Reviewerly/exHarmony huggingface dataset:
Exact Inference Techniques for the Analysis of Bayesian Attack Graphs

Attack graphs are a powerful tool for security risk assessment by analysing network vulnerabilities and the paths attackers can use to compromise network resources.The uncertainty about the attacker's behaviour makes Bayesian networks suitable to model attack graphs to perform static and dynamic analysis.Previous approaches have focused on the formalization of attack graphs into a Bayesian model rather than proposing mechanisms for their analysis.In this paper we propose to use efficient algorithms to make exact inference in Bayesian attack graphs, enabling the static and dynamic network risk assessments.To support the validity of our approach we have performed an extensive experimental evaluation on synthetic Bayesian attack graphs with different topologies, showing the computational advantages in terms of time and memory use of the proposed techniques when compared to existing approaches.
A model-driven approach to automate data visualization in big data analytics

In big data analytics, advanced analytic techniques operate on big datasets aimed at complementing the role of traditional OLAP for decision making. To enable companies to take benefit of these techniques despite the lack of in-house technical skills, the H2020 TOREADOR Project adopts a model-driven architecture for streamlining analysis processes, from data preparation to their visualization. In this article, we propose a new approach named SkyViz focused on the visualization area, in particular on (1) how to specify the user’s objectives and describe the dataset to be visualized, (2) how to translate this specification into a platform-independent visualization type, and (3) how to concretely implement this visualization type on the target execution platform. To support step (1), we define a visualization context based on seven prioritizable coordinates for assessing the user’s objectives and conceptually describing the data to be visualized. To automate step (2), we propose a skyline-based technique that translates a visualization context into a set of most suitable visualization types. Finally, to automate step (3), we propose a skyline-based technique that, with reference to a specific platform, finds the best bindings between the columns of the dataset and the graphical coordinates used by the visualization type chosen by the user. SkyViz can be transparently extended to include more visualization types on one hand, more visualization coordinates on the other. The article is completed by an evaluation of SkyViz based on a case study excerpted from the pilot applications of the TOREADOR Project.
Automated crater detection algorithms from a machine learning perspective in the convolutional neural network era

Convolutional Neural Networks (CNN) offer promising opportunities to automatically glean scientifically relevant information directly from annotated images, without needing to handcraft features for detection. Crater counting started with hand counting hundreds, thousands, or even millions of craters in order to determine the age of geological units on planetary bodies of the solar system. Automated crater detection algorithms have attempted to speed up this process. Previous research has employed computer vision techniques with handcrafted features such as light and shadow patterns, circle finding, or edge detection. This research continues, but now some researchers use techniques like convolutional neural networks that enable the algorithm to develop its own features. As the field of machine learning undergoes exponential growth in terms of paper count and research methods, the crater counting application can benefit from the new research, especially when conducting joint interdisciplinary projects. Despite these advancements, the crater counting community has not yet adopted standard methods for automating the process despite decades of research. This survey enumerates challenges for both planetary geologists and machine learning researchers, looks at the recent automatic crater detection advancements using machine learning techniques (primarily in methods using CNNs), and makes recommendations for the path toward greater automation.
Compact Descriptors for Video Analysis: The Emerging MPEG Standard

This paper provides an overview of the on-going compact descriptors for video analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG). MPEG-CDVA targets at defining a standardized bitstream syntax to enable interoperability in the context of video analysis applications. During the developments of MPEG-CDVA, a series of techniques aiming to reduce the descriptor size and improve the video representation ability have been proposed. This paper describes the new standard that is being developed and reports the performance of these key technical contributions.
Clustering with similarity preserving

Graph-based clustering has shown promising performance in many tasks. A key step of graph-based approach is the similarity graph construction. In general, learning graph in kernel space can enhance clustering accuracy due to the incorporation of nonlinearity. However, most existing kernel-based graph learning mechanisms is not similarity-preserving, hence leads to sub-optimal performance. To overcome this drawback, we propose a more discriminative graph learning method which can preserve the pairwise similarities between samples in an adaptive manner for the first time. Specifically, we require the learned graph be close to a kernel matrix, which serves as a measure of similarity in raw data. Moreover, the structure is adaptively tuned so that the number of connected components of the graph is exactly equal to the number of clusters. Finally, our method unifies clustering and graph learning which can directly obtain cluster indicators from the graph itself without performing further clustering step. The effectiveness of this approach is examined on both single and multiple kernel learning scenarios in several datasets.
A three-way clustering method based on an improved DBSCAN algorithm

