OpenRaiser/Intern-Atlas huggingface dataset:
Commonly Uncommon: Semantic Sparsity in Situation Recognition
Venue: arXiv cs.CV 2016
Year: 2016

Semantic sparsity is a common challenge in structured visual classification problems; when the output space is complex, the vast majority of the possible predictions are rarely, if ever, seen in the training set. This paper studies semantic sparsity in situation recognition, the task of producing structured summaries of what is happening in images, including activities, objects and the roles objects play within the activity. For this problem, we find empirically that most object-role combinations are rare, and current state-of-the-art models significantly underperform in this sparse data regime. We avoid many such errors by (1) introducing a novel tensor composition function that learns to share examples across role-noun combinations and (2) semantically augmenting our training data with automatically gathered examples of rarely observed outputs using web data. When integrated within a complete CRF-based structured prediction model, the tensor-based approach outperforms existing state of the art by a relative improvement of 2.11% and 4.40% on top-5 verb and noun-role accuracy, respectively. Adding 5 million images with our semantic augmentation techniques gives further relative improvements of 6.23% and 9.57% on top-5 verb and noun-role accuracy.
Disruptive innovations in RoboCup 2D Soccer Simulation League: from Cyberoos'98 to Gliders2016
Venue: arXiv cs.MA 2016
Year: 2016

We review disruptive innovations introduced in the RoboCup 2D Soccer Simulation League over the twenty years since its inception, and trace the progress of our champion team (Gliders). We conjecture that the League has been developing as an ecosystem shaped by diverse approaches taken by participating teams, increasing in its overall complexity. A common feature is that different champion teams succeeded in finding a way to decompose the enormous search-space of possible single- and multi-agent behaviours, by automating the exploration of the problem space with various techniques which accelerated the software development efforts. These methods included interactive debugging, machine learning, automated planning, and opponent modelling. The winning approach developed by Gliders is centred on human-based evolutionary computation which optimised several components such as an action-dependent evaluation function, dynamic tactics with Voronoi diagrams, information dynamics, and bio-inspired collective behaviour.
Deep Learning with Energy-efficient Binary Gradient Cameras
Venue: arXiv cs.CV 2016
Year: 2016

Power consumption is a critical factor for the deployment of embedded computer vision systems. We explore the use of computational cameras that directly output binary gradient images to reduce the portion of the power consumption allocated to image sensing. We survey the accuracy of binary gradient cameras on a number of computer vision tasks using deep learning. These include object recognition, head pose regression, face detection, and gesture recognition. We show that, for certain applications, accuracy can be on par or even better than what can be achieved on traditional images. We are also the first to recover intensity information from binary spatial gradient images--useful for applications with a human observer in the loop, such as surveillance. Our results, which we validate with a prototype binary gradient camera, point to the potential of gradient-based computer vision systems.
Mining Spatio-temporal Data on Industrialization from Historical Registries
Venue: arXiv cs.CV 2016
Year: 2016

Despite the growing availability of big data in many fields, historical data on socioevironmental phenomena are often not available due to a lack of automated and scalable approaches for collecting, digitizing, and assembling them. We have developed a data-mining method for extracting tabulated, geocoded data from printed directories. While scanning and optical character recognition (OCR) can digitize printed text, these methods alone do not capture the structure of the underlying data. Our pipeline integrates both page layout analysis and OCR to extract tabular, geocoded data from structured text. We demonstrate the utility of this method by applying it to scanned manufacturing registries from Rhode Island that record 41 years of industrial land use. The resulting spatio-temporal data can be used for socioenvironmental analyses of industrialization at a resolution that was not previously possible. In particular, we find strong evidence for the dispersion of manufacturing from the urban core of Providence, the state's capital, along the Interstate 95 corridor to the north and south.
Improving congestion control for Concurrent Multipath Transfer through bandwidth estimation based resource pooling
Venue: arXiv cs.NI 2016
Year: 2016

Stream Control Transmission Protocol (SCTP) was introduced in 2001 as a multipath variant to traditional transport protocols, i.e. Transmission Control Protocol (TCP) and User Datagram Protocol (UDP). Concurrent Multipath Transfer (CMT) has been proposed as an extension for SCTP to support concurrent usage of available multiple paths. In this paper, we propose a new congestion control algorithm for CMT-SCTP based on the principle of resource pooling. We use the connection bandwidth estimates to obtain the collection of the network resources being used by different flows on multiple paths. Based on these bandwidth estimates, we have used the bandwidth estimation based resource pooling approach to adjust the congestion window of the respective paths. We compare our proposed scheme with CMT-SCTP through ns-2 based simulations.
A Non-Local Means Approach for Gaussian Noise Removal from Images using a Modified Weighting Kernel
Venue: arXiv cs.CV 2016
Year: 2016

