OpenRaiser/Intern-Atlas huggingface dataset:
Provide High-QoS of the High-Speed Railway Mobile Communications in Cyber-Physical Systems
Venue: arXiv cs.NI 2014
Year: 2014

Technical advances in networks, embedded computing, and wireless communications are leading to the next generation of complex intelligent systems called Cyber-Physical Systems (CPS). CPS promises to transform the way we interact with the physical world. Efficient and reliable operation of the CTCS-3 (Chinese Train Control System Level 3) is great protection of the national economy and public safety. The CTCS-3 is based on GSM-R (GSM for Railway) to achieve a continuous and two-way transmission of information between ground and the train. To ensure the growing needs of safety, fastness and service diversity of China's railway, the pursuit of high-QoS has been the key of the relative study. This paper examines the main characteristics of GSM-R and the requirements of all-layers' QoS indicators for GSM-R. Several main technologies of improving QoS indicators of all-layers are summarized. As a solution, a comprehensive scheme is proposed to improve the delay and packet loss indicators. An example is also presented that illustrates the real-time features of the proposed solution. Based on the CPS characteristics are highly correlated with the QoS indicators, conclusions are made that GSM-R can provide a reliable and real-time way for message.
Comparison of Distance Metrics for Hierarchical Data in Medical Databases
Venue: arXiv cs.DB 2014
Year: 2014

Distance metrics are broadly used in different research areas and applications, such as bio-informatics, data mining and many other fields. However, there are some metrics, like pq-gram and Edit Distance used specifically for data with a hierarchical structure. Other metrics used for non-hierarchical data are the geometric and Hamming metrics. We have applied these metrics to The Health Improvement Network (THIN) database which has some hierarchical data. The THIN data has to be converted into a tree-like structure for the first group of metrics. For the second group of metrics, the data are converted into a frequency table or matrix, then for all metrics, all distances are found and normalised. Based on this particular data set, our research question: which of these metrics is useful for THIN data? This paper compares the metrics, particularly the pq-gram metric on finding the similarities of patients' data. It also investigates the similar patients who have the same close distances as well as the metrics suitability for clustering the whole patient population. Our results show that the two groups of metrics perform differently as they represent different structures of the data. Nevertheless, all the metrics could represent some similar data of patients as well as discriminate sufficiently well in clustering the patient population using $k$-means clustering algorithm.
On $\ell_p$-norm Computation over Multiple-Access Channels
Venue: arXiv cs.IT 2014
Year: 2014

This paper addresses some aspects of the general problem of information transfer and distributed function computation in wireless networks. Many applications of wireless technology foresee networks of autonomous devices executing tasks that can be posed as distributed function computation. In today's wireless networks, the tasks of communication and (distributed) computation are performed separately, although an efficient network operation calls for approaches in which the information transfer is dynamically adapted to time-varying computation objectives. Thus, wireless communications and function computation must be tightly coupled and it is shown in this paper that information theory may play a crucial role in the design of efficient computation-aware wireless communication and networking strategies. This is explained in more detail by considering the problem of computing $\ell_p$-norms over multiple access channels.
Hybrid Systems Knowledge Representation Using Modelling Environment System Techniques Artificial Intelligence
Venue: arXiv cs.AI 2014
Year: 2014

Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model ENVIRONMENTAL SYSTEMS. Use of Artificial Intelligence (AI) in environmental modelling has increased with recognition of its potential. In this paper we examine the DIFFERENT TECHNIQUES of Artificial intelligence with profound examples of human perception, learning and reasoning to solve complex problems. However with the increase of complexity better methods are required. Keeping in view of the above some researchers introduced the idea of hybrid mechanism in which two or more methods can be combined which seems to be a positive effort for creating a more complex; advanced and intelligent system which has the capability to in- cooperate human decisions thus driving the landscape changes.
Domain Transfer Structured Output Learning
Venue: arXiv cs.LG 2014
Year: 2014

In this paper, we propose the problem of domain transfer structured output learn- ing and the first solution to solve it. The problem is defined on two different data domains sharing the same input and output spaces, named as source domain and target domain. The outputs are structured, and for the data samples of the source domain, the corresponding outputs are available, while for most data samples of the target domain, the corresponding outputs are missing. The input distributions of the two domains are significantly different. The problem is to learn a predictor for the target domain to predict the structured outputs from the input. Due to the limited number of outputs available for the samples form the target domain, it is difficult to directly learn the predictor from the target domain, thus it is necessary to use the output information available in source domain. We propose to learn the target domain predictor by adapting a auxiliary predictor trained by using source domain data to the target domain. The adaptation is implemented by adding a delta function on the basis of the auxiliary predictor. An algorithm is developed to learn the parameter of the delta function to minimize loss functions associat- ed with the predicted outputs against the true outputs of the data samples with available outputs of the target domain.
Quality of Service Improvement for High-Speed Railway Communications
Venue: arXiv cs.IT 2014
Year: 2014

