Reviewerly/exHarmony huggingface dataset:
Capturing Cellular Topology in Multi-Gigapixel Pathology Images

In computational pathology, multi-gigapixel whole slide images (WSIs) are typically divided into small patches because of their extremely large size and memory requirements. However, following this strategy, one risks losing visual context which is very important in the development of machine learning models aimed at diagnostic and prognostic assessment of WSIs. In this paper, we propose a novel graph convolutional neural network based model (called Slide Graph) which overcomes these limitations by building a graph representation of the cellular architecture in an entire WSI in a bottom-up manner. We evaluate Slide Graph for prediction of the status of human epidermal growth factor receptor 2 (HER2) and progesterone receptor (PR) expression from WSIs of H&E stained tissue slides of breast cancer. We demonstrate that the proposed model outperforms previous state-of-the-art methods and is more computationally efficient. The proposed paradigm of WSI-level graphs can potentially be applied to other problems in computational pathology as well.
Going Beyond Real Data: A Robust Visual Representation for Vehicle Re-identification

In this report, we present the Baidu-UTS submission to the AICity Challenge in CVPR 2020. This is the winning solution to the vehicle re-identification (re-id) track. We focus on developing a robust vehicle re-id system for real-world scenarios. In particular, we aim to fully leverage the merits of the synthetic data while arming with real images to learn a robust representation for vehicles in different views and illumination conditions. By comprehensively investigating and evaluating various data augmentation approaches and popular strong baselines, we analyze the bottleneck restricting the vehicle re-id performance. Based on our analysis, we therefore design a vehicle re-id method with better data augmentation, training and post-processing strategies. Our proposed method has achieved the 1st place out of 41 teams, yielding 84.13% mAP on the private test set. We hope that our practice could shed light on using synthetic and real data effectively in training deep re-id networks and pave the way for real-world vehicle re-id systems.
Video Object Grounding Using Semantic Roles in Language Description

We explore the task of Video Object Grounding (VOG), which grounds objects in videos referred to in natural language descriptions. Previous methods apply image grounding based algorithms to address VOG, fail to explore the object relation information and suffer from limited generalization. Here, we investigate the role of object relations in VOG and propose a novel framework VOGNet to encode multi-modal object relations via self-attention with relative position encoding. To evaluate VOGNet, we propose novel contrasting sampling methods to generate more challenging grounding input samples, and construct a new dataset called ActivityNet-SRL (ASRL) based on existing caption and grounding datasets. Experiments on ASRL validate the need of encoding object relations in VOG, and our VOGNet outperforms competitive baselines by a significant margin.
Lithological and alteration mineral mapping for alluvial gold exploration in the south east of Birao area, Central African Republic using Landsat-8 Operational Land Imager (OLI) data

Geological field mapping and mineral exploration still remain very laborious in many parts of the inter tropical regions of African plate due to remoteness, low security and high cost. Currently, a number of remote sensing satellite imagery are freely available and can be broadly used for lithological mapping and mineral exploration in inaccessible regions of African plate. The Birao region of the Central African Republic is well known for its alluvial deposits of gold and copper. The Birao region is located in subtropical belt of Africa plate. In this study, Landsat-8 Operational Land Imager (OLI) satellite imagery was used for lithological and alteration mapping in the south-eastern part (inaccessible zone) of Birao region for alluvial gold exploration. Image processing techniques such as False Color Composite (FCC), Band Ratio (BR), Principal component analysis (PCA), minimum noise fraction (MNF) and Spectral Angle Mapper (SAM) were executed to extract spectral information related to lithological units and alteration minerals. Clay and carbonate minerals, iron oxides, ferrous silicates and lithological units, including the Neo-Chadian Formation, the Lower Proterozoic Formations and the Recent Alluvial Formation were identified and discriminated in the study area. The band ratio of 6/7, 4/2 and 6/5 were typically used to map clay, iron oxides and ferrous silicates, respectively. The results of band ratio and PCA techniques were integrated with fuzzy logic modeling that considerably highlighted hydrothermal alteration minerals and high perspective zones for alluvial gold exploration. Additionally, an updated lithological-mineralogical map was generated for south-eastern part of the Birao region by fusing the FCC, MNF and SAM techniques. Comparison with previous lithological map of the study area, virtual verification, fieldwork and laboratory data confirmed good rate of accuracy for the result obtained from the Landsat-8 OLI satellite data. The approach presented in this study is a low-cost technique that highly applicable for lithological mapping and mineral exploration in remote subtropical regions of African plate.
VOC-RelD: Vehicle Re-identification based on Vehicle-Orientation-Camera

