OpenRaiser/Intern-Atlas huggingface dataset:
AME Blockchain: An Architecture Design for Closed-Loop Fluid Economy Token System
Venue: arXiv cs.DC 2018
Year: 2018

In this white paper, we propose a blockchain-based system, named AME, which is a decentralized infrastructure and application platform with enhanced security and self-management properties. The AME blockchain technology aims to increase the transaction throughput by adopting various optimizations in network transport and storage layers, and to enhance smart contracts with AI algorithm support. We introduce all major technologies adopted in our system, including blockchain, distributed storage, P2P network, service application framework, and data encryption. To properly provide a cohesive, concise, yet comprehensive introduction to the AME system, we mainly focus on describing the unique definitions and features that guide the system implementation.
Accurate Hand Keypoint Localization on Mobile Devices
Venue: arXiv cs.CV 2018
Year: 2018

We present a novel approach for 2D hand keypoint localization from regular color input. The proposed approach relies on an appropriately designed Convolutional Neural Network (CNN) that computes a set of heatmaps, one per hand keypoint of interest. Extensive experiments with the proposed method compare it against state of the art approaches and demonstrate its accuracy and computational performance on standard, publicly available datasets. The obtained results demonstrate that the proposed method matches or outperforms the competing methods in accuracy, but clearly outperforms them in computational efficiency, making it a suitable building block for applications that require hand keypoint estimation on mobile devices.
Using the decision support algorithms combining different security policies
Venue: arXiv cs.CR 2018
Year: 2018

During the development of the security subsystem of modern information systems, a problem of the joint implementation of several access control models arises quite often. Traditionally, a request for the user's access to resources is granted in case of simultaneous access permission by all active security policies. When there is a conflict between the decisions of the security policies, the issue of granting access remains open. The proposed method of combining multiple security policies is based on the decision support algorithms and provides a response to the access request, even in case of various decisions of active security policies. To construct combining algorithm we determine a number of weight coefficients, use a weighted sum of the clearance levels of individual security policies and apply the analytic hierarchy process. The weight coefficients are adjustable parameters of the algorithm and allow administrator to manage the impact of the various security rules flexibly.
Exposing A Customizable, Decentralized Cryptoeconomy as a Data Type
Venue: arXiv cs.DC 2018
Year: 2018

Purposely modular, this protocol enables customization of several protocol properties, including the consensus properties implemented, blockchain type, the roots used, and virtual machine opcodes, among others. These modules enable implementing parties to control the behavior of their economy, with a minimal amount of effort, and no sacrifice in participant cryptoeconomic quality. This work also demonstrates the simplification of the developer experience by abstracting away all technological details, except basic CRUD-based operations, using various programming languages. We demonstrate the mechanism design approach taken, and formalize a process for deploying populations of blockchain economies at scale. The framework shown includes adequate tooling for simulation, development, deployment, maintenance, and analytic-based decision making. Lastly, we introduce an expressive programming language for the purpose of creating, and interacting with the cryptoeconomy designed by the implementing developer.
Generating Diverse and Meaningful Captions
Venue: arXiv cs.CV 2018
Year: 2018

Image Captioning is a task that requires models to acquire a multi-modal understanding of the world and to express this understanding in natural language text. While the state-of-the-art for this task has rapidly improved in terms of n-gram metrics, these models tend to output the same generic captions for similar images. In this work, we address this limitation and train a model that generates more diverse and specific captions through an unsupervised training approach that incorporates a learning signal from an Image Retrieval model. We summarize previous results and improve the state-of-the-art on caption diversity and novelty. We make our source code publicly available online.
Unconstrained Iris Segmentation using Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

The extraction of consistent and identifiable features from an image of the human iris is known as iris recognition. Identifying which pixels belong to the iris, known as segmentation, is the first stage of iris recognition. Errors in segmentation propagate to later stages. Current segmentation approaches are tuned to specific environments. We propose using a convolution neural network for iris segmentation. Our algorithm is accurate when trained in a single environment and tested in multiple environments. Our network builds on the Mask R-CNN framework (He et al., ICCV 2017). Our approach segments faster than previous approaches including the Mask R-CNN network. Our network is accurate when trained on a single environment and tested with a different sensors (either visible light or near-infrared). Its accuracy degrades when trained with a visible light sensor and tested with a near-infrared sensor (and vice versa). A small amount of retraining of the visible light model (using a few samples from a near-infrared dataset) yields a tuned network accurate in both settings. For training and testing, this work uses the Casia v4 Interval, Notre Dame 0405, Ubiris v2, and IITD datasets.
A Rich-Variant Architecture for a User-Aware multi-tenant SaaS approach
Venue: arXiv cs.SE 2018
Year: 2018

