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Modern mobile operating systems support displaying Web pages in native mobile applications. When an app user navigates to a specific location containing a Web page, the Web page will be loaded and rendered from within the app. Such kind of Web browsing, as we call embedded Web browsing, is different from traditional Web browsing, which involves typing a URL on a browser and loading the Web page. However, little has been known about the prevalence or performance of such embedded Web pages. In this paper, we conduct, to the best of our knowledge, the first measurement study on embedded Web browsing on Android. Our study on 22,521 popular Android apps shows that 57.9 and 73.8 percent of apps embed Web pages on two popular app markets, that is, Google Play and Wandoujia, respectively. We design and implement EWProfiler, a tool that can automatically search for embedded Web pages inside apps, trigger page loads, and retrieve performance metrics to analyze the embedded Web browsing performance at scale. Based on 445 embedded Web pages obtained by EWProfiler in 99 popular apps from the two app markets, we investigate the characteristics and performance of embedded Web pages, and find that embedded Web pages significantly impede the app user experience. We investigate the effectiveness of three techniques, i.e., separating the browser kernel to a different process, loading pages from the local storage, and pre-rendering, to optimize the performance of embedded Web browsing. We believe that our findings could draw the attention of Web developers, browser vendors, app developers, and mobile OS vendors together toward a better performance of embedded Web browsing. [ABSTRACT FROM AUTHOR]

The web has become a ubiquitous application development platform for mobile systems. Yet, web access on mobile devices remains an energy-hungry activity. Prior work in the field mainly focuses on the initial page loading stage, but fails to exploit the opportunities for energy-efficiency optimization while the user is interacting with a loaded page. This paper presents a novel approach for performing energy optimization for interactive mobile web browsing. At the heart of our approach is a set of machine learning models, which estimate at runtime the frames per second for a given user interaction input by running the computation-intensive web render engine on a specific processor core under a given clock speed. We use the learned predictive models as a utility function to quickly search for the optimal processor setting to carefully trade responsive time for reduced energy consumption. We integrate our techniques to the open-source Chromium browser and apply it to two representative mobile user events: scrolling and pinching (i.e., zoom in and out). We evaluate the developed system on the landing pages of the top-100 hottest websites and two big.LITTLE heterogeneous mobile platforms. Our extensive experiments show that the proposed approach reduces the system-wide energy consumption by over 36% on average and up to 70%. This translates to an over 17% improvement on energy-efficiency over a state-of-the-art event-based web browser scheduler, but with significantly fewer violations on the quality of service.

In a variety of network applications, there exists a significant amount of shared data between two end hosts. Examples include data synchronization services that replicate data from one node to another. Given that shared data may have a high correlation with new data to transmit, we question how such shared data can be best utilized to improve the efficiency of data transmission. To answer this, we develop an inter-data encoding technique, SyncCoding, that effectively replaces bit sequences of the data to be transmitted with the pointers to their matching bit sequences in the shared data so called references. By doing so, SyncCoding can reduce data traffic, speed up data transmission, and save energy consumption for transmission. Our evaluations of SyncCoding implemented in Linux show that it outperforms existing popular encoding techniques, Brotli, LZMA, Deflate, and Deduplication. The gains of SyncCoding over those techniques in the perspective of data size after compression in a cloud storage scenario are about 12.5, 20.8, 30.1, and 66.1 percent, and are about 78.4, 80.3, 84.3, and 94.3 percent in a web browsing scenario, respectively. [ABSTRACT FROM AUTHOR]

Web browsing is the main Internet service, and every customer wants the minimum web page loading times. To satisfy this perpetual need, web browsers offer new processing engines and increased functionalities. Web developers make use of new programming languages and paradigms, new transport protocols have been introduced, network operators offer increased bandwidth, and CDN providers deploy web servers closest to end users. To assess the efficiency of these evolutions, delivered performance is often evaluated in a standalone manner, without considering the whole chain of web browsing. We propose Web View, a measurement platform that performs automated web browsing sessions on popular websites in a user-representative environment. Through different configurable parameters, Web View measures a wide set of information in order to evaluate the impact of the different parameters (transport protocols, web browsers, CDN, access networks, etc.). Based on those measurements, to ease the understanding of web browsing and the corresponding evolutions, Web View offers a public visualization website (https://webview. orange.com). For instance, with Web View, we were able to detect that the use of different CDNs at different times of the day can decrease loading times up to 400 percent and that the delivery of content through different transport protocols can decrease loading times up to 79 percent. This article presents the Web View measurement platform as well as remarkable events we noticed during more than one year of measurements. [ABSTRACT FROM AUTHOR]

The quality of experience (QoE) perceived by users is a critical performance measure for Web browsing. “Above-The-Fold” (ATF) time has been recently recognized and widely used as a direct measure of user-end QoE by a number of studies. To reduce the ATF time, the existing works mainly focus on reducing the delay of networking. However, we observe that the webpage structures and content orders can also significantly affect theWeb QoE. In this paper, we propose a novel optimization framework that reorders the webpage objects to minimize the user-end ATF time. Our core idea is to first identify the webpage objects that consume the ATF time but have no impact on the page experience and then change the positions of these objects to achieve the minimum ATF time. We implement this framework and evaluate its performance with popular websites. The results show that the ATF time is greatly reduced compared with the existing works, especially for complex webpages.

