Reviewerly/exHarmony huggingface dataset:
Developing a computationally effective Interval Type-2 TSK Fuzzy Logic Controller1

Type-2 fuzzy logic controllers are capable of handling different types of uncertainties that naturally exist in most practical situations. However, the high computation cost of type-2 fuzzy logic controllers is a bottleneck for practically applying them to real-world applications. This paper introd uces a novel approach for designing a computationally effective type-2 fuzzy logic controller. For this purpose, on the antecedent side, interval type-2 fuzzy sets are employed to capture the signal readings, which significantly reduce the computation costs while preserving the major advantages of general type-2 fuzzy logic systems. On the consequent side, however, the Takagi-Sugeno-Kang (TSK) technique is integrated with the proposed controller to render the control outputs in a parallel way. To further reduce the computation cost, the theory of uncertainty bounds is employed for the output processing of the proposed controller. To develop this control structure, a decomposition technique is integrated to break down the original type-2 fuzzy processes into type-1 and take advantage of type-1 fuzzy techniques, followed by an aggregation mechanism to calculate the collective output. The approach is applied to the control of an inverted pendulum and cart model. The simulation results of the developed interval type-2 fuzzy logic controller is compared with a type-1 TSK fuzzy logic controller and a classical proportional derivative (PD) controller. From the results, we have found a 16.6% and 23.3% improvement in Root Mean Square (RMS) error compared to type-1 TSK fuzzy logic controller and classical PD controller, respectively.
Noise-Robust Voice Conversion Using High-Quefrency Boosting via Sub-Band Cepstrum Conversion and Fusion

This paper presents a noise-robust voice conversion method with high-quefrency boosting via sub-band cepstrum conversion and fusion based on the bidirectional long short-term memory (BLSTM) neural networks that can convert parameters of vocal tracks of a source speaker into those of a target speaker. With the implementation of state-of-the-art machine learning methods, voice conversion has achieved good performance given abundant clean training data. However, the quality and similarity of the converted voice are significantly degraded compared to that of a natural target voice due to various factors, such as limited training data and noisy input speech from the source speaker. To address the problem of noisy input speech, an architecture of voice conversion with statistical filtering and sub-band cepstrum conversion and fusion is introduced. The impact of noises on the converted voice is reduced by the accurate reconstruction of the sub-band cepstrum and the subsequent statistical filtering. By normalizing the mean and variance of the converted cepstrum to those of the target cepstrum in the training phase, a cepstrum filter was constructed to further improve the quality of the converted voice. The experimental results showed that the proposed method significantly improved the naturalness and similarity of the converted voice compared to the baselines, even with the noisy inputs of source speakers.
Mercem: Method Name Recommendation Based on Call Graph Embedding

Software developers must provide meaningful but short names to identifiers because they strongly affect the comprehensibility of source code. On the other hand, identifier naming can be a difficult and time-consuming task, even for experienced developers. To support identifier naming, several techniques to recommend candidate names have been proposed. These techniques have challenges on the goodness of suggested candidates and limitations of applicable situations. This paper proposes a new approach to recommend method names by applying graph embedding techniques to the call graph. An experiment confirms that the proposed technique can suggest more appropriate name candidates in difficult situations than the state-of-the-art approach.
Introduction to the special issue on “Cost‐effective and innovative mineral exploration solutions”

Geophysical ProspectingVolume 68, Issue 1 p. 3-6 Introduction Introduction to the special issue on “Cost-effective and innovative mineral exploration solutions” Alireza Malehmir, Corresponding Author Alireza Malehmir alireza.malehmir@geo.uu.se Department of Earth Sciences, Uppsala University, Uppsala, SwedenE-mail: alireza.malehmir@geo.uu.seSearch for more papers by this authorMusa Manzi, Musa Manzi University of the Witwatersrand, School of Geosciences, Johannesburg, South AfricaSearch for more papers by this authorDeyan Draganov, Deyan Draganov Department of Geoscience and Engineering, TU Delft, Delft, The NetherlandsSearch for more papers by this authorUte Weckmann, Ute Weckmann GFZ German Research Centre for Geosciences, Potsdam, GermanySearch for more papers by this authorEsben Auken, Esben Auken Department of Earth Sciences, Aarhus University, Aarhus, DenmarkSearch for more papers by this author Alireza Malehmir, Corresponding Author Alireza Malehmir alireza.malehmir@geo.uu.se Department of Earth Sciences, Uppsala University, Uppsala, SwedenE-mail: alireza.malehmir@geo.uu.seSearch for more papers by this authorMusa Manzi, Musa Manzi University of the Witwatersrand, School of Geosciences, Johannesburg, South AfricaSearch for more papers by this authorDeyan Draganov, Deyan Draganov Department of Geoscience and Engineering, TU Delft, Delft, The NetherlandsSearch for more papers by this authorUte Weckmann, Ute Weckmann GFZ German Research Centre for Geosciences, Potsdam, GermanySearch for more papers by this authorEsben Auken, Esben Auken Department of Earth Sciences, Aarhus University, Aarhus, DenmarkSearch for more papers by this author First published: 30 December 2019 https://doi.org/10.1111/1365-2478.12915Citations: 6Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume68, Issue1Special Issue: Cost-Effective and Innovative Mineral Exploration SolutionsJanuary 2020Pages 3-6 RelatedInformation
Leveraging Glocality for Fast Failure Recovery in Distributed RAM Storage

