OpenRaiser/Intern-Atlas huggingface dataset:
Enabling Enterprise Blockchain Interoperability with Trusted Data Transfer (industry track)
Venue: arXiv cs.DC 2019
Year: 2019

The adoption of permissioned blockchain networks in enterprise settings has seen an increase in growth over the past few years. While encouraging, this is leading to the emergence of new data, asset and process silos limiting the potential value these networks bring to the broader ecosystem. Mechanisms for enabling network interoperability help preserve the benefits of independent sovereign networks, while allowing for the transfer or sharing of data, assets and processes across network boundaries. However, a naive approach to interoperability based on traditional point-to-point integration is insufficient for preserving the underlying trust decentralized networks provide. In this paper, we lay the foundation for an approach to interoperability based on a communication protocol that derives trust from the underlying network consensus protocol. We present an architecture and a set of building blocks that can be adapted for use in a range of network implementations and demonstrate a proof-of-concept for trusted data-sharing between two independent trade finance and supply-chain networks, each running on Hyperledger Fabric. We show how existing blockchain deployments can be adapted for interoperation and discuss the security and extensibility of our architecture and mechanisms.
Spatial Search Strategies for Open Government Data: A Systematic Comparison
Venue: arXiv cs.IR 2019
Year: 2019

The increasing availability of open government datasets on the Web calls for ways to enable their efficient access and searching. There is however an overall lack of understanding regarding spatial search strategies which would perform best in this context. To address this gap, this work has assessed the impact of different spatial search strategies on performance and user relevance judgment. We harvested machine-readable spatial datasets and their metadata from three English-based open government data portals, performed metadata enhancement, developed a prototype and performed both a theoretical and user-based evaluation. The results highlight that (i) switching between area of overlap and Hausdorff distance for spatial similarity computation does not have any substantial impact on performance; and (ii) the use of Hausdorff distance induces slightly better user relevance ratings than the use of area of overlap. The data collected and the insights gleaned may serve as a baseline against which future work can compare.
Emergence of Numeric Concepts in Multi-Agent Autonomous Communication
Venue: arXiv cs.CL 2019
Year: 2019

With the rapid development of deep learning, most of current state-of-the-art techniques in natural langauge processing are based on deep learning models trained with argescaled static textual corpora. However, we human beings learn and understand in a different way. Thus, grounded language learning argues that models need to learn and understand language by the experience and perceptions obtained by interacting with enviroments, like how humans do. With the help of deep reinforcement learning techniques, there are already lots of works focusing on facilitating the emergence of communication protocols that have compositionalities like natural languages among computational agents population. Unlike these works, we, on the other hand, focus on the numeric concepts which correspond to abstractions in cognition and function words in natural language. Based on a specifically designed language game, we verify that computational agents are capable of transmitting numeric concepts during autonomous communication, and the emergent communication protocols can reflect the underlying structure of meaning space. Although their encodeing method is not compositional like natural languages from a perspective of human beings, the emergent languages can be generalised to unseen inputs and, more importantly, are easier for models to learn. Besides, iterated learning can help further improving the compositionality of the emergent languages, under the measurement of topological similarity. Furthermore, we experiment another representation method, i.e. directly encode numerals into concatenations of one-hot vectors, and find that the emergent languages would become compositional like human natural languages. Thus, we argue that there are 2 important factors for the emergence of compositional languages.
Learning One-Shot Imitation from Humans without Humans
Venue: arXiv cs.RO 2019
Year: 2019

Humans can naturally learn to execute a new task by seeing it performed by other individuals once, and then reproduce it in a variety of configurations. Endowing robots with this ability of imitating humans from third person is a very immediate and natural way of teaching new tasks. Only recently, through meta-learning, there have been successful attempts to one-shot imitation learning from humans; however, these approaches require a lot of human resources to collect the data in the real world to train the robot. But is there a way to remove the need for real world human demonstrations during training? We show that with Task-Embedded Control Networks, we can infer control polices by embedding human demonstrations that can condition a control policy and achieve one-shot imitation learning. Importantly, we do not use a real human arm to supply demonstrations during training, but instead leverage domain randomisation in an application that has not been seen before: sim-to-real transfer on humans. Upon evaluating our approach on pushing and placing tasks in both simulation and in the real world, we show that in comparison to a system that was trained on real-world data we are able to achieve similar results by utilising only simulation data.
Abstractions and automated algorithms for mixed domain finite element methods
Venue: arXiv cs.MS 2019
Year: 2019

