OpenRaiser/Intern-Atlas huggingface dataset:
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
Venue: arXiv cs.CV 2019
Year: 2019

Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera, foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.
On the Importance of Asymmetry and Monotonicity Constraints in Maximal Correlation Analysis
Venue: arXiv cs.IT 2019
Year: 2019

The maximal correlation coefficient is a well-established generalization of the Pearson correlation coefficient for measuring non-linear dependence between random variables. It is appealing from a theoretical standpoint, satisfying Rényi's axioms for a measure of dependence. It is also attractive from a computational point of view due to the celebrated alternating conditional expectation algorithm, allowing to compute its empirical version directly from observed data. Nevertheless, from the outset, it was recognized that the maximal correlation coefficient suffers from some fundamental deficiencies, limiting its usefulness as an indicator of estimation quality. Another well-known measure of dependence is the correlation ratio which also suffers from some drawbacks. Specifically, the maximal correlation coefficient equals one too easily whereas the correlation ratio equals zero too easily. The present work recounts some attempts that have been made in the past to alter the definition of the maximal correlation coefficient in order to overcome its weaknesses and then proceeds to suggest a natural variant of the maximal correlation coefficient as well as a modified conditional expectation algorithm to compute it. The proposed dependence measure at the same time resolves the major weakness of the correlation ratio measure and may be viewed as a bridge between these two classical measures.
Variation Network: Learning High-level Attributes for Controlled Input Manipulation
Venue: arXiv cs.LG 2019
Year: 2019

This paper presents the Variation Network (VarNet), a generative model providing means to manipulate the high-level attributes of a given input. The originality of our approach is that VarNet is not only capable of handling pre-defined attributes but can also learn the relevant attributes of the dataset by itself. These two settings can also be easily considered at the same time, which makes this model applicable to a wide variety of tasks. Further, VarNet has a sound information-theoretic interpretation which grants us with interpretable means to control how these high-level attributes are learned. We demonstrate experimentally that this model is capable of performing interesting input manipulation and that the learned attributes are relevant and meaningful.
An Optical Frontend for a Convolutional Neural Network
Venue: arXiv cs.CV 2019
Year: 2019

The parallelism of optics and the miniaturization of optical components using nanophotonic structures, such as metasurfaces present a compelling alternative to electronic implementations of convolutional neural networks. The lack of a low-power optical nonlinearity, however, requires slow and energy-inefficient conversions between the electronic and optical domains. Here, we design an architecture which utilizes a single electrical to optical conversion by designing a free-space optical frontend unit that implements the linear operations of the first layer with the subsequent layers realized electronically. Speed and power analysis of the architecture indicates that the hybrid photonic-electronic architecture outperforms sole electronic architecture for large image sizes and kernels. Benchmarking of the photonic-electronic architecture on a modified version of AlexNet achieves a classification accuracy of 87% on images from the Kaggle Cats and Dogs challenge database.
A Biologically Inspired Visual Working Memory for Deep Networks
Venue: arXiv cs.CV 2019
Year: 2019

The ability to look multiple times through a series of pose-adjusted glimpses is fundamental to human vision. This critical faculty allows us to understand highly complex visual scenes. Short term memory plays an integral role in aggregating the information obtained from these glimpses and informing our interpretation of the scene. Computational models have attempted to address glimpsing and visual attention but have failed to incorporate the notion of memory. We introduce a novel, biologically inspired visual working memory architecture that we term the Hebb-Rosenblatt memory. We subsequently introduce a fully differentiable Short Term Attentive Working Memory model (STAWM) which uses transformational attention to learn a memory over each image it sees. The state of our Hebb-Rosenblatt memory is embedded in STAWM as the weights space of a layer. By projecting different queries through this layer we can obtain goal-oriented latent representations for tasks including classification and visual reconstruction. Our model obtains highly competitive classification performance on MNIST and CIFAR-10. As demonstrated through the CelebA dataset, to perform reconstruction the model learns to make a sequence of updates to a canvas which constitute a parts-based representation. Classification with the self supervised representation obtained from MNIST is shown to be in line with the state of the art models (none of which use a visual attention mechanism). Finally, we show that STAWM can be trained under the dual constraints of classification and reconstruction to provide an interpretable visual sketchpad which helps open the 'black-box' of deep learning.
Machine Learning Automation Toolbox (MLaut)
Venue: arXiv cs.LG 2019
Year: 2019

