Reviewerly/exHarmony huggingface dataset:
Comparison of Graph-based and Term Weighting Method for Automatic Summarization of Online News

Text summarization is one of the quickest ways to get the gist of a paragraph or story. In text summarization, there are two ways that can be used: extractive approach and abstractive approach. In this research, the summarization was conducted using extractive approach. The extraction process was conducted by taking a few sentences from a document and combining them into a short summary. The most common method used in conducting text summarization is graph-based method. The authors proposed another method for summarization, namely term weighting method. The purpose of this study is to compare between the result of graph-based method and term weighting method in order to determine the best method for text summarization. The text pre-processing phase involves omitting the stopwords and the affixes. Moreover, the researcher utilized the measurement of Precision, Recall and F-Score. Based on the experiment using the proposed method (term weighting method), the result shows that the average values on Precision and F-score for term-weighting method are 0.296 and 0.280 respectively, which are better than the values of graph-based method. In the end, the result shows that the proposed method, which is the term weighting method, produced better summary compared to graph-based method.
Generalization Bounds for Convolutional Neural Networks

Convolutional neural networks (CNNs) have achieved breakthrough performances in a wide range of applications including image classification, semantic segmentation, and object detection. Previous research on characterizing the generalization ability of neural networks mostly focuses on fully connected neural networks (FNNs), regarding CNNs as a special case of FNNs without taking into account the special structure of convolutional layers. In this work, we propose a tighter generalization bound for CNNs by exploiting the sparse and permutation structure of its weight matrices. As the generalization bound relies on the spectral norm of weight matrices, we further study spectral norms of three commonly used convolution operations including standard convolution, depthwise convolution, and pointwise convolution. Theoretical and experimental results both demonstrate that our bounds for CNNs are tighter than existing bounds.
Algorithmic Copyright Enforcement and AI: Issues and Potential Solutions through the Lens of Text and Data Mining

Although digitalization and the emergence of the Internet has caused a long-term crisis for copyright law, technology itself also seems to offer a seemingly ideal solution to the challenges of digital age: copyright has been a major use case for algorithmic enforcement from the early days of digital rights management technologies to the more advanced content recognition algorithms. These technologies identify and filter possibly infringing content automatically, effectively and often in a preventive fashion. These methods have been criticized for their shortcomings, such as the lack of transparency, bias and the possible impairment of fundamental rights. Self-learning machines and semi-autonomous AI have the potential to offer even more sophisticated and expeditious enforcement by code, however, they could also aggravate the aforementioned issues. As the EU legislator envisions to make the use of such technologies essentially obligatory for certain online content sharing service providers (via the infamous Article 17 of the directive on copyright in the digital single market), the assessment of the situation in light of future technological development has become a current topic.This paper aims to identify the main issues and potential long-term consequences of creating legislation that practically requires the employment of such filtering algorithms as well as their solutions. This paper focuses on the potential role a broad copyright exception for text and data mining could play in counterbalancing the issues associated with algorithmic enforcement.
Foundations of Collaborative Task-Oriented Dialogue: What’s in a Slot?

In this paper, we examine the foundations of task-oriented dialogues, in which systems are requested to perform tasks for humans. We argue that the way this dialogue task has been framed has limited its applicability to processing simple requests with atomic “slot-fillers”. However, real task-oriented dialogues can contain more complex utterances that provide non-atomic constraints on slot values. For example, in response to the system’s question “What time do you want me to reserve the restaurant?”, a user should be able to say “the earliest time available,” which cannot be handled by classic “intent + slots” approaches that do not incorporate expressive logical form meaning representations. Furthermore, situations for which it would be desirable to build task-oriented dialogue systems, e.g., to engage in mixed-initiative, collaborative or multiparty dialogues, will require a more general approach. In order to overcome these limitations and to provide such an approach, we give a logical analysis of the “intent+slot” dialogue setting using a modal logic of intention and including a more expansive notion of “dialogue state”. Finally, we briefly discuss our program of research to build a next generation of plan-based dialogue systems that goes beyond “intent + slots”.
Advancing Pancreas Segmentation in Multi-protocol MRI Volumes Using Hausdorff-Sine Loss Function

