OpenRaiser/Intern-Atlas huggingface dataset:
On the Bias of Reed-Muller Codes over Odd Prime Fields
Venue: arXiv cs.DM 2018
Year: 2018

We study the bias of random bounded-degree polynomials over odd prime fields and show that, with probability exponentially close to 1, such polynomials have exponentially small bias. This also yields an exponential tail bound on the weight distribution of Reed-Muller codes over odd prime fields. These results generalize bounds of Ben-Eliezer, Hod, and Lovett who proved similar results over $\mathbb{F}_2$. A key to our bounds is the proof of a new precise extremal property for the rank of sub-matrices of the generator matrices of Reed-Muller codes over odd prime fields. This extremal property is a substantial extension of an extremal property shown by Keevash and Sudakov for the case of $\mathbb{F}_2$. Our exponential tail bounds on the bias can be used to derive exponential lower bounds on the time for space-bounded learning of bounded-degree polynomials from their evaluations over odd prime fields.
Private Text Classification
Venue: arXiv cs.CL 2018
Year: 2018

Confidential text corpora exist in many forms, but do not allow arbitrary sharing. We explore how to use such private corpora using privacy preserving text analytics. We construct typical text processing applications using appropriate privacy preservation techniques (including homomorphic encryption, Rademacher operators and secure computation). We set out the preliminary materials from Rademacher operators for binary classifiers, and then construct basic text processing approaches to match those binary classifiers.
Few Throats to Choke: On the Current Structure of the Internet
Venue: arXiv cs.NI 2018
Year: 2018

The original design of the Internet was a resilient, distributed system, that maybe able to route around (and therefore recover from) massive disruption --- up to and including nuclear war. However, network routing effects and business decisions cause traffic to often be routed through a relatively small set of Autonomous Systems (ASes). This is not merely an academic issue; it has practical implications --- some of these frequently appearing ASes are hosted in censorious nations. Other than censoring their own citizens' network access, such ASes may inadvertently filter traffic for other foreign customer ASes. In this paper, we examine the extent of routing centralization in the Internet; identify the major players who control the "Internet backbone"; and point out how many of these are, in fact, under the jurisdiction of censorious countries (specifically, Russia, China, and India). Further, we show that China and India are not only the two largest nations by number of Internet users, but that many users in free and democratic countries are affected by collateral damage caused due to censorship by such countries.
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogue
Venue: arXiv cs.CL 2018
Year: 2018

In this paper, we propose a self-attentive bidirectional long short-term memory (SA-BiLSTM) network to predict multiple emotions for the EmotionX challenge. The BiLSTM exhibits the power of modeling the word dependencies, and extracting the most relevant features for emotion classification. Building on top of BiLSTM, the self-attentive network can model the contextual dependencies between utterances which are helpful for classifying the ambiguous emotions. We achieve 59.6 and 55.0 unweighted accuracy scores in the \textit{Friends} and the \textit{EmotionPush} test sets, respectively.
Response Generation by Context-aware Prototype Editing
Venue: arXiv cs.CL 2018
Year: 2018

Open domain response generation has achieved remarkable progress in recent years, but sometimes yields short and uninformative responses. We propose a new paradigm for response generation, that is response generation by editing, which significantly increases the diversity and informativeness of the generation results. Our assumption is that a plausible response can be generated by slightly revising an existing response prototype. The prototype is retrieved from a pre-defined index and provides a good start-point for generation because it is grammatical and informative. We design a response editing model, where an edit vector is formed by considering differences between a prototype context and a current context, and then the edit vector is fed to a decoder to revise the prototype response for the current context. Experiment results on a large scale dataset demonstrate that the response editing model outperforms generative and retrieval-based models on various aspects.
5G-CORNET: Platform as a Service
Venue: arXiv cs.NI 2018
Year: 2018

