Query: Literature Fingerprinting: A New Method for Visual Literary Analysis

Relevant passages:
Data Mining : Concepts and Techniques <=SEP=> Association rule mining was first proposed by Agrawal, Imielinski, and Swami [AIS93]. The Apriori algorithm discussed in Section 5.2.1 for frequent itemset mining was presented in Agrawal and Srikant [AS94b]. A variation of the algorithm using a similar pruning heuristic was developed independently by Mannila, Tiovonen, and Verkamo [MTV94]. A joint publication combining these works later appeared in Agrawal, Mannila, Srikant, Toivonen, and Verkamo [AMS96]. A method for generating association rules from frequent itemsets is described in Agrawal and Srikant [AS94a].

Combining Text and Linguistic Document Representations for Authorship Attribution <=SEP=> In this paper, we provide several alternatives to the classical Bag-Of-Words model for automatic authorship attribution. To this end, we consider linguistic and writing style information such as grammatical structures to construct different document representations. Furthermore we describe two techniques to combine the obtained representations: combination vectors and ensemble based meta classification. Our experiments show the viability of our approach.

Finding Interesting Rules from Large Sets of Discovered Association Rules <=SEP=> Association rules, introduced by Agrawal, Imielinski, and Swami, are rules of the form “for 90% of the rows of the relation, if the row has value 1 in the columns in set W, then it has 1 also in column B”. Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules can, however, be so large that browsing the rule set and finding interesting rules from it can be quite difficult for the user. We show how a simple formalism of rule templates makes it possible to easily describe the structure of interesting rules. We also give examples of visualization of rules, and show how a visualization tool interfaces with rule templates.

Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods <=SEP=> 

Automatically Categorizing Written Texts by Author Gender <=SEP=> The problem of automatically determining the gender of a document's author would appear to be a more subtle problem than those of categorization by topic or authorship attribution. Nevertheless, it is shown that automated text categorization techniques can exploit combinations of simple lexical and syntactic features to infer the gender of the author of an unseen formal written document with approximately 80% accuracy. The same techniques can be used to determine if a document is fiction or non-fiction with approximately 98% accuracy.

To Create What You Tell: Generating Videos from Captions <=SEP=> We are creating multimedia contents everyday and everywhere. While automatic content generation has played a fundamental challenge to multimedia community for decades, recent advances of deep learning have made this problem feasible. For example, the Generative Adversarial Networks (GANs) is a rewarding approach to synthesize images. Nevertheless, it is not trivial when capitalizing on GANs to generate videos. The difficulty originates from the intrinsic structure where a video is a sequence of visually coherent and semantically dependent frames. This motivates us to explore semantic and temporal coherence in designing GANs to generate videos. In this paper, we present a novel Temporal GANs conditioning on Captions, namely TGANs-C, in which the input to the generator network is a concatenation of a latent noise vector and caption embedding, and then is transformed into a frame sequence with 3D spatio-temporal convolutions. Unlike the naive discriminator which only judges pairs as fake or real, our discriminator additionally notes whether the video matches the correct caption. In particular, the discriminator network consists of three discriminators: video discriminator classifying realistic videos from generated ones and optimizes video-caption matching, frame discriminator discriminating between real and fake frames and aligning frames with the conditioning caption, and motion discriminator emphasizing the philosophy that the adjacent frames in the generated videos should be smoothly connected as in real ones. We qualitatively demonstrate the capability of our TGANs-C to generate plausible videos conditioning on the given captions on two synthetic datasets (SBMG and TBMG) and one real-world dataset (MSVD). Moreover, quantitative experiments on MSVD are performed to validate our proposal via Generative Adversarial Metric and human study.

