Processing Tasks Research Articles

Measuring Epistemic Trust: Towards a New Lens for Democratic Legitimacy, Misinformation, and Echo Chambers

Trust is crucial for the functioning of complex societies, and an important concern for CSCW. Our purpose is to use research from philosophy, social science, and CSCW to provide a novel account of trust in the 'post-truth' era. Testimony, from one speaker to another, underlies many social systems. Epistemic trust, or testimonial credibility, is the likelihood to accept a speaker's claim due to beliefs about their competence or sincerity. Epistemic trust is closely related to several 'pathological epistemic phenomena': democratic (il)legitimacy, the spread of misinformation, and echo chambers. To the best of our knowledge, this theoretical contribution is novel in the field of social computing. We further argue that epistemic trust is no philosophical novelty: it is measurable. Weakly supervised text classification approaches achieve F_1 scores of around 80 to 85 per cent on detecting epistemic distrust. This is also, to the best of our knowledge, a novel task in natural language processing. We measure expressions of epistemic distrust across 954 political communities on Reddit. We find that expressions of epistemic distrust are relatively rare, although there are substantial differences between communities. Conspiratorial communities and those focused on controversial political topics tend to express more distrust. Communities with strong epistemic norms enforced by moderation are likely to express low levels. While we find users to be an important potential source of contagion of epistemic distrust, community norms appear to dominate. It is likely that epistemic trust is more useful as an aggregated risk factor. Finally, we argue that policymakers should be aware of epistemic trust considering their reliance on legitimacy underwritten by testimony.

Generative named entity recognition framework for Chinese legal domain

Named entity recognition (NER) is a crucial task in natural language processing, particularly challenging in the legal domain due to the intricate and lengthy nature of legal entities. Existing methods often struggle with accurately identifying entity boundaries and types in legal texts. To address these challenges, we propose a novel sequence-to-sequence framework designed specifically for the legal domain. This framework features an entity-type-aware module that leverages contrastive learning to enhance the prediction of entity types. Additionally, we incorporate a decoder with a copy mechanism that accurately identifies complex legal entities without the need for explicit tagging schemas. Our extensive experiments on two legal datasets show that our framework significantly outperforms state-of-the-art methods, achieving notable improvements in precision, recall, and F1 score. This demonstrates the effectiveness of our approach in improving entity recognition in legal texts, offering a promising direction for future research in legal NER.

EDLIoT: A method for decreasing energy consumption and latency using scheduling algorithm in Internet of Things

Decreasing energy consumption in networks with limited resources, such as the Internet of Things, has always been one of the main challenges in guaranteeing network performance. In this article, cooperative game theory is employed to improve the cooperation patterns of fog computing resources. The EDLIoT method consists of two main steps: “Topology Construction” and “Determining Optimal Fog Computing Resources to Process IoT Object Tasks”. In the first step of the proposed method, the set of reliable communications in the network is identified to establish connections between IoT objects and fog computing resources in the form of a tree structure. Then, in the second step, a model based on cooperative game theory and the cost function is used to determine the optimal computing resources in the fog layer for outsourcing the processing tasks of IoT objects. In EDLIoT, active IoT objects perform computation in the fog layer instead of locally, to conserve energy. This is done so that IoT objects, if possible, discover the most suitable processing resources in the fog based on characteristics such as energy consumption, delay, and processing power of the computing resource. The efficiency of the proposed method has been evaluated in a simulated environment, and the results have been compared with those of previous algorithms. The results demonstrate that using the EDLIoT method, in addition to decreasing energy consumption and delay, more computing tasks can be processed through fog resources, thereby increasing the quality of service for IoT users.

A bi-consolidating model for joint relational triple extraction

Current methods to extract relational triples directly make a prediction based on a possible entity pair in a raw sentence without depending on entity recognition. The task suffers from a serious semantic overlapping problem, in which several relation triples may share one or two entities in a sentence. In this paper, based on a two-dimensional sentence representation, a bi-consolidating model is proposed to address this problem by simultaneously reinforcing the local and global semantic features relevant to a relation triple. This model consists of a local consolidation component and a global consolidation component. The first component uses a pixel difference convolution to enhance semantic information of a possible triple representation from adjacent regions and mitigate noise in neighbouring neighbours. The second component strengthens the triple representation based a channel attention and a spatial attention, which has the advantage to learn remote semantic dependencies in a sentence. They are helpful to improve the performance of both entity identification and relation type classification in relation triple extraction. After evaluated on several publish datasets, the bi-consolidating model achieves competitive performance. Analytical experiments demonstrate the effectiveness of our model for relational triple extraction and give motivation for other natural language processing tasks.

