Reviewerly/exHarmony huggingface dataset:
Exploring Interaction Fidelity in Virtual Reality

High degrees of interaction fidelity (IF) in virtual reality (VR) are said to improve user experience and immersion, but there is also evidence of low IF providing comparable experiences. VR games are now increasingly prevalent, yet we still do not fully understand the trade-off between realism and abstraction in this context. We conducted a lab study comparing high and low IF for object manipulation tasks in a VR game. In a second study, we investigated players' experiences of IF for whole-body movements in a VR game that allowed players to crawl underneath virtual boulders and "dangle'' along monkey bars. Our findings show that high IF is preferred for object manipulation, but for whole-body movements, moderate IF can suffice, as there is a trade-off with usability and social factors. We provide guidelines for the development of VR games based on our results.
Using 360‐degree videos in teacher education to improve preservice teachers' professional interpersonal vision

Abstract Noticing and interpreting classroom events (professional vision) is an important element of preservice teachers' (PSTs) interpersonal competence. This paper presents a mixed‐method study about a classroom simulation using 360‐degree videos combined with theoretical lectures in teacher education, intended to improve PSTs' interpretations of noticed events. Furthermore, this study examined how PSTs evaluate technological and educational affordances of 360‐degree videos. Results indicate that participating PSTs improved in noticing classroom events and in applying a more theory‐based terminology to describe these events. PSTs perceived observing other teachers teach as an educational affordance for mastering theory and for developing insights about interpersonal teacher behaviour. Concerning technological affordances, mainly physical discomforts and technical hindrances, was reported by PSTs. The results of this study imply that 360‐degree videos can be useful for teacher education to improve PSTs' interpretation of noticed events.
Chaotic Type-2 Transient-Fuzzy Deep Neuro-Oscillatory Network (CT2TFDNN) for Worldwide Financial Prediction

Over the years, financial engineering ranging from the study of financial signals to the modeling of financial prediction is one of the most exciting topics for both academia and financial community. With the flourishing AI technology in the past 20 years, various hybrid intelligent financial prediction systems with the integration of neural networks, chaos theory, fuzzy logic, and genetic algorithms have been proposed. An interval type-2 fuzzy logic system (IT2FLS) with its remarkable capability for the modeling of highly uncertain events and attributes provides a perfect tool to interpret various financial phenomena and patterns. In this paper, the author proposes a chaotic type-2 transient-fuzzy deep neuro-oscillatory network with retrograde signaling (CT2TFDNN) for worldwide financial prediction. With the extension of author's original work on Lee oscillator-a chaotic discrete-time neural oscillator with profound transient-chaotic property-CT2TFDNN provides: effective modeling of an IT2FLS with a chaotic transient-fuzzy membership function; and effective time-series network training and prediction using a chaotic deep neuro-oscillatory network with retrograde signaling. CT2TFDNN not only provides a fast chaotic fuzzy-neuro deep learning and forecast solution, but also successfully resolves the massive data overtraining and deadlock problems, which are usually imposed by traditional recurrent neural networks using classical sigmoid-based activation functions. From the implementation perspective, CT2TFDNN is integrated with 2048 trading-day time-series financial data and top-10 major financial signals as fuzzy financial signals for the real-time prediction of 129 worldwide financial products that consists of: nine major cryptocurrencies, 84 worldwide forex, 19 major commodities, and 17 worldwide financial indices.
What You Sketch Is What You Get

Augmented Reality(AR) tools are currently primarily targeted at programmers, making designing for AR challenging and time-consuming. We developed an interactive prototype, PintAR, that enables the authoring and rapid-prototyping of situated experiences by allowing designers to bring their ideas to life using a digital pen for sketching and a head-mounted display for visualizing and interacting with virtual content. In this paper, we explore the versatility such a tool could provide through case studies of a researcher, an artist, a ballerina, and a clinician.
Active contours driven by adaptive functions and fuzzy c-means energy for fast image segmentation

