Intelligence Research Research Articles

Интеллектуальная обработка текстовой информации: обзор автоматизированных методов суммаризации

Interest in innovative technological strategies and modern digital tools has increased significantly due to the need to manage large amounts of unstructured data. This paper reviews current paradigms and services for automated summarization, developed based on interdisciplinary research in linguistics, computer technologies, and artificial intelligence. It focuses on syntactic and lexical techniques employed by neural network models for text compression. The paper presents performance examples of such AI-powered services as QuillBot, Summate.it, WordTune, SciSummary, Scholarcy, and OpenAI ChatGPT. The contemporary automated models proved effective in using extractive and abstractive methods to generate summaries of varying quality and length. The extractive approach relies on identifying the most significant sentences from the original text, while abstractive algorithms create new sentence structures that preserve the main idea of the original content. Automated summarizers effectively utilize text compression techniques that are inherent to human approach to text processing, e.g., they exclude redundant information, simplify complex structures, and generalize data. These technologies provide high accuracy and coherence in the generated summaries, though each summarization model has its limitations. Optimal results depend on the specifics of the task at hand: extractive models provide brevity and precision while abstractive ones allow for deeper semantic processing. Automated summarization is becoming an important tool in various fields that require effective analysis and processing of large text data.

Investigating Co‐Authorship Networks of Academic and Industry Researchers in Artificial Intelligence

ABSTRACTResearch teams from the industry, especially big technology companies, have been pushing impactful research work in the field of artificial intelligence (AI), changing the prospects of the field and the careers of many researchers. Research teams from big technology companies usually possess more data, bigger computing infrastructure, and research talent, granting them the advantages in advancing AI research. While most previous work focuses on investigating the advantages the industry has in the field of AI, and how their research publication is different from those published by academic teams, few research has been done to investigate whether working as an industry researcher is beneficial at the individual level. In this work, by analyzing co‐authorship networks of researchers published in AI conferences, we investigate whether working in the industry gives researchers advantages in “intangible” forms, such as social capital, represented by the collaborative relationships they gained or maintained. Our result shows that the many advantages industry researchers possess correlate with the social capital they have, measured by degree centrality, eigenvector centrality, betweenness centrality, and effective size.

Artificial Intelligence: A Twenty First Century International Regulatory Challenge

Artificial Intelligence (AI) is a twenty first century evolution. Certain aspects of AI have been integrated into daily living. AI applications have also been incorporated into the aviation, banking, cyber security, educational, employment, health, and military sectors respectively. However, the unpredictable nature of AI is a cause for concern because ‘In many instances, AI remains under the control of users and designers, but in increasing numbers of applications, the behaviour of a system cannot be predicted by those involved in design and application […]. Newly developed machines are able to teach themselves and even collect data’. Consequently, ‘The potential benefits and harms of AI have led to calls for governments to adapt quickly to the changes AI is already delivering and the potentially transformative changes to come. These include calls to pause AI development and for countries […] to deliver a step-change in regulation’. ‘In March 2023, more than 1,000 artificial intelligence experts, researchers and backers signed an open letter calling for an immediate pause on the creation of “giant” AIs for at least six months, so the capabilities and dangers of such systems can be properly studied and mitigated’. What are the benefits of AI? What are the risks of AI? Which crimes can be committed via AI? What are the regulatory challenges? What has been the international response? In this article, we will explore whether there is a justification for regulating AI from ethical, legal and law enforcement perspectives. Keywords: Artificial intelligence; Ethics; Regulation; Law enforcement

Learning Relation-Enhanced Hierarchical Solver for Math Word Problems.

Automatically solving math word problems (MWPs) is a challenging task for artificial intelligence (AI) and machine learning (ML) research, which aims to answer the problem with a mathematical expression. Many existing solutions simply model the MWP as a sequence of words, which is far from precise solving. To this end, we turn to how humans solve MWPs. Humans read the problem part-by-part and capture dependencies between words for a thorough understanding and infer the expression precisely in a goal-driven manner with knowledge. Moreover, humans can associate different MWPs to help solve the target with related experience. In this article, we present a focused study on an MWP solver by imitating such procedure. Specifically, we first propose a novel hierarchical math solver (HMS) to exploit semantics in one MWP. First, to imitate human reading habits, we propose a novel encoder to learn the semantics guided by dependencies between words following a hierarchical "word-clause-problem" paradigm. Next, we develop a goal-driven tree-based decoder with knowledge application to generate the expression. One step further, to imitate human associating different MWPs for related experience in problem-solving, we extend HMS to the Relation-enHanced Math Solver (RHMS) to utilize the relation between MWPs. First, to capture the structural similarity relation, we develop a meta-structure tool to measure the similarity based on the logical structure of MWPs and construct a graph to associate related MWPs. Then, based on the graph, we learn an improved solver to exploit related experience for higher accuracy and robustness. Finally, we conduct extensive experiments on two large datasets, which demonstrates the effectiveness of the two proposed methods and the superiority of RHMS.

