Reviewerly/exHarmony huggingface dataset:
MRsive: An Augmented Reality Tool for Enhancing Wayfinding and Engagement with Art in Museums

Most museums use printed methods to support indoor navigation and visitor engagement strategies. However, modern museum visitors’ needs are not always met using static and conventional approaches, which are commonly employed in today’s museums. This paper investigates how indoor wayfinding and visitor engagement in the museum might be improved through interactive augmented reality. We designed “MRsive”, a handheld Augmented Reality (AR) tool using a user-centered design approach. The ultimate goal is twofold: the first is to simplify the required cognitive effort in navigating the museum space, and the second goal is to boost visitor engagement with museum artifacts through multisensory interaction. MRsive uses computer vision tools to read visual features in the space and achieve accurate indoor positioning of the directions and virtual cues anchored in the physical space. To evaluate our design, we followed a human-centered design approach. We conducted user testing at the Art Gallery of Ontario (AGO) followed by semi-structured interviews. The observations and answers of participants showed a considerable improvement in the speed, accuracy and ease-of-use when completing a wayfinding or engagement task. We hope our findings and discussion will contribute to the future development of this system and other AR tools that may improve wayfinding in complex indoor spaces and engagement with points of interest in other indoor environments.
Research on the Training Model of Application-Oriented Undergraduates from the Perspective of "Innovation and Entrepreneurship"

Innovation and entrepreneurship education is the necessary requirement of educational reform and development in the new era, and also an important carrier of cultivating talents with comprehensive quality in application-oriented universities.How to reform the mode of talent cultivation, the content of education and the methods of teaching, so as to strengthen the cultivation of college students' innovative spirit and entrepreneurial ability, has become an important research topic in the reform and development of higher education.
Design recommendations for the development of virtual reality focusing on the elderly

Due to the loss of physical and cognitive skills, older people may need special support to help them in daily activities. These activities can be a source of fun and entertainment, and at the same time improve their physical and psychological well-being. One of the options to meet the needs and expectations of the older people may be to use virtual reality (VR) games. For this purpose, it's important to identify design elements that make virtual reality games more suitable for the elderly audience. This study aims to check design recommendations extracted from previous research and also to identify new information that can contribute to the design of VR games focused on older people. In order to achieve this objective, we developed a virtual reality game for user testing with older participants. Participants' experience was recorded for qualitative analysis based on grounded theory. Besides a grounded theory, the results include a list of recommendations for games in virtual reality aimed at the elderly.
Learning Temporal Information From A Single Image For AU Detection

Automatic Facial Action Units (AUs) detection is the recognition of the facial appearance changes caused by the contraction or relaxation of one or more related facial muscles. Compared to the sequence-based methods, a decreased performance is observed for the static image-based AU detection, due to the loss of temporal information. To solve this problem, we propose a novel method that implicitly learns temporal information from a single image for AU detection by adding a hidden optical-flow layer to concatenate two Convolutional Neural Networks (CNNs) models: optical-flow net (OF-Net) and AU detection net (AU-Net). The OF-Net is designed to estimate the facial appearance changes (optical flow) from a single input image through unsupervised learning. The AU-Net accepts the estimated optical-flow as input and predicts the AU occurrence. By training both OF-Net and AU-Net jointly, our model achieves better performance than training them separately, as the AU-Net provides semantic constraints for the optical-flow learning and helps generate more meaningful optical-flow. In return, the estimated optical-flow, which reflects facial appearance changes, benefits the AU-Net. Our proposed method has been evaluated on two benchmarks: BP4D and DISFA, and the experiments show significant performance improvement as compared to the state-of-the-art methods.
Learnings from deploying a voice-based social platform for people with disability

For people with disability living in low-income neighborhoods, access to technology is compounded by inaccessible designs and relative isolation in poverty. To bring together this segment of population, an NGO in India built Enable Vaani, a voice-based social media platform designed for persons with disability living in rural areas without internet connectivity. This system has been in deployment since 2016 and has reached over 25,000 users. We present a mixed-methods analysis utilizing system logs and qualitative interviews, with the goal of understanding Enable Vaani's impact. We find that posts related to employment, education and government programs are the most listened to and shared. Content uploaded by the Enable Vaani team is listened and bookmarked more per post than any user-generated content, indicating the importance of an active role of platform managers. Besides providing useful information, people reported that the platform serves as a medium for social support. Our analysis also finds areas of improvement: people skip a lot of posts, very few people share, and a large fraction of newcomers leave after the first month. In response to these shortcomings, we suggest strategies that can be useful for voice-based systems: active contribution of content, customized content feed, special strategies for encouraging newcomers, and design changes to make sharing easier.
Activity Recognition for Ambient Assisted Living Using Off-the-shelf Motion Sensing Input Devices

