OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Incident Classification for Big Traffic Data by Adaptive Boosting SVM
Venue: arXiv cs.LG 2015
Year: 2015

Modern cities experience heavy traffic flows and congestions regularly across space and time. Monitoring traffic situations becomes an important challenge for the Traffic Control and Surveillance Systems (TCSS). In advanced TCSS, it is helpful to automatically detect and classify different traffic incidents such as severity of congestion, abnormal driving pattern, abrupt or illegal stop on road, etc. Although most TCSS are equipped with basic incident detection algorithms, they are however crude to be really useful as an automated tool for further classification. In literature, there is a lack of research for Automated Incident Classification (AIC). Therefore, a novel AIC method is proposed in this paper to tackle such challenges. In the proposed method, traffic signals are firstly extracted from captured videos and converted as spatial-temporal (ST) signals. Based on the characteristics of the ST signals, a set of realistic simulation data are generated to construct an extended big traffic database to cover a variety of traffic situations. Next, a Mean-Shift filter is introduced to suppress the effect of noise and extract significant features from the ST signals. The extracted features are then associated with various types of traffic data: one normal type (inliers) and multiple abnormal types (outliers). For the classification, an adaptive boosting classifier is trained to detect outliers in traffic data automatically. Further, a Support Vector Machine (SVM) based method is adopted to train the model for identifying the categories of outliers. In short, this hybrid approach is called an Adaptive Boosting Support Vector Machines (AB-SVM) method. Experimental results show that the proposed AB-SVM method achieves a satisfied result with more than 92% classification accuracy on average.
Near-Optimal Bounds for Binary Embeddings of Arbitrary Sets
Venue: arXiv cs.LG 2015
Year: 2015

We study embedding a subset $K$ of the unit sphere to the Hamming cube $\{-1,+1\}^m$. We characterize the tradeoff between distortion and sample complexity $m$ in terms of the Gaussian width $ω(K)$ of the set. For subspaces and several structured sets we show that Gaussian maps provide the optimal tradeoff $m\sim δ^{-2}ω^2(K)$, in particular for $δ$ distortion one needs $m\approxδ^{-2}{d}$ where $d$ is the subspace dimension. For general sets, we provide sharp characterizations which reduces to $m\approx{δ^{-4}}{ω^2(K)}$ after simplification. We provide improved results for local embedding of points that are in close proximity of each other which is related to locality sensitive hashing. We also discuss faster binary embedding where one takes advantage of an initial sketching procedure based on Fast Johnson-Lindenstauss Transform. Finally, we list several numerical observations and discuss open problems.
A Model for Safety Case Confidence Assessment
Venue: arXiv cs.AI 2015
Year: 2015

Building a safety case is a common approach to make expert judgement explicit about safety of a system. The issue of confidence in such argumentation is still an open research field. Providing quantitative estimation of confidence is an interesting approach to manage complexity of arguments. This paper explores the main current approaches, and proposes a new model for quantitative confidence estimation based on Belief Theory for its definition, and on Bayesian Belief Networks for its propagation in safety case networks.
Über die Klassifizierung von Knoten in dynamischen Netzwerken mit Inhalt
Venue: arXiv cs.LG 2015
Year: 2015

This paper explains the DYCOS-Algorithm as it was introduced in by Aggarwal and Li in 2011. It operates on graphs whichs nodes are partially labeled and automatically adds missing labels to nodes. To do so, the DYCOS algorithm makes use of the structure of the graph as well as content which is assigned to the node. Aggarwal and Li measured in an experimental analysis that DYCOS adds the missing labels to a Graph with 19396 nodes of which 14814 are labeled and another Graph with 806635 nodes of which 18999 are labeld on one core of an Intel Xeon 2.5 GHz CPU with 32 G RAM within less than a minute. Additionally, extensions of the DYCOS algorithm are proposed. ----- In dieser Arbeit wird der DYCOS-Algorithmus, wie er 2011 von Aggarwal und Li vorgestellt wurde, erklärt. Er arbeitet auf Graphen, deren Knoten teilweise mit Beschriftungen versehen sind und ergänzt automatisch Beschriftungen für Knoten, die bisher noch keine Beschriftung haben. Dieser Vorgang wird "Klassifizierung" genannt. Dazu verwendet er die Struktur des Graphen sowie textuelle Informationen, die den Knoten zugeordnet sind. Die von Aggarwal und Li beschriebene experimentelle Analyse ergab, dass er auch auf dynamischen Graphen mit 19396 bzw. 806635 Knoten, von denen nur 14814 bzw. 18999 beschriftet waren, innerhalb von weniger als einer Minute auf einem Kern einer Intel Xeon 2.5 GHz CPU mit 32 G RAM ausgeführt werden kann. Zusätzlich wird die Veröffentlichung von Aggarwal und Li kritisch erörtert und und es werden mögliche Erweiterungen des DYCOS-Algorithmus vorgeschlagen.
Quantifying Public Response towards Islam on Twitter after Paris Attacks
Venue: arXiv cs.SI 2015
Year: 2015

