Reviewerly/exHarmony huggingface dataset:
Online and Offline Data Collection of Japanese Handwriting

This paper describes online and offline Japanese character database developed for handwriting identification. A pair of online and offline data of Japanese handwritten characters were collected from four hundred and one participant using Anoto pen and Anoto pen paper. Participants were asked to write five sets of characters. Each set had seven hundred and two characters, which were the same all through the sets but were different in order. Online data was acquired by page, segmented into a character and used for the trajectory drawing of a handwriting. Calculation of writing speed and the difference in pen pressure between two measuring points were also acquired using the online data. Trajectory of each handwriting was acquired by connecting coordinates of measuring points and it was used for indicating stroke order, writing speed and the difference in pen pressure while writing along with indicating rough shape of the handwriting. Trajectories gave an image of the relationship between the writing behavior and the results of the behavior, that is, the rough shape of handwriting. Offline data was processed into image files by scanning at the resolution of 800 dpi by page and segmented into each handwritten character. This database enabled us to know visually intra and inter individual differences in handwritten characters and in the pattern of the pen pressure and the pen speed.
Interactive 3D Segmentation Editing and Refinement via Gated Graph Neural Networks

The extraction of organ and lesion regions is an important yet challenging problem in medical image analysis. The accuracy of the segmentation is essential to the quantitative evaluation in many clinical applications. Nevertheless, automated segmentation approaches often suffer from a variety of errors, e.g., over-segmentation, under-detection, and dull edges, which often requires manual corrections on the algorithm-generated results. Therefore, an efficient segmentation editing and refinement tool is desired due to the need of (1) minimizing the repeated effort of human annotators on similar errors (e.g., under-segmentation cross several slices in 3D volumes); (2) an “intelligent” algorithm that can preserve the correct part of the segmentation while it can also align the erroneous part with the true boundary based on users’ limited input. This paper presents a novel solution that utilizes the gated graph neural networks to refine the 3D image volume segmentation from certain automated methods in an interactive mode. The pre-computed segmentation is converted to polygons in a slice-by-slice manner, and then we construct the graph by defining polygon vertices cross slices as nodes in a directed graph. The nodes are modeled with gated recurrent units to first propagate the features among neighboring nodes. Afterward, our framework outputs the movement prediction of each polygon vertex based on the converged states of nodes. We quantitatively demonstrate the refinement performance of our framework on the artificially degraded segmentation data. Up to $$10\%$$ improvement in IOUs are achieved for the segmentation with a variety of error degrees and percentages.
Resistance of IID Noise in Differentially Private Schemes for Trajectory Publishing

Abstract Although analyzing and mining user’s trajectory data can provide outstanding benefit, data owners may not be willing to upload their trajectory data because of privacy concerns. Recently, differential privacy technology has achieved a good trade-off between data utility and privacy preserving by publishing noisy outputs, and relevant schemes have been proposed for trajectory release. However, we experimentally find that a relatively accurate estimate of the true data value can still be obtained from the noisy outputs by means of a posterior estimation. But there are no practical mechanisms against current schemes to verify their effectiveness and resistance. To fill this gap, we propose a solution to evaluate the resistance performance of differential privacy on trajectory data release, including a notion of correlation-distinguishability filtering (CDF) and a privacy quantification measurement. Specifically, taking advantage of the principle of filtering that independent noise can be filtered out from correlated sequence, CDF is proposed to sanitize the noise added into the trajectory. To conduct this notion in practice, we attempt to apply a Kalman/particle filter to filter out the corresponding Gaussian/Laplace noise added by differential privacy schemes. Furthermore, to quantify the distortion of privacy strength before and after filtering, an entropy-based privacy quantification metric is proposed, which is used to measure the lost uncertainty of the true locations for an adversary. Experimental results show that the resistance performance of current approaches has a degradation to varying degrees under the filtering attack model in our solution. Moreover, the privacy quantification metric can be regarded as a unified criterion to measure the privacy strength introduced by the noise that does not conform to the form required by differential privacy.
Disruptive technologies: Present and future

In this chapter, we provide a smorgasbord of emergent technologies on the watch. We begin with an introduction to telehomecare; continue our discussions on the topic of the use of robots for medical and healthcare applications, artificial intelligence and its usage in health and medical care, and virtual reality and its applications in healthcare and medicine; and conclude with blockchain technology. We discuss these technologies with a view to their present and future potential.
Enhancing the Accuracies of Age Estimation With Heterogeneous Databases Using Modified CycleGAN

