Browsing Systems Research Articles

A34] 伝統空間のデータ化と閲覧システムの有効性と課題：データ化手法・閲覧形態の違いにおける「思い出」想起の差異を通して

A34] 伝統空間のデータ化と閲覧システムの有効性と課題：データ化手法・閲覧形態の違いにおける「思い出」想起の差異を通して

It ain't what you do, it's the way that you do it: Design guidelines to better support online browsing

ABSTRACTMany authors have proposed guidelines for online browsing systems, however recently empirical research has added depth to these requirements. There has also been a spate of development in online browsing systems, particularly for library shelf browsing. These strands of research are clearly in service of providing better browsing options in an increasingly online library environment, however they have not yet been brought together. In this paper we use a recent empirical study and a survey of the literature to generate a comprehensive list of requirements for browsing interfaces. We will then compare that list to the interface features of a range of different book browsing interfaces, demonstrating why they may be more or less effective, and signpost development of more effective online browsing.

Freeweb: P2P-Assisted Collaborative Censorship-Resistant Web Browsing

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.

LIDAB: a user-friendly display system for linked multimedia data and its application in education

The Semantic Web is an extension of the current Web where information is represented in a way that computer programs such as software agents can understand the meaning of the information without human's intervention. Like the Web, huge number of hyperlinked data exist on the Semantic Web which results in the Web of Linked Data. In addition, one can easily encounter hyperlinked multimedia data such as image files, video clips, etc. on the Semantic Web. In this paper, we propose a browsing system called LIDAB which shows user-friendly views of different types of linked multimedia data sources on the Semantic Web. The motivation behind our research is to provide lay users without technical backgrounds on the Semantic Web with intuitive views on the linked multimedia data. While there exist well-known linked data browsing systems such as Tabulator, Disco, etc., using these systems are not always easy for novice users since they do not show intuitive views on browser screens. On the other hand, our system generates a view that looks almost like a normal HTML view that one can see on a Web browser. Our system has two types of audience: non-expert users who browse the information on the Semantic Web and Web designers who can create HTML documents. It is easy to use the proposed system in various educational scenarios where students can surf the Semantic Web to study learning materials.

Modeling Reusable and Interoperable Faceted Browsing Systems with Category Theory.

Faceted browsing has become ubiquitous with modern digital libraries and online search engines, yet the process is still difficult to abstractly model in a manner that supports the development of interoperable and reusable interfaces. We propose category theory as a theoretical foundation for faceted browsing and demonstrate how the interactive process can be mathematically abstracted. Existing efforts in facet modeling are based upon set theory, formal concept analysis, and lightweight ontologies, but in many regards, they are implementations of faceted browsing rather than a specification of the basic, underlying structures and interactions. We will demonstrate that category theory allows us to specify faceted objects and study the relationships and interactions within a faceted browsing system. Implementations can then be constructed through a category-theoretic lens using these models, allowing abstract comparison and communication that naturally support interoperability and reuse.

An EBMC-Based Approach to Selecting Types for Entity Filtering

The quantity of entities in the Linked Data is increasing rapidly. For entity search and browsing systems, filtering is very useful for users to find entities that they are interested in. Type is a kind of widely-used facet and can be easily obtained from knowledge bases, which enables to create filters by selecting at most K types of an entity collection. However, existing approaches often fail to select high-quality type filters due to complex overlap between types. In this paper, we propose a novel type selection approach based upon Budgeted Maximum Coverage (BMC), which can achieve integral optimization for the coverage quality of type filters. Furthermore, we define a new optimization problem called Extended Budgeted Maximum Coverage (EBMC) and propose an EBMC-based approach, which enhances the BMC-based approach by incorporating the relevance between entities and types, so as to create sensible type filters. Our experimental results show that the EBMC-based approach performs best comparing with several representative approaches.

