OpenRaiser/Intern-Atlas huggingface dataset:
Efficient phase retrieval based on dark fringe recognition with an ability of bypassing invalid fringes
Venue: arXiv cs.CV 2016
Year: 2016

This paper discusses the noisy phase retrieval problem: recovering a complex image signal with independent noise from quadratic measurements. Inspired by the dark fringes shown in the measured images of the array detector, a novel phase retrieval approach is proposed and demonstrated both theoretically and experimentally to recognize the dark fringes and bypass the invalid fringes. A more accurate relative phase ratio between arbitrary two pixels is achieved by calculating the multiplicative ratios (or the sum of phase difference) on the path between them. Then the object phase image can be reconstructed precisely. Our approach is a good choice for retrieving high-quality phase images from noisy signals and has many potential applications in the fields such as X-ray crystallography, diffractive imaging, and so on.
Improving Neural Network Generalization by Combining Parallel Circuits with Dropout
Venue: arXiv cs.NE 2016
Year: 2016

In an attempt to solve the lengthy training times of neural networks, we proposed Parallel Circuits (PCs), a biologically inspired architecture. Previous work has shown that this approach fails to maintain generalization performance in spite of achieving sharp speed gains. To address this issue, and motivated by the way Dropout prevents node co-adaption, in this paper, we suggest an improvement by extending Dropout to the PC architecture. The paper provides multiple insights into this combination, including a variety of fusion approaches. Experiments show promising results in which improved error rates are achieved in most cases, whilst maintaining the speed advantage of the PC approach.
Towards an Approximate Conformance Relation for Hybrid I/O Automata
Venue: arXiv cs.LO 2016
Year: 2016

Several notions of conformance have been proposed for checking the behavior of cyber-physical systems against their hybrid systems models. In this paper, we explore the initial idea of a notion of approximate conformance that allows for comparison of both observable discrete actions and (sampled) continuous trajectories. As such, this notion will consolidate two earlier notions, namely the notion of Hybrid Input-Output Conformance (HIOCO) by M. van Osch and the notion of Hybrid Conformance by H. Abbas and G.E. Fainekos. We prove that our proposed notion of conformance satisfies a semi-transitivity property, which makes it suitable for a step-wise proof of conformance or refinement.
Reducing Nondeterministic Tree Automata by Adding Transitions
Venue: arXiv cs.FL 2016
Year: 2016

We introduce saturation of nondeterministic tree automata, a technique that consists of adding new transitions to an automaton while preserving its language. We implemented our algorithm on minotaut - a module of the tree automata library libvata that reduces the size of automata by merging states and removing superfluous transitions - and we show how saturation can make subsequent merge and transition-removal operations more effective. Thus we obtain a Ptime algorithm that reduces the size of tree automata even more than before. Additionally, we explore how minotaut alone can play an important role when performing hard operations like complementation, allowing to both obtain smaller complement automata and lower computation times. We then show how saturation can extend this contribution even further. We tested our algorithms on a large collection of automata from applications of libvata in shape analysis, and on different classes of randomly generated automata.
Sending Money Like Sending E-mails: Cryptoaddresses, The Universal Decentralised Identities
Venue: arXiv cs.CR 2016
Year: 2016

Sending money in cryptocurrencies is majorly based on public keys or their hashed forms -- "addresses." These long random-looking strings are user unfriendly for transferring by other means than via copy-and-paste or QR codes. Replacing such strings with identifiers chosen by users themselves would significantly improve usability of cryptocurrencies. Such identifiers could be memorable, easier to write on paper or to dictate over phone. Main challenge lies in designing a practically usable decentralised system for providing these identifiers. Former solutions have been built as centralised systems or come with nonnegligible limitations. Our solution is reminiscent of a prevalent e-mail system, which is an already user friendly and desirably decentralised system. It is shown that our approach is directly applicable also to other systems that use long cryptographic identifiers.
An early sign of satisfiability
Venue: arXiv cs.LO 2016
Year: 2016

This note considers checking satisfiability of sets of propositional clauses (SAT instances). It shows that "unipolar sets" of clauses (containing no positive or no negative clauses) provide an "early sign" of satisfiability of SAT instances before all the clauses become satisfied in the course of solving SAT problems. At this sign the processing can be terminated by "unipolar set termination", UST thus before it is usually done by SAT solvers (Table 1). An analysis of benchmark SAT instances used at SAT Competitions shows that UST can speed up solving SAT instances stemming from many real-world problems. The efficiency of UST increases with the "skewness" of the SAT set being checked, that is the difference between probabilities of negated and unnegated literals in the set. Many real-world problems, by virtue of their semantics, are skewed (Table 2). The efficiency of UST can be increased by revealing the "hidden skewness" of SAT sets (Table 3).
Ontohub: A semantic repository for heterogeneous ontologies
Venue: arXiv cs.AI 2016
Year: 2016