Age estimation using face images has been widely employed across various fields. Because the characteristics of face images usually vary greatly depending on race, camera type, lighting, and other environmental factors, the recognition ability of untrained heterogeneous face image databases is not accurate by previous methods. Therefore, various attempts have been made where different heterogeneous databases were combined to enable training; however, the training time is extended and diverse environmental variables in databases cannot be sufficiently trained. To address these issues, this study proposes modified cycle-consistent generative adversarial network (CycleGAN) that generates an even distribution of heterogeneous face data, and an age-estimation method that is effective for heterogeneous data based on comparative CNN for age estimation (CCNNAE). In addition, we propose the method of reducing the errors caused by image transformation in modified CycleGAN through adaptive selection between transformed and original face images for the input to CCNNAE, which is based on age similarity between the transformed face image and the original face image. Experiments with two open databases, MORPH and MegaAge databases showed that our method outperformed the state-of-the art methods.
Q2ABR: QoE‐aware adaptive video bit rate solution

Summary In this paper, we propose a new adaptive bit rate (ABR) streaming method. This method is based on estimating and monitoring users' video streaming experience, their quality of experience (QoE). This ensures a good user QoE and optimises bandwidth utilisation by monitoring video buffer fill rate to ensure minimal data traffic. First, we achieve a QoE evaluation model based on network bandwidth, video segment representation, and dropped video frame rate parameters. Second, following our QoE evaluation model, we formulate an ABR method using the reinforcement learning (RL) paradigm to select video representations and using a breakpoint detection mechanism to monitor end‐user QoE variation. The proposed ABR method is called “QoE‐aware adaptive bit rate (Q2ABR)” and is composed of three individual modules, one for QoE estimation using machine learning methods, one for QoE variation monitoring using the breakpoint detection mechanism, and one for video representation selection using reinforcement learning. The design objective of Q2ABR is to ensure the overall QoE of these users while maintaining a minimum variation in the standard deviation of the users' QoE values. Third, the performance of the Q2ABR method is evaluated and compared with several existing ABR approaches in the literature using real traces that we collect on different transport scenarios (such as bus and train, among others). Since this method considers the user's perception of video quality as a regulator for optimising the overall video distribution network, good results are ensured in terms of the user's experience and buffer fill rate.
Deep Learning in Natural Language Processing, edited by Li Deng and Yang Liu. Singapore: Springer, 2018. ISBN 9789811052088. XVII + 329 pages

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Signifier-Based Immersive and Interactive 3D Modeling

Interactive 3D modeling in VR is both aided by immersive 3D input and hampered by model disjunct, tool-based or selection-action user interfaces. We propose a direct, signifier-based approach to the popular interactive technique of creating 3D models through a sequence of extrusion operations. Motivated by handles and signifiers that communicate the affordances of everyday objects, we define a set of design principles for an immersive, signifier-based modeling interface. We then present an interactive 3D modeling system where all modeling affordances are modelessly reachable and signified on the model itself.
Robust Character Recognition For Optical And Natural Images Using Deep Learning

Character recognition is one of the most critical parts of many computer vision system. And many studies have explored character recognition as two subcategories: character recognition in optical images, character recognition natural images while this separation was to achieve high accuracy in each field separated but it needs more hardware resources to operate two models one for each area. In addition to that most researches divided each field into (digits recognition, character recognition) that add extra cost in both time and hardware resources also we found that both areas still have room for accuracy improvement. This paper tackles the problem of the two subclasses by building one robust, accurate classifier using a convolutional neural network that can recognize (characters and digits) accurately in both optical and natural scene images, the proposed model has been trained on a combination of EMNIST and Char74k data sets with a random data augmentation. The proposed model achieved 92% accuracy in EMINST compared to previous works shows that the proposed model has the highest accuracy among all the previous works based on EMNIST data set. We also tested the model on none-seen data sets (ICDAR203) and the obtained results indicate the high generality and the robustness of the classifier.
Preliminary Evaluation of the Usability of a Virtual Reality Game for Mudslide Education for Children

Mudslide education is important for children. In this study, a design-based research approach was used to develop an educational VR mudslide game for children. Eleven children participated in the usability evaluations. The results indicated the importance of intuitive, easy-to-learn controls. Six major refinements of the VR mudslide game were made to increase usabilities. Feedback from the participants will guide future game refinements to increase users’ engagement and interaction.
A logic of efficient and optimal designs

