The Integrated Platform of Digital Cultural Heritage in China: a Proposed Model Based on Public’s Expectations

This poster attempts to propose an integrated platform of digital cultural heritage in China based on the public’s expectations and provide specific suggestions for policy makers. A questionnaire was designed and disseminated through online survey service website. From 6 October to November 2016, a total of 1,076 responses were collected. The data showed that the Chinese users expected a comprehensive, convenient, and unified one-stop online accessible portal to all types of digital cultural heritage from China. Based on user need analysis, an integrated platform model of digital cultural heritage has been proposed. Also the China’s digital cultural heritage integration management system has been proposed. In this system, the corporation between the Ministry of Culture and the State Archives Administration of China can be realized

Evaluating AIGC Detectors on Code Content

Artificial Intelligence Generated Content (AIGC) has garnered considerable attention for its impressive performance, with ChatGPT emerging as a leading AIGC model that produces high-quality responses across various applications, including software development and maintenance. Despite its potential, the misuse of ChatGPT poses significant concerns, especially in education and safetycritical domains. Numerous AIGC detectors have been developed and evaluated on natural language data. However, their performance on code-related content generated by ChatGPT remains unexplored. To fill this gap, in this paper, we present the first empirical study on evaluating existing AIGC detectors in the software domain. We created a comprehensive dataset including 492.5K samples comprising code-related content produced by ChatGPT, encompassing popular software activities like Q&A (115K), code summarization (126K), and code generation (226.5K). We evaluated six AIGC detectors, including three commercial and three open-source solutions, assessing their performance on this dataset. Additionally, we conducted a human study to understand human detection capabilities and compare them with the existing AIGC detectors. Our results indicate that AIGC detectors demonstrate lower performance on code-related data compared to natural language data. Fine-tuning can enhance detector performance, especially for content within the same domain; but generalization remains a challenge. The human evaluation reveals that detection by humans is quite challenging

How Online Patient–Physician Interaction Influences Patient Satisfaction

Online health interaction (OHI) is an effective and increasingly popular method for patients to access health information. Extant literature overlooks such service users’ satisfaction derived from online interactions and the measurement of OHI processes. Based on the relational communication literature and the features of OHI, the present study proposes three dimensions to conceptualize the success of OHI processes (i.e., interaction depth, information intensity, and time breadth) and explores the association between these interaction processes and service satisfaction. Further, two characteristics of OHI, namely information richness and indirect interaction, are identified as contingent factors on those proposed linkages. The research model was tested on the objective data collected from an online healthcare platform. The study findings showed that (1) interaction depth, information intensity, and time breadth positively impact service satisfaction and (2) both information richness and indirect interaction negatively moderate the effects of interaction depth and information intensity and positively moderate the effect of time breadth. The present study contributes to the existing literature by conceptualizing online interaction process and identifying the role of the specific characteristics of online healthcare and also provides implications to practitioners

Attribute Selection Method based on Objective Data and Subjective Preferences in MCDM

Decision attributes are important parameters when choosing an alternative in a multiple criteria decision-making (MCDM) problem. In order to select the optimal set of decision attributes, an analysis framework is proposed to illustrate the attribute selection problem. Then a two-step attribute selection procedure is presented based on the framework: In the first step, attributes are filtered by using correlation algorithm. In the second step, a multi-objective optimization model is constructed to screen attributes from the results of the first step. Finally, a case study is given to illustrate and verify this method. The advantage of this method is that both external attribute data and subjective decision preferences are utilized in a sequential procedure. It enhances the reliability of decision attributes and matches the actual decision-making scenarios better

A scalable bloom filter based prefilter and hardware-oriented predispatcher

Presented in this paper a scalable bloom filter based prefilter and a hardware-oriented predispatcher pattern matching mechanism for content filtering applications, which are scalable in terms of speed, the number of patterns and the pattern length. Prefilter algorithm is based on a memory efficient multi-hashing data structure called bloom filter. According to the statistics of simulations, the filter ratio can reach up to 60% if the whole engine has been trained well. It has been showed that this engine could enhance the capabilities of general-purpose IDS solutions

Tropical storm-induced turbulent mixing and chlorophyll-a enhancement in the continental shelf southeast of Hainan Island

AbstractBased on moored observations and remote sensing data in July and August 2005, energy sources for enhancing turbulent mixing and possible mechanisms of phytoplankton bloom in the continental shelf southeast of Hainan Island under the influence of Washi, a fast-moving and weak tropical storm, are analyzed in this paper. Observations show that strong near-inertial internal waves were generated by the rapidly changing wind stress and the near-inertial energy was dissipated quickly across the thermocline. The strong turbulent mixing associated with the near-inertial baroclinic shear instability occurred with maximum eddy diffusivity above 3.2×10−4m2s−1, and the surface chlorophyll-a (Chl-a) concentration after the storm increased by 22.2%. The Chl-a concentration augment was inferred to be an upper ocean biophysical response to the enhanced near-inertial turbulent mixing which could increase the upward nutrient flux into the surface low eutrophic zone during the passage of Washi

Cross-Lingual Adaptation for Type Inference

Deep learning-based techniques have been widely applied to the program analysis tasks, in fields such as type inference, fault localization, and code summarization. Hitherto deep learning-based software engineering systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label a prohibitively large amount of data. However, most Turing-complete imperative languages share similar control- and data-flow structures, which make it possible to transfer knowledge learned from one language to another. In this paper, we propose cross-lingual adaptation of program analysis, which allows us to leverage prior knowledge learned from the labeled dataset of one language and transfer it to the others. Specifically, we implemented a cross-lingual adaptation framework, PLATO, to transfer a deep learning-based type inference procedure across weakly typed languages, e.g., Python to JavaScript and vice versa. PLATO incorporates a novel joint graph kernelized attention based on abstract syntax tree and control flow graph, and applies anchor word augmentation across different languages. Besides, by leveraging data from strongly typed languages, PLATO improves the perplexity of the backbone cross-programming-language model and the performance of downstream cross-lingual transfer for type inference. Experimental results illustrate that our framework significantly improves the transferability over the baseline method by a large margin

Fluorescent Probes for Molecular Imaging of ROS/RNS Species in Living Systems

Reactive Oxygen Species (ROS) and Reactive Nitrogen Species (RNS) are highly reactive species which play crucial roles in many fundamental physiological processes including cellular signalling pathways. Over-production of these reactive species by various stimuli leads to cellular oxidative stress which is linked to various disease conditions. Therefore, the development of novel detection methods for ROS and RNS is of great interest and indispensable for monitoring the dynamic changes of ROS and RNS in cells and for elucidating their mechanisms of trafficking and connections to diseases. We have been recently developing various fluorescent sensors which can selectively detect metal ions, ROS or RNS species in live cells or animals. Our turn-on profluorescent sensors are capable of imaging oxidative stress promoted by metal and H2O2 (i.e. the Fenton Reaction conditions) in living cells (Chem Commun 2010); our highly selective and sensitive iron sensors can image the endogenous exchangeable iron pools and their dynamic changes with subcellular resolution in living neuronal cells (ChemBioChem 2012 and unpublished data), and so do our superoxide sensors (ChemBioChem 2012 and unpublished data). Moreover, we have recently developed nitric oxide (NO) sensors for molecular imaging of stimulated NO production in live cells with subcellular resolution as well as novel near infra red (NIR) sensors for NO imaging in live animals