Query: A learning-based approach to text image retrieval: using CNN features and improved similarity metrics

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Very Deep Convolutional Networks for Large-Scale Image Recognition <=SEP=> In this work we investigate the effect of the convolutional n etwork depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achi eved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first a nd he second places in the localisation and classification tracks respec tively. We also show that our representations generalise well to other datasets, whe re t y achieve the stateof-the-art results. Importantly, we have made our two bestp rforming ConvNet models publicly available to facilitate further research o n the use of deep visual representations in computer vision.

Going deeper with convolutions <=SEP=> We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

You Only Look Once: Unified, Real-Time Object Detection <=SEP=> We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.

Return of the Devil in the Details: Delving Deep into Convolutional Nets <=SEP=> The latest generation of Convolutional Neural Networks (CNN) have achieved impressive results in challenging benchmarks on image recognition and object detection, significantly raising the interest of the community in these methods. Nevertheless, it is still unclear how different CNN methods compare with each other and with previous state-of-the-art shallow representations such as the Bag-of-Visual-Words and the Improved Fisher Vector. This paper conducts a rigorous evaluation of these new techniques, exploring different deep architectures and comparing them on a common ground, identifying and disclosing important implementation details. We identify several useful properties of CNN-based representations, including the fact that the dimensionality of the CNN output layer can be reduced significantly without having an adverse effect on performance. We also identify aspects of deep and shallow methods that can be successfully shared. In particular, we show that the data augmentation techniques commonly applied to CNN-based methods can also be applied to shallow methods, and result in an analogous performance boost. Source code and models to reproduce the experiments in the paper is made publicly available.

Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks <=SEP=> Recognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief (Dean et al., 2012) implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available SVHN dataset and achieve over 96% accuracy in recognizing complete street numbers. We show that on a per-digit recognition task, we improve upon the state-of-the-art and achieve 97.84% accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over 90% accuracy. Our evaluations further indicate that at specific operating thresholds, the performance of the proposed system is comparable to that of human operators. To date, our system has helped us extract close to 100 million physical street numbers from Street View imagery worldwide.

Delay-constrained throughput maximization in UAV-enabled OFDM systems <=SEP=> The use of unmanned aerial vehicles (UAVs) as aerial base stations (BSs) is of great practical significance in future wireless networks, especially for on-demand deployment during a temporary event and emergency situation. Although prior works have demonstrated the performance improvement brought by the UAV mobility, they mainly focus on the delay-tolerant applications such as file transfer and data collection. As such, it is unknown if the UAV mobility is able to provide performance gain for delay-constrained applications, such as video conferencing and online gaming. Motivated by this, we study in this paper a UAV-enabled downlink orthogonal division multiple access (OFDMA) network where a UAV is dispatched to serve two ground users within a given flight period. By taking into account the delay-specified minimum-rate-ratio constraints of the users, our goal is to maximize the minimum user throughput by jointly optimizing the UAV trajectory and communication resource allocation. We show that the max-min user throughput in general decreases as the minimum-rate-ratio constraints become more stringent, which reveals a fundamental tradeoff between the throughput gain by exploiting the UAV mobility and the user delay requirement. Simulation results verify our theoretical findings and also demonstrate the effectiveness of our proposed design.

Data Encryption Scheme Based on Rules of Cellular Automata and Chaotic Map Function for Information Security <=SEP=> Cryptography has recently played a significant role in secure data transmissions and storages. Most conventional data encryption schemes are relatively complicated and complexity in encrypted keys is insufficient, resulting in long computational time and low degree of security against all types of attacks. Consequently, a highlysecured and robust data encryption scheme is necessary. This paper therefore presents the data encryption scheme based on a combination of Cellular Automata (CA) and a robust chaotic system that employs absolutevalue piecewise-linear nonlinearity. The proposed encryption scheme is not only applicable to image encryption but also extendable to text and Excel files. Simulation results reveal that the entropy of the encrypted image is nearly 8 corresponding to the image entropy theory, and the correlation coefficients of before-and-after encrypted images are close to zero. Besides, the histogram of the encrypted image of pixel values in the range (0-255) is apparently flat, indicating that the color in image is fully distributed. Such results indicate that the proposed data encryption scheme offers a high level of security with fast processing time duration. The implementation of image encryption Android application is demonstrated. Comparisons to other related approaches are also included.

