Reviewerly/exHarmony huggingface dataset:
Aggregation of Linked Data in the Cultural Heritage Domain: A Case Study in the Europeana Network

Online cultural heritage resources are widely available through digital libraries maintained by numerous organizations. In order to improve discoverability in cultural heritage, the typical approach is metadata aggregation, a method where centralized efforts such as Europeana improve the discoverability by collecting resource metadata. The redefinition of the traditional data models for cultural heritage resources into data models based on semantic technology has been a major activity of the cultural heritage community. Yet, linked data may bring new innovation opportunities for cultural heritage metadata aggregation. We present the outcomes of a case study that we conducted within the Europeana cultural heritage network. In this study, the National Library of The Netherlands contributed by providing the role of data provider, while the Dutch Digital Heritage Network contributed as an intermediary aggregator that aggregates datasets and provides them to Europeana, the central aggregator. We identified and analyzed the requirements for an aggregation solution for the linked data, guided by current aggregation practices of the Europeana network. These requirements guided the definition of a workflow that fulfils the same functional requirements as the existing one. The workflow was put into practice within this study and has led to the development of software applications for administrating datasets, crawling the web of data, harvesting linked data, data analysis and data integration. We present our analysis of the study outcomes and analyze the effort necessary, in terms of technology adoption, to establish a linked data approach, from the point of view of both data providers and aggregators. We also present the expertise requirements we identified for cultural heritage data analysts, as well as determining which supporting tools were required to be designed specifically for semantic data.
Signboard Detection and Text Recognition Using Artificial Neural Networks

A sub-field of Artificial Intelligence (AI) is machine learning (ML) which allows the computer to learn and adopt new rules. With the ML algorithm, the computer can identify samples of the observations, figures and other formats, which may describe the model or other structure. The machine observes various events from the world and presents things with/without explaining the pre-programmed rules. This paper presents the recognition of scene text from the outside environment focusing signboards. A framework for text detection and recognition of text from the natural environment is presented. Advance steps from paper-based text recognition; the text recognition from natural scenes are divided into several levels. Firstly, the image is captured from the outside environment with a smart device, followed by detection of the edges of a signboard. The next phase is the detection of text and the recognition of the text into two languages such as Urdu and English. Final phase uses Artificial Neural Network for the classification and recognition of the text extracted from the natural scenes or an outside environment. Experimental results have been generated on the developed image database created as the part of this research. The effort is a multilingual and produces output in Urdu and English. The system performs well as compared to traditional recognition systems and delivers an overall 85% accuracy in image results.
SWIRL: High-performance many-core CPU code generation for deep neural networks

Deep neural networks (DNNs) have demonstrated effectiveness in many domains including object recognition, speech recognition, natural language processing, and health care. Typically, the computations involved in DNN training and inferencing are time consuming and require efficient implementations. Existing frameworks such as TensorFlow, Theano, Torch, Cognitive Tool Kit (CNTK), and Caffe enable Graphics Processing Unit (GPUs) as the status quo devices for DNN execution, leaving Central Processing Unit (CPUs) behind. Moreover, existing frameworks forgo or limit cross layer optimization opportunities that have the potential to improve performance by significantly reducing data movement through the memory hierarchy. In this article, we describe an alternative approach called SWIRL, a compiler that provides high-performance CPU implementations for DNNs. SWIRL is built on top of the existing domain-specific language (DSL) for DNNs called LATTE. SWIRL separates DNN specification and its schedule using predefined transformation recipes for tensors and layers commonly found in DNN layers. These recipes synergize with DSL constructs to generate high-quality fused, vectorized, and parallelized code for CPUs. On an Intel Xeon Platinum 8180M CPU, SWIRL achieves performance comparable with Tensorflow integrated with MKL-DNN; on average 1.00× of Tensorflow inference and 0.99× of Tensorflow training. It also outperforms the original LATTE compiler on average by 1.22× and 1.30× on inference and training, respectively.
Robust three-dimensional face reconstruction by one-shot structured light line pattern

Three-dimensional face reconstruction is important in many intelligent human-machine applications, e.g. automatic face authentication, human computer interface based entertainment and robotic surgery. Most state-of-the-art structured light methods require multiple projections of the designed patterns, which is not desirable for face reconstruction because it is uncomfortable or difficult for a person to keep his/her face rigid for a period of time. In this paper, we propose an approach to reconstruct the three-dimensional face by one-shot of structured light line pattern. The proposed one-shot 3D reconstruction approach calculates the distortion map from the distorted structured light line pattern. It relies on robust image processing methods and the generic features of human face to segment and cluster the projected structured light line pattern from one single captured image. The y coordinates of the clustered lines are extracted to form a low-resolution distortion matrix which is then transformed to full-resolution distortion map by spline interpolation. The 3D face is reconstructed from the distortion map and the calibrated parameters. Experimental results show that the proposed approach is able to reconstruct the three-dimensional human face robustly by one-shot.
A tutorial on uncertainty modeling for machine reasoning