Distributed RAM storage aggregates the RAM of servers in data center networks (DCN) to provide extremely high I/O performance for large-scale cloud systems. For quick recovery of storage server failures, MemCube [53] exploits the proximity of the BCube network to limit the recovery traffic to the recovery servers’ 1-hop neighborhood. However, the previous design is applicable only to the symmetric BCube( n , k ) network with n k +1 nodes and has suboptimal recovery performance due to congestion and contention. To address these problems, in this article, we propose CubeX, which (i) generalizes the “1-hop” principle of MemCube for arbitrary cube-based networks and (ii) improves the throughput and recovery performance of RAM-based key-value (KV) store via cross-layer optimizations. At the core of CubeX is to leverage the glocality (= globality + locality) of cube-based networks: It scatters backup data across a large number of disks globally distributed throughout the cube and restricts all recovery traffic within the small local range of each server node. Our evaluation shows that CubeX not only efficiently supports RAM-based KV store for cube-based networks but also significantly outperforms MemCube and RAMCloud in both throughput and recovery time.
A Beam Search for the Longest Common Subsequence Problem Guided by a Novel Approximate Expected Length Calculation

The longest common subsequence problem (LCS) aims at finding a longest string that appears as subsequence in each of a given set of input strings. This is a well known $$\mathcal {NP}$$-hard problem which has been tackled by many heuristic approaches. Among them, the best performing ones are based on beam search (BS) but differ significantly in various aspects. In this paper we compare the existing BS-based approaches by using a common BS framework making the differences more explicit. Furthermore, we derive a novel heuristic function to guide BS, which approximates the expected length of an LCS of random strings. In a rigorous experimental evaluation we compare all BS-based methods from the literature and investigate the impact of our new heuristic guidance. Results show in particular that our novel heuristic guidance leads frequently to significantly better solutions. New best solutions are obtained for a wide range of the existing benchmark instances.
Applications of Character Computing From Psychology to Computer Science

The integration of Psychology and Computer Science research is one of the main focus points of research into Character Computing. Each field can help further Character Computing and only together can a usable framework for Character Computing be reached. This is done through combining experimental, computational and data-driven approaches. Research into Character Computing can be clustered into three main research modules. (1) Character sensing and profiling through implicit or explicit means while maintaining privacy and security measures. (2) Developing ubiquitous adaptive systems by leveraging character for specific use cases. (3) investigating artificial characters, how they could be achieved and when they should be implemented. This chapter discusses the challenges, opportunities, and possible applications of each module.
Assessing the Effect of Imbalanced Learning on Cross-project Software Defect Prediction

Software Defect Prediction (SDP) identifies the defect-prone modules from software source code, which helps to serve good quality software. Mostly previous cross-project SDP models were built based on single project data, where single project was used to prepare prediction models. However, this investigation represents an empirical study of SDP where multiple projects data have been used to prepare prediction models. In this study, multiple projects data have been used to prepare a balance and an imbalance datasets. After that this datasets have been used in different prediction models with eight different classifier algorithms. The trained models have been cross-checked by one balanced and imbalanced test datasets. Five evaluation metrics have been considered for evaluating the performance of the models. The experimental results show that there was no significant changes observed between balanced and imbalanced training models. Only AUC (Area Under the Curve) scores have increased significantly in terms of balanced training model with imbalanced test datasets. In the same training model with the balanced test, accuracy and AUC score have increased significantly. However, this study covers widely by creating the classification model from multiple projects' historical data. Further, it proves that if the sufficient number of non-defective and defective data are supplied in the prediction model, it can predict balanced and imbalanced both categories dataset alike. Here, recommendation will be to consider the imbalanced learning while building the prediction model for cross-projects.
Strategy for Research Data Management Services in Indonesia

