OpenRaiser/Intern-Atlas huggingface dataset:
BENCHIP: Benchmarking Intelligence Processors
Venue: arXiv cs.PF 2017
Year: 2017

The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in both software and hardware). However, existing benchmarks are unsuitable for benchmarking intelligence processors due to their non-diversity and nonrepresentativeness. Also, the lack of a standard benchmarking methodology further exacerbates this problem. In this paper, we propose BENCHIP, a benchmark suite and benchmarking methodology for intelligence processors. The benchmark suite in BENCHIP consists of two sets of benchmarks: microbenchmarks and macrobenchmarks. The microbenchmarks consist of single-layer networks. They are mainly designed for bottleneck analysis and system optimization. The macrobenchmarks contain state-of-the-art industrial networks, so as to offer a realistic comparison of different platforms. We also propose a standard benchmarking methodology built upon an industrial software stack and evaluation metrics that comprehensively reflect the various characteristics of the evaluated intelligence processors. BENCHIP is utilized for evaluating various hardware platforms, including CPUs, GPUs, and accelerators. BENCHIP will be open-sourced soon.
Content Based Document Recommender using Deep Learning
Venue: arXiv cs.CL 2017
Year: 2017

With the recent advancements in information technology there has been a huge surge in amount of data available. But information retrieval technology has not been able to keep up with this pace of information generation resulting in over spending of time for retrieving relevant information. Even though systems exist for assisting users to search a database along with filtering and recommending relevant information, but recommendation system which uses content of documents for recommendation still have a long way to mature. Here we present a Deep Learning based supervised approach to recommend similar documents based on the similarity of content. We combine the C-DSSM model with Word2Vec distributed representations of words to create a novel model to classify a document pair as relevant/irrelavant by assigning a score to it. Using our model retrieval of documents can be done in O(1) time and the memory complexity is O(n), where n is number of documents.
A Scalable and Adaptive Method for Finding Semantically Equivalent Cue Words of Uncertainty
Venue: arXiv cs.DL 2017
Year: 2017

Scientific knowledge is constantly subject to a variety of changes due to new discoveries, alternative interpretations, and fresh perspectives. Understanding uncertainties associated with various stages of scientific inquiries is an integral part of scientists' domain expertise and it serves as the core of their meta-knowledge of science. Despite the growing interest in areas such as computational linguistics, systematically characterizing and tracking the epistemic status of scientific claims and their evolution in scientific disciplines remains a challenge. We present a unifying framework for the study of uncertainties explicitly and implicitly conveyed in scientific publications. The framework aims to accommodate a wide range of uncertain types, from speculations to inconsistencies and controversies. We introduce a scalable and adaptive method to recognize semantically equivalent cues of uncertainty across different fields of research and accommodate individual analysts' unique perspectives. We demonstrate how the new method can be used to expand a small seed list of uncertainty cue words and how the validity of the expanded candidate cue words are verified. We visualize the mixture of the original and expanded uncertainty cue words to reveal the diversity of expressions of uncertainty. These cue words offer a novel resource for the study of uncertainty in scientific assertions.
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) accessing the frequency band of a Primary User (PU) in an underlay cognitive scenario with a designed PU protection specification. The main idea is that the CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback indicates whether the probing-induced interference is harmful or not and can be used to learn the PU interference constraint. The cognitive part of this sequential probing process is the selection of the power levels of the Secondary Users (SUs) which aims to learn the PU interference constraint with a minimum number of probing attempts while setting a limit on the number of harmful probing-induced interference events or equivalently of NACK packet observations over a time window. This constrained design problem is studied within the Active Learning (AL) framework and an optimal solution is derived and implemented with a sophisticated, accurate and fast Bayesian Learning method, the Expectation Propagation (EP). The performance of this solution is also demonstrated through numerical simulations and compared with modified versions of AL techniques we developed in earlier work.
Listening to the World Improves Speech Command Recognition
Venue: arXiv cs.SD 2017
Year: 2017

We study transfer learning in convolutional network architectures applied to the task of recognizing audio, such as environmental sound events and speech commands. Our key finding is that not only is it possible to transfer representations from an unrelated task like environmental sound classification to a voice-focused task like speech command recognition, but also that doing so improves accuracies significantly. We also investigate the effect of increased model capacity for transfer learning audio, by first validating known results from the field of Computer Vision of achieving better accuracies with increasingly deeper networks on two audio datasets: UrbanSound8k and the newly released Google Speech Commands dataset. Then we propose a simple multiscale input representation using dilated convolutions and show that it is able to aggregate larger contexts and increase classification performance. Further, the models trained using a combination of transfer learning and multiscale input representations need only 40% of the training data to achieve similar accuracies as a freshly trained model with 100% of the training data. Finally, we demonstrate a positive interaction effect for the multiscale input and transfer learning, making a case for the joint application of the two techniques.
Deep Health Care Text Classification
Venue: arXiv cs.CL 2017
Year: 2017

