OpenRaiser/Intern-Atlas huggingface dataset:
French Word Recognition through a Quick Survey on Recurrent Neural Networks Using Long-Short Term Memory RNN-LSTM
Venue: arXiv cs.CV 2018
Year: 2018

Optical character recognition (OCR) is a fundamental problem in computer vision. Research studies have shown significant progress in classifying printed characters using deep learning-based methods and topologies. Among current algorithms, recurrent neural networks with long-short term memory blocks called RNN-LSTM have provided the highest performance in terms of accuracy rate. Using the top 5,000 French words collected from the internet including all signs and accents, RNN-LSTM models were trained and tested for several cases. Six fonts were used to generate OCR samples and an additional dataset that included all samples from these six fonts was prepared for training and testing purposes. The trained RNN-LSTM models were tested and achieved the accuracy rates of 99.98798% and 99.91889% for edit distance and sequence error, respectively. An accurate preprocessing followed by height normalization (standardization methods in deep learning) enabled the RNN-LSTM model to be trained in the most efficient way. This machine learning work also revealed the robustness of RNN-LSTM topology to recognize printed characters.
The New Urban Success: How Culture Pays
Venue: arXiv cs.CY 2018
Year: 2018

Urban economists have put forward the idea that cities that are culturally interesting tend to attract "the creative class" and, as a result, end up being economically successful. Yet it is still unclear how economic and cultural dynamics mutually influence each other. By contrast, that has been extensively studied in the case of individuals. Over decades, the French sociologist Pierre Bourdieu showed that people's success and their positions in society mainly depend on how much they can spend (their economic capital) and what their interests are (their cultural capital). For the first time, we adapt Bourdieu's framework to the city context. We operationalize a neighborhood's cultural capital in terms of the cultural interests that pictures geo-referenced in the neighborhood tend to express. This is made possible by the mining of what users of the photo-sharing site of Flickr have posted in the cities of London and New York over 5 years. In so doing, we are able to show that economic capital alone does not explain urban development. The combination of cultural capital and economic capital, instead, is more indicative of neighborhood growth in terms of house prices and improvements of socio-economic conditions. Culture pays, but only up to a point as it comes with one of the most vexing urban challenges: that of gentrification.
KRB-CCN: Lightweight Authentication & Access Control for Private Content-Centric Networks
Venue: arXiv cs.CR 2018
Year: 2018

Content-Centric Networking (CCN) is an internetworking paradigm that offers an alternative to today's IP-based Internet Architecture. Instead of focusing on hosts and their locations, CCN emphasizes addressable named content. By decoupling content from its location, CCN allows opportunistic in-network content caching, thus enabling better network utilization, at least for scalable content distribution. However, in order to be considered seriously, CCN must support basic security services, including content authenticity, integrity, confidentiality, authorization and access control. Current approaches rely on content producers to perform authorization and access control. This general approach has several disadvantages. First, consumer privacy vis-a-vis producers is not preserved. Second, identity management and access control impose high computational overhead on producers. Also, unnecessary repeated authentication and access control decisions must be made for each content request. These issues motivate our design of KRB-CCN - a complete authorization and access control system for private CCNs. Inspired by Kerberos in IP-based networks, KRB-CCN involves distinct authentication and authorization authorities. By doing so, KRB-CCN obviates the need for producers to make consumer authentication and access control decisions. KRB-CCN preserves consumer privacy since producers are unaware of consumer identities. Producers are also not required to keep any hard state and only need to perform two symmetric key operations to guarantee that sensitive content is confidentially delivered only to authenticated and authorized consumers. Most importantly, unlike prior designs, KRB-CCN leaves the network (i.e., CCN routers) out of any authorization, access control or confidentiality issues. We describe KRB-CCN design and implementation, analyze its security, and report on its performance.
Computing Shapley values in the plane
Venue: arXiv cs.CG 2018
Year: 2018

We consider the problem of computing Shapley values for points in the plane, where each point is interpreted as a player, and the value of a coalition is defined by the area of usual geometric objects, such as the convex hull or the minimum axis-parallel bounding box. For sets of $n$ points in the plane, we show how to compute in roughly $O(n^{3/2})$ time the Shapley values for the area of the minimum axis-parallel bounding box and the area of the union of the rectangles spanned by the origin and the input points. When the points form an increasing or decreasing chain, the running time can be improved to near-linear. In all these cases, we use linearity of the Shapley values and algebraic methods. We also show that Shapley values for the area and the perimeter of the convex hull or the minimum enclosing disk can be computed in $O(n^2)$ and $O(n^3)$ time, respectively. In this case the computation is closely related to the model of stochastic point sets considered in computational geometry, but here we have to consider random insertion orders of the points instead of a probabilistic existence of points.
Discovering the Elite Hypervolume by Leveraging Interspecies Correlation
Venue: arXiv cs.NE 2018
Year: 2018