Abstract Research data management (RDM) ensures the availability of data access and long term data preservation. Its practices are common in developed countries. On the other hand, it is relatively premature in the developing world including Indonesia. To address this problem, we conducted a Systematic Literature Review (SLR) to study best practices for research data management globally. The results become a basis to develop a strategy for research data management services in Indonesia. The SLR synthesis identified the RDM Strengths, Weakness, Opportunities, and Threats (SWOT), which were mapped into the SWOT matrix. It was further analysed to develop strategies to implement RDM services in Indonesia, which suggest provision of national policy and IT/IS infrastructure, as well as improvement of research data awareness among reseachers. The strategies were validated by interviewing three experts in research management.
Using Visualization of Convolutional Neural Networks in Virtual Reality for Machine Learning Newcomers

Software systems and components are increasingly based on machine learning methods, such as Convolutional Neural Networks (CNNs). Thus, there is a growing need for common programmers and machine learning newcomers to understand the general functioning of these algorithms. However, as neural networks are complex in nature, novel presentation means are required to enable rapid access to the functionality. For that purpose, we examine how CNNs can be visualized in Virtual Reality (VR), as it offers the opportunity to focus users on content through effects such as immersion and presence. With a first exploratory study, we confirmed that our visualization approach is both intuitive to use and conductive to learning. Moreover, users indicated an increased motivation to learning due to the unusual virtual environment. Based on our findings, we propose a follow-up study that specifically compares the benefits of a virtual visualization approach to a traditional desktop visualization.
Generating educational mobile applications using UIDPs identified by artificial intelligence techniques

Commercial mobile devices vary in brand, size, and functionalities, but they all allow people to interact with educational applications. In software engineering, application development techniques, approaches, methodologies, and processes (e.g., BBD, FDD, RAD, DDD) are often time consuming, costly, or aim at expert developers – which implies that users outside the software development field (e.g., teachers) need great practice to become experienced application developers. This work proposes an artificial-intelligence-based process for generating educational mobile apps from freehand-generated images. The images’ design is based on User Interface Design Pattern (UIDP) representations. As a proof of concept, we introduce EduMatic, an application development tool. To test our process, we assessed Wiki-Comp, an application built with EduMatic, along with three other external educational applications: Khan Academy, Wiki encyclopedia, and Kiwix. According to the evaluation results, Wiki-Comp outstands in functionality, usability, and performance aspects.
Correlation Filter Tracking via Distractor-Aware Learning and Multi-Anchor Detection

Correlation filter has demonstrated the power in object tracking, benefiting from its superior speed and competitive performance. However, existing correlation filter based trackers (CFTs) are fragile for some inherent defects caused by the boundary effect. To address this issue, we propose a novel correlation filter based tracking framework by integrating three highly collaborative components, including a fast target proposal module, a distractor-aware filter, and a correlation filter based refiner. Specifically, the target proposal aims at determining some target-like regions in contexts efficiently, which provides target-like patches to learn a distractor-aware filter and detect. Multi-region strategy enlarges space fields for learning and prediction. The filter learned from both target and distractors enhances its ability to identify background. Therefore, our method is capable of evaluating multiple candidates in wider context with less risk of drifting to distractors, namely multi-anchor detection. Besides, the proposed Proposal-Detect-Refine hierarchical searching process progressively achieves data alignment between testing and training samples, which benefits for reliable model prediction. A refiner is used to fine-tune positions after multi-anchor detection for lessening error accumulation and preventing model from drifting. Comprehensive experiments on five challenging datasets, i.e. OTB2013, OTB2015, VOT2017, VOT19, and TC128, demonstrate that the proposed method achieves superior performance against the state-of-the-art methods.
Automated Generation of Management Workflows for Applications Based on Deployment Models

To automate the deployment of applications several deployment technologies have been developed. However, the management of deployed applications is only partially covered by existing approaches: While management functionalities such as scaling components or changing their configurations are covered directly by cloud providers or configuration management technologies such as Chef, holistic management processes that affect multiple components probably deployed in different environments cannot be automated using these approaches. For example, testing all deployed components and their communication or backing up the entire application state that is scattered across different components requires custom management logic that needs to be implemented manually, e. g., using scripts. However, a manual implementation of such management processes is errorprone, time-consuming, and requires immense technical expertise. Therefore, we propose an approach that enables automatically generating executable management workflows based on the declarative deployment model of an application. This significantly reduces the effort for automating holistic management processes as no manual implementation is required. We validate the practical feasibility of the approach by a prototypical implementation based on the TOSCA standard and the OpenTOSCA ecosystem.
Fragility of quantum correlations and coherence in a multipartite photonic system

