OpenRaiser/Intern-Atlas huggingface dataset:
Cloud based Real-Time and Low Latency Scientific Event Analysis
Venue: arXiv cs.DB 2018
Year: 2018

Astronomy is well recognized as big data driven science. As the novel observation infrastructures are developed, the sky survey cycles have been shortened from a few days to a few seconds, causing data processing pressure to shift from offline to online. However, existing scientific databases focus on offline analysis of long-term historical data, not real-time and low latency analysis of large-scale newly arriving data. In this paper, a cloud based method is proposed to efficiently analyze scientific events on large-scale newly arriving data. The solution is implemented as a highly efficient system, namely Aserv. A set of compact data store and index structures are proposed to describe the proposed scientific events and a typical analysis pattern is formulized as a set of query operations. Domain aware filter, accuracy aware data partition, highly efficient index and frequently used statistical data designs are four key methods to optimize the performance of Aserv. Experimental results under the typical cloud environment show that the presented optimization mechanism can meet the low latency demand for both large data insertion and scientific event analysis. Aserv can insert 3.5 million rows of data within 3 seconds and perform the heaviest query on 6.7 billion rows of data also within 3 seconds. Furthermore, a performance model is given to help Aserv choose the right cloud resource setup to meet the guaranteed real-time performance requirement.
Sentiment Analysis of Financial News Articles using Performance Indicators
Venue: arXiv cs.IR 2018
Year: 2018

Mining financial text documents and understanding the sentiments of individual investors, institutions and markets is an important and challenging problem in the literature. Current approaches to mine sentiments from financial texts largely rely on domain specific dictionaries. However, dictionary based methods often fail to accurately predict the polarity of financial texts. This paper aims to improve the state-of-the-art and introduces a novel sentiment analysis approach that employs the concept of financial and non-financial performance indicators. It presents an association rule mining based hierarchical sentiment classifier model to predict the polarity of financial texts as positive, neutral or negative. The performance of the proposed model is evaluated on a benchmark financial dataset. The model is also compared against other state-of-the-art dictionary and machine learning based approaches and the results are found to be quite promising. The novel use of performance indicators for financial sentiment analysis offers interesting and useful insights.
Arena Model: Inference About Competitions
Venue: arXiv cs.LG 2018
Year: 2018

The authors propose a parametric model called the arena model for prediction in paired competitions, i.e. paired comparisons with eliminations and bifurcations. The arena model has a number of appealing advantages. First, it predicts the results of competitions without rating many individuals. Second, it takes full advantage of the structure of competitions. Third, the model provides an easy method to quantify the uncertainty in competitions. Fourth, some of our methods can be directly generalized for comparisons among three or more individuals. Furthermore, the authors identify an invariant Bayes estimator with regard to the prior distribution and prove the consistency of the estimations of uncertainty. Currently, the arena model is not effective in tracking the change of strengths of individuals, but its basic framework provides a solid foundation for future study of such cases.
Convexity in scientific collaboration networks
Venue: arXiv cs.SI 2018
Year: 2018

Convexity in a network (graph) has been recently defined as a property of each of its subgraphs to include all shortest paths between the nodes of that subgraph. It can be measured on the scale [0, 1] with 1 being assigned to fully convex networks. The largest convex component of a graph that emerges after the removal of the least number of edges is called a convex skeleton. It is basically a tree of cliques, which has been shown to have many interesting features. In this article the notions of convexity and convex skeletons in the context of scientific collaboration networks are discussed. More specifically, we analyze the co-authorship networks of Slovenian researchers in computer science, physics, sociology, mathematics, and economics and extract convex skeletons from them. We then compare these convex skeletons with the residual graphs (remainders) in terms of collaboration frequency distributions by various parameters such as the publication year and type, co-authors' birth year, status, gender, discipline, etc. We also show the top-ranked scientists by four basic centrality measures as calculated on the original networks and their skeletons and conclude that convex skeletons may help detect influential scholars that are hardly identifiable in the original collaboration network. As their inherent feature, convex skeletons retain the properties of collaboration networks. These include high-level structural properties but also the fact that the same authors are highlighted by centrality measures. Moreover, the most important ties and thus the most important collaborations are retained in the skeletons.
Um Sistema de Aquisição e Análise de Dados para Extração de Conhecimento da Plataforma Ebit
Venue: arXiv cs.CY 2018
Year: 2018

The internet development and the consequent change in communication forms have strengthened as online social networks, increasing the involvement of people with this media and making consumers of products and services, which are more informed and demanding for companies. This context has given rise to Social CRM, which can be put into practice by means of electronic word of mouth platforms, enable web sharing of comments and evaluations about companies, defining their reputation. However, most electronic word of mouth platforms do not provide information for extracting your information, making it difficult to analyze the data. To satisfy this gap, a system was developed to capture and automatically summarize the data of the companies registered in the eBit platform.
LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning
Venue: arXiv cs.LG 2018
Year: 2018

