Reviewerly/exHarmony huggingface dataset:
Analysis of Deep Neural Networks for Automobile Insurance Claim Prediction

Claim prediction is an important process in an automobile insurance industry to prepare the right type of insurance policy for each potential policyholder. The volume of available data to construct the model of the claim prediction is usually large. Nowadays, deep neural networks (DNN) becomes more popular in the machine learning field especially for unstructured data likes image, text, or signal. The DNN model integrates the feature selection into the model in the form of some additional hidden layers. Moreover, DNN is suitable for the large volume of data because of its incremental learning. In this paper, we apply and analyze the accuracy of DNN for the problem of claim prediction which has structured data. First, we show the sensitivity of the hyperparameters on the accuracy of DNN and compare the performance of DNN with standard neural networks. Our simulation shows that the accuracy of DNN is slightly better than the standard neural networks in term of normalized Gini.
Image-to-Image Translation to Unfold the Reality of Artworks: An Empirical Analysis

State-of-the-art Computer Vision pipelines show poor performances on artworks and data coming from the artistic domain, thus limiting the applicability of current architectures to the automatic understanding of the cultural heritage. This is mainly due to the difference in texture and low-level feature distribution between artistic and real images, on which state-of-the-art approaches are usually trained. To enhance the applicability of pre-trained architectures on artistic data, we have recently proposed an unpaired domain translation approach which can translate artworks to photo-realistic visualizations. Our approach leverages semantically-aware memory banks of real patches, which are used to drive the generation of the translated image while improving its realism. In this paper, we provide additional analyses and experimental results which demonstrate the effectiveness of our approach. In particular, we evaluate the quality of generated results in the case of the translation of landscapes, portraits and of paintings coming from four different styles using automatic distance metrics. Also, we analyze the response of pre-trained architecture for classification, detection and segmentation both in terms of feature distribution and entropy of prediction, and show that our approach effectively reduces the domain shift of paintings. As an additional contribution, we also provide a qualitative analysis of the reduction of the domain shift for detection, segmentation and image captioning.
Adversarial point perturbations on 3D objects

The importance of training robust neural network grows as 3D data is increasingly utilized in deep learning for vision tasks, like autonomous driving. We examine this problem from the perspective of the attacker, which is necessary in understanding how neural networks can be exploited, and thus defended. More specifically, we propose adversarial attacks based on solving different optimization problems, like minimizing the perceptibility of our generated adversarial examples, or maintaining a uniform density distribution of points across the adversarial object surfaces. Our four proposed algorithms for attacking 3D point cloud classification are all highly successful on existing neural networks, and we find that some of them are even effective against previously proposed point removal defenses.
Image Structure Subspace Learning Using Structural Similarity Index

Literature has shown that Mean Squared Error is not a promising measure for image fidelity and similarity assessment, and Structural Similarity Index (SSIM) can properly handle this aspect. The existing subspace learning methods in machine learning are based on Euclidean distance or MSE and thus cannot properly capture the structural features of images. In this paper, we define image structure subspace which captures the intrinsic structural features of an image and discriminates the different types of image distortions. Therefore, this paper provides a bridge between image processing and manifold learning opening future research opportunities. In order to learn this subspace, we propose SSIM kernel as a new kernel which can be used in kernel-based machine learning methods.
SAP: Standard Arabic Profiling Toolset for Textual Analysis

This paper defines a Standard Arabic Profiling (SAP) toolset that helps researchers for textual analysis and comparing between different Arabic corpora.Since tools for Arabic language are needed, we present the SAP toolset to simplify the textual analysis process.The approach consists of three profilers: The Part of Speech (POS) profiler that gives statistical analysis for a given document, vocabulary profiler which provides user with an indication out the vocabulary used in a document with reference to Open Source Arabic Corpus (OSAC) of two news agencies (CNN and BBC).The process is accomplished by computing similarity between documents and corpus using Log likelihood measure.Lastly the newly added profiler is the Readability profiler which is used to 1) assess the readability level for a document according to Flesch Reading Ease Readability Formula, and 2) measure the simplicity and ambiguity levels of the document.We described the current part-of-speech for this toolset and how we can extend its functionality to embrace vocabulary and readability profiling.
Upper bounds on the multiplicative complexity of symmetric Boolean functions

