Reviewerly/exHarmony huggingface dataset:
Poisson Twister Generator by Cumulative Frequency Technology

The widely known generators of Poisson random variables are associated with different modifications of the algorithm based on the convergence in probability of a sequence of uniform random variables to the created stochastic number. However, in some situations, this approach yields different discrete Poisson probability distributions and skipping in the generated numbers. This article offers a new approach for creating Poisson random variables based on the complete twister generator of uniform random variables, using cumulative frequency technology. The simulation results confirm that probabilistic and frequency distributions of the obtained stochastic numbers completely coincide with the theoretical Poisson distribution. Moreover, combining this new approach with the tuning algorithm of basic twister generation allows for a significant increase in length of the created sequences without using additional RAM of the computer.
A free database of eye movements watching “Hollywood” videoclips

The provided database of tracked eye movements was collected using an infra-red, video-camera Eyelink 1000 system, from 95 participants as they viewed 'Hollywood' video clips. There are 206 clips of 30-s and eleven clips of 30-min for a total viewing time of about 60 hours. The database also provides the raw 30-s video clip files, a short preview of the 30-min clips, and subjective ratings of the content of the videos for each in categories: (1) genre; (2) importance of human faces; (3) importance of human figures; (4) importance of man-made objects; (5) importance of nature; (6) auditory information; (7) lighting; and (8) environment type. Precise timing of the scene cuts within the clips and the democratic gaze scanpath position (center of interest) per frame are provided. At this time, this eye-movement dataset has the widest age range (22–85 years) and is the third largest (in recorded video viewing time) of those that have been made available to the research community. The data-acquisition procedures are described, along with participant demographics, summaries of some common eye-movement statistics, and highlights of research topics in which the database was used. The dataset is freely available in the Open Science Framework repository (link in the manuscript) and can be used without restriction for educational and research purposes, providing that this paper is cited in any published work.
Can We Overcome the <i>n</i> log <i>n</i> Barrier for Oblivious Sorting?

It is well-known that non-comparison-based techniques can allow us to sort n elements in o(n log n) time on a Random-Access Machine (RAM).On the other hand, it is a long-standing open question whether (non-comparison-based) circuits can sort n elements from the domain [1..2 k ] with o(kn log n) boolean gates.We consider weakened forms of this question: first, we consider a restricted class of sorting where the number of distinct keys is much smaller than the input length; and second, we explore Oblivious RAMs and probabilistic circuit families, i.e., computational models that are somewhat more powerful than circuits but much weaker than RAM.We show that Oblivious RAMs and probabilistic circuit families can sort o(log n)-bit keys in o(n log n) time or o(kn log n) circuit complexity.Our algorithms work in the indivisible model, i.e., not only can they sort an array of numerical keys -if each key additionally carries an opaque ball, our algorithms can also move the balls into the correct order.We further show that in such an indivisible model, it is impossible to sort Ω(log n)-bit keys in o(n log n) time, and thus the o(log n)-bit-key assumption is necessary for overcoming the n log n barrier.Finally, after optimizing the IO efficiency, we show that even the 1-bit special case can solve open questions: our oblivious algorithms solve tight compaction and selection with optimal IO efficiency for the first time.
A New One Round Video Encryption Scheme Based on 1D Chaotic Maps

The express development of communication and technology has created new challenges to transfer data in a secured way. This paper presents a secure cryptosystem with three level security based on chaos for video encryption. The proposed method employs one round permutation and diffusion structure for encryption. The logistic and tent maps are combined (LTS) to generate the initial parameters for the proposed encryption system. The video frame is first selected based on Frame Selection (FS) is encrypted by applying permutation order (PO) and then diffusion is carried out on the permuted frame by diffusion frame (DF) generated. After encryption, the encrypted frame is placed in order, to create a video sequence for transmission. This method provides three- level security (FS, PO and DF) in extracting the encrypted information. Experimental outcomes and analyses confirm that the method is competent.
Revisiting NMT for Normalization of Early English Letters

Mika Hämäläinen, Tanja Säily, Jack Rueter, Jörg Tiedemann, Eetu Mäkelä. Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. 2019.
Digital Literacy in ESL Classroom

