Reviewerly/exHarmony huggingface dataset:
Exploring Effects of Interactivity on Learning with Interactive Storytelling in Immersive Virtual Reality

Immersive virtual reality (VR) holds great potential for learning, but it is unclear how VR experiences should be designed to maximize learning potential. In this study, we explored how the level of interactivity in an educational VR storytelling experience for immunology learning affects a user's learning gains. We created three versions of the VR experience with low (system automates as many actions as possible), medium (a combination of system automation and user-controlled actions), and high (as many user-controlled actions as possible) levels of interactivity. We hypothesized that too much or too little interactivity would result in smaller learning gains than a medium level of interactivity. Although data from pre and post-tests showed no significant difference in students' learning gains due to interactivity level, questionnaire and interview data suggest that interactivity in the experience significantly affects students' engagement in learning, attention, and focus on learning material. Participants also perceived that they could learn better and more effectively in a VR experience with a higher level of interactivity.
PHI

Many applications perform frequent scatter update operations to large data structures. For example, in push-style graph algorithms, processing each vertex requires updating the data of all its neighbors. Neighbors are often scattered over the whole graph, so these scatter updates have poor spatial and temporal locality. In current systems, scatter updates suffer high synchronization costs and high memory traffic. These drawbacks make push-style execution unattractive, and, when algorithms allow it, programmers gravitate towards pull-style implementations based on gather reads instead.
Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples

Deep neural networks have been proved efficient for medical image denoising. Current training methods require both noisy and clean images. However, clean images cannot be acquired for many practical medical applications due to naturally noisy signal, such as dynamic imaging, spectral computed tomography, arterial spin labeling magnetic resonance imaging, etc. In this paper we proposed a training method which learned denoising neural networks from noisy training samples only. Training data in the acquisition domain was split to two subsets and the network was trained to map one noisy set to the other. A consensus loss function was further proposed to efficiently combine the outputs from both subsets. A mathematical proof was provided that the proposed training scheme was equivalent to training with noisy and clean samples when the noise in the two subsets was uncorrelated and zero-mean. The method was validated on Low-dose CT Challenge dataset and NYU MRI dataset and achieved improved performance compared to existing unsupervised methods.
Explainable Product Search with a Dynamic Relation Embedding Model

Product search is one of the most popular methods for customers to discover products online. Most existing studies on product search focus on developing effective retrieval models that rank items by their likelihood to be purchased. However, they ignore the problem that there is a gap between how systems and customers perceive the relevance of items. Without explanations, users may not understand why product search engines retrieve certain items for them, which consequentially leads to imperfect user experience and suboptimal system performance in practice. In this work, we tackle this problem by constructing explainable retrieval models for product search. Specifically, we propose to model the “search and purchase” behavior as a dynamic relation between users and items, and create a dynamic knowledge graph based on both the multi-relational product data and the context of the search session. Ranking is conducted based on the relationship between users and items in the latent space, and explanations are generated with logic inferences and entity soft matching on the knowledge graph. Empirical experiments show that our model, which we refer to as the Dynamic Relation Embedding Model (DREM), significantly outperforms the state-of-the-art baselines and has the ability to produce reasonable explanations for search results.
TilePoP

We present TilePoP, a new type of pneumatically-actuated interface deployed as floor tiles which dynamically pop up by inflating into large shapes constructing proxy objects for whole-body interactions in Virtual Reality. TilePoP consists of a 2D array of stacked cube-shaped airbags designed with specific folding structures, enabling each airbag to be inflated into a physical proxy and then deflated down back to its original tile shape when not in use. TilePoP is capable of providing haptic feedback for the whole body and can even support human body weight. Thus, it allows new interaction possibilities in VR. Herein, the design and implementation of TilePoP are described in detail along with demonstrations of its applications and the results of a preliminary user evaluation conducted to understand the users' experience with TilePoP.
Automated grading of prostate cancer using convolutional neural network and ordinal class classifier

Prostate Cancer (PCa) is one of the most prominent cancer among men. Early diagnosis and treatment planning are significant in reducing the mortality rate due to PCa. Accurate prediction of grade is required to ensure prompt treatment for cancer. Grading of prostate cancer can be considered as an ordinal class classification problem. This paper presents a novel method for the grading of prostate cancer from multiparametric magnetic resonance images using VGG-16 Convolutional Neural Network and Ordinal Class Classifier with J48 as the base classifier. Multiparametric magnetic resonance images of the PROSTATEx-2 2017 grand challenge dataset are employed for this work. The method achieved a moderate quadratic weighted kappa score of 0.4727 in the grading of PCa into 5 grade groups, which is higher than state-of-the-art methods. The method also achieved a positive predictive value of 0.9079 in predicting clinically significant prostate cancer.
Routing Micro-videos via A Temporal Graph-guided Recommendation System

