OpenRaiser/Intern-Atlas huggingface dataset:
CENI: a Hybrid Framework for Efficiently Inferring Information Networks
Venue: arXiv cs.SI 2015
Year: 2015

Nowadays, the message diffusion links among users or websites drive the development of countless innovative applications. However, in reality, it is easier for us to observe the timestamps when different nodes in the network react on a message, while the connections empowering the diffusion of the message remain hidden. This motivates recent extensive studies on the network inference problem: unveiling the edges from the records of messages disseminated through them. Existing solutions are computationally expensive, which motivates us to develop an efficient two-step general framework, Clustering Embedded Network Inference (CENI). CENI integrates clustering strategies to improve the efficiency of network inference. By clustering nodes directly on the timelines of messages, we propose two naive implementations of CENI: Infection-centric CENI and Cascade-centric CENI. Additionally, we point out the critical dimension problem of CENI: instead of one-dimensional timelines, we need to first project the nodes to an Euclidean space of certain dimension before clustering. A CENI adopting clustering method on the projected space can better preserve the structure hidden in the cascades, and generate more accurately inferred links. This insight sheds light on other related work attempting to discover or utilize the latent cluster structure in the disseminated messages. By addressing the critical dimension problem, we propose the third implementation of the CENI framework: Projection-based CENI. Through extensive experiments on two real datasets, we show that the three CENI models only need around 20% $\sim$ 50% of the running time of state-of-the-art methods. Moreover, the inferred edges of Projection-based CENI preserves or even outperforms the effectiveness of state-of-the-art methods.
Implementation and Evaluation of multimodal input/output channels for task-based industrial robot programming
Venue: arXiv cs.RO 2015
Year: 2015

Programming industrial robots is not very intuitive, and the programmer has to be a domain expert for e.g. welding and programming to know how the task is optimally executed. For SMEs such employees are not affordable, nor cost-effective. Therefore a new system is needed where domain experts from a specific area, like welding or assembly, can easily program a robot without knowing anything about programming languages or how to use TeachPads. Such a system needs to be flexible to adapt to new tasks and functions. These requirements can be met by using a task based programming approach where the robot program is built up using a hierarchical structure of process, tasks and skills. It also needs to be intuitive so that domain experts don't need much training time on handling the system. Intuitive interaction is achieved by using different input and output modalities like gesture input, speech input, or touch input which are suitable for the current task. This master thesis focuses on the implementation of a user interface (GUI) for task based industrial robot programming and evaluates different input modalities (gesture, speech, touch, pen input) for the interaction with the system. The evaluation is based on a user study conducted with 30 participants as a Wizard-Of-Oz experiment, where non expert users had to program assembly and welding tasks to an industrial robot, using the previously developed GUI and various input and output modalities. The findings of the task analysis and user study are then used for creating a semantic description which will be used in the cognitive robotics-worker cell for automatically inferring required system components, and to provide the best suited input modality.
Ultra-Fast Shapelets for Time Series Classification
Venue: arXiv cs.LG 2015
Year: 2015

Time series shapelets are discriminative subsequences and their similarity to a time series can be used for time series classification. Since the discovery of time series shapelets is costly in terms of time, the applicability on long or multivariate time series is difficult. In this work we propose Ultra-Fast Shapelets that uses a number of random shapelets. It is shown that Ultra-Fast Shapelets yield the same prediction quality as current state-of-the-art shapelet-based time series classifiers that carefully select the shapelets by being by up to three orders of magnitudes. Since this method allows a ultra-fast shapelet discovery, using shapelets for long multivariate time series classification becomes feasible. A method for using shapelets for multivariate time series is proposed and Ultra-Fast Shapelets is proven to be successful in comparison to state-of-the-art multivariate time series classifiers on 15 multivariate time series datasets from various domains. Finally, time series derivatives that have proven to be useful for other time series classifiers are investigated for the shapelet-based classifiers. It is shown that they have a positive impact and that they are easy to integrate with a simple preprocessing step, without the need of adapting the shapelet discovery algorithm.
Coverage in mmWave Cellular Networks with Base station Cooperation
Venue: arXiv cs.IT 2015
Year: 2015

