Reviewerly/exHarmony huggingface dataset:
Product Bundle Identification using Semi-Supervised Learning

Many sellers on e-commerce platforms offer buyers product bundles, which package together two or more different items. The identification of such bundles is a necessary step to support a variety of related services, from recommendation to dynamic pricing. In this work, we present a comprehensive study of bundle identification on a large e-commerce website. Our analysis of bundle compared to non-bundle listed items reveals several key differentiating characteristics, spanning the listing's title, image, and attributes. Following, we experiment with a multi-modal classifier, which takes advantage of these characteristics as features. Our analysis also shows that a bundle indicator input by sellers tends to be highly noisy and carries only a weak signal. The bundle identification task therefore faces the challenge of having a small set of manually-labeled clean examples and a larger set of noisy-labeled examples, in conjunction with class imbalance due to the relative scarcity of bundles.
DG-FPN: Learning Dynamic Feature Fusion Based on Graph Convolution Network For Object Detection

Feature Pyramid Network (FPN) is one of the most popular feature fusion methods to address the multi-scale issue in object detection. Current FPN-based methods are mostly designed manually, which cannot guarantee the optimal feature fusion. Besides, the predetermined methods generally provide the same strategy to various targets, which are not distinctive among targets with different scales. In this paper, we present a novel dynamic feature fusion method based on the graph convolution network (GCN), called DG-FPN. The proposed GCN-based method can dynamically transfer knowledge with learnable weights across all nodes, making it possible to learn the optimal feature fusion for detectors. Furthermore, the pixel-based adjacency matrix is proposed to offer customized fusion strategy for each target, achieving dynamic feature fusion. To optimize matrix-driven learning, semantic information is introduced to guide the process of fusion. Experiments show that DG-FPN significantly improves the performance of baseline networks on the challenging MS-COCO object benchmark, especially in small objects.
Fully-dynamic planarity testing in polylogarithmic time

Given a dynamic graph subject to insertions and deletions of edges, a natural question is whether the graph presently admits a planar embedding. We give a deterministic fully-dynamic algorithm for general graphs, running in amortized O(log3 n) time per edge insertion or deletion, that maintains a bit indicating whether or not the graph is presently planar. This is an exponential improvement over the previous best algorithm [Eppstein, Galil, Italiano, Spencer, 1996] which spends amortized O(√n) time per update.
The early history of F#

This paper describes the genesis and early history of the F# programming language. I start with the origins of strongly-typed functional programming (FP) in the 1970s, 80s and 90s. During the same period, Microsoft was founded and grew to dominate the software industry. In 1997, as a response to Java, Microsoft initiated internal projects which eventually became the .NET programming framework and the C# language. From 1997 the worlds of academic functional programming and industry combined at Microsoft Research, Cambridge. The researchers engaged with the company through Project 7, the initial effort to bring multiple languages to .NET, leading to the initiation of .NET Generics in 1998 and F# in 2002. F# was one of several responses by advocates of strongly-typed functional programming to the "object-oriented tidal wave" of the mid-1990s. The development of the core features of F# 1.0 happened from 2004-2007, and I describe the decision-making process that led to the "productization" of F# by Microsoft in 2007-10 and the release of F# 2.0. The origins of F#'s characteristic features are covered: object programming, quotations, statically resolved type parameters, active patterns, computation expressions, async, units-of-measure and type providers. I describe key developments in F# since 2010, including F# 3.0-4.5, and its evolution as an open source, cross-platform language with multiple delivery channels. I conclude by examining some uses of F# and the influence F# has had on other languages so far.
Gradient Descent Learning With Floats

The gradient learning descent method is the main workhorse of training tasks in artificial intelligence and machine-learning research. Current theoretical studies of gradient descent only use the continuous domains, which is unreal since electronic computers use the float point numbers to store and deal with data. Although existing results are sufficient for the extremely tiny errors in high-precision machines, they need to be improved for low-precision cases. This article presents an understanding of the learning algorithm in computers with floats. The performances of three gradient descents with the floating domain are investigated when the objective function is smooth. When the function is assumed to have the PŁ condition, the convergence speed can be improved. We proved that for floating gradient descent to obtain an error with ϵ , the iteration is O(1/ϵ) for the general smooth case, and O(ln(1/ϵ)) for the PŁ case. But ϵ should be larger than the s -bit machine epsilon δ(s) in the deterministic case, that is, ϵ ≥ Ω(δ(s)) , while ϵ ≥ Ω(√{δ(s)}) for the stochastic case. Floating stochastic and sign gradient descents can both output an ϵ noised result in O(1/ϵ2) iterations.
Neural Reranking for Dependency Parsing: An Evaluation