Ontohub is a repository engine for managing distributed heterogeneous ontologies. The distributed nature enables communities to share and exchange their contributions easily. The heterogeneous nature makes it possible to integrate ontologies written in various ontology languages. Ontohub supports a wide range of formal logical and ontology languages, as well as various structuring and modularity constructs and inter-theory (concept) mappings, building on the OMG-standardized DOL language. Ontohub repositories are organised as Git repositories, thus inheriting all features of this popular version control system. Moreover, Ontohub is the first repository engine meeting a substantial amount of the requirements formulated in the context of the Open Ontology Repository (OOR) initiative, including an API for federation as well as support for logical inference and axiom selection.
Objective Micro-Facial Movement Detection Using FACS-Based Regions and Baseline Evaluation
Venue: arXiv cs.CV 2016
Year: 2016

Micro-facial expressions are regarded as an important human behavioural event that can highlight emotional deception. Spotting these movements is difficult for humans and machines, however research into using computer vision to detect subtle facial expressions is growing in popularity. This paper proposes an individualised baseline micro-movement detection method using 3D Histogram of Oriented Gradients (3D HOG) temporal difference method. We define a face template consisting of 26 regions based on the Facial Action Coding System (FACS). We extract the temporal features of each region using 3D HOG. Then, we use Chi-square distance to find subtle facial motion in the local regions. Finally, an automatic peak detector is used to detect micro-movements above the newly proposed adaptive baseline threshold. The performance is validated on two FACS coded datasets: SAMM and CASME II. This objective method focuses on the movement of the 26 face regions. When comparing with the ground truth, the best result was an AUC of 0.7512 and 0.7261 on SAMM and CASME II, respectively. The results show that 3D HOG outperformed for micro-movement detection, compared to state-of-the-art feature representations: Local Binary Patterns in Three Orthogonal Planes and Histograms of Oriented Optical Flow.
Graphical RNN Models
Venue: arXiv cs.NE 2016
Year: 2016

Many time series are generated by a set of entities that interact with one another over time. This paper introduces a broad, flexible framework to learn from multiple inter-dependent time series generated by such entities. Our framework explicitly models the entities and their interactions through time. It achieves this by building on the capabilities of Recurrent Neural Networks, while also offering several ways to incorporate domain knowledge/constraints into the model architecture. The capabilities of our approach are showcased through an application to weather prediction, which shows gains over strong baselines.
Feature Learning for Chord Recognition: The Deep Chroma Extractor
Venue: arXiv cs.SD 2016
Year: 2016

We explore frame-level audio feature learning for chord recognition using artificial neural networks. We present the argument that chroma vectors potentially hold enough information to model harmonic content of audio for chord recognition, but that standard chroma extractors compute too noisy features. This leads us to propose a learned chroma feature extractor based on artificial neural networks. It is trained to compute chroma features that encode harmonic information important for chord recognition, while being robust to irrelevant interferences. We achieve this by feeding the network an audio spectrum with context instead of a single frame as input. This way, the network can learn to selectively compensate noise and resolve harmonic ambiguities. We compare the resulting features to hand-crafted ones by using a simple linear frame-wise classifier for chord recognition on various data sets. The results show that the learned feature extractor produces superior chroma vectors for chord recognition.
Transition-based Parsing with Context Enhancement and Future Reward Reranking
Venue: arXiv cs.CL 2016
Year: 2016

This paper presents a novel reranking model, future reward reranking, to re-score the actions in a transition-based parser by using a global scorer. Different to conventional reranking parsing, the model searches for the best dependency tree in all feasible trees constraining by a sequence of actions to get the future reward of the sequence. The scorer is based on a first-order graph-based parser with bidirectional LSTM, which catches different parsing view compared with the transition-based parser. Besides, since context enhancement has shown substantial improvement in the arc-stand transition-based parsing over the parsing accuracy, we implement context enhancement on an arc-eager transition-base parser with stack LSTMs, the dynamic oracle and dropout supporting and achieve further improvement. With the global scorer and context enhancement, the results show that UAS of the parser increases as much as 1.20% for English and 1.66% for Chinese, and LAS increases as much as 1.32% for English and 1.63% for Chinese. Moreover, we get state-of-the-art LASs, achieving 87.58% for Chinese and 93.37% for English.
A Simple Approach to Multilingual Polarity Classification in Twitter
Venue: arXiv cs.CL 2016
Year: 2016

