Artificial Intelligence Tasks Research Articles

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.

Baoulé Related Parallel Corpora for Machine Translation Tasks: Mtbci-1.0

According to the Ethnologue platform, we have 7,164 known living languages in the World, and not all of them have data available over the internet to facilitate Artificial Intelligence (AI) tasks such as Machine Translation (MT). Consequently, there is a need for thorough Data Engineering tasks for most of these languages. Especially, the Baoulé living language normalized as ISO 639-3 (bci) is not yet supported on popular worldwide free translation platform such as Microsoft Translator, nor on the Official Wikipedias. In this paper, we have proposed the "Baoulé Related Parallel Corpora for Machine Translation tasks: mtBCI-1.0" to make parallel Baoulé-related datasets available to the scientific community for AI tasks implying Machine Translation. We have shown that, after a brief presentation of the Baoulé language in the proposed approach, we will focus on the Data Engineering Process itself before providing a baseline proving that the collected data is of scientific interest.

120 GOPS Photonic tensor core in thin-film lithium niobate for inference and in situ training

Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant challenges in constructing large-scale photonic neuromorphic networks. Here, we propose a fully integrated photonic tensor core, consisting of only two thin-film lithium niobate (TFLN) modulators, a III-V laser, and a charge-integration photoreceiver. Despite its simple architecture, it is capable of implementing an entire layer of a neural network with a computational speed of 120 GOPS, while also allowing flexible adjustment of the number of inputs (fan-in) and outputs (fan-out). Our tensor core supports rapid in-situ training with a weight update speed of 60 GHz. Furthermore, it successfully classifies (supervised learning) and clusters (unsupervised learning) 112 × 112-pixel images through in-situ training. To enable in-situ training for clustering AI tasks, we offer a solution for performing multiplications between two negative numbers.

PrescDRL: deep reinforcement learning for herbal prescription planning in treatment of chronic diseases

Treatment planning for chronic diseases is a critical task in medical artificial intelligence, particularly in traditional Chinese medicine (TCM). However, generating optimized sequential treatment strategies for patients with chronic diseases in different clinical encounters remains a challenging issue that requires further exploration. In this study, we proposed a TCM herbal prescription planning framework based on deep reinforcement learning for chronic disease treatment (PrescDRL). PrescDRL is a sequential herbal prescription optimization model that focuses on long-term effectiveness rather than achieving maximum reward at every step, thereby ensuring better patient outcomes. We constructed a high-quality benchmark dataset for sequential diagnosis and treatment of diabetes and evaluated PrescDRL against this benchmark. Our results showed that PrescDRL achieved a higher curative effect, with the single-step reward improving by 117% and 153% compared to doctors. Furthermore, PrescDRL outperformed the benchmark in prescription prediction, with precision improving by 40.5% and recall improving by 63%. Overall, our study demonstrates the potential of using artificial intelligence to improve clinical intelligent diagnosis and treatment in TCM.

Exploring the Role of Artificial Intelligence in Internet of Things Systems: A Systematic Mapping Study.

The use of Artificial Intelligence (AI) in Internet of Things (IoT) systems has gained significant attention due to its potential to improve efficiency, functionality and decision-making. To further advance research and practical implementation, it is crucial to better understand the specific roles of AI in IoT systems and identify the key application domains. In this article we aim to identify the different roles of AI in IoT systems and the application domains where AI is used most significantly. We have conducted a systematic mapping study using multiple databases, i.e., Scopus, ACM Digital Library, IEEE Xplore and Wiley Online. Eighty-one relevant survey articles were selected after applying the selection criteria and then analyzed to extract the key information. As a result, six general tasks of AI in IoT systems were identified: pattern recognition, decision support, decision-making and acting, prediction, data management and human interaction. Moreover, 15 subtasks were identified, as well as 13 application domains, where healthcare was the most frequent. We conclude that there are several important tasks that AI can perform in IoT systems, improving efficiency, security and functionality across many important application domains.

