Reviewerly/exHarmony huggingface dataset:
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection

Nearest-neighbor (NN) procedures are well studied and widely used in both supervised and unsupervised learning problems. In this paper we are concerned with investigating the performance of NN-based methods for anomaly detection. We first show through extensive simulations that NN methods compare favorably to some of the other state-of-the-art algorithms for anomaly detection based on a set of benchmark synthetic datasets. We further consider the performance of NN methods on real datasets, and relate it to the dimensionality of the problem. Next, we analyze the theoretical properties of NN-methods for anomaly detection by studying a more general quantity called distance-to-measure (DTM), originally developed in the literature on robust geometric and topological inference. We provide finite-sample uniform guarantees for the empirical DTM and use them to derive misclassification rates for anomalous observations under various settings. In our analysis we rely on Huber's contamination model and formulate mild geometric regularity assumptions on the underlying distribution of the data.
The Impact of the Gricean Maxims of Quality, Quantity and Manner in Chatbots

Pragmatics between artificial conversational partners in online discussions remain to this day an under-developed field of research despite the rapidly increasing demand for artificial conversational partners. In this study, we evaluate the contribution of violations of conversational pragmatics, in particular, the Gricean Maxims of Quality (truthfulness), of Quantity (no over-describing) and Manner (no ambiguity), on the response times of participants in a protocol inspired by the Turing Test. As previous studies have also shown the influence of gender on the response times in online textual conversations, we evaluate the influence of this factor. We expected violations of the different maxims to increase the response times in two of our conditions: when producing violations of the maxim of Quality and of the maxim of Quantity. We also expected this effect to be more pronounced for males than for females since the literature has shown that females usually have better language skills than males. Our results are coherent with these expectations as only violations of the maxims of Quality and of Quantity had a significant effect, and for the latter only for male participants. Violations of the maxim of Quality also had a substantial effect on the humanness of the conversational partner.
Open Research Knowledge Graph: A System Walkthrough

Despite improved digital access to scholarly literature in the last decades, the fundamental principles of scholarly communication remain unchanged and continue to be largely document-based. Scholarly knowledge remains locked in representations that are inadequate for machine processing. The Open Research Knowledge Graph (ORKG) is an infrastructure for representing, curating and exploring scholarly knowledge in a machine actionable manner. We demonstrate the core functionality of ORKG for representing research contributions published in scholarly articles. A video of the demonstration [7] and the system ( https://labs.tib.eu/orkg/ ) are available online.
Lexicon-Grammar based open information extraction from natural language sentences in Italian

In the last decade, the quantity of readily accessible text has grown rapidly and enormously, long exceeding the capacity of humans to read and understand it. One of the most interesting strategies proposed to fulfill this need is known as Open Information Extraction (OIE). It is essentially devised to read in sentences and rapidly extract one or more domain-independent coherent propositions, each represented by a verb relation and its arguments. Even though many OIE approaches exist for English, no significant research has been conducted about OIE on Italian texts. Due to the usage of language-specific features, OIE systems operating in other languages are not directly applicable for Italian. Therefore, this paper proposes, as first contribution, a novel approach to perform OIE for Italian language, based on standard linguistic structures to analyze sentences and on a set of verbal behavior patterns to extract information from them. These patterns are built combining a solid linguistic theoretical framework, i.e. Lexicon-Grammar (LG), and distributional profiles extracted from a contemporary Italian corpus, i.e. itWaC. Starting from simple sentences, the approach is able to determine elementary tuples, then, all their permutations, by adding complements and adverbials, and, finally, n-ary propositions, by granting syntactic invariance, preserving the overall grammaticality and also respecting some syntactic constraints and selection preferences, thus approximating a first level of semantic acceptability. As second contribution of this work, a gold standard dataset for the Italian language has been built from the itWaC corpus, aimed at being widely used to enable the experimental validation of OIE solutions. It has been manually and independently labeled by four Italian native speakers with all the n-ary propositions that can be extracted, following the criteria of grammaticality and acceptability, i.e. granting syntactic well-formedness and meaningfulness in the context. Finally, the proposed approach has been experimented and quantitatively validated on this gold standard dataset, also in comparison with an indirect approach translating input sentences and output propositions from Italian to English and vice versa and embedding an OIE approach for English, as well as with an OIE system for Italian previously presented by the authors. The results obtained have shown the effectiveness of the proposed approach in generating propositions with respect to these criteria of grammaticality and acceptability. Even if the approach has been evaluated for the Italian language, it is essentially based on linguistic resources produced by LG, which exist for many languages besides Italian and a representative corpus for the language under consideration. Given these premises, it has a general basis from a methodological perspective and can be proficiently extended also to other languages.
Controllable data synthesis method for grammatical error correction