Gaussian noise removal is an interesting area in digital image processing not only to improve the visual quality, but for its impact on other post-processing algorithms like image registration or segmentation. Many presented state-of-the-art denoising methods are based on the self-similarity or patch-based image processing. Specifically, Non-Local Means (NLM) as a patch-based filter has gained increasing attention in recent years. Essentially, this filter tends to obtain the noise-less signal value by computing the Gaussian-weighted Euclidean distance between the patch under-processing and other patches inside the image. However, the NLM filter is sensitive to the outliers (pixels that their intensity values are far away from other pixels) inside the patch, meaning that the pixels with the symmetric locations in the patch are assigned the same weight. This can lead to sub-optimal denoising performance when the destructive nature of noise generates some outliers inside patches. In this paper, we propose a new weighting approach to modify the Gaussian kernel of the NLM filter. Our approach employs the geometric distance between image intensities to come up with new weights for each pixel of a patch, lowering the impact of outliers on the denoising performance. Experiments on a set of standard images and different noise levels show that our proposed method outperforms the other compared denoising filters.
Approximation Algorithms for the Maximum Profit Pick-up Problem with Time Windows and Capacity Constraint
Venue: arXiv cs.DS 2016
Year: 2016

In this paper, we study the Maximum Profit Pick-up Problem with Time Windows and Capacity Constraint (MP-PPTWC). Our main results are 3 polynomial time algorithms, all having constant approximation factors. The first algorithm has an approximation ratio of $~46 (1 + (71/60 + \fracα{\sqrt{10+p}}) ε) \log T$, where: (i) $ε> 0$ and $T$ are constants; (ii) The maximum quantity supplied is $q_{max} = O(n^p) q_{min}$, for some $p > 0$, where $q_{min}$ is the minimum quantity supplied; (iii) $α> 0$ is a constant such that the optimal number of vehicles is always at least $\sqrt{10 + p} / α$. The second algorithm has an approximation ratio of $\simeq 46 (1 + ε+ \frac{(2 + α) ε}{\sqrt{10 + p}}) \log T$. Finally, the third algorithm has an approximation ratio of $\simeq 11 (1 + 2 ε) \log T$. While our algorithms may seem to have quite high approximation ratios, in practice they work well and, in the majority of cases, the profit obtained is at least 1/2 of the optimum.
Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning
Venue: arXiv cs.CV 2016
Year: 2016

Dense object detection and temporal tracking are needed across applications domains ranging from people-tracking to analysis of satellite imagery over time. The detection and tracking of malignant skin cancers and benign moles poses a particularly challenging problem due to the general uniformity of large skin patches, the fact that skin lesions vary little in their appearance, and the relatively small amount of data available. Here we introduce a novel data synthesis technique that merges images of individual skin lesions with full-body images and heavily augments them to generate significant amounts of data. We build a convolutional neural network (CNN) based system, trained on this synthetic data, and demonstrate superior performance to traditional detection and tracking techniques. Additionally, we compare our system to humans trained with simple criteria. Our system is intended for potential clinical use to augment the capabilities of healthcare providers. While domain-specific, we believe the methods invoked in this work will be useful in applying CNNs across domains that suffer from limited data availability.
Enhancing Use Case Points Estimation Method Using Soft Computing Techniques
Venue: arXiv cs.SE 2016
Year: 2016

Software estimation is a crucial task in software engineering. Software estimation encompasses cost, effort, schedule, and size. The importance of software estimation becomes critical in the early stages of the software life cycle when the details of software have not been revealed yet. Several commercial and non-commercial tools exist to estimate software in the early stages. Most software effort estimation methods require software size as one of the important metric inputs and consequently, software size estimation in the early stages becomes essential. One of the approaches that has been used for about two decades in the early size and effort estimation is called use case points. Use case points method relies on the use case diagram to estimate the size and effort of software projects. Although the use case points method has been widely used, it has some limitations that might adversely affect the accuracy of estimation. This paper presents some techniques using fuzzy logic and neural networks to improve the accuracy of the use case points method. Results showed that an improvement up to 22% can be obtained using the proposed approach.
Learning to superoptimize programs - Workshop Version
Venue: arXiv cs.LG 2016
Year: 2016

