OpenRaiser/Intern-Atlas huggingface dataset:
Programming Internet of Things, Service, and People (IoTSP) Applications
Venue: arXiv cs.SE 2016
Year: 2016

Application development for Internet of Things, Service, and People (IoTSP) is challenging because it involves dealing with the heterogeneity that exists both in Physical and Internet worlds. Second, stakeholders involved in the application development have to address issues pertaining to different life-cycles ranging from design, implementation to deployment. Given these, a critical challenge is to enable an application development for IoTSP applications with effectively and efficiently from various stakeholders. Several approaches to tackling this challenge have been proposed in the fields of Wireless Sensor Networks (WSN) and Pervasive Computing, regarded as precursors to the modern day of IoTSP. However, existing approaches only cover limited subsets of the above mentioned challenges when applied to the IoTSP. In view of this, we have built upon existing framework and evolved it into a framework for developing IoTSP applications, with substantial additions and enhancements in high-level modeling languages and their integration into the framework, and we present a comparative evaluation results with existing approaches. This provides the IoTSP community for further benchmarking. The evaluation is carried out on real devices exhibiting heterogeneity. Our experimental analysis and results demonstrate that our approach drastically reduces development effort for IoTSP applications compared to existing approaches.
NxWLAN: Neighborhood eXtensible WLAN
Venue: arXiv cs.NI 2016
Year: 2016

The increased usage of IEEE 802.11 Wireless LAN (WLAN) in residential environments by unexperienced users leads to dense, unplanned and chaotic residential WLAN deployments. Often WLAN Access Points (APs) are deployed unprofitable in terms of radio coverage and interference conditions. In many cases the usage of the neighbor's AP would be beneficial as it would provide better radio coverage in some parts of the residential user's apartment. Moreover, the network performance can be dramatically improved by balancing the network load over spatially co-located APs. We address this problem by presenting Neighborhood extensible WLAN (NxWLAN) which enables the secure extension of user's home WLANs through usage of neighboring APs in residential environments with zero configuration efforts and without revealing WPA2 encryption keys to untrusted neighbor APs. NxWLAN makes use of virtualization techniques utilizing neighboring AP by deploying on-demand a Wireless Termination Point (WTP) on the neighboring AP and by tunneling encrypted 802.11 traffic to the Virtual Access Point (VAP) residing on the home AP. This allows the client devices to always authenticate against the home AP using the WPA2-PSK passphrase already stored in the device without any additional registration process. We implemented NxWLAN prototypically using off-the-shelf hardware and open source software. As the OpenFlow is not suited for forwarding native 802.11 frames, we built software switch using P4 language. The performance evaluation in a small 802.11 indoor testbed showed the feasibility of our approach. NxWLAN is provided to the community as open source.
City-Identification of Flickr Videos Using Semantic Acoustic Features
Venue: arXiv cs.MM 2016
Year: 2016

City-identification of videos aims to determine the likelihood of a video belonging to a set of cities. In this paper, we present an approach using only audio, thus we do not use any additional modality such as images, user-tags or geo-tags. In this manner, we show to what extent the city-location of videos correlates to their acoustic information. Success in this task suggests improvements can be made to complement the other modalities. In particular, we present a method to compute and use semantic acoustic features to perform city-identification and the features show semantic evidence of the identification. The semantic evidence is given by a taxonomy of urban sounds and expresses the potential presence of these sounds in the city- soundtracks. We used the MediaEval Placing Task set, which contains Flickr videos labeled by city. In addition, we used the UrbanSound8K set containing audio clips labeled by sound- type. Our method improved the state-of-the-art performance and provides a novel semantic approach to this task
Tractable Stochastic Geometry Model for IoT Access in LTE Networks
Venue: arXiv cs.IT 2016
Year: 2016

The Internet of Things (IoT) is large-scale by nature. This is not only manifested by the large number of connected devices, but also by the high volumes of traffic that must be accommodated. Cellular networks are indeed a natural candidate for the data tsunami the IoT is expected to generate in conjunction with legacy human-type traffic. However, the random access process for scheduling request represents a major bottleneck to support IoT via LTE cellular networks. Accordingly, this paper develops a mathematical framework to model and study the random access channel (RACH) scalability to accommodate IoT traffic. The developed model is based on stochastic geometry and discrete time Markov chains (DTMC) to account for different access strategies and possible sources of inter-cell and intra-cell interferences. To this end, the developed model is utilized to assess and compare three different access strategies, which incorporate a combination of transmission persistency, back-off, and power ramping. The analysis and the results showcased herewith clearly illustrate the vulnerability of the random access procedure as the IoT intensity grows. Finally, the paper offers insights into effective scenarios for each transmission strategy in terms of IoT intensity and RACH detection thresholds.
Extending Finite Memory Determinacy to Multiplayer Games
Venue: arXiv cs.GT 2016
Year: 2016