Mixed dimensional partial differential equations (PDEs) are equations coupling unknown fields defined over domains of differing topological dimension. Such equations naturally arise in a wide range of scientific fields including geology, physiology, biology and fracture mechanics. Mixed dimensional PDEs are also commonly encountered when imposing non-standard conditions over a subspace of lower dimension e.g. through a Lagrange multiplier. In this paper, we present general abstractions and algorithms for finite element discretizations of mixed domain and mixed dimensional PDEs of co-dimension up to one (i.e. nD-mD with |n-m| <= 1). We introduce high level mathematical software abstractions together with lower level algorithms for expressing and efficiently solving such coupled systems. The concepts introduced here have also been implemented in the context of the FEniCS finite element software. We illustrate the new features through a range of examples, including a constrained Poisson problem, a set of Stokes-type flow models and a model for ionic electrodiffusion.
HARQ-CC Enabled NOMA Designs With Outage Probability Constraints
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we aim to design an adaptive power allocation scheme to minimize the average transmit power of a hybrid automatic repeat request with chase combining (HARQ-CC) enabled non-orthogonal multiple access (NOMA) system under strict outage constraints of users. Specifically, we assume the base station only knows the statistical channel state information of the users. We first focus on the two-user cases. To evaluate the performance of the two-user HARQ-CC enabled NOMA systems, we first analyze the outage probability of each user. Then, an average power minimization problem is formulated. However, the attained expressions of the outage probabilities are nonconvex, and thus make the problem difficult to solve. Thus, we first conservatively approximate it by a tractable one and then use a successive convex approximation based algorithm to handle the relaxed problem iteratively. For more practical applications, we also investigate the HARQ-CC enabled transmissions in multi-user scenarios. The user-paring and power allocation problem is considered. With the aid of matching theory, a low complexity algorithm is presented to first handle the user-paring problem. Then the power allocation problem is solved by the proposed SCA-based algorithm. Simulation results show the efficiency of the proposed transmission strategy and the near-optimality of the proposed algorithms.
Fast Safety Assessment and Correction Framework for Maintenance Work Zones
Venue: arXiv cs.OH 2019
Year: 2019

A framework is proposed to assess the safety of maintenance work zones in a timely manner, show whether there are safety hazards, whether adjustments need to be made and how to adjust it. By means of advanced data acquisition technologies such as multi video detection and portable device based naturalistic driving, the microscopic vehicle behaviour data can be collected. Based on this data, a method for expressing and displaying the distribution of unsafe vehicle behaviour is used to show whether safety hazards exist. Using Vissim, the impacts of the length and speed limit of the warning area, the length and type of the upstream transition area and the length of the work area of the maintenance work zone on the distribution of unsafe vehicle behaviour are simulated to establish the safety correction matrix, which can tell maintenance departments the direction of adjustment when safety hazards exist in maintenance work zones.
Raft Consensus Algorithm: an Effective Substitute for Paxos in High Throughput P2P-based Systems
Venue: arXiv cs.DC 2019
Year: 2019

One of the significant problem in peer-to-peer databases is collision problem. These databases do not rely on a central leader that is a reason to increase scalability and fault tolerance. Utilizing these systems in high throughput computing cause more flexibility in computing system and meanwhile solve the problems in most of the computing systems which are depend on a central nodes. There are limited researches in this scope and they seem are not suitable for using in a large scale. In this paper, we used Cassandra which is a distributed database based on peer-to-peer network as a high throughput computing system. Cassandra uses Paxos to elect central leader by default that causes collision problem. Among existent consensus algorithms Raft separates the key elements of consensus, such as leader election, so enforces a stronger degree of coherency to reduce the number of states that must be considered, such as collision.
Strategic API Analysis and Planning: APIS Technical Report
Venue: arXiv cs.SE 2019
Year: 2019

Traditionally, software APIs (application programming interfaces) have been viewed from a technical perspective, as a means to separate implementation from functional calls, and as a way to define a contract of software functionality. The technical benefits of APIs have been reported in numerous studies. Several reports from industry offer useful practical design considerations for APIs, including advice on collecting usage data, monetization strategies, and at what point to open an API to external parties. Although this advice can be useful, the focus is still often on the API, only without considering the role the API plays in the wider organization or how it fits into an organizational strategy. Our industrial experiences show that more information is needed about the challenges and best practices of API design and management in an organizational context. Furthermore, it has become apparent that APIs are able to play a key role as part of a strategic business plan for software-intensive companies. In this report, we present the results of our work with companies to build a framework that synthesizes and summarizes API strategies from several perspectives, including: strategic API lifecycle, lifecycle stage characteristics, lifecycle use cases and transition points, API layered architecture, BAPO, value modeling, ecosystem mapping with goal models, API metrics, goal models with metrics, and API governance guidance. Our research was carried out with an industry-academic collaboration. The framework was developed iteratively through workshops and discussions with company partners. We show how conceptual frameworks can be used to drive API strategy development, drawing on our research with several companies in practice.
Contextualization of Big Data Quality: A framework for comparison
Venue: arXiv cs.CY 2019
Year: 2019

