Query: AnyDBC: An Efficient Anytime Density-based Clustering Algorithm for Very Large Complex Datasets

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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..)

Using grid for accelerating density-based clustering <=SEP=> Clustering analysis is a primary method for data mining. The ever increasing volumes of data in different applications forces clustering algorithms to cope with it. DBSCAN is a well-known algorithm for density-based clustering. It is both effective so it can detect arbitrary shaped clusters of dense regions and efficient especially in existence of spatial indexes to perform the neighborhood queries efficiently. In this paper we introduce a new algorithm GriDBSCAN to enhance the performance of DBSCAN using grid partitioning and merging, yielding a high performance with the advantage of high degree of parallelism. We verified the correctness of the algorithm theoretically and experimentally, studied the performance theoretically and using experiments on both real and synthetic data. It proved to run much faster than original DBSCAN. We compared the algorithm with a similar algorithm, EnhancedDBSCAN, which is also an enhancement to DBSCAN using partitioning. Experiments showed the new algorithm's superiority in performance and degree of parallelism.

Active Learning Literature Survey <=SEP=> The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer labeled training instances if it is allowed to choose the data from which is learns. An active learner may ask queries in the form of unlabeled instances to be labeled by an oracle (e.g., a human annotator). Active learning is well-motivated in many modern machine learning problems, where unlabeled data may be abundant but labels are difficult, time-consuming, or expensive to obtain. This report provides a general introduction to active learning and a survey of the literature. This includes a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date. An analysis of the empirical and theoretical evidence for active learning, a summary of several problem setting variants, and a discussion of related topics in machine learning research are also presented.

An improved sampling-based DBSCAN for large spatial databases <=SEP=> Spatial data clustering is one of the important data mining techniques for extracting knowledge from large amount of spatial data collected in various applications, such as remote sensing, GIS, computer cartography, environmental assessment and planning, etc. Several useful and popular spatial data clustering algorithms have been proposed in the past decade. DBSCAN is one of them, which can discover clusters of any arbitrary shape and can handle the noise points effectively. However, DBSCAN requires large volume of memory support because it operates on the entire database. This paper presents an improved sampling-based DBSCAN which can cluster large-scale spatial databases effectively. Experimental results included to establish that the proposed sampling-based DBSCAN outperforms DBSCAN as well as its other counterparts, in terms of execution time, without losing the quality of clustering.

Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications <=SEP=> Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distribution, and insensitivity to the order of input records. We present CLIQUE, a clustering algorithm that satisfies each of these requirements. CLIQUE identifies dense clusters in subspaces of maximum dimensionality. It generates cluster descriptions in the form of DNF expressions that are minimized for ease of comprehension. It produces identical results irrespective of the order in which input records are presented and does not presume any specific mathematical form for data distribution. Through experiments, we show that CLIQUE efficiently finds accurate cluster in large high dimensional datasets.

An improved NSGA-III algorithm for feature selection used in intrusion detection <=SEP=> Feature selection can improve classification accuracy and decrease the computational complexity of classification. Data features in intrusion detection systems (IDS) always present the problem of imbalanced classification in which some classifications only have a few instances while others have many instances. This imbalance can obviously limit classification efficiency, but few effort s have been made to address it. In this paper, a scheme for the many-objective problem is proposed for feature selection in IDS, which uses two strategies, namely, a special domination method and a predefined multiple targeted search, for population evolution. It can differentiate traffic not only between normal and abnormal but also by abnormality type. Based on our scheme, NSGA-III is used to obtain an adequate feature subset with good performance. An improved many-objective optimization algorithm (I-NSGA-III) is further proposed using a novel niche preservation procedure. It consists of a bias-selection process that selects the individual with the fewest selected features and a fit-selection process that selects the individual with the maximum sum weight of its objectives. Experimental results show that I-NSGA-III can alleviate the imbalance problem with higher classification accuracy for classes having fewer instances. Moreover, it can achieve both higher classification accuracy and lower computational complexity. © 2016 Published by Elsevier B.V.