We show that under some general conditions the finite memory determinacy of a class of two-player win/lose games played on finite graphs implies the existence of a Nash equilibrium built from finite memory strategies for the corresponding class of multi-player multi-outcome games. This generalizes a previous result by Brihaye, De Pril and Schewe. For most of our conditions we provide counterexamples showing that they cannot be dispensed with. Our proofs are generally constructive, that is, provide upper bounds for the memory required, as well as algorithms to compute the relevant winning strategies.
Incomplete Pivoted QR-based Dimensionality Reduction
Venue: arXiv cs.LG 2016
Year: 2016

High-dimensional big data appears in many research fields such as image recognition, biology and collaborative filtering. Often, the exploration of such data by classic algorithms is encountered with difficulties due to `curse of dimensionality' phenomenon. Therefore, dimensionality reduction methods are applied to the data prior to its analysis. Many of these methods are based on principal components analysis, which is statistically driven, namely they map the data into a low-dimension subspace that preserves significant statistical properties of the high-dimensional data. As a consequence, such methods do not directly address the geometry of the data, reflected by the mutual distances between multidimensional data point. Thus, operations such as classification, anomaly detection or other machine learning tasks may be affected. This work provides a dictionary-based framework for geometrically driven data analysis that includes dimensionality reduction, out-of-sample extension and anomaly detection. It embeds high-dimensional data in a low-dimensional subspace. This embedding preserves the original high-dimensional geometry of the data up to a user-defined distortion rate. In addition, it identifies a subset of landmark data points that constitute a dictionary for the analyzed dataset. The dictionary enables to have a natural extension of the low-dimensional embedding to out-of-sample data points, which gives rise to a distortion-based criterion for anomaly detection. The suggested method is demonstrated on synthetic and real-world datasets and achieves good results for classification, anomaly detection and out-of-sample tasks.
Conversions between Electrical Network Representations
Venue: arXiv cs.SY 2016
Year: 2016

The behaviour of electrical networks can be described with many different representations, each with their distinct benefits. In this paper, we consider Z, Y, G, H, ABCD, S and T parameters. Formulas exist to go from one representation to another, but implementing them is an error-prone procedure. In this paper, we present a more elegant way to implement the transformations based on matrix calculations.
Energy- and Spectral-Efficiency Tradeoff in Full-Duplex Communications
Venue: arXiv cs.IT 2016
Year: 2016

This paper investigates the tradeoff between energyefficiency (EE) and spectral-efficiency (SE) for full-duplex (FD) enabled cellular networks.We assume that small cell base stations are working in the FD mode while user devices still work in the conventional half-duplex (HD) mode. First, a necessary condition for a FD transceiver to achieve better EE-SE tradeoff than a HD one is derived. Then, we analyze the EE-SE relation of a FD transceiver in the scenario of single pair of users and obtain a closed-form expression. Next, we extend the result into the multiuser scenario and prove that EE is a quasi-concave function of SE in general and develop an optimal algorithm to achieve the maximum EE based on the Lagrange dual method. Our analysis is finally verified by extensive numerical results.
Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy
Venue: arXiv cs.AI 2016
Year: 2016

There has been an ever-increasing interest in multidisciplinary research on representing and reasoning with imperfect data. Possibilistic networks present one of the powerful frameworks of interest for representing uncertain and imprecise information. This paper covers the problem of their parameters learning from imprecise datasets, i.e., containing multi-valued data. We propose in the rst part of this paper a possibilistic networks sampling process. In the second part, we propose a likelihood function which explores the link between random sets theory and possibility theory. This function is then deployed to parametrize possibilistic networks.
Data as processes: introducing measurement data into CARMA models
Venue: arXiv cs.PL 2016
Year: 2016

Measurement data provides a precise and detailed description of components within a complex system but it is rarely used directly as a component of a system model. In this paper we introduce a model-based representation of measurement data and use it together with modeller-defined components expressed in the CARMA modelling language. We assess both liveness and safety properties of these models with embedded data.
Application of Convolutional Neural Network for Image Classification on Pascal VOC Challenge 2012 dataset
Venue: arXiv cs.CV 2016
Year: 2016