LogKV: Exploiting Key-Value Stores for Log Processing <=SEP=> Event log processing and analysis play a key role in applications ranging from security management, IT trouble shooting, to user behavior analysis. Recent years have seen a rapid growth in system scales and the corresponding rapid increase in the amount of log event data. At the same time, as logs are found to be a valuable information source, log analysis tasks have become more sophisticated demanding both interactive exploratory query processing and batch computation. Desirable query types include selection with time ranges and value filtering criteria, join within time windows, join between log data and reference tables, and various aggregation types. In such a situation, parallel solutions are necessary, but existing parallel and distributed solutions either support limited query types or perform only batch computations on logs. With a system called LogKV, this paper reports a first study of using Key-Value stores to support log processing and analysis, exploiting the scalability, reliability, and efficiency commonly found in Key-Value store systems. LogKV contains a number of unique techniques that are needed to handle log data in terms of event ingestion, load balancing, storage optimization, and query processing. Preliminary experimental results show that LogKV is a promising solution.

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems <=SEP=> The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing in a wide range of wireless networking applications. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. For instance, UAVs can be deployed to complement existing cellular systems by providing additional capacity to hotspot areas as well as to provide network coverage in emergency and public safety situations. On the other hand, UAVs can operate as flying mobile terminals within the cellular networks. Such cellular-connected UAVs can enable a wide range of key applications expanding from real-time video streaming to item delivery. Despite the several benefits and practical applications of using UAVs as aerial wireless devices, one must address many technical challenges. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional (3D) deployment, performance analysis, air-to-ground channel modeling, and energy efficiency are explored along with representative results. Then, fundamental open problems and potential research directions pertaining to wireless communications and networking with UAVs are introduced. To cope with the open research problems, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.

TCP Ordo: The cost of ordered processing in TCP servers <=SEP=> To achieve scalable, high-throughput, low-latency packet processing, TCP implementations are parallelized across cores in multicore platforms. This, however, significantly affects the order in which packets from different flows are delivered to application processing. Our measurements record up to 75% of the packets are delivered to applications in a way that does not match the order in which they are received on the network interface. For many important classes of applications, such as financial services, bidding and trading engines, game engines, this cross-flow packet reordering affects their ability to provide fairness guarantees. To address this gap, we propose TCP-Ordo - a TCP stack which provides strict ordering of packets across multiple flows, as well as flexibility to control the degree to which ordering is enforced. TCP-Ordo outperforms existing TCP implementations in both latency and throughput. The current prototype is implemented as a user-level TCP stack for Mellanox Connectx3 NICs with 40Gbps Ethernet interfaces. Without ordering guarantees TCP-Ordo delivers one way latency of 4.75usec (a 5x improvement over the Linux kernel) for 150B packets and throughput of 22Gbps (a 2x improvement over mTCP) for 1500B packets. Furthermore, with TCP-Ordo applications are provided with strict packet order delivery, with performance that continues to be superior to the state-of-the-art, even when enforcing packet order across 800,000 connections on a 12-core platform. Finally, TCP-Ordo supports the notion of `ordered domains' that offer flexibility in the degree of ordering that an application will experience, and pay for.

An Ultralow-Power Low-Noise CMOS Biopotential Amplifier for Neural Recording <=SEP=> This brief presents a design strategy for a neural recording amplifier array with ultralow-power low-noise operation that is suitable for large-scale integration. The topology combines a highly efficient but supply-sensitive single-ended first stage with a shared reference channel and a differential second stage to effect feedforward supply noise cancellation, combining the low power of single-ended amplifiers with improved supply rejection. For a two-channel amplifier, the measurements show a midband gain of 58.7 dB and a passband from 490 mHz to 10.5 kHz. The amplifier consumes 2.85 μA per channel from a 1-V supply and exhibits an input-referred noise of 3.04 μVrms from 0.1 Hz to 100 kHz, corresponding to a noise efficiency factor of 1.93. The power supply rejection ratio is better than 50 dB in the passband. The amplifier is fabricated in a 90-nm CMOS process and occupies 0.137 mm2 of chip area.

Design and Verification of Secure Systems <=SEP=> This paper reviews some of the difficulties that arise in the verification of kernelized secure systems and suggests new techniques for their resolution.
 It is proposed that secure systems should be conceived as distributed systems in which security is achieved partly through the physical separation of its individual components and partly through the mediation of trusted functions performed within some of those components. The purpose of a security kernel is simply to allow such a 'distributed' system to actually run within a single processor; policy enforcement is not the concern of a security kernel.
 This approach decouples verification of components which perform trusted functions from verification of the security kernel. This latter task may be accomplished by a new verification technique called 'proof of separability' which explicitly addresses the security relevant aspects of interrupt handling and other issues ignored by present methods.

