OpenRaiser/Intern-Atlas huggingface dataset:
Harmonic-summing Module of SKA on FPGA--Optimising the Irregular Memory Accesses
Venue: arXiv cs.DC 2018
Year: 2018

The Square Kilometre Array (SKA), which will be the world's largest radio telescope, will enhance and boost a large number of science projects, including the search for pulsars. The frequency domain acceleration search is an efficient approach to search for binary pulsars. A significant part of it is the harmonic-summing module, which is the research subject of this paper. Most of the operations in the harmonic-summing module are relatively cheap operations for FPGAs. The main challenge is the large number of point accesses to off-chip memory which are not consecutive but irregular. Although harmonic-summing alone might not be targeted for FPGA acceleration, it is a part of the pulsar search pipeline that contains many other compute-intensive modules, which are efficiently executed on FPGA. Hence having the harmonic-summing also on the FPGA will avoid off-board communication, which could destroy other acceleration benefits. Two types of harmonic-summing approaches are investigated in this paper: 1) storing intermediate data in off-chip memory and 2) processing the input signals directly without storing. For the second type, two approaches of caching data are proposed and evaluated: 1) preloading points that are frequently touched 2) preloading all necessary points that are used to generate a chunk of output points. OpenCL is adopted to implement the proposed approaches. In an extensive experimental evaluation, the same OpenCL kernel codes are evaluated on FPGA boards and GPU cards. Regarding the proposed preloading methods, preloading all necessary points method while reordering the input signals is faster than all the other methods. While in raw performance a single FPGA board cannot compete with a GPU, in terms of energy dissipation, GPU costs up to 2.6x times more energy than that of FPGAs in executing the same NDRange kernels.
Responsive Content-Centric Delivery in Large Urban Communication Networks: A LinkNYC Use-Case
Venue: arXiv cs.NI 2018
Year: 2018

Large urban communication networks such as smart cities are an ecosystem of devices and services cooperating to address multiple issues that greatly benefit end users, cities and the environment. LinkNYC is a first-of-its-kind urban communications network aiming to replace all payphones in the five boroughs of New York City (NYC) with kiosk-like structures providing free public Wi-Fi. We consolidate these networks with standalone edge cloud devices known as cloudlets and introduce geographically distributed content delivery cloudlets (CDCs) to store popular Internet content closer to end users; essential in environments with diverse and dynamic content interests. A content-centric and delivery framework is proposed leveraging NYC's population densities and CDCs for interest-based in-network caching. Analysis shows that although the adoption of multiple CDCs dramatically improves overall network performance, advanced caching policies are needed when considering increased content heterogeneity. Thus, we propose popularity-driven (pLFU) and cooperation-based (sLFU) caching policies at individual CDCs to account for user and content dynamics over time. The amalgamation of urban population densities, multiple CDC placements and smarter caching techniques help exploit the ultimate benefits of a content-centric urban communications network and dramatically improves overall network performance and responsiveness. Our proposed solutions are validated using LinkNYC as a use-case.
Optimized Participation of Multiple Fusion Functions in Consensus Creation: An Evolutionary Approach
Venue: arXiv cs.NE 2018
Year: 2018

Recent studies show that ensemble methods enhance the stability and robustness of unsupervised learning. These approaches are successfully utilized to construct multiple clustering and combine them into a one representative consensus clustering of an improved quality. The quality of the consensus clustering is directly depended on fusion functions used in combination. In this article, the hierarchical clustering ensemble techniques are extended by introducing a new evolutionary fusion function. In the proposed method, multiple hierarchical clustering methods are generated via bagging. Thereafter, the consensus clustering is obtained using the search capability of genetic algorithm among different aggregated clustering methods made by different fusion functions. Putting some popular data sets to empirical study, the quality of the proposed method is compared with regular clustering ensembles. Experimental results demonstrate the accuracy improvement of the aggregated clustering results.
Empirical Evaluation of Character-Based Model on Neural Named-Entity Recognition in Indonesian Conversational Texts
Venue: arXiv cs.CL 2018
Year: 2018