Literacy is more than the ability to decode words and digital literacy is more than the ability to use a computer.This paper will attempt to define digital literacy and the benefits it provides to both the English language learner (ELL) and the English language instructor.It will also explore some of the misconceptions surrounding digital literacy and try to show that there is a need for basic training in this area for all language educators.Schools have taken the first step by providing access to computer technology in the classrooms; however, most have short-changed students in the development of digital skills.
Algebraic geometric codes on minimal Hirzebruch surfaces

We define a linear code Cη(δT,δX) by evaluating polynomials of bidegree (δT,δX) in the Cox ring on Fq-rational points of a minimal Hirzebruch surface over the finite field Fq. We give explicit parameters of the code, notably using Gröbner bases. The minimum distance provides an upper bound of the number of Fq-rational points of a non-filling curve on a Hirzebruch surface.
Alignment of library services with the research lifecycle

Objectives: This study sought to understand the needs of biomedical researchers related to the research lifecycle and the present state of library support for biomedical research.Methods: Qualitative interview data were collected from biomedical researchers who were asked to describe their research activities from identifying a problem to measuring the impact of their findings. Health sciences library leaders were surveyed about the services that they currently provide or plan to provide in supporting biomedical research.Results: Library services were strongest at the beginning and end of the research lifecycle but were weaker in the conducting phase of the research. Co-occurrence of codes from the qualitative data suggests that library services are on the fringe of rather than integrated into the research lifecycle.Discussion: Findings from this study suggest that tradition-based service models of health sciences libraries are insufficient to meet the needs of biomedical researchers. Investments by libraries in services that integrate with the conducting phase of research are needed for libraries to remain relevant in their support of the research lifecycle.
Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque

In this paper, we present a cross-lingual neural coreference resolution system for a less-resourced language such as Basque. To begin with, we build the first neural coreference resolution system for Basque, training it with the relatively small EPEC-KORREF corpus (45,000 words). Next, a cross-lingual coreference resolution system is designed. With this approach, the system learns from a bigger English corpus, using cross-lingual embeddings, to perform the coreference resolution for Basque. The cross-lingual system obtains slightly better results (40.93 F1 CoNLL) than the monolingual system (39.12 F1 CoNLL), without using any Basque language corpus to train it.
Combining <i>n</i>-grams and deep convolutional features for language variety classification

Abstract This paper presents a novel neural architecture capable of outperforming state-of-the-art systems on the task of language variety classification. The architecture is a hybrid that combines character-based convolutional neural network (CNN) features with weighted bag-of- n -grams (BON) features and is therefore capable of leveraging both character-level and document/corpus-level information. We tested the system on the Discriminating between Similar Languages (DSL) language variety benchmark data set from the VarDial 2017 DSL shared task, which contains data from six different language groups, as well as on two smaller data sets (the Arabic Dialect Identification (ADI) Corpus and the German Dialect Identification (GDI) Corpus, from the VarDial 2016 ADI and VarDial 2018 GDI shared tasks, respectively). We managed to outperform the winning system in the DSL shared task by a margin of about 0.4 percentage points and the winning system in the ADI shared task by a margin of about 0.2 percentage points in terms of weighted F1 score without conducting any language group-specific parameter tweaking. An ablation study suggests that weighted BON features contribute more to the overall performance of the system than the CNN-based features, which partially explains the uncompetitiveness of deep learning approaches in the past VarDial DSL shared tasks. Finally, we have implemented our system in a workflow, available in the ClowdFlows platform, in order to make it easily available also to the non-programming members of the research community.
Low Memory Architectures of Fractional Wavelet Filter for Low-Cost Visual Sensors and Wearable Devices

This paper proposes a low memory architecture of fractional wavelet filter for computing two-dimensional discrete wavelet transform of gray-scale images. The proposed architecture uses a data scanning order which mainly contributes to memory reduction. A multiplierless version of the proposed architecture is also designed to reduce the critical path delay equal to the delay of an adder. The proposed architectures and an existing low memory discrete wavelet transform architecture are implemented on the same field programmable gate array board and their performance are compared for different image resolutions. The results show that the proposed architectures (with and without multipliers) uses lesser basic blocks, such as look-up tables (about 85-94% less), flip-flops (about 85-90% less), consumes lesser dynamic power (about 58-75% less), and results in critical path delay reduction of (about 23-77%) compared to one of the best existing low memory discrete wavelet transform architecture for images of $1024\times {1024}$ pixels. These advantages are achieved in addition to reduction in memory requirement over the existing state-of-the-art architectures. Thus, the proposed architectures are viable options for calculating discrete wavelet transform coefficients of gray-scale images on low-cost memory-constrained platforms like visual sensors/wearable devices and Internet of things.
Analysis on Boolean Function in a Restricted (Biased) Domain

