OpenRaiser/Intern-Atlas huggingface dataset:
Modularisation of Sequent Calculi for Normal and Non-normal Modalities
Venue: arXiv cs.LO 2017
Year: 2017

In this work we explore the connections between (linear) nested sequent calculi and ordinary sequent calculi for normal and non-normal modal logics. By proposing local versions to ordinary sequent rules we obtain linear nested sequent calculi for a number of logics, including to our knowledge the first nested sequent calculi for a large class of simply dependent multimodal logics, and for many standard non-normal modal logics. The resulting systems are modular and have separate left and right introduction rules for the modalities, which makes them amenable to specification as bipole clauses. While this granulation of the sequent rules introduces more choices for proof search, we show how linear nested sequent calculi can be restricted to blocked derivations, which directly correspond to ordinary sequent derivations.
A case study on English-Malayalam Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

In this paper we present our work on a case study on Statistical Machine Translation (SMT) and Rule based machine translation (RBMT) for translation from English to Malayalam and Malayalam to English. One of the motivations of our study is to make a three way performance comparison, such as, a) SMT and RBMT b) English to Malayalam SMT and Malayalam to English SMT c) English to Malayalam RBMT and Malayalam to English RBMT. We describe the development of English to Malayalam and Malayalam to English baseline phrase based SMT system and the evaluation of its performance compared against the RBMT system. Based on our study the observations are: a) SMT systems outperform RBMT systems, b) In the case of SMT, English - Malayalam systems perform better than that of Malayalam - English systems, c) In the case RBMT, Malayalam to English systems are performing better than English to Malayalam systems. Based on our evaluations and detailed error analysis, we describe the requirements of incorporating morphological processing into the SMT to improve the accuracy of translation.
RDCL 3D, a Model Agnostic Web Framework for the Design and Composition of NFV Services
Venue: arXiv cs.NI 2017
Year: 2017

We present RDCL 3D, a "model agnostic" web framework for the design and composition of NFV services and components. The framework allows editing and validating the descriptors of services and components both textually and graphically and supports the interaction with external orchestrators or with deployment and execution environments. RDCL 3D is open source and designed with a modular approach, allowing developers to "plug in" the support for new models. We describe several advances with respect to the NFV state of the art, which have been implemented with RDCL 3D. We have integrated in the platform the latest ETSI NFV ISG model specifications for which no parsers/validators were available. We have also included in the platform the support for OASIS TOSCA models, reusing existing parsers. Then we have considered the modelling of components in a modular software router (Click), which goes beyond the traditional scope of NFV. We have further developed this approach by combining traditional NFV components (Virtual Network Functions) and Click elements in a single model. Finally, we have considered the support of this solution using the Unikernels virtualization technology.
A Manifesto for Web Science @ 10
Venue: arXiv cs.CY 2017
Year: 2017

Twenty-seven years ago, one of the biggest societal changes in human history began slowly when the technical foundations for the World Wide Web were defined by Tim Berners-Lee. Ever since, the Web has grown exponentially, reaching far beyond its original technical foundations and deeply affecting the world today - and even more so the society of the future. We have seen that the Web can influence the realization of human rights and even the pursuit of happiness. The Web provides an infrastructure to help us to learn, to work, to communicate with loved ones, and to provide entertainment. However, it also creates an environment affected by the digital divide between those who have and those who do not have access. Additionally, the Web provides challenges we must understand if we are to find a viable balance between data ownership and privacy protection, between over-whelming surveillance and the prevention of terrorism. For the Web to succeed, we need to understand its societal challenges including increased crime, the impact of social platforms and socio-economic discrimination, and we must work towards fairness, social inclusion, and open governance. Ten Yars ago, the field of Web Science was created to explore the science underlying the Web from a socio-technical perspective including its mathematical properties, engineering principles, and social impacts. Ten years later, we are learning much as the interdisciplinary endeavor to understand the Web's global information space continues to grow. In this article we want to elicit the major lessons we have learned through Web Science and make some cautious predictions of what to expect next.
ArrayBridge: Interweaving declarative array processing with high-performance computing
Venue: arXiv cs.DB 2017
Year: 2017

