OpenRaiser/Intern-Atlas huggingface dataset:
On the Maintenance of Classic Modula-2 Compilers
Venue: arXiv cs.PL 2018
Year: 2018

The classic Modula-2 language was specified in [Wir78] by N.Wirth at ETH Zürich in 1978. The last revision [Wir88] was published in 1988. Many computer science books of that era used Modula-2 in programming examples. Many of these are still valuable resources in computer science education today. To compile and run the examples therein, it is essential to have compilers available that follow the classic Modula-2 language definition and run on modern computer hardware and operating systems. Although most Modula-2 compilers of that era have disappeared, a few have since been re-released under open source licenses. Whilst the original authors have long ceased work on these compilers, new maintainers have stepped in. This paper gives recommendations for maintenance on classic Modula-2 compilers while balancing the aim to modernise with the need to maintain the capability to compile programming examples in the literature with minimal effort. Nevertheless, the principles, methods and conclusions presented are adaptable to maintenance on other languages.
Capacity Control of ReLU Neural Networks by Basis-path Norm
Venue: arXiv cs.LG 2018
Year: 2018

Recently, path norm was proposed as a new capacity measure for neural networks with Rectified Linear Unit (ReLU) activation function, which takes the rescaling-invariant property of ReLU into account. It has been shown that the generalization error bound in terms of the path norm explains the empirical generalization behaviors of the ReLU neural networks better than that of other capacity measures. Moreover, optimization algorithms which take path norm as the regularization term to the loss function, like Path-SGD, have been shown to achieve better generalization performance. However, the path norm counts the values of all paths, and hence the capacity measure based on path norm could be improperly influenced by the dependency among different paths. It is also known that each path of a ReLU network can be represented by a small group of linearly independent basis paths with multiplication and division operation, which indicates that the generalization behavior of the network only depends on only a few basis paths. Motivated by this, we propose a new norm \emph{Basis-path Norm} based on a group of linearly independent paths to measure the capacity of neural networks more accurately. We establish a generalization error bound based on this basis path norm, and show it explains the generalization behaviors of ReLU networks more accurately than previous capacity measures via extensive experiments. In addition, we develop optimization algorithms which minimize the empirical risk regularized by the basis-path norm. Our experiments on benchmark datasets demonstrate that the proposed regularization method achieves clearly better performance on the test set than the previous regularization approaches.
Audio Based Disambiguation Of Music Genre Tags
Venue: arXiv cs.IR 2018
Year: 2018

In this paper, we propose to infer music genre embeddings from audio datasets carrying semantic information about genres. We show that such embeddings can be used for disambiguating genre tags (identification of different labels for the same genre, tag translation from a tag system to another, inference of hierarchical taxonomies on these genre tags). These embeddings are built by training a deep convolutional neural network genre classifier with large audio datasets annotated with a flat tag system. We show empirically that they makes it possible to retrieve the original taxonomy of a tag system, spot duplicates tags and translate tags from a tag system to another.
A Game-Theoretic Analysis of Shard-Based Permissionless Blockchains
Venue: arXiv cs.GT 2018
Year: 2018

Low transaction throughput and poor scalability are significant issues in public blockchain consensus protocols such as Bitcoins. Recent research efforts in this direction have proposed shard-based consensus protocols where the key idea is to split the transactions among multiple committees (or shards), which then process these shards or set of transactions in parallel. Such a parallel processing of disjoint sets of transactions or shards by multiple committees significantly improves the overall scalability and transaction throughout of the system. However, one significant research gap is a lack of understanding of the strategic behavior of rational processors within committees in such shard-based consensus protocols. Such an understanding is critical for designing appropriate incentives that will foster cooperation within committees and prevent free-riding. In this paper, we address this research gap by analyzing the behavior of processors using a game-theoretic model, where each processor aims at maximizing its reward at a minimum cost of participating in the protocol. We first analyze the Nash equilibria in an N-player static game model of the sharding protocol. We show that depending on the reward sharing approach employed, processors can potentially increase their payoff by unilaterally behaving in a defective fashion, thus resulting in a social dilemma. In order to overcome this social dilemma, we propose a novel incentive-compatible reward sharing mechanism to promote cooperation among processors. Our numerical results show that achieving a majority of cooperating processors (required to ensure a healthy state of the blockchain network) is easier to achieve with the proposed incentive-compatible reward sharing mechanism than with other reward sharing mechanisms.
Towards Large-Scale Video Video Object Mining
Venue: arXiv cs.CV 2018
Year: 2018

