Reviewerly/exHarmony huggingface dataset:
The Unreasonable Effectiveness of Pattern Generation

Languages are constantly evolving, and so are their hyphenation rules and needs. The effectiveness and utility of TeX's hyphenation have been proven by its usage in almost all typesetting systems in use today. The current Czech hyphenation patterns were generated in 1995, and no hyphenated word database was freely available. We have developed a new Czech word database and have used the patgen program to generate new effective Czech hyphenation patterns efficiently and evaluated their generalization qualities. We have achieved full coverage on the training dataset of 3,000,000 words, and developed a validation procedure of new patterns for Czech based on the testing database of 105,000 words approved by the Czech Academy of Science linguists. Our pattern generation case study exemplifies a practical solution to the widespread dictionary problem. The study has proven the versatility, effectiveness, and extensibility of Liang's approach to hyphenation developed for TeX. The unreasonable effectiveness of the pattern technology has led to applications that are and will be used, even more widely now, nearly 40 years after its inception.
Automated Real-Time Email Classification System Based on Machine Learning

Classification of massive unstructured emails targeted to the organization’s official email address is a tedious task and sometimes involves manual intervention. This research work proposes an automated real-time email classification system using machine learning techniques. Various feature construction and feature representation techniques that are commonly used in the case of text classification problems are tested and compared in this context. Effect of application of different preprocessing steps along with interaction terms and dimension reduction techniques is analyzed. The work also compares the efficacy of different standard classification algorithms against two different datasets. After classification, the system is made capable of routing the incoming emails targeted toward the organization’s official email address to specific teams based on the contextual meaning of text embedded in the mail. Experimental results show that the proposed email classifier gives good performance in real-time classification. The system is interfaced with Gmail to demonstrate its effectiveness in real time.
The Relevance of Using Post-quantum Cryptography on the Isogenies of Elliptic Curves for Mobile Application Protection

The article is a brief overview of the relevance of using cryptographic protocols on isogenies of elliptic curves in mobile applications. The article briefly describes the current situation in the field of cryptography that has arisen in connection with the development of a new generation of computer technology, namely, quantum computers. The article describes the stages of standardization of post-quantum cryptography in accordance with the NIST competition. The article contains a comparison of post-quantum algorithms in terms of cryptographic stability and speed, defines of advantages and disadvantages of protocols on isogenies of elliptic curves, and results of studies on the implementation of the protocols on isogenies of elliptic curves on mobile platforms. The main goal of the article is to consider the possibility of using protocols on the isogeny of elliptic curves for practical purposes to protect confidential information in mobile systems and applications.
Development of an accurate CAD model of femur bone

This research work explores the area of biomedical image processing. A CAD model of human femur structure has been developed from computerized tomographic image taken as input data, which is further modified and segmented to form an accurate CAD model of a human femur by varying the segmentation methods and parameters, multi component model of whole femur structure is obtained as an assembly of bone. The segmentation is performed using an open source code ITK SNAP. This segmented CAD model of human femur structure can be used for engineering analysis such as static structural analysis, Model analysis and also used in manufacturing of handicapped aids, artificial bones, safety in sports equipment, design and implementing the rapid prototyping for Multi scaled model development.
Supersaliency: A Novel Pipeline for Predicting Smooth Pursuit-Based Attention Improves Generalisability of Video Saliency

Predicting attention is a popular topic at the intersection of human and computer vision.However, even though most of the available video saliency data sets and models claim to target human observers' fixations, they fail to differentiate them from smooth pursuits (SPs), a major eye movement type that is unique to perception of dynamic scenes.In this work, we strive for a more meaningful prediction and conceptual understanding of saliency in general.Because of the higher attentional selectivity of smooth pursuit compared to fixations modelled in traditional saliency research, we refer to the problem of SP prediction as ''supersaliency''.To make this distinction explicit, we (i) use algorithmic and manual annotations of SPs and fixations for two well-established video saliency data sets, (ii) train Slicing Convolutional Neural Networks for saliency prediction on either fixation-or SP-salient locations, and (iii) evaluate our and 26 publicly available dynamic saliency models on three data sets against traditional saliency and supersaliency ground truth.Overall, our models outperform the state of the art in both the new supersaliency and the traditional saliency problem settings, for which literature models are optimised.Importantly, on two independent data sets, our supersaliency model shows greater generalisation ability than its counterpart saliency model and outperforms all other models, even for fixation prediction.Furthermore, we tested an end-to-end video saliency model, which also showed systematic improvements when smooth pursuit was predicted either exclusively or together with fixations, with the best performance achieved when the model was trained for the supersaliency problem.This demonstrates the practical benefits and the potential of principled training data selection based on eye movement analysis.
Introducing Hilbert Space and Quantum Cognition to Cyber Security Risk Management

