blockchain systems unsustainable. In this paper, we propose a new Robust Proof of Stake (RPoS) consensus protocol, which uses the amount of coins to select miners and limits the maximum value of the coin age to effectively avoid coin age accumulation attack and Nothing-at-Stake (N@S) attack. Under a comparison framework, we show that the RPoS equals or outperforms Proof of Work (PoW) protocol and Proof of Stake (PoS) protocol in three dimensions: energy consumption, robustness, and transaction processing speed. To compare the three consensus protocols in terms of trade efficiency, we built an agent-based model and find that RPoS protocol has greater or similar trade request-satisfied ratio than PoW and PoS. Hence, we suggest that RPoS is very suitable for building a robust digital economy distributed system.
SR2CNN: Zero-Shot Learning for Signal Recognition

Signal recognition is one of the significant and challenging tasks in the signal processing and communications field. It is often a common situation that there's no training data accessible for some signal classes to perform a recognition task. Hence, as widely-used in image processing field, zero-shot learning (ZSL) is also very important for signal recognition. Unfortunately, ZSL regarding this field has hardly been studied due to inexplicable signal semantics. This paper proposes a ZSL framework, signal recognition and reconstruction convolutional neural networks (SR2CNN), to address relevant problems in this situation. The key idea behind SR2CNN is to learn the representation of signal semantic feature space by introducing a proper combination of cross entropy loss, center loss and reconstruction loss, as well as adopting a suitable distance metric space such that semantic features have greater minimal inter-class distance than maximal intra-class distance. The proposed SR2CNN can discriminate signals even if no training data is available for some signal class. Moreover, SR2CNN can gradually improve itself in the aid of signal detection, because of constantly refined class center vectors in semantic feature space. These merits are all verified by extensive experiments with ablation studies.
Combination of Recursive and Recurrent Neural Networks for Aspect-Based Sentiment Analysis Using Inter-Aspect Relations

Sentiment analysis studies in the literature mostly use either recurrent or recursive neural network models. Recurrent models capture the effect of time and propagate the information of sentiment labels in a review throughout the word sequence. Recursive models, on the other hand, extract syntactic structures from the texts and leverage the sentiment information during training. There are only a few studies that incorporate both of these models into a single neural network for the sentiment classification task. In this paper, we propose a novel neural network framework that combines recurrent and recursive neural models for aspect-based sentiment analysis. By using constituency and dependency parsers, we first divide each review into subreviews that include the sentiment information relevant to the corresponding aspect terms. After generating and training the recursive neural trees built from the parses of the subreviews, we feed their output into the recurrent model. We evaluated our ensemble approach on two datasets in English of different genres. We achieved state-of-the-art results and outperformed the baseline study by a significant margin for both domains.
Susceptibility to phishing on social network sites: A personality information processing model

Today, the traditional approach used to conduct phishing attacks through email and spoofed websites has evolved to include social network sites (SNSs). This is because phishers are able to use similar methods to entice social network users to click on malicious links masquerading as fake news, controversial videos and other opportunities thought to be attractive or beneficial to the victim. SNSs are a phisher's “market” as they offer phishers a wide range of targets and take advantage of opportunities that exploit the behavioural vulnerabilities of their users. As such, it is important to further investigate aspects affecting behaviour when users are presented with phishing. Based on the literature studied, this research presents a theoretical model to address phishing susceptibility on SNSs. Using data collected from 215 respondents, the study examined the mediating role that information processing plays with regard to user susceptibility to social network phishing based on their personality traits, thereby identifying user characteristics that may be more susceptible than others to phishing on SNSs. The results from the structural equation modeling (SEM) analysis revealed that conscientious users were found to have a negative influence on heuristic processing, and are thus less susceptible to phishing on SNSs. The study also confirmed that heuristic processing increases susceptibility to phishing, thus supporting prior studies in this area. This research contributes to the information security discipline as it is one of the first to examine the effect of the relationship between the Big Five personality model and the heuristic-systematic model of information processing.
Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations

