Reviewerly/exHarmony huggingface dataset:
Clinical Accuracy of Holographic Navigation Using Point-Based Registration on Augmented-Reality Glasses

Abstract BACKGROUND As current augmented-reality (AR) smart glasses are self-contained, powerful computers that project 3-dimensional holograms that can maintain their position in physical space, they could theoretically be used as a low-cost, stand-alone neuronavigation system. OBJECTIVE To determine feasibility and accuracy of holographic neuronavigation (HN) using AR smart glasses. METHODS We programmed a fully functioning neuronavigation system on commercially available smart glasses (HoloLens®, Microsoft, Redmond, Washington) and tested its accuracy and feasibility in the operating room. The fiducial registration error (FRE) was measured for both HN and conventional neuronavigation (CN) (Brainlab, Munich, Germany) by using point-based registration on a plastic head model. Subsequently, we measured HN and CN FRE on 3 patients. RESULTS A stereoscopic view of the holograms was successfully achieved in all experiments. In plastic head measurements, the mean HN FRE was 7.2 ± 1.8 mm compared to the mean CN FRE of 1.9 ± 0.45 (mean difference: –5.3 mm; 95% confidence interval [CI]: –6.7 to –3.9). In the 3 patients, the mean HN FRE was 4.4 ± 2.5 mm compared to the mean CN FRE of 3.6 ± 0.5 (mean difference: –0.8 mm; 95% CI: –3.0 to 4.6). CONCLUSION Owing to the potential benefits and promising results, we believe that HN could eventually find application in operating rooms. However, several improvements will have to be made before the device can be used in clinical practice.
A Visual Analytics System for Exploring, Monitoring, and Forecasting Road Traffic Congestion

We present an interactive visual analytics system that enables traffic congestion exploration, surveillance, and forecasting based on vehicle detector data. Through domain expert collaboration, we have extracted task requirements, incorporated the Long Short-Term Memory (LSTM) model for congestion forecasting, and designed a weighting method for detecting the causes of congestion and congestion propagation directions. Our visual analytics system is designed to enable users to explore congestion causes, directions, and severity. Congestion conditions of a city are visualized using a Volume-Speed Rivers (VSRivers) visualization that simultaneously presents traffic volumes and speeds. To evaluate our system, we report performance comparison results, wherein our model is more accurate than other forecasting algorithms. We demonstrate the usefulness of our system in the traffic management and congestion broadcasting domains through three case studies and domain expert feedback.
Mobile Technology and Generation Z in the English Language Classroom – A Preliminary Study

Every generation of students has different characteristics that reflect the conditions of the time period they were growing in. It is important for educators from all fields of study, especially English language teachers, to understand the generational differences and the learning preferences of students in order to create productive learning environments. In today’s technology driven era, students have different learning needs in comparison with their predecessors. To enhance language learning in the new generation, it is recommended to use mobile technology in class. Mobile devices and applications offer a wide range of activities that support language learning. The aim of this study was to discover if the students using mobile devices in the class got higher scores in tests than the students using traditional methods. The methods included pre- and post-tests, as well as a questionnaire survey. The results of this study reveal that the use of a mobile application had a positive effect on students’ achievement results as far as the vocabulary learning was concerned. The students using the app seemed to retain more words than the students in the control group. Furthermore, the results of the questionnaire showed that using the app was more enjoyable for students than the traditional teaching methods. In addition, it contributed to collaborative learning. However, the results also indicate that being a Generation Z student does not make him/her automatically interested in using mobile applications in the process of language acquisition. Considering the small number of participants, further studies are planned to confirm the results from the current preliminary study.
Learning Speaker Representations with Mutual Information

Learning good representations is of crucial importance in deep learning. Mutual Information (MI) or similar measures of statistical dependence are promising tools for learning these representations in an unsupervised way. Even though the mutual information between two random variables is hard to measure directly in high dimensional spaces, some recent studies have shown that an implicit optimization of MI can be achieved with an encoder-discriminator architecture similar to that of Generative Adversarial Networks (GANs). In this work, we learn representations that capture speaker identities by maximizing the mutual information between the encoded representations of chunks of speech randomly sampled from the same sentence. The proposed encoder relies on the SincNet architecture and transforms raw speech waveform into a compact feature vector. The discriminator is fed by either positive samples (of the joint distribution of encoded chunks) or negative samples (from the product of the marginals) and is trained to separate them. We report experiments showing that this approach effectively learns useful speaker representations, leading to promising results on speaker identification and verification tasks. Our experiments consider both unsupervised and semi-supervised settings and compare the performance achieved with different objective functions.
Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks

