Artificial Intelligence Tasks Research Articles

The Method Of Logical Networks For Modeling Adaptive Knowledge Testing Systems

The subject of the research is the development of mathematical and algorithmic support of the intellectual toolkit for the analysis of sets of test tasks and the modeling of the process of interpreting the quality of sets of test tasks, which allows for objective and comprehensive continuous control of the knowledge of subjects of training, subject to the implementation of the concept of virtual distributed training (retraining).
 The purpose of the research is to improve the effectiveness of monitoring the knowledge of subjects of education in the distance form of education through the use of adaptive computer testing methods based on models of logical networks and the algebra of finite predicates.
 The following tasks are solved in the article: the formation of a testing model in a distributed virtual learning environment and a model of validity assessment based on the content of sets of test tasks.
 The following methods are used: methods of logical networks and algebraic programming based on the algebra of finite predicates and predicate operations, intellectual analysis of information.
 The following results were obtained: the principles of intellectual analysis were formulated in the development of a model of a universal logical network and its application to actual tasks of artificial intelligence in the field of informal information processing, namely, in the construction of knowledge testing systems for distributed virtual learning
 Conclusions. Algorithms for optimal multi-stage adaptive testing of knowledge as part of distributed virtual learning models and methods for analyzing the success of training subjects have been improved. The use of conjunctive decomposition with binary predicates achieves the goal of the research, because in this way any multi-place predicate can be represented by a logical network simulating the process of knowledge testing, the model of the subject of learning is described.

Compression and Transmission of Big AI Model Based on Deep Learning

In recent years, big AI models have demonstrated remarkable performance in various artificial intelligence (AI) tasks. However, their widespread use has introduced significant challenges in terms of model transmission and training. This paper addresses these challenges by proposing a solution that involves the compression and transmission of large models using deep learning techniques, thereby ensuring the efficiency of model training. To achieve this objective, we leverage deep convolutional networks to design a novel approach for compressing and transmitting large models. Specifically, deep convolutional networks are employed for model compression, providing an effective means to reduce the size of large models without compromising their representational capacity. The proposed framework also includes carefully devised encoding and decoding strategies to guarantee the restoration of model integrity after transmission. Furthermore, a tailored loss function is designed for model training, facilitating the optimization of both the transmission and training performance within the system. Through experimental evaluation, we demonstrate the efficacy of the proposed approach in addressing the challenges associated with large model transmission and training. The results showcase the successful compression and subsequent accurate reconstruction of large models, while maintaining their performance across various AI tasks. This work contributes to the ongoing research in enhancing the practicality and efficiency of deploying large models in real-world AI applications.

Handling Data Handoff of AI-Based Applications in Edge Computing Systems

Edge computing aims at better supporting low-latency applications. One of its key techniques is computation offloading, the process that outsources computing tasks from resourced-constrained mobile devices and moves them to edge data centers. In this paper, we tackle an emerging problem within the umbrella of computation offloading, i.e., migration of offloaded inference tasks of Artificial Intelligence (AI) trained models. Such context tailors migration aspects of data-sensitive services where i) the value of the updates is inversely proportional to the data age and ii) outage is highly detrimental to accuracy. To tackle this challenge, we propose Mobile Edge Data-handoff (MED) a framework able to relocate inference or online training tasks from one edge datacenter to another by moving only the necessary data to minimize any accuracy drop during the process. We implemented MED in a well-known edge computing emulator, openLEON, and experimentally verified its performance with an AI-based Industry 4.0 application that forecasts the gas flow in a chemical plant. For our experiments, we use a real, open-source dataset that contains sensors readings. Collected results show that MED, employing proactive data handoff algorithms, is able to minimize the packet loss during the handoff thereby providing guarantees on the inference accuracy.

A physics-based predictive model for pulse design to realize high-performance memristive neural networks

Memristive neural networks have extensively been investigated for their capability in handling various artificial intelligence tasks. The training performance of memristive neural networks depends on the pulse scheme applied to the constituent memristors. However, the design of the pulse scheme in most previous studies was approached in an empirical manner or through a trial-and-error method. Here, we choose ferroelectric tunnel junction (FTJ) as a model memristor and demonstrate a physics-based predictive model for the pulse design to achieve high training performance. This predictive model comprises a physical model for FTJ that can adequately describe the polarization switching and memristive switching behaviors of the FTJ and an FTJ-based neural network that uses the long-term potentiation (LTP)/long-term depression (LTD) characteristics of the FTJ for the weight update. Simulation results based on the predictive model demonstrate that the LTP/LTD characteristics with a good trade-off between ON/OFF ratio, nonlinearity, and asymmetry can lead to high training accuracies for the FTJ-based neural network. Moreover, it is revealed that an amplitude-increasing pulse scheme may be the most favorable pulse scheme as it offers the widest ranges of pulse amplitudes and widths for achieving high accuracies. This study may provide useful guidance for the pulse design in the experimental development of high-performance memristive neural networks.

