Search Results (212)

Private information retrieval (PIR) is a typical application scenario of encrypted computing, which allows users to retrieve data from a database by providing only an encrypted index. In an academic research scenario, multiple parties may entrust their data to a third party and require collaborative retrieval. However, due to competitive relationships and mutual distrust between these parties, they do not share public–private keys, making single-key mechanisms inadequate for meeting actual privacy requirements. In this case, based on the multi-key fully homomorphic encryption (MKFHE) algorithm, we construct an efficient PIR scheme with an access permission verification mechanism and dynamic database. Specifically, we design an MKFHE algorithm to protect multi-user privacy information. The vector–matrix multiplication optimization algorithm is adopted to improve computational efficiency, the expand algorithm is used to reduce user communication traffic, and homomorphic multiplication with ciphertext chunking is used to avoid excessive noise caused by direct ciphertext multiplication. Experiments based on the SEAL library show that by transferring part of the computational pressure to the offline stage, the online query response efficiency of our scheme is improved by about 7.69%, and the online computational efficiency of vector–matrix multiplication is improved by about 19.7%. Full article

Background/Objectives: Research raising concerns about the occupational health of signed language interpreters has proliferated in the past two decades. Recent studies examining interpreters’ various work settings find that Video Relay Service (VRS) work is linked to greater health risks than other interpreting settings. This study aimed to shed light on why VRS work appears to be particularly hazardous. Methods: This mixed-methods study reports data from an online survey of 345 American VRS interpreters. Participants were queried about a range of potentially stressful experiences with callers. Quantitative data regarding the types, frequency, patterns, and consequences of stressful calls were further informed by qualitative data reported by participants in free-response survey fields. Results: Incidents of VRS interpreters mediating calls regarding sexual activity, drug deals, and prostitution were reported with notable frequency, as was interpreters’ witnessing abuse of vulnerable individuals. Interpreters also were often the object of callers’ derisive sexual, physical, and racial comments. Yet the incidence of participants reporting these experiences to management or outside authorities was quite limited despite the potential legal jeopardy involved. When reports were made, most participants stated their companies took little or no action. We also examined how factors such as the tenure of VRS, hours worked per week, and work shift times were associated with such caller experiences. Conclusions: This study builds upon prior VRS health risk research by examining external factors, including caller behavior and employer policies, that may contribute to interpreter stress and burnout. Suggestions for remediation and workforce development, involving VRS companies, the Federal Communications Commission (FCC) and state legislation are offered. Full article

Efficient emergency load transfer is crucial for ensuring the power system’s safe operation and reliable power supply. However, traditional load transfer methods that rely on human experience have limitations, such as slow response times and low efficiency, which make it difficult to address complex and diverse fault scenarios effectively. Therefore, this paper proposes an emergency load transfer method based on knowledge graphs to achieve intelligent management and efficient retrieval of emergency knowledge. Firstly, a named entity recognition model based on ERNIE-BiGRU-CRF is constructed to automatically extract key entities and relationships from the load transfer plan texts, obtaining information such as fault names, fault causes, and operation steps. Secondly, a power system emergency load transfer knowledge graph is constructed based on the extracted structured knowledge, which is efficiently stored using a graph database and enables the visualization and interactive query of knowledge. Finally, real power system fault cases prove that the proposed method can effectively improve the retrieval efficiency of fault knowledge and provide intelligent support for online emergency load transfer decisions. Full article

Large language models (LLMs) have become extensively used among users across diverse settings. Yet, with the complex nature of these large-scale artificial intelligence (AI) systems, leveraging their capabilities effectively is yet to be explored. In this study, we looked at the types of communication errors that occur in interactions between humans and ChatGPT-3.5 in Arabic. A corpus of six Arabic-language consultations was collected from an online mental health support forum. For each consultation, the researchers provided the user’s Arabic queries to ChatGPT-3.5 and analyzed the system’s responses. The study identified 102 communication errors, mostly grammatical and repetitions. Other errors involved contradictions, ambiguous language, ignoring questions, and lacking sociality. By examining the patterns and types of communication errors observed in ChatGPT’s responses, the study is expected to provide insights into the challenges and limitations of current conversational AI systems, particularly in the context of sensitive domains like mental health support. Full article

