Reviewerly/exHarmony huggingface dataset:
Residual Component Estimating CNN for Image Super-Resolution

With the success of convolutional neural networks (CNNs) for different computer vision applications, CNNs have been widely applied for single image super-resolution (SR). The recent research line for CNN-based image SR mainly concentrates on exploring the pioneering network architectures such as very deep CNN, ResNet, GAN-net, for enhancing performance of the learned high-resolution (HR) image. Although the impressive performance with the recent CNN-based SR work has been achieved, the non-recovered high-frequency (residual) components are unavoidably existed with the current network architectures. This study aims to explore an unified CNN network architecture for learning not only the HR image but also simultaneously the difficultly recovered residual components in the first network. With one existed CNN architecture for image super-resolution, the HR image can be learned while some high-frequency content in the ground-truth image may not be perfectly recovered. For estimating the non-recovered high-frequency content, this study stacks another CNN architecture on the output of the baseline CNN, and construct an end-to-end residual component learning framework for more accurate image SR. Experimental results on benchmark dataset validate that the proposed residual component estimating CNN can outperform the non-stacked CNN architecture, and demonstrates state-of-the-art restoration quality.
A Survey of Ontology Benchmarks for Semantic Web Ontology Tools

Software engineering employs different benchmarks for a software evaluation. This enables software developers to continuously improve their product. The same needs are intrinsic for software tools in the semantic web field. While there are many different benchmarks already available, there has not been their overview and categorization yet. This work provides such an overview and categorization of benchmarks specifically oriented on benchmarks where an ontology plays an important role. Benchmarks are naturally categorized in line with ontology tool categorization along with an indication which activities those benchmarks are deliberate and which are non-deliberative. Although the article itself can already navigate a reader to an adequate benchmark, we moreover automatically designed a flexible rule-based recommendation tool based on the analysis of existing benchmarks.
An application of the virtual reality 360° concept to the Great Zimbabwe monument

This paper is an application of the virtual reality 360° concept to the Great Zimbabwe monument to produce virtual tour videos of this world-scale heritage site. The paper was prompted by the need to make this tourist magnet accessible to the physically challenged, and those constrained by finance, distance and time. It was also prompted by the need to better inform and educate guests about the monument and possibly increase visitation as several studies have showed that three-dimensional virtual tours may increase actual intention to visit. To produce the virtual videos, spherical images of the monument were first shot with a 360° camera and then stitched in Kolor Autopano software to get high-resolution images. The virtual tour videos are expected to be uploaded on a server for access by the entire world on computers, tablets and smartphones for a fee. The videos allow patrons to navigate the monument’s key archeological sites, such as the Great Enclosure and the Valley Ruins, as well as listen to highly educational and informative commentaries. As this project is the first of its kind in Zimbabwe, this paper suggests that follow-up research on the videos should be undertaken to establish their impact.
Wrapper-based Feature Selection for Imbalanced Data using Binary Queuing Search Algorithm

The non-uniform distribution of classes (imbalanced data) and the presence of irrelevant and/or redundant information are considered as challenging aspects encountered in most real-world domains. In this paper, we propose an efficient software fault prediction (SFP) model based on a wrapper feature selection method combined with Synthetic Minority Oversampling Technique (SMOTE) with the aim of maximizing the prediction accuracy of the learning model. A binary variant of recent optimization algorithm; Queuing Search Algorithm (QSA), is introduced as a search strategy in wrapper FS method. The performance of the proposed model is assessed on 14 real-world benchmarks from the PROMISE repository in terms of three evaluation measures; sensitivity, specificity, and area under the curve (AUC). Experimental results reveal a positive impact of the SMOTE technique in improving the prediction performing in a highly imbalanced data. Moreover, the binary QSA (BQSA) show a superior efficacy on 64.28% of datasets compared with other state-of-the-art algorithms in handling the problem of FS. The combination of BQSA and SMOTE achieved an acceptable AUC results (66.47-87.12%).
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks

The increasing inclusion of Deep Learning (DL) models in safety-critical systems such as autonomous vehicles have led to the development of multiple model-based DL testing techniques. One common denominator of these testing techniques is the automated generation of test cases, e.g., new inputs transformed from the original training data with the aim to optimize some test adequacy criteria. So far, the effectiveness of these approaches has been hindered by their reliance on random fuzzing or transformations that do not always produce test cases with a good diversity. To overcome these limitations, we propose, DeepEvolution, a novel search-based approach for testing DL models that relies on metaheuristics to ensure a maximum diversity in generated test cases. We assess the effectiveness of DeepEvolution in testing computer-vision DL models and found that it significantly increases the neuronal coverage of generated test cases. Moreover, using DeepEvolution, we could successfully find several corner-case behaviors. Finally, DeepEvolution outperformed Tensorfuzz (a coverage-guided fuzzing tool developed at Google Brain) in detecting latent defects introduced during the quantization of the models. These results suggest that search-based approaches can help build effective testing tools for DL systems.
Application of convolution neural network object detection algorithm in logistics warehouse

