| 1. |
- Saarela, J, et al.
(author)
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Logical Structure of a Hypermedia Newspaper
- 1997
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In: Information Processing & Management. - 0306-4573 .- 1873-5371. ; 33:5, s. 599-614
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Journal article (peer-reviewed)abstract
- The OtaOnline project at the Helsinki University of Technology has been deploying the distribution of Finnish newspapers such as Iltalehti, Aamulehti and Kauppalehti on the Internet since 1994, The editors produce the electronic counterpart of these papers by a conversion process from QuarkXpress documents to HyperText Markup Language. The project is about to step into a new phase by introducing an approach which provides many new features not available in the old process. This paper describes an object-oriented approach which implements the logical model of a hypermedia newspaper. This model encapsulates the structure of the hypermedia documents as well as their capability for transforming into different presentation formats. It also provides a semantical rating mechanism to be used with intelligent agents. A distribution scheme which enables efficient use of this model is also presented
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| 2. |
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| 3. |
- Ahlgren, Per, et al.
(author)
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Indexing strategies for Swedish full text retrieval under different user scenarios
- 2007
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In: Information Processing & Management. - : Elsevier BV. - 0306-4573 .- 1873-5371. ; 43:1, s. 81-102
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Journal article (peer-reviewed)abstract
- This paper deals with Swedish full text retrieval and the problem of morphological variation of query terms in the document database. The effects of combination of indexing strategies with query terms on retrieval effectiveness were studied. Three of five tested combinations involved indexing strategies that used conflation, in the form of normalization. Further, two of these three combinations used indexing strategies that employed compound splitting. Normalization and compound splitting were performed by SWETWOL, a morphological analyzer for the Swedish language. A fourth combination attempted to group related terms by right hand truncation of query terms. The four combinations were compared to each other and to a baseline combination, where no attempt was made to counteract the problem of morphological variation of query terms in the document database. The five combinations were evaluated under six different user scenarios, where each scenario simulated a certain user type. The four alternative combinations outperformed the baseline, for each user scenario. The truncation combination had the best performance under each user scenario. The main conclusion of the paper is that normalization and right hand truncation (performed by a search expert) enhanced retrieval effectiveness in comparison to the baseline. The performance of the three combinations of indexing strategies with query terms based on normalization was not far below the performance of the truncation combination. (c) 2006 Elsevier Ltd. All rights reserved.
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| 4. |
- Alavijeh, Soroush Zamani, et al.
(author)
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What users’ musical preference on Twitter reveals about psychological disorders
- 2023
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In: Information Processing & Management. - London : Elsevier. - 0306-4573 .- 1873-5371. ; 60:3
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Journal article (peer-reviewed)abstract
- Previous research found a strong relation between the users’ psychological disorders and their language use in social media posts in terms of vocabulary selection, emotional expressions, and psychometric attributes. However, although studying the association between psychological disorders and musical preference is considered as rather an old tradition in the clinical analysis of health data, it is not explored through the lens of social media analytics. In this study, we investigate which attributes of the music posted on social media are associated with mental health conditions of Twitter users. We created a large-scale dataset of 1519 Twitter users with six self-reported psychological disorders (depression, bipolar, anxiety, panic, post-traumatic stress disorder, and borderline) and matched with 2480 control users. We then conduct an observational study to investigate the relationship between the users’ psychological disorders and their musical preference by analyzing lyrics of the music tracks that the users shared on Twitter from multiple dimensions including word usage, linguistic style, sentiment and emotion patterns, topical interests and underlying semantics. Our findings reveal descriptive differences on the linguistic and semantic features of music tracks of affected users compared to control individuals and among users from different psychological disorders. Additionally, we build a feature-based and an (explainable) deep learning-based binary classifiers trained on disorder and control users and demonstrate that lyrics of the music tracks of users on Twitter can be considered as complementary information to their published posts to improve the accuracy of the disorder detection task. Overall, we find that the music attributes of users on Twitter allow inferences about their mental health status. © 2023 Elsevier Ltd
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| 5. |
- Capannini, Gabriele, et al.
