OpenRaiser/Intern-Atlas huggingface dataset:
Restricted Boltzmann machine to determine the input weights for extreme learning machines
Venue: arXiv cs.NE 2017
Year: 2017

The Extreme Learning Machine (ELM) is a single-hidden layer feedforward neural network (SLFN) learning algorithm that can learn effectively and quickly. The ELM training phase assigns the input weights and bias randomly and does not change them in the whole process. Although the network works well, the random weights in the input layer can make the algorithm less effective and impact on its performance. Therefore, we propose a new approach to determine the input weights and bias for the ELM using the restricted Boltzmann machine (RBM), which we call RBM-ELM. We compare our new approach with a well-known approach to improve the ELM and a state of the art algorithm to select the weights for the ELM. The results show that the RBM-ELM outperforms both methodologies and achieve a better performance than the ELM.
An Annotated Corpus of Relational Strategies in Customer Service
Venue: arXiv cs.CL 2017
Year: 2017

We create and release the first publicly available commercial customer service corpus with annotated relational segments. Human-computer data from three live customer service Intelligent Virtual Agents (IVAs) in the domains of travel and telecommunications were collected, and reviewers marked all text that was deemed unnecessary to the determination of user intention. After merging the selections of multiple reviewers to create highlighted texts, a second round of annotation was done to determine the classes of language present in the highlighted sections such as the presence of Greetings, Backstory, Justification, Gratitude, Rants, or Emotions. This resulting corpus is a valuable resource for improving the quality and relational abilities of IVAs. As well as discussing the corpus itself, we compare the usage of such language in human-human interactions on TripAdvisor forums. We show that removal of this language from task-based inputs has a positive effect on IVA understanding by both an increase in confidence and improvement in responses, demonstrating the need for automated methods of its discovery.
The Storage vs Repair Bandwidth Trade-off for Multiple Failures in Clustered Storage Networks
Venue: arXiv cs.IT 2017
Year: 2017

We study the trade-off between storage overhead and inter-cluster repair bandwidth in clustered storage systems, while recovering from multiple node failures within a cluster. A cluster is a collection of $m$ nodes, and there are $n$ clusters. For data collection, we download the entire content from any $k$ clusters. For repair of $t \geq 2$ nodes within a cluster, we take help from $\ell$ local nodes, as well as $d$ helper clusters. We characterize the optimal trade-off under functional repair, and also under exact repair for the minimum storage and minimum inter-cluster bandwidth (MBR) operating points. Our bounds show the following interesting facts: $1)$ When $t|(m-\ell)$ the trade-off is the same as that under $t=1$, and thus there is no advantage in jointly repairing multiple nodes, $2)$ When $t \nmid (m-\ell)$, the optimal file-size at the MBR point under exact repair can be strictly less than that under functional repair. $3)$ Unlike the case of $t=1$, increasing the number of local helper nodes does not necessarily increase the system capacity under functional repair.
EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity
Venue: arXiv cs.CL 2017
Year: 2017

In this paper we describe a deep learning system that has been designed and built for the WASSA 2017 Emotion Intensity Shared Task. We introduce a representation learning approach based on inner attention on top of an RNN. Results show that our model offers good capabilities and is able to successfully identify emotion-bearing words to predict intensity without leveraging on lexicons, obtaining the 13th place among 22 shared task competitors.
Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution
Venue: arXiv cs.CL 2017
Year: 2017

Recent applications of neural language models have led to an increased interest in the automatic generation of natural language. However impressive, the evaluation of neurally generated text has so far remained rather informal and anecdotal. Here, we present an attempt at the systematic assessment of one aspect of the quality of neurally generated text. We focus on a specific aspect of neural language generation: its ability to reproduce authorial writing styles. Using established models for authorship attribution, we empirically assess the stylistic qualities of neurally generated text. In comparison to conventional language models, neural models generate fuzzier text that is relatively harder to attribute correctly. Nevertheless, our results also suggest that neurally generated text offers more valuable perspectives for the augmentation of training data.
On wrapping the Kalman filter and estimating with the SO(2) group
Venue: arXiv cs.RO 2017
Year: 2017