Abstract Logics of design have been formulated until recently to offer systematic treatments of the way systems express the relation between resources, processes and their outputs. We present a logic of systems design which explicitly formalizes this relation as a decidable checking problem on resource access and define computable efficiency and optimality properties.
A-HA

Session-based recommender system refers to a specific type of recommender system that focuses more on the transactional structure of each session rather than the user and item interactions [16]. It is stated that the users' interactions are mostly homogeneous in the same sessions, while being heterogeneous across different sessions [5]. Therefore, it is essential to extract the interest dynamics of users within each session. The 2019 ACM Recsys Challenge [10] aims to apply session-based recommender systems to the domain of travel metasearch. The goal is to predict which hotels are clicked in the search results based on the context of each session. In this paper, we propose our approach to effectively tackle the challenge. It involves an ensemble of three models, LightGBM, XGBoost, and a Neural Network based on DeepFM [6] that is capable of handling sequential features. Our team, RosettaAI, won the 4th place in this challenge, scoring 0.679933 on the final leaderboard. The source code is available online 1.
Latent cognizance

Despite overwhelming achievements in recognition accuracy, extending an open-set capability---ability to identify when the question is out of scope---remains greatly challenging in a scalable machine learning inference. A recent research has discovered Latent Cognizance (LC)---an insight on a recognition mechanism based on a new probabilistic interpretation, Bayesian theorem, and an analysis of an internal structure of a commonly-used recognition inference structure. The new interpretation emphasizes a latent assumption of an overlooked probabilistic condition on a learned inference model. Viability of LC has been shown on a task of sign language recognition, but its potential and implication can reach far beyond a specific domain and can move object recognition toward a scalable open-set recognition.
A User Study on the Usefulness of Visualization Support for Requirements Monitoring

Many requirements monitoring approaches have been proposed that check key properties of systems and their interactions at runtime. Some of these approaches also visualize monitoring results and provide details on requirements violations to end users. However, only few studies exist about the usefulness of requirements monitoring tools for practitioners, particularly regarding visualization. In this paper, we present a user study we have conducted with both industrial practitioners and researchers to assess the usefulness of visualization capabilities we have been developing for an event-based requirements monitoring tool. These capabilities allow users to monitor the status of the involved systems, to view trends and statistics, and to inspect the events and data that led to specific violations when diagnosing their root cause. We first performed a walkthrough of the tool using the cognitive dimensions of notations framework from the field of human-computer interaction. We then conducted a user study involving five software engineers of a large company from the automation software domain and four researchers. Using the tool's visualization capabilities all subjects succeeded in monitoring a real-world automation system and in diagnosing violations. Subjects regarded the visualization capabilities as essential for understanding the behavior of a complex system. Based on the study results we derive implications, opportunities, and risks of using visualization in requirements monitoring tools.
Rational bicubic Ball for image interpolation

Abstract In this study, a new rational bicubic Ball with six free parameters is proposed for image interpolation. These free parameters can be used to refine the shape of the surface. This rational bicubic Ball function is constructed by using tensor product approach. The proposed scheme is tested to grayscale image upscale. An efficient algorithm is employed to upscales the original image with factor 2 and 4. The effectiveness of proposed scheme is measured in terms of Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Feature SIMilarity (FSIM) index including comparison with some existing schemes. Numerical and graphical results for image interpolation are presented by using MATLAB. We found out the proposed scheme resulting with higher PSNR and FSIM and smaller in RMSE. Thus, the new rational bicubic Ball with six parameters is better than the existing schemes.
Effect of Attention Adaptive Personal Audio Deliverable System on Digital Signage

The purpose of this study is to improve the comfortability of the space using digital signage and the effectiveness of the advertisement to be displayed. So we have developed a system that only reached sound information to people who need it, like people watching a screen.
Deep Learning-Based Pedestrian Detection for Automated Driving: Achievements and Future Challenges

