Reviewerly/exHarmony huggingface dataset:
Isolation and Beyond

System security has historically relied on hardware-provided isolation primitives. However, Meltdown [36] and Spectre [30] demonstrate that basic user/kernel isolation could be bypassed in every widely deployed ISA for decades; they are a caution to system designers who accept hardware isolation guarantees as an article of faith. Hardware isolation is fallible and should be considered fallible by software systems.
Tripartite Active Learning for Interactive Anomaly Discovery

Most existing approaches to anomaly detection focus on statistical features of the data. However, in many cases, users are merely interested in a subset of the statistical outliers depending on the specific domain of interest, e.g., network attacks or financial fraud. The instruction from human experts is therefore indispensable in building predictive models in such applications. However, obtaining labels from human experts is time-consuming and expensive. Obtaining labels from nonexpert labelers are relatively easy and cost-effective. However, the labeling accuracy of a nonexpert is usually difficult to assess. Therefore, it remains open to leverage both the machine intelligence and the knowledge from labelers with diverse backgrounds to construct a machine learning model for domain-specific anomaly detection. To this end, this paper proposes a framework of tripartite active learning for interactive anomaly discovery in large datasets based on crowdsourced labels. This tripartite active learning method consists of two stages. In the first stage, an unsupervised learning algorithm is employed to extract statistical outliers from the dataset. This algorithm is of low computational complexity as well as memory requirement and thus well suited for large datasets. We then develop an iterative algorithm consisting of two steps. The algorithm first evaluates and trains labelers based on gold instances provided by the expert labelers. Then, it assigns the most informative samples to its most confident labeler for relabeling and update the detector based on new labels. The capacity constraints are taken into account in the active learning approach to guarantee the fair allocation of labeling instances as well as robustness against erroneous labels. It is seen through experiments that the proposed algorithm provides an effective means for interactive anomaly detection. As far as we are aware of, this is the first work that considers designing a tripartite machine learning system for domain-specific anomaly detection.
Radial feature descriptors for cell classification and recommendation

This paper introduces computational tools for cell classification into normal and abnormal, as well as content-based-image-retrieval (CBIR) for cell recommendation. It also proposes the radial feature descriptors (RFD), which define evenly interspaced segments around the nucleus, and proportional to the convexity of the nuclear boundary. Experiments consider Herlev and CRIC image databases as input to classification via Random Forest and bootstrap; we compare 14 different feature sets by means of False Negative Rate (FNR) and Kappa (κ), obtaining FNR =0.02 and κ=0.89 for Herlev, and FNR =0.14 and κ=0.78 for CRIC. Next, we sort and rank cell images using convolutional neural networks and evaluate performance with the Mean Average Precision (MAP), achieving MAP =0.84 and MAP =0.82 for Herlev and CRIC, respectively. Cell classification show encouraging results regarding RFD, including its sensitivity to intensity variation around the nuclear membrane as it bypasses cytoplasm segmentation.
Global Regularizer and Temporal-Aware Cross-Entropy for Skeleton-Based Early Action Recognition

In this paper, we propose a new approach to recognize the class label of an action before this action is fully performed based on skeleton sequences. Compared to action recognition which uses fully observed action sequences, early action recognition with partial sequences is much more challenging mainly due to: (1) the global information of a long-term action is not available in the partial sequence, and (2) the partial sequences at different observation ratios of an action contain a number of sub-actions with diverse motion information. To address the first challenge, we introduce a global regularizer to learn a hidden feature space, where the statistical properties of the partial sequences are similar to those of the full sequences. We introduce a temporal-aware cross-entropy to address the second challenge and achieve better prediction performance. We evaluate the proposed method on three challenging skeleton datasets. Experimental results show the superiority of the proposed method for skeleton-based early action recognition.
CNN-Based Dense Image Matching for Aerial Remote Sensing Images