BIGOWL4DQ: Ontology-driven approach for Big Data quality meta-modelling, selection and reasoning

Context:Data quality should be at the core of many Artificial Intelligence initiatives from the very first moment in which data is required for a successful analysis. Measurement and evaluation of the level of quality are crucial to determining whether data can be used for the tasks at hand. Conscientious of this importance, industry and academia have proposed several data quality measurements and assessment frameworks over the last two decades. Unfortunately, there is no common and shared vocabulary for data quality terms. Thus, it is difficult and time-consuming to integrate data quality analysis within a (Big) Data workflow for performing Artificial Intelligence tasks. One of the main reasons is that, except for a reduced number of proposals, the presented vocabularies are neither machine-readable nor processable, needing human processing to be incorporated. Objective:This paper proposes a unified data quality measurement and assessment information model. This model can be used in different environments and contexts to describe data quality measurement and evaluation concerns. Method:The model has been developed as an ontology to make it interoperable and machine-readable. For better interoperability and applicability, this ontology, BIGOWL4DQ, has been developed as an extension of a previously developed ontology for describing knowledge management in Big Data analytics. Conclusions:This extended ontology provides a data quality measurement and assessment framework required when designing Artificial Intelligence workflows and integrated reasoning capacities. Thus, BIGOWL4DQ can be used to describe Big Data analysis and assess the data quality before the analysis. Result:Our proposal has been validated with two use cases. First, the semantic proposal has been assessed using an academic use case. And second, a real-world case study within an Artificial Intelligence workflow has been conducted to endorse our work.

Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions.

Three-dimensional (3D) hetero-integration technology is poised to revolutionize the field of electronics by stacking functional layers vertically, thereby creating novel 3D circuity architectures with high integration density and unparalleled multifunctionality. However, the conventional 3D integration technique involves complex wafer processing and intricate interlayer wiring. Here we demonstrate monolithic 3D integration of two-dimensional, material-based artificial intelligence (AI)-processing hardware with ultimate integrability and multifunctionality. A total of six layers of transistor and memristor arrays were vertically integrated into a 3D nanosystem to perform AI tasks, by peeling and stacking of AI processing layers made from bottom-up synthesized two-dimensional materials. This fully monolithic-3D-integrated AI system substantially reduces processing time, voltage drops, latency and footprint due to its densely packed AI processing layers with dense interlayer connectivity. The successful demonstration of this monolithic-3D-integrated AI system will not only provide a material-level solution for hetero-integration of electronics, but also pave the way for unprecedented multifunctional computing hardware with ultimate parallelism.

Use of deep learning to segment bolus during videofluoroscopic swallow studies

Anatomical segmentations generated using artificial intelligence (AI) have the potential to significantly improve video fluoroscopic swallow study (VFS) analysis. AI segments allow for various metrics to be determined without additional time constraints streamlining and creating new opportunities for analysis. While the opportunity is vast, it is important to understand the challenges and limitations of the underlying AI task. This work evaluates a bolus segmentation network. The first swallow of thin or liquid bolus from 80 unique patients were manually contoured from bolus first seen in the oral cavity to end of swallow motion. The data was split into a 75/25 training and validation set and a 4-fold cross validation was done. A U—Net architecture along with variations were tested with the dice coefficient as the loss function and overall performance metric. The average validation set resulted in a dice coefficient of 0.67. Additional analysis to characterize the variability of images and performance on sub intervals was conducted indicating high variability among the processes required for training the network. It was found that bolus in the oral cavity consistently degrades performance due to misclassification of teeth and unimportant residue. The dice coefficients dependence on structure size can have substantial effects on the reported value. This work shows the efficacy of bolus segmentation and identifies key areas that are detriments to the performance of the network.

