Reviewerly/exHarmony huggingface dataset:
Blind Digital Watermarking Algorithm against Projection Transformation for Vector Geographic Data

Projection transformation is an important part of geographic analysis in geographic information systems, which are particularly common for vector geographic data. However, achieving resistance to projection transformation attacks on watermarking for vector geographic data is still a challenging task. We proposed a digital watermarking against projection transformation based on feature invariants for vector geographic data in this paper. Firstly, the features of projection transformation are analyzed, and the number of vertices, the storage order, and the storage direction of two adjacent objects are designed and used as the feature invariant to projection transformation. Then, the watermark index is calculated by the number of vertices of two adjacent objects, and the embedding rule is determined by the storage direction of two adjacent objects. Finally, the proposed scheme performs blind detection through the storage direction of adjacent features. Experimental results demonstrate that the method can effectively resist arbitrary projection transformation, which indicates the superior performance of the proposed method in comparison to the previous methods.
Learning Pairwise Inter-plane Relations for Piecewise Planar Reconstruction

This paper proposes a novel single-image piecewise planar reconstruction technique that infers and enforces inter-plane relationships. Our approach takes a planar reconstruction result from an existing system, then utilizes convolutional neural network (CNN) to (1) classify if two planes are orthogonal or parallel; and 2) infer if two planes are touching and, if so, where in the image. We formulate an optimization problem to refine plane parameters and employ a message passing neural network to refine plane segmentation masks by enforcing the inter-plane relations. Our qualitative and quantitative evaluations demonstrate the effectiveness of the proposed approach in terms of plane parameters and segmentation accuracy.
AnswerFact: Fact Checking in Product Question Answering

Product-related question answering platforms nowadays are widely employed in many Ecommerce sites, providing a convenient way for potential customers to address their concerns during online shopping.However, the misinformation in the answers on those platforms poses unprecedented challenges for users to obtain reliable and truthful product information, which may even cause a commercial loss in E-commerce business.To tackle this issue, we investigate to predict the veracity of answers in this paper and introduce Answer-Fact, a large scale fact checking dataset from product question answering forums.Each answer is accompanied by its veracity label and associated evidence sentences, providing a valuable testbed for evidence-based fact checking tasks in QA settings.We further propose a novel neural model with tailored evidence ranking components to handle the concerned answer veracity prediction problem.Extensive experiments are conducted with our proposed model and various existing fact checking methods, showing that our method outperforms all baselines on this task.
A classification of permutation polynomials of degree 7 over finite fields

Up to linear transformations, a classification of all permutation polynomials (PPs) of degree 7 over Fq is obtained for any odd prime power q>7. Since all exceptional polynomials of degree 7 are precisely known up to linear transformations, it suffices to work on non-exceptional PPs of degree 7, which exist only when q⩽409 by our previous result. This can be exhausted by the SageMath software running on a personal computer. To facilitate the computation, some requirements after linear transformations and equations by Hermite's criterion are provided for the polynomial coefficients. The main result is that a non-exceptional PP f of degree 7 exists over a finite field Fq of odd order q>7 if and only if q∈{9,11,13,17,19,23,25,27,31,49}, and f is explicitly listed up to linear transformations.
Speculative Backpropagation for CNN Parallel Training

The parallel learning in neural networks can greatly shorten the training time. Its prior efforts were mostly limited to distributing inputs to multiple computing engines. It is because the gradient descent algorithm in the neural network training is inherently sequential. This paper proposes a novel CNN parallel training method for image recognition. It overcomes the sequential property of the gradient descent and enables the parallel training with the speculative backpropagation. We found that the Softmax and ReLU outcomes in the forward propagation for the same labels are likely to be very similar. This characteristic makes it possible to perform the forward and backward propagation simultaneously. We implemented the proposed parallel model with CNNs in both software and hardware, and evaluated its performance. The parallel training reduces the training time by 34% in CIFAR-100 without the loss of the prediction accuracy compared to the sequential training. In many cases, it even improves the accuracy.
Spatial relation learning for explainable image classification and annotation in critical applications

