OpenRaiser/Intern-Atlas huggingface dataset:
Learning Robust Representations for Automatic Target Recognition
Venue: arXiv cs.LG 2018
Year: 2018

Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world. Given the high variability of complex-valued target responses, RF systems are susceptible to attacks masking true target characteristics from accurate identification. In this work, we evaluate different techniques for building robust classification architectures exploiting learned physical structure in received synthetic aperture radar signals of simulated 3D targets.
Environments for Lifelong Reinforcement Learning
Venue: arXiv cs.AI 2018
Year: 2018

To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific task but also to constantly build more complex skills and scaffold their knowledge about the world, without forgetting what has already been learned. In this paper, we discuss the desired characteristics of environments that can support the training and evaluation of lifelong reinforcement learning agents, review existing environments from this perspective, and propose recommendations for devising suitable environments in the future.
DONUT: CTC-based Query-by-Example Keyword Spotting
Venue: arXiv cs.LG 2018
Year: 2018

Keyword spotting--or wakeword detection--is an essential feature for hands-free operation of modern voice-controlled devices. With such devices becoming ubiquitous, users might want to choose a personalized custom wakeword. In this work, we present DONUT, a CTC-based algorithm for online query-by-example keyword spotting that enables custom wakeword detection. The algorithm works by recording a small number of training examples from the user, generating a set of label sequence hypotheses from these training examples, and detecting the wakeword by aggregating the scores of all the hypotheses given a new audio recording. Our method combines the generalization and interpretability of CTC-based keyword spotting with the user-adaptation and convenience of a conventional query-by-example system. DONUT has low computational requirements and is well-suited for both learning and inference on embedded systems without requiring private user data to be uploaded to the cloud.
The Batched Set Cover Problem
Venue: arXiv cs.DS 2018
Year: 2018

We introduce the batched set cover problem, which is a generalization of the online set cover problem. In this problem, the elements of the ground set that need to be covered arrive in batches. Our main technical contribution is a tight $Ω(H_{m - 2^z + 1})$ lower bound on the competitive ratio of any fractional batched algorithm given an adversary that is required to produce batches of VC-dimension at least $z$, for some $z \in \mathbb{N}^0$. This restriction on the adversary is motivated by the fact that, in some real world applications, decisions are made after collecting batches of data of non-trivial VC-dimension. In particular, ridesharing systems rely on the batch assignment of trip requests to vehicles, and some related problems such as that of optimal congregation points for passenger pickups and dropoffs can be modeled as a batched set cover problem with VC-dimension greater than or equal to two. Furthermore, we note that while any online algorithm may be used to solve the batched set cover problem by artificially sequencing the elements in a batch, this procedure may neglect the rich information encoded in the complex interactions between the elements of a batch and the sets that contain them. Therefore, we propose a minor modification to an online algorithm found in [8] to obtain an algorithm that attempts to exploit such information. Unfortunately, we are unable to improve its analysis in a way that reflects this intuition. However, we present computational experiments that provide empirical evidence of a constant factor improvement in the competitive ratio. To the best of our knowledge, we are the first to use the VC-dimension in the context of online (batched) covering problems.
User Support for the Combinator Logic Synthesizer Framework
Venue: arXiv cs.LO 2018
Year: 2018

Usability is crucial for the adoption of software development technologies. This is especially true in development stages, where build processes fail, because software is not yet complete or was incompletely modified. We present early work that aims to improve usability of the Combinatory Logic Synthesizer (CL)S framework, especially in these stages. (CL)S is a publicly available type-based development tool for the automatic composition of software components from a user-specified repository. It provides an implementation of a type inhabitation algorithm for Combinatory Logic with intersection types, which is fully integrated into the Scala programming language. Here, we specifically focus on building a web-based IDE to make potentially incomplete or erroneous input specifications for and decisions of the algorithm understandable for non-experts. A main aspect of this is providing graphical representations illustrating the step-wise search process of the algorithm. We also provide a detailed discussion of possible future work to further improve the understandability of these representations.
A Notebook Format for the Holistic Design of Embedded Systems (Tool Paper)
Venue: arXiv cs.SE 2018
Year: 2018

