Search

Composing coherent and structured music is one of the main challenges in symbolic music generation. Our research aims to propose a user-centric framework design that promotes a collaborative environment between users and knowledge agents. The primary objective is to improve the music creation process by actively involving users who provide qualitative feedback and emotional assessments. The proposed framework design constructs an abstract format in which a musical piece is represented as a sequence of musical samples. It consists of multiple agents that embody the dynamics of musical creation, emphasizing user-driven creativity and control. This user-centric approach can benefit individuals with different musical backgrounds, encouraging creative exploration and autonomy in personalized, adaptive environments. To guide the design of this framework, we investigate several key research questions, including the optimal balance between system autonomy and user involvement, the extraction of rhythmic and melodic features through musical sampling, and the effectiveness of topological and hierarchical data representations. Our discussion will highlight the different aspects of the framework in relation to the research questions, expected outcomes, and its potential effectiveness in achieving objectives. Through establishing a theoretical foundation and addressing the research questions, this work has laid the groundwork for future empirical studies to validate the framework and its potential in symbolic music generation. [ABSTRACT FROM AUTHOR]

For semiconductor manufacturing, automatic optical inspections (AOIs) are important for chip quality inspection. An AOI system contains a robot arm, an industrial camera, a x-y platform, and a visual inspection module. Using the industrial camera, a wafer map can be obtained and then sent to the visual inspection module to compare with qualified chip features. There is a baseline in the x-y platform. Due to the limitations of the robot arm flexibility, it is difficult for the robot arm to control the angles between the chip orientation and the baseline every time, which decreases the defect recognition accuracy. This work aims to improve the defect recognition accuracy and efficiency of the AOI system. Specifically, an efficient method is presented to calculate the angle between the baseline and chip orientation. Then, the wafer map can be rotated, such that the angle equals to zero. Further, a powerful system is established to recode the rotated chip coordinate, such that the unqualified chip positions can be located efficiently. This method is called a central array method. The central array method with deep learning methods forms an AI-based AOI system. Extensive experiments demonstrate that our proposed method performs well in improving the chip quality inspection efficiency and accuracy. Nevertheless, the proposed method still has challenges in implementation since it requires integration with the manufacturing line. [ABSTRACT FROM AUTHOR]

Self-adaptive systems can autonomously adjust their behavior in response to environmental changes. Nowadays, not only can these systems be engineered individually, but they can also be conceived as members of a family based on the approach of dynamic software product lines. Through systematic mapping, we build on the identified gaps in the variability management of self-adaptive systems; we propose a framework that improves the adaptive capability of self-adaptive systems through feature model generation, variation point generation, the selection of a variation point, and runtime variability management using deep learning and the monitor–analysis–plan–execute–knowledge (MAPE-K) control loop. We compute the permutation of domain features and obtain all the possible variation points that a feature model can possess. After identifying variation points, we obtain an adaptation rule for each variation point of the corresponding product line through a two-stage training of an artificial neural network. To evaluate our proposal, we developed a test case in the context of an air quality-based activity recommender system, in which we generated 11 features and 32 possible variations. The results obtained with the proof of concept show that it is possible to manage identifying new variation points at runtime using deep learning. Future research will employ generating and building variation points using artificial intelligence techniques. [ABSTRACT FROM AUTHOR]

Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions. [ABSTRACT FROM AUTHOR]

In the rapidly advancing field of Artificial Intelligence (AI), this study presents a critical evaluation of the resilience and cybersecurity efficacy of leading AI models, including ChatGPT-4, Bard, Claude, and Microsoft Copilot. Central to this research are innovative adversarial prompts designed to rigorously test the content moderation capabilities of these AI systems. This study introduces new adversarial tests and the Response Quality Score (RQS), a metric specifically developed to assess the nuances of AI responses. Additionally, the research spotlights FreedomGPT, an AI tool engineered to optimize the alignment between user intent and AI interpretation. The empirical results from this investigation are pivotal for assessing AI models' current robustness and security. They highlight the necessity for ongoing development and meticulous testing to bolster AI defenses against various adversarial challenges. Notably, this study also delves into the ethical and societal implications of employing advanced "jailbreak" techniques in AI testing. The findings are significant for understanding AI vulnerabilities and formulating strategies to enhance AI technologies' reliability and ethical soundness, paving the way for safer and more secure AI applications. [ABSTRACT FROM AUTHOR]

Coffee crema plays a crucial role in assessing the quality of espresso. In recent years, in response to the rising labor costs, aging population, remote security/authentication needs, civic awareness, and the growing preference for non-face-to-face interactions, robot cafes have emerged. While some people seek sentiment and premium coffee, there are also many who desire quick and affordable options. To align with the trends of this era, there is a need for lightweight artificial intelligence algorithms for easy and quick decision making, as well as monitoring the extraction process in these automated cafes. However, the application of these technologies to actual coffee machines has been limited. In this study, we propose an innovative real-time coffee crema control system that integrates lightweight machine learning algorithms. We employ the GrabCut algorithm to segment the crema region from the rest of the image and use a clustering algorithm to determine the optimal brewing conditions for each cup of espresso based on the characteristics of the crema extracted. Our results demonstrate that our approach can accurately analyze coffee crema in real time. This research proposes a promising direction by leveraging computer vision and machine learning technologies to enhance the efficiency and consistency of coffee brewing. Such an approach enables the prediction of component replacement timing in coffee machines, such as the replacement of water filters, and provides administrators with Before Service. This could lead to the development of fully automated artificial intelligence coffee making systems in the future. [ABSTRACT FROM AUTHOR]