Number : AUTCON-D16-00058 R 1 Title : BIM for Infrastructure : An Overall Review and Constructor Perspective <=SEP=> 6 The subject of Building Information Modelling (BIM) has become a central topic to the improvement of the AECOO 7 (Architecture, Engineering, Construction, Owner and Operator) industry around the world, to the point where the 8 concept is being expanded into domains it was not originally conceived to address. Transitioning BIM into the 9 domain of infrastructure projects has provided challenges and emphasized the constructor perspective of BIM. 10 Therefore, this study aims to collect the relevant literature regarding BIM within the Infrastructure domain and its 11 use from the constructor perspective to review and analyse the current industry positioning and research state of 12 the art, with regards to the set criteria. The review highlighted a developing base of BIM for infrastructure. From 13 the analysis, the related research gaps were identified regarding information integration, alignment of BIM 14 processes to constructor business processes & the effective governance and value of information. From this a 15 unique research strategy utilising a framework for information governance coupled with a graph based distributed 16 data environment is outlined to further progress the integration and efficiency of AECOO Infrastructure projects. 17

Enterprise Database Applications and the Cloud: A Difficult Road Ahead <=SEP=> There is considerable interest in moving DBMS applications from inside enterprise data centers to the cloud, both to reduce cost and to increase flexibility and elasticity. Some of these applications are "green field" projects (i.e., new applications), others are existing legacy systems that must be migrated to the cloud. In another dimension, some are decision support applications while others are update-oriented. In this paper, we discuss the technical and political challenges that these various enterprise applications face when considering cloud deployment. In addition, a requirement for quality-of-service (QoS) guarantees will generate additional disruptive issues. In some circumstances, achieving good DBMS performance on current cloud architectures and future hardware technologies will be non-trivial. In summary, there is a difficult road ahead for enterprise database applications.

Laughbot: Detecting Humor in Spoken Language with Language and Audio Cues <=SEP=> We propose detecting and responding to humor in spoken dialogue by extracting language and audio cues and subsequently feeding these features into a combined recurrent neural network (RNN) and logistic regression model. In this paper, we parse Switchboard phone conversations to build a corpus of punchlines and unfunny lines where punchlines precede laughter tokens in Switchboard transcripts. We create a combined RNN and logistic regression model that uses both acoustic and language cues to predict whether a conversational agent should respond to an utterance with laughter. Our model achieves an F1-score of 63.2 and accuracy of 73.9. This model outperforms our logistic language model (F1-score 56.6) and RNN acoustic model (59.4) as well as the final RNN model of D. Bertero, 2016 (52.9). Using our final model, we create a “laughbot” that audibly responds to a user with laughter when their utterance is classified as a punchline. A conversational agent outfitted with a humorrecognition system such as the one we present in this paper would be valuable as these agents gain utility in everyday life. Keywords—Chatbots; spoken natural language processing; deep learning; machine learning

Reconstruction of dorsal and/or caudal nasal septum deformities with septal battens or by septal replacement: an overview and comparison of techniques. <=SEP=> OBJECTIVES
The objectives of this study were to describe and compare two techniques used to correct nasal septum deviations located in the dorsal and/or caudal septum.


STUDY DESIGN
The authors conducted a retrospective clinical chart review.


METHODS
The authors conducted a comparison of functional and technical results between surgery in the L-strut of the septum in 114 patients with septal battens or by septal replacement by subjective self-evaluation and by examination of the position of the septum during follow up.


RESULTS
There was subjective improvement in nasal breathing in 86% of the septal batten group and in 94% of the septal replacement group. This difference was not statistically significant. The technical result was judged by examining the position of the septum during follow up as midline, slightly deviated, or severely deviated. The septum was significantly more often located in the midline during follow up in the septal replacement group than in the septal batten group.


CONCLUSION
Treatment of deformities located in the structurally important L-strut of the septum may be technically challenging and many functional, structural, and esthetic considerations must be taken into account. On the basis of this series, both septal battens and septal replacement techniques may be considered for correction of deviations in this area. The functional improvement rates were not significantly different between the techniques, although during follow up, the septum appeared to be significantly more often located in the midline in the septal replacement group. The techniques are described and their respective advantages and potential drawbacks are discussed.

