title: Shuffle Index: Efficient and Private Access to Outsourced Data

text:
Data outsourcing and cloud computing have been emerging at an ever-growing rate as successful approaches for allowing users and companies to rely on external services for storing and managing data. As data and access to them are not under the control of the data owner, there is a clear need to provide proper confidentiality protection. Such requirements concern the confidentiality not only of the stored data (content) but also of the specific accesses (or patterns of them) that users make on such data.
 In this article, we address these issues and propose an approach for guaranteeing content, access, and pattern confidentiality in a data outsourcing scenario. The proposed solution is based on the definition of a shuffle index structure, which adapts traditional B +-trees and, by applying a combination of techniques (covers, caches, and shuffling), ensures confidentiality of the data and of queries over them, protecting each single access as well as sequences thereof. The proposed solution also supports update operations over the data, while making reads and writes not recognizable as such by the server. We show that the shuffle index exhibits a limited performance cost, thus resulting effectively usable in practice.


title: A deep learning framework for unsupervised affine and deformable image registration

text: Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.


title: Implementing Efficient, Portable Computations for Machine Learning

text: Implementing E cient, Portable Computations for Machine Learning


title: Identity Mappings in Deep Residual Networks

text: Deep residual networks [1] have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly propagated from one block to any other block, when using identity mappings as the skip connections and after-addition activation. A series of ablation experiments support the importance of these identity mappings. This motivates us to propose a new residual unit, which makes training easier and improves generalization. We report improved results using a 1001-layer ResNet on CIFAR-10 (4.62% error) and CIFAR-100, and a 200-layer ResNet on ImageNet. Code is available at: https://github.com/KaimingHe/ resnet-1k-layers.


title: Face Description with Local Binary Patterns: Application to Face Recognition

text: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed


title: Low-resolution Convolutional Neural Networks for video face recognition

text: Security and safety applications such as surveillance or forensics demand face recognition in low-resolution video data. We propose a face recognition method based on a Convolutional Neural Network (CNN) with a manifold-based track comparison strategy for low-resolution video face recognition. The low-resolution domain is addressed by adjusting the network architecture to prevent bottlenecks or significant upscaling of face images. The CNN is trained with a combination of a large-scale self-collected video face dataset and large-scale public image face datasets resulting in about 1.4M training images. To handle large amounts of video data and for effective comparison, the CNN face descriptors are compared efficiently on track level by local patch means. Our setup achieves 80.3 percent accuracy on a 32×32 pixels low-resolution version of the YouTube Faces Database and outperforms local image descriptors as well as the state-of-the-art VGG-Face network [20] in this domain. The superior performance of the proposed method is confirmed on a self-collected in-the-wild surveillance dataset.


title: Learning From Semi-Supervised Weak-Label Data

text: Multi-label learning deals with data objects associated with multiple labels simultaneously. Previous studies typically assume that for each instance, the full set of relevant labels associated with each training instance is given. In many applications such as image annotation, however, it’s usually difficult to get the full label set for each instance and only a partial or even empty set of relevant labels is available. We call this kind of problem as ‘semi-supervised weak-label learning’ problem. In this work we propose the SSWL (Semi-Supervised Weak-Label) method to address this problem. Both instance similarity and label similarity are considered for the complement of missing labels. Ensemble of multiple models are utilized to improve the robustness when label information is insufficient. We formulate the objective as a bi-convex optimization problem with an efficient block coordinate descent algorithm. Experiments validate the effectiveness of SSWL.


title: A Future-Adaptable Password Scheme

text: Many authentication schemes depend on secret passwords. Unfortunately, the length and randomness of user-chosen passwords remain xed over time. In contrast, hardware improvements constantly give attackers increasing computational power. As a result, password schemes such as the traditional UNIX user-authentication system are failing with time. This paper discusses ways of building systems in which password security keeps up with hardware speeds. We formalize the properties desirable in a good password system, and show that the computational cost of any secure password scheme must increase as hardware improves. We present two algorithms with adaptable cost|eksblow sh, a block cipher with a purposefully expensive key schedule, and bcrypt, a related hash function. Failing a major breakthrough in complexity theory, these algorithms should allow password-based systems to adapt to hardware improvements and remain secure well into the future.


title: The importance of rehabilitation concerning upper extremity amputees: A Systematic review

text:
OBJECTIVE
To evaluate and point out the importance of prosthetic rehabilitation of upper extremity.

METHODS
A systematic literature search was performed to identify studies concerning prosthetic rehabilitation in upper extremity. The PRISMA Statement 2009 was used to establish the study and the methodological quality was assessed.

RESULTS
The literature search identified 620 studies. Of these 620, 9 studies fulfilled the inclusion criteria and were included for data extraction. The studies pointed out the upper limb prosthetic rehabilitation protocols consist of general exercise programme, motor tasks, phantom exercises, Muscle Training System, edema control, functional activities, signal strengthening, prosthetic education exercises, neuromuscular reeducation, virtual image and virtual reality exercises.

CONCLUSIONS
The current systematic literature review has shown that the prosthetic rehabilitation seems promising especially for upper extremity amputees.


title: Comparing ability and self-report trait emotional intelligence , fluid intelligence , and personality traits in career decision

text: This study investigated the role of ability and trait emotional intelligence (EI), fluid intelligence, and personality traits in career decision-making self-efficacy, career indecision and indecisiveness. The Advanced Progressive Matrices, Big Five Questionnaire, Mayer-Salovey-Caruso Emotional Intelligence Test, Bar-On Emotional Intelligence Inventory, Trait Emotional Intelligence Questionnaire, Career Decision SelfEfficacy Scale: Short Form, Career Decision-Making Difficulties Questionnaire, and Indecisiveness Scale were administered to 194 Italian high school students. These results highlighted the potentially important role of self-reported EI in career decisions. 2014 Elsevier Ltd. All rights reserved.


title: Sleep deprivation-induced reduction in cortical functional response to serial subtraction.

text: Thirteen normal volunteers were studied with fMRI during arithmetic performance after a normal night of sleep and following sleep deprivation (SD). Aims included determining whether the prefrontal cortex (PFC) and the parietal lobe arithmetic areas are vulnerable to the effects of SD. After a normal night of sleep, activation localized to the bilateral PFC, parietal lobes and premotor areas. Following SD, activity in these regions decreased markedly, especially in the PFC. Performance also dropped. Data from the serial subtraction task are consistent with Horne's PFC vulnerability hypothesis but, based on this and other studies, we suggest the localized, functional effects of SD in the brain may vary, in part, with the specific cognitive task.


title: Efficient Discovery of Statistically Significant Association Rules

text: Searching statistically significant association rules is an important but neglected problem. Traditional association rules do not capture the idea of statistical dependence and the resulting rules can be spurious, while the most significant rules may be missing. This leads to erroneous models and predictions which often become expensive.The problem is computationally very difficult, because the significance is not a monotonic property. However, in this paper we prove several other properties, which can be used for pruning the search space. The properties are implemented in the StatApriori algorithm, which searches statistically significant, non-redundant association rules. Based on both theoretical and empirical observations, the resulting rules are very accurate compared to traditional association rules. In addition, StatApriori can work with extremely low frequencies, thus finding new interesting rules.


title: Four Text Classification Algorithms Compared on a Dutch Corpus

text: We describe an experiment in applying text classification algorithms to Dutch texts. Four well-known learning algorithms, Rocchio’s algorithm, the Simple Bayesian Classifier (SBC), the Sleeping Experts (SE) and Winnow were implemented. They were tested on a corpus of articles from the Dutch newspaper NRC, pre-classified into four categories. The algorithms are compared on learning speed and error rate. We also investigated the effect of discarding terms, using either a dynamic stoplist or the Winnow heuristic.


title: Adaptive Linked Data-Driven Web Components: Building Flexible and Reusable Semantic Web Interfaces - Building Flexible and Reusable Semantic Web Interfaces

text: Due to the increasing amount of Linked Data openly published on the Web, user-facing Linked Data Applications (LDAs) are gaining momentum. One of the major entrance barriers for Web developers to contribute to this wave of LDAs is the required knowledge of Semantic Web (SW) technologies such as the RDF data model and SPARQL query language. This paper presents an adaptive componentbased approach together with its open source implementation for creating flexible and reusable SW interfaces driven by Linked Data. Linked Data-driven (LD-R) Web components abstract the complexity of the underlying SW technologies in order to allow reuse of existing Web components in LDAs, enabling Web developers who are not experts in SW to develop interfaces that view, edit and browse Linked Data. In addition to the modularity provided by the LD-R components, the proposed RDF-based configuration method allows application assemblers to reshape their user interface for different use cases, by either reusing existing shared configurations or by creating their proprietary configurations.