The Paris terrorist attacks occurred on November 13, 2015 prompted a massive response on social media including Twitter, with millions of posted tweets in the first few hours after the attacks. Most of the tweets were condemning the attacks and showing support to Parisians. One of the trending debates related to the attacks concerned possible association between terrorism and Islam and Muslims in general. This created a global discussion between those attacking and those defending Islam and Muslims. In this paper, we provide quantitative and qualitative analysis of data collection we streamed from Twitter starting 7 hours after the Paris attacks and for 50 subsequent hours that are related to blaming Islam and Muslims and to defending them. We collected a set of 8.36 million tweets in this epoch consisting of tweets in many different of languages. We could identify a subset consisting of 900K tweets relating to Islam and Muslims. Using sampling methods and crowd-sourcing annotation, we managed to estimate the public response of these tweets. Our findings show that the majority of the tweets were in fact defending Muslims and absolving them from responsibility for the attacks. However, a considerable number of tweets were blaming Muslims, with most of these tweets coming from western countries such as the Netherlands, France, and the US.
Semantic-enriched Visual Vocabulary Construction in a Weakly Supervised Context
Venue: arXiv cs.CV 2015
Year: 2015

One of the prevalent learning tasks involving images is content-based image classification. This is a difficult task especially because the low-level features used to digitally describe images usually capture little information about the semantics of the images. In this paper, we tackle this difficulty by enriching the semantic content of the image representation by using external knowledge. The underlying hypothesis of our work is that creating a more semantically rich representation for images would yield higher machine learning performances, without the need to modify the learning algorithms themselves. The external semantic information is presented under the form of non-positional image labels, therefore positioning our work in a weakly supervised context. Two approaches are proposed: the first one leverages the labels into the visual vocabulary construction algorithm, the result being dedicated visual vocabularies. The second approach adds a filtering phase as a pre-processing of the vocabulary construction. Known positive and known negative sets are constructed and features that are unlikely to be associated with the objects denoted by the labels are filtered. We apply our proposition to the task of content-based image classification and we show that semantically enriching the image representation yields higher classification performances than the baseline representation.
Multilevel Diversity Coding with Regeneration: Separate Coding Achieves the MBR Point
Venue: arXiv cs.IT 2015
Year: 2015

The problem of multilevel diversity coding with regeneration is considered in this work. Two new outer bounds on the optimal tradeoffs between the normalized storage capacity and repair bandwidth are established, by which the optimality of separate coding at the minimum-bandwidth-regeneration (MBR) point follows immediately. This resolves a question left open in a previous work by Tian and Liu.
Energy-Efficient Classification for Anomaly Detection: The Wireless Channel as a Helper
Venue: arXiv cs.IT 2015
Year: 2015

Anomaly detection has various applications including condition monitoring and fault diagnosis. The objective is to sense the environment, learn the normal system state, and then periodically classify whether the instantaneous state deviates from the normal one or not. A flexible and cost-effective way of monitoring a system state is to use a wireless sensor network. In the traditional approach, the sensors encode their observations and transmit them to a fusion center by means of some interference avoiding channel access method. The fusion center then decodes all the data and classifies the corresponding system state. As this approach can be highly inefficient in terms of energy consumption, in this paper we propose a transmission scheme that exploits interference for carrying out the anomaly detection directly in the air. In other words, the wireless channel helps the fusion center to retrieve the sought classification outcome immediately from the channel output. To achieve this, the chosen learning model is linear support vector machines. After discussing the proposed scheme and proving its reliability, we present numerical examples demonstrating that the scheme reduces the energy consumption for anomaly detection by up to 53% compared to a strategy that uses time division multiple-access.
Analyzing the Utility of a Support Pin in Sequential Robotic Manipulation
Venue: arXiv cs.RO 2015
Year: 2015

Pick-and-place regrasp is an important manipulation skill for a robot. It helps a robot accomplish tasks that cannot be achieved within a single grasp, due to constraints such as kinematics or collisions between the robot and the environment. Previous work on pick-and-place regrasp only leveraged flat surfaces for intermediate placements, and thus is limited in the capability to reorient an object. In this paper, we extend the reorientation capability of a pick-and-place regrasp by adding a vertical pin on the working surface and using it as the intermediate location for regrasping. In particular, our method automatically computes the stable placements of an object leaning against a vertical pin, finds several force-closure grasps, generates a graph of regrasp actions, and searches for the regrasp sequence. To compare the regrasping performance with and without using pins, we evaluate the success rate and the length of regrasp sequences while performing tasks on various models. Experiments on reorientation and assembly tasks validate the benefit of using support pins for regrasping.
UML4IoT - A UML profile to exploit IoT in cyber-physical manufacturing systems
Venue: arXiv cs.SE 2015
Year: 2015