The shortcoming of geometric active contours is that they are extremely sensitive to positions of initialization. Initial contours of the distance regularized level set evolution model must be set inside or outside target completely. In addition, the model is prone to problems of falling into false boundary, leaking from weak edge and poor anti-noise ability. In this paper, we propose a robust active contour model driven by adaptive functions (including an adaptive edge indicator function and adaptive sign function) and fuzzy c-means energy. Utilize the adaptive edge indicator function which is composed of image intensity information to substitute traditional edge indicator function in the area term. Active contours can expand or shrink from initialization automatically, which improves the disadvantages of poor robustness to initialization and unidirectional movement. Due to the adaptive sign function and fuzzy c-means energy, the problems of slow convergence and leaking from weak edge have been solved. Moreover, a novel distance regularized term (mainly a potential function and evolution speed function) is proposed to make evolution more stable. Experimental results have proved that our model can segment images with intensity inhomogeneity effectively. Compared with other classic models, the proposed model not only shortens time spent and improves segmentation accuracy, but also shows a better robustness to initialization.
Leaf-based plant species recognition based on improved local binary pattern and extreme learning machine

Over the past 15 years, many feature extraction methods have been used and developed for the recognition of plant species. These methods have mostly been performed using separation operations from the background based on a pre-processing stage. However, the Local Binary Patterns (LBP) method, which provides high performance in object recognition, is used to obtain textural features from images without need for a pre-processing stage. In this paper, we propose different approaches based on LBP for the recognition of plant leaves using extracted texture features from plant leaves. While the original LBP converts color images to gray tones, the proposed methods are applied by using the R and G color channel of images. In addition, we evaluate the robustness of the proposed methods against noise such as salt & pepper and Gaussian. Later, the obtained features from the proposed methods were classified and tested using the Extreme Learning Machine (ELM) method. The experimental works were performed using various plant leaf datasets such as Flavia, Swedish, ICL, and Foliage. According to the obtained performance results, the calculated accuracy values for Flavia, Swedish, ICL and Foliage datasets were 98.94%, 99.46%, 83.71%, and 92.92%, respectively. The results demonstrate that the proposed method was more successful when compared to the original LBP, improved LBP methods, and other image descriptors for both noisy and noiseless images.
A Novel Neural Network Classifier Using Beetle Antennae Search Algorithm for Pattern Classification

Traditional training algorithms in artificial neural networks (ANNs) show some inherent weaknesses, such as the possibility of falling into local optimum, slow learning speed, and the inability to determine the optimal neuronal structure. To remedy the deficiencies of traditional neural networks, this paper proposes a novel neural network classifier (NNC) using the beetle antennae search (BAS) algorithm, termed BASNNC. The BAS algorithm is explored to optimize the weights of the NNC. The network of the proposed BASNNC adopts three-layer structure, including an input layer, a hidden layer, and an output layer. Quite differing from the traditional training algorithm using a principle of gradient descent, the weights between the hidden and output layers are optimized by the BAS algorithm, which effectively improves the computational speed of the classifier. The numerical studies, applications to pattern classification and comparisons with an error back-propagation neural network model, show that the proposed BASNNC has faster computational speed and higher classification accuracy.
Automatic Recognition of General LPI Radar Waveform Using SSD and Supplementary Classifier

For low probability of intercept (LPI) radars, frequency-modulated and phase-modulated continuous waveforms are widely used because of their low peak power compared to that of pulse waves (PW). However, there has been a limited number of studies on recognizing continuous wave (CW) LPI radar, in spite of its importance and popularity. In this paper, in order to recognize both PW and CW LPI radar waveforms, we propose an LPI radar waveform recognition technique (LWRT) based on a single-shot multi-box detector (SSD) and a supplementary classifier. It is demonstrated with Monte Carlo simulations that the proposed LWRT achieves classification performance similar to that of the current LWRT with the highest classification performance for PW LPI radar waveforms, even without the prior condition used in existing LWRTs. For CW LPI radar waveforms, on the other hand, with the combination of the SSD and the supplementary classifier, the proposed LWRT achieves extraordinary recognition performance for all 12 LPI radar modulation schemes (i.e., BPSK, Costas, LFM, Frank, P1, P2, P3, P4, T1, T2, T3, and T4) considered in the literature.
Character convolutions for Arabic Named Entity Recognition with Long Short-Term Memory Networks

