OpenRaiser/Intern-Atlas huggingface dataset:
Scalable Architecture for Anomaly Detection and Visualization in Power Generating Assets
Venue: arXiv cs.DC 2017
Year: 2017

Power-generating assets (e.g., jet engines, gas turbines) are often instrumented with tens to hundreds of sensors for monitoring physical and performance degradation. Anomaly detection algorithms highlight deviations from predetermined benchmarks with the goal of detecting incipient faults. We are developing an integrated system to address three key challenges within analyzing sensor data from power-generating assets: (1) difficulty in ingesting and analyzing data from large numbers of machines; (2) prevalence of false alarms generated by anomaly detection algorithms resulting in unnecessary downtime and maintenance; and (3) lack of an integrated visualization that helps users understand and explore the flagged anomalies and relevant sensor context in the energy domain. We present preliminary results and our key findings in addressing these challenges. Our system's scalable event ingestion framework, based on OpenTSDB, ingests nearly 400,000 sensor data samples per seconds using a 30 machine cluster. To reduce false alarm rates, we leverage the False Discovery Rate (FDR) algorithm which significantly reduces the number of false alarms. Our visualization tool presents the anomalies and associated content flagged by the FDR algorithm to inform users and practitioners in their decision making process. We believe our integrated platform will help reduce maintenance costs significantly while increasing asset lifespan. We are working to extend our system on multiple fronts, such as scaling to more data and more compute nodes (70 in total).
Explicit Constructions and Bounds for Batch Codes with Restricted Size of Reconstruction Sets
Venue: arXiv cs.IT 2017
Year: 2017

Linear batch codes and codes for private information retrieval (PIR) with a query size $t$ and a restricted size $r$ of the reconstruction sets are studied. New bounds on the parameters of such codes are derived for small values of $t$ or of $r$ by providing corresponding constructions. By building on the ideas of Cadambe and Mazumdar, a new bound in a recursive form is derived for batch codes and PIR codes.
Coarse-graining and the Blackwell order
Venue: arXiv cs.IT 2017
Year: 2017

Suppose we have a pair of information channels, $κ_{1},κ_{2}$, with a common input. The Blackwell order is a partial order over channels that compares $κ_{1}$ and $κ_{2}$ by the maximal expected utility an agent can obtain when decisions are based on the channel outputs. Equivalently, $κ_{1}$ is said to be Blackwell-inferior to $κ_{2}$ if and only if $κ_{1}$ can be constructed by garbling the output of $κ_{2}$. A related partial order stipulates that $κ_{2}$ is more capable than $κ_{1}$ if the mutual information between the input and output is larger for $κ_{2}$ than for $κ_{1}$ for any distribution over inputs. A Blackwell-inferior channel is necessarily less capable. However, examples are known where $κ_{1}$ is less capable than $κ_{2}$ but not Blackwell-inferior. We show that this may even happen when $κ_{1}$ is constructed by coarse-graining the inputs of $κ_{2}$. Such a coarse-graining is a special kind of "pre-garbling" of the channel inputs. This example directly establishes that the expected value of the shared utility function for the coarse-grained channel is larger than it is for the non-coarse-grained channel. This contradicts the intuition that coarse-graining can only destroy information and lead to inferior channels. We also discuss our results in the context of information decompositions.
Non-Uniformly Coupled LDPC Codes: Better Thresholds, Smaller Rate-loss, and Less Complexity
Venue: arXiv cs.IT 2017
Year: 2017

We consider spatially coupled low-density parity-check codes with finite smoothing parameters. A finite smoothing parameter is important for designing practical codes that are decoded using low-complexity windowed decoders. By optimizing the amount of coupling between spatial positions, we show that we can construct codes with excellent thresholds and small rate loss, even with the lowest possible smoothing parameter and large variable node degrees, which are required for low error floors. We also establish that the decoding convergence speed is faster with non-uniformly coupled codes, which we verify by density evolution of windowed decoding with a finite number of iterations. We also show that by only slightly increasing the smoothing parameter, practical codes with potentially low error floors and thresholds close to capacity can be constructed. Finally, we give some indications on protograph designs.
Private Information Retrieval Schemes for Coded Data with Arbitrary Collusion Patterns
Venue: arXiv cs.IT 2017
Year: 2017