A Low Radar Cross Section and Low Profile Antenna Co-Designed With Absorbent Frequency Selective Radome <=SEP=> A low radar cross section (RCS) and low profile antenna co-designed with absorbent frequency selective radome (AFSR) is investigated. A pair of circular slot resonators is embedded on surface of the AFSR to realize a transmission window in the vertical polarization, while the wide absorption band is still maintained in the horizontal polarization. When a patch antenna is etched within the AFSR, where the metal grounds of the patch antenna and AFSR are co-used, the co-designed antenna with low RCS and low profile is thus realized. For demonstration, an AFSR is designed with its transmission window has a minimal insertion loss of 0.45 dB at 8.9 GHz, and two separate absorption bands (a lower absorption band from 4.8 to 7.5 GHz and an upper absorption band from 10 to 13 GHz) in the vertical polarization, a wide absorption band (from 4.5 to 12.5 GHz) in the horizontal polarization. A patch antenna etched within the AFSR is optimized to operate at 8.9 GHz, then it is simulated and fabricated. The measured results demonstrate that the proposed antenna not only has good radiation patterns, but also obtains significant RCS reduction.

Hierarchical Disentangled Representations <=SEP=> Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner. A number of recent efforts have focused on learning representations that disentangle statistically independent axes of variation, often by introducing suitable modifications of the objective function. We synthesize this growing body of literature by formulating a generalization of the evidence lower bound that explicitly represents the trade-offs between sparsity of the latent code, bijectivity of representations, and coverage of the support of the empirical data distribution. Our objective is also suitable to learning hierarchical representations that disentangle blocks of variables whilst allowing for some degree of correlations within blocks. Experiments on a range of datasets demonstrate that learned representations contain interpretable features, are able to learn discrete attributes, and generalize to unseen combinations of factors.

Stochastic Models , Estimation and Control <=SEP=> 

A lightweight method for virtual machine introspection <=SEP=> A method for the introspection of virtual machines is proposed. The main distinctive feature of this method is that it makes it possible to obtain information about the system operation using the minimum knowledge about its internal organization. The proposed approach uses rarely changing parts of the application binary interface, such as identifiers and parameters of system calls, calling conventions, and the formats of executable files. The lightweight property of the introspection method is due to the minimization of the knowledge about the system and by its high performance. The introspection infrastructure is based on the QEMU emulator, version 2.8. Presently, monitoring the file operations, processes, and API function calls are implemented. The available introspection tools (RTKDSM, Panda, and DECAF) get data for the analysis using kernel structures. All the data obtained (addresses of structures, etc.) is written to special profiles. Since the addresses and offsets strongly depend not only on the version of the operating system but also on the parameters of its assembly, these tools have to store a large number of profiles. We propose to use parts of the application binary interface because they are rarely modified and it is often possible to use one profile for a family of OSs. The main idea underlying the proposed method is to intercept the system and library function calls and read parameters and returned values. The processor provides special instructions for calling system and user defined functions. The capabilities of QEMU are extended by an instrumentation mechanism to enable one to track each executed instruction and find the instructions of interest among them. When a system call occurs, the control is passed to the system call detector that checks the number of the call and decides to which module the job should be forwarded. In the case of an API function call, the situation is similar, but the API function detector checks the function address. An introspection tool consisting of a set of modules is developed. These modules are dynamic libraries that are plugged in QEMU. The modules can interact by exchanging data.

International experiences with the Hospital Anxiety and Depression Scale--a review of validation data and clinical results. <=SEP=> More than 200 published studies from most medical settings worldwide have reported experiences with the Hospital Anxiety and Depression Scale (HADS) which was specifically developed by Zigmond and Snaith for use with physically ill patients. Although introduced in 1983, there is still no comprehensive documentation of its psychometric properties. The present review summarizes available data on reliability and validity and gives an overview of clinical studies conducted with this instrument and their most important findings. The HADS gives clinically meaningful results as a psychological screening tool, in clinical group comparisons and in correlational studies with several aspects of disease and quality of life. It is sensitive to changes both during the course of diseases and in response to psychotherapeutic and psychopharmacological intervention. Finally, HADS scores predict psychosocial and possibly also physical outcome.