Scientists are increasingly turning to datacenter-scale computers to produce and analyze massive arrays. Despite decades of database research that extols the virtues of declarative query processing, scientists still write, debug and parallelize imperative HPC kernels even for the most mundane queries. This impedance mismatch has been partly attributed to the cumbersome data loading process; in response, the database community has proposed in situ mechanisms to access data in scientific file formats. Scientists, however, desire more than a passive access method that reads arrays from files. This paper describes ArrayBridge, a bi-directional array view mechanism for scientific file formats, that aims to make declarative array manipulations interoperable with imperative file-centric analyses. Our prototype implementation of ArrayBridge uses HDF5 as the underlying array storage library and seamlessly integrates into the SciDB open-source array database system. In addition to fast querying over external array objects, ArrayBridge produces arrays in the HDF5 file format just as easily as it can read from it. ArrayBridge also supports time travel queries from imperative kernels through the unmodified HDF5 API, and automatically deduplicates between array versions for space efficiency. Our extensive performance evaluation in NERSC, a large-scale scientific computing facility, shows that ArrayBridge exhibits statistically indistinguishable performance and I/O scalability to the native SciDB storage engine.
Revealing Hidden Potentials of the q-Space Signal in Breast Cancer
Venue: arXiv cs.CV 2017
Year: 2017

Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many of these false-positive findings prior to biopsy. Current approaches estimate tissue properties by means of quantitative parameters taken from generative, biophysical models fit to the q-space encoded signal under certain assumptions regarding noise and spatial homogeneity. This process is prone to fitting instability and partial information loss due to model simplicity. We reveal unexplored potentials of the signal by integrating all data processing components into a convolutional neural network (CNN) architecture that is designed to propagate clinical target information down to the raw input images. This approach enables simultaneous and target-specific optimization of image normalization, signal exploitation, global representation learning and classification. Using a multicentric data set of 222 patients, we demonstrate that our approach significantly improves clinical decision making with respect to the current state of the art.
Combining the $k$-CNF and XOR Phase-Transitions
Venue: arXiv cs.DM 2017
Year: 2017

The runtime performance of modern SAT solvers on random $k$-CNF formulas is deeply connected with the 'phase-transition' phenomenon seen empirically in the satisfiability of random $k$-CNF formulas. Recent universal hashing-based approaches to sampling and counting crucially depend on the runtime performance of SAT solvers on formulas expressed as the conjunction of both $k$-CNF and XOR constraints (known as $k$-CNF-XOR formulas), but the behavior of random $k$-CNF-XOR formulas is unexplored in prior work. In this paper, we present the first study of the satisfiability of random $k$-CNF-XOR formulas. We show empirical evidence of a surprising phase-transition that follows a linear trade-off between $k$-CNF and XOR constraints. Furthermore, we prove that a phase-transition for $k$-CNF-XOR formulas exists for $k = 2$ and (when the number of $k$-CNF constraints is small) for $k > 2$.
Monte Carlo Action Programming
Venue: arXiv cs.AI 2017
Year: 2017

This paper proposes Monte Carlo Action Programming, a programming language framework for autonomous systems that act in large probabilistic state spaces with high branching factors. It comprises formal syntax and semantics of a nondeterministic action programming language. The language is interpreted stochastically via Monte Carlo Tree Search. Effectiveness of the approach is shown empirically.
Local Synchronization of Sampled-Data Systems on Lie Groups
Venue: arXiv cs.SY 2017
Year: 2017

We present a smooth distributed nonlinear control law for local synchronization of identical driftless kinematic agents on a Cartesian product of matrix Lie groups with a connected communication graph. If the agents are initialized sufficiently close to one another, then synchronization is achieved exponentially fast. We first analyze the special case of commutative Lie groups and show that in exponential coordinates, the closed-loop dynamics are linear. We characterize all equilibria of the network and, in the case of an unweighted, complete graph, characterize the settling time and conditions for deadbeat performance. Using the Baker-Campbell-Hausdorff theorem, we show that, in a neighbourhood of the identity element, all results generalize to arbitrary matrix Lie groups.
Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments
Venue: arXiv cs.LG 2017
Year: 2017

