OpenRaiser/Intern-Atlas huggingface dataset:
Service Discovery for Hyperledger Fabric
Venue: arXiv cs.DC 2018
Year: 2018

Hyperledger Fabric (HLF) is a modular and extensible permissioned blockchain platform released to open-source and hosted by the Linux Foundation. The platform's design exhibits principles required by enterprise grade business applications like supply-chains, financial transactions, asset management, food safety, and many more. For that end HLF introduces several innovations, two of which are smart contracts in general purpose languages (\emph{chaincode} in HLF), and flexible endorsement policies, which govern whether a transaction is considered valid. Typical blockchain applications are comprised of two tiers: the first tier focuses on the modelling of the data schema and embedding of business rules into the blockchain by means of smart contracts (\emph{chaincode}) and endorsment policies; and the second tier uses the SDK (Software Development Kit) provided by HLF to implement client side application logic. However there is a gap between the two tiers that hinders the rapid adoption of changes in the chaincode and endorsement policies within the client SDK. Currently, the chaincode location and endorsement policies are statically configured into the client SDK. This limits the reliability and availability of the client in the event of changes in the platform, and makes the platform more difficult to use. In this work we address and bridge the gap by describing the design and implementation of \emph{Service Discovery}. \emph{Service Discovery} provides APIs which allow dynamic discovery of the configuration required for the client SDK to interact with the platform, alleviating the client from the burden of maintaining it. This enables the client to rapidly adapt to changes in the platform, thus significantly improving the reliability of the application layer. It also makes the HLF platform more consumable, simplifying the job of creating blockchain applications.
An Interval Type-2 Fuzzy Approach to Automatic PDF Generation for Histogram Specification
Venue: arXiv cs.CV 2018
Year: 2018

Image enhancement plays an important role in several application in the field of computer vision and image processing. Histogram specification (HS) is one of the most widely used techniques for contrast enhancement of an image, which requires an appropriate probability density function for the transformation. In this paper, we propose a fuzzy method to find a suitable PDF automatically for histogram specification using interval type - 2 (IT2) fuzzy approach, based on the fuzzy membership values obtained from the histogram of input image. The proposed algorithm works in 5 stages which includes - symmetric Gaussian fitting on the histogram, extraction of IT2 fuzzy membership functions (MFs) and therefore, footprint of uncertainty (FOU), obtaining membership value (MV), generating PDF and application of HS. We have proposed 4 different methods to find membership values - point-wise method, center of weight method, area method, and karnik-mendel (KM) method. The framework is sensitive to local variations in the histogram and chooses the best PDF so as to improve contrast enhancement. Experimental validity of the methods used is illustrated by qualitative and quantitative analysis on several images using the image quality index - Average Information Content (AIC) or Entropy, and by comparison with the commonly used algorithms such as Histogram Equalization (HE), Recursive Mean-Separate Histogram Equalization (RMSHE) and Brightness Preserving Fuzzy Histogram Equalization (BPFHE). It has been found out that on an average, our algorithm improves the AIC index by 11.5% as compared to the index obtained by histogram equalisation.
Context Spaces as the Cornerstone of a Near-Transparent & Self-Reorganizing Semantic Desktop
Venue: arXiv cs.AI 2018
Year: 2018

Existing Semantic Desktops are still reproached for being too complicated to use or not scaling well. Besides, a real "killer app" is still missing. In this paper, we present a new prototype inspired by NEPOMUK and its successors having a semantic graph and ontologies as its basis. In addition, we introduce the idea of context spaces that users can directly interact with and work on. To make them available in all applications without further ado, the system is transparently integrated using mostly standard protocols complemented by a sidebar for advanced features. By exploiting collected context information and applying Managed Forgetting features (like hiding, condensation or deletion), the system is able to dynamically reorganize itself, which also includes a kind of tidy-up-itself functionality. We therefore expect it to be more scalable while providing new levels of user support. An early prototype has been implemented and is presented in this demo.
Distributed Joint Offloading Decision and Resource Allocation for Multi-User Mobile Edge Computing: A Game Theory Approach
Venue: arXiv cs.IT 2018
Year: 2018