Addressing the Winograd Schema Challenge as a Sequence Ranking Task <=SEP=> The Winograd Schema Challenge targets pronominal anaphora resolution problems which require the application of cognitive inference in combination with world knowledge. These problems are easy to solve for humans but most difficult to solve for machines. Computational models that previously addressed this task rely on syntactic preprocessing and incorporation of external knowledge by manually crafted features. We address the Winograd Schema Challenge from a new perspective as a sequence ranking task, and design a Siamese neural sequence ranking model which performs significantly better than a random baseline, even when solely trained on sequences of words. We evaluate against a baseline and a state-of-the-art system on two data sets and show that anonymization of noun phrase candidates strongly helps our model to generalize.

Inter-AS traffic engineering with SDN <=SEP=> Egress selection in an Internet Service Provider (ISP) is the process of selecting an egress router to route interdomain traffic across the ISP such that a traffic engineering objective is achieved. In traditional ISP networks, traffic through the ISP is carried through the network and exits via an egress that is closest to the source in an attempt to minimize network resources for transit traffic. This exit strategy is known as Hot Potato Routing (HPR). The emerging field of Software-Defined Networking (SDN) has opened up many possibilities and promised to bring new flexibility to the rigid traditional paradigm of networking. In an ISP network, however, completely replacing legacy network devices with SDN nodes is neither simple nor straightforward. This has led to the idea of incremental and selective deployment of SDN nodes in an ISP network. Such a hybrid network gives us control over traffic flows that pass through the SDN nodes without requiring extensive changes to an existing ISP network. In this paper, we look at the problem of choosing an optimal set of egress routers to route inter-domain transit traffic in a hybrid SDN network such that the maximum link utilization of the egress links is minimized. We formulate the optimization problem, show that it is related to the makespan scheduling problem of unrelated parallel machines, and propose heuristics to solve it. We perform simulations to evaluate our heuristic on a real ISP topology and show that even with a small number of SDN nodes in the network, the maximum link utilization on the egress links can be lower than that of traditional HPR.

