Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep-Learning Algorithms for Video Anomaly Detection: A Mini-Review

With the increasing demand for security in the day-to-day lives of people, especially when in public spaces, surveillance system helps to monitor human behavior, prevent illegal activities and detect anomalous events. Anomaly detection in video involves the process of detecting anomalous behaviors that occur in such video. Due to the inefficiency of humans when it comes to surveillance, various algorithms can help to automatically detect these anomalous events in videos. Deep learning is fast rising, which is very useful as it imitates how humans gain knowledge and this helps in the area of surveillance and detection. This study aims to provide a comprehensive review of deep learning algorithms used for the detection of anomalous events captured in videos. This study also captures the datasets available for anomaly detection, the applications and future research directions.
Medical Relation Extraction Based On Feature Fusion Forest

Medical relation extraction has received extensive research and application. However, due to the high information density of biomedical medical texts, the extraction performance of relation extraction models in the medical field is not satisfactory. This paper proposes a two-dimensional feature fusion method. We define semantic and syntactic information, as well as word-level and sentence-level information, as features on two dimensions, and further use two attention mechanisms to fuse the features on the two dimensions to improve the performance of relation extraction models. We conducted experiments on three datasets and demonstrated that our model performs better than current relation extraction models in biomedical medical texts.
Knowledge-Driven Approach for Quality Assessment of HAR Data Sets: An Automated Tool

The quality of Human Activity Recognition (HAR) data sets can be affected by a number of factors, such as processes for governance, diversity of data collection protocols, information understanding, labelling accuracy, imbalanced data and missing or irrelevant information. These factors can be assessed by the construction of standard quality metrics and quality assessment frameworks and tools. This research outlines a knowledge-driven based approach for the qualitative assessment of HAR data sets, supported by an automated tool. Through the utilization of domain expertise, semantic modelling, and machine learning techniques, we established a set of quality metrics and criteria that effectively encapsulate the fundamental attributes of the HAR data set. The automated tool employs specific methodologies to evaluate quality, such as the Threshold-based technique and the Aggregative-based approach. The proposed research provides a novel method and tool that improves the evaluation of the quality of HAR data set, thereby facilitating more informed decisions and enhancements in the HAR domain.
Exploring the Potential of Blockchain Technology in an IoT-Enabled Environment: A Review

Internet of Things (IoT) plays an essential contribution in connecting devices and enabling seamless data exchange, leading to increased efficiency and convenience. However, security concerns in IoT systems are significant, as compromised devices can lead to data breaches and privacy violations. Blockchain technology can enhance IoT security by providing decentralized consensus, immutability, and transparent transaction records, ensuring secure and trustworthy communication and data integrity. This review article gives a succinct but thorough understanding of blockchain technology, covering architecture of blockchain, working principles, types, applications, platforms, and its role in the IoT environment. The study highlights potential benefits of blockchain like enhanced security and privacy, and explores its integration with IoT. Additionally, the study discusses various real-world applications, examines blockchain platforms, and addresses the limitations and challenges associated with blockchain technology. This review serves as a valuable resource for researchers and practitioners seeking a deeper understanding of blockchain’s potential and its implications in the IoT landscape.
Fishing Posture Detection Model Based on Convolutional Neural Network

Many countries have set up ecological coastal preserves for sustainable oceans and ecological conservation and prohibiting people from angling along the shoreline. Although there are regular patrols by law enforcement officers, there are still many anglers who do not comply with the regulations, and intrude into the ecological coastal preserve. This requires a lot of human resources and time for law enforcement officers. Therefore, we developed a fishing detection system by collecting images of anglers’ fishing behaviors. We used a deep learning model with object detection and posture recognition. When anglers engaged in fishing behaviors, the system immediately transmitted the anglers’ geographic locations to the back-end database and notified the law enforcement officers. The results showed that the system’s accuracy rate in determining angling postures was as high as 90%.
Analysis of fNIRS as a Biometric Modality

Given the popularity and importance of biometric personal identification, new modalities continue to be explored by the research community, including those based on the infrared scans of the target tissues. This paper presents a study of functional near-infrared spectroscopy (fNIRS) as a potential biometric modality. We assess the subject-specificity of the fNIRS’s functional and structural signatures using a multichannel fNIRS dataset collected over the forehead during various motor tasks and rest states. We detail our preprocessing, feature extraction (with Uniform Manifold Approximation and Projection), and feature selection (with permutation feature importance). Using cosine similarity matching between enrollment and validation features from an average of four mental actions, we obtained an area under the ROC curve (AUC) of 0.86. We also compare the efficacy of various classifiers, such as tree-based, neural network-based, and gradient-boosting-based models. An ensemble classifier improved the verification accuracy and AUC over the test data set to 96% and 0.99, respectively.
CrossLMD: Cross-Language Malicious code Detection