(author)
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Quality versus efficiency in document scoring with learning-to-rank models
- 2016
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In: Information Processing & Management. - : Elsevier BV. - 0306-4573 .- 1873-5371. ; 52:6, s. 1161-1177
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Journal article (peer-reviewed)abstract
- Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training data to induce high-quality ranking functions. Given a set of documents and a user query, these functions are able to precisely predict a score for each of the documents, in turn exploited to effectively rank them. Although the scoring efficiency of LtR models is critical in several applications – e.g., it directly impacts on response time and throughput of Web query processing – it has received relatively little attention so far. The goal of this work is to experimentally investigate the scoring efficiency of LtR models along with their ranking quality. Specifically, we show that machine-learned ranking models exhibit a quality versus efficiency trade-off. For example, each family of LtR algorithms has tuning parameters that can influence both effectiveness and efficiency, where higher ranking quality is generally obtained with more complex and expensive models. Moreover, LtR algorithms that learn complex models, such as those based on forests of regression trees, are generally more expensive and more effective than other algorithms that induce simpler models like linear combination of features. We extensively analyze the quality versus efficiency trade-off of a wide spectrum of state-of-the-art LtR, and we propose a sound methodology to devise the most effective ranker given a time budget. To guarantee reproducibility, we used publicly available datasets and we contribute an open source C++ framework providing optimized, multi-threaded implementations of the most effective tree-based learners: Gradient Boosted Regression Trees (GBRT), Lambda-Mart (Λ-MART), and the first public-domain implementation of Oblivious Lambda-Mart (Ωλ-MART), an algorithm that induces forests of oblivious regression trees. We investigate how the different training parameters impact on the quality versus efficiency trade-off, and provide a thorough comparison of several algorithms in the quality-cost space. The experiments conducted show that there is not an overall best algorithm, but the optimal choice depends on the time budget.
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| 6. |
- Huvila, Isto, Professor, 1976-, et al.
(author)
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Anticipating ageing : Older adults reading their medical records
- 2018
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In: Information Processing & Management. - : Elsevier. - 0306-4573 .- 1873-5371. ; 54:3, s. 394-407
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Journal article (peer-reviewed)abstract
- In spite of the general interest in health information behaviour, there is little earlier research on how older adults, who are still active in working life but approaching retirement, differ from other age groups. A survey with Swedish patients who had ordered and read their medical record was conducted to map the preferences and motivations of older adults (born 1946-1960) ordering a copy of their medical record, and using medical records based e-health and information services in the future. The results do not indicate an obvious linear relationship between age and motivation to use online health information but show several differences between the age groups. Older adults were less interested in communication with their medical doctor by e-mail. Yet, they had searched health information in the Internet during the last week more likely than young. They were more inclined to read medical record to get an overview of their health than young, but less confident that they understood most of the content or turn to their family and friends to seek help than the elderly. When compared to younger adults and elderly people, older adults are the least confident and least motivated to use online health information. It is suggested that older adulthood can be seen as a transitory stage of life when the need of health information increases and engagement with health changes. The results agree with prior research on the potential usefulness of (online) medical records as a way to inform citizens. However, specific provision strategies may be necessary to match the needs and motivations of different age groups.
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| 7. |
- Kastrati, Zenun, 1984-, et al.
(author)
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The impact of deep learning on document classification using semantically rich representations
- 2019
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In: Information Processing & Management. - : Elsevier. - 0306-4573 .- 1873-5371. ; 56:5, s. 1618-1632
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Journal article (peer-reviewed)abstract
- This paper presents a semantically rich document representation model for automatically classifying financial documents into predefined categories utilizing deep learning. The model architecture consists of two main modules including document representation and document classification. In the first module, a document is enriched with semantics using background knowledge provided by an ontology and through the acquisition of its relevant terminology. Acquisition of terminology integrated to the ontology extends the capabilities of semantically rich document representations with an in depth-coverage of concepts, thereby capturing the whole conceptualization involved in documents. Semantically rich representations obtained from the first module will serve as input to the document classification module which aims at finding the most appropriate category for that document through deep learning. Three different deep learning networks each belonging to a different category of machine learning techniques for ontological document classification using a real-life ontology are used. Multiple simulations are carried out with various deep neural networks configurations, and our findings reveal that a three hidden layer feedforward network with 1024 neurons obtain the highest document classification performance on the INFUSE dataset. The performance in terms of F1 score is further increased by almost five percentage points to 78.10% for the same network configuration when the relevant terminology integrated to the ontology is applied to enrich document representation. Furthermore, we conducted a comparative performance evaluation using various state-of-the-art document representation approaches and classification techniques including shallow and conventional machine learning classifiers.