Clustering is a fundamental research field and plays an important role in data analysis. To better address the relationship between an element and a cluster, a Three-Way clustering method based on an Improved DBSCAN (3W-DBSCAN) algorithm is proposed in this paper. 3W-DBSCAN represents a cluster by a pair of nested sets called lower bound and upper bound respectively. The two bounds classify objects into three status: belong-to, not belong-to and ambiguity. Objects in lower bound certainly belong to the cluster. Objects in upper bound while not in the lower bound are ambiguous because they are in a boundary region and might belong to one or more clusters. Objects beyond the upper bound certainly do not belong to the same cluster. This clustering representation can well explain the clustering result and consist with human cognitive thinking. By improving similarity calculation, improved DBSCAN is presented to obtain initial clustering results, then three-way decision strategies are used to acquire the positive and boundary regions of a cluster. Three benchmarks Accuracy (Acc), F-measure (F1), NMI and ten datasets including three synthetic datasets, three UCI datasets and four shape datasets are used in experiments to evaluate the effectiveness of 3W-DBSCAN. Experimental results suggest that 3W-DBSCAN has a good performance and is effective in clustering.
A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance

Robust motion planning is a well-studied problem in the robotics literature, yet current algorithms struggle to operate scalably and safely in the presence of other moving agents, such as humans. This paper introduces a novel framework for robot navigation that accounts for high-order system dynamics and maintains safety in the presence of external disturbances, other robots, and humans. Our approach precomputes a tracking error margin for each robot, generates confidence-aware human motion predictions, and coordinates multiple robots with a sequential priority ordering, effectively enabling scalable safe trajectory planning and execution. We demonstrate our approach in hardware with two robots and two humans, and showcase scalability in a larger simulation.
Classifying Pedestrian Actions In Advance Using Predicted Video Of Urban Driving Scenes

Fig. 1.Generating predictions of a future for a pedestrian attempting to cross the street. We pick out two key frames from the (a) input sequence and the (b) ground truth sequence, 16 frames apart. Image (c) shows our prediction at the same time instant as the ground truth.We explore prediction of urban pedestrian actions by generating a video future of the traffic scene, and show promising results in classifying pedestrian behaviour before it is observed. We compare several encoder-decoder network models that predict 16 frames (400-600 milliseconds of video) from the preceding 16 frames. Our main contribution is a method for learning a sequence of representations to iteratively transform features learnt from the input to the future. Then we use a binary action classifier network for determining a pedestrian's crossing intent from predicted video. Our results show an average precision of 81%, significantly higher than previous methods. The model with the best classification performance runs for 117 ms on commodity GPU, giving an effective look-ahead of 416 ms.
Realistic Facial Expression Reconstruction for VR HMD Users

We present a system for sensing and reconstructing facial expressions of the virtual reality (VR) head-mounted display (HMD) user.The HMD occludes a large portion of the user's face, which makes most existing facial performance capturing techniques intractable.To tackle this problem, a novel hardware solution with electromyography (EMG) sensors being attached to the headset frame is applied to track facial muscle movements.For realistic facial expression recovery, we first reconstruct the user's 3D face from a single image and generate the personalized blendshapes associated with seven facial action units (AUs) on the most emotionally salient facial parts (ESFPs).We then utilize preprocessed EMG signals for measuring activations of AU-coded facial expressions to drive pre-built personalized blendshapes.Since facial expressions appear as important nonverbal cues of the subject's internal emotional states, we further investigate the relationship between six basic emotions -anger, disgust, fear, happiness, sadness and surprise, and detected AUs using a fern classifier.Experiments show the proposed system can accurately sense and reconstruct high-fidelity common facial expressions while providing useful information regarding the emotional state of the HMD user. Index Terms-facial expression
ModelOps: Cloud-Based Lifecycle Management for Reliable and Trusted AI

This paper proposes a cloud-based framework and platform for end-to-end development and lifecycle management of artificial intelligence (AI) applications. We build on our previous work on platform-level support for cloud-managed deep learning services, and show how the principles of software lifecycle management can be leveraged and extended to enable automation, trust, reliability, traceability, quality control, and reproducibility of AI pipelines. Based on a discussion of use cases and current challenges, we describe a framework for managingAI application lifecycles and its key components. We also show concrete examples that illustrate how this framework enables managing and executing model training and continuous learning pipelines while infusing trusted AI principles.
Federated Reinforcement Learning for Fast Personalization

