Reviewerly/exHarmony huggingface dataset:
Design and Calculation of Complex Directional-Well Trajectories on the Basis of the Minimum-Curvature Method

Summary The vector-algebra method and the minimum-curvature method were used to solve the trajectory design challenge for intersecting two wells. In accordance with known parameters, we expressed algorithms for intersecting a horizontal well. The geometrical shape of the target area was extended to attain the algorithms needed for landing on spherical and cylindrical surfaces. Combined with the expression for calculating the tool-face angle, the algorithms needed for calculating the closest distance to an existing well and landing surface were also proposed. The algorithms presented here provide a theoretical basis for the design and control of complex directional-well trajectories.
Reinforcing Self-expressive Representation with Constraint Propagation for Face Clustering in Movies

The ability to robustly cluster faces in movies is a necessary step in understanding media content representations of people along dimensions such as gender and age. Building upon the successes of sparse subspace clustering (SSC) in uncovering the underlying structure of the data, in this paper we propose an algorithm called Constraint Propagation Sparse Subspace Clustering (CP-SSC) for applications such as face clustering in videos where pairwise sample constraints (must-link and cannot-link sample pairs) are available in the processing pipeline since detected faces can be tracked locally in time. We learn the subspace structure while simultaneously incorporating the pairwise constraints to construct a similarity matrix needed for clustering. Our joint formulation uses low-rank matrix completion to propagate the initial pairwise constraints, that are used to reinforce the subspace representation during optimization. We evaluate CP-SSC for clustering faces in movies with pre-trained neural network embeddings as features. We first analyze CP-SSC with synthetic data and then show that it can be effectively used to cluster faces in movie videos. We evaluate our method for two movies annotated in-house and two benchmark movies released publicly. We also compare the performance of our algorithm with other clustering approaches that use pairwise constraint information.
Use of Software Process in Research Software Development

Background: Developers face challenges in building high-quality research software due to its inherent complexity. These challenges can reduce the confidence users have in the quality of the result produced by the software. Use of a defined software development process, which divides the development into distinct phases, results in improved design, more trustworthy results, and better project management. Aims: This paper focuses on gaining a better understanding of the use of software development process for research software. Method: We surveyed research software developers to collect information about their use of software development processes. We analyze whether and demographic factors influence the respondents' use of and perceived value in defined process. Results: Based on 98 responses, research software developers appear to follow a defined software development process at least some of the time. The respondents also have a strong positive perception about the value of following processes. Conclusions: To produce high-quality and reliable research software, which is critical for many research domains, research software developers must follow a proper software development process. The results indicate a positive perception of value about using defined development processes that should lead to both short-term benefits through improved results and long-term benefits through more maintainable software.
Saliency detection via multi-view graph based saliency optimization

Saliency detection is an important problem in computer vision and pattern recognition area. Many works have been proposed for addressing the saliency detection task. As a popular method, graph based saliency optimization has been widely studied. However, previous works have universally focussed on single graph optimization which fails to consider multi-view feature representation of image content. In this paper, we first provide a general framework for traditional graph based saliency optimization models. Then, we extend the general framework to the multi-view case and propose our general multi-view graph based saliency optimization model. Finally, we present a particular implementation of our general model and derive an effective updating algorithm to solve it. Experimental results using several benchmark datasets demonstrate the effectiveness of our proposed saliency model.
EvoChef: Show Me What to Cook! Artificial Evolution of Culinary Arts

Computational Intelligence (CI) has proven its artistry in creation of music, graphics, and drawings. EvoChef demonstrates the creativity of CI in artificial evolution of culinary arts. EvoChef takes input from well-rated recipes of different cuisines and evolves new recipes by recombining the instructions, spices, and ingredients. Each recipe is represented as a property graph containing ingredients, their status, spices, and cooking instructions. These recipes are evolved using recombination and mutation operators. The expert opinion (user ratings) has been used as the fitness function for the evolved recipes. It was observed that the overall fitness of the recipes improved with the number of generations and almost all the resulting recipes were found to be conceptually correct. We also conducted a blind-comparison of the original recipes with the EvoChef recipes and the EvoChef was rated to be more innovative. To the best of our knowledge, EvoChef is the first semi-automated, open source, and valid recipe generator that creates easy to follow, and novel recipes.
Model of the Optimal Maneuver Route