The Evolution of Data Centers in the Age of AI

This article explores the evolving landscape of data centers in the era of artificial intelligence (AI). It examines the exponential growth of the global data center market, driven by increasing data generation and AI adoption. The article discusses key technological developments in data centers, including enhanced operational efficiency through AI-powered systems, specialized hardware for AI workloads, advanced cooling technologies, and sustainability initiatives. It also delves into future prospects, such as increased capacity for complex AI tasks, real-time processing of massive datasets, and further improvements in energy efficiency. The symbiotic relationship between AI and data centers is highlighted, emphasizing how this transformation is reshaping digital infrastructure to meet unprecedented demands for computational power and data processing while striving for sustainability.

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.

Using design thinking hands-on learning to improve artificial intelligence application creativity: A study of brainwaves

In an era of rapidly developing artificial intelligence (AI), nurturing AI proficiency and innovation is critical to enhance individual, corporate, and national competitiveness. This study explored the effects of hands-on learning of design thinking (HLDT) on the creative performance and brainwaves of participants involved in an AI task. The study included 67 public university students from two classes who were also pre-service teachers. The experimental group received HLDT, while the comparison group received multimedia lecture-based instruction about DT. We found that HLDT significantly positively affected the novelty, feasibility, and value of the products that were created by the students. The effect on novelty was most substantial. In addition, it significantly positively affected various stages of the creative process, including discovery, definition, ideation, implementation, and refining. The effect on the refining stage was the most pronounced. Finally, it promoted higher levels of meditation during the discovery stage, with individuals feeling more relaxed and exhibiting lower beta and gamma activity. It also encouraged higher levels of attention and meditation during the ideation stage, aiding divergent thinking. Based on these findings, we propose practical teaching recommendations for suitable contexts and key instructional content for HLDT. Additionally, we suggest areas for future research.

Feature/vector entity retrieval and disambiguation techniques to create a supervised and unsupervised semantic table interpretation approach

Recently, there has been an increasing interest in extracting and annotating tables on the Web. This activity allows the transformation of textual data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g., semantic search and dataset extension. Semantic Table Interpretation (STI) is the process of annotating elements in a table. The paper explores Semantic Table Interpretation, addressing the challenges of Entity Retrieval and Entity Disambiguation in the context of Knowledge Graphs (KGs). It introduces LamAPI, an Information Retrieval system with string/type-based filtering and s-elBat, an Entity Disambiguation technique that combines heuristic and ML-based approaches. By applying the acquired know-how in the field and extracting algorithms, techniques and components from our previous STI approaches and the state of the art, we have created a new platform capable of annotating any tabular data, ensuring a high level of quality.

Understanding Users' Acceptance of Artificial Intelligence Applications: A Literature Review.

In recent years, with the continuous expansion of artificial intelligence (AI) application forms and fields, users' acceptance of AI applications has attracted increasing attention from scholars and business practitioners. Although extant studies have extensively explored user acceptance of different AI applications, there is still a lack of understanding of the roles played by different AI applications in human-AI interaction, which may limit the understanding of inconsistent findings about user acceptance of AI. This study addresses this issue by conducting a systematic literature review on AI acceptance research in leading journals of Information Systems and Marketing disciplines from 2020 to 2023. Based on a review of 80 papers, this study made contributions by (i) providing an overview of methodologies and theoretical frameworks utilized in AI acceptance research; (ii) summarizing the key factors, potential mechanisms, and theorization of users' acceptance response to AI service providers and AI task substitutes, respectively; and (iii) proposing opinions on the limitations of extant research and providing guidance for future research.