Understanding user behavior and adapting to it has been an important focus area for applications. That adaptation is commonly called Personalization. Personalization has been sought after in gaming, personal assistants, dialogue managers, and other popular application categories. One of the challenges of personalization methods is the time they take to adapt to the user behavior or reactions. This sometimes is detrimental to user experience. The contribution of this work is twofold: (1) showing the applicability of granular (per user) personalization through the use of reinforcement learning, and (2) proposing a novel mitigation strategy to decrease the personalization time, through federated learning. To our knowledge, this paper is among the first to present an overall architecture for federated reinforcement learning (FRL), which includes the grouping policy, the learning policy, and the federation policy. We demonstrate the efficacy of the proposed architecture on a non-player character in the Atari game Pong, and scale the implementation across 3, 4, and 5 users. We demonstrate the success of the proposal through achieving a median improvement of ~17% on the personalization time.
EzPC: Programmable and Efficient Secure Two-Party Computation for Machine Learning

We present EZPC, a secure two-party computation (2PC) framework that generates efficient 2PC protocols from high-level, easy-to-write programs.EZPC provides formal correctness and security guarantees while maintaining performance and scalability.Previous language frameworks, such as CBMC-GC, ObliVM, SMCL, and Wysteria, generate protocols that use either arithmetic or boolean circuits exclusively.Our compiler is the first to generate protocols that combine both arithmetic and boolean circuits for better performance.We empirically demonstrate that the performance of the protocols generated by EZPC is comparable to or better than (in some cases upto 19x) their state-of-the-art, hand-crafted implementations, while EZPC protocols also outperform their boolean circuits only counterparts by as much as 25x.
Differential Privacy-Based Location Protection in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a location-based outsourcing service whereby SC-server allocates tasks to workers with mobile devices according to the locations outsourced by requesters and workers. Since location information contains individual privacy, the locations should be protected before being submitted to untrusted SC-server. However, the encryption schemes limit data availability, and existing differential privacy (DP) methods do not protect the tasks’ location privacy. In this paper, we propose a differential privacy-based location protection (DPLP) scheme, which protects the location privacy of both workers and tasks, and achieves task allocation with high data utility. Specifically, DPLP splits the exact locations of both workers and tasks into noisy multi-level grids by using adaptive three-level grid decomposition (ATGD) algorithm and DP-based adaptive complete pyramid grid (DPACPG) algorithm, respectively, thereby considering the grid granularity and location privacy. Furthermore, DPLP adopts an optimal greedy algorithm to calculate a geocast region around the task grid, which achieves the trade-off between acceptance rate and system overhead. Detailed privacy analysis demonstrates that our DPLP scheme satisfies <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -differential privacy. The extensive analysis and experiments over two real-world datasets confirm high efficiency and data utility of our scheme.
Let You See in Sand Dust Weather: A Method Based on Halo-Reduced Dark Channel Prior Dehazing for Sand-Dust Image Enhancement

The images that are captured in sand storms often suffer from low contrast and serious color cast that are caused by sand dust, and these issues will have significant negative effects on the performance of an outdoor computer vision system. To address these problems, a method based on halo-reduced dark channel prior (DCP) dehazing for sand dust image enhancement is proposed in this paper. It includes three components in sequence: color correction in the LAB color space based on gray world theory, dust removal using a halo-reduced DCP dehazing method, and contrast stretching in the LAB color space using a Gamma function improved contrast limited adaptive histogram equalization (CLAHE), in which a guided filter is used to improve the artifacts of the histogram equalization. Experiments on a large number of real sand dust images demonstrate that the proposed method can well remove the overall yellowing tone and dust haze effect and obtain normal visual colors and a detailed clear image.
An efficient nonlocal variational method with application to underwater image restoration

Light is absorbed and scattered when it travels though water, which causes the captured underwater images often suffering from blurring, low contrast and color degradation. To overcome these problems, we propose a novel variational model based on nonlocal differential operators, in which the underwater image formation model is successfully integrated into the variational framework. The underwater dark channel prior (UDCP) and quad-tree subdivision methods are applied to the construction of underwater image formation model to estimate the transmission map and the global background light. Furthermore, we employ a fast algorithm based on the alternating direction method of multipliers (ADMM) to speed up the solving procedure. To reproduce color saturation, we perform a Gamma correction operation on the recovered image. Both the real underwater image application test and the simulation experiment demonstrate that the proposed underwater nonlocal total variational (UNLTV) approach achieves superb performance on dehazing, denoising, and improving the visibility of underwater images. In addition, six state-of-the-art algorithms are compared under different challenging scenes to evaluate their effectiveness and robustness. Extensive qualitative and quantitative experimental comparisons further validate the superiority of the proposed method.
Performance and Scalability of Private Ethereum Blockchains

