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Topic Review
Reinforcement Learning, Knowledge Distillation, and Channel Pruning
The methods used for model compression and acceleration are primarily divided into five categories—network pruning, parameter quantization, low-rank decomposition, lightweight network design, and knowledge distillation—such that the scope of actions and design ideas for each method are different.
  • 202
  • 12 Dec 2023
Topic Review
Microseismic Monitoring Signal Waveform Recognition and Classification
Microseismic event identification is of great significance for enhancing our understanding of underground phenomena and ensuring geological safety. Microseismic monitoring entails the continuous surveillance of minuscule seismic events during mining activities. These imperceptible events provide valuable information about evolving geological conditions. They serve as early warning signals, offering crucial insights into potential hazards and enabling timely preventive measures. This not only safeguards the well-being of miners but also enhances the overall efficiency and sustainability of mining practices.
  • 166
  • 11 Dec 2023
Topic Review
Visual Tracking Related to Age or Gender Information
Visual tracking of multiple targets, also referred to as multiple object tracking (MOT), since the target can be any moving object or entity, is a well-investigated computer vision task. Actually, the goal is to detect one or more targets in a time-variate scene and then obtain their trajectories in terms of following their tracklets, for a given video sequence. This is completed by associating newly detected instances with current ones. Typically, the association part assumes a prediction task whose aim is to favor the most possible correspondence among detections of consecutive frames for a given target. When the targets of interest are real people, resulting detections from this procedure are usually post-processed so as to extract useful information related, for instance, with their age or gender. 
  • 158
  • 11 Dec 2023
Topic Review
Social Recommender Systems
Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network data.
  • 168
  • 11 Dec 2023
Topic Review
Adversarial Attacks in Camera-Based Vision Systems
Vision-based perception modules are increasingly deployed in many applications, especially autonomous vehicles and intelligent robots. These modules are being used to acquire information about the surroundings and identify obstacles. Hence, accurate detection and classification are essential to reach appropriate decisions and take appropriate and safe actions at all times. Adversarial attacks can be categorized into digital and physical attacks.
  • 209
  • 11 Dec 2023
Topic Review
Enhancing Ransomware Attack Detection on Cloud-Encrypted Data
Ransomware attacks on cloud-encrypted data pose a significant risk to the security and privacy of cloud-based businesses and their consumers. RANSOMNET+, a state-of-the-art hybrid model that combines Convolutional Neural Networks (CNNs) with pre-trained transformers, to efficiently take on the challenging issue of ransomware attack classification. RANSOMNET+ excels over other models because it combines the greatest features of both architectures, allowing it to capture hierarchical features and local patterns. The findings demonstrate the exceptional capabilities of RANSOMNET+. The model had a fantastic precision of 99.5%, recall of 98.5%, and F1 score of 97.64%, and attained a training accuracy of 99.6% and a testing accuracy of 99.1%. The loss values for RANSOMNET+ were impressively low, ranging from 0.0003 to 0.0035 throughout training and testing. RANSOMNET+ excelled over the other two models in terms of F1 score, accuracy, precision, and recall. The algorithm’s decision-making process was also illuminated by RANSOMNET+’s interpretability analysis and graphical representations. The model’s openness and usefulness were improved by the incorporation of feature distributions, outlier detection, and feature importance analysis. Finally, RANSOMNET+ is a huge improvement in cloud safety and ransomware research. As a result of its unrivaled accuracy and resilience, it provides a formidable line of defense against ransomware attacks on cloud-encrypted data, keeping sensitive information secure and ensuring the reliability of cloud-stored data. Cybersecurity professionals and cloud service providers now have a reliable tool to combat ransomware threats thanks to this research.
  • 166
  • 11 Dec 2023
Topic Review
IoT Security Challenges and Intrusion Detection Systems
Cybersecurity finds widespread applications across diverse domains, encompassing intelligent industrial systems, residential environments, personal gadgets, and automobiles. This has spurred groundbreaking advancements while concurrently posing persistent challenges in addressing security concerns tied to IoT devices. IoT intrusion detection involves using sophisticated techniques, including deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and anomaly detection algorithms, to identify unauthorized or malicious activities within IoT ecosystems. These systems continuously monitor and analyze network traffic and device behavior, seeking patterns that deviate from established norms. When anomalies are detected, security measures are triggered to thwart potential threats. IoT intrusion detection is vital for safeguarding data integrity, ensuring users’ privacy, and maintaining critical systems’ reliability and safety.
  • 474
  • 11 Dec 2023
Topic Review
Sign Language Recognition
Recognition of hand motion capture is an interesting topic. Hand motion can represent many gestures. In particular, sign language plays an important role in the daily lives of hearing-impaired people. Sign language recognition is essential in hearing-impaired people’s communication. Wearable data gloves and computer vision are partially complementary solutions. However, sign language recognition using a general monocular camera suffers from occlusion and recognition accuracy issues.
  • 397
  • 08 Dec 2023
Topic Review
Semantic Textual Similarity
Semantic textual similarity (STS) refers to the degree of similarity between two pieces of text based on their meaning or semantics and assesses how closely related or similar two pieces of text are based on the information they convey, regardless of variations in wording or structure. STS has been explored from both linguistic and computational perspectives. It holds significance in various NLP applications, and in recent years, Transformer-based neural language models have emerged as the state-of-the-art solutions for many of these applications.
  • 224
  • 08 Dec 2023
Topic Review
Multi-Label Classification Based on Associations
Associative classification (AC) has been shown to outperform other methods of single-label classification for over 20 years. In order to create rules that are both more precise and simpler to grasp, AC combines the rules of mining associations with the task of classification.
  • 186
  • 08 Dec 2023
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