Due to the spectrum reuse in small cell network, the inter-cell interference has great effect on MEC's performance. In this paper, for reducing the energy consumption and latency of MEC, we propose a game theory based jointing offloading decision and resource allocation algorithm for multi-user MEC. In this algorithm, the transmission power, offloading decision, and mobile user's CPU capability are determined jointly. We prove that this game is an exact potential game and the NE of this game exists and is unique. For reaching the NE, the best response dynamic is applied. We calculate the best responses of these three variables. Moreover, we investigate the properties of this algorithm, including the convergence, the computation complexity, and the price of anarchy. The theoretical analysis shows that the inter-cell interference has great effect on the performance of MEC. The NE of this game is Pareto efficiency and also the global optimal solution of the proposed optimal issue. Finally, we evaluate the performance of this algorithm by simulation. The simulation results illustrates that this algorithm is effective on improving the performance of the multi-user MEC system.
A Trustworthy Electronic Voting System for Australian Federal Elections
Venue: arXiv cs.CR 2018
Year: 2018

The existing system for determining election results in Australia is, for the most part, secure, accurate and understandable by the average voter. This thesis explores the design of electronic voting systems designed to achieve these same goals, while also improving on the existing system in the areas of counting speed, accuracy, tamper resistance and accessibility. Electronic voting systems have seen limited use within Australian elections, most prominently for State Elections in Victoria (2014), New South Wales (2015) and Western Australia (2017), along with trials of electronic voting systems in federal elections in 2007. This thesis presents an analysis of the iVote electronic voting system used for the 2017 Western Australian State Election (iVote WA), outlining a number of security risks introduced by the use of cloud-based distributed denial of service mitigation. In addition, this thesis presents the results of a cross-sectional survey of Australian voters regarding levels of trust for three voting systems: the existing paper-based system used for Australian federal elections, the iVote WA system, and the vVote system used for the 2014 Victorian State Election. The analysis of iVote, combined with the survey results, are used to inform a recommendation for future research and public policy regarding the use of electronic voting systems in Australian federal elections.
Correlation Heuristics for Constraint Programming
Venue: arXiv cs.AI 2018
Year: 2018

Effective general-purpose search strategies are an important component in Constraint Programming. We introduce a new idea, namely, using correlations between variables to guide search. Variable correlations are measured and maintained by using domain changes during constraint propagation. We propose two variable heuristics based on the correlation matrix, crbs-sum and crbs-max. We evaluate our correlation heuristics with well known heuristics, namely, dom/wdeg, impact-based search and activity-based search. Experiments on a large set of benchmarks show that our correlation heuristics are competitive with the other heuristics, and can be the fastest on many series.
Beamforming Design for Max-Min Fair SWIPT in Green Cloud-RAN with Wireless Fronthaul
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, a joint beamforming design for max-min fair simultaneous wireless information and power transfer (SWIPT) is investigated in a green cloud radio access network (Cloud-RAN) with millimeter wave (mmWave) wireless fronthaul. To achieve a balanced user experience for separately located data receivers (DRs) and energy receivers (ERs) in the network, joint transmit beamforming vectors are optimized to maximize the minimum data rate among all the DRs, while satisfying each ER with sufficient RF energy at the same time. Then, a two-step iterative algorithm is proposed to solve the original non-convex optimization problem with the fronthaul capacity constraint in an $l_0$-norm form. Specifically, the $l_0$-norm constraint can be approximated by the reweighted $l_1$-norm, from which the optimal max-min data rate and the corresponding joint beamforming vector can be derived via semidefinite relaxation (SDR) and bi-section search. Finally, extensive numerical simulations are performed to verify the superiority of the proposed joint beamforming design to other separate beamforming strategies.
Using Humanoid Robot to Instruct and Evaluate Performance of a Physical Task
Venue: arXiv cs.HC 2018
Year: 2018