Current malicious code detection models usually mainly focus on variants of popular programming languages and source codes, while ignoring the inherent semantic information in source codes. These approaches present significant challenges in the face of the vast amount of multi-programming language code that exists in repositories, open-source communities, blogs, and forums. To address this problem, we propose CrossLMD, a cross-language malicious code detection model capable of identifying malicious code across various programming languages using a single model. CrossLMD leverages large-scale pre-trained models to understand semantic dependencies between code written in different languages. We built a neural network classifier on top of the pre-trained model to fine-tune the pre-trained model for cross-programming language malicious code detection tasks. Our experimental results demonstrate that CrossLMD can effectively identify malicious code written in never-encountered programming languages, exhibiting superior performance compared to baseline methods. Our proposed model provides a more comprehensive solution for malicious code detection, taking into account the inherent semantic information of source code, regardless of language or syntax changes. Utilizing a single model to detect malicious code in multiple programming languages simplifies the detection process and improves efficiency and cost-effectiveness.
Learning Customer Behaviors in the Metaverse

This study examines the possibility for businesses to gain a competitive advantage by leveraging the abundant data from the metaverse to better understand customer behavior and enhance decision-making. The main contributions of the paper are to conceptualize the possibility for businesses to yield competitive advantage gained from the metaverse data to augment customer behavioral analytics and subsequently several other advanced analytics use cases. This study is intended to serve as a basis for future research and practice, particularly in the areas of customer behavioral analytics and augmented analytics.
Mutation Testing Techniques for Android Applications: A Comparative Study

Mobile applications are becoming increasingly used to achieve various computing needs. Hence, it is essential to guarantee quality of the applications. Software testing has been the main activity for ensuring the quality of software, however, it is a critical and costly activity. Furthermore, code coverage is commonly used as metric for verifying the effectiveness of testing technique. However, numerous researchers and experts claimed that considering code coverage only to ascertain the testing quality is not enough to ensure quality of apps. In view of this, mutation testing has been introduced to complement the procedure by assuring that apps behave as expected and are released free of faults. Several techniques/tools were proposed based on different syntax and mutation operators. This study presents a comparative study of six (6) mutation techniques/tools for Android applications to highlight their strengths and weakness which will give an insight to researchers on the directions for further research.
An Open RAN Implementation of 5G Network Slicing

Network slicing has been introduced as a fundamental feature of 5G mobile networks. In this paper, the concept of network slicing is first clarified and the type of network slicing that might be achievable in the first deployments is defined. Then, some critical aspects of slicing implementation in open-RAN base-stations are detailed. Given that network slicing resides in resource allocation, the different resources that the base station can associate with slices are reviewed, together with the technologies that can be used for associating resources to slices.
A Framework for Internet Protocol Based Realtime Bottle Inspection System Using Blobanalysis Technique

Image processing is becoming a more significant component of the production process in order to define the efficiency and accuracy of the bottles. This paper presents an automatic vision inspection system that employs a stream of real-time videos recorded from the Android camera and sent to MATLAB through wireless network to improve line quality, In the vision inspection, methods and algorithms were employed to analyze and detect the presence and attributes of a bottle, then extract useful data using a Graphical User Interface. Filtering, segmentation, and blob analysis were used to remove most noise and undesired objects. The system's testing findings demonstrate that it is accurate when the camera is placed in a steady, fixed distance from the bottle and at a fixed motor speed. However, results may be impacted by a high motor speed. Anomaly detection has also advanced to a high level with strong detection accuracy.
Multiple adverse weather removal using adversarial and contrastive learning

Adverse weather removal is significant and valuable for real-world computer vision systems. Most existing algorithms focus on removing one specific type of weather and rely on paired synthesized weather datasets for training. In this paper, to train a multiple weather removal model with unpaired datasets, we introduce multi-dimension contrastive learning mechanism into generative adversarial networks. Specifically, our method consists of two blocks: multi-channel cycle translation (MCCT) block and multi-dimension contrastive learning (MDCL) block. MCCT is used to preserve the color and structural information and gain informative supervisory signals. MDCL is proposed to discriminate feature distribution among different image domains. Results of extensive experiments show that our method is superior over existing unsupervised methods and maintains a decent performance degradation when adapted to multiple weather removal tasks.
Monitoring and Analysis of Encrypted Attack Traffic Based on Machine Learning