Learning query and item representations is important for building large scale recommendation systems. In many real applications where there is a huge catalog of items to recommend, the problem of efficiently retrieving top k items given user’s query from deep corpus leads to a family of factorized modeling approaches where queries and items are jointly embedded into a low-dimensional space. In this paper, we first showcase how to apply a two-tower neural network framework, which is also known as dual encoder in the natural language community, to improve a large-scale, production app recommendation system. Furthermore, we offer a novel negative sampling approach called Mixed Negative Sampling (MNS). In particular, different from commonly used batch or unigram sampling methods, MNS uses a mixture of batch and uniformly sampled negatives to tackle the selection bias of implicit user feedback. We conduct extensive offline experiments using large-scale production dataset and show that MNS outperforms other baseline sampling methods. We also conduct online A/B testing and demonstrate that the two-tower retrieval model based on MNS significantly improves retrieval quality by encouraging more high-quality app installs.
A Survey on Privacy in Social Media

The increasing popularity of social media has attracted a huge number of people to participate in numerous activities on a daily basis. This results in tremendous amounts of rich user-generated data. These data provide opportunities for researchers and service providers to study and better understand users’ behaviors and further improve the quality of the personalized services. Publishing user-generated data risks exposing individuals’ privacy. Users privacy in social media is an emerging research area and has attracted increasing attention recently. These works study privacy issues in social media from the two different points of views: identification of vulnerabilities and mitigation of privacy risks. Recent research has shown the vulnerability of user-generated data against the two general types of attacks, identity disclosure and attribute disclosure. These privacy issues mandate social media data publishers to protect users’ privacy by sanitizing user-generated data before publishing it. Consequently, various protection techniques have been proposed to anonymize user-generated social media data. There is vast literature on privacy of users in social media from many perspectives. In this survey, we review the key achievements of user privacy in social media. In particular, we review and compare the state-of-the-art algorithms in terms of the privacy leakage attacks and anonymization algorithms. We overview the privacy risks from different aspects of social media and categorize the relevant works into five groups: (1) social graphs and privacy, (2) authors in social media and privacy, (3) profile attributes and privacy, (4) location and privacy, and (5) recommendation systems and privacy. We also discuss open problems and future research directions regarding user privacy issues in social media.
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases

Different texts shall by nature correspond to different number of keyphrases.This desideratum is largely missing from existing neural keyphrase generation models.In this study, we address this problem from both modeling and evaluation perspectives.We first propose a recurrent generative model that generates multiple keyphrases as delimiter-separated sequences.Generation diversity is further enhanced with two novel techniques by manipulating decoder hidden states.In contrast to previous approaches, our model is capable of generating diverse keyphrases and controlling number of outputs.We further propose two evaluation metrics tailored towards the variable-number generation.We also introduce a new dataset (ST A C KEX) that expands beyond the only existing genre (i.e., academic writing) in keyphrase generation tasks.With both previous and new evaluation metrics, our model outperforms strong baselines on all datasets.* These authors contributed equally.The order is determined by a fidget spinner.
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance

Models for natural language understanding (NLU) tasks often rely on the idiosyncratic biases of the dataset, which make them brittle against test cases outside the training distribution.Recently, several proposed debiasing methods are shown to be very effective in improving out-of-distribution performance.However, their improvements come at the expense of performance drop when models are evaluated on the in-distribution data, which contain examples with higher diversity.This seemingly inevitable trade-off may not tell us much about the changes in the reasoning and understanding capabilities of the resulting models on broader types of examples beyond the small subset represented in the outof-distribution data.In this paper, we address this trade-off by introducing a novel debiasing method, called confidence regularization, which discourage models from exploiting biases while enabling them to receive enough incentive to learn from all the training examples.We evaluate our method on three NLU tasks and show that, in contrast to its predecessors, it improves the performance on out-of-distribution datasets (e.g., 7pp gain on HANS dataset) while maintaining the original in-distribution accuracy. 1
Efficient Second-Order TreeCRF for Neural Dependency Parsing