The chapter deals with the mathematical model for planning the optimal movement route, which has been implemented in the Tactical Decision Support System (TDSS).The model processes and evaluates the data contained in the five raster layers, which are tactically relevant for planning the movement route of troops or autonomous vehicles on the battlefield.The basis for calculating the optimal movement route is a ground surface layer, which is then modified by algorithmic and criterion relationships with the layers of hypsometry, weather attack, and the activities of enemy and friendly units.The result of mathematical model calculations is a time-optimized and safe movement route displayed on the topographic basis.The experiments realized have verified the function of the optimal movement route model when neither the reconnaissance group nor the autonomous vehicle was observed by the enemy.The total time of the UGV with the use of the TDSS to cover the route of maneuver was 67 minutes shorter than the real time of the BRAVO group movement with the use of the TDSS and 105 minutes shorter than the real time of the ALFA group without the use of the TDSS.The comparison of responses to the attack shows that the BRAVO group using the Maneuver Control System (MCS CZ) as part of the TDSS has destroyed the attackers faster by 71 seconds than the ALFA group without the use of the TDSS.
Co-attention Network and Low-rank Bilinear Pooling for Aspect Based Sentiment Analysis

Aspect Based Sentiment Analysis (ABSA) is an important and challenging task in language understanding. It aims to assign the correct polarity to a given sentence considering the entity on which an opinion is expressed. Extant neural networks usually employ Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), or Attention networks to address the so-called target-sensitive sentiment problem, referring to the fact that sentence polarity is decided by aspect information and surrounding contexts jointly. However, those models usually are complicated and will incur tremendous training cost. Instead of using sophisticated sequential networks, we present a novel co-attention based network to capture the correlation between aspect and contexts. We evaluate our model on three public datasets and the results demonstrate a strong evidence of improved accuracy (up to 2.32% absolute improvement) and efficiency (model converges at least 4 times faster).
Speaker-dependent Wavenet-based Delay-free Adpcm Speech Coding

This paper proposes a WaveNet-based delay-free adaptive differential pulse code modulation (ADPCM) speech coding system. The WaveNet generative model, which is a state-of-the-art model for neural-network-based speech waveform synthesis, is used as the adaptive predictor in ADPCM. To further improve speech quality, mel-cepstrum-based noise shaping and postfiltering were integrated with the proposed ADPCM system. Both objective and subjective evaluation results indicate that the proposed ADPCM system outperformed not only the conventional ADPCM system based on ITU-T Recommendation G.726 but also the ADPCM system based on adaptive mel-cepstral analysis.
Formant-gaps Features for Speaker Verification Using Whispered Speech

In this work, we propose a new feature based on formants for whispered speaker verification (SV) task, where neutral data is used for enrollment and whispered recordings are used for test. Such a mismatch between enrollment and test often degrades the performance of whispered SV systems due to the difference in acoustic characteristics of whispered and neutral speech. We hypothesize that the proposed formant and formant gap (F oG) features are more invariant to the modes of speech in capturing speaker specific information compared to traditional baseline features for SV including mel frequency cepstral coefficients (MFCC) and auditory-inspired amplitude modulation features (AAMF). Whispered SV experiments with 714 speakers comprising 29232 neutral and 22932 whispered recordings reveal that the equal error rate (EER) using the proposed features is lower than that using the best baseline features by ~3.79% (absolute). It was also observed that at least four whispered recordings during enrollment are required for the baseline features to perform at par with the proposed features. However, it was found that the best performing baseline features yield an EER for neutral SV task which is ~1.88% higher than that using the proposed features.
A novel approach to remove outliers for parallel voice conversion

Alignment is a key step before learning a mapping function between a source and a target speaker’s spectral features in various state-of-the-art parallel data Voice Conversion (VC) techniques. After alignment, some corresponding pairs are still inconsistent with the rest of the data and are considered outliers. These outliers shift the parameters of the mapping function from their true value and hence, negatively affect the learning of mapping function during the training phase of the VC task. To the best of the authors’ knowledge, the effect of outliers (and hence, their removal) on quality of the converted voice has not been much explored in the VC literature. Recent research has shown the effectiveness of the outlier removal as a pre-processing step in the VC. In this paper, we extend this study with a detailed theory and analysis. The proposed method uses a score distance that is estimated using Robust Principal Component Analysis (ROBPCA) to detect the outliers. In particular, the outliers are determined using a fixed cut-off on the score distances, based on the degrees of freedom in a chi-squared distribution, which is speaker-pair independent. The fixed cut-off is due to the assumption that the score distances follow the normal (i.e., Gaussian) distribution. However, this is a weak statistical assumption even in the cases where quite many samples are available. Hence, in this paper, we propose to explore speaker-pair dependent cut-offs to detect the outliers. In addition, we have presented our results on two state-of-the-art databases, namely, CMU-ARCTIC and Voice Conversion Challenge (VCC) 2016 by developing various state-of-the-art methods in the VC. In particular, we have presented the effectiveness of the outlier removal on Gaussian Mixture Model (GMM), Artificial Neural Network (ANN), and Deep Neural Network (DNN)-based VC techniques. Furthermore, we have presented subjective and objective evaluations using a 95% confidence interval for the statistical significance of the tests. We obtained an average 0.56% relative reduction in Mel Cepstral Distortion (MCD) with the proposed outlier removal approach as a pre-processing step. In particular, with the proposed speaker-pair dependent cut-off, we have observed relative improvement of 12.24% and 30.51% in the speech quality, and 39.7% and 4.27% absolute improvement in the speaker similarity for the CMU-ARCTIC and the VCC 2016, respectively.
Investigation on Neural Bandwidth Extension of Telephone Speech for Improved Speaker Recognition