title: Elimination of Residual Flux in Transformers by the Application of an Alternating Polarity DC Voltage Source

text: The purpose of core demagnetization is twofold: 1) to reduce the inrush currents when transformers are energized; and 2) to make sure that the frequency-response analysis (FRA) tests are consistent to avoid false diagnoses of damage during transportation. The significance of demagnetizing is presented on field measurements of an 80 MVA unit with FRA measurements. A new demagnetizer device with an alternating polarity dc voltage source is prototyped. Experimental verification of this prototype is presented for the demagnetization of transformers. A nearly complete demagnetization was observed in the laboratory for a small single-phase isolation transformer. The method proposed in this paper is applied to three-phase transformers with different core configurations and connections. Topologically correct modeling and numerical simulations confirm the full demagnetization of all branches of three-phase (three- and five-limb) transformer cores. Inrush current measurements and FRA plots before and after demagnetization confirm the effectiveness of the process.


title: SensoTube: A Scalable Hardware Design Architecture for Wireless Sensors and Actuators Networks Nodes in the Agricultural Domain

text: Wireless Sensor and Actuators Networks (WSANs) constitute one of the most challenging technologies with tremendous socio-economic impact for the next decade. Functionally and energy optimized hardware systems and development tools maybe is the most critical facet of this technology for the achievement of such prospects. Especially, in the area of agriculture, where the hostile operating environment comes to add to the general technological and technical issues, reliable and robust WSAN systems are mandatory. This paper focuses on the hardware design architectures of the WSANs for real-world agricultural applications. It presents the available alternatives in hardware design and identifies their difficulties and problems for real-life implementations. The paper introduces SensoTube, a new WSAN hardware architecture, which is proposed as a solution to the various existing design constraints of WSANs. The establishment of the proposed architecture is based, firstly on an abstraction approach in the functional requirements context, and secondly, on the standardization of the subsystems connectivity, in order to allow for an open, expandable, flexible, reconfigurable, energy optimized, reliable and robust hardware system. The SensoTube implementation reference model together with its encapsulation design and installation are analyzed and presented in details. Furthermore, as a proof of concept, certain use cases have been studied in order to demonstrate the benefits of migrating existing designs based on the available open-source hardware platforms to SensoTube architecture.


title: DivQ: diversification for keyword search over structured databases

text: Keyword queries over structured databases are notoriously ambiguous. No single interpretation of a keyword query can satisfy all users, and multiple interpretations may yield overlapping results. This paper proposes a scheme to balance the relevance and novelty of keyword search results over structured databases. Firstly, we present a probabilistic model which effectively ranks the possible interpretations of a keyword query over structured data. Then, we introduce a scheme to diversify the search results by re-ranking query interpretations, taking into account redundancy of query results. Finally, we propose α-nDCG-W and WS-recall, an adaptation of α-nDCG and S-recall metrics, taking into account graded relevance of subtopics. Our evaluation on two real-world datasets demonstrates that search results obtained using the proposed diversification algorithms better characterize possible answers available in the database than the results of the initial relevance ranking.


title: EMARS: Efficient Management and Allocation of Resources in Serverless

text: We introduce EMARS, an efficient resource management system for serverless cloud computing frameworks with the goal to enhance resource (focus on memory) allocation among containers. We have built our prototype on top of an open-source serverless platform, OpenLambda. It is based upon application workloads and serverless functions' memory needs. As a background motivation we analyzed the latencies and memory requirements of functions running on AWS lambda. The memory limits also lead to variations in number of containers spawned on OpenLambda. We use memory limit settings to propose a model of predictive efficient memory management.


title: Neural Embeddings of Graphs in Hyperbolic Space

text: ABSTRACT Neural embeddings have been used with great success in Natural Language Processing (NLP). They provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks. The success of neural embeddings has prompted signi cant amounts of research into applications in domains other than language. One such domain is graph-structured data, where embeddings of vertices can be learned that encapsulate vertex similarity and improve performance on tasks including edge prediction and vertex labelling. For both NLP and graph based tasks, embeddings have been learned in highdimensional Euclidean spaces. However, recent work has shown that the appropriate isometric space for embedding complex networks is not the at Euclidean space, but negatively curved, hyperbolic space. We present a new concept that exploits these recent insights and propose learning neural embeddings of graphs in hyperbolic space. We provide experimental evidence that embedding graphs in their natural geometry signi cantly improves performance on downstream tasks for several real-world public datasets.


title: Simulink based performance analysis of lead acid batteries with the variation of load current and temperature

text: Storage device in renewable energy applications plays an important role for efficient operation. For solar PV applications, storage device is an integral part during post sun shine hours to supply power to the load. For Solar Home System (SHS), approximately 60% of the total cost is due to storage battery and the battery needs replacement in every five years or so. If the performance of the battery can be enhanced, not only the cost of energy will come down appreciably but the reliability of the system will improve. Battery is needed in PV system to store the excess energy from the sun and release the power to the system again when it is needed. In other words, to enhance a system performance we have to increase the battery performance. In this paper, the parameters which play important role for optimum performance has been studied. The major two parameters that affect the battery performance are load current and temperature. It has been found that, when the discharge current is high, battery capacity reduces and the vice-versa. Again in case of temperature, when temperature increases more than the rated standard value of 25°C, capacity increases but the life time of the battery decreases. On the other hand, when the ambient temperature goes down, capacity of battery decreases but lifetime of the battery is increased. To analyse the effect of temperature and load current on battery performance, the standard equivalent circuit of battery is considered first and then the equivalent circuit is simulated using Simulink under MATLAB environment.


title: Monte Carlo Tree Search for Asymmetric Trees

text: We present an extension of Monte Carlo Tree Search (MCTS) that strongly increases its efficiency for trees with asymmetry and/or loops. Asymmetric termination of search trees introduces a type of uncertainty for which the standard upper confidence bound (UCB) formula does not account. Our first algorithm (MCTS-T), which assumes a non-stochastic environment, backs-up tree structure uncertainty and leverages it for exploration in a modified UCB formula. Results show vastly improved efficiency in a well-known asymmetric domain in which MCTS performs arbitrarily bad. Next, we connect the ideas about asymmetric termination to the presence of loops in the tree, where the same state appears multiple times in a single trace. An extension to our algorithm (MCTS-T+), which in addition to nonstochasticity assumes full state observability, further increases search efficiency for domains with loops as well. Benchmark testing on a set of OpenAI Gym and Atari 2600 games indicates that our algorithms always perform better than or at least equivalent to standard MCTS, and could be first-choice tree search algorithms for non-stochastic, fully-observable environments.


title: Nationalism and patriotism: national identification and out-group rejection.

text: It is argued that the differentiation between nationalism and patriotism proposed in the literature can be seen as analogous to judgments based on different types of comparisons: intergroup comparisons with other nations are associated with intergroup behaviour that corresponds to nationalism, whereas temporal or standard comparisons are linked with behaviour that corresponds to patriotism. Four studies (N = 103, 107, 96 and 105) conducted in Germany and Britain examined the hypothesis that national identification and in-group evaluation only show a reliable relationship with out-group rejection under an intergroup comparison orientation. Participants were primed with either an intergroup comparison, a temporal comparison or no explicit comparison orientation. A subsequent questionnaire assessed in-group (own country) identification, in-group evaluation (i.e. national pride) and rejection of national out-groups. Across all four studies, both in-group identification and in-group evaluation show a stronger correlation with out-group derogation if participants were primed with an intergroup comparison orientation compared to temporal and control conditions. Results are discussed with regard to nationalism and patriotism as well as Hinkle and Brown's (1990) model on relational vs. autonomous orientations.


title: Open Logo Detection Challenge

text: Existing logo detection benchmarks consider artificial deployment scenarios by assuming that large training data with fine-grained bounding box annotations for each class are available for model training. Such assumptions are often invalid in realistic logo detection scenarios where new logo classes come progressively and require to be detected with little or none budget for exhaustively labelling fine-grained training data for every new class. Existing benchmarks are thus unable to evaluate the true performance of a logo detection method in realistic and open deployments. In this work, we introduce a more realistic and challenging logo detection setting, called Open Logo Detection. Specifically, this new setting assumes fine-grained labelling only on a small proportion of logo classes whilst the remaining classes have no labelled training data to simulate the open deployment. We further create an open logo detection benchmark, called QMUL-OpenLogo, to promote the investigation of this new challenge. QMUL-OpenLogo contains 27,083 images from 352 logo classes, built by aggregating/refining 7 existing datasets and establishing an open logo detection evaluation protocol. To address this challenge, we propose a Context Adversarial Learning (CAL) approach to synthesising training data with coherent logo instance appearance against diverse background context for enabling more effective optimisation of contemporary deep learning detection models. Experiments show the performance advantage of CAL over existing state-of-the-art alternative methods on the more realistic and challenging open logo benchmark. The QMUL-OpenLogo benchmark is publicly available at https://qmul-openlogo.github.io/.