Dense stereo matching plays a key role in 3D reconstruction. The capability of using deep learning in the stereo matching of remote sensing data is currently uncertain. This article investigated the application of deep learning–based stereo methods in aerial image series and proposed a deep learning–based multi-view dense matching framework. First, we applied three typical convolutional neural network models, MC-CNN, GC-Net, and DispNet, to aerial stereo pairs and compared the results with those of the SGM and a commercial software, SURE. Second, on different data sets, the generalization ability of each network is evaluated by using direct transfer learning with models pretrained on other data sets and by fine-tuning with a small number of target training data. Third, we present a deep learning–based multi-view dense matching framework where the multi-view geometry is introduced to further refine matching results. Three sets of aerial images as the main data sets and two open-source sets of street images as auxiliary data sets are used for testing. Experiments show that, first, the performance of deep learning–based stereo methods is slightly better than traditional methods. Second, both the GC-Net and the MC-CNN have demonstrated good generalization ability and can obtain satisfactory results on aerial images using a pretrained model on several available stereo benchmarks. Third, multi-view geometry constraints can further improve the performance of deep learning–based methods, which is better than that of the multi-view–based SGM and SURE.
Classification of Fashion Article Images Based on Improved Random Forest and VGG-IE Algorithm

In classification of fashion article images based on e-commerce image recommendation system, the classification accuracy and computation time cannot meet the actual requirements. Herein, for the first time to our knowledge, we present two diverse image recognition approaches for classification of fashion article images called random-forest method based on genetic algorithm (GA-RF) and Visual Geometry Group-Image Enhancement algorithm (VGG-IE) to solve classification accuracy and computation time problem. In GA-RF, the number of segmentation times and the decision trees are the key factors affecting the classification results. Improved genetic algorithm is introduced into the parameter optimization of forests to determine the optimal combination of the two parameters with minimal manual intervention. Finally, we propose six different Deep Neural Network architectures, including VGG-IE, to improve classification accuracy. The VGG-IE algorithm uses batch normalization and seven kinds training-data augmentation for ease and promotion of learning process. We investigate the effectiveness of the proposed method using Fashion-MNIST dataset and 70[Formula: see text]000 pictures, Experimental results demonstrate that, in comparison with the state-of-the-art algorithms for 10 categories of image recognition, our VGG algorithm has the shortest computational time when it satisfies certain classification accuracy. VGG-IE approach has the highest classification accuracy.
3rd KidRec Workshop

Today's children spend considerable time online, searching and receiving information from various websites and apps. While searching for information, e.g. for school or hobbies, children use search systems to locate resources and receive site recommendations that might be useful for them. The call for good, reliable, child-friendly systems has been made many times and the thesis that the algorithms of "adult" information systems are not necessarily suitable or fair for children is widely accepted. However, there is still no clear and balanced view on what makes one search/recommendation system for children good or better than other systems, nor on what content should be considered "good enough to be retrieved" or recommended. The goal of this workshop is to bring together researchers and practitioners in education, child-development, computer science, and more who can address this questions while considering issues related to education, algorithms, ethics, privacy, evaluation.
Towards Continuous Benchmarking

We present an automated performance evaluation framework that enables an automated workflow for testing and performance evaluation of software libraries. Integrating this component into an ecosystem enables sustainable software development, as a community effort, via a web application for interactively evaluating the performance of individual software components. The performance evaluation tool is based exclusively on web technologies, which removes the burden of downloading performance data or installing additional software. We employ this framework for the Ginkgo software ecosystem, but the framework can be used with essentially any software project, including the comparison between different software libraries. The Continuous Integration (CI) framework of Ginkgo is also extended to automatically run a benchmark suite on predetermined HPC systems, store the state of the machine and the environment along with the compiled binaries, and collect results in a publicly accessible performance data repository based on Git. The Ginkgo performance explorer (GPE) can be used to retrieve the performance data from the repository, and visualizes it in a web browser. GPE also implements an interface that allows users to write scripts, archived in a Git repository, to extract particular data, compute particular metrics, and visualize them in many different formats (as specified by the script). The combination of these approaches creates a workflow which enables performance reproducibility and software sustainability of scientific software. In this paper, we present example scripts that extract and visualize performance data for Ginkgo's SpMV kernels that allow users to identify the optimal kernel for specific problem characteristics.
Creating Interactive Scientific Publications using Bindings

Many scientific publications report on computational results based on code and data, but even when code and data are published, the main text is usually provided in a separate, traditional format such as PDF. Since code, data, and text are not linked on a deep level, it is difficult for readers and reviewers to understand and retrace how the authors achieved a specific result that is reported in the main text, e.g. a figure, table, or number. In addition, to make use of new the opportunities afforded by data and code availability, such as re-running analyses with changed parameters, considerable effort is required. In order to overcome this issue and to enable more interactive publications that support scientists in more deeply exploring the reported results, we present the concept, implementation, and initial evaluation of bindings. A binding describes which data subsets, code lines, and parameters produce a specific result that is reported in the main text (e.g. a figure or number). Based on a prototypical implementation of these bindings, we propose a toolkit for authors to easily create interactive figures by connecting specific UI widgets (e.g. a slider) to parameters. In addition to inspecting code and data, readers can then manipulate the parameter and see how the results change. We evaluated the approach by applying it to a set of existing articles. The results provide initial evidence that the concept is feasible and applicable to many papers with moderate effort.
Show, match and segment: Joint learning of semantic matching and object co-segmentation