Internet of Things is changing the world. The manufacturing industry has already identified that the IoT brings great opportunities to retain its leading position in economy and society. However, the adoption of this new technology changes the development process of the manufacturing system and raises many challenges. In this paper the modern manufacturing system is considered as a composition of cyber-physical, cyber and human components and IoT is used as a glue for their integration as far as it regards their cyber interfaces. The key idea is a UML profile for the IoT with an alternative to apply the approach also at the source code level specification of the component in case that a UML design specification is not available. The proposed approach, namely UML4IoT, fully automates the generation process of the IoT-compliant layer that is required for the cyber-physical component to be integrated in the modern IoT manufacturing environment. A prototype implementation of the myLiqueur laboratory system has been developed to demonstrate the applicability and effectiveness of the UML4IoT approach.
MIMO CDMA-based Optical SATCOMs: A New Solution
Venue: arXiv cs.NI 2015
Year: 2015

A new scheme for MIMO CDMA-based optical satellite communications (OSATCOMs) is presented. Three independent problems are described for up-link and down- link in terms of two distinguished optimization problems. At first, in up-link, Pulse-width optimization is proposed to reduce dispersions over fibers as the terrestrial part. This is performed for return-to-zero (RZ) modulation that is supposed to be used as an example in here. This is carried out by solving the first optimization problem, while minimizing the probability of overlapping for the Gaussian pulses that are used to produce RZ. Some constraints are assumed such as a threshold for the peak-to-average power ratio (PAPR). In down-link, the second and the third problems are discussed as follows, jointly as a closed-form solution. Solving the second optimization problem, an objective function is obtained, namely the MIMO CDMA-based satellite weight-matrix as a conventional adaptive beam-former. The Satellite link is stablished over flat un-correlated Nakagami-m/Suzuki fading channels as the second problem. On the other hand, the mentioned optimization problem is robustly solved as the third important problem, while considering inter-cell interferences in the multi-cell scenario. Robust solution is performed due to the partial knowledge of each cell from the others in which the link capacity is maximized. Analytical results are conducted to investigate the merit of system.
Pricing-based Distributed Energy-Efficient Beamforming for MISO Interference Channels
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we consider the problem of maximizing the weighted sum energy efficiency (WS-EE) for multi-input single-output (MISO) interference channels (ICs) which is well acknowledged as general models of heterogeneous networks (HetNets), multicell networks, etc. To address this problem, we develop an efficient distributed beamforming algorithm based on a pricing mechanism. Specifically, we carefully introduce a price metric for distributed beamforming design which fortunately allows efficient closed-form solutions to the per-user beam-vector optimization problem. The convergence of the distributed pricing-based beamforming design is theoretically proven. Furthermore, we present an implementation strategy of the proposed distributed algorithm with limited information exchange. Numerical results show that our algorithm converges much faster than existing algorithms, while yielding comparable, sometimes even better performance in terms of the WS-EE. Finally, by taking the backhaul power consumption into account, it is interesting to show that the proposed algorithm with limited information exchange achieves better WS-EE than the full information exchange based algorithm in some special cases.
Inferring Gene Regulatory Network Using An Evolutionary Multi-Objective Method
Venue: arXiv cs.CE 2015
Year: 2015

Inference of gene regulatory networks (GRNs) based on experimental data is a challenging task in bioinformatics. In this paper, we present a bi-objective minimization model (BoMM) for inference of GRNs, where one objective is the fitting error of derivatives, and the other is the number of connections in the network. To solve the BoMM efficiently, we propose a multi-objective evolutionary algorithm (MOEA), and utilize the separable parameter estimation method (SPEM) decoupling the ordinary differential equation (ODE) system. Then, the Akaike Information Criterion (AIC) is employed to select one inference result from the obtained Pareto set. Taking the S-system as the investigated GRN model, our method can properly identify the topologies and parameter values of benchmark systems. There is no need to preset problem-dependent parameter values to obtain appropriate results, and thus, our method could be applicable to inference of various GRNs models.
Streaming Kernel Principal Component Analysis
Venue: arXiv cs.DS 2015
Year: 2015

Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture non-linear structures within large data sets, and is a central tool in data analysis and learning. To allow for non-linear relations, typically a full $n \times n$ kernel matrix is constructed over $n$ data points, but this requires too much space and time for large values of $n$. Techniques such as the Nyström method and random feature maps can help towards this goal, but they do not explicitly maintain the basis vectors in a stream and take more space than desired. We propose a new approach for streaming KPCA which maintains a small set of basis elements in a stream, requiring space only logarithmic in $n$, and also improves the dependence on the error parameter. Our technique combines together random feature maps with recent advances in matrix sketching, it has guaranteed spectral norm error bounds with respect to the original kernel matrix, and it compares favorably in practice to state-of-the-art approaches.
Novel Outer Bounds and Capacity Results for the Interference Channel with Conferencing Receivers
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, capacity bounds for the two-user Interference Channel (IC) with cooperative receivers via conferencing links of finite capacities is investigated. The capacity results known for this communication scenario are limited to a very few special cases of the one-sided IC. One of the major challenges in analyzing such cooperative networks is how to establish efficient capacity outer bounds for them. In this paper, by applying new techniques, novel capacity outer bounds are presented for the ICs with conferencing users. Using the outer bounds, several new capacity results are proved for interesting channels with unidirectional cooperation in strong and mixed interference regimes. A fact is that the conferencing link (between receivers) may be utilized to provide one receiver with information about its corresponding signal or its non-corresponding signal (interference signal). As a remarkable consequence, it is demonstrated that both strategies can be helpful to achieve the capacity of the channel. Finally, for the case of Gaussian IC, it is mathematically shown that our outer bound is strictly tighter than the previous one derived by Wang and Tse.
From Stars to Patients: Lessons from Space Science and Astrophysics for Health Care Informatics
Venue: arXiv cs.SY 2015
Year: 2015

Big Data are revolutionizing nearly every aspect of the modern society. One area where this can have a profound positive societal impact is the field of Health Care Informatics (HCI), which faces many challenges. The key idea behind this study is: can we use some of the experience and technical and methodological solutions from the fields that have successfully adapted to the Big Data era, namely astronomy and space science, to help accelerate the progress of HCI? We illustrate this with examples from the Virtual Observatory framework, and the NCI EDRN project. An effective sharing and reuse of tools, methods, and experiences from different fields can save a lot of effort, time, and expense. HCI can thus benefit from the proven solutions to big data challenges from other domains.
Why scientific publications should be anonymous
Venue: arXiv cs.DL 2015
Year: 2015

Numerous studies have revealed biases within the scientific communication system and across all scientific fields. For example, already prominent researchers receive disproportional credit compared to their (almost) equally qualified colleagues -- because of their prominence. However, none of those studies has offered a solution as to how to decrease the incidence of these biases. In this paper I argue that by publishing anonymously, we can decrease the incidence of inaccurate heuristics in the current scientific communication system. Specific suggestions are made as to how to implement the changes.
Signal Representations on Graphs: Tools and Applications
Venue: arXiv cs.AI 2015
Year: 2015

We present a framework for representing and modeling data on graphs. Based on this framework, we study three typical classes of graph signals: smooth graph signals, piecewise-constant graph signals, and piecewise-smooth graph signals. For each class, we provide an explicit definition of the graph signals and construct a corresponding graph dictionary with desirable properties. We then study how such graph dictionary works in two standard tasks: approximation and sampling followed with recovery, both from theoretical as well as algorithmic perspectives. Finally, for each class, we present a case study of a real-world problem by using the proposed methodology.
Numerical Demultiplexing of Color Image Sensor Measurements via Non-linear Random Forest Modeling
Venue: arXiv cs.CV 2015
Year: 2015

The simultaneous capture of imaging data at multiple wavelengths across the electromagnetic spectrum is highly challenging, requiring complex and costly multispectral image sensors. In this study, we introduce a comprehensive framework for performing simultaneous multispectral imaging using conventional image sensors with color filter arrays via numerical demultiplexing of the color image sensor measurements. A numerical forward model characterizing the formation of sensor measurements from light spectra hitting the sensor is constructed based on a comprehensive spectral characterization of the sensor. A numerical demultiplexer is then learned via non-linear random forest modeling based on the forward model. Given the learned numerical demultiplexer, one can then demultiplex simultaneously-acquired measurements made by the image sensor into reflectance intensities at discrete selectable wavelengths, resulting in a higher resolution reflectance spectrum. Simulation and real-world experimental results demonstrate the efficacy of such a method for simultaneous multispectral imaging.
A Method of Passage-Based Document Retrieval in Question Answering System
Venue: arXiv cs.IR 2015
Year: 2015

We propose a method for using the scoring values of passages to effectively retrieve documents in a Question Answering system. For this, we suggest evaluation function that considers proximity between each question terms in passage. And using this evaluation function , we extract a documents which involves scoring values in the highest collection, as a suitable document for question. The proposed method is very effective in document retrieval of Korean question answering system.