Design of a Cognitive Knowledge Representation Model to Assess the Reasoning Levels of Primary School Children

In recent days, the research on student’s intelligence level modelling is a challenging Artificial Intelligence (AI) task, which gains more attraction because it provides actionable insights to the tutor by analyzing the intelligence level of the learners. Each learner’s knowledge, comprehension, and intellectual capacities are unique. It is critical to identify these capacities and provide learners, particularly slow learners, with the necessary knowledge. Cognitive Performance Test (CPT) is an essential component for assessing the knowledge level of students. The reasoning level or coefficient deals with the analysis of the thinking capability in a logical way. It also reflects the child’s learning potential. The main aim of the proposed system is to design a Cognitive Knowledge Representation Model (CKRM), which fuses Cognitive Performance Metrics (CPM) calculation and Reasoning Coefficient Calculation (RCC) algorithms to assess the student’s intelligence level. The result of the proposed system is stratification of students to three different ranges of reasoning coefficient. The CKRM consists of the following phases: data collection, statistical Exploratory Data Analysis (EDA), model building and analysis, which involve the assessment of the knowledge level using CPT and calculation of reasoning coefficient using First Order Logic (FOL), and finally model evaluation using cognitive evaluation metrics. CPM and RCC algorithms have been proposed in this paper to calculate the student’s reasoning coefficient by using the forward chaining FOL inference engine. The dataset is a real time data which consists of the academic and cognitive performance details of school students from classes 1 to 6 for the year 2019 to 2020. The academic data are collected from the Educational Management Information System (EMIS) maintained by the school. The cognitive performance data are collected by conducting the tests for the students using the memory training application called Lumosity. The proposed system’s performance is evaluated using ten Machine Learning (ML) algorithms in which the Quadratic Discriminant Analysis achieved an accuracy of 0.97 for classes 1, 2, and 3. For classes 4, 5, and 6, nearly twelve ML algorithms are evaluated in which Random Forest (RF) Classifier achieved an accuracy of 0.98. Six math expert committee teachers concluded that the reasoning coefficient value was acceptable with an average accuracy of 0.92 for classes 1, 2, 3 and 0.9 for classes 4, 5, 6. In comparison to the pre-existing models employed in the prior research, it was determined that the created CKRM (academic and cognitive) was superior. The cognitive metrics such as taskability, Response Time (RT), knowledge capacity and utilization has also been evaluated. The average values of taskability, RT, knowledge capacity and knowledge utilisation are 0.85, 0.81, 0.55, and 0.44. The ultimate goal is to make customised teaching easier; hence, this article involves determining a student’s cognitive level by estimating their reasoning coefficient. The suggested approach analyses and categorises students’ cognitive abilities, such as memory, reasoning, problem solving, thinking, and logical reasoning, using three different reasoning coefficients. This approach assists teachers in determining the degree of intelligence of their students.

Photonic optical accelerators: The future engine for the era of modern AI?

Photonic accelerators for Artificial Intelligence (AI) are rapidly advancing, promising to provide revolutionary computational speed for modern AI architectures. By leveraging photons with a bandwidth higher than 100 THz, photonic accelerators tackle the computational demands of AI tasks that GHz electronics alone cannot meet. Photonics accelerators integrate circuitry for matrix–vector operators and ultra-fast feature extractors, enabling energy-efficient and parallel computations that prove crucial for the training and inference of AI models in various applications, including classification, segmentation, and feature extraction. This Perspective discusses modern challenges and opportunities that optical computations open in AI for research and industry.

Classification of Metal Handwritten Digits Based on Microwave Diffractive Deep Neural Network

AbstractRecently, optical diffractive deep neural networks (D2NNs) have shown unprecedented superiority in terms of processing speed and power consumption. However, in the microwave band, complicated classification based on D2NN needs further investigation, which may accelerate the artificial intelligent tasks and simplify systems. Here, a three‐layer D2NN is constructed for handwritten digit classification in the microwave frequency. The excited electromagnetic wave, which passes through the metal plane engraved with different digit patterns as the input, will focus on the target plane at designated focal points through the D2NN platform. A detector array is deployed to collect the target plane energy for direct digit classification. Each layer of the proposed D2NN is composed of 1024 phase modulating meta‐units, and the phase distribution is generated through the stochastic gradient descent algorithm applied on the dataset. The network realizes an accuracy rate of 90% in numeric simulations, together with a 100% accuracy rate on the eighteen fabricated samples on the built‐up platform. The average focal efficiency reaches 18.7% and 11.7% in the simulation and experiment, respectively. The system can be seen as an alternative method for seamless in situ monitoring of security checks and near‐field sensing.

