Reviewerly/exHarmony huggingface dataset:
Learning DOM Trees of Web Pages by Subpath Kernel and Detecting Fake e-Commerce Sites

The subpath kernel is a class of positive definite kernels defined over trees, which has the following advantages for the purposes of classification, regression and clustering: it can be incorporated into a variety of powerful kernel machines including SVM; It is invariant whether input trees are ordered or unordered; It can be computed by significantly fast linear-time algorithms; And, finally, its excellent learning performance has been proven through intensive experiments in the literature. In this paper, we leverage recent advances in tree kernels to solve real problems. As an example, we apply our method to the problem of detecting fake e-commerce sites. Although the problem is similar to phishing site detection, the fact that mimicking existing authentic sites is harmful for fake e-commerce sites marks a clear difference between these two problems. We focus on fake e-commerce site detection for three reasons: e-commerce fraud is a real problem that companies and law enforcement have been cooperating to solve; Inefficiency hampers existing approaches because datasets tend to be large, while subpath kernel learning overcomes these performance challenges; And we offer increased resiliency against attempts to subvert existing detection methods through incorporating robust features that adversaries cannot change: the DOM-trees of web-sites. Our real-world results are remarkable: our method has exhibited accuracy as high as 0.998 when training SVM with 1000 instances and evaluating accuracy for almost 7000 independent instances. Its generalization efficiency is also excellent: with only 100 training instances, the accuracy score reached 0.996.
Incorporating Memory-Based Preferences and Point-of-Interest Stickiness into Recommendations in Location-Based Social Networks

In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI recommendation methods. In psychological effect-based POI recommendations, the memory-based attenuation of people’s preferences with respect to POIs, e.g., the fact that more attention is paid to POIs that were checked in to recently than those visited earlier, is emphasized. However, the memory effect only reflects the changes in an individual’s check-in trajectory and cannot discover the important POIs that dominate their mobility patterns, which are related to the repeat-visit frequency of an individual at a POI. To solve this problem, in this paper, we developed a novel POI recommendation framework using people’s memory-based preferences and POI stickiness, named U-CF-Memory-Stickiness. First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. Second, we took the visiting frequency of POIs into consideration and introduced the concept of POI stickiness to identify the important POIs that reflect the stable interests of an individual with respect to their mobility behavior decisions. Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. The results of the experiments we conducted on a real LBSN dataset demonstrated that our method outperformed other methods.
How Augmented Reality Could Improve the Student’s Attraction to Learn Mechanisms

Mechanics, along with electronics, is a basic field for the development of high technologies. However, learning mechanics is not an easy task. To meet and adapt to the requirements of students in the digital age, teachers must provide them significant ways to incorporate the latest technologies and applications for their studies. In this study, we explored the application of augmented reality (AR) to improve the learning of the science of Mechanisms. An AR application was implemented and developed for Android-based devices, followed by a qualitative experiment conducted with a sample of 116 students. The study was based on the technology acceptance model and the students’ attitudes towards learning in AR environments were assessed using the structural equation modeling. The results showed that the didactic potential of this application is promising, which is highlighted by the positive attitude about using the application, as well as by the high values obtained for intention to use.
A Case Study on the Design and Implementation of a Platform for Hand Rehabilitation

Rehabilitation aids help people with temporal or permanent disabilities during the rehabilitation process. However, these solutions are usually expensive and, consequently, inaccessible outside of professional medical institutions. Rapid advances in software development, Internet of Things (IoT), robotics, and additive manufacturing open up a way to affordable rehabilitation solutions, even to the general population. Imagine a rehabilitation aid constructed from accessible software and hardware with local production. Many obstacles exist to using such technology, starting with the development of unified software for custom-made devices. In this paper, we address open issues in designing rehabilitation aids by proposing an extensive rehabilitation platform. To demonstrate our concept, we developed a unique platform, RehabHand. The main idea is to use domain-specific language and code generation techniques to enable loosely coupled software and hardware solutions. The main advantage of such separation is support for modular and a higher abstraction level by enabling therapists to write rehabilitation exercises in natural, domain-specific terminology and share them with patients. The same platform provides a hardware-independent part that facilitates the integration of new rehabilitation devices. Experience in implementing RehabHand with three different rehabilitation devices confirms that such rehabilitation technology can be developed, and shows that implementing a hardware-independent rehabilitation platform might not be as challenging as expected.
In Search of the Cognitively Complex Person: Is There a Meaningful Trait Component of Cognitive Complexity?