Health related social media mining is a valuable apparatus for the early recognition of the diverse antagonistic medicinal conditions. Mostly, the existing methods are based on machine learning with knowledge-based learning. This working note presents the Recurrent neural network (RNN) and Long short-term memory (LSTM) based embedding for automatic health text classification in the social media mining. For each task, two systems are built and that classify the tweet at the tweet level. RNN and LSTM are used for extracting features and non-linear activation function at the last layer facilitates to distinguish the tweets of different categories. The experiments are conducted on 2nd Social Media Mining for Health Applications Shared Task at AMIA 2017. The experiment results are considerable; however the proposed method is appropriate for the health text classification. This is primarily due to the reason that, it doesn't rely on any feature engineering mechanisms.
Locally Nameless Permutation Types
Venue: arXiv cs.PL 2017
Year: 2017

We define "Locally Nameless Permutation Types", which fuse permutation types as used in Nominal Isabelle with the locally nameless representation. We show that this combination is particularly useful when formalizing programming languages where bound names may become free during execution ("extrusion"), common in process calculi. It inherits the generic definition of permutations and support, and associated lemmas, from the Nominal approach, and the ability to stay close to pencil-and-paper proofs from the locally nameless approach. We explain how to use cofinite quantification in this setting, show why reasoning about renaming is more important here than in languages without extrusion, and provide results about infinite support, necessary when reasoning about countable choice.
Are Multiagent Systems Resilient to Communication Failures?
Venue: arXiv cs.GT 2017
Year: 2017

A challenge in multiagent control systems is to ensure that they are appropriately resilient to communication failures between the various agents. In many common game-theoretic formulations of these types of systems, it is implicitly assumed that all agents have access to as much information about other agents' actions as needed. This paper endeavors to augment these game-theoretic methods with policies that would allow agents to react on-the-fly to losses of this information. Unfortunately, we show that even if a single agent loses communication with one other weakly-coupled agent, this can cause arbitrarily-bad system states to emerge as various solution concepts of an associated game, regardless of how the agent accounts for the communication failure and regardless of how weakly coupled the agents are. Nonetheless, we show that the harm that communication failures can cause is limited by the structure of the problem; when agents' action spaces are richer, problems are more susceptible to these types of pathologies. Finally, we undertake an initial study into how a system designer might prevent these pathologies, and explore a few limited settings in which communication failures cannot cause harm.
A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features
Venue: arXiv cs.SI 2017
Year: 2017

The emergence of social media as news sources has led to the rise of clickbait posts attempting to attract users to click on article links without informing them on the actual article content. This paper presents our efforts to create a clickbait detector inspired by fake news detection algorithms, and our submission to the Clickbait Challenge 2017. The detector is based almost exclusively on text-based features taken from previous work on clickbait detection, our own work on fake post detection, and features we designed specifically for the challenge. We use a two-level classification approach, combining the outputs of 65 first-level classifiers in a second-level feature vector. We present our exploratory results with individual features and their combinations, taken from the post text and the target article title, as well as feature selection. While our own blind tests with the dataset led to an F-score of 0.63, our final evaluation in the Challenge only achieved an F-score of 0.43. We explore the possible causes of this, and lay out potential future steps to achieve more successful results.
Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search
Venue: arXiv cs.RO 2017
Year: 2017

