OpenRaiser/Intern-Atlas huggingface dataset:
A Deep Learning-based Penetration Testing Framework for Vulnerability Identification in Internet of Things Environments
Venue: arXiv cs.CR 2021
Year: 2021

The Internet of Things (IoT) paradigm has displayed tremendous growth in recent years, resulting in innovations like Industry 4.0 and smart environments that provide improvements to efficiency, management of assets and facilitate intelligent decision making. However, these benefits are offset by considerable cybersecurity concerns that arise due to inherent vulnerabilities, which hinder IoT-based systems' Confidentiality, Integrity, and Availability. Security vulnerabilities can be detected through the application of penetration testing, and specifically, a subset of the information-gathering stage, known as vulnerability identification. Yet, existing penetration testing solutions can not discover zero-day vulnerabilities from IoT environments, due to the diversity of generated data, hardware constraints, and environmental complexity. Thus, it is imperative to develop effective penetration testing solutions for the detection of vulnerabilities in smart IoT environments. In this paper, we propose a deep learning-based penetration testing framework, namely Long Short-Term Memory Recurrent Neural Network-Enabled Vulnerability Identification (LSTM-EVI). We utilize this framework through a novel cybersecurity-oriented testbed, which is a smart airport-based testbed comprised of both physical and virtual elements. The framework was evaluated using this testbed and on real-time data sources. Our results revealed that the proposed framework achieves about 99% detection accuracy for scanning attacks, outperforming other four peer techniques.
Commonsense Knowledge in Word Associations and ConceptNet
Venue: arXiv cs.CL 2021
Year: 2021

Humans use countless basic, shared facts about the world to efficiently navigate in their environment. This commonsense knowledge is rarely communicated explicitly, however, understanding how commonsense knowledge is represented in different paradigms is important for both deeper understanding of human cognition and for augmenting automatic reasoning systems. This paper presents an in-depth comparison of two large-scale resources of general knowledge: ConcpetNet, an engineered relational database, and SWOW a knowledge graph derived from crowd-sourced word associations. We examine the structure, overlap and differences between the two graphs, as well as the extent to which they encode situational commonsense knowledge. We finally show empirically that both resources improve downstream task performance on commonsense reasoning benchmarks over text-only baselines, suggesting that large-scale word association data, which have been obtained for several languages through crowd-sourcing, can be a valuable complement to curated knowledge graphs
Learning Versatile Convolution Filters for Efficient Visual Recognition
Venue: arXiv cs.CV 2021
Year: 2021

This paper introduces versatile filters to construct efficient convolutional neural networks that are widely used in various visual recognition tasks. Considering the demands of efficient deep learning techniques running on cost-effective hardware, a number of methods have been developed to learn compact neural networks. Most of these works aim to slim down filters in different ways, \eg,~investigating small, sparse or quantized filters. In contrast, we treat filters from an additive perspective. A series of secondary filters can be derived from a primary filter with the help of binary masks. These secondary filters all inherit in the primary filter without occupying more storage, but once been unfolded in computation they could significantly enhance the capability of the filter by integrating information extracted from different receptive fields. Besides spatial versatile filters, we additionally investigate versatile filters from the channel perspective. Binary masks can be further customized for different primary filters under orthogonal constraints. We conduct theoretical analysis on network complexity and an efficient convolution scheme is introduced. Experimental results on benchmark datasets and neural networks demonstrate that our versatile filters are able to achieve comparable accuracy as that of original filters, but require less memory and computation cost.
An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator
Venue: arXiv cs.NE 2021
Year: 2021

Differential evolution (DE) is an effective population-based metaheuristic algorithm for solving complex optimisation problems. However, the performance of DE is sensitive to the mutation operator. In this paper, we propose a novel DE algorithm, Clu-DE, that improves the efficacy of DE using a novel clustering-based mutation operator. First, we find, using a clustering algorithm, a winner cluster in search space and select the best candidate solution in this cluster as the base vector in the mutation operator. Then, an updating scheme is introduced to include new candidate solutions in the current population. Experimental results on CEC-2017 benchmark functions with dimensionalities of 30, 50 and 100 confirm that Clu-DE yields improved performance compared to DE.
Flexible User Mapping for Radio Resource Assignment in Advanced Satellite Payloads
Venue: arXiv cs.IT 2021
Year: 2021

