OpenRaiser/Intern-Atlas huggingface dataset:
CentralNet: a Multilayer Approach for Multimodal Fusion
Venue: arXiv cs.AI 2018
Year: 2018

This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media. While most of the past multimodal approaches either work by projecting the features of different modalities into the same space, or by coordinating the representations of each modality through the use of constraints, our approach borrows from both visions. More specifically, assuming each modality can be processed by a separated deep convolutional network, allowing to take decisions independently from each modality, we introduce a central network linking the modality specific networks. This central network not only provides a common feature embedding but also regularizes the modality specific networks through the use of multi-task learning. The proposed approach is validated on 4 different computer vision tasks on which it consistently improves the accuracy of existing multimodal fusion approaches.
Clustering and Labelling Auction Fraud Data
Venue: arXiv cs.LG 2018
Year: 2018

Although shill bidding is a common auction fraud, it is however very tough to detect. Due to the unavailability and lack of training data, in this study, we build a high-quality labeled shill bidding dataset based on recently collected auctions from eBay. Labeling shill biding instances with multidimensional features is a critical phase for the fraud classification task. For this purpose, we introduce a new approach to systematically label the fraud data with the help of the hierarchical clustering CURE that returns remarkable results as illustrated in the experiments.
Invisible Pixels Are Dead, Long Live Invisible Pixels!
Venue: arXiv cs.CY 2018
Year: 2018

Privacy has deteriorated in the world wide web ever since the 1990s. The tracking of browsing habits by different third-parties has been at the center of this deterioration. Web cookies and so-called web beacons have been the classical ways to implement third-party tracking. Due to the introduction of more sophisticated technical tracking solutions and other fundamental transformations, the use of classical image-based web beacons might be expected to have lost their appeal. According to a sample of over thirty thousand images collected from popular websites, this paper shows that such an assumption is a fallacy: classical 1 x 1 images are still commonly used for third-party tracking in the contemporary world wide web. While it seems that ad-blockers are unable to fully block these classical image-based tracking beacons, the paper further demonstrates that even limited information can be used to accurately classify the third-party 1 x 1 images from other images. An average classification accuracy of 0.956 is reached in the empirical experiment. With these results the paper contributes to the ongoing attempts to better understand the lack of privacy in the world wide web, and the means by which the situation might be eventually improved.
Predicting Musical Sophistication from Music Listening Behaviors: A Preliminary Study
Venue: arXiv cs.IR 2018
Year: 2018

Psychological models are increasingly being used to explain online behavioral traces. Aside from the commonly used personality traits as a general user model, more domain dependent models are gaining attention. The use of domain dependent psychological models allows for more fine-grained identification of behaviors and provide a deeper understanding behind the occurrence of those behaviors. Understanding behaviors based on psychological models can provide an advantage over data-driven approaches. For example, relying on psychological models allow for ways to personalize when data is scarce. In this preliminary work we look at the relation between users' musical sophistication and their online music listening behaviors and to what extent we can successfully predict musical sophistication. An analysis of data from a study with 61 participants shows that listening behaviors can successfully be used to infer users' musical sophistication.
Semi-Trained Memristive Crossbar Computing Engine with In-Situ Learning Accelerator
Venue: arXiv cs.ET 2018
Year: 2018

On-device intelligence is gaining significant attention recently as it offers local data processing and low power consumption. In this research, an on-device training circuitry for threshold-current memristors integrated in a crossbar structure is proposed. Furthermore, alternate approaches of mapping the synaptic weights into fully-trained and semi-trained crossbars are investigated. In a semi-trained crossbar a confined subset of memristors are tuned and the remaining subset of memristors are not programmed. This translates to optimal resource utilization and power consumption, compared to a fully programmed crossbar. The semi-trained crossbar architecture is applicable to a broad class of neural networks. System level verification is performed with an extreme learning machine for binomial and multinomial classification. The total power for a single 4x4 layer network, when implemented in IBM 65nm node, is estimated to be ~ 42.16uW and the area is estimated to be 26.48um x 22.35um.
Are we there yet? Understanding the challenges faced in complying with the General Data Protection Regulation (GDPR)
Venue: arXiv cs.CY 2018
Year: 2018