Researchers have long assumed that complex thinking is determined by both situational factors and stable, trait-based differences. However, although situational influences on complexity have been discussed at length in the literature, there is still no comprehensive integration of evidence regarding the theorized trait component of cognitive complexity. To fill this gap, we evaluate the degree that cognitive complexity is attributable to trait variance. Specifically, we review two domains of evidence pertaining to (a) the generalizability of individuals’ complex thinking across domains and the temporal stability of individuals’ complex thinking and (b) the relationship of complex thinking with conceptually related traits. Cumulatively, the literature suggests that persons’ cognitive complexity at any point in time results partially from a stable and generalizable trait component that accounts for a small-to-moderate amount of variance. It further suggests that cognitively complex persons are characterized by chronic trait-based differences in motivation and ability to think complexly.
Quantum simulation of the ground-state Stark effect in small molecules: a case study using IBM Q

As quantum computing approaches its first commercial implementations, quantum simulation emerges as a potentially ground-breaking technology for several domains, including biology and chemistry. However, taking advantage of quantum algorithms in quantum chemistry raises a number of theoretical and practical challenges at different levels, from the conception to its actual execution. We go through such challenges in a case study of a quantum simulation for the hydrogen (H $$_2$$ ) and lithium hydride (LiH) molecules, at an actual commercially available quantum computer, the IBM Q. The former molecule has always been a playground for testing approximate calculation methods in quantum chemistry, while the latter is just a little bit more complex, lacking the mirror symmetry of the former. Using the variational quantum eigensolver method, we study the molecule’s ground state energy versus interatomic distance, under the action of stationary electric fields (Stark effect). Additionally, we review the necessary calculations of the matrix elements of the second quantization Hamiltonian encompassing the extra terms concerning the action of electric fields, using STO-LG-type atomic orbitals to build the minimal basis sets.
Time-Based Roofline for Deep Learning Performance Analysis

Deep learning applications based on neural networks are generating considerable interest in various fields due to their high accuracy. Such an application is usually very compute-intensive thus requires a long run time. Researchers and engineers are actively exploring new solutions to this issue from both hardware and software/algorithm sides. However, little previous work has focused on providing a practical methodology to characterize deep learning performance bottlenecks and potentially guide the following optimization efforts. In this paper, we introduce an extension of the Roofline model and use it to analyze two representative computation kernels in deep learning, 2D convolution and long short-term memory, on NVIDIA GPUs. This new time-based Roofline model incorporates both compute/bandwidth complexity and run time in its formulae to demonstrate performance issues that cannot be reflected by the classic Roofline. Factors such as arithmetic intensity, data transfer, kernel launch overhead, and the Tensor Core usage will be examined by varying different parameters such as batch size and feature size, etc. This work helped form a more systematic way to understand the performance issue of deep learning applications. Last but not least, this generic performance model can be applied to a wide category of applications besides deep learning as well.
Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning

The privacy leakage of the model about the training data can be bounded in the differential privacy mechanism. However, for meaningful privacy parameters, a differentially private model degrades the utility drastically when the model comprises a large number of trainable parameters. In this paper, we propose an algorithm \emph{Gradient Embedding Perturbation (GEP)} towards training differentially private deep models with decent accuracy. Specifically, in each gradient descent step, GEP first projects individual private gradient into a non-sensitive anchor subspace, producing a low-dimensional gradient embedding and a small-norm residual gradient. Then, GEP perturbs the low-dimensional embedding and the residual gradient separately according to the privacy budget. Such a decomposition permits a small perturbation variance, which greatly helps to break the dimensional barrier of private learning. With GEP, we achieve decent accuracy with low computational cost and modest privacy guarantee for deep models. Especially, with privacy bound ϵ=8, we achieve 74.9% test accuracy on CIFAR10 and 95.1% test accuracy on SVHN, significantly improving over existing results.
Detection of Fraud Transactions Using Recurrent Neural Network during COVID-19

