Reviewerly/exHarmony huggingface dataset:
Detection of Multiple Maneuvering Extended Targets by Three-Dimensional Hough Transform and Multiple Hypothesis Tracking

Existing extended target probability hypothesis density (ET-PHD) filters are insufficient in tracking weak extended targets. Hough transform-based track-before-detect methods are designed to detect the weak targets in a straight-line constant-velocity model. Therefore, this paper presents a novel method for detecting and tracking multiple maneuvering weak extended targets by a 3-dimensional Hough transform (3DHT) and multiple hypothesis tracking (MHT). The proposed method consists of two stages. In stage 1, the measurements in multiple scans are partitioned into overlapped time windows. The tracklets in each window can be detected by the 3DHT. In stage 2, the tracklets are associated to get the entire trajectories by the MHT. The tracklets of weak targets can be detected by the 3DHT in stage 1. Association in stage 2 is designed to detect maneuvering targets. Some false alarm tracklets could be built in stage 1. However, the false alarm tracklets are independent and unlikely to form a sequential trajectory in stage 2. Merely, the trajectories whose target likelihood ratio larger than a detection threshold can be confirmed as a target. Both the real data and the synthetic data are performed with the proposed approach and several existing algorithms. The result infers that the proposed approach is superior to the others with much less prior information that is necessary.
Towards the application of recommender systems to secure coding

Secure coding is crucial for the design of secure and efficient software and computing systems. However, many programmers avoid secure coding practices for a variety of reasons. Some of these reasons are lack of knowledge of secure coding standards, negligence, and poor performance of and usability issues with existing code analysis tools. Therefore, it is essential to create tools that address these issues and concerns. This article features the proposal, development, and evaluation of a recommender system that uses text mining techniques, coupled with IntelliSense technology, to recommend fixes for potential vulnerabilities in program code. The resulting system mines a large code base of over 1.6 million Java files using the MapReduce methodology, creating a knowledge base for a recommender system that provides fixes for taint-style vulnerabilities. Formative testing and a usability study determined that surveyed participants strongly believed that a recommender system would help programmers write more secure code.
Fortunettes

Feedback is commonly used to explain what happened in an interface. What if questions, on the other hand, remain mostly unanswered. In this paper, we present the concept of enhanced widgets capable of visualizing their future state, which helps users to understand what will happen without committing to an action. We describe two approaches to extend GUI toolkits to support widget-level feedforward, and illustrate the usefulness of widget-level feedforward in a standardized interface to control the weather radar in commercial aircraft. In our evaluation, we found that users require less clicks to achieve tasks and are more confident about their actions when feedforward information was available. These findings suggest that widget-level feedforward is highly suitable in applications the user is unfamiliar with, or when high confidence is desirable.
Classification of mammogram images using the energy probability in frequency domain and most discriminative power coefficients

Abstract The purpose of this work is to develop a computer‐aided diagnosis (CAD) system to assist radiologists in the classification of mammogram images. The CAD system is composed of three main steps. The first step is image preprocessing and segmentation with the seeded region growing algorithm applied on a localized triangular region to remove only the muscle. In the second step of the CAD system, we proposed a novel features extraction method, which consists of three stages. In the first, the discrete cosine transform (DCT) is applied on all obtained regions of interest and then only the upper left corner (ULC) of DCT coefficients is retained. Second, we have applied the energy probability to the ULCs that is used as a criterion for selecting discriminant information. At the last stage, a new Most Discriminative power coefficient algorithm has been proposed to select the most significant features. In the final step of the CAD, the support vector machines, Naive Bayes, and artificial neural network (ANN) classifiers are used to make an effective classification. The evaluation of the proposed algorithm on the mini‐Mammographic Image Analysis Society database shows its efficiency over other recently proposed CAD systems in the literature, whereas an accuracy of 100% can be achieved using ANN with a small number of features.
Deeply integrating Linked Data with Geographic Information Systems