DroidTrace: A ptrace based Android dynamic analysis system with forward execution capability <=SEP=> Android, being an open source smartphone operating system, enjoys a large community of developers who create new mobile services and applications. However, it also attracts malware writers to exploit Android devices in order to distribute malicious apps in the wild. In fact, Android malware are becoming more sophisticated and they use advanced “dynamic loading” techniques like Java reflection or native code execution to bypass security detection. To detect dynamic loading, one has to use dynamic analysis. Currently, there are only a handful of Android dynamic analysis tools available, and they all have shortcomings in detecting dynamic loading. The aim of this paper is to design and implement a dynamic analysis system which allows analysts to perform systematic analysis of dynamic payloads with malicious behaviors. We propose “DroidTrace”, a ptrace based dynamic analysis system with forward execution capability. Our system uses ptrace to monitor selected system calls of the target process which is running the dynamic payloads, and classifies the payloads behaviors through the system call sequence, e.g., behaviors such as file access, network connection, inter-process communication and even privilege escalation. Also, DroidTrace performs “physical modification” to trigger different dynamic loading behaviors within an app. Using DroidTrace, we carry out a large scale analysis on 36,170 dynamic payloads in 50,000 apps and 294 malware in 10 families (four of them are zero-day) with various dynamic loading behaviors.

Understanding the Dynamic Interplay of Social Buzz and Contribution Behavior within and between Online Platforms - Evidence from Crowdfunding <=SEP=> Motivated by the growing interconnection between online platforms, we examine the dynamic interplay between social buzz and contribution behavior in the crowdfunding context. Since the utility of crowdfunding projects is usually difficult to ascertain, prospective backers draw on quality signals, such as social buzz and prior-contribution behavior, to make their funding decisions. We employ the panel vector autoregression (PVAR) methodology to investigate both intraand cross-platform effects based on data collected from three platforms: Indiegogo, one of the largest crowdfunding platforms on the web, Twitter and Facebook. Our results show a positive influence of social buzz on project backing, but a negative relationship in the reverse direction. Furthermore, we observe strong positive feedback cycles within each platform. Our results are supplemented by split-sample analyses for project orientation (Social, Cause and Entrepreneurial) and project success (Winners vs. Losers), in which Facebook shares were identified as a critical success factor.

Comparing exploration strategies for Q-learning in random stochastic mazes <=SEP=> Balancing the ratio between exploration and exploitation is an important problem in reinforcement learning. This paper evaluates four different exploration strategies combined with Q-learning using random stochastic mazes to investigate their performances. We will compare: UCB-1, softmax, ∈-greedy, and pursuit. For this purpose we adapted the UCB-1 and pursuit strategies to be used in the Q-learning algorithm. The mazes consist of a single optimal goal state and two suboptimal goal states that lie closer to the starting position of the agent, which makes efficient exploration an important part of the learning agent. Furthermore, we evaluate two different kinds of reward functions, a normalized one with rewards between 0 and 1, and an unnormalized reward function that penalizes the agent for each step with a negative reward. We have performed an extensive grid-search to find the best parameters for each method and used the best parameters on novel randomly generated maze problems of different sizes. The results show that softmax exploration outperforms the other strategies, although it is harder to tune its temperature parameter. The worst performing exploration strategy is ∈-greedy.

Character-based movie summarization <=SEP=> A decent movie summary is helpful for movie producer to promote the movie as well as audience to capture the theme of the movie before watching the whole movie. Most exiting automatic movie summarization approaches heavily rely on video content only, which may not deliver ideal result due to the semantic gap between computer calculated low-level features and human used high-level understanding. In this paper, we incorporate script into movie analysis and propose a novel character-based movie summarization approach, which is validated by modern film theory that what actually catches audiences' attention is the character. We first segment scenes in the movie by analysis and alignment of script and movie. Then we conduct substory discovery and content attention analysis based on the scent analysis and character interaction features. Given obtained movie structure and content attention value, we calculate movie attraction scores at both shot and scene levels and adopt this as criterion to generate movie summary. The promising experimental results demonstrate that character analysis is effective for movie summarization and movie content understanding.