Computing pancreatic morphology in 3D radiological scans could provide significant insight about a medical condition. However, segmenting the pancreas in magnetic resonance imaging (MRI) remains challenging due to high inter-patient variability. Also, the resolution and speed of MRI scanning present artefacts that blur the pancreas boundaries between overlapping anatomical structures. This paper proposes a dual-stage automatic segmentation method: (1) a deep neural network is trained to address the problem of vague organ boundaries in high class-imbalanced data. This network integrates a novel loss function to rigorously optimise boundary delineation using the modified Hausdorff metric and a sinusoidal component; (2) Given a test MRI volume, the output of the trained network predicts a sequence of targeted 2D pancreas classes that are reconstructed as a volumetric binary mask. An energy-minimisation approach fuses a learned digital contrast model to suppress the intensities of non-pancreas classes, which, combined with the binary volume performs a refined segmentation in 3D while revealing dense boundary detail. Experiments are performed on two diverse MRI datasets containing 180 and 120 scans, in which the proposed approach achieves a mean Dice score of 84.1 ± 4.6% and 85.7 ± 2.3%, respectively. This approach is statistically stable and outperforms state-of-the-art methods on MRI.
Compositional construction of bounded error over-approximations of acyclic interconnected continuous dynamical systems

We consider the problem of bounded time safety verification of interconnections of input-output continuous dynamical systems. We present a compositional framework for computing bounded error approximations of the complete system from those of the components. The main crux of our approach consists of capturing the input-output signal behaviors of a component using an abstraction predicate that represents the input-output sample behaviors corresponding to the signal behaviors. We define a semantics for the abstraction predicate that captures an over-approximation of the input-output signal behaviors of a component. Next, we define how to compose abstraction predicates of components to obtain an abstraction predicate for the composed system. We instantiate our compositional abstraction construction framework for linear dynamical systems by providing concrete methods for constructing the input-output abstraction predicates for the individual systems.
Detection of Classifier Inconsistencies in Image Steganalysis

In this paper, a methodology to detect inconsistencies in classification-based image steganalysis is presented. The proposed approach uses two classifiers: the usual one, trained with a set formed by cover and stego images, and a second classifier trained with the set obtained after embedding additional random messages into theoriginal training set. When the decisions of these two classifiers are not consistent, we know that the prediction is not reliable. The number of inconsistencies in the predictions of a testing set may indicate that the classifier is not performing correctly in the testing scenario. This occurs, for example, in case of cover source mismatch,or when we are trying to detect a steganographic method that theclassifier is no capable of modelling accurately. We also show how the number of inconsistencies can be used to predict the reliability of the classifier (classification errors).
Neural Machine Translation: A Review of the Approaches

Neural Machine Translation (NMT) has presented promising results in Machine translation, convincingly replacing the traditional Statistical Machine Translation (SMT). This success of NMT in machine translation tasks therefore projects to more translation tasks using NMT. This paper systematically reviews the hitherto proposed NMT systems since 2014. 86 NMT papers have been selected and reviewed. The peak of NMT systems were proposed in 2016 and the same was the case for many machine translation workshops who provided datasets for NMT tasks. Most of the proposed systems covered English, German, French and Chinese translation tasks. BLEU score accompanied by significance tests has been seen to be the best metric for NMT systems evaluation. Human judgement for fluency and adequacy is also important to support the metrics. There is still room for further improvement in translations regarding rich source translations and rare words. There is also need for extensive NMT works in other languages to maximize the apparent capabilities of NMT systems. RNN Search and Moses are basically used to develop SMT baselines for model comparisons. Results provide futuristic and directional insights into further translation tasks.
New labeled dataset of interconnected lexical typos for automatic correction in the bug reports

Large-scale and especially open-source projects use software triage systems like Bugzilla to manage their user’s requests like bugs, suggestions, and requirements. The software triage systems have many tasks like prioritizing, finding duplicate and assigning bug reports to developers automatically, which needs text mining, information retrieval, and natural language processing techniques. We already showed there are many typos in the bug reports which reduce the performance of artificial intelligence techniques. The connected terms were one of the most types of typos in the context of bug reports. Also, we introduce some algorithms to correct the connected terms earlier, but there was not any labeled dataset that can be used to evaluate the accuracy of process of typo correction. Now we made a new labeled dataset including 42,970 typos between 182,096 to can be used for the typo correction evaluation process. There are 52% connected typos in the labeled dataset, which show the previous results about the number of connected typos were correct. Then we used the typo correction algorithms which were introduced in prior studies to evaluate their accuracy. The experimental results show 81.6% and 83.3% accuracy in top-5 and top-10 suggestions of the list of typo corrections, respectively.
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task

In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages. We combine two existing objective functions to make images and captions close in a joint embedding space while adapting the alignment of word embeddings between existing languages in our model. We show that our approach enables better generalization, achieving state-of-the-art performance in text-to-image and image-to-text retrieval task, and caption-caption similarity task. Two multimodal multilingual datasets are used for evaluation: Multi30k with German and English captions and Microsoft-COCO with English and Japanese captions.
Efficient Quantum Algorithms Related to Autocorrelation Spectrum