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| 8. |
- Liu, Zhouying, et al.
(author)
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Exploring askers' switching from free to paid social Q&A services : A perspective on the push-pull-mooring framework
- 2021
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In: Information Processing & Management. - : Elsevier BV. - 0306-4573 .- 1873-5371. ; 58:1
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Journal article (peer-reviewed)abstract
- The purpose of this study is to explore the factors that prompt askers to switch from free to paid social question-and-answer (SQA) services. Prior studies have investigated users' motivations and participation in free and paid SQA services; however, little attention has been paid to askers' switching behavior. We empirically analyzed the content of qualitative interviews from 64 askers on a well-known SQA platform in China. Based on the push-pull-mooring framework, we identified and classified factors that influenced askers' to switch from free to paid Q&A services, using the critical incident technique, after which we calculated the entropy weights of the 16 subcategories before and after the switch, using the entropy weight method. The findings suggest that askers' switching behavior was influenced by push factors (i.e., dissatisfaction with the free SQA service), pull factors (i.e., satisfaction with the paid SQA service), and mooring factors (i.e., social factors, personal factors, situational factors). Moreover, the findings show that the effects of these factors vary significantly before and after a switch. Dissatisfaction with the quality of information from the free SQA service would influence users before a switch, whereas satisfaction with the quality of information from the paid SQA service would influence them after a switch. In terms of mooring factors, the effects of social and personal factors on askers' switching behavior, especially subjective norms and cognitive lock-in, turn out to be less significant after a switch, whereas the effect of trust is more significant. Besides, the effects of situational factors are more or less the same before and after a switch. To the best of our knowledge, this paper is one of the first attempts to explore factors that affect askers' switching behavior and to shed light on the managerial strategies of paid SQA services.
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| 9. |
- Mouda Ye, Edwin, et al.
(author)
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Understanding roles in collaborative information behaviour : a case of Chinese group travelling
- 2021
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In: Information Processing & Management. - : Elsevier BV. - 0306-4573 .- 1873-5371. ; 58:4
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Journal article (peer-reviewed)abstract
- A group trip entails collaborative information behaviour (CIB) of multiple actors seeking, sharing, and using travel-related information. However, there is a lack of investigation on how people choose to assume or be appointed different CIB roles during such leisure projects. Thus, limited information support is provided to travellers involved in group trips. This article investigates role adoption to show how group travellers involved in CIB through different actions. A naturalistic inquiry on CIB was conducted with 20 travel groups from mainland China to Australia. Of these, 36 real tourists participated in the study through initial demographic questionnaires, pre- and post-trip interviews, and self-reported diaries during the travel. Data were analysed using iterative coding guided by the constructivist grounded theory. Results suggested the complexity of CIB among group travellers. Besides searching together as equal peers, most group travellers voluntarily assume different CIB roles which are often implicit. Six distinct CIB roles were identified, including team player, all-rounder, influencer, authoritarian, supporter, and follower. Furthermore, the distribution of such roles in a travel group was examined and classified into five patterns. The findings also contribute to information seeking research in tourism discipline. Practical implications are provided regarding system support for collaborative work and tourism information provision.
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| 10. |
- Ran, Hang, et al.
