Reviewerly/exHarmony huggingface dataset:
Image stitching via deep homography estimation

Image stitching is a well-studied problem and has many applications in a variety of fields. Traditional feature based methods rely heavily on accurate localization or even distribution of hand-crafted features, and may fail for some difficult cases. Although there are robust deep learning based homography estimation or semantic alignment methods, their accuracies are not high enough for image stitching problem. In this paper, we present a deep neural network that estimates homography accurately enough for image stitching of images with small parallax. The key components of our network are feature maps with progressively increased resolution and matching cost volumes constructed in hybrid manner. Both of these designs are illustrated to be helpful for performance improvement. We also propose a new stitching oriented loss function that takes image contents into consideration. To train our network, we prepare a synthesized training dataset, the image pairs in which are more nature and similar to those of real world image stitching problem. Experimental results demonstrate that our method outperforms existing deep learning based methods and traditional feature based method in term of quantitative evaluation, visual stitching result and robustness.
Ontology and rule-based natural language processing approach for interpreting textual regulations on underground utility infrastructure

The nation’s massive underground utility infrastructure must comply with a multitude of regulations. The regulatory compliance checking of underground utilities requires an objective and consistent interpretation of the regulations. However, utility regulations contain a variety of domain-specific terms and numerous spatial constraints regarding the location and clearance of underground utilities. It is challenging for the interpreters to understand both the domain and spatial semantics in utility regulations. To address the challenge, this paper adopts an ontology and rule-based Natural Language Processing (NLP) framework to automate the interpretation of utility regulations – the extraction of regulatory information and the subsequent transformation into logic clauses. Two new ontologies have been developed. The urban product ontology (UPO) is domain-specific to model domain concepts and capture domain semantics on top of heterogeneous terminologies in utility regulations. The spatial ontology (SO) consists of two layers of semantics – linguistic spatial expressions and formal spatial relations – for better understanding the spatial language in utility regulations. Pattern-matching rules defined on syntactic features (captured using common NLP techniques) and semantic features (captured using ontologies) were encoded for information extraction. The extracted information elements were then mapped to their semantic correspondences via ontologies and finally transformed into deontic logic (DL) clauses to achieve the semantic and logical formalization. The approach was tested on the spatial configuration-related requirements in utility accommodation policies. Results show it achieves a 98.2% precision and a 94.7% recall in information extraction, a 94.4% precision and a 90.1% recall in semantic formalization, and an 83% accuracy in logical formalization.
PTP: Parallelized Tracking and Prediction With Graph Neural Networks and Diversity Sampling

Multi-object tracking (MOT) and trajectory prediction are two critical components in modern 3D perception systems that require accurate modeling of multi-agent interaction. We hypothesize that it is beneficial to unify both tasks under one framework in order to learn a shared feature representation of agent interaction. Furthermore, instead of performing tracking and prediction sequentially which can propagate errors from tracking to prediction, we propose a parallelized framework to mitigate the issue. Also, our parallel track-forecast framework incorporates two additional novel computational units. First, we use a feature interaction technique by introducing Graph Neural Networks (GNNs) to capture the way in which agents interact with one another. The GNN is able to improve discriminative feature learning for MOT association and provide socially-aware contexts for trajectory prediction. Second, we use a diversity sampling function to improve the quality and diversity of our forecasted trajectories. The learned sampling function is trained to efficiently extract a variety of outcomes from a generative trajectory distribution and helps avoid the problem of generating duplicate trajectory samples. We evaluate on KITTI and nuScenes datasets showing that our method with socially-aware feature learning and diversity sampling achieves new state-of-the-art performance on 3D MOT and trajectory prediction. Project website is: http://www.xinshuoweng.com/projects/PTP.
Validity and Reliability Analysis of the PlotDigitizer Software Program for Data Extraction from Single-Case Graphs

Access to raw data of graphs presented in original articles to calculate the effect size of single-case research is a challenge for researchers conducting studies such as meta-analysis. Researchers typically use data extraction software programs to extract raw data from the graphs in articles. In this study, we aimed to analyze the validity and reliability of the PlotDigitizer software program, which is widely used in literature and an alternative to other data extraction programs, on computers with different operating systems. We performed the digitization of 6.846 data points on three different computers using 15 hypothetical graphs with 20 data series and 186 graphs with 242 data series from 29 published articles to accomplish the goal. Besides, using the values we digitized, we recalculated the 23 effect sizes presented in the original articles for validity analysis. Based on our sampling, we calculated intercoder and intracoder Pearson correlation coefficients. The results showed that PlotDigitizer could be an alternative to other programs as it is free and can run on many current and outdated systems, and it is valid and reliable as it is nearly perfect. Based on the obtained results and considering the data extraction process, we presented various recommendations for the researchers that will use the PlotDigitizer program for the quantitative analysis of single-case graphs.
Evaluating security and privacy issues of social networks based information systems in Industry 4.0