Because unmanned forklifts need to recognise and locate pallets in warehouses, a detection algorithm based on deep learning framework was proposed. First, the authors collected a large number of pictures including people and pallet in the real warehouse and marked the corresponding label to build a logistics warehouse pallet database. Second, the object detection algorithm based on a single shot multibox detector is improved and trained by the database. In the prediction phase, the network combines the multiscale feature maps with different resolution, which enhances the adaptability of the network to the detection task. Third, the algorithm is an end-to-end detection network, i.e. uses a single network for detection tasks, which can be easily combined with other systems which need detection tasks. The experimental results show that the accuracy of the improved pallet detection algorithm can reach 92.7% and the test rate is 42 frames per second, which can meet the requirements of the efficiency and accuracy of the pallet detection while using the TITAN X GPU.
Measuring elastoplastic strain loops in the near crack-tip region using a Stereo Microscope DIC system

In this work, DIC analysis is used to assess the cyclic plastic deformation response, Δε, at the near crack-tip region of propagating fatigue cracks and to identify the effects of other phenomena that happen during crack propagation, such as crack closure and partial crack opening on the measured elastoplastic strain loops. The DIC measurements use a stereo microscope and a very refined speckle pattern on the specimen surface. Fatigue tests were performed in constant ΔK and Kmax conditions on aluminum and steel specimens. Such measurements can be used to investigate the actual fatigue crack growth driving force.
Direction Concentration Learning: Enhancing Congruency in Machine Learning

One of the well-known challenges in computer vision tasks is the visual diversity of images, which could result in an agreement or disagreement between the learned knowledge and the visual content exhibited by the current observation. In this work, we first define such an agreement in a concepts learning process as congruency. Formally, given a particular task and sufficiently large dataset, the congruency issue occurs in the learning process whereby the task-specific semantics in the training data are highly varying. We propose a Direction Concentration Learning (DCL) method to improve congruency in the learning process, where enhancing congruency influences the convergence path to be less circuitous. The experimental results show that the proposed DCL method generalizes to state-of-the-art models and optimizers, as well as improves the performances of saliency prediction task, continual learning task, and classification task. Moreover, it helps mitigate the catastrophic forgetting problem in the continual learning task. The code is publicly available at https://github.com/luoyan407/congruency.
A Deep Learning Approach to Selecting Representative Time Steps for Time-Varying Multivariate Data

We present a deep learning approach that selects representative time steps from a given time-varying multivariate data set. Our solution leverages an autoencoder that implicitly learns feature descriptors of each individual volume in a latent space. These feature descriptors are used to reconstruct respective volumes for error estimation during network training. We then perform dimensionality reduction of these feature descriptors and select representative time steps in the projected space. Unlike previous approaches, our solution can handle time-varying multivariate data sets where the multivariate features can be learned using a multichannel input to the autoencoder. We demonstrate the effectiveness of our approach using several time-varying multivariate data sets and compare our selection results with those generated using an information-theoretic approach.
Data-Driven Skin Detection in Cluttered Search and Rescue Environments

Locating human victims in cluttered urban search and rescue (USAR) environments is still a challenge. In this paper, we present an approach to generate skin objectness windows to assist human rescuers. We introduce the term skin objectness to denote the task of extracting windows in the scene with a high probability of skin presence for locating victims. Unlike naïve skin segmentation approaches, the presented algorithm accounts for both color and spatial information to extract regions of interest and at the same time, rejects the background clutter. We use temporal information of the video sequence to make the skin objectness windows more reliable. To selectively boost skin regions, the RGB skin pixels are transformed to Gabor space to generate a transformation matrix. The matrix is used to generate skin affinity maps removing the unwanted background clutter. Further, the Bayesian inference and temporal cues from previous frames are used for detecting skin objectness windows. It has real-time applications in image retrieval, action classification, virtual reality, etc. The experimental analysis demonstrates quantitative and qualitative results on a disaster victim dataset showing plausibility and efficiency of the proposed method in cluttered environments.
Immutable autobiography of smart cars leveraging blockchain technology