Vehicle re-identification is a challenging task due to high intra-class variances and small inter-class variances. In this work, we focus on the failure cases caused by similar background and shape. They pose serve bias on similarity, making it easier to neglect fine-grained information. To reduce the bias, we propose an approach named VOC- ReID, taking the triplet vehicle-orientation-camera as a whole and reforming background/shape similarity as camera/orientation re-identification. At first, we train models for vehicle, orientation and camera reidentification respectively. Then we use orientation and camera similarity as penalty to get final similarity. Besides, we propose a high performance baseline boosted by bag of tricks and weakly supervised data augmentation. Our algorithm achieves the second place in vehicle reidentification at the NVIDIA AI City Challenge 2020.
Binarizing MobileNet via Evolution-Based Searching

Binary Neural Networks (BNNs), known to be one among the effectively compact network architectures, have achieved great outcomes in the visual tasks. Designing efficient binary architectures is not trivial due to the binary nature of the network. In this paper, we propose a use of evolutionary search to facilitate the construction and training scheme when binarizing MobileNet, a compact network with separable depth-wise convolution. Being inspired by one-shot architecture search frameworks, we manipulate the idea of group convolution to design efficient 1-Bit Convolutional Neural Networks (CNNs), assuming an approximately optimal trade-off between computational cost and model accuracy. Our objective is to come up with a tiny yet efficient binary neural architecture by exploring the best candidates of the group convolution while optimizing the model performance in terms of complexity and latency. The approach is threefold. First, we modify and train strong baseline binary networks with a wide range of random group combinations at each convolutional layer. This set-up gives the binary neural networks a capability of preserving essential information through layers. Second, to find a good set of hyper-parameters for group convolutions we make use of the evolutionary search which leverages the exploration of efficient 1-bit models. Lastly, these binary models are trained from scratch in a usual manner to achieve the final binary model. Various experiments on ImageNet are conducted to show that following our construction guideline, the final model achieves 60.09% Top-1 accuracy and outperforms the state-of-the-art CI-BCNN with the same computational cost.
Comparing the Difficulty of Factorization and Discrete Logarithm: A 240-Digit Experiment

We report on two new records: the factorization of RSA-240, a 795-bit number, and a discrete logarithm computation over a 795-bit prime field. Previous records were the factorization of RSA-768 in 2009 and a 768-bit discrete logarithm computation in 2016. Our two computations at the 795-bit level were done using the same hardware and software, and show that computing a discrete logarithm is not much harder than a factorization of the same size. Moreover, thanks to algorithmic variants and well-chosen parameters, our computations were significantly less expensive than anticipated based on previous records. The last page of this paper also reports on the factorization of RSA-250.
Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation

The cold start problem is a long-standing challenge in recommender systems. That is, how to recommend for new users and new items without any historical interaction record? Recent ML-based approaches have made promising strides versus traditional methods. These ML approaches typically combine both user-item interaction data of existing warm start users and items (as in CF-based methods) with auxiliary information of users and items such as user profiles and item content information (as in content-based methods). However, such approaches face key drawbacks including the error superimposition issue that the auxiliary-to-CF transformation error increases the final recommendation error; the ineffective learning issue that long distance from transformation functions to model output layer leads to ineffective model learning; and the unified transformation issue that applying the same transformation function for different users and items results in poor transformation.
LRNNET: A Light-Weighted Network with Efficient Reduced Non-Local Operation for Real-Time Semantic Segmentation

The recent development of light-weighted neural networks has promoted the applications of deep learning under resource constraints and mobile applications. Many of these applications need to perform a real-time and efficient prediction for semantic segmentation with a light-weighted network. This paper introduces a light-weighted network with an efficient reduced non-local module (LRNNet) for efficient and realtime semantic segmentation. We proposed a factorized convolutional block in ResNet-Style encoder to achieve more lightweighted, efficient and powerful feature extraction. Meanwhile, our proposed reduced non-local module utilizes spatial regional dominant singular vectors to achieve reduced and more representative non-local feature integration with much lower computation and memory cost. Experiments demonstrate our superior trade-off among light-weight, speed, computation and accuracy. Without additional processing and pretraining, LRNNet achieves 72.2% mIoU on Cityscapes test dataset only using the fine annotation data for training with only 0.68M parameters and with 71 FPS on a GTX 1080Ti card.
Robust 3D Self-Portraits in Seconds