The EU General Data Protection Regulation (GDPR), enforced from 25th May 2018, aims to reform how organisations view and control the personal data of private EU citizens. The scope of GDPR is somewhat unprecedented: it regulates every aspect of personal data handling, includes hefty potential penalties for non-compliance, and can prosecute any company in the world that processes EU citizens' data. In this paper, we look behind the scenes to investigate the real challenges faced by organisations in engaging with the GDPR. This considers issues in working with the regulation, the implementation process, and how compliance is verified. Our research approach relies on literature but, more importantly, draws on detailed interviews with several organisations. Key findings include the fact that large organisations generally found GDPR compliance to be reasonable and doable. The same was found for small-to-medium organisations (SMEs/SMBs) that were highly security-oriented. SMEs with less focus on data protection struggled to make what they felt was a satisfactory attempt at compliance. The main issues faced in their compliance attempts emerged from: the sheer breadth of the regulation; questions around how to enact the qualitative recommendations of the regulation; and the need to map out the entirety of their complex data networks.
Study on Base Station Topology in Cellular Networks: Take Advantage of Alpha Shapes, Betti Numbers, and Euler Characteristics
Venue: arXiv cs.NI 2018
Year: 2018

Faced with the ever-increasing trend of the cellular network scale, how to quantitatively evaluate the effectiveness of the large-scale deployment of base stations (BSs) has become a challenging topic. To this end, a deeper understanding of the cellular network topology is of fundamental significance to be achieved. In this paper, $ α$-Shape, a powerful algebraic geometric tool, is integrated into the analysis of real BS location data for six Asian countries and six European countries, respectively. Firstly, the BS spatial deployments of both Asian and European countries express fractal features based on two different testifying metrics, namely the Betti numbers and the Hurst coefficients. Secondly, it is found out that the log-normal distribution presents the best match to the cellular network topology when the practical BS deployment is characterized by the Euler characteristics.
Dynamic Self-Attention : Computing Attention over Words Dynamically for Sentence Embedding
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we propose Dynamic Self-Attention (DSA), a new self-attention mechanism for sentence embedding. We design DSA by modifying dynamic routing in capsule network (Sabouretal.,2017) for natural language processing. DSA attends to informative words with a dynamic weight vector. We achieve new state-of-the-art results among sentence encoding methods in Stanford Natural Language Inference (SNLI) dataset with the least number of parameters, while showing comparative results in Stanford Sentiment Treebank (SST) dataset.
Clustered Planarity = Flat Clustered Planarity
Venue: arXiv cs.DM 2018
Year: 2018

The complexity of deciding whether a clustered graph admits a clustered planar drawing is a long-standing open problem in the graph drawing research area. Several research efforts focus on a restricted version of this problem where the hierarchy of the clusters is "flat", i.e., no cluster different from the root contains other clusters. We prove that this restricted problem, that we call Flat Clustered Planarity, retains the same complexity of the general Clustered Planarity problem, where the clusters are allowed to form arbitrary hierarchies. We strengthen this result by showing that Flat Clustered Planarity is polynomial-time equivalent to Independent Flat Clustered Planarity, where each cluster induces an independent set. We discuss the consequences of these results.
Weakly-supervised Neural Semantic Parsing with a Generative Ranker
Venue: arXiv cs.CL 2018
Year: 2018

Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as latent. The task is challenging due to the large search space and spuriousness of logical forms. In this paper we introduce a neural parser-ranker system for weakly-supervised semantic parsing. The parser generates candidate tree-structured logical forms from utterances using clues of denotations. These candidates are then ranked based on two criterion: their likelihood of executing to the correct denotation, and their agreement with the utterance semantics. We present a scheduled training procedure to balance the contribution of the two objectives. Furthermore, we propose to use a neurally encoded lexicon to inject prior domain knowledge to the model. Experiments on three Freebase datasets demonstrate the effectiveness of our semantic parser, achieving results within the state-of-the-art range.
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN
Venue: arXiv cs.LG 2018
Year: 2018

Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets. Previous work primarily focused on data augmentation for semi-supervised and supervised tasks. In this paper, we instead focus on unsupervised anomaly detection and propose a novel generative data augmentation framework optimized for this task. In particular, we propose to oversample infrequent normal samples - normal samples that occur with small probability, e.g., rare normal events. We show that these samples are responsible for false positives in anomaly detection. However, oversampling of infrequent normal samples is challenging for real-world high-dimensional data with multimodal distributions. To address this challenge, we propose to use a GAN variant known as the adversarial autoencoder (AAE) to transform the high-dimensional multimodal data distributions into low-dimensional unimodal latent distributions with well-defined tail probability. Then, we systematically oversample at the `edge' of the latent distributions to increase the density of infrequent normal samples. We show that our oversampling pipeline is a unified one: it is generally applicable to datasets with different complex data distributions. To the best of our knowledge, our method is the first data augmentation technique focused on improving performance in unsupervised anomaly detection. We validate our method by demonstrating consistent improvements across several real-world datasets.
Leakage Rate Analysis for Artificial Noise Assisted Massive MIMO with Non-coherent Passive Eavesdropper in Block-fading
Venue: arXiv cs.IT 2018
Year: 2018