This paper proposes the use of notebooks for the design documentation and tool interaction in the rigorous design of embedded systems. Conventionally, a notebook is a sequence of cells alternating between (textual) code and prose to form a document that is meant to be read from top to bottom, in the spirit of literate programming. We extend the use of notebooks to embedded systems specified by pCharts. The charts are visually edited in cells inline. Other cells can contain statements that generate code and analyze the charts qualitatively and quantitatively; in addition, notebook cells can contain other instructions to build the product from the generated code. This allows a notebook to be replayed to re-analyze the design and re-build the product, like a script, but also allows the notebook to be used for presentations, as for this paper, and for the inspection of the design. The interaction with the notebook is done through a web browser that connects to a local or remote server, thus allowing a computationally intensive analysis to run remotely if needed. The pState notebooks are implemented as an extension to Jupyter. The underlying software architecture is described and the issue of proper placement of transition labels in charts embedded in notebooks is discussed.
VECTORS: Video communication through opportunistic relays and scalable video coding
Venue: arXiv cs.MM 2018
Year: 2018

Crowd-sourced video distribution is frequently of interest in the local vicinity. In this paper, we propose a novel design to transfer such content over opportunistic networks with adaptive quality encoding to achieve reasonable delay bounds. The video segments are transmitted between source and destination in a delay tolerant manner using the Nearby Connections Android library. This implementation can be applied to multiple domains, including farm monitoring, wildlife, and environmental tracking, disaster response scenarios, etc. In this work, we present the design of an opportunistic contact based system, and we discuss basic results for the trial runs within our institute.
Maximizing Multivariate Information with Error-Correcting Codes
Venue: arXiv cs.IT 2018
Year: 2018

Multivariate mutual information provides a conceptual framework for characterizing higher-order interactions in complex systems. Two well-known measures of multivariate information---total correlation and dual total correlation---admit a spectrum of measures with varying sensitivity to intermediate orders of dependence. Unfortunately, these intermediate measures have not received much attention due to their opaque representation of information. Here we draw on results from matroid theory to show that these measures are closely related to error-correcting codes. This connection allows us to derive the class of global maximizers for each measure, which coincide with maximum distance separable codes of order $k$. In addition to deepening the understanding of these measures and multivariate information more generally, we use these results to show that previously proposed bounds on information geometric quantities are tight at the extremes.
Robust Artificial Intelligence and Robust Human Organizations
Venue: arXiv cs.AI 2018
Year: 2018

Every AI system is deployed by a human organization. In high risk applications, the combined human plus AI system must function as a high-reliability organization in order to avoid catastrophic errors. This short note reviews the properties of high-reliability organizations and draws implications for the development of AI technology and the safe application of that technology.
Information Technology Utilization in Environmentally Friendly Higher Education
Venue: arXiv cs.CY 2018
Year: 2018

The awareness of an environmentally friendly learning process has been of concern lately. IT offers some applications that are able to provide better green education environment in higher education sectors. This research involves top social information technology applications, DropBox and WordPress. DropBox is the most popular cloud storage in the world. Meanwhile, WordPress is top content management systems at the moment. This research employs the utilization of those two applications in students assignments and presentation medium. The study observed 150 sophomore students in computer science faculty. The results show that, involving DropBox and WordPress in higher education learning activities able to significantly reduce paper and ink usage. The combination of those two top applications create a very handy and comfortable environment for students in higer education. This strategy reduce the consumption of papers and inks and are accepted well by most of the students.
UnDEMoN 2.0: Improved Depth and Ego Motion Estimation through Deep Image Sampling
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we provide an improved version of UnDEMoN model for depth and ego motion estimation from monocular images. The improvement is achieved by combining the standard bi-linear sampler with a deep network based image sampling model (DIS-NET) to provide better image reconstruction capabilities on which the depth estimation accuracy depends in un-supervised learning models. While DIS-NET provides higher order regression and larger input search space, the bi-linear sampler provides geometric constraints necessary for reducing the size of the solution space for an ill-posed problem of this kind. This combination is shown to provide significant improvement in depth and pose estimation accuracy outperforming all existing state-of-the-art methods in this category. In addition, the modified network uses far less number of tunable parameters making it one of the lightest deep network model for depth estimation. The proposed model is labeled as "UnDEMoN 2.0" indicating an improvement over the existing UnDEMoN model. The efficacy of the proposed model is demonstrated through rigorous experimental analysis on the standard KITTI dataset.
Making Agents' Abilities Explicit
Venue: arXiv cs.AI 2018
Year: 2018

