OpenRaiser/Intern-Atlas huggingface dataset:
A Novel Pixel-Averaging Technique for Extracting Training Data from a Single Image, Used in ML-Based Image Enlargement
Venue: arXiv cs.CV 2019
Year: 2019

Size of the training dataset is an important factor in the performance of a machine learning algorithms and tools used in medical image processing are not exceptions. Machine learning tools normally require a decent amount of training data before they could efficiently predict a target. For image processing and computer vision, the number of images determines the validity and reliability of the training set. Medical images in some cases, suffer from poor quality and inadequate quantity required for a suitable training set. The proposed algorithm in this research obviates the need for large or even small image datasets used in machine learning based image enlargement techniques by extracting the required data from a single image. The extracted data was then introduced to a decision tree regressor for upscaling greyscale medical images at different zoom levels. Results from the algorithm are relatively acceptable compared to third-party applications and promising for future research. This technique could be tailored to the requirements of other machine learning tools and the results may be improved by further tweaking of the tools hyperparameters.
Manual Encryption Revisited
Venue: arXiv cs.CR 2019
Year: 2019

This document brings together several articles devoted to manual encryption and introduces new algorithms and ciphers: a permutation algorithm, Spirale (a one-time-pad cipher), a solution to the problem of the ordered route of a table, Diagonales (a cipher by transposition), Carousel (another cipher by transposition), solutions to the problem of creating keys or passwords.
Affect in Tweets Using Experts Model
Venue: arXiv cs.IR 2019
Year: 2019

Estimating the intensity of emotion has gained significance as modern textual inputs in potential applications like social media, e-retail markets, psychology, advertisements etc., carry a lot of emotions, feelings, expressions along with its meaning. However, the approaches of traditional sentiment analysis primarily focuses on classifying the sentiment in general (positive or negative) or at an aspect level(very positive, low negative, etc.) and cannot exploit the intensity information. Moreover, automatically identifying emotions like anger, fear, joy, sadness, disgust etc., from text introduces challenging scenarios where single tweet may contain multiple emotions with different intensities and some emotions may even co-occur in some of the tweets. In this paper, we propose an architecture, Experts Model, inspired from the standard Mixture of Experts (MoE) model. The key idea here is each expert learns different sets of features from the feature vector which helps in better emotion detection from the tweet. We compared the results of our Experts Model with both baseline results and top five performers of SemEval-2018 Task-1, Affect in Tweets (AIT). The experimental results show that our proposed approach deals with the emotion detection problem and stands at top-5 results.
A Weighted Multi-Criteria Decision Making Approach for Image Captioning
Venue: arXiv cs.CV 2019
Year: 2019

Image captioning aims at automatically generating descriptions of an image in natural language. This is a challenging problem in the field of artificial intelligence that has recently received significant attention in the computer vision and natural language processing. Among the existing approaches, visual retrieval based methods have been proven to be highly effective. These approaches search for similar images, then build a caption for the query image based on the captions of the retrieved images. In this study, we present a method for visual retrieval based image captioning, in which we use a multi criteria decision making algorithm to effectively combine several criteria with proportional impact weights to retrieve the most relevant caption for the query image. The main idea of the proposed approach is to design a mechanism to retrieve more semantically relevant captions with the query image and then selecting the most appropriate caption by imitation of the human act based on a weighted multi-criteria decision making algorithm. Experiments conducted on MS COCO benchmark dataset have shown that proposed method provides much more effective results in compare to the state-of-the-art models by using criteria with proportional impact weights .
Boosted Attention: Leveraging Human Attention for Image Captioning
Venue: arXiv cs.CV 2019
Year: 2019

Visual attention has shown usefulness in image captioning, with the goal of enabling a caption model to selectively focus on regions of interest. Existing models typically rely on top-down language information and learn attention implicitly by optimizing the captioning objectives. While somewhat effective, the learned top-down attention can fail to focus on correct regions of interest without direct supervision of attention. Inspired by the human visual system which is driven by not only the task-specific top-down signals but also the visual stimuli, we in this work propose to use both types of attention for image captioning. In particular, we highlight the complementary nature of the two types of attention and develop a model (Boosted Attention) to integrate them for image captioning. We validate the proposed approach with state-of-the-art performance across various evaluation metrics.
Cursive Overlapped Character Segmentation: An Enhanced Approach
Venue: arXiv cs.CV 2019
Year: 2019

