title: 3D convolutional neural networks by modal fusion

text: We propose multi-view and volumetric convolutional neural networks (ConvNets) for 3D shape recognition, which combines surface normal and height fields to capture local geometry and physical size of an object. This strategy helps distinguishing between objects with similar geometries but different sizes. This is especially useful for enhancing volumetric ConvNets and classifying 3D scans with insufficient surface details. Experimental results on CAD and real-world scan datasets showed that our technique outperforms previous approaches.


title: Using ultra-low parasitic hybrid packaging method to reduce high frequency EMI noise for SiC power module

text: In order to reduce high frequency electromagnetic interference (EMI) noise for SiC devices, this paper presents a hybrid structure SiC power module with low parasitic inductance and low ground capacitance that can suppress EMI efficiently. To certify the improvement on EMI, two buck converters, using the same power loop and gate drive, are built for experiment. Two converters use the proposed power module and TO-247 packaged commercial devices respectively. The experimental results show that low parasitic inductance can increase switching speed considerably while have only slight impact on EMI. And low parasitic ground capacitance can decrease the ground current effectively. Considering soft switching can also reduce EMI, this paper further compares EMI noise of two converters under two conditions: continuous current modulation (CCM) and triangle current modulation (TCM). The result shows that the proposed power module can suppress the common mode (CM) noise effectively at both situations. Furthermore, this paper verifies the noise mode transformation from differential mode (DM) EMI to CM EMI.


title: MVR: An Architecture for Computation Offloading in Mobile Edge Computing

text: As communication and sensing capabilities of mobile devices increase, mobile applications are becoming increasingly complex. The ability of computation offloading, which is one of the main features of mobile edge computing gains relevance as a technique to improve the battery lifetime of mobile devices and increase the performance of applications. In this paper, we describe the offloading system model and present an innovative architecture, called "MVR", contributing to computation offloading in mobile edge computing.


title: A Better Method to Analyze Blockchain Consistency

text: The celebrated Nakamoto consensus protocol [16] ushered in several new consensus applications including cryptocurrencies. A few recent works [7, 17] have analyzed important properties of blockchains, including most significantly, consistency, which is a guarantee that all honest parties output the same sequence of blocks throughout the execution of the protocol. To establish consistency, the prior analysis of Pass, Seeman and Shelat [17] required a careful counting of certain combinatorial events that was difficult to apply to variations of Nakamoto. The work of Garay, Kiayas, and Leonardas [7] provides another method of analyzing the blockchain under the simplifying assumption that the network was synchronous. The contribution of this paper is the development of a simple Markov-chain based method for analyzing consistency properties of blockchain protocols. The method includes a formal way of stating strong concentration bounds as well as easy ways to concretely compute the bounds. We use our new method to answer a number of basic questions about consistency of blockchains: Our new analysis provides a tighter guarantee on the consistency property of Nakamoto's protocol, including for parameter regimes which [17] could not consider; We analyze a family of delaying attacks first presented in [17], and extend them to other protocols; We analyze how long a participant should wait before considering a high-value transaction "confirmed"; We analyze the consistency of CliqueChain, a variation of the Chainweb [14] system; We provide the first rigorous consistency analysis of GHOST [20] and also analyze a folklore "balancing"-attack. In each case, we use our framework to experimentally analyze the consensus bounds for various network delay parameters and adversarial computing percentages. We hope our techniques enable authors of future blockchain proposals to provide a more rigorous analysis of their schemes.


title: Cloaking Malware with the Trusted Platform Module

text: The Trusted Platform Module (TPM) is commonly thought of as hardware that can increase platform security. However, it can also be used for malicious purposes. The TPM, along with other hardware, can implement acloaked computation , whose memory state cannot be observed by any other software, including the operating system and hypervisor. We show that malware can use cloaked computations to hide essential secrets (like the target of an attack) from a malware analyst. We describe and implement a protocol that establishes an encryption key under control of the TPM that can only be used by a specific infection program. An infected host then proves the legitimacy of this key to a remote malware distribution platform, and receives and executes an encrypted payload in a way that prevents software visibility of the decrypted payload. We detail how malware can benefit from cloaked computations and discuss defenses against our protocol. Hardening legitimate uses of the TPM against attack improves the resilience of our malware, creating a Catch-22 for secure computing technology.


title: Question Generation Shared Task and Evaluation Challenge - Status Report

text: The First Shared Task Evaluation Challenge on Question Generation took place in 2010 as part of the 3 workshop on Question Generation. The campaign included two tasks: Question Generation from Sentences and Question Generation from Paragraphs. This status report briefly summarizes the motivation, tasks and results. Lessons learned relevant to future QG-STECs are also offered.


title: Sensor observation streams within cloud-based IoT platforms: Challenges and directions

text: Observation streams can be considered as a special case of data streams produced by sensors. With the growth of the Internet of Things (IoT), more and more connected sensors will produce unbounded observation streams. In order to bridge the gap between sensors and observation consumers, we have witnessed the design and the development of Cloud-based IoT platforms. Such systems raise new research challenges, in particular regarding observation collection, processing and consumption. These new research challenges are related to observation streams and should be addressed from the implementation phase by developers to build platforms able to meet other non-functional requirements later. Unlike existing surveys, this paper is intended for developers that would like to design and implement a Cloud-based IoT platform capable of handling sensor observation streams. It provides a comprehensive way to understand main observation-related challenges, as well as non-functional requirements of IoT platforms such as platform adaptation, scalability and availability. Last but not the least, it gives recommendations and compares some relevant open-source software that can speed up the development process.


title: The Effects of Perceived Corporate Social Responsibility on Employee Attitudes

text: We explore the impact on employee attitudes of their perceptions of how others outside the organization are treated (i.e., corporate social responsibility) above and beyond the impact of how employees are directly treated by the organization. Results of a study of 827 employees in eighteen organizations show that employee perceptions of corporate social responsibility (CSR) are positively related to (a) organizational commitment with the relationship being partially mediated by work meaningfulness and perceived organizational support (POS) and (b) job satisfaction with work meaningfulness partially mediating the relationship but not POS. Moreover, in order to address limited micro-level research in CSR, we develop a measure of employee perceptions of CSR through four pilot studies. Employing a bifactor model, we find that social responsibility has an additional effect on employee attitudes beyond environmental responsibility, which we posit is due to the relational component of social responsibility (e.g., relationships with community).


title: Steady-State Analysis of the Series Resonant Converter

text: The series resonant converter is analyzed in steady state, and for constant switching frequency the output current and voltage characteristics are found to be ellipses. The converter operating point can then be easily obtained by superimposing a load line on these elliptical characteristics. Peak resonant capacitor voltage and inductor current are also plotted in the output plane and are dependent to first order only on output current. When peak voltage and current are plotted in this manner, the dependence of component stresses on operating point is clearly revealed. The output characteristics are modified to include the effect of transistor and diode voltage drops, and experimental verification is presented.


title: CSIFT based locality-constrained linear coding for image classification

text: In the past decade, SIFT descriptor has been witnessed as one of the most robust local invariant feature descriptors and widely used in various vision tasks. Most traditional image-classification systems depend on the gray-based SIFT descriptors, which only analyze the gray level variations of the images. Misclassification may happen since their color contents are ignored. In this article, we concentrate on improving the performance of existing image-classification algorithms by adding color information. To achieve this purpose, different kinds of colored SIFT descriptors are introduced and implemented. locality-constrained linear coding (LLC), a state-of-the-art sparse coding technology, is employed to construct the image-classification system for the evaluation. Moreover, we propose a simple $$\ell _2$$ ℓ 2 -norm regularized local distance to improve the traditional LLC method. The real experiments are carried out on several benchmarks. With the enhancements to color SIFT and $$\ell _2$$ ℓ 2 -norm regularization, the proposed image-classification system obtains approximately $$2\,\%$$ 2 % improvement of classification accuracy on the Caltech-101 dataset and approximately $$5\,\%$$ 5 % improvement of classification accuracy on the Caltech-256 dataset.


title: GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild

text: In this work, we introduce a large high-diversity database for generic object tracking, called GOT-10k. GOT-10k is backboned by the semantic hierarchy of WordNet [1]. It populates a majority of 563 object classes and 87 motion patterns in real-world, resulting in a scale of over 10 thousand video segments and 1.5 million bounding boxes. To our knowledge, GOT-10k is by far the richest motion trajectory dataset, and its coverage of object classes is more than a magnitude wider than similar scale counterparts [20], [23]. By publishing GOT-10k, we hope to encourage the development of generic purposed trackers that work for a wide range of moving objects and under diverse real-world scenarios. To promote generalization and avoid the evaluation results biased to seen classes, we follow the one-shot principle [35] in dataset splitting where training and testing classes are zero-overlapped. We also carry out a series of analytical experiments to select a compact while highly representative testing subset – it embodies 84 object classes and 32 motion patterns with only 180 video segments, allowing for efficient evaluation. Finally, we train and evaluate a number of representative trackers on GOT-10k and analyze their performance. The evaluation results suggest that tracking in real-world unconstrained videos is far from being solved, and only 40% of frames are successfully tracked using top ranking trackers. The database and toolkits are publicly available at https://got-10k.github.io.