Spiking-NeRF: Spiking Neural Network for Energy-Efficient Neural Rendering

Artificial Neural Networks (ANNs) have achieved remarkable performance in many artificial intelligence tasks. As the application scenarios become more sophisticated, the computation and energy consumption of ANNs are also constantly increasing, which poses a challenge for deploying ANNs on energy-constrained devices. Spiking Neural Networks (SNNs) provide a promising solution to build energy-efficiency neural networks. However, the current training methods of SNNs cannot output values as precise as ANNs. This limits the applications of SNNs to relatively simple image classification tasks. In this article, we extend the application of SNNs to neural rendering tasks and propose an energy-efficient spiking neural rendering model, called Spiking-NeRF (Spiking Neural Radiance Fields). We first analyze the ANN-to-SNN conversion theory and propose an output scheme for SNNs to obtain the precise scene property values. Then we customize the parameter normalization method for the special network architecture of neural rendering. Furthermore, we present an early termination strategy (ETS) based on the discrete nature of spikes to reduce energy consumption. We evaluate the performance of Spiking-NeRF on both realistic and synthetic scenes. Experimental results show that Spiking-NeRF can achieve comparable rendering performance to ANN-based NeRF with up to \(2.27\times\) energy reduction.

Impact of Generative AI in Academic Integrity and Learning Outcomes: A Case Study in the Upper East Region

With the increasing use of Generative Artificial Intelligence (AI) tools like ChatGPT and Bard, universities face challenges in maintaining academic integrity. This research investigates the impact of these tools on learning outcomes (factual knowledge, comprehension, critical thinking) in selected universities of Ghana's Upper East Region during the 2023-2024 academic year. The study specifically analyzes changes in student comprehension and academic integrity concerns when using Generative AI for content generation, research assistance, and summarizing complex topics. A mixed-methods approach was employed, combining qualitative data from interviews and open-ended questions with quantitative analysis of survey data and academic records. The research focuses on three institutions: C. K. Tedam University of Technology and Applied Sciences, Bolgatanga Technical University, and Regentropfen University College. A purposive sampling technique recruited 150 participants (50 from each university) who had used Generative AI tools. Key findings show that 72% of students reported improved understanding of course material through Generative AI use, yet 75% cited academic integrity as a primary concern. Quantitative analysis revealed a weak to moderate positive correlation (r = 0.45) between AI tool usage and improved grades, with variations depending on the specific AI tasks performed. Qualitative data highlighted concerns about overreliance on AI and its impact on critical thinking skills. This research contributes to the ongoing debate on AI's role in education by providing valuable insights for educators and policymakers worldwide. The findings suggest that while AI tools can enhance comprehension, ethical considerations and potential drawbacks related to critical thinking require careful attention. The study concludes with recommendations for integrating AI literacy programs, developing ethical guidelines, and implementing advanced plagiarism detection systems to harness the benefits of Generative AI while mitigating risks to academic integrity. Although specific to the Upper East Region of Ghana, these insights may be applicable to other educational systems with similar characteristics.

Energy-efficiency optimization for heterogeneous computing-assisted NOMA-MEC edge AI tasks

Edge artificial intelligence (AI) is an emerging paradigm that leverages edge computing to pave the last-mile delivery of AI. To satisfy the increasing demand for high-performance computing and low latency of edge service, heterogeneous computing accelerators, especially Neural Processor Units (NPUs), are widely deployed on edge nodes. However, the edge AI heterogeneous computing system encounters the challenge of energy constraints. In this paper, we investigate the energy efficiency (EE) optimization problem in heterogeneous computing-assisted non-orthogonal multiple access mobile edge computing (NOMA-MEC) network model. Edge devices are configured with CPU-NPU heterogeneous computing processors to improve the AI task processing, and NOMA is applied to edge task offloading to enable massive connectivity. The system-centric energy efficiency maximization problem is studied. We formulate a system-centric energy efficiency maximization problem. To solve this problem, we decouple the original problem into two subproblems, i.e., the optimization problems of heterogeneous computing workload splitting and task offloading. A heuristic algorithm and binary search algorithm are proposed to optimize NOMA user pairing and task offloading delay, respectively. Furthermore, we propose a multi-objective alternative iterative algorithm to optimize system energy efficiency. Numerical results show that the proposed scheme can significantly improve the energy efficiency of heterogeneous computing-assisted NOMA-MEC networks compared to the existing baselines.

Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks.