A special metric of interest about Boolean functions is multiplicative complexity (MC): the minimum number of AND gates sufficient to implement a function with a Boolean circuit over the basis {XOR, AND, NOT}. In this paper we study the MC of symmetric Boolean functions, whose output is invariant upon reordering of the input variables. Based on the Hamming weight method from Muller and Preparata (J. ACM 22(2), 195–201, 1975), we introduce new techniques that yield circuits with fewer AND gates than upper bounded by Boyar et al. (Theor. Comput. Sci. 235(1), 43–57, 2000) and by Boyar and Peralta (Theor. Comput. Sci. 396(1–3), 223–246, 2008). We generate circuits for all such functions with up to 25 variables. As a special focus, we report concrete upper bounds for the MC of elementary symmetric functions ${{\Sigma }^{n}_{k}}$ and counting functions ${E^{n}_{k}}$ with up to n = 25 input variables. In particular, this allows us to answer two questions posed in 2008: both the elementary symmetric ${{\Sigma }^{8}_{4}}$ and the counting ${E^{8}_{4}}$ functions have MC 6. Furthermore, we show upper bounds for the maximum MC in the class of n-variable symmetric Boolean functions, for each n up to 132.
Graph Coloring Using GPUs

Graph coloring is a widely studied problem that is used in a variety of applications, such as task scheduling, register allocation, eigenvalue computations, social network analysis, and so on. Many of the modern day applications deal with large graphs (with millions of vertices and edges) and researchers have exploited the parallelism provided by multi-core systems to efficiently color such large graphs. GPUs provide a promising parallel infrastructure to run large applications. In this paper, we present new schemes to efficiently color large graphs on GPUs. We extend the algorithm of Rokos et al. [21] to efficiently color graphs using GPUs. Their approach has to continually resolve conflicts for color assignment. We present a data driven variation of their algorithm and use an improved scheme for conflict resolution. We also propose two optimizations for our algorithm to reduce both the execution time and memory requirements. We have evaluated our scheme (called SIRG) against the NVIDIA cuSPARSE library and the work of Chen et al. [13], and show that SIRG runs significantly faster: geomean 3.42 $$\times $$ and 1.76 $$\times $$ , respectively. We have also compared SIRG against the scheme of Rokos et al. [21] for CPUs and show that SIRG performs faster on most input graphs: geomean 10.37 $$\times $$ .
Word Similarity Computing Based on HowNet and Synonymy Thesaurus

Based on HowNet, some definitions of entity similarity are unreasonable. The synonymy and similar relationships of each entity are analyzed in great detail in Extended Synonym. But Extended Synonym includes only entity words, not other types of words. In view of the deficiencies of the two dictionaries, a word similarity method based on HowNet and Extended Synonym is proposed. Firstly, a synonym expansion figure is established through the existing tree figure in Extended Synonym. Then based on the two types of figures and six types of words, the entity similarity methods in five different cases are established in detail. The important parameters in the five methods are set based on the existing semantic parameter and large-scale experiments. For the other case that one of two words is at least not an entity, the traditional HowNet-based method is used to compute the similarity between two words. The word similarity method proposed in this paper improves the performance of short text similarity.
KU_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI

In this paper, we describe our system and results submitted for the Natural Language Inference (NLI) track of the MEDIQA 2019 Shared Task. As KU_ai team, we used BERT as our baseline model and pre-processed the MedNLI dataset to mitigate the negative impact of de-identification artifacts. Moreover, we investigated different pre-training and transfer learning approaches to improve the performance. We show that pre-training the language model on rich biomedical corpora has a significant effect in teaching the model domain-specific language. In addition, training the model on large NLI datasets such as MultiNLI and SNLI helps in learning task-specific reasoning. Finally, we ensembled our highest-performing models, and achieved 84.7% accuracy on the unseen test dataset and ranked 10th out of 17 teams in the official results.
COVIZ

We demonstrate COVIZ, an interactive system to visually form and explore patient cohorts. COVIZ seamlessly integrates visual cohort formation and exploration, making it a single destination for hypothesis generation. COVIZ is easy to use by medical experts and offers many features: (1) It provides the ability to isolate patient demographics (e.g., their age group and location), health markers (e.g., their body mass index), and treatments (e.g., Ventilation for respiratory problems), and hence facilitates cohort formation; (2) It summarizes the evolution of treatments of a cohort into health trajectories, and lets medical experts explore those trajectories; (3) It guides them in examining different facets of a cohort and generating hypotheses for future analysis; (4) Finally, it provides the ability to compare the statistics and health trajectories of multiple cohorts at once. COVIZ relies on QDS, a novel data structure that encodes and indexes various data distributions to enable their efficient retrieval. Additionally, COVIZ visualizes air quality data in the regions where patients live to help with data interpretations. We demonstrate two key scenarios, ecological scenario and case cross-over scenario . A video demonstration of COVIZ is accessible via http://bit.ly/video-coviz.
A Middle Game Search Algorithm Applicable to Low-Cost Personal Computer for Go