In Private Information Retrieval (PIR), one wants to download a file from a database without revealing to the database which file is being downloaded. Much attention has been paid to the case of the database being encoded across several servers, subsets of which can collude to attempt to deduce the requested file. With the goal of studying the achievable PIR rates in realistic scenarios, we generalize results for coded data from the case of all subsets of servers of size $t$ colluding, to arbitrary subsets of the servers. We investigate the effectiveness of previous strategies in this new scenario, and present new results in the case where the servers are partitioned into disjoint colluding groups.
Fast and Accurate Time Series Classification with WEASEL
Venue: arXiv cs.DS 2017
Year: 2017

Time series (TS) occur in many scientific and commercial applications, ranging from earth surveillance to industry automation to the smart grids. An important type of TS analysis is classification, which can, for instance, improve energy load forecasting in smart grids by detecting the types of electronic devices based on their energy consumption profiles recorded by automatic sensors. Such sensor-driven applications are very often characterized by (a) very long TS and (b) very large TS datasets needing classification. However, current methods to time series classification (TSC) cannot cope with such data volumes at acceptable accuracy; they are either scalable but offer only inferior classification quality, or they achieve state-of-the-art classification quality but cannot scale to large data volumes. In this paper, we present WEASEL (Word ExtrAction for time SEries cLassification), a novel TSC method which is both scalable and accurate. Like other state-of-the-art TSC methods, WEASEL transforms time series into feature vectors, using a sliding-window approach, which are then analyzed through a machine learning classifier. The novelty of WEASEL lies in its specific method for deriving features, resulting in a much smaller yet much more discriminative feature set. On the popular UCR benchmark of 85 TS datasets, WEASEL is more accurate than the best current non-ensemble algorithms at orders-of-magnitude lower classification and training times, and it is almost as accurate as ensemble classifiers, whose computational complexity makes them inapplicable even for mid-size datasets. The outstanding robustness of WEASEL is also confirmed by experiments on two real smart grid datasets, where it out-of-the-box achieves almost the same accuracy as highly tuned, domain-specific methods.
Neuromorphic computing with nanoscale spintronic oscillators
Venue: arXiv cs.ET 2017
Year: 2017

Neurons in the brain behave as non-linear oscillators, which develop rhythmic activity and interact to process information. Taking inspiration from this behavior to realize high density, low power neuromorphic computing will require huge numbers of nanoscale non-linear oscillators. Indeed, a simple estimation indicates that, in order to fit a hundred million oscillators organized in a two-dimensional array inside a chip the size of a thumb, their lateral dimensions must be smaller than one micrometer. However, despite multiple theoretical proposals, there is no proof of concept today of neuromorphic computing with nano-oscillators. Indeed, nanoscale devices tend to be noisy and to lack the stability required to process data in a reliable way. Here, we show experimentally that a nanoscale spintronic oscillator can achieve spoken digit recognition with accuracies similar to state of the art neural networks. We pinpoint the regime of magnetization dynamics leading to highest performance. These results, combined with the exceptional ability of these spintronic oscillators to interact together, their long lifetime, and low energy consumption, open the path to fast, parallel, on-chip computation based on networks of oscillators.
Object Database Benchmarks
Venue: arXiv cs.DB 2017
Year: 2017

