OpenRaiser/Intern-Atlas huggingface dataset:
Least-squares Optimal Relative Planar Motion for Vehicle-mounted Cameras
Venue: arXiv cs.CV 2019
Year: 2019

A new closed-form solver is proposed minimizing the algebraic error optimally, in the least-squares sense, to estimate the relative planar motion of two calibrated cameras. The main objective is to solve the over-determined case, i.e., when a larger-than-minimal sample of point correspondences is given - thus, estimating the motion from at least three correspondences. The algorithm requires the camera movement to be constrained to a plane, e.g. mounted to a vehicle, and the image plane to be orthogonal to the ground. The solver obtains the motion parameters as the roots of a 6-th degree polynomial. It is validated both in synthetic experiments and on publicly available real-world datasets that using the proposed solver leads to results superior to the state-of-the-art in terms of geometric accuracy with no noticeable deterioration in the processing time.
Architectural Stability Reasoning using Self-Awareness Principles: Case of Self-Adaptive Cloud Architectures
Venue: arXiv cs.SE 2019
Year: 2019

With the increased dependence on software, there is a pressing need for engineering long-lived software. As architectures have a profound effect on the life-span of the software and the provisioned quality of service, stable architectures are significant assets. Architectural stability tends to reflect the success of the system in supporting continuous changes without phasing-out. The \textit{behavioural} aspect of stability is essential for seamless operation, to continuously keep the provision of quality requirements stable and prevent architecture's drifting and phasing-out. In this paper, we present a framework for reasoning about stability during runtime, leveraging on self-awareness principles. Specifically, we employ runtime goals for managing stability goals, online learning for reasoning about stability on the long-run, and stochastic games for managing associated trade-offs. We evaluate the proposed work using the case of cloud architectures for its highly dynamics during runtime. The experimental results have shown the efficiency of self-awareness techniques in realising the expected behaviour stable during runtime operation.
Augmenting Fiat Currency with an Integrated Managed Cryptocurrency
Venue: arXiv cs.CR 2019
Year: 2019

In this work, we investigate how the governance features of a managed currency (e.g., a fiat currency) can be built into a cryptocurrency in order to leverage potential benefits found in the use of blockchain technology and smart contracts. The resulting managed cryptocurrency can increase transparency and integrity, while potentially enabling the emergence of novel monetary instruments. It has similarities to cash in that it enables the general public to immediately transfer funds to a recipient without intermediary systems being involved. However, our system is account-based, unlike circulating bank notes that are self-contained. Our design would allow one to satisfy know your customer laws and be subject to law enforcement actions following legal due process (e.g., account freezing and fund seizure), while mitigating counterparty risk with checks and balances. Funds can thus be transferred only between approved and authenticated users. Our system has on-chain governance capabilities using smart contracts deployed on a dedicated, permissioned blockchain that has different sets of control mechanisms for who can read data, write data, and publish blocks. To enable the governance features, only authorized identity proofed entities can submit transactions. To enable privacy, only the block publishers can read the blockchain; the publishers maintain dedicated nodes that provide access controlled partial visibility of the blockchain data. Being permissioned, we can use a simple consensus protocol with no transaction fees. A separate security layer prevents denial of service and a balance of power mechanism prevents any small group of entities from having undue control. While permissioned, we ensure that no one entity controls the blockchain data or block publishing capability through a voting system with publicly visible election outcomes.
Computer Viruses: The Abstract Theory Revisited
Venue: arXiv cs.CR 2019
Year: 2019

Identifying new viral threats, and developing long term defences against current and future computer viruses, requires an understanding of their behaviour, structure and capabilities. This paper aims to advance this understanding by further developing the abstract theory of computer viruses. A method of providing abstract definitions for classes of viruses is presented in this paper, which addresses inadequacies of previous techniques. Formal definitions for some classes of viruses are then provided, which correspond to existing informal definitions. To relate the abstract theory to the real world, the connection between the abstract definitions and concrete virus implementations is examined. The use of the proposed method in studying the fundamental properties of computer viruses is discussed.
CIS-Net: A Novel CNN Model for Spatial Image Steganalysis via Cover Image Suppression
Venue: arXiv cs.MM 2019
Year: 2019