Boolean functions are usually studied under the assumption that each input bit is considered independent and identically distributed. However, in the case of some stream ciphers, a keystream bit is generated by using a nonlinear Boolean function with inputs from a restricted domain. At Eurocrypt 2016, one such stream cipher (FLIP) has been proposed, where a Boolean function on n variables was exploited with inputs of weight n/2 only. Recently, Carlet et al. studied several properties of such functions and obtained certain bounds on linear approximations of direct sum in the restricted domain. In this paper, we observe that for a direct sum like f = f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> + f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , the inputs to each sub-function f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> do not follow a uniform distribution in the restricted domain. In this regard, we study the properties of the Boolean functions by considering a general probability distribution on the inputs. We further obtain several bounds related to the biases of direct sums. Finally, we obtain a lower bound on the bias of the nonlinear filter function of FLIP. Our results provide a general framework to study security parameters of ciphers over restricted domain.
Fast Feature Selection for Linear Value Function Approximation

Linear value function approximation is a standard approach to solving reinforcement learning problems with large state spaces. Since designing good approximation features is difficult, automatic feature selection is an important research topic. We propose a new method for feature selection that is based on a low-rank factorization of the transition matrix. Our approach derives features directly from high-dimensional raw inputs, such as image data. The method is easy to implement using SVD, and our experiments show that it is faster and more stable than alternative methods.
Property-Specific Aesthetic Assessment With Unsupervised Aesthetic Property Discovery

We propose the property-specific aesthetic assessment (PSAA) algorithm with unsupervised aesthetic property discovery. The proposed PSAA algorithm uses an aesthetic feature extractor, an aesthetic property classifier, and multiple property-specific assessment networks. The aesthetic feature extractor analyzes aesthetics of images to generate features. Using such aesthetic features, we discover diverse aesthetic properties in an unsupervised manner and develop the aesthetic property classifier to predict the aesthetic property of each image. For each discovered aesthetic property, we train a property-specific assessment network. Thus, we can assess the aesthetic quality of an image using the property-specific network that corresponds to its property. Experimental results on a large dataset show that the proposed PSAA algorithm achieves state-of-the-art aesthetic assessment performance. Furthermore, we demonstrate that PSAA is useful for improving aesthetic qualities of images in two applications: contrast enhancement and image cropping.
Using Relational Concept Networks for Explainable Decision Support

In decision support systems, information from many different sources must be integrated and interpreted to aid the process of gaining situational understanding. These systems assist users in making the right decisions, for example when under time pressure. In this work, we discuss a controlled automated support tool for gaining situational understanding, where multiple sources of information are integrated. In the domain of operational safety and security, available data is often limited and insufficient for sub-symbolic approaches such as neural networks. Experts generally have high level (symbolic) knowledge but may lack the ability to adapt and apply that knowledge to the current situation. In this work, we combine sub-symbolic information and technologies (machine learning) with symbolic knowledge and technologies (from experts or ontologies). This combination offers the potential to steer the interpretation of the little data available with the knowledge of the expert. We created a framework that consists of concepts and relations between those concepts, for which the exact relational importance is not necessarily specified. A machine-learning approach is used to determine the relations that fit the available data. The use of symbolic concepts allows for properties such as explainability and controllability. The framework was tested with expert rules on an attribute dataset of vehicles. The performance with incomplete inputs or smaller training sets was compared to a traditional fully-connected neural network. The results show it as a viable alternative when data is limited or incomplete, and that more semantic meaning can be extracted from the activations of concepts.
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization

We propose a novel adaptive, accelerated algorithm for the stochastic constrained convex optimization setting.Our method, which is inspired by the Mirror-Prox method, \emph{simultaneously} achieves the optimal rates for smooth/non-smooth problems with either deterministic/stochastic first-order oracles. This is done without any prior knowledge of the smoothness nor the noise properties of the problem. To the best of our knowledge, this is the first adaptive, unified algorithm that achieves the optimal rates in the constrained setting. We demonstrate the practical performance of our framework through extensive numerical experiments.
A Mobile Application for Keyword Search in Real-World Scenes

