OpenRaiser/Intern-Atlas huggingface dataset:
Descriptional Complexity of Non-Unary Self-Verifying Symmetric Difference Automata
Venue: arXiv cs.FL 2017
Year: 2017

Previously, self-verifying symmetric difference automata were defined and a tight bound of 2^n-1-1 was shown for state complexity in the unary case. We now consider the non-unary case and show that, for every n at least 2, there is a regular language L_n accepted by a non-unary self-verifying symmetric difference nondeterministic automaton with n states, such that its equivalent minimal deterministic finite automaton has 2^n-1 states. Also, given any SV-XNFA with n states, it is possible, up to isomorphism, to find at most another |GL(n,Z_2)|-1 equivalent SV-XNFA.
Golden Years, Golden Shores: A Study of Elders in Online Travel Communities
Venue: arXiv cs.CL 2017
Year: 2017

In this paper we present our exploratory findings related to extracting knowledge and experiences from a community of senior tourists. By using tools of qualitative analysis as well as review of literature, we managed to verify a set of hypotheses related to the content created by senior tourists when participating in on-line communities. We also produced a codebook, representing various themes one may encounter in such communities. This codebook, derived from our own qualitative research, as well a literature review will serve as a basis for further development of automated tools of knowledge extraction. We also managed to find that older adults more often than other poster in tourists forums, mention their age in discussion, more often share their experiences and motivation to travel, however they do not differ in relation to describing barriers encountered while traveling.
Towards Object Life Cycle-Based Variant Generation of Business Process Models
Venue: arXiv cs.SE 2017
Year: 2017

Variability management of process models is a major challenge for Process-Aware Information Systems. Process model variants can be attributed to any of the following reasons: new technologies, governmental rules, organizational context or adoption of new standards. Current approaches to manage variants of process models address issues such as reducing the huge effort of modeling from scratch, preventing redundancy, and controlling inconsistency in process models. Although the effort to manage process model variants has been exerted, there are still limitations. Furthermore, existing approaches do not focus on variants that come from change in organizational perspective of process models. Organizational-driven variant management is an important area that still needs more study that we focus on in this paper. Object Life Cycle (OLC) is an important aspect that may change from an organization to another. This paper introduces an approach inspired by real life scenario to generate consistent process model variants that come from adaptations in the OLC.
An Architecture for Establishing Legal Semantic Workflows in the Context of Integrated Law Enforcement
Venue: arXiv cs.CY 2017
Year: 2017

Traditionally the integration of data from multiple sources is done on an ad-hoc basis for each analysis scenario and application. This is a solution that is inflexible, incurs in high costs, leads to "silos" that prevent sharing data across different agencies or tasks, and is unable to cope with the modern environment, where workflows, tasks, and priorities frequently change. Operating within the Data to Decision Cooperative Research Centre (D2D CRC), the authors are currently involved in the Integrated Law Enforcement Project, which has the goal of developing a federated data platform that will enable the execution of integrated analytics on data accessed from different external and internal sources, thereby providing effective support to an investigator or analyst working to evaluate evidence and manage lines of inquiries in the investigation. Technical solutions should also operate ethically, in compliance with the law, and subject to good governance principles.
AACT: Application-Aware Cooperative Time Allocation for Internet of Things
Venue: arXiv cs.NI 2017
Year: 2017

As the number of Internet of Things (IoT) devices keeps increasing, data is required to be communicated and processed by these devices at unprecedented rates. Cooperation among wireless devices by exploiting Device-to-Device (D2D) connections is promising, where aggregated resources in a cooperative setup can be utilized by all devices, which would increase the total utility of the setup. In this paper, we focus on the resource allocation problem for cooperating IoT devices with multiple heterogeneous applications. In particular, we develop Application-Aware Cooperative Time allocation (AACT) framework, which optimizes the time that each application utilizes the aggregated system resources by taking into account heterogeneous device constraints and application requirements. AACT is grounded on the concept of Rolling Horizon Control (RHC) where decisions are made by iteratively solving a convex optimization problem over a moving control window of estimated system parameters. The simulation results demonstrate significant performance gains.
Emotion Detection Using Noninvasive Low Cost Sensors
Venue: arXiv cs.HC 2017
Year: 2017