In this paper we present MLaut (Machine Learning AUtomation Toolbox) for the python data science ecosystem. MLaut automates large-scale evaluation and benchmarking of machine learning algorithms on a large number of datasets. MLaut provides a high-level workflow interface to machine algorithm algorithms, implements a local back-end to a database of dataset collections, trained algorithms, and experimental results, and provides easy-to-use interfaces to the scikit-learn and keras modelling libraries. Experiments are easy to set up with default settings in a few lines of code, while remaining fully customizable to the level of hyper-parameter tuning, pipeline composition, or deep learning architecture. As a principal test case for MLaut, we conducted a large-scale supervised classification study in order to benchmark the performance of a number of machine learning algorithms - to our knowledge also the first larger-scale study on standard supervised learning data sets to include deep learning algorithms. While corroborating a number of previous findings in literature, we found (within the limitations of our study) that deep neural networks do not perform well on basic supervised learning, i.e., outside the more specialized, image-, audio-, or text-based tasks.
Implementation of the 3GPP LTE-WLAN Interworking Protocols in NS-3
Venue: arXiv cs.NI 2019
Year: 2019

The next generation wireless standard, called Fifth Generation (5G), is being designed to encompass Heterogeneous Networks (HetNets) architectures consisting of a single holistic network with Multiple Radio Access Technologies (Multi-RAT). Multiple connectivity protocols and spectrum would be managed from a common core (management system) handling both: i) traditional macro cellular systems (such as LTE), that can provide long-range, outdoor coverage, as well as ii) low-power wireless systems with high capacity (such as Wi-Fi), that can be deployed to cater indoor traffic needs. 5G HetNets are expected to achieve ubiquitous connectivity that would guarantee Quality of Service (QoS), Quality of Experience (QoE) along with efficient use of spectrum and energy at low cost. Tightly coupled LTE-Wi-Fi networks have emerged as one of the promising solutions in the 5G era to boost network capacity and improve end user's quality of experience. LTE/Wi-Fi Link Aggregation (LWA) and LTE WLAN Radio Level Integration with IPSec Tunnel (LWIP) are two approaches put forward by the 3rd Generation Partnership Project (3GPP) to enable flexible, general, and scalable LTE-WLAN inter-working. These techniques enable operator-controlled access of licensed and unlicensed spectrum and allow transparent access of operator's evolved core. The most important aspect of these techniques is that they could be enabled with straightforward software upgrades and can utilize the already existing Wi-Fi networks. This article presents and motivates the design details of LWA and LWIP protocols. We also present the first NS-3 LWA and LWIP implementations over Network Simulator 3 (NS-3). In particular, this work focuses on the adaptation and concurrent usage of different NS-3 modules and protocols of different technologies to enable the support of these interworking schemes.
Vehicular Edge Cloud Computing: Depressurize the Intelligent Vehicles Onboard Computational Power
Venue: arXiv cs.DC 2019
Year: 2019

Recently, with the rapid development of autonomous vehicles and connected vehicles, the demands of vehicular computing keep continuously growing. We notice a constant and limited onboard computational ability can hardly keep up with the rising requirements of the vehicular system and software application during their long-term lifetime, and also at the same time, the vehicles onboard computation causes an increasingly higher vehicular energy consumption. Therefore, we suppose to build a vehicular edge cloud computing (VECC) framework to resolve such a vehicular computing dilemma. In this framework, potential vehicular computing tasks can be executed remotely in an edge cloud within their time latency constraints. Simultaneously, an effective wireless network resources allocation scheme is one of the essential and fundamental factors for the QoS (quality of Service) on the VECC. In this paper, we adopted a stochastic fair allocation (SFA) algorithm to randomly allocate minimum required resource blocks to admitted vehicular users. The numerical results show great effectiveness of energy efficiency in VECC.
Using Scene Graph Context to Improve Image Generation
Venue: arXiv cs.CV 2019
Year: 2019

Generating realistic images from scene graphs asks neural networks to be able to reason about object relationships and compositionality. As a relatively new task, how to properly ensure the generated images comply with scene graphs or how to measure task performance remains an open question. In this paper, we propose to harness scene graph context to improve image generation from scene graphs. We introduce a scene graph context network that pools features generated by a graph convolutional neural network that are then provided to both the image generation network and the adversarial loss. With the context network, our model is trained to not only generate realistic looking images, but also to better preserve non-spatial object relationships. We also define two novel evaluation metrics, the relation score and the mean opinion relation score, for this task that directly evaluate scene graph compliance. We use both quantitative and qualitative studies to demonstrate that our pro-posed model outperforms the state-of-the-art on this challenging task.
Byzantine Consensus under Local Broadcast Model: Tight Sufficient Condition
Venue: arXiv cs.DC 2019
Year: 2019

