OpenRaiser/Intern-Atlas huggingface dataset:
Two-Timescale Hybrid Compression and Forward for Massive MIMO Aided C-RAN
Venue: arXiv cs.IT 2019
Year: 2019

We consider the uplink of a cloud radio access network (C-RAN), where massive MIMO remote radio heads (RRHs) serve as relays between users and a centralized baseband unit (BBU). Although employing massive MIMO at RRHs can improve the spectral efficiency, it also significantly increases the amount of data transported over the fronthaul links between RRHs and BBU, which becomes a performance bottleneck. Existing fronthaul compression methods for conventional C-RAN are not suitable for the massive MIMO regime because they require fully-digital processing and/or real-time full channel state information (CSI), incurring high implementation cost for massive MIMO RRHs. To overcome this challenge, we propose to perform a two-timescale hybrid analog-and-digital spatial filtering at each RRH to reduce the fronthaul consumption. Specifically, the analog filter is adaptive to the channel statistics to achieve massive MIMO array gain, and the digital filter is adaptive to the instantaneous effective CSI to achieve spatial multiplexing gain. Such a design can alleviate the performance bottleneck of limited fronthaul with reduced hardware cost and power consumption, and is more robust to the CSI delay. We propose an online algorithm for the two-timescale non-convex optimization of analog and digital filters, and establish its convergence to stationary solutions. Finally, simulations verify the advantages of the proposed scheme.
Self-triggered distributed k-order coverage control
Venue: arXiv cs.MA 2019
Year: 2019

A k-order coverage control problem is studied where a network of agents must deploy over a desired area. The objective is to deploy all the agents in a decentralized manner such that a certain coverage performance metric of the network is maximized. Unlike many prior works that consider multi-agent deployment, we explicitly consider applications where more than one agent may be required to service an event that randomly occurs anywhere in the domain. The proposed method ensures the distributed agents autonomously cover the area while simultaneously relaxing the requirement of constant communication among the agents. In order to achieve the stated goals, a self-triggered coordination method is developed that both determines how agents should move without having to continuously acquire information from other agents, as well as exactly when to communicate and acquire new information. Through analysis, the proposed strategy is shown to provide asymptotic convergence similar to that of continuous or periodic methods. Simulation results demonstrate that the proposed method can reduce the number of messages exchanged as well as the amount of communication power necessary to accomplish the deployment task.
Syllable-based Neural Named Entity Recognition for Myanmar Language
Venue: arXiv cs.CL 2019
Year: 2019

Named Entity Recognition (NER) for Myanmar Language is essential to Myanmar natural language processing research work. In this work, NER for Myanmar language is treated as a sequence tagging problem and the effectiveness of deep neural networks on NER for Myanmar language has been investigated. Experiments are performed by applying deep neural network architectures on syllable level Myanmar contexts. Very first manually annotated NER corpus for Myanmar language is also constructed and proposed. In developing our in-house NER corpus, sentences from online news website and also sentences supported from ALT-Parallel-Corpus are also used. This ALT corpus is one part of the Asian Language Treebank (ALT) project under ASEAN IVO. This paper contributes the first evaluation of neural network models on NER task for Myanmar language. The experimental results show that those neural sequence models can produce promising results compared to the baseline CRF model. Among those neural architectures, bidirectional LSTM network added CRF layer above gives the highest F-score value. This work also aims to discover the effectiveness of neural network approaches to Myanmar textual processing as well as to promote further researches on this understudied language.
Extracting localized information from a Twitter corpus for flood prevention
Venue: arXiv cs.IR 2019
Year: 2019

In this paper, we discuss the collection of a corpus associated to tropical storm Harvey, as well as its analysis from both spatial and topical perspectives. From the spatial perspective, our goal here is to get a first estimation of the quality and precision of the geographical information featured in the collected corpus. From a topical perspective, we discuss the representation of Twitter posts, and strategies to process an initially unlabeled corpus of tweets.
Occlusion-guided compact template learning for ensemble deep network-based pose-invariant face recognition
Venue: arXiv cs.CV 2019
Year: 2019

Concatenation of the deep network representations extracted from different facial patches helps to improve face recognition performance. However, the concatenated facial template increases in size and contains redundant information. Previous solutions aim to reduce the dimensionality of the facial template without considering the occlusion pattern of the facial patches. In this paper, we propose an occlusion-guided compact template learning (OGCTL) approach that only uses the information from visible patches to construct the compact template. The compact face representation is not sensitive to the number of patches that are used to construct the facial template and is more suitable for incorporating the information from different view angles for image-set based face recognition. Instead of using occlusion masks in face matching (e.g., DPRFS [38]), the proposed method uses occlusion masks in template construction and achieves significantly better image-set based face verification performance on a challenging database with a template size that is an order-of-magnitude smaller than DPRFS.
Paradox in Deep Neural Networks: Similar yet Different while Different yet Similar
Venue: arXiv cs.CV 2019
Year: 2019

