OpenRaiser/Intern-Atlas huggingface dataset:
Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans
Venue: arXiv cs.CV 2020
Year: 2020

In the last two decades, baggage scanning has globally become one of the prime aviation security concerns. Manual screening of the baggage items is tedious, error-prone, and compromise privacy. Hence, many researchers have developed X-ray imagery-based autonomous systems to address these shortcomings. This paper presents a cascaded structure tensor framework that can automatically extract and recognize suspicious items in heavily occluded and cluttered baggage. The proposed framework is unique, as it intelligently extracts each object by iteratively picking contour-based transitional information from different orientations and uses only a single feed-forward convolutional neural network for the recognition. The proposed framework has been rigorously evaluated using a total of 1,067,381 X-ray scans from publicly available GDXray and SIXray datasets where it outperformed the state-of-the-art solutions by achieving the mean average precision score of 0.9343 on GDXray and 0.9595 on SIXray for recognizing the highly cluttered and overlapping suspicious items. Furthermore, the proposed framework computationally achieves 4.76\% superior run-time performance as compared to the existing solutions based on publicly available object detectors
Extrapolation in Gridworld Markov-Decision Processes
Venue: arXiv cs.LG 2020
Year: 2020

Extrapolation in reinforcement learning is the ability to generalize at test time given states that could never have occurred at training time. Here we consider four factors that lead to improved extrapolation in a simple Gridworld environment: (a) avoiding maximum Q-value (or other deterministic methods) for action choice at test time, (b) ego-centric representation of the Gridworld, (c) building rotational and mirror symmetry into the learning mechanism using rotational and mirror invariant convolution (rather than standard translation-invariant convolution), and (d) adding a maximum entropy term to the loss function to encourage equally good actions to be chosen equally often.
Interpretable Safety Validation for Autonomous Vehicles
Venue: arXiv cs.RO 2020
Year: 2020

An open problem for autonomous driving is how to validate the safety of an autonomous vehicle in simulation. Automated testing procedures can find failures of an autonomous system but these failures may be difficult to interpret due to their high dimensionality and may be so unlikely as to not be important. This work describes an approach for finding interpretable failures of an autonomous system. The failures are described by signal temporal logic expressions that can be understood by a human, and are optimized to produce failures that have high likelihood. Our methodology is demonstrated for the safety validation of an autonomous vehicle in the context of an unprotected left turn and a crosswalk with a pedestrian. Compared to a baseline importance sampling approach, our methodology finds more failures with higher likelihood while retaining interpretability.
Beamforming and Resource Allocation for Multi-User Full-Duplex Wireless Powered Communications in IoT Networks
Venue: arXiv cs.IT 2020
Year: 2020

For a self-sustaining wireless communication system in Internet-of-Things (IoT) networks, energy harvesting (EH) can be implemented at each user node as a constant renewable power supply source. Hence, an investigation into the use of wireless powered communication network (WPCN) protocols to facilitate communication between an access point (AP) and multiple mobile users (MUs) is presented in this paper. The AP has multiple antennas and operates in the full-duplex (FD) mode. The MUs, on the other hand, have single antennas and works in the half-duplex (HD) mode. Each MU communicating with the FD-AP is assigned to one of two groups, based on the time allocation and channel access for either uplink (UL) or downlink (DL) communication. The channel assignment, time resource, and power resource allocations are optimized to maximize the UL weighted sum-rate. The sum-rate optimization problem is found to be non-convex. Therefore, an iterative algorithm is investigated to optimize the UL weighted sum-rate of the propose FD-WPCN system. Next, the proposed FD-WPCN algorithm is modified for a HD-WPCN enabled communication between the AP and multiple MUs. Extensive simulations are conducted to verify the proposed algorithm for FD-WPCN and compare its performance with the HD-WPCN counterpart. From the simulation results, FD-WPCN outperformed HD-WPCN at a low AP transmit signal-to-noise ratio (SNR) region. The opposite behavior is observed for high AP transmit SNR due to increasing residual self-interference at the FD-AP.
Mirror Ritual: Human-Machine Co-Construction of Emotion
Venue: arXiv cs.HC 2020
Year: 2020

Mirror Ritual is an interactive installation that challenges the existing paradigms in our understanding of human emotion and machine perception. In contrast to prescriptive interfaces, the work's real-time affective interface engages the audience in the iterative conceptualisation of their emotional state through the use of affectively-charged machine generated poetry. The audience are encouraged to make sense of the mirror's poetry by framing it with respect to their recent life experiences, effectively `putting into words' their felt emotion. This process of affect labelling and contextualisation works to not only regulate emotion, but helps to construct the rich personal narratives that constitute human identity.
How to Securely Prune Bitcoin's Blockchain
Venue: arXiv cs.CR 2020
Year: 2020