Smart contracts provide promising use cases for the public and the private sector by combining cryptographically secure blockchains and the versatility of software. In contrast to public blockchains, private ones can be tailored by configuring blockchain-specific parameters like the time passing between two consecutive blocks, the size of blocks, the hardware of the nodes running the blockchain software, or simply the size of the network. However, the effects of parameters on the performance of private smart contract platforms are not well studied. In this work, we systematically examine to which extent the performance of private Ethereum blockchains scales with various parameters, and which parameters constitute bottlenecks. We introduce a concept for measuring the performance and scalability of private Ethereum smart contract platforms, as well as a framework for the automatic deployment of differently configured private Ethereum blockchains on the cloud. Based on the collected performance-related data, we visualize the impact of parameter changes on performance. Our results show that the effect of variations in one parameter is highly dependent on the configuration of other parameters, especially when running the system near its limits. Moreover, we identify a structure for the bottlenecks of current private Ethereum smart contract platforms.
Cyber hygiene: The concept, its measure, and its initial tests

While policy makers to cyber security experts call for improving cyber hygiene, no one really knows what it means. In fact, there exists no scholarly research explicating the concept or its measurement. This research makes an important contribution by conceptualizing cyber hygiene, operationalizing it, empirically identifying its sub-dimensions, and developing an inventory for it. The research achieves this with a mixed-methods approach, where using a combination of experts and a convenience sample of Internet users, it develops the initial items reflecting the construct, empirically refines the items, confirms its dimensions, and validates its fit. The outcome is an 18-item Cyber Hygiene Inventory (CHI) that measures five distinct dimensions of user cyber hygiene. Finally, the research demonstrates why cyber hygiene matters. Using the CHI it shows how cyber hygiene significantly predicts aspects of human cyber interaction that are pivotal to cyber safety including user self-beliefs about technology, how they cognitively process information online, and their online banking behavior.
Exploring Unsupervised Learning Techniques for the Internet of Things

Nowadays, machine learning (ML) techniques can provide new perspectives to identify hidden patterns and classes inside data. Applying ML to the Internet of Things (IoT) and its produced data represents a great challenge in every application domain, since analyzing IoT data increasingly requires the use of advanced mathematical algorithms, novel computational techniques, and services. In this article, we present and discuss the application of unsupervised learning techniques on IoT data collected in a cultural heritage framework. Behavioral data have been gathered in a noninvasive way in order to achieve an ML classification that can be exploited by cultural stakeholders in terms of the medium- to long-term strategy and also in terms of strictly operational decisions. The application of ML and other learning techniques will acquire a key role to complement the more traditional services with new intelligent ones able to satisfy the needs of companies, stakeholders, and consumers.
HWDCNN: Multi-class recognition in breast histopathology with Haar wavelet decomposed image based convolution neural network

Among the predominant cancers, breast cancer is one of the main causes of cancer deaths impacting women worldwide. However, breast cancer classification is challenging due to numerous morphological and textural variations that appeared in intra-class images. Also, the direct processing of high resolution histological images is uneconomical in terms of GPU memory. In the present study, we have proposed a new approach for breast histopathological image classification that uses a deep convolution neural network (CNN) with wavelet decomposed images. The original microscopic image patches of 2048 × 1536 × 3 pixels are decomposed into 512 × 384 × 3 using 2-level Haar wavelet and subsequently used in proposed CNN model. The image decomposition step considerably reduces convolution time in deep CNNs and computational resources, without any performance downgrade. The CNN model extracts the deep features from Haar wavelet decomposed images and incorporates multi-scale discriminant features for precise prognostication of class labels. This paper also solves the demand for massive histopathology dataset by means of transfer learning and data augmentation techniques. The efficacy of proposed approach is corroborated on two publicly available breast histology datasets-(a) one provided as a part of international conference on image analysis and recognition (ICIAR 2018) grand challenge and (b) on BreakHis data. On the ICIAR 2018 validation data, our model showed an accuracy of 98.2% for both 4-class and 2-class recognition. Further, on hidden test data of the ICIAR 2018, we achieved an accuracy of 91%, outperforming existing state-of-the-art results significantly. Besides, on BreakHis dataset, the model achieved competing performance with 96.85% multi-class accuracy.