Justifying a privacy guardian in discourse and behaviour : the People’s Republic of China’s strategic framing in data governance

The People’s Republic of China’s (PRC) approach to data governance, centred on data sovereignty, is much debated in academic literature. However, it remains unclear how the PRC’s different state actors justify this approach. Based on an analysis of the discourse and behaviour of the PRC’s state actors through strategic framing theory, their role as a privacy guardian can arguably be described as strategically constructed. The Chinese government and legislative bodies have tailored their communications to present themselves as champions of individual privacy, aiming to secure support for state policies. This strategic framing encompasses four mechanisms: the reframing of privacy threats through political narratives; legal ambiguities; selective framing; and the implementation of censorship to influence public discourse. An examination of how the Chinese government responded differently to data breaches in the cases of Didi and the Shanghai National Police Database leak highlights the Chinese government’s efforts in maintaining framing consistency to construct itself as a guardian, rather than a violator, of individual privacy.Peer reviewe

DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion

We present DiverseMotion, a new approach for synthesizing high-quality human motions conditioned on textual descriptions while preserving motion diversity.Despite the recent significant process in text-based human motion generation,existing methods often prioritize fitting training motions at the expense of action diversity. Consequently, striking a balance between motion quality and diversity remains an unresolved challenge. This problem is compounded by two key factors: 1) the lack of diversity in motion-caption pairs in existing benchmarks and 2) the unilateral and biased semantic understanding of the text prompt, focusing primarily on the verb component while neglecting the nuanced distinctions indicated by other words.In response to the first issue, we construct a large-scale Wild Motion-Caption dataset (WMC) to extend the restricted action boundary of existing well-annotated datasets, enabling the learning of diverse motions through a more extensive range of actions. To this end, a motion BLIP is trained upon a pretrained vision-language model, then we automatically generate diverse motion captions for the collected motion sequences. As a result, we finally build a dataset comprising 8,888 motions coupled with 141k text.To comprehensively understand the text command, we propose a Hierarchical Semantic Aggregation (HSA) module to capture the fine-grained semantics.Finally,we involve the above two designs into an effective Motion Discrete Diffusion (MDD) framework to strike a balance between motion quality and diversity. Extensive experiments on HumanML3D and KIT-ML show that our DiverseMotion achieves the state-of-the-art motion quality and competitive motion diversity. Dataset, code, and pretrained models will be released to reproduce all of our results.Comment: 12 pages, 7 figure

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

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

The influence of 1-MCP on the fruit quality and flesh browning of ‘Red Fuji’ apple after long-term cold storage

This study assessed the influence of 1-MCP treatment on the fruit quality and flesh browning of ‘Red Fuji’ apple at shelf life after long-term cold storage. The ‘Red Fuji’ fruit were stored at 0±0.5 °C for 270 days after treating with 1.0 μL L-1 1-methylcyclopropylene (1-MCP). Fruit quality, browning rate of stem-end flesh, chlorogenic acid content, polyphenol oxidase (PPO) activity were analyzed at shelf-life under 20±0.5 °C, the expression profile of ethylene receptors (MdERS1), phenylalnine ammonia lyase genes (MdPA L1, MdPA L2), quinate hydroxycinnamoyl/hydrxycinnamoyl CoA shi-kimate gene (MdHCT3), polyphenol oxidase genes (MdPPO1, MdPPO5)and lipoxygenase gene (MdLOX) were measured by real-time quantitative PCR. 1-MCP treatment improved the fruit storage quality, decreased stem-end flesh tissue browning, and fruit decay. In addition, the fruit respiration rate and ethylene production rate increased at shelf-life, but this increase could be inhibited by 1-MCP. The same rule was observed in the changes of chlorogenic acid content and PPO activity, the expression of MdERS1, MdPA L1, MdPPO1 and MdLOX were inhibited by 1-MCP as well in the stem-end flesh. Thus, 1-MCP treatment improves the fruit quality of ‘Red Fuji’ apple at shelf-life after long-term cold storage, and inhibits the browning of stem-end flesh by decreasing the chlorogenic acid content and PPO activity. MdPA L1, MdHCT3, MdPPO1 and MdLOX participate in the flesh browning progress

AdaEvo: Edge-Assisted Continuous and Timely DNN Model Evolution for Mobile Devices

Mobile video applications today have attracted significant attention. Deep learning model (e.g. deep neural network, DNN) compression is widely used to enable on-device inference for facilitating robust and private mobile video applications. The compressed DNN, however, is vulnerable to the agnostic data drift of the live video captured from the dynamically changing mobile scenarios. To combat the data drift, mobile ends rely on edge servers to continuously evolve and re-compress the DNN with freshly collected data. We design a framework, AdaEvo, that efficiently supports the resource-limited edge server handling mobile DNN evolution tasks from multiple mobile ends. The key goal of AdaEvo is to maximize the average quality of experience (QoE), e.g. the proportion of high-quality DNN service time to the entire life cycle, for all mobile ends. Specifically, it estimates the DNN accuracy drops at the mobile end without labels and performs a dedicated video frame sampling strategy to control the size of retraining data. In addition, it balances the limited computing and memory resources on the edge server and the competition between asynchronous tasks initiated by different mobile users. With an extensive evaluation of real-world videos from mobile scenarios and across four diverse mobile tasks, experimental results show that AdaEvo enables up to 34% accuracy improvement and 32% average QoE improvement.Comment: Accepted by IEEE Transactions on Mobile Computing 202