The challenge of motivating potential donors to contribute is the primary hurdle to the success of online charitable crowdfunding. Anchoring on warm glow theory, we investigate how fundraisers’ self-donations influence donors to contribute to online charitable crowdfunding projects and examine the underlying mechanism through activating potential donors’ warm glow feeling. Using a large-scale dataset from a leading crowdfunding platform in China and a laboratory experiment, we find that fundraisers’ self-donations have a significant positive impact on potential donors’ contributions through developing their warm glow feeling. Our results show that charitable crowdfunding projects with fundraisers’ self-donations perform better in attracting donations from donors. Fundraisers’ self-donations not only improve the probability of success for charitable crowdfunding projects but also facilitates the collection of additional donations. Implications for theory and practice are discussed.

Impulsive buying accounts for a significant portion of online transactions. However, it is challenging for retailers to stimulate consumers’ impulsive buying in the quick decision-making environment of online shopping, which is often driven by promotional events. As consumers often regret when shopping online, a key factor influencing their impulsive buying is the anticipation of that regret. Yet few studies have examined the role of anticipated regret in impulsive buying. Drawing on regret theory, we explore how online review valence shapes two forms of anticipated regret: anticipated action regret (for buying) and anticipated inaction regret (for not buying). Furthermore, we examine how quantity- and time-based scarcity messages moderate the relationship between review valence and anticipated regret. Findings from two laboratory experiments show that positive review valence reduces anticipated action regret while increasing anticipated inaction regret, with scarcity messages amplifying these effects. We further find that anticipated inaction regret, rather than anticipated action regret, plays a pivotal role in driving impulsive buying. Our qualitative survey provides rich evidence to validate our theoretical arguments and findings. This study enriches the literature by highlighting the affective mechanism of anticipated regret in impulsive buying and offering a nuanced understanding of its dual nature. It provides actionable suggestions to online retailers for optimizing the use of review valence and scarcity messages to enhance sales in fast-paced online shopping contexts.

The recent surge in contrastive learning has intensified the interest in understanding the generalization of non-pointwise learning paradigms. While information-theoretic analysis achieves remarkable success in characterizing the generalization behavior of learning algorithms, its applicability is largely confined to pointwise learning, with extensions to the simplest pairwise settings remaining unexplored due to the challenges of non-i.i.d losses and dimensionality explosion. In this paper, we develop the first series of information-theoretic bounds extending beyond pointwise scenarios, encompassing pointwise, pairwise, triplet, quadruplet, and higher-order scenarios, all within a unified framework. Specifically, our hypothesis-based bounds elucidate the generalization behavior of iterative and noisy learning algorithms via gradient covariance analysis, and our prediction-based bounds accurately estimate the generalization gap with computationally tractable low-dimensional information metrics. Comprehensive numerical studies then demonstrate the effectiveness of our bounds in capturing the generalization dynamics across diverse learning scenarios.

Dialogue state tracking is responsible for predicting the user’s dialogue state during the whole dialogue process. In practical applications, values for different slots exist in individual utterances of the dialog history. With the accumulation of the dialogue history, it becomes extremely difficult to accurately predict slots and corresponding values from the lengthy dialogue history. To solve the problem of the interference caused by lengthy dialogue history, we propose a dual hierarchical selective augmentation method, which makes use of two hierarchical level information selection strategy to generate slot values. In the encoding phase, we first extract word-level matching features between the slot and each dialogue turn, and then build turn-level context relevance. In the decoding phase, first of all, from a global perspective, the dialogue turn information is selected multiple according to the dialogue context and slot, so that the model focuses more on the turn containing slot value. Secondly, our model performs weighted context attention to capture the critical words of dialogue turn from the local view. This dual hierarchical context selection alleviates the interference caused by excessive redundant information in the dialogue history and enhances the judgment ability of the model for vital turns and words. Furthermore, to enhance the copying ability of the model, we use the turn selection-guided pointer network to copy slot values from the dialogue. Experimental results show that our model significantly outperforms multiple baselines on the released MultiWOZ benchmark.