With the fast development of high-speed railways, a call for fulfilling the notion of communication at "anytime, anywhere" for high-speed train passengers in the Train Operating Control System is on the way. In order to make a realization of that, new railway wireless communication networks are needed. The most promising one is the Long Term Evolution for Railway which will provide broadband access, fast handover, and reliable communication for high mobility users. However, with the increase of speed, the system is subjected to high bit error rate, Doppler frequency shift and handover failure just like other system does. This paper is trying to solve these problems by employing MIMO technique. Specifically, the goal is to provide higher data rate, higher reliability, less delay, and other relative quality of services for passengers. MIMO performance analysis, resource allocation, and access control for handover and various services in a two-hop model are proposed in this paper. Analytical results and simulation results show that the proposed model and schemes perform well in improving the system performances.
Feasibility of In-band Full-Duplex Radio Transceivers with Imperfect RF Components: Analysis and Enhanced Cancellation Algorithms
Venue: arXiv cs.IT 2014
Year: 2014

In this paper we provide an overview regarding the feasibility of in-band full-duplex transceivers under imperfect RF components. We utilize results and findings from the recent research on full-duplex communications, while introducing also transmitter-induced thermal noise into the analysis. This means that the model of the RF impairments used in this paper is the most comprehensive thus far. By assuming realistic parameter values for the different transceiver components, it is shown that IQ imaging and transmitter-induced nonlinearities are the most significant sources of distortion in in-band full-duplex transceivers, in addition to linear self-interference. Motivated by this, we propose a novel augmented nonlinear digital self-interference canceller that is able to model and hence suppress all the essential transmitter imperfections jointly. This is also verified and demonstrated by extensive waveform simulations.
Unsynthesizable Cores - Minimal Explanations for Unsynthesizable High-Level Robot Behaviors
Venue: arXiv cs.RO 2014
Year: 2014

With the increasing ubiquity of multi-capable, general-purpose robots arises the need for enabling non-expert users to command these robots to perform complex high-level tasks. To this end, high-level robot control has seen the application of formal methods to automatically synthesize correct-by-construction controllers from user-defined specifications; synthesis fails if and only if there exists no controller that achieves the specified behavior. Recent work has also addressed the challenge of providing easy-to-understand feedback to users when a specification fails to yield a corresponding controller. Existing techniques provide feedback on portions of the specification that cause the failure, but do so at a coarse granularity. This work presents techniques for refining this feedback, extracting minimal explanations of unsynthesizability.
Accurate, fully-automated NMR spectral profiling for metabolomics
Venue: arXiv cs.AI 2014
Year: 2014

Many diseases cause significant changes to the concentrations of small molecules (aka metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile". This information can be extracted from a biofluid's NMR spectrum. Today, this is often done manually by trained human experts, which means this process is relatively slow, expensive and error-prone. This paper presents a tool, Bayesil, that can quickly, accurately and autonomously produce a complex biofluid's (e.g., serum or CSF) metabolic profile from a 1D1H NMR spectrum. This requires first performing several spectral processing steps then matching the resulting spectrum against a reference compound library, which contains the "signatures" of each relevant metabolite. Many of these steps are novel algorithms and our matching step views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures, show that Bayesil can autonomously find the concentration of all NMR-detectable metabolites accurately (~90% correct identification and ~10% quantification error), in <5minutes on a single CPU. These results demonstrate that Bayesil is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively -- with an accuracy that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. Available at http://www.bayesil.ca.
Synchronizing Edits in Distributed Storage Networks
Venue: arXiv cs.IT 2014
Year: 2014

We consider the problem of synchronizing data in distributed storage networks under an edit model that includes deletions and insertions. We present two modifications of MDS, regenerating and locally repairable codes that allow updates in the parity-check values to be performed with one round of communication at low bit rates and using small storage overhead. Our main contributions are novel protocols for synchronizing both hot and semi-static data and protocols for data deduplication applications, based on intermediary permutation, Vandermonde and Cauchy matrix coding.
Evaluation of High-speed Train Communication Handover Models Based on DEA
Venue: arXiv cs.NI 2014
Year: 2014

Broadband communications for high speed train is becoming a main trend in high mobility communications. The main bottleneck of this communication network is handover, since the handover occurs so frequently and the delays are so long that broadband real-time communication cannot apply. Various handover models have been developed and studied recently. However, no comprehensive evaluation method for these models is employed. To this end, we borrow Data Envelopment Analysis (DEA) method to evaluate six typical handover system models. Handover models that to be evaluated are introduced. A brief presentation of DEA and its characters is provided. A specific procedure of the evaluation is proposed. Then the results of the evaluation are obtained by running the DEA. Finally, we give our comments and conclusions to all the handover models. We hope our work will supply a gap in the system evaluation area.
Polymorphic Worms Collection in Cloud Computing
Venue: arXiv cs.DC 2014
Year: 2014