With the advent of big data applications and the increasing amount of data being produced in these applications, the importance of efficient methods for big data analysis has become highly evident. However, the success of any such method will be hindered should the data lacks the required quality. Big data quality assessment is therefore a major requirement for any organization or business that use big data analytics for its decision making. On the other hand, using contextual information is advantageous in many analysis tasks in various domains, e.g. user behavior analysis in the social networks. However, the big data quality assessment has benefited less from this potential. There is a vast variety of data sources in the big data domain that can be utilized to improve the quality evaluation of big data. Including contextual information provided by these sources into the big data quality assessment process is an emerging trend towards more advanced techniques aimed at enhancing the performance and accuracy of quality assessment. This paper presents a context classification framework for big data quality, categorizing the context features into four primary dimensions: 1) context category, 2) data source type that contextual features come from, 3) discovery and extraction method of context, and 4) the quality factors affected by the contextual data. The proposed model introduces new context features and dimensions that need to be taken into consideration in quality assessment of big data. The initial evaluation demonstrates that the model is more understandable, more comprehensive, richer, and more useful compared to existing models.
Fast sampling and counting k-SAT solutions in the local lemma regime
Venue: arXiv cs.DS 2019
Year: 2019

We give new algorithms based on Markov chains to sample and approximately count satisfying assignments to $k$-uniform CNF formulas where each variable appears at most $d$ times. For any $k$ and $d$ satisfying $kd<n^{o(1)}$ and $k\ge 20\log k + 20\log d + 60$, the new sampling algorithm runs in close to linear time, and the counting algorithm runs in close to quadratic time. Our approach is inspired by Moitra (JACM, 2019) which remarkably utilizes the Lovász local lemma in approximate counting. Our main technical contribution is to use the local lemma to bypass the connectivity barrier in traditional Markov chain approaches, which makes the well developed MCMC method applicable on disconnected state spaces such as SAT solutions. The benefit of our approach is to avoid the enumeration of local structures and obtain fixed polynomial running times, even if $k=ω(1)$ or $d=ω(1)$.
Synthetic Video Generation for Robust Hand Gesture Recognition in Augmented Reality Applications
Venue: arXiv cs.CV 2019
Year: 2019

Hand gestures are a natural means of interaction in Augmented Reality and Virtual Reality (AR/VR) applications. Recently, there has been an increased focus on removing the dependence of accurate hand gesture recognition on complex sensor setup found in expensive proprietary devices such as the Microsoft HoloLens, Daqri and Meta Glasses. Most such solutions either rely on multi-modal sensor data or deep neural networks that can benefit greatly from abundance of labelled data. Datasets are an integral part of any deep learning based research. They have been the principal reason for the substantial progress in this field, both, in terms of providing enough data for the training of these models, and, for benchmarking competing algorithms. However, it is becoming increasingly difficult to generate enough labelled data for complex tasks such as hand gesture recognition. The goal of this work is to introduce a framework capable of generating photo-realistic videos that have labelled hand bounding box and fingertip that can help in designing, training, and benchmarking models for hand-gesture recognition in AR/VR applications. We demonstrate the efficacy of our framework in generating videos with diverse backgrounds.
Examining UK drill music through sentiment trajectory analysis
Venue: arXiv cs.SI 2019
Year: 2019

This paper presents how techniques from natural language processing can be used to examine the sentiment trajectories of gang-related drill music in the United Kingdom (UK). This work is important because key public figures are loosely making controversial linkages between drill music and recent escalations in youth violence in London. Thus, this paper examines the dynamic use of sentiment in gang-related drill music lyrics. The findings suggest two distinct sentiment use patterns and statistical analyses revealed that lyrics with a markedly positive tone attract more views and engagement on YouTube than negative ones. Our work provides the first empirical insights into the language use of London drill music, and it can, therefore, be used in future studies and by policymakers to help understand the alleged drill-gang nexus.
Affective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups more Therapeutic than Twitter?
Venue: arXiv cs.HC 2019
Year: 2019

