Reviewerly/exHarmony huggingface dataset:
Knowledge discovery using SPARQL property path: The case of disease data set

The Semantic Web allows knowledge discovery on graph-based data sets and facilitates answering complex queries that are extremely difficult to achieve using traditional database approaches. Intuitively, the Semantic Web query language (SPARQL) has a ‘property path’ feature that enables knowledge discovery in a knowledgebase using its reasoning engine. In this article, we utilise the property path of SPARQL and the other Semantic Web technologies to answer sophisticated queries posed over a disease data set. To this aim, we transform data from a disease web portal to a graph-based data set by designing an ontology, present a template to define the queries and provide a set of conjunctive queries on the data set. We illustrate how the reasoning engine of ‘property path’ feature of SPARQL can retrieve the results from the designed knowledgebase. The results of this study were verified by two domain experts as well as authors’ manual exploration on the disease web portal.
Partial Correctness of a Factorial Algorithm

Summary In this paper we present a formalization in the Mizar system [3],[1] of the partial correctness of the algorithm: i := val.1 j := val.2 n := val.3 s := val.4 while (i &lt;&gt; n) i := i + j s := s * i return s computing the factorial of given natural number n, where variables i, n, s are located as values of a V-valued Function, loc , as: loc/.1 = i, loc/.3 = n and loc/.4 = s , and the constant 1 is located in the location loc/.2 = j (set V represents simple names of considered nominative data [16]). This work continues a formal verification of algorithms written in terms of simple-named complex-valued nominative data [6],[8],[14],[10],[11],[12]. The validity of the algorithm is presented in terms of semantic Floyd-Hoare triples over such data [9]. Proofs of the correctness are based on an inference system for an extended Floyd-Hoare logic [2],[4] with partial pre- and post-conditions [13],[15],[7],[5].
Interaction-and-Aggregation Network for Person Re-identification

Person re-identification (reID) benefits greatly from deep convolutional neural networks (CNNs) which learn robust feature embeddings. However, CNNs are inherently limited in modeling the large variations in person pose and scale due to their fixed geometric structures. In this paper, we propose a novel network structure, Interaction-and-Aggregation (IA), to enhance the feature representation capability of CNNs. Firstly, Spatial IA (SIA) module is introduced. It models the interdependencies between spatial features and then aggregates the correlated features corresponding to the same body parts. Unlike CNNs which extract features from fixed rectangle regions, SIA can adaptively determine the receptive fields according to the input person pose and scale. Secondly, we introduce Channel IA (CIA) module which selectively aggregates channel features to enhance the feature representation, especially for smallscale visual cues. Further, IA network can be constructed by inserting IA blocks into CNNs at any depth. We validate the effectiveness of our model for person reID by demonstrating its superiority over state-of-the-art methods on three benchmark datasets.
Development of mobile augmented-reality and virtual-reality simulated training systems for marine ecology education

The paper is a case study for the effectiveness of hand-held and head-mounted AR interaction for increasing teaching and learning, in the specific field of marine ecology education. This system will serve as an assisting tool for the teaching activity of marine ecology guide to improve the learning effectiveness of the learners. A total of 191 questionnaires were collected, there were 161 valid questionnaires, among which including 70 AR questionnaires, 91 AR2VR questionnaires. There were 30 incomplete invalid questionnaires. This study adopts the pre-test and post-test variance design of the quasi-experimental research method to explore the learners’ flow experience effect, technology acceptance model, activity involvement and learning effect under different guidance modes, as well as their attitudes towards the use and acceptance of the guidance system. The experiments are performed and analyzed in detail. The study results indicate that (a) the learning achievements of the students differed between before and after the AR and VR mobile guidance activities, (b) both the AR and VR guidance modes enhanced the flow experience of visitors, (c) all aspects of the technology acceptance models in both groups had positive effects on the flow experience, (d) all aspects of the activity involvement of both groups had positive effects on flow experience, and (e) most learners exhibited positive attitudes toward and acceptance of using AR and VR mobile guidance systems.
Improved Calibration Technique for Quasi-Monostatic Polarimetric Measurement System Using a Dihedral as the Calibration Reference

The alluring polarimetric characteristics of a dihedral corner reflector make it specially useful as a polarimetric calibration reference. However, for a radar cross section (RCS) range with quasi-monostatic geometry where there exists a small bistatic angle, the polarimetric calibration error rapidly increases as the bistatic angle increases when a dihedral is used as the polarimetric calibration reference. In this work, an improved calibration technique for quasi-monostatic polarimetric measurement system is proposed. The scattering mechanism of a rectangular dihedral corner reflector in a quasi-monostatic radar system is analyzed, where the bistatic angle constrains to be within a few degrees. The polarimetric characteristic of the dihedral is evaluated by means of physical optics (POs) approximation. Combining the evaluation formulation with a nonlinear calibration technique, the polarimetric measurement error model can be adapted for the quasi-monostatic geometry so that accurate polarimetric calibration may be accomplished. The experimental results are presented to validate the proposed technique with essential improvement in the calibrated cross polarimetric measurements.
Understanding Kernel Size in Blind Deconvolution