title: Weighted Coverage based Reviewer Assignment

text: Peer reviewing is a standard process for assessing the quality of submissions at academic conferences and journals. A very important task in this process is the assignment of reviewers to papers. However, achieving an appropriate assignment is not easy, because all reviewers should have similar load and the subjects of the assigned papers should be consistent with the reviewers' expertise. In this paper, we propose a generalized framework for fair reviewer assignment. We first extract the domain knowledge from the reviewers' published papers and model this knowledge as a set of topics. Then, we perform a group assignment of reviewers to papers, which is a generalization of the classic Reviewer Assignment Problem (RAP), considering the relevance of the papers to topics as weights. We study a special case of the problem, where reviewers are to be found for just one paper (Journal Assignment Problem) and propose an exact algorithm which is fast in practice, as opposed to brute-force solutions. For the general case of having to assign multiple papers, which is too hard to be solved exactly, we propose a greedy algorithm that achieves a 1/2-approximation ratio compared to the exact solution. This is a great improvement compared to the 1/3-approximation solution proposed in previous work for the simpler coverage-based reviewer assignment problem, where there are no weights on topics. We theoretically prove the approximation bound of our solution and experimentally show that it is superior to the current state-of-the-art.


title: Fine-grained change detection in structured text documents

text:
Detecting and understanding changes between document revisions is an important task. The acquired knowledge can be used to classify the nature of a new document revision or to support a human editor in the review process. While purely textual change detection algorithms offer fine-grained results, they do not understand the syntactic meaning of a change. By representing structured text documents as XML documents we can apply tree-to-tree correction algorithms to identify the syntactic nature of a change.
 Many algorithms for change detection in XML documents have been propsed but most of them focus on the intricacies of generic XML data and emphasize speed over the quality of the result. Structured text requires a change detection algorithm to pay close attention to the content in text nodes, however, recent algorithms treat text nodes as black boxes.
 We present an algorithm that combines the advantages of the purely textual approach with the advantages of tree-to-tree change detection by redistributing text from non-overlapping common substrings to the nodes of the trees. This allows us to not only spot changes in the structure but also in the text itself, thus achieving higher quality and a fine-grained result in linear time on average. The algorithm is evaluated by applying it to the corpus of structured text documents that can be found in the English Wikipedia.


title: Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning

text: In distributed training of deep neural networks, parallel minibatch SGD is widely used to speed up the training process by using multiple workers. It uses multiple workers to sample local stochastic gradient in parallel, aggregates all gradients in a single server to obtain the average, and update each worker’s local model using a SGD update with the averaged gradient. Ideally, parallel mini-batch SGD can achieve a linear speed-up of the training time (with respect to the number of workers) compared with SGD over a single worker. However, such linear scalability in practice is significantly limited by the growing demand for gradient communication as more workers are involved. Model averaging, which periodically averages individual models trained over parallel workers, is another common practice used for distributed training of deep neural networks since (Zinkevich et al. 2010) (McDonald, Hall, and Mann 2010). Compared with parallel mini-batch SGD, the communication overhead of model averaging is significantly reduced. Impressively, tremendous experimental works have verified that model averaging can still achieve a good speed-up of the training time as long as the averaging interval is carefully controlled. However, it remains a mystery in theory why such a simple heuristic works so well. This paper provides a thorough and rigorous theoretical study on why model averaging can work as well as parallel mini-batch SGD with significantly less communication overhead. Introduction Consider the distributed training of deep neural networks over multiple workers (Dean et al. 2012), where all workers can access all or partial training data and aim to find a common model that yields the minimum training loss. Such a scenario can be modeled as the following distributed parallel non-convex optimization min x∈Rm f(x) ∆ = 1 N N ∑


title: Algorithmic Aspects of Vertex Elimination on Graphs

text: We consider a graph-theoretic elimination process which is related to performing Gaussian elimination on sparse symmetric positive definite systems of linear equations. We give a new linear-time algorithm to calculate the fill-in produced by any elimination ordering, and we give two new related algorithms for finding orderings with special properties. One algorithm, based on breadth-first search, finds a perfect elimination ordering, if any exists, in O(n + e) time, if the problem graph has n vertices and e edges. An extension of this algorithm finds a minimal (but not necessarily minimum) ordering in O(ne) time. We conjecture that the problem of finding a minimum ordering is


title: An Algorithm to Find Overlapping Community Structure in Networks

text: Abstract. Recent years have seen the development of many graph clustering algorithms, which can identify community structure in networks. The vast majority of these only find disjoint communities, but in many real-world networks communities overlap to some extent. We present a new algorithm for discovering overlapping communities in networks, by extending Girvan and Newman’s well-known algorithm based on the betweenness centrality measure. Like the original algorithm, ours performs hierarchical clustering — partitioning a network into any desired number of clusters — but allows them to overlap. Experiments confirm good performance on randomly generated networks based on a known overlapping community structure, and interesting results have also been obtained on a range of real-world networks.


title: Constructing popular routes from uncertain trajectories

text: The advances in location-acquisition technologies have led to a myriad of spatial trajectories. These trajectories are usually generated at a low or an irregular frequency due to applications' characteristics or energy saving, leaving the routes between two consecutive points of a single trajectory uncertain (called an uncertain trajectory). In this paper, we present a Route Inference framework based on Collective Knowledge (abbreviated as RICK) to construct the popular routes from uncertain trajectories. Explicitly, given a location sequence and a time span, the RICK is able to construct the top-k routes which sequentially pass through the locations within the specified time span, by aggregating such uncertain trajectories in a mutual reinforcement way (i.e., uncertain + uncertain → certain). Our work can benefit trip planning, traffic management, and animal movement studies. The RICK comprises two components: routable graph construction and route inference. First, we explore the spatial and temporal characteristics of uncertain trajectories and construct a routable graph by collaborative learning among the uncertain trajectories. Second, in light of the routable graph, we propose a routing algorithm to construct the top-k routes according to a user-specified query. We have conducted extensive experiments on two real datasets, consisting of Foursquare check-in datasets and taxi trajectories. The results show that RICK is both effective and efficient.


title: Friendship and mobility: user movement in location-based social networks

text: Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.


title: Event-based social networks: linking the online and offline social worlds

text:
Newly emerged event-based online social services, such as Meetup and Plancast, have experienced increased popularity and rapid growth. From these services, we observed a new type of social network - event-based social network (EBSN). An EBSN does not only contain online social interactions as in other conventional online social networks, but also includes valuable offline social interactions captured in offline activities. By analyzing real data collected from Meetup, we investigated EBSN properties and discovered many unique and interesting characteristics, such as heavy-tailed degree distributions and strong locality of social interactions.
 We subsequently studied the heterogeneous nature (co-existence of both online and offline social interactions) of EBSNs on two challenging problems: community detection and information flow. We found that communities detected in EBSNs are more cohesive than those in other types of social networks (e.g. location-based social networks). In the context of information flow, we studied the event recommendation problem. By experimenting various information diffusion patterns, we found that a community-based diffusion model that takes into account of both online and offline interactions provides the best prediction power.
 This paper is the first research to study EBSNs at scale and paves the way for future studies on this new type of social network. A sample dataset of this study can be downloaded from http://www.largenetwork.org/ebsn.


title: Trajectory pattern mining

text: The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we move towards this direction and develop an extension of the sequential pattern mining paradigm that analyzes the trajectories of moving objects. We introduce trajectory patterns as concise descriptions of frequent behaviours, in terms of both space (i.e., the regions of space visited during movements) and time (i.e., the duration of movements). In this setting, we provide a general formal statement of the novel mining problem and then study several different instantiations of different complexity. The various approaches are then empirically evaluated over real data and synthetic benchmarks, comparing their strengths and weaknesses.


title: LARS: A Location-Aware Recommender System

text: This paper proposes LARS, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items, LARS, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS can apply these techniques separately, or in concert, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the Movie Lens movie recommendation system reveals that LARS is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.


