Query: Albatross: Lightweight Elasticity in Shared Storage Databases for the Cloud using Live Data Migration

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Adapting microsoft SQL server for cloud computing <=SEP=> Cloud SQL Server is a relational database system designed to scale-out to cloud computing workloads. It uses Microsoft SQL Server as its core. To scale out, it uses a partitioned database on a shared-nothing system architecture. Transactions are constrained to execute on one partition, to avoid the need for two-phase commit. The database is replicated for high availability using a custom primary-copy replication scheme. It currently serves as the storage engine for Microsoft's Exchange Hosted Archive and SQL Azure.

The Part-Time Parliament <=SEP=> Recent archaeological discoveries on the island of Paxos reveal that the parliament functioned despite the peripatetic propensity of its part-time legislators. The legislators maintained consistent copies of the parliamentary record, despite their frequent forays from the chamber and the forgetfulness of their messengers. The Paxon parliament's protocol provides a new way of implementing the state machine approach to the design of distributed systems.

Xen and the art of virtualization <=SEP=> Numerous systems have been designed which use virtualization to subdivide the ample resources of a modern computer. Some require specialized hardware, or cannot support commodity operating systems. Some target 100% binary compatibility at the expense of performance. Others sacrifice security or functionality for speed. Few offer resource isolation or performance guarantees; most provide only best-effort provisioning, risking denial of service.This paper presents Xen, an x86 virtual machine monitor which allows multiple commodity operating systems to share conventional hardware in a safe and resource managed fashion, but without sacrificing either performance or functionality. This is achieved by providing an idealized virtual machine abstraction to which operating systems such as Linux, BSD and Windows XP, can be ported with minimal effort.Our design is targeted at hosting up to 100 virtual machine instances simultaneously on a modern server. The virtualization approach taken by Xen is extremely efficient: we allow operating systems such as Linux and Windows XP to be hosted simultaneously for a negligible performance overhead --- at most a few percent compared with the unvirtualized case. We considerably outperform competing commercial and freely available solutions in a range of microbenchmarks and system-wide tests.

Dynamo: amazon's highly available key-value store <=SEP=> Reliability at massive scale is one of the biggest challenges we face at Amazon.com, one of the largest e-commerce operations in the world; even the slightest outage has significant financial consequences and impacts customer trust. The Amazon.com platform, which provides services for many web sites worldwide, is implemented on top of an infrastructure of tens of thousands of servers and network components located in many datacenters around the world. At this scale, small and large components fail continuously and the way persistent state is managed in the face of these failures drives the reliability and scalability of the software systems.
 This paper presents the design and implementation of Dynamo, a highly available key-value storage system that some of Amazon's core services use to provide an "always-on" experience. To achieve this level of availability, Dynamo sacrifices consistency under certain failure scenarios. It makes extensive use of object versioning and application-assisted conflict resolution in a manner that provides a novel interface for developers to use.

An end-to-end approach to host mobility <=SEP=> We present the design and implementation of an end-to-end architecture for Internet host mobility using dynamic updates to the Domain Name System (DNS) to track host location. Existing TCP connections are retained using secure and efficient connection migration, enabling established connections to seamlessly negotiate a change in endpoint IP addresses without the need for a third party. Our architecture is secure—name updates are effected via the secure DNS update protocol, while TCP connection migration uses a novel set of Migrate options—and provides a pure end-system alternative to routing-based approaches such as Mobile IP.
Mobile IP was designed under the principle that fixed Internet hosts and applications were to remain unmodified and only the underlying IP substrate should change. Our architecture requires no changes to the unicast IP substrate, instead modifying transport protocols and applications at the end hosts. We argue that this is not a hindrance to deployment; rather, in a significant number of cases, it allows for an easier deployment path than Mobile IP, while simultaneously giving better performance. We compare and contrast the strengths of end-to-end and network-layer mobility schemes, and argue that end-to-end schemes are better suited to many common mobile applications. Our performance experiments show that hand-off times are governed by TCP migrate latencies, and are on the order of a round-trip time of the communicating peers.

