Miracle-dz/UniFAR-FTUs huggingface dataset:
Malicious Variant Text Alignment Algorithm Based on Improved Soundshape Codes

In order to improve the extraction ability of illegal information from various platforms in the network, this paper proposes a non-equal-length malicious foreign characters alignment algorithm based on improved soundshape code combined with Needleman-Wunsch algorithm. The algorithm first uses the improved soundshape code to transform Chinese characters into a special coding sequence that integrates soundshape, morphological and semantic features, and then according to the principle of maximizing the sum of the similarity of characters at each position in the sequence, the Needleman-Wunsch sequence comparison algorithm combining the character similarity function is utilized to complete the alignment of the original text with the deformed text by correspondence and padding. Experiments were conducted on a constructed dataset containing malicious variants, and the results show that the algorithm improves the alignment effect due to reduced character padding and accurate text correspondence.
Cross-Domain Heterogeneous Hyperspectral Image Classification Based on Meta-Learning with Task-Adaptive Loss Function

The scarcity of labeled samples poses a significant challenge in hyperspectral image (HSI) classification. Cross-domain classification offers a potential solution by leveraging information from a source domain with abundant labeled samples to classify the target domain, even when only limited labeled samples are available. Our work is to classify the target domain where there are classes that are not in the source domain, which is a difficult task. This paper presents a method called cross-domain meta-learning with task-adaptive loss function (CD-MTALF) for heterogeneous HSI classification. CD-MTALF is a transfer learning-based method specifically designed to address the issue of limited labeled samples in the target domain. CD-MTALF adopts a meta-learning approach based on optimization. It consists of two optimization tasks: 1) Inner-loop optimization: adapting the base learner to a new task using a learning algorithm. 2) Outer-loop optimization: training the base learner, meta-learner, and meta-learnable loss function and employing an adaptive weighting strategy to balance the losses between the source and target domains. CD-MTALF aims to enhance the model’s generalization performance by incorporating both inner-loop and outer-loop optimizations. Experiments on publicly available HSI datasets demonstrate the efficacy of CD-MTALF in improving classification performance.
Unlocking The Power Of Deep Learning: A Guide To Automatic License Number Recognition

An image processing technique called Automatic Number Plate Recognition (ANPR) utilizes the vehicle’s number plate to identify it. The goal is to create a successful automatic authorized car identification system that makes use of the license plate. In the majority of applications involving the movement of vehicles, the detection and recognition of a vehicle’s license plate (LP) is a crucial method. Additionally, it is a hot research subject in the area of image processing. To find and identify LPs, numerous techniques, methods, and algorithms have been created. Prior to taking a picture of the car, the developed system first recognizes it. The region of an image that contains the car number plate is retrieved using image segmentation. An optical character recognition technique is used to recognize characters. However, due to the LP’s characteristics which include the numbering system, colors, character languages, fonts, and size varying from one nation to the next. To make the detection and recognition procedure very effective, more research in this area is still required. Despite the fact that many scholars have studied this field, many current systems function in defined and regulated environments. For instance, some frameworks need sophisticated hardware in order to produce high-quality images or record video from cars moving slowly. Because of this, it is still challenging to successfully discover and identify LPs under various environmental circumstances and climatic variations. This article presents a deep learning-based automatic system for LP detection and recognition that is divided into three sections: character recognition, segmentation and detection.
Pixel-Based Vertex Clustering for Spectral Data Enrichment of Planar Point Clouds

Application of both hyperspectral imaging and 3D point clouds have been utilized in various fields that investigates a surface’s geometrical properties and its material characteristics. The two modalities are acquired using different sensors and must subsequently be fused in order to be analyzed in a common dataspace. This fusion is a complex and time-consuming process, and has only recently been investigated properly with remote sensing. We propose an approachable and fast workflow that highlights the most important aspect of each method for applications with proximity imaging, meant to be used for quick investigations of an objects geometric surface and its material characteristics. Data is collected using commercial instruments, and simplifies the data while retaining and highlighting the most important geometrical and spectral features.
Comparative Analysis of Traditional and Machine Learning Based Face Recognition Models