We extend our previous work on training mixed-bandwidth (BW) speaker recognition system by predicting missing information in upperband (UB) of upsampled telephone speech. Mixed-BW systems combine speech from narrowband (NB) and wideband (WB) speech corpora by basic upsampling of NB speech with low-pass filter interpolator, resulting in no information loss in the original WB speech. In this work, we explore the usage of a deep residual full-convolutional neural network (CNN) and a bidirectional long short term memory (BLSTM) network along with a previously proposed deep neural network (DNN) for bandwidth extension (BWE) of NB telephone speech. Speaker recognition systems trained with bandwidth extended features improved in performance over mixed-BW and NB baseline systems. In terms of detection cost function (DCF), the CNN-BWE system improved by 10.78% and 15.96% (relative) in the Speakers In The Wild (SITW) eval core and assist-multi-speaker condition respectively w.r.t. the NB baseline; and improved by 3.21% and 4.13% w.r.t. to the mixed-BW baseline.
Utilizing Recurrent Neural Network for topic discovery in short text scenarios1

The volume of short text data increases rapidly these years. Data examples include tweets and online Q&A pairs. It is essential to organize and summarize these data automatically. Topic model is one of the effective approaches, whose application domains include text mining, personalized recommendat ion and so on. Conventional models like pLSA and LDA are designed for long text data. However, these models may suffer from the sparsity problem brought by lacking words in short text scenarios. Recent studies such as BTM show that using word co-occurrent pairs is effective to relieve the sparsity problem. However, both BTM and extended models ignore the quantifiable relationship between words. From our perspectives, two more related words should occur in the same topic. Based on this idea, we introduce a model named RIBS, which makes use of RNN to learn relationship. By using the learned relationship, we introduce a model named RIBS-Bigrams, which can display topics with bigrams. Through experiments on two open-source and real-world datasets, RIBS achieves better coherence in topic discovery, and RIBS-Bigrams achieves better readability in topic display. In the document characterization task, the document representation of RIBS can lead better purity and entropy in clustering, higher accuracy in classification.
An Evolutionary Strategy For Concept-Based Multi-Domain Sentiment Analysis

Inferencing the sentiment expressed within a document is still a challenging task, especially when it is necessary to consider the domain dimension. In order to improve inference algorithm effectiveness, one of the main challenges is to learn polarity values representing the concept-domain pair. In this paper, we present an approach which relies on evolutionary algorithms and exploiting semantic relationships for estimating domain-dependent polarities of opinion concepts. The SenticNet resource is used as a starting point for extracting both concepts and common-sense expression relevant to the sentiment analysis topic. Subsequently, the creation of semantic relations is performed by exploiting the alignments between SenticNet and WordNet. Finally, an evolutionary strategy has been implemented for learning the polarity values of concept-domain pairs. Our approach has been validated by following the Dranziera protocol and obtained results demonstrated the suitability of the proposed solution.
Crowdsourcing Platform Toward Seismic Disaster Reduction: The Taiwan Scientific Earthquake Reporting (TSER) System

We initiated an earthquake reporting project in 2016 to collect field observations of ground damages caused by large earthquakes from trained volunteers and interested citizens. After a potentially damaging earthquake occurs in the Taiwan area, our system, the Taiwan Scientific Earthquake Reporting System (TSER), would send a notice to the participants, who are encouraged to visit the epicentral area to survey and describe as in detail as possible the variations of the ground damages using a Usahidi-based mapping platform. They may also upload relevant images in the field when the condition permitted (i.e., good mobile signal). This collective information will be shared with the public after a quick check by the on-duty scientists. Statistically in Taiwan, damaging inland earthquakes, e.g., magnitude greater than 6, occurred every 2-3 years. During the intermittent time, the platform serves to share educational materials such as pictures of geological structures and landscapes, which are beneficial to many of the volunteers, who are high school science teachers. This experimental, science-oriented crowdsourcing system was first tested during the February 6, 2018 Mw 6.4 offshore Hualien, Taiwan earthquake. We received 19 field reports in the first 3 days after the earthquake. Most of these reports provide surface damage details along the Milun fault, which also ruptured during the 1951 ML 7.1 Longitudinal Valley earthquake sequence. The crowdsourcing approach of TSER has proven to be effective in enhancing public awareness and the potential for scientific advancement in hazard mitigation.
Granular counting of uncertain data