Despite the long history of named-entity recognition (NER) task in the natural language processing community, previous work rarely studied the task on conversational texts. Such texts are challenging because they contain a lot of word variations which increase the number of out-of-vocabulary (OOV) words. The high number of OOV words poses a difficulty for word-based neural models. Meanwhile, there is plenty of evidence to the effectiveness of character-based neural models in mitigating this OOV problem. We report an empirical evaluation of neural sequence labeling models with character embedding to tackle NER task in Indonesian conversational texts. Our experiments show that (1) character models outperform word embedding-only models by up to 4 $F_1$ points, (2) character models perform better in OOV cases with an improvement of as high as 15 $F_1$ points, and (3) character models are robust against a very high OOV rate.
Stackelberg Game Approaches for Anti-jamming Defence in Wireless Networks
Venue: arXiv cs.GT 2018
Year: 2018

This article investigates the anti-jamming communications problem in wireless networks from a Stackelberg game perspective. By exploring and analyzing the inherent characteristics of the anti-jamming problem, we present and discuss some technical challenges and fundamental requirements to address them. To be specific, the adversarial characteristic, incomplete information constraints, dynamics, uncertainty, dense deployment, and heterogeneous feature bring technical challenges to anti-jamming communications in wireless networks. Then, for the purpose of improving system performance, four requirements for anti-jamming communications are presented and discussed. Following the advantages of the Stackelberg game model in anti-jamming field, we formulate an anti-jamming decision-making framework based on the Stackelberg game for anti-jamming defence in wireless networks. Moreover, two preliminary case studies are presented and discussed for better understanding of the anti-jamming Stackelberg game problem. Finally, some future research directions are also provided.
Skyblocking for Entity Resolution
Venue: arXiv cs.DB 2018
Year: 2018

In this paper, for the first time, we introduce the concept of skyblocking, which aims to efficiently identify the "most preferred" blocking scheme in terms of a given set of selection criteria for entity resolution blocking. To capture all possible preferred blocking schemes, scheme skyline (i.e. blocking schemes on the skyline) has been studied in a multi-dimensional scheme space with dimensions corresponding to selection criteria for blocking (e.g. PC and PQ). However, applying traditional skyline techniques to learn scheme skylines is a non-trivial task. Due to the unique characteristics of blocking schemes, we face several challenges, such as: how to find a balanced number of match and non-match labels to effectively approximate a block scheme in a scheme space, and how to design efficient skyline algorithms to explore a scheme space for finding scheme skylines. To overcome these challenges, we propose a scheme skyline learning approach, which incorporates skyline techniques into an active learning process of scheme skylines. We have conducted experiments over four real-world datasets. The experimental results show that our approach is able to efficiently identify scheme skylines in a large scheme space only using a limited number of labels. Our approach also outperforms the state-of-the-art approaches for learning blocking schemes in several aspects, including: label efficiency, blocking quality and learning efficiency.
Agents and Devices: A Relative Definition of Agency
Venue: arXiv cs.LG 2018
Year: 2018

According to Dennett, the same system may be described using a `physical' (mechanical) explanatory stance, or using an `intentional' (belief- and goal-based) explanatory stance. Humans tend to find the physical stance more helpful for certain systems, such as planets orbiting a star, and the intentional stance for others, such as living animals. We define a formal counterpart of physical and intentional stances within computational theory: a description of a system as either a device, or an agent, with the key difference being that `devices' are directly described in terms of an input-output mapping, while `agents' are described in terms of the function they optimise. Bayes' rule can then be applied to calculate the subjective probability of a system being a device or an agent, based only on its behaviour. We illustrate this using the trajectories of an object in a toy grid-world domain.
KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings
Venue: arXiv cs.LG 2018
Year: 2018