Critical review of the e-loyalty literature: a purchase-centred framework <=SEP=> 

Empathy circuits <=SEP=> The social neuroscientific investigation of empathy has revealed that the same neural networks engaged during first-hand experience of affect subserve empathic responses. Recent meta-analyses focusing on empathy for pain for example reliably identified a network comprising anterior insula and anterior midcingulate cortex. Moreover, recent studies suggest that the generation of empathy is flexibly supported by networks involved in action simulation and mentalizing depending on the information available in the environment. Further, empathic responses are modulated by many factors including the context they occur in. Recent work shows how this modulation can be afforded by the engagement of antagonistic motivational systems or by cognitive control circuits, and these modulatory systems can also be employed in efforts to regulate one's empathic responses.

A new word sense similarity measure in wordnet <=SEP=> Recognizing similarities between words is a basic element of computational linguistics and artificial intelligence applications. This paper presents a new approach for measuring semantic similarity between words via concepts. Our proposed measure is a hybrid system based on using a new Information content metric and edge counting-based tuning function. In proposed system, hierarchical structure is used to present information content instead of text corpus and our result will be improved by edge counting-based tuning function. The result of the system is evaluated against human similarity ratings demonstration and shows significant improvement in compare with traditional similarity measures.

Performance evaluation of pattern classifiers for handwritten character recognition <=SEP=> This paper describes a performance evaluation study in which some efficient classifiers are tested in handwritten digit recognition. The evaluated classifiers include a statistical classifier (modified quadratic discriminant function, MQDF), three neural classifiers, and an LVQ (learning vector quantization) classifier. They are efficient in that high accuracies can be achieved at moderate memory space and computation cost. The performance is measured in terms of classification accuracy, sensitivity to training sample size, ambiguity rejection, and outlier resistance. The outlier resistance of neural classifiers is enhanced by training with synthesized outlier data. The classifiers are tested on a large data set extracted from NIST SD19. As results, the test accuracies of the evaluated classifiers are comparable to or higher than those of the nearest neighbor (1-NN) rule and regularized discriminant analysis (RDA). It is shown that neural classifiers are more susceptible to small sample size than MQDF, although they yield higher accuracies on large sample size. As a neural classifier, the polynomial classifier (PC) gives the highest accuracy and performs best in ambiguity rejection. On the other hand, MQDF is superior in outlier rejection even though it is not trained with outlier data. The results indicate that pattern classifiers have complementary advantages and they should be appropriately combined to achieve higher performance.

Using web security scanners to detect vulnerabilities in web services <=SEP=> Although web services are becoming business-critical components, they are often deployed with critical software bugs that can be maliciously explored. Web vulnerability scanners allow detecting security vulnerabilities in web services by stressing the service from the point of view of an attacker. However, research and practice show that different scanners have different performance on vulnerabilities detection. In this paper we present an experimental evaluation of security vulnerabilities in 300 publicly available web services. Four well known vulnerability scanners have been used to identify security flaws in web services implementations. A large number of vulnerabilities has been observed, which confirms that many services are deployed without proper security testing. Additionally, the differences in the vulnerabilities detected and the high number of false-positives (35% and 40% in two cases) and low coverage (less than 20% for two of the scanners) observed highlight the limitations of web vulnerability scanners on detecting security vulnerabilities in web services.

Optimizing Regular Path Expressions Using Graph Schemas <=SEP=> Several languages, such as LOREL and UnQL, support querying of semi-structured data. Others, such as WebSQL and WebLog, query Web sites. All these languages model data as labeled graphs and use regular path expressions to express queries that traverse arbitrary paths in graphs. Naive execution of path expressions is ineecient, however, because it often requires exhaustive graph search. We describe two optimization techniques for queries with regular path expressions, which we call regular queries. Both rely on graph schemas, which specify partial knowledge of a graph's structure. Query pruning restricts search to a fragment of the graph; we give an eecient algorithm for rewriting any regular query into a pruned one. Query rewriting using state extents can entirely eliminate or substantially reduce graph traversal; it is reminiscent of optimizing relational queries using indices. There may be several ways to optimize a query using state extents ; we give an exponential-time algorithm that nds all such optimizations. On special forms of regular queries, the algorithm is provably eecient. We also give an eecient approximation algorithm that works on all regular queries. Our target databases are \intranet" sites, i.e., Web sites associated with a company or organization. Intranet sites are good targets for our techniques, because they often reeect the structure of their organization, hence their structure is partially known. Preliminary results show that regular queries applied to an intranet site produce more precise results than keyword-only queries and that our optimizations substantially reduce the cost of evaluating regular queries, indicating regular queries could be evaluated eeciently by Web-site search engines.

