In industrial cyber-physical systems (CPS), maintaining large-scale, long-lived software stacks demands rapid retrieval of protocol-compatible and system-context-aware code segments to avoid costly downtime and safety issues. Existing code search methods lack system-aware reasoning and suffer from rigid semantic fusion, leading to fragmented understanding of intercomponent dependencies in industrial software. To tackle these pain points, this article proposes an industrial-oriented hierarchical feature and graph-based code search (HFGCS) framework with sample-aware hierarchical fusion and hub-centric heterogeneous graph reasoning, customized for CPS maintenance requirements. It integrates a dynamic layer aggregation (DLA) module for adaptive multigranularity semantic fusion (capturing syntax-to-logic features based on query intent) and an integrated hub context encoder (IHCE) module that constructs a heterogeneous CPS graph with a global hub node to propagate cross-component dependencies (control logic, communication protocols, sensor/actuator bindings, and runtime constraints). A learnable gating network dynamically balances these representations to achieve intent-aligned and system-consistent code retrieval. Extensive experiments on CodeSearchNet-C show HFGCS improves MRR by 7.3%–15.9% over state-of-the-art baselines with strong cross-backbone robustness. Industrial validation confirms it shortens development cycles, enhances efficiency and accuracy, and prevents protocol mismatches in safety-critical CPS software. Feasible for deployment with topology-aware reasoning, HFGCS serves as a scalable and reliable retrieval engine, providing high-quality compatible code references to underpin the stable operation of industrial CPS and aligning with the core scope of Industrial Informatics. The code and data used in our study are available at online.

Stable Diffusion has become a crucial technology in supporting the development of generative art, allowing users to generate detailed and diverse images from text prompts. Generative art is at the infant stage, but already attracted many researchers to develop the related technologies, which are quite diversified in terms of quality of images as well as algorithms and computing power that needed. Many versions of Stable Diffusion are available, but four versions will be selected in our study. Stable Diffusion-1.5 is relatively lightweight and requires low computing power, is good for quick prototyping and basic image generation. Stable Diffusion-2.1 with an improved CLIP text encoder for better prompt alignment and higher resolution. Stable Diffusion-XL supports 4K images and multi-modal input, aims at the market of professional and high-end needs. Stable Diffusion- 3.5 is a dynamic version, which uses super-resolution technologies to balance speed and quality. This study compares the above versions in terms of architectures, performance like generation speed and image quality, as well as application domains from digital art to scientific research. The results of this study can be used to support users choosing the best Stable Diffusion version for the tasks they have. Technologies that support Generative art is not just focus on the elements described in the prompt can be generated, but also the how the artistic components or art work can be generated that meet the user’s expectation.

Analyzing the market sentiment and forecasting movements in asset prices is extremely important and has been attempted by researchers and market practitioners. Asset volatilities, regardless of historical ones or implied from the option prices, are crucial barometers of the market. And this research proposes a new approach that combines image processing and machine learning to capture the relationships between sentiment-related features and asset movement. The proposed research is based on the tick-level SPY options transactions, and the dataset contains around 1.5 million trading records. Specially, we obtained the gap between the call surface and the put surface as the second-level implied volatility surface (IVS). After adopting the traditional convolutional neural network (CNN) to compare the predictive effects among implied volatility (IV) call surface, IV put surface, and IV gap surface, the results indicate that the IV gap provides the most significant predictability. Besides, our project creatively proposes the interframe difference approach to optimized CNN and a recurrent CNN (RCNN) to fully utilize spatial and temporal features of the IV surface data. According to experiment results, the directional accuracy of prediction ranges from 67.20% to 72.05% for asset movement forecasting at the millisecond level.

As Internet of Things devices become widely used in the real-world, it is crucial to protect them from malicious intrusions. However, the data scarcity of IoT limits the applicability of traditional intrusion detection methods, which are highly data-dependent. To address this, in this article, we propose the Open-Set Dandelion Network (OSDN) based on unsupervised heterogeneous domain adaptation in an open-set manner. The OSDN model performs intrusion knowledge transfer from the knowledge-rich source network intrusion domain to facilitate more accurate intrusion detection for the data-scarce target IoT intrusion domain. Under the open-set setting, it can also detect newly-emerged target domain intrusions that are not observed in the source domain. To achieve this, the OSDN model forms the source domain into a dandelion-like feature space in which each intrusion category is compactly grouped and different intrusion categories are separated, i.e., simultaneously emphasising inter-category separability and intra-category compactness. The dandelion-based target membership mechanism then forms the target dandelion. Then, the dandelion angular separation mechanism achieves better inter-category separability, and the dandelion embedding alignment mechanism further aligns both dandelions in a finer manner. To promote intra-category compactness, the discriminating sampled dandelion mechanism is used. Assisted by the intrusion classifier trained using both known and generated unknown intrusion knowledge, a semantic dandelion correction mechanism emphasises easily-confused categories and guides better inter-category separability. Holistically, these mechanisms form the OSDN model that effectively performs intrusion knowledge transfer to benefit IoT intrusion detection. Comprehensive experiments on several intrusion datasets verify the effectiveness of the OSDN model, outperforming three state-of-the-art baseline methods by 16.9%. The contribution of each OSDN constituting component, the stability and the efficiency of the OSDN model are also verified.