This work explores the flexible assignment of users to beams in order to match the non-uniform traffic demand in satellite systems, breaking the conventional cell boundaries and serving users potentially by non-dominant beams. The additional degree of freedom from the flexible beam-user mapping brings about a user-centric framework, with a two-step optimization process as a practical strategy. The smart beam-user mapping is jointly explored with adjustable bandwidth allocation per beam, and tested against different techniques for payloads with flexible radio resource allocation. Numerical results are obtained for various non-uniform traffic distributions to evaluate the performance of the solutions. The traffic profile across beams is shaped by the Dirichlet distribution, which can be conveniently parameterized, and makes simulations easily reproducible. Even with ideal conditions for the power allocation, both flexible beam-user mapping and adjustable power allocation similarly enhance the flexible assignment of the bandwidth on average. Results show that a smart pairing of users and beams provides significant advantages in highly asymmetric demand scenarios, with improvements up to 10\% and 30\% in terms of the offered and the minimum user rates, respectively, in hot-spot like cases with no impact on the user equipment.
Modality and Negation in Event Extraction
Venue: arXiv cs.CL 2021
Year: 2021

Language provides speakers with a rich system of modality for expressing thoughts about events, without being committed to their actual occurrence. Modality is commonly used in the political news domain, where both actual and possible courses of events are discussed. NLP systems struggle with these semantic phenomena, often incorrectly extracting events which did not happen, which can lead to issues in downstream applications. We present an open-domain, lexicon-based event extraction system that captures various types of modality. This information is valuable for Question Answering, Knowledge Graph construction and Fact-checking tasks, and our evaluation shows that the system is sufficiently strong to be used in downstream applications.
Struct-MRT: Immersive Learning and Teaching of Design and Verification in Structural Civil Engineering using Mixed Reality
Venue: arXiv cs.HC 2021
Year: 2021

Our goal is to transform traditional paper-based instruction into an immersive lesson. This paper presents the conception, workflow and deployment of two MR applications for verification of typical yet geometrically complex structural members: a reinforced concrete corbel and a steel frame. The aim of this research is threefold: (i) to develop and implement the technological feasibility of such applications, (ii) to demonstrate possible use cases in the context of structural engineering lectures and (iii) to evaluate the presented MR examples and the future potential of such MR applications in structural engineering lectures through a survey. The workflow and MR teaching applications were developed with Apple's ARKit. The verification process was reproduced in the MR applications based on conventional exercises taught on paper. Users can navigate independently through the applications and review every single step, including a true-to-scale, spatial representation of the specific component as well as associated verification formulas in the respective step. The applications were used to assess the demand and expectations for immersive teaching techniques among students and instructors through a survey. The participants were asked to test the MR applications on their devices or watch pre-recorded video demonstrations, afterwards perception was elicited through a questionnaire. The results of subsequent data analysis show generally positive judgement of the MR application over the six questioned categories (style, usefulness, ease of use, enjoyment, attitude as well as intention towards using). The statistical analysis revealed (positivity) biases for users with prior XR experience w.r.t. to usage and navigation, while inexperienced users underlined increased enjoyment or excitement with this learning format. The outlook covers identified shortcomings and future developments in this field.
Unsupervised domain adaptation with non-stochastic missing data
Venue: arXiv cs.LG 2021
Year: 2021

We consider unsupervised domain adaptation (UDA) for classification problems in the presence of missing data in the unlabelled target domain. More precisely, motivated by practical applications, we analyze situations where distribution shift exists between domains and where some components are systematically absent on the target domain without available supervision for imputing the missing target components. We propose a generative approach for imputation. Imputation is performed in a domain-invariant latent space and leverages indirect supervision from a complete source domain. We introduce a single model performing joint adaptation, imputation and classification which, under our assumptions, minimizes an upper bound of its target generalization error and performs well under various representative divergence families (H-divergence, Optimal Transport). Moreover, we compare the target error of our Adaptation-imputation framework and the "ideal" target error of a UDA classifier without missing target components. Our model is further improved with self-training, to bring the learned source and target class posterior distributions closer. We perform experiments on three families of datasets of different modalities: a classical digit classification benchmark, the Amazon product reviews dataset both commonly used in UDA and real-world digital advertising datasets. We show the benefits of jointly performing adaptation, classification and imputation on these datasets.
Landmark Policy Optimization for Object Navigation Task
Venue: arXiv cs.AI 2021
Year: 2021

This work studies object goal navigation task, which involves navigating to the closest object related to the given semantic category in unseen environments. Recent works have shown significant achievements both in the end-to-end Reinforcement Learning approach and modular systems, but need a big step forward to be robust and optimal. We propose a hierarchical method that incorporates standard task formulation and additional area knowledge as landmarks, with a way to extract these landmarks. In a hierarchy, a low level consists of separately trained algorithms to the most intuitive skills, and a high level decides which skill is needed at this moment. With all proposed solutions, we achieve a 0.75 success rate in a realistic Habitat simulator. After a small stage of additional model training in a reconstructed virtual area at a simulator, we successfully confirmed our results in a real-world case.
Scaling TensorFlow to 300 million predictions per second
Venue: arXiv cs.LG 2021
Year: 2021

