OpenRaiser/Intern-Atlas huggingface dataset:
Composing Music with Grammar Argumented Neural Networks and Note-Level Encoding
Venue: arXiv cs.LG 2016
Year: 2016

Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for artificial intelligence research. Despite recent successes of long-short term memory (LSTM) recurrent neural networks (RNNs) in sequential learning, LSTM neural networks have not, by themselves, been able to generate natural-sounding music conforming to music theory. To transcend this inadequacy, we put forward a novel method for music composition that combines the LSTM with Grammars motivated by music theory. The main tenets of music theory are encoded as grammar argumented (GA) filters on the training data, such that the machine can be trained to generate music inheriting the naturalness of human-composed pieces from the original dataset while adhering to the rules of music theory. Unlike previous approaches, pitches and durations are encoded as one semantic entity, which we refer to as note-level encoding. This allows easy implementation of music theory grammars, as well as closer emulation of the thinking pattern of a musician. Although the GA rules are applied to the training data and never directly to the LSTM music generation, our machine still composes music that possess high incidences of diatonic scale notes, small pitch intervals and chords, in deference to music theory.
Investigating Factors Influencing the Latency of Cyberbullying Detection
Venue: arXiv cs.SI 2016
Year: 2016

Cyberbullying in online social networks has become a critical problem, especially among teenagers who are social networks' prolific users. As a result, researchers have focused on identifying distinguishing features of cyberbullying and developing techniques to automatically detect cyberbullying incidents. While this research has resulted in developing highly accurate classifiers, two key practical issues related to identifying cyberbullying have largely been ignored, namely scalability of cyberbullying detection services and timeliness of raising alerts whenever a cyberbullying incident is suspected. These two issues are the subject of this paper. We propose a multi-stage cyberbullying detection solution that drastically reduces the classification time and the time to raise cyberbullying alerts. The proposed solution is highly scalable, does not sacrifice accuracy for scalability, and is highly responsive in raising alerts. The solution is comprised of three novel components, an initial predictor, a multilevel priority scheduler, and an incremental classification mechanism. We have implemented this solution and utilized data obtained from the Vine online social network to demonstrate the utility of each of these components via a detailed performance evaluation. We show that our complete solution is significantly more scalable and responsive than the current state-of-the-art.
An Efficient Framework for Floor-plan Prediction of Dynamic Runtime Reconfigurable Systems
Venue: arXiv cs.OH 2016
Year: 2016

Several embedded application domains for reconfigurable systems tend to combine frequent changes with high performance demands of their workloads such as image processing, wearable computing and network processors. Time multiplexing of reconfigurable hardware resources raises a number of new issues, ranging from run-time systems to complex programming models that usually form a Reconfigurable hardware Operating System (ROS). The Operating System performs online task scheduling and handles resource management. There are many challenges in adaptive computing and dynamic reconfigurable systems. One of the major understudied challenges is estimating the required resources in terms of soft cores, Programmable Reconfigurable Regions (PRRs), the appropriate communication infrastructure, and to predict a near optimal layout and floorplan of the reconfigurable logic fabric. Some of these issues are specific to the application being designed, while others are more general and relate to the underlying run-time environment. Static resource allocation for Run- Time Reconfiguration (RTR) often leads to inferior and unacceptable results. In this paper, we present a novel adaptive and dynamic methodology, based on a Machine Learning approach, for predicting and estimating the necessary resources for an application based on past historical information. An important feature of the proposed methodology is that the system is able to learn and generalize and, therefore, is expected to improve its accuracy over time. The goal of the entire process is to extract useful hidden knowledge from the data. This knowledge is the prediction and estimation of the necessary resources for an unknown or not previously seen application.
On the Exploration of Convolutional Fusion Networks for Visual Recognition
Venue: arXiv cs.CV 2016
Year: 2016

Despite recent advances in multi-scale deep representations, their limitations are attributed to expensive parameters and weak fusion modules. Hence, we propose an efficient approach to fuse multi-scale deep representations, called convolutional fusion networks (CFN). Owing to using 1$\times$1 convolution and global average pooling, CFN can efficiently generate the side branches while adding few parameters. In addition, we present a locally-connected fusion module, which can learn adaptive weights for the side branches and form a discriminatively fused feature. CFN models trained on the CIFAR and ImageNet datasets demonstrate remarkable improvements over the plain CNNs. Furthermore, we generalize CFN to three new tasks, including scene recognition, fine-grained recognition and image retrieval. Our experiments show that it can obtain consistent improvements towards the transferring tasks.
Swap Equilibria under Link and Vertex Destruction
Venue: arXiv cs.GT 2016
Year: 2016