The presence of signal outage, due to shadowing and blockage, is expected to be the main bottleneck in millimeter wave (mmWave) networks. Moreover, with the anticipated vision that mmWave networks would have a dense deployment of base stations, interference from strong line-of-sight base stations increases too, thus further increasing the probability of outage. To address the issue of reducing outage, this paper explores the possibility of base station cooperation in the downlink of a mmWave heterogenous network. The main focus of this work is showing that, in a stochastic geometry framework, cooperation from randomly located base stations decreases outage probability. With the presumed vision that less severe fading will be experienced due to highly directional transmissions, one might expect that cooperation would increase the coverage probability; our numerical examples suggest that is in fact the case. Coverage probabilities are derived accounting for: different fading distributions, antenna directionality and blockage. Numerical results suggest that coverage with base station cooperation in dense mmWave systems and with no small scale fading considerably exceeds coverage with no cooperation. In contrast, an insignificant increase is reported when mmWave networks are less dense with a high probability of signal blockage and with Rayleigh fading.
An SMDP-based Resource Management Scheme for Distributed Cloud Systems
Venue: arXiv cs.NI 2015
Year: 2015

In this paper, the resource management problem in geographically distributed cloud systems is considered. The Follow Me Cloud concept which enables service migration across federated data centers (DCs) is adopted. Therefore, there are two types of service requests to the DC, i.e., new requests (NRs) initiated in the local service area and migration requests (MRs) generated when mobile users move across service areas. A novel resource management scheme is proposed to help the resource manager decide whether to accept the service requests (NRs or MRs) or not and determine how much resources should be allocated to each service (if accepted). The optimization objective is to maximize the average system reward and keep the rejection probability of service requests under a certain threshold. Numerical results indicate that the proposed scheme can significantly improve the overall system utility as well as the user experience compared with other resource management schemes.
Model Checking AORTA: Verification of Organization-Aware Agents
Venue: arXiv cs.MA 2015
Year: 2015

As agent systems grow larger and more complex, there is an increasing need to formally verify them. Furthermore, it is often suggested that complex systems can be regulated using organizational models, imposing constraints on the agents in the systems. Agents that can understand the organizational model and constraints in a system is said to be organization-aware. This paper is concerned with verification of organization-aware agents. We show how agents using AORTA, a framework for making agents organization-aware, can be formally verified using an extended version of the Agent Java PathFinder (AJPF), a model checking system designed specifically for agent programming languages. We integrate AORTA with the Agent Infrastructure Layer (AIL), which is an intermediate layer on top of which APLs can be implemented, and use our extension of AJPF to verify a system of agents aiming to write a paper together by using an organization for coordination.
YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes
Venue: arXiv cs.NI 2015
Year: 2015

YouTube relies on a massively distributed Content Delivery Network (CDN) to stream the billions of videos in its catalogue. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet Service Providers (ISPs), which are compelled to optimize end-users' Quality of Experience (QoE) while having no control on the CDN decisions. This paper presents YouLighter, an unsupervised technique to identify changes in the YouTube CDN. YouLighter leverages only passive measurements to cluster co-located identical caches into edge-nodes. This automatically unveils the structure of YouTube's CDN. Further, we propose a new metric, called Constellation Distance, that compares the clustering obtained from two different time snapshots, to pinpoint sudden changes. While several approaches allow comparison between the clustering results from the same dataset, no technique allows to measure the similarity of clusters from different datasets. Hence, we develop a novel methodology, based on the Constellation Distance, to solve this problem. By running YouLighter over 10-month long traces obtained from two ISPs in different countries, we pinpoint both sudden changes in edge-node allocation, and small alterations to the cache allocation policies which actually impair the QoE that the end-users perceive.
Fundamental Analysis of a Developer Support Chat Log for Identifying Process Improvement Opportunities
Venue: arXiv cs.SE 2015
Year: 2015

In this report analysis of a support chat log of a development team is shown. Developer support chat is used to provide internal support to other development teams. The report shows how a fundamental data analysis helped to identify gaps and action items to boost performance of a development team by reducing time spent on developer support chat and minimizing interrupts from other developer teams. The report also shows an example of how a root cause analysis can be supported by simple data analysis in finding process improvement opportunities.
Learning to Search for Dependencies
Venue: arXiv cs.CL 2015
Year: 2015

We demonstrate that a dependency parser can be built using a credit assignment compiler which removes the burden of worrying about low-level machine learning details from the parser implementation. The result is a simple parser which robustly applies to many languages that provides similar statistical and computational performance with best-to-date transition-based parsing approaches, while avoiding various downsides including randomization, extra feature requirements, and custom learning algorithms.
Compute-Compress-and-Forward: Exploiting Asymmetry of Wireless Relay Networks
Venue: arXiv cs.IT 2015
Year: 2015