Image steganalysis is a special binary classification problem that aims to classify natural cover images and suspected stego images which are the results of embedding very weak secret message signals into covers. How to effectively suppress cover image content and thus make the classification of cover images and stego images easier is the key of this task. Recent researches show that Convolutional Neural Networks (CNN) are very effective to detect steganography by learning discriminative features between cover images and their stegos. Several deep CNN models have been proposed via incorporating domain knowledge of image steganography/steganalysis into the design of the network and achieve state of the art performance on standard database. Following such direction, we propose a novel model called Cover Image Suppression Network (CIS-Net), which improves the performance of spatial image steganalysis by suppressing cover image content as much as possible in model learning. Two novel layers, the Single-value Truncation Layer (STL) and Sub-linear Pooling Layer (SPL), are proposed in this work. Specifically, STL truncates input values into a same threshold when they are out of a predefined interval. Theoretically, we have proved that STL can reduce the variance of input feature map without deteriorating useful information. For SPL, it utilizes sub-linear power function to suppress large valued elements introduced by cover image contents and aggregates weak embedded signals via average pooling. Extensive experiments demonstrate the proposed network equipped with STL and SPL achieves better performance than rich model classifiers and existing CNN models on challenging steganographic algorithms.
An Energy-Efficient Heterogeneous Memory Architecture for Future Dark Silicon Embedded Chip-Multiprocessors
Venue: arXiv cs.AR 2019
Year: 2019

Main memories play an important role in overall energy consumption of embedded systems. Using conventional memory technologies in future designs in nanoscale era causes a drastic increase in leakage power consumption and temperature-related problems. Emerging non-volatile memory (NVM) technologies offer many desirable characteristics such as near-zero leakage power, high density and non-volatility. They can significantly mitigate the issue of memory leakage power in future embedded chip-multiprocessor (eCMP) systems. However, they suffer from challenges such as limited write endurance and high write energy consumption which restrict them for adoption in modern memory systems. In this article, we present a convex optimization model to design a 3D stacked hybrid memory architecture in order to minimize the future embedded systems energy consumption in the dark silicon era. This proposed approach satisfies endurance constraint in order to design a reliable memory system. Our convex model optimizes numbers and placement of eDRAM and STT-RAM memory banks on the memory layer to exploit the advantages of both technologies in future eCMPs. Energy consumption, the main challenge in the dark silicon era, is represented as a major target in this work and it is minimized by the detailed optimization model in order to design a dark silicon aware 3D Chip-Multiprocessor. Experimental results show that in comparison with the Baseline memory design, the proposed architecture improves the energy consumption and performance of the 3D CMP on average about 61.33 and 9 percent respectively.
Dota 2 with Large Scale Deep Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task.
Private Federated Learning with Domain Adaptation
Venue: arXiv cs.LG 2019
Year: 2019

Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other parties, offering advantages in both scale and privacy. We propose a framework to augment this collaborative model-building with per-user domain adaptation. We show that this technique improves model accuracy for all users, using both real and synthetic data, and that this improvement is much more pronounced when differential privacy bounds are imposed on the FL model.
Optimal PAC-Bayesian Posteriors for Stochastic Classifiers and their use for Choice of SVM Regularization Parameter
Venue: arXiv cs.LG 2019
Year: 2019

PAC-Bayesian set up involves a stochastic classifier characterized by a posterior distribution on a classifier set, offers a high probability bound on its averaged true risk and is robust to the training sample used. For a given posterior, this bound captures the trade off between averaged empirical risk and KL-divergence based model complexity term. Our goal is to identify an optimal posterior with the least PAC-Bayesian bound. We consider a finite classifier set and 5 distance functions: KL-divergence, its Pinsker's and a sixth degree polynomial approximations; linear and squared distances. Linear distance based model results in a convex optimization problem. We obtain closed form expression for its optimal posterior. For uniform prior, this posterior has full support with weights negative-exponentially proportional to number of misclassifications. Squared distance and Pinsker's approximation bounds are possibly quasi-convex and are observed to have single local minimum. We derive fixed point equations (FPEs) using partial KKT system with strict positivity constraints. This obviates the combinatorial search for subset support of the optimal posterior. For uniform prior, exponential search on a full-dimensional simplex can be limited to an ordered subset of classifiers with increasing empirical risk values. These FPEs converge rapidly to a stationary point, even for a large classifier set when a solver fails. We apply these approaches to SVMs generated using a finite set of SVM regularization parameter values on 9 UCI datasets. These posteriors yield stochastic SVM classifiers with tight bounds. KL-divergence based bound is the tightest, but is computationally expensive due to non-convexity and multiple calls to a root finding algorithm. Optimal posteriors for all 5 distance functions have lowest 10% test error values on most datasets, with linear distance being the easiest to obtain.
SemEval-2013 Task 2: Sentiment Analysis in Twitter
Venue: arXiv cs.CL 2019
Year: 2019