Graption: A graph-based P2P traffic classification framework for the internet backbone <=SEP=> 1 The authors thank CAIDA for providing this set of traffic traces. Additional information for these traces can be found in the DatCat, Internet Measurement Data Catalog [8], indexed under the label ‘‘PAIX’’. 1910 M. Iliofotou et al. / Computer Networks 55 (2011) 1909–1920 behavior. Flow-level and payload-based classification methods require per application training and will thus not detect P2P traffic from emerging protocols. Behavioral-host-based approaches such as BLINC [25] can detect traffic from new protocols [25], but have weak performance when applied at the backbone [26]. In addition, most tools including BLINC [25] require fine-tuning and careful selection of parameters [26]. We discuss the limitations of previous methods in more detail in Section 4. In this paper, we use the network-wide behavior of an application to assist in classifying its traffic. To model this behavior, we use graphs where each node is an IP address, and each edge represents a type of interaction between two nodes. We use the term Traffic Dispersion Graph or TDG to refer to such a graph [19]. Intuitively, with TDGs we enable the detection of network-wide behavior (e.g., highly connected graphs) that is common among P2P applications and different from other traffic (e.g., Web). While we recognize that some previous efforts [6,9] have used graphs to detect worm activity, they have not explored the full capabilities of TDGs for application classification. This paper is an extension of a workshop paper [18] and the differences will be clarified in the related work section (Section 4). We propose a classification framework, dubbed Graption (Graph-based classification), as a systematic way to combine network-wide behavior and flow-level characteristics of network applications. Graption first groups flows using flow-level features, in an unsupervised and agnostic way, i.e., without using application-specific knowledge. It then uses TDGs to classify each group of flows. As a proof of concept, we instantiate our framework and develop a P2P detection method, which we call Graption-P2P. Compared to other methods (e.g., BLINC [25]), Graption-P2P is easier to configure and requires fewer parameters. The highlights of our work can be summarized in the following points: Distinguishing between P2P and client–server TDGs. We use real-world backbone traces and derive graph theoretic metrics that can distinguish between the TDGs formed by client–server (e.g., Web) and P2P (e.g., eDonkey) applications (Section 2.2). Practical considerations for TDGs. We show that even a single backbone link contains enough information to generate TDGs that can be used to classify traffic. In addition, TDGs of the same application seem fairly consistent across time (Section 2.3). High P2P classification accuracy. Our framework instantiation (Graption-P2P) classifies 90% of P2P traffic with 95% accuracy when applied at the backbone. Such traces are particularly challenging for other methods (Section 3.2.2). Comparison with a behavioral-host-based method. Graption-P2P performs better than BLINC [25] in P2P identification at the backbone. For example, Graption-P2P identifies 95% of BitTorrent traffic while BLINC identifies only 25% (Section 3.3). Identifying the unknown. Using Graption, we identified a P2P overlay of the Slapper worm. The TDG of Slapper was never used to train our classifier. This is a promising result showing that our approach can be used to detect both known and unknown P2P applications (Section 3.4). The rest of the paper is organized as follows. In Section 2 we define TDGs, and identify TDG-based metrics that differentiate between applications. In Section 3 we present the Graption framework and our instantiation, GraptionP2P. In Section 5 we discuss various practical issues. In Section 4 we discuss related work. Finally, in Section 6 we conclude the paper. 2. Studying the TDGs of P2P applications 2.1. Traffic dispersion graphs (TDGs) Definition. Throughout this paper, we assume that packets can be grouped into flows using the standard 5-tuple {srcIP, srcPort, dstIP, dstPort, protocol}. Given a group of flows S, collected over a fixed-length time interval, we define the corresponding TDG to be a directed graph G(V,E), where the set of nodes V corresponds to the set of IP addresses in S, and there is a link (u,v) 2 E from u to v if there is a flow f 2 S between them. In this paper, we consider bidirectional flows. We define a TCP flow to start on the first packet with the SYN-flag set and the ACK-flag not set, so that the initiator and the recipient of the flow are defined for the purposes of direction. For UDP flows, direction is decided upon the first packet of the flow. Visualization examples. In Fig. 1, we show TDG examples from two different applications. In order to motivate the discussion in the rest of the paper, we show the contrast between a P2P and a client–server TDG. From the figure we see that P2P traffic forms more connected and more dense graphs compared to client–server TDGs. In Section 2.2, we show how we can translate the visual intuition of Fig. 1 into quantitative measures that can be used to classify TDGs that correspond to different applications. Data set. To study TDGs, we use three backbone traces from a Tier-1 ISP and the Abilene (Internet2) network. These traces are summarized in Table 1. All data are IP anonymized and contain traffic from both directions of the link. The TR-PAY1 and TR-PAY2 traces were collected from an OC48 link of a commercial US Tier-1 ISP at the Palo Alto Internet eXchange (PAIX). To the best of our knowledge, these are the most recent backbone traces with payload that are available to researchers by CAIDA [5]. The TR-ABIL trace is a publicly available data set collected from the Abilene (Internet2) academic network connecting Indianapolis with Kansas City. The Abilene trace consists of five randomly selected five-minute samples taken every day for one month, and covers both day and night hours as well as weekdays and weekends. Extractingground truth. We used a Payload-based Classifier (PC) to establish the ground truth of flows for the 1 2 2 1 0 3 1 9 4 3 3 4 5 4 8 1 4 8 2 9 3 4 1 4 6 1 1 0 7 1 1 5 5 5 6 6 3 4 7 4 5 1 0 5 8 1 1 6 9 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 8 6 3 1 0 3 2 4 4 1 8 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 7 7 1 1 0 9 6 2 5 2 6 4 1 2 4 6 3 2 7 2 8 2 9 3 0 3 1 3 2 3 3 3 4 3 5 3 6 1 0 1 3 7 3 8 7 3 1 6 6 2 0 6 2 3 2 3 4 2 3 4 5 4 0 9 4 8 6 5 3 0 6 3 0 6 3 5 7 3 4 7 4 8 7 4 9 7 9 4 7 9 6 8 5 2 8 5 5 9 1 6 1 0 7 0 1 1 3 4 1 2 4 2 3 9 4 0 8 1 8 5 1 1 6 1 2 3 1 3 0 1 3 2 1 3 5 1 3 9 1 4 8 1 6 1 1 6 4 1 6 5 1 8 6 1 9 1 2 2 5 2 2 7 2 3 5 2 4 3 2 5 7 2 6 8 2 9 2 3 0 3 3 1 3 3 1 6 3 4 9 3 5 0 3 6 2 3 7 6 3 8 3 4 0 3 4 1 3 4 1 4 4 4 5 4 5 5 4 6 1 4 7 7 4 9 3 5 1 7 5 2 4 5 3 7 5 7 0 5 9 1 6 1 4 6 1 9 6 4 0 6 5 7 6 6 6 6 6 8 6 7 4 7 3 1 7 3 6 7 3 7 8 0 0 8 0 7 8 0 8 8 3 1 8 3 9 8 4 1 8 4 4 8 5 6 8 7 0 8 7 6 8 8 8 8 9 2 8 9 8 9 0 0 9 0 8 9 1 4 9 1 5 9 2 5 9 7 4 1 0 0 0 1 0 0 5 1 0 0 9 1 0 1 8 1 0 2 2 1 0 2 7 1 0 3 5 1 0 5 6 1 0 9 2 1 0 9 9 1 1 0 3 1 1 0 5 1 1 0 6 1 1 2 8 1 1 3 3 1 1 6 5 1 1 6 6 1 1 7 0 1 1 8 3 1 1 8 6 1 2 0 9 1 2 5 6 1 2 6 7 1 2 7 2 1 2 8 8 1 2 8 9 1 2 9 1 1 3 3 4 1 3 5 2 1 3 7 2 4 1 4 2 3 0 9 3 2 0 3 5 6 6 0 2 6 3 2 7 1 4 8 7 2 1 0 6 5 4 3 4 4 4 5 4 6 1 1 5 4 7 4 8 2 5 6 7 7 2 4 9 5 0 1 3 7 1 6 0 1 8 1 5 4 5 7 1 5 7 4 1 1 0 0 4 5 1 5 2 5 3 5 4 1 4 2 1 0 0 7 5 5 5 6 5 7 2 9 1 7 6 0 8 8 3 5 8 5 9 6 0 6 1 2 7 7 4 3 2 6 2 6 3 2 9 9 5 7 2 7 3 5 1 3 7 0 6 4 6 5 9 0 2 6 1 2 8 2 3 4 1 5 8 2 6 3 3 1 3 6 4 6 6 6 7 9 2 9 1 0 8 0 1 0 8 2 6 8 7 5 5 1 1 3 5 1 2 2 8 6 9 7 0 7 1 2 7 0 7 2 7 4 7 5 7 6 7 7 7 8 7 9 8 0 8 2 8 3 7 2 3 8 4 8 7 3 6 1 8 8 8 9 1 7 5 9 1 9 2