Emerging digital technologies are transforming how consumers participate in financial markets, yet their benefits depend critically on the speed, reliability, and transparency of the underlying platforms. Online stock trading platforms must maintain high efficiency underload to ensure a good user experience. This paper presents performance analysis under various load conditions based on the containerized stock exchange system. A comprehensive data logging pipeline was implemented, capturing metrics such as API response times, database query times, and resource utilization. We analyze the collected data to identify performance patterns, using both statistical analysis and machine learning techniques. Preliminary analysis reveals correlations between application processing time and database load, as well as the impact of user behavior on system performance. Association rule mining is applied to uncover relationships among performance metrics, and multiple classification algorithms are evaluated for their ability to predict user activity class patterns from system metrics. The insights from this work can guide optimizations in similar distributed web applications to improve scalability and reliability under a heavy load. By framing performance not merely as a technical property but as a determinant of financial decision-making and well-being, the study contributes actionable insights for designers of consumer-facing fintech services seeking to meet sustainable development goals through trustworthy, resilient digital infrastructure. Full article

Differential privacy is a robust framework for private data analysis and query processing, which achieves privacy preservation by introducing controlled noise to query results in a centralized setting. The sensitivity of a query, defined as the maximum change in query output resulting from the addition or removal of a single data record, directly influences the magnitude of noise to be introduced. Computing sensitivity for simple queries, such as count queries, is straightforward, but it becomes significantly more challenging for complex queries involving join operations. In such cases, the global sensitivity can be unbounded, which substantially impacts the accuracy of query results. While existing measures like elastic sensitivity and residual sensitivity provide upper bounds on local sensitivity to reduce noise, they often struggle with either low utility or high computational overhead when applied to complex join queries. In this paper, we propose two novel sensitivity estimation methods based on sampling and sketching techniques, which provide competitive utility while achieving higher efficiency compared to existing state-of-the-art approaches. Experiments on real-world and benchmark datasets confirm that both methods enable efficient differentially private joins, significantly enhancing the usability of online interactive query systems. Full article

The variance function (VF) is central to natural exponential family (NEF) theory. Prompted by an online query about whether, beyond the classical normal NEF, other real-line NEFs with bounded VFs exist, we establish three complementary sets of sufficient conditions that yield many such families. One set imposes a polynomial-growth bound on the NEF’s generating measure, ensuring rapid tail decay and a uniformly bounded VF. A second set relies on the Legendre duality, requiring a uniform positive lower bound on the second derivative of the generating function, which likewise ensures a bounded VF. The third set starts from the standard normal distribution and constructs an explicit sequence of NEFs whose Laplace transforms and VFs remain bounded. Collectively, these results reveal a remarkably broad class of NEFs whose Laplace transforms are not expressible in elementary form (apart from those stemming from the standard normal case), yet can be handled easily using modern symbolic and numerical software. Worked examples show that NEFs with bounded VFs are far more varied than previously recognized, offering practical alternatives to the normal and other classical models for real-data analysis across many fields. Full article

This paper presents Sillcom, a high-performance secure indoor localization scheme designed to minimize both communication and computational costs while preserving participants’ privacy. Unlike existing privacy-preserving indoor localization techniques, which suffer from high computational overhead and excessive communication, Sillcom integrates replicated secret sharing and function secret sharing in an outsourcing model to achieve significantly lower online communication overhead. A multi-branch tree structure and multi-thread parallelism further optimize both the offline and online phases. Experimental results demonstrate that Silcom outperforms the state-of-the-art online-efficient scheme FAPRIL, reducing online communication by a factor of 15 and end-to-end query time by 75%. Full article