The fifth version of the Reduced Instruction Set Computer (RISC-V) is a popular instruction set architecture (ISA) featured for low energy consumption. Currently, a growing number of industrial applications are based on RISC-V platforms, especially Internet of Things (IoT) devices. Those applications pursue low power and just sufficient computing capacity. However, low power consumption shall not be directly regarded as weak in computation. Recent advancement in RISC- V shows the potential for building a computing platform capable of handling tasks requiring considerable computing power. Traditional financial computing platforms are generally based on Complex Instruction Set Computers (CISC), like x86 platforms. As green computing is sweeping, it is meaningful to handle financial computing tasks with less energy consumption. To explore the potential of RISC- V in financial computing, we set up a typical financial computing task - American option implied volatility calculation, and examine the performance and power consumption of x86 and RISC-V platforms. The result shows that the RISC-V CPU is sufficient for some financial computing scenarios considering actual requirements. A heterogeneous computing system composed of x86 and RISC-V platforms could significantly improve energy efficiency.

With the fast growth of mobile edge computing (MEC), the deep neural network (DNN) has gained more opportunities in application to various mobile services. Given the tremendous number of learning parameters and large model size, the DNN model is often trained in cloud center and then dispatched to end devices for inference via edge network. Therefore, maximizing the cost-efficiency of learned model dispatch in the edge network would be a critical problem for the model serving in various application contexts. To reach this goal, in this article we focus mainly on reducing the total model dispatch cost in the edge network while maintaining the efficiency of the model inference. We first study this problem in its off-line form as a baseline where a sequence of$n$requests can be pre-defined in advance and exploit dynamic programming techniques to obtain a fast optimal algorithm in time complexity of$O(m^{2}n)$under a semi-homogeneous cost model in a$m$-sized network. Then, we design and implement a 2.5-competitive algorithm for its online case with a provable lower bound of 2 for any deterministic online algorithm. We verify our results through careful algorithmic analysis and validate their actual performance via a trace-based study based on a public open international mobile network dataset.

Nowadays, people are showing growing attention to the market movements. With more demand for market sentiment analysis and risk management, advanced investment tools are needed to assist the high frequency trading activities. Machine learning as a fast-growing tool provides people a new perspective to handle complex problems. Although financial data contains various information and is usually regarded as hard to concentrate into one unified dimension, our research aims to fuse the image processing method with the high frequency implied-volatility-based market sentiment analysis. In this way, our research implemented the real-time processing of the market data and proposes an innovative idea, applying the machine learning method to regress the market price using the two-dimensional discrete financial data, which is traditionally viewed as images. The proposed method shows satisfying performance in testing with tick-level S&P500 option dataset containing around 1.5 million trading record. To go further with the improvement of the economic image classification and represent the momentum factors of the implied volatility surface images, we also introduce the speed and acceleration of sequence images. Overall, we have reached 61.23% accuracy for implied volatility image classification, and 63.22% & 65.52% accuracy for financial image considering velocity and acceleration.

In financial field, predicting the future price of an asset has always been a hot topic. There are mainly two existing methods: One is to model the trend of asset prices in price prediction. Therefore, this method inevitably has a lag at the inflection point of the asset sequence. The other is to mine market opinion information from the internet to predict the future direction of prices. The challenge with this approach is that unstructured data processing and analysis is difficult. Therefore, we propose a method for asset movement prediction based on SABR [3] model. On the one hand, the market’s prediction of asset trends implied in options can be used to solve the hysteresis problem. On the other hand, options data is easy to process and analyze. In this article, we try to use a neural network model to capture the market’s view of the future trend of assets hidden in the stochastic volatility surface generated by the stochastic volatility model and establish a mapping relationship with asset prices. The results show that our methods can effectively eliminate the lag of price prediction and improve the accuracy of the prediction.

Digital libraries store materials in electronic format. Research and development in digital libraries includes content creation, conversion, indexing, organization, and dissemination. The key technological issues are how to search and display desired selections from and across large collections effectively [Schatz & Chen, 1996]. Digital library research projects (DLI-1) sponsored by NSF/DARPA/NASA have a common theme of bringing search to the net, which is the flagship research effort for the National Information Infrastructure (NII) in the United States. A repository is an indexed collection of objects. Indexing is an important task for searching. The better the indexing, the better the searching result. Developing a universal digital library has been the dream of many researchers, however, there are still many problems to be solved before such a vision is fulfilled. The most critical is to support a cross-lingual retrieval or multilingual digital library. Much work has been done on English information retrieval, however, there is relatively less work on Chinese information retrieval. In this article, we focus on Chinese indexing, which is the foundation of Chinese and cross-lingual information retrieval. The smallest indexing units in Chinese digital libraries are words, while the smallest units in a Chinese sentence are characters. However, Chinese text has no delimiter to mark word boundaries as it is in English text. In English or other languages using Roman or Greek-based orthographies, often, spacing reliably indicates word boundaries. In Chinese, a number of characters are placed together without any delimiters indicating the boundaries between consecutive characters. In this article, we investigate the combination and boundary detection approaches based on mutual information for segmentation. The combination approach combines n-grams to form words with more number of characters. In the combination approach Algorithm 1 does not allow overlapping of n-grams while Algorithm 2 does. The boundary detection approach detects the segmentation points on a sentence based on the values and the change of values of the mutual information. Experiments are conducted to evaluate their performances. An interface of the system is also presented to show how a Chinese web page is downloaded, the text in the page filtered, and segmented into words. The segmented words can be submitted for indexing or new unknown words can be identified and submitted to a dictionary.