The need for performance measurement tools appeared soon after the emergence of the first Object-Oriented Database Management Systems (OODBMSs), and proved important for both designers and users (Atkinson \& Maier, 1990). Performance evaluation is useful to designers to determine elements of architecture and more generally to validate or refute hypotheses regarding the actual behavior of an OODBMS. Thus, performance evaluation is an essential component in the development process of well-designed and efficient systems. Users may also employ performance evaluation, either to compare the efficiency of different technologies before selecting an OODBMS or to tune a system.Performance evaluation by experimentation on a real system is generally referred to as benchmarking. It consists in performing a series of tests on a given OODBMS to estimate its performance in a given setting. Benchmarks are generally used to compare the global performance of OODBMSs, but they can also be exploited to illustrate the advantages of one system or another in a given situation, or to determine an optimal hardware configuration. Typically, a benchmark is constituted of two main elements: a workload model constituted of a database and a set of read and write operations to apply on this database, and a set of performance metrics.
Dynamic time warping distance for message propagation classification in Twitter
Venue: arXiv cs.AI 2017
Year: 2017

Social messages classification is a research domain that has attracted the attention of many researchers in these last years. Indeed, the social message is different from ordinary text because it has some special characteristics like its shortness. Then the development of new approaches for the processing of the social message is now essential to make its classification more efficient. In this paper, we are mainly interested in the classification of social messages based on their spreading on online social networks (OSN). We proposed a new distance metric based on the Dynamic Time Warping distance and we use it with the probabilistic and the evidential k Nearest Neighbors (k-NN) classifiers to classify propagation networks (PrNets) of messages. The propagation network is a directed acyclic graph (DAG) that is used to record propagation traces of the message, the traversed links and their types. We tested the proposed metric with the chosen k-NN classifiers on real world propagation traces that were collected from Twitter social network and we got good classification accuracies.
Contextual Consent: Ethical Mining of Social Media for Health Research
Venue: arXiv cs.HC 2017
Year: 2017

Social media are a rich source of insight for data mining and user-centred research, but the question of consent arises when studying such data without the express knowledge of the creator. Case studies that mine social data from users of online services such as Facebook and Twitter are becoming increasingly common. This has led to calls for an open discussion into how researchers can best use these vast resources to make innovative findings while still respecting fundamental ethical principles. In this position paper we highlight some key considerations for this topic and argue that the conditions of informed consent are often not being met, and that using social media data that some deem free to access and analyse may result in undesirable consequences, particularly within the domain of health research and other sensitive topics. We posit that successful exploitation of online personal data, particularly for health and other sensitive research, requires new and usable methods of obtaining consent from the user.
Match-Tensor: a Deep Relevance Model for Search
Venue: arXiv cs.IR 2017
Year: 2017

The application of Deep Neural Networks for ranking in search engines may obviate the need for the extensive feature engineering common to current learning-to-rank methods. However, we show that combining simple relevance matching features like BM25 with existing Deep Neural Net models often substantially improves the accuracy of these models, indicating that they do not capture essential local relevance matching signals. We describe a novel deep Recurrent Neural Net-based model that we call Match-Tensor. The architecture of the Match-Tensor model simultaneously accounts for both local relevance matching and global topicality signals allowing for a rich interplay between them when computing the relevance of a document to a query. On a large held-out test set consisting of social media documents, we demonstrate not only that Match-Tensor outperforms BM25 and other classes of DNNs but also that it largely subsumes signals present in these models.
Bounds for Substituting Algebraic Functions into D-finite Functions
Venue: arXiv cs.SC 2017
Year: 2017

It is well known that the composition of a D-finite function with an algebraic function is again D-finite. We give the first estimates for the orders and the degrees of annihilating operators for the compositions. We find that the analysis of removable singularities leads to an order-degree curve which is much more accurate than the order-degree curve obtained from the usual linear algebra reasoning.
Intelligent Topic Selection for Low-Cost Information Retrieval Evaluation: A New Perspective on Deep vs. Shallow Judging
Venue: arXiv cs.IR 2017
Year: 2017