The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question answering (QA) has been recently released. ARC only contains natural science questions authored for human exams, which are hard to answer and require advanced logic reasoning. On the ARC Challenge Set, existing state-of-the-art QA systems fail to significantly outperform random baseline, reflecting the difficult nature of this task. In this paper, we propose a novel framework for answering science exam questions, which mimics human solving process in an open-book exam. To address the reasoning challenge, we construct contextual knowledge graphs respectively for the question itself and supporting sentences. Our model learns to reason with neural embeddings of both knowledge graphs. Experiments on the ARC Challenge Set show that our model outperforms the previous state-of-the-art QA systems.
On Prefix Normal Words
Venue: arXiv cs.FL 2018
Year: 2018

We present a new class of binary words: the prefix normal words. They are defined by the property that for any given length $k$, no factor of length $k$ has more $a$'s than the prefix of the same length. These words arise in the context of indexing for jumbled pattern matching (a.k.a. permutation matching or Parikh vector matching), where the aim is to decide whether a string has a factor with a given multiplicity of characters, i.e., with a given Parikh vector. Using prefix normal words, we give the first non-trivial characterization of binary words having the same set of Parikh vectors of factors. We prove that the language of prefix normal words is not context-free and is strictly contained in the language of pre-necklaces, which are prefixes of powers of Lyndon words. We discuss further properties and state open problems.
From Model Checking to Runtime Verification and Back
Venue: arXiv cs.SE 2018
Year: 2018

We describe a novel approach for adapting an existing software model checker to perform precise runtime verification. The software under test is allowed to communicate with the wider environment (including the file system and network). The modifications to the model checker are small and self-contained, making this a viable strategy for re-using existing model checking tools in a new context. Additionally, from the data that is gathered during a single execution in the runtime verification mode, we automatically re-construct a description of the execution environment which can then be used in the standard, full-blown model checker. This additional verification step can further improve coverage, especially in the case of parallel programs, without introducing substantial overhead into the process of runtime verification.
New Feature Detection Mechanism for Extended Kalman Filter Based Monocular SLAM with 1-Point RANSAC
Venue: arXiv cs.CV 2018
Year: 2018

We present a different approach of feature point detection for improving the accuracy of SLAM using single, monocular camera. Traditionally, Harris Corner detection, SURF or FAST corner detectors are used for finding feature points of interest in the image. We replace this with another approach, which involves building a non-linear scale-space representation of images using Perona and Malik Diffusion equation and computing the scale normalized Hessian at multiple scale levels (KAZE feature). The feature points so detected are used to estimate the state and pose of a mono camera using extended Kalman filter. By using accelerated KAZE features and a more rigorous feature rejection routine combined with 1-point RANSAC for outlier rejection, short baseline matching of features are significantly improved, even with a lesser number of feature points, especially in the presence of motion blur. We present a comparative study of our proposal with FAST and show improved localization accuracy in terms of absolute trajectory error.
Simulation of Random Variables under Rényi Divergence Measures of All Orders
Venue: arXiv cs.IT 2018
Year: 2018

