Query: Research on data mining models for the internet of things

Relevant passages:
Trajectory clustering: a partition-and-group framework <=SEP=> Existing trajectory clustering algorithms group similar trajectories as a whole, thus discovering common trajectories. Our key observation is that clustering trajectories as a whole could miss common sub-trajectories. Discovering common sub-trajectories is very useful in many applications, especially if we have regions of special interest for analysis. In this paper, we propose a new partition-and-group framework for clustering trajectories, which partitions a trajectory into a set of line segments, and then, groups similar line segments together into a cluster. The primary advantage of this framework is to discover common sub-trajectories from a trajectory database. Based on this partition-and-group framework, we develop a trajectory clustering algorithm TRACLUS. Our algorithm consists of two phases: partitioning and grouping. For the first phase, we present a formal trajectory partitioning algorithm using the minimum description length(MDL) principle. For the second phase, we present a density-based line-segment clustering algorithm. Experimental results demonstrate that TRACLUS correctly discovers common sub-trajectories from real trajectory data.

The Internet of Things in an Enterprise Context <=SEP=> This paper puts the Internet of Things in a wider context: How it relates to the Future Internet overall, and where the business value lies so that it will become interesting for enterprises to invest in it. Real-World Awareness and Business Process Decomposition are the two major paradigms regarding future business value. The major application domains where the Internet of Things will play an important role and where there are concrete business opportunities are highlighted. But there are also many technical challenges that need to be addressed. These are listed and it is shown how they are tackled by existing research projects with industrial participation.

Challenges for Database Management in the Internet of Things <=SEP=> This article discusses the challenges for Database Management in the Internet of Things. We provide scenarios to illustrate the new world that will be produced by the Internet of Things, where physical objects are fully integrated into the information highway. We discuss the different types of data that will be part of the Internet of Things. These include identification, positional, environmental, historical, and descriptive data. We consider the challenges brought by the need to manage vast quantities of data across heterogeneous systems. In particular, we consider the areas of querying, indexing, process modeling, transaction handling, and integration of heterogeneous systems. We refer to the earlier work that might provide solutions for these challenges. Finally we discuss a road map for the Internet of Things and respective technical priorities.

Models and Issues in Data Stream Systems <=SEP=> In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work relevant to data stream systems and current projects in the area, the paper explores topics in stream query languages, new requirements and challenges in query processing, and algorithmic issues.

A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise <=SEP=> Spatial Databases Require to detect knowledge from great amount of data Need to handle with arbitrary shape Requirements of Clustering in Data Mining • Scalability • Dealing with different types of attributes • Discovery of Clusters with arbitrary shape • Minimal requirements for domain knowledge to determine input parameters • Able to deal with noise and outliers • Insensitive to the order of input data • High dimensionality of data • Interpretability and usability Introduction(cont..)

Monitoring road traffic with a UAV-based system <=SEP=> Unmanned Aerial Vehicles (UAVs) are becoming an attractive solution for road traffic monitoring because of their mobility, low cost, and broad view range. Up to now, existing traffic monitoring systems based on UAVs only use one UAV with fixed trajectory to extract information about vehicles. In this paper, we propose a road traffic monitoring system using multiple UAVs. We develop a method to generate adaptive UAVs trajectories, which is based on the tracking of moving points in the UAV field of view. Also we generate UAVs trajectories using mobility models that are usually used to model vehicles mobility. UAVs monitor the traffic on a city road, they are responsible for collecting and sending, in real time, vehicle information to a traffic processing center for traffic regulation purposes. We show that the performance of our system is better than the performance of the fixed UAV trajectory traffic monitoring system in terms of coverage rates and events detection rates.

A novel explicit multi-focus image fusion method <=SEP=> We propose a multi-focus image fusion method. The energy of Laplacian of input images is obtained to decide which portions of the input images are in better focus, and the majority filter offers a means of spreading the focused regions to neighborhood. The edge-preserving guided image filter is applied to reduce the block effect. We conduct several experiments which demonstrate that it provided a better performance than the state of the art fusion methods in visual and quantitative evaluations.