In this paper, we propose efficient probabilistic algorithms for several problems regarding the autocorrelation spectrum. First, we present a quantum algorithm that samples from the Walsh spectrum of any derivative of f(). Informally, the autocorrelation coefficient of a Boolean function f() at some point a measures the average correlation among the values f(x) and \(f(x \oplus a)\). The derivative of a Boolean function is an extension of autocorrelation to correlation among multiple values of f(). The Walsh spectrum is well-studied primarily due to its connection to the quantum circuit for the Deutsch-Jozsa problem. We extend the idea to “Higher-order Deutsch-Jozsa” quantum algorithm to obtain points corresponding to large absolute values in the Walsh spectrum of a certain derivative of f(). Further, we design an algorithm to sample the input points according to squares of the autocorrelation coefficients. Finally we provide a different set of algorithms for estimating the square of a particular coefficient or cumulative sum of their squares.
Slurry nebulisation ICP-MS direct determination of high field strength elements (Nb, Ta, Zr, and Hf) in silicate rocks

A simple, rapid, and reliable method was developed for the direct determination of four high field strength elements (HFSEs, namely, Nb, Ta, Zr, and Hf) in refractory silicate rocks using slurry nebulisation inductively coupled plasma mass spectrometry (ICP-MS). Samples were dispersed in a 0.2% Triton X-100 solution by wet-grinding with a tissue cell-destroyer, and 90% of the particles in the slurry were found to be less than 1.5 μm in diameter during 90 s of milling. Fine slurry was directly introduced into ICP and detected by mass spectrometry. The behaviors of different particle size distributions in the transportation and ionisation processes were investigated. Results indicated that the cut-off size for particle efficient transport (in the sample introduction system) and complete ionization (in the ICP) was approximately 8 μm and 4 μm, respectively. Our fine slurries (mean particle size, <1.0 μm) showed comparable mass transfer efficiency and ionisation efficiency to aqueous standards. Calibration can be carried out via aqueous standards, with a limit of quantification (LOQ) in the range of 18-30 ng g-1. Satisfactory accuracy (recovery, 91 to 103%) and precision (RSD, 0.9 to 6.3%) were verified by analysis of a series of silicate rock certified reference materials (CRMs).
Robust Ordinal VAE: Employing Noisy Pairwise Comparisons for Disentanglement

Recent work by Locatello et al. (2018) has shown that an inductive bias is required to disentangle factors of interest in Variational Autoencoder (VAE). Motivated by a real-world problem, we propose a setting where such bias is introduced by providing pairwise ordinal comparisons between instances, based on the desired factor to be disentangled. For example, a doctor compares pairs of patients based on the level of severity of their illnesses, and the desired factor is a quantitive level of the disease severity. In a real-world application, the pairwise comparisons are usually noisy. Our method, Robust Ordinal VAE (ROVAE), incorporates the noisy pairwise ordinal comparisons in the disentanglement task. We introduce non-negative random variables in ROVAE, such that it can automatically determine whether each pairwise ordinal comparison is trustworthy and ignore the noisy comparisons. Experimental results demonstrate that ROVAE outperforms existing methods and is more robust to noisy pairwise comparisons in both benchmark datasets and a real-world application.
Towards a Social and Human Factor Classification Related to Productivity in Software Development Teams

Software product development is characterized as an activity that focuses on social and human factors. In fact, studying these factors may be particularly appealing to software organizations seeking initiatives for fostering team productivity. In this light, the classification of the factors that may have an impact on the productivity of the software development team becomes the point of departure for the selection and definition of improvement strategies. As part of this research, we designed a methodology grounded in systematic literature review processes to approach a classification for social and human factors and develop a validation plan. In addition, the definition of inclusion and exclusion criteria yielded an approximation to the proposed classification, which may be used as the input for the definition of improvement actions. Finally, this work reports on the preliminary advances of the validation plan.
Enhancing code clone detection using control flow graphs

Code clones are syntactically or semantically equivalent code fragments of source code. Copy-and-paste programming allows software developers to improve development productivity, but it could produce code clones that can introduce non-trivial difficulties in software maintenance. In this paper, a code clone detection framework is presented with a feature extractor and a clone classifier using deep learning. The clone classifier is trained with true and false clones and then is tested with a test dataset to evaluate the performance of the proposed approach to clone detection. In particular, the proposed approach to clone detection uses Control Flow Graphs (CFGs) to extract features of a given code snippet. The selected features are used to compute similarity scores for comparing two code fragments. The clone classifier is trained and tested with similarity scores that quantify the degree of how similar two code fragments are. The experimental results demonstrate that using CFG features is a viable methodology in terms of the effectiveness of clone detection for both syntactic and semantic clones.
An Improved Linear Programming Bound on the Average Distance of a Binary Code