The present study aimed to analyse the main risks related to security and privacy of social networks based information systems in Industry 4.0. The methodology we used is an innovative exploratory data-driven process divided into three steps. First, we performed sentiment analysis to divide the database composed of 67, 206 tweets into feelings. Second, we applied a topic-modelling algorithm to extract topics. Third, we applied textual analysis to collect insights. A total of 10 topics related to security and privacy issues were identified as results. The paper concludes with a discussion of the challenges and main concerns related to the identified topics.
Sparse Graph Attention Networks

Graph Neural Networks (GNNs) have proved to be an effective representation learning framework for graph-structured data, and have achieved state-of-the-art performance on many practical predictive tasks. Among the variants of GNNs, Graph Attention Networks (GATs) improve the performance of many graph learning tasks through a dense attention mechanism. However, real-world graphs are often very large and noisy, and GATs are prone to overfitting if not regularized properly. In this paper, we propose Sparse Graph Attention Networks (SGATs) that learn sparse attention coefficients under an L0-norm regularization, and the learned sparse attentions are then used for all GNN layers, resulting in an edge-sparsified graph. By doing so, we can identify noisy/task-irrelevant edges, and thus perform feature aggregation on most informative neighbors. Extensive experiments on synthetic and real-world (assortative and disassortative) graph learning benchmarks demonstrate the superior performance of SGATs. Furthermore, the removed edges can be interpreted intuitively and quantitatively. To the best of our knowledge, this is the first graph learning algorithm that shows significant redundancies in graphs and edge-sparsified graphs can achieve similar (on assortative graphs) or sometimes higher (on disassortative graphs) predictive performances than original graphs. Our code is available at https://github.com/Yangyeeee/SGAT.
Robust feature matching using guided local outlier factor

• We propose a novel non-iterative approach for robust feature matching. • Heavy outliers can be detected and removed by the guided local outlier factor. • Multi-granularity neighborhood structure-preserving prevents the matching collapse. Matching local features on two or more images is fundamental for many applications in the field of computer vision and pattern recognition. Identifying and rejecting mismatches is an important part in the framework of feature matching, due to the putative correspondences always contaminated by mismatches with the error-prone local feature detectors. In this paper, we introduce a novel method, namely Guided Local Outlier Factor (GLOF) for feature matching with gross mismatches under multi-granularity neighborhood structure-preserving. We first construct a tentative correspondence set by matching multi-features. Then, we identify and remove mismatches. Inspired by the anomaly detection technique, putative correspondences are assigned to a particular score, so abnormal instances, i.e. , mismatches can be classified by a user-defined threshold. More specially, the neighborhood preserving guides the local searching procedure. Moreover, to eliminate the fluctuation of the matching results with different sizes of local neighbors, we use the multi-granularity algorithm to average out the deviation. Experimental results demonstrate that the introduced approach is superior to several state-of-the-art methods in terms of mismatch rejection on publicly available datasets.
An efficient anisotropic diffusion model for image denoising with edge preservation

Anisotropic diffusion filtering is a widely used partial differential equation (PDE) based technique, which works effectively for removal of noise and preserving edges. This technique is highly dependent on diffusion coefficient and threshold parameter. In this work, we propose a new diffusion coefficient and image dependent threshold parameter which has a faster rate of convergence than those found in literature. The new technique outperforms the traditional models of anisotropic diffusion. Experimental results show the superiority of the proposed work over traditional anisotropic diffusion models.
A decision-making framework for evaluating appropriate business blockchain platforms using multiple preference formats and VIKOR

The blockchain is a shared, reliable, public transaction ledger that can be programmed for virtually anything valuable. Blockchains in terms of permissions and transactions with private ledgers are relatively new. The goal of this study is to present a decision-making process to guide organizations in the evaluation of blockchains as an enterprise platform service. Enterprise platforms can be selected by decision makers who voice their opinions in diverse forms by considering a range of criteria. Multiple preference formats offer suitable methods for evaluating such criteria. In this study, the VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) technique is employed for evaluating the alternatives and solving the selection problem. It contributes to the blockchain literature by combining multiple preference formats with VIKOR for the first time for the blockchain platform selection problem. The findings suggest that the proposed evaluation framework is powerful for selecting the most suitable enterprise blockchain platform among alternatives.
Category-aware Collaborative Sequential Recommendation