Increasingly we rely on machine intelligence for reasoning and decision making under uncertainty. This tutorial reviews the prevalent methods for model-based autonomous decision making based on observations and prior knowledge, primarily in the context of classification. Both observations and the knowledge-base available for reasoning are treated as being uncertain. Accordingly, the central themes of this tutorial are quantitative modeling of uncertainty, the rules required to combine such uncertain information, and the task of decision making under uncertainty. The paper covers the main approaches to uncertain knowledge representation and reasoning, in particular, Bayesian probability theory, possibility theory, reasoning based on belief functions and finally imprecise probability theory. The main feature of the tutorial is that it illustrates various approaches with several testing scenarios, and provides MATLAB solutions for them as a supplementary material for an interested reader.
A significance-based trust-aware recommendation approach

Trust-aware recommender systems have been widely used in recent years to improve the performance of traditional collaborative filtering systems. A common assumption of existing trust models is that all items have the same importance for all users. However, it is reasonable to expect that some items are more significant than others in making recommendations. Furthermore, the significance of an item is not the same for all users but varies depending on many factors such as the demographic characteristics of users. For example, an item that is important to women may not be important to men. Also, the significance of an item for an individual user is not static and can change throughout the life cycle (from childhood to old age). Thus, items that are currently important to a user may become less important in the future. In this paper, we propose a Significance-Based Trust-Aware Recommendation (SBTAR) approach, which uses a new trust measure based on the concept of item significance. The significance of an item for a user is measured with respect to the demographic context of the user. Thus, SBTAR can adapt to dynamic changes in user preferences. To model demographic context, SBTAR uses Shuffled Frog Leaping Algorithm (SFLA), which is a meta-heuristic optimization technique based on the social behavior of frogs. SFLA has the advantages of simplicity, fast convergence, strong global search ability and easy implementation. Experimental results show that the proposed approach is more effective and efficient than several state-of-the-art recommendation approaches.
An Enhanced Lightning Attachment Procedure Optimization with Quasi-Opposition-Based Learning and Dimensional Search Strategies

Lightning attachment procedure optimization (LAPO) is a new global optimization algorithm inspired by the attachment procedure of lightning in nature. However, similar to other metaheuristic algorithms, LAPO also has its own disadvantages. To obtain better global searching ability, an enhanced version of LAPO called ELAPO has been proposed in this paper. A quasi-opposition-based learning strategy is incorporated to improve both exploration and exploitation abilities by considering an estimate and its opposite simultaneously. Moreover, a dimensional search enhancement strategy is proposed to intensify the exploitation ability of the algorithm. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are utilized to test the effectiveness of the proposed algorithm. Numerical results indicate that ELAPO can provide better or competitive performance compared with the basic LAPO and other five state-of-the-art optimization algorithms.
Data Driven Intelligent Diagnostics for Parkinson’s Disease

Parkinson's disease (PD) is a common chronic occult neurological degeneration disease which is most likely to occur among middle-aged and old-aged people.Neuroprotective methods can significantly slow the PD progression, therefore early diagnosis and the treatment of the PD is crucial.As the second most common neurodegenerative disorder, Parkinson's disease has its identification based on clinical diagnoses.Early diagnosis is especially important due to the lack of radical treatment.In all early diagnostic methods, results of imaging diagnosis based on positron emission tomography (PET) imaging has the most outstanding accuracy.Deep learning methods based on convolutional neural networks performed well in diagnosing different diseases.To address the demand of early diagnosis, this paper applies several preprocessing methods to image data such as gray level transformation, histogram equalization, improved wavelet soft-threshold denoising and image enhancement, and proposes a deep learning model based on U-Net architecture with deformable convolution kernels.After comparing the results of both methods, we found out that deformable U-Net outperformed the previously built improved VGG-Net.These results show that U-Net with deformable convolution structure has a good diagnostic capability and a better performance than the improved VGG-Net.
Fast Automatic Optimisation of CNN Architectures for Image Classification Using Genetic Algorithm