This paper analyzes directional tracking in 2D with the extended Kalman filter on Lie groups (LG-EKF). The study stems from the problem of tracking objects moving in 2D Euclidean space, with the observer measuring direction only, thus rendering the measurement space and object position on the circle---a non-Euclidean geometry. The problem is further inconvenienced if we need to include higher-order dynamics in the state space, like angular velocity which is a Euclidean variables. The LG-EKF offers a solution to this issue by modeling the state space as a Lie group or combination thereof, e.g., SO(2) or its combinations with Rn. In the present paper, we first derive the LG-EKF on SO(2) and subsequently show that this derivation, based on the mathematically grounded framework of filtering on Lie groups, yields the same result as heuristically wrapping the angular variable within the EKF framework. This result applies only to the SO(2) and SO(2)xRn LG-EKFs and is not intended to be extended to other Lie groups or combinations thereof. In the end, we showcase the SO(2)xR2 LG-EKF, as an example of a constant angular acceleration model, on the problem of speaker tracking with a microphone array for which real-world experiments are conducted and accuracy is evaluated with ground truth data obtained by a motion capture system.
Ultra-Sparse Non-Binary LDPC Codes for Probabilistic Amplitude Shaping
Venue: arXiv cs.IT 2017
Year: 2017

This work shows how non-binary low-density parity-check codes over GF($2^p$) can be combined with probabilistic amplitude shaping (PAS) (Böcherer, et al., 2015), which combines forward-error correction with non-uniform signaling for power-efficient communication. Ultra-sparse low-density parity-check codes over GF(64) and GF(256) gain 0.6 dB in power efficiency over state-of-the-art binary LDPC codes at a spectral efficiency of 1.5 bits per channel use and a blocklength of 576 bits. The simulation results are compared to finite length coding bounds and complemented by density evolution analysis.
Constructive Modalities with Provability Smack
Venue: arXiv cs.LO 2017
Year: 2017

I overview the work of the Tbilisi school on intuitionistic modal logics of well-founded/scattered structures and its connections with contemporary theoretical computer science. Fixed-point theorems and their consequences are of particular interest.
Online Service with Delay
Venue: arXiv cs.DS 2017
Year: 2017

In this paper, we introduce the online service with delay problem. In this problem, there are $n$ points in a metric space that issue service requests over time, and a server that serves these requests. The goal is to minimize the sum of distance traveled by the server and the total delay in serving the requests. This problem models the fundamental tradeoff between batching requests to improve locality and reducing delay to improve response time, that has many applications in operations management, operating systems, logistics, supply chain management, and scheduling. Our main result is to show a poly-logarithmic competitive ratio for the online service with delay problem. This result is obtained by an algorithm that we call the preemptive service algorithm. The salient feature of this algorithm is a process called preemptive service, which uses a novel combination of (recursive) time forwarding and spatial exploration on a metric space. We hope this technique will be useful for related problems such as reordering buffer management, online TSP, vehicle routing, etc. We also generalize our results to $k > 1$ servers.
Asymptotic Analysis of Plausible Tree Hash Modes for SHA-3
Venue: arXiv cs.CR 2017
Year: 2017

Discussions about the choice of a tree hash mode of operation for a standardization have recently been undertaken. It appears that a single tree mode cannot address adequately all possible uses and specifications of a system. In this paper, we review the tree modes which have been proposed, we discuss their problems and propose remedies. We make the reasonable assumption that communicating systems have different specifications and that software applications are of different types (securing stored content or live-streamed content). Finally, we propose new modes of operation that address the resource usage problem for the three most representative categories of devices and we analyse their asymptotic behavior.
An Improved Residual LSTM Architecture for Acoustic Modeling
Venue: arXiv cs.CL 2017
Year: 2017

Long Short-Term Memory (LSTM) is the primary recurrent neural networks architecture for acoustic modeling in automatic speech recognition systems. Residual learning is an efficient method to help neural networks converge easier and faster. In this paper, we propose several types of residual LSTM methods for our acoustic modeling. Our experiments indicate that, compared with classic LSTM, our architecture shows more than 8% relative reduction in Phone Error Rate (PER) on TIMIT tasks. At the same time, our residual fast LSTM approach shows 4% relative reduction in PER on the same task. Besides, we find that all this architecture could have good results on THCHS-30, Librispeech and Switchboard corpora.
On the Contribution of Discourse Structure on Text Complexity Assessment
Venue: arXiv cs.CL 2017
Year: 2017

This paper investigates the influence of discourse features on text complexity assessment. To do so, we created two data sets based on the Penn Discourse Treebank and the Simple English Wikipedia corpora and compared the influence of coherence, cohesion, surface, lexical and syntactic features to assess text complexity. Results show that with both data sets coherence features are more correlated to text complexity than the other types of features. In addition, feature selection revealed that with both data sets the top most discriminating feature is a coherence feature.
Measuring the Effect of Discourse Relations on Blog Summarization
Venue: arXiv cs.CL 2017
Year: 2017