The random variable simulation problem consists in using a $k$-dimensional i.i.d. random vector $X^{k}$ with distribution $P_{X}^{k}$ to simulate an $n$-dimensional i.i.d. random vector $Y^{n}$ so that its distribution is approximately $Q_{Y}^{n}$. In contrast to previous works, in this paper we consider the standard Rényi divergence and two variants of all orders to measure the level of approximation. These two variants are the max-Rényi divergence $D_α^{\mathsf{max}}(P,Q)$ and the sum-Rényi divergence $D_α^{+}(P,Q)$. When $α=\infty$, these two measures are strong because for any $ε>0$, $D_{\infty}^{\mathsf{max}}(P,Q)\leqε$ or $D_{\infty}^{+}(P,Q)\leqε$ implies $e^{-ε}\leq\frac{P(x)}{Q(x)}\leq e^ε$ for all $x$. Under these Rényi divergence measures, we characterize the asymptotics of normalized divergences as well as the Rényi conversion rates. The latter is defined as the supremum of $\frac{n}{k}$ such that the Rényi divergences vanish asymptotically. In addition, when the Rényi parameter is in the interval $(0,1)$, the Rényi conversion rates equal the ratio of the Shannon entropies $\frac{H\left(P_{X}\right)}{H\left(Q_{Y}\right)}$, which is consistent with traditional results in which the total variation measure was adopted. When the Rényi parameter is in the interval $(1,\infty]$, the Rényi conversion rates are, in general, smaller than $\frac{H\left(P_{X}\right)}{H\left(Q_{Y}\right)}$. When specialized to the case in which either $P_{X}$ or $Q_{Y}$ is uniform, the simulation problem reduces to the source resolvability and intrinsic randomness problems. The preceding results are used to characterize the asymptotics of Rényi divergences and the Rényi conversion rates for these two cases.
On GANs and GMMs
Venue: arXiv cs.CV 2018
Year: 2018

A longstanding problem in machine learning is to find unsupervised methods that can learn the statistical structure of high dimensional signals. In recent years, GANs have gained much attention as a possible solution to the problem, and in particular have shown the ability to generate remarkably realistic high resolution sampled images. At the same time, many authors have pointed out that GANs may fail to model the full distribution ("mode collapse") and that using the learned models for anything other than generating samples may be very difficult. In this paper, we examine the utility of GANs in learning statistical models of images by comparing them to perhaps the simplest statistical model, the Gaussian Mixture Model. First, we present a simple method to evaluate generative models based on relative proportions of samples that fall into predetermined bins. Unlike previous automatic methods for evaluating models, our method does not rely on an additional neural network nor does it require approximating intractable computations. Second, we compare the performance of GANs to GMMs trained on the same datasets. While GMMs have previously been shown to be successful in modeling small patches of images, we show how to train them on full sized images despite the high dimensionality. Our results show that GMMs can generate realistic samples (although less sharp than those of GANs) but also capture the full distribution, which GANs fail to do. Furthermore, GMMs allow efficient inference and explicit representation of the underlying statistical structure. Finally, we discuss how GMMs can be used to generate sharp images.
Simulation Of Logic Circuit Tests On Android-Based Mobile Devices
Venue: arXiv cs.HC 2018
Year: 2018

In this study, an application that can run on Android and Windows-based mobile devices was developed to allow students attending such classes as Numerical/Digital Electronics, Logic Circuits, Basic Electronics Measurement and Electronic Systems in Turkey's Vocation and Technical Education Schools to easily carry out the simulation of logic gates, as well as logic circuit tests performed using logic gates. A 2D-mobile application that runs on both platforms was developed using the C# language on the Unity3D editor. To assess the usability of the mobile application, a one-hour training session was administered in March of the 2017-2018 academic year to two groups of students from a single class in the sixth grade of an Imam Hatip Secondary School affiliated to the Ministry of National Education. Each of the two groups contained 12 students who were assumed to be equivalent, and who had no prior knowledge of the subject. The training of the first group began with a lecture on basic logic gates using a blackboard, and involved no simulations, while the second group, in addition to being given the same the lecture, received additional training involving demonstrations of the developed mobile application and its simulations. Following the lectures, a written exam was applied to both groups. An evaluation of the exam results revealed that 83 percent of the students who had been given demonstrations of the mobile application were able to perform the circuit task completely, whereas only 50 percent of the other were able to complete the task. It was concluded that the application was both useful and facilitating for to the students, and it was also noted that students who were supported by the mobile application had gained a better grasp of the topic by being able to see and practice the simulations first hand.
An Integrated View on the Future of Logistics and Information Technology
Venue: arXiv cs.OH 2018
Year: 2018