We present an approach for jointly matching and segmenting object instances of the same category within a collection of images. In contrast to existing algorithms that tackle the tasks of semantic matching and object co-segmentation in isolation, our method exploits the complementary nature of the two tasks. The key insights of our method are two-fold. First, the estimated dense correspondence fields from semantic matching provide supervision for object co-segmentation by enforcing consistency between the predicted masks from a pair of images. Second, the predicted object masks from object co-segmentation in turn allow us to reduce the adverse effects due to background clutters for improving semantic matching. Our model is end-to-end trainable and does not require supervision from manually annotated correspondences and object masks. We validate the efficacy of our approach on five benchmark datasets: TSS, Internet, PF-PASCAL, PF-WILLOW, and SPair-71k, and show that our algorithm performs favorably against the state-of-the-art methods on both semantic matching and object co-segmentation tasks.
Wootz: a compiler-based framework for fast CNN pruning via composability

Convolutional Neural Networks (CNN) are widely used for Deep Learning tasks. CNN pruning is an important method to adapt a large CNN model trained on general datasets to fit a more specialized task or a smaller device. The key challenge is on deciding which filters to remove in order to maximize the quality of the pruned networks while satisfying the constraints. It is time-consuming due to the enormous configuration space and the slowness of CNN training.
STEAM as an Innovative Educational Technology.

The purpose of this work is to develop a model for the construction of STEAM-education, which is based on project training in the so-called spaces. Creative spaces are integration areas of schoolchildren, students and graduate students working in the format of joint work on projects initiated by various structures of a society and business. The authors of the article analyzed the experience of different countries in the implementation of STEM and STEAM-education and identified the effective ways of structuring technical disciplines, and creative activity into a single integration program. During 2017-2018 years within the framework of informal and nonformal education, an experimental work was carried out with 32 schoolchildren and 34 students of the Kazan Federal University, aimed at determining the level of the development of such competences as: the ability to manage projects and processes, system thinking, the ability to creativity, the ability to work within the teams, groups and individuals, the ability to work in a regime of high uncertainty and a rapid change in the conditions of tasks. Analysis of the results of experimental work has shown that the use of spaces for the implementation of project activities of schoolchildren and students, the inclusion of the category art in its content allows students to form skills and competencies necessary for the Industry 4.0.
Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models

Structured latent attribute models (SLAMs) are a special family of discrete latent variable models widely used in social and biological sciences. This paper considers the problem of learning significant attribute patterns from a SLAM with potentially high-dimensional configurations of the latent attributes. We address the theoretical identifiability issue, propose a penalized likelihood method for the selection of the attribute patterns, and further establish the selection consistency in such an overfitted SLAM with diverging number of latent patterns. The good performance of the proposed methodology is illustrated by simulation studies and two real datasets in educational assessment.
Joint Waveform and Guidance Control Optimization for Target Rendezvous

The algorithm developed in this paper jointly selects the optimal transmitted waveform and the control input so that a radar sensor on a moving platform with linear dynamics can reach a target by minimizing a predefined cost. The cost proposed in this paper accounts for the energy of the transmitted radar signal, the energy of the platform control input, and the relative position error between the platform and the target, which is a function of the waveform design and control input. Similarly to the linear quadratic Gaussian control problem, we demonstrate that the optimal solution satisfies the separation principle between filtering and optimization and, therefore, the optimum can be found analytically. The performance of the proposed solution is assessed with a set of simulations for a pulsed Doppler radar transmitting linearly frequency modulated chirps. Results show the effectiveness of the proposed approach for optimal waveform design and optimal guidance control.
Distributionally Robust Submodular Maximization