title: Thermal Facial Analysis for Deception Detection

text: Thermal imaging technology can be used to detect stress levels in humans based on the radiated heat from their face. In this paper, we use thermal imaging to monitor the periorbital region's thermal variations and test whether it can offer a discriminative signature for detecting deception. We start by presenting an overview on automated deception detection and propose a novel methodology, which we validate experimentally on 492 thermal responses (249 lies and 243 truths) extracted from 25 participants. The novelty of this paper lies in scoring a larger number of questions per subject, emphasizing a within-person approach for learning from data, proposing a framework for validating the decision making process, and correct evaluation of the generalization performance. A $k$ -nearest neighbor classifier was used to classify the thermal responses using different strategies for data representation. We report an 87% ability to predict the lie/truth responses based on a within-person methodology and fivefold cross validation. Our results also show that the between-person approach for modeling deception does not generalize very well across the training data.


title: Deception detection using a multimodal approach

text: In this paper we address the automatic identification of deceit by using a multimodal approach. We collect deceptive and truthful responses using a multimodal setting where we acquire data using a microphone, a thermal camera, as well as physiological sensors. Among all available modalities, we focus on three modalities namely, language use, physiological response, and thermal sensing. To our knowledge, this is the first work to integrate these specific modalities to detect deceit. Several experiments are carried out in which we first select representative features for each modality, and then we analyze joint models that integrate several modalities. The experimental results show that the combination of features from different modalities significantly improves the detection of deceptive behaviors as compared to the use of one modality at a time. Moreover, the use of non-contact modalities proved to be comparable with and sometimes better than existing contact-based methods. The proposed method increases the efficiency of detecting deceit by avoiding human involvement in an attempt to move towards a completely automated non-invasive deception detection process.


Multiple View Geometry in Computer Vision


The quintessence of the diagnosis in traditional Chinese medicine is syndrome differentiation and treatment. Syndrome differentiation consists of four methods: observing, hearing as well as smelling, asking, and touching. The examination of the observing is the most important procedure in the method of "tongue." In recent years, numerous medical studies have identified the close relations between sublingual veins and human organs. Therefore, sublingual pathological symptoms, as well as demographical information of patients, imply pathological changes in the organs, and early diagnosis is beneficial for early treatment. However, the diagnosis of sublingual pathological symptoms is usually influenced by the doctor's subjective interpretation, experience, and environmental factors. The results can easily be limited by subjective factors such as knowledge, experience, mentality, diagnostic techniques, color perception and interpretation. Different doctors may make different judgments on the same tongue, presenting less than desirable repeatability. Therefore, assisting doctors' diagnoses with scientific methods and standardizing the differentiating process to obtain reliable diagnoses and enhance the clinical applicability of Chinese medicine is an important issue. In its wake, this study aims to construct an Automatic Sublingual Vein Feature Extraction System based on image processing technologies to allow objective and quantified computer readings. The extraction of sublingual vein features mainly captures the back of the tongue and extract the sublingual vein area for feature expression analysis. Firstly, the patient's back of the tongue is photographed and color-graded to compensate for color distortion, and then the tongue-back area is extracted. This study extracts tongue-back imagery by analyzing the RGB color expression of the back of the tongue, lips, teeth and skin, translating it into the HSI color space easily perceived by the human eye, along with skin area removal, rectangle detection, teeth area removal, black area removal and control point detection. The captured tongue-back image goes through histogram equalization and hue shift to enhance color contrast. Sublingual veins are extracted through analyzing RGB color component shift, hues, saturation and brightness. Then the sublingual vein color information and positioning are used to differentiate hues, lengths and branches. Thinning analysis is used to determine the presence of varicose veins. At the same time, the surrounding features of sublingual veins, such as columnar vein, bubbly vein, petechiae and bloodshot, are extracted. The information regarding features and lingual vein conditions are integrated and analyzed for doctors' clinical reference. This study utilizes 199 lingual images for statistic testing and three lingual diagnostic experts in Chinese medicine for lingual reading. The accuracy for the extractions are: tongue back 86%, sublingual vein 80%, varicose veins 90%, branches 87%, and the accuracy rates for columnar veins and bubbly veins are 87%, 88% and 73% respectively.


title: Robust camera calibration of soccer video using genetic algorithm

text: This paper proposes an exact and robust genetic algorithm-based method for the calibration of soccer camera. According to the FIFA official soccer field layout we defined a field model for the soccer court. Camera calibration is done through finding the homography transform between the field model and the input frame which is followed with DLT decomposition. The intersection of lines in the field model and input frame form feature points and by means of a genetic algorithm, we found the correspondence between those points. Our algorithm was applied to a couple of soccer video frames and the achieved results demonstrate its robustness, accuracy and high performance.


title: You are what you include: large-scale evaluation of remote javascript inclusions

text:
JavaScript is used by web developers to enhance the interactivity of their sites, offload work to the users' browsers and improve their sites' responsiveness and user-friendliness, making web pages feel and behave like traditional desktop applications. An important feature of JavaScript, is the ability to combine multiple libraries from local and remote sources into the same page, under the same namespace. While this enables the creation of more advanced web applications, it also allows for a malicious JavaScript provider to steal data from other scripts and from the page itself. Today, when developers include remote JavaScript libraries, they trust that the remote providers will not abuse the power bestowed upon them.
 In this paper, we report on a large-scale crawl of more than three million pages of the top 10,000 Alexa sites, and identify the trust relationships of these sites with their library providers. We show the evolution of JavaScript inclusions over time and develop a set of metrics in order to assess the maintenance-quality of each JavaScript provider, showing that in some cases, top Internet sites trust remote providers that could be successfully compromised by determined attackers and subsequently serve malicious JavaScript. In this process, we identify four, previously unknown, types of vulnerabilities that attackers could use to attack popular web sites. Lastly, we review some proposed ways of protecting a web application from malicious remote scripts and show that some of them may not be as effective as previously thought.


title: Memory engram storage and retrieval

text: A great deal of experimental investment is directed towards questions regarding the mechanisms of memory storage. Such studies have traditionally been restricted to investigation of the anatomical structures, physiological processes, and molecular pathways necessary for the capacity of memory storage, and have avoided the question of how individual memories are stored in the brain. Memory engram technology allows the labeling and subsequent manipulation of components of specific memory engrams in particular brain regions, and it has been established that cell ensembles labeled by this method are both sufficient and necessary for memory recall. Recent research has employed this technology to probe fundamental questions of memory consolidation, differentiating between mechanisms of memory retrieval from the true neurobiology of memory storage.


title: Dimension Reduction and Data Visualization Using Neural Networks

text: The problem of visual presentation of multidimensional data is discussed. The projection methods for dimension reduction are reviewed. The chapter deals with the artificial neural networks that may be used for reducing dimension and data visualization, too. The stress is put on combining the selforganizing map (SOM) and Sammon mapping and on the neural network for Sammon’s mapping SAMANN. Large scale applications are discussed: environmental data analysis, statistical analysis of curricula, comparison of schools, analysis of the economic and social conditions of countries, analysis of data on the fundus of eyes and analysis of physiological data on men’s health.


title: Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking

text: The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.


title: Adaptive bootstrapping of recommender systems using decision trees

text: Recommender systems perform much better on users for which they have more information. This gives rise to a problem of satisfying users new to a system. The problem is even more acute considering that some of these hard to profile new users judge the unfamiliar system by its ability to immediately provide them with satisfying recommendations, and may quickly abandon the system when disappointed. Rapid profiling of new users by a recommender system is often achieved through a bootstrapping process - a kind of an initial interview - that elicits users to provide their opinions on certain carefully chosen items or categories. The elicitation process becomes particularly effective when adapted to users' responses, making best use of users' time by dynamically modifying the questions to improve the evolving profile. In particular, we advocate a specialized version of decision trees as the most appropriate tool for this task. We detail an efficient tree learning algorithm, specifically tailored to the unique properties of the problem. Several extensions to the tree construction are also introduced, which enhance the efficiency and utility of the method. We implemented our methods within a movie recommendation service. The experimental study delivered encouraging results, with the tree-based bootstrapping process significantly outperforming previous approaches.