In the deep learning (DL) era, parsing models are extremely simplified with little hurt on performance, thanks to the remarkable capability of multi-layer BiLSTMs in context representation.As the most popular graphbased dependency parser due to its high efficiency and performance, the biaffine parser directly scores single dependencies under the arc-factorization assumption, and adopts a very simple local token-wise cross-entropy training loss.This paper for the first time presents a second-order TreeCRF extension to the biaffine parser.For a long time, the complexity and inefficiency of the inside-outside algorithm hinder the popularity of TreeCRF.To address this issue, we propose an effective way to batchify the inside and Viterbi algorithms for direct large matrix operation on GPUs, and to avoid the complex outside algorithm via efficient back-propagation.Experiments and analysis on 27 datasets from 13 languages clearly show that techniques developed before the DL era, such as structural learning (global TreeCRF loss) and high-order modeling are still useful, and can further boost parsing performance over the state-of-the-art biaffine parser, especially for partially annotated training data.
Graph-to-Tree Learning for Solving Math Word Problems

While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well.This results in poor quantity representations and incorrect solution expressions.In this paper, we propose Graph2Tree, a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions.Included in our Graph2Tree framework are two graphs, namely the Quantity Cell Graph and Quantity Comparison Graph, which are designed to address limitations of existing methods by effectively representing the relationships and order information among the quantities in MWPs.We conduct extensive experiments on two available datasets.Our experiment results show that Graph2Tree outperforms the state-of-the-art baselines on two benchmark datasets significantly.We also discuss case studies and empirically examine Graph2Tree's effectiveness in translating the MWP text into solution expressions 1 .
Region-edge-based active contours driven by hybrid and local fuzzy region-based energy for image segmentation

This paper raises a region-edge-based active contour driven by the hybrid and local fuzzy region-based energy to segment images with high noise and intensity inhomogeneity. The energy functional consists of region energy and edge energy. The region energy is made up of hybrid fuzzy region term and local fuzzy region term. Its aim is to motivate initial contour to move toward the exact object boundary. What’s more, it is proved to be convex and ensures the segmentation results independent of initialization. The hybrid fuzzy region term can balance the importance of the object and background while the local fuzzy region term by incorporating spatial and local information can decrease the effect of intensity inhomogeneity in given images. The edge energy is used to regularize the pseudo level set function (LSF) and maintain the appearance of the smoothness during the curve evolution. Inspired by the fuzzy energy-based active contour (FEAC), a more direct and simpler method is developed to calculate the difference between the old and new energy functions to update the pseudo LSF during the curve evolution. Experimental results on synthetic and real images with high noise and intensity inhomogeneity show that the proposed model can obtain better performance than the state-of-the-art active contour models. The code is available at: https://github.com/fangchj2002/HLFRA.
CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs

Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other.In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5% across different language pairs.We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other.We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English.In addition to curating this massive dataset, we introduce baseline methods that leverage crosslingual representations to identify aligned documents based on their textual content.Finally, we demonstrate the value of this parallel documents dataset through a downstream task of mining parallel sentences and measuring the quality of machine translations from models trained on this mined data.Our objective in releasing this dataset is to foster new research in cross-lingual NLP across a variety of low, medium, and high-resource languages.
UNIQ

We present a novel method for neural network quantization. Our method, named UNIQ , emulates a non-uniform k -quantile quantizer and adapts the model to perform well with quantized weights by injecting noise to the weights at training time. As a by-product of injecting noise to weights, we find that activations can also be quantized to as low as 8-bit with only a minor accuracy degradation. Our non-uniform quantization approach provides a novel alternative to the existing uniform quantization techniques for neural networks. We further propose a novel complexity metric of number of bit operations performed (BOPs), and we show that this metric has a linear relation with logic utilization and power. We suggest evaluating the trade-off of accuracy vs. complexity (BOPs). The proposed method, when evaluated on ResNet18/34/50 and MobileNet on ImageNet, outperforms the prior state of the art both in the low-complexity regime and the high accuracy regime. We demonstrate the practical applicability of this approach, by implementing our non-uniformly quantized CNN on FPGA.
PairRE: Knowledge Graph Embeddings via Paired Relation Vectors