In this paper, we propose an efficient method for robust 3D self-portraits using a single RGBD camera. Benefiting from the proposed PIFusion and lightweight bundle adjustment algorithm, our method can generate detailed 3D self-portraits in seconds and shows the ability to handle subjects wearing extremely loose clothes. To achieve highly efficient and robust reconstruction, we propose PIFusion, which combines learning-based 3D recovery with volumetric non-rigid fusion to generate accurate sparse partial scans of the subject. Moreover, a non-rigid volumetric deformation method is proposed to continuously refine the learned shape prior. Finally, a lightweight bundle adjustment algorithm is proposed to guarantee that all the partial scans can not only ``loop'' with each other but also remain consistent with the selected live key observations. The results and experiments show that the proposed method achieves more robust and efficient 3D self-portraits compared with state-of-the-art methods.
IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning

Medicine is an important application area for deep learning models. Research in this field is a combination of medical expertise and data science knowledge. In this paper, instead of 2D medical images, we introduce an open-access 3D intracranial aneurysm dataset, IntrA, that makes the application of points-based and mesh-based classification and segmentation models available. Our dataset can be used to diagnose intracranial aneurysms and to extract the neck for a clipping operation in medicine and other areas of deep learning, such as normal estimation and surface reconstruction. We provide a large-scale benchmark of classification and part segmentation by testing state-of-the-art networks. We also discuss the performance of each method and demonstrate the challenges of our dataset. The published dataset can be accessed here: https://github.com/intra2d2019/IntrA.
AR-CF

Cold-start problems are arguably the biggest challenges faced by collaborative filtering (CF) used in recommender systems. When few ratings are available, CF models typically fail to provide satisfactory recommendations for cold-start users or to display cold-start items on users' top-N recommendation lists. Data imputation has been a popular choice to deal with such problems in the context of CF, filling empty ratings with inferred scores. Different from (and complementary to) data imputation, this paper presents AR-CF, which stands for Augmented Reality CF, a novel framework for addressing the cold-start problems by generating virtual, but plausible neighbors for cold-start users or items and augmenting them to the rating matrix as additional information for CF models. Notably, AR-CF not only directly tackles the cold-start problems, but is also effective in improving overall recommendation qualities. Via extensive experiments on real-world datasets, AR-CF is shown to (1) significantly improve the accuracy of recommendation for cold-start users, (2) provide a meaningful number of the cold-start items to display in top-N lists of users, and (3) achieve the best accuracy as well in the basic top-N recommendations, all of which are compared with recent state-of-the-art methods.
Learning When and Where to Zoom With Deep Reinforcement Learning

While high resolution images contain semantically more useful information than their lower resolution counterparts, processing them is computationally more expensive, and in some applications, e.g. remote sensing, they can be much more expensive to acquire. For these reasons, it is desirable to develop an automatic method to selectively use high resolution data when necessary while maintaining accuracy and reducing acquisition/run-time cost. In this direction, we propose PatchDrop a reinforcement learning approach to dynamically identify when and where to use/acquire high resolution data conditioned on the paired, cheap, low resolution images. We conduct experiments on CIFAR10, CIFAR100, ImageNet and fMoW datasets where we use significantly less high resolution data while maintaining similar accuracy to models which use full high resolution images.
One-Shot Domain Adaptation for Face Generation

In this paper, we propose a framework capable of generating face images that fall into the same distribution as that of a given one-shot example. We leverage a pre-trained StyleGAN model that already learned the generic face distribution. Given the one-shot target, we develop an iterative optimization scheme that rapidly adapts the weights of the model to shift the output's high-level distribution to the target's. To generate images of the same distribution, we introduce a style-mixing technique that transfers the low-level statistics from the target to faces randomly generated with the model. With that, we are able to generate an unlimited number of faces that inherit from the distribution of both generic human faces and the one-shot example. The newly generated faces can serve as augmented training data for other downstream tasks. Such setting is appealing as it requires labeling very few, or even one example, in the target domain, which is often the case of real-world face manipulations that result from a variety of unknown and unique distributions, each with extremely low prevalence. We show the effectiveness of our one-shot approach for detecting face manipulations and compare it with other few-shot domain adaptation methods qualitatively and quantitatively.
FingerTrak

In this paper, we present FingerTrak, a minimal-obtrusive wristband that enables continuous 3D finger tracking and hand pose estimation with four miniature thermal cameras mounted closely on a form-fitting wristband. FingerTrak explores the feasibility of continuously reconstructing the entire hand postures (20 finger joints positions) without the needs of seeing all fingers. We demonstrate that our system is able to estimate the entire hand posture by observing only the outline of the hand, i.e., hand silhouettes from the wrist using low-resolution (32 x 24) thermal cameras. A customized deep neural network is developed to learn to "stitch" these multi-view images and estimate 20 joints positions in 3D space. Our user study with 11 participants shows that the system can achieve an average angular error of 6.46° when tested under the same background, and 8.06° when tested under a different background. FingerTrak also shows encouraging results with the re-mounting of the device and has the potential to reconstruct some of the complicated poses. We conclude this paper with further discussions of the opportunities and challenges of this technology.
Interpretable confidence measures for decision support systems