Due to the lack of parallel data in current grammatical error correction (GEC) task, models based on sequence to sequence framework cannot be adequately trained to obtain higher performance. We propose two data synthesis methods which can control the error rate and the ratio of error types on synthetic data. The first approach is to corrupt each word in the monolingual corpus with a fixed probability, including replacement, insertion and deletion. Another approach is to train error generation models and further filtering the decoding results of the models. The experiments on different synthetic data show that the error rate is 40% and that the ratio of error types is the same can improve the model performance better. Finally, we synthesize about 100 million data and achieve comparable performance as the state of the art, which uses twice as much data as we use.
Region-based image registration for remote sensing imagery

We propose an automatic region-based registration method for remote sensing imagery. In this method, we aim to register two images by matching region properties to address possible errors caused by local feature estimators. We apply automated image segmentation to identify the regions and calculate regional Fourier descriptors and standardized regional intensity descriptors for each region. We define a joint matching cost, as a linear combination of Euclidean distances, to establish and extract correspondences between regions. The segmentation technique utilizes kernel density estimators for edge localization, followed by morphological reconstruction and the watershed transform. We evaluated the registration performance of our method on synthetic and real datasets. We measured the registration accuracy by calculating the root-mean-squared error (RMSE) between the estimated transformation and the ground truth transformation. The results obtained using the joint intensity-Fourier descriptor were compared to the results obtained using Harris, Minimum eigenvalue, Features accelerated segment test (FAST), speeded-up robust features (SURF), binary robust invariant scalable keypoints (BRISK) and KAZE keypoint descriptors. The joint intensity-Fourier descriptor yielded average RMSE of 0.446±0.359 pixels and 1.152±0.488 pixels on two satellite imagery datasets consisting of 35 image pairs in total. These results indicate the capacity of the proposed technique for high accuracy. Our method also produces a lower registration error than the compared feature-based methods.
Photonic scheme of quantum phase estimation for quantum algorithms via cross-Kerr nonlinearities under decoherence effect

Quantum phase estimation (QPE) is the key procedure in various quantum algorithms. The main aim of the QPE scheme is to estimate the phase of an unknown eigenvalue, corresponding to an eigenstate of an arbitrary unitary operation. The QPE scheme can be applied as a subroutine to design many quantum algorithms. In this paper, we propose the basic structure of a QPE scheme that could be applied in quantum algorithms, with feasibility by utilizing cross-Kerr nonlinearities (controlled-unitary gates) and linearly optical devices. Subsequently, we analyze the efficiency and the performance of the controlled-unitary gate. This gate consists of a controlled-path gate and a merging-path gate via cross-Kerr nonlinearities under the decoherence effect. Also shown in this paper is a method by which to enhance robustness against the decoherence effect to provide a reliable QPE scheme based on controlled-unitary gates.
Thoughtfully Using Artificial Intelligence in Earth Science

Deriving scientific insights from artificial intelligence methods requires adhering to best practices and moving beyond off-the-shelf approaches.
Uncertainty-based modulation for lifelong learning

The creation of machine learning algorithms for intelligent agents capable of continuous, lifelong learning is a critical objective for algorithms being deployed on real-life systems in dynamic environments. Here we present an algorithm inspired by neuromodulatory mechanisms in the human brain that integrates and expands upon Stephen Grossberg's ground-breaking Adaptive Resonance Theory proposals. Specifically, it builds on the concept of uncertainty, and employs a series of "neuromodulatory" mechanisms to enable continuous learning, including self-supervised and one-shot learning. Algorithm components were evaluated in a series of benchmark experiments that demonstrate stable learning without catastrophic forgetting. We also demonstrate the critical role of developing these systems in a closed-loop manner where the environment and the agent's behaviors constrain and guide the learning process. To this end, we integrated the algorithm into an embodied simulated drone agent. The experiments show that the algorithm is capable of continuous learning of new tasks and under changed conditions with high classification accuracy (>94%) in a virtual environment, without catastrophic forgetting. The algorithm accepts high dimensional inputs from any state-of-the-art detection and feature extraction algorithms, making it a flexible addition to existing systems. We also describe future development efforts focused on imbuing the algorithm with mechanisms to seek out new knowledge as well as employ a broader range of neuromodulatory processes.
SQL Injection Detection Based on Deep Belief Network

