Reviewerly/exHarmony huggingface dataset:
Sense Equivalence in plWordNet to Princeton WordNet Mapping

Abstract Though the interest in use of wordnets for lexicography is (gradually) growing, no research has been conducted so far on equivalence between lexical units (or senses) in inter-linked wordnets. In this paper, we present and validate a procedure of sense-linking between plWordNet and Princeton WordNet. The proposed procedure employs a continuum of three equivalence types: strong, regular and weak, distinguished by a custom-designed set of formal, semantic and translational features. To validate the procedure, three independent samples of 120 sense pairs were manually analysed with respect to the features. The results show that synsets from the two wordnets linked by interlingual synonymy relation have a greater number of equivalents than those linked through interlingual partial synonymy or interlingual hyponymy relations. Even synsets linked via interlingual synonymy may have pairs of lexical units which are only weak equivalents. More-fine grained sense linking enhances the usefulness of the mapped wordnets as a bilingual lexicon for translators or researchers.
You say Potato, I say Po-Data

Providing data visualization authoring tools for the general public remains an ongoing challenge. Inspired by block-printing, we explore how visualization stamps as a physical visualization authoring tool could leverage both visual freedom and ease of repetition. We conducted a workshop with two groups---visualization experts and non-experts---where participants authored visualizations on paper using hand-carved stamps made from potatoes and sponges. The low-fidelity medium freed participants to test new stamp patterns and accept mistakes. From the created visualizations, we observed several unique traits and uses of block-printing tools for visualization authoring, including: modularity of patterns, annotation guides, creation of multiple patterns from one stamp, and various techniques to apply data onto paper. We discuss the issues around expressivity and effectiveness of block-printed stamps in visualization authoring, and identify implications for the design and assembly of primitives in potential visualization stamp kits, as well as applications for future use in non-digital environments.
Intelligent Methods in Digital Forensics: State of the Art

This paper contains a review of modern intelligent methods’ applications for digital forensics. One of the main problems of digital forensics, which was investigated by authors, is related to the fact that a huge volume of data needs to be analysed for evidence of crime. The primary aim of this work is to improve this challenging forensic process through application of intelligent methods for analysis of digital evidences. The desired outcome of this work is to encourage advancing these methods in a forensic science discipline.
A Digital Random Number Generator Based on Irregular Sampling of Regular Waveform

There is an increasing demand for the security of information over the past few decades. Random Number Generators (RNGs) play a significant role in many cryptographic applications for generating unpredictable bit-streams. Despite the fact that Application Specific Integrated Circuit (ASIC) implementation of RNGs are usually preferred for their high performance, fully digital circuits implemented on Field Programmable Gate Array (FPGA) platforms are more attractive for designers considering their ability of rapid-reconfigurable prototyping, and their easy integration to other digital circuits. In this study, a new improvement based on irregular sampling of regular waveform is proposed for ring oscillator based RNGs. It is subjected to National Institute of Standards and Technology (NIST) 800-22 test suite and it passes full tests without any post-processing. An attack circuit is also implemented to see the robustness of the designed RNG against coupling-based attacks. No effective correlation can be obtained between the outputs of the target and the attack circuit; in spite of, their extremely close placement to each other inside the FPGA chip. The feasibility of the proposed method is given in experimental results.
Performance evaluation of speaker recognition system

AbstractAbstractSignificance of accuracy in Speaker Recognition System (SRS) is still a challenging area of research. In this paper, the accuracy of a SRS based on Mel Frequency Cepstral Coefficient (MFCC) using Correlation is analysed on a limited amount of speech corpus. The experiments have been performed on 2 Bilingual Speech Corpus. A set of two different experiments has been carried out to study the impact of recording text, recording languages, duration of speech sample and recording devices on the accuracy of SRS. On the basis of the performed experiments, it has been concluded that recognition rate degrades due to acoustic mismatch in training and testing phase. The SRS achieved accuracy of 96.67% for text-dependent and device dependent while it degrades by 30% for Language independent and device independent.Keywords: Speaker Recognition SystemSpeech CorpusMel Frequency Cepstral CoefficientCorrelation Coefficient
Specification of a UX Process Reference Model towards the Strategic Planning of UX Activities