We propose to leverage a generic object tracker in order to perform object mining in large-scale unlabeled videos, captured in a realistic automotive setting. We present a dataset of more than 360'000 automatically mined object tracks from 10+ hours of video data (560'000 frames) and propose a method for automated novel category discovery and detector learning. In addition, we show preliminary results on using the mined tracks for object detector adaptation.
A general framework for secondary constructions of bent and plateaued functions
Venue: arXiv cs.IT 2018
Year: 2018

In this work, we employ the concept of {\em composite representation} of Boolean functions, which represents an arbitrary Boolean function as a composition of one Boolean function and one vectorial function, for the purpose of specifying new secondary constructions of bent/plateaued functions. This representation gives a better understanding of the existing secondary constructions and it also allows us to provide a general construction framework of these objects. This framework essentially gives rise to an {\em infinite number} of possibilities to specify such secondary construction methods (with some induced sufficient conditions imposed on initial functions) and in particular we solve several open problems in this context. We provide several explicit methods for specifying new classes of bent/plateaued functions and demonstrate through examples that the imposed initial conditions can be easily satisfied. Our approach is especially efficient when defining new bent/plateaued functions on larger variable spaces than initial functions. For instance, it is shown that the indirect sum methods and Rothaus' construction are just special cases of this general framework and some explicit extensions of these methods are given. In particular, similarly to the basic indirect sum method of Carlet, we show that it is possible to derive (many) secondary constructions of bent functions without any additional condition on initial functions apart from the requirement that these are bent functions. In another direction, a few construction methods that generalize the secondary constructions which do not extend the variable space of the employed initial functions are also proposed.
A Coupled Evolutionary Network for Age Estimation
Venue: arXiv cs.CV 2018
Year: 2018

Age estimation of unknown persons is a challenging pattern analysis task due to the lacking of training data and various aging mechanisms for different people. Label distribution learning-based methods usually make distribution assumptions to simplify age estimation. However, age label distributions are often complex and difficult to be modeled in a parameter way. Inspired by the biological evolutionary mechanism, we propose a Coupled Evolutionary Network (CEN) with two concurrent evolutionary processes: evolutionary label distribution learning and evolutionary slack regression. Evolutionary network learns and refines age label distributions in an iteratively learning way. Evolutionary label distribution learning adaptively learns and constantly refines the age label distributions without making strong assumptions on the distribution patterns. To further utilize the ordered and continuous information of age labels, we accordingly propose an evolutionary slack regression to convert the discrete age label regression into the continuous age interval regression. Experimental results on Morph, ChaLearn15 and MegaAge-Asian datasets show the superiority of our method.
PP-DBLP: Modeling and Generating Attributed Public-Private Networks with DBLP
Venue: arXiv cs.DB 2018
Year: 2018

In many online social networks (e.g., Facebook, Google+, Twitter, and Instagram), users prefer to hide her/his partial or all relationships, which makes such private relationships not visible to public users or even friends. This leads to a new graph model called public-private networks, where each user has her/his own perspective of the network including the private connections. Recently, public-private network analysis has attracted significant research interest in the literature. A great deal of important graph computing problems (e.g., shortest paths, centrality, PageRank, and reachability tree) has been studied. However, due to the limited data sources and privacy concerns, proposed approaches are not tested on real-world datasets, but on synthetic datasets by randomly selecting vertices as private ones. Therefore, real-world datasets of public-private networks are essential and urgently needed for such algorithms in the evaluation of efficiency and effectiveness. In this paper, we generate four public-private networks from real-world DBLP records, called PPDBLP. We take published articles as public information and regard ongoing collaborations as the hidden information, which is only known by the authors. Our released datasets of PPDBLP offer the prospects for verifying various kinds of efficient public-private analysis algorithms in a fair way. In addition, motivated by widely existing attributed graphs, we propose an advanced model of attributed public-private graphs where vertices have not only private edges but also private attributes. We also discuss open problems on attributed public-private graphs. Preliminary experimental results on our generated real-world datasets verify the effectiveness and efficiency of public-private models and state-of-the-art algorithms.
$L_1$ Shortest Path Queries in Simple Polygons
Venue: arXiv cs.CG 2018
Year: 2018