Alternating-time temporal logics (ATL/ATL*) represent a family of modal logics for reasoning about agents' strategic abilities in multiagent systems (MAS). The interpretations of ATL/ATL* over the semantic model Concurrent Game Structures (CGS) usually vary depending on the agents' abilities, for instance, perfect vs. imperfect information, perfect vs. imperfect recall, resulting in a variety of variants which have been studied extensively in literature. However, they are defined at the semantic level, which may limit modeling flexibilities and may give counter-intuitive interpretations. To mitigate these issues, in this work, we propose to extend CGS with agents' abilities and study the new semantics of ATL/ATL* under this model. We give PSACE/2EXPTIME model-checking algorithms for ATL/ATL* and implement them as a prototype tool. Experiment results show the practical feasibility of the approach.
Robust active attacks on social graphs
Venue: arXiv cs.SI 2018
Year: 2018

In order to prevent the disclosure of privacy-sensitive data, such as names and relations between users, social network graphs have to be anonymised before publication. Naive anonymisation of social network graphs often consists in deleting all identifying information of the users, while maintaining the original graph structure. Various types of attacks on naively anonymised graphs have been developed. Active attacks form a special type of such privacy attacks, in which the adversary enrols a number of fake users, often called sybils, to the social network, allowing the adversary to create unique structural patterns later used to re-identify the sybil nodes and other users after anonymisation. Several studies have shown that adding a small amount of noise to the published graph already suffices to mitigate such active attacks. Consequently, active attacks have been dubbed a negligible threat to privacy-preserving social graph publication. In this paper, we argue that these studies unveil shortcomings of specific attacks, rather than inherent problems of active attacks as a general strategy. In order to support this claim, we develop the notion of a robust active attack, which is an active attack that is resilient to small perturbations of the social network graph. We formulate the design of robust active attacks as an optimisation problem and we give definitions of robustness for different stages of the active attack strategy. Moreover, we introduce various heuristics to achieve these notions of robustness and experimentally show that the new robust attacks are considerably more resilient than the original ones, while remaining at the same level of feasibility.
Neural Non-Stationary Spectral Kernel
Venue: arXiv cs.LG 2018
Year: 2018

Standard kernels such as Matérn or RBF kernels only encode simple monotonic dependencies within the input space. Spectral mixture kernels have been proposed as general-purpose, flexible kernels for learning and discovering more complicated patterns in the data. Spectral mixture kernels have recently been generalized into non-stationary kernels by replacing the mixture weights, frequency means and variances by input-dependent functions. These functions have also been modelled as Gaussian processes on their own. In this paper we propose modelling the hyperparameter functions with neural networks, and provide an experimental comparison between the stationary spectral mixture and the two non-stationary spectral mixtures. Scalable Gaussian process inference is implemented within the sparse variational framework for all the kernels considered. We show that the neural variant of the kernel is able to achieve the best performance, among alternatives, on several benchmark datasets.
Generating Responses Expressing Emotion in an Open-domain Dialogue System
Venue: arXiv cs.CL 2018
Year: 2018

Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances. The generated responses are typically acceptable as a sentence but are often dull, generic, and certainly devoid of any emotion. In this paper, we present neural models that learn to express a given emotion in the generated response. We propose four models and evaluate them against 3 baselines. An encoder-decoder framework-based model with multiple attention layers provides the best overall performance in terms of expressing the required emotion. While it does not outperform other models on all emotions, it presents promising results in most cases.
An explicit representation and enumeration for self-dual cyclic codes over $\mathbb{F}_{2^m}+u\mathbb{F}_{2^m}$ of length $2^s$
Venue: arXiv cs.IT 2018
Year: 2018

Let $\mathbb{F}_{2^m}$ be a finite field of cardinality $2^m$ and $s$ a positive integer. Using properties for Kronecker product of matrices and calculation for linear equations over $\mathbb{F}_{2^m}$, an efficient method for the construction of all distinct self-dual cyclic codes with length $2^s$ over the finite chain ring $\mathbb{F}_{2^m}+u\mathbb{F}_{2^m}$ $(u^2=0)$ is provided. On that basis, an explicit representation for every self-dual cyclic code of length $2^s$ over $\mathbb{F}_{2^m}+u\mathbb{F}_{2^m}$ and an exact formula to count the number of all these self-dual cyclic codes are given.
Translating and Evolving: Towards a Model of Language Change in DisCoCat
Venue: arXiv cs.CL 2018
Year: 2018