title: Coprime array adaptive beamforming based on compressive sensing virtual array signal

text: In this paper, we propose a novel adaptive beamforming algorithm for coprime array by compressive sensing the virtual uniform linear array signal. Based on the idea of coprime sampling, a much longer virtual uniform linear array can be generated from a coprime array. With a compressive sensing matrix, a connection can be built between the coprime array with fewer physical sensors and the virtual uniform linear array with much more virtual sensors. Hence, the proposed adaptive beamforming algorithm takes full advantage of the longer virtual array. The performance increment provided by the virtual array is much larger than the performance loss due to the introduced compressive sensing. Hence, the beam-former using the virtual array is expected to obtain much better performance than those using the coprime array directly. Simulation results demonstrate the effectiveness of the proposed adaptive beamforming algorithm.


title: Location Systems for Ubiquitous Computing

text: Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.


title: Efficient Active Learning for Image Classification and Segmentation Using a Sample Selection and Conditional Generative Adversarial Network

text: Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods.


title: An Algorithm for Differential File Comparison

text: The programdiff reports differences between tw o files, expressed as a minimal list of line changes to bring either file into agreement with the other . Diff has been engineered to mak e efficient use of time and space on typical inputs that arise in vetting v ersion-to-version changes in computer-maintained or computer-generated documents. T ime and space usage are observed to v ary about as the sum of the file lengths on real data, although they are known to vary as the product of the file lengths in the worst case. The central algorithm of diff solves the ‘longest common subsequence problem’ to find the lines that do not change between files. Practical ef ficiency is gained by attending only to certain critical ‘candidate’ matches between the files, the breaking of which would shorten the longest subsequence common to some pair of initial segments of the two files. Various techniques of hashing, presorting into equi valence classes, merging by binary search, and dynamic storage allocation are used to obtain good performance. [This document was scanned from Bell Laboratories Computing Science T e hnical Report #41, dated July 1976. Te xt was converted by OCR and hand-corrected (last changed June, 2012). Figures were reconstructed. Some OCR errors may remain, especially in tables and equations. Please report them to doug@cs.dartmouth.edu.] The programdiff creates a list of what lines of one file ha ve to be changed to bring it into agreement with a second file or vice versa. It is based on ideas from se veral sources[1,2,7,8]. As an example of its work, consider the two files, listed horizontally for brevity: a b c d e f g w a b x y z e It is easy to see that the first file can be made into the second by the follo wing prescription, in which an imaginary line 0 is understood at the beginning of each: append after line 0: w, change lines 3 through 4, which were: c d into: x y z, delete lines 6 through 7, which were: f g. Going the other way , the first file can be made from the second this way: delete line 1, which was: w, change lines 4 through 6, which were: x y z into: c d, append after line 7: f g. Delete, change and append are the only operations a vailable to diff . It indicates them by 1-letter


title: Hair animation with collision detection

text: We propose an efficient method for hair animation. The motion of hairs is modeled by a simplified physical simulation. Collision between hair and a human body or other objects is used to make a realistic hair animation. Fast collision detection is required because the number of hairs is very large. Therefore, a cylindrical representation of human body or other objects is created for efficient collision detection. Because of the cylindrical representation, collision is detected with table look-up and interpolation, and computation complexity is independent of the complexity of the body. Reaction constraint method is applied for the collision response to simulate inelastic contact. This method is successfully applied to generate hair animation.


title: A system model for energy efficient green-IoT network

text: The notion of IoT has ignited umpteen possibilities of different heterogeneous devices to blend within a network. In IoT, especially sensor devices are mostly deployed in an extremely resource constrained environment and thus pose the necessity to extend the capability and life expectancy of these kind of devices in terms of energy consumption. Since, the devices in IoT have limited energy sources they often run on battery with a certain energy capability, the deployment of green communication and system model in IoT has been a core challenging issue. This paper addresses the energy efficiency issues across diverse IoT driven networks by proposing a system model for G-IoT and energy efficient scheme for the IoT devices to extend the life expectancy of the whole IoT network.


title: A User's Guide to Compressed Sensing for Communications Systems

text: This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on 1 optimization, which plays the central role in compressed sensing, with some intuitive explanations on the optimization problem. Moreover, we introduce some important properties of the sensing matrix in order to establish the guarantee of the exact recovery of sparse signals from the underdetermined system. After summarizing several major algorithms to obtain a sparse solution focusing on the 1 optimization and the greedy approaches, we introduce applications of compressed sensing to communications systems, such as wireless channel estimation, wireless sensor network, network tomography, cognitive radio, array signal processing, multiple access scheme, and networked control. key words: compressed sensing, sparse signal, compressible signal, 1norm, underdetermined system


title: Phoenix: A Self-Optimizing Chess Engine

text: Since the advent of computers, many tasks which required humans to spend a lot of time and energy have been trivialized by the computers' ability to perform repetitive tasks extremely quickly. However there are still many areas in which humans excel in comparison with the machines. One such area is chess. Even with great advances in the speed and computational power of modern machines, Grandmasters often beat the best chess programs in the world with relative ease1. This may be due to the fact that a game of chess cannot be won by pure calculation. There is more to the goodness of a chess position than some numerical value which apparently can be seen only by the human brain. Here an effort has been made to improve current chess engines by letting themselves evolve over a period of time. Firstly, the problem of learning is reduced into an optimization problem by defining Position Evaluation in terms of Positional Value Tables (PVTs). Next, the PVTs are optimized using Multi-Niche Crowding which successfully identifies the optima in a multimodal function, thereby arriving at distinctly different solutions which are close to the global optimum.


title: Selection of Optimal Stopping Time for Nonlinear Diffusion Filtering

text: We develop a novel time-selection strategy for iterative image restoration techniques: the stopping time is chosen so that the correlation of signal and noise in the filtered image is minimized. The new method is applicable to any images where the noise to be removed is uncorrelated with the signal, under the assumptions that the filter used is suitable for the given type of data, and that neither the additive noise nor the filtering procedure alter the average gray value; no other knowledge (e.g. the noise variance, training data etc.) is needed. We analyse the theoretical properties of the method, then test the performance of our time estimation procedure experimentally, and demonstrate that it yields near-optimal results for a wide range of noise levels and for various filtering methods.


title: Automatic generation of high-performance modular multipliers for arbitrary mersenne primes on FPGAs

text: Modular multiplication is a fundamental and performance determining operation in various public-key cryptosystems. High-performance modular multipliers on FPGAs are commonly realized by several small-sized multipliers, an adder tree for summing up the digit-products, and a reduction circuit. While small-sized multipliers are available in pre-fabricated high-speed DSP slices, the adder tree and the reduction circuit are implemented in standard logic. The latter operations represent the performance bottleneck to high-performance implementations. Previous works attempted to minimize the critical path of the adder tree by rearranging digit-products on digit-level. We report improved performance by regrouping digit-products on bit-level, while incorporating the reduction for Mersenne primes. Our approach leads to very fast modular multipliers, whose latency and throughput characteristics outperform all previous results. We formalize our approach and provide algorithms to automatically generate high-performance modular multipliers for arbitrary Mersenne primes from any small-sized multipliers.


title: Recognition of thalidomide defects.

