OpenRaiser/Intern-Atlas huggingface dataset:
Negative-Unlabeled Tensor Factorization for Location Category Inference from Highly Inaccurate Mobility Data
Venue: arXiv cs.LG 2017
Year: 2017

Identifying significant location categories visited by mobile users is the key to a variety of applications. This is an extremely challenging task due to the possible deviation between the estimated location coordinate and the actual location, which could be on the order of kilometers. To estimate the actual location category more precisely, we propose a novel tensor factorization framework, through several key observations including the intrinsic correlations between users, to infer the most likely location categories within the location uncertainty circle. In addition, the proposed algorithm can also predict where users are even in the absence of location information. In order to efficiently solve the proposed framework, we propose a parameter-free and scalable optimization algorithm by effectively exploring the sparse and low-rank structure of the tensor. Our empirical studies show that the proposed algorithm is both efficient and effective: it can solve problems with millions of users and billions of location updates, and also provides superior prediction accuracies on real-world location updates and check-in data sets.
Probabilistic Complex Event Recognition: A Survey
Venue: arXiv cs.DB 2017
Year: 2017

Complex Event Recognition applications exhibit various types of uncertainty, ranging from incomplete and erroneous data streams to imperfect complex event patterns. We review Complex Event Recognition techniques that handle, to some extent, uncertainty. We examine techniques based on automata, probabilistic graphical models and first-order logic, which are the most common ones, and approaches based on Petri Nets and Grammars, which are less frequently used. A number of limitations are identified with respect to the employed languages, their probabilistic models and their performance, as compared to the purely deterministic cases. Based on those limitations, we highlight promising directions for future work.
Differential Geometric Retrieval of Deep Features
Venue: arXiv cs.IR 2017
Year: 2017

Comparing images to recommend items from an image-inventory is a subject of continued interest. Added with the scalability of deep-learning architectures the once `manual' job of hand-crafting features have been largely alleviated, and images can be compared according to features generated from a deep convolutional neural network. In this paper, we compare distance metrics (and divergences) to rank features generated from a neural network, for content-based image retrieval. Specifically, after modelling individual images using approximations of mixture models or sparse covariance estimators, we resort to their information-theoretic and Riemann geometric comparisons. We show that using approximations of mixture models enable us to compute a distance measure based on the Wasserstein metric that requires less effort than other computationally intensive optimal transport plans; finally, an affine invariant metric is used to compare the optimal transport metric to its Riemann geometric counterpart -- we conclude that although expensive, retrieval metric based on Wasserstein geometry is more suitable than information theoretic comparison of images. In short, we combine GPU scalability in learning deep feature vectors with statistically efficient metrics that we foresee being utilised in a commercial setting.
Splitarchitecture: SDN for the carrier domain
Venue: arXiv cs.NI 2017
Year: 2017

The concept of SDN has emerged as a way to address numerous challenges with traditional network architectures by decoupling network control and forwarding. So far, the use cases for SDN mainly targeted data-center applications. This article considers SDN for network carriers, facing operation of large-scale networks with millions of customers, multiple technologies, and high availability demands. With specific carrier-grade features such as scalability, reliability, flexibility, modularity, and virtualization in mind, the SPARC EU project has developed the SPARC SplitArchitecture concept. The SplitArchitecture control plane allows hierarchical layering of several control plane layers which can be flexibly mapped to data plane layers. Between control layers open interfaces are used. Furthermore, SplitArchitecture proposes an additional split of forwarding and processing functions in data path elements, enabling switch based OAM functionality and handling of tunneling techniques. The SplitArchitecture concept is evaluated in a prototype demonstrating an SDN version of BRAS: the floating BRAS. The floating BRAS allows creation of residential Internet access services with dynamically managed BRAS instances. The demo is based on a controller enabling protected MPLS transport connections spanning SDN-controlled aggregation and IP/MPLS-based core domains. The prototype showcases how SplitArchitecture enables virtualization of service nodes in an SDN-controlled network, leading to increased flexibility in configuration and placement of service creation functions. Overall, the results show that it is technically and economically beneficial to apply SDN, and specifically the SplitArchitecture concept, to the carrier domain.
A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks
Venue: arXiv cs.DC 2017
Year: 2017