While test collections provide the cornerstone for Cranfield-based evaluation of information retrieval (IR) systems, it has become practically infeasible to rely on traditional pooling techniques to construct test collections at the scale of today's massive document collections. In this paper, we propose a new intelligent topic selection method which reduces the number of search topics needed for reliable IR evaluation. To rigorously assess our method, we integrate previously disparate lines of research on intelligent topic selection and deep vs. shallow judging. While prior work on intelligent topic selection has never been evaluated against shallow judging baselines, prior work on deep vs. shallow judging has largely argued for shallowed judging, but assuming random topic selection. We argue that for evaluating any topic selection method, ultimately one must ask whether it is actually useful to select topics, or should one simply perform shallow judging over many topics? In seeking a rigorous answer to this over-arching question, we conduct a comprehensive investigation over a set of relevant factors never previously studied together 1) topic selection method 2) the effect of topic familiarity on human judging speed and 3) how different topic generation processes impact (i) budget utilization and (ii) the resultant quality of judgments. Experiments on NIST TREC Robust 2003 and Robust 2004 test collections show that not only can we reliably evaluate IR systems with fewer topics, but also that 1) when topics are intelligently selected, deep judging is often more cost-effective than shallow judging in evaluation reliability and 2) topic familiarity and topic generation costs greatly impact the evaluation cost vs. reliability trade-off. Our findings challenge conventional wisdom in showing that deep judging is often preferable to shallow judging when topics are selected intelligently.
Human-Robot Mutual Adaptation in Shared Autonomy
Venue: arXiv cs.RO 2017
Year: 2017

Shared autonomy integrates user input with robot autonomy in order to control a robot and help the user to complete a task. Our work aims to improve the performance of such a human-robot team: the robot tries to guide the human towards an effective strategy, sometimes against the human's own preference, while still retaining his trust. We achieve this through a principled human-robot mutual adaptation formalism. We integrate a bounded-memory adaptation model of the human into a partially observable stochastic decision model, which enables the robot to adapt to an adaptable human. When the human is adaptable, the robot guides the human towards a good strategy, maybe unknown to the human in advance. When the human is stubborn and not adaptable, the robot complies with the human's preference in order to retain their trust. In the shared autonomy setting, unlike many other common human-robot collaboration settings, only the robot actions can change the physical state of the world, and the human and robot goals are not fully observable. We address these challenges and show in a human subject experiment that the proposed mutual adaptation formalism improves human-robot team performance, while retaining a high level of user trust in the robot, compared to the common approach of having the robot strictly following participants' preference.
Statistical Analysis on Bangla Newspaper Data to Extract Trending Topic and Visualize Its Change Over Time
Venue: arXiv cs.IR 2017
Year: 2017

Trending topic of newspapers is an indicator to understand the situation of a country and also a way to evaluate the particular newspaper. This paper represents a model describing few techniques to select trending topics from Bangla Newspaper. Topics that are discussed more frequently than other in Bangla newspaper will be marked and how a very famous topic loses its importance with the change of time and another topic takes its place will be demonstrated. Data from two popular Bangla Newspaper with date and time were collected. Statistical analysis was performed after on these data after preprocessing. Popular and most used keywords were extracted from the stream of Bangla keyword with this analysis. This model can also cluster category wise news trend or a list of news trend in daily or weekly basis with enough data. A pattern can be found on their news trend too. Comparison among past news trend of Bangla newspapers will give a visualization of the situation of Bangladesh. This visualization will be helpful to predict future trending topics of Bangla Newspaper.
Stability Analysis of DC Microgrids with Constant Power Load under Distributed Control Method
Venue: arXiv cs.SY 2017
Year: 2017