In this paper, we present a tool to assess users ability to change tasks. To do this, we use a variation of the Box and Blocks Test. In this version, a humanoid robot instructs a user to perform a task involving the movement of certain colored blocks. The robot changes randomly change the color of blocks that the user is supposed to move. Canny Edge Detection and Hough Transformation are used to assess user perform the robot's built-in camera. This will allow the robot to inform the user and keep a log of their progress. We present this method for monitoring user progress by describing how the moved blocks are detected. We also present the results of a pilot study where users used this system to perform the task. Preliminary results show that users do not perform differently when the task is changed in this scenario.
Russian word sense induction by clustering averaged word embeddings
Venue: arXiv cs.CL 2018
Year: 2018

The paper reports our participation in the shared task on word sense induction and disambiguation for the Russian language (RUSSE-2018). Our team was ranked 2nd for the wiki-wiki dataset (containing mostly homonyms) and 5th for the bts-rnc and active-dict datasets (containing mostly polysemous words) among all 19 participants. The method we employed was extremely naive. It implied representing contexts of ambiguous words as averaged word embedding vectors, using off-the-shelf pre-trained distributional models. Then, these vector representations were clustered with mainstream clustering techniques, thus producing the groups corresponding to the ambiguous word senses. As a side result, we show that word embedding models trained on small but balanced corpora can be superior to those trained on large but noisy data - not only in intrinsic evaluation, but also in downstream tasks like word sense induction.
Automated Diagnosis of Clinic Workflows
Venue: arXiv cs.AI 2018
Year: 2018

Outpatient clinics often run behind schedule due to patients who arrive late or appointments that run longer than expected. We sought to develop a generalizable method that would allow healthcare providers to diagnose problems in workflow that disrupt the schedule on any given provider clinic day. We use a constraint optimization problem to identify the least number of appointment modifications that make the rest of the schedule run on-time. We apply this method to an outpatient clinic at Vanderbilt. For patient seen in this clinic between March 27, 2017 and April 21, 2017, long cycle times tended to affect the overall schedule more than late patients. Results from this workflow diagnosis method could be used to inform interventions to help clinics run smoothly, thus decreasing patient wait times and increasing provider utilization.
Incorporating Privileged Information to Unsupervised Anomaly Detection
Venue: arXiv cs.LG 2018
Year: 2018

We introduce a new unsupervised anomaly detection ensemble called SPI which can harness privileged information - data available only for training examples but not for (future) test examples. Our ideas build on the Learning Using Privileged Information (LUPI) paradigm pioneered by Vapnik et al. [19,17], which we extend to unsupervised learning and in particular to anomaly detection. SPI (for Spotting anomalies with Privileged Information) constructs a number of frames/fragments of knowledge (i.e., density estimates) in the privileged space and transfers them to the anomaly scoring space through "imitation" functions that use only the partial information available for test examples. Our generalization of the LUPI paradigm to unsupervised anomaly detection shepherds the field in several key directions, including (i) domain knowledge-augmented detection using expert annotations as PI, (ii) fast detection using computationally-demanding data as PI, and (iii) early detection using "historical future" data as PI. Through extensive experiments on simulated and real datasets, we show that augmenting privileged information to anomaly detection significantly improves detection performance. We also demonstrate the promise of SPI under all three settings (i-iii); with PI capturing expert knowledge, computationally expensive features, and future data on three real world detection tasks.
Multi-Domain Neural Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

We present an approach to neural machine translation (NMT) that supports multiple domains in a single model and allows switching between the domains when translating. The core idea is to treat text domains as distinct languages and use multilingual NMT methods to create multi-domain translation systems, we show that this approach results in significant translation quality gains over fine-tuning. We also explore whether the knowledge of pre-specified text domains is necessary, turns out that it is after all, but also that when it is not known quite high translation quality can be reached.
The State of the Art in Developing Fuzzy Ontologies: A Survey
Venue: arXiv cs.AI 2018
Year: 2018