Complimentary Memory Technologies to Reform the Homogeneity , Energy Landscape , and Volatility of Future Memory Hierarchies <=SEP=> With the limits of transistor size rapidly approaching and the memory wall approaching us at unfathomable speeds, a need to revisit our existing memory technologies has arisen. Fortunately, there are new non-volatile memory (NVM) technologies on the horizon showing great promise. In this paper we will examine some of the new NVM technologies being developed. We will present the basic characteristics of these new technologies as well as the properties that make them unique and the possibilities for the future they offer. Keywords—non-volatile; volatile; memory; memristor; RRAM; MRAM; SRAM; STT-RAM; PCM; PRAM

Common Ground in Dialogue with a Gendered Humanoid Robot * <=SEP=> s of the 2004 Conference on Human Factors and Computing Systems, pp. 1485-1488, April 2004.

Flexible dry surface-electrodes for ECG long-term monitoring <=SEP=> In this paper a new generation of dry electrodes for ECG-recording purposes is presented. These electrodes are flexible, biocompatible, biostable and perform a dry signal acquisition without the use of any electrolyte gel or adhesive. Thereby no skin preparation is needed. The material properties allow integration into textiles to form intelligent clothes for long-term monitoring duties. The electrodes can be washed together with the carrier textiles and provide a very good wearing comfort resulting in wide acceptance by the patients. The Department Medical Engineering and Neuroprosthetics of the Fraunhofer Institute for Biomedical Engineering developed these electrodes mainly to process the Electrocardiogram, however, in first tests they proved their capability even for other biosignal requirements like surface-EMG. In either case the signal quality of the dry electrodes is comparable to commercial Ag/AgCl gel-electrodes.

Secure data processing framework for mobile cloud computing <=SEP=> In mobile cloud computing, mobile devices can rely on cloud computing and information storage resource to perform computationally intensive operations such as searching, data mining, and multimedia processing. In addition to providing traditional computation services, mobile cloud also enhances the operation of traditional ad hoc network by treating mobile devices as service nodes, e.g., sensing services. The sensed information, such as location coordinates, health related information, should be processed and stored in a secure fashion to protect user's privacy in the cloud. To this end, we present a new mobile cloud data processing framework through trust management and private data isolation. Finally, an implementation pilot for improving teenagers' driving safety, which is called FocusDrive, is presented to demonstrate the solution.

A Combined Corner and Edge Detector <=SEP=> The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work. For example, we desire to obtain an understanding of natural scenes, containing roads, buildings, trees, bushes, etc., as typified by the two frames from a sequence illustrated in Figure 1. The solution to this problem that we are pursuing is to use a computer vision system based upon motion analysis of a monocular image sequence from a mobile camera. By extraction and tracking of image features, representations of the 3D analogues of these features can be constructed.

Threatened egotism, narcissism, self-esteem, and direct and displaced aggression: does self-love or self-hate lead to violence? <=SEP=> It has been widely asserted that low self-esteem causes violence, but laboratory evidence is lacking, and some contrary observations have characterized aggressors as having favorable self-opinions. In 2 studies, both simple self-esteem and narcissism were measured, and then individual participants were given an opportunity to aggress against someone who had insulted them or praised them or against an innocent third person. Self-esteem proved irrelevant to aggression. The combination of narcissism and insult led to exceptionally high levels of aggression toward the source of the insult. Neither form of self-regard affected displaced aggression, which was low in general. These findings contradict the popular view that low self-esteem causes aggression and point instead toward threatened egotism as an important cause.

Rank-based multiple change-point detection in multivariate time series <=SEP=> In this paper, we propose a Bayesian approach for multivariate time series segmentation. A robust non-parametric test, based on rank statistics, is derived in a Bayesian framework to yield robust distribution-independent segmentations of piecewise constant multivariate time series for which mutual dependencies are unknown. By modelling rank-test p-values, a pseudo-likelihood is proposed to favour change-points detection for significant p-values. A vague prior is chosen for dependency structure between time series, and a MCMC method is applied to the resulting posterior distribution. The Gibbs sampling strategy makes the method computationally efficient. The algorithm is illustrated on simulated and real signals in two practical settings. It is demonstrated that change-points are robustly detected and localized, through implicit dependency structure learning or explicit structural prior introduction.