In this work we consider Byzantine Consensus on undirected communication graphs under the local broadcast model. In the classical point-to-point communication model the messages exchanged between two nodes $u, v$ on an edge $uv$ of $G$ are private. This allows a faulty node to send conflicting information to its different neighbours, a property called equivocation. In contrast, in the local broadcast communication model considered here, a message sent by node $u$ is received identically by all of its neighbours. This restriction to broadcast messages provides non-equivocation even for faulty nodes. In prior results [10, 11] it was shown that in the local broadcast model the communication graph must be $(\lfloor 3f/2 \rfloor +1)$-connected and have degree at least $2f$ to achieve Byzantine Consensus. In this work we show that this network condition is tight.
What comes next? Extractive summarization by next-sentence prediction
Venue: arXiv cs.CL 2019
Year: 2019

Existing approaches to automatic summarization assume that a length limit for the summary is given, and view content selection as an optimization problem to maximize informativeness and minimize redundancy within this budget. This framework ignores the fact that human-written summaries have rich internal structure which can be exploited to train a summarization system. We present NEXTSUM, a novel approach to summarization based on a model that predicts the next sentence to include in the summary using not only the source article, but also the summary produced so far. We show that such a model successfully captures summary-specific discourse moves, and leads to better content selection performance, in addition to automatically predicting how long the target summary should be. We perform experiments on the New York Times Annotated Corpus of summaries, where NEXTSUM outperforms lead and content-model summarization baselines by significant margins. We also show that the lengths of summaries produced by our system correlates with the lengths of the human-written gold standards.
Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data
Venue: arXiv cs.SD 2019
Year: 2019

Continuous Speech Keyword Spotting (CSKS) is the problem of spotting keywords in recorded conversations, when a small number of instances of keywords are available in training data. Unlike the more common Keyword Spotting, where an algorithm needs to detect lone keywords or short phrases like "Alexa", "Cortana", "Hi Alexa!", "Whatsup Octavia?" etc. in speech, CSKS needs to filter out embedded words from a continuous flow of speech, ie. spot "Anna" and "github" in "I know a developer named Anna who can look into this github issue." Apart from the issue of limited training data availability, CSKS is an extremely imbalanced classification problem. We address the limitations of simple keyword spotting baselines for both aforementioned challenges by using a novel combination of loss functions (Prototypical networks' loss and metric loss) and transfer learning. Our method improves F1 score by over 10%.
All-Instances Restricted Chase Termination
Venue: arXiv cs.DB 2019
Year: 2019

The chase procedure is a fundamental algorithmic tool in database theory with a variety of applications. A key problem concerning the chase procedure is all-instances termination: for a given set of tuple-generating dependencies (TGDs), is it the case that the chase terminates for every input database? In view of the fact that this problem is undecidable, it is natural to ask whether known well-behaved classes of TGDs ensure decidability. We consider here the main paradigms that led to robust TGD-based formalisms, that is, guardedness and stickiness. Although all-instances termination is well-understood for the oblivious version of the chase, the more subtle case of the restricted (a.k.a. the standard) chase is rather unexplored. We show that all-instances restricted chase termination for guarded and sticky single-head TGDs is decidable.
Automated Deep Photo Style Transfer
Venue: arXiv cs.CV 2019
Year: 2019

Photorealism is a complex concept that cannot easily be formulated mathematically. Deep Photo Style Transfer is an attempt to transfer the style of a reference image to a content image while preserving its photorealism. This is achieved by introducing a constraint that prevents distortions in the content image and by applying the style transfer independently for semantically different parts of the images. In addition, an automated segmentation process is presented that consists of a neural network based segmentation method followed by a semantic grouping step. To further improve the results a measure for image aesthetics is used and elaborated. If the content and the style image are sufficiently similar, the result images look very realistic. With the automation of the image segmentation the pipeline becomes completely independent from any user interaction, which allows for new applications.
Image retrieval method based on CNN and dimension reduction
Venue: arXiv cs.CV 2019
Year: 2019