In the past few years, micro-videos have become the dominant trend in the social media era. Meanwhile, as the number of microvideos increases, users are frequently overwhelmed by their uninterested ones. Despite the success of existing recommendation systems developed for various communities, they cannot be applied to routing micro-videos, since users in micro-video platforms have their unique characteristics: diverse and dynamic interest, multilevel interest, as well as true negative samples. To address these problems, we present a temporal graph-guided recommendation system. In particular, we first design a novel graph-based sequential network to simultaneously model users' dynamic and diverse interest.Similarly, uninterested information can be captured from users'true negative samples. Beyond that, we introduce users' multi-level interest into our recommendation model via a user matrix that is able to learn the enhanced representation of users' interest. Finally, the system can make accurate recommendation by considering the above characteristics. Experimental results on two public datasets verify the effectiveness of our proposed model.
BinaryDenseNet: Developing an Architecture for Binary Neural Networks

Binary Neural Networks (BNNs) show promising progress in reducing computational and memory costs, but suffer from substantial accuracy degradation compared to their real-valued counterparts on large-scale datasets, e.g., ImageNet. In this work we study existing BNN architectures and revisit the commonly used technique to include scaling factors. We suggest several architectural design principles for BNNs, based on our studies on architectures. Guided by our principles we develop a novel BNN architecture BinaryDenseNet, which is the first architecture specifically created for BNNs to the best of our knowledge. In our experiments, BinaryDenseNet achieves 18.6% and 7.6% relative improvement over the well-known XNOR-Network and the current state-of-the-art Bi-Real Net in terms of top-1 accuracy on ImageNet, respectively. Further, we show the competitiveness of our BinaryDenseNet regarding memory requirements and computational complexity.
Autoethnography of a Hard of Hearing Traveler

Travel experiences offer a diverse view into an individual's interactions with different cultures, societies, and places. In this paper, we present a 2.5-year autoethnographic travel account of a hard of hearing individual-Jain. Through retrospective journals and field notes, we reveal the tensions and nuances in his travel, including the magnified difficulty of social conversations, issues with navigating unfamiliar environments and cultural contexts, and changes in the relationship to personal assistive technologies. By exploring the longitudinal travel experiences of a single individual, we uncover evocative and personal insights rarely available through participant-based research methods. Based on these lived experiences and post hoc reflections, we present two design explorations of personalized technology the autoethnographer created for aiding his travel. Finally, we offer reflections for customized travel technologies for deaf and hard of hearing users, and methodological guidelines for performing first-person research in the context of disability.
Extracting and tracking hot topics of micro-blogs based on improved Latent Dirichlet Allocation

Micro-blog has changed people’s life, study, and work styles. Every day, we want to know what public opinion news happens and how it evolves. Extracting and tracking these topics correctly help us better understand the latest public opinions and pay attention to their evolution. To extract topics from Microblog posts accurately, we adopt five unique features of micro-blogs to drive the joint probability distributions of all words and topics, and improve LDA into our topic extraction model(named MF-LDA). To track evolution trend of the topic, we propose a hot topic life cycle model (named HTLCM). We divide the HTLCM into five stages, namely, birth, growth, maturity, decline, and disappearance. The HTLCM determines whether a topic is the candidate hot topic or not and estimates hot topic evolution stages. On the other hand, we propose a hot topic tracking (shorten for HTT) algorithm which integrates MF-LDA and HTLCM. First, the HTT assigns candidate hot topics, which are labeled by HTLCM, to the corresponding time window according to the release time. Second, to obtain the hot topic in each time window, we input Micro-blog posts of each time window into MF-LDA in order. By analyzing changes in these hot topics, we track the changes in their contents. The experiment results show that MF-LDA has a lower perplexity and higher coverage rate than LDA under the same conditions. We conclude parameters of the Transition regions of our proposed HTLCM model. The MR, FR of our proposed HTLCM model are lower than 18%. The average P, R, F of the HTT algorithm are 85.64%, 84.97%, 85.66%, respectively. A practical application on topicFemale driver beats male driver in chengdu shows an excellent effect and practical significance of HTLCM model and HTT algorithm in extracting and tracking hot topics.
Artificial intelligence in academic libraries: An environmental scan