Most blind deconvolution methods usually pre-define a large kernel size to guarantee the support domain. Blur kernel estimation error is likely to be introduced, yielding severe artifacts in deblurring results. In this paper, we first theoretically and experimentally analyze the mechanism to estimation error in oversized kernel, and show that it holds even on blurry images without noises. Then to suppress this adverse effect, we propose a low rank-based regularization on blur kernel to exploit the structural information in degraded kernels, by which larger-kernel effect can be effectively suppressed. And we propose an efficient optimization algorithm to solve it. Experimental results on benchmark datasets show that the proposed method is comparable with the state-of-the-arts by accordingly setting proper kernel size, and performs much better in handling larger-size kernels quantitatively and qualitatively. The deblurring results on real-world blurry images further validate the effectiveness of the proposed method.
Advanced Super-Resolution Using Lossless Pooling Convolutional Networks

In this paper, we present a novel deep learning-based approach for still image super-resolution, that unlike the mainstream models does not rely solely on the input low resolution image for high quality upsampling, and takes advantage of a set of artificially created auxiliary self-replicas of the input image that are incorporated in the neural network to create an enhanced and accurate upscaling scheme. Inclusion of the proposed lossless pooling layers, and the fusion of the input self-replicas enable the model to exploit the high correlation between multiple instances of the same content, and eventually result in significant improvements in the quality of the super-resolution, which is confirmed by extensive evaluations.
Generalized Median Graph via Iterative Alternate Minimizations

Computing a graph prototype may constitute a core element for clustering or classification tasks. However, its computation is an NP-Hard problem, even for simple classes of graphs. In this paper, we propose an efficient approach based on block coordinate descent to compute a generalized median graph from a set of graphs. This approach relies on a clear definition of the optimization process and handles labeling on both edges and nodes. This iterative process optimizes the edit operations to perform on a graph alternatively on nodes and edges. Several experiments on different datasets show the efficiency of our approach.
AffectiveHMD

We propose a facial expression mapping technology between virtual avatars and Head-Mounted Display (HMD) users. HMDs allow people to enjoy an immersive Virtual Reality (VR) experience. A virtual avatar can be a representative of the user in the virtual environment. However, the synchronization of the virtual avatar's expressions with those of the HMD user is limited. The major problem of wearing an HMD is that a large portion of the user's face is occluded, making facial recognition difficult in an HMD-based virtual environment. To overcome this problem, we propose a facial expression mapping technology using photo-reflective sensors. The sensors attached inside the HMD measure the reflection intensity between the sensors and the user's face. The intensity values of five basic facial expressions (Neutral, Happy, Angry, Surprised, and Sad) are used for training a classifier to estimate the facial expression of a user. In Siggraph 2019, the user can enjoy two application, the facial expression synchronization with the avatar, and simple manipulation experience for a virtual environment by facial expressions.
Correction: Vocal imitation of percussion sounds: On the perceptual similarity between imitations and imitated sounds

[This corrects the article DOI: 10.1371/journal.pone.0219955.].
Virtual Reality-Based Simulation of Age-Related Visual Deficiencies: Implementation and Evaluation in the Design Process

Age-related changes significantly affect elderly users’ interaction with specific products and services. Nevertheless, the challenges experienced by elderly users are difficult to be perceived and understood, especially by younger people. Inspired by the concept of aging suits, we propose a Virtual Reality-based approach, where age-related visual impairments are simulated in virtual environments. The aim is to provide an approximation of the experience of viewing and interacting with a product, from the perspective of elderly persons with specific age-related visual problems. The effectiveness of the proposed approach is examined through an experiment involving package design evaluation. The experimental results demonstrate that Virtual Reality can play an important role on understanding the challenges that elderly users face, thus support the design of elderly-friendly products.
GFLIB: an Open Source Library for Genetic Folding Solving Optimization Problems

This paper aims at presenting GFLIB, a Genetic Folding MATLAB toolbox for supervised learning problems. In essence, the goal of GFLIB is to build a concise model of supervised learning, and a free open source MATLAB toolbox for performing classification and regression. The GFLIB is specifically designed for most of the traditionally used features, to evolve in applications of mathematical models. The toolbox suits all kinds of users; from the users who implemented GFLIB as “black box”, to advanced researchers who want to generate and test new functionalities and parameters of GF algorithm. The toolbox and its documentation are freely available for download at: https://github.com/mohabedalgani/gflib.git
Partial Correctness of a Power Algorithm