Scalable storyboards in handheld devices: applications and evaluation metrics

Summaries are an essential component of video retrieval and browsing systems. Most research in video summarization has focused on content analysis to obtain compact yet comprehensive representations of video items. However, important aspects such as how they can be effectively integrated in mobile interfaces and how to predict the quality and usability of the summaries have not been investigated. Conventional summaries are limited to a single instance with certain length (i.e. a single scale). In contrast, scalable summaries target representations with multiple scales, that is, a set of summaries with increasing length in which longer summaries include more information about the video. Thus, scalability provides high flexibility that can be exploited in devices such as smartphones or tablets to provide versions of the summary adapted to the limited visualization area. In this paper, we explore the application of scalable storyboards to summary adaptation and zoomable video navigation in handheld devices. By introducing a new adaptation dimension related with the summarization scale, we can formulate navigation and adaptation in a two-dimensional adaptation space, where different navigation actions modify the trajectory in that space. We also describe the challenges to evaluate scalable summaries and some usability issues that arise from having multiple scales, proposing some objective metrics that can provide useful insight about their potential quality and usability without requiring very costly user studies. Experimental results show a reasonable agreement with the trends shown in subjective evaluations. Experiments also show that content-based scalable storyboards are less redundant and useful than the content-blind baselines.

Corpus Development for Affective Video Indexing

Affective video indexing is the area of research that develops techniques to automatically generate descriptions of video content that encode the emotional reactions which the video content evokes in viewers. This paper provides a set of corpus development guidelines based on state-of-the-art practice intended to support researchers in this field. Affective descriptions can be used for video search and browsing systems offering users affective perspectives. The paper is motivated by the observation that affective video indexing has yet to fully profit from the standard corpora (data sets) that have benefited conventional forms of video indexing. Affective video indexing faces unique challenges, since viewer-reported affective reactions are difficult to assess. Moreover affect assessment efforts must be carefully designed in order to both cover the types of affective responses that video content evokes in viewers and also capture the stable and consistent aspects of these responses. We first present background information on affect and multimedia and related work on affective multimedia indexing, including existing corpora. Three dimensions emerge as critical for affective video corpora, and form the basis for our proposed guidelines: the context of viewer response, personal variation among viewers, and the effectiveness and efficiency of corpus creation. Finally, we present examples of three recent corpora and discuss how these corpora make progressive steps towards fulfilling the guidelines.

Savanna browse production. 1: Determinants and measurement

With the rapid expansion of wildlife ranching and conservation in South African savannas and the resultant increase in multi-species grazing and browsing systems, information on browse production potential is essential for determination of carrying capacity. This study, conducted on three game reserves in the northern Zululand coastal plains of KwaZulu-Natal, explored the biotic (measurable tree dimensions, and tree species) and abiotic (climatic and soil) factors influencing the rate and quantity of quarterly browse production of seven key savanna tree species (Acacia nilotica [Vachellia nilotica], Carissa bispinosa, Dichrostachys cinerea, Euclea divinorum, Gymnosporia senegalensis, Spirostachys africana and Ziziphus mucronata). Available canopy volume, mean maximum daily temperature and tree species were the most significant determinants of browse production. Available canopy volume proved to be the most reliable predictor of browse production per species and per tree, with quarterly browse production being greater on trees with larger available canopy volumes (up to a threshold). Browse production differed significantly between quarters. Growing season onset occurred during the sampling period prior to the rainy season when mean maximum daily temperatures peaked. Quarterly browse production was greatest in evergreen E. divinorum, followed by the semi-deciduous species, S. africana and Z. mucronata.

A Multimedia Recommender System

The extraordinary technological progress we have witnessed in recent years has made it possible to generate and exchange multimedia content at an unprecedented rate. As a consequence, massive collections of multimedia objects are now widely available to a large population of users. As the task of browsing such large collections could be daunting, Recommender Systems are being developed to assist users in finding items that match their needs and preferences. In this article, we present a novel approach to recommendation in multimedia browsing systems, based on modeling recommendation as a social choice problem. In social choice theory, a set of voters is called to rank a set of alternatives, and individual rankings are aggregated into a global ranking. In our formulation, the set of voters and the set of alternatives both coincide with the set of objects in the data collection. We first define what constitutes a choice in the browsing domain and then define a mechanism to aggregate individual choices into a global ranking. The result is a framework for computing customized recommendations by originally combining intrinsic features of multimedia objects, past behavior of individual users, and overall behavior of the entire community of users. Recommendations are ranked using an importance ranking algorithm that resembles the well-known PageRank strategy. Experiments conducted on a prototype of the proposed system confirm the effectiveness and efficiency of our approach.