With the recent successes of black-box models in Artificial Intelligence (AI) and the growing interactions between humans and AIs, explainability issues have risen. In this article, in the context of high-stake applications, we propose an approach for explainable classification and annotation of images. It is based on a transparent model, whose reasoning is accessible and human understandable, and on interpretable fuzzy relations that enable to express the vagueness of natural language. The knowledge about relations is set beforehand by an expert and thus training instances do not need to be annotated. The most relevant relations are extracted using a fuzzy frequent itemset mining algorithm in order to build rules, for classification, and constraints, for annotation. We also present two heuristics that make the process of evaluating relations faster. Since the strengths of our approach are the transparency of the model and the interpretability of the relations, an explanation in natural language can be generated. Supported by experimental results, we show that, given a segmentation of the input, our approach is able to successfully perform the target task and generate explanations that were judged as consistent and convincing by a set of participants.
A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment

Cross-lingual entity alignment, which aims to match equivalent entities in KGs with different languages, has attracted considerable focus in recent years.Recently, many graph neural network (GNN) based methods are proposed for entity alignment and obtain promising results.However, existing GNN-based methods consider the two KGs independently and learn embeddings for different KGs separately, which ignore the useful pre-aligned links between two KGs.In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments.We conduct extensive experiments on three benchmark cross-lingual entity alignment datasets.The experimental results demonstrate that our proposed method obtains remarkable performance gains compared to state-of-the-art methods.
WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization

In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents based on the given query.However, one major challenge for this task is the lack of availability of labeled training datasets.To overcome this issue, in this paper, we propose a novel weakly supervised learning approach via utilizing distant supervision.In particular, we use datasets similar to the target dataset as the training data where we leverage pre-trained sentence similarity models to generate the weak reference summary of each individual document in a document set from the multidocument gold reference summaries.Then, we iteratively train our summarization model on each single-document to alleviate the computational complexity issue that occurs while training neural summarization models in multiple documents (i.e., long sequences) at once.Experimental results on the Document Understanding Conferences (DUC) datasets show that our proposed approach sets a new state-of-the-art result in terms of various evaluation metrics.
Path Planning Based on Improved Hybrid A<sup>*</sup> Algorithm

Hybrid A * algorithm has been widely used in mobile robots to obtain paths that are collision-free and drivable. However, the outputs of hybrid A * algorithm always contain unnecessary steering actions and are close to the obstacles. In this paper, the artificial potential field (APF) concept is applied to optimize the paths generated by the hybrid A * algorithm. The generated path not only satisfies the non-holonomic constraints of the vehicle, but also is smooth and keeps a comfortable distance to the obstacle at the same time. Through the robot operating system (ROS) platform, the path planning experiments are carried out based on the hybrid A * algorithm and the improved hybrid A * algorithm, respectively. In the experiments, the results show that the improved hybrid A * algorithm greatly reduces the number of steering actions and the maximum curvature of the paths in many different common scenarios. The paths generated by the improved algorithm nearly do not have unnecessary steering or sharp turning before the obstacles, which are safer and smoother than the paths generated by the hybrid A * algorithm for the autonomous ground vehicle.
Weighted Error Entropy-Based Information Theoretic Learning for Robust Subspace Representation

In most of the existing representation learning frameworks, the noise contaminating the data points is often assumed to be independent and identically distributed ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.i.d.</i> ), where the Gaussian distribution is often imposed. This assumption, though greatly simplifies the resulting representation problems, may not hold in many practical scenarios. For example, the noise in face representation is usually attributable to local variation, random occlusion, and unconstrained illumination, which is essentially structural, and hence, does not satisfy the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.i.d.</i> property or the Gaussianity. In this article, we devise a generic noise model, referred to as independent and piecewise identically distributed ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.p.i.d.</i> ) model for robust presentation learning, where the statistical behavior of the underlying noise is characterized using a union of distributions. We demonstrate that our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.p.i.d.</i> model can better describe the complex noise encountered in practical scenarios and accommodate the traditional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.i.d.</i> one as a special case. Assisted by the proposed noise model, we then develop a new information-theoretic learning framework for robust subspace representation through a novel minimum weighted error entropy criterion. Thanks to the superior modeling capability of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.p.i.d.</i> model, our proposed learning method achieves superior robustness against various types of noise. When applying our scheme to the subspace clustering and image recognition problems, we observe significant performance gains over the existing approaches.
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving

In learning-assisted theorem proving, one of the most critical challenges is to generalize to theorems unlike those seen at training time. In this paper, we introduce INT, an INequality Theorem proving benchmark designed to test agents’ generalization ability. INT is based on a theorem generator, which provides theoretically infinite data and allows us to measure 6 different types of generalization, each reflecting a distinct challenge, characteristic of automated theorem proving. In addition, provides a fast theorem proving environment with sequence-based and graph-based interfaces, conducive to performing learning-based research. We introduce base-lines with architectures including transformers and graph neural networks (GNNs)for INT. Using INT, we find that transformer-based agents achieve stronger test performance for most of the generalization tasks, despite having much larger out-of-distribution generalization gaps than GNNs. We further find that the addition of Monte Carlo Tree Search (MCTS) at test time helps to prove new theorems.
Integrated Geochemical Assessment of Soils and Stream Sediments to Evaluate Source-Sink Relationships and Background Variations in the Parauapebas River Basin, Eastern Amazon

This study aims to handle an integrated evaluation of soil and stream sediment geochemical data to evaluate source apportionment and to establish geochemical threshold variations for Fe, Al, and 20 selected Potentially Toxic Elements (PTE) in the Parauapebas River Basin (PB), Eastern Amazon. The data set used in this study is from the Itacaiúnas Geochemical Mapping and Background Project (ItacGMBP), which collected 364 surface soil (0–10 cm) samples and 189 stream sediments samples in the entire PB. The &lt;0.177 mm fraction of these samples were analyzed for 51 elements by ICP-MS and ICP-AES, following an aqua regia digestion. The geochemical maps of many elements revealed substantial differences between the north (NPB) and the south (SPB) of PB, mainly due to the geological setting. The new statistically derived threshold values of the NPB and SPB regions were compared to the threshold of the whole PB, reported in previous studies, and to quality guidelines proposed by Brazilian environmental agencies. The natural variation of geochemical background in soils and stream sediments of PB should be considered prior to defining new guideline values. At the regional scale, the local anomalies are mostly influenced by the predominant lithology rather than any anthropogenic impact.
Utilization of multispectral landsat-8 remote sensing data for lithological mapping of southwestern Saudi Arabia

The upper plutonic rocks in southwestern Saudi Arabia has been mapped through Landsat 8 Operational Land Imager (OLI) data. The gathered field rock samples were subjected to spectral measurements (in the range of 0.4–2.5 µm) in the laboratory using a GER 3700 spectroradiometer. Integrated remote sensing techniques, including the band ratio, principal component analysis (PCA), and Gram-Schmidt spectral sharpening have been applied through this study. The red–greenblue composites of the pan-sharpened original bands (3, 4, and 7), principal components (PC3, PC1, and PC2), and band ratios 5/6 and 5/7 were found to be the most useful to delineate the study area’s Upper Proterozoic plutonic rocks. The results of image processing methods, spectral reflectance analyses are in good agreement with the field data. The spectral reflectance curves of the collected samples and the corresponding Landsat 8 pixels match well. It is indicated that Landsat-8 image data has great capability to identify lithological units and could be applicable in several areas in arid and semiarid environments.
Smart contract service migration mechanism based on container in edge computing

In edge computing, smart contracts usually run in the form of containers on edge nodes. However, the container has process nesting and strong association with image files, which leads to insufficient real-time migration. This article studies the container-based service migration problem in edge computing. In order to reduce the service delay of nodes, we propose a service migration mechanism based on mobility awareness. The service migration mechanism based on mobility awareness triggers service migration according to the service density of the current node, and selects the most suitable service set for migration in the current node with the optimization goal of minimizing service delay, and finally the corresponding destination node is selected for migration according to the migration cost and the moving direction of the device to which the service belongs. Experimental results show that our proposed service migration mechanism based on container can effectively reduce the waiting delay and migration delay in the service process, and optimize the service quality of edge nodes.
Symmetry-Induced Error Filtering in a Photonic Lieb Lattice

Quantum computation promises intrinsically parallel information processing capacity by harnessing the superposition and entanglement of quantum states. However, it is still challenging to realize universal quantum computation due that the reliability and scalability are limited by unavoidable noises on qubits. Nontrivial topological properties like quantum Hall phases are found capable of offering protection, but require stringent conditions of topological band gaps and broken time-reversal symmetry. Here, we propose and experimentally demonstrate a symmetry-induced error filtering scheme, showing a more general role of geometry in protection mechanism and applications. We encode qubits in a superposition of two spatial modes on a photonic Lieb lattice. The geometric symmetry endows the system with topological properties featuring a flat band touching, leading to distinctive transmission behaviors of $\ensuremath{\pi}$-phase qubits and 0-phase qubits. The geometry exhibits a significant effect on filtering phase errors, which also enables it to monitor phase deviations in real time. The symmetry-induced error filtering can be a key element for encoding and protecting quantum states, suggesting an emerging field of symmetry-protected universal quantum computation and noisy intermediate-scale quantum technologies.
Software Engineering Applications Enabled by Blockchain Technology: A Systematic Mapping Study