Keyword search in a cluttered environment is difficult in general, and even more challenging for people with low vision. While magnification can help in reading for low vision people, it does not facilitate efficient visual search due to the constriction of the field of view. The motivating observation for this study is that, in a large number of visual search tasks, people know what are they looking for (i.e., they know the keywords), they just do not know where to find them in the scene. We have developed a mobile application that allows the users to input keywords (by voice or by typing), uses an optical character recognition (OCR) engine to search for the provided keyword in the scene captured by the smartphone camera, and zooms in on the instances of the keyword detected in the captured images, to facilitate efficient information acquisition. In this paper we describe the development and evaluation of various aspects of the application, including comparing the various mainstream OCR engines that power the app, and an evaluation study comparing the app to the conventional optical magnifier vision aid. Normally sighted adults, while wearing blur glasses to lower their visual acuity, performed keyword searches for a series of items ranging from easy to difficult with the app and with a handheld magnifier. While there was no difference in the search times between the two methods for the easier tasks, the app was significantly faster than the magnifier for the difficult tasks.
A Refined Fuzzy Min–Max Neural Network With New Learning Procedures for Pattern Classification

The fuzzy min-max (FMM) neural network stands as a useful model for solving pattern classification problems. FMM has many important features, such as online learning and one-pass learning. It, however, has certain limitations, especially in its learning algorithm, which consists of the expansion, overlap test, and contraction procedures. This article proposes a refined fuzzy min-max (RFMM) neural network with new procedures for tackling the key limitations of FMM. RFMM has a number of contributions. First, a new expansion procedure for overcoming the problems of overlap leniency and irregularity of hyperbox expansion is introduced. It avoids the overlap cases between hyperboxes from different classes, reducing the number of overlap cases to one (containment case). Second, a new formula that simplifies the original rules in the overlap test is proposed. It has two important features: (i) identifying the overlap leniency problem during the expansion procedure; (ii) activating the contraction procedure to eliminate the containment case. Third, a new contraction procedure for overcoming the data distortion problem and providing more accurate decision boundaries for the contracted hyperboxes is proposed. Fourth, a new prediction strategy that combines both membership function and distance measure to prevent any possible random decision-making during the test stage is proposed. The performance of RFMM is evaluated with the UCI benchmark datasets. The results demonstrate the effectiveness of the proposed modifications in making RFMM a useful model for solving pattern classification problems, as compared with other existing FMM and non-FMM classifiers.
Novelty Detection in Social Media by Fusing Text and Image Into a Single Structure

This work aims to propose an approach for detecting novelties, taking into account the temporal flow of data streams in social media. To this end, we present a completely new architecture for novelty detection. This new architecture entails three new contributions. First, we propose a new concept for novelty definition based on temporal windows. Second, we formulate an expression to determine the quality of a novelty. Third, we introduce a new approach to the fusion of heterogeneous data (image + text), using the COCO dataset and the MASK-RCNN convolutional neural network, which transforms image and text from social media into a single data format ready to be identified by machine learning algorithms. Since novelty detection is a task in which labeled samples are scarce or inexistent, unsupervised algorithms are used, and thus, the following baseline and state-of-the-art algorithms have been chosen: kNN, HBOS, FBagging, IForesting, and autoencoders. The new fusion approach is also compared to a state-of-the-art approach to outlier detection named AOM. Because of temporal particularities and the data types being fused, a new dataset was created, containing 27,494 tweets collected from Twitter. Our experiments show that data classification of social media using data fusion is superior to using only text or only images as input data.
Combining evidence in complex cases - a practical approach to interdisciplinary casework

Activity level evaluations, although still a major challenge for many disciplines, bring a wealth of possibilities for a more formal approach to the evaluation of interdisciplinary forensic evidence. This paper proposes a practical methodology for combining evidence from different disciplines within the likelihood ratio framework. Evidence schemes introduced in this paper make the process of combining evidence more insightful and intuitive thereby assisting experts in their interdisciplinairy evaluation and in explaining this process to the courts. When confronted with two opposing scenarios and multiple types of evidence, the likelihood ratio approach allows experts to combine this evidence in a probabilistic manner. Parts of the prosecution and defence scenarios for which forensic science is expected to be informative are identified. For these so called core elements, activity level propositions are formulated. Afterwards evidence schemes are introduced to assist the expert in combining the evidence in a logical manner. Two types of evidence relations are identified: serial and parallel evidence. Practical guidelines are given on how to deal with both types of evidence relations when combining the evidence.