Distributed learning systems have enabled training large-scale models over large amount of data in significantly shorter time. In this paper, we focus on decentralized distributed deep learning systems and aim to achieve differential privacy with good convergence rate and low communication cost. To achieve this goal, we propose a new learning algorithm LEASGD (Leader-Follower Elastic Averaging Stochastic Gradient Descent), which is driven by a novel Leader-Follower topology and a differential privacy model.We provide a theoretical analysis of the convergence rate and the trade-off between the performance and privacy in the private setting.The experimental results show that LEASGD outperforms state-of-the-art decentralized learning algorithm DPSGD by achieving steadily lower loss within the same iterations and by reducing the communication cost by 30%. In addition, LEASGD spends less differential privacy budget and has higher final accuracy result than DPSGD under private setting.
CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss
Venue: arXiv cs.NE 2018
Year: 2018

Fully-convolutional neural networks have achieved superior performance in a variety of image segmentation tasks. However, their training requires laborious manual annotation of large datasets, as well as acceleration by parallel processors with high-bandwidth memory, such as GPUs. We show that simple models can achieve competitive accuracy for organ segmentation on CT images when trained with extensive data augmentation, which leverages existing graphics hardware to quickly apply geometric and photometric transformations to 3D image data. On 3 mm^3 CT volumes, our GPU implementation is 2.6-8X faster than a widely-used CPU version, including communication overhead. We also show how to automatically generate training labels using rudimentary morphological operations, which are efficiently computed by 3D Fourier transforms. We combined fully-automatic labels for the lungs and bone with semi-automatic ones for the liver, kidneys and bladder, to create a dataset of 130 labeled CT scans. To achieve the best results from data augmentation, our model uses the intersection-over-union (IOU) loss function, a close relative of the Dice loss. We discuss its mathematical properties and explain why it outperforms the usual weighted cross-entropy loss for unbalanced segmentation tasks. We conclude that there is no unique IOU loss function, as the naive one belongs to a broad family of functions with the same essential properties. When combining data augmentation with the IOU loss, our model achieves a Dice score of 78-92% for each organ. The trained model, code and dataset will be made publicly available, to further medical imaging research.
Edge Cloud System Evaluation
Venue: arXiv cs.NI 2018
Year: 2018

Real-time applications in the next generation networks often rely upon offloading the computational task to a \textit{nearby} server to achieve ultra-low latency. Augmented reality applications for instance have strict latency requirements which can be fulfilled by an interplay between cloud and edge servers. In this work, we study the impact of load on a hybrid edge cloud system. The resource distribution between central cloud and edge affects the capacity of the network. Optimizing delay and capacity constraints of this hybrid network is similar to maximum cardinal bin packing problem which is NP-hard. We design a simulation framework using a city-scale access point dataset to propose an enhanced capacity edge cloud network while answering following questions: (a) how much load an edge cloud network can support without affecting the performance of an application, (b) how is application delay-constraint limit affects the capacity of the network, (c) what is the impact of load and resource distribution on goodput, (d) under what circumstances, cloud can perform better than edge network and (e) what is the impact of inter-edge networking bandwidth on the system capacity. An evaluation system and model is developed to analyze the tradeoffs of different edge cloud deployments and results are shown to support the claims.
Questioning the assumptions behind fairness solutions
Venue: arXiv cs.CY 2018
Year: 2018

In addition to their benefits, optimization systems can have negative economic, moral, social, and political effects on populations as well as their environments. Frameworks like fairness have been proposed to aid service providers in addressing subsequent bias and discrimination during data collection and algorithm design. However, recent reports of neglect, unresponsiveness, and malevolence cast doubt on whether service providers can effectively implement fairness solutions. These reports invite us to revisit assumptions made about the service providers in fairness solutions. Namely, that service providers have (i) the incentives or (ii) the means to mitigate optimization externalities. Moreover, the environmental impact of these systems suggests that we need (iii) novel frameworks that consider systems other than algorithmic decision-making and recommender systems, and (iv) solutions that go beyond removing related algorithmic biases. Going forward, we propose Protective Optimization Technologies that enable optimization subjects to defend against negative consequences of optimization systems.
Improved Speech Enhancement with the Wave-U-Net
Venue: arXiv cs.SD 2018
Year: 2018