Two-dimensional materials memory devices with floating metal gate for neuromorphic applications

Emerging technologies such as neuromorphic computing and nonvolatile memories based on floating gate field-effect transistors (FETs) hold promise for addressing a wide range of artificial intelligence tasks. For example, neuromorphic computing seeks to emulate the human brain's functionality and employs a device that mimics the role of a synapse in the brain. However, achieving a high current ON/OFF ratio for the program and erase states of nonvolatile memory and neuromorphic computing device with a metal gate is necessary. This study demonstrates a multi-functional device based on heterostructures of transition metal dichalcogenides (TMDCs) with a metal floating gate. Five different channel materials (SnS2, WSe2, MoS2, WS2, and MoTe2) were employed, and hexagonal boron nitride (h-BN) was used as a tunneling layer. The study found that n-type SnS2 exhibits high endurance (15,000 cycles), good retention (2.4 × 105 s), and the highest current ON/OFF ratio (∼2.58 × 108) among the materials for the program and erase states. Moreover, the SnS2 device exhibits synaptic behavior and offers highly stable operation at room temperature. Furthermore, the device shows high linearity in both potentiation and depression, with good retention time and repeatable results with low cycle-to-cycle variations. Additionally, the study used an artificial neural network (ANN) for MNIST simulation of image recognition and achieved the highest accuracy of ∼92 % based on the SnS2 synaptic device experimental results. These findings pave the way for developing nonvolatile memory devices and their applications in brain-inspired neuromorphic computing and artificial intelligence systems.

Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation.

The development of artificial intelligence (AI) within nuclear imaging involves several ethically fraught components at different stages of the machine learning pipeline, including during data collection, model training and validation, and clinical use. Drawing on the traditional principles of medical and research ethics, and highlighting the need to ensure health justice, the AI task force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks: privacy of data subjects, data quality and model efficacy, fairness toward marginalized populations, and transparency of clinical performance. We provide preliminary recommendations to developers of AI-driven medical devices for mitigating the impact of these risks on patients and populations.

4TH INDUSTRIAL REVOLUTION AND ARTIFICIAL INTELLIGENCE

An overview of industrial revolutions is made. Then the analysis of the fourth industrial revolution. The main major components of Industry 4.0 are identified, and their essence is revealed. The tasks of artificial intelligence within Industry 4.0 are also considered. Developed software in Python language about data and machine learning (ML) to visualize objects and table data. In order to use the capabilities of AI to the maximum benefit for business, it is necessary to hire data scientists. Data science combines statistics, computer science and business knowledge to extract value from various data sources. Developers use artificial intelligence to more effectively perform tasks that would otherwise have to be done manually, interact with customers, identify patterns and solve problems. To start working with AI, developers will need mathematical knowledge and the ability to use algorithms. There are several stages in the development and deployment of machine learning models, including training and inference. AI learning and inference refers to the process of experimenting with machine learning models to solve a problem. For example, a machine learning engineer can experiment with various candidate models to solve a computer vision problem, such as detecting bone fractures in X-ray images. Keywords: fourth industrial revolution, artificial intelligence.

Hierarchical relation and entity rotation-based multi-feature knowledge graph embedding

Knowledge graphs are knowledge bases that represent entities and relations in the objective world through graph structure, and they have a boosting effect on many artificial intelligence tasks. To facilitate the development of downstream artificial intelligence tasks, knowledge graph embedding (KGE) is proposed. It aims to express semantic information for each entity and relation in the knowledge graph within a low-dimensional space. However, when it comes to semantic hierarchy, multiple relation patterns and multi-fold relational structures in knowledge graphs, most of the existing models tend to focus on only one or two aspects, often neglecting the importance of considering all three simultaneously. Therefore, we propose a new knowledge graph embedding model, Hierarchical relation and Entity Rotation-based Multi-Feature knowledge graph Embedding (HERotMFE). Concerning hierarchical relation rotation and entity rotation, it can represent semantic hierarchy, multiple relation patterns and multi-fold relations simultaneously. Self-attention mechanism is used to learn the weights of the two-part rotation to further enhance the model’s performance. According to the findings of the experiments, HERotMFE outperforms existing models on most metrics and achieves state-of-the-art results.

Morphologic for knowledge dynamics: revision, fusion and abduction

Several tasks in artificial intelligence require the ability to find models about knowledge dynamics. They include belief revision, fusion and belief merging, and abduction. In this paper, we exploit the algebraic framework of mathematical morphology in the context of propositional logic and define operations such as dilation or erosion of a set of formulas. We derive concrete operators, based on a semantic approach, that have an intuitive interpretation and that are formally well behaved, to perform revision, fusion and abduction. Computation and tractability are addressed, and simple examples illustrate the main results.

LPN: Label-Enhanced Prototypical Network for Legal Judgment Prediction.