Racial/ethnic disparities, artificial intelligence, and cutting-edge research: Proceedings from the 2023 Florida cardio-oncology symposium

Racial/ethnic disparities, artificial intelligence, and cutting-edge research: Proceedings from the 2023 Florida cardio-oncology symposium

Challenging large language models’ “intelligence” with human tools: A neuropsychological investigation in Italian language on prefrontal functioning

The Artificial Intelligence (AI) research community has used ad-hoc benchmarks to measure the “intelligence” level of Large Language Models (LLMs). In humans, intelligence is closely linked to the functional integrity of the prefrontal lobes, which are essential for higher-order cognitive processes. Previous research has found that LLMs struggle with cognitive tasks that rely on these prefrontal functions, highlighting a significant challenge in replicating human-like intelligence. In December 2022, OpenAI released ChatGPT, a new chatbot based on the GPT-3.5 model that quickly gained popularity for its impressive ability to understand and respond to human instructions, suggesting a significant step towards intelligent behaviour in AI. Therefore, to rigorously investigate LLMs' level of “intelligence,” we evaluated the GPT-3.5 and GPT-4 versions through a neuropsychological assessment using tests in the Italian language routinely employed to assess prefrontal functioning in humans. The same tests were also administered to Claude2 and Llama2 to verify whether similar language models perform similarly in prefrontal tests. When using human performance as a reference, GPT-3.5 showed inhomogeneous results on prefrontal tests, with some tests well above average, others in the lower range, and others frankly impaired. Specifically, we have identified poor planning abilities and difficulty in recognising semantic absurdities and understanding others' intentions and mental states. Claude2 exhibited a similar pattern to GPT-3.5, while Llama2 performed poorly in almost all tests. These inconsistent profiles highlight how LLMs' emergent abilities do not yet mimic human cognitive functioning. The sole exception was GPT-4, which performed within the normative range for all the tasks except planning. Furthermore, we showed how standardised neuropsychological batteries developed to assess human cognitive functions may be suitable for challenging LLMs’ performance.

Bibliometric Analysis of Studies on the Artificial Intelligence in Science Education with VOSviewer

Understanding the trends and developments in artificial intelligence research in science education, which has rapidly advanced in recent years, is crucial for technological innovations and applications in education. Therefore, this study examines research on artificial intelligence in science education conducted between 2019 and 2023 (the last five years) through bibliometric analysis, utilizing the Web of Science database. VOSviewer was used for the analysis. As a result of scanning the Web of Science database under these criteria, 867 studies were identified. Analyzing the distribution of these 867 publications by year reveals a concentration in 2023 and 2022. In terms of publication distribution by country, the leading contributors were the USA, China and Australia. The most prolific authors were identified as D. Gasevic, Z. Xiaoming and S. S. Oyelere. Regarding the number of documents and citations in journals, "Computers & Education" ranked first. The most cited used keywords were "Machine Learning" and "Artificial Intelligence", followed by "Learning Analytics", "Data Science" and "Higher Education". The findings illuminate recent research on artificial intelligence in science education. This study is expected to assist researchers in identifying trends within the field and to provide guidance for future studies.

A Motion Planner Based on Mask-D3QN of Quadruped Robot Motion for Steam Generator.

Crawling robots are the focus of intelligent inspection research, and the main feature of this type of robot is the flexibility of in-plane attitude adjustment. The crawling robot HIT_Spibot is a new type of steam generator heat transfer tube inspection robot with a unique mobility capability different from traditional quadrupedal robots. This paper introduces a hierarchical motion planning approach for HIT_Spibot, aiming to achieve efficient and agile maneuverability. The proposed method integrates three distinct planners to handle complex motion tasks: a nonlinear optimization-based base motion planner, a TOPSIS-based base orientation planner, and a Mask-D3QN (MD3QN) algorithm-based gait motion planner. Initially, the robot's base and foot workspace were delineated through envelope analysis, followed by trajectory computation using Larangian methods. Subsequently, the TOPSIS algorithm was employed to establish an evaluation framework conducive to foundational turning planning. Finally, the MD3QN algorithm trained foot-points to facilitate robot movement along predefined paths. Experimental results demonstrated the method's adaptability across diverse tube structures, showcasing robust performance even in environments with random obstacles. Compared to the D3QN algorithm, MD3QN achieved a 100% success rate, enhanced average overall scores by 6.27%, reduced average stride lengths by 39.04%, and attained a stability rate of 58.02%. These results not only validate the effectiveness and practicality of the method but also showcase the significant potential of HIT_Spibot in the field of industrial inspection.