In the past few years, computer worms are seen as one of significant challenges of cloud computing. Worms are rapidly changing and getting more sophisticated to evade detection. One major issue to defend against computer worms is collecting worms' payloads to generate their signature and study their behavior. To collect worms' payloads, we identified challenges for detecting and collecting worms' payloads and proposed high-interactive honeypot to collect payloads of zero-day polymorphic worms in homogeneous and heterogeneous cloud computing platforms. Virtual machine (VM) memory and VM disk image are inspected from outside using open-source forensics tools and VMWare Virtual Disk Development Kit. Our experiments show that the proposed approach overcomes the identified challenges.
Throughput Optimal Routing in Overlay Networks
Venue: arXiv cs.NI 2014
Year: 2014

Maximum throughput requires path diversity enabled by bifurcating traffic at different network nodes. In this work, we consider a network where traffic bifurcation is allowed only at a subset of nodes called \emph{routers}, while the rest nodes (called \emph{forwarders}) cannot bifurcate traffic and hence only forward packets on specified paths. This implements an overlay network of routers where each overlay link corresponds to a path in the physical network. We study dynamic routing implemented at the overlay. We develop a queue-based policy, which is shown to be maximally stable (throughput optimal) for a restricted class of network scenarios where overlay links do not correspond to overlapping physical paths. Simulation results show that our policy yields better delay over dynamic policies that allow bifurcation at all nodes, such as the backpressure policy. Additionally, we provide a heuristic extension of our proposed overlay routing scheme for the unrestricted class of networks.
V2V Propagation Modeling with Imperfect RSSI Samples
Venue: arXiv cs.OH 2014
Year: 2014

We describe three in-field data collection efforts yielding a large database of RSSI values vs. time or distance from vehicles communicating with each other via DSRC. We show several data processing schemes we have devised to develop Vehicle-to-Vehicle (V2V) propagation models from such data. The database is limited in several important ways, not least, the presence of a high noise floor that limits the distance over which good modeling is feasible. Another is the presence of interference from multiple active transmitters. Our methodology makes it possible to obtain, despite these limitations, accurate models of median path loss vs. distance, shadow fading, and fast fading caused by multipath. We aim not to develop a new V2V model, but to show the methods enabling such a model to be obtained from in-field RSSI data.
An NLP Assistant for Clide
Venue: arXiv cs.CL 2014
Year: 2014

This report describes an NLP assistant for the collaborative development environment Clide, that supports the development of NLP applications by providing easy access to some common NLP data structures. The assistant visualizes text fragments and their dependencies by displaying the semantic graph of a sentence, the coreference chain of a paragraph and mined triples that are extracted from a paragraph's semantic graphs and linked using its coreference chain. Using this information and a logic programming library, we create an NLP database which is used by a series of queries to mine the triples. The algorithm is tested by translating a natural language text describing a graph to an actual graph that is shown as an annotation in the text editor.
Introducing Molly: Distributed Memory Parallelization with LLVM
Venue: arXiv cs.PL 2014
Year: 2014

Programming for distributed memory machines has always been a tedious task, but necessary because compilers have not been sufficiently able to optimize for such machines themselves. Molly is an extension to the LLVM compiler toolchain that is able to distribute and reorganize workload and data if the program is organized in statically determined loop control-flows. These are represented as polyhedral integer-point sets that allow program transformations applied on them. Memory distribution and layout can be declared by the programmer as needed and the necessary asynchronous MPI communication is generated automatically. The primary motivation is to run Lattice QCD simulations on IBM Blue Gene/Q supercomputers, but since the implementation is not yet completed, this paper shows the capabilities on Conway's Game of Life.
Streaming Lower Bounds for Approximating MAX-CUT
Venue: arXiv cs.DS 2014
Year: 2014