Power densities of modern mobile system-on-a-chip designs can quickly exceed the thermal design limits during typical application use such as gaming or Web browsing. Resulting high temperatures lead to frequent thermal throttling and significant loss in quality-of-service (QoS) delivered to users. Thus, a joint consideration of thermal constraints and QoS requirements is essential to maximize the overall user experience. Prior techniques either rely on users to determine the best tradeoff point between QoS and temperature, or greedily utilize the thermal headroom to maximize performance, causing QoS to drop below user tolerable levels over extended durations of use. This paper introduces the MAESTRO framework to automatically manage QoS at runtime depending on application characteristics and thermal constraints. MAESTRO builds on the observation that increased temperatures can be tolerated for applications with bursty compute patterns due to idle periods between activities, while causing large QoS degradations for long-running applications with continuous computations. MAESTRO: 1) detects such continuous computations that are susceptible to throttling; 2) proactively finds a QoS level to balance user experience and temperature; and 3) performs closed-loop DVFS and thermally efficient thread mapping to meet the target QoS on a heterogeneous multicore CPU. Such application-adaptive control of QoS-temperature tradeoffs allows MAESTRO to sustain a target QoS level within a user tolerable range for longer durations without sacrificing the performance of latency-sensitive bursty computations. Evaluations on a real system prototype validates MAESTRO’s ability to accurately detect potential throttling-induced QoS degradations and demonstrates 41% to $6.7\boldsymbol {\times }$ longer durations of sustained QoS compared to state-of-the-art for a set of mobile applications. [ABSTRACT FROM AUTHOR]

The excessive use of digital devices such as cameras and smartphones in smart cities has produced huge data repositories that require automatic tools for efficient browsing, searching, and management. Data prioritization (DP) is a technique that produces a condensed form of the original data by analyzing its contents. Current DP studies are either concerned with data collected through stable capturing devices or focused on prioritization of data of a certain type such as surveillance, sports, or industry. This necessitates the need for DP tools that intelligently and cost-effectively prioritize a large variety of data for detecting abnormal events and hence effectively manage them, thereby making the current smart cities greener. In this article, we first carry out an in-depth investigation of the recent approaches and trends of DP for data of different natures, genres, and domains of two decades in green smart cities. Next, we propose an energy-efficient DP framework by intelligent integration of the Internet of Things, artificial intelligence, and big data analytics. Experimental evaluation on real-world surveillance data verifies the energy efficiency and applicability of this framework in green smart cities. Finally, this article highlights the key challenges of DP, its future requirements, and propositions for integration into green smart cities. [ABSTRACT FROM AUTHOR]

One of the main goals of the future wireless networks is improving the users’ quality of experience (QoE). In this paper, we consider the problem of the QoE-based resource allocation in the downlink of a massive multiple-input multiple-output heterogeneous network. The network consists of a macrocell with a number of small cells embedded in it. The small cells’ base stations (BSs) are equipped with a few antennas, while the macro BS is equipped with a massive number of antennas. We consider the two services Video and Web Browsing and design the beamforming vectors at the BSs. The objective is to maximize the aggregated mean opinion score (MOS) of the users under constraints on the BSs’ powers and the required quality of service of the users. We also consider extra constraints on the QoE of users to more strongly enforce the QoE in the beamforming design. To reduce the complexity of the optimization problem, we suggest suboptimal and computationally efficient solutions. Our results illustrate that increasing the number of antennas at the BSs and also increasing the number of small cells’ antennas in the network leads to a higher user satisfaction. [ABSTRACT FROM AUTHOR]

With the rapid advancement of mobile devices, people have become more attached to them than ever. This rapid growth combined with millions of applications (apps) make smart phones a favorite means of communication among users. In general, the available contents on smart phones, apps, and Web, come in two versions: (1) free content that is monetized via advertisements (ads) and (2) paid content that is monetized by user subscription fees. However, the resources, namely, energy, bandwidth, and processing power, on-board are limited, and the existence of ads in Web sites and free apps can significantly increase the usage of these resources. These issues necessitate a good understanding of the mobile advertising eco-system and how such limited resources can be efficiently used. In this paper, we present the results of a novel Web browsing technique that adapts the Web pages delivered to smart phone, based on the smart phone's current battery level and the network type. Web pages are adapted by controlling the amount of ads to be displayed. Validation tests confirm that the system can extend smart phone battery life by up to 30% and save wireless bandwidth up to ~44%.