Performance Evaluation of SVM and K-Nearest Neighbor Algorithm over Medical Data set <=SEP=> In this age of computer science each and every thing becomes intelligent and perform task as human. For that purpose there are various tools, techniques and methods are proposed. Support vector machine is a model for statistics and computer science, to perform supervised learning, methods that are used to make analysis of data and recognize patterns. SVM is mostly used for classification and regression analysis. And in the same way k-nearest neighbor algorithm is a classification algorithm used to classify data using training examples. In this paper we use SVM and KNN algorithm to classify data and get prediction (find hidden patterns) for target. Here we use medical patients nominal data to classify and discover the data pattern to predict future disease, Uses data mining which is use to classify text analysis in future.

2-Point-based outlier rejection for camera-IMU systems with applications to micro aerial vehicles <=SEP=> This paper presents a novel method to perform the outlier rejection task between two different views of a camera rigidly attached to an Inertial Measurement Unit (IMU). Only two feature correspondences and gyroscopic data from IMU measurerments are used to compute the motion hypothesis. By exploiting this 2-point motion parametrization, we propose two algorithms to remove wrong data associations in the feature-matching process for case of a 6DoF motion. We show that in the case of a monocular camera mounted on a quadrotor vehicle, motion priors from IMU can be used to discard wrong estimations in the framework of a 2-point-RANSAC based approach. The proposed methods are evaluated on both synthetic and real data.

Phoneme visem mapping for Marathi language using linguistic approach <=SEP=> Visual speech is transcribed using visems. Visem is a set of visibly different phonemes in a language. There is high correlation between Phoneme and visem and phoneme to visem mapping is many to one because of the fact that many phonemes cannot be distinguished visually. Since each language has its own phonetic set, visem classification becomes language dependant process. Most of the phoneme visem maps available in literature are developed for English language. This paper presents phoneme visem mapping of consonants and vowels of Marathi language based on linguistic approach. This is the first attempt towards Marathi phoneme visem mapping and will be useful for visual speech recognition in Marathi language.

Adoption and use of Java generics <=SEP=> Support for generic programming was added to the Java language in 2004, representing perhaps the most significant change to one of the most widely used programming languages today. Researchers and language designers anticipated this addition would relieve many long-standing problems plaguing developers, but surprisingly, no one has yet measured how generics have been adopted and used in practice. In this paper, we report on the first empirical investigation into how Java generics have been integrated into open source software by automatically mining the history of 40 popular open source Java programs, traversing more than 650 million lines of code in the process. We evaluate five hypotheses and research questions about how Java developers use generics. For example, our results suggest that generics sometimes reduce the number of type casts and that generics are usually adopted by a single champion in a project, rather than all committers. We also offer insights into why some features may be adopted sooner and others features may be held back.

SVM classifier on chip for melanoma detection <=SEP=> Support Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM-based diagnosis system for use in primary care for early detection of melanoma. In this paper, an optimized SVM classifier is implemented onto a recent FPGA platform using the latest design methodology to be embedded into the proposed device for realizing online efficient melanoma detection on a single system on chip/device. The hardware implementation results demonstrate a high classification accuracy of 97.9% and a significant acceleration factor of 26 from equivalent software implementation on an embedded processor, with 34% of resources utilization and 2 watts for power consumption. Consequently, the implemented system meets crucial embedded systems constraints of high performance and low cost, resources utilization and power consumption, while achieving high classification accuracy.

Hardware Trojan Detection by Multiple-Parameter Side-Channel Analysis <=SEP=> Hardware Trojan attack in the form of malicious modification of a design has emerged as a major security threat. Sidechannel analysis has been investigated as an alternative to conventional logic testing to detect the presence of hardware Trojans. However, these techniques suffer from decreased sensitivity toward small Trojans, especially because of the large process variations present in modern nanometer technologies. In this paper, we propose a novel noninvasive, multiple-parameter side-channel analysisbased Trojan detection approach. We use the intrinsic relationship between dynamic current and maximum operating frequency of a circuit to isolate the effect of a Trojan circuit from process noise. We propose a vector generation approach and several design/test techniques to improve the detection sensitivity. Simulation results with two large circuits, a 32-bit integer execution unit (IEU) and a 128-bit advanced encryption standard (AES) cipher, show a detection resolution of 1.12 percent amidst ±20 percent parameter variations. The approach is also validated with experimental results. Finally, the use of a combined side-channel analysis and logic testing approach is shown to provide high overall detection coverage for hardware Trojan circuits of varying types and sizes.