We study the use of the Wave-U-Net architecture for speech enhancement, a model introduced by Stoller et al for the separation of music vocals and accompaniment. This end-to-end learning method for audio source separation operates directly in the time domain, permitting the integrated modelling of phase information and being able to take large temporal contexts into account. Our experiments show that the proposed method improves several metrics, namely PESQ, CSIG, CBAK, COVL and SSNR, over the state-of-the-art with respect to the speech enhancement task on the Voice Bank corpus (VCTK) dataset. We find that a reduced number of hidden layers is sufficient for speech enhancement in comparison to the original system designed for singing voice separation in music. We see this initial result as an encouraging signal to further explore speech enhancement in the time-domain, both as an end in itself and as a pre-processing step to speech recognition systems.
Constructions of involutions over finite fields
Venue: arXiv cs.IT 2018
Year: 2018

An involution over finite fields is a permutation polynomial whose inverse is itself. Owing to this property, involutions over finite fields have been widely used in applications such as cryptography and coding theory. As far as we know, there are not many involutions, and there isn't a general way to construct involutions over finite fields. This paper gives a necessary and sufficient condition for the polynomials of the form $x^rh(x^s)\in \bF_q[x]$ to be involutions over the finite field~$\bF_q$, where $r\geq 1$ and $s\,|\, (q-1)$. By using this criterion we propose a general method to construct involutions of the form $x^rh(x^s)$ over $\bF_q$ from given involutions over the corresponding subgroup of $\bF_q^*$. Then, many classes of explicit involutions of the form $x^rh(x^s)$ over $\bF_q$ are obtained.
Unsupervised Control Through Non-Parametric Discriminative Rewards
Venue: arXiv cs.LG 2018
Year: 2018

Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsupervised learning algorithm to train agents to achieve perceptually-specified goals using only a stream of observations and actions. Our agent simultaneously learns a goal-conditioned policy and a goal achievement reward function that measures how similar a state is to the goal state. This dual optimization leads to a co-operative game, giving rise to a learned reward function that reflects similarity in controllable aspects of the environment instead of distance in the space of observations. We demonstrate the efficacy of our agent to learn, in an unsupervised manner, to reach a diverse set of goals on three domains -- Atari, the DeepMind Control Suite and DeepMind Lab.
Alea Iacta Est: Auctions, Persuasion, Interim Rules, and Dice
Venue: arXiv cs.GT 2018
Year: 2018

To select a subset of samples or "winners" from a population of candidates, order sampling [Rosen 1997] and the k-unit Myerson auction [Myerson 1981] share a common scheme: assign a (random) score to each candidate, then select the k candidates with the highest scores. We study a generalization of both order sampling and Myerson's allocation rule, called winner-selecting dice. The setting for winner-selecting dice is similar to auctions with feasibility constraints: candidates have random types drawn from independent prior distributions, and the winner set must be feasible subject to certain constraints. Dice (distributions over scores) are assigned to each type, and winners are selected to maximize the sum of the dice rolls, subject to the feasibility constraints. We examine the existence of winner-selecting dice that implement prescribed probabilities of winning (i.e., an interim rule) for all types. Our first result shows that when the feasibility constraint is a matroid, then for any feasible interim rule, there always exist winner-selecting dice that implement it. Unfortunately, our proof does not yield an efficient algorithm for constructing the dice. In the special case of a 1-uniform matroid, i.e., only one winner can be selected, we give an efficient algorithm that constructs winner-selecting dice for any feasible interim rule. Furthermore, when the types of the candidates are drawn in an i.i.d.~manner and the interim rule is symmetric across candidates, unsurprisingly, an algorithm can efficiently construct symmetric dice that only depend on the type but not the identity of the candidate.
Exploiting "Quantum-like Interference" in Decision Fusion for Ranking Multimodal Documents
Venue: arXiv cs.IR 2018
Year: 2018

Fusing and ranking multimodal information remains always a challenging task. A robust decision-level fusion method should not only be dynamically adaptive for assigning weights to each representation but also incorporate inter-relationships among different modalities. In this paper, we propose a quantum-inspired model for fusing and ranking visual and textual information accounting for the dependency between the aforementioned modalities. At first, we calculate the text-based and image-based similarity individually. Two different approaches have been applied for computing each unimodal similarity. The first one makes use of the bag-of-words model. For the second one, a pre-trained VGG19 model on ImageNet has been used for calculating the image similarity, while a query expansion approach has been applied to the text-based query for improving the retrieval performance. Afterward, the local similarity scores fit the proposed quantum-inspired model. The inter-dependency between the two modalities is captured implicitly through "quantum interference". Finally, the documents are ranked based on the proposed similarity measurement. We test our approach on ImageCLEF2007photo data collection and show the effectiveness of the proposed approach. A series of interesting findings are discussed, which would provide theoretical and empirical foundations for future development of this direction.
Robust Dynamic Programming for Temporal Logic Control of Stochastic Systems
Venue: arXiv cs.SY 2018
Year: 2018