Emotion recognition from biometrics is relevant to a wide range of application domains, including healthcare. Existing approaches usually adopt multi-electrodes sensors that could be expensive or uncomfortable to be used in real-life situations. In this study, we investigate whether we can reliably recognize high vs. low emotional valence and arousal by relying on noninvasive low cost EEG, EMG, and GSR sensors. We report the results of an empirical study involving 19 subjects. We achieve state-of-the- art classification performance for both valence and arousal even in a cross-subject classification setting, which eliminates the need for individual training and tuning of classification models.
Exploring the Ideological Nature of Journalists' Social Networks on Twitter and Associations with News Story Content
Venue: arXiv cs.SI 2017
Year: 2017

The present work proposes the use of social media as a tool for better understanding the relationship between a journalists' social network and the content they produce. Specifically, we ask: what is the relationship between the ideological leaning of a journalist's social network on Twitter and the news content he or she produces? Using a novel dataset linking over 500,000 news articles produced by 1,000 journalists at 25 different news outlets, we show a modest correlation between the ideologies of who a journalist follows on Twitter and the content he or she produces. This research can provide the basis for greater self-reflection among media members about how they source their stories and how their own practice may be colored by their online networks. For researchers, the findings furnish a novel and important step in better understanding the construction of media stories and the mechanics of how ideology can play a role in shaping public information.
Reflection Separation and Deblurring of Plenoptic Images
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we address the problem of reflection removal and deblurring from a single image captured by a plenoptic camera. We develop a two-stage approach to recover the scene depth and high resolution textures of the reflected and transmitted layers. For depth estimation in the presence of reflections, we train a classifier through convolutional neural networks. For recovering high resolution textures, we assume that the scene is composed of planar regions and perform the reconstruction of each layer by using an explicit form of the plenoptic camera point spread function. The proposed framework also recovers the sharp scene texture with different motion blurs applied to each layer. We demonstrate our method on challenging real and synthetic images.
Implementation of SNR estimation based Energy Detection on USRP and GNU Radio for Cognitive Radio Networks
Venue: arXiv cs.NI 2017
Year: 2017

Development of smart spectrum sensing techniques is the most important task in the design of a cognitive radio system which uses the available spectrum efficiently. The adaptive SNR estimation based energy detection technique has the dual benefit of improving the efficiency of spectrum usage by capitalizing on the underutilization of the spectrum in an adaptive and iterative fashion, as well as reducing the hardware resources leading to easy implementation on a versatile and diverse group of cognitive radio infrastructures. The use of adaptive threshold for energy detection based on SNR estimation improves the spectrum sensing performance and efficiency of the cognitive radio by many folds, especially in low SNR as well as high noise variance situations. The proposed method is implemented on the USRP B200 and results show significant improvement in the detection rate of primary users as compared to conventional energy detection techniques.
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems
Venue: arXiv cs.LG 2017
Year: 2017

The Koopman operator has recently garnered much attention for its value in dynamical systems analysis and data-driven model discovery. However, its application has been hindered by the computational complexity of extended dynamic mode decomposition; this requires a combinatorially large basis set to adequately describe many nonlinear systems of interest, e.g. cyber-physical infrastructure systems, biological networks, social systems, and fluid dynamics. Often the dictionaries generated for these problems are manually curated, requiring domain-specific knowledge and painstaking tuning. In this paper we introduce a deep learning framework for learning Koopman operators of nonlinear dynamical systems. We show that this novel method automatically selects efficient deep dictionaries, outperforming state-of-the-art methods. We benchmark this method on partially observed nonlinear systems, including the glycolytic oscillator and show it is able to predict quantitatively 100 steps into the future, using only a single timepoint, and qualitative oscillatory behavior 400 steps into the future.
Min-Max Theorems for Packing and Covering Odd $(u,v)$-trails
Venue: arXiv cs.DM 2017
Year: 2017

We investigate the problem of packing and covering odd $(u,v)$-trails in a graph. A $(u,v)$-trail is a $(u,v)$-walk that is allowed to have repeated vertices but no repeated edges. We call a trail odd if the number of edges in the trail is odd. Let $ν(u,v)$ denote the maximum number of edge-disjoint odd $(u,v)$-trails, and $τ(u,v)$ denote the minimum size of an edge-set that intersects every odd $(u,v)$-trail. We prove that $τ(u,v)\leq 2ν(u,v)+1$. Our result is tight---there are examples showing that $τ(u,v)=2ν(u,v)+1$---and substantially improves upon the bound of $8$ obtained in [Churchley et al 2016] for $τ(u,v)/ν(u,v)$. Our proof also yields a polynomial-time algorithm for finding a cover and a collection of trails satisfying the above bounds. Our proof is simple and has two main ingredients. We show that (loosely speaking) the problem can be reduced to the problem of packing and covering odd $(uv,uv)$-trails losing a factor of 2 (either in the number of trails found, or the size of the cover). Complementing this, we show that the odd-$(uv,uv)$-trail packing and covering problems can be tackled by exploiting a powerful min-max result of [Chudnovsky et al 2006] for packing vertex-disjoint nonzero $A$-paths in group-labeled graphs.
Generating Visual Representations for Zero-Shot Classification
Venue: arXiv cs.CV 2017
Year: 2017