In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hindered by the lack of suitable datasets, complicating the comparison between approaches. To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a message-level subtask. We used crowdsourcing on Amazon Mechanical Turk to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks. All datasets used in the evaluation are released to the research community. The task attracted significant interest and a total of 149 submissions from 44 teams. The best-performing team achieved an F1 of 88.9% and 69% for subtasks A and B, respectively.
Cerberus: A Blockchain-Based Accreditation and Degree Verification System
Venue: arXiv cs.CR 2019
Year: 2019

Credential fraud is a widespread practice that undermines investment and confidence in higher education systems and bears significant economic and social costs. Legacy credential verification systems are typically time-consuming, costly, and bureaucratic, and struggle against certain classes of credential fraud. In this paper, we propose a comprehensive blockchain-based credential verification solution, Cerberus, which is considerably more efficient, easy and intuitive to use, and effectively mitigates widespread manifestations of credential fraud. Cerberus also improves significantly upon other blockchain-based solutions in the research literature: it adheres closely to the existing credential verification ecosystem, it addresses a threat model informed by real-world fraud scenarios. Moreover, Cerberus uses on-chain smart contracts for credential revocation, and it does not entail students or employers to manage digital identities or cryptographic credentials to use the system. We prototype our solution and describe our attempt to design an online verification service with a rich feature set, including data privacy, transcript verification, and selective disclosure of data. We hope this effort contributes positively to towards alleviating the problem of fake credentials.
LScDC-new large scientific dictionary
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we present a scientific corpus of abstracts of academic papers in English -- Leicester Scientific Corpus (LSC). The LSC contains 1,673,824 abstracts of research articles and proceeding papers indexed by Web of Science (WoS) in which publication year is 2014. Each abstract is assigned to at least one of 252 subject categories. Paper metadata include these categories and the number of citations. We then develop scientific dictionaries named Leicester Scientific Dictionary (LScD) and Leicester Scientific Dictionary-Core (LScDC), where words are extracted from the LSC. The LScD is a list of 974,238 unique words (lemmas). The LScDC is a core list (sub-list) of the LScD with 104,223 lemmas. It was created by removing LScD words appearing in not greater than 10 texts in the LSC. LScD and LScDC are available online. Both the corpus and dictionaries are developed to be later used for quantification of meaning in academic texts. Finally, the core list LScDC was analysed by comparing its words and word frequencies with a classic academic word list 'New Academic Word List (NAWL)' containing 963 word families, which is also sampled from an academic corpus. The major sources of the corpus where NAWL is extracted are Cambridge English Corpus (CEC), oral sources and textbooks. We investigate whether two dictionaries are similar in terms of common words and ranking of words. Our comparison leads us to main conclusion: most of words of NAWL (99.6%) are present in the LScDC but two lists differ in word ranking. This difference is measured.
Conquering the Extensional Scalability Problem for Value-Flow Analysis Frameworks
Venue: arXiv cs.SE 2019
Year: 2019

With an increasing number of value-flow properties to check, existing static program analysis still tends to have scalability issues when high precision is required. We observe that the key design flaw behind the scalability problem is that the core static analysis engine is oblivious of the mutual synergies among different properties being checked and, thus, inevitably loses many optimization opportunities. Our approach is inter-property-aware and able to capture possible overlaps and inconsistencies among different properties. Thus, before analyzing a program, we can make optimization plans which decide how to reuse the specific analysis results of a property to speed up checking other properties. Such a synergistic interaction among the properties significantly improves the analysis performance. We have evaluated our approach by checking twenty value-flow properties in standard benchmark programs and ten real-world software systems. The results demonstrate that our approach is more than 8x faster than existing ones but consumes only 1/7 memory. Such a substantial improvement in analysis efficiency is not achieved by sacrificing the effectiveness: at the time of writing, 39 bugs found by our approach have been fixed by developers and four of them have been assigned CVE IDs due to their security impact.
Spatial Influence-aware Reinforcement Learning for Intelligent Transportation System
Venue: arXiv cs.MA 2019
Year: 2019