Named Entity Recognition (NER) is a significant information extraction task since it is an important component of many natural language processing applications, such as Information Retrieval, Question Answering and Speech Recognition. The complexity and morphological richness of the Arabic language is the main reason why most existing Arabic NER systems rely strongly on hand-crafted feature engineering. In this paper, we propose to augment the existing LSTM neural tagging model for Arabic NER with a Convolutional Neural Network (CNN) for the extraction of relevant character-level features. By operating on the character-level, the proposed model is able to handle out-of-vocabulary words. Our results show that character CNN is able to outperform the previously used character-level Bi-directional Long Short-Term Memory Networks (BiLSTM) in many settings. Moreover, our observations indicate that CNNs tend to perform better than BiLSTM on relatively longer tokens. In addition, we conduct a comparison of four different pre-trained word vector models for Arabic NER and results show that a Skip-Gram Word2vec model, pre-trained on a subset of the Arabic Gigaword corpus, is generally sufficient to obtain acceptable Arabic NER performance.
Adaptive and large-scale service composition based on deep reinforcement learning

In a service-oriented system, simple services are combined to form value-added services to meet users' complex requirements. As a result, service composition has become a common practice in service computing. With the rapid development of web service technology, a massive number of web services with the same functionality but different non-functional attributes (e.g., QoS) are emerging. The increasingly complex user requirements and the large number of services lead to a significant challenge to select the optimal services from numerous candidates to achieve an optimal composition. Meanwhile, web services accessible via computer networks are inherently dynamic and the environment of service composition is also complex and unstable. Thus, service composition solutions need to be adaptable to the dynamic environment. To address these key challenges, we propose a new service composition scheme based on Deep Reinforcement Learning (DRL) for adaptive and large-scale service composition. The proposed approach is more suitable for the partially observable service environment, making it work better for real-world settings. A recurrent neural network is adopted to improve reinforcement learning, which can predict the objective function and enhance the ability to express and generalize. In addition, we employ the heuristic behavior selection strategy, in which the state set is divided into the hidden and fully observable state sets, to perform the targeted behavior selection strategy when facing with different types of states. The experimental results justify the effectiveness and efficiency, scalability, and adaptability of our methods by showing obvious advantages in composition results and efficiency for service composition.
Augmented Reality Map Navigation with Freehand Gestures

Freehand gesture interaction has long been proposed as a `natural' input method for Augmented Reality (AR) applications, yet has been little explored for intensive applications like multiscale navigation. In multiscale navigation, such as digital map navigation, pan and zoom are the predominant interactions. A position-based input mapping (e.g. grabbing metaphor) is intuitive for such interactions, but is prone to arm fatigue. This work focuses on improving digital map navigation in AR with mid-air hand gestures, using a horizontal intangible map display. First, we conducted a user study to explore the effects of handedness (unimanual and bimanual) and input mapping (position-based and rate-based). From these findings we designed DiveZoom and TerraceZoom, two novel hybrid techniques that smoothly transition between position- and rate-based mappings. A second user study evaluated these designs. Our results indicate that the introduced input-mapping transitions can reduce perceived arm fatigue with limited impact on performance.
Robust Image-Based Visual Servoing With Prescribed Performance Under Field of View Constraints

In this paper, we propose a visual servoing scheme that imposes predefined performance specifications on the image feature coordinate errors and satisfies the visibility constraints that inherently arise owing to the camera's limited field of view, despite the inevitable calibration and depth measurement errors. Its efficiency is demonstrated via comparative experimental and simulation studies.
Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.
Real-World Evidence, Causal Inference, and Machine Learning

The current focus on real world evidence (RWE) is occurring at a time when at least two major trends are converging. First, is the progress made in observational research design and methods over the past decade. Second, the development of numerous large observational healthcare databases around the world is creating repositories of improved data assets to support observational research. OBJECTIVE: This paper examines the implications of the improvements in observational methods and research design, as well as the growing availability of real world data for the quality of RWE. These developments have been very positive. On the other hand, unstructured data, such as medical notes, and the sparcity of data created by merging multiple data assets are not easily handled by traditional health services research statistical methods. In response, machine learning methods are gaining increased traction as potential tools for analyzing massive, complex datasets. CONCLUSIONS: Machine learning methods have traditionally been used for classification and prediction, rather than causal inference. The prediction capabilities of machine learning are valuable by themselves. However, using machine learning for causal inference is still evolving. Machine learning can be used for hypothesis generation, followed by the application of traditional causal methods. But relatively recent developments, such as targeted maximum likelihood methods, are directly integrating machine learning with causal inference.
Multi-label classification using a cascade of stacked autoencoder and extreme learning machines