Face recognition technology uses methodologies to process images, register them, and compare and verify it with a set of faces it has already registered and labelled. Recent development in visual recognition showcases the immense power of utilising machine learning techniques like deep neural networks, convolutional neural networks to achieve face recognition. Based on a comprehensive literature review it was found that some of the algorithms that provide great performance metrics in a variety of conditions are MTCNN, FaceNet model, Haar cascade, LBPH, hence it was chosen to be the subject of comparison under various conditions in the paper. A machine learning model was constructed which included MTCNN for face detection and FaceNet for face recognition.The Performance of KNN and SVM classifiers has been studied. The second model uses a combination of Haar cascade for face detection and LBPH for face recognition, a traditional face recognition technique. The results highlight that MTCNN, FaceNet and KNN perform exceptionally well. The performance of the other models are also discussed in the results and discussion section. The research not only guides researchers and practitioners in choosing suitable face recognition techniques but also offers ground for future improvements in accuracy and reliability in the field of algorithm developments.
Multimodal Cyberbullying Detection on Social Media: Review and Challenges

Cyberbullying is a prevalent issue on social media platforms, where people employ various digital means to harass and intimidate others, particularly affecting young individuals. According to a study by the Cyberbullying Research Center, 34% of students aged 12–17 have experienced cyberbullying. Unfortunately, cyberbullying is frequently underreported, with only 10% of affected students reporting it to adults. To tackle this problem, machine learning-based automated detection systems have been developed. However, single modality detection systems that focus on analyzing only one type of data, like text or images, may have limited accuracy due to the complex and variable nature of cyberbullying behavior. This paper explores the importance of multimodal cyberbullying detection on social media and the challenges involved in creating effective detection systems. It examines existing literature on multimodal cyberbullying detection, emphasizing the advantages of analyzing multiple modalities. The paper also addresses the ethical considerations associated with analyzing personal data to identify cyberbullying behavior.
Single-Switch Isolated Buck-Flyback PFCC for BLDCM Ceiling-Fan

This paper presents a sensorless single-switch isolated buck-flyback (SSSIB-flyback) power-factor-correction converter (PFCC) with a change in output-voltage to minimize harmonics distortion at the input-mains for the brushless-direct-current-motor driven ceiling-fan (BLDCM-CF). Converter operates at high-frequency switching, and it’s both inductors are designed in discontinuous conduction mode (DCM) to reduce their size and for an auto power factor correction (PFC) at the input-mains. The low values of inductors with fewer components count of converter have low losses. For the sensorless operation of front-end converter, an auxiliary-wdg voltage ($V_{a}$) is estimated as a feedback, and reference voltage for the CF-speed. The Hall-signal estimated with back-EMF control to get the sensorless operation of BLDCM. The CF-motor speed is controlled with voltage-speed linearity and inverter fundamental switching. The design, analysis, and performance of the front-end converter fed CF are verified with MATLAB/Simulink results. It shows satisfactory performance with low supply current harmonics distortion within the limits of IEC standard 61000-3-2.
An Automated Rice Leaf Disease Identification using Deep Learning

Extensive research and modern advancements have proven that Artificial Intelligence (AI) has brought a drastic change for all the sectors of the economy including the rural agricultural sector. This research paper is focused at applying the method of Deep Learning (DL) for rice leaf disease identification - a major problem in the farming industry. This research project incorporates methods like data preprocessing, model training, and evaluation with the help of deep learning architectures like Tensorflow, transfer learning, Conv2D and BatchNormalization. Thus, with a combination of these intelligent techniques, the contribution of this research paper is that the proposed custom model has acquired an accuracy of 95% while using noticeably lesser number of layers when compared to some of the popular pre-trained AI models like MobileNet, ResNet and InceptionV3.Deep Learning (DL) has immense potential to demonstrate the benefits of AI in farming and agriculture. There are countless opportunities to detect rice leaf diseases early with the objective to improve crop management and productivity through Artificial Intelligence (AI). Keeping this in mind, the data for training AI model has been optimized using ImageDataGenerator followed by strategic callbacks. The result speaks for itself as it provides precise accuracy to identify rice leaf disease. Additionally, this research paper talks about precision agriculture and its importance in the modern agricultural industry. It highlights the prospective to increase farming efficiency, reduce resource wastage, and foster sustainability in agriculture. Through this research, it is evident that the utilization of AI in the agricultural sector can facilitate informed decision-making, resource optimization, and the early detection of plant diseases.
Early Detection of Rolling Bearing Faults Using Long Short-Term Memory

Bearings are the vital components of rotating machines in wind turbines and industrial productions. Any bearing fault might result in system failures, interruptions in industry process and huge losses of productivity. Therefore, detecting bearing faults in early stages is essential to prevent system outages and reduce maintenance costs. This paper presents a data driven fault detection method, based on long short-term memory (LSTM) and fault characteristic frequencies. Within the framework, both time series data and frequency domain features are fed to the LSTM model-based autoencoder to train the model, aiming to detect early abnormal bearing states through the learning method. Further, supervised learning methods of classifying the bearing faults are compared based on accuracy. The proposed method can detect incipient bearing faults, allowing the operators to have right actions to prevent a large-scale failure in energy systems. The suggested scheme is validated through experimental data, being able to discriminate anomalies from the observed multivariate data.
Satellite Video Object Detection Network based on DLA-34