Practical testing of the latest wireless communications standards requires the availability of flexible radio frequency hardware, networking and computing resources. We are providing a Cloud-based infrastructure which offers the necessary resources to carry out tests of the latest 5G standards. The testbed provides a Cloud-based Infrastructure as a Service. The research community can access hardware and software resources through a virtual plat-form that enables isolation and customization of experiments. In other words, researchers have control over the preferred experimental architecture and can run concurrent experiments on the same testbed. This paper introduces the resources that can be used to develop 5G testbeds and experiments.
Restricted Boltzmann Machines: Introduction and Review
Venue: arXiv cs.LG 2018
Year: 2018

The restricted Boltzmann machine is a network of stochastic units with undirected interactions between pairs of visible and hidden units. This model was popularized as a building block of deep learning architectures and has continued to play an important role in applied and theoretical machine learning. Restricted Boltzmann machines carry a rich structure, with connections to geometry, applied algebra, probability, statistics, machine learning, and other areas. The analysis of these models is attractive in its own right and also as a platform to combine and generalize mathematical tools for graphical models with hidden variables. This article gives an introduction to the mathematical analysis of restricted Boltzmann machines, reviews recent results on the geometry of the sets of probability distributions representable by these models, and suggests a few directions for further investigation.
A New COLD Feature based Handwriting Analysis for Ethnicity/Nationality Identification
Venue: arXiv cs.CV 2018
Year: 2018

Identifying crime for forensic investigating teams when crimes involve people of different nationals is challenging. This paper proposes a new method for ethnicity (nationality) identification based on Cloud of Line Distribution (COLD) features of handwriting components. The proposed method, at first, explores tangent angle for the contour pixels in each row and the mean of intensity values of each row in an image for segmenting text lines. For segmented text lines, we use tangent angle and direction of base lines to remove rule lines in the image. We use polygonal approximation for finding dominant points for contours of edge components. Then the proposed method connects the nearest dominant points of every dominant point, which results in line segments of dominant point pairs. For each line segment, the proposed method estimates angle and length, which gives a point in polar domain. For all the line segments, the proposed method generates dense points in polar domain, which results in COLD distribution. As character component shapes change, according to nationals, the shape of the distribution changes. This observation is extracted based on distance from pixels of distribution to Principal Axis of the distribution. Then the features are subjected to an SVM classifier for identifying nationals. Experiments are conducted on a complex dataset, which show the proposed method is effective and outperforms the existing method
Diffeomorphic brain shape modelling using Gauss-Newton optimisation
Venue: arXiv cs.CV 2018
Year: 2018

Shape modelling describes methods aimed at capturing the natural variability of shapes and commonly relies on probabilistic interpretations of dimensionality reduction techniques such as principal component analysis. Due to their computational complexity when dealing with dense deformation models such as diffeomorphisms, previous attempts have focused on explicitly reducing their dimension, diminishing de facto their flexibility and ability to model complex shapes such as brains. In this paper, we present a generative model of shape that allows the covariance structure of deformations to be captured without squashing their domain, resulting in better normalisation. An efficient inference scheme based on Gauss-Newton optimisation is used, which enables processing of 3D neuroimaging data. We trained this algorithm on segmented brains from the OASIS database, generating physiologically meaningful deformation trajectories. To prove the model's robustness, we applied it to unseen data, which resulted in equivalent fitting scores.
Facing Multiple Attacks in Adversarial Patrolling Games with Alarmed Targets
Venue: arXiv cs.AI 2018
Year: 2018