We propose a definition of granular count realized in the presence of uncertain data modeled through possibility distributions. We show that the resulting counts are fuzzy intervals in the domain of natural numbers. Based on this result, we devise two algorithms for granular counting: an exact counting algorithm with quadratic-time complexity and an approximate counting algorithm with linear-time complexity. We compare the two algorithms on synthetic data and show their application to a Bioinformatics scenario concerning the assessment of gene expressions in cells.
Efficient Hardware Implementation of 256-bit ECC Processor Over Prime Field

Exchange of private information over the public domain is very much susceptible to unauthorized access, therefore this necessitates the need for a cryptosystem to ensure the protection of information against forthcoming threats. Elliptical curve cryptography (ECC) has attracted the eyes of many scientists due to its smaller key size and high-speed operation. In this paper, an efficient hardware implementation using field programmable gate array (FPGA) on elliptical curve processor (ECP) over a prime field has been proposed. Jacobian coordinate has been used to avoid modular inversion, which is regarded as the most costly operation. To minimize the area and delay in modular multiplication, an interleaved modular multiplier has been proposed. Point doubling (PD) and point addition (PA) architecture was designed with the minimum arithmetic unit using the efficient modular multiplier algorithm. Furthermore, an efficient elliptical curve point multiplication (ECPM) module has been proposed using the high-performance PD and PA architecture. It was found that the proposed ECPM module has a minimal delay and is very area efficient and thus it has immense potentiality in its application in modern day cryptosystem.
Toward the Quantum Internet: A Directional-dependent Noise Model for Quantum Signal Processing

After decades of pure science phase, the research on quantum technologies is finally reaching the engineering phase, getting out of the labs into business reality. Quantum technologies relies on quantum bits, aka qubits, which are the equivalent of classical bits used in classical information processing. Similarly to bits, the information stored in qubits can be corrupted by classical noise. Differently from bits, qubits are also vulnerable to quantum noise, a type of noise with no counterpart in the classical world. Hence, it becomes crucial to understand, from an engineering perspective, how the quantum noise corrupts the information stored within a qubit. To this aim, in this invited paper, we overview the effects of the quantum noise on an arbitrary qubit from a signal-processing perspective.
Multi-classification of Breast Cancer Histology Images by Using Gravitation Loss

The scarcity of professional doctors stimulates the progress of breast cancer classification. However, there are still numerous challenges such as varied appearances (color, texture etc.) of microscopy images and the ambiguous category boundaries. In this paper, we propose an efficient and effective method to achieve multi-classification for H&E stained breast cancer images. Firstly, to restrain color noises in the staining stage, data augmentation in HSV color space is used to increase the diversity of color distribution. In addition, inspired by the principle of gravitation, a Gravitation Loss (G-loss) is proposed to maximize inter-class difference and minimize intra-class variance. The experimental results on public BACH 2018 dataset indicate that the proposed algorithm achieves the state-of-the-art performance, which demonstrates its effectiveness.
A Micro-Aggregation Algorithm Based on Density Partition Method for Anonymizing Biomedical Data

Objective: Biomedical data can be de-identified via micro-aggregation achieving privacy. However, the existing micro-aggregation algorithms result in low similarity within the equivalence classes, and thus, produce low-utility anonymous data when dealing with a sparse biomedical dataset. To balance data utility and anonymity, we develop a novel microaggregation framework. Methods: Combining a density-based clustering method and classical micro-aggregation algorithm, we propose a density-based second division micro-aggregation framework called DBTP . The framework allows the anonymous sets to achieve the optimal k- partition with an increased homogeneity of the tuples in the equivalence class. Based on the proposed framework, we propose a k − anonymity algorithm DBTP − MDAV and an l − diversity algorithm DBTP − l − MDAV to respond to different attacks. Conclusion: Experiments on real-life biomedical datasets confirm that the anonymous algorithms under the framework developed in this paper are superior to the existing algorithms for achieving high utility.
Knowledge Management for Cybersecurity in Business Organizations: A Case Study

Knowledge management (KM) plays important roles in cybersecurity. This study collects five real-life cases of good practices of KM for the domain of cybersecurity in business organizations. Through an iterative process of team-based qualitative data analysis of the five cases, the study develops a model of KM for cybersecurity that conceptualizes three common aspects of KM practices for cybersecurity in business organizations. First, KM for cybersecurity in business organizations has clear specialized organizational structures that involve three inter-organizational tiers across the organization boundaries. Second, the knowledge flows of KM for cybersecurity in business organizations emphasize on explicit, declarative, and specific knowledge. Third, in comparison with KM for other domains, KM for cybersecurity has well-defined objective measures to assess the effectiveness of KM. The domain-specific KM model based on the good practice cases provides a road-map for KM practices in the domain of cybersecurity in business organizations.