Forecasting hand poses is a challenging task due to inherent uncertainties, occlusions, and inaccuracies in 3D pose estimation. Diffusion models provide a promising direction for predicting precise 3D hand poses under noisy conditions. In this work, we introduce Dual-diffusion, an innovative framework for the precise prediction of future hand poses. Our approach leverages the strengths of diffusion models by framing hand pose forecasting as a reverse diffusion process, effectively addressing the complexities of noisy hand joint movements and their subtleties. To enhance the learning of hand pose representations, we propose a unique neural architecture that simultaneously captures both local and global features. This is achieved through the deployment of Global and Local Diffusion (GLD) blocks within our network, which facilitate the exchange of information between local and global features. This diffusion-based interaction enables the integration of global hand actions and local finger actions, leading to a more powerful representation learning approach. We evaluate the effectiveness of our Dual-diffusion method on three public 3D hand pose estimation datasets (MSRA, F-PHAB, and BigHand2.2M), and our approach outperforms previous methods.

Vision Large Language Models (VLLMs) exhibit promising potential for multi-modal understanding, yet their application to video-based emotion recognition remains limited by insufficient spatial and contextual awareness. Traditional approaches, which prioritize isolated facial features, often neglect critical non-verbal cues such as body language, environmental context, and social interactions, leading to reduced robustness in real-world scenarios. To address this gap, we propose Set-of-Vision-Text Prompting (SoVTP), a novel framework that enhances zero-shot emotion recognition by integrating spatial annotations (e.g., bounding boxes, facial landmarks), physiological signals (facial action units), and contextual cues (body posture, scene dynamics, others’ emotions) into a unified prompting strategy. SoVTP preserves holistic scene information while enabling fine-grained analysis of facial muscle movements and interpersonal dynamics. Extensive experiments show that SoVTP achieves substantial improvements over existing visual prompting methods, demonstrating its effectiveness in enhancing VLLMs’ video emotion recognition capabilities.

This study introduces LRDif, a novel diffusion-based framework designed specifically for facial expression recognition (FER) within the context of under-display cameras (UDC). To address the inherent challenges posed by UDC’s image degradation, such as reduced sharpness and increased noise, LRDif employs a two-stage training strategy that integrates a condensed preliminary extraction network (FPEN) and an agile transformer network (UDCformer) to effectively identify emotion labels from UDC images. By harnessing the robust distribution mapping capabilities of Diffusion Models (DMs) and the spatial dependency modeling strength of transformers, LRDif effectively overcomes the obstacles of noise and distortion inherent in UDC environments. Comprehensive experiments on standard FER datasets including RAFDB, KDEF, and FERPlus, LRDif demonstrate state-of-the-art performance, underscoring its potential in advancing FER applications. This work not only addresses a significant gap in the literature by tackling the UDC challenge in FER but also sets a new benchmark for future research in the field.

Facial expression is related to facial muscle contractions and different muscle movements correspond to different emotional states. For micro-expression recognition, the muscle movements are usually subtle, which has a negative impact on the performance of current facial emotion recognition algorithms. Most existing methods use self-attention mechanisms to capture relationships between tokens in a sequence, but they do not take into account the inherent spatial relationships between facial landmarks. This can result in sub-optimal performance on micro-expression recognition tasks. Therefore, learning to recognize facial muscle movements is a key challenge in the area of micro-expression recognition. In this paper, we propose a Hierarchical Transformer Network (HTNet) to identify critical areas of facial muscle movement. HTNet includes two major components: a transformer layer that leverages the local temporal features and an aggregation layer that extracts local and global semantical facial features. Specifically, HTNet divides the face into four different facial areas: left lip area, left eye area, right eye area and right lip area. The transformer layer is used to focus on representing local minor muscle movement with local self-attention in each area. The aggregation layer is used to learn the interactions between eye areas and lip areas. The experiments on four publicly available micro-expression datasets show that the proposed approach outperforms previous methods by a large margin. The codes and models are available at: https://github.com/wangzhifengharrison/HTNet.

Future affective computing research could be enhanced by enabling the computer to recognise a displayer's mental state from an observer's reaction (measured by physiological signals), using this information to improve recognition algorithms, and eventually to computer systems which are more responsive to human emotions. In this paper, an observer's physiological signals are analysed to distinguish displayers' genuine from fake smiles. Overall, thirty smile videos were collected from four benchmark database and classified as showing genuine or fake smiles. Overall, forty observers viewed videos. We generally recorded four physiological signals: pupillary response (PR), electrocardiogram (ECG), galvanic skin response (GSR), and blood volume pulse (BVP). A number of temporal features were extracted after a few processing steps, and minimally correlated features between genuine and fake smiles were selected using the NCCA (canonical correlation analysis with neural network) system. Finally, classification accuracy was found to be as high as 98.8 percent from PR features using a leave-one-observer-out process. In comparison, the best current image processing technique [1] on the same video data was 95 percent correct. Observers were 59 percent (on average) to 90 percent (by voting) correct by their conscious choices. Our results demonstrate that humans can non-consciously (or emotionally) recognise the quality of smiles 4 percent better than current image processing techniques and 9 percent better than the conscious choices of groups.