title: Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Point Clouds

text: The ability to segment unknown objects in depth images has potential to enhance robot skills in grasping and object tracking. Recent computer vision research has demonstrated that Mask R-CNN can be trained to segment specific categories of objects in RGB images when massive handlabeled datasets are available. As generating these datasets is time-consuming, we instead train with synthetic depth images. Many robots now use depth sensors, and recent results suggest training on synthetic depth data can generalize well to the real world. We present a method for automated dataset generation and rapidly generate a training dataset of 50k depth images and 320k object masks synthetically using simulated scenes of 3D CAD models. We train a variant of Mask R-CNN on the generated dataset to perform category-agnostic instance segmentation without hand-labeled data. We evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution images of challenging, denselycluttered bins containing objects with highly-varied geometry. SD Mask R-CNN outperforms point cloud clustering baselines by an absolute 15% in Average Precision and 20% in Average Recall, and achieves performance levels similar to a Mask RCNN trained on a massive, hand-labeled RGB dataset and fine-tuned on real images from the experimental setup. The network also generalizes well to a lower-resolution depth sensor. We deploy the model in an instance-specific grasping pipeline to demonstrate its usefulness in a robotics application. Code, the synthetic training dataset, and supplementary material are available at https://bit.ly/2letCuE.


title: Disruption of Large-Scale Brain Systems in Advanced Aging

text: Cognitive decline is commonly observed in advanced aging even in the absence of disease. Here we explore the possibility that normal aging is accompanied by disruptive alterations in the coordination of large-scale brain systems that support high-level cognition. In 93 adults aged 18 to 93, we demonstrate that aging is characterized by marked reductions in normally present functional correlations within two higher-order brain systems. Anterior to posterior components within the default network were most severely disrupted with age. Furthermore, correlation reductions were severe in older adults free from Alzheimer's disease (AD) pathology as determined by amyloid imaging, suggesting that functional disruptions were not the result of AD. Instead, reduced correlations were associated with disruptions in white matter integrity and poor cognitive performance across a range of domains. These results suggest that cognitive decline in normal aging arises from functional disruption in the coordination of large-scale brain systems that support cognition.


title: Use of physiological signals to predict cybersickness

text: http://dx.doi.org/10.1016/j.displa.2016.07.002 0141-9382/ 2016 Elsevier B.V. All rights reserved. q This paper was recommended for publication by Richard H.Y. So. ⇑ Corresponding author at: University of California, Irvine, 2201 Social & Behavioral Sciences Gateway Building, Irvine, CA 92697-5100, USA. E-mail addresses: mdenniso@uci.edu (M.S. Dennison), awisti@uci.edu (A.Z. Wisti), mdzmura@uci.edu (M. D’Zmura). Mark S. Dennison ⇑, A. Zachary Wisti, Michael D’Zmura


title: Secure Fine-Grained Access Control and Data Sharing for Dynamic Groups in the Cloud

text: Cloud computing is an emerging computing paradigm that enables users to store their data in a cloud server to enjoy scalable and on-demand services. Nevertheless, it also brings many security issues, since cloud service providers (CSPs) are not in the same trusted domain as users. To protect data privacy against untrusted CSPs, existing solutions apply cryptographic methods (e.g., encryption mechanisms) and provide decryption keys only to authorized users. However, sharing cloud data among authorized users at a fine-grained level is still a challenging issue, especially when dealing with dynamic user groups. In this paper, we propose a secure and efficient fine-grained access control and data sharing scheme for dynamic user groups by: 1) defining and enforcing access policies based on the attributes of the data; 2) permitting the key generation center to efficiently update user credentials for dynamic user groups; and 3) allowing some expensive computation tasks to be performed by untrusted CSPs without requiring any delegation key. Specifically, we first design an efficient revocable attribute-based encryption (ABE) scheme with the property of ciphertext delegation by exploiting and uniquely combining techniques of identity-based encryption, ABE, subset-cover framework, and ciphertext encoding mechanism. We then present a fine-grained access control and data sharing system for on-demand services with dynamic user groups in the cloud. The experimental data show that our proposed scheme is more efficient and scalable than the state-of-the-art solution.


title: Machine Learning-Based Software Quality Prediction Models: State of the Art

text: Quantification of parameters affecting the software quality is one of the important aspects of research in the field of software engineering. In this paper, we present a comprehensive literature survey of prominent quality molding studies. The survey addresses two views: (1) quantification of parameters affecting the software quality; and (2) using machine learning techniques in predicting the software quality. The paper concludes that, model transparency is a common shortcoming to all the surveyed studies.


title: Design and Implementation of Energy Management System With Fuzzy Control for DC Microgrid Systems

text: This paper presents the design and implementation of an energy management system (EMS) with fuzzy control for a dc microgrid system. Modeling, analysis, and control of distributed power sources and energy storage devices with MATLAB/Simulink are proposed, and the integrated monitoring EMS is implemented with LabVIEW. To improve the life cycle of the battery, fuzzy control manages the desired state of charge. The RS-485/ZigBee network has been designed to control the operating mode and to monitor the values of all subsystems in the dc microgrid system.


title: 3-Level high power converter with press pack IGBT

text: Converteam recently has introduced the MV7000 series of medium voltage drives, designed with Press Pack IGBT (PPI) in 3-level NPC topology in the power range from 4 MW to 32 MW [3]. PPI combines the benefits of disk type device technology with state-of-the-art semiconductor technology. This paper explains relevant technical features and design issues related to MV7000 including direct series connection of devices.


title: Bayesian Unsupervised Topic Segmentation

text: This paper describes a novel Bayesian approach to unsupervised topic segmentation. Unsupervised systems for this task are driven by lexical cohesion: the tendency of wellformed segments to induce a compact and consistent lexical distribution. We show that lexical cohesion can be placed in a Bayesian context by modeling the words in each topic segment as draws from a multinomial language model associated with the segment; maximizing the observation likelihood in such a model yields a lexically-cohesive segmentation. This contrasts with previous approaches, which relied on hand-crafted cohesion metrics. The Bayesian framework provides a principled way to incorporate additional features such as cue phrases, a powerful indicator of discourse structure that has not been previously used in unsupervised segmentation systems. Our model yields consistent improvements over an array of state-of-the-art systems on both text and speech datasets. We also show that both an entropy-based analysis and a well-known previous technique can be derived as special cases of the Bayesian framework.1


title: Block diagonalization for multi-user MIMO with other-cell interference

text: Block diagonalization is one approach for linear preceding in the multiple-input multiple-output broadcast channel that sends multiple interference free data streams to different users in the same cell. Unfortunately, block diagonalization neglects other-cell interference (OCI), which limits the performance of users at the edge of the cell. This paper presents an OCI-aware enhancement to block diagonalization that uses a whitening filter for interference suppression at the receiver and a novel precoder using the interference-plus-noise covariance matrix for each user at the transmitter. For complex Gaussian matrix channels, the asymptotic sum rate of the proposed system is analyzed under a large antenna assumption for isotropic inputs and compared to conventional block diagonalization. The capacity loss due to OCI is quantified in terms of results from single-user MIMO capacity. Several numerical examples compare achievable sum rates, the proposed asymptotic rates, and the capacity loss, in low and high interference regimes.


title: Design and deployment of Aqua monitoring system using wireless sensor networks and IAR-Kick

text: In Aquaculture, the yields (shrimp, fish etc.) depend on the water characteristics of the aquaculture pond. For maximizing fish yields, the parameters which are to be kept at certain optimal levels in water. The parameters can vary a lot during the period of a day and can rapidly change depending on the external environmental conditions. Hence it is necessary to monitor these parameters with high frequency . Wireless sensor networks are used to monitor aqua farms for relevant parameters this system consists of two modules which are transmitter station and receiver station. The data transmits through GSM to the Database at receiver station. The graphical user interface was designed, to convey the data in the form of a message to the farmers in their respective local languages to their Mobile Phones and alerts them in unhygienic environmental conditions, in order to take suitable actions. Keywords; aquaculture; wireless sensor networks; IAR-Kick;pH;


title: A Comparison over Focused Web Crawling Strategies

text: In this paper we review and compare focused crawling strategies, studied and published during the past decade. Despite giant leaps in communication, storage and computing power in recent years, crawlers have always struggled to keep up with Web content generation and modification. Focused crawlers attempt to i) accelerate the crawling process, ii) maximize the harvest of high quality pages, iii) assign appropriate credit to different documents along a crawling path, such that short-term gains are not pursued at the expense of less obvious paths that ultimately yield larger sets of valuable pages. Beyond the review and comparison of the focused crawling strategies, we additionally propose additions to the corresponding architectures for further research.


title: Penetration Testing with Improved Input Vector Identification

text: Penetration testing is widely used to help ensure the security of web applications. It discovers vulnerabilities by simulating attacks from malicious users on a target application. Identifying the input vectors of a web application and checking the results of an attack are important parts of penetration testing, as they indicate where an attack could be introduced and whether an attempted attack was successful. Current techniques for identifying input vectors and checking attack results are typically ad-hoc and incomplete, which can cause parts of an application to be untested and leave vulnerabilities undiscovered. In this paper, we propose a new approach to penetration testing that addresses these limitations by leveraging two recently-developed analysis techniques. The first is used to identify a web application's possible input vectors, and the second is used to automatically check whether an attack resulted in an injection. To empirically evaluate our approach, we compare it against a state-of-the-art, alternative technique. Our results show that our approach performs a more thorough penetration testing and leads to the discovery of more vulnerabilities.