Recent work has shown that neural rerankers can improve results for dependency parsing over the top k trees produced by a base parser.However, all neural rerankers so far have been evaluated on English and Chinese only, both languages with a configurational word order and poor morphology.In the paper, we re-assess the potential of successful neural reranking models from the literature on English and on two morphologically rich(er) languages, German and Czech.In addition, we introduce a new variation of a discriminative reranker based on graph convolutional networks (GCNs).We show that the GCN not only outperforms previous models on English but is the only model that is able to improve results over the baselines on German and Czech.We explain the differences in reranking performance based on an analysis of a) the gold tree ratio and b) the variety in the k-best lists.
Relational and Fine-Grained Argument Mining

Abstract In our project ReMLAV , funded within the DFG Priority Program RATIO ( http://www.spp-ratio.de/ ), we focus on relational and fine-grained argument mining. In this article, we first introduce the problems we address and then summarize related work. The main part of the article describes our research on argument mining, both coarse-grained and fine-grained methods, and on same-side stance classification, a relational approach to the problem of stance classification. We conclude with an outlook.
Robust Design of Deep Neural Networks Against Adversarial Attacks Based on Lyapunov Theory

Deep neural networks (DNNs) are vulnerable to subtle adversarial perturbations applied to the input. These adversarial perturbations, though imperceptible, can easily mislead the DNN. In this work, we take a control theoretic approach to the problem of robustness in DNNs. We treat each individual layer of the DNN as a nonlinear system and use Lyapunov theory to prove stability and robustness locally. We then proceed to prove stability and robustness globally for the entire DNN. We develop empirically tight bounds on the response of the output layer, or any hidden layer, to adversarial perturbations added to the input, or the input of hidden layers. Recent works have proposed spectral norm regularization as a solution for improving robustness against l2 adversarial attacks. Our results give new insights into how spectral norm regularization can mitigate the adversarial effects. Finally, we evaluate the power of our approach on a variety of data sets and network architectures and against some of the well-known adversarial attacks.
Rankvqa: Answer Re-Ranking For Visual Question Answering

Visual Question Answering (VQA) is a comprehensive task to answer questions about the visual contents of an image. However, a number of studies have pointed out that VQA models rely heavily on superficial correlations between question and answer, and predict the answer just according to the textual statistical correlations without truly understanding the visual contents. To address this issue, we propose an answer re-ranking VQA model, called as RankVQA, in which the roles of the input image are re-examined to select the most relevant answer from a set of candidate answers generated by a typical VQA model. Specifically, we rank the candidate answers with their relevance to visual content of the input image and some question-related image captions respectively. Extensive experiments on the two datasets, i.e., VQA v2 and VQACP v2, demonstrate the effectiveness of the proposed model, and the state-of-the-art performance on both the datasets are achieved.
Aspect-based sentiment analysis search engine for social media data

Extraction of positive or negative opinions from any online content has received more consideration from researchers during the past decade, since the number of internet users that actively use online review sites, social networks and personal blogs to express their opinions has been growing. Sentiment analysis is the study of automated techniques for extracting sentiments from written languages making use of natural language processing tasks to thoroughly pre-process the data and extract polarity from the data. Customers who want to purchase products or services as well as business organizations, often rely on online reviews for knowing the overall user sentiment. Based on the sentiment, customers can choose whether or not to purchase a product while the organizations get an overall picture of their product. Aspect-based sentiment analysis helps in extraction of important features called aspects because knowing the polarity only is not sufficient. The proposed aspect-based sentiment analysis model uses polarity classification and sentiment extraction on reviews, and extracts the most interesting polarity aspects preferred by the customers automatically using both machine learning and deep learning algorithms. A search engine to pull out tweets and reviews relevant to user specified keyword is developed and corresponding interesting aspects are displayed.
Differentially Private Knowledge Distillation for Mobile Analytics

The increasing demand for on-device deep learning necessitates the deployment of deep models on mobile devices. However, directly deploying deep models on mobile devices presents both capacity bottleneck and prohibitive privacy risk. To address these problems, we develop a Differentially Private Knowledge Distillation (DPKD) framework to enable on-device deep learning as well as preserve training data privacy. We modify the conventional Private Aggregation of Teacher Ensembles (PATE) paradigm by compressing the knowledge acquired by the ensemble of teachers into a student model in a differentially private manner. The student model is then trained on both the labeled, public data and the distilled knowledge by adopting a mixed training algorithm. Extensive experiments on popular image datasets, as well as the real implementation on a mobile device show that DPKD can not only benefit from the distilled knowledge but also provide a strong differential privacy guarantee (ε=2$) with only marginal decreases in accuracy.
Integrating Image-Based and Knowledge-Based Representation Learning