Due to the ever-increasing presence of artificial intelligence (AI), an environmental scan on academic libraries engagement with AI was conducted.The authors reviewed scholarly articles, university libraries' strategic plans, and library programming to determine if any reference to AI was being made and in what context.Top research universities in the United States and Canada were considered.The primary goal was to discover what role the librarian will play in an AI-dominant future, as well as how libraries are responding to this change.Findings indicated a lack of response or awareness to the current AI trend, though a small number of institutions were found to be participating in or creating their own AI hubs.
Improving the management of medical imaging by using robust and secure dual watermarking

Nowadays, the management of medical imaging could be affected by several issues related to information security. Among others, a critical issue is related to information authentication. A promissory way to solve this issue is the use of digital watermarking to improve the management of medical imaging. In this paper, we propose a robust and secure medical image watermarking method that involves invisible and zero watermarking techniques that provide a proper link between clinical information, medical image, and patient identity. Concealing an encoded and encrypted watermark signal in the frequency domain of the medical image, the invisible watermarking component avoids the detachment between the medical images and their corresponding electronic patient record. Based on a modified spread spectrum technique, the response of the invisible watermarking detector and the watermark information, the zero watermarking stage authenticates in a confidential form the identity of the patient. Experimental results show the effectiveness of the proposed method in terms of transparency, security, reliability, and robustness of the watermark against several image processing operations. A comparison with state of the art proposals with similar purposes to the proposed method is also provided. The presented work may find applications in the secure and effective management of medical imaging.
Value-added tax fraud detection with scalable anomaly detection techniques

The tax fraud detection domain is characterized by very few labelled data (known fraud/legal cases) that are not representative for the population due to sample selection bias. We use unsupervised anomaly detection (AD) techniques, which are uncommon in tax fraud detection research, to deal with these domain issues. We analyse a unique dataset containing the VAT declarations and client listings of all Belgian VAT numbers pertaining to ten sectors. Our methodology consists in applying AD methods to firms belonging to the same sector and enables an efficient auditing strategy that can be adopted by tax authorities worldwide. The high lifts and hit rates observed in most sectors demonstrate the success of this approach. Sectoral differences exist due to varying market conditions and legal requirements across sectors and we show that the optimal AD method is sector dependent. We focus on three methodological problems that show issues in the related literature. (1) Can we design suitable input features? We develop new fraud indicators from specific fields of the VAT form and client listings and show the predictive value of the combination of these features. (2) Can we design fast algorithms to deal with the large data sizes that can occur in the tax domain? New methods are developed and we demonstrate their scalability both theoretically as well as empirically. (3) How should fraud detection performance be assessed? A new evaluation methodology is proposed that provides reliable performance indications and guarantees that fraud cases are effectively detected by the proposed methods.
Lowering the latency of data processing pipelines through FPGA based hardware acceleration

Web search engines often involve a complex pipeline of processing stages including computing, scoring, and ranking potential answers plus returning the sorted results. The latency of such pipelines can be improved by minimizing data movement, making stages faster, and merging stages. The throughput is determined by the stage with the smallest capacity and it can be improved by allocating enough parallel resources to each stage. In this paper we explore the possibility of employing hardware acceleration (an FPGA) as a way to improve the overall performance when computing answers to search queries. With a real use case as a baseline and motivation, we focus on accelerating the scoring function implemented as a decision tree ensemble, a common approach to scoring and classification in search systems. Our solution uses a novel decision tree ensemble implementation on an FPGA to: 1) increase the number of entries that can be scored per unit of time, and 2) provide a compact implementation that can be combined with previous stages. The resulting system, tested in Amazon F1 instances, significantly improves the quality of the search results and improves performance by two orders of magnitude over the existing CPU based solution.
Monocular Neural Image Based Rendering With Continuous View Control

We propose a method to produce a continuous stream of novel views under fine-grained (e.g., 1 degree step-size) camera control at interactive rates. A novel learning pipeline determines the output pixels directly from the source color. Injecting geometric transformations, including perspective projection, 3D rotation and translation into the network forces implicit reasoning about the underlying geometry. The latent 3D geometry representation is compact and meaningful under 3D transformation, being able to produce geometrically accurate views for both single objects and natural scenes. Our experiments show that both proposed components, the transforming encoder-decoder and depth-guided appearance mapping, lead to significantly improved generalization beyond the training views and in consequence to more accurate view synthesis under continuous 6-DoF camera control. Finally, we show that our method outperforms state-of-the-art baseline methods on public datasets.
Earth observation tools and services to increase the effectiveness of humanitarian assistance