Green grass and high tides: grazing lawns in terrestrial and aquatic ecosystems (commentary on Burkepile 2013)

Deron Burkepile advances a review of compelling empirical examples arguing that common mechanisms may structure grazing systems in both terrestrial and aquatic biomes. We welcome this synthesis, and on many specific points we concur. In this commentary we further develop the characteristics of grazing systems, grazing lawns in particular, reviewing their prevalence across systems wet and dry. Then, we use studies from terrestrial browsing systems – and their aquatic parallels – to explore major contrasts that are still prominent in cross-system comparisons. We rely heavily on quantitative reviews and meta-analyses to assess the prevalence and generality of the evidence for common mechanisms across grazing ecosystems. Although this approach sacrifices the detail from singular studies, it avoids undue focus on singular exceptions, instead highlighting key similarities and contrasts across biomes.

Instance based personalized multi-form image browsing and retrieval

It is important to adapt and personalize image browsing and retrieval systems based on users' preferences for improved user experience and satisfaction. In this paper, we present a novel instance based personalized multi-form image representation with implicit relevance feedback and adaptive weighting approach for image browsing and retrieval systems. In the proposed system, images are grouped into forms, which represent different information on images such as location, content etc. We conducted user interviews on image browsing, sharing and retrieval systems for understanding image browsing and searching behaviors of users. Based on the insights gained from the user interview study we propose an adaptive weighting method and implicit relevance feedback for multi-form structures that aim to improve the efficiency and accuracy of the system. Statistics of the past actions are considered for modeling the target of the users. Thus, on each iteration weights of the forms are updated adaptively. Moreover, retrieval results are modified according to the users' preferences on iterations in order to improve personalized user experience. The proposed method has been evaluated and results are illustrated in the paper. It is shown that, satisfactory improvements can be achieved with proposed approaches in the multi-form scheme.

Predicted Packet Padding for Anonymous Web Browsing Against Traffic Analysis Attacks

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.

Collaborative browsing system based on semantic mashup with open APIs

Due to a large amount of information available on world wide web, it has been difficult for users to effectively find relevant information. Many web browsing methods and systems have been investigated to apply adaptive approaches which can extract personal interests of the users. In this paper, we propose a semantic mashup-based collaborative browsing (co-browsing) platform for supporting knowledge sharing with other partners. Especially, the semantic mashup scheme can integrate heterogeneous information collected by various Open APIs, and help users to determine which partners should be selected. For evaluating the proposed method, we have implemented a co-browsing platform which can exchange bookmarks, and measured whether the semantic mashup scheme make a positive influence on improving the performance of the co-browsing process.

Attacking Anonymous Web Browsing at Local Area Networks Through Browsing Dynamics

The majority of current anonymous systems focus on improving anonymity at the network and website level in order to defend against traffic analysis attacks. However, the vulnerability of the connections between end users and the anonymous network do not attract any attention yet. For the first time, we reveal an end user browsing dynamics based attack on anonymous browsing systems at the LAN where the victim locates. This new attack method is fundamentally different from existing attack methodologies. In general, web surfers browse the web following certain patterns, such as requesting a web page, viewing it and requesting another page. The browsing pattern of a victim can be clearly observed by a local adversary when the victim is viewing the web without protection. Unfortunately, browsing dynamics releases rich information for attacking even though the web page content is encrypted. In order to show how a local eavesdropper can decipher which pages have been viewed with the knowledge of user browsing dynamics and the public information of a given website, we established a specific hidden Markov model to represent browsing dynamics for the website. By using this model, we can then identify the optimal of the accessed pages using the Viterbi algorithm. In order to confirm the effectiveness of the revealed attack method, we have conducted extensive experiments on a real data set. The results demonstrated that the attack accuracy can be more than 80%. A few possible counter-attack strategies are discussed at the end of the paper.