Sequential recommendation is the task of predicting the next items for users based on their interaction history. Modeling the dependence of the next action on the past actions accurately is crucial to this problem. Moreover, sequential recommendation often faces serious sparsity of item-to-item transitions in a user's action sequence, which limits the practical utility of such solutions.
TILDE: Term Independent Likelihood moDEl for Passage Re-ranking

Deep language models (deep LMs) are increasingly being used for full text retrieval or within cascade retrieval pipelines as later-stage re-rankers. A problem with using deep LMs is that, at query time, a slow inference step needs to be performed -- this hinders the practical adoption of these powerful retrieval models, or limits sensibly how many documents can be considered for re-ranking.
Three-dimensional Shape Reconstruction from Single-shot Speckle Image Using Deep Convolutional Neural Networks

Three-dimensional (3D) shape reconstruction from a monocular two-dimensional (2D) image has emerged as a highly demanded tool in many applications. This paper presents a novel 3D shape reconstruction technique that employs an end-to-end deep convolutional neural network (CNN) to transform a single speckle-pattern image into its corresponding 3D point cloud. In the proposed approach, three CNN models are explored for comparison to find the best capable network. To train the models with reliable datasets in the learning process, a multi-frequency fringe projection profilometry technique is adopted to prepare high-accuracy ground-truth 3D labels. Unlike the conventional 3D imaging and shape reconstruction techniques which often involve complicated algorithms and intensive computation, the proposed technique is simple, yet very fast and robust. A few experiments have been conducted to assess and validate the proposed approach, and its capability provides promising solutions to the ever-increasing scientific research and engineering applications.
TFROM

At present, most research on the fairness of recommender systems is conducted either from the perspective of customers or from the perspective of product (or service) providers. However, such a practice ignores the fact that when fairness is guaranteed to one side, the fairness and rights of the other side are likely to reduce. In this paper, we consider recommendation scenarios from the perspective of two sides (customers and providers). From the perspective of providers, we consider the fairness of the providers' exposure in recommender system. For customers, we consider the fairness of the reduced quality of recommendation results due to the introduction of fairness measures. We theoretically analyzed the relationship between recommendation quality, customers fairness, and provider fairness, and design a two-sided fairness-aware recommendation model (TFROM) for both customers and providers. Specifically, we design two versions of TFROM for offline and online recommendation. The effectiveness of the model is verified on three real-world data sets. The experimental results show that TFROM provides better two-sided fairness while still maintaining a higher level of personalization than the baseline algorithms.
CrawlPhish: Large-scale Analysis of Client-side Cloaking Techniques in Phishing

Phishing is a critical threat to Internet users. Although an extensive ecosystem serves to protect users, phishing websites are growing in sophistication, and they can slip past the ecosystem’s detection systems—and subsequently cause real-world damage—with the help of evasion techniques. Sophisticated client-side evasion techniques, known as cloaking, leverage JavaScript to enable complex interactions between potential victims and the phishing website, and can thus be particularly effective in slowing or entirely preventing automated mitigations. Yet, neither the prevalence nor the impact of client-side cloaking has been studied.In this paper, we present CrawlPhish, a framework for automatically detecting and categorizing client-side cloaking used by known phishing websites. We deploy CrawlPhish over 14 months between 2018 and 2019 to collect and thoroughly analyze a dataset of 112,005 phishing websites in the wild. By adapting state-of-the-art static and dynamic code analysis, we find that 35,067 of these websites have 1,128 distinct implementations of client-side cloaking techniques. Moreover, we find that attackers’ use of cloaking grew from 23.32% initially to 33.70% by the end of our data collection period. Detection of cloaking by our framework exhibited low false-positive and false-negative rates of 1.45% and 1.75%, respectively. We analyze the semantics of the techniques we detected and propose a taxonomy of eight types of evasion across three high-level categories: User Interaction, Fingerprinting, and Bot Behavior.Using 150 artificial phishing websites, we empirically show that each category of evasion technique is effective in avoiding browser-based phishing detection (a key ecosystem defense). Additionally, through a user study, we verify that the techniques generally do not discourage victim visits. Therefore, we propose ways in which our methodology can be used to not only improve the ecosystem’s ability to mitigate phishing websites with client-side cloaking, but also continuously identify emerging cloaking techniques as they are launched by attackers.
Predictive performance of machine and statistical learning methods: Impact of data-generating processes on external validity in the “large N, small p” setting