Now, satellite video data faces challenges such as complex backgrounds, limited spatial features of targets, and artifacts caused by satellite motion, all of which increase the difficulty of target detection. We have designed a Satellite Video Object Detection Network (SVODNet). The algorithm consists primarily of a temporal dynamic feature extraction module and a grouped channel attention module. The temporal dynamic feature extraction module captures the changes of targets over time by utilizing both global and local information, effectively overcoming the influences from complex backgrounds, limited spatial features, and artifacts. Additionally, to further analyze the dynamic changes of targets throughout the entire video sequence, we employ a grouped channel attention mechanism to integrate global and local information. The SVODNet has been evaluated on Jilin-1 satellite data with competitive results.
Bidirectional Multi-Stack RNNs with Attention for Machine Translation

This project developed a novel attention algorithm for multi-stack bidirectional encoder-decoder RNN (including GRU and LSTM) sequence-to-sequence models, particularly for language translation tasks. The attention mechanism utilizes matrix rearranging and multiplication to compute the significance of the vectors in the encoder output to the vectors in the current decoder hidden states when predicting each word. Our approach achieved 98% of the performance of fine-tuned pretrained T5-small, with 30% to 50% fewer parameters depending on vocabulary size, making our model an ideal choice in cases of single-processor training, low processor resource, limited memory, small dataset, or tasks not supported by pretrained transformers.
GUI Testing Using Random Event-Based Test Cases

This paper presents an approach for test case generation of Graphical User Interface (GUI) based applications. Test case generation is a challenging issue for the software industries. In many cases, the test case generated may not be presented in the actually developed software. This type of problem is mostly experienced in the case of random test case generation. This paper uses the idea of first indexing all the events for execution based on the graph generated from the event interactions. These graph events are heuristically tested to get the desired results. In this paper, we have proposed the heuristic search technique based on genetic algorithms for testing. We have made a comparison of the test cases generated from the heuristic search technique with the iteration-based approach like the Hill Climbing approach. From the deviation in the test cases generated by the proposed approach, it is found to be better in comparison to the hill climbing-based approach. If we have more deviation in the test case, then this implies we are targeting the different areas of software for testing rather than iterating in the same module of the software.
Machine Learning-based Software Bug Classification through Mining Open Source Repositories

A taxonomy of expected bugs in a typical program can be useful while performing several software engineering tasks such as test case design, mutation testing, and fault localization. In this context, we have analyzed the bug characteristics available in two open source projects: Eclipse and NetBeans. On these bug repositories, we have used a discriminative classical algorithm and three machine learning techniques to classify 126536 bugs into 9 different categories. Consequently, we could classify several hidden patterns over time in different classes. Our experimental results show an 29.73% increase in classifier performance in terms of AUC-ROC as compared to state-of-the-art results.
A new species of "Pseudocellus" Platnick, 1980 (Ricinulei: Ricinoididae) from Guantanamo Bay, southeastern Cuba

The ricinulid Pseudocellus aridus n.sp. is described from a single adult male collected in a xerophytic locality east of Guantanamo Bay, southeastern Cuba. This new taxon is very easy to distinguish from all its described Cuban congeners by the morphology of the leg II and a unique combination of other characters such as the degree of attenuation and sculpture of the body and appendages, size, coloration, and structure of the copulatory organ. This represents the fourth species described from Cuba, as well as the third from its eastern region and the second from Guantanamo province.
Dealing with Black-hole Attacks in Inter-vehicle Networks Using the Packet Delivery Rate Algorithm

The VANET is a type of case mobile networks (a decentralized type of wireless networks) which includes moving vehicles as nodes in the network that establishes communication among adjacent vehicles as well as stationary vehicles and equipment which are usually installed along the roads. Therefore, reducing network overhead and traffic and increasing the data transmission security as well as the Packet Delivery Rate (PDR) are the most important issues related to VANETs. One of the most important challenges in the inter-vehicle networks is the presence of security attacks such as black-hole attacks in which the malicious node eliminates data packets and reduces the quality of the network. Therefore, this paper proposes a method to detect and eliminate the black-hole attacks in three phases. The so called DABHA-VANET in the first phase confirms the new join requests and verifies valid vehicles by checking the previous database and number of the steps. In the second phase, it applies the PDR algorithm to detect malicious nodes of the black-hole and then eliminates these nodes from the network and routing. Finally, the NS-2 simulator is used to compare DABHA-VANET with the PFDSA method. The provided results indicated an acceptable performance of the proposed approach.
Modified-Distributed Energy-Efficient Clustering (M-DEEC) for Heterogeneous Wireless Sensor Network