Recently, recommender systems play a pivotal role in alleviating the problem of information overload. Latent factor models have been widely used for recommendation. Most existing latent factor models mainly utilize the interaction information between users and items, although some recently extended models utilize some auxiliary information to learn a unified latent factor for users and items. The unified latent factor only represents the characteristics of users and the properties of items from the aspect of purchase history. However, the characteristics of users and the properties of items may stem from different aspects, e.g., the brand-aspect and category-aspect of items. Moreover, the latent factor models usually use the shallow projection, which cannot capture the characteristics of users and items well. Deep neural network has shown tremendous potential to model the non-linearity relationship between users and items. It can be used to replace shallow projection to model the complex correlation between users and items. In this paper, we propose a Neural network based Aspect-level Collaborative Filtering model (NeuACF) to exploit different aspect latent factors. Through modelling the rich object properties and relations in recommender system as a heterogeneous information network, NeuACF first extracts different aspect-level similarity matrices of users and items, respectively, through different meta-paths, and then feeds an elaborately designed deep neural network with these matrices to learn aspect-level latent factors. Finally, the aspect-level latent factors are fused for the top-N recommendation. Moreover, to fuse information from different aspects more effectively, we further propose NeuACF++ to fuse aspect-level latent factors with self-attention mechanism. Extensive experiments on three real world datasets show that NeuACF and NeuACF++ significantly outperform both existing latent factor models and recent neural network models.
Multi-View Deep Network: A Deep Model Based on Learning Features From Heterogeneous Neural Networks for Sentiment Analysis

By the development of social media, sentiment analysis has changed to one of the most remarkable research topics in the field of natural language processing which tries to dig information from textual data containing users' opinions or attitudes toward a particular topic.In this regard, deep neural networks have emerged as promising techniques that have been extensively used for this aim in recent years and obtained significant results.Considering the fact that deep neural networks can automatically extract features from data, it can be claimed that intermediate representations extracted from these networks can be also used as appropriate features.While different deep neural networks are able to extract various types of features due to their distinct structures, we decided to combine features extracted from heterogeneous neural networks using multi-view classifiers to enhance the overall performance of document-level sentiment analysis by considering the correlation between them.The proposed multi-view deep network makes use of intermediate features extracted from convolutional and recursive neural networks to perform classification.Based on the results of the experiments, the proposed multi-view deep network not only outperforms singleview deep neural networks but also has superior efficiency and generalization performance.
Re-evaluating Evaluation in Text Summarization

Automated evaluation metrics as a stand-in for manual evaluation are an essential part of the development of text-generation tasks such as text summarization.However, while the field has progressed, our standard metrics have not -for nearly 20 years ROUGE has been the standard evaluation in most summarization papers.In this paper, we make an attempt to re-evaluate the evaluation method for text summarization: assessing the reliability of automatic metrics using top-scoring system outputs, both abstractive and extractive, on recently popular datasets for both systemlevel and summary-level evaluation settings.We find that conclusions about evaluation metrics on older datasets do not necessarily hold on modern datasets and systems.We release a dataset of human judgments that are collected from 25 top-scoring neural summarization systems (14 abstractive and 11 extractive):
RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images

Image enhancement (IE) technology can help enhance the brightness of remote-sensing images to obtain better interpretation and visualization effects. Convolutional neural networks (CNN), such as the Low-light CNN (LLCNN) and Super-resolution CNN (SRCNN), have achieved great success in image enhancement, image super resolution, and other image-processing applications. Therefore, we adopt CNN to propose a new neural network architecture with end-to-end strategy for low-light remote-sensing IE, named remote-sensing CNN (RSCNN). In RSCNN, an upsampling operator is adopted to help learn more multi-scaled features. With respect to the lack of labeled training data in remote-sensing image datasets for IE, we use real natural image patches to train firstly and then perform fine-tuning operations with simulated remote-sensing image pairs. Reasonably designed experiments are carried out, and the results quantitatively show the superiority of RSCNN in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) over conventional techniques for low-light remote-sensing IE. Furthermore, the results of our method have obvious qualitative advantages in denoising and maintaining the authenticity of colors and textures.
Sharpness-aware Minimization for Efficiently Improving Generalization