Segmentation of highly slanted and horizontally overlapped characters is a challenging research area that is still fresh. Several techniques are reported in the state of art, but produce low accuracy for the highly slanted characters segmentation and cause overall low handwriting recognition precision. Accordingly, this paper presents a simple yet effective approach for character segmentation of such difficult slanted cursive words without using any slant correction technique. Rather a new concept of core-zone is introduced for segmenting such difficult slanted handwritten words. However, due to the inherent nature of cursive words, few characters are over-segmented and therefore, a threshold is selected heuristically to overcome this problem. For fair comparison, difficult words are extracted from the IAM benchmark database. Experiments thus performed exhibit promising result and high speed.
Polysemy and brevity versus frequency in language
Venue: arXiv cs.CL 2019
Year: 2019

The pioneering research of G. K. Zipf on the relationship between word frequency and other word features led to the formulation of various linguistic laws. The most popular is Zipf's law for word frequencies. Here we focus on two laws that have been studied less intensively: the meaning-frequency law, i.e. the tendency of more frequent words to be more polysemous, and the law of abbreviation, i.e. the tendency of more frequent words to be shorter. In a previous work, we tested the robustness of these Zipfian laws for English, roughly measuring word length in number of characters and distinguishing adult from child speech. In the present article, we extend our study to other languages (Dutch and Spanish) and introduce two additional measures of length: syllabic length and phonemic length. Our correlation analysis indicates that both the meaning-frequency law and the law of abbreviation hold overall in all the analyzed languages.
k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training
Venue: arXiv cs.CV 2019
Year: 2019

For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identifiable data is restricted and prohibited by law. Then, "how can we ensure the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification?" becomes a challenge issue. In this work, we propose an approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN, which leverages the k-Same-Anonymity mechanism, the Generative Adversarial Network, and the hyperparameter tuning methods. Moreover, to speed up model training and reduce memory consumption, the mixed precision training technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, to validate its applicability, the proposed work has been applied to actual datasets - RafD and CelebA for performance testing. Besides protecting privacy of high-resolution facial images, the proposed system is also justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters.
Tree Search Network for Sparse Regression
Venue: arXiv cs.LG 2019
Year: 2019

We consider the classical sparse regression problem of recovering a sparse signal $x_0$ given a measurement vector $y = Φx_0+w$. We propose a tree search algorithm driven by the deep neural network for sparse regression (TSN). TSN improves the signal reconstruction performance of the deep neural network designed for sparse regression by performing a tree search with pruning. It is observed in both noiseless and noisy cases, TSN recovers synthetic and real signals with lower complexity than a conventional tree search and is superior to existing algorithms by a large margin for various types of the sensing matrix $Φ$, widely used in sparse regression.
An Approach to Identity Management in Clouds without Trusted Third Parties
Venue: arXiv cs.CR 2019
Year: 2019

The management of sensitive data, including identity management (IDM), is an important problem in cloud computing, fundamental for authentication and fine-grained service access control. Our goal is creating an efficient and robust IDM solution that addresses critical issues in cloud computing. The proposed IDM scheme does not rely on trusted third parties (TTPs) or trusted dealers. The scheme is a multiparty interactive solution that combines RSA distributed key generation and attribute-based encryption. We believe that it will be a robust IDM privacy-preserving solution in cloud computing, because it has the following features: (i) protects sensitive data on untrusted hosts using active bundle; (ii) supports the minimum disclosure property; (iii) minimizes authentication overhead by providing single sign-on; (iv) supports authentication with encrypted credentials; (v) avoids using trusted third parties (TTPs_, incl. using TTPs for key management; (vi) supports revocation and delegation of access right; and (vii) supports revocation of user credentials. The scheme should also be efficient because it exploits parallelism.
Unsupervised Abbreviation Disambiguation Contextual disambiguation using word embeddings
Venue: arXiv cs.CL 2019
Year: 2019

Abbreviations often have several distinct meanings, often making their use in text ambiguous. Expanding them to their intended meaning in context is important for Machine Reading tasks such as document search, recommendation and question answering. Existing approaches mostly rely on manually labeled examples of abbreviations and their correct long-forms. Such data sets are costly to create and result in trained models with limited applicability and flexibility. Importantly, most current methods must be subjected to a full empirical evaluation in order to understand their limitations, which is cumbersome in practice. In this paper, we present an entirely unsupervised abbreviation disambiguation method (called UAD) that picks up abbreviation definitions from unstructured text. Creating distinct tokens per meaning, we learn context representations as word vectors. We demonstrate how to further boost abbreviation disambiguation performance by obtaining better context representations using additional unstructured text. Our method is the first abbreviation disambiguation approach with a transparent model that allows performance analysis without requiring full-scale evaluation, making it highly relevant for real-world deployments. In our thorough empirical evaluation, UAD achieves high performance on large real-world data sets from different domains and outperforms both baseline and state-of-the-art methods. UAD scales well and supports thousands of abbreviations with multiple different meanings within a single model. In order to spur more research into abbreviation disambiguation, we publish a new data set, that we also use in our experiments.
LUTNet: Rethinking Inference in FPGA Soft Logic
Venue: arXiv cs.LG 2019
Year: 2019