The work presented in this paper attempts to evaluate and quantify the use of discourse relations in the context of blog summarization and compare their use to more traditional and factual texts. Specifically, we measured the usefulness of 6 discourse relations - namely comparison, contingency, illustration, attribution, topic-opinion, and attributive for the task of text summarization from blogs. We have evaluated the effect of each relation using the TAC 2008 opinion summarization dataset and compared them with the results with the DUC 2007 dataset. The results show that in both textual genres, contingency, comparison, and illustration relations provide a significant improvement on summarization content; while attribution, topic-opinion, and attributive relations do not provide a consistent and significant improvement. These results indicate that, at least for summarization, discourse relations are just as useful for informal and affective texts as for more traditional news articles.
Minimum Hidden Guarding of Histogram Polygons
Venue: arXiv cs.CG 2017
Year: 2017

A hidden guard set $ G $ is a set of point guards in polygon $ P $ that all points of the polygon are visible from some guards in $ G $ under the constraint that no two guards may see each other. In this paper, we consider the problem for finding minimum hidden guard sets in histogram polygons under orthogonal visibility. Two points $ p $ and $ q $ are orthogonally visible if the orthogonal bounding rectangle for $ p $ and $ q $ lies within $ P $. It is known that the problem is NP-hard for simple polygon with general visibility and it is true for simple orthogonal polygon. We proposed a linear time exact algorithm for finding minimum hidden guard set in histogram polygons under orthogonal visibility. In our algorithm, it is allowed that guards place everywhere in the polygon.
High Voltage Insulator Surface Evaluation Using Image Processing
Venue: arXiv cs.CV 2017
Year: 2017

High voltage insulators are widely deployed in power systems to isolate the live- and dead-part of overhead lines as well as to support the power line conductors mechanically. Permanent, secure and safe operation of power transmission lines require that the high voltage insulators are inspected and monitor, regularly. Severe environment conditions will influence insulator surface and change creepage distance. Consequently, power utilities and transmission companies face significant problem in operation due to insulator damage or contamination. In this study, a new technique is developed for real-time inspection of insulator and estimating the snow, ice and water over the insulator surface which can be a potential risk of operation breakdown. To examine the proposed system, practical experiment is conducted using ceramic insulator for capturing the images with snow, ice and wet surface conditions. Gabor and Standard deviation filters are utilized for image feature extraction. The best achieved recognition accuracy rate was 87% using statistical approach the Standard deviation.
Dependences in Strategy Logic
Venue: arXiv cs.LO 2017
Year: 2017

Strategy Logic (SL) is a very expressive logic for specifying and verifying properties of multi-agent systems: in SL, one can quantify over strategies, assign them to agents, and express properties of the resulting plays. Such a powerful framework has two drawbacks: first, model checking SL has non-elementary complexity; second, the exact semantics of SL is rather intricate, and may not correspond to what is expected. In this paper, we focus on strategy dependences in SL, by tracking how existentially-quantified strategies in a formula may (or may not) depend on other strategies selected in the formula. We study different kinds of dependences, refining the approach of [Mogavero et al., Reasoning about strategies: On the model-checking problem, 2014], and prove that they give rise to different satisfaction relations. In the setting where strategies may only depend on what they have observed, we identify a large fragment of SL for which we prove model checking can be performed in 2EXPTIME.
A Cointuitionistic Adjoint Logic
Venue: arXiv cs.LO 2017
Year: 2017

One leading question with respect to Bi-intuitionistic logic (BINT) is, what does BINT look like across the three arcs -- logic, typed $λ$-calculi, and category theory -- of the Curry-Howard-Lambek correspondence? Categorically, BINT can be seen as a mixing of two worlds: the first being intuitionistic logic (IL), which is modeled by a cartesian closed category, and the second being the dual to intuitionistic logic called cointuitionistic logic (coIL), which is modeled by a cocartesian coclosed category. Crolard showed that combining these two categories into the same category results in it degenerating to a poset. However, this degeneration does not occur when both logics are linear. We propose that IL and coIL need to be separated, and then mixed in a controlled way using the modalities from linear logic. This separation can be ultimately achieved by an adjoint formalization of bi-intuitionistic logic. This formalization consists of three worlds instead of two: the first is intuitionistic logic, the second is linear bi-intuitionistic (Bi-ILL), and the third is cointuitionistic logic. They are then related via two adjunctions. The adjunction between IL and ILL is known as a Linear/Non-linear model (LNL model) of ILL, and is due to Benton. However, the dual to LNL models which would amount to the adjunction between coILL and coIL has yet to appear in the literature. In this paper we fill this gap by studying the dual to LNL models which we call dual LNL models. We show that dual LNL models correspond to dual linear categories, the dual to Bierman's linear categories proposed by Bellin. Then we give the definition of bi-LNL models by combining our model with LNL models to obtain a new model of bi-intuitionistic logic. Finally, we give a corresponding sequent calculus, natural deduction, and term assignment for dual LNL models.
Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
Venue: arXiv cs.CV 2017
Year: 2017