Massive MIMO is one of the salient techniques for achieving high spectral efficiency in next generation wireless networks. Recently, a combined strategy of the massive MIMO and the artificial noise (AN), namely, {\it AN assisted massive MIMO (ANAM)} has recently been actively investigated for security enhancement. However, most of previous studies on the ANAM have been built upon the full channel state information (CSI) assumption at the eavesdropper (ED), which may be too pessimistic to provide meaningful information on the security since the channel uncertainty of the ED may degrade its decoding ability. In this paper, we provide more sophisticated investigation on the performance of the ANAM system assuming that the CSI of the ED channels are unknown to both the BS and the ED or partially known to the ED. We measure the secrecy in terms of both the leakage rate to the ED and the secrecy rate to the legitimate users, and characterize their upper and lower bounds in the high SNR regime as a function of the number of ED antennas, the number of data and AN signal dimensions, and coherence time. Finally, from numerical results, we demonstrate the accuracy of our analysis and highlight the security potential of the ANAM system against the passive eavesdropping attack.
FMS: Functional Programming as a Modelling Language
Venue: arXiv cs.PL 2018
Year: 2018

In this paper we introduce the Functional Modelling System (FMS). The system introduces the Functional Modelling Language (FML), which is a modelling language for NP-complete search problems based on concepts of functional programming. Internally, we translate FML specifications to an Answer Set Program to obtain models. We give a general overview of the new FML language, and how this language is handled in the system. We give a step-by-step walkthrough of the system, pointing out what features are in place, and what improvements are still possible.
StretchDenoise: Parametric Curve Reconstruction with Guarantees by Separating Connectivity from Residual Uncertainty of Samples
Venue: arXiv cs.GR 2018
Year: 2018

We reconstruct a closed denoised curve from an unstructured and highly noisy 2D point cloud. Our proposed method uses a two- pass approach: Previously recovered manifold connectivity is used for ordering noisy samples along this manifold and express these as residuals in order to enable parametric denoising. This separates recovering low-frequency features from denoising high frequencies, which avoids over-smoothing. The noise probability density functions (PDFs) at samples are either taken from sensor noise models or from estimates of the connectivity recovered in the first pass. The output curve balances the signed distances (inside/outside) to the samples. Additionally, the angles between edges of the polygon representing the connectivity become minimized in the least-square sense. The movement of the polygon's vertices is restricted to their noise extent, i.e., a cut-off distance corresponding to a maximum variance of the PDFs. We approximate the resulting optimization model, which consists of higher-order functions, by a linear model with good correspondence. Our algorithm is parameter-free and operates fast on the local neighborhoods determined by the connectivity. We augment a least-squares solver constrained by a linear system to also handle bounds. This enables us to guarantee stochastic error bounds for sampled curves corrupted by noise, e.g., silhouettes from sensed data, and we improve on the reconstruction error from ground truth. Open source to reproduce figures and tables in this paper is available at: https://github.com/stefango74/stretchdenoise
Exploring Parallel Execution Strategies for Constraint Handling Rules - Work-in-Progress Report
Venue: arXiv cs.DC 2018
Year: 2018

Constraint Handling Rules (CHR) is a declarative rule-based formalism and language. Concurrency is inherent as rules can be applied to subsets of constraints in parallel. Parallel implementations of CHR, be it in software, be it in hardware, use different execution strategies for parallel execution of CHR programs depending on the implementation language. In this report, our goal is to analyze parallel execution of CHR programs from a more general conceptual perspective. We want to experimentally see what is possible when CHR programs are automatically parallelized. For this purpose, a sequential simulation of parallel CHR execution is used to systematically encode different parallel execution strategies. In exhaustive experiments on some typical examples from the literature, parallel and sequential execution can be compared to each other. The number of processors can be bounded or unbounded for a more theoretical analysis. As a result, some preliminary but indicative observations on the influence of the execution strategy can be made for the different problem classes and in general.
High frame-rate cardiac ultrasound imaging with deep learning
Venue: arXiv cs.CV 2018
Year: 2018