Bitcoin was the first successful decentralized cryptocurrency and remains the most popular of its kind to this day. Despite the benefits of its blockchain, Bitcoin still faces serious scalability issues, most importantly its ever-increasing blockchain size. While alternative designs introduced schemes to periodically create snapshots and thereafter prune older blocks, already-deployed systems such as Bitcoin are often considered incapable of adopting corresponding approaches. In this work, we revise this popular belief and present CoinPrune, a snapshot-based pruning scheme that is fully compatible with Bitcoin. CoinPrune can be deployed through an opt-in velvet fork, i.e., without impeding the established Bitcoin network. By requiring miners to publicly announce and jointly reaffirm recent snapshots on the blockchain, CoinPrune establishes trust into the snapshots' correctness even in the presence of powerful adversaries. Our evaluation shows that CoinPrune reduces the storage requirements of Bitcoin already by two orders of magnitude today, with further relative savings as the blockchain grows. In our experiments, nodes only have to fetch and process 5 GiB instead of 230 GiB of data when joining the network, reducing the synchronization time on powerful devices from currently 5 h to 46 min, with even more savings for less powerful devices.
Intelligent Slicing of Radio Resource Control Layer for Cellular IoT: Design and Implementation
Venue: arXiv cs.IT 2020
Year: 2020

The cellular internet of things (CIoT) has become an important branch to cater various applications of IoT devices. Within CIoT, the radio resource control (RRC) layer is responsible for fundamental functionalities such as connection control and bearer establishment in radio access network (RAN). The emergence of various IoT scenarios and diversified service requirements have made both RAN slicing and intelligent control imperative requirement in RRC layer. This paper focuses on enhancing standardized capabilities of CIoT RRC layer, by designing and implementing a new architecture which accommodate RRC slicing and intelligent controller. The architecture aims to realize functionalities of creating, modifying, and deleting slices in RRC layer, while the intelligent controller is added to satisfy various and dynamic service requirements of different IoT devices smartly. The proposed architecture is further implemented on an open-source software platform OpenAirInterface (OAI), on top of which the effectiveness of RRC slicing is validated and one proof-of-concept case to adopt reinforcement learning to dynamically tune discontinuous reception parameters therein is presented. Simulation results have demonstrated the effectiveness of the proposed intelligent RRC slicing architecture.
Advanced Evasion Attacks and Mitigations on Practical ML-Based Phishing Website Classifiers
Venue: arXiv cs.CR 2020
Year: 2020

Machine learning (ML) based approaches have been the mainstream solution for anti-phishing detection. When they are deployed on the client-side, ML-based classifiers are vulnerable to evasion attacks. However, such potential threats have received relatively little attention because existing attacks destruct the functionalities or appearance of webpages and are conducted in the white-box scenario, making it less practical. Consequently, it becomes imperative to understand whether it is possible to launch evasion attacks with limited knowledge of the classifier, while preserving the functionalities and appearance. In this work, we show that even in the grey-, and black-box scenarios, evasion attacks are not only effective on practical ML-based classifiers, but can also be efficiently launched without destructing the functionalities and appearance. For this purpose, we propose three mutation-based attacks, differing in the knowledge of the target classifier, addressing a key technical challenge: automatically crafting an adversarial sample from a known phishing website in a way that can mislead classifiers. To launch attacks in the white- and grey-box scenarios, we also propose a sample-based collision attack to gain the knowledge of the target classifier. We demonstrate the effectiveness and efficiency of our evasion attacks on the state-of-the-art, Google's phishing page filter, achieved 100% attack success rate in less than one second per website. Moreover, the transferability attack on BitDefender's industrial phishing page classifier, TrafficLight, achieved up to 81.25% attack success rate. We further propose a similarity-based method to mitigate such evasion attacks, Pelican. We demonstrate that Pelican can effectively detect evasion attacks. Our findings contribute to design more robust phishing website classifiers in practice.
Exploration of Indoor Environments Predicting the Layout of Partially Observed Rooms
Venue: arXiv cs.RO 2020
Year: 2020