Conceptual formalism supported by typical ontologies may not be sufficient to represent uncertainty information which is caused due to the lack of clear cut boundaries between concepts of a domain. Fuzzy ontologies are proposed to offer a way to deal with this uncertainty. This paper describes the state of the art in developing fuzzy ontologies. The survey is produced by studying about 35 works on developing fuzzy ontologies from a batch of 100 articles in the field of fuzzy ontologies.
A Hierarchical Matcher using Local Classifier Chains
Venue: arXiv cs.CV 2018
Year: 2018

This paper focuses on improving the performance of current convolutional neural networks in visual recognition without changing the network architecture. A hierarchical matcher is proposed that builds chains of local binary neural networks after one global neural network over all the class labels, named as Local Classifier Chains based Convolutional Neural Network (LCC-CNN). The signature of each sample as two components: global component based on the global network; local component based on local binary networks. The local networks are built based on label pairs created by a similarity matrix and confusion matrix. During matching, each sample travels through one global network and a chain of local networks to obtain its final matching to avoid error propagation. The proposed matcher has been evaluated with image recognition, character recognition and face recognition datasets. The experimental results indicate that the proposed matcher achieves better performance when compared with methods using only a global deep network. Compared with the UR2D system, the accuracy is improved significantly by 1% and 0.17% on the UHDB31 dataset and the IJB-A dataset, respectively.
Multimodal Machine Translation with Reinforcement Learning
Venue: arXiv cs.CL 2018
Year: 2018

Multimodal machine translation is one of the applications that integrates computer vision and language processing. It is a unique task given that in the field of machine translation, many state-of-the-arts algorithms still only employ textual information. In this work, we explore the effectiveness of reinforcement learning in multimodal machine translation. We present a novel algorithm based on the Advantage Actor-Critic (A2C) algorithm that specifically cater to the multimodal machine translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT18). We experiment our proposed algorithm on the Multi30K multilingual English-German image description dataset and the Flickr30K image entity dataset. Our model takes two channels of inputs, image and text, uses translation evaluation metrics as training rewards, and achieves better results than supervised learning MLE baseline models. Furthermore, we discuss the prospects and limitations of using reinforcement learning for machine translation. Our experiment results suggest a promising reinforcement learning solution to the general task of multimodal sequence to sequence learning.
Acyclic Strategy for Silent Self-Stabilization in Spanning Forests
Venue: arXiv cs.DC 2018
Year: 2018

In this paper, we formalize design patterns, commonly used in the self-stabilizing area, to obtain general statements regarding both correctness and time complexity guarantees. Precisely, we study a general class of algorithms designed for networks endowed with a sense of direction describing a spanning forest (e.g., a directed tree or a network where a directed spanning tree is available) whose characterization is a simple (i.e., quasi-syntactic) condition. We show that any algorithm of this class is (1) silent and self-stabilizing under the distributed unfair daemon, and (2) has a stabilization time which is polynomial in moves and asymptotically optimal in rounds. To illustrate the versatility of our method, we review several existing works where our results apply.
QARC: Video Quality Aware Rate Control for Real-Time Video Streaming via Deep Reinforcement Learning
Venue: arXiv cs.MM 2018
Year: 2018

Due to the fluctuation of throughput under various network conditions, how to choose a proper bitrate adaptively for real-time video streaming has become an upcoming and interesting issue. Recent work focuses on providing high video bitrates instead of video qualities. Nevertheless, we notice that there exists a trade-off between sending bitrate and video quality, which motivates us to focus on how to get a balance between them. In this paper, we propose QARC (video Quality Awareness Rate Control), a rate control algorithm that aims to have a higher perceptual video quality with possibly lower sending rate and transmission latency. Starting from scratch, QARC uses deep reinforcement learning(DRL) algorithm to train a neural network to select future bitrates based on previously observed network status and past video frames, and we design a neural network to predict future perceptual video quality as a vector for taking the place of the raw picture in the DRL's inputs. We evaluate QARC over a trace-driven emulation. As excepted, QARC betters existing approaches.
Non-Monochromatic and Conflict-Free Coloring on Tree Spaces and Planar Network Spaces
Venue: arXiv cs.DM 2018
Year: 2018