The novel, yet disruptive blockchain technology has witnessed growing attention, due to its intrinsic potential. Besides the conventional domains that benefit from such potential, such as finance, supply chain and healthcare, blockchain use cases in software engineering have emerged recently. In this study, we aim to contribute to the body of knowledge of blockchain-oriented software engineering by providing an adequate overview of the software engineering applications enabled by blockchain technology. To do so, we carried out a systematic mapping study and identified 22 primary studies. Then, we extracted data within the research type, research topic and contribution type facets. Findings suggest an increasing trend of studies since 2018. Additionally, findings reveal the potential of using blockchain technologies as an alternative to centralized systems, such as GitHub, Travis CI, and cloud-based package managers, and also to establish trust between parties in collaborative software development. We also found out that smart contracts can enable the automation of a variety of software engineering activities that usually require human reasoning, such as the acceptance phase, payments to software engineers, and compliance adherence. In spite of the fact that the field is not yet mature, we believe that this systematic mapping study provides a holistic overview that may benefit researchers interested in bringing blockchain to the software industry, and practitioners willing to understand how blockchain can transform the software development industry.
Phenotyping coronavirus disease 2019 during a global health pandemic: Lessons learned from the characterization of an early cohort

From the start of the coronavirus disease 2019 (COVID-19) pandemic, researchers have looked to electronic health record (EHR) data as a way to study possible risk factors and outcomes. To ensure the validity and accuracy of research using these data, investigators need to be confident that the phenotypes they construct are reliable and accurate, reflecting the healthcare settings from which they are ascertained. We developed a COVID-19 registry at a single academic medical center and used data from March 1 to June 5, 2020 to assess differences in population-level characteristics in pandemic and non-pandemic years respectively. Median EHR length, previously shown to impact phenotype performance in type 2 diabetes, was significantly shorter in the SARS-CoV-2 positive group relative to a 2019 influenza tested group (median 3.1 years vs 8.7; Wilcoxon rank sum P = 1.3e-52). Using three phenotyping methods of increasing complexity (billing codes alone and domain-specific algorithms provided by an EHR vendor and clinical experts), common medical comorbidities were abstracted from COVID-19 EHRs, defined by the presence of a positive laboratory test (positive predictive value 100%, recall 93%). After combining performance data across phenotyping methods, we observed significantly lower false negative rates for those records billed for a comprehensive care visit (p = 4e-11) and those with complete demographics data recorded (p = 7e-5). In an early COVID-19 cohort, we found that phenotyping performance of nine common comorbidities was influenced by median EHR length, consistent with previous studies, as well as by data density, which can be measured using portable metrics including CPT codes. Here we present those challenges and potential solutions to creating deeply phenotyped, acute COVID-19 cohorts.
Unsupervised Monocular Depth Estimation for Colonoscope System Using Feedback Network

A colonoscopy is a medical examination used to check disease or abnormalities in the large intestine. If necessary, polyps or adenomas would be removed through the scope during a colonoscopy. Colorectal cancer can be prevented through this. However, the polyp detection rate differs depending on the condition and skill level of the endoscopist. Even some endoscopists have a 90% chance of missing an adenoma. Artificial intelligence and robot technologies for colonoscopy are being studied to compensate for these problems. In this study, we propose a self-supervised monocular depth estimation using spatiotemporal consistency in the colon environment. It is our contribution to propose a loss function for reconstruction errors between adjacent predicted depths and a depth feedback network that uses predicted depth information of the previous frame to predict the depth of the next frame. We performed quantitative and qualitative evaluation of our approach, and the proposed FBNet (depth FeedBack Network) outperformed state-of-the-art results for unsupervised depth estimation on the UCL datasets.
Tesseract

Tesseract is the first distributed system for executing general graph mining algorithms on evolving graphs. Tesseract scales out by decomposing a stream of graph updates into per-update mining tasks and dynamically assigning these tasks to a set of distributed workers. We present a novel approach to change detection that efficiently determines the exact modifications to the algorithm's output for each update to the input graph. We use a disaggregated, multiversioned graph store to allow workers to process updates independently, without producing duplicates. Moreover, Tesseract provides interactive mining insights for complex applications using an incremental aggregation API. Finally, we implement and evaluate Tesseract and demonstrate that it achieves orders-of-magnitude improvements over state-of-the-art systems.