In this conceptual paper, we present a UX process reference model (UXPRM), explain how it builds on the related work and report our experience using it. The UXPRM includes a description of primary UX lifecycle processes, and a classification of UX methods and artifacts. This work draws an accurate picture of UX base practices and allows the reader to compare and select methods for different purposes. Building on that basis, our future work consists of developing a UX Capability/Maturity Model (UXCMM) intended for UX activity planning according to the organization’s UX capabilities. Ultimately, the UXCMM aims to facilitate the integration of UX processes in software engineering, which should contribute to reducing the gap between UX research and UX practice.
Uncertainties of genuinely incompatible triple measurements based on statistical distance

We investigate the measurement uncertainties of a triple of positive operator-valued measures (POVMs) based on statistical distance, and formulate state-independent tight uncertainty inequalities satisfied by the three measurements in terms of triple-wise joint measurability. Particularly, uncertainty inequalities for three unbiased qubit measurements are presented with analytical lower bounds which relates to the necessary and sufficient condition of the triple-wise joint measurability of the given triple. We show that the measurement uncertainties for a triple measurement are essentially different from the ones obtained by pair wise measurement uncertainties by comparing the lower bounds of different measurement uncertainties.
Towards an Audio-Locative Mobile Application for Immersive Storytelling

We live in an age in which digital media is omnipresent and augmented reality is beginning to find its way into our everyday lives, GPS allows us to determine our position with meter precision and the sensor capabilities of smartphones are increasing. All these technologies in combination enable us to explore one of the oldest human art forms in a new way: storytelling. The presented work aims at developing approaches that combine different elements of audio media playback, geolocation and other sensor capabilities of smartphones to allow the creation of immersive geolocated narratives within a mobile application. Thus, the narrative can be precisely adjusted to the specific spatial and temporal context of the user. One of the main goals of such a geolocated narrative would be to influence the cognitive processing of the users. As the visual perception of the location stays the same while using the app, it is only the auditory cognition that causes a subtle enhancement and thus alteration of the reality which could result in a very strong sense of immersion. There are many location-based audio applications on the market. Most of them are intended for tourism in the form of an audio tour guide. Some of those applications have been analyzed before developing our own ideas on how to intertwine sound, space and time into an immersive mobile application for storytelling with semi-linear narrative structures including a map. Possible features could be influenceability of the narrative by, e.g., speed or approach direction of the user, sound movement over time on predetermined routes, etc. The first prototype implemented some audiovisual presentations among others.
Corpus-Driven Annotation Enrichment

A reference library can be described as a corpus of an individual composition of documents containing related work of research, documents of favorite authors, or proceedings of a conference. Enriching documents with meaningful annotations is beneficial for the performance of applications like semantic search, content aggregation, automated relationship discovery, query answering and information retrieval. Available (semi-) automatic annotation tools ignore the individual composition of documents in corpora by annotating documents with generic named-entity related data. In this paper, we present and unsupervised corpus-driven annotation enrichment approach considering the composition of documents and use an EM-like algorithm to enrich weakly annotated documents with meaningful annotations of related documents from the same corpus.
Review of convolutional neural network optimization and training in image processing

Major breakthroughs in the fields of image processing, pattern recognition, and scene classification have recently been made through the using of convolutional neural networks (CNNs) and deep learning. If the training set is sufficient, a CNN performs better than traditional machine learning algorithms and differs from them in many ways. In CNNs, feature extraction is more intelligent, so researchers no longer require extensive knowledge about the specific topic. They can focus on the CNN itself, including the structural design of the network, the model optimization, and the numerical solution. The many articles on CNNs published over the past few years propose many neural network models. Methods of training and optimization for CNNs have also been proposed. This paper reviews the history of CNNs, introduces the commonly established CNN structure, and summarizes methods and tips for CNN training.
A System Architecture of Cybersecurity Information Exchange with Privacy (CYBEX-P)

Rapid evolution of cyber threats and recent trends in the increasing number of cyber-attacks call for adopting robust and agile cybersecurity techniques. Cybersecurity information sharing is expected to play an effective role in detecting and defending against new attacks. However, reservations and or-ganizational policies centering the privacy of shared data have become major setbacks in large-scale collaboration in cyber defense. The situation is worsened by the fact that the benefits of cyber-information exchange are not realized unless many actors participate. In this paper, we argue that privacy preservation of shared threat data will motivate entities to share threat data. Accordingly, we propose a framework called CYBersecurity information EXchange with Privacy (CYBEX-P) to achieve this. CYBEX-P is a structured information sharing platform with integrating privacy-preserving mechanisms. We propose a complete system architecture for CYBEX-P that guarantees maximum security and privacy of data. CYBEX-P outlines the details of a cybersecurity information sharing platform. The adoption of blind processing, privacy preservation, and trusted computing paradigms make CYBEX-P a versatile and secure information exchange platform.
Outlier detection and disparity refinement in stereo matching