text: Thalidomide: a brief history Thalidomide (alpha-phthalimido-glutarimide) was developed by the German firm Chemie Grunenthal as an anticonvulsant drug. Early trials showed it to be unsuitable for this purpose but indicated that it had sedative properties. Furthermore, it had one remarkable property: overdoses simply caused prolonged sleep, not death. The drug was first marketed in Germany in 1957 under the name Contergan, and in the UK in April 1958 as Distaval. Later, compound preparations which combined thalidomide with other drugs were marketed for a wide variety of indications: Asmaval for asthma, Tensival for hypertension, Valgraine for migraine, and so forth. The promotion of these products laid great stress on the safety of thalidomide, based on the remarkable property described above. German paediatricians and geneticists began to see children with gross limb malformations of a most unusual pattern. When two cases were shown at a paediatric meeting in Kassel by Kosenow and Pfeiffer in October 1960, few people present had ever seen similar limb defects. Wiedemann in 1961 described 13 affected infants who had been referred to him over a period of 10 months, and noted that this amounted to an epidemic. He drew attention to a number of associated malformations in these children, including congenital heart disease, microphthalmos and coloboma, intestinal atresia, renal malformations, abnormal pinnae, and facial naevus. In November 1961, Lenz suggested that these deformities resulted from the mothers having taken thalidomide. By a remarkable coincidence, the same suggestion was made at much the same time by McBride in Australia. Confirmation of this suggestion came rapidly from all parts of the British Isles, Kenya, Japan, Sweden, Belgium, Switzerland, Lebanon, Israel, Peru, Canada, Brazil, the Netherlands, and the USA. The drug had been released only for clinical trials in the USA because of concerns following reports from Europe of irreversible peripheral neuritis as a side effect of thalidomide. Consequently there were very few cases. By contrast, it had been on sale over the counter in Germany, and there were consequently more affected children there than anywhere else. In the UK the drug was available on prescription only, but it was used very widely for, among other problems, common symptoms of early pregnancy. Thirty years later, subjects are still coming forward (albeit, in small numbers) with claims that they have birth defects which have (or may have) been caused by thalidomide taken by their mothers during early pregnancy, and that they should therefore be accepted as beneficiaries of whatever forms of financial assistance may be available. In the UK, this is the Thalidomide Trust. Thalidomide caused a wide variety of birth defects, not one of which was unique to that drug. Nevertheless, the nature and pattern of the defects are, in most cases, characteristic enough to be recognisable to an experienced eye. Indeed, many of the present beneficiaries of the Trust have been accepted on the basis of clinical judgements, without direct evidence of thalidomide exposure. As the number of doctors with wide experience of this problem is small (possibly only three in the UK) and will become smaller with the passage of time, and as new claims continue to arise, it seems timely to record, in as much detail as possible, the observations which underlie these clinical judgements. As well as describing the defects and patterns associated with thalidomide, it will be appropriate to discuss 'differential diagnosis', that is, recognisable defects and syndromes which, to a greater or lesser degree, resemble thalidomide defects. Some of these are unlikely to confuse an experienced eye: others can present considerable difficulty. In considering a new claim, attention will obviously be paid to the claimant's date of birth. In the UK, thalidomide was first marketed in April 1958, initially in very small quantities, so it is not to be expected that it would damage anybody born before January 1959 (unless supplies had been obtained from West Germany). Sales in the UK stopped in November 1961. Had consumption stopped at the same time, no 'thalidomide babies' should have been born after August 1962. However, many people still had tablets containing thalidomide in their homes, and either did not hear promptly of its dangers, or did not realise that their tablets contained thalidomide. In fact, children with defects accepted as attributable to thalidomide (though not necessarily with documentary evidence of prescription) continued to be born up to about May 1963, and, very exceptionally, beyond this date. In trying to establish a yardstick or benchmark for bona fide thalidomide defects, it is necessary to start with cases in which there is 5 North Grange Mews, North Grange Road, Leeds LS6 2EW. R W Smithells


title: Research of security metric architecture for Next Generation Network

text: Constructing a security metric architecture in Next Generation Network is a critical part for network security assessment. In this paper, a three-dimensional security metric architecture model is given. In this model, four comprehensive metrics, which are vulnerability, threat, stability and survivability, are first presented. Then based on these four metrics, a hierarchical security metric architecture is devised, and instanced in a prototype system of NGN. Compared with qualitative metrics, the presented metrics are not only accurate, but also measurable and easy to collect based on the quantitative parameters. Since there is limited research result of the security metric architecture for NGN at present, this model can be referenced to a certain extent.


title: Stereo Panorama with a Single Camera

text: Full panoramic images, covering 360 degrees, can be created either by using panoramic cameras or by mosaicing together many regular images. Creating panoramic views instereo, where one panorama is generated for the left eye, and another panorama is generated for the right eye is more problematic. Earlier attempts to mosaic images from a rotating pair of stereo cameras faced severe problems of parallax and of scale changes. A new family of multiple viewpoint image projections, the Circular Projections, is developed. Two panoramic images taken using such projections can serve as a panoramic stereo pair. A system is described to generates a stereo panoramic image using circular projections from images or video taken by a single rotating camera. The system works in real-time on a PC. It should be noted that the stereo images are created without computation of 3D structure, and the depth effects are created only in the viewer's brain.


title: ParaBox: Exploiting Parallelism for Virtual Network Functions in Service Chaining

text: Service Function Chains (SFCs) comprise a sequence of Network Functions (NFs) that are typically traversed in-order by data flows. Consequently, SFC delay grows linearly with the length of the SFC. Yet, for highly latency sensitive applications, this delay may be unacceptable---particularly when the constituent NFs are virtualized, running on commodity servers. In this paper, we investigate how SFC latency may be reduced by exploiting opportunities for parallel packet processing across NFs. We propose ParaBox, a novel hybrid packet processing architecture that, when possible, dynamically distributes packets to VNFs in parallel and merges their outputs intelligently to ensure the preservation of correct sequential processing semantics. To demonstrate the feasibility of our approach, we implement a ParaBox prototype on top of the DPDK-enabled Berkeley Extensible Software Switch. Our preliminary experiment results show that ParaBox can not only significantly reduce the service chaining latency, but also improve throughput.


title: Cognitive radio: making software radios more personal

text: Software radios arc cmcrging as platforms for multiband multimode personal communications systems. Radio etiqucttc is the set of RF bands, air interfaces, protocols, and spatial and temporal patterns that modcrate the use of the radio spectrum. Cognitive radio extends the software radio with radio-domain model-based rcasoning about such ctiquettes. Cognitivc radio enhances the flexibility of personal services through a Radio Knowlcdge Representation Language. This language reprcsents knowledge of radio etiquettc, devices, software modules, propagation, nctworks, user needs, and application scenarios in a way that supports automated reasoning about thc needs of the user. This empowers software radios to conduct expressivc ncgotiations among peers about the use of radio spectrum across fluents of space, time, and uscr context. With RKRL, cognitivc radio agents may actively manipulate the protocol stack to adapt known etiquettes to better satis$ the LISCT’S nccds. This transforms radio nodes from blind executors of predefined protocols to radio-domain-aware intclligent agents that scarch out ways to dclivcr the services thc uscr wants even if that uscr docs not know how to obtain them. Softwarc radio [l] provides an idcal platform for thc rcalization of cognitive radio. Cognitive Radio: Making Software Radios More Personal J O S E P H MITOLA 111 A N D GERALD Q. M A G U I R E , J R . R O Y A L INSTITUTE OF TECHNOLOGY Global System for Mobile Communications (GSM) radio’s equalizer taps reflcct the channel multipath structure. A network might want to ask a handset, “How many distinguishable multipath components are you seeing?” Knowledge of thc internal states of the equalizer could be useful bccause in some reception areas, thcrc may be little or no multipath and 20 dB of extra signal-to-noisc ratio (SNR). Software radio processing capacity is wasted running a computationally intcnsive equalizer algorithm when no cqualizer is necessary. That processing capacity could be diverted to better use, or part of the processor might be put to sleep, saving battery life. In addition, the radio and network could agree to put data bits in the superfluous embedded training scquence, enhancing the payload data rate accordingly.’ Two problems arise. First, the network has no standard language with which to posc a question about cqualizer taps. Sccand, the handset has the answer in the time-domain structure of its equalizer taps, but cannot access this information. It has no computational description of its own structure. Thus, it does not “know what it knows.” Standards-setting bodies have been gradually making such internal data available to networks through specific air interfaces, as the needs of the technology dictate. This labor-intensive process takes ycars to accomplish. Radio Knowledge Represcntation Language (RKRL), on the other hand, provides a standard languagc within which such unanticipated data exchanges can be defined dynamically. Why might the need for such unanticipatcd exchanges arise? Debugging new software radio downloads might require access to internal software parameters. Creating personal services that diffcrentiate one servicc provider from another might be enhanced if the provider does not need to expose new ideas to the competition in the standards-setting process. And the time to deploy those personalized services could be reduced. Cognitive radio, through RKRL, knows that the natural lanThis raise.$ a host of questions about the control of such complex adaptive agents, network ,stabiliQ, and the like. guage phrase equalizer taps refers to specific parameters of a tapped delay-line structure. This structure may be implemented in an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or an algorithm in a software radio. Since a cognitive radio has a model of its own internal structurc, it can check the model to find out how the equalizer has been implemented. It then may retrieve thc register values from the ASIC (e.g., using a JTAG port) or find the taps in the proper memory location of its software implementation. A radio that knows its own internal structure to this degree does not have to wait for a consortium, forum, or standards body to define a level H33492.x7 radio as one that can access its equalizcr taps. The network can pose such an unanticipated question in (a standard) RKRL, and any RKRL-capable radio can answer it. To enable such a scenario, cognitive radio has an RKRL model of itself that includes the equalizer’s structure and function, as illustrated in Fig. 1. In this example, the radio hardware consists of the antenna, the radio frequency (RF) conversion module, the modem, and the other modules shown in the hardware part of the figure. The baseband processor includes a baseband modem and a back-end control protocol stack. In addition, this processor contains a cognition cngine and a set of computational models. The models consist of RKRL frames that describe the radio itself, including the equalizer, in the context of a comprehensive ontology, also written in RKRL. Using this ontology, the radio can track the user’s environment over time and space. Cognitive radio, then, matches its internal models to cxternal observations to understand what it means to commute to and from work, take a business trip to Europe, go on vacation, and so on. Clearly, significant memory, computational resources, and communications bandwidth are needed for cognitive radio, so this technology might not be deployable for somc time. In addition, a collcction of cognitivc radios may not require human intervention to develop their own protocols. Initially, intervention will be required in order to ensurd that networks of such radios remain stable (or that we know who to blame if this is not the case). Networks of such radios are complex Authorized licensed use limited to: CHONGQING UNIVERSITY. Downloaded on April 8, 2009 at 21:50 from IEEE Xplore. Restrictions apply. ___ ................. ............ .. Cognition -.