Prolonged healing time of acute burn wounds is associated with increased pain, infection, risk of scarring, poorer mobility and higher financial and emotional burden. Electrical stimulation (ES) reduces healing time in chronic wounds; however, its reported use on acute burn wounds is limited. This systematic review (SR) aimed to evaluate the relative benefit of ES compared to routine wound care on the healing time of acute burn wounds in adults. The online databases queried included Cochrane Database of SR’s, MEDLINE, EMBASE, PUBMED and CINAHL. The search criteria included RCTs involving the application of ES of varying voltage, duration and modality in acute burn patients aged ≥18 years. The primary outcome investigated was days to burn wound closure, while the secondary outcomes included edema and infection. Four RCTs were discovered, involving a total of 143 participants with a mean age 35.5 years. Two RCTs demonstrated (a) 36% (2.6 days) reduction in time to wound closure with ES (p < 0.001); and (b) significant reduction in wound area with ES (11.2 ± 3.2 cm², p < 0.001) compared to controls at 21 days. Two RCTs found ES promoted better wound-healing environments, reducing edema, bacterial infection, and biofilm. This review highlighted low-risk wound-healing benefits with ES as a feasible adjunct to routine burn care. Full article

Associations between age-related diseases and behavioral alterations have been highlighted in previous studies. This study investigates the prevalence of diseases and behavioral changes in non-diseased and diseased senior cats before and after diagnosis, concentrating on four prevalent diseases: 1. osteoarthritis, 2. chronic kidney disease, 3. hyperthyroidism, and 4. cognitive dysfunction syndrome. An online survey was performed by 594 German cat-owners with a cat older than nine years; prevalent diseases, related medications, and scaled behavioral questions before and after diagnosis were queried. Chi-Quadrat-Test and Spearman’s rank correlation were used to detect correlations between behavioral changes and diseases. Multiple linear regression was used to determine dependencies between behavioral changes and each disease pre- and post-diagnosis. Half of the cats had at least one disease diagnosed (54.6%). The most prevalent diseases were osteoarthritis (18.9%), chronic kidney disease (12.3%), and hyperthyroidism (8.9%). Cognitive dysfunction syndrome was diagnosed in 2.9% of the cats. With increasing age, the likelihood of developing at least one disease rose (r_s = 0.204, p < 0.001). Disease-associated behavioral changes were found in the four mentioned diseases, with some behavioral changes occurring before diagnosis. These findings underscore the relevance of early detection of underlying diseases to decelerate ongoing behavioral changes in treatable diseases. Full article

The Global Quality Score (GQS) is one of the most frequently used tools to evaluate the content quality of online health information. To the author’s knowledge, it is frequently misnamed as the Global Quality Scale, and occasionally secondary sources are cited as the original source of the tool. This work aimed to reveal the current situation especially regarding the citations among published studies. Web of Science, Scopus, and PubMed were queried to identify papers that mentioned the use of the GQS. Among a total of 411 analyzed papers, 45.0% misnamed it as Global Quality Scale, and 46.5% did not cite the primary source published in 2007 to credit it as the original source. Another 80 references were also cited from time to time as the source of the GQS, led by a secondary source published in 2012. There was a decreasing trend in citing the primary source when using the GQS. Among the 12 papers that claimed that the GQS was validated, half of them cited the primary source to justify the claim, but in fact the original publication did not mention anything about its validation. To conclude, future studies should name and cite the GQS properly to minimize confusion. Full article

With the increasing popularity of online education platforms, the use frequency of students and teachers has gradually increased. A large volume of data is generated and analyzed daily on these platforms including course information and student learning status. However, traditional analysis methods often require substantial manpower and expertise. Large language models Chat GPT-4 offer a potential solution to this problem. This study aims to address this challenge by utilizing the large-scale language model framework LangChain and the database of the OpenEdu online education platform. We designed an interface capable of querying educational data in natural language. When a user queries in natural language, the large language model generates structured query language to query the database and converts the query back into natural language to respond to the user’s question. Two different query methods were developed based on LangChain components: the sequential query version and the Agent query version. Based on these methods, four different versions of prompt and model combinations were created. The accuracy in converting natural language to SQL was estimated, and error type analysis was conducted to enhance the system’s performance and accuracy. The execution accuracy reached up to 85.7%, with the primary error type in natural language-generated SQL being Schema Linking. By integrating large-scale language models into conversational query systems, a promising approach was developed for handling large-scale data queries on educational platforms. Full article