FPGA-based hardware accelerators for convolutional neural networks (CNNs) have obtained great attentions due to their higher energy efficiency than GPUs. However, it is challenging for FPGA-based solutions to achieve a higher throughput than GPU counterparts. In this paper, we demonstrate that FPGA acceleration can be a superior solution in terms of both throughput and energy efficiency when a CNN is trained with binary constraints on weights and activations. Specifically, we propose an optimized FPGA accelerator architecture tailored for bitwise convolution and normalization that features massive spatial parallelism with deep pipelines stages. A key advantage of the FPGA accelerator is that its performance is insensitive to data batch size, while the performance of GPU acceleration varies largely depending on the batch size of the data. Experiment results show that the proposed accelerator architecture for binary CNNs running on a Virtex-7 FPGA is 8.3x faster and 75x more energy-efficient than a Titan X GPU for processing online individual requests in small batch sizes. For processing static data in large batch sizes, the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9.5x higher energy efficiency.
Delving Deeper into MOOC Student Dropout Prediction
Venue: arXiv cs.AI 2017
Year: 2017

In order to obtain reliable accuracy estimates for automatic MOOC dropout predictors, it is important to train and test them in a manner consistent with how they will be used in practice. Yet most prior research on MOOC dropout prediction has measured test accuracy on the same course used for training the classifier, which can lead to overly optimistic accuracy estimates. In order to understand better how accuracy is affected by the training+testing regime, we compared the accuracy of a standard dropout prediction architecture (clickstream features + logistic regression) across 4 different training paradigms. Results suggest that (1) training and testing on the same course ("post-hoc") can overestimate accuracy by several percentage points; (2) dropout classifiers trained on proxy labels based on students' persistence are surprisingly competitive with post-hoc training (87.33% versus 90.20% AUC averaged over 8 weeks of 40 HarvardX MOOCs); and (3) classifier performance does not vary significantly with the academic discipline. Finally, we also research new dropout prediction architectures based on deep, fully-connected, feed-forward neural networks and find that (4) networks with as many as 5 hidden layers can statistically significantly increase test accuracy over that of logistic regression.
Admissibility in Concurrent Games
Venue: arXiv cs.GT 2017
Year: 2017

In this paper, we study the notion of admissibility for randomised strategies in concurrent games. Intuitively, an admissible strategy is one where the player plays `as well as possible', because there is no other strategy that dominates it, i.e., that wins (almost surely) against a super set of adversarial strategies. We prove that admissible strategies always exist in concurrent games, and we characterise them precisely. Then, when the objectives of the players are omega-regular, we show how to perform assume-admissible synthesis, i.e., how to compute admissible strategies that win (almost surely) under the hypothesis that the other players play admissible
Crowd Sourcing Image Segmentation with iaSTAPLE
Venue: arXiv cs.CV 2017
Year: 2017

We propose a novel label fusion technique as well as a crowdsourcing protocol to efficiently obtain accurate epithelial cell segmentations from non-expert crowd workers. Our label fusion technique simultaneously estimates the true segmentation, the performance levels of individual crowd workers, and an image segmentation model in the form of a pairwise Markov random field. We term our approach image-aware STAPLE (iaSTAPLE) since our image segmentation model seamlessly integrates into the well-known and widely used STAPLE approach. In an evaluation on a light microscopy dataset containing more than 5000 membrane labeled epithelial cells of a fly wing, we show that iaSTAPLE outperforms STAPLE in terms of segmentation accuracy as well as in terms of the accuracy of estimated crowd worker performance levels, and is able to correctly segment 99% of all cells when compared to expert segmentations. These results show that iaSTAPLE is a highly useful tool for crowd sourcing image segmentation.
Characterization on Practical Photon Counting Receiver in Optical Scattering Communication
Venue: arXiv cs.IT 2017
Year: 2017

We characterize the practical photon-counting receiver in optical scattering communication with finite sampling rate and electrical noise. In the receiver side, the detected signal can be characterized as a series of pulses generated by photon-multiplier (PMT) detector and held by the pulse-holding circuits, which are then sampled by the analog-to-digit convertor (ADC) with finite sampling rate and counted by a rising-edge pulse detector. However, the finite small pulse width incurs the dead time effect that may lead to sub-Poisson distribution on the recorded pulses. We analyze first-order and second-order moments on the number of recorded pulses with finite sampling rate at the receiver side under two cases where the sampling period is shorter than or equal to the pulse width as well as longer than the pulse width. Moreover, we adopt the maximum likelihood (ML) detection. In order to simplify the analysis, we adopt binomial distribution approximation on the number of recorded pulses in each slot. A tractable holding time and decision threshold selection rule is provided aiming to maximize the minimal Kullback-Leibler (KL) distance between the two distributions. The performance of proposed sub-Poisson distribution and the binomial approximation are verified by the experimental results. The equivalent arrival rate and holding time predicted by the of sub-Poisson model and the associated proposed binomial distribution on finite sampling rate and the electrical noise are validated by the simulation results. The proposed the holding time and decision threshold selection rule performs close to the optimal one.
An Integer Programming Model for Binary Knapsack Problem with Value-Related Dependencies among Elements
Venue: arXiv cs.AI 2017
Year: 2017