We present the process of transitioning machine learning models to the TensorFlow framework at a large scale in an online advertising ecosystem. In this talk we address the key challenges we faced and describe how we successfully tackled them; notably, implementing the models in TF and serving them efficiently with low latency using various optimization techniques.
MeetDot: Videoconferencing with Live Translation Captions
Venue: arXiv cs.CL 2021
Year: 2021

We present MeetDot, a videoconferencing system with live translation captions overlaid on screen. The system aims to facilitate conversation between people who speak different languages, thereby reducing communication barriers between multilingual participants. Currently, our system supports speech and captions in 4 languages and combines automatic speech recognition (ASR) and machine translation (MT) in a cascade. We use the re-translation strategy to translate the streamed speech, resulting in caption flicker. Additionally, our system has very strict latency requirements to have acceptable call quality. We implement several features to enhance user experience and reduce their cognitive load, such as smooth scrolling captions and reducing caption flicker. The modular architecture allows us to integrate different ASR and MT services in our backend. Our system provides an integrated evaluation suite to optimize key intrinsic evaluation metrics such as accuracy, latency and erasure. Finally, we present an innovative cross-lingual word-guessing game as an extrinsic evaluation metric to measure end-to-end system performance. We plan to make our system open-source for research purposes.
Resilient Level Ancestor, Bottleneck, and Lowest Common Ancestor Queries in Dynamic Trees
Venue: arXiv cs.DS 2021
Year: 2021

We study the problem of designing a \emph{resilient} data structure maintaining a tree under the Faulty-RAM model [Finocchi and Italiano, STOC'04] in which up to $δ$ memory words can be corrupted by an adversary. Our data structure stores a rooted dynamic tree that can be updated via the addition of new leaves, requires linear size, and supports \emph{resilient} (weighted) level ancestor queries, lowest common ancestor queries, and bottleneck vertex queries in $O(δ)$ worst-case time per operation.
Parameter Decoupling Strategy for Semi-supervised 3D Left Atrium Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Consistency training has proven to be an advanced semi-supervised framework and achieved promising results in medical image segmentation tasks through enforcing an invariance of the predictions over different views of the inputs. However, with the iterative updating of model parameters, the models would tend to reach a coupled state and eventually lose the ability to exploit unlabeled data. To address the issue, we present a novel semi-supervised segmentation model based on parameter decoupling strategy to encourage consistent predictions from diverse views. Specifically, we first adopt a two-branch network to simultaneously produce predictions for each image. During the training process, we decouple the two prediction branch parameters by quadratic cosine distance to construct different views in latent space. Based on this, the feature extractor is constrained to encourage the consistency of probability maps generated by classifiers under diversified features. In the overall training process, the parameters of feature extractor and classifiers are updated alternately by consistency regularization operation and decoupling operation to gradually improve the generalization performance of the model. Our method has achieved a competitive result over the state-of-the-art semi-supervised methods on the Atrial Segmentation Challenge dataset, demonstrating the effectiveness of our framework. Code is available at https://github.com/BX0903/PDC.
Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Play
Venue: arXiv cs.CL 2021
Year: 2021

Task-oriented dialog systems are often trained on human/human dialogs, such as collected from Wizard-of-Oz interfaces. However, human/human corpora are frequently too small for supervised training to be effective. This paper investigates two approaches to training agent-bots and user-bots through self-play, in which they autonomously explore an API environment, discovering communication strategies that enable them to solve the task. We give empirical results for both reinforcement learning and game-theoretic equilibrium finding.
On Generalization in Coreference Resolution
Venue: arXiv cs.CL 2021
Year: 2021

While coreference resolution is defined independently of dataset domain, most models for performing coreference resolution do not transfer well to unseen domains. We consolidate a set of 8 coreference resolution datasets targeting different domains to evaluate the off-the-shelf performance of models. We then mix three datasets for training; even though their domain, annotation guidelines, and metadata differ, we propose a method for jointly training a single model on this heterogeneous data mixture by using data augmentation to account for annotation differences and sampling to balance the data quantities. We find that in a zero-shot setting, models trained on a single dataset transfer poorly while joint training yields improved overall performance, leading to better generalization in coreference resolution models. This work contributes a new benchmark for robust coreference resolution and multiple new state-of-the-art results.
Metamorphic Relation Prioritization for Effective Regression Testing
Venue: arXiv cs.SE 2021
Year: 2021