Design and development of a low cost EMG signal acquisition system using surface EMG electrode <=SEP=> Electromyogram or EMG signal is a very small signal; it requires a system to enhance for display purpose or for further analysis process. This paper presents the development of low cost physiotherapy EMG signal acquisition system with two channel input. In the acquisition system, both input signals are amplified with a differential amplifier and undergo signal pre-processing to obtain the linear envelope of EMG signal. Obtained EMG signal is then digitalized and sent to the computer to be plotted.

Security and Privacy in the Internet of Vehicles <=SEP=> Internet of Vehicles (IoV) is a typical application of Internet of things in the field of transportation, which aims at achieving an integrated intelligent transportation system to enhance traffics, to avoid accidents, to ensure road safety, and to improve driving experiences. Due to its characteristics of dynamic topological structures, huge network scale, non-uniform distribution of nodes, and mobile limitation, IoV systems face various types of attacks, such as authentication and identification attacks, availability attacks, confidentiality attacks, routing attacks, data authenticity attacks and etc., which result in several challenging requirements in security and privacy. Many security scientists made numerous efforts to ensure the security and privacy for the Internet of Vehicles in recent years. This paper aims to review the advances on issues of security and privacy in IoV, including security and privacy requirements, attack types, and the relevant solutions, and discuss challenges and future trends in this area.