Utilizing quantitative users' reactions to represent affective meanings of an image

AbstractEmotional meaning is critical for users to retrieve relevant images. However, because emotional meanings are subject to the individual viewer's interpretation, they are considered difficult to implement when designing image retrieval systems. With the intent of making an image's emotional messages more readily accessible, this study aims to test a new approach designed to enhance the accessibility of emotional meanings during the image search process. This approach utilizes image searchers' emotional reactions, which are quantitatively measured. Broadly used quantitative measurements for emotional reactions, Semantic Differential (SD) and Self‐Assessment Manikin (SAM), were selected as tools for gathering users' reactions. Emotional representations obtained from these two tools were compared with three image perception tasks: searching, describing, and sorting. A survey questionnaire with a set of 12 images was administered to 58 participants, which were tagged with basic emotions. Results demonstrated that the SAM represents basic emotions on 2‐dimensional plots (pleasure and arousal dimensions), and this representation consistently corresponded to the three image perception tasks. This study provided experimental evidence that quantitative users' reactions can be a useful complementary element of current image retrieval/indexing systems. Integrating users' reactions obtained from the SAM into image browsing systems would reduce the efforts of human indexers as well as improve the effectiveness of image retrieval systems.

Web-Based Intelligent Photograph Management System Enhancing Browsing Experience

We developed a web-based intelligent photograph browsing system that enables automatic clustering of unstructured digital photograph collection. We conducted a user study to assess the effectiveness of developed photograph browsing system em APC and APCCT). The user task adopted here was finding some target photographs indicated by the experimenter. The results suggest that the clustering method of the photograph browsing systems should be changed according to whether or not the photographs had been taken by the user.

Multimedia surrogates for video gisting: Toward combining spoken words and imagery

Good surrogates that allow people to quickly derive the gist of videos without taking the time to view the full video are crucial to video retrieval and browsing systems. Although there are many kinds of textual and visual surrogates used in video retrieval systems, there are few audio surrogates in practice. To evaluate the effectiveness of audio surrogates alone and in combination with one kind of visual surrogate, fast forwards, a user study with 48 participants was conducted. The study investigated the effects of manually and automatically generated spoken keywords and spoken descriptions, using a text-to-speech synthesizer, on six specific video gisting tasks. Results demonstrate that manually generated spoken descriptions are better than both manually generated spoken keywords and fast forwards for video gisting. Both spoken keywords, whether manually or automatically generated, and fast forwards are better than automatically extracted descriptions. High quality spoken summaries were found very effective for video gisting. Combining fast forwards with either type of spoken text was not significantly better than any of the individual spoken surrogates; however, the visual elements added subjective value to the user experience. Adding spoken descriptions or keywords as surrogates to video retrieval and browsing systems is recommended.

Fault-Tolerant Text Data Compression Algorithms

There has been an unparalleled explosion of textual information flow over the internet through electronic mail, web browsing, digital library and information retrieval systems, etc. Since there is a persistent increase in the amount of data that needs to be transmitted or archived, the importance of data compression is likely to increase in the near future. Virtually, all modern compression methods are adaptive models and generate variable-bit-length codes that must be decoded sequentially from beginning to end. If there is any error during transmission, the entire file cannot be retrieved safely. In this article we propose few fault-tolerant methods of text compression that facilitate decoding to begin with any part of compressed file not necessarily from the beginning. If any sequence of one or more bytes is changed during transmission of compressed file due to various reasons, the remaining data can be retrieved safely. These algorithms also support reversible decompression.

Nutritive value of edible forage from two Leucaena leucocephala cultivars with different growth habit and morphology

The nutritive value of edible forage from Leucaena leucocephala cv. Cunningham (CUNN) and cv. CNIA-250 (CNIA) was determined during the rainy (R) and dry (D) seasons of Cuba without fertilization or irrigation. Forage was supplied ad libitum and the French system of total faeces collection was used for nutritive value determination and expression by using six adult castrated Pelibuey wethers for each determination. There were noticeable differences in the chemical composition and nutritive value between the two cultivars which were also influenced by year season. The highest protein and energy content were found in CNIA and during the rainy season which could also explain its higher forage intake and digestibility, while CUNN expressed higher DM and nutrient yield throughout the year, mainly due to advantages in tree size and morphology (higher ramification and proportion of leaves, and acceptable branch length). Quality and nutritive value results indicated that these Leucaena cultivars have a suitable potential as supplements for sustainable ruminant nutrition strategies during both seasons of the year; CNIA, because of its smaller height, is strongly recommended for agrosilvopastoral small ruminant browsing systems.