Ocular Pain and Impending Blindness During Facial Cosmetic Injections: Is Your Office Prepared? <=SEP=> Soft tissue filler injections are the second most common non-surgical procedure performed by the plastic surgeon. Embolization of intravascular material after facial injection is a rare but terrifying outcome due to the high likelihood of long-term sequela such as blindness and cerebrovascular accident. The literature is replete with examples of permanent blindness caused by injection with autologous fat, soft tissue fillers such as hyaluronic acid, PLLA, calcium hydroxyl-apatite, and even corticosteroid suspensions. However, missing from the discussion is an effective treatment algorithm that can be quickly and safely followed by injecting physicians in the case of an intravascular injection with impending blindness. In this report, we present the case of a 64-year-old woman who suffered from blindness and hemiparesis after facial cosmetic injections performed by a family physician. We use this case to create awareness that this complication has become more common as the number of injectors and patients seeking these treatments have increased exponentially over the past few years. We share in this study our experience with the incorporation of a “blindness safety kit” in each of our offices to promptly initiate treatment in someone with embolization and impending blindness. The kit contains a step-by-step protocol to follow in the event of arterial embolization of filler material associated with ocular pain and impending loss of vision. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

CATS: Collection and Analysis of Tweets Made Simple <=SEP=> Twitter presents an unparalleled opportunity for researchers from various ï¬elds to gather valuable and genuine textual data from millions of people. However, the collection pro-cess, as well as the analysis of these data require different kinds of skills (e.g. programing, data mining) which can be an obstacle for people who do not have this background. In this paper we present CATS, an open source, scalable, Web application designed to support researchers who want to carry out studies based on tweets. The purpose of CATS is twofold: (i) allow people to collect tweets (ii) enable them to analyze these tweets thanks to efï¬cient tools (e.g. event detection, named-entity recognition, topic modeling, word-clouds). What is more, CATS relies on a distributed imple-mentation which can deal with massive data streams.

Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation <=SEP=> In optimization, multiple objectives and constraints cannot be handled independently of the underlying optimizer. Requirements such as continuity and di erentiability of the cost surface add yet another con icting element to the decision process. While \better" solutions should be rated higher than \worse" ones, the resulting cost landscape must also comply with such requirements. Evolutionary algorithms (EAs), which have found application in many areas not amenable to optimization by other methods, possess many characteristics desirable in a multiobjective optimizer, most notably the concerted handling of multiple candidate solutions. However, EAs are essentially unconstrained search techniques which require the assignment of a scalar measure of quality, or tness, to such candidate solutions. After reviewing current evolutionary approaches to multiobjective and constrained optimization, the paper proposes that tness assignment be interpreted as, or at least related to, a multicriterion decision process. A suitable decision making framework based on goals and priorities is subsequently formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies. Finally, the ranking of an arbitrary number of candidates is considered. The e ect of preference changes on the cost surface seen by an EA is illustrated graphically for a simple problem. The paper concludes with the formulation of a multiobjective genetic algorithm based on the proposed decision strategy. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape.

Model-Driven Design for the Visual Analysis of Heterogeneous Data <=SEP=> As heterogeneous data from different sources are being increasingly linked, it becomes difficult for users to understand how the data are connected, to identify what means are suitable to analyze a given data set, or to find out how to proceed for a given analysis task. We target this challenge with a new model-driven design process that effectively codesigns aspects of data, view, analytics, and tasks. We achieve this by using the workflow of the analysis task as a trajectory through data, interactive views, and analytical processes. The benefits for the analysis session go well beyond the pure selection of appropriate data sets and range from providing orientation or even guidance along a preferred analysis path to a potential overall speedup, allowing data to be fetched ahead of time. We illustrate the design process for a biomedical use case that aims at determining a treatment plan for cancer patients from the visual analysis of a large, heterogeneous clinical data pool. As an example for how to apply the comprehensive design approach, we present Stack'n'flip, a sample implementation which tightly integrates visualizations of the actual data with a map of available data sets, views, and tasks, thus capturing and communicating the analytical workflow through the required data sets.