With the rapid growth of digital computing, communication, and storage devices applied in various real-world scenarios, more and more data have been collected and stored to drive the development of machine learning techniques. It is also noted that the data that emerge in real-world applications tend to become more complex. In this study, we regard a complex data type, i.e., multi-label data, acquired with a time constraint in a dynamic online scenario. Under such conditions, constructing a learning model has to face two challenges: it requires dynamically adapting the variances in label correlations and imbalanced data distributions and it requires more labeling consumptions. To solve these two issues, we propose a novel online multi-label active learning (OMAL) algorithm that considers simultaneously adopting uncertainty (using the average entropy of prediction probabilities) and diversity (using the average cosine distance between feature vectors) as an active query strategy. Specifically, to focus on label correlations, we use a classifier chain (CC) as the multi-label learning model and design a label co-occurrence ranking strategy to arrange label sequence in CC. To adapt the naturally imbalanced distribution of the multi-label data, we select weight extreme learning machine (WELM) as the basic binary-class classifier in CC. The experimental results on ten benchmark multi-label datasets that were transformed into streams show that our proposed method is superior to several popular static multi-label active learning algorithms in terms of both the Macro-F1 and Micro-F1 metrics, indicating its specifical adaptions in the dynamic data stream environment. Full article

Regulatory technologies (RegTechs) are a set of electronic and digital technologies applied to check compliance in industrial processes. Such applications also aim to simplify the process of data collection and exchange according to the expected format over the cloud or the internet. Industrial processes are required to meet basic regulatory requirements according to law and follow a set of industry practices. Industry practices must be compliant with the basic regulatory requirements. Such applications also need a high level of privacy to protect the individual participant’s data from competitors but are revealed to the relevant regulatory agencies. However, there cannot be a standard data procurement method, as the industrial processes are different for individual businesses and often involve various stages of data collection with different aims. Also, the regulatory requirements may be changed over time. These challenges can be addressed over an online system that uses large language models (LLM) to perform continuous compliance checks. With LLMs, RegTech can be easily scaled up to meet new requirements. It can also help with data analysis and reformatting for different stakeholders in RegTech, such as producers, supply chains, regulators, and financial institutions. It can check for acceptable values with regards to RegTech through either numeric comparisons or enumerations matching. In this paper, we propose a comprehensive RegTech framework backed by LLM and web services. We propose a method to measure the accuracy of LLM in returning appropriate responses for RegTech queries and herein analyze several LLMs to conclude that they are satisfactory for basic tasks, but a dedicated LLM is needed for RegTech. Furthermore, we test the LLM’s tool-calling capabilities to identify and use dedicated functions in the form of web services to enhance the analytical accuracy and consistency of RegTech-related prompts. Full article

To achieve carbon neutrality, thermal power plants must undertake heavier peak shaving tasks; consequently, utility boilers will be required to operate under frequently changing operating conditions. In light of this new circumstance, a combustion optimization decision that is executed more rapidly is necessary. A novel online combustion optimization framework is proposed for the combustion system of utility boilers. First, a robust filter for extracting high-quality steady-state data samples is designed and executed. Then, the K-means algorithm is used to divide the cleaned sample space and construct the working condition case library. Based on the constructed library, the boiler combustion model is constructed using the XGBoost algorithm. Therefore, the corresponding optimization case library can be established using the multiobjective optimization algorithm and working condition case library. To further capture the phenomenon of data distribution migrating as the operating conditions change, an adaptive update strategy for the combustion system is proposed, including online querying and data and model updates. The findings of this study conducted on a 660 MW utility boiler show that the proposed online optimization method can effectively decrease NO_x emissions and improve combustion efficiency in approximately 2 milliseconds. Full article