We consider the problem of estimating the value of max cut in a graph in the streaming model of computation. At one extreme, there is a trivial $2$-approximation for this problem that uses only $O(\log n)$ space, namely, count the number of edges and output half of this value as the estimate for max cut value. On the other extreme, if one allows $\tilde{O}(n)$ space, then a near-optimal solution to the max cut value can be obtained by storing an $\tilde{O}(n)$-size sparsifier that essentially preserves the max cut. An intriguing question is if poly-logarithmic space suffices to obtain a non-trivial approximation to the max-cut value (that is, beating the factor $2$). It was recently shown that the problem of estimating the size of a maximum matching in a graph admits a non-trivial approximation in poly-logarithmic space. Our main result is that any streaming algorithm that breaks the $2$-approximation barrier requires $\tildeΩ(\sqrt{n})$ space even if the edges of the input graph are presented in random order. Our result is obtained by exhibiting a distribution over graphs which are either bipartite or $\frac{1}{2}$-far from being bipartite, and establishing that $\tildeΩ(\sqrt{n})$ space is necessary to differentiate between these two cases. Thus as a direct corollary we obtain that $\tildeΩ(\sqrt{n})$ space is also necessary to test if a graph is bipartite or $\frac{1}{2}$-far from being bipartite. We also show that for any $ε> 0$, any streaming algorithm that obtains a $(1 + ε)$-approximation to the max cut value when edges arrive in adversarial order requires $n^{1 - O(ε)}$ space, implying that $Ω(n)$ space is necessary to obtain an arbitrarily good approximation to the max cut value.
Tracing Analytic Ray Curves for Light and Sound Propagation in Non-linear Media
Venue: arXiv cs.GR 2014
Year: 2014

The physical world consists of spatially varying media, such as the atmosphere and the ocean, in which light and sound propagates along non-linear trajectories. This presents a challenge to existing ray-tracing based methods, which are widely adopted to simulate propagation due to their efficiency and flexibility, but assume linear rays. We present a novel algorithm that traces analytic ray curves computed from local media gradients, and utilizes the closed-form solutions of both the intersections of the ray curves with planar surfaces, and the travel distance. By constructing an adaptive unstructured mesh, our algorithm is able to model general media profiles that vary in three dimensions with complex boundaries consisting of terrains and other scene objects such as buildings. We trace the analytic ray curves using the adaptive unstructured mesh, which considerably improves the efficiency over prior methods. We highlight the algorithm's application on simulation of sound and visual propagation in outdoor scenes.
State-Based Modeling of Buildings and Facilities
Venue: arXiv cs.SE 2014
Year: 2014

Research on energy efficiency of today's buildings focuses on the monitoring of a building's behavior while in operation. But without a formalized description of the data measured, including their correlations and in particular the expected measurements, the full potential of the collected data can not necessarily be exploited. Who knows if a measured value is good or bad? This problem becomes more virulent as smart control systems sometimes exhibit intelligent, but unexpected behavior (e.g. starting heating at unconventional times). Therefore we defined a methodology starting already at the design of the building leading to a formalized specification of the implementation of a building's management system, which seamlessly integrates to an intelligent monitoring. DIN EN ISO 16484 proposes a method to describe functional requirements in an easy to understand way. We extended its use of state machines to our proposed concept of state based modeling. This proved to be a wholesome approach to easily model buildings and facilities according to the DIN EN ISO 16484 while providing the possibility to apply sophisticated and meaningful analysis methods during monitoring.
A Domain Specific Transformation Language
Venue: arXiv cs.SE 2014
Year: 2014

Domain specific languages (DSLs) allow domain experts to model parts of the system under development in a problem-oriented notation that is well-known in the respective domain. The introduction of a DSL is often accompanied the desire to transform its instances. Although the modeling language is domain specific, the transformation language used to describe modifications, such as model evolution or refactoring operations, on the underlying model, usually is a rather domain independent language nowadays. Most transformation languages use a generic notation of model patterns that is closely related to typed and attributed graphs or to object diagrams (the abstract syntax). A notation that reflects the transformed elements of the original DSL in its own concrete syntax would be strongly preferable, because it would be more comprehensible and easier to learn for domain experts. In this paper we present a transformation language that reuses the concrete syntax of a textual modeling language for hierarchical automata, which allows domain experts to describe models as well as modifications of models in a convenient, yet precise manner. As an outlook, we illustrate a scenario where we generate transformation languages from existing textual languages.
Delta-oriented Architectural Variability Using MontiCore
Venue: arXiv cs.SE 2014
Year: 2014

Modeling of software architectures is a fundamental part of software development processes. Reuse of software components and early analysis of software topologies allow the reduction of development costs and increases software quality. Integrating variability modeling concepts into architecture description languages (ADLs) is essential for the development of diverse software systems with high demands on software quality. In this paper, we present the integration of delta modeling into the existing ADL MontiArc. Delta modeling is a language-independent variability modeling approach supporting proactive, reactive and extractive product line development. We show how ?-MontiArc, a language for explicit modeling of architectural variability based on delta modeling, is implemented as domain-specific language (DSL) using the DSL development framework MontiCore. We also demonstrate how MontiCore's language reuse mechanisms provide efficient means to derive an implementation of ?-MontiArc tool implementation. We evaluate ?-Monti-Arc by comparing it with annotative variability modeling.