We initiate the study of the \emph{destruction model} (\aka \emph{adversary model}) introduced by Kliemann (2010), using the stability concept of \emph{swap equilibrium} introduced by Alon et. al (2010). The destruction model is a network formation game incorporating the robustness of a network under a more or less targeted attack. In addition to bringing in the swap equilibrium (SE) concept, we extend the model from an attack on the edges of the network to an attack on its vertices. Vertex destruction can generally cause more harm and tends to be more difficult to analyze. We prove structural results and linear upper bounds or super-linear lower bounds on the social cost of SE under different attack scenarios. The most complex case is when the vertex to be destroyed is chosen uniformly at random from the set of those vertices where each causes a maximum number of player pairs to be separated (called a max-sep vertex). We prove a lower bound on the social cost of $Ω(n^{3/2})$ for this case and initiate an understanding of the structural properties of SE in this scenario. Namely, we prove that there is no SE that is a tree and has only one max-sep vertex. We conjecture that this result can be generalized, in particular we conjecture that there is no SE that is a tree. On the other hand, we prove that if the vertex to be destroyed is chosen uniformly at random from the set of \emph{all} vertices, then each SE is a tree (unless it is two-connected). Our conjecture would imply that moving from the uniform probability measure to a measure concentrated on the max-sep vertices, means moving from no SE having a cycle (unless two-connected) to each SE having a cycle. This would ask for a more detailed study of this transition in future work.
The Matrix Chain Algorithm to Compile Linear Algebra Expressions
Venue: arXiv cs.DS 2016
Year: 2016

The matrix chain problem consists in finding the parenthesization of a matrix product $M := A_1 A_2 \cdots A_n$ that minimizes the number of scalar operations. In practical applications, however, one frequently encounters more complicated scenarios, where expressions involve transposition, inversion, matrices with given properties, and sequences. The computation of such expressions makes use of a set of computational kernels that offer functionality well beyond the simple matrix product. The challenge then shifts from finding an optimal parenthesization to finding an optimal mapping of the input expression to the available kernels. Furthermore, it is often the case that a solution based on the minimization of scalar operations does not result in the optimal solution in terms of execution time, and/or might be numerically unstable. In this paper, we introduce a number of generalizations of the matrix chain problem--including kernels, properties, sequences, and cost functions--and present corresponding algorithmic solutions. The motivation for this work comes from the fact that--despite great advances in the development of compilers--the task of mapping linear algebra problems to optimized kernels is still to be done manually. In order to relieve the user from this complex task, new techniques for the compilation of linear algebra expressions have to be developed.
AutoScaler: Scale-Attention Networks for Visual Correspondence
Venue: arXiv cs.CV 2016
Year: 2016

Finding visual correspondence between local features is key to many computer vision problems. While defining features with larger contextual scales usually implies greater discriminativeness, it could also lead to less spatial accuracy of the features. We propose AutoScaler, a scale-attention network to explicitly optimize this trade-off in visual correspondence tasks. Our network consists of a weight-sharing feature network to compute multi-scale feature maps and an attention network to combine them optimally in the scale space. This allows our network to have adaptive receptive field sizes over different scales of the input. The entire network is trained end-to-end in a siamese framework for visual correspondence tasks. Our method achieves favorable results compared to state-of-the-art methods on challenging optical flow and semantic matching benchmarks, including Sintel, KITTI and CUB-2011. We also show that our method can generalize to improve hand-crafted descriptors (e.g Daisy) on general visual correspondence tasks. Finally, our attention network can generate visually interpretable scale attention maps.
Answering Image Riddles using Vision and Reasoning through Probabilistic Soft Logic
Venue: arXiv cs.CV 2016
Year: 2016

In this work, we explore a genre of puzzles ("image riddles") which involves a set of images and a question. Answering these puzzles require both capabilities involving visual detection (including object, activity recognition) and, knowledge-based or commonsense reasoning. We compile a dataset of over 3k riddles where each riddle consists of 4 images and a groundtruth answer. The annotations are validated using crowd-sourced evaluation. We also define an automatic evaluation metric to track future progress. Our task bears similarity with the commonly known IQ tasks such as analogy solving, sequence filling that are often used to test intelligence. We develop a Probabilistic Reasoning-based approach that utilizes probabilistic commonsense knowledge to answer these riddles with a reasonable accuracy. We demonstrate the results of our approach using both automatic and human evaluations. Our approach achieves some promising results for these riddles and provides a strong baseline for future attempts. We make the entire dataset and related materials publicly available to the community in ImageRiddle Website (http://bit.ly/22f9Ala).
Robust and Scalable Column/Row Sampling from Corrupted Big Data
Venue: arXiv cs.LG 2016
Year: 2016