This work proposes a process for efficiently searching over combinations of individual object 6D pose hypotheses in cluttered scenes, especially in cases involving occlusions and objects resting on each other. The initial set of candidate object poses is generated from state-of-the-art object detection and global point cloud registration techniques. The best-scored pose per object by using these techniques may not be accurate due to overlaps and occlusions. Nevertheless, experimental indications provided in this work show that object poses with lower ranks may be closer to the real poses than ones with high ranks according to registration techniques. This motivates a global optimization process for improving these poses by taking into account scene-level physical interactions between objects. It also implies that the Cartesian product of candidate poses for interacting objects must be searched so as to identify the best scene-level hypothesis. To perform the search efficiently, the candidate poses for each object are clustered so as to reduce their number but still keep a sufficient diversity. Then, searching over the combinations of candidate object poses is performed through a Monte Carlo Tree Search (MCTS) process that uses the similarity between the observed depth image of the scene and a rendering of the scene given the hypothesized pose as a score that guides the search procedure. MCTS handles in a principled way the tradeoff between fine-tuning the most promising poses and exploring new ones, by using the Upper Confidence Bound (UCB) technique. Experimental results indicate that this process is able to quickly identify in cluttered scenes physically-consistent object poses that are significantly closer to ground truth compared to poses found by point cloud registration methods.
On the differences between citations and altmetrics: An investigation of factors driving altmetrics vs. citations for Finnish articles
Venue: arXiv cs.DL 2017
Year: 2017

This study examines a range of factors associating with future citation and altmetric counts to a paper. The factors include journal impact factor, individual collaboration, international collaboration, institution prestige, country prestige, research funding, abstract readability, abstract length, title length, number of cited references, field size, and field type and will be modelled in association with citation counts, Mendeley readers, Twitter posts, Facebook posts, blog posts, and news posts. The results demonstrate that eight factors are important for increased citation counts, seven different factors are important for increased Mendeley readers, eight factors are important for increased Twitter posts, three factors are important for increased Facebook posts, six factors are important for increased blog posts, and five factors are important for increased news posts. Journal impact factor and international collaboration are the two factors that significantly associate with increased citation counts and with all altmetric scores. Moreover, it seems that the factors driving Mendeley readership are similar to those driving citation counts. However, the altmetric events differ from each other in terms of a small number of factors; for instance, institution prestige and country prestige associate with increased Mendeley readers and blog and news posts, but it is an insignificant factor for Twitter and Facebook posts. The findings contribute to the continued development of theoretical models and methodological developments associated with capturing, interpreting, and understanding altmetric events.
Comparison of Signaling and Media Approaches to Detect VoIP SPIT Attack
Venue: arXiv cs.CR 2017
Year: 2017

IP networks became the most dominant type of information networks nowadays. It provides a number of services and makes it easy for users to be connected. IP networks provide an efficient way with a large number of services compared to other ways of voice communication. This leads to the migration to make voice calls via IP networks. Despite the wide range of IP networks services, availability, and its capabilities, there still a large number of security threats that affect IP networks and for sure affecting other services based on it and voice is one of them. This paper discusses reasons of migration from making voice calls via IP networks and leaving legacy networks, requirements to be available in IP networks to support voice transport, and concentrating on SPIT attack and its detection methods. Experiments took place to compare the different approaches used to detect spam over VoIP networks.
Confidence intervals for normalised citation counts: Can they delimit underlying research capability?
Venue: arXiv cs.DL 2017
Year: 2017

Normalised citation counts are routinely used to assess the average impact of research groups or nations. There is controversy over whether confidence intervals for them are theoretically valid or practically useful. In response, this article introduces the concept of a group's underlying research capability to produce impactful research. It then investigates whether confidence intervals could delimit the underlying capability of a group in practice. From 123120 confidence interval comparisons for the average citation impact of the national outputs of ten countries within 36 individual large monodisciplinary journals, moderately fewer than 95% of subsequent indicator values fall within 95% confidence intervals from prior years, with the percentage declining over time. This is consistent with confidence intervals effectively delimiting the research capability of a group, although it does not prove that this is the cause of the results. The results are unaffected by whether internationally collaborative articles are included.
Bottom-up automata on data trees and vertical XPath
Venue: arXiv cs.DB 2017
Year: 2017

A data tree is a finite tree whose every node carries a label from a finite alphabet and a datum from some infinite domain. We introduce a new model of automata over unranked data trees with a decidable emptiness problem. It is essentially a bottom-up alternating automaton with one register that can store one data value and can be used to perform equality tests with the data values occurring within the subtree of the current node. We show that it captures the expressive power of the vertical fragment of XPath - containing the child, descendant, parent and ancestor axes - obtaining thus a decision procedure for its satisfiability problem.
EagleMine: Vision-Guided Mining in Large Graphs
Venue: arXiv cs.SI 2017
Year: 2017