Performance Evaluation of Concurrent Lock-free Data Structures on GPUs <=SEP=> Graphics processing units (GPUs) have emerged as a strong candidate for high-performance computing. While regular data-parallel computations with little or no synchronization are easy to map on the GPU architectures, it is a challenge to scale up computations on dynamically changing pointer-linked data structures. The traditional lock-based implementations are known to offer poor scalability due to high lock contention in the presence of thousands of active threads, which is common in GPU architectures. In this paper, we present a performance evaluation of concurrent lock-free implementations of four popular data structures on GPUs. We implement a set using lock-free linked list, hash table, skip list, and priority queue. On the first three data structures, we evaluate the performance of different mixes of addition, deletion, and search operations. The priority queue is designed to support retrieval and deletion of the minimum element and addition operations to the set. We evaluate the performance of these lock-free data structures on a Tesla C2070 Fermi GPU and compare it with the performance of multi-threaded lock-free implementations for CPU running on a 24-core Intel Xeon server. The linked list, hash table, skip list, and priority queue implementations achieve speedup of up to 7.4, 11.3, 30.7, and 30.8, respectively on the GPU compared to the Xeon server.

The meaning of compassion fatigue to student nurses: an interpretive phenomenological study <=SEP=> Background: Compassion fatigue is a form of occupational stress which occurs when individuals are exposed to suffering and trauma on an ongoing basis. The purpose of this study was to explore the experiences of compassion fatigue among student nurses following their first clinical placement in a UK health care setting during 2015. Methods: The aim of this study was to explore students’ thoughts and feelings about compassion fatigue using reflective poems as a source of data. An interpretive phenomenological approach was taken using a purposeful sampling strategy which aimed to explore in depth meaning of the concept as experienced by the students. Results: From this study it is clear that students experience compassion fatigue and this has a psychological effect on their wellbeing and ability to learn in the clinical practice setting. Reflective poetry writing enabled articulation of feelings which were at times negative and linked to the student’s status as a novice nurse. Conclusions: Students experience compassion fatigue and educators need to find ways to provide support in both clinical and university settings. Positive practices such as shared reflection and the use of creative teaching methods might be beneficial, to support exploration of feelings, build resilience and effective ways of coping.

A New Framework for Distributed Submodular Maximization <=SEP=> A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems. A lot of recent effort has been devoted to developing distributed algorithms for these problems. However, these results suffer from high number of rounds, suboptimal approximation ratios, or both. We develop a framework for bringing existing algorithms in the sequential setting to the distributed setting, achieving near optimal approximation ratios for many settings in only a constant number of MapReduce rounds. Our techniques also give a fast sequential algorithm for non-monotone maximization subject to a matroid constraint.

Image Retrieval using Harris Corners and Histogram of Oriented Gradients <=SEP=> Content based image retrieval is the technique to retrieve similar images from a database that are visually similar to a given query image. It is an active and emerging research field in computer vision. In our proposed system, the Interest points based Histogram of Oriented Gradients (HOG) feature descriptor is used to retrieve the relevant images from the database. The dimensionality of the HOG feature vector is reduced by Principle Component analysis (PCA). To improve the retrieval accuracy of the system the Colour Moments along with HOG feature descriptor are used in this system. The Interest points are detected using the Harris-corner detector in order to extract the image features. The KD-tree is used for matching and indexing the features of the query image with the database images.

HanGuard: SDN-driven protection of smart home WiFi devices from malicious mobile apps <=SEP=> A new development of smart-home systems is to use mobile apps to control IoT devices across a Home Area Network (HAN). As verified in our study, those systems tend to rely on the Wi-Fi router to authenticate other devices. This treatment exposes them to the attack from malicious apps, particularly those running on authorized phones, which the router does not have information to control. Mitigating this threat cannot solely rely on IoT manufacturers, which may need to change the hardware on the devices to support encryption, increasing the cost of the device, or software developers who we need to trust to implement security correctly. In this work, we present a new technique to control the communication between the IoT devices and their apps in a unified, backward-compatible way. Our approach, called HanGuard, does not require any changes to the IoT devices themselves, the IoT apps or the OS of the participating phones. HanGuard uses an SDN-like approach to offer fine-grained protection: each phone runs a non-system userspace Monitor app to identify the party that attempts to access the protected IoT device and inform the router through a control plane of its access decision; the router enforces the decision on the data plane after verifying whether the phone should be allowed to talk to the device. We implemented our design over both Android and iOS (> 95% of mobile OS market share) and a popular router. Our study shows that HanGuard is both efficient and effective in practice.