Imperfect Web resource loading prevents mobile Web browsing from providing satisfactory user experience. In this article, we design and implement the SWAROVsky system to address three main issues of current inefficient Web resource loading: (1) on-demand and thus slow loading of sub-resources of webpages; (2) duplicated loading of resources with different URLs but the same content; and (3) redundant loading of the same resource due to improper cache configurations. SWAROVsky employs a dual-proxy architecture that comprises a remote cloud-side proxy and a local proxy on mobile devices. The remote proxy proactively loads webpages from their original Web servers and maintains a resource loading graph for every single webpage. Based on the graph, the remote proxy is capable of deciding which resources are “really” needed for the webpage and their loading orders, and thus can synchronize these needed resources with the local proxy of a client efficiently and timely. The local proxy also runs an intelligent and light-weight algorithm to identify resources with different URLs but the same content, and thus can avoid duplicated downloading of the same content via network. Our system can be used with existing Web browsers and Web servers, and does not break the normal semantics of a webpage. Evaluations with 50 websites show that on average our system can reduce the page load time by 43.1 percent and the network data transmission by 57.6 percent, while imposing marginal system overhead. [ABSTRACT FROM AUTHOR]

We study the problem of finding related forum posts to a post at hand. In contrast to traditional approaches for finding related documents that perform content comparisons across the content of the posts as a whole, we consider each post as a set of segments, each written with a different goal in mind. We advocate that the relatedness between two posts should be based on the similarity of their respective segments that are intended for the same goal, i.e., are conveying the same intention. This means that it is possible for the same terms to weigh differently in the relatedness score depending on the intention of the segment in which they are found. We have developed a segmentation method that by monitoring a number of text features can identify the parts of a post where significant jumps occur indicating a point where a segmentation should take place. The generated segments of all the posts are clustered to form intention clusters and then similarities across the posts are calculated through similarities across segments with the same intention. We experimentally illustrate the effectiveness and efficiency of our segmentation method and our overall approach of finding related forum posts. [ABSTRACT FROM PUBLISHER]

Redundant transfer of resources is a critical issue for compromising the performance of mobile Web applications (a.k.a., apps) in terms of data traffic, load time, and even energy consumption. Evidence demonstrates that the current cache mechanisms are far from satisfactory. With lessons learned from how native apps manage their resources, in this article, we present the ReWAP approach to fundamentally reducing redundant transfers by restructuring the resource loading of mobile Web apps. ReWAP is based on an efficient resource-packaging mechanism where stable resources are encapsulated and maintained into a package, and such a package shall be loaded always from the local storage and updated by explicitly refreshing. By retrieving and analyzing the update of resources, ReWAP maintains resource packages that can accurately identify which resources can be loaded from the local storage for a considerably long period. ReWAP also provides a wrapper for mobile Web apps to enable loading and updating resource packages in the local storage as well as loading resources from resource packages. ReWAP can be easily and seamlessly deployed into existing mobile Web architectures with minimal modifications, and is transparent to end-users. We evaluate ReWAP based on continuous 15-day access traces of 50 mobile Web apps randomly chosen from Alexa top 500 ranking list. Compared to the original mobile Web apps with cache enabled, ReWAP can significantly reduce the data traffic, with the median saving up to 51 percent. In addition, ReWAP can incur only very minor runtime overhead of the client-side browsers and thus does not compromise user experiences. [ABSTRACT FROM AUTHOR]

Mobile specific webpages differ significantly from their desktop counterparts in content, layout, and functionality. Accordingly, existing techniques to detect malicious websites are unlikely to work for such webpages. In this paper, we design and implement kAYO, a mechanism that distinguishes between malicious and benign mobile webpages. kAYO makes this determination based on static features of a webpage ranging from the number of iframes to the presence of known fraudulent phone numbers. First, we experimentally demonstrate the need for mobile specific techniques and then identify a range of new static features that highly correlate with mobile malicious webpages. We then apply kAYO to a dataset of over 350,000 known benign and malicious mobile webpages and demonstrate 90 percent accuracy in classification. Moreover, we discover, characterize, and report a number of webpages missed by Google Safe Browsing and VirusTotal, but detected by kAYO. Finally, we build a browser extension using kAYO to protect users from malicious mobile websites in real-time. In doing so, we provide the first static analysis technique to detect malicious mobile webpages. [ABSTRACT FROM AUTHOR]

In many countries, the Internet is under stringent censorship for political or religious reasons which severely undermines the free flow of information. A censorship-resistant web browsing system must be scalable, blocking resistant, and tracing resistant. However, current censorship-resistant web browsing systems, which use a group of dedicated proxies to bypass censorship, fail to meet these requirements. To tackle these challenges, we propose Freeweb, which relies on widely-distributed peer-to-peer (P2P) nodes in a decentralized manner rather than specified proxies in a centralized manner. We also proposed enhancement methods to reduce file access delay and avoid node overloads in Freeweb. Freeweb is built on top of a Distributed Hash Table (DHT)-based P2P network, where nodes not under censorship help nodes under censorship to access blocked webpages. Freeweb has a web browser front-end whose user interface resembles existing web browsers. The underlying complex process of retrieving blocked webpages is therefore hidden from users. We implemented an open-sourced Freeweb and conducted extensive real-world experiments on PlanetLab. The experimental results show that Freeweb has a high success rate and reasonable browsing latency, and its enhancement reduces much network load and file access latency, and avoids node overloads. [ABSTRACT FROM PUBLISHER]