DC microgrids are becoming popular as effective means to integrate various renewable energy resources. Constant power loads (CPLs) may yield instability due to the negative impedance characteristic. This paper analyzes the stability of the DC microgrid in presence of CPL. Distributed generations (DGs) are controlled by using a distributed controller which aims at current sharing and voltage recovery. For simplicity, a reduced order model is derived on the fundamental of neglecting the transient state of the DC/DC converter. The purpose of this paper is to analyze stability conditions and give the suggestions to design control parameters. The stability conditions are obtained by using inertia theorem. Moreover, this paper makes a further detailed research based on the existed theorems. Simulation results are provided to verify the effectiveness and validity of the proposed theorem.
Novel processes and metrics for a scientific evaluation rooted in the principles of science - Version 1
Venue: arXiv cs.DL 2017
Year: 2017

Scientific evaluation is a determinant of how scientists, institutions and funders behave, and as such is a key element in the making of science. In this article, we propose an alternative to the current norm of evaluating research with journal rank. Following a well-defined notion of scientific value, we introduce qualitative processes that can also be quantified and give rise to meaningful and easy-to-use article-level metrics. In our approach, the goal of a scientist is transformed from convincing an editorial board through a vertical process to convincing peers through an horizontal one. We argue that such an evaluation system naturally provides the incentives and logic needed to constantly promote quality, reproducibility, openness and collaboration in science. The system is legally and technically feasible and can gradually lead to the self-organized reappropriation of the scientific process by the scholarly community and its institutions. We propose an implementation of our evaluation system with the platform "the Self-Journals of Science" (www.sjscience.org).
Erasure Coding for Small Objects in In-Memory KV Storage
Venue: arXiv cs.DB 2017
Year: 2017

We present MemEC, an erasure-coding-based in-memory key-value (KV) store that achieves high availability and fast recovery while keeping low data redundancy across storage servers. MemEC is specifically designed for workloads dominated by small objects. By encoding objects in entirety, MemEC is shown to incur 60% less storage redundancy for small objects than existing replication- and erasure-coding-based approaches. It also supports graceful transitions between decentralized requests in normal mode (i.e., no failures) and coordinated requests in degraded mode (i.e., with failures). We evaluate our MemEC prototype via testbed experiments under read-heavy and update-heavy YCSB workloads. We show that MemEC achieves high throughput and low latency in both normal and degraded modes, and supports fast transitions between the two modes.
Existence of Evolutionarily Stable Strategies Remains Hard to Decide for a Wide Range of Payoff Values
Venue: arXiv cs.CC 2017
Year: 2017

The concept of an evolutionarily stable strategy (ESS), introduced by Smith and Price, is a refinement of Nash equilibrium in 2-player symmetric games in order to explain counter-intuitive natural phenomena, whose existence is not guaranteed in every game. The problem of deciding whether a game possesses an ESS has been shown to be $Σ_{2}^{P}$-complete by Conitzer using the preceding important work by Etessami and Lochbihler. The latter, among other results, proved that deciding the existence of ESS is both NP-hard and coNP-hard. In this paper we introduce a "reduction robustness" notion and we show that deciding the existence of an ESS remains coNP-hard for a wide range of games even if we arbitrarily perturb within some intervals the payoff values of the game under consideration. In contrast, ESS exist almost surely for large games with random and independent payoffs chosen from the same distribution.
Rotated Eigenstructure Analysis for Source Localization without Energy-decay Models
Venue: arXiv cs.IT 2017
Year: 2017

Herein, the problem of simultaneous localization of two sources given a modest number of samples is examined. In particular, the strategy does not require knowledge of the target signatures of the sources a priori, nor does it exploit classical methods based on a particular decay rate of the energy emitted from the sources as a function of range. General structural properties of the signatures such as unimodality are exploited. The algorithm localizes targets based on the rotated eigenstructure of a reconstructed observation matrix. In particular, the optimal rotation can be found by maximizing the ratio of the dominant singular value of the observation matrix over the nuclear norm of the optimally rotated observation matrix. It is shown that this ratio has a unique local maximum leading to computationally efficient search algorithms. Moreover, analytical results are developed to show that the squared localization error decreases at a rate faster than the baseline scheme.