Machine learning approaches are increasingly suggested as tools to improve prediction of clinical outcomes. We aimed to identify when machine learning methods perform better than a classical learning method. We hereto examined the impact of the data-generating process on the relative predictive accuracy of six machine and statistical learning methods: bagged classification trees, stochastic gradient boosting machines using trees as the base learners, random forests, the lasso, ridge regression, and unpenalized logistic regression. We performed simulations in two large cardiovascular datasets which each comprised an independent derivation and validation sample collected from temporally distinct periods: patients hospitalized with acute myocardial infarction (AMI, n = 9484 vs. n = 7000) and patients hospitalized with congestive heart failure (CHF, n = 8240 vs. n = 7608). We used six data-generating processes based on each of the six learning methods to simulate outcomes in the derivation and validation samples based on 33 and 28 predictors in the AMI and CHF data sets, respectively. We applied six prediction methods in each of the simulated derivation samples and evaluated performance in the simulated validation samples according to c-statistic, generalized R 2 , Brier score, and calibration. While no method had uniformly superior performance across all six data-generating process and eight performance metrics, (un)penalized logistic regression and boosted trees tended to have superior performance to the other methods across a range of data-generating processes and performance metrics. This study confirms that classical statistical learning methods perform well in low-dimensional settings with large data sets.
Bootstrapping User and Item Representations for One-Class Collaborative Filtering

The goal of one-class collaborative filtering (OCCF) is to identify the user-item pairs that are positively-related but have not been interacted yet, where only a small portion of positive user-item interactions (e.g., users' implicit feedback) are observed. For discriminative modeling between positive and negative interactions, most previous work relied on negative sampling to some extent, which refers to considering unobserved user-item pairs as negative, as actual negative ones are unknown. However, the negative sampling scheme has critical limitations because it may choose "positive but unobserved" pairs as negative. This paper proposes a novel OCCF framework, named as BUIR, which does not require negative sampling. To make the representations of positively-related users and items similar to each other while avoiding a collapsed solution, BUIR adopts two distinct encoder networks that learn from each other; the first encoder is trained to predict the output of the second encoder as its target, while the second encoder provides the consistent targets by slowly approximating the first encoder. In addition, BUIR effectively alleviates the data sparsity issue of OCCF, by applying stochastic data augmentation to encoder inputs. Based on the neighborhood information of users and items, BUIR randomly generates the augmented views of each positive interaction each time it encodes, then further trains the model by this self-supervision. Our extensive experiments demonstrate that BUIR consistently and significantly outperforms all baseline methods by a large margin especially for much sparse datasets in which any assumptions about negative interactions are less valid.
Weakly-Supervised Salient Object Detection With Saliency Bounding Boxes

In this paper, we propose a novel form of weak supervision for salient object detection (SOD) based on saliency bounding boxes, which are minimum rectangular boxes enclosing the salient objects. Based on this idea, we propose a novel weakly-supervised SOD method, by predicting pixel-level pseudo ground truth saliency maps from just saliency bounding boxes. Our method first takes advantage of the unsupervised SOD methods to generate initial saliency maps and addresses the over/under prediction problems, to obtain the initial pseudo ground truth saliency maps. We then iteratively refine the initial pseudo ground truth by learning a multi-task map refinement network with saliency bounding boxes. Finally, the final pseudo saliency maps are used to supervise the training of a salient object detector. Experimental results show that our method outperforms state-of-the-art weakly-supervised methods.
AutoML for Video Analytics with Edge Computing

Video analytics constitute a core component of many wireless services that require processing of voluminous data streams emanating from handheld devices. Multi-Access Edge Computing (MEC) is a promising solution for supporting such resource-hungry services, but there is a plethora of configuration parameters affecting their performance in an unknown and possibly time-varying fashion. To overcome this obstacle, we propose an Automated Machine Learning (AutoML) framework for jointly configuring the service and wireless network parameters, towards maximizing the analytics' accuracy subject to minimum frame rate constraints. Our experiments with a bespoke prototype reveal the volatile and system/data-dependent performance of the service, and motivate the development of a Bayesian online learning algorithm which optimizes on-the-fly the service performance. We prove that our solution is guaranteed to find a near-optimal configuration using safe exploration, i.e., without ever violating the set frame rate thresholds. We use our testbed to further evaluate this AutoML framework in a variety of scenarios, using real datasets.