Discrete-time stochastic systems are an essential modelling tool for many engineering systems. We consider stochastic control systems that are evolving over continuous spaces. For this class of models, methods for the formal verification and synthesis of control strategies are computationally hard and generally rely on the use of approximate abstractions. Building on approximate abstractions, we compute control strategies with lower- and upper-bounds for satisfying unbounded temporal logic specifications. Firstly, robust dynamic programming mappings over the abstract system are introduced to solve the control synthesis and verification problem. These mappings yield a control strategy and a unique lower bound on the satisfaction probability for temporal logic specifications that is robust to the incurred approximation errors. Secondly, upper-bounds on the satisfaction probability are quantified, and properties of the mappings are analysed and discussed. Finally, we show the implications of these results for linear stochastic dynamic systems with a continuous state space. This abstraction-based synthesis framework is shown to be able to handle infinite-horizon properties. Approximation errors expressed as deviations in the outputs of the models and as deviations in the probabilistic transitions are allowed and are quantified using approximate stochastic simulation relations.
Potential applications of Distributed Ledger Technology to the Defence Support Network
Venue: arXiv cs.CY 2018
Year: 2018

Proponents of Distributed Ledger Technology (DLT) claim it could have an impact greater than the internet; a breakthrough defying organisational boundaries by securely storing data across trustless entities. This would allow decisions to be made on verifiable data in an automated manner without the costs imposed by middlemen, with a corresponding economy-wide impact. Despite this potential, real-world application is embryonic with public and private sectors rapidly seeking exploitation opportunities. This research seeks to understand how DLT might apply to the Defence Support Network (DSN), the mechanism used to sustain UK Armed Forces with materiel and equipment. Drawing on academic and commercial models, a framework was produced for evaluating DLT use cases which measures utility, ease of implementation and impact. Using a functionalist research paradigm, interviews were conducted with DLT and DSN experts on potential use cases, the data from which was then analysed against a lightweight version of the evaluation framework. Results show that use cases involving codification, certification and supply chain provenance merit further investigation. The research concluded with recommendations that the DSN should pilot DLT use cases, but these should be carefully selected utilising an evaluation framework due to DLT's emergent nature.
The L-CSC cluster: Optimizing power efficiency to become the greenest supercomputer in the world in the Green500 list of November 2014
Venue: arXiv cs.PF 2018
Year: 2018

The L-CSC (Lattice Computer for Scientific Computing) is a general purpose compute cluster built with commodity hardware installed at GSI. Its main operational purpose is Lattice QCD (LQCD) calculations for physics simulations. Quantum Chromo Dynamics (QCD) is the physical theory describing the strong force, one of the four known fundamental interactions in the universe. L-CSC leverages a multi-GPU design accommodating the huge demand of LQCD for memory bandwidth. In recent years, heterogeneous clusters with accelerators such as GPUs have become more and more powerful while supercomputers in general have shown enormous increases in power consumption making electricity costs and cooling a significant factor in the total cost of ownership. Using mainly GPUs for processing, L-CSC is very power-efficient, and its architecture was optimized to provide the greatest possible power efficiency. This paper presents the cluster design as well as optimizations to improve the power efficiency. It examines the power measurements performed for the Green500 list of the most power-efficient supercomputers in the world which led to the number 1 position as the greenest supercomputer in November 2014.
$HS^2$: Active Learning over Hypergraphs
Venue: arXiv cs.LG 2018
Year: 2018

We propose a hypergraph-based active learning scheme which we term $HS^2$, $HS^2$ generalizes the previously reported algorithm $S^2$ originally proposed for graph-based active learning with pointwise queries [Dasarathy et al., COLT 2015]. Our $HS^2$ method can accommodate hypergraph structures and allows one to ask both pointwise queries and pairwise queries. Based on a novel parametric system particularly designed for hypergraphs, we derive theoretical results on the query complexity of $HS^2$ for the above described generalized settings. Both the theoretical and empirical results show that $HS^2$ requires a significantly fewer number of queries than $S^2$ when one uses $S^2$ over a graph obtained from the corresponding hypergraph via clique expansion.
WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices
Venue: arXiv cs.LG 2018
Year: 2018

We present a logarithmic-scale efficient convolutional neural network architecture for edge devices, named WaveletNet. Our model is based on the well-known depthwise convolution, and on two new layers, which we introduce in this work: a wavelet convolution and a depthwise fast wavelet transform. By breaking the symmetry in channel dimensions and applying a fast algorithm, WaveletNet shrinks the complexity of convolutional blocks by an O(logD/D) factor, where D is the number of channels. Experiments on CIFAR-10 and ImageNet classification show superior and comparable performances of WaveletNet compared to state-of-the-art models such as MobileNetV2.