A snippet (content summary for a web page) is one of the main elements in a search result page. Search engines have been improved to reduce users' effort in web search, e.g., providing flexible snippet sizes by considering the purpose of the search and suggesting predicted answers. In most cases, search engines for mobile devices present two or three lines of snippet for each result link. Some studies suggest that long snippets provide a better search experience on desktop screens, but this may not be true for mobile devices because of the smaller screen.

Preparing Software Engineering graduates with skills to satisfy the demands of industry has always been a challenge for universities. One way to overcome this challenge is to provide students with an authentic team project experience. Authentic student projects are those that are as close as possible to real projects in terms of uncertainty, complexity and diversity. In order to achieve this, collaboration with business and other external stakeholders is essential. However, such collaboration is often hindered by constraints around team structure, team and individual student assessment, the use of specific processes and technology, providing technical and other support, and fixed project durations. In this paper, we present a simple course structure and assessment process that has allowed us to remove the majority of such constraints, while maintaining integrity around student learning and assessment. By removing these constraints, we are providing students with authentic team project experiences that prepare them well for future employment. Preliminary evaluations of our approach indicate that despite several challenges, the experience has led to high levels of satisfaction among students and our external partners including employers.

Vertical scrolling is the standard method of exploring search results pages. For touch-enabled mobile devices that are not equipped with a mouse or keyboard, we adopt other methods of controlling the viewport with the aim of investigating user interaction. From the intuition that people are used to reading books by turning pages horizontally, we conducted a user experiment to investigate the effects of horizontal and vertical control types (pagination versus scrolling) on a touch-enabled mobile phone. Our findings suggest that participants using pagination were more likely to find relevant documents, especially those over the fold; spent more time attending to relevant results; and were faster to click while spending less time on the search result pages overall. We also found that the main reason for the difference in search speed is the time taken for the scroll itself. We conclude that search engines need to provide different viewport controls to allow better search experiences on touch-enabled mobile devices.

Compared to the early versions of smart phones, recent mobile devices have bigger screens that can present more web search results. Several previous studies have reported differences in user interaction between conventional desktop computer and mobile device‐based web searches, so it is imperative to consider the differences in user behavior for web search engine interface design on mobile devices. However, it is still unknown how the diversification of screen sizes on hand‐held devices affects how users search. In this article, we investigate search performance and behavior on three different small screen sizes: early smart phones, recent smart phones, and phablets. We found no significant difference with respect to the efficiency of carrying out tasks, however participants exhibited different search behaviors: less eye movement within top links on the larger screen, fast reading with some hesitation before choosing a link on the medium, and frequent use of scrolling on the small screen. This result suggests that the presentation of web search results for each screen needs to take into account differences in search behavior. We suggest several ideas for presentation design for each screen size.

In recent years, searching the web on mobile devices has become enormously popular. Because mobile devices have relatively small screens and show fewer search results, search behavior with mobile devices may be different from that with desktops or laptops. Therefore, examining these differences may suggest better, more efficient designs for mobile search engines. In this experiment, we use eye tracking to explore user behavior and performance. We analyze web searches with 2 task types on 2 differently sized screens: one for a desktop and the other for a mobile device. In addition, we examine the relationships between search performance and several search behaviors to allow further investigation of the differences engendered by the screens. We found that users have more difficulty extracting information from search results pages on the smaller screens, although they exhibit less eye movement as a result of an infrequent use of the scroll function. However, in terms of search performance, our findings suggest that there is no significant difference between the 2 screens in time spent on search results pages and the accuracy of finding answers. This suggests several possible ideas for the presentation design of search results pages on small devices.

Serious games are beneficial for education in various computer science areas. Numerous works have reported the experiences of using games (not only playing but also development) in teaching and learning. Considering it could be difficult for teachers/students to prepare/develop a game from scratch during one semester, assistant educational materials would be crucial in the corresponding courses. Unfortunately, the literature shows that not many materials from educational game projects are shared. To help different educators identify suitable courseware and help students implement game development, it is worth further investigating and accumulating the educational resources from individual game projects. Following such an idea, this paper proposes a game development project of an object-oriented Sokoban solver, and exposes relevant educational materials. The documented system design can be viewed as a ready-to-use resource for education in object-oriented analysis and design (OOAD), while the Sokoban solver itself may be used as an assignment platform for teaching artificial intelligence (AI). Further documentation, platform, and APIs will be realized and shared in the future to facilitate others' educational activities. Overall, this work is supposed to inspire and encourage other researchers and educators to post available materials of more game projects for the purpose of sharing and reuse.