Distributed Denial-of-Service (DDoS) Threat in Collaborative Environment - A Survey on DDoS Attack Tools and Traceback Mechanisms <=SEP=> Collaborative applications are feasible nowadays and are becoming more popular due to the advancement in internetworking technology. The typical collaborative applications, in India include the Space research, Military applications, Higher learning in Universities and Satellite campuses, State and Central government sponsored projects, e-governance, e-healthcare systems, etc. In such applications, computing resources for a particular institution/organization spread across districts and states and communication is achieved through internetworking. Therefore the computing and communication resources must be protected against security attacks as any compromise on these resources would jeopardize the entire application/mission. Collaborative environment is prone for various threats, of which Distributed Denial of Service (DDoS) attacks are of major concern. DDoS attack prevents legitimate access to critical resources. A survey by Arbor networks reveals that approximately 1,200 DDoS attacks occur per day. As the DDoS attack is coordinated, the defense for the same has to be a collaborative one. To counter DDoS attacks in a collaborative environment, all the routers need to work collaboratively by exchanging their caveat messages with their neighbors. This paper analyses the security measures in a collaborative environment, identifles the popular DDoS attack tools, and surveys the existing traceback mechanisms to trace the real attacker.

Geolocation of RF Emitters by Many UAVs <=SEP=> This paper presents an approach to using a large team of UAVs to find radio frequency (RF) emitting targets in a large area. Small, inexpensive UAVs that can collectively and rapidly determine the approximate location of intermittently broadcasting and mobile RF emitters have a range of applications in both military, e.g., for finding SAM batteries, and civilian, e.g., for finding lost hikers, domains. Received Signal Strength Indicator (RSSI) sensors on board the UAVs measure the strength of RF signals across a range of frequencies. The signals, although noisy and ambiguous due to structural noise, e.g., multipath effects, overlapping signals and sensor noise, allow estimates to be made of emitter locations. Generating a probability distribution over emitter locations requires integrating multiple signals from different UAVs into a Bayesian filter, hence requiring cooperation between the UAVs. Once likely target locations are identified, EO-camera equipped UAVs must be tasked to provide a video stream of the area to allow a user to identify the emitter.

75–85 GHz flip-chip phased array RFIC with simultaneous 8-transmit and 8-receive paths for automotive radar applications <=SEP=> This paper presents the first simultaneous 8-transmit and 8-receive paths 75-85 GHz phased array RFIC for FMCW automotive radars. The receive path has two separate I/Q mixers each connected to 4-element phased arrays for RF and digital beamforming. The chip also contains a build-in-self-test system (BIST) for the transmit and receive paths. Measurements on a flip-chip prototype show a gain >24 dB at 77 GHz, -25 dB coupling between adjacent channels in the transmit and receive paths (<;-45 dB between non-adjacent channels), and <;-50 dB coupling between the transmit and receive portions of the chip.

Globally and locally consistent image completion <=SEP=> We present a novel approach for image completion that results in images that are both locally and globally consistent. With a fully-convolutional neural network, we can complete images of arbitrary resolutions by filling-in missing regions of any shape. To train this image completion network to be consistent, we use global and local context discriminators that are trained to distinguish real images from completed ones. The global discriminator looks at the entire image to assess if it is coherent as a whole, while the local discriminator looks only at a small area centered at the completed region to ensure the local consistency of the generated patches. The image completion network is then trained to fool the both context discriminator networks, which requires it to generate images that are indistinguishable from real ones with regard to overall consistency as well as in details. We show that our approach can be used to complete a wide variety of scenes. Furthermore, in contrast with the patch-based approaches such as PatchMatch, our approach can generate fragments that do not appear elsewhere in the image, which allows us to naturally complete the images of objects with familiar and highly specific structures, such as faces.