Digital Beamforming on Receive: Techniques and Optimization Strategies for High-Resolution Wide-Swath SAR Imaging <=SEP=> Synthetic Aperture Radar (SAR) is a well-proven imaging technique for remote sensing of the Earth. However, conventional SAR systems are not capable of fulfilling the increasing demands for improved spatial resolution and wider swath coverage. To overcome these inherent limitations, several innovative techniques have been suggested which employ multiple receive-apertures to gather additional information along the synthetic aperture. These digital beamforming (DBF) on receive techniques are reviewed with particular emphasis on the multi-aperture signal processing in azimuth and a multi-aperture reconstruction algorithm is presented that allows for the unambiguous recovery of the Doppler spectrum. The impact of Doppler aliasing is investigated and an analytic expression for the residual azimuth ambiguities is derived. Further, the influence of the processing on the signal-to-noise ratio (SNR) is analyzed, resulting in a pulse repetition frequency (PRF) dependent factor describing the SNR scaling of the multi-aperture beamforming network. The focus is then turned to a complete high-resolution wide-swath SAR system design example which demonstrates the intricate connection between multi-aperture azimuth processing and the system architecture. In this regard, alternative processing approaches are compared with the multi-aperture reconstruction algorithm. In a next step, optimization strategies are discussed as pattern tapering, prebeamshaping-on-receive, and modified processing algorithms. In this context, the analytic expressions for both the residual ambiguities and the SNR scaling factor are generalized to cascaded beamforming networks. The suggested techniques can moreover be extended in many ways. Examples discussed are a combination with ScanSAR burst mode operation and the transfer to multistatic sparse array configurations.

Effects of domain on measures of semantic relatedness <=SEP=> Measures of semantic relatedness have been used in a variety of applications in information retrieval and language technology, such as measuring document similarity and cohesion of text. Definitions of such measures have ranged from using distance-based calculations over WordNet or other taxonomies to statistical distributional metrics over document collections such as Wikipedia or the Web. Existing measures do not explicitly consider the domain associations of terms when calculating relatedness: This article demonstrates that domain matters. We construct a data set of pairs of terms with associated domain information and extract pairs that are scored nearly identical by a sample of existing semantic-relatedness measures. We show that human judgments reliably score those pairs containing terms from the same domain as significantly more related than cross-domain pairs, even though the semantic-relatedness measures assign the pairs similar scores. We provide further evidence for this result using a machine learning setting by demonstrating that domain is an informative feature when learning a metric. We conclude that existing relatedness measures do not account for domain in the same way or to the same extent as do human judges.

A Lossy Image Codec Based on Index Coding <=SEP=> In this paper we propose a new lossy image codec based on index coding. Both J. Shapiro’s embedded zerotree wavelet algorithm, and A. Said and W. A. Pearlman’s codetree algorithm (which is the state-of-the-art method today) use spatial orientation tree structures to implicitly locate the significant wavelet transform coefficients. Here a direct approach to find the positions of these significant coefficients is presented. The new algorithm combines the discrete wavelet transform, differential coding, variable-length coding of integers, ordered bit plane transmission, and adaptive arithmetic coding. The encoding can be stopped at any point, which allows a target rate or distortion metric to be met exactly. The bits in the bit stream are generated in the order of importance, yielding a fully embedded code to successively approximate the original image source; thus it’s well suited for progressive image transmission. The decoder can also terminate the decoding at any point, and produce a lower bit rate reconstruction image. Our algorithm is very simple in its form (which will make the encoding and decoding very fast), requires no training of any kind or prior knowledge of image sources, and has a clear geometric structure. The image coding results of it are quite competitive with almost all previous reported image compression algorithms on standard test images. 456 1068.0314/96$5.00

Dynamic Facial Expression Recognition With Atlas Construction and Sparse Representation <=SEP=> In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.