Convolutional Neural Networks (CNNs) are currently the most prominent deep neural network models and have been used with great success for image classification and other applications. The performance of CNNs depends on their architecture and hyperparameter settings. Early CNN models like LeNet and AlexNet were manually designed by experienced researchers. The empirical design and optimisation of a new CNN architecture require a lot of expertise and can be very time-consuming. In this paper, we propose a genetic algorithm that can, for a given image processing task, efficiently explore a defined space of potentially suitable CNN architectures and simultaneously optimise their hyperparameters. We named this fast automatic optimisation model fast-CNN and employed it to find competitive CNN architectures for image classification on CIFAR10. In a series of comparative simulation experiments we could demonstrate that the network designed by fast-CNN achieved nearly as good accuracy as some of the other best network models available but fast-CNN took significantly less time to evolve. The trained fast-CNN network model also generalised well to CIFAR100.
Stacked dense networks for single-image snow removal

Single image snow removal is important since snowy images usually degrade the performance of computer vision systems. In this paper, we deduce a physics-based snow model and propose a novel snow removal method based on the snow model and deep neural networks. Our model decomposes a snowy image into a nonlinear combination of a snow-free image and dynamic snowflakes. Inspired by our model and DenseNet connectivity pattern, we design a novel Multi-scale Stacked Densely Connected Convolutional Network (MS-SDN) to simultaneously detect and remove snowflakes in an image. The MS-SDN is composed of a multi-scale convolutional sub-net for extracting feature maps and two stacked modified DenseNets for snowflakes detection and removal. The snowflake detection sub-net guides snow removal through forward transmission, and the snowflake removal sub-net adjusts snow detection through back transmission. In this way, snowflake detection and removal mutually improve the final results. For training and testing our method, we constructed a large-scale benchmark synthesis dataset which contains 3000 triplets of snowy images, snowflakes, and snow-free images. Specifically, the snow-free images are captured from snow scenes, and the snowy images are synthesized by using our deduced snow model. Our extensive quantitative and qualitative experimental results show that our MS-SDN performs better than several state-of-the-art methods, and the stacked structure is better than multi-branch structures in terms of snow removal.
Scale - Unexplored Opportunities for Immersive Technologies in Place-based Learning

Immersive technologies have the potential to overcome physical limitations and virtually deliver field site experiences, for example, into the classroom. Yet, little is known about the features of immersive technologies that contribute to successful place-based learning. Immersive technologies afford embodied experiences by mimicking natural embodied interactions through a user's egocentric perspective. Additionally, they allow for beyond reality experiences integrating contextual information that cannot be provided at actual field sites. The current study singles out one aspect of place-based learning: Scale. In an empirical evaluation, scale was manipulated as part of two immersive virtual field trip (iVFT) experiences in order to disentangle its effect on place-based learning. Students either attended an actual field trip (AFT) or experienced one of two iVFTs using a head-mounted display. The iVFTs either mimicked the actual field trip or provided beyond reality experiences offering access to the field site from an elevated perspective using pseudo-aerial 360° imagery. Results show that students with access to the elevated perspective had significantly better scores, for example, on their spatial situation model (SSM). Our findings provide first results on how an increased (geographic) scale, which is accessible through an elevated perspective, boosts the development of SSMs. The reported study is part of a larger immersive education effort. Inspired by the positive results, we discuss our plan for a more rigorous assessment of scale effects on both self- and objectively assessed performance measures of spatial learning.
A new adaptive weighted imbalanced data classifier via improved support vector machines with high-dimension nature

The standard support vector machine (SVM) models are widely used in various fields, but we show that they are not rationally defined from the perspective of geometric point, which is likely to degrade the models’ performances theoretically, especially under the high-dimensional cases. In this paper, we consider a composite penalty and propose an elastic net support vector machine (ENSVM). Unlike the doubly regularized support vector machine (DrSVM, Wang et al. (2006)), we impose the penalty to the slack variables rather than the normal vectors of the hyperplane. Then, we prove that ENSVM is more rationally defined than standard SVM and DrSVM (in section 3.2.1). Moreover, the ENSVM demonstrates a more stable and high-dimension nature inherently, while the simulation results cogently support these merits. Besides, we also combine fused weights with ENSVM and propose an adaptive weighted elastic net support vector machine (AWENSVM), to make the primal model more adaptive and robust to the imbalanced data. Compared with the other popular SVMs, the AWENSVM model proposed in this paper performs better obviously.
A Hybrid Genetic Programming Hyper-Heuristic Approach for Online Two-level Resource Allocation in Container-based Clouds

