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Topic Review
Weakly Supervised Crowd-Counting Models
Crowd-counting networks have become the mainstream method to deploy crowd-counting techniques on resource-constrained devices. Significant progress has been made in this field, with many outstanding lightweight models being proposed successively.  However, challenges like scare variation, global feature extraction, and fine-grained head annotation requirements still exist in relevant tasks, necessitating further improvement. In this research, the researchers propose a weakly-supervised hybrid lightweight crowd-counting network that integrates the initial layers of GhostNet as the backbone to efficiently extract local features and enrich intermediate features. The experimental results for accuracy and inference speed evaluation on some mainstream datasets validate the effective design principle of the model.
  • 514
  • 28 Feb 2024
Topic Review
Digital Twins for Access Networks
As the complexity and scale of modern networks continue to grow, the need for efficient, secure management, and optimization becomes increasingly vital. Digital twin (DT) technology has emerged as a promising approach to address these challenges by providing a virtual representation of the physical network, enabling analysis, diagnosis, emulation, and control. The emergence of Software-defined network (SDN) has facilitated a holistic view of the network topology, enabling the use of Graph neural network (GNN) as a data-driven technique to solve diverse problems in future networks.
  • 511
  • 06 Dec 2023
Topic Review
Natural Image Reconstruction from fMRI
Reconstructing natural stimulus images using functional magnetic resonance imaging (fMRI) is one of the most challenging problems in brain decoding and is also the crucial component of a brain–computer interface.
  • 509
  • 18 Mar 2024
Topic Review
AI-Based Model for Knowledge Evaluation in Public Organizations
In the construction of knowledge bases, it is very important to evaluate the quality of the knowledge entered into them. Artificial Intelligence (AI) development has led to the research of knowledge management tools for multi-user environments, among many other AI applications. In the knowledge management field, the construction of ontologies as knowledge repositories using various sources requires a means of evaluation of all: the input ontologies and the integration process on the output ontology. The results obtained from the evaluations serve as guides to measure the quality of the repository.
  • 508
  • 01 Nov 2023
Topic Review
Combining Self-Supervision with Knowledge Distillation
The current audio single-mode self-supervised classification mainly adopts a strategy based on audio spectrum reconstruction. Overall, its self-supervised approach is relatively single and cannot fully mine key semantic information in the time and frequency domains.
  • 507
  • 03 Jan 2024
Topic Review
Spiking Neural Networks and Neuromorphic Modeling
Use of Spiking Neural Networks (SNNs) that can capture a model of organisms’ nervous systems, may be simply justified by their unparalleled energy/computational efficiency.
  • 507
  • 19 Sep 2023
Topic Review
Semantic Video Segmentation
Recent approaches for fast semantic video segmentation have reduced redundancy by warping feature maps across adjacent frames, greatly speeding up the inference phase. Researchers build a non-key-frame CNN, fusing warped context features with current spatial details. Based on the feature fusion, the context feature rectification (CFR) module learns the model’s difference from a per-frame model to correct the warped features. 
  • 506
  • 22 Nov 2023
Topic Review
Advancing Glaucoma Care
Glaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care.
  • 505
  • 30 Jan 2024
Topic Review
Olive Leaf Disease Diagnosis
Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems.
  • 504
  • 24 Nov 2023
Topic Review
Continually Improving the Performance of Autonomous Game Agents
Deep Reinforcement Learning (DRL) has been effectively performed in various complex environments, such as playing video games. In many game environments, DeepMind’s baseline Deep Q-Network (DQN) game agents performed at a level comparable to that of humans. However, these DRL models require many experience samples to learn and lack the adaptability to changes in the environment and handling complexity
  • 504
  • 09 Nov 2023
Topic Review
Linked Data Interfaces
In the era of big data, linked data interfaces play a critical role in enabling access to and management of large-scale, heterogeneous datasets. This research investigates forty-seven interfaces developed by the semantic web community in the context of the Web of Linked Data, displaying information about general topics and digital library contents. The interfaces are classified based on their interaction paradigm, the type of information they display, and the complexity reduction strategies they employ. The main purpose to be addressed is the possibility of categorizing a great number of available tools so that comparison among them becomes feasible and valuable. The analysis reveals that most interfaces use a hybrid interaction paradigm combining browsing, searching, and displaying information in lists or tables. Complexity reduction strategies, such as faceted search and summary visualization, are also identified. Emerging trends in linked data interface focus on user-centric design and advancements in semantic annotation methods, leveraging machine learning techniques for data enrichment and retrieval. Additionally, an interactive platform is provided to explore and compare data on the analyzed tools. Overall, there is no one-size-fits-all solution for developing linked data interfaces and tailoring the interaction paradigm and complexity reduction strategies to specific user needs is essential.