At present, the ways for detecting SQL injection attacks include pre-compilation of SQL statements, filtering user input at the WEB layer to prevent SQL injection, such as filtering global parameters with Filter, strictly restricting the operation authority of the database, and trying to satisfy all. The lowest permissions for the operation, etc. The detection method for the SQL injection attack is mainly to analyze the incoming parameters to determine whether an illegal parameter is passed in. However, due to the poor real-time performance of the analyzed traffic content and the accuracy, the false positive rate is not ideal. Therefore, this paper proposes a deep learning-based approach to find SQL injection aggression. It does not need to analyze and extract all the content. It only needs to find out the features needed by the model. Entering these features into the model that is trained in advance can detect SQL injection attacks traffic in real time. This paper use deep learning to identify SQL injection attacks in network traffic. We select the target features according to the attack characteristics of the SQL injection attack and get request from url or post packet as train data; use the deep belief network (DBN) model to train the selected features and the collected sample data, and finally get an identifiable SQL Injection attack model. Finally find a best model for Detecting SQL injection, and achieve online and real-time detection.
What I See Is What You See

In recent years, more and more videos are captured from the first-person viewpoint by wearable cameras. Such first-person video provides additional information besides the traditional third-person video, and thus has a wide range of applications. However, techniques for analyzing the first-person video can be fundamentally different from those for the third-person video, and it is even more difficult to explore the shared information from both viewpoints. In this paper, we propose a novel method for first- and third-person video co-analysis. At the core of our method is the notion of "joint attention'', indicating the learnable representation that corresponds to the shared attention regions in different viewpoints and thus links the two viewpoints. To this end, we develop a multi-branch deep network with a triplet loss to extract the joint attention from the first- and third-person videos via self-supervised learning. We evaluate our method on the public dataset with cross-viewpoint video matching tasks. Our method outperforms the state-of-the-art both qualitatively and quantitatively. We also demonstrate how the learned joint attention can benefit various applications through a set of additional experiments.
Randomized Projection Methods for Linear Systems with Arbitrarily Large Sparse Corruptions

In applications like medical imaging, error correction, and sensor networks, one needs to solve large-scale linear systems that may be corrupted by a small number of arbitrarily large corruptions. We consider solving such large-scale systems of linear equations $A\mathbf{x}=\mathbf{b}$ that are inconsistent due to corruptions in the measurement vector $\mathbf{b}$. With this as our motivating example, we develop an approach for this setting that allows detection of the corrupted entries and thus convergence to the "true" solution of the original system. We provide analytical justification for our approaches as well as experimental evidence on real and synthetic systems.
Classification of Benign and Malignant Breast Cancer using Supervised Machine Learning Algorithms Based on Image and Numeric Datasets

Abstract Breast cancer has been identified as the second leading cause of death among women worldwide after lung cancer and hence, it becomes extremely crucial to identify it at an early stage, which can considerably increase the chances of survival. The most important part in cancer detection is to be able to differentiate between benign and malignant tumors and this is where the work of Machine Learning comes in. Taking all the dependent features upon consideration, Supervised Machine Learning methods allow for classification with higher degree of accuracy and improve upon the misdiagnosis of the physicians, which might occur almost 20% of the time. In our paper, we are focusing towards understanding the shortcomings of digital mammograms in detection of breast cancer and utilize Machine Learning classifiers for the classification of benign and malignant tumors using image analysis. Apart from this, we are also looking into implementing Supervised Machine Learning classifiers such as Decision Tree, K Nearest Neighbour (KNN), Random Forest and Gaussian Naive Bayes classifiers for assessing the risks involved with breast cancer by analyzing the biomarkers that are involved with it. Our aim is to provide a comprehensive view on prediction of breast cancer through Machine Learning through both image and data analyses, which can play a pivotal role in prevention of misdiagnosis in future. Fig. 1. gives a layout for the breast cancer prediction using Supervised Machine learning classifiers.
Hierarchical coded matrix multiplication

Slow working nodes, known as stragglers, can greatly reduce the speed of distributed computation. Coded matrix multiplication is a recently introduced technique that enables straggler-resistant distributed multiplication of large matrices. A key property is that the finishing time depends only on the work completed by a set of the fastest workers, while the work done by the slowest workers is ignored completely. This paper is motivated by the observation that in real-world commercial cloud computing systems such as Amazon's Elastic Compute Cloud (EC2) the distinction between fast and slow nodes is often a soft one. Thus, if we could also exploit the work completed by stragglers we may realize substantial performance gains. To realize such gains, in this paper we use the idea of hierarchical coding (Ferdinand and Draper, IEEE Int. Symp. Inf. Theory, 2018). We decompose the overall matrix multiplication task into a hierarchy of heterogeneously sized subtasks. The duty to complete each subtask is shared amongst all workers and each subtask is (generally) of a different complexity. The motivation for the hierarchical decomposition is the recognition that more workers will finish the first subtask than the second (or third, forth, etc.). Connecting to error correction coding, earlier subtasks can therefore be designed to be of a higher rate than later subtasks. Through this hierarchical design our scheme exploits the work completed by stragglers, rather than ignoring it, even if that amount is much less than that completed by the fastest workers. We numerically show that our method realizes a 60% improvement in the expected finishing time for a widely studied statistical model of the speed of computation and, on Amazon EC2, the gain is 35%.
Fast and Space Efficient Spectral Sparsification in Dynamic Streams