Conventional sampling techniques fall short of drawing descriptive sketches of the data when the data is grossly corrupted as such corruptions break the low rank structure required for them to perform satisfactorily. In this paper, we present new sampling algorithms which can locate the informative columns in presence of severe data corruptions. In addition, we develop new scalable randomized designs of the proposed algorithms. The proposed approach is simultaneously robust to sparse corruption and outliers and substantially outperforms the state-of-the-art robust sampling algorithms as demonstrated by experiments conducted using both real and synthetic data.
DeepVO: A Deep Learning approach for Monocular Visual Odometry
Venue: arXiv cs.CV 2016
Year: 2016

Deep Learning based techniques have been adopted with precision to solve a lot of standard computer vision problems, some of which are image classification, object detection and segmentation. Despite the widespread success of these approaches, they have not yet been exploited largely for solving the standard perception related problems encountered in autonomous navigation such as Visual Odometry (VO), Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM). This paper analyzes the problem of Monocular Visual Odometry using a Deep Learning-based framework, instead of the regular 'feature detection and tracking' pipeline approaches. Several experiments were performed to understand the influence of a known/unknown environment, a conventional trackable feature and pre-trained activations tuned for object classification on the network's ability to accurately estimate the motion trajectory of the camera (or the vehicle). Based on these observations, we propose a Convolutional Neural Network architecture, best suited for estimating the object's pose under known environment conditions, and displays promising results when it comes to inferring the actual scale using just a single camera in real-time.
Fast low-level pattern matching algorithm
Venue: arXiv cs.CV 2016
Year: 2016

This paper focuses on pattern matching in the DNA sequence. It was inspired by a previously reported method that proposes encoding both pattern and sequence using prime numbers. Although fast, the method is limited to rather small pattern lengths, due to computing precision problem. Our approach successfully deals with large patterns, due to our implementation that uses modular arithmetic. In order to get the results very fast, the code was adapted for multithreading and parallel implementations. The method is reduced to assembly language level instructions, thus the final result shows significant time and memory savings compared to the reference algorithm.
Team-maxmin equilibrium: efficiency bounds and algorithms
Venue: arXiv cs.AI 2016
Year: 2016

The Team-maxmin equilibrium prescribes the optimal strategies for a team of rational players sharing the same goal and without the capability of correlating their strategies in strategic games against an adversary. This solution concept can capture situations in which an agent controls multiple resources-corresponding to the team members-that cannot communicate. It is known that such equilibrium always exists and it is unique (unless degeneracy) and these properties make it a credible solution concept to be used in real-world applications, especially in security scenarios. Nevertheless, to the best of our knowledge, the Team-maxmin equilibrium is almost completely unexplored in the literature. In this paper, we investigate bounds of (in)efficiency of the Team-maxmin equilibrium w.r.t. the Nash equilibria and w.r.t. the Maxmin equilibrium when the team members can play correlated strategies. Furthermore, we study a number of algorithms to find and/or approximate an equilibrium, discussing their theoretical guarantees and evaluating their performance by using a standard testbed of game instances.
End-to-End Subtitle Detection and Recognition for Videos in East Asian Languages via CNN Ensemble with Near-Human-Level Performance
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we propose an innovative end-to-end subtitle detection and recognition system for videos in East Asian languages. Our end-to-end system consists of multiple stages. Subtitles are firstly detected by a novel image operator based on the sequence information of consecutive video frames. Then, an ensemble of Convolutional Neural Networks (CNNs) trained on synthetic data is adopted for detecting and recognizing East Asian characters. Finally, a dynamic programming approach leveraging language models is applied to constitute results of the entire body of text lines. The proposed system achieves average end-to-end accuracies of 98.2% and 98.3% on 40 videos in Simplified Chinese and 40 videos in Traditional Chinese respectively, which is a significant outperformance of other existing methods. The near-perfect accuracy of our system dramatically narrows the gap between human cognitive ability and state-of-the-art algorithms used for such a task.
Minimizing cyber sickness in head mounted display systems: design guidelines and applications
Venue: arXiv cs.HC 2016
Year: 2016

We are experiencing an upcoming trend of using head mounted display systems in games and serious games, which is likely to become an established practice in the near future. While these systems provide highly immersive experiences, many users have been reporting discomfort symptoms, such as nausea, sickness, and headaches, among others. When using VR for health applications, this is more critical, since the discomfort may interfere a lot in treatments. In this work we discuss possible causes of these issues, and present possible solutions as design guidelines that may mitigate them. In this context, we go deeper within a dynamic focus solution to reduce discomfort in immersive virtual environments, when using first-person navigation. This solution applies an heuristic model of visual attention that works in real time. This work also discusses a case study (as a first-person spatial shooter demo) that applies this solution and the proposed design guidelines.
Determining the Veracity of Rumours on Twitter
Venue: arXiv cs.SI 2016
Year: 2016

