Perception of meaning and usage motivations of emoticons among Americans and Chinese users

Do people of different cultures agree on the meaning and use of emoticons? This study addresses this question from an inter-cultural perspective and explores the use of emoticons in the American and Chinese Computer-mediated communication (CMC) communities. The research indicates that both the Americans and Chinese participants use emoticons for entertaining, informational and social interaction motivations but the Americans are more likely to use emoticons for information motivations than the Chinese and the Chinese participants are more likely to use emoticons for social interactions than the Americans participants. The results correspond to the cultural differences between the two countries in low-/ high-context and individualism/collectivism dimensions. Moreover, the results also show that the Americans and the Chinese disagree on the meaning of most emoticons used in the study

Lyapunov-type inequalities for (m+1)(m+1)th order half-linear differential equations with anti-periodic boundary conditions

In this work, we will establish several new Lyapunov-type inequalities for $(m+1)$th order half-linear differential equations with anti-periodic boundary conditions, the results of this paper are new and generalize and improve some early results in the literature

Dual Relation Alignment for Composed Image Retrieval

Composed image retrieval, a task involving the search for a target image using a reference image and a complementary text as the query, has witnessed significant advancements owing to the progress made in cross-modal modeling. Unlike the general image-text retrieval problem with only one alignment relation, i.e., image-text, we argue for the existence of two types of relations in composed image retrieval. The explicit relation pertains to the reference image & complementary text-target image, which is commonly exploited by existing methods. Besides this intuitive relation, the observations during our practice have uncovered another implicit yet crucial relation, i.e., reference image & target image-complementary text, since we found that the complementary text can be inferred by studying the relation between the target image and the reference image. Regrettably, existing methods largely focus on leveraging the explicit relation to learn their networks, while overlooking the implicit relation. In response to this weakness, We propose a new framework for composed image retrieval, termed dual relation alignment, which integrates both explicit and implicit relations to fully exploit the correlations among the triplets. Specifically, we design a vision compositor to fuse reference image and target image at first, then the resulted representation will serve two roles: (1) counterpart for semantic alignment with the complementary text and (2) compensation for the complementary text to boost the explicit relation modeling, thereby implant the implicit relation into the alignment learning. Our method is evaluated on two popular datasets, CIRR and FashionIQ, through extensive experiments. The results confirm the effectiveness of our dual-relation learning in substantially enhancing composed image retrieval performance

Efficient Attribute-Based Encryption with Privacy-Preserving Key Generation and Its Application in Industrial Cloud

Due to the rapid development of new technologies such as cloud computing, Internet of Things (IoT), and mobile Internet, the data volumes are exploding. Particularly, in the industrial field, a large amount of data is generated every day. How to manage and use industrial Big Data primely is a thorny challenge for every industrial enterprise manager. As an emerging form of service, cloud computing technology provides a good solution. It receives more and more attention and support due to its flexible configuration, on-demand purchase, and easy maintenance. Using cloud technology, enterprises get rid of the heavy data management work and concentrate on their main business. Although cloud technology has many advantages, there are still many problems in terms of security and privacy. To protect the confidentiality of the data, the mainstream solution is encrypting data before uploading. In order to achieve flexible access control to encrypted data, attribute-based encryption (ABE) is an outstanding candidate. At present, more and more applications are using ABE to ensure data security. However, the privacy protection issues during the key generation phase are not considered in the current ABE systems. That is to say, the key generation center (KGC) knows both of attributes and corresponding keys of each user. This problem is especially serious in the industrial big data scenario, because it will cause great damage to the business secrets of industrial enterprises. In this paper, we design a new ABE scheme that protects user\u27s privacy during key issuing. In our new scheme, we separate the functionality of attribute auditing and key generating to ensure that the KGC cannot know user\u27s attributes and that the attribute auditing center (AAC) cannot obtain the user\u27s secret key. This is ideal for many privacy-sensitive scenarios, such as industrial big data scenario

Influence of Laserâ Microtextured Surface Collar on Marginal Bone Loss and Periâ Implant Soft Tissue Response: A Systematic Review and Metaâ Analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142176/1/jper0651-sup-0003.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142176/2/jper0651.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142176/3/jper0651-sup-0004.pd

Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark

In this paper, we introduce a large Multi-Attribute and Language Search dataset for text-based person retrieval, called MALS, and explore the feasibility of performing pre-training on both attribute recognition and image-text matching tasks in one stone. In particular, MALS contains 1,510,330 image-text pairs, which is about 37.5 times larger than prevailing CUHK-PEDES, and all images are annotated with 27 attributes. Considering the privacy concerns and annotation costs, we leverage the off-the-shelf diffusion models to generate the dataset. To verify the feasibility of learning from the generated data, we develop a new joint Attribute Prompt Learning and Text Matching Learning (APTM) framework, considering the shared knowledge between attribute and text. As the name implies, APTM contains an attribute prompt learning stream and a text matching learning stream. (1) The attribute prompt learning leverages the attribute prompts for image-attribute alignment, which enhances the text matching learning. (2) The text matching learning facilitates the representation learning on fine-grained details, and in turn, boosts the attribute prompt learning. Extensive experiments validate the effectiveness of the pre-training on MALS, achieving state-of-the-art retrieval performance via APTM on three challenging real-world benchmarks. In particular, APTM achieves a consistent improvement of +6.96%, +7.68%, and +16.95% Recall@1 accuracy on CUHK-PEDES, ICFG-PEDES, and RSTPReid datasets by a clear margin, respectively