The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especially in interpreting radiographic images. However, there is a knowledge gap among endodontists regarding the fundamentals of machine learning and deep learning, hindering the full utilization of AI in this field. This narrative review aims to: (A) elaborate on the basic principles of machine learning and deep learning and present the basics of neural network architectures; (B) explain the workflow for developing AI solutions, from data collection through clinical integration; (C) discuss specific AI tasks and applications relevant to endodontic diagnosis and treatment. The article shows that AI offers diverse practical applications in endodontics. Computer vision methods help analyse images while natural language processing extracts insights from text. With robust validation, these techniques can enhance diagnosis, treatment planning, education, and patient care. In conclusion, AI holds significant potential to benefit endodontic research, practice, and education. Successful integration requires an evolving partnership between clinicians, computer scientists, and industry.

A Mathematical Model to Enhance Creativity in Generative AI Systems

People often think it will be hard for artificial intelligence (AI) systems to copy creativity because it is a complicated and important part of human intelligence. Generative AI, which tries to make new and useful things, has shown promise in being able to copy creative processes. However, generative AI systems that are already in use often have trouble making results that are truly original. This is mostly because there isn't a clear scientific framework to help people be creative. We present a new mathematical model to improve creativity in creative AI systems in this work. Our model is based on the ideas of lateral and convergent thinking, which are important parts of how people think creatively. Divergent thinking means coming up with many ideas or solutions, while convergent thinking means picking the best ones and making them even better. To put our model into action, we come up with the idea of a "creativity score," which measures how new and useful the results are. The creativity score is determined by looking at a number of things, such as the variety of outputs, how different they are from current solutions, and how useful they are for fixing the problem at hand. Finding the right balance between divergent and convergent thinking is one of the hardest parts of making generative AI systems more creative. Divergent thinking is important for coming up with many ideas, but convergent thinking is needed to pick out the best ones and make them even better. This problem is solved by our model, which uses both divergent and convergent thought. To make sure our model works, we test it on different creative AI tasks, such as making images and writing text. Our results show that our model can greatly improve the creativity of generative AI systems, resulting in more varied, unique, and useful outputs.

A deep learning approach for overall survival prediction in lung cancer with missing values

Background and Objective:In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical domain, our primary objective is to develop an AI model capable of dynamically handling this missing data. Additionally, we aim to leverage all accessible data, effectively analyzing both uncensored patients who have experienced the event of interest and censored patients who have not, by embedding a specialized technique within our AI model, not commonly utilized in other AI tasks. Through the realization of these objectives, our model aims to provide precise OS predictions for non-small cell lung cancer (NSCLC) patients, thus overcoming these significant challenges. Methods:We present a novel approach to survival analysis with missing values in the context of NSCLC, which exploits the strengths of the transformer architecture to account only for available features without requiring any imputation strategy. More specifically, this model tailors the transformer architecture to tabular data by adapting its feature embedding and masked self-attention to mask missing data and fully exploit the available ones. By making use of ad-hoc designed losses for OS, it is able to account for both censored and uncensored patients, as well as changes in risks over time. Results:We compared our method with state-of-the-art models for survival analysis coupled with different imputation strategies. We evaluated the results obtained over a period of 6 years using different time granularities obtaining a Ct-index, a time-dependent variant of the C-index, of 71.97, 77.58 and 80.72 for time units of 1 month, 1 year and 2 years, respectively, outperforming all state-of-the-art methods regardless of the imputation method used. Conclusions:The results show that our model not only outperforms the state-of-the-art’s performance but also simplifies the analysis in the presence of missing data, by effectively eliminating the need to identify the most appropriate imputation strategy for predicting OS in NSCLC patients.