We focus on adversarial patrolling games on arbitrary graphs, where the Defender can control a mobile resource, the targets are alarmed by an alarm system, and the Attacker can observe the actions of the mobile resource of the Defender and perform different attacks exploiting multiple resources. This scenario can be modeled as a zero-sum extensive-form game in which each player can play multiple times. The game tree is exponentially large both in the size of the graph and in the number of attacking resources. We show that when the number of the Attacker's resources is free, the problem of computing the equilibrium path is NP-hard, while when the number of resources is fixed, the equilibrium path can be computed in poly-time. We provide a dynamic-programming algorithm that, given the number of the Attacker's resources, computes the equilibrium path requiring poly-time in the size of the graph and exponential time in the number of the resources. Furthermore, since in real-world scenarios it is implausible that the Defender knows the number of attacking resources, we study the robustness of the Defender's strategy when she makes a wrong guess about that number. We show that even the error of just a single resource can lead to an arbitrary inefficiency, when the inefficiency is defined as the ratio of the Defender's utilities obtained with a wrong guess and a correct guess. However, a more suitable definition of inefficiency is given by the difference of the Defender's utilities: this way, we observe that the higher the error in the estimation, the higher the loss for the Defender. Then, we investigate the performance of online algorithms when no information about the Attacker's resources is available. Finally, we resort to randomized online algorithms showing that we can obtain a competitive factor that is twice better than the one that can be achieved by any deterministic online algorithm.
SMarTplan: a Task Planner for Smart Factories
Venue: arXiv cs.AI 2018
Year: 2018

Smart factories are on the verge of becoming the new industrial paradigm, wherein optimization permeates all aspects of production, from concept generation to sales. To fully pursue this paradigm, flexibility in the production means as well as in their timely organization is of paramount importance. AI is planning a major role in this transition, but the scenarios encountered in practice might be challenging for current tools. Task planning is one example where AI enables more efficient and flexible operation through an online automated adaptation and rescheduling of the activities to cope with new operational constraints and demands. In this paper we present SMarTplan, a task planner specifically conceived to deal with real-world scenarios in the emerging smart factory paradigm. Including both special-purpose and general-purpose algorithms, SMarTplan is based on current automated reasoning technology and it is designed to tackle complex application domains. In particular, we show its effectiveness on a logistic scenario, by comparing its specialized version with the general purpose one, and extending the comparison to other state-of-the-art task planners.
Belousov-Zhabotinsky reaction in liquid marbles
Venue: arXiv cs.ET 2018
Year: 2018

This paper reports on a new method of encapsulating BZ solution droplets in a powder coating of either polyethylene (PE) or polytetrafluoroethylene (PTFE), to produce BZ liquid marbles (LMs). The BZ LMs have solid--liquid interfaces in contrast to the previously reported BZ encapsulation systems used to study pattern formations and wave transfers, such as BZ emulsions and BZ vesicles. All these systems are important to study as they can be seen as analogues for information transfer in excitable systems in nature. Due to the powder coating of the LMs, LMs do not wet the underlying substrate, making the LMs mobile, enabling them to be easily arranged in arrays. The preparation of complex LMs, containing an acidic oscillating solution, proves the resilience of LMs and their application in transporting a wide range of chemical cargoes and in playing a mediating role in chemical reactions. PTFE-coated LMs were harder to prepare and not as robust as PE-coated LMs, therefore oscillation studies of BZ LMs positioned in arrays only focused on PE-coated LMs. Sporadic transfer of excitation waves was observed between LMs placed in close proximity to each other in ordered and disordered arrays, suggesting the transfer of liquid species possibly arises from contact between imperfectly coated areas at the LM--LM interface or capillary action, where solution is actively transported to the marble surface through the coating. Propagation pathways of the excitation waves in both the disordered and ordered arrays of BZ LMs are reported. These results lay the foundations for future studies on information transmission and processing arrays of BZ LMs, in addition to observing BZ wave propagation in a LM and the effect of other physical stimuli such as heat, light and chemical environments on the dynamics of excitation in arrays of BZ Lms.
Agent-Mediated Social Choice
Venue: arXiv cs.AI 2018
Year: 2018