title: Crawling AJAX by Inferring User Interface State Changes

text: AJAX is a very promising approach for improving rich interactivity and responsiveness of web applications. At the same time, AJAX techniques shatter the metaphor of a web "page" upon which general search crawlers are based. This paper describes a novel technique for crawling AJAX applications through dynamic analysis and reconstruction of user interface state changes. Our method dynamically infers a state-flow graph modeling the various navigation paths and states within an AJAX application. This reconstructed model can be used to generate linked static pages. These pages could be used to expose AJAX sites to general search engines. Moreover, we believe that the crawling techniques that are part of our solution have other applications, such as within general search engines, accessibility improvements, or in automatically exercising all user interface elements and conducting state-based testing of AJAX applications. We present our open source tool called CRAWLJAX which implements the concepts discussed in this paper. Additionally, we report a case study in which we apply our approach to a number of representative AJAX applications and elaborate on the obtained results.


title: Flow-Sensitive Type Qualifiers

text: We present a system for extending standard type systems with flow-sensitive type qualifiers. Users annotate their programs with type qualifiers, and inference checks that the annotations are correct. In our system only the type qualifiers are modeled flow-sensitively---the underlying standard types are unchanged, which allows us to obtain an efficient constraint-based inference algorithm that integrates flow-insensitive alias analysis, effect inference, and ideas from linear type systems to support strong updates. We demonstrate the usefulness of flow-sensitive type qualifiers by finding a number of new locking bugs in the Linux kernel.


title: A Comparison of Push and Pull Techniques for AJAX

text: AJAX applications are designed to have high user interactivity and low user-perceived latency. Real-time dynamic Web data such as news headlines, stock tickers, and auction updates need to be propagated to the users as soon as possible. However, AJAX still suffers from the limitations of the Web's request/response architecture which prevents servers from pushing real-time dynamic web data. Such applications usually use a pull style to obtain the latest updates, where the client actively requests the changes based on a predefined interval. It is possible to overcome this limitation by adopting a push style of interaction where the server broadcasts data when a change occurs on the server side. Both these options have their own trade-offs. This paper explores the fundamental limits of browser-based applications and analyzes push solutions for AJAX technology. It also shows the results of an empirical study comparing push and pull.


title: Tractable Queries for Lightweight Description Logics

text: It is a classic result in database theory that conjunctive query (CQ) answering, which is NP-complete in general, is feasible in polynomial time when restricted to acyclic queries. Subsequent results identified more general structural properties of CQs (like bounded treewidth) which ensure tractable query evaluation. In this paper, we lift these tractability results to knowledge bases formulated in the lightweight description logics DL-Lite and ELH. The proof exploits known properties of query matches in these logics and involves a querydependent modification of the data. To obtain a more practical approach, we propose a concrete polynomial-time algorithm for answering acyclic CQs based on rewriting queries into datalog programs. A preliminary evaluation suggests the interest of our approach for handling large acyclic CQs.


title: Twitter Sentiment Classification on Sanders Data using Hybrid Approach

text: Sentiment analysis is very perplexing and massive issue in the field of social data mining. Twitter is one of the mostly used social media where people discuss on various issues in a dense way. The tweets about a particular topic give peoples’ views, opinions, orientations, inclinations about that topic. In this work, we have used pre-labeled (with positive, negative and neutral opinion) tweets on particular topics for sentiment classification. Opinion score of each tweet is calculated using feature vectors. These opinion score is used to classify the tweets into positive, negative and neutral classes. Then using various machine learning classifiers the accuracy of predicted classification with respect to actual classification is being calculated and compared using supervised learning model. Along with building a sentiment classification model, analysis of tweets is being carried out by visualizing the wordcloud of tweets using R.


title: Multiple Kernel Fuzzy Clustering

text: While fuzzy c-means is a popular soft-clustering method, its effectiveness is largely limited to spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to address this problem by mapping data with nonlinear relationships to appropriate feature spaces. Kernel combination, or selection, is crucial for effective kernel clustering. Unfortunately, for most applications, it is uneasy to find the right combination. We propose a multiple kernel fuzzy c-means (MKFC) algorithm that extends the fuzzy c-means algorithm with a multiple kernel-learning setting. By incorporating multiple kernels and automatically adjusting the kernel weights, MKFC is more immune to ineffective kernels and irrelevant features. This makes the choice of kernels less crucial. In addition, we show multiple kernel k-means to be a special case of MKFC. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed MKFC algorithm.


title: Helicopter induced propeller injuries.

text: Following routine maintenance a military helicopter was returned to service. Due to negligence some hoses had not been connected correctly. The wrongly connected hoses resulted in a malfunction, causing the rotor blades of the helicopter to smash through the windows of the helicopter cockpit. Both the pilot and the co-pilot were hit by the rotor blades and decapitated. Other passengers in the helicopter were injured. Due to vibrations the helicopter fell to the ground and its tail partly broke away (Figs. 1, 2). The bodies and body parts of the pilot and co-pilot were found on the ground outside the helicopter cockpit.


title: Sonoliards: Rendering Audible Sound Spots by Reflecting the Ultrasound Beams

text: This paper proposes a dynamic acoustic field generation system for a spot audio towards particular person indoors. Spot audio techniques have been explored by generating the ultrasound beams toward the target person in certain area however everyone in this area can hear the sound. Our system recognizes the positions of each person indoor using motion capture and 3D model data of the room. After that we control direction of parametric speaker in real-time so that sound reach only particular person by calculating the reflection of sound on surfaces such as wall and ceiling. We calculate direction of parametric speaker using a beam tracing method. We present generating methods of dynamic acoustic field in our system and conducted the experiments on human factor to evaluate performance of proposed system.


title: Emotions , Partisanship , and Misperceptions : How Anger and Anxiety Moderate the Effect of Partisan Bias on Susceptibility to Political Misinformation

text: Citizens are frequently misinformed about political issues and candidates but the circumstances under which inaccurate beliefs emerge are not fully understood. This experimental study demonstrates that the independent experience of two emotions, anger and anxiety, in part determines whether citizens consider misinformation in a partisan or open-minded fashion. Anger encourages partisan, motivated evaluation of uncorrected misinformation that results in beliefs consistent with the supported political party, while anxiety at times promotes initial beliefs based less on partisanship and more on the information environment. However, exposure to corrections improves belief accuracy, regardless of emotion or partisanship. The results indicate that the unique experience of anger and anxiety can affect the accuracy of political beliefs by strengthening or attenuating the influence of partisanship.


title: Sensation seeking in England and America: cross-cultural, age, and sex comparisons.

text: This study compared the factor structure of the Sensation-Seeking Scale (SSS) in English and American samples, and a new form of the SSS, applicable to both samples, was constructed. Three of the four factors showed good crossnational and cross-sex reliability. English and American males did not differ on the total SSS score, but American females scored higher than English females. Males in both countries scored higher than females on the total SSS score and on the Thrill and Adventure-Seeking and Disinhibition subscales. Significant age declines occurred for both sexes, particularly on Thrill and Adventure Seeking and Disinhibition.


title: A Better Baseline for AVA

text: We introduce a simple baseline for action localization on the AVA dataset. The model builds upon the Faster R-CNN bounding box detection framework, adapted to operate on pure spatiotemporal features – in our case produced exclusively by an I3D model pretrained on Kinetics. This model obtains 21.9% average AP on the validation set of AVA v2.1, up from 14.5% for the best RGB spatiotemporal model used in the original AVA paper (which was pretrained on Kinetics and ImageNet), and up from 11.3% of the publicly available baseline using a ResNet101 image feature extractor, that was pretrained on ImageNet. Our final model obtains 22.8%/21.9% mAP on the val/test sets and outperforms all submissions to the AVA challenge at CVPR 2018.


title: Deep predictive policy training using reinforcement learning

text: Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small subnetwork with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.


title: Piriformis syndrome, diagnosis and treatment.

text: Piriformis syndrome (PS) is an uncommon cause of sciatica that involves buttock pain referred to the leg. Diagnosis is often difficult, and it is one of exclusion due to few validated and standardized diagnostic tests. Treatment for PS has historically focused on stretching and physical therapy modalities, with refractory patients also receiving anesthetic and corticosteroid injections into the piriformis muscle origin, belly, muscle sheath, or sciatic nerve sheath. Recently, the use of botulinum toxin (BTX) to treat PS has gained popularity. Its use is aimed at relieving sciatic nerve compression and inherent muscle pain from a tight piriformis. BTX is being used increasingly for myofascial pain syndromes, and some studies have demonstrated superior efficacy to corticosteroid injection. The success of BTX in treating PS supports the prevailing pathoanatomic etiology of the condition and suggests a promising future for BTX in the treatment of other myofascial pain syndromes.