Summary This work continues a formal verification of algorithms written in terms of simple-named complex-valued nominative data [6],[8],[15],[11],[12],[13]. In this paper we present a formalization in the Mizar system [3],[1] of the partial correctness of the algorithm: i := val.1 j := val.2 b := val.3 n := val.4 s := val.5 while (i &lt;&gt; n) i := i + j s := s * b return s computing the natural n power of given complex number b, where variables i, b, n, s are located as values of a V-valued Function, loc, as: loc/.1 = i, loc/.3 = b, loc/.4 = n and loc/.5 = s, and the constant 1 is located in the location loc/.2 = j (set V represents simple names of considered nominative data [17]). The validity of the algorithm is presented in terms of semantic Floyd-Hoare triples over such data [9]. Proofs of the correctness are based on an inference system for an extended Floyd-Hoare logic [2],[4] with partial pre- and post-conditions [14],[16],[7],[5].
Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory.

Deep learning based knowledge tracing model has been shown to outperform traditional knowledge tracing model without the need for human-engineered features, yet its parameters and representations have long been criticized for not being explainable. In this paper, we propose Deep-IRT which is a synthesis of the item response theory (IRT) model and a knowledge tracing model that is based on the deep neural network architecture called dynamic key-value memory network (DKVMN) to make deep learning based knowledge tracing explainable. Specifically, we use the DKVMN model to process the student's learning trajectory and estimate the student ability level and the item difficulty level over time. Then, we use the IRT model to estimate the probability that a student will answer an item correctly using the estimated student ability and the item difficulty. Experiments show that the Deep-IRT model retains the performance of the DKVMN model, while it provides a direct psychological interpretation of both students and items.
KiPN: a visual notation for knowledge-intensive processes

Modelling knowledge-intensive processes (KiP) is not an easy task, due to their essential characteristics of being unstructured, based on social interactions, experience and intentions of the actors involved. The knowledge-intensive process ontology (KiPO) provides a structural conceptualisation for KiPs, precisely specifying all concepts and relations that are necessary to make a KiP explicit. However, KiPO does not provide a graphical notation, which is crucial for organisation managers, as well as process managers, modellers and executors. Some visual notations exist for the visual representation of KiP, but they miss at least one of these relevant features in the KiP representation. In this paper, we propose the knowledge-intensive process notation (KiPN), a notation for modelling KiP. KiPN is based on the principles of the theory of visual notations to develop the cognitive efficacy of a visual representation. The evaluation of KiPN indicates the cognitive effectiveness and perceived usefulness of KiPN.
The sequential and conditional nature of 21st-century digital skills

The importance of 21st-century digital skills has been well established. However, research often fails to examine how various skills relate to each other. Through a survey of a sample of 1,222 professionals working in the creative industries, we tested, by using path analysis, whether six 21st-century digital skills have a sequential and conditional nature. The sequence of the model starts with information and communication digital skills, followed by collaboration, critical thinking, and creative digital skills. All skills lead to problem-solving digital skills. The results confirm that the analyzed skills build on each other sequentially. To understand what interventions might be successful, the relations among various digital skills should be considered.
Learning Options with Interest Functions

Learning temporal abstractions which are partial solutions to a task and could be reused for solving other tasks is an ingredient that can help agents to plan and learn efficiently. In this work, we tackle this problem in the options framework. We aim to autonomously learn options which are specialized in different state space regions by proposing a notion of interest functions, which generalizes initiation sets from the options framework for function approximation. We build on the option-critic framework to derive policy gradient theorems for interest functions, leading to a new interest-option-critic architecture.
Meta-Interpretive Learning Using HEX-Programs

Meta-Interpretive Learning (MIL) is a recent approach for Inductive Logic Programming (ILP) implemented in Prolog. Alternatively, MIL-problems can be solved by using Answer Set Programming (ASP), which may result in performance gains due to efficient conflict propagation. However, a straightforward MIL-encoding results in a huge size of the ground program and search space. To address these challenges, we encode MIL in the HEX-extension of ASP, which mitigates grounding issues, and we develop novel pruning techniques.
RBF based sliding mode control method for lumber drying system

Lumber is an indispensable raw material for people’s daily life. Drying process is a crucial stage in lumber manufacture. As to ensure a suitable and usable end product of lumber, most of its MC must be removed by drying. Improving the quality of lumber drying requires efficient control scheme. This article presents a design of radical basis function (RBF) neural network–based sliding mode controller for lumber drying system. RBF neural network is introduced to optimize the conventional slidingmode controller. The proposed strategy has been theoretically and experimentally investigated to demonstrate the applicability for lumber drying process. Comparative study of conventional sliding mode control (SMC) scheme and proposed control scheme is also presented. It was found that the control performance of RBF-based sliding mode controller was superior to the conventional slidingmode controller in computer simulation. Furthermore, in the field conditions, time and energy consumption reduction were noticed with RBF-based sliding mode controller compared with conventional SMC strategy using the same drying schedule, although the drying quality using the two control methods were similar.
InteractionNN: A Neural Network for Learning Hidden Features in Sparse Prediction