A Systematic Investigation Based on BCI and EEG Implemented using Machine Learning Algorithms

BCI is a strong tool that improves human-system communication. It improves the brain's ability to interact with its surroundings. Recent decades have seen substantial advances in neuroscience and computer science. This has made BCI a leader in computational neuroscience and intelligence research. Recent technological advances including wearable sensing devices, real-time data streaming, machine learning, and deep learning have raised the need for electroencephalographic (EEG)-based brain-computer interface (BCI) in clinical and translational applications. EEG-based Brain-Computer Interfaces (BCIs) detect cognitive state variations throughout laborious tasks, making them advantageous for individuals. To fill in the gaps in the wide overview of the past five years (2019-2024), we surveyed the newest research on EEG signal detection and computational intelligence in brain-computer interfaces. To provide a more accurate account, we will begin by reviewing Brain-Computer Interface (BCI) technology and its main challenges. Modern signal detection and enhancement techniques for EEG signal collection and refinement follow. We also provide advanced computational intelligence methods for tracking, maintaining, and monitoring human cognitive and operational performance in everyday applications. Combinations, interpretable fuzzy models, transfer learning, and deep learning are used. We conclude with a sample of cutting-edge BCI-driven healthcare applications and explore future EEG-based BCI research.

Real-Time Identification of Strawberry Pests and Diseases Using an Improved YOLOv8 Algorithm

Strawberry crops are susceptible to a wide range of pests and diseases, some of which are insidious and diverse due to the shortness of strawberry plants, and they pose significant challenges to accurate detection. Although deep learning-based techniques to detect crop pests and diseases are effective in addressing these challenges, determining how to find the optimal balance between accuracy, speed, and computation remains a key issue for real-time detection. In this paper, we propose a series of improved algorithms based on the YOLOv8 model for strawberry disease detection. These include improvements to the Convolutional Block Attention Module (CBAM), Super-Lightweight Dynamic Upsampling Operator (DySample), and Omni-Dimensional Dynamic Convolution (ODConv). In experiments, the accuracy of these methods reached 97.519%, 98.028%, and 95.363%, respectively, and the F1 evaluation values reached 96.852%, 97.086%, and 95.181%, demonstrating significant improvement compared to the original YOLOv8 model. Among the three improvements, the improved model based on CBAM has the best performance in training stability and convergence, and the change in each index is relatively smooth. The model is accelerated by TensorRT, which achieves fast inference through highly optimized GPU computation, improving the real-time identification of strawberry diseases. The model has been deployed in the cloud, and the developed client can be accessed by calling the API. The feasibility and effectiveness of the system have been verified, providing an important reference for the intelligent research and application of strawberry disease identification.

Optimizing Convolutional Neural Network Architectures

Convolutional neural networks (CNNs) are commonly employed for demanding applications, such as speech recognition, natural language processing, and computer vision. As CNN architectures become more complex, their computational demands grow, leading to substantial energy consumption and complicating their use on devices with limited resources (e.g., edge devices). Furthermore, a new line of research seeking more sustainable approaches to Artificial Intelligence development and research is increasingly drawing attention: Green AI. Motivated by an interest in optimizing Machine Learning models, in this paper, we propose Optimizing Convolutional Neural Network Architectures (OCNNA). It is a novel CNN optimization and construction method based on pruning designed to establish the importance of convolutional layers. The proposal was evaluated through a thorough empirical study including the best known datasets (CIFAR-10, CIFAR-100, and Imagenet) and CNN architectures (VGG-16, ResNet-50, DenseNet-40, and MobileNet), setting accuracy drop and the remaining parameters ratio as objective metrics to compare the performance of OCNNA with the other state-of-the-art approaches. Our method was compared with more than 20 convolutional neural network simplification algorithms, obtaining outstanding results. As a result, OCNNA is a competitive CNN construction method which could ease the deployment of neural networks on the IoT or resource-limited devices.

Don't jump the gun quite yet: aiming for the true target in plant neurobiology research.

