Recently, promises were added to ECMAScript 6, the JavaScript standard, in order to provide better support for the asynchrony that arises in user interfaces, network communication, and non-blocking I/O. Using promises, programmers can avoid common pitfalls of event-driven programming such as event races and the deeply nested counterintuitive control ow referred to as “callback hell”. Unfortunately, promises have complex semantics and the intricate control– and data- ow present in promise-based code hinders program comprehension and can easily lead to bugs. The promise graph was proposed as a graphical aid for understanding and debugging promise-based code. However, it did not cover all promise-related features in ECMAScript 6, and did not present or evaluate any technique for constructing the promise graphs. In this paper, we extend the notion of promise graphs to include all promise-related features in ECMAScript 6, including default reactions, exceptions, and the synchronization operations race and all. Furthermore, we report on the construction and evaluation of PromiseKeeper, which performs a dynamic analysis to create promise graphs and infer common promise anti-patterns. We evaluate PromiseKeeper by applying it to 12 open source promise-based Node.js applications. Our results suggest that the promise graphs constructed by PromiseKeeper can provide developers with valuable information about occurrences of common anti-patterns in their promise-based code, and that promise graphs can be constructed with acceptable run-time overhead.

Many geometric problems of interest to mathematical visualization applications involve changing structures requiring mathematical simulations, such as the moves that transform one knot into an equivalent knot. In this paper, we propose to explore a unique paradigm that makes use of self-deformable object models embedded in mathematical space, supplemented by energy-driven relaxation and user-defined motion constraints to guide the simulation through the configuration space. Furthermore, we exploit web-based user interfaces and parallelization to accelerate mathematical simulations, and to extract key moments where successive terms in the sequence differ by one critical change to represent and analyze various mathematical evolutions. The presented work leverages the nature of the interrelationship between mathematics and computer science, especially computer graphics, graph algorithms, user interfaces, and accelerated computation.

Confidentiality and authentication are two main security goals in secure electronic mail (e-mail). Pretty good privacy (PGP) and secure/multipurpose internet mail extensions (S/MIME) are two famous secure e-mail solutions. Both PGP and S/MIME use digital envelope to provide message confidentiality and digital signature to provide message authentication. However, these methods have the following two weaknesses: 1) digital signature provides non-repudiation evidence of sender that is not desired in some e-mail applications and 2) efficiency is low, since these methods use two kinds of public key cryptographic primitives: public key encryption and digital signature. To overcome the above two weaknesses, we introduce a new concept called deniably authenticated encryption that can achieve confidentiality, integrity, and deniable authentication in a logical single step. We first propose a deniably authenticated encryption scheme and prove its security in the random oracle model. Then, we design a secure e-mail protocol using the proposed deniably authenticated encryption scheme. The deniable authentication property protects senders' privacy.

Abstract The MPEG‐7 standard supports the description of both the structure and the semantics of multimedia; however, the generation and consumption of MPEG‐7 structural and semantic descriptions are outside the scope of the standard. This article presents two research prototype systems that demonstrate the generation and consumption of MPEG‐7 structural and semantic descriptions in retrieval applications. The active system for MPEG‐4 video object simulation (AMOS) is a video object segmentation and retrieval system that segments, tracks, and models objects in videos (e.g., person, car) as a set of regions with corresponding visual features and spatiotemporal relations. The region‐based model provides an effective base for similarity retrieval of video objects. The second system, the Intelligent Multimedia Knowledge Application (IMKA), uses the novel MediaNet framework for representing semantic and perceptual information about the world using multimedia. MediaNet knowledge bases can be constructed automatically from annotated collections of multimedia data and used to enhance the retrieval of multimedia.

The success of object-based media representation and description (e.g., MPEG-4 and -7) depends largely on effective object segmentation tools. We expand our previous work on automatic video region tracking and develop a robust-moving objects detection system. In our system, we first utilize innovative methods of combining color and edge information in improving the object motion estimation results. Then we use the long-term spatio-temporal constraints to achieve reliable object tracking over long sequences. Our extensive experiments demonstrate excellent results in handling challenging cases in general domains (e.g., stock footage) including depth-varying multi-layer background and fast camera motion.

In this paper, we present an effective framework for scene detection and structure analysis for sports videos, using tennis and baseball as examples. Sports video can be characterized by its predictable temporal syntax, recurrent events with consistent features, and a fixed number of views. Our approach combines domain-specific knowledge, supervised machine learning techniques, and automatic feature analysis at multiple levels. Real time processing performance is achieved by utilizing compressed-domain processing techniques. High accuracy in view recognition is achieved by using compressed-domain global features as prefilters and object-level refined analysis in the latter verification stage. Applications include high-level structure browsing/navigation, highlight generation, and mobile

We demonstrate a real-time fully automated software system for filtering important events in sports video. Events represent occurrences of actions or state changes in video content. In the current prototype, we demonstrate detection of pitching in baseball and serving in tennis. For wireless video applications, we propose and apply a unique notion of content-based adaptive streaming, in which video encoding rate and media modality is dynamically varied according to the event filtering results. Our system includes an event detection module, an adaptive encoding module, and a buffer management module for adaptive streaming. We achieve the real-time performance by exploring compresseddomain techniques and multi-stage multi-resolution contentanalysis processes.