title: A high-level 3D visualization API for Java and ImageJ

text: Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set. Here we present a platform-independent framework based on Java and Java 3D for accelerated rendering of biological images. Our framework is seamlessly integrated into ImageJ, a free image processing package with a vast collection of community-developed biological image analysis tools. Our framework enriches the ImageJ software libraries with methods that greatly reduce the complexity of developing image analysis tools in an interactive 3D visualization environment. In particular, we provide high-level access to volume rendering, volume editing, surface extraction, and image annotation. The ability to rely on a library that removes the low-level details enables concentrating software development efforts on the algorithm implementation parts. Our framework enables biomedical image software development to be built with 3D visualization capabilities with very little effort. We offer the source code and convenient binary packages along with extensive documentation at http://3dviewer.neurofly.de .


title: Psychological first-aid: a practical aide-memoire.

text: Despite advances made in recent years in medical first aid, psychiatric intervention, survival training and equipment design, many people still perish quickly during and immediately following a disastrous event. In this study, individuals and groups of survivors of life-threatening events were debriefed and the behavior of those who coped well during such a threat to life were compared with those who did not. The behaviors of those who coped well were distilled into a set of principles for psychological first aid; that is, a series of simple actions for use within a disaster which serves to recover victims to functional behavior as quickly as possible, thus increasing their chance for survival. These principles of psychological first aid have recently been introduced into basic first aid and survival training courses for both military and civilian units.


title: Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation

text: The `1-regularized maximum likelihood estimation problem has recently become a topic of great interest within the machine learning, statistics, and optimization communities as a method for producing sparse inverse covariance estimators. In this paper, a proximal gradient method (G-ISTA) for performing `1-regularized covariance matrix estimation is presented. Although numerous algorithms have been proposed for solving this problem, this simple proximal gradient method is found to have attractive theoretical and numerical properties. G-ISTA has a linear rate of convergence, resulting in an O(log ε) iteration complexity to reach a tolerance of ε. This paper gives eigenvalue bounds for the G-ISTA iterates, providing a closed-form linear convergence rate. The rate is shown to be closely related to the condition number of the optimal point. Numerical convergence results and timing comparisons for the proposed method are presented. G-ISTA is shown to perform very well, especially when the optimal point is well-conditioned.


title: A gene-coexpression network for global discovery of conserved genetic modules.

text: To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.


title: Sparse inverse covariance estimation with the graphical lasso.

text: We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.


title: Anatomical and biomechanical mechanisms of subacromial impingement syndrome.

text: Subacromial impingement syndrome is the most common disorder of the shoulder, resulting in functional loss and disability in the patients that it affects. This musculoskeletal disorder affects the structures of the subacromial space, which are the tendons of the rotator cuff and the subacromial bursa. Subacromial impingement syndrome appears to result from a variety of factors. Evidence exists to support the presence of the anatomical factors of inflammation of the tendons and bursa, degeneration of the tendons, weak or dysfunctional rotator cuff musculature, weak or dysfunctional scapular musculature, posterior glenohumeral capsule tightness, postural dysfunctions of the spinal column and scapula and bony or soft tissue abnormalities of the borders of the subacromial outlet. These entities may lead to or cause dysfunctional glenohumeral and scapulothoracic movement patterns. These various mechanisms, singularly or in combination may cause subacromial impingement syndrome.


title: Artificial Intelligence in Education

text: In a life-long learning society, learning scenarios can be categorized into five types, which are “classroom learning”, “self-learning”, “inquiry learning”, “learning in doing” and “learning in working”. From a life-wide learning perspective, all these scenarios play vital roles for personal development. How to recognize these learning scenarios (including learning time, learning place, learning peers, learning activities, etc.) and provide the matched learning ways (including learning path, resources, peers, teachers, etc.) are the basis for smart learning environments, however few research could be found to address this problem. In order to solve this problem, we propose a conceptual framework of smart learning engine that is the core of integrated, interactive and intelligent (i) learning environments. The smart learning engine consists of three main functions. The first function is to identify data from student, teacher, subject area, and the environment using wireless sensors, the established learning resources and scenarios, and a learner modeling technology. The acquired data includes prior knowledge, theme-based context, leaner/teacher profile, physical environments, etc. The second function is to compute the best ways of learning based on the learning scenario and learning preference. In detail, this function includes modeling learner’s affective data, building knowledge structure, optimizing knowledge module, and connecting learners. The third function is to deploy personalized and adaptive strategy, resources and tools for students and teachers based on the computed results in the second function. Deploy interactive strategies, learning paces, learning resources, and delivery approaches are the core elements for this function.


title: Evolutionary reinforcement learning in FX order book and order flow analysis

text: As macroeconomic fundamentals based modelling of FX timeseries have been shown not to fit the empirical evidence at horizons of less than one year, interest has moved towards microstructure-based approaches. Order flow data has recently been receiving an increasing amount of attention in equity market analyses and thus increasingly in foreign exchange as well. In this paper, order flow data is coupled with order book derived indicators and we explore whether pattern recognition techniques derived from computational learning can be applied to successfully infer trading strategies on the underlying timeseries. Due to the limited amount of data available the results are preliminary. However, the approach demonstrates promise and it is shown that using order flow and order book data is usually superior to trading on technical signals alone.


title: Enterprise Architecture for Digital Transformation

text: Digital transformation requires an altogether new institutional logic and effective response at a requisite organizational level. Sensing and seizing fleeting market opportunities and reconfiguring the business in line with the shifting value proposition requires increasingly specialized resources, more dynamic capabilities, and in-built resilience in the face of change. While Enterprise Architecture (EA) has been suggested to facilitate enterprise transformation, the focus has traditionally been on process standardization and integration rather than on continuous adaptation to the changing business and technological landscape. For EA to have a desired impact, more adaptive conceptualizations of EA that address the requirements of the new digital environment are called for. In this conceptual paper, we explore the implications of digital transformation on enterprise architecture. In particular, we note that existing approaches to EA address integration and coherence within a single organization but typically fall short in the face of complexities pertaining to digital ecosystems. We suggest a vertical typology of organizational capabilities and postulate that today's digital environment increasingly requires adaptive capabilities that transcend the traditional notion of dynamic capabilities. We then investigate how EA can help build flexibility and resilience in the organization.


title: Looking under the bonnet : factors affecting student adoption of e-learning systems in

text: The primary questions addressed in this paper are the following: what are the factors that affect students‟ adoption of an e-learning system and what are the relationships among these factors? This paper investigates and identifies some of the major factors affecting students‟ adoption of an e-learning system in a university in Jordan. E-learning adoption is approached from the information systems acceptance point of view. This suggests that a prior condition for learning effectively using e-learning systems is that students must actually use them. Thus, a greater knowledge of the factors that affect IT adoption and their interrelationships is a pre-cursor to a better understanding of student acceptance of e-learning systems. In turn, this will help and guide those who develop, implement, and deliver e-learning systems. In this study, an extended version of the Technology Acceptance Model (TAM) was developed to investigate the underlying factors that influence students‟ decisions to use an e-learning system. The TAM was populated using data gathered from a survey of 486 undergraduate students, who were using the Moodle based e-learning system at the Arab Open University. The model was estimated using Structural Equation Modelling (SEM). A path model was developed to analyze the relationships between the factors to explain students‟ adoption of the e-learning system. Whilst findings support existing literature about prior experience affecting perceptions, they also point to surprising group effects, which may merit future exploration.


title: Video semantic object segmentation by self-adaptation of DCNN

text: This paper proposes a new framework for semantic segmentation of objects in videos. We address the label inconsistency problem of deep convolutional neural networks (DCNNs) by exploiting the fact that videos have multiple frames; in a few frames the object is confidently-estimated (CE) and we use the information in them to improve labels of the other frames. Given the semantic segmentation results of each frame obtained from DCNN, we sample several CE frames to adapt the DCNN model to the input video by focusing on specific instances in the video rather than general objects in various circumstances. We propose offline and online approaches under different supervision levels. In experiments our method achieved great improvement over the original model and previous state-of-the-art methods. c © 2016 Elsevier Ltd. All rights reserved.


title: Vision: towards real time epidemic vigilance through online social networks: introducing SNEFT -- social network enabled flu trends

text: Our vision is to achieve faster and near real time detection and prediction of the emergence and spread of an influenza epidemic, through sophisticated data collection and analysis of Online Social Networks (OSNs) such as Facebook, MySpace, and Twitter. In particular, we present the design of a system called SNEFT (Social Network Enabled Flu Trends), which will be developed in a 12-month SBIR (Small Business Innovation Research) project funded by the National Institutes of Health (NIH). We describe the innovative technologies that will be developed in this project for collecting and aggregating OSN data, extracting information from it, and integrating it with mathematical models of influenza. One of the monitoring tools used by the Centers for Disease Control and Prevention (CDC) is reports of Influenza-Like Illness (ILI) cases; these reports are authoritative but typically have a delay of one to two weeks due to the largely manual process. We describe the SNEFT prototype in the context of predicting ILI cases well in advance of the CDC reports. We observe that OSN data is individually noisy but collectively revealing, and speculate on other applications that can potentially be enabled by OSN data collection and analysis.