Developing healthcare language model embedding spaces

Pre-trained Large Language Models (LLMs) have revolutionised Natural Language Processing (NLP) tasks, but often struggle when applied to specialised domains such as healthcare. The traditional approach of pre-training on large datasets followed by task-specific fine-tuning is resource-intensive and poorly aligned with the constraints of many healthcare settings. This presents a significant challenge for deploying LLM-based NLP solutions in medical contexts, where data privacy, computational resources, and domain-specific language pose unique obstacles.This study aims to develop and evaluate efficient methods for adapting smaller LLMs to healthcare-specific datasets and tasks. We seek to identify pre-training approaches that can effectively instil healthcare competency in compact LLMs under tight computational budgets, a crucial capability for responsible and sustainable deployment in local healthcare settings.We explore three specialised pre-training methods to adapt smaller LLMs to different healthcare datasets: traditional Masked Language modelling (MLM), Deep Contrastive Learning for Unsupervised Textual Representations (DeCLUTR), and a novel approach utilising metadata categories from healthcare settings. These methods are assessed across multiple healthcare datasets, with a focus on downstream document classification tasks. We evaluate the performance of the resulting LLMs through classification accuracy and analysis of the derived embedding spaces.Contrastively trained models consistently outperform other approaches on classification tasks, delivering strong performance with limited labelled data and fewer model parameter updates. While our novel metadata-based pre-training does not further improve classifications across datasets, it yields interesting embedding cluster separability. Importantly, all domain-adapted LLMs outperform their publicly available, general-purpose base models, validating the importance of domain specialisation.This research demonstrates the efficacy of specialised pre-training methods in adapting compact LLMs to healthcare tasks, even under resource constraints. We provide guidelines for pre-training specialised healthcare LLMs and motivate continued inquiry into contrastive objectives. Our findings underscore the potential of these approaches for aligning small LLMs with privacy-sensitive medical tasks, offering a path toward more efficient and responsible NLP deployment in healthcare settings. This work contributes to the broader goal of making advanced NLP techniques accessible and effective in specialised domains, particularly where resource limitations and data sensitivity are significant concerns.

Fault-Tolerant Scheduling Mechanism for Dynamic Edge Computing Scenarios Based on Graph Reinforcement Learning

With the proliferation of Internet of Things (IoT) devices and edge nodes, edge computing has taken on much of the real-time data processing and low-latency response tasks which were previously managed by cloud computing. However, edge computing often encounters challenges such as network instability and dynamic resource variations, which can lead to task interruptions or failures. To address these issues, developing a fault-tolerant scheduling mechanism is crucial to ensure that a system continues to operate efficiently even when some nodes experience failures. In this paper, we propose an innovative fault-tolerant scheduling model based on asynchronous graph reinforcement learning. This model incorporates a deep reinforcement learning framework built upon a graph neural network, allowing it to accurately capture the complex communication relationships between computing nodes. The model generates fault-tolerant scheduling actions as output, ensuring robust performance in dynamic environments. Additionally, we introduce an asynchronous model update strategy, which enhances the model’s capability of real-time dynamic scheduling through multi-threaded parallel interactions with the environment and frequent model updates via running threads. The experimental results demonstrate that the proposed method outperformed the baseline algorithms in terms of quality of service (QoS) assurance and fault-tolerant scheduling capabilities.

Classification of breast cancer histopathology images using a modified supervised contrastive learning method.

Deep neural networks have reached remarkable achievements in medical image processing tasks, specifically in classifying and detecting various diseases. However, when confronted with limited data, these networks face a critical vulnerability, often succumbing to overfitting by excessively memorizing the limited information available. This work addresses the challenge mentioned above by improving the supervised contrastive learning method leveraging both image-level labels and domain-specific augmentations to enhance model robustness. This approach integrates self-supervised pre-training with a two-stage supervised contrastive learning strategy. In the first stage, we employ a modified supervised contrastive loss that not only focuses on reducing false negatives but also introduces an elimination effect to address false positives. In the second stage, a relaxing mechanism is introduced that refines positive and negative pairs based on similarity, ensuring that only relevant image representations are aligned. We evaluate our method on the BreakHis dataset, which consists of breast cancer histopathology images, and demonstrate an increase in classification accuracy by 1.45% in the image level, compared to the state-of-the-art method. This improvement corresponds to 93.63% absolute accuracy, highlighting the effectiveness of our approach in leveraging properties of data to learn more appropriate representation space. The code implementation of this study is accessible on GitHub https://github.com/matinamehdizadeh/Breast-Cancer-Detection .