An image retrieval method based on convolution neural network and dimension reduction is proposed in this paper. Convolution neural network is used to extract high-level features of images, and to solve the problem that the extracted feature dimensions are too high and have strong correlation, multilinear principal component analysis is used to reduce the dimension of features. The features after dimension reduction are binary hash coded for fast image retrieval. Experiments show that the method proposed in this paper has better retrieval effect than the retrieval method based on principal component analysis on the e-commerce image datasets.
Serverless architecture efficiency: an exploratory study
Venue: arXiv cs.SE 2019
Year: 2019

Cloud service provider propose services to insensitive customers to use their platform. Different services can achieve the same result at different cost. In this paper, we study the efficiency of a serverless architecture for running highly parallelizable tasks to compare theses services in order to find the most efficient in term of performance and cost. More precisely, we look at the compute time and at the cost per task for a given task. The tasks studied is the count of the occurrence of a given word in a corpus. We compare the serverless architecture to the Apache Spark map reduce technique commonly used for this type of task. Using AWS Lambda for the serverless architecture and Amazon EMR for the Apache Spark map reduce, with similar compute power, we show that the serverless technique achieve comparable performance in term of compute time and cost. We observed that the lambda function is a great approach for real time computing, while EMR is preferable for task that require long compute time.
Implementing DMZ in Improving Network Security of Web Testing in STMIK AKBA
Venue: arXiv cs.CR 2019
Year: 2019

The aims of this research are to design and to implement network security system in internal web testing using DeMilitarized Zone Method and Microtic Router on Siakad server of STMIK AKBA. Data analysis techniques that possible to use is descriptive method. The significances of the study are 1) to avoid the attack of cracker who intend to access the system without permissions and 2) to improve network securityon web testing services on Siakad server of STMIK AKBA. The data are obtained by having literature review and observation. Literature review assists the researchers to collect the theory on DeMilitarised Zone Method and the previous studies which were used as comparison to the recent study. Obervation was carried out directly to the field to observe the running system. Based on the results and discussion, it is shown that the aplication of DeMilitarized Zone Method on microtic can secure the web testing on Siakad server of STMIK AKBA and can maintain the whole series of online services that are available in the server.
Optimal Strategies of Blotto Games: Beyond Convexity
Venue: arXiv cs.GT 2019
Year: 2019

The Colonel Blotto game, first introduced by Borel in 1921, is a well-studied game theory classic. Two colonels each have a pool of troops that they divide simultaneously among a set of battlefields. The winner of each battlefield is the colonel who puts more troops in it and the overall utility of each colonel is the sum of weights of the battlefields that s/he wins. Over the past century, the Colonel Blotto game has found applications in many different forms of competition from advertisements to politics to sports. Two main objectives have been proposed for this game in the literature: (i) maximizing the guaranteed expected payoff, and (ii) maximizing the probability of obtaining a minimum payoff $u$. The former corresponds to the conventional utility maximization and the latter concerns scenarios such as elections where the candidates' goal is to maximize the probability of getting at least half of the votes (rather than the expected number of votes). In this paper, we consider both of these objectives and show how it is possible to obtain (almost) optimal solutions that have few strategies in their support. One of the main technical challenges in obtaining bounded support strategies for the Colonel Blotto game is that the solution space becomes non-convex. This prevents us from using convex programming techniques in finding optimal strategies which are essentially the main tools that are used in the literature. However, we show through a set of structural results that the solution space can, interestingly, be partitioned into polynomially many disjoint convex polytopes that can be considered independently. Coupled with a number of other combinatorial observations, this leads to polynomial time approximation schemes for both of the aforementioned objectives.
Technologies for promoting social participation in later life
Venue: arXiv cs.HC 2019
Year: 2019

Social participation is known to bring great benefits to the health and well-being of people as they age. From being in contact with others to engaging in group activities, keeping socially active can help slow down the effects of age-related declines, reduce risks of loneliness and social isolation and even mortality in old age. There are unfortunately a variety of barriers that make it difficult for older adults to engage in social activities in a regular basis. In this chapter, we give an overview of the challenges to social participation and discuss how technology can help overcome these barriers and promote participation in social activities. We examine two particular research threads and designs, exploring ways in which technology can support co-located and virtual participation: i) an application that motivates the virtual participation in group training programs, and ii) a location-based game that supports co-located intergenerational ICT training classes. We discuss the effectiveness and limitations of various design choices in the two use cases and outline the lessons learned