title: Randomized controlled trial of yoga and exercise in multiple sclerosis.

text:
OBJECTIVE
To determine the effect of yoga and of aerobic exercise on cognitive function, fatigue, mood, and quality of life in multiple sclerosis (MS).

METHODS
Subjects with clinically definite MS and Expanded Disability Status Score less than or equal to 6.0 were randomly assigned to one of three groups lasting 6 months: weekly Iyengar yoga class along with home practice, weekly exercise class using a stationary bicycle along with home exercise, or a waiting-list control group. Outcome assessments performed at baseline and at the end of the 6-month period included a battery of cognitive measures focused on attention, physiologic measures of alertness, Profile of Mood States, State-Trait Anxiety Inventory, Multi-Dimensional Fatigue Inventory (MFI), and Short Form (SF)-36 health-related quality of life.

RESULTS
Sixty-nine subjects were recruited and randomized. Twelve subjects did not finish the 6-month intervention. There were no adverse events related to the intervention. There were no effects from either of the active interventions on either of the primary outcome measures of attention or alertness. Both active interventions produced improvement in secondary measures of fatigue compared to the control group: Energy and Fatigue (Vitality) on the SF-36 and general fatigue on the MFI. There were no clear changes in mood related to yoga or exercise.

CONCLUSION
Subjects with MS participating in either a 6-month yoga class or exercise class showed significant improvement in measures of fatigue compared to a waiting-list control group. There was no relative improvement of cognitive function in either of the intervention groups.


title: An Algorithmic Approach to Concept Exploration in a Large Knowledge Network (Automatic Thesaurus Consultation): Symbolic Branch-and-Bound Search vs. Connectionist Hopfield Net Activation

text: This paper presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge-based systems and to alleviate the limitations of the manual browsing approach, we have developed two spreading activation-based algorithms for concept exploration in large, heterogeneous networks of concepts (e.g., multiple thesauri). One algorithm, which is based on the symbolic Al paradigm, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The second algorithm, which is based on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify “convergent” concepts for some initial queries (a parallel, heuristic search process). Both algorithms can be adopted for automatic, multiple-thesauri consultation. We tested these two algorithms on a large text-based knowledge network of about 13,000 nodes (terms) and 80,000 directed links in the area of computing technologies. This knowledge network was created from two external thesauri and one automatically generated thesaurus. We conducted experiments to compare the behaviors and performances of the two algorithms with the hypertext-like browsing process. Our experiment revealed that manual browsing achieved higher-term recall but lower-term precision in comparison to the algorithmic systems. However, it was also a much more laborious and cognitively demanding process. In document retrieval, there were no statistically significant differences in document recall and precision between the algorithms and the manual browsing process. In light of the effort required by the manual browsing process, our proposed algorithmic approach presents a viable option for efficiently traversing largescale, multiple thesauri (knowledge network).


title: AndroTaint: An efficient android malware detection framework using dynamic taint analysis

text: Android operating system is widespread in today's smart-phone market due to its open source model, its easy functionality and huge number of Apps and App-Stores (the official and others). The coverage of some 90% of the market, it is also the place-holder for user's personal as well as sensitive information (IMEI, IMSI, etc.). There is a tendency of Apps user to trust on Android OS for securing his/her data but it has been proved that Android OS is more vulnerable to exploitation either for fun or for monetary purpose. Malware detection for Android OS has become an upcoming research problem of interest. Dynamic Taint Analysis is an efficient analysis in existing Android malware detection analysis. The aim of this paper is to work towards Dynamic Taint Analysis of Android malware using automatic tagging and with out modification in Android platform. In order to do this, this paper surveys exhaustively the available literature and works related to dynamic taint analysis. This paper proposes some novel ideas to improve the existing solution with more accuracy. There are two phases of our novel algorithm, the first phase is training phase for feature extraction and the second phase is the analysis phase for automatic tagging and tainting. We have developed a framework named AndroTaint, which is working on Dynamic Taint Analysis. AndroTaint follows a novel supervised and unsupervised Anomaly Detection technique with high precision, recall and harmonic mean. Our Dynamic Taint Analysis algorithm categorizes the App is risky, benign, malicious or aggressive as per their feature and behaviour. We have measured AndroTaint effectiveness on the basis of timeline for building dataset and 10-fold cross validation. AndroTaint covers 90% of malware and benign in analysis phase with less false positive and false negative.


title: Object Detection using Deep Learning

text: Autonomous vehicles, surveillance systems, face detection systems lead to the development of accurate object detection system [1]. These systems recognize, classify and localize every object in an image by drawing bounding boxes around the object [2]. These systems use existing classification models as backbone for Object Detection purpose. Object detection is the process of finding instances of real-world objects such as human faces, animals and vehicles etc., in pictures, images or in videos. An Object detection algorithm uses extracted features and learning techniques to recognize the objects in an image. In this paper, various Object Detection techniques have been studied and some of them are implemented. As a part of this paper, three algorithms for object detection in an image were implemented and their results were compared. The algorithms are “Object Detection using Deep Learning Framework by OpenCV”, “Object Detection using Tensorflow” and “Object Detection using Keras models”.


title: Comdb2: Bloomberg's Highly Available Relational Database System

text: Comdb2 is a distributed database system designed for geographical replication and high availability. In contrast with the latest trends in this field, Comdb2 o↵ers full transactional support, a standard relational model, and the expressivity of SQL. Moreover, the system allows for rich stored procedures using a dialect of Lua. Comdb2 implements a serializable system in which reads from any node always return current values. Comdb2 provides transparent High Availability through built-in service discovery and sophisticated retry logic embedded in the standard API. In addition to the relational data model, Comdb2 implements queues for publisher-to-subscriber message delivery. Queues can be combined with table triggers for timeconsistent log distribution, providing functionality commonly needed in modern OLTP. In this paper we give an overview of our last twelve years of work. We focus on the design choices that have made Comdb2 the primary database solution within our company, Bloomberg LP (BLP).


title: DLM: Decentralized Linearized Alternating Direction Method of Multipliers

text: This paper develops the Decentralized Linearized Alternating Direction Method of Multipliers (DLM) that minimizes a sum of local cost functions in a multiagent network. The algorithm mimics operation of the decentralized alternating direction method of multipliers (DADMM) except that it linearizes the optimization objective at each iteration. This results in iterations that, instead of successive minimizations, implement steps whose cost is akin to the much lower cost of the gradient descent step used in the distributed gradient method (DGM). The algorithm is proven to converge to the optimal solution when the local cost functions have Lipschitz continuous gradients. Its rate of convergence is shown to be linear if the local cost functions are further assumed to be strongly convex. Numerical experiments in least squares and logistic regression problems show that the number of iterations to achieve equivalent optimality gaps are similar for DLM and ADMM and both much smaller than those of DGM. In that sense, DLM combines the rapid convergence of ADMM with the low computational burden of DGM.


title: Table of Contents Recognition and Extraction for Heterogeneous Book Documents

text: Existing work on book table of contents (TOC) recognition has been almost all on small size, application-dependent, and domain-specific datasets. However, TOC of books from different domains differ significantly in their visual layout and style, making TOC recognition a challenging problem for a large scale collection of heterogeneous books. We observed that TOCs can be placed into three basic styles, namely "flat", "ordered", and "divided", giving insights into how to achieve effective TOC parsing. As such, we propose a new TOC recognition approach which adaptively decides the most appropriate TOC parsing rules based on the classification of these three TOC styles. Evaluation on large number, over 25,000, of book documents from various domains demonstrates its effectiveness and efficiency.