With the growing significance of network security, an increasing number of websites and applications are opting to encrypt their transmitted traffic. In order to provide an effective method for analyzing encrypted traffic, this paper uses the C4.5 decision tree algorithm to establish a model for classifying encrypted traffic. Based on the Moore feature set, we perform feature extraction for monitoring and analyzing encrypted traffic. We use Cfssubseteval, Gainratioattributeeval and Infogainattributeeval as evaluation functions to remove redundant features to obtain the optimal feature subset, thereby improving the performance of model detection. Experimental results show that the C4.5 decision tree algorithm after feature extraction can reduce the training time of the original algorithm by 50% while ensuring a certain accuracy. In addition, compared to other deep learning models, although the traditional C4.5 decision tree method may not have high accuracy, it is more efficient in processing large-scale data and holds potential research value in the future.
Joint Task Offloading and Migration Scheme Based on Optimal System Benefit in Mobility-aware Multi-Access Edge Computing Network

Multi-access edge computing (MEC) is considered as an effective method to provide computing resources to nearby users. However, it is difficult to find a suitable offloading and migration scheme for computing task in high-speed movement scenario. Considering the impact of connection time between mobile user and MEC server on system performance, a MEC task processing model is constructed. A task offloading and migration scheme based on the optimal system benefit is designed by defining the system benefit function. A task offloading algorithm based on particle swarm optimization (PSO) is proposed in order to maximize the system benefit. Comprehensive consideration of communication resource, calculation resource, energy consumption, connection time, and amount of task, the best MEC servers are selected to provide services for users, and the optimal task offloaded and migration is also given. Simulation results demonstrate that the system benefit of the scheme proposed in this paper is up to 12% higher than that of its several existing schemes.
Fault Detection and Classification in Parallel and semi-parallel Transmission Lines Connected to Large-Scale Photovoltaic Power Plants Using Artificial Neural Networks and Support Vector Machines

Sustainable energy supply and the increasing use of large-scale photovoltaic power plants (LSPPPs) demand stronger and more reliable transmission systems. Parallel transmission lines (PTL) are extensively utilized in contemporary power systems to augment the reliability and security of transmission line. With the need for energy transfer from LSPPPs and the impact of LSPPPs on changing the nature of faults, PTLs face new challenges. This paper investigates the fault detection (FD) and fault classification (FC) with various characteristics in parallel and semi-parallel transmission lines using Artificial Neural Networks (ANNs) and Support Vector Machines (SWM). This approach facilitates the enhancement of efficiency and confidence in transmission systems and contributes to organizing stable and reliable performance in these systems. For simulation and analysis, the PSCAD/EMTDC software is employed to simulate the model of parallel and semi-parallel transmission lines connected to LSPPPs. Subsequently, the three-phase current signal values and the positive sequence voltage angle signals of the two buses connected to the transmission line are extracted, processed, and divided into training and testing data using MATLAB software. Then, these two methods have been compared. The results demonstrate that ANN and SWM are capable of accurately FD and FC with high precision.
Dynamic Abnormal Detection in Smart Contracts Using Opcode Sequences

While blockchain technology brings a trusted execution environment to smart contracts, it also brings new challenges to smart contracts. Once a smart contract is deployed on the blockchain, it is meant to be permanently executed and immutable. If a smart contract is vulnerable, this can cause significant financial losses to users. In this paper, we propose a model for detecting the abnormal behavior using opcode sequences. It can detect the potential abnormal behavior in smart contracts by detecting whether the control flow paths of smart contracts triggered by vulnerability transactions are inside a normal set of control flow paths. In order to obtain the control flow path information of smart contracts, we use the method of Geth instrumentation to obtain the opcode sequences during the execution of smart contract transactions as the control flow paths, and then detect the abnormal behavior of smart contracts. Due to the abnormal detection, it will add some detection time overhead. The experimental results show that this model has high detection accuracy in abnormal behavior for four common vulnerabilities while the increased time overhead is very small inside the limited set of normal control flow paths.
Metaverse User Portrait Construction Based on Human Gaze-tracking Data