The web browser is one of the most significant applications on mobile devices such as smartphones. However, the user experience of mobile web browsing is undesirable because of the slow resource loading. To improve the performance of web resource loading, client-side cache has been adopted as a key mechanism. However, the existing passive measurement studies cannot comprehensively characterize the “client-side” cache performance of mobile web browsing. For example, most of these studies mainly focus on client-side implementations but not server-side configurations, suffer from biased user behaviors, and fail to study “miscached” resources. To address these issues, in this article, we present a proactive approach to making a comprehensive measurement study on client-side cache performance. The key idea of our approach is to proactively crawl resources from hundreds of websites periodically with a fine-grained time interval. Thus, we are able to uncover the resource update history and cache configurations at the server side, and analyze the cache performance in various time granularities. Based on our collected data, we build a new cache analysis model and study the upper bound of how high percentage of resources could potentially be cached and how effectively the caching works in practice. We report detailed analysis results of different websites and various types of web resources, and identify the problems caused by unsatisfactory cache performance. In particular, we identify two major problems— Redundant Transfer and Miscached Resource, which lead to unsatisfactory cache performance. We investigate three main root causes: Same Content , Heuristic Expiration, and Conservative Expiration Time, and discuss what mobile web developers can do to mitigate those problems. [ABSTRACT FROM PUBLISHER]

Internet Service Provider design of service tiers are modeled and analyzed, based on demand for web browsing and video streaming. A basic model that considers user willingness to pay, network capacity, and application performance is formulated to determine when multiple tiers maximize profit. An extended model that also considers the time that users devote to each application is formulated to determine the optimal network capacity, tier rates, and tier prices. We show that an Internet Service Provider may simplify tier and capacity design by allowing its engineering department to set network capacity, its marketing department to set tier prices, and both to jointly set tier rates. Numerical results are presented to illustrate the magnitude of the decrease in profit compared to the optimal profit resulting from such a simplified design. [ABSTRACT FROM PUBLISHER]

A centralized and structured management framework for biosignals is proposed. This framework is dedicated to design biomedical studies, acquire and store biosignals and the associated metadata, as well as validate, synchronize, view, and evaluate these signals from an individual study-related perspective. We introduce a five-layer model as the basis of the framework to facilitate data management, which includes the acquisition, validation, postprocessing, and statistics layers, as well as the vertical presentation layer. The strategic interaction between external data (i.e., metadata) and internal data (i.e., recorded biosignals) is defined to favor high-quality results and sustainability of recorded data. For the first time, a systematic software solution was designed to mimic the proposed layer model and to reasonably combine internal and external data management using microservices within a private cloud. Researchers can individually access and manipulate all data using a client implementation or a Web browser in order to address research-related questions, whereas developers can easily implement modular functions and algorithms. A simplified implementation of the proposed framework shows its strong potential in the unification and structuring of biomedical studies as well as in the associated acquisition, storage, and analysis of biosignals. [ABSTRACT FROM AUTHOR]

TDMA based wireless mesh networks have gained prominence as some of the recent standards such as WiMAX and 802.11s have proposed the use of TDMA based MAC protocol for mesh networks. Even though HTTP web browsing traffic constitutes a significant percentage of Internet traffic, but as of yet there have been no attempts to study the performance of HTTP based web browsing traffic in TDMA mesh networks. HTTP web browsing traffic has different characteristics compared with other types of traffic. In particular, as HTTP traffic consists of large number of small sized file transfers (median file size is 10 KB), it can impose high scheduling overhead. As we highlight, HTTP traffic requires that RTT (round trip time) be small and also it requires that large sized flows be allocated higher share of bandwidth. Given these characteristics of HTTP traffic, it is not clear what protocol design for TDMA mesh networks performs best. In this work, we have studied the performance comparison of four different TDMA MAC protocols for HTTP web browsing traffic. Two of these protocols follow distributed scheduling, one centralized and the other naive static fixed schedule approach. We compare the TDMA MAC protocols for various aspects such as optimum values of different TDMA MAC parameters, design of TDMA scheduling algorithm, performance under varying load, in presence of wireless loss, etc. Our study highlights a crucial aspect that the two distributed protocols specified by the recent WiMAX and 802.11s standards, are incapable of handling wireless packet loss and perform poorly in presence of large HTTP file downloads. As against this, the centralized protocol performs well for the different experiment cases that we have considered. However, our analysis reveals that the performance of the centralized protocol can be further improved if we eliminate the flow set-up phase and minimize the delay in propagating the TDMA schedule. Likewise, we carry out required modifications to the centralized protocol and observe that the performance of the modified protocol, which we term as the sf-centralized (set-up free centralized) protocol, improves by as much as 19 percent under moderate load and 47 percent under high load. [ABSTRACT FROM PUBLISHER]

