OpenRaiser/Intern-Atlas huggingface dataset:
Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration
Venue: arXiv cs.NE 2017
Year: 2017

This article expands on research that has been done to develop a recurrent neural network (RNN) capable of predicting aircraft engine vibrations using long short-term memory (LSTM) neurons. LSTM RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, making this approach ungeneralizable across multiple engines. In initial work, multiple LSTM RNN architectures were proposed, evaluated and compared. This research improves the performance of the most effective LSTM network design proposed in the previous work by using a promising neuroevolution method based on ant colony optimization (ACO) to develop and enhance the LSTM cell structure of the network. A parallelized version of the ACO neuroevolution algorithm has been developed and the evolved LSTM RNNs were compared to the previously used fixed topology. The evolved networks were trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. Results were obtained using MPI (Message Passing Interface) on a high performance computing (HPC) cluster, evolving 1000 different LSTM cell structures using 168 cores over 4 days. The new evolved LSTM cells showed an improvement of 1.35%, reducing prediction error from 5.51% to 4.17% when predicting excessive engine vibrations 10 seconds in the future, while at the same time dramatically reducing the number of weights from 21,170 to 11,810.
Indoor Massive MIMO Deployments for Uniformly High Wireless Capacity
Venue: arXiv cs.IT 2017
Year: 2017

Providing consistently high wireless capacity is becoming increasingly important to support the applications required by future digital enterprises. In this paper, we propose Eigen-direction-aware ZF (EDA-ZF) with partial coordination among base stations (BSs) and distributed interference suppression as a practical approach to achieve this objective. We compare our solution with Zero Forcing (ZF), entailing neither BS coordination or inter-cell interference mitigation, and Network MIMO (NeMIMO), where full BS coordination enables centralized inter-cell interference management. We also evaluate the performance of said schemes for three sub-6 GHz deployments with varying BS densities -- sparse, intermediate, and dense -- all with fixed total number of antennas and radiated power. Extensive simulations show that: (i) indoor massive MIMO implementing the proposed EDA-ZF provides uniformly good rates for all users; (ii) indoor network densification is detrimental unless full coordination is implemented; (iii) deploying NeMIMO pays off under strong outdoor interference, especially for cell-edge users.
A Lambda Calculus for Transfinite Arrays: Unifying Arrays and Streams
Venue: arXiv cs.PL 2017
Year: 2017

Array programming languages allow for concise and generic formulations of numerical algorithms, thereby providing a huge potential for program optimisation such as fusion, parallelisation, etc. One of the restrictions that these languages typically have is that the number of elements in every array has to be finite. This means that implementing streaming algorithms in such languages requires new types of data structures, with operations that are not immediately compatible with existing array operations or compiler optimisations. In this paper, we propose a design for a functional language that natively supports infinite arrays. We use ordinal numbers to introduce the notion of infinity in shapes and indices. By doing so, we obtain a calculus that naturally extends existing array calculi and, at the same time, allows for recursive specifications as they are found in stream- and list-based settings. Furthermore, the main language construct that can be thought of as an $n$-fold cons operator gives rise to expressing transfinite recursion in data, something that lists or streams usually do not support. This makes it possible to treat the proposed calculus as a unifying theory of arrays, lists and streams. We give an operational semantics of the proposed language, discuss design choices that we have made, and demonstrate its expressibility with several examples. We also demonstrate that the proposed formalism preserves a number of well-known universal equalities from array/list/stream theories, and discuss implementation-related challenges.
The power of A/B testing under interference
Venue: arXiv cs.SI 2017
Year: 2017

In this paper, we address the fundamental statistical question: how can you assess the power of an A/B test when the units in the study are exposed to interference? This question is germane to many scientific and industrial practitioners that rely on A/B testing in environments where control over interference is limited. We begin by proving that interference has a measurable effect on its sensitivity, or power. We quantify the power of an A/B test of equality of means as a function of the number of exposed individuals under any interference mechanism. We further derive a central limit theorem for the number of exposed individuals under a simple Bernoulli switching interference mechanism. Based on these results, we develop a strategy to estimate the power of an A/B test when actors experience interference according to an observed network model. We demonstrate how to leverage this theory to estimate the power of an A/B test on units sharing any network relationship, and highlight the utility of our method on two applications - a Facebook friendship network as well as a large Twitter follower network. These results yield, for the first time, the capacity to understand how to design an A/B test to detect, with a specified confidence, a fixed measurable treatment effect when the A/B test is conducted under interference driven by networks.
When is Network Lasso Accurate: The Vector Case
Venue: arXiv cs.LG 2017
Year: 2017