THE ANALYSIS OF THE EFFICIENCY OF GENERATIVE AI ALGORITHMS FOR CREATING A NATURAL DIALOGUE

In the modern world, artificial intelligence (AI) plays an increasingly important role in various fields of human activity. One of the most promising areas of AI application is the generation of natural dialogue. The purpose of this work is to analyze the efficiency of generative AI algorithms for creating natural dialogue. The relevance of this topic is due to the growing interest in the use of AI to create dialogue systems capable of interacting with people in a natural way. The results of the study can be useful for developers of dialogue systems, researchers in the field of AI, as well as anyone interested in the application of AI in their everyday life. Natural language generation is a fundamental task in artificial intelligence, with applications ranging from chatbots to virtual assistants. This study provides a comprehensive analysis of the efficiency of various generative artificial intelligence algorithms for creating a natural dialogue. Their performance is assessed in generating consistent and contextually appropriate responses by evaluating modern models using quantitative metrics and human evaluation. Additionally, the study explores the impact of various training data sizes and techniques on the quality of a generated dialogue. The results provide insight into the strengths and weaknesses of current generative AI approaches in the generation of a dialogue.

The role of student motivation in integrating AI into web design education: A longitudinal study

Amidst the current wave studies of artificial intelligence (AI) in education, this longitudinal case study, spanning Spring 2023 to Spring 2024, delves into the integration of AI in the UI/UX web design classroom. By introducing both text-based and image-based AI tools to students with varying levels of skill in introductory web design and user experience (UX) courses, the study observed a significant enhancement in student creative capabilities and project outcomes. The utilization of text-based generators markedly improved writing efficiency and coding, while image-based tools facilitated better ideation and color selection. These findings underscore the potential to augment traditional educational methods, providing students with novel avenues for creativity and innovation. At the same time, the goal of this study was also to ascertain the factors that led to the adoption of AI tools in the educational workflow, specifically focusing on student major and background, thereby illuminating how AI can be tailored to meet diverse educational needs and foster a more adaptive and innovative learning environment. The findings reveal that students were more receptive to integrating AI tasks into their workflows when these tasks did not directly relate to their major field of study. For example, Computer Science students exhibited less resistance to using AI for selecting color palettes, a task outside their primary focus, compared to utilizing AI for coding. Additionally, the study observed a significant growth in both awareness and usage of AI tools among students throughout the duration of the research. This trend suggests an increasing incorporation of AI technologies into their standard toolkit, highlighting a broader acceptance and integration of AI in educational practices.

Harnessing ferroic ordering in thin film devices for analog memory and neuromorphic computing applications down to deep cryogenic temperatures

The future computing beyond von Neumann era relies heavily on emerging devices that can extensively harness material and device physics to bring novel functionalities and can perform power-efficient and real time computing for artificial intelligence (AI) tasks. Additionally, brain-like computing demands large scale integration of synapses and neurons in practical circuits that requires the nanotechnology to support this hardware development, and all these should come at an affordable process complexity and cost to bring the solutions close to market rather soon. For bringing AI closer to quantum computing and space technologies, additional requirements are operation at cryogenic temperatures and radiation hardening. Considering all these requirements, nanoelectronic devices utilizing ferroic ordering has emerged as one promising alternative. The current review discusses the basic architectures of spintronic and ferroelectric devices for their integration in neuromorphic and analog memory applications, ferromagnetic and ferroelectric domain structures and control of their dynamics for reliable multibit memory operation, synaptic and neuronal leaky-integrate-and-fire (LIF) functions, concluding with their large-scale integration possibilities, challenges and future research directions.

The accelerated tensor Kaczmarz algorithm with adaptive parameters for solving tensor systems

Solving tensor systems is a common task in scientific computing and artificial intelligence. In this paper, we propose a tensor randomized average Kaczmarz method with adaptive parameters that exponentially converges to the unique least Frobenius norm solution of a given consistent tensor system under the t-product structure. In order to accelerate convergence, a tensor average Kaczmarz method based on stochastic heavy ball momentum technique (tAKSHBM) is proposed. The tAKSHBM method utilizes iterative information to update parameters instead of relying on prior information, addressing the problem in the adaptive learning of parameters. Additionally, the tAKSHBM method based on Fourier transform is proposed, which can be effectively implemented in a distributed environment. It is proven that the iteration sequences generated by all the proposed methods are convergent for given consistent tensor systems. Finally, we conduct experiments on both synthetic data and practical applications to support our theoretical results and demonstrate the effectiveness of the proposed algorithms.