Browsing the web has become a common task performed using personal mobile devices, resulting in significant access network and battery limitation challenges. Efforts to alleviate these challenges are commonly based around approaches incorporating elements of on-device and network optimizations. Energy-efficient mobile web content delivery has, in turn, attracted a significant body of research and practical developments. However, the efforts put forth today might not result in long-term applicable results should the underlying characteristics of the mobile content change drastically over time. As caching frequently used data locally is a common initial approach employed to limit network traffic and energy expenditures while ?on the go,? we evaluate the long-term suitability of approximating a basic set of parameters for a cache and request behavior model using a popular large data set. We present a convenient approach that can employ a general approximation of parameters over time. Our long-term modeling of the underlying factors results in an acceptable level of peak inaccuracies in simulations for more than a year?s time horizon. In turn, practitioners and researchers are enabled to readily employ modeling and simulation approaches over a significant period of time with only slight impacts on their approaches and results. [ABSTRACT FROM PUBLISHER]

Although cooperative Web caching has been widely researched, comparatively little has been done to reduce inter-proxy network overhead whilst allowing for a high percentage of requested documents to be retrieved from the cache. Alleviating these issues can substantially reduce Web traffic, increase scalability and enhance a user’s browsing experience. This paper introduces a novel cache sharing system employing data structures called Dynamic Interest-Tagged Filtered Bloom Filters (DITFBFs). DITFBFs are capable of representing the cache content of a proxy in a compact form, which is then shared with other proxies in the cooperative Web caching system. What distinguishes the proposed system from others is that DITFBFs only represent the portion of a proxy’s cache content that will be of interest to another proxy. This then results in a reduction of inter-proxy overhead. Experimental simulations indicate that, when compared with existing protocols, the proposed system is capable of multiple improvements. Namely, lowering the number of remote cache search messages by at least 60 percent, decreasing user-perceived latency by at least 65 percent and appreciably reducing the overall inter-proxy network overhead. The proposed system accomplishes this whilst maintaining a cache hit ratio as high as the other protocols. [ABSTRACT FROM PUBLISHER]

In collaborative environments, members may try to acquire similar information on the web in order to gain knowledge in one domain. For example, in a company several departments may successively need to buy business intelligence software and employees from these departments may have studied online about different business intelligence tools and their features independently. It will be productive to get them connected and share learned knowledge. We investigate fine-grained knowledge sharing in collaborative environments. We propose to analyze members’ web surfing data to summarize the fine-grained knowledge acquired by them. A two-step framework is proposed for mining fine-grained knowledge: (1) web surfing data is clustered into tasks by a nonparametric generative model; (2) a novel discriminative infinite Hidden Markov Model is developed to mine fine-grained aspects in each task. Finally, the classic expert search method is applied to the mined results to find proper members for knowledge sharing. Experiments on web surfing data collected from our lab at UCSB and IBM show that the fine-grained aspect mining framework works as expected and outperforms baselines. When it is integrated with expert search, the search accuracy improves significantly, in comparison with applying the classic expert search method directly on web surfing data. [ABSTRACT FROM AUTHOR]

The effectiveness of data loss prevention (DLP) solutions remains obscure, especially with respect to the types of attacks they claim to protect against. A systematic analysis of HTTP data leakage vectors and a test of three content-based DLP solutions give insight into these solutions' vulnerabilities. [ABSTRACT FROM PUBLISHER]

Mozilla's decision to release a new version of its products every six weeks (instead of every year) profoundly affected developers and users and was accompanied by significant changes in the release process. Were such changes enough to allow Mozilla to move faster without breaking things? What lessons can be learned from Mozilla's adoption of rapid releases? To answer these questions, researchers analyzed tens of thousands of commits and bug reports from Firefox and talked to its developers. The results show that, because of integration repositories, build sheriffs, and better testing tools, broken patches were backed out (reverted) earlier, rendering the release process more stable. [ABSTRACT FROM PUBLISHER]

Smartphone based web browsing wastes a lot of power when downloading webpages due to the special characteristics of the wireless radio interface. In this paper, we identify these special characteristics, and address power consumption issues through two novel techniques. First, we reorganize the computation sequence of the web browser when loading a webpage, so that the web browser can first run the computations that will generate new data transmissions and retrieve these data from the web server. Then, the web browser can put the wireless radio interface into low power state, release the radio resource, and then run the remaining computations. Second, we introduce a practical data mining based method to predict the user reading time of webpages, based on which the smartphone can switch to low power state when the reading time is longer than a threshold. To demonstrate the effectiveness of our energy-aware approaches, we develop a testbed with Android phones on T-Mobile UMTS network. Experimental results show that our approach can reduce the power consumption of smartphone by more than 30 percent during web browsing. Moreover, our solution can further reduce the webpage loading time and increase the network capacity. [ABSTRACT FROM AUTHOR]