Degraded dot matrix character recognition using CSM-based feature extraction <=SEP=> This paper presents an OCR method for degraded character recognition applied to a reference number (RN) of 15 printed characters of an invoice document produced by dot-matrix printer. First, the paper deals with the problem of the reference number localization and extraction, in which the characters tops or bottoms are or not touched with a printed reference line of the electrical bill. In case of touched RN, the extracted characters are severely degraded leading to missing parts in the characters tops or bottoms. Secondly, a combined recognition method based on the complementary similarity measure (CSM) method and MLP-based classifier is used. The CSM is used to accept or reject an incoming character. In case of acceptation, the CSM acts as a feature extractor and produces a feature vector of ten component features. The MLP is then trained using these feature vectors. The use of the CSM as a feature extractor tends to make the MLP very powerful and very well suited for rejection. Experimental results on electrical bills show the ability of the model to yield relevant and robust recognition on severely degraded printed characters.

Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health. <=SEP=> BACKGROUND
In the present paper, we describe a model of neurovisceral integration in which a set of neural structures involved in cognitive, affective, and autonomic regulation are related to heart rate variability (HRV) and cognitive performance.


METHODS
We detail the pathways involved in the neural regulation of the cardiovascular system and provide pharmacological and neuroimaging data in support of the neural structures linking the central nervous system to HRV in humans. We review a number of studies from our group showing that individual differences in HRV are related to performance on tasks associated with executive function and prefrontal cortical activity. These studies include comparisons of executive- and nonexecutive-function tasks in healthy participants, in both threatening and nonthreatening conditions. In addition, we show that manipulating resting HRV levels is associated with changes in performance on executive-function tasks. We also examine the relationship between HRV and cognitive performance in ecologically valid situations using a police shooting simulation and a naval navigation simulation. Finally, we review our studies in anxiety patients, as well as studies examining psychopathy.


CONCLUSION
These findings in total suggest an important relationship among cognitive performance, HRV, and prefrontal neural function that has important implications for both physical and mental health. Future studies are needed to determine exactly which executive functions are associated with individual differences in HRV in a wider range of situations and populations.

Visuo-acoustic stimulation that helps you to relax: A virtual reality setup for patients in the intensive care unit <=SEP=> After prolonged stay in an intensive care unit (ICU) patients often complain about cognitive impairments that affect health-related quality of life after discharge. The aim of this proof-of-concept study was to test the feasibility and effects of controlled visual and acoustic stimulation in a virtual reality (VR) setup in the ICU. The VR setup consisted of a head-mounted display in combination with an eye tracker and sensors to assess vital signs. The stimulation consisted of videos featuring natural scenes and was tested in 37 healthy participants in the ICU. The VR stimulation led to a reduction of heart rate (p = 0. 049) and blood pressure (p = 0.044). Fixation/saccade ratio (p < 0.001) was increased when a visual target was presented superimposed on the videos (reduced search activity), reflecting enhanced visual processing. Overall, the VR stimulation had a relaxing effect as shown in vital markers of physical stress and participants explored less when attending the target. Our study indicates that VR stimulation in ICU settings is feasible and beneficial for critically ill patients.

A Practical and Provably Secure Coalition-Resistant Group Signature Scheme <=SEP=> A group signature scheme allows a group member to sign messages anonymously on behalf of the group. However, in the case of a dispute, the identity of a signature’s originator can be revealed (only) by a designated entity. The interactive counterparts of group signatures are identity escrow schemes or group identification scheme with revocable anonymity. This work introduces a new provably secure group signature and a companion identity escrow scheme that are significantly more efficient than the state of the art. In its interactive, identity escrow form, our scheme is proven secure and coalition-resistant under the strong RSA and the decisional Diffie-Hellman assumptions. The security of the noninteractive variant, i.e., the group signature scheme, relies additionally on the Fiat-Shamir heuristic (also known as the random oracle model).

Thirty Years Of Graph Matching In Pattern Recognition <=SEP=> A recent paper posed the question: “Graph Matching: What are we really talking about?”. Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented and discussed. The first includes almost all the graph matching algorithms proposed from the late seventies, and describes the different classes of algorithms. The second taxonomy considers the types of common applications of graph-based techniques in the Pattern Recognition and Machine Vision field.