With the rapid growth of virtual reality technology in the Metaverse, where a digital virtual world is integrated with the real world, users are exposed to a variety of content in different scenarios. Similar to the Web2 scenario, accurately profiling users in Metaverse applications is critical for improving user satisfaction. This involves collecting and inferring information such as users’ basic attributes, interests, and consumption habits. In this study, we propose a new approach for profiling users in virtual worlds based on gaze-tracking. We developed a Metaverse shopping experiment platform and recruited volunteers to record their attention time on different commodities in 3-D scenes by wearing eye-tracking devices. We treat this gaze-tracking data as implicit feedback to capture users’ interests, thereby predicting users’ attributes. We tried different ways of extracting features from collected gaze-tracking data. To our knowledge, we are the first to mine user portraits using gaze-tracking sequences. Our experimental results demonstrate that the fusion of sequence and image features significantly improves the accuracy of user gender prediction, reaching 88.89%, which is better than using sequence or image features only. These findings demonstrated the practical and effective advantages of using gaze-tracking data to build user portraits in the Metaverse.
Deep Learning Models for NEAT Activity Detection on Smartwatch

Our goal of this research focuses on exploring deep learning classification models for recognizing and differentiating Non-Exercise Activity Thermogenesis (NEAT) activities commonly conducted in home settings. NEAT encompasses energy expenditure in activities beyond sleep, eating, and traditional exercise. Our study identifies and classifies NEAT and non-NEAT activities, including cooking, sweeping, mopping, walking, climbing stairs, watching television, and desk work. Our aim is to create a smartwatch-compatible classification model that offers users regular activity updates. By examining various parameters like window length, we assess their impact on battery depletion rate and classification accuracy. Our research diverges from current activity recognition practices that overlook NEAT activities like cooking, sweeping, and mopping while prioritizing high-frequency data. Instead, we utilize a lower frequency data sampling rate of 10 Hz for accurate classification. Findings indicate that artificial neural networks (ANN) achieve the highest accuracy, followed by 1D-CNN, bidirectional long short-term memory (LSTM), and LSTM. Bidirectional LSTM exhibits the lowest battery depletion rate across most window lengths.
Natural Language Processing for Supply Chain with Chat GPT

We researched how an aircraft manufacturer found keywords for component specifications in the order document. The component specifications in the document were unstructured and manually reviewed by employees. This often caused problems such as overtime work and missing several specifications. Through a literature review using GPT-3, we extracted keywords of component specifications. We established a GPT-3 model for keyword extraction. First, we created training samples with prompts and responses. Then, this training sample was used to create fine-tuned models. The results showed the high accuracy of the model and the possibility of GPT-3 for the keyword extraction.
Graph Convolutional Network with Gated Multi-mode Fusion for Traffic Forecasting

As a crucial problem in Intelligent Transportation Systems (ITS), traffic forecasting has attracted attention from an increasing number of researchers in recent years. Currently, the most promising strategy is spatio-temporal graph neural networks, which leverage graph neural networks for spatial dependency and sequence learning modules for temporal dynamics simultaneously. However, previous studies omit that there are complex relations between multi-mode features. And the temporal patterns captured by previous models are always limited or time-consuming. To address the above issues, this paper proposes Multi-mode Fusion Graph Neural Network (MFGNN). Different from the majority of previous studies that simply concatenate different features together, the proposed method adopts a framework that explicitly considers relations between primary features and auxiliary features. To handle the complex relations between different features, a gate-based fusion module is specially designed to filter unnecessary information. The module can control how much information from the auxiliary branch is fused and learned automatically with the data. Moreover, a graph-based temporal relation learning module is proposed. Different from convolutional neural networks or recurrent neural networks, the module establishes parameterized relations between every pair of input time slots directly, which can learn flexible yet efficient temporal patterns of traffic. Extensive experiments are conducted on three real-world datasets and two popular traffic forecasting tasks. The experimental results demonstrate the superiority of the proposed method.
Biologically-Inspired Multi-Objective Optimization for UAV Path Planning

Planning an optimal path for UAVs (Unmanned Aerial Vehicle) is one of critical and challenging tasks in many applications. Biologically-inspired multi-objective optimization is a promising optimization method using the concepts from natural computing. The feasibility of applying multi-objective optimization algorithms inspired by biology in UAV path planning is investigated in this research by analyzing algorithm performance including the accuracy, processing time, and precision. A mathematical model for optimizing the UAV path is first developed. Then two objective functions are built using the mathematical model to formulate a Multi-Objective Optimization problem properly. Afterwards, the optimization problem is solved using three bio-inspired multi-objective optimization algorithms separately (SPEA2, NSGA2 and MOEA/D) and their performance is comprehensively compared. The experimental results obtained from the simulations show that multi-objective optimization algorithms used can generate a feasible and effective path for the UAV successfully. Among the three algorithms used in this research, it suggests that all the three algorithms have the comparable performance and would be good bio-inspired multi-objective optimization approach for UAV offline path planning applications.