Let $P$ be a simple polygon of $n$ vertices. We consider two-point $L_1$ shortest path queries in $P$. We build a data structure of $O(n)$ size in $O(n)$ time such that given any two query points $s$ and $t$, the length of an $L_1$ shortest path from $s$ to $t$ in $P$ can be computed in $O(\log n)$ time, or in $O(1)$ time if both $s$ and $t$ are vertices of $P$, and an actual shortest path can be output in additional linear time in the number of edges of the path. To achieve the result, we propose a mountain decomposition of simple polygons, which may be interesting in its own right. Most importantly, our approach is much simpler than the previous work on this problem.
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data
Venue: arXiv cs.LG 2018
Year: 2018

The Industrial Internet of Things drastically increases connectivity of devices in industrial applications. In addition to the benefits in efficiency, scalability and ease of use, this creates novel attack surfaces. Historically, industrial networks and protocols do not contain means of security, such as authentication and encryption, that are made necessary by this development. Thus, industrial IT-security is needed. In this work, emulated industrial network data is transformed into a time series and analysed with three different algorithms. The data contains labeled attacks, so the performance can be evaluated. Matrix Profiles perform well with almost no parameterisation needed. Seasonal Autoregressive Integrated Moving Average performs well in the presence of noise, requiring parameterisation effort. Long Short Term Memory-based neural networks perform mediocre while requiring a high training- and parameterisation effort.
Machine Learning for semi linear PDEs
Venue: arXiv cs.LG 2018
Year: 2018

Recent machine learning algorithms dedicated to solving semi-linear PDEs are improved by using different neural network architectures and different parameterizations. These algorithms are compared to a new one that solves a fixed point problem by using deep learning techniques. This new algorithm appears to be competitive in terms of accuracy with the best existing algorithms.
Autonomous Driving System Design for Formula Student Driverless Racecar
Venue: arXiv cs.RO 2018
Year: 2018

This paper summarizes the work of building the autonomous system including detection system and path tracking controller for a formula student autonomous racecar. A LIDAR-vision cooperating method of detecting traffic cone which is used as track mark is proposed. Detection algorithm of the racecar also implements a precise and high rate localization method which combines the GPS-INS data and LIDAR odometry. Besides, a track map including the location and color information of the cones is built simultaneously. Finally, the system and vehicle performance on a closed loop track is tested. This paper also briefly introduces the Formula Student Autonomous Competition (FSAC) in 2017.
Designing a blockchain-based IoT infrastructure with Ethereum, Swarm and LoRa
Venue: arXiv cs.CR 2018
Year: 2018

Today, the number of IoT devices in all aspects of life is exponentially increasing. The cities we are living in are getting smarter and informing us about our surroundings in a contextual manner. However, there lay significant challenges of deploying, managing and collecting data from these devices, in addition to the problem of storing and mining that data for higher-quality IoT services. Blockchain technology, even in today's nascent form, contains the pillars to create a common, distributed, trustless and autonomous infrastructure system. This paper describes a standardized IoT infrastructure; where data is stored on a DDOS-resistant, fault-tolerant, distributed storage service and data access is managed by a decentralized, trustless blockchain. The illustrated system used LoRa as the emerging network technology, Swarm as the distributed data storage and Ethereum as the blockchain platform. Such a data backend will ensure high availability with minimal security risks while replacing traditional backend systems with a single "smart contract".
Automatic Parallelization of Sequential Programs
Venue: arXiv cs.DC 2018
Year: 2018

Prior work on Automatically Scalable Computation (ASC) suggests that it is possible to parallelize sequential computation by building a model of whole-program execution, using that model to predict future computations, and then speculatively executing those future computations. Although that prior work demonstrated scaling, it did not demonstrate speedup, because it ran entirely in emulation. We took this as a challenge to construct a hardware prototype that embodies the ideas of ASC, but works on a broader range of programs and runs natively on hardware. The resulting system is similar in spirit to the original work, but differs in practically every respect. We present an implementation of the ASC architecture that runs natively on x86 hardware and achieves near-linear speedup up to 44-cores (the size of our test platform) for several classes of programs, such as computational kernels, map-style programs, and matrix operations. We observe that programs are either completely predictable, achieving near-perfect predictive accuracy, or totally unpredictable, and therefore not amenable to scaling via ASC-like techniques. We also find that in most cases, speedup is limited only by implementation details: the overhead of our dependency tracking infrastructure and the manipulation of large state spaces. We are able to automatically parallelize programs with linked data structures that are not amenable to other forms of automatic parallelization.
The Empirical Network Analysis of Website Visitors
Venue: arXiv cs.SI 2018
Year: 2018