Disparity estimation in ill-posed regions, such as occlusions, repetitive patterns and textureless regions, is a challenging problem in stereo matching. The initial disparities obtained in these regions tend to be regarded as outliers that must be detected and addressed. In this paper, two outlier detection methods are proposed, i.e., the efficient approach and the accurate approach. The efficient approach detects outliers by exploring the disparity map for the left image only and reduces runtime and memory costs. First, the match fixed point jumps (MFPJ) algorithm is proposed as an initial solution to detect outliers. Then, a high-probability outlier detection algorithm is proposed to accomplish denser outlier detection with less noise. The accurate approach first classifies outliers as occlusions or mismatches. Then, 3D label assignment is performed for occlusion outliers and normal-based plane fitting is conducted for mismatch outliers to refine the disparities of the outliers and to achieve an accurate stereo matching result. Evaluations of the Middlebury datasets demonstrate that the proposed methods effectively improve the stereo matching performance.
Linear- and Linear-Matrix-Inequality-Constrained State Estimation for Nonlinear Systems

This paper considers nonlinear state estimation subject to inequality constraints in the form of linear and linear-matrix inequalities. Rewriting the standard maximum likelihood objective function used to derive the Kalman filter allows the Kalman gain to be found by solving a constrained optimization problem with a linear objective function subject to a linear-matrix-inequality constraint. Additional constraints, such as weighted-norm- or linear-inequality constraints, that the state estimate must satisfy are easily augmented to the constrained optimization problem. The proposed constrained estimation methodology is applied in the extended Kalman filter (EKF) and sigma point Kalman filter (SPKF) frameworks. Motivated by estimation problems involving a vehicle that can rotate and translate in space, multiplicative versions of the constrained EKF and SPKF formulations are discussed. Simulation results for a ground-based mobile robot operating in a constrained three-dimensional terrain are presented and are compared to results that use the traditional multiplicative EKF and SPKF, as well as filters that enforce inequality constraints by simply projecting the state estimate into the constrained domain along the shortest Euclidean distance.
Presenting Tendermint: Idiosyncrasies, Weaknesses, and Good Practices

In this paper, we present Tendermint: a Byzantine Fault Tolerant (BFT) application-based blockchain. We show that Tendermint promotes another perception of blockchain programming. Unlike Ethereum which is a blockchain holding many applications, Tendermint proposes to have one application per blockchain. We discuss the idiosyncrasies of Tendermint and how it could, potentially, ease blockchain programming and improve performance. We finish by showing weaknesses of Tendermint, good practices to adopt to hinder security attacks when handling Tendermint nodes, and some potential adjustments in the IBC protocol - an interoperability protocol designed for Tendermint. Our goal is to introduce Tendermint by showing its main characteristics over traditional blockchain platforms such as Bitcoin or Ethereum.
A new algorithm for image inpainting in Fourier transform domain

One of the aims of image inpainting is recovering an image some of which Fourier transform coefficients are lost. In this paper, we present a new algorithm for image inpainting in Fourier transform domain. We consider the effect of spectrum and phase angle of the Fourier transform, separately. Hence, two regularization parameters are generated; therefore, we have two degree of freedom. Some numerical examples confirm our proposed method.
Weighted Posets and Digraphs Admitting the Extended Hamming Code to be a Perfect Code

Recently, Etzion et al. introduced metrics on F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> based on directed graphs on n vertices and developed some basic coding theory on directed graph metric spaces. In this paper, we consider the problem of classifying directed graphs, which admit the extended Hamming codes to be a perfect code. We first consider weighted poset metrics as a natural generalization of poset metrics and investigate interrelation between the weighted poset metrics and the directed graph-based metrics. In the next, we classify weighted posets on a set with eight elements and directed graphs on eight vertices, which admit the extended Hamming code H̃ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> to be a two-perfect code. We also construct some families of such structures for any k ≥ 3, which can be viewed as generalizations of some results presented by Etzion et al. and Hyun and Kim. Those families enable us to construct packing or covering codes of radius 2 under certain maps.
Studying emotions in Romanian words using Formal Concept Analysis