Meaningful evaluation of web search must take account of spam. Here we conduct a user experiment to investigate whether satisfaction with search engine result pages as a whole is harmed more by spam or by irrelevant documents. On some measures, search result pages are differentially harmed by the insertion of spam and irrelevant documents. Additionally we find that when users are given two documents of equal utility, the one with the lower spam score will be preferred; a result page without any spam documents will be preferred to one with spam; and an irrelevant document high in a result list is surprisingly more damaging to user satisfaction than a spam document. We conclude that web ranking and evaluation should consider both utility (relevance) and "spamminess" of documents.

Software for scientific research purposes has received increased attention in recent years. Case studies have noted development practices, limitations, and problems in the development of scientific software. However, applicability of the results of these studies to improving the wider scientific software development practices is not known. This paper presents a survey of 60 scientific software developers. The survey was conducted online from August--September 2009, and aims to identify where improvements to scientific software practices can be made. While our results generally confirm previous work, we have found some notable differences. The use of IDEs and version control tools among the surveyed scientific software developers has increased, and trace-ability of scientific software is not as important to scientific software developers as it is to scientific software users. Documentation also appears to be more widely produced than previous studies indicate. However, there remains room for improvement in tool use, documentation, testing, and verification activities for scientific software development.

There are significant barriers to academic research into user Web search preferences. Academic researchers are unable to manipulate the results shown by a major search engine to users and would have no access to the interaction data collected by the engine. Our initial approach to overcoming this was to ask participants to submit queries to an experimental search engine rather than their usual search tool. Over several different experiments we found that initial user buy-in was high but that people quickly drifted back to their old habits and stopped contributing data. Here, we report our investigation of possible reasons why this occurs. An alternative approach is exemplified by the Lemur browser toolbar, which allows local collection of user interaction data from search engine sessions, but does not allow result pages to be modified. We will demonstrate a new Firefox toolbar that we have developed to support experiments in which search results may be arbitrarily manipulated. Using our toolbar, academics can set up the experiments they want to conduct, while collecting (subject to human experimentation guidelines) queries, clicks and dwell times as well as optional explicit judgments.

Web search engines discover indexable documents by recursively 'crawling' from a seed URL. Their rankings take into account link popularity. While this works well, it introduces biases towards older documents. Older documents are more likely to be the target of links, while new documents with few, or no, incoming links are unlikely to rank highly in search results.

In real world use of test collection methods, it is essential that the query test set be representative of the work load expected in the actual application. Using a random sample of queries from a media company's query log as a 'gold standard' test set we demonstrate that biases in sitemap-derived and top n query sets can lead to significant perturbations in engine rankings and big differences in estimated performance levels.

Internet voting systems, catering to different requirements, have been widely implemented and used. Some have been successful, while others have experienced unexpected problems. This paper overviews the concerns of using Internet voting and tries to give solutions. Tradeoffs between the advantages and disadvantages of employing Internet voting are also addressed. A simple protocol is designed to show that technical problems identified in the literature can be solved fairly easily, though some seem subtle. This study reveals that the main concerns of adopting Internet voting for critical elections are not cryptographic tools, but hardware and software reliability, and social and political issues. After discussing possible concerns about Internet voting and possible solutions or comments, we propose steps to enable Internet voting to be adopted even for critical elections.

Certain fundamental graph problems like recognition, dominating set, Hamiltonian cycle and path, and clique partition, which are hard for well-covered graphs in general, can be solved efficiently for very well covered graphs. We address the question of how far the class of very well covered graphs can be generalized before such problems become hard. In this context, we examine the complexity of such problems for two subclasses of well-covered graphs, one properly containing the other, with the smaller one properly containing the family of very well covered graphs. We show that the problems studied algorithmically separate the subclasses from each other and from the families of well-covered and very well covered graphs. © 1997 John Wiley & Sons, Inc.

Abstract A graph with n vertices is well covered if every maximal independent set is a maximum independent set and very well covered if every maximal independent set has size n/2. In this work, we study these graphs from an algorithmic complexity point of view. We show that well‐covered graph recognition is co‐NP‐complete and that several other problems are NP‐complete for well‐covered graphs. A number of these problems remain NP‐complete on very well covered graphs, while some admit polynomial time solutions for the smaller class. For both families, the isomorphism problem is as hard as general graph isomorphism.