In the realms of the Internet of Things (IoT) and artificial intelligence (AI) security, ensuring the integrity and quality of visual data becomes paramount, especially under low-light conditions, where low-light image enhancement emerges as a crucial technology. However, the current methods for enhancing images under low-light conditions still face some challenging issues, including the inability to effectively handle uneven illumination distribution, suboptimal denoising performance, and insufficient correlation among a branch network. Addressing these issues, the Multi-Scale Branch Network is proposed. It utilizes multi-scale feature extraction to handle uneven illumination distribution, introduces denoising functions to mitigate noise issues arising from image enhancement, and establishes correlations between network branches to enhance information exchange. Additionally, our approach incorporates a vision transformer to enhance feature extraction and context understanding. The process begins with capturing raw RGB data, which are then optimized through sophisticated image signal processor (ISP) techniques, resulting in a refined visual output. This method significantly improves image brightness and reduces noise, achieving remarkable improvements in low-light image enhancement compared to similar methods. Using the LOL-V2-real dataset, we achieved improvements of 0.255 in PSNR and 0.23 in SSIM, with decreases of 0.003 in MAE and 0.009 in LPIPS, compared to the state-of-the-art methods. Rigorous experimentation confirmed the reliability of this approach in enhancing image quality under low-light conditions. [ABSTRACT FROM AUTHOR]

This review article provides an in-depth analysis of the growing field of AI-assisted programming tasks, specifically focusing on the use of code embeddings and transformers. With the increasing complexity and scale of software development, traditional programming methods are becoming more time-consuming and error-prone. As a result, researchers have turned to the application of artificial intelligence to assist with various programming tasks, including code completion, bug detection, and code summarization. The utilization of artificial intelligence for programming tasks has garnered significant attention in recent times, with numerous approaches adopting code embeddings or transformer technologies as their foundation. While these technologies are popular in this field today, a rigorous discussion, analysis, and comparison of their abilities to cover AI-assisted programming tasks is still lacking. This article discusses the role of code embeddings and transformers in enhancing the performance of AI-assisted programming tasks, highlighting their capabilities, limitations, and future potential in an attempt to outline a future roadmap for these specific technologies. [ABSTRACT FROM AUTHOR]

In this work, an intelligent hybrid model is proposed to identify hazardous or inattentive driving manoeuvres on roads, with the final goal being to increase and ensure travellers' safety and comfort. The estimation is based on the effects that road geometry may have on vehicle accelerations, displacements and dynamics. The outputs of the intelligent systems proposed are how the type of driving can be characterized as normal, careless or distracted. The intelligent system consists of an LSTM (Long Short-Term Memory) neural network in a first step that distinguishes between normal and abnormal driving behaviour and then a second module that classifies abnormal forms of driving as aggressive or inattentive, with the latter implemented with another LSTM, a CNN (convolutional neural network) or the Hotelling transform. They are applied to some of the characteristics of vehicle dynamics to estimate the driving behaviour. Smartphone inertial sensors such as GPS, accelerometers and gyroscopes are used to measure these vehicle characteristics and to identify driving events in manoeuvres. Specifically, the critical acceleration due to the influence of the road geometry can be measured with inertial sensors, and then, this road acceleration with the lateral acceleration allows us to estimate the driver's perceived acceleration. This perceived acceleration affects the driving style and, consequently, the estimation of the appropriate speed to travel on that road. There is use of both a traditional two-lane and a motorway route located in the Madrid region of Spain. Driving behaviour is determined by considering how changes in road geometry may affect one's driving style and, consequently, the estimation of the proper speed. The results obtained with some of the proposed configurations of the intelligent hybrid system reach an accuracy of 97.21% in detecting dangerous driving or driving with a certain risk. This could allow generating real-time alerts for potentially dangerous or inattentive manoeuvres, leading to safer and more appropriate driving. [ABSTRACT FROM AUTHOR]

Speech emotion recognition (SER) is a key branch in the field of artificial intelligence, focusing on the analysis and understanding of emotional content in human speech. It involves a multidisciplinary knowledge of acoustics, phonetics, linguistics, pattern recognition, and neurobiology, aiming to establish a connection between human speech and emotional expression. This technology has shown broad application prospects in the medical, educational, and customer service fields. With the evolution of deep learning and neural network technologies, SER research has shifted from relying on manually designed low-level descriptors (LLDs) to utilizing complex neural network models for extracting high-dimensional features. A perennial challenge for researchers has been how to comprehensively capture the rich emotional features. Given that emotional information is present in both time and frequency domains, our study introduces a novel time–frequency domain convolution module (TFCM) based on Mel-frequency cepstral coefficient (MFCC) features to deeply mine the time–frequency information of MFCCs. In the deep feature extraction phase, for the first time, we have introduced hybrid dilated convolution (HDC) into the SER field, significantly expanding the receptive field of neurons, thereby enhancing feature richness and diversity. Furthermore, we innovatively propose the residual attention-gated multilayer perceptron (RA-GMLP) structure, which combines the global feature recognition ability of GMLP with the concentrated weighting function of the multihead attention mechanism, effectively focusing on the key emotional information within the speech sequence. Through extensive experimental validation, we have demonstrated that TFCM, HDC, and RA-GMLP surpass existing advanced technologies in enhancing the accuracy of SER tasks, fully showcasing the powerful advantages of the modules we proposed. [ABSTRACT FROM AUTHOR]