Cache-oblivious streaming B-trees <=SEP=> A <b><i>streaming B-tree</i></b> is a dictionary that efficiently implements insertions and range queries. We present two cache-oblivious streaming B-trees, the <b><i>shuttle tree</i></b>, and the <b><i>cache-oblivious lookahead array (COLA)</i></b>.
 For block-transfer size <i>B</i> and on <i>N</i> elements, the shuttle tree implements searches in optimal <i>O</i>(log <sub><i>B</i>+1</sub><i>N</i>) transfers, range queries of <i>L</i> successive elements in optimal <i>O</i>(log <sub><i>B</i>+1</sub><i>N</i> +<i>L/B</i>) transfers, and insertions in <i>O</i>((log <sub><i>B</i>+1</sub><i>N</i>)/<i>B</i><sup>Θ(1/(log log <i>B</i>)<sup>2</sup>)</sup>+(log<sup>2</sup><i>N</i>)/<i>B</i>) transfers, which is an asymptotic speedup over traditional B-trees if <i>B</i> ≥ (log <i>N</i>)<sup>1+<i>c</i> log log log<sup>2</sup> <i>N</i></sup> for any constant <i>c</i> >1.
 A COLA implements searches in <i>O</i>(log <i>N</i>) transfers, range queries in O(log <i>N</i> + <i>L/B</i>) transfers, and insertions in amortized <i>O</i>((log <i>N</i>)/<i>B</i>) transfers, matching the bounds for a (cache-aware) buffered repository tree. A partially deamortized COLA matches these bounds but reduces the worst-case insertion cost to <i>O</i>(log <i>N</i>) if memory size <i>M</i> = Ω(log <i>N</i>). We also present a cache-aware version of the COLA, the <b><i>lookahead array</i></b>, which achieves the same bounds as Brodal and Fagerberg's (cache-aware) B<sup>ε</sup>-tree.
 We compare our COLA implementation to a traditional B-tree. Our COLA implementation runs 790 times faster for random inser-tions, 3.1 times slower for insertions of sorted data, and 3.5 times slower for searches.

Dynamic Behaviour of an Electronically Commutated ( Brushless DC ) Motor Drive with Back-emf Sensing <=SEP=> Conventionally, BLDC motors are commutated in six-step pattern with commutation controlled by position sensors. To reduce cost and complexity of the drive system, sensorless drive is preferred. The existing sensorless control scheme with the conventional back EMF sensing based on motor neutral voltage for BLDC has certain drawbacks, which limit its applications. This paper presents the dynamic behaviour of an analytical and circuit model of a Brushless DC (BLDC) motors with back emf sensing. The circuit model was simulated using LTspice and the results obtained were compared with the experimental results. The value of the motor constant and the back emf measured from the experiment agreed with the simulated model. The starting behaviour of the motor, changing of load torque when current are varied and disturbance of sensing method at peak load shows that the dynamic behaviour results of the experiment obtained from oscilloscope are similar to the simulated value.

A Computational Model of Plan-Based Narrative Conflict at the Fabula Level <=SEP=> Conflict is an essential element of interesting stories. In this paper, we operationalize a narratological definition of conflict and extend established narrative planning techniques to incorporate this definition. The conflict partial order causal link planning algorithm (CPOCL) allows narrative conflict to arise in a plan while maintaining causal soundness and character believability. We also define seven dimensions of conflict in terms of this algorithm's knowledge representation. The first three-participants, reason, and duration-are discrete values which answer the “who?” “why?” and “when?” questions, respectively. The last four-balance, directness, stakes, and resolution-are continuous values which describe important narrative properties that can be used to select conflicts based on the author's purpose. We also present the results of two empirical studies which validate our operationalizations of these narrative phenomena. Finally, we demonstrate the different kinds of stories which CPOCL can produce based on constraints on the seven dimensions.