The Dialogue State Tracking (DST) module tracks the user’s intent by populating multiple predefined slots related to the dialogue task. In recent years, various graph neural network-based DST methods have been proposed to establish graph structures capturing the correlations between domains and slots, thereby enhancing model performance. However, these methods may involve redundant connections in the graph structure. To better construct relationships between domains and slots, we introduce a graph neural network-based dialogue state tracking method called Dynamic Domain Selection Graph DST (DDSG-DST). Specifically, (1) we employ Graphormer to establish hierarchical relationships between domains and slots; (2) we propose an additional domain prediction auxiliary task to predict the domain relevant to the dialogue context; (3) based on the predicted relevant domain from the auxiliary task, we dynamically select domain node information in the graph and perform dialogue state prediction. Experimental results demonstrate that we effectively establish hierarchical relationships between domains and slots, mitigate the negative impact of redundant connections in the graph structure, and enhance model performance.

In our dynamic world where data arrives in a continuous stream, continual learning enables us to incrementally add new tasks/domains without the need to retrain from scratch. A major challenge in continual learning of language model is catastrophic forgetting, the tendency of models to forget knowledge from previously trained tasks/domains when training on new ones. This paper studies dialog generation under the continual learning setting. We propose a novel method that 1) uses Text-Mixup as data augmentation to avoid model overfitting on replay memory and 2) leverages Batch-Nuclear Norm Maximization (BNNM) to alleviate the problem of mode collapse. Experiments on a 37-domain task-oriented dialog dataset and DailyDialog (a 10-domain chitchat dataset) demonstrate that our proposed approach outperforms the state-of-the-art in continual learning.

The importance of encrypted traffic classification for network management and security is self-evident. The emergence of programmable data plane (PDP) technology makes it possible to directly implement encrypted traffic classification in the data plane, which can classify network traffics in line-rate. In this paper, we propose a two-phase encrypted traffic classification (TP-ETC) scheme in programmable data plane. In TP-ETC, Convolutional Neural Network (CNN) is employed for classifying highly similar traffic with high accuracy in the first phase, and Long Short-Term Memory (LSTM) model is responsible for classifying all remaining traffic with low storage overhead in the second phase, achieving the best balance between accuracy and storage overhead. We also design a feature extraction method suitable for PDP, effectively reducing the overhead of feature storage. In addition, we design a table segmentation algorithm to reduce the growth rate of table entries to a linear level. The experimental results demonstrate the superiority of the proposed scheme TP-ETC.

Open-domain multi-modal dialogue system heavily relies on visual information to generate contextually relevant responses. The existing open-domain multi-modal dialog generation methods ignore the complementary relationship between multiple modalities, and are difficult to integrate with LLMs. To tackle these challenges, we introduce AutoGraph, an innovative method for constructing visual context graphs automatically. We aim to structure complex information and seamlessly integrate it with large language models (LLMs), aligning information from multiple modalities at both semantic and structural levels. Specifically, we fully connect the text graphs and scene graphs, and then trim unnecessary edges via LLMs to automatically construct a visual context graph. Next, we design several graph sampling grammar for the first time to convert graph structures into sequence which is suitable for LLMs. Finally, we propose a two-stage fine-tuning strategy to allow LLMs to understand graph sampling grammar and generate responses. We validate our proposed method on text-based LLMs, and visual-based LLMs, respectively. Experimental results show that our proposed method achieves state-of-the-art performance on multiple public datasets.

Website fingerprinting has emerged as a prominent topic in the area of network management. However, the proliferation of encrypted network traffic poses new challenges for website fingerprinting. In this paper, we analyze the behavior and correlations among the network flows generated by browsing a webpage and conclude that there exist specific spatio-temporal correlations among these network flows. Based on this finding, we propose the construction of an inter-flow spatio-temporal correlation graph (STCG) to model these correlations. In the STCG, each node represents a flow, with its features capturing the properties of the flow itself, and each edge with a weight vector represents the spatio-temporal correlation between two flows. Subsequently, we propose a graph neural network-based website fingerprinting method (STC-WF) by considering the inter-flow spatio-temporal correlations, in which the Graph Attention Network (GAT) and Self-Attention Graph Pooling (SAGPool) mechanisms are employed to acquire a comprehensive representation of the STCG. To evaluate the performance of STC-WF, we construct a real-world traffic dataset and conduct comprehensive evaluations. The experimental results demonstrate that STC-WF outperforms state-of-the-art methods in terms of accuracy and time consumption.

