Reviewerly/exHarmony huggingface dataset:
Bidirectional Potential Guided RRT* for Motion Planning

Requirement for high accuracy and speed of grasping operation for motion planning is very important. Motion planning algorithms for avoiding obstacles in narrow channels play a vital role for robotic arm effectively operating grasp tasks. The potential function-based RRT*-connect (P-RRT*-connect) algorithm for motion planning is presented by combining the bidirectional artificial potential field into the rapidly exploring random tree star (RRT*) in order to enhance the performance of the RRT*. The motion path is found out by exploring two path trees from the start node and destination node, respectively, with the rapidly exploring random tree star. Two trees advance each other at the same time according to the attractive potential field and the repulsion potential field generated by the artificial potential field method of sampled nodes until they meet. The P-RRT*-connect algorithm is especially suitable for solving the problem of narrow channels. The simulation results prove that the P-RRT*-connect algorithm is more efficient than potential Function-based RRT* (P-RRT*) regardless of the number of iterations or the running time. The experimental data show that the time for the P-RRT*-connect to find the optimal path from the starting node to the target node is half than that of the P-RRT*, and the number of iterations of the P-RRT*-connect is also about one-third less than that of the P-RRT* which is useful for real time.
Difficulties in the Incorporation of Augmented Reality in University Education: Visions from the Experts

“Augmented reality” (AR) is presented as technology that harmonizes, in real time and with user collaboration, digital information with physical information through different technological supports. Such are AR possibilities, that when it is incorporated into teaching, students show high satisfaction rates and positive attitudes for its use, but at the same time, a series of limitations and obstacles can be noticed. As an attempt to investigate the technical, curricular and organizational difficulties of AR incorporation in training contexts, an instrument “AR obstacle questionnaire” (CORA) was designed, which in turn was administered to a total of 264 subject experts for validation through the Delphi method, subsequently applying the “K coefficient” (K = ½ Kc (knowledge coefficient) + Ka (argumentation coefficient)), selecting those experts who obtained a ≥ 0,8 score in the coefficient. Among the results obtained in the study we manage to point out the main obstacles involving AR implementation into university education: the lack of teacher training and improvement, the few educational experiences found, lack of conceptual foundation, limited educational research and lack of institutional support.
What's Mine Is a Hologram? How Shared Augmented Reality Augments Psychological Ownership

Augmented Reality (AR) holograms are 3D digital objects projected into a customer's physical environment through mobile technology. Applied as potential substitutes to physical products, AR holograms pose a unique challenge for conventional configurations of product ownership. Taking a socially situated cognition perspective, we demonstrate how customers' shared experience of AR holograms leads to distinct perspectives on psychological ownership. In Study 1, we demonstrate how customization of AR holograms lets customers feel psychological ownership of digital products. In Study 2, we highlight the mechanisms of social adaptation related to assimilation and differentiation that drive the relationship between customization and psychological ownership of AR holograms in social settings. In Study 3, we illustrate how these mechanisms are influenced by the affordances of AR technology when customers switch between personal or shared devices. We discuss implications for theory and marketing practice of this potentially novel class of digital consumer products.
Generalizing Eye Tracking With Bayesian Adversarial Learning

Existing appearance-based gaze estimation approaches with CNN have poor generalization performance. By systematically studying this issue, we identify three major factors: 1) appearance variations; 2) head pose variations and 3) over-fitting issue with point estimation. To improve the generalization performance, we propose to incorporate adversarial learning and Bayesian inference into a unified framework. In particular, we first add an adversarial component into traditional CNN-based gaze estimator so that we can learn features that are gaze-responsive but can generalize to appearance and pose variations. Next, we extend the point-estimation based deterministic model to a Bayesian framework so that gaze estimation can be performed using all parameters instead of only one set of parameters. Besides improved performance on several benchmark datasets, the proposed method also enables online adaptation of the model to new subjects/environments, demonstrating the potential usage for practical real-time eye tracking applications.
Progressive Spatial Recurrent Neural Network for Intra Prediction