As one of the most critical tasks in legal artificial intelligence, legal judgment prediction (LJP) has garnered growing attention, especially in the civil law system. However, current methods often overlook the challenge of imbalanced label distributions, treating each label with equal importance, which can lead the model to be biased toward labels with high frequency. In this paper, we propose a label-enhanced prototypical network (LPN) suitable for LJP, that adopts a strategy of uniform encoding and separate decoding. Specifically, LPN adopts a multi-scale convolutional neural network to uniformly encode case factual description to capture long-distance features of the document. At the decoding end, a prototypical network incorporating label semantic features is used to guide the learning of prototype representations of high-frequency and low-frequency labels, respectively. At the same time, we also propose a prototype-prototype loss to optimize the prototypical representation. We conduct extensive experiments on two real datasets and show that our proposed method effectively improves the performance of LJP, with an average F1 of 1.23% and 1.13% higher than the state-of-the-art model on two subtasks, respectively.

The unintended consequences of artificial intelligence in paediatric radiology.

Over the past decade, there has been a dramatic rise in the interest relating to theapplication of artificial intelligence (AI) in radiology. Originally only 'narrow' AI tasks were possible; however, with increasing availability of data, teamed with ease of access to powerful computer processing capabilities, we are becoming more able to generate complex and nuanced prediction models and elaborate solutions for healthcare. Nevertheless, these AI models are not without their failings, and sometimes the intended use for these solutions may not lead to predictable impacts for patients, society or those working within the healthcare profession. In this article, we provide an overview of the latest opinions regarding AI ethics, bias, limitations, challenges and considerations that we should all contemplate in this exciting and expanding field, with a special attention to how this applies to the unique aspects of a paediatric population. By embracing AI technology and fostering a multidisciplinary approach, it is hoped that we can harness the power AI brings whilst minimising harm and ensuring a beneficial impact on radiology practice.

A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions

In recent years, there has been rapid development in machine learning for solving artificial intelligence tasks in various fields, including translation, speech, and image processing. These AI tasks are often interconnected rather than independent. One specific type of relationship is known as structural duality, which exists between multiple pairs of artificial intelligence tasks. The concept of dual learning has gained significant attention in the fields of machine learning, computer vision, and natural language processing. Dual learning involves using primitive tasks (mapping from domains X to Y) and dual tasks (mapping from domains Y to X) to enhance the performance of both tasks. In this study, we propose a general framework called Bi-Dual Inference by combining the principles of dual inference and dual learning. Our framework generates multiple dual models and a primal model by utilizing two dual tasks: sentiment analysis of input text and sentence generation of sentiment labels. We create these model pairs (primal model f, dual model g) by employing different initialization seeds and data access sequences. Each primal and dual model is considered as a distinct LSTM model. By reasoning about a single task with multiple similar models in the same direction, our framework achieves improved classification results. To validate the effectiveness of our proposed model, we conduct experiments on two datasets, namely NLPCC2013 and NLPCC2014. The results demonstrate that our model outperforms the optimal baseline model in terms of the F1 score, achieving an improvement of approximately 5%. Additionally, we provide parameter values for our proposed model, including model iteration analysis, α parameter analysis, λ parameter analysis, batch size analysis, training sentence length analysis, and hidden layer size setting. These experimental results further confirm the effectiveness of our proposed model.

Semantic Communications With Artificial Intelligence Tasks: Reducing Bandwidth Requirements and Improving Artificial Intelligence Task Performance

Semantic Communications With Artificial Intelligence Tasks: Reducing Bandwidth Requirements and Improving Artificial Intelligence Task Performance

Parallel adder-subtractor on elements of neural logic

Modern universal computing machines are capable of implementing any algorithm, but when solving certain tasks in terms of processing speed, they cannot compete with specialized computing modules that can increase the speed of performing arithmetic operations by using parallelism in data processing. The reliability of arithmetic calculators increases due to the use of neural-like elements. Specialized devices effectively solve array processing tasks, artificial intelligence tasks, and are used as control devices. The developed structure of a parallel adder-subtractor based on neural logic elements ensures high performance and reliability. The structural and functional schemes of the adder/subtractor blocks allow to increase the speed in comparison with the known sequential action adder when added in additional codes by at least 2 times, and with a parallel adder with sequential transfer by at least 1.5 times. It is shown that the hardware complexity of the presented adder-subtractor is 1.3 times lower compared to the sequential action adder and 2.8 times lower compared to the parallel adder with sequential transfer. The proposed structure of the adder-subtractor provides an increase in the calculation speed of arithmetic operations due to the implementation of parallelism in data processing.