OpenRaiser/Intern-Atlas huggingface dataset:
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security
Venue: arXiv cs.CR 2020
Year: 2020

Despite the potential of Machine learning (ML) to learn the behavior of malware, detect novel malware samples, and significantly improve information security (InfoSec) we see few, if any, high-impact ML techniques in deployed systems, notwithstanding multiple reported successes in open literature. We hypothesize that the failure of ML in making high-impacts in InfoSec are rooted in a disconnect between the two communities as evidenced by a semantic gap---a difference in how executables are described (e.g. the data and features extracted from the data). Specifically, current datasets and representations used by ML are not suitable for learning the behaviors of an executable and differ significantly from those used by the InfoSec community. In this paper, we survey existing datasets used for classifying malware by ML algorithms and the features that are extracted from the data. We observe that: 1) the current set of extracted features are primarily syntactic, not behavioral, 2) datasets generally contain extreme exemplars producing a dataset in which it is easy to discriminate classes, and 3) the datasets provide significantly different representations of the data encountered in real-world systems. For ML to make more of an impact in the InfoSec community requires a change in the data (including the features and labels) that is used to bridge the current semantic gap. As a first step in enabling more behavioral analyses, we label existing malware datasets with behavioral features using open-source threat reports associated with malware families. This behavioral labeling alters the analysis from identifying intent (e.g. good vs bad) or malware family membership to an analysis of which behaviors are exhibited by an executable. We offer the annotations with the hope of inspiring future improvements in the data that will further bridge the semantic gap between the ML and InfoSec communities.
Factorize Factorization
Venue: arXiv cs.LO 2020
Year: 2020

Factorization -- a simple form of standardization -- is concerned with reduction strategies, i.e. how a result is computed. We present a new technique for proving factorization theorems for compound rewriting systems in a modular way, which is inspired by the Hindley-Rosen technique for confluence. Specifically, our technique is well adapted to deal with extensions of the call-by-name and call-by-value lambda-calculi. The technique is first developed abstractly. We isolate a sufficient condition (called linear swap) for lifting factorization from components to the compound system, and which is compatible with beta-reduction. We then closely analyze some common factorization schemas for the lambda-calculus. Concretely, we apply our technique to diverse extensions of the lambda-calculus, among which de' Liguoro and Piperno's non-deterministic lambda-calculus and -- for call-by-value -- Carraro and Guerrieri's shuffling calculus. For both calculi the literature contains factorization theorems. In both cases, we give a new proof which is neat, simpler than the original, and strikingly shorter.
Neural Subdivision
Venue: arXiv cs.GR 2020
Year: 2020

This paper introduces Neural Subdivision, a novel framework for data-driven coarse-to-fine geometry modeling. During inference, our method takes a coarse triangle mesh as input and recursively subdivides it to a finer geometry by applying the fixed topological updates of Loop Subdivision, but predicting vertex positions using a neural network conditioned on the local geometry of a patch. This approach enables us to learn complex non-linear subdivision schemes, beyond simple linear averaging used in classical techniques. One of our key contributions is a novel self-supervised training setup that only requires a set of high-resolution meshes for learning network weights. For any training shape, we stochastically generate diverse low-resolution discretizations of coarse counterparts, while maintaining a bijective mapping that prescribes the exact target position of every new vertex during the subdivision process. This leads to a very efficient and accurate loss function for conditional mesh generation, and enables us to train a method that generalizes across discretizations and favors preserving the manifold structure of the output. During training we optimize for the same set of network weights across all local mesh patches, thus providing an architecture that is not constrained to a specific input mesh, fixed genus, or category. Our network encodes patch geometry in a local frame in a rotation- and translation-invariant manner. Jointly, these design choices enable our method to generalize well, and we demonstrate that even when trained on a single high-resolution mesh our method generates reasonable subdivisions for novel shapes.
A multi-component framework for the analysis and design of explainable artificial intelligence
Venue: arXiv cs.AI 2020
Year: 2020

The rapid growth of research in explainable artificial intelligence (XAI) follows on two substantial developments. First, the enormous application success of modern machine learning methods, especially deep and reinforcement learning, which have created high expectations for industrial, commercial and social value. Second, the emergence of concern for creating trusted AI systems, including the creation of regulatory principles to ensure transparency and trust of AI systems.These two threads have created a kind of "perfect storm" of research activity, all eager to create and deliver it any set of tools and techniques to address the XAI demand. As some surveys of current XAI suggest, there is yet to appear a principled framework that respects the literature of explainability in the history of science, and which provides a basis for the development of a framework for transparent XAI. Here we intend to provide a strategic inventory of XAI requirements, demonstrate their connection to a history of XAI ideas, and synthesize those ideas into a simple framework to calibrate five successive levels of XAI.
Toward Equilibria and Solvability of Blockchain Pooling Strategies: A Topological Approach
Venue: arXiv cs.CR 2020
Year: 2020