Recently, sentiment analysis has received a lot of attention due to the interest in mining opinions of social media users. Sentiment analysis consists in determining the polarity of a given text, i.e., its degree of positiveness or negativeness. Traditionally, Sentiment Analysis algorithms have been tailored to a specific language given the complexity of having a number of lexical variations and errors introduced by the people generating content. In this contribution, our aim is to provide a simple to implement and easy to use multilingual framework, that can serve as a baseline for sentiment analysis contests, and as starting point to build new sentiment analysis systems. We compare our approach in eight different languages, three of them have important international contests, namely, SemEval (English), TASS (Spanish), and SENTIPOLC (Italian). Within the competitions our approach reaches from medium to high positions in the rankings; whereas in the remaining languages our approach outperforms the reported results.
Catalyzing Cloud-Fog Interoperation in 5G Wireless Networks: An SDN Approach
Venue: arXiv cs.NI 2016
Year: 2016

The piling up storage and compute stacks in cloud data center are expected to accommodate the majority of internet traffic in the future. However, as the number of mobile devices significantly increases, getting massive data into and out of the cloud wirelessly inflicts high pressure on the bandwidth, and meanwhile induces unpredictable latency. Fog computing, which advocates extending clouds to network edge, guarantees low latency and location-aware service provisioning. In this article, we consider fog computing as an ideal complement rather than a substitute of cloud computing, and we propose a software defined networking (SDN) enabled framework for cloud-fog interoperation, aiming at improving quality of experience and optimizing network resource usage. Two case studies are provided to illuminate the feasibility and advantage of the proposed framework. At last, potential research issues are presented for further investigation.
Output Constraint Transfer for Kernelized Correlation Filter in Tracking
Venue: arXiv cs.CV 2016
Year: 2016

Kernelized Correlation Filter (KCF) is one of the state-of-the-art object trackers. However, it does not reasonably model the distribution of correlation response during tracking process, which might cause the drifting problem, especially when targets undergo significant appearance changes due to occlusion, camera shaking, and/or deformation. In this paper, we propose an Output Constraint Transfer (OCT) method that by modeling the distribution of correlation response in a Bayesian optimization framework is able to mitigate the drifting problem. OCT builds upon the reasonable assumption that the correlation response to the target image follows a Gaussian distribution, which we exploit to select training samples and reduce model uncertainty. OCT is rooted in a new theory which transfers data distribution to a constraint of the optimized variable, leading to an efficient framework to calculate correlation filters. Extensive experiments on a commonly used tracking benchmark show that the proposed method significantly improves KCF, and achieves better performance than other state-of-the-art trackers. To encourage further developments, the source code is made available https://github.com/bczhangbczhang/OCT-KCF.
Software Defined Adaptive MIMO Visible Light Communications after an Obstruction
Venue: arXiv cs.IT 2016
Year: 2016

We experimentally demonstrate a software-defined 2x2 MIMO VLC system employing link adaptation of spatial multiplexing and diversity. The average error-free spectral efficiency of 12 b/s/Hz is achieved over 2 meters indoor transmission after an obstruction.
A Generalized Approximation Framework for Fractional Network Flow and Packing Problems
Venue: arXiv cs.DS 2016
Year: 2016

We generalize the fractional packing framework of Garg and Koenemann to the case of linear fractional packing problems over polyhedral cones. More precisely, we provide approximation algorithms for problems of the form $\max\{c^T x : Ax \leq b, x \in C \}$, where the matrix $A$ contains no negative entries and $C$ is a cone that is generated by a finite set $S$ of non-negative vectors. While the cone is allowed to require an exponential-sized representation, we assume that we can access it via one of three types of oracles. For each of these oracles, we present positive results for the approximability of the packing problem. In contrast to other frameworks, the presented one allows the use of arbitrary linear objective functions and can be applied to a large class of packing problems without much effort. In particular, our framework instantly allows to derive fast and simple fully polynomial-time approximation algorithms (FPTASs) for a large set of network flow problems, such as budget-constrained versions of traditional network flows, multicommodity flows, or generalized flows. Some of these FPTASs represent the first ones of their kind, while others match existing results but offer a much simpler proof.
Two-weight codes from trace codes over $R_k$
Venue: arXiv cs.IT 2016
Year: 2016

We construct a family of two-Lee-weight codes over the ring $R_k,$ which is defined as trace codes with algebraic structure of abelian codes. The Lee weight distribution of the two-weight codes is given. Taking the Gray map, we obtain optimal abelian binary two-weight codes by using the Griesmer bound. An application to secret sharing schemes is also given.
Models, networks and algorithmic complexity
Venue: arXiv cs.LG 2016
Year: 2016