A recently proposed learning algorithm for massive network-structured data sets (big data over networks) is the network Lasso (nLasso), which extends the well- known Lasso estimator from sparse models to network-structured datasets. Efficient implementations of the nLasso have been presented using modern convex optimization methods. In this paper, we provide sufficient conditions on the network structure and available label information such that nLasso accurately learns a vector-valued graph signal (representing label information) from the information provided by the labels of a few data points.
Alternate Derivation of Geometric Extended Kalman Filter by MEKF Approach
Venue: arXiv cs.RO 2017
Year: 2017

This note is devoted to deriving the measurement update of the geometric extended Kalman filter using the multiplicative extended Kalman filtering approach, resulting in the attitude estimator referred as geometric multiplicative extended Kalman filter. The equivalence of the derived geometric multiplicative extended Kalman filter and geometric extended Kalman filter is also demonstrated in this note.
Do Social Bots Dream of Electric Sheep? A Categorisation of Social Media Bot Accounts
Venue: arXiv cs.HC 2017
Year: 2017

So-called 'social bots' have garnered a lot of attention lately. Previous research showed that they attempted to influence political events such as the Brexit referendum and the US presidential elections. It remains, however, somewhat unclear what exactly can be understood by the term 'social bot'. This paper addresses the need to better understand the intentions of bots on social media and to develop a shared understanding of how 'social' bots differ from other types of bots. We thus describe a systematic review of publications that researched bot accounts on social media. Based on the results of this literature review, we propose a scheme for categorising bot accounts on social media sites. Our scheme groups bot accounts by two dimensions - Imitation of human behaviour and Intent.
Stream Graphs and Link Streams for the Modeling of Interactions over Time
Venue: arXiv cs.SI 2017
Year: 2017

Graph theory provides a language for studying the structure of relations, and it is often used to study interactions over time too. However, it poorly captures the both temporal and structural nature of interactions, that calls for a dedicated formalism. In this paper, we generalize graph concepts in order to cope with both aspects in a consistent way. We start with elementary concepts like density, clusters, or paths, and derive from them more advanced concepts like cliques, degrees, clustering coefficients, or connected components. We obtain a language to directly deal with interactions over time, similar to the language provided by graphs to deal with relations. This formalism is self-consistent: usual relations between different concepts are preserved. It is also consistent with graph theory: graph concepts are special cases of the ones we introduce. This makes it easy to generalize higher-level objects such as quotient graphs, line graphs, k-cores, and centralities. This paper also considers discrete versus continuous time assumptions, instantaneous links, and extensions to more complex cases.
How Deterministic are Good-For-Games Automata?
Venue: arXiv cs.FL 2017
Year: 2017

In GFG automata, it is possible to resolve nondeterminism in a way that only depends on the past and still accepts all the words in the language. The motivation for GFG automata comes from their adequacy for games and synthesis, wherein general nondeterminism is inappropriate. We continue the ongoing effort of studying the power of nondeterminism in GFG automata. Initial indications have hinted that every GFG automaton embodies a deterministic one. Today we know that this is not the case, and in fact GFG automata may be exponentially more succinct than deterministic ones. We focus on the typeness question, namely the question of whether a GFG automaton with a certain acceptance condition has an equivalent GFG automaton with a weaker acceptance condition on the same structure. Beyond the theoretical interest in studying typeness, its existence implies efficient translations among different acceptance conditions. This practical issue is of special interest in the context of games, where the Buchi and co-Buchi conditions admit memoryless strategies for both players. Typeness is known to hold for deterministic automata and not to hold for general nondeterministic automata. We show that GFG automata enjoy the benefits of typeness, similarly to the case of deterministic automata. In particular, when Rabin or Streett GFG automata have equivalent Buchi or co-Buchi GFG automata, respectively, then such equivalent automata can be defined on a substructure of the original automata. Using our typeness results, we further study the place of GFG automata in between deterministic and nondeterministic ones. Specifically, considering automata complementation, we show that GFG automata lean toward nondeterministic ones, admitting an exponential state blow-up in the complementation of a Streett automaton into a Rabin automaton, as opposed to the constant blow-up in the deterministic case.
Aprendendo Programacao Orientada a Objetos com uma Abordagem Ludica Baseada em Greenfoot e Robocode
Venue: arXiv cs.CY 2017
Year: 2017