We study the problem of causal structure learning over a set of random variables when the experimenter is allowed to perform at most $M$ experiments in a non-adaptive manner. We consider the optimal learning strategy in terms of minimizing the portions of the structure that remains unknown given the limited number of experiments in both Bayesian and minimax setting. We characterize the theoretical optimal solution and propose an algorithm, which designs the experiments efficiently in terms of time complexity. We show that for bounded degree graphs, in the minimax case and in the Bayesian case with uniform priors, our proposed algorithm is a $ρ$-approximation algorithm, where $ρ$ is independent of the order of the underlying graph. Simulations on both synthetic and real data show that the performance of our algorithm is very close to the optimal solution.
Progress Estimation and Phase Detection for Sequential Processes
Venue: arXiv cs.LG 2017
Year: 2017

Process modeling and understanding are fundamental for advanced human-computer interfaces and automation systems. Most recent research has focused on activity recognition, but little has been done on sensor-based detection of process progress. We introduce a real-time, sensor-based system for modeling, recognizing and estimating the progress of a work process. We implemented a multimodal deep learning structure to extract the relevant spatio-temporal features from multiple sensory inputs and used a novel deep regression structure for overall completeness estimation. Using process completeness estimation with a Gaussian mixture model, our system can predict the phase for sequential processes. The performance speed, calculated using completeness estimation, allows online estimation of the remaining time. To train our system, we introduced a novel rectified hyperbolic tangent (rtanh) activation function and conditional loss. Our system was tested on data obtained from the medical process (trauma resuscitation) and sports events (Olympic swimming competition). Our system outperformed the existing trauma-resuscitation phase detectors with a phase detection accuracy of over 86%, an F1-score of 0.67, a completeness estimation error of under 12.6%, and a remaining-time estimation error of less than 7.5 minutes. For the Olympic swimming dataset, our system achieved an accuracy of 88%, an F1-score of 0.58, a completeness estimation error of 6.3% and a remaining-time estimation error of 2.9 minutes.
Learning What Data to Learn
Venue: arXiv cs.LG 2017
Year: 2017

Machine learning is essentially the sciences of playing with data. An adaptive data selection strategy, enabling to dynamically choose different data at various training stages, can reach a more effective model in a more efficient way. In this paper, we propose a deep reinforcement learning framework, which we call \emph{\textbf{N}eural \textbf{D}ata \textbf{F}ilter} (\textbf{NDF}), to explore automatic and adaptive data selection in the training process. In particular, NDF takes advantage of a deep neural network to adaptively select and filter important data instances from a sequential stream of training data, such that the future accumulative reward (e.g., the convergence speed) is maximized. In contrast to previous studies in data selection that is mainly based on heuristic strategies, NDF is quite generic and thus can be widely suitable for many machine learning tasks. Taking neural network training with stochastic gradient descent (SGD) as an example, comprehensive experiments with respect to various neural network modeling (e.g., multi-layer perceptron networks, convolutional neural networks and recurrent neural networks) and several applications (e.g., image classification and text understanding) demonstrate that NDF powered SGD can achieve comparable accuracy with standard SGD process by using less data and fewer iterations.
Scaffolding Networks: Incremental Learning and Teaching Through Questioning
Venue: arXiv cs.CL 2017
Year: 2017

We introduce a new paradigm of learning for reasoning, understanding, and prediction, as well as the scaffolding network to implement this paradigm. The scaffolding network embodies an incremental learning approach that is formulated as a teacher-student network architecture to teach machines how to understand text and do reasoning. The key to our computational scaffolding approach is the interactions between the teacher and the student through sequential questioning. The student observes each sentence in the text incrementally, and it uses an attention-based neural net to discover and register the key information in relation to its current memory. Meanwhile, the teacher asks questions about the observed text, and the student network gets rewarded by correctly answering these questions. The entire network is updated continually using reinforcement learning. Our experimental results on synthetic and real datasets show that the scaffolding network not only outperforms state-of-the-art methods but also learns to do reasoning in a scalable way even with little human generated input.
A Data-driven Approach for Furniture and Indoor Scene Colorization
Venue: arXiv cs.GR 2017
Year: 2017

We present a data-driven approach that colorizes 3D furniture models and indoor scenes by leveraging indoor images on the internet. Our approach is able to colorize the furniture automatically according to an example image. The core is to learn image-guided mesh segmentation to segment the model into different parts according to the image object. Given an indoor scene, the system supports colorization-by-example, and has the ability to recommend the colorization scheme that is consistent with a user-desired color theme. The latter is realized by formulating the problem as a Markov random field model that imposes user input as an additional constraint. We contribute to the community a hierarchically organized image-model database with correspondences between each image and the corresponding model at the part-level. Our experiments and a user study show that our system produces perceptually convincing results comparable to those generated by interior designers.
Answering FO+MOD queries under updates on bounded degree databases
Venue: arXiv cs.DB 2017
Year: 2017