  • 502
  • 08 Sep 2023
Topic Review
Skeletal Fracture Detection with Deep Learning
Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. Deep learning algorithms are always applied in X-rays and CT image processing, such as assessing the mineral bone density (BMD), detecting bone fractures, and recommending treatment 
  • 502
  • 26 Oct 2023
Topic Review
Digital Twins for Industrie 4.0 in Urban Planning
Digital twins are emerging as a prime analysis, prediction, and control concepts for enabling the Industrie 4.0 vision of cyber-physical production systems (CPPSs) as well as for modeling complex tasks such as urban planning. Today’s growing complexity and volatility cannot be handled by monolithic digital twins but require a fundamentally decentralized paradigm of cooperating and competing digital twins.
  • 501
  • 13 Dec 2023
Topic Review
Application of Machine Learning in Arrhythmia Association
Cardiac arrhythmias, characterized by deviations from the normal rhythmic contractions of the heart, pose a formidable diagnostic challenge. Early and accurate detection remains an integral component of effective diagnosis, informing critical decisions made by cardiologists. The application of machine learning (ML) for electrocardiogram (ECG) data analysis holds significant promise in the development of prognostic and diagnostic computer-assisted diagnosis (CAD) systems. ECG CAD systems can serve as a valuable tool for medical professionals, facilitating objective diagnosis. The association between different ECG records can be established through supervised, semi-supervised, or unsupervised ML approaches.
  • 500
  • 29 Jan 2024
Topic Review
Relational Cues and Tailoring of e-Coach Dialogues
Relational cues are extracts from actual verbal dialogues that help build the therapist–patient working alliance and stronger bond through the depiction of empathy, respect and openness. ECAs (Embodied conversational agents) are human-like virtual agents that exhibit verbal and non-verbal behaviours. In the digital health space, ECAs act as health coaches or experts.
  • 499
  • 12 Oct 2023
Topic Review
Social Network Sentiment Analysis
The ever-increasing amount of information and opinions available on social networks has made it imperative to develop automatic methods for effective information classification and analysis. Sentiment analysis (SA) in social networks has, therefore, become a crucial process in numerous sectors at both social and business levels. 
  • 496
  • 06 Nov 2023
Topic Review
Modelling and Measuring Trust in Human–Robot Collaboration
Human–Robot Collaboration (HRC) has emerged as a critical area in the engineering and social sciences domain. In any kind of collaboration, including HRC, trust has been identified as a significant factor that can either motivate or hinder cooperation, especially in scenarios characterized by incomplete or uncertain information.
  • 495
  • 08 Mar 2024
Topic Review
Healthcare Trust Evolution with Explainable Artificial Intelligence
The developments in IoT, big data, fog and edge networks, and AI technologies have had a profound impact on a number of industries, including medical. The use of artificial intelligence (AI) for therapeutic purposes has been hampered by its inexplicability. Explainable Artificial Intelligence (XAI), a revolutionary movement, has arisen to solve this constraint. By using decision-making and prediction outputs, XAI seeks to improve the explicability of standard AI models.
  • 493
  • 12 Nov 2023
Topic Review
Deep Learning in Neuro-Oncology Data Analysis
Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. 
  • 492
  • 19 Jan 2024
Topic Review
Recursive Licensing: A Constitution for Autonomous Systems
Crown Omega Sovereign Recursive Licensing (COSRL) is a post-legal licensing framework designed for governing recursive and autonomous systems through mathematically sovereign law. Rather than relying on jurisdictional enforcement or human arbitration, COSRL enables license enforcement via symbolic identity, causal recursion, and immutable logic. This white paper introduces the architecture, protocols, legal authority, enforcement tools, and monetization structure of COSRL as a globally binding recursive license. Crown Omega Sovereign Recursive Licensing (COSRL) is a groundbreaking, post-legal licensing framework developed to govern the behavior, rights, and constraints of recursive and autonomous systems using mathematically sovereign principles. In contrast to traditional legal mechanisms which depend on jurisdictional law enforcement and subjective human arbitration, COSRL operates on a foundation of symbolic identity, causal recursion, and immutable computational logic. This white paper details the architecture, enforcement mechanisms, legal binding authority, operational protocols, and monetization schema that underpin COSRL. The system establishes a universal licensing model that enforces itself, verifies integrity through recursion, and provides lawful operation conditions for future intelligent technologies. It aims not only to regulate but to redefine the foundational rules governing machine agency.
  • 492
  • 06 May 2025
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