In 2015, Eyal proposed the first game-theoretical model for analyzing the equilibrium of blockchain pooling: when the blockchain pools are abstracted as a non-cooperative game, two pools can reach a Nash equilibrium with a closed-form formula; Moreover, an arbitrary number of pools still exhibit an equilibrium as long as the pools have an equal number of miners. Nevertheless, whether an equilibrium exists for three or more pools of distinct sizes remains an open problem. To this end, this paper studies the equilibrium in a blockchain of arbitrary pools. First, we show that the equilibrium among $q$ identical pools, coinciding the result demonstrated by Eyal through game theory, can be constructed using a topological approach. Second, if the pools are of different size, we show that (i) if the blockchain's pools exhibit two distinct sizes, an equilibrium can be reached, and (ii) if the blockchain has at least three distinct pool sizes, there does not exist an equilibrium.
Modal features for image texture classification
Venue: arXiv cs.CV 2020
Year: 2020

Feature extraction is a key step in image processing for pattern recognition and machine learning processes. Its purpose lies in reducing the dimensionality of the input data through the computing of features which accurately describe the original information. In this article, a new feature extraction method based on Discrete Modal Decomposition (DMD) is introduced, to extend the group of space and frequency based features. These new features are called modal features. Initially aiming to decompose a signal into a modal basis built from a vibration mechanics problem, the DMD projection is applied to images in order to extract modal features with two approaches. The first one, called full scale DMD, consists in exploiting directly the decomposition resulting coordinates as features. The second one, called filtering DMD, consists in using the DMD modes as filters to obtain features through a local transformation process. Experiments are performed on image texture classification tasks including several widely used data bases, compared to several classic feature extraction methods. We show that the DMD approach achieves good classification performances, comparable to the state of the art techniques, with a lower extraction time.
Characterizing Triviality of the Exponent Lattice of A Polynomial through Galois and Galois-Like Groups
Venue: arXiv cs.SC 2020
Year: 2020

The problem of computing \emph{the exponent lattice} which consists of all the multiplicative relations between the roots of a univariate polynomial has drawn much attention in the field of computer algebra. As is known, almost all irreducible polynomials with integer coefficients have only trivial exponent lattices. However, the algorithms in the literature have difficulty in proving such triviality for a generic polynomial. In this paper, the relations between the Galois group (respectively, \emph{the Galois-like groups}) and the triviality of the exponent lattice of a polynomial are investigated. The $\bbbq$\emph{-trivial} pairs, which are at the heart of the relations between the Galois group and the triviality of the exponent lattice of a polynomial, are characterized. An effective algorithm is developed to recognize these pairs. Based on this, a new algorithm is designed to prove the triviality of the exponent lattice of a generic irreducible polynomial, which considerably improves a state-of-the-art algorithm of the same type when the polynomial degree becomes larger. In addition, the concept of the Galois-like groups of a polynomial is introduced. Some properties of the Galois-like groups are proved and, more importantly, a sufficient and necessary condition is given for a polynomial (which is not necessarily irreducible) to have trivial exponent lattice.
Low Power In-Memory Implementation of Ternary Neural Networks with Resistive RAM-Based Synapse
Venue: arXiv cs.ET 2020
Year: 2020

The design of systems implementing low precision neural networks with emerging memories such as resistive random access memory (RRAM) is a major lead for reducing the energy consumption of artificial intelligence (AI). Multiple works have for example proposed in-memory architectures to implement low power binarized neural networks. These simple neural networks, where synaptic weights and neuronal activations assume binary values, can indeed approach state-of-the-art performance on vision tasks. In this work, we revisit one of these architectures where synapses are implemented in a differential fashion to reduce bit errors, and synaptic weights are read using precharge sense amplifiers. Based on experimental measurements on a hybrid 130 nm CMOS/RRAM chip and on circuit simulation, we show that the same memory array architecture can be used to implement ternary weights instead of binary weights, and that this technique is particularly appropriate if the sense amplifier is operated in near-threshold regime. We also show based on neural network simulation on the CIFAR-10 image recognition task that going from binary to ternary neural networks significantly increases neural network performance. These results highlight that AI circuits function may sometimes be revisited when operated in low power regimes.
DPN -- Dependability Priority Numbers
Venue: arXiv cs.SE 2020
Year: 2020