Binary Knapsack Problem (BKP) is to select a subset of an element (item) set with the highest value while keeping the total weight within the capacity of the knapsack. This paper presents an integer programming model for a variation of BKP where the value of each element may depend on selecting or ignoring other elements. Strengths of such Value-Related Dependencies are assumed to be imprecise and hard to specify. To capture this imprecision, we have proposed modeling value-related dependencies using fuzzy graphs and their algebraic structure.
Unsupervised Diverse Colorization via Generative Adversarial Networks
Venue: arXiv cs.CV 2017
Year: 2017

Colorization of grayscale images has been a hot topic in computer vision. Previous research mainly focuses on producing a colored image to match the original one. However, since many colors share the same gray value, an input grayscale image could be diversely colored while maintaining its reality. In this paper, we design a novel solution for unsupervised diverse colorization. Specifically, we leverage conditional generative adversarial networks to model the distribution of real-world item colors, in which we develop a fully convolutional generator with multi-layer noise to enhance diversity, with multi-layer condition concatenation to maintain reality, and with stride 1 to keep spatial information. With such a novel network architecture, the model yields highly competitive performance on the open LSUN bedroom dataset. The Turing test of 80 humans further indicates our generated color schemes are highly convincible.
Data Distillation for Controlling Specificity in Dialogue Generation
Venue: arXiv cs.CL 2017
Year: 2017

People speak at different levels of specificity in different situations. Depending on their knowledge, interlocutors, mood, etc.} A conversational agent should have this ability and know when to be specific and when to be general. We propose an approach that gives a neural network--based conversational agent this ability. Our approach involves alternating between \emph{data distillation} and model training : removing training examples that are closest to the responses most commonly produced by the model trained from the last round and then retrain the model on the remaining dataset. Dialogue generation models trained with different degrees of data distillation manifest different levels of specificity. We then train a reinforcement learning system for selecting among this pool of generation models, to choose the best level of specificity for a given input. Compared to the original generative model trained without distillation, the proposed system is capable of generating more interesting and higher-quality responses, in addition to appropriately adjusting specificity depending on the context. Our research constitutes a specific case of a broader approach involving training multiple subsystems from a single dataset distinguished by differences in a specific property one wishes to model. We show that from such a set of subsystems, one can use reinforcement learning to build a system that tailors its output to different input contexts at test time.
Portability Analysis for Axiomatic Memory Models. PORTHOS: One Tool for all Models
Venue: arXiv cs.PL 2017
Year: 2017

We present Porthos, the first tool that discovers porting bugs in performance-critical code. Porthos takes as input a program and the memory models of the source architecture for which the program has been developed and the target model to which it is ported. If the code is not portable, Porthos finds a bug in the form of an unexpected execution - an execution that is consistent with the target but inconsistent with the source memory model. Technically, Porthos implements a bounded model checking method that reduces the portability analysis problem to satisfiability modulo theories (SMT). There are two main problems in the reduction that we present novel and efficient solutions for. First, the formulation of the portability problem contains a quantifier alternation (consistent + inconsistent). We introduce a formula that encodes both in a single existential query. Second, the supported memory models (e.g., Power) contain recursive definitions. We compute the required least fixed point semantics for recursion (a problem that was left open in [47]) efficiently in SMT. Finally we present the first experimental analysis of portability from TSO to Power.
Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings
Venue: arXiv cs.CL 2017
Year: 2017

Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a broad set of types. Distant supervision paradigm is extensively used to generate training data for this task. However, generated training data assigns same set of labels to every mention of an entity without considering its local context. Existing FETC systems have two major drawbacks: assuming training data to be noise free and use of hand crafted features. Our work overcomes both drawbacks. We propose a neural network model that jointly learns entity mentions and their context representation to eliminate use of hand crafted features. Our model treats training data as noisy and uses non-parametric variant of hinge loss function. Experiments show that the proposed model outperforms previous state-of-the-art methods on two publicly available datasets, namely FIGER (GOLD) and BBN with an average relative improvement of 2.69% in micro-F1 score. Knowledge learnt by our model on one dataset can be transferred to other datasets while using same model or other FETC systems. These approaches of transferring knowledge further improve the performance of respective models.
Improving a Strong Neural Parser with Conjunction-Specific Features
Venue: arXiv cs.CL 2017
Year: 2017