The increase in the prevalence of mental health problems has coincided with a growing popularity of health related social networking sites. Regardless of their therapeutic potential, On-line Support Groups (OSGs) can also have negative effects on patients. In this work we propose a novel methodology to automatically verify the presence of therapeutic factors in social networking websites by using Natural Language Processing (NLP) techniques. The methodology is evaluated on On-line asynchronous multi-party conversations collected from an OSG and Twitter. The results of the analysis indicate that therapeutic factors occur more frequently in OSG conversations than in Twitter conversations. Moreover, the analysis of OSG conversations reveals that the users of that platform are supportive, and interactions are likely to lead to the improvement of their emotional state. We believe that our method provides a stepping stone towards automatic analysis of emotional states of users of online platforms. Possible applications of the method include provision of guidelines that highlight potential implications of using such platforms on users' mental health, and/or support in the analysis of their impact on specific individuals.
Active Status Update Packet Drop Control in an Energy Harvesting Node
Venue: arXiv cs.IT 2019
Year: 2019

This paper considers an energy harvesting sensor node with battery size $B_{max}$ that recharges its battery through an incremental energy harvesting process and receives updates from a single information source in slotted time. The node actively decides to power down (OFF) or up (ON) the communication circuitry for a portion of its operation time in order to maintain energy efficiency. Update packets arriving in ON (OFF) periods are received (discarded). A deterministic energy cost per time is paid during ON periods. The power down decision can be in partial or full nature, yielding various options for deciding ON-OFF intervals. We develop age-threshold based power ON-OFF schemes to minimize age of information at the node subject to energy harvesting constraints with partial and full power down options for $B_{max}=1$ and $B_{max}=\infty$ cases.
Improving Supervised Phase Identification Through the Theory of Information Losses
Venue: arXiv cs.LG 2019
Year: 2019

This paper considers the problem of Phase Identification in power distribution systems. In particular, it focuses on improving supervised learning accuracies by focusing on exploiting some of the problem's information theoretic properties. This focus, along with recent advances in Information Theoretic Machine Learning (ITML), helps us to create two new techniques. The first transforms a bound on information losses into a data selection technique. This is important because phase identification data labels are difficult to obtain in practice. The second interprets the properties of distribution systems in the terms of ITML. This allows us to obtain an improvement in the representation learned by any classifier applied to the problem. We tested these two techniques experimentally on real datasets and have found that they yield phenomenal performance in every case. In the most extreme case, they improve phase identification accuracy from $51.7\%$ to $97.3\%$. Furthermore, since many problems share the physical properties of phase identification exploited in this paper, the techniques can be applied to a wide range of similar problems.
On Compositionality in Neural Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

We investigate two specific manifestations of compositionality in Neural Machine Translation (NMT) : (1) Productivity - the ability of the model to extend its predictions beyond the observed length in training data and (2) Systematicity - the ability of the model to systematically recombine known parts and rules. We evaluate a standard Sequence to Sequence model on tests designed to assess these two properties in NMT. We quantitatively demonstrate that inadequate temporal processing, in the form of poor encoder representations is a bottleneck for both Productivity and Systematicity. We propose a simple pre-training mechanism which alleviates model performance on the two properties and leads to a significant improvement in BLEU scores.
DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

DeepRacer is a platform for end-to-end experimentation with RL and can be used to systematically investigate the key challenges in developing intelligent control systems. Using the platform, we demonstrate how a 1/18th scale car can learn to drive autonomously using RL with a monocular camera. It is trained in simulation with no additional tuning in physical world and demonstrates: 1) formulation and solution of a robust reinforcement learning algorithm, 2) narrowing the reality gap through joint perception and dynamics, 3) distributed on-demand compute architecture for training optimal policies, and 4) a robust evaluation method to identify when to stop training. It is the first successful large-scale deployment of deep reinforcement learning on a robotic control agent that uses only raw camera images as observations and a model-free learning method to perform robust path planning. We open source our code and video demo on GitHub: https://git.io/fjxoJ.
Deep Collaborative Discrete Hashing with Semantic-Invariant Structure
Venue: arXiv cs.IR 2019
Year: 2019

Existing deep hashing approaches fail to fully explore semantic correlations and neglect the effect of linguistic context on visual attention learning, leading to inferior performance. This paper proposes a dual-stream learning framework, dubbed Deep Collaborative Discrete Hashing (DCDH), which constructs a discriminative common discrete space by collaboratively incorporating the shared and individual semantics deduced from visual features and semantic labels. Specifically, the context-aware representations are generated by employing the outer product of visual embeddings and semantic encodings. Moreover, we reconstruct the labels and introduce the focal loss to take advantage of frequent and rare concepts. The common binary code space is built on the joint learning of the visual representations attended by language, the semantic-invariant structure construction and the label distribution correction. Extensive experiments demonstrate the superiority of our method.