In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability. Indeed, optimizing only the training loss value, as is commonly done, can easily lead to suboptimal model quality. Motivated by prior work connecting the geometry of the loss landscape and generalization, we introduce a novel, effective procedure for instead simultaneously minimizing loss value and loss sharpness. In particular, our procedure, Sharpness-Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this formulation results in a min-max optimization problem on which gradient descent can be performed efficiently. We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets (e.g., CIFAR-{10, 100}, ImageNet, finetuning tasks) and models, yielding novel state-of-the-art performance for several. Additionally, we find that SAM natively provides robustness to label noise on par with that provided by state-of-the-art procedures that specifically target learning with noisy labels.
A Blockchain-Based Trust Model for the Internet of Things Supply Chain Management

Accurate data and strategic business processes are crucial to all parties in a supply chain system. However, the absence of mutual trust can create a barrier to implementation. Several studies have shown that supply chains face challenges arising from a lack of trust with respect to the sharing of data. How well each party trusts the data they receive can have a profound influence on management decisions. Blockchain technology has been widely used to process cryptocurrency transactions. Recently, it has also proved to be effective in creating trust in the Internet of things (IoT) domain. Blockchain technology can facilitate mutual trust between parties who would otherwise have been doubtful of each other’s data, allowing for more effective and secure sharing of data. However, if the blockchain is not IoT-optimized, companies can experience significant delays and the need for extensive computational capacity. Moreover, there are still some limitations regarding the consensus between the nodes in the traditional consensus approaches. Here, we propose an alternative approach to creating trust in supply chains with diverse IoT elements. Our streamlined trust model simplifies data sharing and reduces computational, storage, and latency requirements while increasing the security of the IoT-based supply chain management. We evaluate the suggested model using simulations and highlight its viability.
Independent real‐world application of a clinical‐grade automated prostate cancer detection system

Artificial intelligence (AI)-based systems applied to histopathology whole-slide images have the potential to improve patient care through mitigation of challenges posed by diagnostic variability, histopathology caseload, and shortage of pathologists. We sought to define the performance of an AI-based automated prostate cancer detection system, Paige Prostate, when applied to independent real-world data. The algorithm was employed to classify slides into two categories: benign (no further review needed) or suspicious (additional histologic and/or immunohistochemical analysis required). We assessed the sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) of a local pathologist, two central pathologists, and Paige Prostate in the diagnosis of 600 transrectal ultrasound-guided prostate needle core biopsy regions ('part-specimens') from 100 consecutive patients, and to ascertain the impact of Paige Prostate on diagnostic accuracy and efficiency. Paige Prostate displayed high sensitivity (0.99; CI 0.96-1.0), NPV (1.0; CI 0.98-1.0), and specificity (0.93; CI 0.90-0.96) at the part-specimen level. At the patient level, Paige Prostate displayed optimal sensitivity (1.0; CI 0.93-1.0) and NPV (1.0; CI 0.91-1.0) at a specificity of 0.78 (CI 0.64-0.89). The 27 part-specimens considered by Paige Prostate as suspicious, whose final diagnosis was benign, were found to comprise atrophy (n = 14), atrophy and apical prostate tissue (n = 1), apical/benign prostate tissue (n = 9), adenosis (n = 2), and post-atrophic hyperplasia (n = 1). Paige Prostate resulted in the identification of four additional patients whose diagnoses were upgraded from benign/suspicious to malignant. Additionally, this AI-based test provided an estimated 65.5% reduction of the diagnostic time for the material analyzed. Given its optimal sensitivity and NPV, Paige Prostate has the potential to be employed for the automated identification of patients whose histologic slides could forgo full histopathologic review. In addition to providing incremental improvements in diagnostic accuracy and efficiency, this AI-based system identified patients whose prostate cancers were not initially diagnosed by three experienced histopathologists. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
Automatic Detection and Classification System of Domestic Waste via Multimodel Cascaded Convolutional Neural Network

Domestic waste classification was incorporated into legal provisions recently in China. However, relying on manpower to detect and classify domestic waste is highly inefficient. To that end, in this article, we propose a multimodel cascaded convolutional neural network (MCCNN) for domestic waste image detection and classification. MCCNN combined three subnetworks (DSSD, YOLOv4, and Faster-RCNN) to obtain the detections. Moreover, to suppress the false-positive predicts, we utilized a classification model cascaded with the detection part to judge whether the detection results are correct. To train and evaluate MCCNN, we designed a large-scale waste image dataset (LSWID), containing 30 000 domestic waste multilabeled images with 52 categories. To the best of our knowledge, the LSWID is the largest dataset on domestic waste images. Furthermore, a smart trash can is designed and applied to a Shanghai community, which helped to make waste recycling more efficient. Experimental results showed a state-of-the-art performance, with an average improvement of 10% in detection precision.
Adversarial Attacks on Node Embeddings via Graph Poisoning