Quasi-Cyclic LDPC Codes: Influence of Proto- and Tanner-Graph Structure on Minimum Hamming Distance Upper Bounds <=SEP=> Quasi-cyclic (QC) low-density parity-check (LDPC) codes are an important instance of proto-graph-based LDPC codes. In this paper we present upper bounds on the minimum Hamming distance of QC LDPC codes and study how these upper bounds depend on graph structure parameters (like variable degrees, check node degrees, girth) of the Tanner graph and of the underlying proto-graph. Moreover, for several classes of proto-graphs we present explicit QC LDPC code constructions that achieve (or come close to) the respective minimum Hamming distance upper bounds. Because of the tight algebraic connection between QC codes and convolutional codes, we can state similar results for the free Hamming distance of convolutional codes. In fact, some QC code statements are established by first proving the corresponding convolutional code statements and then using a result by Tanner that says that the minimum Hamming distance of a QC code is upper bounded by the free Hamming distance of the convolutional code that is obtained by “unwrapping” the QC code.

Piranha: a scalable architecture based on single-chip multiprocessing <=SEP=> The microprocessor industry is currently struggling with higher development costs and longer design times that arise from exceedingly complex processors that are pushing the limits of instruction-level parallelism. Meanwhile, such designs are especially ill suited for important commercial applications, such as on-line transaction processing (OLTP), which suffer from large memory stall times and exhibit little instruction-level parallelism. Given that commercial applications constitute by far the most important market for high-performance servers, the above trends emphasize the need to consider alternative processor designs that specifically target such workloads. The abundance of explicit thread-level parallelism in commercial workloads, along with advances in semiconductor integration density, identify chip multiprocessing (CMP) as potentially the most promising approach for designing processors targeted at commercial servers.
 This paper describes the Piranha system, a research prototype being developed at Compaq that aggressively exploits chip multi-processing by integrating eight simple Alpha processor cores along with a two-level cache hierarchy onto a single chip. Piranha also integrates further on-chip functionality to allow for scalable multiprocessor configurations to be built in a glueless and modular fashion. The use of simple processor cores combined with an industry-standard ASIC design methodology allow us to complete our prototype within a short time-frame, with a team size and investment that are an order of magnitude smaller than that of a commercial microprocessor. Our detailed simulation results show that while each Piranha processor core is substantially slower than an aggressive next-generation processor, the integration of eight cores onto a single chip allows Piranha to outperform next-generation processors by up to 2.9 times (on a per chip basis) on important workloads such as OLTP. This performance advantage can approach a factor of five by using full-custom instead of ASIC logic. In addition to exploiting chip multiprocessing, the Piranha prototype incorporates several other unique design choices including a shared second-level cache with no inclusion, a highly optimized cache coherence protocol, and a novel I/O architecture.

Short-Circuit Detection for Electrolytic Processes Employing Optibar Intercell Bars <=SEP=> This paper presents a method to detect metallurgical short circuits suitable for Optibar intercell bars in copper electrowinning and electrorefining processes. One of the primary achievements of this bar is to limit short-circuit currents to a maximum of 1.5 p.u. of the actual process current. However, low-current short circuits are more difficult to detect. Thus, conventional short-circuit detection instruments like gaussmeters and infrared cameras become ineffective. To overcome this problem, the proposed method is based on detecting the voltage drop across anode-cathode pairs. The method does not affect the operation of the process and does not require modifications of the industrial plant. In order to verify the performance of this proposal, experimental measurements done over a period of four months at a copper refinery are presented. A 100% success rate was obtained.

Rating Image Aesthetics Using Deep Learning <=SEP=> This paper investigates unified feature learning and classifier training approaches for image aesthetics assessment . Existing methods built upon handcrafted or generic image features and developed machine learning and statistical modeling techniques utilizing training examples. We adopt a novel deep neural network approach to allow unified feature learning and classifier training to estimate image aesthetics. In particular, we develop a double-column deep convolutional neural network to support heterogeneous inputs, i.e., global and local views, in order to capture both global and local characteristics of images . In addition, we employ the style and semantic attributes of images to further boost the aesthetics categorization performance . Experimental results show that our approach produces significantly better results than the earlier reported results on the AVA dataset for both the generic image aesthetics and content -based image aesthetics. Moreover, we introduce a 1.5-million image dataset (IAD) for image aesthetics assessment and we further boost the performance on the AVA test set by training the proposed deep neural networks on the IAD dataset.