Research has shown that deep neural networks contain significant redundancy, and that high classification accuracies can be achieved even when weights and activations are quantised down to binary values. Network binarisation on FPGAs greatly increases area efficiency by replacing resource-hungry multipliers with lightweight XNOR gates. However, an FPGA's fundamental building block, the K-LUT, is capable of implementing far more than an XNOR: it can perform any K-input Boolean operation. Inspired by this observation, we propose LUTNet, an end-to-end hardware-software framework for the construction of area-efficient FPGA-based neural network accelerators using the native LUTs as inference operators. We demonstrate that the exploitation of LUT flexibility allows for far heavier pruning than possible in prior works, resulting in significant area savings while achieving comparable accuracy. Against the state-of-the-art binarised neural network implementation, we achieve twice the area efficiency for several standard network models when inferencing popular datasets. We also demonstrate that even greater energy efficiency improvements are obtainable.
Retracing and assessing the CEP project
Venue: arXiv cs.GL 2019
Year: 2019

The last decade witnessed a renewed interest in the development of the Italian computer industry and in the role of the Fifties pioneers in Rome, Milan, Ivrea, and Pisa. The aim of the paper is to retrace some steps of the CEP project, carried out by the University of Pisa in collaboration with Olivetti, by reassessing the documents preserved in the University archives. The project was a seminal enterprise for Italy, and among its accomplishments it delivered in 1957 the first Italian computer. The mix of public sector funding and industrial foretelling witnessed by the project is one of the leading examples in Italy of best practices, and its success paved the way for the birth of Computer Science in the country as an industry as well as a scientific discipline.
Some variations on Lyndon words
Venue: arXiv cs.DM 2019
Year: 2019

In this paper we compare two finite words $u$ and $v$ by the lexicographical order of the infinite words $u^ω$ and $v^ω$. Informally, we say that we compare $u$ and $v$ by the infinite order. We show several properties of Lyndon words expressed using this infinite order. The innovative aspect of this approach is that it allows to take into account also non trivial conditions on the prefixes of a word, instead that only on the suffixes. In particular, we derive a result of Ufnarovskij [V. Ufnarovskij, "Combinatorial and asymptotic methods in algebra", 1995] that characterizes a Lyndon word as a word which is greater, with respect to the infinite order, than all its prefixes. Motivated by this result, we introduce the prefix standard permutation of a Lyndon word and the corresponding (left) Cartesian tree. We prove that the left Cartesian tree is equal to the left Lyndon tree, defined by the left standard factorization of Viennot [G. Viennot, "Algèbres de Lie libres et monoïdes libres", 1978]. This result is dual with respect to a theorem of Hohlweg and Reutenauer [C. Hohlweg and C. Reutenauer, "Lyndon words, permutations and trees", 2003].
Complexity and Algorithms for Semipaired Domination in Graphs
Venue: arXiv cs.DM 2019
Year: 2019

For a graph $G=(V,E)$ with no isolated vertices, a set $D\subseteq V$ is called a semipaired dominating set of G if $(i)$ $D$ is a dominating set of $G$, and $(ii)$ $D$ can be partitioned into two element subsets such that the vertices in each two element set are at distance at most two. The minimum cardinality of a semipaired dominating set of $G$ is called the semipaired domination number of $G$, and is denoted by $γ_{pr2}(G)$. The \textsc{Minimum Semipaired Domination} problem is to find a semipaired dominating set of $G$ of cardinality $γ_{pr2}(G)$. In this paper, we initiate the algorithmic study of the \textsc{Minimum Semipaired Domination} problem. We show that the decision version of the \textsc{Minimum Semipaired Domination} problem is NP-complete for bipartite graphs and split graphs. On the positive side, we present a linear-time algorithm to compute a minimum cardinality semipaired dominating set of interval graphs and trees. We also propose a $1+\ln(2Δ+2)$-approximation algorithm for the \textsc{Minimum Semipaired Domination} problem, where $Δ$ denote the maximum degree of the graph and show that the \textsc{Minimum Semipaired Domination} problem cannot be approximated within $(1-ε) \ln|V|$ for any $ε> 0$ unless NP $\subseteq$ DTIME$(|V|^{O(\log\log|V|)})$.
Estimation and Prediction of technical debt: a proposal
Venue: arXiv cs.SE 2019
Year: 2019