Intra prediction is an important component of modern video codecs, which is able to efficiently squeeze out the spatial redundancy in video frames. With preceding pixels as the context, traditional intra prediction schemes generate linear predictions based on several predefined directions (i.e., modes) for blocks to be encoded. However, these modes are relatively simple and their predictions may fail when facing blocks with complex textures, which leads to additional bits encoding the residue. In this paper, we design a progressive spatial recurrent neural network (PS-RNN) that learns to conduct intra prediction. Specifically, our PS-RNN consists of three spatial recurrent units and progressively generates predictions by passing information along from preceding contents to blocks to be encoded. To make our network generate predictions considering both distortion and bit rate, we propose using sum of absolute transformed difference (SATD) as the loss function to train PS-RNN since SATD is able to measure rate-distortion cost of encoding a residue block. Moreover, our method supports variable-block-size for intra prediction, which is more practical in real coding conditions. The proposed intra prediction scheme achieves on average 2.5% bit-rate reduction on variable-block-size settings under the same reconstruction quality compared with HEVC.
Utilization of DenseNet201 for diagnosis of breast abnormality

As one of the leading killers of females, breast cancer has become one of the heated research topics in the community of clinical medical science and computer science. In the clinic, mammography is a publicly accepted technique to detect early abnormalities such as masses and distortions in breast leading to cancer. Interpreting the images, however, is time-consuming and error-prone for radiologists considering artificial factors including potential fatigue. To improve radiologists’ working efficiency, we developed a semi-automatic computer-aided diagnosis system to classify mammograms into normality and abnormality and thus to ease the process of making a diagnosis of breast cancer. Through transferring deep convolutional neural network DenseNet201 on the basis of suspicious regions provided by radiologists into our system, we obtained the network we termed DenseNet201-C, which achieved a high diagnostic accuracy of 92.73%. The comparison results between our method and the other five methods show that our method achieved the highest accuracy.
Joint Multi-View Face Alignment in the Wild

The de facto algorithm for facial landmark estimation involves running a face detector with a subsequent deformable model fitting on the bounding box. This encompasses two basic problems: i) the detection and deformable fitting steps are performed independently, while the detector might not provide best-suited initialization for the fitting step, ii) the face appearance varies hugely across different poses, which makes the deformable face fitting very challenging and thus distinct models have to be used (e.g., one for profile and one for frontal faces). In this work, we propose the first, to the best of our knowledge, joint multi-view convolutional network to handle large pose variations across faces in-the-wild, and elegantly bridge face detection and facial landmark localization tasks. Existing joint face detection and landmark localization methods focus only on a very small set of landmarks. By contrast, our method can detect and align a large number of landmarks for semi-frontal (68 landmarks) and profile (39 landmarks) faces. We evaluate our model on a plethora of datasets including standard static image datasets such as IBUG, 300W, COFW, and the latest Menpo Benchmark for both semi-frontal and profile faces. Significant improvement over state-of-the-art methods on deformable face tracking is witnessed on 300VW benchmark. We also demonstrate state-ofthe- art results for face detection on FDDB and MALF datasets.
A Collaborative Filtering Approach Based on Naïve Bayes Classifier

Recommender system is an information filtering tool used to alleviate information overload for users on the web. Collaborative filtering recommends items to users based on their historical rating information. There are two approaches: memory-based, which usually provides inaccurate but explainable recommendations; and model-based, whose recommendations are more precise but hard to understand. Here we propose a Bayesian model that not only provides us with recommendations as good as matrix factorization models, but these predictions can also be explained. The model is based on both user-based and item-based collaborative filtering approaches, which recommends items by using similar users' and items' information, respectively. Experiments carried out using four datasets present good results compared to several state-of-the-art baselines, achieving the best performance using the Normalized Discounted Cumulative Gain (nDCG) quality measure and also improving the prediction's accuracy in some datasets.
A Content Transformation Block for Image Style Transfer