Vehicles Capable of Dynamic Vision <=SEP=> A survey is given on two decades of developments in the field, encompassing an increase in computing power by four orders of magnitude. The '4-D approach' integrating expectation-based methods from systems dynamics and control engineering with methods from AI has allowed to create vehicles with unprecedented capabilities in the technical realm: Autonomous road vehicle guidance in public traffic on freeways at speeds beyond 130 km/h, on-board-autonomous landing approaches of aircraft, and landmark navigation for AGV's, for road vehicles including turn-offs onto cross-roads, and for helicopters in low-level flight (real-time, hardware-in-the-loop simulations in the latter case).

DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map <=SEP=> For applications such as augmented reality, autonomous driving, self-localization/camera pose estimation and scene parsing are crucial technologies. In this paper, we propose a unified framework to tackle these two problems simultaneously. The uniqueness of our design is a sensor fusion scheme which integrates camera videos, motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robustness and efficiency of the system. Specifically, we first have an initial coarse camera pose obtained from consumer-grade GPS/IMU, based on which a label map can be rendered from the 3D semantic map. Then, the rendered label map and the RGB image are jointly fed into a pose CNN, yielding a corrected camera pose. In addition, to incorporate temporal information, a multi-layer recurrent neural network (RNN) is further deployed improve the pose accuracy. Finally, based on the pose from RNN, we render a new label map, which is fed together with the RGB image into a segment CNN which produces perpixel semantic label. In order to validate our approach, we build a dataset with registered 3D point clouds and video camera images. Both the point clouds and the images are semantically-labeled. Each video frame has ground truth pose from highly accurate motion sensors. We show that practically, pose estimation solely relying on images like PoseNet [25] may fail due to street view confusion, and it is important to fuse multiple sensors. Finally, various ablation studies are performed, which demonstrate the effectiveness of the proposed system. In particular, we show that scene parsing and pose estimation are mutually beneficial to achieve a more robust and accurate system.

The learning curve in microtia surgery. <=SEP=> Reconstruction of the auricle is known to be complex. Our objective was to evaluate the improvement of the outcome of the lobulus-type microtia reconstruction. Patient satisfaction was also evaluated. There are no previous reports of the learning process in this field. Postoperative photographs of 51 microtia reconstructions were assessed and rated by a panel made up of six surgeons. The ratings were gathered to generate learning curves. Twenty-two patients assessed the outlook of their reconstructed ears, and the results were analyzed as a self-assessment group. The reliability of the rating by a panel was tested by intraclass correlations. There is a highly significant increasing trend in learning ( P = 0.000001). This trend is not constantly upward, and the steady state was not reached during the study. In the self-assessment group, females were significantly more critical than males ( P = 0.014). Intraclass correlation for six panel members was 0.90, and the rating was considered reliable. Thus, a long and gentle learning curve does exist in microtia reconstruction. To secure good quality and continuity, centralization of the operations and trainee arrangements are highly advisable. Outcomes of plastic surgery can reliably be rated by an evaluation panel.

Adolescent coping style and behaviors: conceptualization and measurement. <=SEP=> The developmental tasks associated with adolescence pose a unique set of stressors and strains. Included in the normative tasks of adolescence are developing and identity, differentiating from the family while still staying connected, and fitting into a peer group. The adolescent's adaptation to these and other, often competing demands is achieved through the process of coping which involves cognitive and behavioral strategies directed at eliminating or reducing demands, redefining demands so as to make them more manageable, increasing resources for dealing with demands, and/or managing the tension which is felt as a result of experiencing demands. In this paper, individual copying theory and family stress theory are reviewed to provide a theoretical foundation for assessing adolescent coping. In addition, the development and testing of an adolescent self-report coping inventory, Adolescent Coping Orientation for Problem Experiences (A-COPE) is presented. Gender differences in coping style are presented and discussed. These coping patterns were validated against criterion indices of adolescents' use of cigarettes, liquor, and marijuana using data from a longitudinal study of 505 families with adolescents. The findings are discussed in terms of coping theory and measurement and in terms of adolescent development and substance use.