We investigate the query evaluation problem for fixed queries over fully dynamic databases, where tuples can be inserted or deleted. The task is to design a dynamic algorithm that immediately reports the new result of a fixed query after every database update. We consider queries in first-order logic (FO) and its extension with modulo-counting quantifiers (FO+MOD), and show that they can be efficiently evaluated under updates, provided that the dynamic database does not exceed a certain degree bound. In particular, we construct a data structure that allows to answer a Boolean FO+MOD query and to compute the size of the result of a non-Boolean query within constant time after every database update. Furthermore, after every update we are able to immediately enumerate the new query result with constant delay between the output tuples. The time needed to build the data structure is linear in the size of the database. Our results extend earlier work on the evaluation of first-order queries on static databases of bounded degree and rely on an effective Hanf normal form for FO+MOD recently obtained by Heimberg, Kuske, and Schweikardt (LICS 2016).
Lipschitz Optimisation for Lipschitz Interpolation
Venue: arXiv cs.LG 2017
Year: 2017

Techniques known as Nonlinear Set Membership prediction, Kinky Inference or Lipschitz Interpolation are fast and numerically robust approaches to nonparametric machine learning that have been proposed to be utilised in the context of system identification and learning-based control. They utilise presupposed Lipschitz properties in order to compute inferences over unobserved function values. Unfortunately, most of these approaches rely on exact knowledge about the input space metric as well as about the Lipschitz constant. Furthermore, existing techniques to estimate the Lipschitz constants from the data are not robust to noise or seem to be ad-hoc and typically are decoupled from the ultimate learning and prediction task. To overcome these limitations, we propose an approach for optimising parameters of the presupposed metrics by minimising validation set prediction errors. To avoid poor performance due to local minima, we propose to utilise Lipschitz properties of the optimisation objective to ensure global optimisation success. The resulting approach is a new flexible method for nonparametric black-box learning. We provide experimental evidence of the competitiveness of our approach on artificial as well as on real data.
Physically unclonable function using initial waveform of ring oscillators on 65 nm CMOS technology
Venue: arXiv cs.CR 2017
Year: 2017

A silicon physically unclonable function (PUF) using ring oscillators (ROs) has the advantage of easy application in both an application specific integrated circuit (ASIC) and a field-programmable gate array (FPGA). Here, we provide a RO-PUF using the initial waveform of the ROs based on 65 nm CMOS technology. Compared with the conventional RO-PUF, the number of ROs is greatly reduced and the time needed to generate an ID is within a couple of system clocks.
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Venue: arXiv cs.CV 2017
Year: 2017

Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total image number, (ii) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion, and (iii) has high within-class diversity and between-class similarity. The creation of this dataset will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed dataset and the results are reported as a useful baseline for future research.
Revisiting Unsupervised Learning for Defect Prediction
Venue: arXiv cs.SE 2017
Year: 2017

Collecting quality data from software projects can be time-consuming and expensive. Hence, some researchers explore "unsupervised" approaches to quality prediction that does not require labelled data. An alternate technique is to use "supervised" approaches that learn models from project data labelled with, say, "defective" or "not-defective". Most researchers use these supervised models since, it is argued, they can exploit more knowledge of the projects. At FSE'16, Yang et al. reported startling results where unsupervised defect predictors outperformed supervised predictors for effort-aware just-in-time defect prediction. If confirmed, these results would lead to a dramatic simplification of a seemingly complex task (data mining) that is widely explored in the software engineering literature. This paper repeats and refutes those results as follows. (1) There is much variability in the efficacy of the Yang et al. predictors so even with their approach, some supervised data is required to prune weaker predictors away. (2)Their findings were grouped across $N$ projects. When we repeat their analysis on a project-by-project basis, supervised predictors are seen to work better. Even though this paper rejects the specific conclusions of Yang et al., we still endorse their general goal. In our our experiments, supervised predictors did not perform outstandingly better than unsupervised ones for effort-aware just-in-time defect prediction. Hence, they may indeed be some combination of unsupervised learners to achieve comparable performance to supervised ones. We therefore encourage others to work in this promising area.