While dependency parsers reach very high overall accuracy, some dependency relations are much harder than others. In particular, dependency parsers perform poorly in coordination construction (i.e., correctly attaching the "conj" relation). We extend a state-of-the-art dependency parser with conjunction-specific features, focusing on the similarity between the conjuncts head words. Training the extended parser yields an improvement in "conj" attachment as well as in overall dependency parsing accuracy on the Stanford dependency conversion of the Penn TreeBank.
Joint Spectrum Reuse and Power Control for Multi-Sharing Device-to-Device Communication
Venue: arXiv cs.NI 2017
Year: 2017

Compared to current mobile networks, next-generation mobile networks are expected to support higher numbers of simultaneously connected devices and to achieve higher system spectrum efficiency and lower power consumption. To achieve these goals, we study the multi-sharing device-to-device (D2D) communication, which allows any cellular user equipment to share its radio resource with multiple D2D devices. We jointly consider resource block reuse and power control and then develop the MISS algorithm. Simulation results show that MISS performs very well in terms of transmission power consumption, system throughput, and the number of permitted D2D devices.
Reinventing NetFlow for OpenFlow Software-Defined Networks
Venue: arXiv cs.NI 2017
Year: 2017

Obtaining flow-level measurements, similar to those provided by Netflow/IPFIX, with OpenFlow is challenging as it requires the installation of an entry per flow in the flow tables. This approach does not scale well with the number of concurrent flows in the traffic as the number of entries in the flow tables is limited and small. Flow monitoring rules may also interfere with forwarding or other rules already present in the switches, which are often defined at different granularities than the flow level. In this paper, we present a transparent and scalable flow-based monitoring solution that is fully compatible with current off-the-shelf OpenFlow switches. As in NetFlow/IPFIX, we aggregate packets into flows directly in the switches and asynchronously send traffic reports to an external collector. In order to reduce the overhead, we implement three different traffic sampling methods depending on the OpenFlow features available in the switch. We developed our complete flow monitoring solution within OpenDaylight and evaluated its accuracy in a testbed with Open vSwitch. Our experimental results using real-world traffic traces show that the proposed sampling methods are accurate and can effectively reduce the resource requirements of flow measurements in OpenFlow.
Embedded real-time monitoring using SystemC in IMA network
Venue: arXiv cs.SE 2017
Year: 2017

Avionics is one kind of domain where prevention prevails. Nonetheless fails occur. Sometimes due to pilot misreacting, flooded in information. Sometimes information itself would be better verified than trusted. To avoid some kind of failure, it has been thought to add,in midst of the ARINC664 aircraft data network, a new kind of monitoring.
RenderMap: Exploiting the Link Between Perception and Rendering for Dense Mapping
Venue: arXiv cs.CV 2017
Year: 2017

We introduce an approach for the real-time (2Hz) creation of a dense map and alignment of a moving robotic agent within that map by rendering using a Graphics Processing Unit (GPU). This is done by recasting the scan alignment part of the dense mapping process as a rendering task. Alignment errors are computed from rendering the scene, comparing with range data from the sensors, and minimized by an optimizer. The proposed approach takes advantage of the advances in rendering techniques for computer graphics and GPU hardware to accelerate the algorithm. Moreover, it allows one to exploit information not used in classic dense mapping algorithms such as Iterative Closest Point (ICP) by rendering interfaces between the free space, occupied space and the unknown. The proposed approach leverages directly the rendering capabilities of the GPU, in contrast to other GPU-based approaches that deploy the GPU as a general purpose parallel computation platform. We argue that the proposed concept is a general consequence of treating perception problems as inverse problems of rendering. Many perception problems can be recast into a form where much of the computation is replaced by render operations. This is not only efficient since rendering is fast, but also simpler to implement and will naturally benefit from future advancements in GPU speed and rendering techniques. Furthermore, this general concept can go beyond addressing perception problems and can be used for other problem domains such as path planning.
Mean Birds: Detecting Aggression and Bullying on Twitter
Venue: arXiv cs.CY 2017
Year: 2017

In recent years, bullying and aggression against users on social media have grown significantly, causing serious consequences to victims of all demographics. In particular, cyberbullying affects more than half of young social media users worldwide, and has also led to teenage suicides, prompted by prolonged and/or coordinated digital harassment. Nonetheless, tools and technologies for understanding and mitigating it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of cyberbullies and aggressors, and what features distinguish them from regular users. We find that bully users post less, participate in fewer online communities, and are less popular than normal users, while aggressors are quite popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, achieving over 90% AUC.