Certain quantum states are well known to be particularly fragile in the presence of decoherence, as illustrated by Schr\"odinger's famous gedanken cat experiment. More recently it has been considered that quantum states can be characterized through a hierarchy of quantum quantities such as entanglement, quantum correlations, and quantum coherence. It has been conjectured that each of these quantities has various degrees of fragility in the presence of decoherence. Here we experimentally confirm this conjecture by preparing tripartite photonic states and subjecting them to controlled amounts of dephasing. When the dephasing is applied to all the qubits, we find that the entanglement is the most fragile quantity, followed by the quantum coherence and then mutual information. This is in agreement with the widely held expectation that multipartite quantum correlations are a highly fragile manifestation of quantumness. We also perform dephasing on one of the three qubits on star and $W\overline{W}$ states. Here the distribution of the correlations and coherence in the star state becomes more important in relation to the dephasing location.
Flood Detection in Social Media Images using Visual Features and Metadata

Images uploaded to social media platforms such as Twitter and Flickr have become a potential source of information about natural disasters. However, due to their lower reliability and noisy nature, it is challenging to automatically identify social media images that genuinely contain evidence of natural disasters. Visual features have been popular for classifying these images while the associated metadata are often ignored or exploited only to a limited extent. To test their potential, we employed them separately to identify social media images with flooding evidence. For visual feature extraction, we utilized three advanced Convolutional Neural Networks (CNNs) pre-trained on two different types of datasets and used a simple neural network for the classification. The results demonstrate that the combination of two types of visual features have a positive impact on distinguishing natural disaster images. From metadata, we considered only the textual metadata. Here, we combined all textual metadata and extracted bi-gram features. Then we employed a Support Vector Machine (SVM) for the classification task. The results show that the combination of the textual metadata can improve the classification accuracy compared to their individual counterparts. The results also demonstrate that although the visual feature approach outperforms metadata approach, metadata have certain capability to classify these images. For instance, the proposed visual feature approach achieved a similar result (MAP = 95.15) compared to the top visual feature approaches presented in MediaEval 2017, the metadata approach outperformed (MAP = 84.52) presented metadata methods. For the experiments, we utilized dataset from MediaEval 2017 Disaster Image Retrieval from Social Media (DIRSM) task and compared the achieved results with the other methods presented (Number of participants = 11) of the task.
Research on Deep Learning Expression Recognition Algorithm Based on Multi-Model Fusion

As an important form of human emotion expression, expression has broad application prospects in the fields of human-computer interaction and emotion calculation. Aiming at the fact that the facial expressions in reality are affected by illumination and attitude angle, and the traditional expression extraction method can't effectively extract the features of strong expressive power, a deep learning expression recognition algorithm model based on multi-model fusion is proposed. The residual feature net-work is used to extract the expression features. Secondly, the feature weights of different feature maps are learned by embeding the squeeze and excitation network module in the residual network unit, and the feature re-calibration method is used to improve the network expression capability. Experiments show that this method has achieved good recognition results in the KDEF database.
Restoration of Lossy JPEG-Compressed Brain MR Images Using Cross-Domain Neural Networks

Lossy image compression allows for efficient storage and transfer of image data with varying degrees of image degradation. However, lossy compression is not commonly used in medical imaging as the process may irreversibly remove information that defines clinically important image features. The lossy component of JPEG compression is represented as lost precision in the discrete cosine transform (DCT) domain after quantization on 8 × 8 image blocks and results in degradation of the image. We propose a cross-domain cascade of U-nets called the W-net. This network operates in the DCT domain to restore discarded DCT coefficients that leverages information from adjacent blocks, and the image domain to suppress compression artifacts at the image pixel level. For comparison, we adapted the Automated Transform by Manifold Approximation (AUTOMAP) method for JPEG decompression by learning the dequantization of individual 8 × 8 DCT coefficient blocks. These results were then transformed to the image domain and processed by a U-net. The deep learning models were able to suppress common compression artifacts at the expense of high spatial frequency detail. Both the W-net and AUTOMAP network structures were quantitatively superior to standard JPEG decompression, with the W-net outperforming AUTOMAP, suggesting that leveraging DCT coefficients from adjacent blocks improves JPEG decompression performance.