Abstract The realization that knowledge often forms a densely interconnected graph has fueled the development of graph databases, Web‐scale knowledge graphs and query languages for them, novel visualization and query paradigms, as well as new machine learning methods tailored to graphs as data structures. One such example is the densely connected and global Linked Data cloud that contains billions of statements about numerous domains, including life science and geography. While Linked Data has found its way into everyday applications such as search engines and question answering systems, there is a growing disconnect between the classical ways in which Geographic Information Systems (GIS) are still used today and the open‐ended, exploratory approaches used to retrieve and consume data from knowledge graphs such as Linked Data. In this work, we conceptualize and prototypically implement a Linked Data connector framework as a set of toolboxes for Esri's ArcGIS to close this gap and enable the retrieval, integration, and analysis of Linked Data from within GIS. We discuss how to connect to Linked Data endpoints, how to use ontologies to probe data and derive appropriate GIS representations on the fly, how to make use of reasoning, how to derive data that are ready for spatial analysis out of RDF triples, and, most importantly, how to utilize the link structure of Linked Data to enable analysis. The proposed Linked Data connector framework can also be regarded as the first step toward a guided geographic question answering system over geographic knowledge graphs.
CardioCam

With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving $25$ subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over $99%$ average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as $4%$.
Study on Text Classification using Capsule Networks

Deep Neural Networks are being used in different domains, from Image Classification to Natural language Processing. Research has been done on Artificial Neural Networks and different types of Neural Networks such as CNN and RNN have been studied and developed. They have been applied in different applications. In 2017, a new concept has been introduced in the Neural Networks Architecture by Geoffrey Hinton - Capsule Networks.Capsule Networks bring an improvement in the old neural network architecture and it has worked better than the Convolutional Neural Networks (CNN). There are certain disadvantages of using the convolutional neural networks, few areas where CNN lacks, Capsule Networks have overcome those limitations and now it is better neural network architecture for developing models to solve the problems in different domains. Capsule Networks are primarily used for Image Classification. They can be applied in the areas of Natural Language Processing and Recommender Systems to utilize the textual information in a more efficient manner. Neural Networks can be trained to learn numeric representations of various words and phrases using models such as Word2Vec and Glove and can be applied to classify the data in different categories. This paper presents a review on text classification using the newly introduced capsule networks.
Weakly supervised label distribution learning based on transductive matrix completion with sample correlations

Label distribution learning (LDL) is one of the paradigms for dealing with label ambiguity, and it can learn the relative importance of each label to a particular instance. Most of the existing LDL approaches require strong supervision information, which always involves high cost of the data-labeling process. To this end, we propose a novel transductive weakly supervised LDL algorithm based on matrix completion, which simultaneously explores the discriminant knowledge of both training instances and testing instances. Moreover, the manifold regularization is exploited to capture the samples’ relevance to enhance the performance of matrix completion. Experimental results on real-world data sets with various missing percentages validate the effectiveness of our method.
Theoretically-Efficient and Practical Parallel In-Place Radix Sorting

Parallel radix sorting has been extensively studied in the literature for decades. However, the most efficient implementations require auxiliary memory proportional to the input size, which can be prohibitive for large inputs. The standard serial in-place radix sorting algorithm is based on swapping each key to its correct place in the array on each level of recursion. However, it is not straightforward to parallelize this algorithm due to dependencies among the swaps. This paper presents Regions Sort, a new parallel in-place radix sorting algorithm that is efficient both in theory and in practice. Our algorithm uses a graph structure to model dependencies across elements that need to be swapped, and generates independent tasks from this graph that can be executed in parallel. For sorting n integers from a range r, and a parameter K, Regions Sort requires only O(Kłog rłog n) auxiliary memory. Our algorithm requires O(nłog r) work and O((n/K+łog K)łog r) span, making it work-efficient and highly parallel. In addition, we present several optimizations that significantly improve the empirical performance of our algorithm. We compare the performance of Regions Sort to existing parallel in-place and out-of-place sorting algorithms on a variety of input distributions and show that Regions Sort is faster than optimized out-of-place radix sorting and comparison sorting algorithms, and is almost always faster than the fastest publicly-available in-place sorting algorithm.
Multi‐representational convolutional neural networks for text classification