Evolution has produced an astonishing diversity of species, each filling a different niche. Algorithms like MAP-Elites mimic this divergent evolutionary process to find a set of behaviorally diverse but high-performing solutions, called the elites. Our key insight is that species in nature often share a surprisingly large part of their genome, in spite of occupying very different niches; similarly, the elites are likely to be concentrated in a specific "elite hypervolume" whose shape is defined by their common features. In this paper, we first introduce the elite hypervolume concept and propose two metrics to characterize it: the genotypic spread and the genotypic similarity. We then introduce a new variation operator, called "directional variation", that exploits interspecies (or inter-elites) correlations to accelerate the MAP-Elites algorithm. We demonstrate the effectiveness of this operator in three problems (a toy function, a redundant robotic arm, and a hexapod robot).
How vulnerable are the Indian banks: A cryptographers' view
Venue: arXiv cs.CR 2018
Year: 2018

With the advent of e-commerce and online banking it has become extremely important that the websites of the financial institutes (especially, banks) implement up-to-date measures of cyber security (in accordance with the recommendations of the regulatory authority) and thus circumvent the possibilities of financial frauds that may occur due to vulnerabilities of the website. Here, we systematically investigate whether Indian banks are following the above requirement. To perform the investigation, recommendations of Reserve Bank of India (RBI), National Institute of Standards and Technology (NIST), European Union Agency for Network and Information Security (ENISA) and Internet Engineering Task Force (IETF) are considered as the benchmarks. Further, the validity and quality of the security certificates of various Indian banks have been tested with the help of a set of tools (e.g., SSL Certificate Checker provided by Digicert and SSL server test provided by SSL Labs). The analysis performed by using these tools and a comparison with the benchmarks, have revealed that the security measures taken by a set of Indian banks are not up-to-date and are vulnerable under some known attacks.
Deep Learning For Computer Vision Tasks: A review
Venue: arXiv cs.CV 2018
Year: 2018

Deep learning has recently become one of the most popular sub-fields of machine learning owing to its distributed data representation with multiple levels of abstraction. A diverse range of deep learning algorithms are being employed to solve conventional artificial intelligence problems. This paper gives an overview of some of the most widely used deep learning algorithms applied in the field of computer vision. It first inspects the various approaches of deep learning algorithms, followed by a description of their applications in image classification, object identification, image extraction and semantic segmentation in the presence of noise. The paper concludes with the discussion of the future scope and challenges for construction and training of deep neural networks.
Attention U-Net: Learning Where to Look for the Pancreas
Venue: arXiv cs.CV 2018
Year: 2018

We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN architectures such as the U-Net model with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed Attention U-Net architecture is evaluated on two large CT abdominal datasets for multi-class image segmentation. Experimental results show that AGs consistently improve the prediction performance of U-Net across different datasets and training sizes while preserving computational efficiency. The code for the proposed architecture is publicly available.
Non-Malleable Extractors and Non-Malleable Codes: Partially Optimal Constructions
Venue: arXiv cs.CC 2018
Year: 2018

The recent line of study on randomness extractors has been a great success, resulting in exciting new techniques, new connections, and breakthroughs to long standing open problems in several seemingly different topics. These include seeded non-malleable extractors, privacy amplification protocols with an active adversary, independent source extractors (and explicit Ramsey graphs), and non-malleable codes in the split state model. However, in all cases there is still a gap to optimum and the motivation to close this gap remains strong. In this paper, we introduce a set of new techniques to further push the frontier in the above questions. Our techniques lead to improvements in all of the above questions, and in several cases partially optimal constructions. Specifically, we obtain: 1. A seeded non-malleable extractor with seed length $O(log n)+log^{1+o(1)}(1/ε) and entropy requirement O(log log n+log(1/ε)), where the entropy requirement is asymptotically optimal by a recent result of Gur and Shinkar \cite{GurS17}; 2. A two-round privacy amplification protocol with optimal entropy loss for security parameter up to Ω(k), which solves the privacy amplification problem completely; 3. A two-source extractor for entropy O(\frac{log n log log n}{log log log n}), which also gives an explicit Ramsey graph on N vertices with no clique or independent set of size (log N)^{O(\frac{log log log N}{log log log log N})}; and 4. The first explicit non-malleable code in the 2-split state model with \emph{constant} rate, which has been a major goal in the study of non-malleable codes for quite some time. One small caveat is that the error of this code is only (an arbitrarily small) constant, but we can also achieve negligible error with rate Ω(log log log n/log log n), which already improves the rate in \cite{Li17} exponentially.
QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAM
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we use 2D object detections from multiple views to simultaneously estimate a 3D quadric surface for each object and localize the camera position. We derive a SLAM formulation that uses dual quadrics as 3D landmark representations, exploiting their ability to compactly represent the size, position and orientation of an object, and show how 2D object detections can directly constrain the quadric parameters via a novel geometric error formulation. We develop a sensor model for object detectors that addresses the challenge of partially visible objects, and demonstrate how to jointly estimate the camera pose and constrained dual quadric parameters in factor graph based SLAM with a general perspective camera.
Constraint-Based Synthesis of Coupling Proofs
Venue: arXiv cs.PL 2018
Year: 2018