In their recent paper, Kingsland and Taiz argue that proponents of plant intelligence and plant neurobiology misuse historical sources to support their claims, suggesting a pattern of bias. They critique the reliance on subjective judgments and the systematic misuse of past literature by notable scientists. This response addresses their criticisms while adhering to Rapoport's rules to foster constructive academic dialogue. We emphasize the importance of evidence-based research and highlight areas of agreement, including the fallacy of appealing to authority and the necessity for more robust empirical evidence. However, we also challenge their selective citation practices and argue that their narrative itself is subject to the same criticisms they levy. By examining recent works and pointing out overlooked rebuttals, we aim to clarify misconceptions and advocate for a more nuanced understanding of plant intelligence research. This dialogue underscores the need for rigorous, respectful scientific discourse to advance the field.

The editorial for special issue “Russian Research in Artificial Intelligence for Cybersecurity” of Journal of Computer Virology and Hacking Techniques (JICV) highlights the studies of Russian researchers in the field of artificial intelligence to ensure cybersecurity

The editorial for special issue “Russian Research in Artificial Intelligence for Cybersecurity” of Journal of Computer Virology and Hacking Techniques (JICV) highlights the studies of Russian researchers in the field of artificial intelligence to ensure cybersecurity

Intelligent vehicle lateral control strategy research based on feedforward + predictive LQR algorithm with GA optimisation and PID compensation

Targeting the lateral motion control problem in the intelligent vehicle autopilot structural system, this paper proposes a feedforward + predictive LQR algorithm for lateral motion control based on Genetic Algorithm (GA) parameter optimisation and PID steering angle compensation. Firstly, based on the vehicle dynamics tracking error model, the intelligent vehicle LQR lateral motion controller as well as the feedforward controller are designed, and upon which the predictive controller is added to eliminate the system lag.Subsequently, exploiting the advantage that the PID algorithm is not model-based, a PID steering angle compensation controller that can directly control and correct the lateral error is designed. Second, a LQR controller based on path tracking deviation is designed by using the parameter rectification method of genetic algorithm (GA), which optimizes the control parameters of the lateral motion controller and improves the adaptivity of the control accuracy. Finally, Based on the Carsim-Simulink co-simulation platform, the simulation validation and analysis of double lane change (DLC) test and circular condition test (CCT) are carried out, and the results indicate that compared with the other two LQR controllers, the optimised controllers improved more than 50% in lateral error and heading error control, and the vehicle sideslip angle and vehicle yaw rate are in the range of −0.05° to 0.05° and − 0.15 rad/s to 0.10 rad/s, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.

A Review of Facial Micro Expression Recognition

With facial expression recognition gradually becoming a hot topic in the fields of image processing and artificial intelligence research, more and more scholars are paying attention to the fact that the micro expressions instantly revealed on the face can better reflect human inner emotions and thoughts. In this paper, firstly, the research status of micro expression and the commonly used micro expression datasets are described, and the advantages and disadvantages of each dataset are analyzed. Then the feature extraction of micro expression is analyzed from the two algorithms. Finally, the application fields of micro expression research and the challenges facing the future development are discussed.

Complexity and phase transitions in citation networks: insights from artificial intelligence research.

In this study, we analyze the changes over time in the complexity and structure of words used in article titles and the connections between articles in citation networks, focusing on the topic of artificial intelligence (AI) up to 2020. By measuring unpredictability in word usage and changes in the connections between articles, we gain insights into shifts in research focus and diversity of themes. Our investigation reveals correspondence between fluctuations in word complexity and changes in the structure of citation networks, highlighting links between thematic evolution and network dynamics. This approach not only enhances our understanding of scientific progress but also may help in anticipating emerging fields and fostering innovation, providing a quantitative lens for studying scientific domains beyond AI.

Application of Fuzzy Reasoning with Fusion Rules in Intelligent Information Processing

The core of artificial intelligence (AI) research is intelligent information processing, and fuzzy reasoning and fuzzy neural networks are significant research areas in models of intelligent information processing. The study examines the robustness of fuzzy reasoning by using the property characteristics of triangular modal operators and implication operators, which are used to analyse the Lipschitz property of fuzzy operators and its corresponding property characteristics of triangular modal operators and implication operators. Inference operators are used to investigate the robustness and error-control capabilities of fuzzy operators for fuzzy reasoning. The results showed that the robustness of quasi-copula fuzzy reasoning with fusion rules is better, and the corresponding maximum output perturbation is lowest at 0.36 when the fuzzy operator is T[Formula: see text]&R[Formula: see text] and the corresponding maximum output error is lowest at 0.41 when the fuzzy operators are T[Formula: see text]&R[Formula: see text] and T[Formula: see text]&R[Formula: see text]. This shows the optimal performance of the fuzzy associative memory model with fuzzy rule for quasi-copula fuzzy operators and can improve the theoretical technology. The best-performance fuzzy copula operator of the fuzzy associative memory model can enhance the theoretical foundation and technical support for the processing of intelligent information.