Machine learning is advancing towards a data-science approach, implying a necessity to a line of investigation to divulge the knowledge learnt by deep neuronal networks. Limiting the comparison among networks merely to a predefined intelligent ability, according to ground truth, does not suffice, it should be associated with innate similarity of these artificial entities. Here, we analysed multiple instances of an identical architecture trained to classify objects in static images (CIFAR and ImageNet data sets). We evaluated the performance of the networks under various distortions and compared it to the intrinsic similarity between their constituent kernels. While we expected a close correspondence between these two measures, we observed a puzzling phenomenon. Pairs of networks whose kernels' weights are over 99.9% correlated can exhibit significantly different performances, yet other pairs with no correlation can reach quite compatible levels of performance. We show implications of this for transfer learning, and argue its importance in our general understanding of what intelligence is, whether natural or artificial.
Secured by Blockchain: Safeguarding Internet of Things Devices
Venue: arXiv cs.CR 2019
Year: 2019

Blockchain is a disruptive technology that has been characterised to be the next big thing and has already gained a broad recognition by experts in diverse fields. In this paper, we consider possible use cases and applications of the blockchain for the consumer electronics (CE) industry and its interplay with the Internet of things. Instead of discussing how the blockchain can revolutionise the supply chain, we focus on how it could be employed for enhancing the security of networked CE devices. This work is motivated by the large number of recent attacks that use easily hackable devices as a weaponry. Towards this direction, privacy and data protection aspects of blockchain solutions are also presented and are linked to regulatory framework provisions. Information on existing blockchain solutions is also provided.
Estimation of Photovoltaic Generation Forecasting Models using Limited Information
Venue: arXiv cs.SY 2019
Year: 2019

This work deals with the problem of estimating a photovoltaic generation forecasting model in scenarios where measurements of meteorological variables (i.e. solar irradiance and temperature) at the plant site are not available. A novel algorithm for the estimation of the parameters of the well-known PVUSA model of a photovoltaic plant is proposed. Such a method is characterized by a low computational complexity, and efficiently exploits only power generation measurements, a theoretical clear-sky irradiance model, and temperature forecasts provided by a meteorological service. An extensive experimental validation of the proposed method on real data is also presented.
Hierarchical Autoregressive Image Models with Auxiliary Decoders
Venue: arXiv cs.CV 2019
Year: 2019

Autoregressive generative models of images tend to be biased towards capturing local structure, and as a result they often produce samples which are lacking in terms of large-scale coherence. To address this, we propose two methods to learn discrete representations of images which abstract away local detail. We show that autoregressive models conditioned on these representations can produce high-fidelity reconstructions of images, and that we can train autoregressive priors on these representations that produce samples with large-scale coherence. We can recursively apply the learning procedure, yielding a hierarchy of progressively more abstract image representations. We train hierarchical class-conditional autoregressive models on the ImageNet dataset and demonstrate that they are able to generate realistic images at resolutions of 128$\times$128 and 256$\times$256 pixels. We also perform a human evaluation study comparing our models with both adversarial and likelihood-based state-of-the-art generative models.
Temporal Logics Over Finite Traces with Uncertainty (Technical Report)
Venue: arXiv cs.LO 2019
Year: 2019

Temporal logics over finite traces have recently seen wide application in a number of areas, from business process modelling, monitoring, and mining to planning and decision making. However, real-life dynamic systems contain a degree of uncertainty which cannot be handled with classical logics. We thus propose a new probabilistic temporal logic over finite traces using superposition semantics, where all possible evolutions are possible, until observed. We study the properties of the logic and provide automata-based mechanisms for deriving probabilistic inferences from its formulas. We then study a fragment of the logic with better computational properties. Notably, formulas in this fragment can be discovered from event log data using off-the-shelf existing declarative process discovery techniques.
Character Eyes: Seeing Language through Character-Level Taggers
Venue: arXiv cs.CL 2019
Year: 2019

Character-level models have been used extensively in recent years in NLP tasks as both supplements and replacements for closed-vocabulary token-level word representations. In one popular architecture, character-level LSTMs are used to feed token representations into a sequence tagger predicting token-level annotations such as part-of-speech (POS) tags. In this work, we examine the behavior of POS taggers across languages from the perspective of individual hidden units within the character LSTM. We aggregate the behavior of these units into language-level metrics which quantify the challenges that taggers face on languages with different morphological properties, and identify links between synthesis and affixation preference and emergent behavior of the hidden tagger layer. In a comparative experiment, we show how modifying the balance between forward and backward hidden units affects model arrangement and performance in these types of languages.
Empirical analysis of the variability in the flow-density relationship for smart motorways
Venue: arXiv cs.CY 2019
Year: 2019

