This paper studies the beamforming design for jamming assisted multi-user covert communications under the surveillance of an eavesdropper. Different from existing work, the impact of multi-user interference as well as the tradeoff between the jamming and the confidential signal power allocation are taken into consideration. To degrade the eavesdropper's signal detection capability, the power of the jamming signal is often randomized, which introduces uncertainty to the achievable transmission rate. In light of this, we propose to carry out the beamforming design with the concept of quantile transmission rate (QTR) so that the expected covert transmission rate can be achieved with high probability. With QTR, the fairness aware multi-user beamforming design problem is formulated to maximize the minimum QTR among all legitimate users while ensuring the covertness constraints are satisfied. Given the nonconvexity of the constraints, we propose a successive convex approximation (SCA)-based iterative algorithm for effective solution finding. The validity of the obtained covert beamforming scheme is evaluated through various experiments.

Driven by the pursuit of enhanced performance, deep learning has recently seen rapid developments in the scaling of network architectures and parameters. However, this advancement has led to extremely high computational costs, undesirable in real-time and resource-limited scenarios. To address these challenges, we propose SwiftNet, a cost-efficient deep learning framework. Our novelty lies in SwiftNet's innovative multidimensional early-exit strategy that integrates seamlessly with existing neural network architectures. The framework includes additional branch classifiers concatenated to the backbone network, allowing high-confidence samples to exit early, thereby, reducing computational load. Unlike traditional methods, SwiftNet dynamically assesses confidence levels, ensuring only low-confidence samples proceed to subsequent classifiers or the final layer, optimizing resource usage without compromising accuracy. We have validated SwiftNet on multiple neural network models and datasets, demonstrating its ability to significantly reduce the computational cost of models while maintaining neural network performance.

A prevailing assumption in existing deep learning research posits that data across source and target domains adhere to the independent and identically distributed (i.i.d.) assumption. However, this assumption often proves inadequate in real-world scenarios, leading to significant performance degradation when models encounter data with divergent distributions. To address this challenge, a novel unsupervised domain adaptation (UDA) algorithm, decoupling feature alignment (DFA), is introduced. The approach begins with the establishment of a robust theoretical framework, serving as the foundation for the mean-covariance adjustment feature alignment (MCAFA) algorithm. Simultaneously, a data decoupling (DD) module is introduced, effectively segregating target domain data into two subsets: one that mirrors the source domain and another that diverges markedly. Furthermore, a multidimensional alignment module is employed, leveraging the MCAFA algorithm and the DD module to align target data with source data across various layers and categories. Comprehensive evaluations on multiple data sets underscore the superiority of DFA.

Understanding the influence of users’ opinions on their search behavior together with their inherent biases in web search has garnered widespread interest in recent times. This is largely due to the implications of promoting critical thinking, explaining phenomena such as political polarization, or the manifestation of echo chambers. It is important to understand how personal opinions can bias users’ interaction with search results. Moreover, there is a lack of understanding of the impact of user search intents, namely non-purposeful browsing versus searching with a pre-defined goal, on users’ interactions with search results. We take a step towards bridging this knowledge gap through an empirical study in this paper. To do so, we select two controversial topics in abortion and gun control, and invite users to learn about them through ‘Purposeless’ and ‘Purposeful’ web searching. Our findings suggest that users with strong personal opinions exhibit biased interactions with the search results. However, the effect of users’ opinions on their interactions with search results can differ depending on whether users search purposelessly or with a purpose. Our findings advance the current understanding of the effect of users’ opinions in web search sessions, and show that users’ search intents shape their interaction with search results. This work has broad design implications on dealing with bias in interactive information retrieval systems.

Abstract Evaluation of interactive search systems and study of users’ struggling search behaviors require a significant number of search tasks. However, generation of such tasks is inherently difficult, as each task is supposed to trigger struggling search behavior rather than simple search behavior. To the best of our knowledge, there has not been a commonly used task set for research in struggling search. Moreover, the everchanging landscape of information needs would render old task sets less ideal if not unusable for evaluation. To deal with this problem, we propose a crowd-powered task generation method and develop a platform to efficiently generate struggling search tasks on basis of online wikis such as Wikipedia. Our experiments and analysis show that the generated tasks are qualified to emulate struggling search behaviors consisting of “repeated similar queries” and “quick-back clicks”; tasks of diverse topics, high quality and difficulty can be created using this method. For benefit of the community, we publicly released a task generation platform TaskGenie, a task set of 80 topically diverse struggling search tasks with “baselines,” and the corresponding anonymized user behavior logs.

Interactive information-seeking systems are designed to help users with their struggling during the searching for complex fact checking tasks, where a searcher may have clear information needs but experience difficulty in collecting required information. However, evaluation and comparison of such systems requires a large number of such tasks, which are difficult to collect or make up. To the best of our knowledge, there has not been a commonly used task set for evaluating struggling search of this kind. This paper proposes a convenient method to generate complex fact checking tasks. Each of the generated task has a clearly defined goal, which however takes an average searcher a significant amount of effort to reach. We conducted lab user studies to verify the feasibility of this method. The results confirmed its feasibility and efficiency.

User-centered approaches have been extensively studied and used in the area of struggling search. Related research has targeted key aspects of users such as user satisfaction or frustration, and search success or failure, using a variety of experimental methods including laboratory user studies, in-situ explicit feedback from searchers and by using crowdsourcing. Such studies are valuable in advancing the understanding of search difficulty from a user's perspective, and yield insights that can directly improve search systems and their evaluation. However, little is known about how user moods influence their interactions with a search system or their perception of struggling. In this work, we show that a user's own mood. can systematically bias the user's perception, and experience while interacting with a search system and trying to satisfy an information need. People who are in activated-(un)pleasant moods tend to issue more queries than people in deactivated or neutral moods. Those in an unpleasant mood perceive a higher level of difficulty. Our insights extend the current understanding of struggling search tasks and have important implications on the design and evaluation of search systems supporting such tasks.

In this demo paper, we introduce LogCanvas, a platform for user search history visualization.Different from the existing visualization tools, LogCanvas focuses on helping users re-construct the semantic relationship among their search activities. LogCanvas segments a user's search history into different sessions and generates a knowledge graph to represent the information exploration process in each session.A knowledge graph is composed of the most important concepts or entities discovered by each search query as well as their relationships. It thus captures the semantic relationship among the queries.LogCanvas offers a session timeline viewer and a snippets viewer to enable users to re-find their previous search results efficiently. LogCanvas also provides a collaborative perspective to support a group of users in sharing search results and experience.