Sentiment Analyzer with Rich Features for Ironic and Sarcastic Tweets <=SEP=> Sentiment Analysis of tweets is a complex task, because these short messages employ unconventional language to increase the expressiveness. This task becomes even more difficult when people use figurative language (e.g. irony, sarcasm and metaphors) because it causes a mismatch between the literal meaning and the actual expressed sentiment. In this paper, we describe a sentiment analysis system designed for handling ironic and sarcastic tweets. Features grounded on several linguistic levels are proposed and used to classify the tweets in a 11-scale range, using a decision tree. The system is evaluated on the dataset released by the organizers of the SemEval 2015, task 11. The results show that our method largely outperforms the systems proposed by the participants of the task on ironic and sarcastic tweets.

Using neural network ensembles for bankruptcy prediction and credit scoring <=SEP=> Bankruptcy prediction and credit scoring have long been regarded as critical topics and have been studied extensively in the accounting and finance literature. Artificial intelligence and machine learning techniques have been used to solve these financial decision-making problems. The multilayer perceptron (MLP) network trained by the back-propagation learning algorithm is the mostly used technique for financial decision-making problems. In addition, it is usually superior to other traditional statistical models. Recent studies suggest combining multiple classifiers (or classifier ensembles) should be better than single classifiers. However, the performance of multiple classifiers in bankruptcy prediction and credit scoring is not fully understood. In this paper, we investigate the performance of a single classifier as the baseline classifier to compare with multiple classifiers and diversified multiple classifiers by using neural networks based on three datasets. By comparing with the single classifier as the benchmark in terms of average prediction accuracy, the multiple classifiers only perform better in one of the three datasets. The diversified multiple classifiers trained by not only different classifier parameters but also different sets of training data perform worse in all datasets. However, for the Type I and Type II errors, there is no exact winner. We suggest that it is better to consider these three classifier architectures to make the optimal financial decision. 2007 Elsevier Ltd. All rights reserved.

Simplified Optimal Trajectory Control (SOTC) for LLC Resonant Converters <=SEP=> In this paper, a simplified optimal trajectory control (SOTC) for the LLC resonant converter is proposed. During the steady state, a linear compensator such as proportional-integral or proportional-integral-derivative (PID) is used, controlling the switching frequency fs to eliminate the steady-state error. However, during load transients, the SOTC method takes over, immediately changing the pulse widths of the gate-driving signals. Using the state-plane analysis, the pulse widths are estimated, letting the state variables track the optimal trajectory locus in the minimum period of time. The proposed solution is implemented in a digital controller, and experimental results show that while the digital logic requirement is very small, the performance improvement is significant.

PARS: A Uniform and Open-source Password Analysis and Research System <=SEP=> In this paper, we introduce an open-source and modular password analysis and research system, PARS, which provides a uniform, comprehensive and scalable research platform for password security. To the best of our knowledge, PARS is the first such system that enables researchers to conduct fair and comparable password security research. PARS contains 12 state-of-the-art cracking algorithms, 15 intra-site and cross-site password strength metrics, 8 academic password meters, and 15 of the 24 commercial password meters from the top-150 websites ranked by Alexa. Also, detailed taxonomies and large-scale evaluations of the PARS modules are presented in the paper.

Design and fabrication of a lightweight additive-manufactured Ka-band horn antenna array <=SEP=> In this paper a 2×2 Ka-band lightweight horn antenna array demonstrator is designed and fabricated by means of an additive manufacturing process optimized for complex RF, microwave and mm-wave antenna components. The proposed waveguide feeding network aims to exploit the flexibility offered by this novel fabrication technique and can be easily translated to antenna arrays with more elements. The electromagnetic performance of the featured structure is validated by means of full-wave numerical simulations. The horn antenna array is fabricated and under experimental RF characterization.

Microblog Entity Linking with Social Temporal Context <=SEP=> Nowadays microblogging sites, such as Twitter and Chinese Sina Weibo, have established themselves as an invaluable information source, which provides a huge collection of manually-generated tweets with broad range of topics from daily life to breaking news. Entity linking is indispensable for understanding and maintaining such information, which in turn facilitates many real-world applications such as tweet clustering and classification, personalized microblog search, and so forth. However, tweets are short, informal and error-prone, rendering traditional approaches for entity linking in documents largely inapplicable. Recent work addresses this problem by utilising information from other tweets and linking entities in a batch manner. Nevertheless, the high computational complexity makes this approach infeasible for real-time applications given the high arrival rate of tweets. In this paper, we propose an efficient solution to link entities in tweets by analyzing their social and temporal context. Our proposed framework takes into consideration three features, namely entity popularity, entity recency, and user interest information embedded in social interactions to assist the entity linking task. Effective indexing structures along with incremental algorithms have also been developed to reduce the computation and maintenance costs of our approach. Experimental results based on real tweet datasets verify the effectiveness and efficiency of our proposals.