(author)
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Learning optimal inter-class margin adaptively for few-shot class-incremental learning via neural collapse-based meta-learning
- 2024
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In: Information Processing & Management. - London : Elsevier. - 0306-4573 .- 1873-5371. ; 61:3
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Journal article (peer-reviewed)abstract
- Few-Shot Class-Incremental Learning (FSCIL) aims to learn new classes incrementally with a limited number of samples per class. It faces issues of forgetting previously learned classes and overfitting on few-shot classes. An efficient strategy is to learn features that are discriminative in both base and incremental sessions. Current methods improve discriminability by manually designing inter-class margins based on empirical observations, which can be suboptimal. The emerging Neural Collapse (NC) theory provides a theoretically optimal inter-class margin for classification, serving as a basis for adaptively computing the margin. Yet, it is designed for closed, balanced data, not for sequential or few-shot imbalanced data. To address this gap, we propose a Meta-learning- and NC-based FSCIL method, MetaNC-FSCIL, to compute the optimal margin adaptively and maintain it at each incremental session. Specifically, we first compute the theoretically optimal margin based on the NC theory. Then we introduce a novel loss function to ensure that the loss value is minimized precisely when the inter-class margin reaches its theoretically best. Motivated by the intuition that “learn how to preserve the margin” matches the meta-learning's goal of “learn how to learn”, we embed the loss function in base-session meta-training to preserve the margin for future meta-testing sessions. Experimental results demonstrate the effectiveness of MetaNC-FSCIL, achieving superior performance on multiple datasets. The code is available at https://github.com/qihangran/metaNC-FSCIL. © 2024 The Author(s)
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| 11. |
- Sjöbergh, Jonas
(author)
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Older versions of the ROUGEeval summarization evaluation system were easier to fool
- 2007
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In: Information Processing & Management. - : Elsevier BV. - 0306-4573 .- 1873-5371. ; 43:6, s. 1500-1505
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Journal article (peer-reviewed)abstract
- We show some limitations of the ROUGE evaluation method for automatic summarization. We present a method for automatic summarization based on a Markov model of the source text. By a simple greedy word selection strategy, summaries with high ROUGE-scores are generated. These summaries would however not be considered good by human readers. The method can be adapted to trick different settings of the ROUGEeval package.
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| 12. |
- Wang, W., et al.
(author)
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Data information processing of traffic digital twins in smart cities using edge intelligent federation learning
- 2023
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In: Information Processing & Management. - : Elsevier. - 0306-4573 .- 1873-5371. ; 60:2
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Journal article (peer-reviewed)abstract
- The present work analyzes the application of deep learning in the context of digital twins (DTs) to promote the development of smart cities. According to the theoretical basis of DTs and the smart city construction, the five-dimensional DTs model is discussed to propose the conceptual framework of the DTs city. Then, edge computing technology is introduced to build an intelligent traffic perception system based on edge computing combined with DTs. Moreover, to improve the traffic scene recognition accuracy, the Single Shot MultiBox Detector (SSD) algorithm is optimized by the residual network, form the SSD-ResNet50 algorithm, and the DarkNet-53 is also improved. Finally, experiments are conducted to verify the effects of the improved algorithms and the data enhancement method. The experimental results indicate that the SSD-ResNet50 and the improved DarkNet-53 algorithm show fast training speed, high recognition accuracy, and favorable training effect. Compared with the original algorithms, the recognition time of the SSD-ResNet50 algorithm and the improved DarkNet-53 algorithm is reduced by 6.37ms and 4.25ms, respectively. The data enhancement method used in the present work is not only suitable for the algorithms reported here, but also has a good influence on other deep learning algorithms. Moreover, SSD-ResNet50 and improved DarkNet-53 algorithms have significant applicable advantages in the research of traffic sign target recognition. The rigorous research with appropriate methods and comprehensive results can offer effective reference for subsequent research on DTs cities.
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| 13. |
- Xu, Xiaoqiong, et al.