Go Artificial Intellects(AIs) using deep reinforcement learning and neural networks have achieved superhuman performance, but they rely on powerful computing resources.They are not applicable to low-cost personal computer(PC).In our life, most entertainment programs of Go run on the general PC.A human Go master consider different strategies for different stages, especially for the middle stage that has a significant impact on winning or losing.To study arguably a more humanlike approach that is applicable to low-cost PC while not reducing chess power, this paper proposes a new search algorithm based on hypothesis testing and dynamic randomization for the middle stage of the game Go.Firstly, a new method to decide the intervals of different playing stages more reasonable based on hypothesis testing is proposed.Secondly, a new search algorithm including a layered pruning branch method, a comprehensive evaluation function and a new selecting node method is proposed.The pruning method based on domain knowledge and upper confidence bound formula(UCB) are all applied to subtract the branches from the lower evaluation score, which was ranked behind 20%.The comprehensive evaluation function with adjustable parameters is proposed to evaluate the tree nodes after pruning.The new selecting node method based on dynamic randomization is used to expand the tree by selecting a node randomly from the high-quality node interval.Finally, the experimental results show that the designed algorithm outperforms Gnugo3.6 and Gnugo3.8 in chess power while reducing average search time and average RAM cost for one move effectively on a 19×19 board.
The Implicit Bias of AdaGrad on Separable Data

We study the implicit bias of AdaGrad on separable linear classification problems. We show that AdaGrad converges to a direction that can be characterized as the solution of a quadratic optimization problem with the same feasible set as the hard SVM problem. We also give a discussion about how different choices of the hyperparameters of AdaGrad may impact this direction. This provides a deeper understanding of why adaptive methods do not seem to have the generalization ability as good as gradient descent does in practice.
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers

In deep neural nets, lower level embedding layers account for a large portion of the total number of parameters. Tikhonov regularization, graph-based regularization, and hard parameter sharing are approaches that introduce explicit biases into training in a hope to reduce statistical complexity. Alternatively, we propose stochastically shared embeddings (SSE), a data-driven approach to regularizing embedding layers, which stochastically transitions between embeddings during stochastic gradient descent (SGD). Because SSE integrates seamlessly with existing SGD algorithms, it can be used with only minor modifications when training large scale neural networks. We develop two versions of SSE: SSE-Graph using knowledge graphs of embeddings; SSE-SE using no prior information. We provide theoretical guarantees for our method and show its empirical effectiveness on 6 distinct tasks, from simple neural networks with one hidden layer in recommender systems, to the transformer and BERT in natural languages. We find that when used along with widely-used regularization methods such as weight decay and dropout, our proposed SSE can further reduce overfitting, which often leads to more favorable generalization results.
Analysing Rhetorical Structure as a Key Feature of Summary Coherence

We present a model for automatic scoring of coherence based on comparing the rhetorical structure (RS) of college student summaries in L2 (English) against expert summaries. Coherence is conceptualised as a construct consisting of the rhetorical relation and its arguments. Comparison with expert-assigned scores shows that RS scores correlate with both cohesion and coherence. Furthermore, RS scores improve the accuracy of a regression model for cohesion score prediction.
Dietcoin

Distributed ledgers are among the most replicated data repositories in the world. They offer data consistency, immutability, and auditability, based on the assumption that each participating node locally verifies their entire content. Although their content, currently extending up to a few hundred gigabytes, can be accommodated by dedicated commodity hard disks, downloading it, processing it, and storing it in general-purpose desktop and laptop computers can prove largely impractical. Even worse, this becomes a prohibitive restriction for smartphones, mobile devices, and resource-constrained IoT devices. In this demo, we present an implementation of Dietcoin, a Bitcoin protocol extension that allows nodes to perform secure local verification of Bitcoin transactions with small bandwidth and storage requirements. This demo presents and benchmarks the main features of Dietcoin that are important for today's cryptocurrencies and smart contract systems, but are missing in the current state-of-the-art: (i) allowing resource-constrained devices to verify the correctness of selected blocks locally without having to download the complete ledger; (ii) enabling devices to join a blockchain quickly yet securely, dropping bootstrap time from days down to a matter of seconds; (iii) providing a generic solution that can be applied to other distributed ledgers secured with Proof-of-Work.
Video tempering detection assessment in full reference mode using difference matrices