We consider exploration tasks in which an autonomous mobile robot incrementally builds maps of initially unknown indoor environments. In such tasks, the robot makes a sequence of decisions on where to move next that, usually, are based on knowledge about the observed parts of the environment. In this paper, we present an approach that exploits a prediction of the geometric structure of the unknown parts of an environment to improve exploration performance. In particular, we leverage an existing method that reconstructs the layout of an environment starting from a partial grid map and that predicts the shape of partially observed rooms on the basis of geometric features representing the regularities of the indoor environment. Then, we originally employ the predicted layout to estimate the amount of new area the robot would observe from candidate locations in order to inform the selection of the next best location and to early stop the exploration when no further relevant area is expected to be discovered. Experimental activities show that our approach is able to effectively predict the layout of partially observed rooms and to use such knowledge to speed up the exploration.
Exploring Probabilistic Soft Logic as a framework for integrating top-down and bottom-up processing of language in a task context
Venue: arXiv cs.CL 2020
Year: 2020

This technical report describes a new prototype architecture designed to integrate top-down and bottom-up analysis of non-standard linguistic input, where a semantic model of the context of an utterance is used to guide the analysis of the non-standard surface forms, including their automated normalization in context. While the architecture is generally applicable, as a concrete use case of the architecture we target the generation of semantically-informed target hypotheses for answers written by German learners in response to reading comprehension questions, where the reading context and possible target answers are given. The architecture integrates existing NLP components to produce candidate analyses on eight levels of linguistic modeling, all of which are broken down into atomic statements and connected into a large graphical model using Probabilistic Soft Logic (PSL) as a framework. Maximum a posteriori inference on the resulting graphical model then assigns a belief distribution to candidate target hypotheses. The current version of the architecture builds on Universal Dependencies (UD) as its representation formalism on the form level and on Abstract Meaning Representations (AMRs) to represent semantic analyses of learner answers and the context information provided by the target answers. These general choices will make it comparatively straightforward to apply the architecture to other tasks and other languages.
Modular-topology optimization with Wang tilings: An application to truss structures
Venue: arXiv cs.CE 2020
Year: 2020

Modularity is appealing for solving many problems in optimization. It brings the benefits of manufacturability and reconfigurability to structural optimization, and enables a trade-off between the computational performance of a Periodic Unit Cell (PUC) and the efficacy of non-uniform designs in multi-scale material optimization. Here, we introduce a novel strategy for concurrent minimum-compliance design of truss modules topologies and their macroscopic assembly encoded using Wang tiling, a formalism providing independent control over the number of modules and their interfaces. We tackle the emerging bilevel optimization problem with a combination of meta-heuristics and mathematical programming. At the upper level, we employ a genetic algorithm to optimize module assemblies. For each assembly, we obtain optimal module topologies as a solution to a convex second-order conic program that exploits the underlying modularity, incorporating stress constraints, multiple load cases, and reuse of module(s) for various structures. Merits of the proposed strategy are illustrated with three representative examples, clearly demonstrating that the best designs obtained by our method exhibited decreased compliance: from 56% to 69% compared to the PUC designs.
Caching Structures for Distributed Data Management in P2P-based Social Networks
Venue: arXiv cs.SI 2020
Year: 2020

Distributed applications require novel solutions to tackle problems that arise due to the scarcity of resources such as bandwidth, memory and processing power. One of these challenges is seen in distributed data management. The challenge is the two part problem of ensuring that the content is valid when accessed and updating it immediately when changed. This is especially difficult when considering p2p-based distributed online social networks, which aim to build reliable, secure social networking platforms on top of often unreliable and unsecure devices. In this paper, we propose three selection strategies, random, trend and social score, for a social caching mechanism. They consider the social interaction patterns in the social network. We implement and evaluate them in a DHT-based distributed online social networks called LibreSocial and show that the social score is the best strategy. Further we implement the social caching solution and also show that when used in combination with the existing caching solution almost all requests can be serviced via cache while retaining the consistency of data during updates.
Distributed Data Verification Protocols in Cloud Computing
Venue: arXiv cs.NI 2020
Year: 2020

Recently, storage of huge volume of data into Cloud has become an effective trend in modern day Computing due to its dynamic nature. After storing, users deletes their original copy of the data files. Therefore users, cannot directly control over that data. This lack of control introduces security issues in Cloud data storage, one of the most important security issue is integrity of the remotely stored data. Here, we propose a Distributed Algorithmic approach to address this problem with publicly probabilistic verifiable scheme. Due to heavy workload at the Third Party Auditor side, we distributes the verification task among various SUBTPAs. We uses Sobol Random Sequences to generates the random block numbers that maintains the uniformity property. In addition, our method provides uniformity for each subtasks also. To makes each subtask uniform, we uses some analytical approach. For this uniformity, our protocols verify the integrity of the data very efficiently and quickly. Also, we provides special care about critical data by using Overlap Task Distribution Keys.
Nouvelles représentations concises exactes des motifs rares
Venue: arXiv cs.IR 2020
Year: 2020