Software as a Service cloud computing model favorites the Multi-Tenancy as a key factor to exploit economies of scale. However Multi-Tenancy present several disadvantages. Therein, our approach comes to assign instances to multi-tenants with an optimal solution while ensuring more economies of scale and avoiding tenants hesitation to share resources. The present paper present the architecture of our user-aware multi-tenancy SaaS approach based on the use of rich-variant components. The proposed approach seek to model services functional customization as well as automation of computing the optimal distribution of instances by tenants. The proposed model takes into consideration tenants functional requirements and tenants deployment requirements to deduce an optimal distribution using essentially a specific variability engine and a graph-based execution framework.
Factorization Machines for Data with Implicit Feedback
Venue: arXiv cs.IR 2018
Year: 2018

In this work, we propose FM-Pair, an adaptation of Factorization Machines with a pairwise loss function, making them effective for datasets with implicit feedback. The optimization model in FM-Pair is based on the BPR (Bayesian Personalized Ranking) criterion, which is a well-established pairwise optimization model. FM-Pair retains the advantages of FMs on generality, expressiveness and performance and yet it can be used for datasets with implicit feedback. We also propose how to apply FM-Pair effectively on two collaborative filtering problems, namely, context-aware recommendation and cross-domain collaborative filtering. By performing experiments on different datasets with explicit or implicit feedback we empirically show that in most of the tested datasets, FM-Pair beats state-of-the-art learning-to-rank methods such as BPR-MF (BPR with Matrix Factorization model). We also show that FM-Pair is significantly more effective for ranking, compared to the standard FMs model. Moreover, we show that FM-Pair can utilize context or cross-domain information effectively as the accuracy of recommendations would always improve with the right auxiliary features. Finally we show that FM-Pair has a linear time complexity and scales linearly by exploiting additional features.
Coroutines with Higher Order Functions
Venue: arXiv cs.PL 2018
Year: 2018

Coroutines are non-preemptive concurrent subroutines that, unlike preemptive threads, voluntarily transfer control between each others. Introduced in the 60s before loosing in popularity in the 80s, they have seen a regain of interest in recent years, thanks to how elegantly they can solve numerous algorithmic problems. Unfortunately, some mainstream languages still lack support for coroutines, hence requiring either the use of non-standard interpreter/compilers, or elaborate hacks in thrid-party libraries. In this short paper, we propose a very simple way to implement coroutine-like components on the top of any language that support or can emulate higher order functions. We accompany our explanations with a handful of examples in JavaScript.
Multisource and Multitemporal Data Fusion in Remote Sensing
Venue: arXiv cs.LG 2018
Year: 2018

The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary datasets, however, opens up the possibility of utilizing multimodal datasets in a joint manner to further improve the performance of the processing approaches with respect to the application at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several spaceborne sensors, the integration of the temporal information with the spatial and/or spectral/backscattering information of the remotely sensed data is possible and helps to move from a representation of 2D/3D data to 4D data structures, where the time variable adds new information as well as challenges for the information extraction algorithms. There are a huge number of research works dedicated to multisource and multitemporal data fusion, but the methods for the fusion of different modalities have expanded in different paths according to each research community. This paper brings together the advances of multisource and multitemporal data fusion approaches with respect to different research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references.
Efficient logic architecture in training gradient boosting decision tree for high-performance and edge computing
Venue: arXiv cs.LG 2018
Year: 2018

This study proposes a logic architecture for the high-speed and power efficiently training of a gradient boosting decision tree model of binary classification. We implemented the proposed logic architecture on an FPGA and compared training time and power efficiency with three general GBDT software libraries using CPU and GPU. The training speed of the logic architecture on the FPGA was 26-259 times faster than the software libraries. The power efficiency of the logic architecture was 90-1,104 times higher than the software libraries. The results show that the logic architecture suits for high-performance and edge computing.
Energy-aware virtual machine selection method for cloud data center resource allocation
Venue: arXiv cs.DC 2018
Year: 2018