DEFO-NET: Learning Body Deformation Using Generative Adversarial Networks <=SEP=> Modelling the physical properties of everyday objects is a fundamental prerequisite for autonomous robots. We present a novel generative adversarial network (DEFO-NET), able to predict body deformations under external forces from a single RGB-D image. The network is based on an invertible conditional Generative Adversarial Network (IcGAN) and is trained on a collection of different objects of interest generated by a physical finite element model simulator. Defo-netinherits the generalisation properties of GANs. This means that the network is able to reconstruct the whole 3-D appearance of the object given a single depth view of the object and to generalise to unseen object configurations. Contrary to traditional finite element methods, our approach is fast enough to be used in real-time applications. We apply the network to the problem of safe and fast navigation of mobile robots carrying payloads over different obstacles and floor materials. Experimental results in real scenarios show how a robot equipped with an RGB-D camera can use the network to predict terrain deformations under different payload configurations and use this to avoid unsafe areas.

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples <=SEP=> We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat iterative optimizationbased attacks, we find defenses relying on this effect can be circumvented. We describe characteristic behaviors of defenses exhibiting the effect, and for each of the three types of obfuscated gradients we discover, we develop attack techniques to overcome it. In a case study, examining noncertified white-box-secure defenses at ICLR 2018, we find obfuscated gradients are a common occurrence, with 7 of 9 defenses relying on obfuscated gradients. Our new attacks successfully circumvent 6 completely, and 1 partially, in the original threat model each paper considers.

Graphical SLAM - a self-correcting map <=SEP=> We describe an approach to simultaneous localization and mapping, SLAM. This approach has the highly desirable property of robustness to data association errors. Another important advantage of our algorithm is that non-linearities are computed exactly, so that global constraints can be imposed even if they result in large shifts to the map. We represent the map as a graph and use the graph to find an efficient map update algorithm. We also show how topological consistency can be imposed on the map, such as, closing a loop. The algorithm has been implemented on an outdoor robot and we have experimental validation of our ideas. We also explain how the graph can be simplified leading to linear approximations of sections of the map. This reduction gives us a natural way to connect local map patches into a much larger global map.

A novel approach to American sign language recognition using MAdaline neural network <=SEP=> Sign language interpretation is gaining a lot of research attention because of its social contributions which is proved to be extremely beneficial for the people suffering from hearing or speaking disabilities. This paper proposes a novel image processing sign language detection framework that employs MAdaline network for classification purpose. This paper mainly highlights two novel aspects, firstly it introduces an advanced feature set comprising of seven distinct features that has not been used widely for sign language interpretation purpose, more over utilization of such features negates the cumbersome step of cropping of irrelevant background image, thus reducing system complexity. Secondly it suggests a possible solution of the concerned problem can be obtained using an extension of the traditional Adaline network, formally termed as MAdaline Network. Although the concept of MAdaline network has originated much earlier, the provision of application of this framework in this domain definitely help in designing an improved sign language interpreting interface. The newly formulated framework has been implemented to recognize standardized American sign language containing 26 English alphabets from ‘A’ to ‘Z’. The performance of the proposed algorithm has also been compared with the standardized algorithms, and in each case the former one outperformed its contender algorithms by a large margin establishing the efficiency of the same.