This paper explores how to analyze empirically a network of website visitors from several countries in the world. While exploring this huge network of website visitors worldwide, this paper shows an empirical data analysis with a visualization of how data has been analyzed and interpreted. By evaluating the methods used in analyzing and interpreting these data, this paper provides the required knowledge to empirically analyze a set of various obtained data from website visitors with different browsers and IP-addresses. Keywords: Website Data Analysis, Website Communities, Visualization
On the Fly Orchestration of Unikernels: Tuning and Performance Evaluation of Virtual Infrastructure Managers
Venue: arXiv cs.DC 2018
Year: 2018

Network operators are facing significant challenges meeting the demand for more bandwidth, agile infrastructures, innovative services, while keeping costs low. Network Functions Virtualization (NFV) and Cloud Computing are emerging as key trends of 5G network architectures, providing flexibility, fast instantiation times, support of Commercial Off The Shelf hardware and significant cost savings. NFV leverages Cloud Computing principles to move the data-plane network functions from expensive, closed and proprietary hardware to the so-called Virtual Network Functions (VNFs). In this paper we deal with the management of virtual computing resources (Unikernels) for the execution of VNFs. This functionality is performed by the Virtual Infrastructure Manager (VIM) in the NFV MANagement and Orchestration (MANO) reference architecture. We discuss the instantiation process of virtual resources and propose a generic reference model, starting from the analysis of three open source VIMs, namely OpenStack, Nomad and OpenVIM. We improve the aforementioned VIMs introducing the support for special-purpose Unikernels and aiming at reducing the duration of the instantiation process. We evaluate some performance aspects of the VIMs, considering both stock and tuned versions. The VIM extensions and performance evaluation tools are available under a liberal open source licence.
Fighting Redundancy and Model Decay with Embeddings
Venue: arXiv cs.SI 2018
Year: 2018

Every day, hundreds of millions of new Tweets containing over 40 languages of ever-shifting vernacular flow through Twitter. Models that attempt to extract insight from this firehose of information must face the torrential covariate shift that is endemic to the Twitter platform. While regularly-retrained algorithms can maintain performance in the face of this shift, fixed model features that fail to represent new trends and tokens can quickly become stale, resulting in performance degradation. To mitigate this problem we employ learned features, or embedding models, that can efficiently represent the most relevant aspects of a data distribution. Sharing these embedding models across teams can also reduce redundancy and multiplicatively increase cross-team modeling productivity. In this paper, we detail the commoditized tools, algorithms and pipelines that we have developed and are developing at Twitter to regularly generate high quality, up-to-date embeddings and share them broadly across the company.
A Visual Query System for Scholar Networks
Venue: arXiv cs.IR 2018
Year: 2018

Large scholar networks is quite popular in the academic domain, like Aminer. It offers to display the academic social network, including profile search, expert finding, conference analysis, course search, sub-graph search, topic browser, academic ranks and user management. Usually the search results are listed as items, while the relations among them are hidden to the users. Visualization is a feasible way to help users explore the hidden relations and discover more useful information. This article aim to visualize the search results in Aminer in a more user-friendly way and help them better utilize the tool. We provided three different designs to visualize the results and tested them in user study. The empirical results of our research show that the designed graphs help users better understand the area they intend to know and make their search more effective.
Implementing Adaptive Separable Convolution for Video Frame Interpolation
Venue: arXiv cs.CV 2018
Year: 2018

As Deep Neural Networks are becoming more popular, much of the attention is being devoted to Computer Vision problems that used to be solved with more traditional approaches. Video frame interpolation is one of such challenges that has seen new research involving various techniques in deep learning. In this paper, we replicate the work of Niklaus et al. on Adaptive Separable Convolution, which claims high quality results on the video frame interpolation task. We apply the same network structure trained on a smaller dataset and experiment with various different loss functions, in order to determine the optimal approach in data-scarce scenarios. The best resulting model is still able to provide visually pleasing videos, although achieving lower evaluation scores.
Rapid Customization for Event Extraction
Venue: arXiv cs.CL 2018
Year: 2018

We present a system for rapidly customizing event extraction capability to find new event types and their arguments. The system allows a user to find, expand and filter event triggers for a new event type by exploring an unannotated corpus. The system will then automatically generate mention-level event annotation automatically, and train a Neural Network model for finding the corresponding event. Additionally, the system uses the ACE corpus to train an argument model for extracting Actor, Place, and Time arguments for any event types, including ones not seen in its training data. Experiments show that with less than 10 minutes of human effort per event type, the system achieves good performance for 67 novel event types. The code, documentation, and a demonstration video will be released as open source on github.com.