The existing computational cognitive models in cyber security risk management have major limitations in the analysis of cyber security behaviors. To address this issue, we introduce Hilbert space and quantum cognition to cyber security risk management. We compare some key axioms and definitions of classical cognition and quantum cognition. We provide examples on how some unique principles of Hilbert space and quantum cognition such as compatibility can help with the event representation, and capturing cognitive biases that are not possible with classical probability. We shed light on how the mathematical formalism of quantum probability can model the observed deviations from the classicality in human reasoning. We also shed light on how quantum cognition can contribute to the science and practice of cyber security. We argue that Hilbert space and quantum cognition can reconcile violations of classical probability theory in cyber security risk management with a formal theory, and examine whether it is possible to express formally some of the key heuristics in cyber security behaviors.
Multi-UAV Formation Transformation Based on Improved Heuristically-Accelerated Reinforcement Learning

Addressing the shortcomings of the classical reinforcement learning algorithms in solving the problem of multi-UAV formation transformation, such as large consumption of computing resources and slow convergence speed, this paper introduces heuristics and uses back propagation algorithms to construct a heuristic function required for multi-UAV formation convergence. When designing the action selection function of reinforcement learning algorithms, the idea of simulated annealing is also introduced, which enables the algorithm to fully explore the combination of convergence actions in the early stage so as to jump out of the local optimal trap, while ensuring the convergence of the algorithm in the later stage. The data of the final simulation experiment show that the improved heuristically-accelerated reinforcement learning algorithms can effectively reduce the consumption of UAV computing resources, improve the convergence speed the convergence quality when dealing with the problem of multi-UAV formation transformation.
Future Trends in Wearable Computing

This chapter concludes the book by presenting a critical view about novel interfacesInterface for wearableWearability technologiesTechnology, focusing on the miniaturizationMiniaturization of devicesDevices as well as on-body interfacesInterface that employ multiple modalities. The examplesExample of technologyTechnology presented include electronicElectronic tattoos and implantedImplanted devicesDevices that are seamlesslySeamless connected to the usersUser’ body to facilitate dataData collection and userUser interactionInteraction.
First-Degree Entailment and Structural Reasoning

In this paper it is shown how the logic of first-degree entailment of Anderson and Belnap can be represented by a variety of deductively equivalent binary (Fmla-Fmla) consequence systems, up to a system with transitivity as the only inference rule. Some possible extensions of these systems are briefly considered as well.
History Coupling Space: A New Model to Represent Evolutionary Relations

During software evolution, files are usually changed together for accommodating modifications. Although co-change analysis has been widely adopted for difference studies, such as defect prediction, impact analysis, architectural relations identification etc., there has been little work characterizing co-changed files as a group and modeling the evolution of these files to represent software maintenance and evolution. In this paper, we present the concept of history coupling dependency, based on which, we propose a novel model, history coupling space (HCSpace), to link the co-changed files and represent how files are historically connected as a group. Our investigations on seven open source projects show that each HCSpace could be treated as a maintenance unit where the involved files are more likely to evolve together. The results also show that the HCSpaces have important impacts on a project's maintenance. In particular, we demonstrate that identified HCSpaces consumes a relatively large portion of maintenance effort spent on a project, and these HCSpaces are still actively evolving.
Time Series and Its Characteristics Variability Analysis Using the Forecasting Model Based on the Basis of Interval Discrete Fuzzy Sets of the Second Type

The approach to the problem of analyzing the variability of the time series and its characteristics in the context of developing a decision on the advisability of implementing the short-term forecasting procedure for one step forward has been considered. The Hurst exponent and fractal dimension are used as the characteristics of the time series. It is proposed to use the forecasting model on the basis of interval discrete fuzzy sets of the second type with the aim of analyzing the parameters of the generated “footprint of uncertainty” for the initial time series and the time series describing one or another of its characteristics, and deciding on the alleged success or failure of the short-term forecasting procedure for one step forward. The examples that confirm the effectiveness of the proposed approach to the problem of analyzing the variability of the time series have been given.
Designing Sound Security Metrics

This article begins with an introduction to security metrics, describing the need for security metrics, followed by a discussion of the nature of security metrics, including the challenges found with some security metrics used in the past. The article then discusses what makes a sound security metric and proposes a rigorous step-by-step method that can be applied to design sound security metrics, and to test existing security metrics to see if they are sound metrics. This is followed by a discussion of the feasibility of having scientifically-based security metrics and whether or not such metrics are sound. Application examples are included to illustrate the design and testing of sound security metrics.
Deep Learning in the Domain of Near-Duplicate Document Detection