In wireless sensor networks sensor nodes are distributed randomly within a defined monitoring region. Employing clustering techniques greatly assists in lowering energy usage, therefore successfully prolonging the total lifespan of the WSN. This research provides a unique clustering approach termed the Modified Distributed Energy Efficient Clustering Strategy for heterogeneous WSN. The technique dynamically modifies the Cluster Head election probability depending on the threshold residual energy value. Through simulation findings, it is proven that M-DEEC beats both LEACH and EDEEC in terms of boosting the network’s lifetime and efficient energy use. Additionally, our technique exhibits remarkable potential for improving the sustainable and sustained functioning of WSNs in varied application settings.
Visual-Based Hearing-Dog Robots For Users With Hearing Impairment

With the advancement of robotics and related cross-technologies, robots are becoming ubiquitous not only in industrial applications but also in social services. Socially service robot aims to assist vulnerable people with rehabilitation training and life adaption. For example the number of people with hearing impairment, reaching 466 million across the world in 2018. One promising way to help is to use hearing-dog robots. Some research has shown that touch-based hearing dog robots have positive impacts on altering users. However, few studies have verified the effectiveness of visually attracting users’ attention by a hearing-dog robot and of a variety of interactive ways to convey the information. To fill this gap, this paper will build a robot system that is capable of visually prompting users, including sound recognition, body movement, LED screen, cross barriers, and face detection. The experimental tests involving 16 participants were conducted to assess the effectiveness of this robot, proving that our proposed robot system can provide effective assistance within the appropriate field of view, in an interactive way to convey sound for people with hearing impairment.
Music and Emotions - A tool to create new playlists for a musical listening device in seclusion rooms in psychiatric hospitals

This article presents research designed to demonstrate the impact of a musical listening device in psychiatric intensive care. The results described in this work are based on statistical data on the effect of listening to music, and on interviews with patients and caregivers. This contribution focuses on patients’ listening strategies, as well as on the way caregivers appropriate the device. We elaborate the case study first. Finally, the article points out several improvements to the system suggested by the research findings, particularly through the use of artificial intelligence.
Multi-Domain Semantic Segmentation in Enhanced DeepLabV3+

Semantic information in images can be extracted using semantic segmentation, an essential task in the field of computer vision. Semantic segmentation, however, faces several challenges across a wide variety of domains, including textiles, cartoons, and real images. The semantic segmentation models need better contextual information comprehension and target segmentation abilities due to the complexity and abstraction of patterns in the textile domain, the variety of cartoon drawing styles, and the complexity and variety of image backgrounds in real scenes. In this work, we first define the semantic labels of patterns and construct multi-domain datasets such as textiles, cartoon images, and daily scenes; then, based on the DeepLabV3+ model, we propose the optimized model MDE-DeepLabV3+, which introduces the channel spatial attention mechanism to obtain more feature information and improve network performance ability. The experimental results show that the mean Intersection over Union of this paper's method on the multi-domain dataset is 79.00%, and when compared to the traditional method, this paper's model effectively improves the accuracy and richness of feature extraction while reducing feature detail information loss.
A compact RGB-D map representation dedicated to autonomous navigation

Our aim is concentrated around building ego-centric topometric maps represented as a graph of keyframe nodes which can be efficiently used by autonomous agents. The keyframe nodes which combines a spherical image and a depth map (augmented visual sphere) synthesises information collected in a local area of space by an embedded acquisition system. The representation of the global environment consists of a collection of augmented visual spheres that provide the necessary coverage of an operational area. A "pose" graph that links these spheres together in six degrees of freedom, also defines the domain potentially exploitable for navigation tasks in real time. As part of this research, an approach to map-based representation has been proposed by considering the following issues : how to robustly apply visual odometry by making the most of both photometric and ; geometric information available from our augmented spherical database ; how to determine the quantity and optimal placement of these augmented spheres to cover an environment completely ; how tomodel sensor uncertainties and update the dense infomation of the augmented spheres ; how to compactly represent the information contained in the augmented sphere to ensure robustness, accuracy and stability along an explored trajectory by making use of saliency maps.