Until a present, the majority of work in data mining were interested in the extraction of the frequent itemsets and the generation of the frequent association rules from these itemsets. Sometimes, the frequent of associations rules can revealed not-interesting in the direction where a frequent behavior is in general a normal behavior in the database. These last years, some work was focused on the exploitation and the extraction of rare itemset and shows them interest. However, the very important size of those itemset was the handicap of algorithms that exploit the rare pattern. In order to relieve this problem, the present report proposes two exact concise representations of the rare itemset, one based on the minimal generators and the other based on the closed itemset. In this context, we introduce two new algorithms called GMRare and MFRare which extract these two exact concise representations.
A Novel CNN-based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box
Venue: arXiv cs.CV 2020
Year: 2020

Currently, reliable and accurate ship detection in optical remote sensing images is still challenging. Even the state-of-the-art convolutional neural network (CNN) based methods cannot obtain very satisfactory results. To more accurately locate the ships in diverse orientations, some recent methods conduct the detection via the rotated bounding box. However, it further increases the difficulty of detection, because an additional variable of ship orientation must be accurately predicted in the algorithm. In this paper, a novel CNN-based ship detection method is proposed, by overcoming some common deficiencies of current CNN-based methods in ship detection. Specifically, to generate rotated region proposals, current methods have to predefine multi-oriented anchors, and predict all unknown variables together in one regression process, limiting the quality of overall prediction. By contrast, we are able to predict the orientation and other variables independently, and yet more effectively, with a novel dual-branch regression network, based on the observation that the ship targets are nearly rotation-invariant in remote sensing images. Next, a shape-adaptive pooling method is proposed, to overcome the limitation of typical regular ROI-pooling in extracting the features of the ships with various aspect ratios. Furthermore, we propose to incorporate multilevel features via the spatially-variant adaptive pooling. This novel approach, called multilevel adaptive pooling, leads to a compact feature representation more qualified for the simultaneous ship classification and localization. Finally, detailed ablation study performed on the proposed approaches is provided, along with some useful insights. Experimental results demonstrate the great superiority of the proposed method in ship detection.
A 5G NR based System Architecture for Real-Time Control with Batteryless RFID Sensors
Venue: arXiv cs.NI 2020
Year: 2020

The fifth-generation wireless networking (5G) technologies have been developed to meet various time-critical use cases with ultra-reliable, low-latency and massive machine-type communications which are indispensable for tactile Internet applications. Recent advancements in very low-cost and batteryless radio-frequency identification (RFID) sensors have given promises of deploying a massive amount of such sensors for real-time sensing and control applications on a 5G New Radio (NR) network. However, the system design and performance of such applications have not been well studied. This paper proposes a novel system architecture for the representative batteryless RFID touch sensors in generic real-time control applications in a 5G NR mmWave environment. We will discuss the solution using edge computing nodes on the 5G NR base station to the implementation of the proposed system architecture. The real-time performance evaluation with the comparison of the Long-Term Evolution (LTE) networks has shown the effectiveness of the proposed system architecture.
Standardisation of practices in Open Source Hardware
Venue: arXiv cs.CY 2020
Year: 2020

Standardisation is an important component in the maturation of any field of technology. It contributes to the formation of a recognisable identity and enables interactions with a wider community. This article reviews past and current standardisation initiatives in the field of Open Source Hardware (OSH). While early initiatives focused on aspects such as licencing, intellectual property and documentation formats, recent efforts extend to ways for users to exercise their rights under open licences and to keep OSH projects discoverable and accessible online. We specifically introduce two standards that are currently being released and call for early users and contributors, the DIN SPEC 3105 and the Open Know How Manifest Specification. Finally, we reflect on challenges around standardisation in the community and relevant areas for future development such as an open tool chain, modularity and hardware specific interface standards.
Bootstrapped model learning and error correction for planning with uncertainty in model-based RL
Venue: arXiv cs.LG 2020
Year: 2020

Having access to a forward model enables the use of planning algorithms such as Monte Carlo Tree Search and Rolling Horizon Evolution. Where a model is unavailable, a natural aim is to learn a model that reflects accurately the dynamics of the environment. In many situations it might not be possible and minimal glitches in the model may lead to poor performance and failure. This paper explores the problem of model misspecification through uncertainty-aware reinforcement learning agents. We propose a bootstrapped multi-headed neural network that learns the distribution of future states and rewards. We experiment with a number of schemes to extract the most likely predictions. Moreover, we also introduce a global error correction filter that applies high-level constraints guided by the context provided through the predictive distribution. We illustrate our approach on Minipacman. The evaluation demonstrates that when dealing with imperfect models, our methods exhibit increased performance and stability, both in terms of model accuracy and in its use within a planning algorithm.