Saving energy is an important issue for cloud providers to reduce energy cost in a data center. With the increasing popularity of cloud computing, it is time to examine various energy reduction methods for which energy consumption could be reduced and lead us to green cloud computing. In this paper, our aim is to propose a virtual machine selection algorithm to improve the energy efficiency of a cloud data center. We are also presenting experimental results of the proposed algorithm in a cloud computing based simulation environment. The proposed algorithm dynamically took the virtual machines' allocation, deallocation, and reallocation action to the physical server. However, it depends on the load and heuristics based on the analysis placement of a virtual machine which is decided over time. From the results obtained from the simulation, we have found that our proposed virtual machine selection algorithm reduces the total energy consumption by 19% compared to the existing one. Therefore, the energy consumption cost of a cloud data center reduces and also lowers the carbon footprint. Simulation-based experimental results show that the proposed heuristics which are based on resource provisioning algorithms reduce the energy consumption of the cloud data center and decrease the virtual machine's migration rate.
Context, Attention and Audio Feature Explorations for Audio Visual Scene-Aware Dialog
Venue: arXiv cs.CL 2018
Year: 2018

With the recent advancements in AI, Intelligent Virtual Assistants (IVA) have become a ubiquitous part of every home. Going forward, we are witnessing a confluence of vision, speech and dialog system technologies that are enabling the IVAs to learn audio-visual groundings of utterances and have conversations with users about the objects, activities and events surrounding them. As a part of the 7th Dialog System Technology Challenges (DSTC7), for Audio Visual Scene-Aware Dialog (AVSD) track, We explore `topics' of the dialog as an important contextual feature into the architecture along with explorations around multimodal Attention. We also incorporate an end-to-end audio classification ConvNet, AclNet, into our models. We present detailed analysis of the experiments and show that some of our model variations outperform the baseline system presented for this task.
A benchmark library for parametric timed model checking
Venue: arXiv cs.LO 2018
Year: 2018

Verification of real-time systems involving hard timing constraints and concurrency is of utmost importance. Parametric timed model checking allows for formal verification in the presence of unknown timing constants or uncertainty (e.g. imprecision for periods). With the recent development of several techniques and tools to improve the efficiency of parametric timed model checking, there is a growing need for proper benchmarks to test and compare fairly these tools. We present here a benchmark library for parametric timed model checking made of benchmarks accumulated over the years. Our benchmarks include academic benchmarks, industrial case studies and examples unsolvable using existing techniques.
Atomic Appends: Selling Cars and Coordinating Armies with Multiple Distributed Ledgers
Venue: arXiv cs.DC 2018
Year: 2018

The various applications using Distributed Ledger Technologies (DLT) or blockchains, have led to the introduction of a new `marketplace' where multiple types of digital assets may be exchanged. As each blockchain is designed to support specific types of assets and transactions, and no blockchain will prevail, the need to perform interblockchain transactions is already pressing. In this work we examine the fundamental problem of interoperable and interconnected blockchains. In particular, we begin by introducing the Multi-Distributed Ledger Objects (MDLO), which is the result of aggregating multiple Distributed Ledger Objects -- DLO (a DLO is a formalization of the blockchain) and that supports append and get operations of records (e.g., transactions) in them from multiple clients concurrently. Next, we define the AtomicAppends problem, which emerges when the exchange of digital assets between multiple clients may involve appending records in more than one DLO. Specifically, AtomicAppend requires that either all records will be appended on the involved DLOs or none. We examine the solvability of this problem assuming rational and risk-averse clients that may fail by crashing, and under different client utility and append models, timing models, and client failure scenarios. We show that for some cases the existence of an intermediary is necessary for the problem solution. We propose the implementation of such intermediary over a specialized blockchain, we term Smart DLO (SDLO), and we show how this can be used to solve the AtomicAppends problem even in an asynchronous, client competitive environment, where all the clients may crash.
Environment Perception Framework Fusing Multi-Object Tracking, Dynamic Occupancy Grid Maps and Digital Maps
Venue: arXiv cs.RO 2018
Year: 2018

Autonomously driving vehicles require a complete and robust perception of the local environment. A main challenge is to perceive any other road users, where multi-object tracking or occupancy grid maps are commonly used. The presented approach combines both methods to compensate false positives and receive a complementary environment perception. Therefore, an environment perception framework is introduced that defines a common representation, extracts objects from a dynamic occupancy grid map and fuses them with tracks of a Labeled Multi-Bernoulli filter. Finally, a confidence value is developed, that validates object estimates using different constraints regarding physical possibilities, method specific characteristics and contextual information from a digital map. Experimental results with real world data highlight the robustness and significance of the presented fusing approach, utilizing the confidence value in rural and urban scenarios.
Efficient and scalable data structures and algorithms for goal-oriented adaptivity of space-time FEM codes
Venue: arXiv cs.MS 2018
Year: 2018