Abstract Various studies have demonstrated that convolutional neural networks (CNNs) can be directly applied to different levels of text embedding, such as character‐, word‐, or document‐levels. However, the effectiveness of different embeddings is limited in the reported result and there is a lack of clear guidance on some aspects of their use, including choosing the proper level of embedding and switching word semantics from one domain to another when appropriate. In this paper, we propose a new architecture of CNN based on multiple representations for text classification, by constructing multiple planes so that more information can be dumped into the networks, such as different parts of text obtained through named entity recognizer or part‐of‐speech tagging tools, different levels of text embedding, or contextual sentences. Various large‐scale, domain‐specific datasets are used to validate the proposed architecture. Tasks analyzed include ontology document classification, biomedical event categorization, and sentiment analysis, showing that multi‐representational CNNs, which learns to focus attention to specific representations of text, can obtain further gains in performance over state‐of‐the‐art deep neural network models.
Robust Shelf Monitoring Using Supervised Learning for Improving On-Shelf Availability in Retail Stores

This paper proposes a method to robustly monitor shelves in retail stores using supervised learning for improving on-shelf availability. To ensure high on-shelf availability, which is a key factor for improving profits in retail stores, we focus on understanding changes in products regarding increases/decreases in product amounts on the shelves. Our method first detects changed regions of products in an image by using background subtraction followed by moving object removal. It then classifies the detected change regions into several classes representing the actual changes on the shelves, such as "product taken (decrease)" and "product replenished/returned (increase)", by supervised learning using convolutional neural networks. It finally updates the shelf condition representing the presence/absence of products using classification results and computes the product amount visible in the image as on-shelf availability using the updated shelf condition. Three experiments were conducted using two videos captured from a surveillance camera on the ceiling in a real store. Results of the first and second experiments show the effectiveness of the product change classification in our method. Results of the third experiment show that our method achieves a success rate of 89.6% for on-shelf availability when an error margin is within one product. With high accuracy, store clerks can maintain high on-shelf availability, enabling retail stores to increase profits.
SoK: Benchmarking Flaws in Systems Security

Properly benchmarking a system is a difficult and intricate task. Even a seemingly innocuous mistake can compromise the guarantees provided by a systems security defense and threaten reproducibility and comparability. Moreover, as many modern defenses trade security for performance, the damage caused by benchmarking mistakes is increasingly worrying. To analyze the magnitude of the phenomenon, we identify 22 benchmarking flaws that threaten the validity of systems security evaluations, and survey 50 defense papers published in top venues. We show that benchmarking flaws are widespread even in papers published at tier-1 venues; tier-1 papers contain an average of five benchmarking flaws and we find only a single paper in our sample without any benchmarking flaws. Moreover, the scale of the problem appears constant over time, suggesting that the community is not yet taking sufficient countermeasures. This threatens the scientific process, which relies on reproducibility and comparability to ensure that published research advances the state of the art. We hope to raise awareness and provide recommendations for improving benchmarking quality and safeguard the scientific process in our community.
Whole Page Optimization with Global Constraints

The Amazon video homepage is the primary gateway for customers looking to explore the large collection of content, and finding something interesting to watch. Typically, the page is personalized for a customer, and consists of a series of widgets or carousels, with each widget containing multiple items (e.g., movies, TV shows etc). Ranking the widgets needs to maximize relevance, and maintain diversity, while simultaneously satisfying business constraints. We present the first unified framework for dealing with relevance, diversity, and business constraints simultaneously. Towards this end, we derive a novel primal-dual algorithm which incorporates local diversity constraints as well as global business constraints for whole page optimization. Through extensive offline experiments and an online A/B test, we show that our proposed method achieves significantly higher user engagement compared to existing methods, while also simultaneously satisfying business constraints. For instance, in an online A/B test, our framework improved key metrics such as customer streaming minutes by 0.77% and customer distinct streaming days by 0.32% over a state-of-the-art submodular diversity model.
Secure Multi-party Computation for Cloud-Based Control

In this chapter, we will explore the cloud-outsourced privacy-preserving computation of a controller on encrypted measurements from a (possibly distributed) system, taking into account the challenges introduced by the dynamical nature of the data. The privacy notion used in this work is that of cryptographic multi-party privacy, i.e., the computation of a functionality should not reveal anything more than what can be inferred only from the inputs and outputs of the functionality. The main theoretical concept used towards this goal is Homomorphic Encryption, which allows the evaluation of sums and products on encrypted data, and, when combined with other cryptographic techniques, such as Secret Sharing, results in a powerful tool for solving a wide range of secure multi-party problems. We will rigorously define these concepts and discuss how multi-party privacy can be enforced in the implementation of a Model Predictive Controller, which encompasses computing stabilizing control actions by solving an optimization problem on encrypted data.
Beyond ImageNet: Deep Learning in Industrial Practice