Increasing of web users due to the popularity of the internet increases the digital documents on the web, and among them many are duplicates and near-duplicates. Identifying duplicate and near-duplicate documents in a huge collection is a significant problem with widespread application and hence, detection and elimination of those documents are the need of the day. This paper proposes a technique to detect the near-duplicate documents on the web which has four main aspects: the first aspect is related to the selection of the important terms from a corpus of documents by developing a new correlation-based feature selection (CBFS) mechanism which enhance the performance of the classifier. The second aspect is to compute the similarity scores between each pair of documents of the corpus. The third aspect concerns with combining these similarity scores with the class label of each pair of documents to generate the feature vector for training the Multi-layer ELM (deep learning architecture) and other established classifiers and the fourth and final aspect introduces a heuristics method to rank the near-duplicate documents based on their similarity scores. The empirical results on DUC datasets witness the effectiveness of the proposed approach using Multi-layer ELM as highly appreciable compared to other state-of-the-art classifiers including the deep learning classifiers.
TK-Text: Multi-shaped Scene Text Detection via Instance Segmentation

Benefit from the development of deep neural networks, scene text detectors have progressed rapidly over the past few years and achieved outstanding performance on several standard benchmarks. However, most existing methods adopt quadrilateral bounding boxes to represent texts, which are usually inadequate to deal with multi-shaped texts such as the curved ones. To keep consist detection performance on both quadrilateral and curved texts, we present a novel representation, i.e., text kernel, for multi-shaped texts. On the basis of text kernel, we propose a simple yet effective scene text detection method, named as TK-Text. The proposed method consists of three steps, namely text-context-aware network, segmentation map generation and text kernel based post-clustering. During text-context-aware network, we construct a segmentation-based network to extract feature map from natural scene images, which are further enhanced with text context information extracted from an attention scheme TKAB. In segmentation map generation, text kernels and rough boundaries of text instances are segmented based on the enhanced feature map. Finally, rough text instances are gradually refined to generate accurate text instances by performing clustering based on text kernel. Experiments on public benchmarks including SCUT-CTW1500, ICDAR 2015 and ICDAR 2017 MLT demonstrate that the proposed method achieves competitive detection performance comparing with the existing methods.
Communication and Technological Skills to Improve University Teaching Performance

The present work carries out a literature review that allows to explain how new technologies have developed to be applied to teaching-learning processes in universities, this has been done by means of overviewing achievements and limitations from previous research, bearing in mind the ubiquitous use of mass social media. Experiences in Ecuador and Peru show that an actual planning that encourages the use and development of technology aimed to improve students and teachers’ competences, even if almost every institution of higher education is provided with online education systems. This leads to a discussion that intends to achieve a synergy that advocates for efficient knowledge generation by using popular social media such as Facebook, Twitter, Instagram and Linkedin.
'Who Knows What'

Social networking sites are becoming increasingly popular venues for seeking information. To the extent that users can understand who knows what in their networks and target those friends appropriately, they can make effective use of the site as a knowledge base of information. This paper explores how targeting one's Facebook network when asking questions influences the breadth and quality of answers they receive. An experiment (N = 64) was conducted in which participants posted questions to their Facebook networks in four broadcast level conditions: status update to their full networks, status update to a custom subset of their networks, posting on a friend's Timeline, and sending a direct message. Results indicate that posting a question more broadly results in more information, which is moderated by perceptions of Facebook as a transactive memory system and as a source of social capital. However, informational and social value of responses is greatest when posting to a custom subset of their network. These results suggest that while targeting specific individuals may be the most effective means of gathering information in offline networks, the broadcast affordance of Facebook may be a more useful way to gather information on the site.?
Communication Signal Recognition Technique Based on Fusion Deep Belief Network

The increasingly complex electromagnetic environment and limited spectrum resources pose serious challenges for the recognition of communication signal. The technique of manually feature extraction no longer meets the actual needs. In order to solve the problem of high error rate of communication signal recognition under low Signal-to-Noise Ratio (SNR), we propose a recognition technique that can recognize eight types of modulations. First, we propose Fusion Deep Belief Network (FDBN) that extracts shape features while extracting the essential features of the signal by self-training. Furthermore, considering the problem that the features extracted by FDBN at low SNR are susceptible to noise, we explore the denoising technique based on Variational Mode Decomposition (VMD). The simulation results show that when the SNR is 0dB, the overall recognition rate of the proposed algorithm reaches 98.78%. The proposed recognition algorithm can also be used to recognize the modulation of radar signal.
Implementation of ANN Training Module on Field Programmable Gate Arrays