Abstract The popularity of smart cars is increasing around the world as they offer a wide range of services and conveniences. These smart cars are equipped with a variety of sensors generating a large amount of data, many of which are critical. Besides, there are multiple parties involved in the lifespan of a smart car, such as manufacturers, car owners, government agencies, and third-party service providers who also generate data about the vehicle. In addition to managing and sharing data among these entities in a secure and privacy-friendly way which is a great challenge itself, there exists a trust deficit about some types of data as they remain under the custody of the car owner (e.g. satellite navigation and mileage data) and can easily be manipulated. In this article, we propose a blockchain-assisted architecture enabling the owner of a smart car to create an immutable record of every data, called the autobiography of a car, generated within its lifespan. We also explain how the trust about this record is guaranteed by the immutability characteristic of the blockchain. Furthermore, the article describes how the proposed architecture enables a secure and privacy-preserving mechanism for sharing of smart car data among different parties.
Validity of Test Instruments

Abstract The two basic principles of the problem in assessment are determining whether a test has measured what is being measured and whether a test has been appropriately used to make a decision about the test taker. It is possible that the test developers argue that math tests for example can predict a person’s ability in physics. Validity or validity comes from the word validity, which means the extent of accuracy and accuracy of a measuring instrument in performing its measuring function. In other words, validity is a concept related to the extent to which the test has measured what should be measured. In this study discusses the meaning of validity, types of validity, testing the validity of tests, testing the validity of tests rationally, construct validity, content validity, testing the validity of tests empirically, forecast validity, comparative validity, question validity, and validity testing calculations. items of measurement results. With the method of quantitative data analysis, the results of the research from the distribution of questionnaires to 20 (twenty) respondents, 10 (ten) of them were tested for validity and it was found that 7 (seven) items were valid and 3 (three) invalid.
Multi-feature fusion for image retrieval using constrained dominant sets

Aggregating different image features for image retrieval has recently shown its effectiveness. While highly effective, though, the question of how to uplift the impact of the best features for a specific query image persists as an open computer vision problem. In this paper, we propose a computationally efficient approach to fuse several hand-crafted and deep features, based on the probabilistic distribution of a given membership score of a constrained cluster in an unsupervised manner. First, we introduce an incremental nearest neighbor (NN) selection method, whereby we dynamically select k-NN to the query. We then build several graphs from the obtained NN sets and employ constrained dominant sets (CDS) on each graph G to assign edge weights which consider the intrinsic manifold structure of the graph, and detect false matches to the query. Finally, we elaborate the computation of feature positive-impact weight (PIW) based on the dispersive degree of the characteristics vector. To this end, we exploit the entropy of a cluster membership-score distribution. In addition, the final NN set bypasses a heuristic voting scheme. Experiments on several retrieval benchmark datasets show that our method can improve the state-of-the-art result.
Multi-Stream End-to-End Speech Recognition

Attention-based methods and Connectionist Temporal Classification (CTC) network have been promising research directions for end-to-end (E2E) Automatic Speech Recognition (ASR). The joint CTC/Attention model has achieved great success by utilizing both architectures during multi-task training and joint decoding. In this article, we present a multi-stream framework based on joint CTC/Attention E2E ASR with parallel streams represented by separate encoders aiming to capture diverse information. On top of the regular attention networks, the Hierarchical Attention Network (HAN) is introduced to steer the decoder toward the most informative encoders. A separate CTC network is assigned to each stream to force monotonic alignments. Two representative framework have been proposed and discussed, which are Multi-Encoder Multi-Resolution (MEM-Res) framework and Multi-Encoder Multi-Array (MEM-Array) framework, respectively. In MEM-Res framework, two heterogeneous encoders with different architectures, temporal resolutions and separate CTC networks work in parallel to extract complementary information from same acoustics. Experiments are conducted on Wall Street Journal (WSJ) and CHiME-4, resulting in relative Word Error Rate (WER) reduction of 18.0-32.1% and the best WER of 3.6% in the WSJ eval92 test set. The MEM-Array framework aims at improving the far-field ASR robustness using multiple microphone arrays which are activated by separate encoders. Compared with the best single-array results, the proposed framework has achieved relative WER reduction of 3.7% and 9.7% in AMI and DIRHA multi-array corpora, respectively, which also outperforms conventional fusion strategies.
Towards Quantifying Multiple View Layouts in Visualisation as Seen from Research Publications