title: Modeling online reviews with multi-grain topic models

text: In this paper we present a novel framework for extracting the ratable aspects of objects from online user reviews. Extracting such aspects is an important challenge in automatically mining product opinions from the web and in generating opinion-based summaries of user reviews [18, 19, 7, 12, 27, 36, 21]. Our models are based on extensions to standard topic modeling methods such as LDA and PLSA to induce multi-grain topics. We argue that multi-grain models are more appropriate for our task since standard models tend to produce topics that correspond to global properties of objects (e.g., the brand of a product type) rather than the aspects of an object that tend to be rated by a user. The models we present not only extract ratable aspects, but also cluster them into coherent topics, e.g., 'waitress' and 'bartender' are part of the same topic 'staff' for restaurants. This differentiates it from much of the previous work which extracts aspects through term frequency analysis with minimal clustering. We evaluate the multi-grain models both qualitatively and quantitatively to show that they improve significantly upon standard topic models.


title: Comparing Twitter and Traditional Media Using Topic Models

text: Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to compare these Twitter topics with topics from New York Times, taking into consideration topic categories and types. We also study the relation between the proportions of opinionated tweets and retweets and topic categories and types. Our comparisons show interesting and useful findings for downstream IR or DM applications.


title: Earthquake shakes Twitter users: real-time event detection by social sensors

text: Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.


title: TwitterReporter: Breaking News Detection and Visualization through the Geo-Tagged Twitter Network

text: The Twitter social network provides a constant stream of concise data, useful within both geospatial and temporal domains. Previous studies have attempted to mine and find recent news topics within the data set. Although the efforts were a step in the right direction, we believe more is needed to accomplish a useful and interesting task: using live Twitter data to automatically identify breaking news events in near real-time. In this paper, we present methods to collect data, identify breaking news topics, and display results in a geo-temporal visualization. Our goal is to provide a solution that discovers breaking news faster than traditional reporting mediums.


title: A Resource-Driven Approach for Anchoring Linguistic Resources to Conceptual Spaces

text: In this paper we introduce the TTCS system, so named after Terms To Conceptual Spaces, that exploits a resource-driven approach relying on BabelNet, NASARI and ConceptNet. TTCS takes in input a term and its context of usage and produces as output a specific type of vector-based semantic representation, where conceptual information is encoded through the Conceptual Spaces (a geometric framework for common-sense knowledge representation and reasoning). The system has been evaluated in a twofold experimentation. In the first case we assessed the quality of the extracted common-sense conceptual information with respect to human judgments with an online questionnaire. In the second one we compared the performances of a conceptual categorization system that was run twice, once fed with extracted annotations and once with hand-crafted annotations. In both cases the results are encouraging and provide precious insights to make substantial improvements.


title: Quickly Boosting Decision Trees - Pruning Underachieving Features Early

text: Boosted decision trees are among the most popular learning techniques in use today. While exhibiting fast speeds at test time, relatively slow training renders them impractical for applications with real-time learning requirements. We propose a principled approach to overcome this drawback. We prove a bound on the error of a decision stump given its preliminary error on a subset of the training data; the bound may be used to prune unpromising features early in the training process. We propose a fast training algorithm that exploits this bound, yielding speedups of an order of magnitude at no cost in the final performance of the classifier. Our method is not a new variant of Boosting; rather, it is used in conjunction with existing Boosting algorithms and other sampling methods to achieve even greater speedups.


title: Holographic Photolysis for Multiple Cell Stimulation in Mouse Hippocampal Slices

text:
BACKGROUND
Advanced light microscopy offers sensitive and non-invasive means to image neural activity and to control signaling with photolysable molecules and, recently, light-gated channels. These approaches require precise and yet flexible light excitation patterns. For synchronous stimulation of subsets of cells, they also require large excitation areas with millisecond and micrometric resolution. We have recently developed a new method for such optical control using a phase holographic modulation of optical wave-fronts, which minimizes power loss, enables rapid switching between excitation patterns, and allows a true 3D sculpting of the excitation volumes. In previous studies we have used holographic photololysis to control glutamate uncaging on single neuronal cells. Here, we extend the use of holographic photolysis for the excitation of multiple neurons and of glial cells.

METHODS/PRINCIPAL FINDINGS
The system combines a liquid crystal device for holographic patterned photostimulation, high-resolution optical imaging, the HiLo microscopy, to define the stimulated regions and a conventional Ca(2+) imaging system to detect neural activity. By means of electrophysiological recordings and calcium imaging in acute hippocampal slices, we show that the use of excitation patterns precisely tailored to the shape of multiple neuronal somata represents a very efficient way for the simultaneous excitation of a group of neurons. In addition, we demonstrate that fast shaped illumination patterns also induce reliable responses in single glial cells.

CONCLUSIONS/SIGNIFICANCE
We show that the main advantage of holographic illumination is that it allows for an efficient excitation of multiple cells with a spatiotemporal resolution unachievable with other existing approaches. Although this paper focuses on the photoactivation of caged molecules, our approach will surely prove very efficient for other probes, such as light-gated channels, genetically encoded photoactivatable proteins, photoactivatable fluorescent proteins, and voltage-sensitive dyes.


title: Efficient RANSAC for Point-Cloud Shape Detection

text: In this work we present an automatic algorithm to detect basic shapes in unorganized point clouds. The algorithm decomposes the point cloud into a concise, hybrid structure of inherent shapes and a set of remaining points. Each detected shape serves as a proxy for a set of corresponding points. Our method is based on random sampling and detects planes, spheres, cylinders, cones and tori. For models with surfaces composed of these basic shapes only, e.g. CAD models, we automatically obtain a representation solely consisting of shape proxies. We demonstrate that the algorithm is robust even in the presence of many outliers and a high degree of noise. The proposed method scales well with respect to the size of the input point cloud and the number and size of the shapes within the data. Even point sets with several millions of samples are robustly decomposed within less than a minute. Moreover the algorithm is conceptually simple and easy to implement. Application areas include measurement of physical parameters, scan registration, surface compression, hybrid rendering, shape classification, meshing, simplification, approximation and reverse engineering.


title: Multi-Camera Multi-Person Tracking for EasyLiving

text: While intelligent environments are often cited as a reason for doing work on visual person-tracking, really making an intelligent environment exposes many realworld problems in visual tracking that must be solved to make the technology practical. In the context of our EasyLiving project in intelligent environments, we created a practical person-tracking system that solves most of the real-world problems. It uses two sets of color stereo cameras for tracking multiple people during live demonstrations in a living room. The stereo images are used for locating people, and the color images are used for maintaining their identities. The system runs quickly enough to make the room feel responsive, and it tracks multiple people standing, walking, sitting, occluding, and entering and leaving the


title: Early neurologic assessment in preterm-infants: integration of traditional neurologic examination and observation of general movements.

text:
OBJECTIVE
To evaluate the possible additional benefit in terms of prognostic accuracy of an integrated application of a traditional scorable method of neurologic examination and the Prechtl's method of qualitative assessment of general movements (GMs) in a large population of 903 consecutive preterm infants.

STUDY DESIGN
Infants were enrolled from the Intensive Care Unit of the University of Catania. Inclusion criteria were a gestational age below 37 weeks and the absence of genetic disorders. All infants underwent serial ultrasound and at 3 months performed both the GMs assessment and the Hammersmith Infant Neurologic Examination (HINE). Outcome was assessed at 2 years by the Touwen neurologic examination and the Clinical Adaptive Test/Clinical, Linguistic and Auditory Milestone Scale.

RESULTS
The integration of the two methods was shown to be more effective than the single assessments in predicting neurologic outcome. The additional benefit of combining the two approaches was particularly clear for the discrimination between unilateral and bilateral cerebral palsy.