This study provides an application-specific integrated circuit (ASIC) diagram of Artificial Neural Networks (ANN) with module design for 32-bit floating point operations on Field Programmable Gate Array (FPGA). It is aimed that ANNs train operations are moved from software to hardware and calculations are made by using IEEE 754 single precision floating point number format. The proposed architecture is designed with combination of Verilog and Very High Speed Integrated Circuits Hardware Description Language (VHDL). Sigmoidal non-linear function was used as the activation function of the train and lookup table (LUT) was created for process efficiency of the designed circuit. Natural parallelisms were used in the calculation of the operations, which are implemented on FPGA, thus the system operations was accelerated by performing independent operations during the same clock cycle. The results obtained from FPGA were compared with the results obtained from MATLAB R2016b.
POCS-Based Super-Resolution Image Reconstruction Using Local Gradient Constraint

In order to suppress and reduce the ringing artifacts of the super resolution reconstruction results, an improved POCSbased reconstruction algorithm is presented in this paper. Local gradient consistency constraint is adopted to reduce the estimation error of traditional results computed with gray value consistency. The fi xed threshold of convex sets constraint is also replaced by an adaptive version. Finally, the advanced algorithm is implemented with a relax factor of projection operation. The accuracy and effectiveness of the proposed algorithm is verified by some experiments.
Applying Linguistic G2P Knowledge on a Statistical Grapheme-to-phoneme Conversion in Khmer

A Grapheme-to-Phoneme (G2P) convertor generates the pronunciation given a word. G2P is an important module in a speech synthesis system and an automatic speech recognition system. Two main G2P approaches are: knowledge-based and data-driven. The knowledge based G2P is built based on linguist knowledge. The data-driven approach such as the statistical approach on the other hand does not need expert knowledge, but it requires data to learn the rules. In this research, we propose an approach that combines linguistic knowledge into a statistical-based G2P convertor for Khmer. We examined a simple way of adding linguistic knowledge into the statistical G2P convertor by simply inserting vowel tags into a Khmer word. Three types of vowel tags were used. The main strength of this approach is it combines the strength of linguistic knowledge and statistical-based approach, to build a robust G2P model. The information allows better modeling and prediction of the phoneme sequence, thus improving the phoneme error rate (PER) and word error rate (WER). The PER and WER of our proposed Khmer G2P improve from 23.2% and 69.6% to 11.1% and 51.4% respectively.
Clinical Scene Segmentation with Tiny Datasets

Many clinical procedures could benefit from automatic scene segmentation and subsequent action recognition. Using Convolutional Neural Networks to semantically segment meaningful parts of an image or video is still an unsolved problem. This becomes even more apparent when only a small dataset is available. Whilst using RGB as the input is sufficient for a large labelled dataset, achieving high accuracy on a small dataset directly from RGB is difficult. This is because the ratio of free image dimensions to the number of training images is very high, resulting in unavoidable underfitting. We show that the addition of superpixels to represent an image in our network improves the semantic segmentation, and that superpixels can be learned to be detected by Convolutional Neural Networks if those superpixels are appropriately represented. Here we present a novel representation for superpixels, multichannel connected graphs (MCGs). We show how using pre-trained deep learned superpixels used in an end-to-end manner achieve good semantic segmentation results without the need for large quantities of labelled data, by training with only 20 instances for 23 classes.
Comparison of Spatial Clustering Techniques for Location Privacy

Location privacy was born to deal with protection privacy issues which came with the massification of georeferenced data due to the frequent use of phones, social media, GPS services and other applications. This georeferenced data can be directly connected to users' personal information like religion, health and tracking, and can be used for different purposes, such as local analysis or selling it to third party companies, which represents a risk for individuals when the information is published or robbed without any protection through a location privacy protection mechanism - LPPMs. Many LPPMs have been proposed in different papers, one of them is called VoKA, a K-Aggregation offline technique. The methodology explained in this paper takes the first part of VoKA, a gridification process, and then applies two different spatial clustering algorithms, K-Means and DBSCAN, in order to protect each point of a dataset. To explain how this mechanism works, a dataset of Dengue registers in Barranquilla-Colombia and its outskirts was used, taking into account that this kind of data is considered sensitive. The results explain how this dataset can fit better with one of the algorithms and its respective metrics using squared error, point loss and heatmap comparisons.