A variety of brain areas is involved in language understanding and generation, accounting for the scope of language that can refer to many real-world matters. In this paper, we investigate how regularities among real-world entities impact emergent language representations. Specifically, we consider knowledge bases, which represent entities and their relations as structured triples, and image representations, which are obtained via deep convolutional networks. We combine these sources of information to learn representations of an image-based knowledge representation learning (IKRL) model. An attention mechanism lets more informative images contribute more to the image-based representations. Evaluation results show that the model outperforms all baselines on the tasks of knowledge graph (KG) completion and triple classification. In analyzing the learned models, we found that the structure-based and image-based representations integrate different aspects of the entities and the attention mechanism provides robustness during learning.
Neural Concept Map Generation for Effective Document Classification with Interpretable Structured Summarization

Concept maps provide concise structured representations for documents regarding their important concepts and interaction links, which have been widely used for document summarization and downstream tasks. However, the construction of concept maps often relies heavily on heuristic design and auxiliary tools. Recent popular neural network models, on the other hand, are shown effective in tasks across various domains, but are short in interpretability and prone to overfitting. In this work, we bridge the gap between concept map construction and neural network models, by designing doc2graph, a novel weakly-supervised text-to-graph neural network, which generates concept maps in the middle and is trained towards document-level tasks like document classification. In our experiments, doc2graph outperforms both its traditional baselines and neural counterparts by significant margins in document classification, while producing high-quality interpretable concept maps as document structured summarization.
Factors Influencing the Adoption of Cloud Computing in Small and Medium Enterprises in Jordan

The factors of Cloud computing (CC) adoption among small and medium enterprises (SMEs) have not been identified in the Jordanian context. Therefore, the aim of this study is to examine the factors influencing the adoption of CC in SMEs in Jordan. Through an extensive review of the literature, the research model was constructed based on the TOE framework. In order to validate the research model and test the proposed hypotheses, a quantitative research methodology followed in which a survey questionnaire was utilized to collect the data. Out of the 350 distributed questionnaires, 92 questionnaires were valid for the analysis. The PLS-SEM technique was used for the data analysis. The results approved the influence of six variables on the adoption of CC by manufacturing SMEs in Jordan, namely; compatibility, security, top management support, technological readiness, prior IT experience, and competitive pressure. Only the influence of the perceived benefits has not been supported. The study has theoretical implications and practical implications to SMEs in Jordan.
Cold Boot Attacks on LUOV

This research article assesses the feasibility of cold boot attacks on the lifted unbalanced oil and Vinegar (LUOV) scheme, a variant of the UOV signature scheme. This scheme is a member of the family of asymmetric cryptographic primitives based on multivariable polynomials over a finite field K and has been submitted as candidate to the ongoing National Institute of Standards and Technology (NIST) standardisation process of post-quantum signature schemes. To the best of our knowledge, this is the first time that this scheme is evaluated in this setting. To perform our assessment of the scheme in this setting, we review two implementations of this scheme, the reference implementation and the libpqcrypto implementation, to learn the most common in-memory private key formats and next develop a key recovery algorithm exploiting the structure of this scheme. Since the LUOV’s key generation algorithm generates its private components and public components from a 256-bit seed, the key recovery algorithm works for all the parameter sets recommended for this scheme. Additionally, we tested the effectiveness and performance of the key recovery algorithm through simulations and found the key recovery algorithm may retrieve the private seed when α = 0.001 (probability that a 0 bit of the original secret key will flip to a 1 bit) and β (probability that a 1 bit of the original private key will flip to a 0 bit) in the range { 0.001 , 0.01 , 0.02 , … , 0.15 } by enumerating approximately 2 40 candidates.
ECCPoW: Error-Correction Code based Proof-of-Work for ASIC Resistance

Bitcoin is the first cryptocurrency to participate in a network and receive compensation for online remittance and mining without any intervention from a third party, such as financial institutions. Bitcoin mining is done through proof of work (PoW). Given its characteristics, the higher hash rate results in a higher probability of mining, leading to the emergence of a mining pool, called a mining organization. Unlike central processing units or graphics processing units, high-cost application-specific integrated circuit miners have emerged with performance efficiency. The problem is that the obtained hash rate exposes Bitcoin’s mining monopoly and causes the risk of a double-payment attack. To solve this problem, we propose the error-correction code PoW (ECCPoW), combining the low-density parity-check decoder and hash function. The ECCPoW contributes to the phenomenon of symmetry in the proof of work (PoW) blockchain. This paper proposes the implementation of ECCPoW, replacing the PoW in Bitcoin. Finally, we compare the mining centralization, security, and scalability of ECCPoW and Bitcoin.
Probabilistically Guaranteed Path Planning for Safe Urban Air Mobility Using Chance Constrained RRT*