Proof by coupling is a classical technique for proving properties about pairs of randomized algorithms by carefully relating (or coupling) two probabilistic executions. In this paper, we show how to automatically construct such proofs for probabilistic programs. First, we present f-coupled postconditions, an abstraction describing two correlated program executions. Second, we show how properties of f-coupled postconditions can imply various probabilistic properties of the original programs. Third, we demonstrate how to reduce the proof-search problem to a purely logical synthesis problem of the form $\exists f\ldotp \forall X\ldotp ϕ$, making probabilistic reasoning unnecessary. We develop a prototype implementation to automatically build coupling proofs for probabilistic properties, including uniformity and independence of program expressions.
Seed-Point Detection of Clumped Convex Objects by Short-Range Attractive Long-Range Repulsive Particle Clustering
Venue: arXiv cs.CV 2018
Year: 2018

Locating the center of convex objects is important in both image processing and unsupervised machine learning/data clustering fields. The automated analysis of biological images uses both of these fields for locating cell nuclei and for discovering new biological effects or cell phenotypes. In this work, we develop a novel clustering method for locating the centers of overlapping convex objects by modeling particles that interact by a short-range attractive and long-range repulsive potential and are confined to a potential well created from the data. We apply this method to locating the centers of clumped nuclei in cultured cells, where we show that it results in a significant improvement over existing methods (8.2% in F$_1$ score); and we apply it to unsupervised learning on a difficult data set that has rare classes without local density maxima, and show it is able to well locate cluster centers when other clustering techniques fail.
Ranking CGANs: Subjective Control over Semantic Image Attributes
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we investigate the use of generative adversarial networks in the task of image generation according to subjective measures of semantic attributes. Unlike the standard (CGAN) that generates images from discrete categorical labels, our architecture handles both continuous and discrete scales. Given pairwise comparisons of images, our model, called RankCGAN, performs two tasks: it learns to rank images using a subjective measure; and it learns a generative model that can be controlled by that measure. RankCGAN associates each subjective measure of interest to a distinct dimension of some latent space. We perform experiments on UT-Zap50K, PubFig and OSR datasets and demonstrate that the model is expressive and diverse enough to conduct two-attribute exploration and image editing.
End-to-end Deep Learning of Optical Fiber Communications
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we implement an optical fiber communication system as an end-to-end deep neural network, including the complete chain of transmitter, channel model, and receiver. This approach enables the optimization of the transceiver in a single end-to-end process. We illustrate the benefits of this method by applying it to intensity modulation/direct detection (IM/DD) systems and show that we can achieve bit error rates below the 6.7\% hard-decision forward error correction (HD-FEC) threshold. We model all componentry of the transmitter and receiver, as well as the fiber channel, and apply deep learning to find transmitter and receiver configurations minimizing the symbol error rate. We propose and verify in simulations a training method that yields robust and flexible transceivers that allow---without reconfiguration---reliable transmission over a large range of link dispersions. The results from end-to-end deep learning are successfully verified for the first time in an experiment. In particular, we achieve information rates of 42\,Gb/s below the HD-FEC threshold at distances beyond 40\,km. We find that our results outperform conventional IM/DD solutions based on 2 and 4 level pulse amplitude modulation (PAM2/PAM4) with feedforward equalization (FFE) at the receiver. Our study is the first step towards end-to-end deep learning-based optimization of optical fiber communication systems.
Understanding Actors and Evaluating Personae with Gaussian Embeddings
Venue: arXiv cs.CY 2018
Year: 2018