Integrated convolutional kernel based on two-dimensional photonic crystals.

Optical neural networks (ONNs) exhibit significant potential for accelerating artificial intelligence task processing due to their low latency, high bandwidth, and parallel processing capabilities. Photonic crystals (PhCs) are extensively utilized in integrated optoelectronics because of their unique photonic bandgap properties and precise control of light waves. In this study, we propose an optical reconfigurable convolutional kernel based on PhCs. This kernel can perform convolutional operations on weights by constructing a PhC weight bank. The convolutional kernel demonstrates exceptional performance within the developed optical convolutional neural network framework, successfully realizing various image edge processing tasks. It achieves blind recognition accuracies of 97.81% for the MNIST dataset and 80.31% for the Fashion-MNIST dataset. This study not only demonstrates the feasibility of constructing optical neural networks based on PhCs but to our knowledge, also offers new avenues for the future development of optical computing.

Arabic word tokenization system using the maximum matching model

Word tokenization is the first stage for higher-order Natural Language Processing (NLP) tasks like Part-of-Speech (PoS) tagging, parsing, and named entity recognition. The amount of text on the World Wide Web is growing daily in the present era of technology, necessitating the use of advanced instruments. Since more and more people speak Arabic around the world, Arabic language processing systems must be improved. Due to the writing style of Arabic with a lack of support for capitalization features and the use of compound words, it is difficult to perform word tokenization. This research paper proposes a novel Arabic word tokenization system based on the knowledge. To develop this system, a maximum matching model with its two variations, namely forward and reverse maximum matching is used. The proposed system is implemented in Python. The results produced during system evaluation report high performance.

Postselection Technique for Optical Quantum Key Distribution with Improved de Finetti Reductions

The postselection technique is an important proof technique for proving the security of quantum key distribution protocols against coherent attacks via the uplift of any security proof against independent identically distributed collective attacks. In this work, we go through multiple steps to rigorously apply the postselection technique to optical quantum key distribution protocols. First, we place the postselection technique on a rigorous mathematical foundation by fixing a technical flaw in the original postselection paper. Second, we extend the applicability of the postselection technique to prepare-and-measure protocols by using a de Finetti reduction with a fixed marginal. Third, we show how the postselection technique can be used for decoy-state protocols by tagging the source. Finally, we extend the applicability of the postselection technique to realistic optical setups by developing a new variant of the flag-state squasher. We also improve existing de Finetti reductions, which reduce the effect of using the postselection technique on the key rate. These improvements can be more generally applied to other quantum information processing tasks. As an example to demonstrate the applicability of our work, we apply our results to the time-bin-encoded three-state protocol. We observe that the postselection technique performs better than all other known proof techniques against coherent attacks. Published by the American Physical Society 2024

DuINet: A dual-branch network with information exchange and perceptual loss for enhanced image denoising

Image denoising is a fundamental task in image processing and low-level computer vision, often necessitating a delicate balance between noise removal and the preservation of fine details. In recent years, deep learning approaches, particularly those utilizing various neural network architectures, have shown significant promise in addressing this challenge. In this study, we propose DuINet, a novel dual-branch network specifically designed to capture complementary aspects of image information. DuINet integrates an information exchange module that facilitates effective feature sharing between the branches, and it incorporates a perceptual loss function aimed at enhancing the visual quality of the denoised images. Extensive experimental results demonstrate that DuINet surpasses existing dual-branch models and several state-of-the-art convolutional neural network (CNN)-based methods, particularly under conditions of severe noise where preserving fine details and textures is critical. Moreover, DuINet maintains competitive performance in terms of the LPIPS index when compared to deeper or larger networks such as Restormer and MIRNet, underscoring its ability to deliver high visual quality in denoised images.

Leveraging ChatGPT for systematic reviews: how accurate is it for screening by title and abstract?