title: Speaking in tongues: SQL access to NoSQL systems

text: Non-relational data stores, which are usually called NoSQL systems, have become an important class of data management systems. They often outperform the relational systems. Yet there is no common way to interface with NoSQL systems. The Structured Query Language (SQL) has already proven to be useful to provide a uniform query language for all the relational systems. We identify a subset of SQL for access to NoSQL systems. Our extensible middleware translates SQL queries to the query languages of the connected NoSQL systems. The migration between these systems is thereby greatly simplified as well as the comparison of the supported NoSQL systems.


title: Upgrading Wireless Home Routers for Enabling Large-Scale Deployment of Cloudlets

text: Smartphones become more and more popular over recent years due to their small form factors. However, such mobile systems are resource-constrained in view of computational power, storage and battery life. Offloading resource-intensive tasks (aka mobile cloud computing) to distant (e.g., cloud computing) or closely located data centers (e.g., cloudlet) overcomes these issues. Especially, cloudlets provide computational power with low latency for responsive applications due to their proximity to mobile users. However, a large-scale deployment of rangerestricted cloudlets is still an open challenge. In this paper, we propose a novel concept for a large-scale deployment of cloudlets by upgrading wireless home routers. Beside router’s native purpose of routing data packets through the network, it can now offer computing resources with low latency and high bandwidth without additional hardware. Proving our concept, we conducted comprehensive benchmark tests against existing concepts. As result, the feasibility of this concept is shown and provide a promising way to large-scale deploy cloudlets in existing infrastructures.


title: Sentiment expression via emoticons on social media

text: Emoticons (e.g., :) and :( ) have been widely used in sentiment analysis and other NLP tasks as features to machine learning algorithms or as entries of sentiment lexicons. In this paper, we argue that while emoticons are strong and common signals of sentiment expression on social media the relationship between emoticons and sentiment polarity are not always clear. Thus, any algorithm that deals with sentiment polarity should take emoticons into account but extreme caution should be exercised in which emoticons to depend on. First, to demonstrate the prevalence of emoticons on social media, we analyzed the frequency of emoticons in a large recent Twitter data set. Then we carried out four analyses to examine the relationship between emoticons and sentiment polarity as well as the contexts in which emoticons are used. The first analysis surveyed a group of participants for their perceived sentiment polarity of the most frequent emoticons. The second analysis examined clustering of words and emoticons to better understand the meaning conveyed by the emoticons. The third analysis compared the sentiment polarity of microblog posts before and after emoticons were removed from the text. The last analysis tested the hypothesis that removing emoticons from text hurts sentiment classification by training two models with and without emoticons in the text, respectively. The results confirms the arguments that: 1) a few emoticons are strong and reliable signals of sentiment polarity and one should take advantage of them in any sentiment analysis; 2) a large group of the emoticons conveys complicated sentiment hence they should be treated with extreme caution.


The Propulsion Committee Final Report and Recommendations to the 25 th ITTC 1


title: Thalamic relays and cortical functioning.

text: Studies on the visual thalamic relays, the lateral geniculate nucleus and pulvinar, provide three key properties that have dramatically changed the view that the thalamus serves as a simple relay to get information from subcortical sites to cortex. First, the retinal input, although a small minority (7%) in terms of numbers of synapses onto geniculate relay cells, dominates receptive field properties of these relay cells and strongly drives them; 93% of input thus is nonretinal and modulates the relay in dynamic and important ways related to behavioral state, including attention. We call the retinal input the driver input and the nonretinal, modulator input, and their unique morphological and functional differences allow us to recognize driver and modulator input to many other thalamic relays. Second, much of the modulation is related to control of a voltage-gated, low threshold Ca(2+) conductance that determines response properties of relay cells -burst or tonic - and this, among other things, affects the salience of information relayed. Third, the lateral geniculate nucleus and pulvinar (a massive but generally mysterious and ignored thalamic relay), are examples of two different types of relay: the LGN is a first order relay, transmitting information from a subcortical driver source (retina), while the pulvinar is mostly a higher order relay, transmitting information from a driver source emanating from layer 5 of one cortical area to another area. Higher order relays seem especially important to general corticocortical communication, and this view challenges the conventional dogma that such communication is based on direct corticocortical connections. In this sense, any new information reaching a cortical area, whether from a subcortical source or another cortical area, benefits from a thalamic relay. Other examples of first and higher order relays also exist, and generally higher order relays represent the majority of thalamus. A final property of interest emphasized in chapter 17 by Guillery (2005) is that most or all driver inputs to thalamus, whether from a subcortical source or from layer 5 of cortex, are axons that branch, with the extrathalamic branch innervating a motor or premotor region in the brainstem, or in some cases, spinal cord. This suggests that actual information relayed by thalamus to cortex is actually a copy of motor instructions (Guillery, 2005). Overall, these features of thalamic relays indicate that the thalamus not only provides a behaviorally relevant, dynamic control over the nature of information relayed, it also plays a key role in basic corticocortical communication.


title: The CRISPR/Cas9 system produces specific and homozygous targeted gene editing in rice in one generation.

text: The CRISPR/Cas9 system has been demonstrated to efficiently induce targeted gene editing in a variety of organisms including plants. Recent work showed that CRISPR/Cas9-induced gene mutations in Arabidopsis were mostly somatic mutations in the early generation, although some mutations could be stably inherited in later generations. However, it remains unclear whether this system will work similarly in crops such as rice. In this study, we tested in two rice subspecies 11 target genes for their amenability to CRISPR/Cas9-induced editing and determined the patterns, specificity and heritability of the gene modifications. Analysis of the genotypes and frequency of edited genes in the first generation of transformed plants (T0) showed that the CRISPR/Cas9 system was highly efficient in rice, with target genes edited in nearly half of the transformed embryogenic cells before their first cell division. Homozygotes of edited target genes were readily found in T0 plants. The gene mutations were passed to the next generation (T1) following classic Mendelian law, without any detectable new mutation or reversion. Even with extensive searches including whole genome resequencing, we could not find any evidence of large-scale off-targeting in rice for any of the many targets tested in this study. By specifically sequencing the putative off-target sites of a large number of T0 plants, low-frequency mutations were found in only one off-target site where the sequence had 1-bp difference from the intended target. Overall, the data in this study point to the CRISPR/Cas9 system being a powerful tool in crop genome engineering.


title: Learning Inverse Dynamics for Robot Manipulator Control

text: Model-based control strategies for robot manipulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such a model is a challenging task which involves modeling such physical processes as friction, which may not be well understood and difficult to model. Furthermore, uncertainties in the physical parameters of a system may be introduced from significant discrepancies between the manufacturer data and the actual system. Traditionally, adaptive and robust control strategies have been developed to deal with parametric uncertainty in the dynamic model, but often require knowledge of the structure of the dynamics. Recent approaches to model-based manipulator control involve data-driven learning of the inverse dynamics relationship, eliminating the need for any a-priori knowledge of the system model. Locally Weighted Projection Regression (LWPR) has been proposed for learning the inverse dynamics function of a manipulator. Due to its use of simple local, linear models, LWPR is suitable for online and incremental learning. Although global regression techniques such as Gaussian Process Regression (GPR) have been shown to outperform LWPR in terms of accuracy, due to its heavy computational requirements, GPR has been applied mainly to offline learning of inverse dynamics. More recent efforts in making GPR computationally tractable for real-time control have resulted in several approximations which operate on a select subset, or sparse representation of the entire training data set. Despite the significant advancements that have been made in the area of learning control, there has not been much work in recent years to evaluate these newer regression techniques against traditional model-based control strategies such as adaptive control. Hence, the first portion of this thesis provides a comparison between a fixed model-based control strategy, an adaptive controller and the LWPR-based learning controller. Simulations are carried out in order to evaluate the position and orientation tracking performance of each controller under varied end effector loading, velocities and inaccuracies in the known dynamic parameters. Both the adaptive controller and LWPR controller are shown to have comparable performance in the presence of parametric uncertainty. However, it is shown that the learning controller is unable to generalize well outside of the regions in which it has been trained. Hence, achieving good performance requires significant amounts of training in the anticipated region of operation. In addition to poor generalization performance, most learning controllers commence learning entirely from ‘scratch,’ making no use of any a-priori knowledge which may be available from the well-known rigid body dynamics (RBD) formulation. The second portion of this thesis develops two techniques for online, incremental learning algorithms which incorporate prior knowledge to improve generalization performance. First, prior knowledge