Comparison of human and computer performance across face recognition experiments <=SEP=> a r t i c l e i n f o Since 2005, human and computer performance has been systematically compared as part of face recognition competitions, with results being reported for both still and video imagery. The key results from these competitions are reviewed. To analyze performance across studies, the cross-modal performance analysis (CMPA) framework is introduced. The CMPA framework is applied to experiments that were part of face a recognition competition. The analysis shows that for matching frontal faces in still images, algorithms are consistently superior to humans. For video and difficult still face pairs, humans are superior. Finally, based on the CMPA framework and a face performance index, we outline a challenge problem for developing algorithms that are superior to humans for the general face recognition problem.

Facilitating relational governance through service level agreements in IT outsourcing: An application of the commitment-trust theory <=SEP=> Article history: Received 18 June 2007 Received in revised form 9 June 2008 Accepted 20 June 2008 Available online 25 June 2008 Firms increasingly rely on outsourcing for strategic IT decisions, and themany sophisticated forms of outsourcing require significant management attention to ensure their success. Two forms of interorganizational governance–formal control and relational–have been used to examine the management of IT outsourcing relationships. Contrary to the conventional substitution view, recent studies have found that these two governance modes are complementary; however, the dynamics of their interactions remain unexplored. Based on the commitment–trust theory, this paper focuses on how the formal controlmechanism can influence the relational governance in an outsourcing engagement. Using service level agreements (SLAs) as a proxy for formal control, this studyfinds that eleven contractual elements, characterized as foundation, governance, and change management variables in an SLA, are positively related to the trust and relationship commitment among the parties. Trust and commitment, in turn, positively influence relational outcomes that we theorize would contribute to outsourcing success. Both research and practical implications of the results are discussed. © 2008 Elsevier B.V. All rights reserved.

Plan 9 , A Distributed System <=SEP=> Plan 9 is a computing environment physically distributed across many machines. The distribution itself is transparent to most programs giving both users and administrators wide latitude in configuring the topology of the environment. Two properties make this possible: a per process group name space and uniform access to all resources by representing them as files.

Preventing Cell Phone Intrusion and Theft using Biometrics <=SEP=> Most cell phones use a password, PIN, or visual pattern to secure the phone. With these types of security methods being used, there is much vulnerability. Another alternative is biometric authentication. Biometric security systems have been researched for many years. Some mobile manufacturers have implemented fingerprint scanners into their phones, such as the old Fujitsu F505i [7] and the current Motorola Atrix. Since theft of cell phones is becoming more common every day, there is a real need for a security system that not only protects the data, but the phone itself. It is proposed through this research that a biometric security system be the alternative to knowledge-based and password-based authentication. Furthermore, a device dongle must be implemented into this infrastructure to establish a reliable security system that deters theft for the majority; biometrics alone is not sufficient. Cell phones need power and must be charged almost daily. A biometric phone charger that acts as a dongle with a solid state relay, will be presented as a viable solution to theft in this research. Additionally, it will be shown through the results of this research that a system dependant only on biometrics is unreliable and unsecure. Essentially, a mobile security system that combines biometrics with dongle technology is believed to be the ideal solution for limiting the black market of stolen cell phones; without the biometric charger/dongle, the stolen cell phone would be rendered useless.

Measuring the Diversity of Automatic Image Descriptions <=SEP=> Automatic image description systems typically produce generic sentences that only make use of a small subset of the vocabulary available to them. In this paper, we consider the production of generic descriptions as a lack of diversity in the output, which we quantify using established metrics and two new metrics that frame image description as a word recall task. This framing allows us to evaluate system performance on the head of the vocabulary, as well as on the long tail, where system performance degrades. We use these metrics to examine the diversity of the sentences generated by nine state-of-the-art systems on the MS COCO data set. We find that the systems trained with maximum likelihood objectives produce less diverse output than those trained with additional adversarial objectives. However, the adversarially-trained models only produce more types from the head of the vocabulary and not the tail. Besides vocabulary-based methods, we also look at the compositional capacity of the systems, specifically their ability to create compound nouns and prepositional phrases of different lengths. We conclude that there is still much room for improvement, and offer a toolkit to measure progress towards the goal of generating more diverse image descriptions.