I aim to show that models, classification or generating functions, invariances and datasets are algorithmically equivalent concepts once properly defined, and provide some concrete examples of them. I then show that a) neural networks (NNs) of different kinds can be seen to implement models, b) that perturbations of inputs and nodes in NNs trained to optimally implement simple models propagate strongly, c) that there is a framework in which recurrent, deep and shallow networks can be seen to fall into a descriptive power hierarchy in agreement with notions from the theory of recursive functions. The motivation for these definitions and following analysis lies in the context of cognitive neuroscience, and in particular in Ruffini (2016), where the concept of model is used extensively, as is the concept of algorithmic complexity.
Machine Learning, Linear and Bayesian Models for Logistic Regression in Failure Detection Problems
Venue: arXiv cs.LG 2016
Year: 2016

In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition Bosch Production Line Performance. We considered the use of machine learning, linear and Bayesian models. For machine learning approach, we analyzed XGBoost tree based classifier to obtain high scored classification. Using the generalized linear model for logistic regression makes it possible to analyze the influence of the factors under study. The Bayesian approach for logistic regression gives the statistical distribution for the parameters of the model. It can be useful in the probabilistic analysis, e.g. risk assessment.
Patent Portfolio Analysis of Cities: Statistics and Maps of Technological Inventiveness
Venue: arXiv cs.DL 2016
Year: 2016

Cities are engines of the knowledge-based economy, because they are the primary sites of knowledge production activities that subsequently shape the rate and direction of technological change and economic growth. Patents provide a wealth of information to analyse the knowledge specialization at specific places, such as technological details and information on inventors and entities involved, including address information. The technology codes on each patent document indicate the specialization and scope of the underlying technological knowledge of a given invention. In this paper we introduce tools for portfolio analysis in terms of patents that provide insights into the technological specialization of cities. The mapping and analysis of patent portfolios of cities using data of the Unites States Patent and Trademark Office (USPTO) website (at http://www.uspto.gov) and dedicated tools (at http://www.leydesdorff.net/portfolio) can be used to analyse the specialisation patterns of inventive activities among cities. The results allow policy makers and other stakeholders to identify promising areas of further knowledge development and 'smart specialisation' strategies.
The geometric lattice of embedded subsets
Venue: arXiv cs.DM 2016
Year: 2016

This work proposes an alternative approach to the so-called lattice of embedded subsets, which is included in the product of the subset and partition lattices of a finite set, and whose elements are pairs consisting of a subset and a partition where the former is a block of the latter. The lattice structure proposed in a recent contribution relies on ad-hoc definitions of both the join operator and the bottom element, while also including join-irreducible elements distinct from atoms. Conversely, here embedded subsets obtain through a closure operator defined over the product of the subset and partition lattices, where elements are generic pairs of a subset and a partition. Those such pairs that coincide with their closure are precisely embedded subsets, and since the Steinitz exchange axiom is also satisfied, what results is a geometric (hence atomic) lattice given by a simple matroid (or combinatorial geometry) included in the product of the subset and partition lattices (as the partition lattice itself is the polygon matroid defined on the edges of a complete graph). By focusing on its Möbius function, this geometric lattice of embedded subsets of a n-set is shown to be isomorphic to the lattice of partitions of a n+1-set.
Graph-based Cyber Security Analysis of State Estimation in Smart Power Grid
Venue: arXiv cs.SY 2016
Year: 2016

Smart power grid enables intelligent automation at all levels of power system operation, from electricity generation at power plants to power usage at households. The key enabling factor of an efficient smart grid is its built-in information and communication technology (ICT) that monitors the real-time system operating state and makes control decisions accordingly. As an important building block of the ICT system, power system state estimation is of critical importance to maintain normal operation of the smart grid, which, however, is under mounting threat from potential cyber attacks. In this article, we introduce a graph-based framework for performing cyber-security analysis in power system state estimation. Compared to conventional arithmetic-based security analysis, the graphical characterization of state estimation security provides intuitive visualization of some complex problem structures and enables efficient graphical solution algorithms, which are useful for both defending and attacking the ICT system of smart grid. We also highlight several promising future research directions on graph-based security analysis and its applications in smart power grid.
A Coordinate-Descent Framework to Design Low PSL/ISL Sequences
Venue: arXiv cs.IT 2016
Year: 2016

This paper is focused on the design of phase sequences with good (aperiodic) autocorrelation properties in terms of Peak Sidelobe Level (PSL) and Integrated Sidelobe Level (ISL). The problem is formulated as a bi-objective Pareto optimization forcing either a continuous or a discrete phase constraint at the design stage. An iterative procedure based on the coordinate descent method is introduced to deal with the resulting optimization problems which are non-convex and NP-hard in general. Each iteration of the devised method requires the solution of a non-convex min-max problem. It is handled either through a novel bisection or an FFT-based method for the continuous and the discrete phase constraint, respectively. Additionally, a heuristic approach to initialize the procedures employing the lp-norm minimization technique is proposed. Simulation results illustrate that the proposed methodologies can outperform some counterparts providing sequences with good autocorrelation features especially in the discrete phase/binary case.