This paper proposes a novel model-based approach to combine the quantitative dependability (safety, reliability, availability, maintainability and IT security) analysis and trade-off analysis. The proposed approach is called DPN (Dependability Priority Numbers) and allows the comparison of different actual dependability characteristics of a systems with its target values and evaluates them regarding trade-off analysis criteria. Therefore, the target values of system dependability characteristics are taken as requirements, while the actual value of a specific system design are provided by quantitative and qualitative dependability analysis (FHA, FMEA, FMEDA, of CFT-based FTA). The DPN approach evaluates the fulfillment of individual target requirements and perform trade-offs between analysis objectives. We present the workflow and meta-model of the DPN approach, and illustrate our approach using a case study on a brake warning contact system. Hence, we demonstrate how the model-based DPNs improve system dependability by selecting the project crucial dependable design alternatives or measures.
Mechanisms of intermediary platforms
Venue: arXiv cs.CY 2020
Year: 2020

In the current digital age of the Internet, with ever-growing networks and data-driven business models, digital platforms and especially marketplaces are becoming increasingly important. These platforms focus primarily on digital businesses by offering services that bring together consumers and producers. Due to added value created for consumers, the profit-driven operators of these platforms Matchmakers are extremely successful and have come to dominate their respective markets. The aim of this article is to understand how Matchmakers and coordination networks gain market dominance. The following sections will take a closer look at network and coordination effects as well as intermediary platform mechanisms and entailing disadvantages for users. Considering strategic and business challenges, we suggest a possible solution and strategy to avoid dependencies on individual players in the digital economy. We present a cooperative approach towards a fair and open Economy of Things (EoT) based on decentralized technologies.
Importance of Data Loading Pipeline in Training Deep Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Training large-scale deep neural networks is a long, time-consuming operation, often requiring many GPUs to accelerate. In large models, the time spent loading data takes a significant portion of model training time. As GPU servers are typically expensive, tricks that can save training time are valuable.Slow training is observed especially on real-world applications where exhaustive data augmentation operations are required. Data augmentation techniques include: padding, rotation, adding noise, down sampling, up sampling, etc. These additional operations increase the need to build an efficient data loading pipeline, and to explore existing tools to speed up training time. We focus on the comparison of two main tools designed for this task, namely binary data format to accelerate data reading, and NVIDIA DALI to accelerate data augmentation. Our study shows improvement on the order of 20% to 40% if such dedicated tools are used.
Human Factors in Biocybersecurity Wargames
Venue: arXiv cs.CR 2020
Year: 2020

Within the field of biocybersecurity, it is important to understand what vulnerabilities may be uncovered in the processing of biologics as well as how they can be safeguarded as they intersect with cyber and cyberphysical systems, as noted by the Peccoud Lab, to ensure not only product and brand integrity, but protect those served. Recent findings have revealed that biological systems can be used to compromise computer systems and vice versa. While regular and sophisticated attacks are still years away, time is of the essence to better understand ways to deepen critique and grasp intersectional vulnerabilities within bioprocessing as processes involved become increasingly digitally accessible. Wargames have been shown to be successful with-in improving group dynamics in response to anticipated cyber threats, and they can be used towards addressing possible threats within biocybersecurity. Within this paper, we discuss the growing prominence of biocybersecurity, the importance of biocybersecurity to bioprocessing , with respect to domestic and international contexts, and reasons for emphasizing the biological component in the face of explosive growth in biotechnology and thus separating the terms biocybersecurity and cyberbiosecurity. Additionally, a discussion and manual is provided for a simulation towards organizational learning to sense and shore up vulnerabilities that may emerge within an organization's bioprocessing pipeline
Psychophysical Evaluation of Deep Re-Identification Models
Venue: arXiv cs.CV 2020
Year: 2020

Pedestrian re-identification (ReID) is the task of continuously recognising the sameindividual across time and camera views. Researchers of pedestrian ReID and theirGPUs spend enormous energy producing novel algorithms, challenging datasets,and readily accessible tools to successfully improve results on standard metrics.Yet practitioners in biometrics, surveillance, and autonomous driving have not re-alized benefits that reflect these metrics. Different detections, slight occlusions,changes in perspective, and other banal perturbations render the best neural net-works virtually useless. This work makes two contributions. First, we introducethe ReID community to a budding area of computer vision research in model eval-uation. By adapting established principles of psychophysical evaluation from psy-chology, we can quantify the performance degradation and begin research thatwill improve the utility of pedestrian ReID models; not just their performance ontest sets. Second, we introduce NuscenesReID, a challenging new ReID datasetdesigned to reflect the real world autonomous vehicle conditions in which ReIDalgorithms are used. We show that, despite performing well on existing ReIDdatasets, most models are not robust to synthetic augmentations or to the morerealistic NuscenesReID data.
BlackBox: Generalizable Reconstruction of Extremal Values from Incomplete Spatio-Temporal Data
Venue: arXiv cs.LG 2020
Year: 2020