CONCLUSIONS
The integrated use of a scorable neurological examination and Prechtl's assessment of GMs can improve early prediction of neurodevelopmental outcome in preterm infants and should complement other clinical and instrumental exams in follow-up programs.


title: Physics-based hand interaction with virtual objects

text: We propose an intuitive user interaction system with virtual objects by directly using user's hands through a physics simulation. This system aims to provide a more direct way to manipulate and interact with virtual objects and to induce more realistic reactions of the virtual objects in the interaction than the existing methods. The key contribution of this paper is to model the deformations of the user's hands in two ways: global deformation by the hand poses and the local deformation by contacts with objects. By the two deformation models, objects can be manipulated with better stability and robustness without any predefinition of interaction methods depending on the object shapes, by solely using a physics simulation. The experiment shows that the proposed method enables a user to intuitively manipulate virtual object accurately.


title: Multiscale Shear-Lag Analysis of Stiffness Enhancement in Polymer-Graphene Nanocomposites.

text: Graphene and other two-dimensional (2D) materials are of emerging interest as functional fillers in polymer-matrix composites. In this study, we present a multiscale atomistic-to-continuum approach for modeling interfacial stress transfer in graphene-high-density polyethylene (HDPE) nanocomposites. Via detailed characterization of atomic-level stress profiles in submicron graphene fillers, we develop a modified shear-lag model for short fillers. A key feature of our approach lies in the correct accounting of stress concentration at the ends of fillers that exhibits a power-law dependence on filler ("flaw") size, determined explicitly from atomistic simulations, without any ad hoc modeling assumptions. In addition to two parameters that quantify the end stress concentration, only one additional shear-lag parameter is required to quantify the atomic-level stress profiles in graphene fillers. This three-parameter model is found to be reliable for fillers with dimensions as small as ∼10 nm. Our model predicts accurately the elastic response of aligned graphene-HDPE composites and provides appropriate upper bounds for the elastic moduli of nanocomposites with more realistic randomly distributed and oriented fillers. This study provides a systematic approach for developing hierarchical multiscale models of 2D material-based nanocomposites and is of particular relevance for short fillers, which are, currently, typical of solution-processed 2D materials.


title: Embedded System for Motion Control of an Omnidirectional Mobile Robot

text: In this paper, an embedded system for motion control of omnidirectional mobile robots is presented. An omnidirectional mobile robot is a type of holonomic robots. It can move simultaneously and independently in translation and rotation. The RoboCup small-size league, a robotic soccer competition, is chosen as the research platform in this paper. The first part of this research is to design and implement an embedded system that can communicate with a remote server using a wireless link, and execute received commands. Second, a fuzzy-tuned proportional–integral (PI) path planner and a related low-level controller are proposed to attain optimal input for driving a linear discrete dynamic model of the omnidirectional mobile robot. To fit the planning requirements and avoid slippage, velocity, and acceleration filters are also employed. In particular, low-level optimal controllers, such as a linear quadratic regulator (LQR) for multiple-input-multiple-output acceleration and deceleration of velocity are investigated, where an LQR controller is running on the robot with feedback from motor encoders or sensors. Simultaneously, a fuzzy adaptive PI is used as a high-level controller for position monitoring, where an appropriate vision system is used as a source of position feedback. A key contribution presented in this research is an improvement in the combined fuzzy-PI LQR controller over a traditional PI controller. Moreover, the efficiency of the proposed approach and PI controller are also discussed. Simulation and experimental evaluations are conducted with and without external disturbance. An optimal result to decrease the variances between the target trajectory and the actual output is delivered by the onboard regulator controller in this paper. The modeling and experimental results confirm the claim that utilizing the new approach in trajectory-planning controllers results in more precise motion of four-wheeled omnidirectional mobile robots.


title: Automatic inspection of pavement cracking distress

text: 15. Supplementary Notes Project performed in cooperation with the Texas Department of Transportation and the Federal Highway Administration. Project Title: Implementation of Automated Pavement Distress Rating Systems 16. Abstract This paper presents the image-processing algorithm customized for high-speed, real-time inspection of pavement cracking. In the algorithm, a pavement image is divided into grid cells of 8 x 8 pixels, and each cell is classified as a non-crack or crack cell using the grayscale information of the border pixels. Whether a crack cell can be regarded as a basic element (or seed) depends on its contrast to the neighboring cells. A number of crack seeds can be called a crack cluster if they fall on a linear string. A crack cluster corresponds to a dark strip in the original image that may or may not be a section of a real crack. Additional conditions to verify a crack cluster include the requirements in the contrast, width, and length of the strip. If verified crack clusters are oriented in similar directions, they will be joined to become one crack. Because many operations are performed on crack seeds rather than on the original image, crack detection can be executed simultaneously when the frame grabber is forming a new image, which permits a real-time, online pavement survey. The trial test results show good repeatability and accuracy when multiple surveys were conducted in different driving conditions.


title: Human activity analysis: A review

text:
Human activity recognition is an important area of computer vision research. Its applications include surveillance systems, patient monitoring systems, and a variety of systems that involve interactions between persons and electronic devices such as human-computer interfaces. Most of these applications require an automated recognition of high-level activities, composed of multiple simple (or atomic) actions of persons. This article provides a detailed overview of various state-of-the-art research papers on human activity recognition. We discuss both the methodologies developed for simple human actions and those for high-level activities. An approach-based taxonomy is chosen that compares the advantages and limitations of each approach.
 Recognition methodologies for an analysis of the simple actions of a single person are first presented in the article. Space-time volume approaches and sequential approaches that represent and recognize activities directly from input images are discussed. Next, hierarchical recognition methodologies for high-level activities are presented and compared. Statistical approaches, syntactic approaches, and description-based approaches for hierarchical recognition are discussed in the article. In addition, we further discuss the papers on the recognition of human-object interactions and group activities. Public datasets designed for the evaluation of the recognition methodologies are illustrated in our article as well, comparing the methodologies' performances. This review will provide the impetus for future research in more productive areas.


title: High speed complementary drive of a hybrid MOSFET and IGBT power switch

text: The hybrid integration of an unipolar and a bipolar power semiconductor in parallel combines their advantages whilst avoiding their disadvantages. The drawback of this approach is an increased complexity of the gate drive. This paper proposes a simple hardware solution utilising a transductor to transmit both drive and control energy together with a slow optocoupler providing an enable signal for the respective hybrid switch. Questions regarding the choice of an optimal switching frequency are addressed


title: A new estimation model of the lightning shielding performance of transmission lines using a fractal approach

text: The path of the lightning discharge shows characteristics of branching and tortuosity, and the lightning shielding failure of the transmission line also has statistical features. In this paper, an improved leader progression model using a fractal approach is introduced, which considers both the deterministic and stochastic features of the downward lightning leader. Based on the proposed model, the statistical features of lightning shielding failure on transmission lines will be analyzed. First, the fractal dimensions of the downward lightning trajectory in simulation are calculated and the value of breakdown probability constant η will be determined based on the fractal dimensions of natural lightning. Second, using the proposed model the shielding failure probability contour around the transmission line space is also obtained and used to describe the shielding failure zone and then the contour curve is applied to analyzing the characteristics of transmission lines that suffer from direct lightning strokes. Moreover, a method to estimate the lightning shielding failure rate of transmission lines is put forward, and the influence of the line height and the shielding angle of ground wires on the shielding failure rate of transmission lines are also investigated.


title: Thermal management consumption and its effect on remaining range estimation of electric vehicles

text: This paper investigates the significance of thermal management energy consumption on the range of electric vehicles. It is necessary to combine consumption models of powertrain components such as the electric machine, power electronics and high voltage battery that take into account trip-based information containing a predicted speed profile and elevation data with a thermal management system that has the ability to incorporate predicted thermal losses of the powertrain. The thermal management system refers to the sum of all components within the refrigeration cycle of the vehicle as well as the associated control strategy. Based on the results of a real world drive study in an electric vehicle, it is demonstrated, that the range uncertainties introduced by the consumption of the thermal management are significant and should be considered when developing predictive range estimation algorithms. These uncertainties are further investigated by an energy flow based thermal system model. In order to minimize range uncertainties, a system architecture for the prediction of thermal management consumption is proposed.


title: Developing pervasive multi-agent systems with nature-inspired coordination

text: Pervasive computing systems can bemodelled effectively as populations of interacting autonomous components. The key challenge to realizing suchmodels is in getting separatelyspecified and -developed sub-systems to discover and interoperate with each other in an open and extensible way, supported by appropriate middleware services. In this paper, we argue that nature-inspired coordination models offer a promising way of addressing this challenge.We first frame the various dimensions alongwhich nature-inspired coordination models can be defined, and survey themost relevant proposals in the area.We describe the nature-inspired coordination model developed within the SAPERE project as a synthesis of existing approaches, and show how it can effectively support the multifold requirements of modern and emerging pervasive services. We conclude by identifying what we think are the open research challenges in this area, and identify some research directions that we believe are promising. © 2014 Elsevier B.V. All rights reserved.