title: The Impact of Interactive Touchscreens on Physics Education in Upper Secondary School – A systematic literature review

text: Interactive touchscreens such as tablet PCs (TPC) and interactive whiteboards (IWB) are becoming more and more common in classrooms around the world. To date, very little research has been conducted on the impact of the new technology on physics education. This systematic literature review aims to investigate research on what impact tablet PCs and interactive whiteboards might have on the education in upper Secondary School. The review was performed in response to the following questions: 1. What is the influence of IWBs and TPCs on students’ active participation in physics education? 2. How can an IWB or TPC improve students’ learning about physics concepts? 3. How can educational research on touchscreen technology help inform effective teaching strategies in physics education? To respond to the questions of the study, relevant research about interactive whiteboards and/or tablet PCs was consulted and analysed. Twelve articles were located, mainly through the ERIC and Scopus databases, but also through Google Scholar. The included articles reported empirical research about physics education with interactive whiteboards or tablet PCs. The results from the articles indicate that interactive touchscreens might help improve learners’ active participation in physics education. Interactive whiteboards can, for example, be used to display interactive simulations during group work, something students are found to appreciate and easily engage in. A tablet PC can be used in the same way, but also allows students to receive anonymous support and feedback from the teacher during class which seems to be beneficial for learning. Results show that it is possible to improve students’ understanding of physics concepts by using interactive whiteboards or tablet PCs. However, further research is required to compare results from students using touch technology and students taught in traditional manner to be able to draw any general conclusions about observed learning effects.


title: Mining Frequent Patterns by Pattern-Growth: Methodology and Implications

text: Mining frequent patterns has been a focused topic in data mining research in recent years, with the developmeht of numerous interesting algorithms for mining association, correlation, causality, sequential patterns, partial periodicity, constraint-based frequent pattern mining, associative classification, emerging patterns, etc. Most of the previous studies adopt an Apriori-like, candidate generation-and-test approach. However, based on our analysis, candidate generation and test may still be expensive, especially when encountering long and numerous patterns. A new methodology, called f r e q u e n t p a t t e r n g rowth , which mines frequent patterns without candidate generation, has been developed. The method adopts a divide-andconquer philosophy to project and partition databases based on the currently discovered frequent patterns and grow such patterns to longer ones in the projected databases. Moreover, efficient data structures have been developed for effective database compression and fast in-memory traversal. Such a methodology may eliminate or substantially reduce the number of candidate sets to be generated and also reduce the size of the database to be iteratively examined, and, therefore, lead to high performance. In this paper, we provide an overview of this approach and examine its methodology and implications for mining several kinds of frequent patterns, including association, frequent closed itemsets, max-patterns, sequential patterns, and constraint-based mining of frequent patterns. We show that frequent pattern growth is efficient at mining large databases and its further development may lead to scalable mining of many other kinds of patterns as well.


title: Alike people, alike interests? Inferring interest similarity in online social networks

text: Understanding how much two individuals are alike in their interests (i.e., interest similarity) has become virtually essential for many applications and services in Online Social Networks (OSNs). Since users do not always explicitly elaborate their interests in OSNs like Facebook, how to determine users’ interest similarity without fully knowing their interests is a practical problem. In this paper, we investigate how users’ interest similarity relates to various social features (e.g. geographic distance); and accordingly infer whether the interests of two users are alike or unalike where one of the users’ interests are unknown. Relying on a large Facebook dataset, which contains 479, 048 users and 5, 263, 351 user-generated interests, we present comprehensive empirical studies and verify the homophily of interest similarity across three interest domains (movies, music and TV shows). The homophily reveals that people tend to exhibit more similar tastes if they have similar demographic information (e.g., age, location), or if they are friends. It also shows that the individuals with a higher interest entropy usually share more interests with others. Based on these results, we provide a practical prediction model under a real OSN environment. For a given user with no interest information, this model can select some individuals who not only exhibit many interests but also probably achieve high interest similarities with the given user. Eventually, we illustrate a use case to demonstrate that the proposed prediction model could facilitate decision-making for OSN applications and services.


title: Synthesizing Training Images for Boosting Human 3D Pose Estimation

text: Human 3D pose estimation from a single image is a challenging task with numerous applications. Convolutional Neural Networks (CNNs) have recently achieved superior performance on the task of 2D pose estimation from a single image, by training on images with 2D annotations collected by crowd sourcing. This suggests that similar success could be achieved for direct estimation of 3D poses. However, 3D poses are much harder to annotate, and the lack of suitable annotated training images hinders attempts towards end-to-end solutions. To address this issue, we opt to automatically synthesize training images with ground truth pose annotations. Our work is a systematic study along this road. We find that pose space coverage and texture diversity are the key ingredients for the effectiveness of synthetic training data. We present a fully automatic, scalable approach that samples the human pose space for guiding the synthesis procedure and extracts clothing textures from real images. Furthermore, we explore domain adaptation for bridging the gap between our synthetic training images and real testing photos. We demonstrate that CNNs trained with our synthetic images out-perform those trained with real photos on 3D pose estimation tasks.


title: "Constant, constant, multi-tasking craziness": managing multiple working spheres

text: Most current designs of information technology are based on the notion of supporting distinct tasks such as document production, email usage, and voice communication. In this paper we present empirical results that suggest that people organize their work in terms of much larger and thematically connected units of work. We present results of fieldwork observation of information workers in three different roles: analysts, software developers, and managers. We discovered that all of these types of workers experience a high level of discontinuity in the execution of their activities. People average about three minutes on a task and somewhat more than two minutes using any electronic tool or paper document before switching tasks. We introduce the concept of working spheres to explain the inherent way in which individuals conceptualize and organize their basic units of work. People worked in an average of ten different working spheres. Working spheres are also fragmented; people spend about 12 minutes in a working sphere before they switch to another. We argue that design of information technology needs to support people's continual switching between working spheres.


title: A Knowledge Hunting Framework for Common Sense Reasoning

text: We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge. Our method uses a knowledge hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine, then extracts and classifies knowledge from the returned results and weighs them to make a resolution. Our approach improves F1 performance on the full WSC by 0.21 over the previous best and represents the first system to exceed 0.5 F1. We further demonstrate that the approach is competitive on the Choice of Plausible Alternatives (COPA) task, which suggests that it is generally applicable.


title: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

text: , doi: 10.1098/rspa.1998.0193 454 1998 Proc. R. Soc. Lond. A Yen, Chi Chao Tung and Henry H. Liu Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan nonlinear and non-stationary time series analysis The empirical mode decomposition and the Hilbert spectrum for References http://rspa.royalsocietypublishing.org/content/454/1971/903#related-urls Article cited in: Email alerting service here corner of the article or click Receive free email alerts when new articles cite this article sign up in the box at the top right-hand


title: NFC Antenna With Nonuniform Meandering Line and Partial Coverage Ferrite Sheet for Metal Cover Smartphone Applications

text: This paper proposes a near-field communication (NFC) antenna solution for metal-cover smartphone applications. In this NFC antenna solution, a narrow slot is initially loaded into the metal cover, and the position of this slot can be altered (with flexibility) according to the design of the smartphone’s external appearance. Next, an unconventional six-turn coil (with a six-sided irregular hexagonal shape) is designed that has a nonuniform linewidth and a nonuniform line gap between two lines, and it is partially loaded with a rectangular ferrite composite. In this design, an enhanced magnetic line of forces can be realized in certain specific locations, and an excellent inductively coupled near-field receiver is achieved. Notably, this proposed NFC antenna can pass the tests required by NFC forum certification, and its performances are comparable with the traditional NFC antenna that has nonmetallic cover.


title: Miniaturized NFC Antenna Design for a Tablet PC With a Narrow Border and Metal Back-Cover

text: A novel structure of the near-field communication (NFC) antenna design for a tablet PC is proposed. This tablet PC has a narrow border and full metallic back-cover. A miniaturized loop antenna design is achieved by attaching ferrite sheets on both sides of the loop antenna. The ferrite sheets may reduce eddy currents induced on the adjacent metallic back-cover by the loop antenna to improve the communication range of the NFC. Only the edge of the tablet PC allows the antenna to radiate due to the full metallic back-cover. Thus, the NFC antenna needs to be narrow to be installed on the edge of the tablet PC. Therefore, we propose a miniaturized NFC antenna with the dimensions of 41.5 (L) × 7.5 (W) × 0.45 (T) mm3 only. Simulated magnetic field distributions are consistent with measured voltage distributions. This design has a good communication range more than 6 cm in front of the touchscreen panel and reaches 2 cm over the other side above the metal back-cover.