Technical debt is a metaphor used to convey the idea that doing things in a "quick and dirty" way when designing and constructing a software leads to a situation where one incurs more and more deferred future expenses. Similarly to financial debt, technical debt requires payment of interest in the form of the additional development effort that could have been avoided if the quick and dirty design choices have not been made. Technical debt applies to all the aspects of software development, spanning from initial requirements analysis to deployment, and software evolution. Technical debt is becoming very popular from scientific and industrial perspectives. In particular, there is an increase in the number of related papers over the years. There is also an increase in the number of related tools and of their adoption in the industry, especially since technical debt is very pricey and therefore needs to be managed. However, techniques to estimate technical debt are inadequate, insufficient since they mostly focus on requirements, code, and test, disregarding key artifacts such as the software architecture and the technologies used by the software at hand. Besides, despite its high relevance, technical debt prediction is one of the least explored aspects of technical debt. To address these shortcomings, it is mandatory that I undertake research to: 1) improve existing techniques to properly estimate technical debt; 2) to determine the extent to which the use of prediction techniques to foresee and therefore avoid technical debt could help companies save money and avoid a potential bankruptcy in the subsequent years. The proposed research can have an important economic impact by helping companies save several millions. It can have a major scientific impact by leading to key findings that will be disseminated through patents, well-established scientific journals and conferences.
Grounds for trust: Essential Epistemic Opacity and Computational Reliabilism
Venue: arXiv cs.OH 2019
Year: 2019

Several philosophical issues in connection with computer simulations rely on the assumption that results of simulations are trustworthy. Examples of these include the debate on the experimental role of computer simulations \cite{Parker2009, Morrison2009}, the nature of computer data \cite{Barberousse2013, Humphreys2013}, and the explanatory power of computer simulations \cite{Krohs2008, Duran2017}. The aim of this article is to show that these authors are right in assuming that the results of computer simulations are to be trusted when computer simulations are reliable processes. After a short reconstruction of the problem of \textit{epistemic opacity}, the article elaborates extensively on \textit{computational reliabilism}, a specified form of process reliabilism with computer simulations located at the center. The article ends with a discussion of four sources for computational reliabilism, namely, verification and validation, robustness analysis for computer simulations, a history of (un)successful implementations, and the role of expert knowledge in simulations.
Multimodal Sparse Classifier for Adolescent Brain Age Prediction
Venue: arXiv cs.LG 2019
Year: 2019

The study of healthy brain development helps to better understand the brain transformation and brain connectivity patterns which happen during childhood to adulthood. This study presents a sparse machine learning solution across whole-brain functional connectivity (FC) measures of three sets of data, derived from resting state functional magnetic resonance imaging (rs-fMRI) and task fMRI data, including a working memory n-back task (nb-fMRI) and an emotion identification task (em-fMRI). These multi-modal image data are collected on a sample of adolescents from the Philadelphia Neurodevelopmental Cohort (PNC) for the prediction of brain ages. Due to extremely large variable-to-instance ratio of PNC data, a high dimensional matrix with several irrelevant and highly correlated features is generated and hence a pattern learning approach is necessary to extract significant features. We propose a sparse learner based on the residual errors along the estimation of an inverse problem for the extreme learning machine (ELM) neural network. The purpose of the approach is to overcome the overlearning problem through pruning of several redundant features and their corresponding output weights. The proposed multimodal sparse ELM classifier based on residual errors (RES-ELM) is highly competitive in terms of the classification accuracy compared to its counterparts such as conventional ELM, and sparse Bayesian learning ELM.
Guarding a Subspace in High-Dimensional Space with Two Defenders and One Attacker
Venue: arXiv cs.SY 2019
Year: 2019

This paper considers a subspace guarding game in high-dimensional space which consists of a play subspace and a target subspace. Two faster defenders cooperate to protect the target subspace by capturing an attacker which strives to enter the target subspace from the play subspace without being captured. A closed-form solution is provided from the perspectives of kind and degree. Contributions of the work include the use of the attack subspace (AS) method to construct the barrier, by which the game winner can be perfectly predicted before the game starts. In addition to this inclusion, with the priori information about the game result, a critical payoff function is designed when the defenders can win the game. Then, the optimal strategy for each player is explicitly reformulated as a saddle-point equilibrium. Finally, we apply these theoretical results to a half-space guarding game in three-dimensional space. Since the whole achieved developments are analytical, they require a little memory without computational burden and allow for real-time updates, beyond the capacity of traditional Hamilton-Jacobi-Isaacs method. It is worth noting that this is the first time in the current work to consider the target guarding games for arbitrary high-dimensional space, and in a fully analytical form.