Mobile Web browsing advancements have largely been driven by the single-minded pursuit of performance. This article takes a different stance and examines the energy efficiency of mobile Web browsing. Focusing on both the network and CPU, the authors find that generational advancements in network technology have reached a point where the mobile CPU is starting to have noticeable impact on Web browsing performance and energy consumption. Achieving energy-efficient mobile Web browsing requires considering both CPU and network capabilities (that is, latency). The authors show that under different network latency conditions, achieving energy-efficient mobile Web browsing mandates different CPU performance capabilities. It is important to understand and leverage such interactions between the network and CPU to deliver high mobile Web performance while maintaining a low energy footprint, which current OS dynamic voltage and frequency scaling governors fail to achieve. The authors' findings suggest that designing future high-performance and energy-efficient mobile Web clients implies looking beyond individual components and taking a full system perspective. [ABSTRACT FROM PUBLISHER]

When glancing at a magazine, or browsing the Internet, we are continuously exposed to photographs. Despite this overflow of visual information, humans are extremely good at remembering thousands of pictures along with some of their visual details. But not all images are equal in memory. Some stick in our minds while others are quickly forgotten. In this paper, we focus on the problem of predicting how memorable an image will be. We show that memorability is an intrinsic and stable property of an image that is shared across different viewers, and remains stable across delays. We introduce a database for which we have measured the probability that each picture will be recognized after a single view. We analyze a collection of image features, labels, and attributes that contribute to making an image memorable, and we train a predictor based on global image descriptors. We find that predicting image memorability is a task that can be addressed with current computer vision techniques. While making memorable images is a challenging task in visualization, photography, and education, this work is a first attempt to quantify this useful property of images. [ABSTRACT FROM AUTHOR]

Recently, building automation and management systems, BASs and BMSs, have shifted from using proprietary protocols and specialized hardware toward widespread adoption of IP-based open standard technologies. While the IP protocol suite improves software and hardware interoperability, practical large-scale BMS deployments face challenges, including the complexity of network addressing and other configuration, reliance on middleware for even relatively simple tasks, and a lack of security. In this article, we propose a data-centric BMS design that uses named data networking, one of the proposed information-centric networking architecture designs. Our sensor data acquisition system uses a hierarchical namespace for data, encryption keys, and access control lists, implements encryption-based access control, and provides a web browser-based data visualization interface that communicates in NDN. Our design has been deployed on a UCLA campus testbed that captures, archives, and visualizes data from industry standard sensors. [ABSTRACT FROM AUTHOR]

The prototype O system seamlessly integrates Web searching and browsing on touch-enabled tablets using two-handed gesture interaction, much like handling and marking content on a piece of paper. The system also leverages contextual information to infer user intent and significantly improve search results over traditional keyword-based approaches, enabling users to go beyond websites' "walled garden"' and more fully explore information objects. [ABSTRACT FROM PUBLISHER]

Smart TV has been an increasingly important multimedia device in recent years due to its powerful functionalities that facilitate consuming multimedia contents. In smart TV environment where an interface for controlling multimedia contents is very limited, how to minimize user efforts while interacting with multimedia contents (e.g., searching for a specific video or browsing through images) is the most critical issue. To address this issue, various approaches using voice communication, gesture control, and gaze tracking systems have been proposed; however, their performance is still not satisfactory. In this paper, a new approach for providing multimedia contents to users with minimized human efforts in smart TV environment is proposed. The proposed approach is based on the recent smart TV equipped with a standard mouse as a remote controller and exploits a novel algorithm to infer user's search intent from mouse movement patterns, thereby improving the speed and accuracy of the contents search in smart TV environment. The experimental results show the strength and potentials of the proposed approach and further research directions. [ABSTRACT FROM AUTHOR]

In recent years, image adaptation has attracted more and more attention during the evolution of the integration of broadcasting, Internet and telecommunications. The diversity of display devices requires images to be resized for optimal display on different terminals. In this paper, an image adaptation scheme and a dynamic browsing strategy are proposed, which are based on the visual attention model (VAM) with two-layer saliency optimization. First, a VAM is constructed by optimizing the image saliency according to the global and local layers by simulated annealing and the saliency of focus of attention (FOA) can be ranked by the obtained saliency map. Then an image adaptation scheme is designed based on the obtained saliency map. In the proposed adaptation scheme, each predominant FOA is modeled as a rectangular region, and the image is adjusted corresponding to the display terminal and the rank of FOA. Based on the principle of global features precedence, a dynamic browsing strategy is developed for browsing large images on small display devices. Experiments on saliency map show that the cross-layer saliency detection algorithm has advantages on detecting salient regions accurately and reflecting the shift of FOA implicitly, which are useful for image adaptation. In addition, subjective evaluation experiments demonstrate the proposed image adaptation scheme and dynamic browsing strategy are feasible and have promising applications in practice. [ABSTRACT FROM PUBLISHER]