It is well known that any set of n intervals in $\mathbb{R}^1$ admits a non-monochromatic coloring with two colors and a conflict-free coloring with three colors. We investigate generalizations of this result to colorings of objects in more complex 1-dimensional spaces, namely so-called tree spaces and planar network spaces.
On Completely Reachable Automata and Subset Reachability
Venue: arXiv cs.FL 2018
Year: 2018

This article focuses on subset reachability in synchronizing automata. First, we provide families of synchronizing automata with subsets which cannot be reached with short words. These families do not fulfil Don's Conjecture about subset reachability. Moreover, they show that some subsets need exponentially long words to be reached, and that the restriction of the conjecture to included subsets also does not hold. Second, we analyze completely reachable automata and provide a counterexample to the conjecture of Bondar and Volkov about the so-called $Γ_1$-graph. We finally prove an alternative version of this conjecture.
A Data-Driven Approach to Smooth Pitch Correction for Singing Voice in Pop Music
Venue: arXiv cs.SD 2018
Year: 2018

In this paper, we present a machine-learning approach to pitch correction for voice in a karaoke setting, where the vocals and accompaniment are on separate tracks and time-aligned. The network takes as input the time-frequency representation of the two tracks and predicts the amount of pitch-shifting in cents required to make the voice sound in-tune with the accompaniment. It is trained on examples of semi-professional singing. The proposed approach differs from existing real-time pitch correction methods by replacing pitch tracking and mapping to a discrete set of notes---for example, the twelve classes of the equal-tempered scale---with learning a correction that is continuous both in frequency and in time directly from the harmonics of the vocal and accompaniment tracks. A Recurrent Neural Network (RNN) model provides a correction that takes context into account, preserving expressive pitch bending and vibrato. This method can be extended into unsupervised pitch correction of a vocal performance---popularly referred to as autotuning.
Computing the Shattering Coefficient of Supervised Learning Algorithms
Venue: arXiv cs.LG 2018
Year: 2018

The Statistical Learning Theory (SLT) provides the theoretical guarantees for supervised machine learning based on the Empirical Risk Minimization Principle (ERMP). Such principle defines an upper bound to ensure the uniform convergence of the empirical risk Remp(f), i.e., the error measured on a given data sample, to the expected value of risk R(f) (a.k.a. actual risk), which depends on the Joint Probability Distribution P(X x Y) mapping input examples x in X to class labels y in Y. The uniform convergence is only ensured when the Shattering coefficient N(F,2n) has a polynomial growing behavior. This paper proves the Shattering coefficient for any Hilbert space H containing the input space X and discusses its effects in terms of learning guarantees for supervised machine algorithms.
Multichannel Distributed Local Pattern for Content Based Indexing and Retrieval
Venue: arXiv cs.CV 2018
Year: 2018

A novel color feature descriptor, Multichannel Distributed Local Pattern (MDLP) is proposed in this manuscript. The MDLP combines the salient features of both local binary and local mesh patterns in the neighborhood. The multi-distance information computed by the MDLP aids in robust extraction of the texture arrangement. Further, MDLP features are extracted for each color channel of an image. The retrieval performance of the MDLP is evaluated on the three benchmark datasets for CBIR, namely Corel-5000, Corel-10000 and MIT-Color Vistex respectively. The proposed technique attains substantial improvement as compared to other state-of- the-art feature descriptors in terms of various evaluation parameters such as ARP and ARR on the respective databases.