Direct democracy is often proposed as a possible solution to the 21st-century problems of democracy. However, this suggestion clashes with the size and complexity of 21st-century societies, entailing an excessive cognitive burden on voters, who would have to submit informed opinions on an excessive number of issues. In this paper I argue for the development of voting avatars, autonomous agents debating and voting on behalf of each citizen. Theoretical research from artificial intelligence, and in particular multiagent systems and computational social choice, proposes 21st-century techniques for this purpose, from the compact representation of a voter's preferences and values, to the development of voting procedures for autonomous agents use only.
Formal verification of the YubiKey and YubiHSM APIs in Maude-NPA
Venue: arXiv cs.CR 2018
Year: 2018

In this paper, we perform an automated analysis of two devices developed by Yubico: YubiKey, designed to authenticate a user to network-based services, and YubiHSM, Yubicos hardware security module. Both are analyzed using the Maude-NPA cryptographic protocol analyzer. Although previous work has been done applying automated tools to these devices, to the best of our knowledge there has been no completely automated analysis to date. This is not surprising, because both YubiKey and YubiHSM, which make use of cryptographic APIs, involve a number of complex features: (i) discrete time in the form of Lamport clocks, (ii) a mutable memory for storing previously seen keys or nonces, (iii) event-based properties that require an analysis of sequences of actions, and (iv) reasoning modulo exclusive-or. In this work, we have been able to both prove properties of YubiKey and find the known attacks on the YubiHSM, in a completely automated way beyond the capabilities of previous work in the literature.
Self-adaptive Privacy Concern Detection for User-generated Content
Venue: arXiv cs.CR 2018
Year: 2018

To protect user privacy in data analysis, a state-of-the-art strategy is differential privacy in which scientific noise is injected into the real analysis output. The noise masks individual's sensitive information contained in the dataset. However, determining the amount of noise is a key challenge, since too much noise will destroy data utility while too little noise will increase privacy risk. Though previous research works have designed some mechanisms to protect data privacy in different scenarios, most of the existing studies assume uniform privacy concerns for all individuals. Consequently, putting an equal amount of noise to all individuals leads to insufficient privacy protection for some users, while over-protecting others. To address this issue, we propose a self-adaptive approach for privacy concern detection based on user personality. Our experimental studies demonstrate the effectiveness to address a suitable personalized privacy protection for cold-start users (i.e., without their privacy-concern information in training data).
An Integrated Framework for Process Discovery Algorithm Evaluation
Venue: arXiv cs.SE 2018
Year: 2018

Process mining offers techniques to exploit event data by providing insights and recommendations to improve business processes. The growing amount of algorithms for process discovery has raised the question of which algorithms perform best on a given event log. Current evaluation frameworks for empirically evaluating discovery techniques depend on the notation used (behavioral identical models may give different results) and cannot provide more general statements about populations of models. Therefore, this paper proposes a new integrated evaluation framework that uses a classification approach to make it modeling notation independent. Furthermore, it is founded on experimental design to ensure the generalization of results. It supports two main evaluation objectives: benchmarking process discovery algorithms and sensitivity analysis, i.e. studying the effect of model and log characteristics on a discovery algorithm's accuracy. The framework is designed as a scientific workflow which enables automated, extendable and shareable evaluation experiments. An extensive experiment including four discovery algorithms and six control-flow characteristics validates the relevance and flexibility of the framework. Ultimately, the paper aims to advance the state-of-the-art for evaluating process discovery techniques.
G-BAM: A Generalized Bandwidth Allocation Model for IP/MPLS/DS-TE Networks
Venue: arXiv cs.NI 2018
Year: 2018