(author)
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Latency performance modeling and analysis for hyperledger fabric blockchain network
- 2021
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In: Information Processing & Management. - : Elsevier. - 0306-4573 .- 1873-5371. ; 58:1
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Journal article (peer-reviewed)abstract
- Blockchain has been one of the most attractive technologies for many modern and even future applications. Fabric, an open-source framework to implement the permissioned enterprise-grade blockchain, is getting increasing attention from innovators. The latency performance is crucial to the Fabric blockchain in assessing its effectiveness. Many empirical studies were conducted to analyze this performance based on different hardware platforms. These experimental results are not comparable as they are highly dependent on the underlying networks. Moreover, theoretical analysis on the latency of Fabric blockchain still receives much less attention. This paper provides a novel theoretical model to calculate the transaction latency under various network configurations such as block size, block interval, etc. Subsequently, we validate the proposed latency model with experiments, and the results show that the difference between analytical and experimental results is as low as 6.1%. We also identify some performance bottlenecks and give insights from the developer’s perspective.
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| 14. |
- Dhamal, Swapnil Vilas, 1988, et al.
(author)
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A Two Phase Investment Game for Competitive Opinion Dynamics in Social Networks
- 2020
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In: Information Processing and Management. - : Elsevier BV. - 0306-4573. ; 57:2
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Journal article (peer-reviewed)abstract
- We propose a setting for two-phase opinion dynamics in social networks, where a node's final opinion in the first phase acts as its initial biased opinion in the second phase. In this setting, we study the problem of two camps aiming to maximize adoption of their respective opinions, by strategically investing on nodes in the two phases. A node's initial opinion in the second phase naturally plays a key role in determining the final opinion of that node, and hence also of other nodes in the network due to its influence on them. However, more importantly, this bias also determines the effectiveness of a camp's investment on that node in the second phase. In order to formalize this two-phase investment setting, we propose an extension of Friedkin-Johnsen model, and hence formulate the utility functions of the camps. We arrive at a decision parameter which can be interpreted as two-phase Katz centrality. There is a natural tradeoff while splitting the available budget between the two phases. A lower investment in the first phase results in worse initial biases in the network for the second phase. On the other hand, a higher investment in the first phase spares a lower available budget for the second phase, resulting in an inability to fully harness the influenced biases. We first analyze the non-competitive case where only one camp invests, for which we present a polynomial time algorithm for determining an optimal way to split the camp's budget between the two phases. We then analyze the case of competing camps, where we show the existence of Nash equilibrium and that it can be computed in polynomial time under reasonable assumptions. We conclude our study with simulations on real-world network datasets, in order to quantify the effects of the initial biases and the weightage attributed by nodes to their initial biases, as well as that of a camp deviating from its equilibrium strategy. Our main conclusion is that, if nodes attribute high weightage to their initial biases, it is advantageous to have a high investment in the first phase, so as to effectively influence the biases to be harnessed in the second phase.
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| 15. |
- Nagy-György, Tamas, et al.
(author)
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Experimental and numerical assessment of the effectiveness of FRP-based strengthening configurations for dapped-end RC beams
- 2012
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In: Engineering structures. - : Elsevier BV. - 0141-0296 .- 1873-7323. ; 44, s. 291-303
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Journal article (peer-reviewed)abstract
- This paper presents experimental and numerical assessments of the effectiveness of strengthening dapped-end reinforced concrete beams using externally bonded carbon fiber reinforced polymers (CFRP). The research was prompted by a real application, in which the dapped-ends of several precast/prestressed concrete beams developed diagonal cracks due to errors during assembly. Hence, the dapped-ends were strengthened on-site using CFRP plates to limit further crack opening. In the empirical phase of the study, four similar specimens were tested: one unstrengthened reference specimen, two strengthened with high-strength CFRP plates, and one with high-modulus CFRP sheets. The specimens strengthened with plates had slightly higher load carrying capacity than the reference element, but failed by debonding, while the specimens strengthened with sheets showed no increase of capacity and failed by the fibers rupturing. Nonlinear finite element analysis of the specimens under the test conditions indicated that: a) debonding is more likely to occur at the inner end of dapped-ends, and b) the capacity could have been increased by up to 20% if the plates had been mechanically anchored.
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