This paper addresses the task of learning an image clas-sifier when some categories are defined by semantic descriptions only (e.g. visual attributes) while the others are defined by exemplar images as well. This task is often referred to as the Zero-Shot classification task (ZSC). Most of the previous methods rely on learning a common embedding space allowing to compare visual features of unknown categories with semantic descriptions. This paper argues that these approaches are limited as i) efficient discrimi-native classifiers can't be used ii) classification tasks with seen and unseen categories (Generalized Zero-Shot Classification or GZSC) can't be addressed efficiently. In contrast , this paper suggests to address ZSC and GZSC by i) learning a conditional generator using seen classes ii) generate artificial training examples for the categories without exemplars. ZSC is then turned into a standard supervised learning problem. Experiments with 4 generative models and 5 datasets experimentally validate the approach, giving state-of-the-art results on both ZSC and GZSC.
Adaptive Plant Propagation Algorithm for Solving Economic Load Dispatch Problem
Venue: arXiv cs.CE 2017
Year: 2017

Optimization problems in design engineering are complex by nature, often because of the involvement of critical objective functions accompanied by a number of rigid constraints associated with the products involved. One such problem is Economic Load Dispatch (ED) problem which focuses on the optimization of the fuel cost while satisfying some system constraints. Classical optimization algorithms are not sufficient and also inefficient for the ED problem involving highly nonlinear, and non-convex functions both in the objective and in the constraints. This led to the development of metaheuristic optimization approaches which can solve the ED problem almost efficiently. This paper presents a novel robust plant intelligence based Adaptive Plant Propagation Algorithm (APPA) which is used to solve the classical ED problem. The application of the proposed method to the 3-generator and 6-generator systems shows the efficiency and robustness of the proposed algorithm. A comparative study with another state-of-the-art algorithm (APSO) demonstrates the quality of the solution achieved by the proposed method along with the convergence characteristics of the proposed approach.
Neighborhood-Based Label Propagation in Large Protein Graphs
Venue: arXiv cs.DC 2017
Year: 2017

Understanding protein function is one of the keys to understanding life at the molecular level. It is also important in several scenarios including human disease and drug discovery. In this age of rapid and affordable biological sequencing, the number of sequences accumulating in databases is rising with an increasing rate. This presents many challenges for biologists and computer scientists alike. In order to make sense of this huge quantity of data, these sequences should be annotated with functional properties. UniProtKB consists of two components: i) the UniProtKB/Swiss-Prot database containing protein sequences with reliable information manually reviewed by expert bio-curators and ii) the UniProtKB/TrEMBL database that is used for storing and processing the unknown sequences. Hence, for all proteins we have available the sequence along with few more information such as the taxon and some structural domains. Pairwise similarity can be defined and computed on proteins based on such attributes. Other important attributes, while present for proteins in Swiss-Prot, are often missing for proteins in TrEMBL, such as their function and cellular localization. The enormous number of protein sequences now in TrEMBL calls for rapid procedures to annotate them automatically. In this work, we present DistNBLP, a novel Distributed Neighborhood-Based Label Propagation approach for large-scale annotation of proteins. To do this, the functional annotations of reviewed proteins are used to predict those of non-reviewed proteins using label propagation on a graph representation of the protein database. DistNBLP is built on top of the "akka" toolkit for building resilient distributed message-driven applications.
Classification via Tensor Decompositions of Echo State Networks
Venue: arXiv cs.LG 2017
Year: 2017

This work introduces a tensor-based method to perform supervised classification on spatiotemporal data processed in an echo state network. Typically when performing supervised classification tasks on data processed in an echo state network, the entire collection of hidden layer node states from the training dataset is shaped into a matrix, allowing one to use standard linear algebra techniques to train the output layer. However, the collection of hidden layer states is multidimensional in nature, and representing it as a matrix may lead to undesirable numerical conditions or loss of spatial and temporal correlations in the data. This work proposes a tensor-based supervised classification method on echo state network data that preserves and exploits the multidimensional nature of the hidden layer states. The method, which is based on orthogonal Tucker decompositions of tensors, is compared with the standard linear output weight approach in several numerical experiments on both synthetic and natural data. The results show that the tensor-based approach tends to outperform the standard approach in terms of classification accuracy.
Lossless Image and Intra-frame Compression with Integer-to-Integer DST
Venue: arXiv cs.MM 2017
Year: 2017