Industrial Big Data Analysis in Smart Factory: Current Status and Research Strategies <=SEP=> Under the background of cyber-physical systems and Industry 4.0, intelligent manufacturing has become an orientation and produced a revolutionary change. Compared with the traditional manufacturing environments, the intelligent manufacturing has the characteristics as highly correlated, deep integration, dynamic integration, and huge volume of data. Accordingly, it still faces various challenges. In this paper, we summarize and analyze the current research status in both domestic and aboard, including industrial big data collection, modeling of the intelligent product lines based on ontology, the predictive diagnosis based on industrial big data, group learning of product line equipment and the product line reconfiguration of intelligent manufacturing. Based on the research status and the problems, we propose the research strategies, including acquisition schemes of industrial big data under the environment of intelligent, ontology modeling and deduction method based intelligent product lines, predictive diagnostic methods on production lines based on deep neural network, deep learning among devices based on cloud supplements and 3-D self-organized reconfiguration mechanism based on the supplements of cloud. In our view, this paper will accelerate the implementation of smart factory.

Graph construction and b-matching for semi-supervised learning <=SEP=> Graph based semi-supervised learning (SSL) methods play an increasingly important role in practical machine learning systems. A crucial step in graph based SSL methods is the conversion of data into a weighted graph. However, most of the SSL literature focuses on developing label inference algorithms without extensively studying the graph building method and its effect on performance. This article provides an empirical study of leading semi-supervised methods under a wide range of graph construction algorithms. These SSL inference algorithms include the Local and Global Consistency (LGC) method, the Gaussian Random Field (GRF) method, the Graph Transduction via Alternating Minimization (GTAM) method as well as other techniques. Several approaches for graph construction, sparsification and weighting are explored including the popular k-nearest neighbors method (kNN) and the b-matching method. As opposed to the greedily constructed kNN graph, the b-matched graph ensures each node in the graph has the same number of edges and produces a balanced or regular graph. Experimental results on both artificial data and real benchmark datasets indicate that b-matching produces more robust graphs and therefore provides significantly better prediction accuracy without any significant change in computation time.

The effect of "open access" on citation impact: An analysis of ArXiv's condensed matter section <=SEP=> This article statistically analyses how the citatio n impact of articles deposited in the Condensed Matter section of the preprint server ArX iv (hosted by Cornell University), and subsequently published in a scientific journal, compares to that of articles in the same journal that were not deposited in that archive. Its principal aim is to further illustrate and roughly estimate the effect of two factors, ‘early view’ and ‘quality bias’, upon differences in citation impact between these two se ts of papers, using citation data from Thomson Scientific’s Web of Science. It presents es imates for a number of journals in the field of condensed matter physics. In order to discriminate between an ‘open access’ effect and an early view effect, longitudinal citat ion data was analysed covering a time period as long as 7 years. Quality bias was measure d by calculating ArXiv citation impact differentials at the level of individual aut hors publishing in a journal, taking into account co-authorship. The analysis provided evide nce of a strong quality bias and early view effect. Correcting for these effects, there is in a sample of 6 condensed matter physics journals studied in detail, no sign of a general ‘open access advantage’ of paper s deposited in ArXiv. The study does provide evidence that ArXiv accelerates citation, due to the fact that that ArXiv makes papers arlier available rather than that it makes papers freely available.

A Faceted Approach to Building Ontologies <=SEP=> Rubén Prieto-Díaz Commonwealth Information Security Center James Madison University prietodiaz@cisat.jmu.edu

Electronic Commerce: Definition, Theory, and Context <=SEP=> 

Modeling and Mitigating Impact of False Data Injection Attacks on Automatic Generation Control <=SEP=> This paper studies the impact of false data injection (FDI) attacks on automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions, such as disconnecting customer loads or generators, leading to blackouts, and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack, consisting of a series of FDIs that minimizes the remaining time until the onset of disruptive remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of the FDIs on power grids, and provides an analysis framework to guide the protection of sensor data links. For countermeasures, we develop efficient algorithms to detect the attack, estimate which sensor data links are under attack, and mitigate attack impact. Our analysis and algorithms are validated by experiments on a physical 16-bus power system test bed and extensive simulations based on a 37-bus power system model.