Container technology has become a new trend in both the software industry and cloud computing. Containers support the fast development of web applications and they have the potential to reduce energy consumption in data centers. Containers are usually first allocated to virtual machines (VMs) and VMs are allocated to physical machines. The container allocation is a challenging task which involves a two-level allocation problem. Current research overly simplifies the container allocation into a one-level allocation problem and uses simple rule-based approaches to solve the problem. As a result, the resource is not allocated efficiently which leads to high energy consumption. This paper provides a novel definition of the two-level container allocation problem. Then, we develop a hybrid approach using genetic programming hyper-heuristics combined with human-designed rules to solve the problem. The experiments show that our hybrid approach is able to significantly reduce energy consumption than solely using human-designed rules.
The right kind of unnatural

Any system using voice to communicate becomes personified by that voice. For robots, where the form and non-vocal behaviour also strongly personify the system, we can see a clash between the two technologies. The many challenges in building responsive and interactive robots mean that language systems are often designed in a vacuum and when they are finally brought together can ruin the look, feel and sound of the completed system. This problem is intensified by natural language processing technology which can further add inappropriate behaviours following mythical business use cases, rather than exploring how users really would like to relate and use embodied artificial systems. In this positional paper, we present two studies in robot voice design and a non-vocal use case of the Honda Research Institute robot Haru. Finally, we ask the question what sort of voice should Haru have?
Penalized Fuzzy C-Means Enabled Hybrid Region Growing in Segmenting Medical Images

Segmentation is considered as one of the challenging and important processes in the field of digital image processing, and there are numerous applications like medical image analysis and satellite data processing where the digital image processing can beneficial. Various algorithms have been developed by many researchers to analyze different medical images like MRI and X-rays. Nuclear image analysis and interpretation also a promising research topic in medical image analysis. For example, positron emission tomography (PET) image can be used to accurately localize disease to help doctors in providing the right treatment and saving valuable time. In recent years, there is a significant advancement in the biomedical imaging domain with the increasing accessibility of computational power as well as automated systems; medical image analysis has become one of the most interesting research areas. Microscopic image analysis is also valuable in the domain of medical imaging as well as medicine. For example, different cell determination, identification, and counting are an important and almost unavoidable step that assists to diagnose some precise diseases. Many computer vision and digital image analysis instances require a basic segmentation phase to find different objects or separate the test image into distinct segments, which can be treated homogeneous depending upon a given property, such as color and texture. Region growing and fuzzy C-Means are two efficient and popular segmentation techniques. In this chapter, we have proposed a hybrid scheme for image segmentation using fuzzy C-Means clustering, region growing method, and thresholding. The fuzzy C-Means (FCM) clustering is used as a preprocessing step. It helps to process the image more accurately in further stages. To eliminate the noise sensitivity property of fuzzy C-Means (FCM), we have used the PFCM, i.e., penalized FCM clustering algorithm. In this work, to find the appropriate region, we have used the region growing segmentation technique coupled with the thresholding. In the proposed work, a similarity feature depending on pixel intensity is used. The threshold value can be calculated using different techniques, such as iterative approach, Otsu’s technique, local thresholding, and manual selection, to determine the optimal threshold. The results are obtained and derived by applying the proposed method on different images obtained from publicly available benchmark datasets of human brain MRI images. Experimental results indicated that the PFCM supported hybrid model is far more superior in segmenting medical images than traditional image segmentation methods.
Mining Semantic Knowledge Graphs to Add Explainability to Black Box Recommender Systems

Recommender systems are being increasingly used to predict the preferences of users on online platforms and recommend relevant options that help them cope with information overload.In particular, modern model-based collaborative filtering algorithms, such as latent factor models, are considered stateof-the-art in recommendation systems.Unfortunately, these black box systems lack transparency, as they provide little information about the reasoning behind their predictions.White box systems, in contrast, can, by nature, easily generate explanations.However, their predictions are less accurate than sophisticated black box models.Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of big data and machine learning methods to a mass audience without compromising trust.Explanations can take a variety of formats, depending on the recommendation domain and the machine learning model used to make predictions.The objective of this work is to build a recommender system that can generate both accurate predictions and semantically rich explanations that justify the predictions.We propose a novel approach to build an explanation generation mechanism into a latent factor-based black box recommendation model.The designed model is trained to learn to make predictions that are accompanied by explanations that are automatically mined from the semantic web.Our evaluation experiments, which carefully study the trade-offs between the quality of predictions and explanations, show that our proposed approach succeeds in producing explainable predictions without a significant sacrifice in prediction accuracy.