Abstract Background Systematic reviews are at the top of the evidence pyramid, but the whole process can be labor-intensive, especially in the screening by title and abstract of retrieved records. Large language models (LLMs), such as ChatGPT, are proficient in natural language processing tasks and offer potential for accelerating this phase. The aim of this study is to assess ChatGPT’s performance in screening records for an already published systematic review (doi: 10.1080/21645515.2023.2300848). Methods 1601 records with title and abstract were evaluated with ChatGPT 4, using two different prompts to instruct the model: one asking the model to classify each record as included or excluded (Prompt A), and one asking the model to rate each record from 1 to 5 based on inclusion confidence (Prompt B), to compute the model’s performance metrics. Results The review included 64 records after title and abstract screening, of which 18 after full text screening. Using records included by title and abstract as a reference, prompt B with a rating cut-off of 3 provided 82% sensitivity, 88% specificity and 99% negative predictive value (NPV). With articles included by full text as a reference, 100% sensitivity, 86% specificity, and 100% NPV. An 85% workload saving was reached. Prompts A showed higher workload savings (∼93%), high NPVs (∼100%) and specificity (∼96%), but lower sensitivity (62-72%). Conclusions Prompts with a rating cut-off of 3 achieved a better performance, and still relevant workload savings compared to binary classification prompts. These findings can inform prompt-engineering strategies to instruct LLMs and reach both high sensitivity and workload savings. Key messages • Systematic reviews provide evidence synthesis to guide policy and decision-making in healthcare and public health. Such process can be time-consuming, and need for timely evidence is often stringent. • LLMs can help reduce workload and speed up the title and abstract screening phase of a systematic review, which is considered a major bottleneck in the review process.

Self-Powered Dye-Sensitized Solar-Cell-Based Synaptic Devices for Multi-Scale Time-Series Data Processing in Physical Reservoir Computing.

Physical reservoir computing (PRC) using synaptic devices has attracted attention as a promising edge artificial intelligence device. To handle time-series data on various time scales, it is necessary to fabricate devices with the desired time scale. In this study, we fabricated a dye-sensitized solar-cell-based synaptic device with controllable time constants by changing the light intensity. This device showed synaptic features, such as paired-pulse facilitation and paired-pulse depression, in response to light intensity. Moreover, we found that the high computational performance of the time-series data processing task was achieved by changing the light intensity, even when the input pulse width was varied. In addition, the fabricated device can be used for motion recognition tasks. This study paves the way for realizing multiple time-scale PRC.

Analyzing Semantic Properties of Word Embeddings Using Eigenvectors

Dense word vectors have demonstrated their efficacy in several downstream natural language processing (NLP) tasks in recent years. Nevertheless, the interpretability of these embeddings' dimensions remains challenging. In this paper, we investigate how eigenvectors can reveal different semantic properties captured by word embedding models. Hence, we train word embeddings (e.g., Word2Vec) on English Wikipedia corpus to analyze the top eigenvectors, identify specific semantic properties (e.g., sentiment, formality) associated with each, and explore how these properties are encoded in the embedding space. This paper also discussed the limitations and potential benefits of this approach compared to other methods for analyzing word embeddings.

Robust projective measurements through measuring code-inspired observables

Quantum measurements are ubiquitous in quantum information processing tasks, but errors can render their outputs unreliable. Here, we present a scheme that implements a robust projective measurement through measuring code-inspired observables. Namely, given a projective POVM, a classical code, and a constraint on the number of measurement outcomes each observable can have, we construct commuting observables whose measurement is equivalent to the projective measurement in the noiseless setting. Moreover, we can correct t errors on the classical outcomes of the observables’ measurement if the classical code corrects t errors. Since our scheme does not require the encoding of quantum data onto a quantum error correction code, it can help construct robust measurements for near-term quantum algorithms that do not use quantum error correction. Moreover, our scheme works for any projective POVM, and hence can allow robust syndrome extraction procedures in non-stabilizer quantum error correction codes.