Decision support systems (DSS) have improved significantly but are more complex due to recent advances in Artificial Intelligence. Current XAI methods generate explanations on model behaviour to facilitate a user’s understanding, which incites trust in the DSS. However, little focus has been on the development of methods that establish and convey a system’s confidence in the advice that it provides. This paper presents a framework for Interpretable Confidence Measures (ICMs). We investigate what properties of a confidence measure are desirable and why, and how an ICM is interpreted by users. In several data sets and user experiments, we evaluate these ideas. The presented framework defines four properties: 1) accuracy or soundness, 2) transparency, 3) explainability and 4) predictability. These characteristics are realized by a case-based reasoning approach to confidence estimation. Example ICMs are proposed for -and evaluated on- multiple data sets. In addition, ICM was evaluated by performing two user experiments. The results show that ICM can be as accurate as other confidence measures, while behaving in a more predictable manner. Also, ICM’s underlying idea of case-based reasoning enables generating explanations about the computation of the confidence value, and facilitates user’s understandability of the algorithm.
WR-ELM: Weighted Regularization Extreme Learning Machine for Imbalance Learning in Software Fault Prediction

Imbalanced data is a significant issue in software fault prediction. It is very challenging for software engineers to handle imbalanced software fault data for the early prediction of software faults. In the last two decades, many researchers have used synthetic minority oversampling technique (SMOTE), SMOTE for regression and other such techniques to preprocess the imbalanced software fault data. However, these preprocessing techniques do not produce consistently good accuracy, especially in inter release, and cross project fault prediction. The learning of imbalanced fault data for prediction of the number of software faults has not been explored in depth so far. To deal with this scenario, we have explored an efficient machine learning technique, namely extreme learning machine (ELM) for prediction of the number of software faults. Furthermore, a new variant of ELM, namely weighted regularization ELM, is proposed to generalize the imbalanced data to balanced data. To validate the proposed imbalanced learning model, we have used 26 open source PROMISE software fault datasets and three prediction scenarios, intra release, inter release, and cross project. We have conducted the experiments for prediction of the number of faults. The experimental results showed that the proposed approach led to improved performance.
Decolonizing Participatory Design: Memory Making in Namibia

Participatory Design (PD) approaches seem particularly well suited to contribute to debates over power and decolonization in design, yet often lack considerations of cultural situatedness and underlying ontological entanglements. In this paper we identify theoretical and methodological gaps in PD relating to contemporary discourses of decolonizing design. We integrate perspectives from PD and postcolonial discourse to explore how we can create more far-reaching examples of decolonizing design in practice. We present a study in which young Namibians are at the forefront of knowledge production on postcolonial memories and contribute to discussions of how decolonizing PD practices may be developed through contextualized, transdisciplinary, and transcultural approaches. In particular, we argue there is a need for a “safe space,” as well as continuing reflection on methods and de-linking of knowledge and epistemologies within the PD process itself.
Personalized learning in iSTART: Past modifications and future design

(2020). Personalized learning in iSTART: Past modifications and future design. Journal of Research on Technology in Education: Vol. 52, Personalization in Technology-Enhanced Learning Environments, pp. 301-321.
Discriminative sparse embedding based on adaptive graph for dimension reduction

The traditional manifold learning methods usually utilize the original observed data to directly define the intrinsic structure among data. Because the original samples often contain a deal of redundant information or it is corrupted by noises, it leads to the unreliability of the obtained intrinsic structure. In addition, the intrinsic structure learning and subspace learning are completely separated. For solving above problems, this paper presents a novel dimension reduction method termed discriminative sparse embedding (DSE) based on adaptive graph. By projecting the original samples into a low-dimensional subspace, DSE learns a sparse weight matrix, which can reduce the effects of redundant information and noises of the original data, and uncover essential structural relationship among the data. In DSE, the robust subspace is learned from the original data. Meanwhile, the intrinsic local structure and the optimal subspace can be simultaneously learned, in which they are mutually improved, and the accurate structure can be captured, and the optimal subspace can be obtained. We propose an alternative and iterative method to solve the DSE model. In order to evaluate the performance of DSE, it is compared with some state-of-the-art feature extraction algorithms. Various experiments show that our DSE is effective and feasible.