Understanding narrative content has become an increasingly popular topic. Nonetheless, research on identifying common types of narrative characters, or personae, is impeded by the lack of automatic and broad-coverage evaluation methods. We argue that computationally modeling actors provides benefits, including novel evaluation mechanisms for personae. Specifically, we propose two actor-modeling tasks, cast prediction and versatility ranking, which can capture complementary aspects of the relation between actors and the characters they portray. For an actor model, we present a technique for embedding actors, movies, character roles, genres, and descriptive keywords as Gaussian distributions and translation vectors, where the Gaussian variance corresponds to actors' versatility. Empirical results indicate that (1) the technique considerably outperforms TransE (Bordes et al. 2013) and ablation baselines and (2) automatically identified persona topics (Bamman, O'Connor, and Smith 2013) yield statistically significant improvements in both tasks, whereas simplistic persona descriptors including age and gender perform inconsistently, validating prior research.
A Review of Augmented Reality Applications for Building Evacuation
Venue: arXiv cs.OH 2018
Year: 2018

Evacuation is one of the main disaster management solutions to reduce the impact of man-made and natural threats on building occupants. To date, several modern technologies and gamification concepts, e.g. immersive virtual reality and serious games, have been used to enhance building evacuation preparedness and effectiveness. Those tools have been used both to investigate human behavior during building emergencies and to train building occupants on how to cope with building evacuations. Augmented Reality (AR) is novel technology that can enhance this process providing building occupants with virtual contents to improve their evacuation performance. This work aims at reviewing existing AR applications developed for building evacuation. This review identifies the disasters and types of building those tools have been applied for. Moreover, the application goals, hardware and evacuation stages affected by AR are also investigated in the review. Finally, this review aims at identifying the challenges to face for further development of AR evacuation tools.
Automatically Infer Human Traits and Behavior from Social Media Data
Venue: arXiv cs.SI 2018
Year: 2018

Given the complexity of human minds and their behavioral flexibility, it requires sophisticated data analysis to sift through a large amount of human behavioral evidence to model human minds and to predict human behavior. People currently spend a significant amount of time on social media such as Twitter and Facebook. Thus many aspects of their lives and behaviors have been digitally captured and continuously archived on these platforms. This makes social media a great source of large, rich and diverse human behavioral evidence. In this paper, we survey the recent work on applying machine learning to infer human traits and behavior from social media data. We will also point out several future research directions.
Controlling Swarms: A Programming Paradigm with Minimalistic Communication
Venue: arXiv cs.MA 2018
Year: 2018

Inspired by natural swarms, numerous control schemes enabling robotic swarms, mobile sensor networks and other multi-agent systems to exhibit various self-organized behaviors have been suggested. In this work, we present a Wave Oriented Swarm Programming Paradigm (WOSPP) enabling the control of swarms with minimalistic communication bandwidth in a simple manner, yet allowing the emergence of diverse complex behaviors and autonomy of the swarm. Communi cation in the proposed paradigm is based on "ping"-signals inspired by strategies for communication and self organization of slime mold (dictyostelium discoideum) and fireflies (lampyridae). Signals propagate as information-waves throughout the swarm. We show that even with 1-bit bandwidth communication between agents suffices for the design of a substantial set of behaviors in the domain of essential behaviors of a collective. Ultimately, the reader will be enabled to develop and design a control scheme for individual swarms.
Word2Vec applied to Recommendation: Hyperparameters Matter
Venue: arXiv cs.IR 2018
Year: 2018

Skip-gram with negative sampling, a popular variant of Word2vec originally designed and tuned to create word embeddings for Natural Language Processing, has been used to create item embeddings with successful applications in recommendation. While these fields do not share the same type of data, neither evaluate on the same tasks, recommendation applications tend to use the same already tuned hyperparameters values, even if optimal hyperparameters values are often known to be data and task dependent. We thus investigate the marginal importance of each hyperparameter in a recommendation setting through large hyperparameter grid searches on various datasets. Results reveal that optimizing neglected hyperparameters, namely negative sampling distribution, number of epochs, subsampling parameter and window-size, significantly improves performance on a recommendation task, and can increase it by an order of magnitude. Importantly, we find that optimal hyperparameters configurations for Natural Language Processing tasks and Recommendation tasks are noticeably different.
Market Making via Reinforcement Learning
Venue: arXiv cs.AI 2018
Year: 2018

Market making is a fundamental trading problem in which an agent provides liquidity by continually offering to buy and sell a security. The problem is challenging due to inventory risk, the risk of accumulating an unfavourable position and ultimately losing money. In this paper, we develop a high-fidelity simulation of limit order book markets, and use it to design a market making agent using temporal-difference reinforcement learning. We use a linear combination of tile codings as a value function approximator, and design a custom reward function that controls inventory risk. We demonstrate the effectiveness of our approach by showing that our agent outperforms both simple benchmark strategies and a recent online learning approach from the literature.