Аналіз застосування технологій ПЛІС в складі IoT

The subject of study in this article and work is the modern technologies of programmable logic devices (PLD) classified as FPGA, and the peculiarities of its application in Internet-of-Things domain at different architectural layers of the implementation, as well as the elements of decision making during choosing of appropriate solutions based on FPGA in the context of implementation of IoT tasks with the requirements of intensive computations. The goal is to analyze the possibilities and peculiarities of the application of technology of programmable logic devices as part of the modern architecture of the Internet of Things with the improvement of decision making process during the choosing of appropriate technical solutions for the use of FPGA with taking into account the types of tasks. Tasks: to analyze the current state of demands regarding the use of FPGA technology in IoT projects; to analyze the challenges and limitations for the application of FPGA technology at different layers during the prototyping of IoT infrastructure; to propose ways of the use of FPGA technology in the IoT infrastructure with the use of high-speed computations; to analyze the advantages and disadvantages of the proposed approaches of integration of FPGA technology; to provide recommendations and determine the further direction of the research of the use of FPGA technology as a part of the IoT infrastructure considering of AI/ML tasks; to provide a practical example of the use of FPGA technology during the creation of a high-performance IoT system. According to the tasks, the following results were obtained. The analysis of the current state of the use of FPGA technology in IoT projects is performed, that showed the significant potential of these technologies for the implementation of high-speed computations in Internet of Things systems. A classification of the ways of applying FPGA in the tasks of real-time data processing and implementation of neural networks is proposed. The set of limitations of the possibility of FPGA technology applying at different layers of IoT infrastructure is defined. It includes high equipment costs, the need for specific design skills, limitations in energy consumption, and the complexity of the integration with existing systems. The practical example of the use of the IoT system with the implementation of high-intensive computations is provided. Conclusions. The main contribution and scientific novelty of the obtained results is that the conducted analysis allows to make a decision about the possibility of applying programmable logic in the construction of IoT projects and home automation systems. Based on the proposed classification of types of IoT tasks, there is a possibility of making a decision on the application of FPGA technology as a separate integrated circuit in the system or using a complete instance of FPGA as a Service in the cloud environment.

CPEQA: A Large Language Model Based Knowledge Base Retrieval System for Chinese Confidentiality Knowledge Question Answering

Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, particularly in the construction of the intelligent question-answering system. These systems, especially in specialized fields, usually rely on NLP through the retrieval of corpus and answering databases to efficiently provide accurate and concise answers. This paper focuses on the national confidentiality publicity and education field, aiming to address the dilemma of inaccurate knowledge retrieval in this field. Therefore, we design an intelligent confidentiality question-answering system CPEQA by comprehensively utilizing the LLMs platform and information retrieval technique. CPEQA is capable of providing professional answers to questions about Chinese confidentiality publicity and education raised by users. Additionally, we also integrate the conventional database retrieval technique and LLMs into the database query construction, enabling CPEQA to perform real-time queries and data analysis for both single-table and multi-table querying tasks. Through extensive experiments with generated query sentences, we show both methodological comparisons and empirical evaluations of CPEQA’s performance. Experimental results indicate that CPEQA has achieved competitive results on answering precision, recall rate and other metrics. Finally, we explore the challenges of the CPEQA system associated with these techniques and outline potential avenues for future research in this emerging field.

Topic aware probing: From sentence length prediction to idiom identification how reliant are neural language models on topic?

Abstract Transformer-based neural language models achieve state-of-the-art performance on various natural language processing tasks. However, an open question is the extent to which these models rely on word-order/syntactic or word co-occurrence/topic-based information when processing natural language. This work contributes to this debate by addressing the question of whether these models primarily use topic as a signal, by exploring the relationship between Transformer-based models’ (BERT and RoBERTa’s) performance on a range of probing tasks in English, from simple lexical tasks such as sentence length prediction to complex semantic tasks such as idiom token identification, and the sensitivity of these tasks to the topic information. To this end, we propose a novel probing method which we call topic-aware probing. Our initial results indicate that Transformer-based models encode both topic and non-topic information in their intermediate layers, but also that the facility of these models to distinguish idiomatic usage is primarily based on their ability to identify and encode topic. Furthermore, our analysis of these models’ performance on other standard probing tasks suggests that tasks that are relatively insensitive to the topic information are also tasks that are relatively difficult for these models.

Optimization of artificial intelligence in localized big data real-time query processing task scheduling algorithm

Optimization of artificial intelligence in localized big data real-time query processing task scheduling algorithm

Mitigating Catastrophic Forgetting in Continual Learning for Natural Language Processing Tasks

Mitigating Catastrophic Forgetting in Continual Learning for Natural Language Processing Tasks