In this project we work on creating a model to classify images for the Pascal VOC Challenge 2012. We use convolutional neural networks trained on a single GPU instance provided by Amazon via their cloud service Amazon Web Services (AWS) to classify images in the Pascal VOC 2012 data set. We train multiple convolutional neural network models and finally settle on the best model which produced a validation accuracy of 85.6% and a testing accuracy of 85.24%.
Fitting a Simplicial Complex using a Variation of k-means
Venue: arXiv cs.LG 2016
Year: 2016

We give a simple and effective two stage algorithm for approximating a point cloud $\mathcal{S}\subset\mathbb{R}^m$ by a simplicial complex $K$. The first stage is an iterative fitting procedure that generalizes k-means clustering, while the second stage involves deleting redundant simplices. A form of dimension reduction of $\mathcal{S}$ is obtained as a consequence.
Graph Isomorphism Restricted by Lists
Venue: arXiv cs.DM 2016
Year: 2016

The complexity of graph isomorphism (GraphIso) is a famous unresolved problem in theoretical computer science. For graphs $G$ and $H$, it asks whether they are the same up to a relabeling of vertices. In 1981, Lubiw proved that list restricted graph isomorphism (ListIso) is NP-complete: for each $u \in V(G)$, we are given a list ${\mathfrak L}(u) \subseteq V(H)$ of possible images of $u$. After 35 years, we revive the study of this problem and consider which results for GraphIso translate to ListIso. We prove the following: 1) When GraphIso is GI-complete for a class of graphs, it translates into NP-completeness of ListIso. 2) Combinatorial algorithms for GraphIso translate into algorithms for ListIso: for trees, planar graphs, interval graphs, circle graphs, permutation graphs, bounded genus graphs, and bounded treewidth graphs. 3) Algorithms based on group theory do not translate: ListIso remains NP-complete for cubic colored graphs with sizes of color classes bounded by 8. Also, ListIso allows to classify results for the graph isomorphism problem. Some algorithms are robust and translate to ListIso. A fundamental problem is to construct a combinatorial polynomial-time algorithm for cubic graph isomorphism, avoiding group theory. By the 3rd result, ListIso is NP-hard for them, so no robust algorithm for cubic graph isomorphism exists, unless P = NP.
Resource Planning For Rescue Operations
Venue: arXiv cs.AI 2016
Year: 2016

After an earthquake, disaster sites pose a multitude of health and safety concerns. A rescue operation of people trapped in the ruins after an earthquake disaster requires a series of intelligent behavior, including planning. For a successful rescue operation, given a limited number of available actions and regulations, the role of planning in rescue operations is crucial. Fortunately, recent developments in automated planning by artificial intelligence community can help different organization in this crucial task. Due to the number of rules and regulations, we believe that a rule based system for planning can be helpful for this specific planning problem. In this research work, we use logic rules to represent rescue and related regular regulations, together with a logic based planner to solve this complicated problem. Although this research is still in the prototyping and modeling stage, it clearly shows that rule based languages can be a good infrastructure for this computational task. The results of this research can be used by different organizations, such as Iranian Red Crescent Society and International Institute of Seismology and Earthquake Engineering (IISEE).
State complexity of multiple catenation
Venue: arXiv cs.FL 2016
Year: 2016

We improve some results relative to the state complexity of the multiple catenation described by Gao and Yu. In particular we nearly divide by 2 the size of the alphabet needed for witnesses. We also give some refinements to the algebraic expression of the state complexity, which is especially complex with this operation. We obtain these results by using peculiar DFAs defined by Brzozowski.
Orthogonal Design of Cyclic Block Filtered Multitone Modulation
Venue: arXiv cs.IT 2016
Year: 2016

The orthogonal design of a Cyclic Block Filtered Multititone Modulation (CB-FMT) system is addressed. CB-FMT is a filter bank modulation scheme that uses frequency confined prototype pulses, similarly to Filtered Multitone Modulation (FMT). Differently from FMT, where the linear convolution is used, the cyclic convolution is exploited in CB-FMT. This allows to efficiently implement the system via a concatenation of discrete Fourier transforms (DFT). The necessary and sufficient orthogonality conditions are derived in time domain and frequency domain. Then, these conditions are expressed in matrix form and the prototype pulse coefficients are parameterized with hyper-spherical coordinates. The effect of a linear time-variant transmission medium is discussed. In such a scenario, the optimal filter bank orthogonal design is considered with the objective of maximizing either the in-band-to-out-band sub-channel energy ratio or the achievable rate. Numerical results and comparisons show the performance improvements attainable with several designed optimal pulses also w.r.t. the use of the baseline root-raised-cosine pulse.
Citation count distributions for large monodisciplinary journals
Venue: arXiv cs.DL 2016
Year: 2016