Parameterization before Meta-Analysis: Cross-Modal Embedding Clustering for Forest Ecology Question-Answering

In the field of forestry ecology, image data capture factual information, while literature is rich with expert knowledge. The corpus within the literature can provide expert-level annotations for images, and the visual information within images naturally serves as a clustering center for the textual corpus. However, both image data and literature represent large and rapidly growing, unstructured datasets of heterogeneous modalities. To address this challenge, we propose cross-modal embedding clustering, a method that parameterizes these datasets using a deep learning model with relatively few annotated samples. This approach offers a means to retrieve relevant factual information and expert knowledge from the database of images and literature through a question-answering mechanism. Specifically, we align images and literature across modalities using a pair of encoders, followed by cross-modal information fusion, and feed these data into an autoregressive generative language model for question-answering with user feedback. Experiments demonstrate that this cross-modal clustering method enhances the performance of image recognition, cross-modal retrieval, and cross-modal question-answering models. Our method achieves superior performance on standardized tasks in public datasets for image recognition, cross-modal retrieval, and cross-modal question-answering, notably achieving a 21.94% improvement in performance on the cross-modal question-answering task of the ScienceQA dataset, thereby validating the efficacy of our approach. Essentially, our method targets cross-modal information fusion, combining perspectives from multiple tasks and utilizing cross-modal representation clustering of images and text. This approach effectively addresses the interdisciplinary complexity of forestry ecology literature and the parameterization of unstructured heterogeneous data encapsulating species diversity in conservation images. Building on this foundation, intelligent methods are employed to leverage large-scale data, providing an intelligent research assistant tool for conducting forestry ecological studies on larger temporal and spatial scales.

A deep learning dataset for metal multiaxial fatigue life prediction

Multiaxial fatigue failure of metals, a common issue in industrial production, often leads to significant losses. Recently, many researchers have applied deep learning methods to predict the multiaxial fatigue life of metals, achieving promising results. Due to the high costs of fatigue testing, training data for deep learning is scarce and labor-intensive to collect. This study meets this need by creating a large-scale, high-quality dataset for multiaxial fatigue life prediction, consisting of 1167 samples from 40 materials collected from literature. The dataset includes key mechanical properties (elastic modulus, yield strength, tensile strength, Poisson’s ratio) and 48 loading paths, along with additional relevant information (composition ratios, processing conditions). Common deep learning models validated the dataset’s effectiveness. This dataset aims to support researchers applying deep learning to fatigue life prediction, addressing the long-standing issue of data scarcity, thereby advancing the intersection of artificial intelligence and metal fatigue research.

A Framework for Prediction of Type II Diabetes through Ensemble Stacking Model

In order to prevent long term complications of diabetes its early diagnosis is crucial. With Increasing advances in Artifical Intelligence (AI) and Machine Learning(ML) researchers are increasingly focusing on using them for early diagnosis of diseases.AI and ML has significant potential for early prediction of type 2 diabetes. In this article we have described a ML based framework for prediction of type 2 diabetes -Improved Ensemble Learning with Dimensionality Reduction Model (IELDR) and discussed its result. An IELDR algorithm is an Auto encoder-based feature extraction method with ensemble learning. The experiments were carried out using the LS_diabetes dataset. LS_diabetes dataset containing 374 records with 35 features related to lifestyle and stress. Accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) were measured. After this results were tested and validated using Diabetes_2019 dataset and PIMA diabetes dataset. The IELDR model showed results in terms accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) of 98.67%, 95.24%, 100%, 98.18%, 97.56%, 99.09% and 0.97 respectively. In comparison with PIMA diabetes dataset, LS_diabetes dataset showed an accuracy, precision, sensitivity, specificity, f1-score,roc and mcc value by 17.96%,13.15% 40.22%,5.59%,28.38%,22.09% and 0.4 respectively. The IELDR model achieved the best result on the LS_diabetes dataset showed an accuracy, sensitivity, roc and mcc value improved by 1.82%, 1.58%, 3.01%and 0.04 % compared to the Diabetes_2019 dataset .This proposed IELDR system predicts the risk of type 2 diabetes in a healthy person based on the person’s current lifestyle pattern. This system can be helpful for early prediction of type2 diabetes.