The categorical compositional distributional (DisCoCat) model of meaning developed by Coecke et al. (2010) has been successful in modeling various aspects of meaning. However, it fails to model the fact that language can change. We give an approach to DisCoCat that allows us to represent language models and translations between them, enabling us to describe translations from one language to another, or changes within the same language. We unify the product space representation given in (Coecke et al., 2010) and the functorial description in (Kartsaklis et al., 2013), in a way that allows us to view a language as a catalogue of meanings. We formalize the notion of a lexicon in DisCoCat, and define a dictionary of meanings between two lexicons. All this is done within the framework of monoidal categories. We give examples of how to apply our methods, and give a concrete suggestion for compositional translation in corpora.
Detection and Prevention Against RTS Attacks in Wireless LAN
Venue: arXiv cs.NI 2018
Year: 2018

Widely deployed wireless network devices use a shared medium to communicate among mobile nodes. WLAN uses virtual carrier sensing mechanism to solve issues like hidden node problem, while this mechanism is vulnerable to DOS attacks e.g., RTS attack. This paper discusses RTS (Request to Send) attack where malicious nodes reserve the medium unnecessarily for overdue period of time. The effects of such attacks on performance of WLAN are observed, proposed a mitigation technique to restore network performance and analyzed the improvement in performance.
The Capacity of Private Information Retrieval from Decentralized Uncoded Caching Databases
Venue: arXiv cs.IT 2018
Year: 2018

We consider the private information retrieval (PIR) problem from decentralized uncoded caching databases. There are two phases in our problem setting, a caching phase, and a retrieval phase. In the caching phase, a data center containing all the $K$ files, where each file is of size $L$ bits, and several databases with storage size constraint $μK L$ bits exist in the system. Each database independently chooses $μK L$ bits out of the total $KL$ bits from the data center to cache through the same probability distribution in a decentralized manner. In the retrieval phase, a user (retriever) accesses $N$ databases in addition to the data center, and wishes to retrieve a desired file privately. We characterize the optimal normalized download cost to be $\frac{D}{L} = \sum_{n=1}^{N+1} \binom{N}{n-1} μ^{n-1} (1-μ)^{N+1-n} \left( 1+ \frac{1}{n} + \dots+ \frac{1}{n^{K-1}} \right)$. We show that uniform and random caching scheme which is originally proposed for decentralized coded caching by Maddah-Ali and Niesen, along with Sun and Jafar retrieval scheme which is originally proposed for PIR from replicated databases surprisingly result in the lowest normalized download cost. This is the decentralized counterpart of the recent result of Attia, Kumar and Tandon for the centralized case. The converse proof contains several ingredients such as interference lower bound, induction lemma, replacing queries and answering string random variables with the content of distributed databases, the nature of decentralized uncoded caching databases, and bit marginalization of joint caching distributions.
Node Diversification in Complex Networks by Decentralized Coloring
Venue: arXiv cs.SI 2018
Year: 2018

We develop a decentralized coloring approach to diversify the nodes in a complex network. The key is the introduction of a local conflict index that measures the color conflicts arising at each node which can be efficiently computed using only local information. We demonstrate via both synthetic and real-world networks that the proposed approach significantly outperforms random coloring as measured by the size of the largest color-induced connected component. Interestingly, for scale-free networks further improvement of diversity can be achieved by tuning a degree-biasing weighting parameter in the local conflict index.
CMD: A Multi-Channel Coordination Scheme for Emergency Message Dissemination in IEEE 1609.4
Venue: arXiv cs.NI 2018
Year: 2018

In the IEEE 1609.4 legacy standard for multi-channel communications in vehicular ad hoc networks(VANETs), the control channel (CCH) is dedicated to broadcast safety messages while the service channels (SCH's) are dedicated to transmit infotainment service content. However, the SCH can be used as an alternative to transmit high priority safety messages in the event that they are invoked during the service channel interval (SCHI). This implies that there is a need to transmit safety messages across multiple available utilized channels to ensure that all vehicles receive the safety message. Transmission across multiple SCH's using the legacy IEEE 1609.4 requires multiple channel switching and therefore introduces further end-to-end delays. Given that safety messaging is a life critical application, it is important that optimal end-to-end delay performance is derived in multi-channel VANET scenarios to ensure reliable safety message dissemination. To tackle this challenge, three primary contributions are in this article: first, a channel coordinator selection approach based on the least average separation distance (LAD) to the vehicles that expect to tune to other SCH's and operates during the control channel interval (CCHI) is proposed. Second, a model to determine the optimal time intervals in which CMD operates during the CCHI is proposed. Third, a contention back-off mechanism for safety message transmission during the SCHI is proposed. Computer simulations and mathematical analysis show that CMD performs better than the legacy IEEE 1609.4 and a selected state-of-the-art multi-channel message dissemination schemes in terms of end-to-end delay and packet reception ratio.