The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despite the proliferation of such methods, there is currently no study of their robustness to adversarial attacks. We provide the first adversarial vulnerability analysis on the widely used family of methods based on random walks. We derive efficient adversarial perturbations that poison the network structure and have a negative effect on both the quality of the embeddings and the downstream tasks. We further show that our attacks are transferable since they generalize to many models and are successful even when the attacker is restricted.
Deep Learning-based Human Motion Prediction considering Context Awareness for Human-Robot Collaboration in Manufacturing

The interest of human-robot collaboration (HRC) for intelligent manufacturing service system is gradually increasing. Fluent human-robot coexistence in manufacturing requires accurate estimation of the human motion intention so that the efficiency and safety of HRC can be guaranteed. Human motion is mainly defined as the sequential positions of the joints of human skeletons among traditional motion prediction solutions, which lead to a deficiency of tools or product components holding in hand. Context awareness based temporal processing is the key to evaluating human motion before the accomplishment of it, so as to save time as well as recognize the intention of the human. In this paper, a deep learning system combing convolutional neural network (CNN) and long short-term memory network (LSTM) towards vision signals is explored to predict human motion accurately. Creatively, this paper utilizes LSTM to extract temporal patterns of human motion automatically outputting the prediction result before motion takes place. Not only does it avoid complex feature extraction due to its end-to-end characteristic, but provide a natural interaction between human and robot without wearable devices or tags that may become a burden for the former. A case study of desktop computer product disassembly is executed to demonstrate the feasibility of the recommended method. Experimental performance proves that our method outperforms the other three optimization algorithms on the prediction accuracy.
Semi-Supervised Non-Negative Matrix Factorization With Dissimilarity and Similarity Regularization

In this article, we propose a semi-supervised non-negative matrix factorization (NMF) model by means of elegantly modeling the label information. The proposed model is capable of generating discriminable low-dimensional representations to improve clustering performance. Specifically, a pair of complementary regularizers, i.e., similarity and dissimilarity regularizers, is incorporated into the conventional NMF to guide the factorization. And, they impose restrictions on both the similarity and dissimilarity of the low-dimensional representations of data samples with labels as well as a small number of unlabeled ones. The proposed model is formulated as a well-posed constrained optimization problem and further solved with an efficient alternating iterative algorithm. Moreover, we theoretically prove that the proposed algorithm can converge to a limiting point that meets the Karush-Kuhn-Tucker conditions. Extensive experiments as well as comprehensive analysis demonstrate that the proposed model outperforms the state-of-the-art NMF methods to a large extent over five benchmark data sets, i.e., the clustering accuracy increases to 82.2% from 57.0%.
Privacy-preserving big data analytics a comprehensive survey

In this paper, we present a comprehensive survey of privacy-preserving big data analytics. We introduce well-designed taxonomies which offer both systematic views and a detailed classification of this challenging research field. We give insights into recent studies on existing active topics in the field. Furthermore, we identify open future research directions for privacy-preserving big data analytics. This survey can serve as a good reference source for the development of modern privacy-preserving techniques to address various privacy-related scenarios to be encountered in practice.
An IoT based device-type invariant fall detection system

As the world elderly population is increasing rapidly, the use of technology for the development of accurate and fast automatic fall detection systems has become a necessity. Most of the fall detection systems are developed for specific devices which reduces the versatility of the fall detection system. This paper proposes a centralized unobtrusive IoT based device-type invariant fall detection and rescue system for monitoring of a large population in real-time. Any type of devices such as Smartphones, Raspberry Pi, Arduino, NodeMcu, and Custom Embedded Systems can be used to monitor a large population in the proposed system. The devices are placed into the users’ left or right pant pocket. The accelerometer data from the devices are continuously sent to a multithreaded server which hosts a pre-trained machine learning model that analyzes the data to determine whether a fall has occurred or not. The server sends the classification results back to the corresponding devices. If a fall is detected, the server notifies the mediator of the user's location via an SMS. As a failsafe, the corresponding device alerts nearby individuals by sounding the buzzer and contacts emergency medical services and mediators via SMS for immediate medical assistance, thus saving the user's life. The proposed system achieved 99.7% accuracy, 96.3% sensitivity, and 99.6% specificity. Finally, the proposed system can be implemented on a variety of devices and used to reliably monitor a large population with low false alarm rate, without obstructing the users’ daily living, as no external connections are required.