Hand Gesture Recognition for Sign Language Recognition: A Review <=SEP=> For deaf and dumb people, Sign language is the only way of communication. With the help of sign language, these physical impaired people express their emotions and thoughts to other person. Since for the common person, it is difficult to understand these language therefore often these physically challenged has to keep the translator along with them to communicate with the world. Hence sign language recognition has become empirical task. Since sign language consist of various movement and gesture of hand therefore the accuracy of sign language depends on the accurate recognition of hand gesture. This paper present various method of hand gesture and sign language recognition proposed in the past by various researchers. Keywords—SL, DCT, Self organizing Map, Camshift

Delaunay Meshing of Isosurfaces <=SEP=> We present an isosurface meshing algorithm, DelIso, based on the Delaunay refinement paradigm. This paradigm has been successfully applied to mesh a variety of domains with guarantees for topology, geometry, mesh gradedness, and triangle shape. A restricted Delaunay tri- angulation, dual of the intersection between the surface and the three dimensional Voronoi diagram, is often the main ingredient in Delaunay refinement. Computing and storing three dimensional Voronoi/Delaunay diagrams become bottlenecks for Delaunay refinement techniques since isosurface computations generally have large input datasets and output meshes. A highlight of our algorithm is that we find a simple way to recover the restricted Delaunay triangulation of the surface without computing the full 3D structure. We employ techniques for efficient ray tracing of isosurfaces to generate surface sample points, and demonstrate the effectiveness of our implementation using a variety of volume datasets.

The Many Faces of Formative Assessment. <=SEP=> In this research paper we consider formative assessment (FA) and discuss ways in which it has been implemented in four different university courses. We illustrate the different aspects of FA by deconstructing it and then demonstrating effectiveness in improving both teaching and student achievement. It appears that specifically “what is done” was less important since there were positive achievement gains in each study. While positive gains were realized with use of technology, gains were also realized with implementation of nontechnology dependent techniques. Further, gains were independent of class size or subject matter.

A review of issues and challenges in designing Iris recognition Systems for noisy imaging environment <=SEP=> Iris recognition is a challenging task in a noisy imaging environment. Nowadays researcher's primary focus is to develop reliable Iris recognition System that can work in noisy imaging environment and to increase the iris recognition rate on different iris database. But there are major issues involved in designing such systems like occlusion by eyelashes, eyelids, glass frames, off-angle imaging, presence of contact lenses, poor illumination, motion blur, close-up of iris image acquired at a large standoff distance and specular reflections etc. Because of these issues the quality of acquired iris image gets affected. The performance of the iris based recognition system will deteriorate abruptly, when the iris mask is not accurate. This results in lower recognition rate. In this review paper different challenges in designing iris recognition systems for noisy imaging environment are reviewed and methodologies involved in overcoming these issues are discussed. At the end, some measures to improve the accuracy of such systems are suggested.

Mining Multi-label Data <=SEP=> A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label. Textual data, such as documents and web pages, are frequently annotated with more than a single label. For example, a news article concerning the reactions of the Christian church to the release of the “Da Vinci Code” film can be labeled as both religion and movies. The categorization of textual data is perhaps the dominant multi-label application. Recently, the issue of learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of new applications, such as semantic annotation of images [1, 2, 3] and video [4, 5], functional genomics [6, 7, 8, 9, 10], music categorization into emotions [11, 12, 13, 14] and directed marketing [15]. Table 1 presents a variety of applications that are discussed in the literature. This chapter reviews past and recent work on the rapidly evolving research area of multi-label data mining. Section 2 defines the two major tasks in learning from multi-label data and presents a significant number of learning methods. Section 3 discusses dimensionality reduction methods for multi-label data. Sections 4 and 5 discuss two important research challenges, which, if successfully met, can significantly expand the real-world applications of multi-label learning methods: a) exploiting label structure and b) scaling up to domains with large number of labels. Section 6 introduces benchmark multi-label datasets and their statistics, while Section 7 presents the most frequently used evaluation measures for multi-label learn-