Compute-and-forward (CF) harnesses interference in a wireless networkby allowing relays to compute combinations of source messages. The computed message combinations at relays are correlated, and so directly forwarding these combinations to a destination generally incurs information redundancy and spectrum inefficiency. To address this issue, we propose a novel relay strategy, termed compute-compress-and-forward (CCF). In CCF, source messages are encoded using nested lattice codes constructed on a chain of nested coding and shaping lattices. A key difference of CCF from CF is an extra compressing stage inserted in between the computing and forwarding stages of a relay, so as to reduce the forwarding information rate of the relay. The compressing stage at each relay consists of two operations: first to quantize the computed message combination on an appropriately chosen lattice (referred to as a quantization lattice), and then to take modulo on another lattice (referred to as a modulo lattice). We study the design of the quantization and modulo lattices and propose successive recovering algorithms to ensure the recoverability of source messages at destination. Based on that, we formulate a sum-rate maximization problem that is in general an NP-hard mixed integer program. A low-complexity algorithm is proposed to give a suboptimal solution. Numerical results are presented to demonstrate the superiority of CCF over the existing CF schemes.
Rule Optimization for Real-Time Query Service in Software-Defined Internet of Vehicles
Venue: arXiv cs.NI 2015
Year: 2015

Internet of Vehicles (IoV) has recently gained considerable attentions from both industry and research communities since the development of communication technology and smart city. However, a proprietary and closed way of operating hardwares in network equipments slows down the progress of new services deployment and extension in IoV. Moreover, the tightly coupled control and data planes in traditional networks significantly increase the complexity and cost of network management. By proposing a novel architecture, called Software-Defined Internet of Vehicles (SDIV), we adopt the software-defined network (SDN) architecture to address these problems by leveraging its separation of the control plane from the data plane and a uniform way to configure heterogeneous switches. However, the characteristics of IoV introduce the very challenges in rule installation due to the limited size of Flow Tables at OpenFlow-enabled switches which are the main component of SDN. It is necessary to build compact Flow Tables for the scalability of IoV. Accordingly, we develop a rule optimization approach for real-time query service in SDIV. Specifically, we separate wired data plane from wireless data plane and use multicast address in wireless data plane. Furthermore, we introduce a destination-driven model in wired data plane for reducing the number of rules at switches. Experiments show that our rule optimization strategy reduces the number of rules while keeping the performance of data transmission.
An approach to improving edge detection for facial and remotely sensed images using vector order statistics
Venue: arXiv cs.CV 2015
Year: 2015

This paper presents an improved edge detection algorithm for facial and remotely sensed images using vector order statistics. The developed algorithm processes colored images directly without been converted to gray scale. A number of the existing algorithms converts the colored images into gray scale before detection of edges. But this process leads to inaccurate precision of recognized edges, thus producing false and broken edges in the output edge map. Facial and remotely sensed images consist of curved edge lines which have to be detected continuously to prevent broken edges. In order to deal with this, a collection of pixel approach is introduced with a view to minimizing the false and broken edges that exists in the generated output edge map of facial and remotely sensed images.
Performance Analysis of Random Linear Network Coding in Two-Source Single-Relay Networks
Venue: arXiv cs.IT 2015
Year: 2015

This paper considers the multiple-access relay channel in a setting where two source nodes transmit packets to a destination node, both directly and via a relay node, over packet erasure channels. Intra-session network coding is used at the source nodes and inter-session network coding is employed at the relay node to combine the recovered source packets of both source nodes. In this work, we investigate the performance of the network-coded system in terms of the probability that the destination node will successfully recover the source packets of the two source nodes. We build our analysis on fundamental probability expressions for random matrices over finite fields and we derive upper bounds on the system performance for the case of systematic and non-systematic network coding. Simulation results show that the upper bounds are very tight and accurately predict the decoding probability at the destination node. Our analysis also exposes the clear benefits of systematic network coding at the source nodes compared to non-systematic transmission.
A software controlled voltage tuning system using multi-purpose ring oscillators
Venue: arXiv cs.OH 2015
Year: 2015