New video representations based on semantic objects (e.g., MPEG-4) provide great potential for content-based video searching. In this paper we present an efficient query model for similarity searching of video objects based on localized region features and spatial-temporal structures. The query model is barred on an existing framework on region-level video query, but addresses several new issues like right spatio-temporal relationships effective performance of the proposed approach.

Object-based video data representations enable unprecedented functionalities of content access and manipulation. We present an integrated approach using region-based analysis for semantic video object segmentation and retrieval. We first present an active system that combines low-level region segmentation with user inputs for defining and tracking semantic video objects. The proposed technique is novel in using an integrated feature fusion framework for tracking and segmentation at both region and object levels. Experimental results and extensive performance evaluation show excellent results compared to existing systems. Building upon the segmentation framework, we then present a unique region-based query system for semantic video object. The model facilitates powerful object search, such as spatio-temporal similarity searching at multiple levels.

Object segmentation and tracking is a fundamental step for many digital video applications. In this paper, we present an active system (AMOS) which combines low level automatic region segmentation with an active method for defining and tracking high-level semantic video objects. The system contains two stages: an initial object segmentation stage where user input in the starting frame is used to create a semantic object; and an object tracking stage where underlying regions of the semantic object are tracked and grouped through successive frames. Experiments with different types of videos show very good performance.

The rapidity with which digital information, particularly video, is being generated has necessitated the development of tools for efficient search of these media. Content-based visual queries have been primarily focused on still image retrieval. In this paper, we propose a novel, interactive system on the Web, based on the visual paradigm, with spatiotemporal attributes playing a key role in video retrieval. We have developed innovative algorithms for automated video object segmentation and tracking, and use real-time video editing techniques while responding to user queries. The resulting system, called VideoQ , is the first on-line video search engine supporting automatic object-based indexing and spatiotemporal queries. The system performs well, with the user being able to retrieve complex video clips such as those of skiers and baseball players with ease.

Object-based video representation provides great promises for new search and editing functionalities. Feature regions in video sequences are automatically segmented, tracked, and grouped to form the basis for content-based video search and higher levels of abstraction. We present a new system for video object segmentation and tracking using feature fusion and region grouping. We also present efficient techniques for spatio-temporal video query based on the automatically segmented video objects.

The rapidity with which digitat information, particularly video, is being generated, has necessitated the development of tools for efficient search of these media.Content based visual queries have been primarily focussed on still image retrieval.In this papel; we propose a novel, real-time, interactive system on the Web, based on the visual paradigm, with spatio-temporal attributesplaying a key role in video retrieval.We have developed algorithms for automated video object segmentation and tracking and use real-time video editing techniques while responding to user queries.The resulting system pe$orms well, with the user being able to retrieve complex video clips such as those of skiers, baseball players, with ease.Penlli~iollto m&e digitnlhrd copies of ail or pa11 ofthis iilfiterinl for personal or clmsroom use is granted without fee provided tht (112 copiare not made or distributed for profit or commercial ndvrmtnge.the COPYridtt notice, tile title ofthe publicntion and its date appear.and notice is given tl]ntcopyrigllt is by permission ofthe ACM, hC.TO Copy othWh& to republisl), to post on servers or to redistribule to lists, requires specific peniiission nnd/or fee.ACM Multimedia 97 ,~en/f/e I~TfJ.~/lingfO17iJX?I

We present a new framework for distributed video editing and browsing over the network. This framework uses a distributed client-server model including a server engine for content analysis/editing, and clients for interactive controls of video browsing/editing. It utilizes several unique features, including compressed-domain video manipulation, multi-resolution video access, content based video browsing/retrieval, and a distributed network architecture. We have developed a complete functional prototype, CVEPS, which includes a compressed domain video analysis and editing engine, and a JAVA based user interface. It has been incorporated into a World Wide Web application, WebClip, for editing compressed video over the Web.

WebClip is a complete working prototype for editing/browsing MPEG-1 and MPEG-2 compressed video distributively over the World Wide Web. It uses a general system architecture to store, retrieve, and edit MPEG-1 or MPEG-2 compressed video over the network. It uses an innovative distributed network support architecture. It also uses our unique CVEPS (Compressed video editing, parsing, and search) technologies.

No abstract available.