Style transfer has recently received a lot of attention, since it allows to study fundamental challenges in image understanding and synthesis. Recent work has significantly improved the representation of color and texture and computational speed and image resolution. The explicit transformation of image content has, however, been mostly neglected: while artistic style affects formal characteristics of an image, such as color, shape or texture, it also deforms, adds or removes content details. This paper explicitly focuses on a content-and style-aware stylization of a content image. Therefore, we introduce a content transformation module between the encoder and decoder. Moreover, we utilize similar content appearing in photographs and style samples to learn how style alters content details and we generalize this to other class details. Additionally, this work presents a novel normalization layer critical for high resolution image synthesis. The robustness and speed of our model enables a video stylization in real-time and high definition. We perform extensive qualitative and quantitative evaluations to demonstrate the validity of our approach.
Simulation of groundwater level using MODFLOW, extreme learning machine and Wavelet-Extreme Learning Machine models

Due to the need to forecast changes in groundwater systems and environment, groundwater modeling has been emerged. In this study, the groundwater level of the Kabodarahang aquifer located in Iran, Hamadan Province is simulated by means of three models: MODFLOW, Extreme Learning Machine (ELM) and Wavelet-Extreme Learning Machine (WA-ELM). At the beginning, the groundwater level simulation is carried out by MODFLOW with reasonable accuracy so that the correlation coefficient (R2) and the scatter index (SI) are calculated 0.917 and 0.0004, respectively. After that, through different input combinations as well as the stepwise selection, 10 different models are developed as different lags for the ELM and WA-ELM models. Based on the numerical results yielded by all three models, WA-ELM is introduced as the superior model in simulating the groundwater level. For instance, the correlation coefficient (R2) and the Nash-Sutcliffe efficiency coefficient (NSC) are computed 0.959 and 0.915, respectively. According to the uncertainty analysis, it is proved that the superior model has an underestimated performance. Furthermore, the by-products of the model such as weights and biases are then utilized to develop an explicit method for estimating the groundwater level. The developed model can be easily employed by an engineer with sufficient knowledge of matrix operation without prior information about the extreme learning machine. It is as accurate as the developed WA-ELM hybrid model and more precise than ELM and MODFLOW.
Close-range, ground-based hyperspectral imaging for mining applications at various scales: Review and case studies

Detailed mapping of mineral phases at centimeter scale can be useful for geological investigation, including resource exploration. This work reviews case histories of ground-based close-range hyperspectral imaging for mining applications. Studies of various economic deposits are discussed, as well as techniques used for data correction, integration with other datasets, and validation of spectral mapping results using geochemical techniques. Machine learning algorithms suggested for automation of the mining workflow are reviewed, as well as systems for environmental monitoring such as gas leak detection. Three new case studies that use a ground-based hyperspectral scanning system with sensors collecting data in the Visible Near-Infrared and Short-Wave Infrared portions of the electromagnetic spectrum in active and abandoned mines are presented. Vertical exposures in a Carlin Style sediment-hosted gold deposit, an active Cu-Au-Mo mine, and an active asphalt quarry are studied to produce images that delineate the extent of alteration minerals at centimeter scale to demonstrate an efficient method of outcrop characterization, which increases understanding of petrogenesis for mining applications. In the Carlin-style gold deposit, clay, iron oxide, carbonate, and jarosite minerals were mapped. In the copper porphyry deposit, different phases of alteration are delineated, some of which correspond to greater occurrence of ore deposits. A limestone quarry was also imaged, which contains bitumen deposits used for road paving aggregate. Review of current literature suggests use of this technology for automation of mining activities, thus reducing physical risk for workers in evaluating vertical mine faces.
Human Activity Recognition using LSTM-RNN Deep Neural Network Architecture

Using raw sensor data to model and train networks for Human Activity Recognition can be used in many different applications, from fitness tracking to safety monitoring applications. These models can be easily extended to be trained with different data sources for increased accuracies or an extension of classifications for different prediction classes. This paper goes into the discussion on the available dataset provided by WISDM and the unique features of each class for the different axes. Furthermore, the design of a Long Short Term Memory (LSTM) architecture model is outlined for the application of human activity recognition. An accuracy of above 94% and a loss of less than 30% has been reached in the first 500 epochs of training.
DeePr-ESN: A deep projection-encoding echo-state network