One the major challenges in undergraduate computing programs is the learning of object-oriented programming (OOP). This paradigm has a variety of concepts with an abstraction level usually high for most beginners, even the ones who already code in an imperative language. Furthermore, transitioning from imperative programming to OOP is a complex issue, with various inappropriate side effects. A significant effort has been pursued in the search of motivating and attractive solutions for such issues. One of those is the use of playful environments that merge games with learning. In this work, we report our experience with OOP learning workshops by means of games, challenges and competitions, supported by Greenfoot and Robocode learning environments. A workshop with sophomore students in a Computer Engineering program is presented here. Lessons learning to motive students include: design of motivating examples, use of competitive challenges, and an appropriate ratio between tutors and students. Results suggest that the workshop was a practical and effective way to introduce OOP and motivate students to learn it.
Pengaruh Perangkat Server Terhadap Kualitas Pengontrolan Jarak Jauh Melalui Internet
Venue: arXiv cs.CY 2017
Year: 2017

Internet greatly assist people in improving their quality of life. Almost all areas of human life can be accessed using the internet. Human aided by the internet that provides all sorts of information that they need. Along with the development of the Internet network infrastructure remotely control began to change using the internet. In this study using notebooks and servers Raspberry Pi to find out the quality control of each device server used. In this study we investigate the possibility of improving the quality of web-based remote control to implement Raspberry Pi as a web server and how much improvement the quality of web-based remote control obtained in this research.
Counterfactual Conditionals in Quantified Modal Logic
Venue: arXiv cs.AI 2017
Year: 2017

We present a novel formalization of counterfactual conditionals in a quantified modal logic. Counterfactual conditionals play a vital role in ethical and moral reasoning. Prior work has shown that moral reasoning systems (and more generally, theory-of-mind reasoning systems) should be at least as expressive as first-order (quantified) modal logic (QML) to be well-behaved. While existing work on moral reasoning has focused on counterfactual-free QML moral reasoning, we present a fully specified and implemented formal system that includes counterfactual conditionals. We validate our model with two projects. In the first project, we demonstrate that our system can be used to model a complex moral principle, the doctrine of double effect. In the second project, we use the system to build a data-set with true and false counterfactuals as licensed by our theory, which we believe can be useful for other researchers. This project also shows that our model can be computationally feasible.
Synkhronos: a Multi-GPU Theano Extension for Data Parallelism
Venue: arXiv cs.DC 2017
Year: 2017

We present Synkhronos, an extension to Theano for multi-GPU computations leveraging data parallelism. Our framework provides automated execution and synchronization across devices, allowing users to continue to write serial programs without risk of race conditions. The NVIDIA Collective Communication Library is used for high-bandwidth inter-GPU communication. Further enhancements to the Theano function interface include input slicing (with aggregation) and input indexing, which perform common data-parallel computation patterns efficiently. One example use case is synchronous SGD, which has recently been shown to scale well for a growing set of deep learning problems. When training ResNet-50, we achieve a near-linear speedup of 7.5x on an NVIDIA DGX-1 using 8 GPUs, relative to Theano-only code running a single GPU in isolation. Yet Synkhronos remains general to any data-parallel computation programmable in Theano. By implementing parallelism at the level of individual Theano functions, our framework uniquely addresses a niche between manual multi-device programming and prescribed multi-GPU training routines.
Explaining Trained Neural Networks with Semantic Web Technologies: First Steps
Venue: arXiv cs.AI 2017
Year: 2017

The ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains. In this paper, we provide a conceptual approach that leverages such data in order to explain the input-output behavior of trained artificial neural networks. We apply existing Semantic Web technologies in order to provide an experimental proof of concept.
Seed selection for information cascade in multilayer networks
Venue: arXiv cs.SI 2017
Year: 2017

Information spreading is an interesting field in the domain of online social media. In this work, we are investigating how well different seed selection strategies affect the spreading processes simulated using independent cascade model on eighteen multilayer social networks. Fifteen networks are built based on the user interaction data extracted from Facebook public pages and tree of them are multilayer networks downloaded from public repository (two of them being Twitter networks). The results indicate that various state of the art seed selection strategies for single-layer networks like K-Shell or VoteRank do not perform so well on multilayer networks and are outperformed by Degree Centrality.
Provably Fair Representations
Venue: arXiv cs.LG 2017
Year: 2017