This article introduces a cascade of neural networks for classification of multi-label data. Two types of networks, namely, stacked autoencoder (SAE) and extreme learning machine (ELM) have been incorporated in the proposed system. ELM is a compact and efficient single-label classifier which seems to lose its efficiency while dealing with multi-label data. This happens due to the complex nature of the multi-label data, which makes it difficult for the smaller networks to interpret it accurately. In our proposed work, we attempt to deal with few of the bottlenecks faced while handling multi-label data. Thus, we aim to enhance the performance of a stand-alone multi-label extreme learning machine (MLELM) by collaborating it with other networks. There are three basic phases in the proposed method: feature encoding, soft classification and class score approximation. In the first step, an SAE network is employed to generate a discriminating and reduced input representation of the multi-label data. This makes the data compact and more manageable for the successive stages. This data in turn is used by an MLELM in the next phase for the prediction of soft labels. In the final step, to improve the prediction capability of the previous network, a novel approach of approximating the class score is proposed using an additional MLELM. Comprehensive experimental evaluation of the proposed approach has been performed on seven datasets against eleven relevant algorithms, and overall it displays a promising performance.
A novel extreme learning Machine-based Hammerstein-Wiener model for complex nonlinear industrial processes

In this paper, a novel extreme learning machine (ELM) based Hammerstein-Wiener model is developed for the approximation of complex nonlinear systems. The proposed model has two static ELM networks surround a dynamic linear part. Estimation of this model can be summarized into the following three aspects: The first one is to approximate the static nonlinear elements of Hammerstein-Wiener model with two independent ELM networks, which are single-hidden layer feedforward network (SLFN) essentially. The second one is to estimate the structure of linear part of the proposed model using lipschitz quotient criterion with respect to the measurements. The final one is to determine the parameters of the two SLFNs and the linear block using ELM algorithm. To evaluate the generalization performance, Rademacher complexity will be used to give the generalization bound of the proposed model with theoretical proof. The proposed model can track and handle the strong nonlinearity and time-varying dynamics over the whole operating domain for its inherent two nonlinear structure. Furthermore, with ELM algorithms, the proposed model can achieve fast learning speed and less computation complexity. Simulations on two typical industrial thermal processes demonstrate the accuracy and efficiency of the researched model.
A sparse regularized nuclear norm based matrix regression for face recognition with contiguous occlusion

Nuclear norm based matrix regression (NMR) method has been proposed to alleviate the influence of contiguous occlusion on face recognition problems. NMR considers that the error image of a test sample has low-rank structure due to the contiguous nature of occlusion. Based on the observation that l1-norm can uncover more natural sparsity of representations than l2-norm, we propose a sparse regularized NMR (SR_NMR) algorithm by imposing the l1-norm constraint rather than l2-norm on the representations of NMR framework. SR_NMR seamlessly integrates the nuclear norm based error matrix regression and l1-norm based sparse representation into one joint framework. Finally, we use the training samples to learn a linear classifier to implement efficient classification. Extensive experiments on three face databases show the proposed SR_NMR can achieve better recognition performance compared with the traditional NMR and other regression methods which indicates that sparse representations are very helpful to recover low-rank error images in the presence of severe occlusion and illumination changes.
FlowSAN: Privacy-Enhancing Semi-Adversarial Networks to Confound Arbitrary Face-Based Gender Classifiers

Privacy concerns in the modern digital age have prompted researchers to develop techniques that allow users to selectively suppress certain information in collected data while allowing for other information to be extracted. In this regard, semi-adversarial networks (SANs) have recently emerged as a method for imparting soft-biometric privacy to face images. SAN enables modifications of input face images so that the resulting face images can still be reliably used by arbitrary conventional face matchers for recognition purposes, while attribute classifiers, such as gender classifiers, are confounded. However, the generalizability of SANs across arbitrary gender classifiers has remained an open concern. In this paper, we propose a new method, FlowSAN, for allowing SANs to generalize to multiple gender classifiers. We propose stacking a diverse set of SAN models to compensate each other's weaknesses, thereby forming a robust model with improved generalization capability. Extensive experiments using different unseen gender classifiers and face matchers demonstrate the efficacy of the proposed paradigm in imparting gender privacy to face images.