Online transactions are becoming more popular in present situation where the globe is facing an unknown disease COVID-19. Now authorities of several countries have requested people to use cashless transaction as far as possible. Practically, it is not always possible to use it in all transactions. Since number of such cashless transactions has been increasing during lockdown period due to COVID-19, fraudulent transactions are also increasing in a rapid way. Fraud can be analysed by viewing a series of customer transactions data that was done in his/ her previous transactions. Normally banks or other transaction authorities warn their customers about the transaction, if they notice any deviation from available patterns; the authorities consider it as a possibly fraudulent transaction. For detection of fraud during COVID-19, banks and credit card companies are applying various methods such as data mining, decision tree, rule based mining, neural network, fuzzy clustering approach and machine learning methods. The approach tries to find out normal usage pattern of customers based on their former activities. The objective of this paper is to propose a method to detect such fraud transactions during such unmanageable situation of the pandemic. Digital payment schemes are often threatened by fraudulent activities. Detecting fraud transactions during money transfer may save customers from financial loss. Mobile-based money transactions are focused in this paper for fraud detection. A Deep Learning (DL) framework is suggested in the paper that monitors and detects fraudulent activities. Implementing and applying Recurrent Neural Network on PaySim generated synthetic financial dataset, deceptive transactions are identified. The proposed method is capable to detect deceptive transactions with an accuracy of 99.87%, F1-Score of 0.99 and MSE of 0.01.
Application of 3D laser scanning technology in engineering field

With the continuous progress of computer and laser measurement technology, non-contact measurement based on laser scanning technology has been more and more applied in the industrial production process. Since the 1990s, 3D laser scanning technology used as a new technology has been developed rapidly. The 3D laser scanning technology, also known as High Definition Surveying (HDS), was based on the principle of laser ranging. The technology on the surface of a measured object can record some features of large dense points, including 3d coordinates, reflectance and texture information, so as to rapidly reconstruct 3D models of a measured target and various map data such as the line, the surface and the body. Meanwhile, it is a revolutionary leap that the technology improves the traditional single point measurement to the surface measurement, and realizes the digital reconstruction of the objects. Especially, for some conditions such as contact measurement and complex component detection that cannot be measured, 3D laser scanning technology has a natural advantage in the field of industrial design and measurement .
Steganographic visual story with mutual-perceived joint attention

Abstract Social media plays an increasingly important role in providing information and social support to users. Due to the easy dissemination of content, as well as difficulty to track on the social network, we are motivated to study the way of concealing sensitive messages in this channel with high confidentiality. In this paper, we design a steganographic visual stories generation model that enables users to automatically post stego status on social media without any direct user intervention and use the mutual-perceived joint attention (MPJA) to maintain the imperceptibility of stego text. We demonstrate our approach on the visual storytelling (VIST) dataset and show that it yields high-quality steganographic texts. Since the proposed work realizes steganography by auto-generating visual story using deep learning, it enables us to move steganography to the real-world online social networks with intelligent steganographic bots.
CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction

Automatic extraction of chemical-induced disease (CID) relation from unstructured text is of essential importance for disease treatment and drug development. In this task, some relational facts can only be inferred from the document rather than single sentence. Recently, researchers investigate graph-based approaches to extract relations across sentences. It iteratively combines the information from neighbor nodes to model the interactions in entity mentions that exist in different sentences. Despite their success, one severe limitation of the graph-based approaches is the over-smoothing problem, which decreases the model distinguishing ability. In this paper, we propose CID-GCN, an effective Graph Convolutional Networks (GCNs) with gating mechanism, for CID relation extraction. Specifically, we construct a heterogeneous graph which contains mention, sentence and entity nodes. Then, the graph convolution operation is employed to aggregate interactive information on the constructed graph. Particularly, we combine gating mechanism with the graph convolution operation to address the over-smoothing problem. The experimental results demonstrate that our approach significantly outperforms the baselines.
Improved Action Recognition with Separable Spatio-Temporal Attention Using Alternative Skeletal and Video Pre-Processing

The potential benefits of recognising activities of daily living from video for active and assisted living have yet to be fully untapped. These technologies can be used for behaviour understanding, and lifelogging for caregivers and end users alike. The recent publication of realistic datasets for this purpose, such as the Toyota Smarthomes dataset, calls for pushing forward the efforts to improve action recognition. Using the separable spatio-temporal attention network proposed in the literature, this paper introduces a view-invariant normalisation of skeletal pose data and full activity crops for RGB data, which improve the baseline results by 9.5% (on the cross-subject experiments), outperforming state-of-the-art techniques in this field when using the original unmodified skeletal data in dataset. Our code and data are available online.
Multiview Multi-Instance Multilabel Active Learning