RoEmoLex (Romanian Emotion Lexicon) is a resource developed for emotion detection in Romanian language, now at its third version. Initially translated automatically from an English version and containing terms annotated with eight primary emotions and two polarity tags, this lexicon has undergone a series of changes. New tags were attached to words, including derived emotions, part-of-speech, additional polarity scores and conceptual category information. Moreover, the lexicon was expanded with synonyms of the original terms and words and phrases from similar resources. This paper presents the current version of the emotion lexicon, describing improvements over the previous forms and presenting summary statistics. Through Formal Concept Analysis, a knowledge discovery technique, we examine the structure and content of the lexicon. Dependencies between words at the emotional level are discovered, conceptual hierarchies are built and comparisons with information from another Romanian lexical resource (RoWordNet) are made. The results encourage further research and the possibility to integrate RoEmoLex as a subcomponent of emotion analysis systems for Romanian text.
Measuring the Wave Height Based on Binocular Cameras

The wave is an important hydrological element in marine research. Accurately describing the characteristics of waves is therefore significant to the study of marine power. The contents of this article are as follows: (1) a wave height measurement system using binocular cameras is proposed, and the small tank experiments are conducted to prove the efficacy of the proposed system; (2) based on the scale invariant feature transition (SIFT) algorithm, sub-pixel Harris corners are calculated in the difference-of-Gaussian (DOG) space to locate key points more accurately; and (3) a bi-directional epipolar constraint is employed to decrease the mismatch rate and computation time.
Hidden States Exploration for 3D Skeleton-Based Gesture Recognition

3D skeletal data has recently attracted wide attention in human behavior analysis for its robustness to variant scenes, while accurate gesture recognition is still challenging. The main reason lies in the high intra-class variance caused by temporal dynamics. A solution is resorting to the generative models, such as the hidden Markov model (HMM). However, existing methods commonly assume fixed anchors for each hidden state, which is hard to depict the explicit temporal structure of gestures. Based on the observation that a gesture is a time series with distinctly defined phases, we propose a new formulation to build temporal compositions of gestures by the low-rank matrix decomposition. The only assumption is that the gesture's "hold" phases with static poses are linearly correlated among each other. As such, a gesture sequence could be segmented into temporal states with semantically meaningful and discriminative concepts. Furthermore, different to traditional HMMs which tend to use specific distance metric for clustering and ignore the temporal contextual information when estimating the emission probability, the Long Short-Term Memory (LSTM) is utilized to learn probability distributions over states of HMM. The proposed method is validated on two challenging datasets. Experiments demonstrate that our approach can effectively work on a wide range of gestures and actions, and achieve state-of-the-art performance.
Feature Selection Algorithm Based on Association Rules

Feature selection is one of the key issues in pattern recognition. The quality of the feature selection has a direct impact on the classification accuracy and generalization performance of the classifier. In order to reduce the size of the feature subset and improve the efficiency of the algorithm without reducing the accuracy, this paper proposes a feature selection algorithm based on association rules, ARFS. The algorithm uses association rules to mine the frequent 2-items set of the feature attributes and category in the dataset. Then the algorithm sorts the features according to the confidence of the frequent 2-items set, and then combines the sequential forward selection method, and uses the classification performance of the decision tree classifier as the evaluation criteria of the feature subsets. In terms of experiments, this paper selects five public datasets in the UCI Machine Learning Repository to conducted three comparison experiments of feature subset selection, learning algorithm accuracy and runtime for four feature selection algorithms. Experimental results show that the ARFS is superior to the contrast method in the feature subset size and the accuracy. However, the ARFS is slightly inferior to the ReliefF algorithm in the runtime.
Client Diversity Factor in HTTPS Webpage Fingerprinting

Webpage fingerprinting methods infer the webpages visited in a traffic trace and are serious threats to the privacy of web users. Prior work evaluates webpage fingerprinting methods using traffic samples from a single client and does not consider the client diversity factor---webpages can be visited using different browsers, operating systems and devices. In this paper, we study the impact of client diversity on HTTPS webpage fingerprinting. First, we evaluate 5 prominent fingerprinting methods using traffic samples from 19 different clients. We show that the best performing methods overfit to the traffic patterns of a single client and do not generalize when they are evaluated using the samples from a different client (even if the clients use the same browser and operating system and only differ in device). Then, we investigate the traffic patterns of the clients and find differences in the HTTP messages generated, servers communicated and implementation of HTTP/2 across the clients. Finally, we show that the robustness of the methods can be increased by training them using the samples from a diverse set of clients. This study informs the community towards a realistic threat model for HTTPS webpage fingerprinting and presents an analysis of modern HTTPS traffic.