Traditional search methods try to obtain the most relevant information and rank it according to the degree of similarity to the queries. Diversity in query results is also preferred by a variety of applications since results very similar to each other cannot capture all aspects of the queried topic. In this paper, we focus on the \lambda-diverse k-nearest neighbor search problem on spatial and multidimensional data. Unlike the approach of diversifying query results in a postprocessing step, we naturally obtain diverse results with the proposed geometric and index-based methods. We first make an analogy with the concept of Natural Neighbors (NatN) and propose a natural neighbor-based method for 2D and 3D data and an incremental browsing algorithm based on Gabriel graphs for higher dimensional spaces. We then introduce a diverse browsing method based on the distance browsing feature of spatial index structures, such as R-trees. The algorithm maintains a Priority Queue with mindivdist of the objects depending on both relevancy and angular diversity and efficiently prunes nondiverse items and nodes. We experiment with a number of spatial and high-dimensional data sets, including Factual's (http://www.factual.com/) US points-of-interest data set of 13M entries. On the experimental setup, the diverse browsing method is shown to be more efficient (regarding disk accesses) than k-NN search on R-trees, and more effective (regarding Maximal Marginal Relevance (MMR)) than the diverse nearest neighbor search techniques found in the literature. [ABSTRACT FROM AUTHOR]

Station-Access Point (STA-AP) association is an important function in IEEE 802.11 Wireless LAN (WLAN) management. We obtain an association policy that can be implemented in centralized WLAN management devices, or in STAs, by taking into account explicitly two aspects of practical importance: (a) TCP-controlled short file downloads interspersed with read times (motivated by web browsing), and (b) different STAs associated with an Access Point (AP) at possibly different rates (depending on distance from the AP). Our approach is based on two steps. First, we consider an analytical model to obtain the aggregate AP throughput for long TCP-controlled file downloads when STAs are associated at k different rates r_1, r_2, ..., r_k; this extends earlier work in the literature. Second, we present a 2-node closed queueing network model to approximate the expected average-sized file download time for a user who shares the AP with other users associated at a multiplicity of rates. These analytical results motivate the proposed association policy, called the Estimated Delay based Association (EDA) policy: Associate with the AP at which the expected file download time is the least. Simulations indicate that for a web-browsing type traffic scenario, EDA performs substantially better than other policies that have been proposed earlier. Crucially, the improved performance is sustained even in realistic evaluation scenarios, where the assumptions underpinning the analytical model do not hold. To the best of our knowledge, this is the first work that proposes an association policy tailored specifically for web browsing. Apart from this, our analytical results could be of independent interest. [ABSTRACT FROM PUBLISHER]

Anonymous communication has become a hot research topic in order to meet the increasing demand for web privacy protection. However, there are few such systems which can provide high level anonymity for web browsing. The reason is the current dominant dummy packet padding method for anonymization against traffic analysis attacks. This method inherits huge delay and bandwidth waste, which inhibits its use for web browsing. In this paper, we propose a predicted packet padding strategy to replace the dummy packet padding method for anonymous web browsing systems. The proposed strategy mitigates delay and bandwidth waste significantly on average. We formulated the traffic analysis attack and defense problem, and defined a metric, cost coefficient of anonymization (CCA), to measure the performance of anonymization. We thoroughly analyzed the problem with the characteristics of web browsing and concluded that the proposed strategy is better than the current dummy packet padding strategy in theory. We have conducted extensive experiments on two real world data sets, and the results confirmed the advantage of the proposed method. [ABSTRACT FROM PUBLISHER]

Web Real-Time Communication (WebRTC) is an upcoming standard that aims to enable real-time communication among Web browsers in a peer-to-peer fashion. The IETF RTCWeb and W3C WebRTC working groups are jointly defining both the APIs and the underlying communication protocols for setting up and managing a reliable communication channel between any pair of next-generation Web browsers. [ABSTRACT FROM AUTHOR]

Web prediction is a classification problem in which we attempt to predict the next set of Web pages that a user may visit based on the knowledge of the previously visited pages. Predicting user's behavior while serving the Internet can be applied effectively in various critical applications. Such application has traditional tradeoffs between modeling complexity and prediction accuracy. In this paper, we analyze and study Markov model and all- Kth Markov model in Web prediction. We propose a new modified Markov model to alleviate the issue of scalability in the number of paths. In addition, we present a new two-tier prediction framework that creates an example classifier EC, based on the training examples and the generated classifiers. We show that such framework can improve the prediction time without compromising prediction accuracy. We have used standard benchmark data sets to analyze, compare, and demonstrate the effectiveness of our techniques using variations of Markov models and association rule mining. Our experiments show the effectiveness of our modified Markov model in reducing the number of paths without compromising accuracy. Additionally, the results support our analysis conclusions that accuracy improves with higher orders of all- Kth model. [ABSTRACT FROM AUTHOR]