Multiview multi-instance multilabel learning (M3L) is a framework for modeling complex objects. In this framework, each object (or bag) contains one or more instances, is represented with different feature views, and simultaneously annotated with a set of nonexclusive semantic labels. Given the multiplicity of the studied objects, traditional M3L methods generally demand a large number of labeled bags to train a predictive model to annotate bags (or instances) with semantic labels. However, annotating sufficient bags is very expensive and often impractical. In this article, we present an active learning-based M3L approach (M3AL) to reduce the labeling costs of bags and to improve the performance as much as possible. M3AL first adapts the multiview self-representation learning to evacuate the shared and individual information of bags and to learn the shared/individual similarities between bags across/within views. Next, to avoid scrutinizing all the possible labels, M3AL introduces a new query strategy that leverages the shared and individual information, and the diverse instance distribution of bags across views, to select the most informative bag-label pair for the query. Experimental studies on benchmark data sets show that M3AL can significantly reduce the query costs while achieving a better performance than other related competitive methods at the same cost.
Pneumonia Outcome Prediction Using Structured And Unstructured Data From EHR

In Intensive Care Unit (ICU), it is important to anticipate interventions for patients at a high-risk of death. This requires identifying those patients ideally at the time of their admission in ICU, and update their initial risk rate every time new data is available. This predictive task can be performed by analyzing structured and unstructured routine data to make sure that we can initiate a prediction for every patient. Traditional statistical tools have been used to assess disease like pneumonia and predict the outcome of a patient. Recently, machine learning models emerged and have shown better performances on such tasks. Although authors have published various results, their works rely on a single datatype, either structured or unstructured data. Using the Medical Information Mart for intensive Care data-set, we are proposing an ensemble model, that aggregates different data-types to predict the outcome of a pneumonia patient admitted in ICU using limited data that can be available at the very early stage of his stay. To demonstrate the importance of this approach, we compared it with 2 different models, one based only on structured data, and another one based on narratives text from caregivers, and we were able to show that our ensemble model can perform way better with an accuracy of 0.98 of F1-score(0.97 MCC), while a model using only structured data had 0.79 of F1-score and where text notes predicted the outcome with an accuracy of 0.89 of Matthews Correlation Coefficient. In addition to showing how ensemble learning models can outperform other models on this task, we demonstrated the importance and usefulness of interpreting the predictions pointing out the leading factors that are determining the global and individual outcome predictions.
Concentration of kernel matrices with application to kernel spectral clustering

We study the concentration of random kernel matrices around their mean. We derive nonasymptotic exponential concentration inequalities for Lipschitz kernels assuming that the data points are independent draws from a class of multivariate distributions on $\mathbb{R}^{d}$, including the strongly log-concave distributions under affine transformations. A feature of our result is that the data points need not have identical distributions or zero mean, which is key in certain applications such as clustering. Our bound for the Lipschitz kernels is dimension-free and sharp up to constants. For comparison, we also derive the companion result for the Euclidean (inner product) kernel for a class of sub-Gaussian distributions. A notable difference between the two cases is that, in contrast to the Euclidean kernel, in the Lipschitz case, the concentration inequality does not depend on the mean of the underlying vectors. As an application of these inequalities, we derive a bound on the misclassification rate of a kernel spectral clustering (KSC) algorithm, under a perturbed nonparametric mixture model. We show an example where this bound establishes the high-dimensional consistency (as $d\to \infty $) of the KSC, when applied with a Gaussian kernel, to a noisy model of nested nonlinear manifolds.
W2VV++ BERT Model at VBS 2021

The W2VV++ model BoW variant integrated to VIRET and SOMHunter systems has proven its effectiveness in the previous Video Browser Showdown competition in 2020. As a next experimental interactive search prototype to benchmark, we consider a simple system relying on the more complex BERT variant of the W2VV++ model, accepting a rich text input. The input can be provided by keyboard or by speech processed by a third-party cloud service. The motivation for the more complex BERT variant is its good performance for rich text descriptions that can be provided for known-item search tasks. At the same time, users will be instructed to specify as rich text description about the searched scene as possible.