In recent years, healthcare and safety monitoring for older adults has become a worldwide concern. As older adults want to age at home, sensor technology has the potential to support their choice by monitoring health status, detecting emergency situations and notifying caregivers in case of emergency situations. Moreover, using new technologies such as the Kinect sensor could bring new ways to build in-home intelligent systems, having already demonstrated performance and accuracy when monitoring older adults. The purpose of this paper is to present an application that allows healthcare providers to take care remotely of older adults by providing live monitoring of the patients. Using a Kinect sensor, the application evaluates the active life of the tracked person, determines the hydration status and notifies the caregiver if the patient is calling for help or needs an ambulance.
Parallel Optimal Transport GAN

Although Generative Adversarial Networks (GANs) are known for their sharp realism in image generation, they often fail to estimate areas of the data density. This leads to low modal diversity and at times distorted generated samples. These problems essentially arise from poor estimation of the distance metric responsible for training these networks. To address these issues, we introduce an additional regularisation term which performs optimal transport in parallel within a low dimensional representation space. We demonstrate that operating in a low dimension representation of the data distribution benefits from convergence rate gains in estimating the Wasserstein distance, resulting in more stable GAN training. We empirically show that our regulariser achieves a stabilising effect which leads to higher quality of generated samples and increased mode coverage of the given data distribution. Our method achieves significant improvements on the CIFAR-10, Oxford Flowers and CUB Birds datasets over several GAN baselines both qualitatively and quantitatively.
MC-SSM: Nonparametric Semantic Image Segmentation With the ICM Algorithm

In the last few years, there has been considerable interest in scene parsing. This task consists of assigning a predefined class label to each pixel (or pre-segmented region) in an image. To best address the complexity challenge of this task, first, we propose a new geometric retrieval strategy to select nearest neighbors from a database containing fully segmented and annotated images. Then, we introduce a novel and simple energy-minimization model. The proposed cost function of this model combines efficiently different global nonparametric semantic likelihood energy terms. These terms are computed from the (pre-)segmented regions of the (query) image and their structural properties (location, texture, color, context, and shape). Different from the traditional approaches, we use a simple and local optimization procedure derived from the iterative conditional modes algorithm to optimize our energy-based model. Experimental results on two challenging datasets: 1) microsoft research Cambridge dataset and 2) Stanford background dataset demonstrate the feasibility and the success of the proposed approach. Compared to existing annotation methods that require training classifiers for each object and learning many parameters, our method is easy to implement, has a few parameters, and combines different criteria.
MagmaDNN

MagmaDNN [17] is a deep learning framework driven using the highly optimized MAGMA dense linear algebra package. The library offers comparable performance to other popular frameworks, such as TensorFlow, PyTorch, and Theano. C++ is used to implement the framework providing fast memory operations, direct cuda access, and compile time errors. Common neural network layers such as Fully Connected, Convolutional, Pooling, Flatten, and Dropout are included. Hyperparameter tuning is performed with a parallel grid search engine.
Grounding natural language commands to StarCraft II game states for narration-guided reinforcement learning

While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniques are still susceptible to the longstanding problem of reward sparsity. This is especially true for tasks such as training an agent to play StarCraft II, a real-time strategy game where reward is only given at the end of a game which is usually very long. While this problem can be addressed through reward shaping, such approaches typically require a human expert with specialized knowledge. Inspired by the vision of enabling reward shaping through the more-accessible paradigm of natural-language narration, we investigate to what extent we can contextualize these narrations by grounding them to the goal-specific states. We present a mutual-embedding model using a multi-input deep-neural network that projects a sequence of natural language commands into the same high-dimensional representation space as corresponding goal states. We show that using this model we can learn an embedding space with separable and distinct clusters that accurately maps natural-language commands to corresponding game states . We also discuss how this model can allow for the use of narrations as a robust form of reward shaping to improve RL performance and efficiency.
Affine Cartesian codes with complementary duals