We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule classification focusing on (i) usefulness of gradient tree boosting (XGBoost) and (ii) effectiveness of parameter optimization using Bayesian optimization (Tree Parzen Estimator, TPE) and random search. 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of local binary pattern was used for calculating feature vectors. Support vector machine (SVM) or XGBoost was trained using the feature vectors and their labels. TPE or random search was used for parameter optimization of SVM and XGBoost. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost were 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. In conclusion, XGBoost was better than SVM for classifying lung nodules. TPE was more efficient than random search for parameter optimization.
The Universal Process
Venue: arXiv cs.LO 2017
Year: 2017

A universal process of a process calculus is one that, given the Gödel index of a process of a certain type, produces a process equivalent to the encoded process. This paper demonstrates how universal processes can be formally defined and how a universal process of the value-passing calculus can be constructed. The existence of such a universal process in a process model can be explored to implement higher order communications, security protocols, and programming languages in the process model. A process version of the S-m-n theorem is stated to showcase how to embed the recursion theory in a process calculus.
On the approximation by single hidden layer feedforward neural networks with fixed weights
Venue: arXiv cs.NE 2017
Year: 2017

Feedforward neural networks have wide applicability in various disciplines of science due to their universal approximation property. Some authors have shown that single hidden layer feedforward neural networks (SLFNs) with fixed weights still possess the universal approximation property provided that approximated functions are univariate. But this phenomenon does not lay any restrictions on the number of neurons in the hidden layer. The more this number, the more the probability of the considered network to give precise results. In this note, we constructively prove that SLFNs with the fixed weight $1$ and two neurons in the hidden layer can approximate any continuous function on a compact subset of the real line. The applicability of this result is demonstrated in various numerical examples. Finally, we show that SLFNs with fixed weights cannot approximate all continuous multivariate functions.
Prevalence and citation advantage of gold open access in the subject areas of the Scopus database
Venue: arXiv cs.DL 2017
Year: 2017

The potential benefit of open access (OA) in relation to citation impact has been discussed in the literature in depth. The methodology used to test the OA citation advantage includes comparing OA vs. non-OA journal impact factors and citations of OA versus non-OA articles published in the same non-OA journals. However, one problem with many studies is that they are small -restricted to a discipline or set of journals-. Moreover, conclusions are not entirely consistent among research areas and 'early view' and 'selection bias' have been suggested as possible explications. In the present paper, an analysis of gold OA from across all areas of research -the 27 subject areas of the Scopus database- is realized. As a novel contribution, this paper takes a journal-level approach to assessing the OA citation advantage, whereas many others take a paper-level approach. Data were obtained from Scimago Lab, sorted using Scopus database, and tagged as OA/non-OA using the DOAJ list. Jointly with the OA citation advantage, the OA prevalence as well as the differences between access types (OA vs. non-OA) in production and referencing are tested. A total of 3,737 OA journals (16.8%) and 18,485 non-OA journals (83.2%) published in 2015 are considered. As the main conclusion, there is no generalizable gold OA citation advantage at journal level.
Network Model Selection for Task-Focused Attributed Network Inference
Venue: arXiv cs.SI 2017
Year: 2017

Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection to evaluate network representations from data, focusing on fundamental predictive tasks on networks. We present a modular methodology using general, interpretable network models, task neighborhood functions found across domains, and several criteria for robust model selection. We demonstrate our methodology on three online user activity datasets and show that network model selection for the appropriate network task vs. an alternate task increases performance by an order of magnitude in our experiments.
Robust Optimal Planning and Control of Non-Periodic Bipedal Locomotion with A Centroidal Momentum Model
Venue: arXiv cs.RO 2017
Year: 2017

This study presents a theoretical method for planning and controlling agile bipedal locomotion based on robustly tracking a set of non-periodic keyframe states. Based on centroidal momentum dynamics, we formulate a hybrid phase-space planning and control method which includes the following key components: (i) a step transition solver that enables dynamically tracking non-periodic keyframe states over various types of terrains, (ii) a robust hybrid automaton to effectively formulate planning and control algorithms, (iii) a steering direction model to control the robot's heading, (iv) a phase-space metric to measure distance to the planned locomotion manifolds, and (v) a hybrid control method based on the previous distance metric to produce robust dynamic locomotion under external disturbances. Compared to other locomotion methodologies, we have a large focus on non-periodic gait generation and robustness metrics to deal with disturbances. Such focus enables the proposed control method to robustly track non-periodic keyframe states over various challenging terrains and under external disturbances as illustrated through several simulations.