(2020). Earth observation tools and services to increase the effectiveness of humanitarian assistance. European Journal of Remote Sensing: Vol. 53, EARSeL Supplement: Earth Observation Supporting Sustainability Research, pp. 67-85.
Channel and filter parallelism for large-scale CNN training

Accelerating large-scale CNN training is needed to keep training times reasonable as datasets grow larger and models become more complex. Existing frameworks primarily scale using data-parallelism, but this is limited by the mini-batch size, which cannot grow arbitrarily. We introduce three algorithms that partition channel or filter data to exploit parallelism beyond the sample dimension. Further, they partition the parameters of convolutional layers, replacing global all reduces with segmented allreduces---smaller, concurrent allreduces among disjoint processor sets. These algorithms enable strong scaling, reduced communication overhead, and reduced memory pressure, enabling training of very wide CNNs.
Multitask Learning of Height and Semantics From Aerial Images

Aerial or satellite imagery is a great source for land surface analysis, which might yield land use maps or elevation models. In this investigation, we present a neural network framework for learning semantics and local height together. We show how this joint multi-task learning benefits to each task on the large dataset of the 2018 Data Fusion Contest. Moreover, our framework also yields an uncertainty map which allows assessing the prediction of the model. Code is available at https://github.com/marcelampc/mtl_aerial_images .
Oblique Decision Tree Ensemble via Twin Bounded SVM

Ensemble methods with "perturb and combine" strategy have shown improved performance in the classification problems. Recently, random forest algorithm was ranked one among 179 classifiers evaluated on 121 UCI datasets. Motivated by this, we propose a new approach for the generation of oblique decision trees. At each non-leaf node, the training data samples are grouped in two categories based on the Bhattachrayya distance with randomly selected feature subset. Then, twin bounded support vector machine (TBSVM) is used to get two clustering hyperplanes such that each hyperplane is closer to data points of one group and as far as possible from the data points of other group. Based on these hyperplanes, each non-leaf node is splitted to generate the decision tree. In this paper, we used different base models like random forest (RaF), rotation forest (RoF), random sub rotation forest (RRoF) to generate the different oblique decision tree forests named as TBRaF, TBRoF and TBRRoF, respectively. In earlier oblique decision trees, like multisurface proximal support vector machine (MPSVM) based oblique decision trees, matrices are semi-positive definite and hence different regularization methods are required. However, no explicit regularization techniques need to be applied to the primal problems as the matrices in the proposed TBRaF, TBRoF and TBRRoF are positive definite. We evaluated the performance of the proposed models (TBRaF, TBRoF and TBRRoF) on 49 datasets taken from the UCI repository and on some real-world biological datasets (not in UCI). The experimental results and statistical tests conducted show that TBRaF and TBRRoF outperform other baseline methods.
OPERA

Intel Software Guard Extensions (SGX) remote attestation enables enclaves to authenticate hardware inside which they run, and attest the integrity of their enclave memory to the remote party. To enforce direct control of attestation, Intel mandates attestation to be verified by Intel's attestation service. This Intel-centric attestation model, however, neither protects privacy nor performs efficiently when distributed and frequent attestation is required. This paper presents OPERA, an Open Platform for Enclave Remote Attestation. Without involving Intel's attestation service while conducting attestation, OPERA is unchained from Intel, although it relies on Intel to establish a chain of trust whose anchor point is the secret rooted in SGX hardware. OPERA is open, as the implementation of its attestation service is completely open, allowing any enclave developer to run her own OPERA service, and its execution is publicly verifiable and hence trustworthy; OPERA is privacy-preserving, as the attestation service does not learn which enclave is being attested or when the attestation takes place; OPERA is performant, as it does not rely on a single-point-of-verification and also reduces the latency of verification.
Forward Stability of ResNet and Its Variants

The residual neural network (ResNet) is a popular deep network architecture which has the ability to obtain high-accuracy results on several image processing problems. In order to analyze the behavior and structure of ResNet, recent work has been on establishing connections between ResNets and continuous-time optimal control problems. In this work, we show that the post-activation ResNet is related to an optimal control problem with differential inclusions and provide continuous-time stability results for the differential inclusion associated with ResNet. Motivated by the stability conditions, we show that alterations of either the architecture or the optimization problem can generate variants of ResNet which improves the theoretical stability bounds. In addition, we establish stability bounds for the full (discrete) network associated with two variants of ResNet, in particular, bounds on the growth of the features and a measure of the sensitivity of the features with respect to perturbations. These results also help to show the relationship between the depth, regularization, and stability of the feature space. Computational experiments on the proposed variants show that the accuracy of ResNet is preserved and that the accuracy seems to be monotone with respect to the depth and various corruptions.