The fundamental diagram is an assumed functional relationship between traffic flow and traffic density. In practice, this relationship is noisy and exhibits significant statistical variability. On smart motorways, this variability is increased by variable speed limits that are not captured by the fundamental diagram. To study this variability, it is appropriate to consider the joint probability distribution function (pdf) of density and flow. We perform an empirical study of the variability in the relationship between flow and density using 74 days of data from 64 sections of London's M25. The objectives are to determine how much of the variability in the flow-density relationship results from variable speed limits and to assess whether particular functional forms of the fundamental diagram are systematically preferred. Empirically, the joint pdf of flow and density is strongly bimodal, illustrating that traffic flows are often found in high-density or low-density regimes but rarely in between. We find that the high-density regime is strongly affected by variable speed limits whereas the low-density regime is not. The Daganzo-Newell (triangular) model of the fundamental diagram systematically fits best to the data. However, the optimal parameters vary with location. Clustering analysis of these parameters suggests three qualitatively different types of flow-density relationships applying to different sections of the M25. These clusters have natural interpretations in terms of the frequency and severity of flow breakdown. Accident rates also depend on cluster type suggesting possible links to other properties of traffic flows beyond the flow-density relationship.
Arithmetic-Geometric Mean Robustness for Control from Signal Temporal Logic Specifications
Venue: arXiv cs.RO 2019
Year: 2019

We present a new average-based robustness score for Signal Temporal Logic (STL) and a framework for optimal control of a dynamical system under STL constraints. By averaging the scores of different specifications or subformulae at different time points, our new definition highlights the frequency of satisfaction, as well as how robustly each specification is satisfied at each time point. We show that this definition provides a better score for how well a specification is satisfied. Its usefulness in monitoring and control synthesis problems is illustrated through case studies.
Teaching Programming Concepts by Developing Games
Venue: arXiv cs.CY 2019
Year: 2019

Learning to program could possibly be analogous to acquiring expertise in abstract mathematics, which may be boring or dull for a majority of students. Thus, among the countless options to approach learning coding [1-14], acquiring concepts through game creation could possibly be the most enriching experience for students. Consequently, it is important to select a lucid and familiar game for students. Then, the following step is to choose a language that introduces the basic concepts of object-oriented programming really well. For this paper, we chose the game of Tic-Tac-Toe, which is straight-forward for most people. The programming language chosen here is C++.
TACAN: Transmitter Authentication through Covert Channels in Controller Area Networks
Venue: arXiv cs.CR 2019
Year: 2019

Nowadays, the interconnection of automotive systems with modern digital devices offers advanced user experiences to drivers. Electronic Control Units (ECUs) carry out a multitude of operations using the insecure Controller Area Network (CAN) bus in automotive Cyber-Physical Systems (CPSs). Therefore, dangerous attacks, such as disabling brakes, are possible and the safety of passengers is at risk. In this paper, we present TACAN (Transmitter Authentication in CAN), which provides secure authentication of ECUs by exploiting the covert channels without introducing CAN protocol modifications or traffic overheads (i.e., no extra bits or messages are used). TACAN turns upside-down the originally malicious concept of covert channels and exploits it to build an effective defensive technique that facilitates transmitter authentication via a trusted Monitor Node. TACAN consists of three different covert channels for ECU authentication: 1) Inter-Arrival Time (IAT)-based, leveraging the IATs of CAN messages; 2) offset-based, exploiting the clock offsets of CAN messages; 3) Least Significant Bit (LSB)-based, concealing authentication messages into the LSBs of normal CAN data. We implement the covert channels on the University of Washington (UW) EcoCAR testbed and evaluate their performance through extensive experiments. We demonstrate the feasibility of TACAN, highlighting no traffic overheads and attesting the regular functionality of ECUs. In particular, the bit error ratios are within 0.1% and 0.42% for the IAT-based and offset-based covert channels, respectively. Furthermore, the bit error ratio of the LSB-based covert channel is equal to that of a normal CAN bus, which is 3.1x10^-7%.
Situational Awareness, Drivers Trust in Automated Driving Systems and Secondary Task Performance
Venue: arXiv cs.HC 2019
Year: 2019