We describe our submission to the Extreme Value Analysis 2019 Data Challenge in which teams were asked to predict extremes of sea surface temperature anomaly within spatio-temporal regions of missing data. We present a computational framework which reconstructs missing data using convolutional deep neural networks. Conditioned on incomplete data, we employ autoencoder-like models as multivariate conditional distributions from which possible reconstructions of the complete dataset are sampled using imputed noise. In order to mitigate bias introduced by any one particular model, a prediction ensemble is constructed to create the final distribution of extremal values. Our method does not rely on expert knowledge in order to accurately reproduce dynamic features of a complex oceanographic system with minimal assumptions. The obtained results promise reusability and generalization to other domains.
Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization
Venue: arXiv cs.CL 2020
Year: 2020

We describe Artemis (Annotation methodology for Rich, Tractable, Extractive, Multi-domain, Indicative Summarization), a novel hierarchical annotation process that produces indicative summaries for documents from multiple domains. Current summarization evaluation datasets are single-domain and focused on a few domains for which naturally occurring summaries can be easily found, such as news and scientific articles. These are not sufficient for training and evaluation of summarization models for use in document management and information retrieval systems, which need to deal with documents from multiple domains. Compared to other annotation methods such as Relative Utility and Pyramid, Artemis is more tractable because judges don't need to look at all the sentences in a document when making an importance judgment for one of the sentences, while providing similarly rich sentence importance annotations. We describe the annotation process in detail and compare it with other similar evaluation systems. We also present analysis and experimental results over a sample set of 532 annotated documents.
II-20: Intelligent and pragmatic analytic categorization of image collections
Venue: arXiv cs.MM 2020
Year: 2020

We introduce II-20 (Image Insight 2020), a multimedia analytics approach for analytic categorization of image collections. Advanced visualizations for image collections exist, but they need tight integration with a machine model to support analytic categorization. Directly employing computer vision and interactive learning techniques gravitates towards search. Analytic categorization, however, is not machine classification (the difference between the two is called the pragmatic gap): a human adds/redefines/deletes categories of relevance on the fly to build insight, whereas the machine classifier is rigid and non-adaptive. Analytic categorization that brings the user to insight requires a flexible machine model that allows dynamic sliding on the exploration-search axis, as well as semantic interactions. II-20 brings 3 major contributions to multimedia analytics on image collections and towards closing the pragmatic gap. Firstly, a machine model that closely follows the user's interactions and dynamically models her categories of relevance. II-20's model, in addition to matching and exceeding the state of the art w. r. t. relevance, allows the user to dynamically slide on the exploration-search axis without additional input from her side. Secondly, the dynamic, 1-image-at-a-time Tetris metaphor that synergizes with the model. It allows the model to analyze the collection by itself with minimal interaction from the user and complements the classic grid metaphor. Thirdly, the fast-forward interaction, allowing the user to harness the model to quickly expand ("fast-forward") the categories of relevance, expands the multimedia analytics semantic interaction dictionary. Automated experiments show that II-20's model outperforms the state of the art and also demonstrate Tetris's analytic quality. User studies confirm that II-20 is an intuitive, efficient, and effective multimedia analytics tool.
A User Study to Investigate Semantically Relevant Contextual Information of WWW Images
Venue: arXiv cs.IR 2020
Year: 2020

The contextual information of Web images is investigated to address the issue of enriching their index characterizations with semantic descriptors and therefore bridge the semantic gap (i.e. the gap between the low-level content-based description of images and their semantic interpretation). Although we are highly motivated by the availability of rich knowledge on the Web and the relative success achieved by commercial search engines in indexing images using surrounding text-based information in webpages, we are aware that the unpredictable quality of the surrounding text is a major limiting factor. In order to improve its quality, we highlight contextual information which is relevant for the semantic characterization of Web images and study its statistical properties in terms of its location and nature considering a classification into five semantic concept classes: signal, object, scene, abstract and relational. A user study is conducted to validate the results. The results suggest that there are several locations that consistently contain relevant textual information with respect to the image. The importance of each location is influenced by the type of webpage as the results show the different distribution of relevant contextual information across the locations for different webpage types. The frequently found semantic concept classes are object and abstract. Another important outcome of the user study shows that a webpage is not an atomic unit and can be further partitioned into smaller segments. Segments containing images are of interest and termed as image segments. We observe that users typically single out textual information which they consider relevant to the image from the textual information bounded within the image segment.