Linlin Chao, Jianshan He, Taifeng Wang, Wei Chu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation

Session-based recommendation plays a central role in a wide spectrum of online applications, ranging from e-commerce to online advertising services. However, the majority of existing session-based recommendation techniques (e.g., attention-based recurrent network or graph neural network) are not well-designed for capturing the complex transition dynamics exhibited with temporally-ordered and multi-level interdependent relation structures. These methods largely overlook the relation hierarchy of item transitional patterns. In this paper, we propose a multi-task learning framework with Multi-level Transition Dynamics (MTD), which enables the jointly learning of intra- and inter-session item transition dynamics in automatic and hierarchical manner. Towards this end, we first develop a position-aware attention mechanism to learn item transitional regularities within individual session. Then, a graph-structured hierarchical relation encoder is proposed to explicitly capture the cross-session item transitions in the form of high-order connectivities by performing embedding propagation with the global graph context. The learning process of intra- and inter-session transition dynamics are integrated, to preserve the underlying low- and high-level item relationships in a common latent space. Extensive experiments on three real-world datasets demonstrate the superiority of MTD as compared to state-of-the-art baselines.
An Overview of Augmented Reality

Modern society is increasingly permeated by realities parallel to the real one. The so-called virtual reality is now part of both current habits and many activities carried out during the day. Virtual reality (VR) is, in turn, related to the concept of augmented reality (AR). It represents a technology still in solid expansion but which was created and imagined several decades ago. This paper presents an overview of augmented reality, starting from its conception, passing through its main applications, and providing essential information. Part of the article will be devoted to hardware and software components used in AR systems. The last part of the paper highlights the limitations related to the design of these systems, the shortcomings in this area, and the possible future fields of application of this extraordinary technological innovation.
Visualizing Instagram: Tracing Cultural Visual Rhythms

Picture-taking has never been easier. We now use our phones to snap photos and instantly share them with friends, family and strangers all around the world. Consequently, we seek ways to visualize, analyze and discover concealed socio-cultural characteristics and trends in this ever-growing flow of visual information. How do we then trace global and local patterns from the analysis of visual planetary–scale data? What types of insights can we draw from the study of these massive visual materials? In this study we use Cultural Analytics visualization techniques for the study of approximately 550,000 images taken by users of the location-based social photo sharing application Instagram. By analyzing images from New York City and Tokyo, we offer a comparative visualization research that indicates differences in local color usage, cultural production rate, and varied hue’s intensities— all form a unique, local, ‘Visual Rhythm’: a framework for the analysis of location-based visual information flows.
On the diffusion approximation of nonconvex stochastic gradient descent

We study the Stochastic Gradient Descent (SGD) method in nonconvex optimization problems from the point of view of approximating diffusion processes. We prove rigorously that the diffusion process can approximate the SGD algorithm weakly using the weak form of master equation for probability evolution. In the small step size regime and the presence of omnidirectional noise, our weak approximating diffusion process suggests the following dynamics for the SGD iteration starting from a local minimizer (resp.~saddle point): it escapes in a number of iterations exponentially (resp.~almost linearly) dependent on the inverse stepsize. The results are obtained using the theory for random perturbations of dynamical systems (theory of large deviations for local minimizers and theory of exiting for unstable stationary points). In addition, we discuss the effects of batch size for the deep neural networks, and we find that small batch size is helpful for SGD algorithms to escape unstable stationary points and sharp minimizers. Our theory indicates that one should increase the batch size at later stage for the SGD to be trapped in flat minimizers for better generalization.