Video coding standards are primarily designed for efficient lossy compression, but it is also desirable to support efficient lossless compression within video coding standards using small modifications to the lossy coding architecture. A simple approach is to skip transform and quantization, and simply entropy code the prediction residual. However, this approach is inefficient at compression. A more efficient and popular approach is to skip transform and quantization but also process the residual block with DPCM, along the horizontal or vertical direction, prior to entropy coding. This paper explores an alternative approach based on processing the residual block with integer-to-integer (i2i) transforms. I2i transforms can map integer pixels to integer transform coefficients without increasing the dynamic range and can be used for lossless compression. We focus on lossless intra coding and develop novel i2i approximations of the odd type-3 DST (ODST-3). Experimental results with the HEVC reference software show that the developed i2i approximations of the ODST-3 improve lossless intra-frame compression efficiency with respect to HEVC version 2, which uses the popular DPCM method, by an average 2.7% without a significant effect on computational complexity.
Understanding Modern Intrusion Detection Systems: A Survey
Venue: arXiv cs.CR 2017
Year: 2017

Intrusion detection systems (IDS) help detect unauthorized activities or intrusions that may compromise the confidentiality, integrity or availability of a resource. This paper presents a general overview of IDSs, the way they are classified, and the different algorithms used to detect anomalous activities. It attempts to compare the various methods of intrusion techniques. It also describes the various approaches and the importance of IDSs in information security.
A Survey of Runtime Monitoring Instrumentation Techniques
Venue: arXiv cs.LO 2017
Year: 2017

Runtime Monitoring is a lightweight and dynamic verification technique that involves observing the internal operations of a software system and/or its interactions with other external entities, with the aim of determining whether the system satisfies or violates a correctness specification. Compilation techniques employed in Runtime Monitoring tools allow monitors to be automatically derived from high-level correctness specifications (aka. properties). This allows the same property to be converted into different types of monitors, which may apply different instrumentation techniques for checking whether the property was satisfied or not. In this paper we compare and contrast the various types of monitoring methodologies found in the current literature, and classify them into a spectrum of monitoring instrumentation techniques, ranging from completely asynchronous monitoring on the one end and completely synchronous monitoring on the other.
Exploring the Link Between Test Suite Quality and Automatic Specification Inference
Venue: arXiv cs.SE 2017
Year: 2017

While no one doubts the importance of correct and complete specifications, many industrial systems still do not have formal specifications written out -- and even when they do, it is hard to check their correctness and completeness. This work explores the possibility of using an invariant extraction tool such as Daikon to automatically infer specifications from available test suites with the idea of aiding software engineers to improve the specifications by having another version to compare to. Given that our initial experiments did not produce satisfactory results, in this paper we explore which test suite attributes influence the quality of the inferred specification. Following further study, we found that instruction, branch and method coverage are correlated to high recall values, reaching up to 97.93%.
Inaudible Voice Commands
Venue: arXiv cs.CR 2017
Year: 2017

Voice assistants like Siri enable us to control IoT devices conveniently with voice commands, however, they also provide new attack opportunities for adversaries. Previous papers attack voice assistants with obfuscated voice commands by leveraging the gap between speech recognition system and human voice perception. The limitation is that these obfuscated commands are audible and thus conspicuous to device owners. In this paper, we propose a novel mechanism to directly attack the microphone used for sensing voice data with inaudible voice commands. We show that the adversary can exploit the microphone's non-linearity and play well-designed inaudible ultrasounds to cause the microphone to record normal voice commands, and thus control the victim device inconspicuously. We demonstrate via end-to-end real-world experiments that our inaudible voice commands can attack an Android phone and an Amazon Echo device with high success rates at a range of 2-3 meters.
NNVLP: A Neural Network-Based Vietnamese Language Processing Toolkit
Venue: arXiv cs.CL 2017
Year: 2017

This paper demonstrates neural network-based toolkit namely NNVLP for essential Vietnamese language processing tasks including part-of-speech (POS) tagging, chunking, named entity recognition (NER). Our toolkit is a combination of bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), Conditional Random Field (CRF), using pre-trained word embeddings as input, which achieves state-of-the-art results on these three tasks. We provide both API and web demo for this toolkit.