Design and Optimization a Circular Shape Network Antenna Micro Strip for Some Application <=SEP=> To meet the demands of high speed required by mobile communication of past generations ,one solution is to increase the number of antennas to the show and the reception of the wireless link this is called MIMO (Multiple input ,Multiple output )technology .however ,the integration of multiple antennas on the same PCB is delicate because of the small volume that require some applications and electromagnetic antenna between the coupling ,phenomena that we cannot neglect them .indeed a strong isolation between them has been reached to reduce fading of the signal caused by the electromagnetic antenna reached to reduce fading of the signal caused by the electromagnetic coupling and maximize the overall gain .in this article we are interested then integration on the same printed circuit of eight antennas MIMO are not operation in the same frequency band .the first antenna of this last work at 2.4GHz .other antennas have resonance frequency folling each with 20MHz offset this device is characterized by its original form that keeps is highly isolated antennas from the point of view electromagnetic coupling . INDEX TERMS MIMO, Technology Micro-strip, Microwave, Network Antenna

Network Traffic Anomaly Detection <=SEP=> This paper presents a tutorial for network anomaly detection, focusing on non-signature-based approaches. Network traffic anomalies are unusual and significant changes in the traffic of a network. Networks play an important role in today’s social and economic infrastructures. The security of the network becomes crucial, and network traffic anomaly detection constitutes an important part of network security. In this paper, we present three major approaches to non-signature-based network detection: PCA-based, sketch-based, and signal-analysis-based. In addition, we introduce a framework that subsumes the three approaches and a scheme for network anomaly extraction. We believe network anomaly detection will become more important in the future because of the increasing importance of network security.

Forward-Private Dynamic Searchable Symmetric Encryption with Efficient Search <=SEP=> Dynamic Searchable Symmetric Encryption (DSSE) allows to delegate keyword search and file update over an encrypted database via encrypted indexes, and therefore provides opportunities to mitigate the data privacy and utilization dilemma in cloud storage platforms. Despite its merits, recent works have shown that efficient DSSE schemes are vulnerable to statistical attacks due to the lack of forward-privacy, whereas forward-private DSSE schemes suffers from practicality concerns as a result of their extreme computation overhead. Due to significant practical impacts of statistical attacks, there is a critical need for new DSSE schemes that can achieve the forward-privacy in a more practical and efficient manner. We propose a new DSSE scheme that we refer to as Forward-private Sublinear DSSE (FS-DSSE). FS-DSSE harnesses special secure update strategies and a novel caching strategy to reduce the computation cost of repeated queries. Therefore, it achieves forward-privacy, sublinear search complexity, low end-to-end delay, and parallelization capability simultaneously. We fully implemented our proposed method and evaluated its performance on a real cloud platform. Our experimental evaluation results showed that the proposed scheme is highly secure and highly efficient compared with state-of-the-art DSSE techniques. Specifically, FS-DSSE is up to three magnitude of times faster than forward-secure DSSE counterparts, depending on the frequency of the searched keyword in the database.

Performance Analysis of Classification Algorithms on Medical Diagnoses-a Survey <=SEP=> Corresponding Author: Vanaja, S., Research Scholar and Research guide, Research and Development, Bharathiar University, Coimbatore, Tamil Nadu, India Email: vanajasha@yahoo.com Abstract: The aim of this research paper is to study and discuss the various classification algorithms applied on different kinds of medical datasets and compares its performance. The classification algorithms with maximum accuracies on various kinds of medical datasets are taken for performance analysis. The result of the performance analysis shows the most frequently used algorithms on particular medical dataset and best classification algorithm to analyse the specific disease. This study gives the details of different classification algorithms and feature selection methodologies. The study also discusses about the data constraints such as volume and dimensionality problems. This research paper also discusses the new features of C5.0 classification algorithm over C4.5 and performance of classification algorithm on high dimensional datasets. This research paper summarizes various reviews and technical articles which focus on the current research on Medical diagnosis.