As a recurrent neural network that requires no training, the reservoir computing (RC) model has attracted widespread attention in the last decade, especially in the context of time series prediction. However, most time series have a multiscale structure, which a single-hidden-layer RC model may have difficulty capturing. In this paper, we propose a novel multiple projection-encoding hierarchical reservoir computing framework called Deep Projection-encoding Echo State Network (DeePr-ESN). The most distinctive feature of our model is its ability to learn multiscale dynamics through stacked ESNs, connected via subspace projections. Specifically, when an input time series is projected into the high-dimensional echo-state space of a reservoir, a subsequent encoding layer (e.g., an autoencoder or PCA) projects the echo-state representations into a lower-dimensional feature space. These representations are the principal components of the echo-state representations, which removes the high frequency components of the representations. These can then be processed by another ESN through random connections. By using projection layers and encoding layers alternately, our DeePr-ESN can provide much more robust generalization performance than previous methods, and also fully takes advantage of the temporal kernel property of ESNs to encode the multiscale dynamics of time series. In our experiments, the DeePr-ESNs outperform both standard ESNs and existing hierarchical reservoir computing models on some artificial and real-world time series prediction tasks.
Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics

Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users' behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions. In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis.
Sensors Technologies for Human Activity Analysis Based on SVM Optimized by PSO Algorithm

The rapid growth of wearable sensors have increased the importance of human activity analysis in different areas of information technologies. Motion artifacts often degrade the performance of wearable sensors. Several wearable sensors have been used since the last decades in order to recognize physical activity detection. The wearable sensors could have numerous applications in medical and daily life routine activities like human gait analysis, health care, fitness, etc. In this paper, accelerometer and gyroscope sensors dataset has been used to propose an efficient model for physical activity detection. We designed a new feature extraction algorithm, Mel-frequency cepstral coefficient and statistical features to extract valuable features. Then, classification of different daily life activities is performed via Particle Swarm Optimization (PSO) together with SVM algorithm over bench mark motion-sense dataset. The results of our model shows that pre-classifier as PSO and SVM along with feature extraction module excel in term of accuracy and efficiency. Our experimental results have shown accuracy of 87.50% over motion-sense dataset. This model is recommended for the system associating in physical activity detection, especially in medical fitness field.
Blockchain-based data privacy management with Nudge theory in open banking

Open banking brings both opportunities and challenges to banks all over the world especially in data management. A blockchain as a continuously growing list of records managed by a peer-to-peer network is widely used in various application scenarios; and it is commonly agreed that the blockchain technology can improve the protection of financial data privacy. However, current blockchain technology still poses some challenges in fully meeting the needs of financial data privacy protection. In order to address the existing problems, this paper proposes a new data privacy management framework based on the blockchain technology for the financial sector. The framework consists of three components: (1) a data privacy classification method according to the characteristics of financial data; (2) a new collaborative-filtering-based model; and (3) a data disclosure confirmation scheme for customer strategies based on the Nudge Theory. We implement a prototype and propose a set of algorithms for this framework. The framework is validated through field experiments and laboratory experiments.
How do People Type on Mobile Devices?

This paper presents a large-scale dataset on mobile text entry collected via a web-based transcription task performed by 37,370 volunteers. The average typing speed was 36.2 WPM with 2.3% uncorrected errors. The scale of the data enables powerful statistical analyses on the correlation between typing performance and various factors, such as demographics, finger usage, and use of intelligent text entry techniques. We report effects of age and finger usage on performance that correspond to previous studies. We also find evidence of relationships between performance and use of intelligent text entry techniques: auto-correct usage correlates positively with entry rates, whereas word prediction usage has a negative correlation. To aid further work on modeling, machine learning and design improvements in mobile text entry, we make the code and dataset openly available.