While social networks can provide an ideal platform for up-to-date information from individuals across the world, it has also proved to be a place where rumours fester and accidental or deliberate misinformation often emerges. In this article, we aim to support the task of making sense from social media data, and specifically, seek to build an autonomous message-classifier that filters relevant and trustworthy information from Twitter. For our work, we collected about 100 million public tweets, including users' past tweets, from which we identified 72 rumours (41 true, 31 false). We considered over 80 trustworthiness measures including the authors' profile and past behaviour, the social network connections (graphs), and the content of tweets themselves. We ran modern machine-learning classifiers over those measures to produce trustworthiness scores at various time windows from the outbreak of the rumour. Such time-windows were key as they allowed useful insight into the progression of the rumours. From our findings, we identified that our model was significantly more accurate than similar studies in the literature. We also identified critical attributes of the data that give rise to the trustworthiness scores assigned. Finally we developed a software demonstration that provides a visual user interface to allow the user to examine the analysis.
A Survey of Methods for Collective Communication Optimization and Tuning
Venue: arXiv cs.DC 2016
Year: 2016

New developments in HPC technology in terms of increasing computing power on multi/many core processors, high-bandwidth memory/IO subsystems and communication interconnects, pose a direct impact on software and runtime system development. These advancements have become useful in producing high-performance collective communication interfaces that integrate efficiently on a wide variety of platforms and environments. However, number of optimization options that shows up with each new technology or software framework has resulted in a \emph{combinatorial explosion} in feature space for tuning collective parameters such that finding the optimal set has become a nearly impossible task. Applicability of algorithmic choices available for optimizing collective communication depends largely on the scalability requirement for a particular usecase. This problem can be further exasperated by any requirement to run collective problems at very large scales such as in the case of exascale computing, at which impractical tuning by brute force may require many months of resources. Therefore application of statistical, data mining and artificial Intelligence or more general hybrid learning models seems essential in many collectives parameter optimization problems. We hope to explore current and the cutting edge of collective communication optimization and tuning methods and culminate with possible future directions towards this problem.
Ordinal Constrained Binary Code Learning for Nearest Neighbor Search
Venue: arXiv cs.CV 2016
Year: 2016

Recent years have witnessed extensive attention in binary code learning, a.k.a. hashing, for nearest neighbor search problems. It has been seen that high-dimensional data points can be quantized into binary codes to give an efficient similarity approximation via Hamming distance. Among existing schemes, ranking-based hashing is recent promising that targets at preserving ordinal relations of ranking in the Hamming space to minimize retrieval loss. However, the size of the ranking tuples, which shows the ordinal relations, is quadratic or cubic to the size of training samples. By given a large-scale training data set, it is very expensive to embed such ranking tuples in binary code learning. Besides, it remains a dificulty to build ranking tuples efficiently for most ranking-preserving hashing, which are deployed over an ordinal graph-based setting. To handle these problems, we propose a novel ranking-preserving hashing method, dubbed Ordinal Constraint Hashing (OCH), which efficiently learns the optimal hashing functions with a graph-based approximation to embed the ordinal relations. The core idea is to reduce the size of ordinal graph with ordinal constraint projection, which preserves the ordinal relations through a small data set (such as clusters or random samples). In particular, to learn such hash functions effectively, we further relax the discrete constraints and design a specific stochastic gradient decent algorithm for optimization. Experimental results on three large-scale visual search benchmark datasets, i.e. LabelMe, Tiny100K and GIST1M, show that the proposed OCH method can achieve superior performance over the state-of-the-arts approaches.
Active and Transfer Learning of Grasps by Sampling from Demonstration
Venue: arXiv cs.RO 2016
Year: 2016

We guess humans start acquiring grasping skills as early as at the infant stage by virtue of two key processes. First, infants attempt to learn grasps for known objects by imitating humans. Secondly, knowledge acquired during this process is reused in learning to grasp novel objects. We argue that these processes of active and transfer learning boil down to a random search of grasps on an object, suitably biased by prior experience. In this paper we introduce active learning of grasps for known objects as well as transfer learning of grasps for novel objects grounded on kernel adaptive, mode-hopping Markov Chain Monte Carlo. Our experiments show promising applicability of our proposed learning methods.