Semantic MEDLINE: An advanced information management application for biomedicine <=SEP=> Semantic MEDLINE integrates information retrieval, advanced natural language processing, automatic summarization, and visualization into a single Web portal. The application is intended to help manage the results of PubMed searches by condensing core semantic content in the citations retrieved. Output is presented as a connected graph of semantic relations, with links to the original MEDLINE citations. The ability to connect salient information across documents helps users keep up with the research literature and discover connections which might otherwise go unnoticed. Semantic MEDLINE can make an impact on biomedicine by supporting scientific discovery and the timely translation of insights from basic research into advances in clinical practice and patient care. Marcelo Fiszman has an M.D. from the State University of Rio de Janeiro and a Ph.D. in biomedical informatics from the University of Utah. He was awarded a postdoctoral fellowship in biomedical informatics at the National Library of Medicine (NLM) and is currently a research scientist there. His work focuses on natural language processing algorithms that exploit symbolic, rule-based techniques for semantic interpretation of biomedical text. He is also interested in using extracted semantic information for automatic abstraction summarization and literaturebased discovery. These efforts underpin Semantic MEDLINE, which is currently under development at NLM. This innovative biomedical information management application combines document retrieval, semantic interpretation, automatic summarization, and knowledge visualization into a single application.

Effects of Professional Development on Teachers ' Instruction : Results from a Three-Year Longitudinal Study <=SEP=> JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. American Educational Research Association is collaborating with JSTOR to digitize, preserve and extend access to Educational Evaluation and Policy Analysis. This article examines the effects of professional development on teachers' instruction. Using a purposefully selected sample of about 207 teachers in 30 schools, in 10 districts infive states, we examine features of teachers' professional development and its effects on changing teaching practice in mathematics and science from 1996-1999. We found that professional developmentfocused on specific instructional practices increases teachers' use of those practices in the classroom. Furthermore, we found that specificfeatures, such as active learning opportunities, increase the effect of the professional development on teacher's instruction. What are the characteristics of professional development that affect teaching practice? This study adds to the knowledge base on effective professional development. The success of standards-based reform depends on teachers' ability to foster both basic knowledge and advanced thinking and problem solving among their students (Loucks-Desimone et al. Education 1999a), and surveys of teachers about their preservice preparation and in-service professional development experiences (e.g., Carey & Frechtling, 1997). In addition, there is a considerable amount of literature describing "best practices" in professional development, drawing on expert experiences (e.g., Loucks-Horsley et al., 1998). A professional consensus is emerging about particular characteristics of "high quality" professional development. These characteristics include a focus on content and how students learn content; in-depth, active learning opportunities; links to high standards, opportunities for teachers to engage in leadership roles; extended duration ; and the collective participation of groups of teachers from the same school, grade, or department. Although lists of characteristics such as these commonly appear in the literature on effective professional development, there is little direct evidence on the extent to which these characteristics are related to better teaching and increased student achievement. Some studies conducted over the past decade suggest that professional development experiences that share all or most of these characteristics can have a substantial, positive influence on teachers' classroom practice and student achieve-A few recent studies have begun to examine the relative importance of specific characteristics of professional development. Several studies have found that the intensity and …

"It's alive, it's magic, it's in love with you": opportunities, challenges and open questions for actuated interfaces <=SEP=> Actuated Interfaces are receiving a great deal of interest from the research community. The field can now present a range of point designs, illustrating the potential design space of Actuated Interfaces. However, despite the increasing interest in Actuated Interfaces, the research carried out is nevertheless primarily preoccupied with the technical challenges and potential application areas, rather than how users actually approach, experience, interpret and understand Actuated Interfaces. Based on three case studies, investigating how people experience Actuated Interfaces, we point to; magic, movement and ambiguity as fruitful perspectives for understanding users' experiences with Actuated Interfaces. The three perspectives are employed to reflect upon opportunities and challenges, as well as point to open questions and relevant areas for future research for Actuated Interfaces.

Automatic Airspace Sectorisation: A Survey <=SEP=> Airspace sectorisation provides a partition of a given airspace into sectors, subject to geometric constraints and workload constraints, so that some cost metric is minimised. We survey the algorithmic aspects of methods for automatic airspace sectorisation, for an intended readership of experts on air traffic management.