In this position paper, we present our vision on the future of the logistics business domain and the use of information technology (IT) in this domain. The vision is based on extensive experience with Dutch and European logistics in various contexts and from various perspectives. We expect that the vision also holds for logistics outside Europe. We build our vision in a number of steps. First, we make an inventory of the most important trends in the logistics domain - we call these mega-trends. Next, we do the same for the information technology domain, restricted to technologies that have relevance for logistics. Then, we introduce logistics meta-concepts that we use to describe our vision and relate them to business engineering. We use these three ingredients to analyze leading concepts that we currently observe in the logistics domain. Next, we consolidate all elements into a model that represents our vision of the integrated future of logistics and IT. We elaborate on the role of data platforms and open standards in this integrated vision.
Sequential Attacks on Agents for Long-Term Adversarial Goals
Venue: arXiv cs.LG 2018
Year: 2018

Reinforcement learning (RL) has advanced greatly in the past few years with the employment of effective deep neural networks (DNNs) on the policy networks. With the great effectiveness came serious vulnerability issues with DNNs that small adversarial perturbations on the input can change the output of the network. Several works have pointed out that learned agents with a DNN policy network can be manipulated against achieving the original task through a sequence of small perturbations on the input states. In this paper, we demonstrate furthermore that it is also possible to impose an arbitrary adversarial reward on the victim policy network through a sequence of attacks. Our method involves the latest adversarial attack technique, Adversarial Transformer Network (ATN), that learns to generate the attack and is easy to integrate into the policy network. As a result of our attack, the victim agent is misguided to optimise for the adversarial reward over time. Our results expose serious security threats for RL applications in safety-critical systems including drones, medical analysis, and self-driving cars.
Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)
Venue: arXiv cs.CV 2018
Year: 2018

Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis. Inspired by the recent success in applying deep learning for functional brain decoding and encoding, in this work we propose a spatio-temporal convolutional neural network (ST-CNN)to jointly learn the spatial and temporal patterns of targeted network from the training data and perform automatic, pin-pointing functional network identification. The proposed ST-CNN is evaluated by the task of identifying the Default Mode Network (DMN) from fMRI data. Results show that while the framework is only trained on one fMRI dataset,it has the sufficient generalizability to identify the DMN from different populations of data as well as different cognitive tasks. Further investigation into the results show that the superior performance of ST-CNN is driven by the jointly-learning scheme, which capture the intrinsic relationship between the spatial and temporal characteristic of DMN and ensures the accurate identification.
Strategy Logic with Imperfect Information
Venue: arXiv cs.LO 2018
Year: 2018

We introduce an extension of Strategy Logic for the imperfect-information setting, called SLii, and study its model-checking problem. As this logic naturally captures multi-player games with imperfect information, the problem turns out to be undecidable. We introduce a syntactical class of "hierarchical instances" for which, intuitively, as one goes down the syntactic tree of the formula, strategy quantifications are concerned with finer observations of the model. We prove that model-checking SLii restricted to hierarchical instances is decidable. This result, because it allows for complex patterns of existential and universal quantification on strategies, greatly generalises previous ones, such as decidability of multi-player games with imperfect information and hierarchical observations, and decidability of distributed synthesis for hierarchical systems. To establish the decidability result, we introduce and study QCTL*ii, an extension of QCTL* (itself an extension of CTL* with second-order quantification over atomic propositions) by parameterising its quantifiers with observations. The simple syntax of QCTL* ii allows us to provide a conceptually neat reduction of SLii to QCTL*ii that separates concerns, allowing one to forget about strategies and players and focus solely on second-order quantification. While the model-checking problem of QCTL*ii is, in general, undecidable, we identify a syntactic fragment of hierarchical formulas and prove, using an automata-theoretic approach, that it is decidable. The decidability result for SLii follows since the reduction maps hierarchical instances of SLii to hierarchical formulas of QCTL*ii .