The cost- and memory-efficient numerical simulation of coupled volume-based multi-physics problems like flow, transport, wave propagation and others remains a challenging task with finite element method (FEM) approaches. Goal-oriented space and time adaptive methods derived from the dual weighted residual (DWR) method appear to be a shiny key technology to generate optimal space-time meshes to minimise costs. Current implementations for challenging problems of numerical screening tools including the DWR technology broadly suffer in their extensibility to other problems, in high memory consumption or in missing system solver technologies. This work contributes to the efficient embedding of DWR space-time adaptive methods into numerical screening tools for challenging problems of physically relevance with a new approach of flexible data structures and algorithms on them, a modularised and complete implementation as well as illustrative examples to show the performance and efficiency.
Impact of Social Media Posts in Real life Violence: A Case Study in Bangladesh
Venue: arXiv cs.HC 2018
Year: 2018

Social Networking Site (SNS) is a great innovation of modern times. Facebook, Twitter etc. have become an everyday part of peoples' life. Among all SNSs, Facebook is the most popular social network all over the world. Bangladesh is no exception. People of Bangladesh use Facebook for social communication, online shopping, business, knowledge and experience sharing etc. As well as the various uses of SNSs, people sometimes find themselves involved in real life violence, provoked by some social media posts or activities. In this paper, we discussed some case studies in which real life violence is originated based on Facebook activities in Bangladesh. Facebook was used in these incidents intentionally or unintentionally mostly as a tool to trigger hatred and violence. We analyzed and discussed the real-world consequences of these virtual activities in social media. Lastly, we recommended possible future measurements to prevent such violence.
Deep Paper Gestalt
Venue: arXiv cs.CV 2018
Year: 2018

Recent years have witnessed a significant increase in the number of paper submissions to computer vision conferences. The sheer volume of paper submissions and the insufficient number of competent reviewers cause a considerable burden for the current peer review system. In this paper, we learn a classifier to predict whether a paper should be accepted or rejected based solely on the visual appearance of the paper (i.e., the gestalt of a paper). Experimental results show that our classifier can safely reject 50% of the bad papers while wrongly reject only 0.4% of the good papers, and thus dramatically reduce the workload of the reviewers. We also provide tools for providing suggestions to authors so that they can improve the gestalt of their papers.
Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
Venue: arXiv cs.CL 2018
Year: 2018

Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the language generator should generate sentences that convey desired information. Traditional template-based generators can produce sentences with all necessary information, but these sentences are not sufficiently diverse. With RNN-based models, the diversity of the generated sentences can be high, however, in the process some information is lost. In this work, we improve an RNN-based generator by considering latent information at the sentence level during generation using the conditional variational autoencoder architecture. We demonstrate that our model outperforms the original RNN-based generator, while yielding highly diverse sentences. In addition, our model performs better when the training data is limited.
Nearly-Linear Time Spectral Graph Reduction for Scalable Graph Partitioning and Data Visualization
Venue: arXiv cs.DS 2018
Year: 2018

This paper proposes a scalable algorithmic framework for spectral reduction of large undirected graphs. The proposed method allows computing much smaller graphs while preserving the key spectral (structural) properties of the original graph. Our framework is built upon the following two key components: a spectrum-preserving node aggregation (reduction) scheme, as well as a spectral graph sparsification framework with iterative edge weight scaling. We show that the resulting spectrally-reduced graphs can robustly preserve the first few nontrivial eigenvalues and eigenvectors of the original graph Laplacian. In addition, the spectral graph reduction method has been leveraged to develop much faster algorithms for multilevel spectral graph partitioning as well as t-distributed Stochastic Neighbor Embedding (t-SNE) of large data sets. We conducted extensive experiments using a variety of large graphs and data sets, and obtained very promising results. For instance, we are able to reduce the "coPapersCiteseer" graph with 0.43 million nodes and 16 million edges to a much smaller graph with only 13K (32X fewer) nodes and 17K (950X fewer) edges in about 16 seconds; the spectrally-reduced graphs also allow us to achieve up to 1100X speedup for spectral graph partitioning and up to 60X speedup for t-SNE visualization of large data sets.
The language preservation problem is undecidable for parametric event-recording automata
Venue: arXiv cs.FL 2018
Year: 2018

Parametric timed automata (PTA) extend timed automata with unknown constants ("parameters"), at the price of undecidability of most interesting problems. The (untimed) language preservation problem ("given a parameter valuation, can we find at least one other valuation with the same untimed language?") is undecidable for PTAs. We prove that this problem remains undecidable for parametric event-recording automata (PERAs), a subclass of PTAs that considerably restrains the way the language can be used; we also show it remains undecidable even for slightly different definitions of the language, i.e., finite sequences of actions ending in or passing infinitely often through accepting locations, or just all finite untimed words (without accepting locations).