A linear code C with the property that C∩C⊥={0} is said to be a linear complementary dual, or LCD, code. In this paper, we consider generalized affine Cartesian codes which are LCD. Generalized affine Cartesian codes arise naturally as the duals of affine Cartesian codes in the same way that generalized Reed–Solomon codes arise as duals of Reed–Solomon codes. Generalized affine Cartesian codes are evaluation codes constructed by evaluating multivariate polynomials of bounded degree at points in an m-dimensional Cartesian set over a finite field K and scaling the coordinates. The LCD property depends on the scalars used. Because Reed–Solomon codes are a special case, we obtain a characterization of those generalized Reed–Solomon codes which are LCD along with the more general result for generalized affine Cartesian codes. These results are independent of the characteristic of the underlying field.
Blockchain-Based Bidirectional Updates on Fine-Grained Medical Data

Electronic medical data sharing between stakeholders, such as patients, doctors, and researchers, can promote more effective medical treatment collaboratively. These sensitive and private data should only be accessed by authorized users. Given a total medical data, users may care about parts of them and other unrelated information might interfere with the user-interested data search and increase the risk of exposure. Besides accessing these data, users may want to update them and propagate to other sharing peers so that all peers keep identical data after each update. To satisfy these requirements, in this paper we propose a medical data sharing architecture that addresses the permission control using smart contracts on blockchain and splits data into fined-grained pieces shared with different peers then synchronize full data and these pieces with bidirectional transformations. Medical data reside on each user's local database and permission-related data are stored on smart contracts. Only all peers have gained the newest shared data after updates can they start to do next operations on it, which are enforced by smart contracts. Blockchain-based immutable shared ledge enables users to trace data updates history. This paper can provide a new perspective to view full medical data as different slices to be shared with various peers but consistency after updates between them are still promised, which can protect privacy and improve data search efficiency.
Lossless CFA Image Compression Chip Design for Wireless Capsule Endoscopy

This paper presents a hardware-oriented lossless color filter array (CFA) image compression algorithm for very-large-scale integration (VLSI) circuit design. In order to achieve high performance, low complexity and low memory requirement, a novel lossless CFA image compression algorithm based on JPEG-LS is proposed for the VLSI implementation. A previous study showed the usage of a context table with its memory consuming more than 81% of the chip area for a JPEG-LS encoder design. The proposed algorithm implements a JPEG-LS-based lossless image compression algorithm that eliminates the use of the context technique and its memory in order to reduce the chip area while still maintaining its high performance. The proposed algorithm includes a pixel restoration, an adaptive Golomb-Rice parameter prediction and an improved Golomb-Rice coding technique. This paper was realized using a 0.18μ m CMOS process with synthesized gate counts and core area of 4.8 k and 57,625μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The synthesized operating frequency of this design reached 200 MHz by using a pipeline scheduling technique. Compared with the previous JPEG-LS-based designs, this paper reduced the gate count to at least 28% and increased the average compression ratio by over 17.15% using the video endoscopy images from the Gastro Gastroenterologist Hospital.
A Comparative Study of Object Recognition Techniques

This paper presents a comparison study on using softmax, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) for object recognition. The least effort is needed for hyper-parameter tuning or selection for all such three classifiers. Convolutional neural network (CNN), using feed-forward-architecture deep learning neural network is employed here for efficient feature extraction and reduction. Then, the extracted, reduced features are fed into the classification comparison. The experiments rely on a small-image CIFAR-10 dataset such that a simple, four convolutional-layer CNN architecture can possibly handle effective feature extraction with hardly over-fitting. Recognition performance evaluations rely on averages of precision, recall, F1 scores and accuracy rates, based on 10-fold cross validation for bias reduction purpose. Such performance measures are implemented under balanced as well as unbalanced --class data, respectively referred to equal and uniform-random-sampling unequal --size class dataset. The results indicate a few bits of recognition performance differences regarding F1 scores as well as accuracy rates among the CNN-LDA, CNN-QDA and CNN-softmax, where the balanced-class and unbalanced-class are separately determined. However, the lowest and the highest of the largest wrong prediction cases are generated by CNN-QDA and CNN-softmax respectively for both balanced and unbalanced-class data.
Performance Optimizations and Analysis of Distributed Deep Learning with Approximated Second-Order Optimization Method