Bandwidth Allocation Models (BAMs) configure and handle resource allocation (bandwidth, LSPs, fiber) in networks in general (IP/MPLS/DS-TE, optical domain, other). BAMs currently available for IP/MPLS/DS-TE networks (MAM, RDM, G-RDM and AllocTC-Sharing) basically define resource restrictions (bandwidth) by class (traffic class, application class, user class or other grouping criteria) and allocate on demand this resource. There is a BAM allocation policy inherent for each existing model which behaves differently under distinct network state, such as heavy traffic loads and dynamic traffic and/or application scenarios. A generalized Bandwidth Allocation Model (G-BAM) is proposed in this paper. G-BAM, firstly, incorporates the inherent behavior of currently used BAMs such as MAM, RDM, G-RDM and AllocTC-Sharing in IP/MPLS/DS-TE context. G-BAM, secondly, proposes a new policy/ behavior allocation in addition to existing ones in which additional private resources are incorporated. G-BAM, thirdly, allows a smoother BAM policy transition among existing policy alternatives resulting from MAM, RDM and AllocTC-Sharing adoption independently. The paper focuses on the first characteristics of G-BAM which is to reproduce MAM, RDM and AllocTC-Sharing behaviors. As such, the required configuration to achieve MAM, RDM and AllocTC-Sharing behaviors is presented followed by a proof of concept. Authors argue that the G-BAM reproducibility characteristics may improve overall network resource utilization under distinct traffic profiles.
A Novel Mobile Data Contract Design with Time Flexibility
Venue: arXiv cs.GT 2018
Year: 2018

In conventional mobile data plans, the data is associated with a fixed period (e.g., one month) and the unused data will be cleared at the end of each period. To take advantage of consumers' heterogeneous demands across different periods and meanwhile to provide more time flexibility, some mobile data service providers (SP) have offered data plans with different lengths of period. In this paper, we consider the data plan design problem for a single SP, who provides data plans with different lengths of period for consumers with different characteristics of data demands. We propose a contract-theoretic approach, wherein the SP offers a period-price data plan contract which consists of a set of period and price combinations, indicating the prices for data with different periods. We study the optimal data plan contract designs under two different models: discrete and continuous consumer-type models, depending on whether the consumer type is discrete or continuous. In the former model, each type of consumers are assigned with a specific period-price combination. In the latter model, the consumers are first categorized into a finite number of groups, and each group of consumers (possibly with different types) are assigned with a specific period-price combination. We systematically analyze the incentive compatibility (IC) constraint and individual rationality (IR) constraint, which ensure each consumer to choose the data plan with the period-price combination intended for his type. We further derive the optimal contract that maximizes the SP's expected profit, meanwhile satisfying the IC and IR constraints of consumers. Our numerical results show that the proposed optimal contract can increase the SP's profit by 35%, comparing with the conventional fixed monthly-period data plan.
Hybrid Planning and Control for Multiple Fixed-Wing Aircraft under Input Constraints
Venue: arXiv cs.SY 2018
Year: 2018

This paper presents a novel hybrid control protocol for de-conflicting multiple vehicles with constraints on control inputs. We consider turning rate and linear speed constraints to represent fixed-wing or car-like vehicles. A set of state-feedback controllers along with a state-dependent switching logic are synthesized in a hybrid system to generate collision-free trajectories that converge to the desired destinations of the vehicles. The switching law is designed so that the safety can be guaranteed while no Zeno behavior can occur. A novel temporary goal assignment technique is also designed to guarantee convergence. We analyze the individual modes for safety and the closed-loop hybrid system for convergence. The theoretical developments are demonstrated via simulation results.
Multilingual Scene Character Recognition System using Sparse Auto-Encoder for Efficient Local Features Representation in Bag of Features
Venue: arXiv cs.CV 2018
Year: 2018

The recognition of texts existing in camera-captured images has become an important issue for a great deal of research during the past few decades. This give birth to Scene Character Recognition (SCR) which is an important step in scene text recognition pipeline. In this paper, we extended the Bag of Features (BoF)-based model using deep learning for representing features for accurate SCR of different languages. In the features coding step, a deep Sparse Auto-encoder (SAE)-based strategy was applied to enhance the representative and discriminative abilities of image features. This deep learning architecture provides more efficient features representation and therefore a better recognition accuracy. Our system was evaluated extensively on all the scene character datasets of five different languages. The experimental results proved the efficiency of our system for a multilingual SCR.