Given a graph with millions of nodes, what patterns exist in the distributions of node characteristics, and how can we detect them and separate anomalous nodes in a way similar to human vision? In this paper, we propose a vision-guided algorithm, EagleMine, to summarize micro-cluster patterns in two-dimensional histogram plots constructed from node features in a large graph. EagleMine utilizes a water-level tree to capture cluster structures according to vision-based intuition at multi-resolutions. EagleMine traverses the water-level tree from the root and adopts statistical hypothesis tests to determine the optimal clusters that should be fitted along the path, and summarizes each cluster with a truncated Gaussian distribution. Experiments on real data show that our method can find truncated and overlapped elliptical clusters, even when some baseline methods split one visual cluster into pieces with Gaussian spheres. To identify potentially anomalous microclusters, EagleMine also a designates score to measure the suspiciousness of outlier groups (i.e. node clusters) and outlier nodes, detecting bots and anomalous users with high accuracy in the real Microblog data.
A survey of robot learning from demonstrations for Human-Robot Collaboration
Venue: arXiv cs.RO 2017
Year: 2017

Robot learning from demonstration (LfD) is a research paradigm that can play an important role in addressing the issue of scaling up robot learning. Since this type of approach enables non-robotics experts can teach robots new knowledge without any professional background of mechanical engineering or computer programming skills, robots can appear in the real world even if it does not have any prior knowledge for any tasks like a new born baby. There is a growing body of literature that employ LfD approach for training robots. In this paper, I present a survey of recent research in this area while focusing on studies for human-robot collaborative tasks. Since there are different aspects between stand-alone tasks and collaborative tasks, researchers should consider these differences to design collaborative robots for more effective and natural human-robot collaboration (HRC). In this regard, many researchers have shown an increased interest in to make better communication framework between robots and humans because communication is a key issue to apply LfD paradigm for human-robot collaboration. I thus review some recent works that focus on designing better communication channels/methods at the first, then deal with another interesting research method, Interactive/Active learning, after that I finally present other recent approaches tackle a more challenging problem, learning of complex tasks, in the last of the paper.
Post-Prognostics Decision for Optimizing the Commitment of Fuel Cell Systems
Venue: arXiv cs.CE 2017
Year: 2017

In a post-prognostics decision context, this paper addresses the problem of maximizing the useful life of a platform composed of several parallel machines under service constraint. Application on multi-stack fuel cell systems is considered. In order to propose a solution to the insufficient durability of fuel cells, the purpose is to define a commitment strategy by determining at each time the contribution of each fuel cell stack to the global output so as to satisfy the demand as long as possible. A relaxed version of the problem is introduced, which makes it potentially solvable for very large instances. Results based on computational experiments illustrate the efficiency of the new approach, based on the Mirror Prox algorithm, when compared with a simple method of successive projections onto the constraint sets associated with the problem.
On the Complexity of Sampling Nodes Uniformly from a Graph
Venue: arXiv cs.DS 2017
Year: 2017

We study a number of graph exploration problems in the following natural scenario: an algorithm starts exploring an undirected graph from some seed node; the algorithm, for an arbitrary node $v$ that it is aware of, can ask an oracle to return the set of the neighbors of $v$. (In social network analysis, a call to this oracle corresponds to downloading the profile page of user $v$ in a social network.) The goal of the algorithm is to either learn something (e.g., average degree) about the graph, or to return some random function of the graph (e.g., a uniform-at-random node), while accessing/downloading as few nodes of the graph as possible. Motivated by practical applications, we study the complexities of a variety of problems in terms of the graph's mixing time and average degree -- two measures that are believed to be quite small in real-world social networks, and that have often been used in the applied literature to bound the performance of online exploration algorithms. Our main result is that the algorithm has to access $Ω\left(t_{\rm mix} d_{\rm avg} ε^{-2} \ln δ^{-1}\right)$ nodes to obtain, with probability at least $1-δ$, an $ε$-additive approximation of the average of a bounded function on the nodes of a graph -- this lower bound matches the performance of an algorithm that was proposed in the literature. We also give tight bounds for the problem of returning a close-to-uniform-at-random node from the graph. Finally, we give lower bounds for the problems of estimating the average degree of the graph, and the number of nodes of the graph.
Macrogeneration and Automata Libraries For COSMA design environment
Venue: arXiv cs.SE 2017
Year: 2017

In ICS, WUT a COSMA design environment is being developed. COSMA is based on Concurrent State Machines (CSM) formalism of system specification. It contains a graphical tool for system design, various tools for the analysis (including a temporal model checker), simulator and code generator. In many projects, some common susbsystems take place. This concerns both complicated modules and simple counters. In the report, a concept of macrogeneration technique for building of libraries of automata is presented. The new technique will support a compactness of projects and reusability of modules.