Submodular functions have applications throughout machine learning, but in many settings, we do not have direct access to the underlying function f. We focus on stochastic functions that are given as an expectation of functions over a distribution P. In practice, we often have only a limited set of samples f_i from P. The standard approach indirectly optimizes f by maximizing the sum of f_i. However, this ignores generalization to the true (unknown) distribution. In this paper, we achieve better performance on the actual underlying function f by directly optimizing a combination of bias and variance. Algorithmically, we accomplish this by showing how to carry out distributionally robust optimization (DRO) for submodular functions, providing efficient algorithms backed by theoretical guarantees which leverage several novel contributions to the general theory of DRO. We also show compelling empirical evidence that DRO improves generalization to the unknown stochastic submodular function.
Resolution and signal-to-noise ratio enhancement for synthetic coded aperture imaging via varying pinhole array

In traditional coded aperture imaging (CAI), the main problem with the deconvolution method is that the noise may dominate the decoding process. Focusing on this problem, a synthetic CAI (s-CAI) method is proposed with a potential to improve the signal-to-noise ratio (SNR) and spatial resolution of CAI. A series of raw coded images are recorded while the pinhole array is varied to different structures, and an iterative decoding algorithm is developed to reconstruct the object image by synthetically using all of these pinhole array structures and corresponding decoded images. Because the proposed iterative decoding algorithm has prominent advantage in noise immunization, and more spatial light components are acquired in comparison with common single-exposure CAI, both the image resolution and SNR are remarkably enhanced.
TEXTile: Eyes-Free Text Input on Smart Glasses Using Touch Enabled Textile on the Forearm

Smart glasses are autonomous and efficient computers that can perform complex tasks through mobile applications. This paper focuses on text input for mobile context. We present a new connected fabric to smart glasses as a device for text entry. This device, integrated into clothing, provides a new interaction technique called TEXTile. It allows typing without requiring users to hold a device. Users can put fingers anywhere on the fabric surface and release them, without needing to look at the fabric or using markers on the fabric. The text entry technique is based on eight combinations of fingers in contact or released, identified as pleasant among 15 in a survey involving 74 participants. A first user’s study with 20 participants establishes that the eight combinations for TEXTile were significantly reliable (98.95% recognition rate). A second study with nine participants evaluates the learning curve of TEXTile. Users achieved a mean typing of 8.11 WPM at the end of ten 12-min sessions, which can be slow, but sufficient with short text compared to other advantages of the technique. Results show low error rates for tasks completed and good usability (76% in SUS questionnaire). The NASA-TLX questionnaire establishes there is no important mental or physical workload to accomplish the task.
Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss

We tackle the task of question generation over knowledge bases.Conventional methods for this task neglect two crucial research issues: 1) the given predicate needs to be expressed;2) the answer to the generated question needs to be definitive.In this paper, we strive toward the above two issues via incorporating diversified contexts and answer-aware loss.Specifically, we propose a neural encoder-decoder model with multi-level copy mechanisms to generate such questions.Furthermore, the answer aware loss is introduced to make generated questions corresponding to more definitive answers.Experiments demonstrate that our model achieves state-of-the-art performance.Meanwhile, such generated question can express the given predicate and correspond to a definitive answer.
A Split-and-Recombine Approach for Follow-up Query Analysis

Context-dependent semantic parsing has proven to be an important yet challenging task.To leverage the advances in contextindependent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information.To accomplish the task, we propose STAR, a novel approach with a well-designed two-phase process.It is parser-independent and able to handle multifarious follow-up scenarios in different domains.Experiments on the FollowUp dataset show that STAR outperforms the state-of-the-art baseline by a large margin of nearly 8%.The superiority on parsing results verifies the feasibility of follow-up query analysis.We also explore the extensibility of STAR on the SQA dataset, which is very promising.
Sample Efficient Active Learning of Causal Trees

We consider the problem of experimental design for learning causal graphs that have a tree structure. We propose an adaptive framework that determines the next intervention based on a Bayesian prior updated with the outcomes of previous experiments, focusing on the setting where observational data is cheap (assumed infinite) and interventional data is expensive. While information greedy approaches are popular in active learning, we show that in this setting they can be exponentially suboptimal (in the number of interventions required), and instead propose an algorithm that exploits graph structure in the form of a centrality measure. If infinite interventional data is available, we show that the algorithm requires a number of interventions less than or equal to a factor of 2 times the minimum achievable number. We show that the algorithm and the associated theory can be adapted to the setting where each performed intervention yields finitely many samples. Several extensions are also presented, to the case where a specified set of nodes cannot be intervened on, to the case where $K$ interventions are scheduled at once, and to the fully adaptive case where each experiment yields only one sample. In the case of finite interventional data, through simulated experiments we show that our algorithms outperform different adaptive baseline algorithms.