title: A silver inkjet printed ferrite NFC antenna

text: This paper presents a near field communication (NFC) antenna that is printed directly on a ferrite substrate using a silver inkjet printing process. Such a silver inkjet printed ferrite NFC antenna provides a minimum substrate thickness, a good operation on metal objects, and a small assembly effort. The NFC antenna performance is analysed by measurements based on the ISO/IEC standard 10373-6 for proximity identification cards test methods. For this, the antenna is connected to an NFC microchip. Measurements in a non-metal environment show that the ferrite antenna performs equally good a custom-built NFC antenna printed on photo paper substrate, despite additional losses in the ferrite substrate. In a metal environment the ferrite antenna clearly outperforms the photo paper antenna.


title: NFC Antenna Design for Low-Permeability Ferromagnetic Material

text: A novel structure of a near-field communication (NFC) antenna for a mobile handset is proposed by changing the sequence of loop winding and modifying the inner loop structure. In general, the sintered ferrite sheets with higher relative permeability (μr ≈ 200) have been used to reduce the performance deterioration of NFC antennas due to the eddy current in the battery pack of a mobile handset. However, their costs are high, and they are considerably breakable. In this letter, we effectively enhance the performance of an NFC antenna by employing the ferrite-polymer composite with lower relative permeability (μr ≈ 55). The proposed antenna improves up to 23% more reading range and up to 65% better load modulation performance.


title: Material property of on-metal magnetic sheet attached on NFC/HF-RFID antenna and research of its proper pattern and size on

text: When a payment system with NFC/HF-RFID is installed in smart-phone, their communication with 13.56MHz is blocked by such metal as battery, coil and PCB. To solve this problem, magnetic sheet with high permeability is set between NFC/HF-RFID antenna and near-by metal. So far thin compound magnetic sheet or thin sintered ferrite has been used. In order to make thickness of these materials thinner, we propose amorphous magnetic sheet. Besides this amorphous magnetic sheet causes to increase the transmission effect.


On neural networks and learning systems for business computing


title: Going wireless: behavior & practice of new mobile phone users

text: We report on the results of a study in which 19 new mobile phone users were closely tracked for the first six weeks after service acquisition. Results show that new users tend to rapidly modify their perceptions of social appropriateness around mobile phone use, that actual nature of use frequently differs from what users initially predict, and that comprehension of service-oriented technologies can be problematic. We describe instances and features of mobile telephony practice. When in use, mobile phones occupy multiple social spaces simultaneously, spaces with norms that sometimes conflict: the physical space of the mobile phone user and the virtual space of the conversation.


title: Evolution of Architectural Floor Plans

text: Layout planning is a process of sizing and placing rooms (e.g. in a house) while attempting to optimize various criteria. Often there are conflicting criteria such as construction cost, minimizing the distance between related activities, and meeting the area requirements for these activities. The process of layout planning has mostly been done by hand, with a handful of attempts to automate the process. This thesis explores some of these past attempts and describes several new techniques for automating the layout planning process using evolutionary computation. These techniques are inspired by the existing methods, while adding some of their own innovations. Additional experiments are done to test the possibility of allowing polygonal exteriors with rectilinear interior walls. Several multi-objective approaches are used to evaluate and compare fitness. The evolutionary representation and requirements specification used provide great flexibility in problem scope and depth and is worthy of considering in future layout and design attempts. The system outlined in this thesis is capable of evolving a variety of floor plans conforming to functional and geometric specifications. Many of the resulting plans look reasonable even when compared to a professional floor plan. Additionally polygonal and multi-floor buildings were also generated.


title: Overview of the TREC 2017 Real-Time Summarization Track

text: Jimmy Lin, Adam Roegiest, Luchen Tan, Richard McCreadie, Ellen Voorhees, and Fernando Diaz 1 David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada 2 School of Computing Science, University of Glasgow, Scotland, the United Kingdom 3 National Institute for Standards and Technology, Maryland, USA 4 Microsoft Research, New York, USA {jimmylin, aroegies, luchen.tan}@uwaterloo.ca richard.mccreadie@glasgow.ac.uk, ellen.voorhees@nist.gov, fdiaz@microsoft.com


title: Human-comfortable navigation for an autonomous robotic wheelchair

text: Reliable autonomous navigation is an active research topic that has drawn the attention for decades, however, human factors such as navigational comfort has not received the same level of attention. This work proposes the concept of “comfortable map” and presents a navigation approach for autonomous passenger vehicles which in top of being safe and reliable is comfortable. In our approach we first extract information from users preference related to comfort while sitting on a robotic wheelchair under different conditions in an indoor corridor environment. Human-comfort factors are integrated to a geometric map generated by SLAM framework. Then a global planner computes a safe and comfortable path which is followed by the robotic wheelchair. Finally, an evaluation with 29 participants using a fully autonomous robotic wheelchair, showed that more than 90% of them found the proposed approach more comfortable than a shortest-path state of the art approach.


title: An ant system based on moderate search for TSP

text: Ant Colony Optimization (ACO) algorithms often suffer from criticism for the local optimum and premature convergence. In order to overcome these inherent shortcomings shared by most ACO algorithms, we divide the ordinary ants into two types: the utilizationoriented ants and the exploration-oriented ants. The utilization-oriented ants focus on constructing solutions based on the learned experience like ants in many other ACO algorithms. On the other hand, inspired by the adaptive behaviors of some real-world Monomorium ant species who tend to select paths with moderate pheromone concentration, a novel search strategy, that is, a completely new transition rule is designed for the exploration-oriented ants to explore more unknown solutions. In addition, a new corresponding update strategy is also employed. Moreover, applying the new search strategy and update strategy, we propose an improved version of ACO algorithm— Moderate Ant System. This improved algorithm is experimentally turned out to be effective and competitive.


title: A new ART-counterpropagation neural network for solving a forecasting problem

text: This study presents a novel Adaptive resonance theory-Counterpropagation neural network (ART-CPN) for solving forecasting problems. The network is based on the ART concept and the CPN learning algorithm for constructing the neural network. The vigilance parameter is used to automatically generate the nodes of the cluster layer for the CPN learning process. This process improves the initial weight problem and the adaptive nodes of the cluster layer (Kohonen layer). ART-CPN involves real-time learning and is capable of developing a more stable and plastic prediction model of input patterns by self-organization. The advantages of ART-CPN include the ability to cluster, learn and construct the network model for forecasting problems. The network was applied to solve the real forecasting problems. The learning algorithm revealed better learning efficiency and good prediction performance. q 2004 Elsevier Ltd. All rights reserved.


title: Search-Free DOD, DOA and Range Estimation for Bistatic FDA-MIMO Radar

text: Frequency diverse array (FDA) is an emerging technology, the hybrid of FDA and multiple-input-multiple-output (FDA-MIMO) under monostatic scenarios has received much attention in recent years. However, little work have been done for bistatic FDA-MIMO radar. In this paper, we investigate strategies on estimating direction-of-departure (DOD), direction-of-arrival and range for bistatic FDA-MIMO radar. Our strategies have two aspects. First, nonlinear frequency increments including both subarray and random modes are employed to overcome the problem that the DOD and range parameters of FDA transmitting steering vectors are coupled. Second, in order to reduce the computational complexity associated with the 3-D spectral peak searching algorithms, estimation of signal parameters via rotational invariance technique and parallel factor algorithms with their corresponding phase ambiguity resolving methods, are proposed for subarray and random modes, respectively. Both of the two algorithms perform well while the range parameter of targets satisfy a range constraint criterion. This criterion can also be used for designing frequency increments of bistatic FDA-MIMO radar. Additionally, the Cramér-Rao bound of bistatic FDA-MIMO radar and the algorithm performance analysis consist of identifiability and complexity are derived. All the proposed methods are verified by both theoretical analysis and numerical simulations. And satisfactory results are achieved.


title: Yoga for anxiety: a systematic review of the research evidence.

text: Between March and June 2004, a systematic review was carried out of the research evidence on the effectiveness of yoga for the treatment of anxiety and anxiety disorders. Eight studies were reviewed. They reported positive results, although there were many methodological inadequacies. Owing to the diversity of conditions treated and poor quality of most of the studies, it is not possible to say that yoga is effective in treating anxiety or anxiety disorders in general. However, there are encouraging results, particularly with obsessive compulsive disorder. Further well conducted research is necessary which may be most productive if focused on specific anxiety disorders.


title: A fully portable robot system for cleaning solar panels

text: Dust and dirt particles accumulating on PV panels decrease the solar energy reaching the cells, thereby reducing their overall power output. Hence, cleaning the PV panels is a problem of great practical engineering interest in solar PV power generation. In this paper, the problem is reviewed and methods for dust removal are discussed. A portable robotic cleaning device is developed and features a versatile platform which travels the entire length of a panel. An Arduino microcontroller is used to implement the robot's control system. Initial testing of the robot has provided favorable results and shows that such a system is viable. Future improvements on the design are discussed, especially the different methods of transporting the robot from one panel to another. In conclusion, it is found that robotic cleaning solution is practical and can help in maintaining the clean PV panel efficiency.