Flying safety is a critical concern for the successful operation of urban air mobility. This paper proposes a novel path planning algorithm based on the Rapidly-exploring Random Tree Star (RRT*) in conjunction with chance constrained formulation to handle uncertain environmental obstacles. Chance constrained formulation for uncertain obstacles under Gaussian noise is developed by converting the probabilistic constraints into deterministic constraints equivalently. The probabilistically feasible region at every time step can be established through the simulation of the system state and the evaluation of probabilistic constraints. By combining chance constrained formulation with RRT* algorithm, our proposed strategy not only enjoys the benefits of sampling-based algorithms but also incorporates uncertainty into the formulation. Simulation results demonstrate that the proposed algorithm can generate probabilistically guaranteed collision-free paths for urban air mobility operations.
On the perceptual influence of shape overlap on data-comparison using scatterplots

Scatterplots can be used for a wide range of visual analysis tasks, for example comparing correlations or variances of clusters across potentially multiple classes of data, in order to find answers to higher-level questions. Comparing classes of data in one scatterplot demands additional visual channels to encode this dimension. While perception research suggests colors as rather perceptually dominant, other studies show that shapes can also be visually salient. However, with an increasing amount of data, overlapping shapes can cause perceptual difficulties and obscure data. Even though shapes in scatterplots have been investigated extensively, the overlap between these shapes has usually been avoided by using synthetic scatterplots. To overcome this limitation, we investigate the perceptual implications of overlap when comparing data using scatterplots using a series of crowd-sourced user studies. These studies include common visual analysis tasks, like comparing the number of points, comparing mean values, and determine the set of points that is more clustered. To support our investigations, we introduced and compared four metrics for overlap in scatterplots. Our results provide insight into the overlap in scatterplots, recommend combinations of shapes that are less prone to overlap, and outline how our metrics could be used to optimize future scatterplot design.
Single-Shot Monocular RGB-D Imaging Using Uneven Double Refraction

Cameras that capture color and depth information have become an essential imaging modality for applications in robotics, autonomous driving, virtual, and augmented reality. Existing RGB-D cameras rely on multiple sensors or active illumination with specialized sensors. In this work, we propose a method for monocular single-shot RGB-D imaging. Instead of learning depth from single-image depth cues, we revisit double-refraction imaging using a birefractive medium, measuring depth as the displacement of differently refracted images superimposed in a single capture. However, existing double-refraction methods are orders of magnitudes too slow to be used in real-time applications, e.g., in robotics, and provide only inaccurate depth due to correspondence ambiguity in double reflection. We resolve this ambiguity optically by leveraging the orthogonality of the two linearly polarized rays in double refraction -- introducing uneven double refraction by adding a linear polarizer to the birefractive medium. Doing so makes it possible to develop a real-time method for reconstructing sparse depth and color simultaneously in real-time. We validate the proposed method, both synthetically and experimentally, and demonstrate 3D object detection and photographic applications.
SIBERIAN SCIENTISTS’ GROWTH OF INTEREST IN OPEN RESOURCES

Open access as a new model of scientific information dissemination affects the changing role of academic libraries in scientific research information support. The results of the data analysis collected during the 2017 online survey allows us to indicate the preferences of scientists and the difficulties they face when using open access resources, to draw conclusions about the promising areas of academic libraries work with open access resources.
I Don’t Have That Much Data! Reusing User Behavior Models for Websites from Different Domains

User behavior models see increased usage in automated evaluation and design of user interfaces (UIs). Obtaining training data for the models is costly, since it generally requires the involvement of human subjects. For interaction's subjective quality parameters, like aesthetic impressions, it is even inevitable. In our paper, we study applicability of trained user behavior models between different domains of websites. We collected subjective assessments of Aesthetics, Complexity and Orderliness from 137 human participants for more than 3000 homepages from 7 domains, and used them to train 21 artificial neural network (ANN) models. The input neurons were 32 quantitative metrics obtained via computer vision-based analysis of the homepages screenshots. Then, we tested how well each ANN model can predict subjective assessments for websites from other domains, and correlated the changes in prediction accuracies with the pairwise distances between the domains. We found that the Complexity scale was rather domain-independent, whereas "foreign-domain" models for Aesthetics and Orderliness had on average greater prediction errors for other domains, by 60% and 45%, respectively. The results of our study provide web designers and engineers with a first framework to assess the reusability and difference in prediction accuracy of the models, for more informed decisions.