Superoptimization requires the estimation of the best program for a given computational task. In order to deal with large programs, superoptimization techniques perform a stochastic search. This involves proposing a modification of the current program, which is accepted or rejected based on the improvement achieved. The state of the art method uses uniform proposal distributions, which fails to exploit the problem structure to the fullest. To alleviate this deficiency, we learn a proposal distribution over possible modifications using Reinforcement Learning. We provide convincing results on the superoptimization of "Hacker's Delight" programs.
Cache-Enabled Physical-Layer Security for Video Streaming in Wireless Networks with Limited Backhaul
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we investigate for the first time the benefits of wireless caching for the physical layer security (PLS) of wireless networks. In particular, a caching scheme enabling power-efficient PLS is proposed for cellular video streaming with constrained backhaul capacity. By sharing video data across a subset of base stations (BSs) through both caching and backhaul loading, secure cooperative transmission of several BSs is dynamically enabled in accordance with the cache status, the channel conditions, and the backhaul capacity. Thereby, caching reduces the data sharing overhead over the capacity-constrained backhaul links. More importantly, caching introduces additional secure degrees of freedom and enables a power-efficient design. We investigate the optimal caching and transmission policies for minimizing the total transmit power while providing quality of service (QoS) and guaranteeing secrecy during video delivery. A two-stage non-convex mixed-integer optimization problem is formulated, which optimizes the caching policy in an offline video caching stage and the cooperative transmission policy in an online video delivery stage. As the problem is NP-hard, suboptimal polynomial-time algorithms are proposed for low-complexity cache training and delivery control, respectively. Sufficient optimality conditions, under which the proposed schemes attain global optimal solutions, are also provided. Simulation results show that the proposed schemes achieve low secrecy outage probability and high power efficiency simultaneously.
Deep Multi-Modal Image Correspondence Learning
Venue: arXiv cs.CV 2016
Year: 2016

Inference of correspondences between images from different modalities is an extremely important perceptual ability that enables humans to understand and recognize cross-modal concepts. In this paper, we consider an instance of this problem that involves matching photographs of building interiors with their corresponding floorplan. This is a particularly challenging problem because a floorplan, as a stylized architectural drawing, is very different in appearance from a color photograph. Furthermore, individual photographs by themselves depict only a part of a floorplan (e.g., kitchen, bathroom, and living room). We propose the use of a number of different neural network architectures for this task, which are trained and evaluated on a novel large-scale dataset of 5 million floorplan images and 80 million associated photographs. Experimental evaluation reveals that our neural network architectures are able to identify visual cues that result in reliable matches across these two quite different modalities. In fact, the trained networks are able to even outperform human subjects in several challenging image matching problems. Our result implies that neural networks are effective at perceptual tasks that require long periods of reasoning even for humans to solve.
Turning an Urban Scene Video into a Cinemagraph
Venue: arXiv cs.CV 2016
Year: 2016

This paper proposes an algorithm that turns a regular video capturing urban scenes into a high-quality endless animation, known as a Cinemagraph. The creation of a Cinemagraph usually requires a static camera in a carefully configured scene. The task becomes challenging for a regular video with a moving camera and objects. Our approach first warps an input video into the viewpoint of a reference camera. Based on the warped video, we propose effective temporal analysis algorithms to detect regions with static geometry and dynamic appearance, where geometric modeling is reliable and visually attractive animations can be created. Lastly, the algorithm applies a sequence of video processing techniques to produce a Cinemagraph movie. We have tested the proposed approach on numerous challenging real scenes. To our knowledge, this work is the first to automatically generate Cinemagraph animations from regular movies in the wild.
Panoramic Structure from Motion via Geometric Relationship Detection
Venue: arXiv cs.CV 2016
Year: 2016

This paper addresses the problem of Structure from Motion (SfM) for indoor panoramic image streams, extremely challenging even for the state-of-the-art due to the lack of textures and minimal parallax. The key idea is the fusion of single-view and multi-view reconstruction techniques via geometric relationship detection (e.g., detecting 2D lines as coplanar in 3D). Rough geometry suffices to perform such detection, and our approach utilizes rough surface normal estimates from an image-to-normal deep network to discover geometric relationships among lines. The detected relationships provide exact geometric constraints in our line-based linear SfM formulation. A constrained linear least squares is used to reconstruct a 3D model and camera motions, followed by the bundle adjustment. We have validated our algorithm on challenging datasets, outperforming various state-of-the-art reconstruction techniques.
Real-time Collision Handling in Railway Network:An Agent-based Approach
Venue: arXiv cs.MA 2016
Year: 2016