Many different citation-based indicators are used by researchers and research evaluators to help evaluate the impact of scholarly outputs. Although the appropriateness of individual citation indicators depends in part on the statistical properties of citation counts, there is no universally agreed best-fitting statistical distribution against which to check them. The two current leading candidates are the discretised lognormal and the hooked or shifted power law. These have been mainly tested on sets of articles from a single field and year but these collections can include multiple specialisms that might dilute their properties. This article fits statistical distributions to 50 large subject-specific journals in the belief that individual journals can be purer than subject categories and may therefore give clearer findings. The results show that in most cases the discretised lognormal fits significantly better than the hooked power law, reversing previous findings for entire subcategories. This suggests that the discretised lognormal is the more appropriate distribution for modelling pure citation data. Thus future analytical investigations of the properties of citation indicators can use the lognormal distribution to analyse their basic properties. This article also includes improved software for fitting the hooked power law.
Adaptable Precomputation for Random Walker Image Segmentation and Registration
Venue: arXiv cs.CV 2016
Year: 2016

The random walker (RW) algorithm is used for both image segmentation and registration, and possesses several useful properties that make it popular in medical imaging, such as being globally optimizable, allowing user interaction, and providing uncertainty information. The RW algorithm defines a weighted graph over an image and uses the graph's Laplacian matrix to regularize its solutions. This regularization reduces to solving a large system of equations, which may be excessively time consuming in some applications, such as when interacting with a human user. Techniques have been developed that precompute eigenvectors of a Laplacian offline, after image acquisition but before any analysis, in order speed up the RW algorithm online, when segmentation or registration is being performed. However, precomputation requires certain algorithm parameters be fixed offline, limiting their flexibility. In this paper, we develop techniques to update the precomputed data online when RW parameters are altered. Specifically, we dynamically determine the number of eigenvectors needed for a desired accuracy based on user input, and derive update equations for the eigenvectors when the edge weights or topology of the image graph are changed. We present results demonstrating that our techniques make RW with precomputation much more robust to offline settings while only sacrificing minimal accuracy.
Timed Supervisory Control for Operational Planning and Scheduling under Multiple Job Deadlines
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, we model an operational planning and scheduling problem under multiple job deadlines in a time-weighted automaton framework. We first present a method to determine whether all given job specifications and deadlines can be met by computing a supremal controllable job satisfaction sublanguage. When this supremal sublanguage is not empty, we compute one of its controllable sublanguages that ensures the minimum total job earliness by adding proper delays. When this supremal sublanguage is empty, we will determine the minimal sets of job deadlines that need to be relaxed.
Understanding Citizen Reactions and Ebola-Related Information Propagation on Social Media
Venue: arXiv cs.SI 2016
Year: 2016

In severe outbreaks such as Ebola, bird flu and SARS, people share news, and their thoughts and responses regarding the outbreaks on social media. Understanding how people perceive the severe outbreaks, what their responses are, and what factors affect these responses become important. In this paper, we conduct a comprehensive study of understanding and mining the spread of Ebola-related information on social media. In particular, we (i) conduct a large-scale data-driven analysis of geotagged social media messages to understand citizen reactions regarding Ebola; (ii) build information propagation models which measure locality of information; and (iii) analyze spatial, temporal and social properties of Ebola-related information. Our work provides new insights into Ebola outbreak by understanding citizen reactions and topic-based information propagation, as well as providing a foundation for analysis and response of future public health crises.
Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
Venue: arXiv cs.IR 2016
Year: 2016

Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences. On the one hand one has to overcome `standard' recommender systems challenges, such as dealing with large, sparse, and long-tailed datasets. On the other, several new challenges present themselves, such as the need to model content in terms of its visual appearance, or even social dynamics, such as a preference toward a particular artist that is independent of the art they create. In this paper we build large-scale recommender systems to model the dynamics of a vibrant digital art community, Behance, consisting of tens of millions of interactions (clicks and `appreciates') of users toward digital art. Methodologically, our main contributions are to model (a) rich content, especially in terms of its visual appearance; (b) temporal dynamics, in terms of how users prefer `visually consistent' content within and across sessions; and (c) social dynamics, in terms of how users exhibit preferences both towards certain art styles, as well as the artists themselves.
DeepQA: Improving the estimation of single protein model quality with deep belief networks
Venue: arXiv cs.AI 2016
Year: 2016

Protein quality assessment (QA) by ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiment demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/.