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Fast and Space Efficient Spectral Sparsification in Dynamic StreamsMichael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri, Aaron Sidford, and Jakab TardosMichael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri, Aaron Sidford, and Jakab Tardospp.1814 - 1833Chapter DOI:https://doi.org/10.1137/1.9781611975994.111PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In this paper, we resolve the complexity problem of spectral graph sparcification in dynamic streams up to polylogarithmic factors. Using a linear sketch we design a streaming algorithm that uses Õ(n) space, and with high probability, recovers a spectral sparsifier from the sketch in Õ(n) time.1 Prior results either achieved near optimal Õ(n) space, but Ω(n2) recovery time [Kapralov et al. ‘14], or ran in o(n2) time, but used polynomially suboptimal space [Ahn et al ‘13]. Our main technical contribution is a novel method for recovering graph edges with high effective resistance from a linear sketch. We show how to do so in nearly linear time by ‘bucketing’ vertices of the input graph into clusters using a coarse approximation to the graph's effective resistance metric. A second main contribution is a new pseudorandom generator (PRG) for linear sketching algorithms. Constructed from a locally computable randomness extractor, our PRG stretches a seed of Õ(n) random bits polynomially in length with just logO(1) n run-time cost per evaluation. This improves on Nisan's commonly used PRG, which in our setting would require Õ(n) time per evaluation. Our faster PRG is essential to simultaneously achieving near optimal space and time complexity. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Audio Splicing Detection using Convolutional Neural Network

In an audio forensics scenario includes audio authentication in which major investigation topic is audio tampering detection. In this paper, we present a novel method of splicing detection using a convolutional neural network. As high-level features of audio are effectively estimated by convolutional neural network, the frequency spectrogram of audio is directly fed as an input to the convolutional neural network. The proposed work uses 10-13 sec of an anechoic audio signal of an audio slice of 1sec, 2sec, and 3sec insertion at the middle of the audio. Results show that the insertion of 3 sec part gives better accuracy than the other two parts. Whereas slice_1 part insertion gives 82.80% accuracy, slice_2 part gives 87.54% accuracy, and slice_3 part gives 96.67% accuracy. Also, the proposed method is robust to the audio compression as well as to the Additive White Gaussian Noise.
Formulating Ensemble Learning of SVMs Into a Single SVM Formulation by Negative Agreement Learning

When a fixed number of support vector machines (SVMs) are taken as the base learners, an attempt to diversify them should be encouraged to achieve a satisfactory ensemble. In this article, by means of a negative agreement learning (NAL) strategy, a new SVM-based ensemble framework is proposed to simultaneously enhance the diversity of SVMs in the ensemble and suppress the training error of the ensemble. The proposed ensemble framework is theoretically derived to have distinctive merits: 1) the ensemble and each of its individual SVM base learner are trained in a joint manner rather than in an independent manner and 2) the NAL strategy facilitates the formulation of the ensemble of SVMs as one single SVM; thus, abundant advances in the training of SVM can be conveniently applied to the proposed ensemble learning of SVMs and there is no need to design special optimization techniques for the involved ensemble learning. Extensive experimental studies demonstrate the effectiveness of the proposed ensemble framework of SVMs.
Learning from Explanations with Neural Execution Tree

While deep neural networks have achieved impressive performance on a range of NLP tasks, these data-hungry models heavily rely on labeled data, which restricts their applications in scenarios where data annotation is expensive. Natural language (NL) explanations have been demonstrated very useful additional supervision, which can provide sufficient domain knowledge for generating more labeled data over new instances, while the annotation time only doubles. However, directly applying them for augmenting model learning encounters two challenges: (1) NL explanations are unstructured and inherently compositional, which asks for a modularized model to represent their semantics, (2) NL explanations often have large numbers of linguistic variants, resulting in low recall and limited generalization ability. In this paper, we propose a novel Neural Execution Tree (NExT) framework to augment training data for text classification using NL explanations. After transforming NL explanations into executable logical forms by semantic parsing, NExT generalizes different types of actions specified by the logical forms for labeling data instances, which substantially increases the coverage of each NL explanation. Experiments on two NLP tasks (relation extraction and sentiment analysis) demonstrate its superiority over baseline methods. Its extension to multi-hop question answering achieves performance gain with light annotation effort.