Advancement in intelligent transportation systems with complex operations requires autonomous planning and management to avoid collisions in day-to-day traffic. As failure and/or inadequacy in traffic safety system are life-critical, such collisions must be detected and resolved in an efficient way to manage continuously rising traffic. In this paper, we address different types of collision scenarios along with their early detection and resolution techniques in a complex railway system. In order to handle collisions dynamically in distributed manner, a novel agent based solution approach is proposed using the idea of max-sum algorithm, where each agent (train agent, station agent, and junction agent) communicates and cooperates with others to generate a good feasible solution that keeps the system in a safe state, i.e., collision free. We implement the proposed mechanism in Java Agent DEvelopment Framework (JADE). The results are evaluated with exhaustive experiments and compared with different existing collision handling methods to show the efficiency of our proposed approach.
New Generation Value Networks for Content Delivery
Venue: arXiv cs.NI 2016
Year: 2016

In this paper we paint a broad picture of the Internet content delivery market, by taking into consideration both economical and technical challenges that might drive the interactions among the stakeholders in the future. We focus on a few disrupting factors, namely ubiquitous encryption, traffic boost, network scalability, latency needs and network control; and try to figure out whether the current model (CDN) is robust against their variation, which optimization can be envisioned and how the most accredited option (ICN) can be of help.
Message Passing Multi-Agent GANs
Venue: arXiv cs.CV 2016
Year: 2016

Communicating and sharing intelligence among agents is an important facet of achieving Artificial General Intelligence. As a first step towards this challenge, we introduce a novel framework for image generation: Message Passing Multi-Agent Generative Adversarial Networks (MPM GANs). While GANs have recently been shown to be very effective for image generation and other tasks, these networks have been limited to mostly single generator-discriminator networks. We show that we can obtain multi-agent GANs that communicate through message passing to achieve better image generation. The objectives of the individual agents in this framework are two fold: a co-operation objective and a competing objective. The co-operation objective ensures that the message sharing mechanism guides the other generator to generate better than itself while the competing objective encourages each generator to generate better than its counterpart. We analyze and visualize the messages that these GANs share among themselves in various scenarios. We quantitatively show that the message sharing formulation serves as a regularizer for the adversarial training. Qualitatively, we show that the different generators capture different traits of the underlying data distribution.
Highly Efficient Regression for Scalable Person Re-Identification
Venue: arXiv cs.CV 2016
Year: 2016

Existing person re-identification models are poor for scaling up to large data required in real-world applications due to: (1) Complexity: They employ complex models for optimal performance resulting in high computational cost for training at a large scale; (2) Inadaptability: Once trained, they are unsuitable for incremental update to incorporate any new data available. This work proposes a truly scalable solution to re-id by addressing both problems. Specifically, a Highly Efficient Regression (HER) model is formulated by embedding the Fisher's criterion to a ridge regression model for very fast re-id model learning with scalable memory/storage usage. Importantly, this new HER model supports faster than real-time incremental model updates therefore making real-time active learning feasible in re-id with human-in-the-loop. Extensive experiments show that such a simple and fast model not only outperforms notably the state-of-the-art re-id methods, but also is more scalable to large data with additional benefits to active learning for reducing human labelling effort in re-id deployment.
A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Dataset
Venue: arXiv cs.LG 2016
Year: 2016

Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due to the nonlinear and heterogeneous nature of the subsurface physical properties. In this context, present study proposes a generalized one class classification framework based on Support Vector Data Description (SVDD) to classify a reservoir characteristic water saturation into two classes (Class high and Class low) from four logs namely gamma ray, neutron porosity, bulk density, and P sonic using an imbalanced dataset. A comparison is carried out among proposed framework and different supervised classification algorithms in terms of g metric means and execution time. Experimental results show that proposed framework has outperformed other classifiers in terms of these performance evaluators. It is envisaged that the classification analysis performed in this study will be useful in further reservoir modeling.
In Quest for Proper Mediums for Technology Transfer in Software Engineering
Venue: arXiv cs.SE 2016
Year: 2016

Successful transfer of the results of research projects into practice is of great interest to all project participants. It can be assumed that different transfer mediums fulfill technology transfer (TT) with different levels of success and that they are impaired by different kinds of barriers. The goal of this study is to gain a better understanding about the different mediums used for TT in software engineering, and to identify barriers weakening the success of the application of such mediums. We conducted an exploratory study implemented by a survey in the context of a German research project with a broad range of used mediums. The main reported barriers were low expectations of usefulness, no awareness of existence, lack of resources, or inadequateness in terms of outdated material or being in an immature state. We interpreted our results as symptoms of a lack of a dissemination plan in the project. Further work will be needed to explore the implications for the transfer of research results (knowledge and techniques) to practice.