Cloud Computing (CC) is an emerging technology that can potentially revolutionise the application and delivery of IT. There has been little research, however, into the adoption of CC in Small and Medium-Sized Enterprises (SMEs). The indicators show that CC has been adopted very slowly. There is also a significant research gap in the investigation of the adoption of this innovation in SMEs. This article explores how the adoption of CC in Australia is related to technological factors, risk factors, and environmental factors. The study provides useful insights that can be utilised practically by SMEs, policymakers, and cloud vendors.

Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved Business-IT strategy misalignment is increasingly an important area of concern and interest in organisations around the world including Saudi Arabia (SA). Indeed, the SA government has launched the National Digital Transformation Strategy for 2030 to support all public-sector organisations to improve efficiency and performance. This research aimed to identify and analyse the factors that contribute to business/IT strategy misalignment in Saudi public-sector organisations. This research focused emerged from the need to better understand the business and IT models incorporated in the organisations Saudi Arabia to achieve high performance, quality of service (QoS) and return of investment (ROI). Using a qualitative study design that included semi-structured interviews with eight executive and managerial staff from five public-sector organisations in Saudi Arabia, this study found human, operational and IT system factors all have the potential to contribute to business-IT strategy misalignment. It also found the approaches to misalignment avoidance in Saudi public-sector organisations sometimes lack structure and consistency.

In this study, the trusted third party (TTP) in Australia's B2C marketplace is studied and the factors influencing consumers' trust behaviour are examined from the perspective of consumers' online trust. Based on the literature review and combined with the development status and background of Australia's e-commerce, underpinned by the Theory of Planned Behaviour (TPB) and a conceptual trust model, this paper expatiates the specific factors and influence mechanism of TTP on consumers' trust behaviour. Also this paper explains two different functions of TTP to solve the online trust problem faced by consumers. Meanwhile, this paper summarizes five different types of services provided by TTPs during the establishment of the trust relationship. Finally, the present study selects 100 B2C enterprises by the simple random sampling method and makes a detailed analysis of their TTPs, to verify the services and functions of the proposed TTP in the trust model. This study is of some significance for comprehending the influence mechanism, functions and services of TTPs on consumers' trust behaviour in the realistic Australian B2C environment.

Usually, a head-eye system comprises of a pair of cameras mounted on a platform. Calibration of such a system can be divided into two parts. The first part is concerned with calibration of intrinsic parameters of the cameras where as the second part deals with calibration of extrinsic parameters of the cameras which is realized through kinematic calibration of the system. In this paper, we solve this kinematic calibration problem. First we formulate the problem for a 6-degree-of-freedom (DOF) head-eye system. It turns out that this problem is very similar to the hand-eye calibration problem, i.e., to solve an equation system of AX=XB, where X is the unknown transformation matrix which contains a rotation and a translation. In a special case, where the system has only rotational motion, the rotation and translation of X are decomposed into two independent equations which are solved separately. We propose a nonlinear optimization solution for the rotation. Algorithms from early work have also be implemented for the purpose of comparison. Experiments and tests are performed on both synthetic and real data. Results are compared and presented in this paper.

Zoom lens camera calibration is an important and difficult problem for two reasons at least. First, the intrinsic parameters of such a camera change over time, it is difficult to calibrate them on-line. Secondly, the pin-hole model for single lens system can not be applied directly to a zoom lens system. In this paper, we address some aspects of this problem, such as determining principal point by zooming, modeling and calibration of lens distortion and focal length, as well as some practical aspects. Experimental results on calibrating cameras with computer controlled zoom, focus and aperture are presented.

We deal with the calibration problem of an active head-eye system, which consists of a pair of cameras mounted on a head with 13 degrees of freedom. The aim of the calibration is to establish relative positions of different 3D systems: between camera and neck, eye and neck, etc., so that we can keep track of the camera position in a fixed (calibration) reference system as a function of the visual parameters of the head-eye system. We formulate the problem and propose both closed-form and nonlinear optimization approaches to solve it. Experiments were carried out and comparison of results with other algorithms were made on both simulated and real data.>

The problem of 2-D shape description, particularly with contour partitioning, grouping, and classification in terms of straight and curved, based on the minimum description length (MDL) criterion and shape-fitting techniques, is discussed. The MDL criterion is used to detect outliers in connection with shape fitting. Using the MDL criterion, it is possible to derive for a given data set and a class of models a description which best explains the data. A new algorithm for fitting 2-D points to an ellipse is presented.>