Fooling an Unbounded Adversary with a Short Key, Repeatedly: The Honey Encryption Perspective

This article is motivated by the classical results from Shannon that put the simple and elegant one-time pad away from practice: key length has to be as large as message length and the same key could not be used more than once. In particular, we consider encryption algorithm to be defined relative to specific message distributions in order to trade for unconditional security. Such a notion named honey encryption (HE) was originally proposed for achieving best possible security for password based encryption where secrete key may have very small amount of entropy. Exploring message distributions as in HE indeed helps circumvent the classical restrictions on secret keys.We give a new and very simple honey encryption scheme satisfying the unconditional semantic security (for the targeted message distribution) in the standard model (all previous constructions are in the random oracle model, even for message recovery security only). Our new construction can be paired with an extremely simple yet "tighter" analysis, while all previous analyses (even for message recovery security only) were fairly complicated and require stronger assumptions. We also show a concrete instantiation further enables the secret key to be used for encrypting multiple messages

Particle swarm optimization with state-based adaptive velocity limit strategy

Velocity limit (VL) has been widely adopted in many variants of particle swarm optimization (PSO) to prevent particles from searching outside the solution space. Several adaptive VL strategies have been introduced with which the performance of PSO can be improved. However, the existing adaptive VL strategies simply adjust their VL based on iterations, leading to unsatisfactory optimization results because of the incompatibility between VL and the current searching state of particles. To deal with this problem, a novel PSO variant with state-based adaptive velocity limit strategy (PSO-SAVL) is proposed. In the proposed PSO-SAVL, VL is adaptively adjusted based on the evolutionary state estimation (ESE) in which a high value of VL is set for global searching state and a low value of VL is set for local searching state. Besides that, limit handling strategies have been modified and adopted to improve the capability of avoiding local optima. The good performance of PSO-SAVL has been experimentally validated on a wide range of benchmark functions with 50 dimensions. The satisfactory scalability of PSO-SAVL in high-dimension and large-scale problems is also verified. Besides, the merits of the strategies in PSO-SAVL are verified in experiments. Sensitivity analysis for the relevant hyper-parameters in state-based adaptive VL strategy is conducted, and insights in how to select these hyper-parameters are also discussed.Comment: 33 pages, 8 figure

Feature-aware conditional GAN for category text generation

Category text generation receives considerable attentions since it is beneficial for various natural language processing tasks. Recently, the generative adversarial network (GAN) has attained promising performance in text generation, attributed to its adversarial training process. However, there are several issues in text GANs, including discreteness, training instability, mode collapse, lack of diversity and controllability etc. To address these issues, this paper proposes a novel GAN framework, the feature-aware conditional GAN (FA-GAN), for controllable category text generation. In FA-GAN, the generator has a sequence-to-sequence structure for improving sentence diversity, which consists of three encoders including a special feature-aware encoder and a category-aware encoder, and one relational-memory-core-based decoder with the Gumbel SoftMax activation function. The discriminator has an additional category classification head. To generate sentences with specified categories, the multi-class classification loss is supplemented in the adversarial training. Comprehensive experiments have been conducted, and the results show that FA-GAN consistently outperforms 10 state-of-the-art text generation approaches on 6 text classification datasets. The case study demonstrates that the synthetic sentences generated by FA-GAN can match the required categories and are aware of the features of conditioned sentences, with good readability, fluency, and text authenticity.Comment: 27 pages, 8 figure

A public health study on the participation mechanism of social capital in the governance of public sports space in dilapidated urban communities – a case study of Changsha City, Hunan Province

IntroductionChina’s aging population, mobile population, low-income families, and other vulnerable groups congregate in dilapidated urban communities serving as public health spaces. As a result, managing public sports spaces in aging urban areas is a significant public health project in China, an essential strategy for raising residents’ quality of life, and a significant effort to support the active aging of the older adult.MethodsThe study used mathematical and statistical techniques, questionnaires, and logical deduction to conduct a public health study on the participation mechanism of social capital in the governance of public sports spaces in dilapidated urban communities. It chose 11 old Changsha, Hunan Province, communities as the research objects.ResultsPersonal social capital was found to boost the availability of public sports spaces in older populations through social connections. Collective social capital improves the availability of public sports spaces in aging populations through social trust and stabilizes the order of public sports spaces in aging communities through social involvement.DiscussionTo improve the governance efficiency of public sports spaces in aging urban communities, the study aims to actively mobilize and accumulate social capital through cultivating the public spirit, reshaping the concept of sports governance, appropriately decentralizing and empowering, strengthening sports governance structures, enhancing communication and collaboration, and building sports governance. This is essential for China to fully implement the policies of active aging, a healthy China, and creating a community for global public health