Driver assistance systems, also called automated driving systems, allow drivers to immerse themselves in non-driving-related tasks. Unfortunately, drivers may not trust the automated driving system, which prevents either handing over the driving task or fully focusing on the secondary task. We assert that enhancing situational awareness can increase trust in automation. Situational awareness should increase trust and lead to better secondary task performance. This study manipulated situational awareness by providing them with different types of information: the control condition provided no information to the driver, the low condition provided a status update, while the high condition provided a status update and a suggested course of action. Data collected included measures of trust, trusting behavior, and task performance through surveys, eye-tracking, and heart rate data. Results show that situational awareness both promoted and moderated the impact of trust in the automated vehicle, leading to better secondary task performance. This result was evident in measures of self-reported trust and trusting behavior.
Hardware/Software Security Patches for Internet of Trillions of Things
Venue: arXiv cs.CR 2019
Year: 2019

With the rapid development of the Internet of Things, there are many interacting devices and applications. One crucial challenge is how to provide security. Our proposal for a new direction is to create "smart buttons" and collections of them called "smart blankets" as hardware/software security patches rather than software-only patches.
Task-oriented Design through Deep Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

We propose a new low-cost machine-learning-based methodology which assists designers in reducing the gap between the problem and the solution in the design process. Our work applies reinforcement learning (RL) to find the optimal task-oriented design solution through the construction of the design action for each task. For this task-oriented design, the 3D design process in product design is assigned to an action space in Deep RL, and the desired 3D model is obtained by training each design action according to the task. By showing that this method achieves satisfactory design even when applied to a task pursuing multiple goals, we suggest the direction of how machine learning can contribute to the design process. Also, we have validated with product designers that this methodology can assist the creative part in the process of design.
Correct-by-construction control synthesis for buck converters with event-triggered state measurement
Venue: arXiv cs.SY 2019
Year: 2019

In this paper, we illustrate a new correct-by-construction switching controller for a power converter with event-triggered measurements. The event-triggered measurement scheme is beneficial for high frequency power converters because it requires relatively low-speed sampling hardware and is immune to unmodeled switching transients. While providing guarantees on the closed-loop system behavior is crucial in this application, off-the-shelf abstraction-based techniques cannot be directly employed to synthesize a controller in this setting because controller cannot always get instantaneous access to the current state. As a result, the switching action has to be based on slightly out-of-date measurements. To tackle this challenge, we introduce the out-of-date measurement as an extra state variable and project out the inaccessible real state to construct a belief space abstraction. The properties preserved by this belief space abstraction are analyzed. Finally, an abstraction-based synthesis method is applied to this abstraction. We demonstrate the controller on a constant on-time buck voltage regulator plant with an event-triggered sampler. The simulation verifies the effectiveness of our controller.
Activity-Based Analysis of Open Source Software Contributors: Roles and Dynamics
Venue: arXiv cs.SE 2019
Year: 2019

Contributors to open source software (OSS) communities assume diverse roles to take different responsibilities. One major limitation of the current OSS tools and platforms is that they provide a uniform user interface regardless of the activities performed by the various types of contributors. This paper serves as a non-trivial first step towards resolving this challenge by demonstrating a methodology and establishing knowledge to understand how the contributors' roles and their dynamics, reflected in the activities contributors perform, are exhibited in OSS communities. Based on an analysis of user action data from 29 GitHub projects, we extracted six activities that distinguished four Active roles and five Supporting roles of OSS contributors, as well as patterns in role changes. Through the lens of the Activity Theory, these findings provided rich design guidelines for OSS tools to support diverse contributor roles.
Zero-shot Domain Adaptation Based on Attribute Information
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we propose a novel domain adaptation method that can be applied without target data. We consider the situation where domain shift is caused by a prior change of a specific factor and assume that we know how the prior changes between source and target domains. We call this factor an attribute, and reformulate the domain adaptation problem to utilize the attribute prior instead of target data. In our method, the source data are reweighted with the sample-wise weight estimated by the attribute prior and the data themselves so that they are useful in the target domain. We theoretically reveal that our method provides more precise estimation of sample-wise transferability than a straightforward attribute-based reweighting approach. Experimental results with both toy datasets and benchmark datasets show that our method can perform well, though it does not use any target data.
Safety Analysis for Vehicle Guidance Systems with Dynamic Fault Trees
Venue: arXiv cs.SE 2019
Year: 2019

This paper considers the design-phase safety analysis of vehicle guidance systems. The proposed approach constructs dynamic fault trees (DFTs) to model a variety of safety concepts and E/E architectures for drive automation. The fault trees can be used to evaluate various quantitative measures by means of model checking. The approach is accompanied by a large-scale evaluation: The resulting DFTs with up to 300 elements constitute larger-than-before DFTs, yet the concepts and architectures can be evaluated in a matter of minutes.