Cardiac ultrasound imaging requires a high frame rate in order to capture rapid motion. This can be achieved by multi-line acquisition (MLA), where several narrow-focused received lines are obtained from each wide-focused transmitted line. This shortens the acquisition time at the expense of introducing block artifacts. In this paper, we propose a data-driven learning-based approach to improve the MLA image quality. We train an end-to-end convolutional neural network on pairs of real ultrasound cardiac data, acquired through MLA and the corresponding single-line acquisition (SLA). The network achieves a significant improvement in image quality for both $5-$ and $7-$line MLA resulting in a decorrelation measure similar to that of SLA while having the frame rate of MLA.
Style Transfer as Unsupervised Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

Language style transferring rephrases text with specific stylistic attributes while preserving the original attribute-independent content. One main challenge in learning a style transfer system is a lack of parallel data where the source sentence is in one style and the target sentence in another style. With this constraint, in this paper, we adapt unsupervised machine translation methods for the task of automatic style transfer. We first take advantage of style-preference information and word embedding similarity to produce pseudo-parallel data with a statistical machine translation (SMT) framework. Then the iterative back-translation approach is employed to jointly train two neural machine translation (NMT) based transfer systems. To control the noise generated during joint training, a style classifier is introduced to guarantee the accuracy of style transfer and penalize bad candidates in the generated pseudo data. Experiments on benchmark datasets show that our proposed method outperforms previous state-of-the-art models in terms of both accuracy of style transfer and quality of input-output correspondence.
Decentralized Control of a Hexapod Robot Using a Wireless Time Synchronized Network
Venue: arXiv cs.RO 2018
Year: 2018

Robots and control systems rely upon precise timing of sensors and actuators in order to operate intelligently. We present a functioning hexapod robot that walks with a dual tripod gait; each tripod is actuated using its own local controller running on a separate wireless node. We compare and report the results of operating the robot using two different decentralized control schemes. With the first scheme, each controller relies on its own local clock to generate control signals for the tripod it controls. With the second scheme, each controller relies on a variable that is local to itself but that is necessarily the same across controllers as a by-product of their host nodes being part of a time synchronized IEEE802.15.4e network. The gait synchronization error (time difference between what both controllers believe is the start of the gait period) grows linearly when the controllers use their local clocks, but remains bounded to within 112 microseconds when the controllers use their nodes' time synchronized local variable.
Proximal Policy Optimization and its Dynamic Version for Sequence Generation
Venue: arXiv cs.CL 2018
Year: 2018

In sequence generation task, many works use policy gradient for model optimization to tackle the intractable backpropagation issue when maximizing the non-differentiable evaluation metrics or fooling the discriminator in adversarial learning. In this paper, we replace policy gradient with proximal policy optimization (PPO), which is a proved more efficient reinforcement learning algorithm, and propose a dynamic approach for PPO (PPO-dynamic). We demonstrate the efficacy of PPO and PPO-dynamic on conditional sequence generation tasks including synthetic experiment and chit-chat chatbot. The results show that PPO and PPO-dynamic can beat policy gradient by stability and performance.
Looking for a Black Cat in a Dark Room: Security Visualization for Cyber-Physical System Design and Analysis
Venue: arXiv cs.HC 2018
Year: 2018

Today, there is a plethora of software security tools employing visualizations that enable the creation of useful and effective interactive security analyst dashboards. Such dashboards can assist the analyst to understand the data at hand and, consequently, to conceive more targeted preemption and mitigation security strategies. Despite the recent advances, model-based security analysis is lacking tools that employ effective dashboards---to manage potential attack vectors, system components, and requirements. This problem is further exacerbated because model-based security analysis produces significantly larger result spaces than security analysis applied to realized systems---where platform specific information, software versions, and system element dependencies are known. Therefore, there is a need to manage the analysis complexity in model-based security through better visualization techniques. Towards that goal, we propose an interactive security analysis dashboard that provides different views largely centered around the system, its requirements, and its associated attack vector space. This tool makes it possible to start analysis earlier in the system lifecycle. We apply this tool in a significant area of engineering design---the design of cyber-physical systems---where security violations can lead to safety hazards.
Different but Equal: Comparing User Collaboration with Digital Personal Assistants vs. Teams of Expert Agents
Venue: arXiv cs.HC 2018
Year: 2018

This work compares user collaboration with conversational personal assistants vs. teams of expert chatbots. Two studies were performed to investigate whether each approach affects accomplishment of tasks and collaboration costs. Participants interacted with two equivalent financial advice chatbot systems, one composed of a single conversational adviser and the other based on a team of four experts chatbots. Results indicated that users had different forms of experiences but were equally able to achieve their goals. Contrary to the expected, there were evidences that in the teamwork situation that users were more able to predict agent behavior better and did not have an overhead to maintain common ground, indicating similar collaboration costs. The results point towards the feasibility of either of the two approaches for user collaboration with conversational agents.