We present initial results of a quantitative analysis of how developers layout the visualisations in their multiple view systems. Many developers create multiple view systems and the technique is commonly used by the visualisation community. Each visualisation shows data in a different way, and often user interaction is coordinated between the views. But it is not always clear to know how many views a developer should use, or what would be the best layout. We extract images of visualisation tools, across TVCG journal, conference, posters and workshop papers 2012-2018 to analyse the quantity and layout of the views in these visualisation systems. Focusing on view juxtaposition, we code the layout of 491 images and analyse view topology in juxtaposed views. Our analysis acts as a starting point to help designers create better visualisations, acts as a taxonomy of visualisation layouts, and provides a quantitative analysis of how many views developers have used in their visualisation systems.
Towards a Periodic Table of conceptualization and formalization on State, Style, Structure, Pattern, Framework, Architecture, Service and so on

Conceptual identification and semantic formalization of concepts are necessary for sharing thought among individuals and accumulating cognition of concepts in discrete minds. The extent of how well concepts are conceptually identified, clarified and semantically formalized essentially determines in positive ratio to how well these concepts can function effectively, and perform efficiently. However, after a long struggle with various effort, we still are not fully satisfied with the conceptual revelation and semantic formalization of some fundamental concepts in natural language, e.g., software Style, Structure, Pattern, Framework, Architecture, Service, etc. Based on our proposed Existence Computation, which enables conceptual revelation and formalizing semantics of concepts from the extreme beginning of cognition on existence, and the ideology of Relationship Defined Everything of Semantic, which provides a global unification of constructive semantic expression, we proposed a Periodic Table like conceptualization and formalization mechanism of concepts, called Existence Periodic Table, based on a two level existence evaluation architecture: atomic existence level referring to independence semantic, and typed existence level bundled with semantic of completeness. We show the cases of utilization of our proposed Existence Periodic table in identification and formalization of concepts.
Web-Based Environment in the Integrated Learning Model for CLIL-Learners: Examination of Students’ and Teacher’s Satisfaction

In this paper we examine the students’ and teacher’s satisfaction with the newly introduced web-based environment in integrated learning model. For the analysis both quantitative and qualitative methods are used. The results of survey show that students level of satisfaction is much higher than average, especially such advantages were noted as the availability, simplicity and clarity of use, as well as the presence of useful theoretical material. According to the interview with coordinator the process of preparing for classroom activities has become more time-consuming for students, but their final results on discipline became much higher. Both coordinator and students noticed that one of the problems was cross-cultural communications during the online project, in this case next semester it is extremely important to try alleviate it allocating participants from the same time zone in working teams. Overall, the developed web-based environment can be confirmed effective and successful.
Instagram Sentiment Analysis with Naive Bayes and KNN: Exploring Customer Satisfaction of Digital Payment Services in Indonesia

Internet penetration and trends of social media have generated a load of data and many researchers have done sentiment analysis to know user's customer satisfaction of a particular brand through textual data form. This research was conducted to determine the customer satisfaction of digital payment services (Go-Pay, Ovo, and LinkAja) in Indonesia, analyzed from the result of Instagram sentiment analysis, in which the textual data form used is the Instagram comment. The research model was built with sentiment analysis classification algorithm: Naïve Bayes and K-Nearest Neighbors (KNN), and customer satisfaction theory. The result of this research with 3800 training data and 200 test data by using 20 k-fold cross validation shows that GO-PAY has all kinds of customers who satisfied, not satisfied and both. As for OVO and LinkAja have a more neutral sentiment. The most accurate classification algorithm implemented in this research is the KNN classifier algorithm, up to 90%. The final sentiment analysis result of this research contributed to the current state of customer satisfaction towards Go-Pay, Ovo, and LinkAja as a digital payment service provider in Indonesia and maybe an input to the relevant stakeholder.
Adaptive Markov‐based approach for dynamic virtual machine consolidation in cloud data centers with quality‐of‐service constraints

Summary Dynamic virtual machine (VM) consolidation is one of the emerging technologies that has been considered for low‐cost computing in cloud data centers. Quality‐of‐service (QoS) assurance is one of the challenging issues in the VM consolidation problem since it is directly affected by the increase of resource utilization due to the consolidations. In this paper, we take advantage of Markov chain models to propose a novel approach for VM consolidation that can be used to explicitly set a desired level of QoS constraint in a data center to ensure the QoS goals while improving system utilization. For this purpose, an energy‐efficient and QoS‐aware best fit decreasing algorithm for VM placement is proposed, which considers QoS objective when determining the location of a migrating VM. This algorithm employs an online transition matrix estimator method to deal with the nonstationary nature of real workload data. We also propose new policies for detecting overloaded and underloaded hosts. The performance of our proposed algorithms is evaluated through simulations. The results show that the proposed VM consolidation algorithms in this paper outperforms the benchmark algorithms in terms of energy consumption, service‐level agreement violations, and other cost factors.