Intelligent transportation systems (ITSs) are envisioned to be crucial for smart cities, which aims at improving traffic flow to improve the life quality of urban residents and reducing congestion to improve the efficiency of commuting. However, several challenges need to be resolved before such systems can be deployed, for example, conventional solutions for Markov decision process (MDP) and single-agent Reinforcement Learning (RL) algorithms suffer from poor scalability, and multi-agent systems suffer from poor communication and coordination. In this paper, we explore the potential of mutual information sharing, or in other words, spatial influence based communication, to optimize traffic light control policy. First, we mathematically analyze the transportation system. We conclude that the transportation system does not have stationary Nash Equilibrium, thereby reinforcement learning algorithms offer suitable solutions. Secondly, we describe how to build a multi-agent Deep Deterministic Policy Gradient (DDPG) system with spatial influence and social group utility incorporated. Then we utilize the grid topology road network to empirically demonstrate the scalability of the new system. We demonstrate three types of directed communications to show the effect of directions of social influence on the entire network utility and individual utility. Lastly, we define "selfish index" and analyze the effect of it on total group utility.
Sensor-Independent Illumination Estimation for DNN Models
Venue: arXiv cs.CV 2019
Year: 2019

While modern deep neural networks (DNNs) achieve state-of-the-art results for illuminant estimation, it is currently necessary to train a separate DNN for each type of camera sensor. This means when a camera manufacturer uses a new sensor, it is necessary to retrain an existing DNN model with training images captured by the new sensor. This paper addresses this problem by introducing a novel sensor-independent illuminant estimation framework. Our method learns a sensor-independent working space that can be used to canonicalize the RGB values of any arbitrary camera sensor. Our learned space retains the linear property of the original sensor raw-RGB space and allows unseen camera sensors to be used on a single DNN model trained on this working space. We demonstrate the effectiveness of this approach on several different camera sensors and show it provides performance on par with state-of-the-art methods that were trained per sensor.
Perception-aware Autonomous Mast Motion Planning for Planetary Exploration Rovers
Venue: arXiv cs.RO 2019
Year: 2019

Highly accurate real-time localization is of fundamental importance for the safety and efficiency of planetary rovers exploring the surface of Mars. Mars rover operations rely on vision-based systems to avoid hazards as well as plan safe routes. However, vision-based systems operate on the assumption that sufficient visual texture is visible in the scene. This poses a challenge for vision-based navigation on Mars where regions lacking visual texture are prevalent. To overcome this, we make use of the ability of the rover to actively steer the visual sensor to improve fault tolerance and maximize the perception performance. This paper answers the question of where and when to look by presenting a method for predicting the sensor trajectory that maximizes the localization performance of the rover. This is accomplished by an online assessment of possible trajectories using synthetic, future camera views created from previous observations of the scene. The proposed trajectories are quantified and chosen based on the expected localization performance. In this work, we validate the proposed method in field experiments at the Jet Propulsion Laboratory (JPL) Mars Yard. Furthermore, multiple performance metrics are identified and evaluated for reducing the overall runtime of the algorithm. We show how actively steering the perception system increases the localization accuracy compared to traditional fixed-sensor configurations.
Long-length Legal Document Classification
Venue: arXiv cs.CL 2019
Year: 2019

One of the principal tasks of machine learning with major applications is text classification. This paper focuses on the legal domain and, in particular, on the classification of lengthy legal documents. The main challenge that this study addresses is the limitation that current models impose on the length of the input text. In addition, the present paper shows that dividing the text into segments and later combining the resulting embeddings with a BiLSTM architecture to form a single document embedding can improve results. These advancements are achieved by utilising a simpler structure, rather than an increasingly complex one, which is often the case in NLP research. The dataset used in this paper is obtained from an online public database containing lengthy legal documents with highly domain-specific vocabulary and thus, the comparison of our results to the ones produced by models implemented on the commonly used datasets would be unjustified. This work provides the foundation for future work in document classification in the legal field.