Metamorphic testing (MT) is widely used for testing programs that face the oracle problem. It uses a set of metamorphic relations (MRs), which are relations among multiple inputs and their corresponding outputs to determine whether the program under test is faulty. Typically, MRs vary in their ability to detect faults in the program under test, and some MRs tend to detect the same set of faults. In this paper, we propose approaches to prioritize MRs to improve the efficiency and effectiveness of MT for regression testing. We present two MR prioritization approaches: (1) fault-based and (2) coverage-based. To evaluate these MR prioritization approaches, we conduct experiments on three complex open-source software systems. Our results show that the MR prioritization approaches developed by us significantly outperform the current practice of executing the source and follow-up test cases of the MRs in an ad-hoc manner in terms of fault detection effectiveness. Further, fault-based MR prioritization leads to reducing the number of source and follow-up test cases that needs to be executed as well as reducing the average time taken to detect a fault, which would result in saving time and cost during the testing process.
Counterfactual Instances Explain Little
Venue: arXiv cs.AI 2021
Year: 2021

In many applications, it is important to be able to explain the decisions of machine learning systems. An increasingly popular approach has been to seek to provide \emph{counterfactual instance explanations}. These specify close possible worlds in which, contrary to the facts, a person receives their desired decision from the machine learning system. This paper will draw on literature from the philosophy of science to argue that a satisfactory explanation must consist of both counterfactual instances and a causal equation (or system of equations) that support the counterfactual instances. We will show that counterfactual instances by themselves explain little. We will further illustrate how explainable AI methods that provide both causal equations and counterfactual instances can successfully explain machine learning predictions.
Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope
Venue: arXiv cs.CL 2021
Year: 2021

Abstract Meaning Representation (AMR) is a graphical meaning representation language designed to represent propositional information about argument structure. However, at present it is unable to satisfyingly represent non-veridical intensional contexts, often licensing inappropriate inferences. In this paper, we show how to resolve the problem of non-veridicality without appealing to layered graphs through a mapping from AMRs into Simply-Typed Lambda Calculus (STLC). At least for some cases, this requires the introduction of a new role :content which functions as an intensional operator. The translation proposed is inspired by the formal linguistics literature on the event semantics of attitude reports. Next, we address the interaction of quantifier scope and intensional operators in so-called de re/de dicto ambiguities. We adopt a scope node from the literature and provide an explicit multidimensional semantics utilizing Cooper storage which allows us to derive the de re and de dicto scope readings as well as intermediate scope readings which prove difficult for accounts without a scope node.
Robustness Analysis of Deep Learning Frameworks on Mobile Platforms
Venue: arXiv cs.LG 2021
Year: 2021

With the recent increase in the computational power of modern mobile devices, machine learning-based heavy tasks such as face detection and speech recognition are now integral parts of such devices. This requires frameworks to execute machine learning models (e.g., Deep Neural Networks) on mobile devices. Although there exist studies on the accuracy and performance of these frameworks, the quality of on-device deep learning frameworks, in terms of their robustness, has not been systematically studied yet. In this paper, we empirically compare two on-device deep learning frameworks with three adversarial attacks on three different model architectures. We also use both the quantized and unquantized variants for each architecture. The results show that, in general, neither of the deep learning frameworks is better than the other in terms of robustness, and there is not a significant difference between the PC and mobile frameworks either. However, in cases like Boundary attack, mobile version is more robust than PC. In addition, quantization improves robustness in all cases when moving from PC to mobile.
Comparative Analysis of Cryptographic Key Management Systems
Venue: arXiv cs.CR 2021
Year: 2021

Managing cryptographic keys can be a complex task for an enterprise and particularly difficult to scale when an increasing number of users and applications need to be managed. In order to address scalability issues, typical IT infrastructures employ key management systems that are able to handle a large number of encryption keys and associate them with the authorized requests. Given their necessity, recent years have witnessed a variety of key management systems, aligned with the features, quality, price and security needs of specific organisations. While the spectrum of such solutions is welcome and demonstrates the expanding nature of the market, it also makes it time consuming for IT managers to identify the appropriate system for their respective company needs. This paper provides a list of key management tools which include a minimum set of features, such as availability of secure database for managing keys, an authentication, authorization, and access control model for restricting and managing access to keys, effective logging of actions with keys, and the presence of an API for accessing functions directly from the application code. Five systems were comprehensively compared by evaluating the attributes related to complexity of the implementation, its popularity, linked vulnerabilities and technical performance in terms of response time and network usage. These were Pinterest Knox, Hashicorp Vault, Square Keywhiz, OpenStack Barbican, and Cyberark Conjur. Out of these five, Hachicorp Vault was determined to be the most suitable system for small businesses.