Contextual feature based one-class classifier approach for detecting video response spam on YouTube <=SEP=> YouTube is one of the largest video sharing websites (with social networking features) on the Internet. The immense popularity of YouTube, anonymity and low publication barrier has resulted in several forms of misuse and video pollution such as uploading of malicious, copyright violated and spam video or content. YouTube has a popular and commonly used feature called as video response which allows users to post a video response to an uploaded or existing video. Some of the popular videos on YouTube receive thousands of video responses. We observe presence of opportunistic users posting unrelated, promotional, pornographic videos (spam videos posted manually or using automated scripts) as video responses to existing videos. We present a method of mining YouTube to automatically detect video response spam. We formulate the problem of video response spam detection as a one-class classification problem (a recognition task) and divide the problem into three sub-problems: promotional video recognition, pornographic or dirty video recognition and automated script or botnet uploader recognition. We create a sample dataset of target class videos for each of the three sub-problems and identify contextual features (meta-data based or non-content based features) characterizing the target class. Our empirical analysis reveals that certain linguistic features (presence of certain terms in the title or description of the YouTube video), temporal features, popularity based features, time based features can be used to predict the video type. We identify features with discriminatory powers and use it within a one-class classification framework to recognize video response spam. We conduct a series of experiments to validate the proposed approach and present evidences to demonstrate the effectiveness of the proposed solution with more than 80% accuracy.

A Generic Toolkit for the Successful Management of Delphi Studies <=SEP=> This paper presents the case of a non-traditional use of the Delphi method for theory evaluation. On the basis of experience gained through secondary and primary research, a generic decision toolkit for Delphi studies is proposed, comprising of taxonomy of Delphi design choices, a stage model and critical methodological decisions. These research tools will help to increase confidence when adopting the Delphi alternative and allow for a wider and more comprehensive recognition of the method within both scientific and interpretivist studies.

A Developmental Model of Critical Thinking <=SEP=> The critical thinking movement, it is suggested, has much to gain from conceptualizing its subject matter in a developmental framework. Most instructional programs designed to teach critical thinking do not draw on contemporary empirical research in cognitive development as a potential resource. The developmental model of critical thinking outlined here derives from contemporary empirical research on directions and processes of intellectual development in children and adolescents. It identifies three forms of second-order cognition (meta-knowing)--metacognitive, metastrategic, and epistemological--that constitute an essential part of what develops cognitively to make critical thinking possible.

Extension of Soft-Switching Region of Dual-Active-Bridge Converter by a Tunable Resonant Tank <=SEP=> Hard-switching-induced switching loss can contribute significantly to the power loss of an isolated bidirectional dual-active-bridge (DAB) dc–dc converter operating at high frequency. An <inline-formula> <tex-math notation="LaTeX">$LC$</tex-math></inline-formula>-type series resonant DAB converter based on a switch-controlled-inductor (SCI) is proposed to mitigate the loss arising from hard switching under wide-range variations in output voltage and current. Zero-voltage switching is achieved at the primary side (high voltage), while at the secondary side (low voltage), zero-current switching is preferred to reduce excessive ringing due to circulating current and switching loss. In order to achieve reduced conduction loss, a nominal operating point is chosen where the root-mean-square resonant tank current is the minimum. To validate the proposed topology and modulation scheme, an <inline-formula><tex-math notation="LaTeX">$LC$</tex-math></inline-formula>-type series resonant DAB converter based on SCI operating at 100 kHz is designed to interface a 400-V dc bus to a supercapacitor-based energy storage. Simulation and experimental results validate the effectiveness of the proposed topology for charging/discharging a supercapacitor with an output voltage variation of between 10 and 48 V and a maximum rated power of 480 W. A maximum efficiency of 94.6% is achieved using the proposed topology and modulation scheme.

WordNet Atlas : a web application for visualizing WordNet as a zoomable map <=SEP=> The English WordNet is a lexical database containing more than 206,900 wordconcept pairs and more than 377,500 semantic and lexical links. Trying to visualize them all at once as a node-link diagram results in a representation which can be too big and complex for the user to grasp, and which cannot be easily processed by current web technologies. We propose a visualization technique based on the concept of semantic zooming, usually found in popular web map applications. This technique makes it feasible to handle large and complex graphs, and display them in an interactive representation. By zooming, it is possible to switch from an overview visualization, quickly and clearly presenting the global characteristics of the structure, to a detailed one, displaying a classic node-link diagram. WordNet Atlas is a web application that leverages this method to aid the user in the exploration of the WordNet data set, from its global taxonomy structure to the detail of words and synonym sets.