Ahlswede and Katona (1977) posed the following isodiametric problem in Hamming spaces: For every $n$ and $1\le M\le2^{n}$, determine the minimum average Hamming distance of binary codes with length $n$ and size $M$. Fu, Wei, and Yeung (2001) used linear programming duality to derive a lower bound on the minimum average distance. However, their linear programming approach was not completely exploited. In this paper, we improve Fu-Wei-Yeung's bound by finding a better feasible solution to their dual program. For fixed $0
Discriminator optimal transport

Within a broad class of generative adversarial networks, we show that discriminator optimization process increases a lower bound of the dual cost function for the Wasserstein distance between the target distribution $p$ and the generator distribution $p_G$. It implies that the trained discriminator can approximate optimal transport (OT) from $p_G$ to $p$.Based on some experiments and a bit of OT theory, we propose a discriminator optimal transport (DOT) scheme to improve generated images. We show that it improves inception score and FID calculated by un-conditional GAN trained by CIFAR-10, STL-10 and a public pre-trained model of conditional GAN by ImageNet.
Data Insertion in Blockchain For Legal Purposes. How to Sign Contracts Using Blockchain

Data Insertion in Blockchain For Legal Purposes. How to Sign Contracts Using BlockchainThe use of blockchain technology, in particular, data insertion (anchoring, hashing) in the blockchain as a way of signing documents or imparting legal properties to facts is researched.A comparative analysis of the known methods of using electronic digital signature with the method of inserting data into the blockchain is carried out.The following issues were addressed.What is the data insertion in the blockchain and what properties do the data acquire?What is the difference between insertion, anchoring, and hashing on the blockchain?What is the difference between blockchain hashing and a digital signature on a document?Will the document be legally binding if it is anchored in the blockchain?What conditions must be met to give legal force for the document?How can anchoring be used to sign contracts, certify evidence that has legal value, denote time stamps, confirm authorship and copyrights, as well as transfer them, issue, and transfer power of attorney and delegate other rights, issue and transfer bearer instruments?
Linguistic Evaluation of German-English Machine Translation Using a Test Suite

We present the results of the application of a grammatical test suite for German→English MT on the systems submitted at WMT19, with a detailed analysis for 107 phenomena organized in 14 categories.The systems still translate wrong one out of four test items in average.Low performance is indicated for idioms, modals, pseudo-clefts, multi-word expressions and verb valency.When compared to last year, there has been a improvement of function words, non verbal agreement and punctuation.More detailed conclusions about particular systems and phenomena are also presented.
Where is the Risk? Analysis of Government Reported Patient Medical Data Breaches

Managing healthcare organizational cybersecurity risk is complex. This work examines government reported patient health data breaches to learn more about trends in reported breaches to inform organizational risk budgeting, trends and focus areas. In many cases, organizations only have enough time to survive daily risk management activities. They all too often have little, if any, time for actual risk management research beyond third-party vendor threat intelligence. Our research fills this gap by analyzing the breach data reported to the United States (US) Health and Human Services (HHS) Office of Civil Rights (OCR) from May 1, 2018 until May 1, 2019. The analysis reports on trends from breach factors reported to the government to further inform cybersecurity patient health data risk management.
Classification of RBC and WBC in Noisy Microscopic Images of Blood Smear

Blood cell counts are one of the vital signs that play a major role in medical diagnostics. The process of estimating complete blood count (CBC) is either expensive or time taking depending on the technique that is used for counting. A manual differential technique, that is often used to get authentic count and abnormalities present in a stained blood smear, requires a lot of human expertise and time. Dedicated efforts are given to make the classification and counting process of blood profiles automatic and robust. In this paper, first, we build a neural network-based multiclass classifier that can classify between RBC, WBC, and platelets. However, we demonstrate that the presence of a small amount of random noise can drastically decrease the performance of the designed classifier. Thus, we design a preprocessing network to enhance the quality of the input image so that the classifier can faithfully classify the blood cells even in adverse imaging conditions.
Information-theoretic metrics for Local Differential Privacy protocols

Local Differential Privacy (LDP) protocols allow an aggregator to obtain population statistics about sensitive data of a userbase, while protecting the privacy of the individual users. To understand the tradeoff between aggregator utility and user privacy, we introduce new information-theoretic metrics for utility and privacy. Contrary to other LDP metrics, these metrics highlight the fact that the users and the aggregator are interested in fundamentally different domains of information. We show how our metrics relate to $\varepsilon$-LDP, the \emph{de facto} standard privacy metric, giving an information-theoretic interpretation to the latter. Furthermore, we use our metrics to quantitatively study the privacy-utility tradeoff for a number of popular protocols.