No-Frills Human-Object Interaction Detection: Factorization, Appearance and Layout Encodings, and Training Techniques <=SEP=> We show that with an appropriate factorization, and encodings of layout and appearance constructed from outputs of pretrained object detectors, a relatively simple model outperforms more sophisticated approaches on humanobject interaction detection. Our model includes factors for detection scores, human and object appearance, and coarse (box-pair configuration) and optionally fine-grained layout (human pose). We also develop training techniques that improve learning efficiency by: (i) eliminating train-inference mismatch; (ii) rejecting easy negatives during mini-batch training; and (iii) using a ratio of negatives to positives that is two orders of magnitude larger than existing approaches while constructing training mini-batches. We conduct a thorough ablation study to understand the importance of different factors and training techniques using the challenging HICO-Det dataset [4].

The nude image identification with adaptive skin chromatic distribution matching scheme <=SEP=> This paper presents a new nude image identification system. We propose an adaptive chroma-distribution matching scheme based on face detection to on-line determine the image's skin chromatic distribution such that it can tolerate the color deviation coming from special lighting without increasing false alarm. The object detection based on machine learning approach is used to locate the face region in the test image. According to the color information of face, the matched skin chromatic distribution is selected to detect the skin objects in the test image. The texture feature, namely coarseness, is used to acquire accurate skin segmentation. The low-level but reliable geometrical constraints and the mug shot exclusion procedure are employed to further examine the skin regions. Experimental results that the overall detection rate is 86.3% show our method can achieve satisfactory performance for detecting nude images under special lighting conditions.

Robust analysis of feature spaces: color image segmentation <=SEP=> A general technique for the recovery of signi cant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image segmentation is discussed. The segmentation is completely autonomous, only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or provide, by extracting all the signi cant colors, a preprocessor for content-based query systems. A 512 512 color image is analyzed in less than 10 seconds on a standard workstation. Gray level images are handled as color images having only the lightness coordinate.

Locality-Sensitive Bloom Filter for Approximate Membership Query <=SEP=> In many network applications, Bloom filters are used to support exact-matching membership query for their randomized space-efficient data structure with a small probability of false answers. In this paper, we extend the standard Bloom filter to Locality-Sensitive Bloom Filter (LSBF) to provide Approximate Membership Query (AMQ) service. We achieve this by replacing uniform and independent hash functions with locality-sensitive hash functions. Such replacement makes the storage in LSBF to be locality sensitive. Meanwhile, LSBF is space efficient and query responsive by employing the Bloom filter design. In the design of the LSBF structure, we propose a bit vector to reduce False Positives (FP). The bit vector can verify multiple attributes belonging to one member. We also use an active overflowed scheme to significantly decrease False Negatives (FN). Rigorous theoretical analysis (e.g., on FP, FN, and space overhead) shows that the design of LSBF is space compact and can provide accurate response to approximate membership queries. We have implemented LSBF in a real distributed system to perform extensive experiments using real-world traces. Experimental results show that LSBF, compared with a baseline approach and other state-of-the-art work in the literature (SmartStore and LSB-tree), takes less time to respond AMQ and consumes much less storage space.

FULLY AUTOMATIC STEREO-TO-MULTIVIEW CONVERSION IN AUTOSTEREOSCOPIC DISPLAYS <=SEP=> Watching 3D without glasses will be the future of 3D home entertainment. However, current stereoscopic 3D content is not suited to drive autostereoscopic displays and a 3D format conversion is required. In this paper a system is presented which is capable of high quality stereo-tomultiview conversion in real-time. This allows playback of 3D Blu-ray or any other stereoscopic 3D video content without prior adaptation on most existing autostereoscopic displays in real-time. The algorithms described include image rectification, fast and reliable disparity estimation, adaptive post-processing and up-sampling of disparity maps, rendering of virtual views and interweaving these views into a format suitable for autostereoscopic displays.