Frequent availability of low-speed internet connections together with easy availability of video capturing in the form of low-cost cameras and digital camcorders, and free video editing software running on low-cost machines, creates the possibility of real vulnerability in the video. To make improvements to images and videos, it is becoming easier for ordinary people to use malicious purposes to access digital doctor tools. This means that television, such as videos and videos on YouTube’s popular Internet websites that have been shown to be images and videos, have been scattered and their honesty cannot always be allowed. On this paper, a video tempering detection technique is proposed under the entire reference scheme. This means that the original video file is compared to the analysis activity. The proposed project uses a general data structure which is called the difference matrix which is used as a purpose. The proposed project takes steps without traditional comparison because it indicates that video edits are made to include objects or it is simply a result of video brightness and contrast enhancement.
The use of educational video materials in educational process of a higher educational institution

This article is devoted to the creation and use of educational video clips in higher educational institutions. This article analyzes the results of studies on the impact of video content on listeners, which showed a large degree of positive impact, presents a theoretical analysis of the basics of developing video materials. The purpose of the article is to create a technology for developing instructional videos in a higher educational institution. The authors presented the experience of implementing video materials in the classroom on pedagogical disciplines. The stages of developing video materials were proposed, recommendations for creating video were highlighted, the conditions and tasks of using video materials in the educational process were substantiated. Experience allowed us to identify the possibilities of video materials in the modern learning process, which can significantly increase the effectiveness of the teaching activities of the teacher and increase the level of students' perception of the material.
Proprioceptive Robot Collision Detection through Gaussian Process Regression

This paper proposes a proprioceptive collision detection algorithm based on Gaussian Regression. Compared to sensor-based collision detection and other proprioceptive algorithms, the proposed approach has minimal sensing requirements, since only the currents and the joint configurations are needed. The algorithm extends the standard Gaussian Process models adopted in learning the robot inverse dynamics, using a more rich set of input locations and an ad-hoc kernel structure to model the complex and non-linear behaviors due to frictions in quasi-static configurations. Tests performed on a Universal Robots UR10 show the effectiveness of the proposed algorithm to detect when a collision has occurred.
Data Set for Stance and Sentiment Analysis from User Comments on Croatian News

Nowadays it is becoming more important than ever to find new ways of extracting useful information from the evergrowing amount of user-generated data available online. In this paper, we describe the creation of a data set that contains news articles and corresponding comments from Croatian news outlet 24 sata. Our annotation scheme is specifically tailored for the task of detecting stances and sentiment from user comments as well as assessing if commentator claims are verifiable. Through this data, we hope to get a better understanding of the publics viewpoint on various events. In addition, we also explore the potential of applying supervised machine learning models toautomate annotation of more data.
Learning to Evaluate Color Similarity for Histopathology Images using Triplet Networks

Stain normalization is a crucial pre-processing step for histopathological image processing, and can help improve the accuracy of downstream tasks such as segmentation and classification. To evaluate the effectiveness of stain normalization methods, various metrics based on color-perceptual similarity and stain color evaluation have been proposed. However, there still exists a huge gap between metric evaluation and human perception, given the limited explainability power of existing metrics and inability to combine color and semantic information efficiently. Inspired by the effectiveness of deep neural networks in evaluating perceptual similarity of natural images, in this paper, we propose TriNet-P, a color-perceptual similarity metric for whole slide images, based on deep metric embeddings. We evaluate the proposed approach using four publicly available breast cancer histological datasets. The benefit of our approach is its representation efficiency of the perceptual factors associated with H&E stained images with minimal human intervention. We show that our metric can capture the semantic similarities, both at subject (patient) and laboratory levels, and leads to better performance in image retrieval and clustering tasks.
Statistical Voice Conversion with Quasi-periodic WaveNet Vocoder

In this paper, we investigate the effectiveness of a quasi-periodic WaveNet (QPNet) vocoder combined with a statistical spectral conversion technique for a voice conversion task. The WaveNet (WN) vocoder has been applied as the waveform generation module in many different voice conversion frameworks and achieves significant improvement over conventional vocoders. However, because of the fixed dilated convolution and generic network architecture, the WN vocoder lacks robustness against unseen input features and often requires a huge network size to achieve acceptable speech quality. Such limitations usually lead to performance degradation in the voice conversion task. To overcome this problem, the QPNet vocoder is applied, which includes a pitch-dependent dilated convolution component to enhance the pitch controllability and attain a more compact network than the WN vocoder. In the proposed method, input spectral features are first converted using a framewise deep neural network, and then the QPNet vocoder generates converted speech conditioned on the linearly converted prosodic and transformed spectral features. The experimental results confirm that the QPNet vocoder achieves significantly better performance than the same-size WN vocoder while maintaining comparable speech quality to the double-size WN vocoder. Index Terms: WaveNet, vocoder, voice conversion, pitch-dependent dilated convolution, pitch controllability