Faster training of deep neural networks is desired to speed up the research and development cycle in deep learning. Distributed deep learning and second-order optimization methods are two different techniques to accelerate the training of deep neural networks. In the previous work, researchers show that an approximated second-order optimization method, called K-FAC, can mitigate each other drawbacks of the two techniques. However, there was no detailed discussion on the performance, which is critical for the usage in practice. In this work, we propose several performance optimization techniques to reduce the overheads of K-FAC and to accelerate the overall training. Applying all performance optimizations, we are able to speed up the training 1.64 times per iteration compared to a baseline. Additional to the performance optimizations, we construct a simple performance model to predict model training performance to help the users to determine whether distributed K-FAC is appropriate or not for their training in terms of wall-time.
Handwritten Mathematical Symbol Recognition Using Machine Learning Techniques: Review

Handwritten character/symbol recognition is an important area of research in the present digital world. The problems such as recognition of handwritten characters/symbols, which may be written in different styles when it is recognized can makes job of the human easier. Mathematical expression recognition using machines has become a subject of serious research. The main motivation for this review work is both recognizing of the handwritten mathematical symbol, digits and characters which will be used for mathematical expression recognition.
Joint latent factors and attributes to discover interpretable preferences in recommendation

Latent factor model (LFM), which uses a dot product between the resulting user and item latent factors to rank candidate items, is the most popular collaborative filtering (CF) based method in recommender systems, due to its better generalization and requiring little expert knowledge. However, LFM suffers from cold-start problems in which no historical interactions are available for new users or items and explanation problems in which no clear explanations of the recommended items are provided for users. Meanwhile, side information like user attributes (e.g. gender, age, occupation, etc.) and item attributes (e.g. category, price, popularity, comments, etc.) is usually rich in a recommender system. To better cope with the cold-start and explanation problems in LFM at the same time, we propose a method which combines latent factors and attribute information to discover interpretable preferences. First, we adapt the sampling strategy of pairwise learning in Bayesian personalized ranking - matrix factorization (BPR-MF) to incorporate explicit feedback into our rating-aware framework. Next, latent factors, preference and attribute factors are fused into the user-item affinity scores’ computation. Therefore, collaborative and content information are better utilized in a hybrid method. In this way our method can effectively improve the recommendation performance under conventional recommender settings. Furthermore, the devised mapping functions learn how to map attribute factors to latent, preference and bias factors, which can deal with the cold-start problems, yielding accurate and fast recommendation results. Besides, the learned preference factors are interpretable. The preference-based explanations for recommendation results can be generated automatically to improve user experience. Our algorithm is evaluated on several large-scale datasets with comparisons to state-of-the-art methods. The experimental results demonstrate that our approach is more effective for recommendation, as well as dealing with cold-start scenarios. Moreover, the results also show that our method can provide satisfactory explanations.
The lead recovery prediction from lead concentrate by an artificial neural network and particle swarm optimization

Prediction of lead recovery during the leaching process is required to increase the process efficiency by proper modeling. In this study, a new artificial neural network predictive model based on the particle swarm optimization (ANN-PSO) was developed to predict the lead recovery by a hydrometallurgical method of lead concentrate leaching using fluoroboric acid. A multi-layer ANN-PSO model was trained for developing a predictive model based on the main effective parameters on the lead leaching process. The input parameters of the ANN-PSO model were leaching time, liquid/solid ratio, stirring speed, temperature and fluoroboric acid concentration, while the lead recovery during leaching was the output. The results indicate that the proposed ANN-PSO model can be effectively predicted the lead recovery during lead concentrate leaching using fluoroboric acid.
Deep embedding kernel

In this paper, we propose a novel supervised learning method that is called Deep Embedding Kernel (DEK). DEK combines the advantages of deep learning and kernel methods in a unified framework. More specifically, DEK is a learnable kernel represented by a newly designed deep architecture. Compared with predefined kernels, this kernel can be explicitly trained to map data to an optimized high-level feature space where data may have favorable features toward the application. Compared with typical deep learning using SoftMax or logistic regression as the top layer, DEK is expected to be more generalizable to new data. Experimental results show that DEK has superior performance than typical machine learning methods in identity detection and classification, and transfer learning, on different types of data including images, sequences, and regularly structured data.