Data-Driven Modeling with Experimental Augmentation for the Modulation Strategy of the Dual-Active-Bridge Converter

For the performance modeling of power converters, the mainstream approaches are essentially knowledge-based, suffering from heavy manpower burden and low modeling accuracy. Recent emerging data-driven techniques greatly relieve human reliance by automatic modeling from simulation data. However, model discrepancy may occur due to unmodeled parasitics, deficient thermal and magnetic models, unpredictable ambient conditions, etc. These inaccurate data-driven models based on pure simulation cannot represent the practical performance in physical world, hindering their applications in power converter modeling. To alleviate model discrepancy and improve accuracy in practice, this paper proposes a novel data-driven modeling with experimental augmentation (D2EA), leveraging both simulation data and experimental data. In D2EA, simulation data aims to establish basic functional landscape, and experimental data focuses on matching actual performance in real world. The D2EA approach is instantiated for the efficiency optimization of a hybrid modulation for neutral-point-clamped dual-active-bridge (NPC-DAB) converter. The proposed D2EA approach realizes 99.92% efficiency modeling accuracy, and its feasibility is comprehensively validated in 2-kW hardware experiments, where the peak efficiency of 98.45% is attained. Overall, D2EA is data-light and can achieve highly accurate and highly practical data-driven models in one shot, and it is scalable to other applications, effortlessly.Comment: 11 page

Localization Accuracy of Ultrasound-Actuated Needle with Color Doppler Imaging

An ultrasonic needle-actuating device for tissue biopsy and regional anaesthesia offers enhanced needle visibility with color Doppler imaging. However, its specific performance is not yet fully determined. This work investigated the influence on needle visibility of the insertion angle and drive voltage, as well as determined the accuracy and agreement of needle tip localization by comparing color Doppler measurements with paired photographic and B-mode ultrasound measurements. Needle tip accuracy measurements in a gelatin phantom gave a regression trend, where the slope of trend is 0.8808; coefficient of determination (R2) is 0.8877; bias is −0.50 mm; and the 95% limits of agreement are from −1.31 to 0.31 mm when comparing color Doppler with photographic measurements. When comparing the color Doppler with B-mode ultrasound measurements, the slope of the regression trend is 1.0179; R2 is 0.9651; bias is −0.16 mm; and the 95% limits of agreement are from −1.935 to 1.605 mm. The results demonstrate the accuracy of this technique and its potential for application to biopsy and ultrasound guided regional anaesthesia

Unlock Multi-Modal Capability of Dense Retrieval via Visual Module Plugin

This paper proposes Multi-modAl Retrieval model via Visual modulE pLugin (MARVEL) to learn an embedding space for queries and multi-modal documents to conduct retrieval. MARVEL encodes queries and multi-modal documents with a unified encoder model, which helps to alleviate the modality gap between images and texts. Specifically, we enable the image understanding ability of a well-trained dense retriever, T5-ANCE, by incorporating the image features encoded by the visual module as its inputs. To facilitate the multi-modal retrieval tasks, we build the ClueWeb22-MM dataset based on the ClueWeb22 dataset, which regards anchor texts as queries, and exact the related texts and image documents from anchor linked web pages. Our experiments show that MARVEL significantly outperforms the state-of-the-art methods on the multi-modal retrieval dataset WebQA and ClueWeb22-MM. Our further analyses show that the visual module plugin method is tailored to enable the image understanding ability for an existing dense retrieval model. Besides, we also show that the language model has the ability to extract image semantics from image encoders and adapt the image features in the input space of language models. All codes are available at https://github.com/OpenMatch/MARVEL