In this paper, we present a neural network (InteractionNN) for sparse predictive analysis where hidden features of sparse data can be learned by multilevel feature interaction. To characterize multilevel interaction of features, InteractionNN consists of three modules, namely, nonlinear interaction pooling, layer-lossing, and embedding. Nonlinear interaction pooling (NI pooling) is a hierarchical structure and, by shortcut connection, constructs low-level feature interactions from basic dense features to elementary features. Layer-lossing is a feed-forward neural network where high-level feature interactions can be learned from low-level feature interactions via correlation of all layers with target. Moreover, embedding is to extract basic dense features from sparse features of data which can help in reducing our proposed model computational complex. Finally, our experiment evaluates on the two benchmark datasets and the experimental results show that InteractionNN performs better than most of state-of-the-art models in sparse regression.
A Framework for Annotating ‘Related Works’ to Support Feedback to Novice Writers

Understanding what is expected of academic writing can be difficult for novice writers to assimilate, and recent years have seen several automated tools become available to support academic writing.Our work presents a framework for annotating features of the Related Work section of academic writing, that supports writer feedback.
Cybernetic systems of music creation

Simplicity of thought and operation can help to define complex end results, with cybernetic systems being a useful means of defining this within songwriting practices. This study outlines utilization of cybernetic practices by key popular music composers, including David Byrne and Brian Eno, who benefited from an art school education which supported these practices. As postmodern creation became more evident within art colleges, systemized processes of creation, where hierarchies were delineated, supported freedom and experimentation within the creative process. The non-musician was able to express their musical creativity due to the rise of new technologies and the reduction of hierarchies, as exposed from interviews with Eno, his art school tutor Roy Ascott, and experimental composer Gavin Bryars. These elements of art school education that they discussed, helped a new generation of musicians to develop original and dynamic work in the 1970s; the results of this research suggest that these are practices that should be introduced and acknowledged within HPME.
MoBiNet: A Mobile Binary Network for Image Classification

MobileNet and Binary Neural Networks are two among the most widely used techniques to construct deep learning models for performing a variety of tasks on mobile and embedded platforms.In this paper, we present a simple yet efficient scheme to exploit MobileNet binarization at activation function and model weights. However, training a binary network from scratch with separable depth-wise and point-wise convolutions in case of MobileNet is not trivial and prone to divergence. To tackle this training issue, we propose a novel neural network architecture, namely MoBiNet - Mobile Binary Network in which skip connections are manipulated to prevent information loss and vanishing gradient, thus facilitate the training process. More importantly, while existing binary neural networks often make use of cumbersome backbones such as Alex-Net, ResNet, VGG-16 with float-type pre-trained weights initialization, our MoBiNet focuses on binarizing the already-compressed neural networks like MobileNet without the need of a pre-trained model to start with. Therefore, our proposal results in an effectively small model while keeping the accuracy comparable to existing ones. Experiments on ImageNet dataset show the potential of the MoBiNet as it achieves 54.40% top-1 accuracy and dramatically reduces the computational cost with binary operators.
Entanglement Classification for a Three-qubit System using Special Unitary Groups, SU(2) and SU(4)

Entanglement is a physical phenomenon that links a pair, or a set of particles that correlates with each other, regardless of the distance between them.Recent researches conducted on entanglement are mostly focused on measurement and classification in multiqubit systems.Classification of two qubits will only distinguish the quantum state as either separable or entangled, and it can be done by measurement.Meanwhile, in a three-qubit system, it becomes more complex because of the structure of the three qubits itself.It is not sufficient to do measurement because the states are divided into three types, including fully separable state, biseparable state, and genuine entangled state.Therefore, the classification is needed to distinguish the type of states in the three-qubit system.This study aims to classify the entanglement of three-qubit pure states using a combination model of special unitary groups, SU(2) and SU(4), by changing the angle of selected parameters in SU(4) and acting on the separable pure state.The matrix represents SU( 2) is matrix while matrix for SU(4) is 4 matrix.Hence, the combination of SU(2) and SU(4) represent 8 matrix.This classification uses the von Neumann entropy and three tangle measurements to classify the class, respectively.The results of this study have indicated that the three-qubit pure state has been successfully classified into different classes, namely, A-B-C, A-BC, C-AB, GHZ, andW, with A-B-C being a fully separable state, A-BC and C-AB are biseparable states, and GHZ and W are genuine entangled states.The results show that this model can change separable pure states to other entanglement class after transformation is done.