Deep learning is considered as a key technology for the development of advanced driver assistance systems and future automated driving. Focus lies especially on the perception of the environment by camera, Radar, and Lidar sensors and fusion concepts. Camera-based perception includes the detection of road users. Highest detection performance is especially required for detecting vulnerable road users such as pedestrians and bicycle drivers. Here, tremendous improvement in vision-based object detection has been achieved within the past decade. Research on object detection has been stimulated by public datasets. The results on public benchmarks show the progress of pedestrian detectors from hand-crafted features, over part-based models towards deep learning. The gap between human and machine performance becomes smaller, leading to the question whether pedestrian detection is solved when the detection performance reaches human performance? As false detections can lead to hazardous situations in traffic scenarios, the expectations on the performance of artificial intelligence for advanced driver assistance systems and automated driving often go beyond human performance. Challenges are precise localization, occlusion, distant objects, and corner cases, where only little or no training data is available. To foster research in this direction, a new comprehensive dataset for pedestrian detection at night has been released. This chapter first introduces vision-based perception of road users as a safety-critical application with increasing demand on detection performance. In the second part, it summarizes concepts for pedestrian detection, including an overview on public datasets and evaluation metrics. The dependency between task complexity and task performance is discussed. Based on this discussion, challenges in pedestrian detection are identified and future directions are outlined. Further improvements in performance can be achieved by including components such as tracking, scene understanding, and sensor fusion. In conclusion, the application of deep learning to advanced driver assistance systems and automated driving is driven by the goal of achieving safe maneuvering in any traffic scene, any weather condition, and under real-time constraints. This places high demands on the development of deep network architectures.
Online Unsupervised Learning of the 3D Kinematic Structure of Arbitrary Rigid Bodies

This work addresses the problem of 3D kinematic structure learning of arbitrary articulated rigid bodies from RGB-D data sequences. Typically, this problem is addressed by offline methods that process a batch of frames, assuming that complete point trajectories are available. However, this approach is not feasible when considering scenarios that require continuity and fluidity, for instance, human-robot interaction. In contrast, we propose to tackle this problem in an online unsupervised fashion, by recursively maintaining the metric distance of the scene's 3D structure, while achieving real-time performance. The influence of noise is mitigated by building a similarity measure based on a linear embedding representation and incorporating this representation into the original metric distance. The kinematic structure is then estimated based on a combination of implicit motion and spatial properties. The proposed approach achieves competitive performance both quantitatively and qualitatively in terms of estimation accuracy, even compared to offline methods.
Hierarchical Topic Modeling for Urdu Text Articles

Digital text is increasing rapidly on the Internet with the excessive use of social media. For this reason, it is very challenging to extract effective information from the digital text due its high dimensionality, sparseness and big data. In this paper, we study the powerful nonparametric Bayesian topic model which is Hierarchical Latent Dirichlet Allocation (hLDA). We deal the issue of learning topics hierarchies from Urdu text data. The presented Topic Model for Urdu is combined with preprocessing activities, hLDA model, and Gibbs Sampling (GS) algorithm. We present hLDA base topic model called Urdu Hierarchical Latent Dirichlet Allocation (uhLDA). Empirical study showed that uhLDA effectively learns the topics hierarchies from 5000 Urdu text documents. Furthermore, we evaluated the results using Pointwise Mutual information (PMI) and it shows that uhLDA outperforms as compared to existing standard topic model LDA.
Cursive Script Textline Image Transformation for Improving OCR Accuracy

Over the years, there has been significant progress in field of OCR for cursive script, such as italic and handwritten text. However, complex cursive script is still a major challenging factor to parse OCR correctly from the image. Additionally, most of the OCR systems trained on straight scripts textline images, could not produce reasonable results for cursive script textline images. In this paper, our main focus is to transform cursive script textline images into straight scripts textline images with the intention of equally using straight script OCR systems for both straight and cursive scripts textline images. We use generative adversarial networks (GANs) models in conditional settings to transform cursive scripts into straight scripts. Our experiment shows that the transformed cursive script performs better than the original cursive script on open-source straight script OCR systems to parse character.
EAN: Error Attenuation Network for Long-term Human Motion Prediction

Human motion prediction is an important problem in human-robot interaction, computer graphics, and autonomous driving. Currently, there are still some difficulties. Firstly, the error of reasoning about temporal relations accumulates over time. Secondly, the local optimal caused by unbalanced data. For example, leg walking is more common than knee bending or standing. Thirdly, the problem of mean pose is easy to occur in long sequence prediction. In this work, we propose a novel prediction method named Error Attenuation Network (EAN) by taking the Recursive Attenuation Mechanism into consideration with attention model. Firstly, an error attenuation wrapper for optimal function is introduced to alleviate the effect of error accumulation and mean pose. Secondly, the attention model is introduced to restrain incidental actions to balance motions, which aims to mitigate the impact of unbalanced data and mean pose. Experimental results demonstrate that our method predicts the future human motion more accurately, which outperforms the related state-of-the-art approaches on long-term prediction in most cases while having a comparable performance on short-term prediction.