Association Link Network (ALN) aims to establish associated relations among various resources. By extending the hyperlink network World Wide Web to an association-rich network, ALN is able to effectively support Web intelligence activities such as Web browsing, Web knowledge discovery, and publishing, etc. Since existing methods for building semantic link on Web resources cannot effectively and automatically organize loose Web resources, effective Web intelligence activities are still challenging. In this paper, a discovery algorithm of associated resources is first proposed to build original ALN for organizing loose Web resources. Second, three schemas for constructing kernel ALN and connection-rich ALN (C-ALN) are developed gradually to optimize the organizing of Web resources. After that, properties of different types of ALN are discussed, which show that C-ALN has good performances to support Web intelligence activities. Moreover, an evaluation method is presented to verify the correctness of C-ALN for semantic link on documents. Finally, an application using C-ALN to organize Web services is presented, which shows that C-ALN is an effective and efficient tool for building semantic link on the resources of Web services. [ABSTRACT FROM PUBLISHER]

An electroencephalographic (EEG) brain–computer interface (BCI) internet browser was designed and evaluated with 10 healthy volunteers and three individuals with advanced amyotrophic lateral sclerosis (ALS), all of whom were given tasks to execute on the internet using the browser. Participants with ALS achieved an average accuracy of 73% and a subsequent information transfer rate (ITR) of 8.6 bits/min and healthy participants with no prior BCI experience over 90% accuracy and an ITR of 14.4 bits/min. We define additional criteria for unrestricted internet access for evaluation of the presented and future internet browsers, and we provide a review of the existing browsers in the literature. The P300-based browser provides unrestricted access and enables free web surfing for individuals with paralysis. [ABSTRACT FROM AUTHOR]

Probabilistic topic models were originally developed and utilized for document modeling and topic extraction in Information Retrieval. In this paper, we describe a new approach for automatic learning of terminological ontologies from text corpus based on such models. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-document interpreted in terms of probability distributions. We propose two algorithms for learning terminological ontologies using the principle of topic relationship and exploiting information theory with the probabilistic topic models learned. Experiments with different model parameters were conducted and learned ontology statements were evaluated by the domain experts. We have also compared the results of our method with two existing concept hierarchy learning methods on the same data set. The study shows that our method outperforms other methods in terms of recall and precision measures. The precision level of the learned ontology is sufficient for it to be deployed for the purpose of browsing, navigation, and information search and retrieval in digital libraries. [ABSTRACT FROM AUTHOR]

Geotagging personal data such as photos and videos are continuously becoming easier and more popular. Nevertheless, browsing such data on general purpose maps can be difficult, due to the frequent zoom and pan operations as well as visual components unnecessary for the task. This paper presents Placegram, a compact diagrammatic map visualization for personal geotagged data browsing based on cognitive map theories. An evaluation using real-life data sets shows that the speed of finding and pointing to places from the participants' own data increased by a factor of 2.1-2.9, and the number of interesting places discovered from others' data within a time limit increased by 48.8 percent in Placegram compared to a general purpose map. Placegram was even slightly faster than a simple text list, while at the same time, preserving the geographic senses of direction and location. Subjective ratings and comments from participants support these results, indicating that Placegram is significantly preferred over both a general map and a text list. [ABSTRACT FROM AUTHOR]

The information about the blur and noise of an original image is lost when a standard image thumbnail is generated filtering and subsampling. Image browsing becomes difficult since the standard thumbnails do not distinguish between high-quality and low-quality originals. In this paper, an efficient algorithm with a blur-generating component and a noise-generating component preserves the local blur and the noise of the originals. The local blur is rapidly estimated using a scale-space expansion of the standard thumbnail and subsequently used to apply a space-varying blur the thumbnail. The noise is estimated and rendered by using multirate signal transformations that allow most of the processing occur at the lower spatial sampling rate of the thumbnail. The new thumbnails provide a quick, natural way for users to identify images of good quality. A subjective evaluation shows the new thumbnails are more representative of their originals for blurry images. The noise generating component improves the results for noisy images, but degrades the results for textured images. The blur generating component of the new thumbnails may always be used advantage. The decision to use the noise generating component the new thumbnails should be based on testing with the particular image mix expected for the application. [ABSTRACT FROM AUTHOR]

We present a metric for quantitatively assessing the quality of Web searches. The Relevance-of-Searching-on-Target index measures how relevant a search result is with respect to the searcher's interest and intention. The measurement is established on the basis of the cognitive characteristics of common user's online Web-browsing behavior and processes. We evaluated the accuracy of the index function with respect to a set of surveys conducted on several groups of our college students. While the index is primarily intended to be used to compare the Web-search results and tell which is more relevant, it can be extended to other applications. For example, it can be used to evaluate the techniques that people apply to improve the Web-search quality (including the quality of search engines), as well as other factors such as the expressiveness of search queries and the effectiveness of result-filtering processes. [ABSTRACT FROM AUTHOR]