Deep learning (DL) methods have gained considerable attention since 2014. In this chapter we briefly review the state of the art in DL and then give several examples of applications from diverse areas of application. We will focus on convolutional neural networks (CNNs), which have since the seminal work of Krizhevsky et al. (ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25, pp. 1097–1105, 2012) revolutionized image classification and even started surpassing human performance on some benchmark data sets (Ciresan et al., Multi-column deep neural network for traffic sign classification, 2012a; He et al., Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. CoRR, Vol. 1502.01852, 2015a). While deep neural networks have become popular primarily for image classification tasks, they can also be successfully applied to other areas and problems with some local structure in the data. We will first present a classical application of CNNs on image-like data, in particular, phenotype classification of cells based on their morphology, and then extend the task to clustering voices based on their spectrograms. Next, we will describe DL applications to semantic segmentation of newspaper pages into their corresponding articles based on clues in the pixels, and outlier detection in a predictive maintenance setting. We conclude by giving advice on how to work with DL having limited resources (e.g., training data).
Iterated Deep Reinforcement Learning in Games

Deep reinforcement learning (RL) is a powerful method for generating policies in complex environments, and recent breakthroughs in game-playing have leveraged deep RL as part of an iterative multiagent search process. We build on such developments and present an approach that learns progressively better mixed strategies in complex dynamic games of imperfect information, through iterated use of empirical game-theoretic analysis (EGTA) with deep RL policies. We apply the approach to a challenging cybersecurity game defined over attack graphs. Iterating deep RL with EGTA to convergence over dozens of rounds, we generate mixed strategies far stronger than earlier published heuristic strategies for this game. We further refine the strategy-exploration process, by fine-tuning in a training environment that includes out-of-equilibrium but recently seen opponents. Experiments suggest this history-aware approach yields strategies with lower regret at each stage of training.
Moving from value sensitive design to virtuous practice design

Purpose The purpose of this paper is to develop a critique of value sensitive design (VSD) and to propose an alternative approach that does not depart from a heuristic of value(s), but from virtue ethics, called virtuous practice design (VPD). Design/methodology/approach This paper develops a philosophical argument, draws from a philosophical method (i.e. virtue ethics) and applies this method to a particular case study that draws from a narrative interview. Findings In this paper, authors show how an approach that takes virtue instead of value as the central notion for aiming at a design that is sensitive to ethical concerns can be fruitful both in theory and in practice. Originality/value This paper presents the first attempt to ground an approach aimed at ethical technology design on the tradition of virtue ethics. As such, it presents VPD as a potentially fruitful alternative to VSD.
Neural networks and deep learning

This chapter introduces the key concepts of artificial neural networks and their extension to deep learning. Topics include neural networks, network architecture, optimizers, convolutionary neural network, and Boltzmann machines.
Early detection and risk assessment for chronic disease with irregular longitudinal data analysis

Early detection and risk assessment of complex chronic disease based on longitudinal clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. Disease diagnosis with computer-aided methods has been extensively studied. However, early detection and contemporaneous risk assessment based on partially labeled irregular longitudinal measurements is relatively unexplored. In this paper, we propose a flexible mixed-kernel framework for training a contemporaneous disease risk detector to predict the onset of disease and monitor the disease progression. Moreover, we address the label insufficiency problem by identifying the pattern of disease-induced progression over time with longitudinal data. Our method is based on a Structured Output Support Vector Machine (SOSVM), extended to longitudinal data analysis. Extensive experiments are conducted on several datasets of varying complexity, including the contemporaneous risk assessment with simulated irregular longitudinal data; the identification of the onset of Type 1 Diabetes (T1D) with irregularly sampled longitudinal RNA-Seq gene expression dataset; as well as the monitoring of the drug long-term effects on patients using longitudinal RNA-Seq dataset containing missing time points, demonstrating that our method enhances the accuracy in both early diagnosis and risk estimation with partially labeled irregular longitudinal clinical data.