This paper presents a novel software driven voltage tuning method that utilises multi-purpose Ring Oscillators (ROs) to provide process variation and environment sensitive energy reductions. The proposed technique enables voltage tuning based on the observed frequency of the ROs, taken as a representation of the device speed and used to estimate a safe minimum operating voltage at a given core frequency. A conservative linear relationship between RO frequency and silicon speed is used to approximate the critical path of the processor. Using a multi-purpose RO not specifically implemented for critical path characterisation is a unique approach to voltage tuning. The parameters governing the relationship between RO and silicon speed are obtained through the testing of a sample of processors from different wafer regions. These parameters can then be used on all devices of that model. The tuning method and software control framework is demonstrated on a sample of XMOS XS1-U8A-64 embedded microprocessors, yielding a dynamic power saving of up to 25% with no performance reduction and no negative impact on the real-time constraints of the embedded software running on the processor.
A General Framework for Multi-focal Image Classification and Authentication: Application to Microscope Pollen Images
Venue: arXiv cs.CV 2015
Year: 2015

In this article, we propose a general framework for multi-focal image classification and authentication, the methodology being demonstrated on microscope pollen images. The framework is meant to be generic and based on a brute force-like approach aimed to be efficient not only on any kind, and any number, of pollen images (regardless of the pollen type), but also on any kind of multi-focal images. All stages of the framework's pipeline are designed to be used in an automatic fashion. First, the optimal focus is selected using the absolute gradient method. Then, pollen grains are extracted using a coarse-to-fine approach involving both clustering and morphological techniques (coarse stage), and a snake-based segmentation (fine stage). Finally, features are extracted and selected using a generalized approach, and their classification is tested with four classifiers: Weighted Neighbor Distance, Neural Network, Decision Tree and Random Forest. The latter method, which has shown the best and more robust classification accuracy results (above 97\% for any number of pollen types), is finally used for the authentication stage.
A Formal Traffic Characterization of LTI Event-triggered Control Systems
Venue: arXiv cs.SY 2015
Year: 2015

Unnecessary communication and computation in the periodic execution of control tasks lead to over-provisioning in hardware design (or underexploitation in hardware utilization) in control applications, such as networked control systems. To address these issues, researchers have proposed a new class of strategies, named event-driven strategies. Despite of their beneficiary effects, matters like task scheduling and appropriate dimensioning of communication components have become more complicated with respect to traditional periodic strategies. In this paper, we present a formal approach to derive an abstracted system that captures the sampling behavior of a family of event-triggered strategies for the case of LTI systems. This structure approximately simulates the sampling behavior of the aperiodic control system. Furthermore, the resulting quotient system is equivalent to a timed automaton. In the construction of the abstraction, the state space is confined to a finite number of convex regions, each of which represents a mode in the quotient system. An LMI-based technique is deployed to derive a sampling time interval associated to each region. Finally, reachability analysis is leveraged to find the transitions of the quotient system.
Deep Transform: Time-Domain Audio Error Correction via Probabilistic Re-Synthesis
Venue: arXiv cs.SD 2015
Year: 2015

In the process of recording, storage and transmission of time-domain audio signals, errors may be introduced that are difficult to correct in an unsupervised way. Here, we train a convolutional deep neural network to re-synthesize input time-domain speech signals at its output layer. We then use this abstract transformation, which we call a deep transform (DT), to perform probabilistic re-synthesis on further speech (of the same speaker) which has been degraded. Using the convolutive DT, we demonstrate the recovery of speech audio that has been subject to extreme degradation. This approach may be useful for correction of errors in communications devices.
Divisible Load Scheduling in Mobile Grid based on Stackelberg Pricing Game
Venue: arXiv cs.DC 2015
Year: 2015

Nowadays, it has become feasible to use mobile nodes as contributing entities in computing systems. In this paper, we consider a computational grid in which the mobile devices can share their idle resources to realize parallel processing. The overall computing task can be arbitrarily partitioned into multiple subtasks to be distributed to mobile resource providers (RPs). In this process, the computation load scheduling problem is highlighted. Based on the optimization objective, i.e., minimizing the task makespan, a buyer-seller model in which the task sponsor can inspire the SPs to share their computing resources by paying certain profits, is proposed. The Stackelberg Pricing Game (SPG) is employed to obtain the optimal price and shared resource amount of each SP. Finally, we evaluate the performance of the proposed algorithm by system simulation and the results indicate that the SPG-based load scheduling algorithm can significantly improve the time gain in mobile grid systems.