Machine learning systems are increasingly used to make decisions about people's lives, such as whether to give someone a loan or whether to interview someone for a job. This has led to considerable interest in making such machine learning systems fair. One approach is to transform the input data used by the algorithm. This can be achieved by passing each input data point through a representation function prior to its use in training or testing. Techniques for learning such representation functions from data have been successful empirically, but typically lack theoretical fairness guarantees. We show that it is possible to prove that a representation function is fair according to common measures of both group and individual fairness, as well as useful with respect to a target task. These provable properties can be used in a governance model involving a data producer, a data user and a data regulator, where there is a separation of concerns between fairness and target task utility to ensure transparency and prevent perverse incentives. We formally define the 'cost of mistrust' of using this model compared to the setting where there is a single trusted party, and provide bounds on this cost in particular cases. We present a practical approach to learning fair representation functions and apply it to financial and criminal justice datasets. We evaluate the fairness and utility of these representation functions using measures motivated by our theoretical results.
Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Automatic segmentation of liver and its tumors is an essential step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis and assessment of tumor response to treatment. MICCAI 2017 Liver Tumor Segmentation Challenge (LiTS) provides a common platform for comparing different automatic algorithms on contrast-enhanced abdominal CT images in tasks including 1) liver segmentation, 2) liver tumor segmentation, and 3) tumor burden estimation. We participate this challenge by developing a hierarchical framework based on deep fully convolutional-deconvolutional neural networks (CDNN). A simple CDNN model is firstly trained to provide a quick but coarse segmentation of the liver on the entire CT volume, then another CDNN is applied to the liver region for fine liver segmentation. At last, the segmented liver region, which is enhanced by histogram equalization, is employed as an additional input to the third CDNN for tumor segmentation. Jaccard distance is used as loss function when training CDNN models to eliminate the need of sample re-weighting. Our framework is trained using the 130 challenge training cases provided by LiTS. The evaluation on the 70 challenge testing cases resulted in a mean Dice Similarity Coefficient (DSC) of 0.963 for liver segmentation, a mean DSC of 0.657 for tumor segmentation, and a root mean square error (RMSE) of 0.017 for tumor burden estimation, which ranked our method in the first, fifth and third place, respectively
The Trees of Hanoi
Venue: arXiv cs.FL 2017
Year: 2017

The game of the Towers of Hanoi is generalized to binary trees. First, a straightforward solution of the game is discussed. Second, a shorter solution is presented, which is then shown to be optimal.
Power Aware Visual Sensor Network for Wildlife Habitat Monitoring
Venue: arXiv cs.SY 2017
Year: 2017

One of the fundamental issue in wireless sensor network is conserving energy and thus extending the lifetime of the network. In this paper we investigate the coverage problem in camera sensor networks by developing two algorithms which consider network lifetime. Also, it is assumed that camera sensors spread randomly over a large area in order to monitor a designated air space. To increase the lifetime of the network, the density of distributed sensors could be such that a subset of sensors can cover the required air space. As a sensor dies another sensor should be selected to compensate for the dead one and reestablish the complete coverage. This process should be continued until complete coverage is not achievable by the existing sensors. Thereafter, a graceful degradation of the coverage is desirable. The goal is to elongate the lifetime of the network while maintaining a maximum possible coverage of the designated air space. Since the selection of a subset of sensors for complete coverage of the target area is an NP-complete problem we present a class of heuristics for this case. This is done by prioritizing the sensors based on their visual and communicative properties.
Additivity of Information in Multilayer Networks via Additive Gaussian Noise Transforms
Venue: arXiv cs.IT 2017
Year: 2017

Multilayer (or deep) networks are powerful probabilistic models based on multiple stages of a linear transform followed by a non-linear (possibly random) function. In general, the linear transforms are defined by matrices and the non-linear functions are defined by information channels. These models have gained great popularity due to their ability to characterize complex probabilistic relationships arising in a wide variety of inference problems. The contribution of this paper is a new method for analyzing the fundamental limits of statistical inference in settings where the model is known. The validity of our method can be established in a number of settings and is conjectured to hold more generally. A key assumption made throughout is that the matrices are drawn randomly from orthogonally invariant distributions. Our method yields explicit formulas for 1) the mutual information; 2) the minimum mean-squared error (MMSE); 3) the existence and locations of certain phase-transitions with respect to the problem parameters; and 4) the stationary points for the state evolution of approximate message passing algorithms. When applied to the special case of models with multivariate Gaussian channels our method is rigorous and has close connections to free probability theory for random matrices. When applied to the general case of non-Gaussian channels, our method provides a simple alternative to the replica method from statistical physics. A key observation is that the combined effects of the individual components in the model (namely the matrices and the channels) are additive when viewed in a certain transform domain.