Miracle-dz/UniFAR-FTUs huggingface dataset:
A high quality low-light image enhancement method

Factors such as insufficient lighting, backlighting, and failed focusing can lead to low brightness, poor contrast, and noise in the collected images. These issues seriously affect the further analysis and processing of images, especially for color images with more information, which often result in color distortion after enhancement. In order to improve the quality of low light color images, this paper proposes a high-quality low light color image enhancement method. According to Retinex theory, the attention map and reflection map are initialized by constructing weight functions for three color channels, and used as inputs to the network. At the same time, using a dual discriminator structure and attention mechanism, an enhanced image is generated to achieve clearer image enhancement effects. Experimental results show that the method proposed in this paper can improve the quality of low light images with higher quality and effectiveness compared to mainstream enhancement algorithms.
Automatic Tooth Enumeration on Panoramic Radiographs Using Deep Learning

Accurate tooth numbering is essential for dental procedures, treatment planning, and patient record management. Automatic tooth enumeration in dental panoramic radiographs is crucial in modern dental image analysis and diagnosis. Tooth enumeration in panoramic radiographs has relied on handcrafted features and traditional image processing techniques. Still, these methods often lack the accuracy and robustness required for complex cases and varied image qualities. Despite the advancements in deep learning-based object detection algorithms, a significant research gap remains in the specific domain of automatic tooth enumeration on panoramic radiographs. This research paper presents an innovative approach for automatic tooth enumeration on panoramic radiographs using the state-of-the-art You Only Look Once (YOLO) object detection framework focused on implementing the YOLOv5 library. The YOLOv5 model is evaluated as an efficient system capable of accurately detecting and enumerating individual teeth from panoramic radiographs. The 612 panoramic radiograph images evaluation shows the bounding-box results show the best tooth detection rate in the YOLOv5x model. The YOLOv5 model is generally very good at predicting tooth enumeration on panoramic radiographs in a relatively small dataset. This advancement is expected to enhance dental diagnosis and treatment planning, benefiting dental professionals and patients.
Disentangled and Parallel Fusion Graph Neural Network for Human Motion Prediction

Human motion prediction is an essential problem for many applications in machine intelligence which needs forecasting future body poses given observed pose sequence. Many approaches to this problem tend to look at gestures as a whole, which complicates the problem in elaborate scenarios. In this paper, we propose a disentangled gragh neural network that decouples the whole body into several components to obtain an accurate representation of different human motions. A disentangled multi-scale human representation is proposed to extract multi-scale features of each body part. In order to match the disentangled multi-scale human representation, we propose a disentangled multi-stream graph neural network. To model the motion correlation of different parts of the human body, feature fusion is performed in parallel between different scales of the whole multi-stream network. In order to solve the problem of network degradation, a residual fusion module is proposed to strengthen the feature fusion. On the H3.6M motion prediction dataset, the proposed disentangled and parallel fusion graph neural network improves the human motion prediction performance compared with similar algorithms.
Decentralized Multi-Authority Ciphertext-Policy Attribute-Based Encryption with Hidden Policy for Cloud Data Sharing

Cloud storage services employ multi-authority attribute-based encryption (MA-ABE) with policy hiding extensively. However, in the existing MA-ABE schemes with policy hiding, all attributes are managed by a centralized authority (CA), which is also responsible for providing the user with the partial attribute keys needed for decryption. Thus, by calculating the required secret keys, CA may decrypt any ciphertext present in the cloud system. Moreover, it will increase the security risk of encrypted data in cloud. This paper proposes a decentralized multi-authority ciphertext-policy attribute-based encryption scheme that supports policy hiding. Participation in the proposed scheme does not require the centralized authority (CA), but rather numerous attribute authorities (AAS). Every AA manages several attribute sets and offers attribute keys for data users. In the encryption computation and key generation phase, we add an exponential operation to better protect the user’s sensitive data. Our scheme outperforms the current schemes in terms of functionality, as demonstrated by the performance analysis. Furthermore, the standard model has demonstrated the full security of our scheme.
Rice Yield Prediction in Sumatra Indonesia Using Machine Learning and Climate Data

Sumatra Island has over 50% of agricultural land in each province, with rice as the dominant food crop. Climate change poses a significant threat to agricultural production, impacting crop patterns, planting times, and yield quality. Rising temperatures and climate variations can further jeopardize agriculture in Sumatra. This study addresses rice yield prediction across eight Sumatran provinces in Indonesia. In this study, four combination climate and crop production data were tested to predict rice yields across eight Sumatran provinces in Indonesia using machine learning (ML) regression-based method. Six regression algorithms are employed for predictive modeling. We employed hyperparameter tunning methodologies such as Grid Search CV or Randomized Search CV for each model to improve its performance. Cross-validation (CV) with k-fold splitting is used to evaluate unbiased models. XGBRegressor appears to be the best-performing model among the six, with 88.48 R2-score, followed by Random Forest Regressor with 86.39 and Support Vector Regressor with 85.21. The XGBRegressor also has the lowest Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), which implies it explains more variance and makes more accurate predictions. This indicates its potential as the most dependable model among those examined. The research aims to provide actionable insights to enhance food security in Sumatra amidst climate challenges, aiding both farmers and policymakers in making informed decisions for stable agricultural production and food security in the region.
Real Time Drones Detection Based on Cameras and Recent YOLO Algorithm Versions

The present paper focused on the real time drones detection using the computer vision algorithm You Only Look Once (YOLO), profiting of its several versions, such as YOLOv5 and YOLOv8. In our work, we used the smallest YOLO versions in order to have the real time aspect (small (s) versions). Training, validation and test processes were done on a customized dataset. The investigated YOLO versions was compared using different metrics such as precision, recall, mAP and box-loss. Furthermore, models were optimized in order to ensure the real time aspect. OpenVino and ONNX optimizer were used, the optimization results of different models are presented, in order to find the best model for the real-time requirements. A comparative synthesis between the different models in terms of precision and inference time is carried out, in order to have the best model in terms of drones detection efficiency and in real time. YOLOv5s has a mAP of 98% and a inference time of 31F P S and YOLOv8s has a mAP of 98.6% and an inference time of 20F P S. Which makes YOLOv5s is the best model for real time drones detection.
IoTAEG: Automatic Exploit Generation of IoT Devices

Automatic exploit generation (AEG) refers to the process of automatically finding the path in the program that can trigger vulnerabilities and generate exploits. Generally speaking, the process of finding vulnerabilities needs to apply fuzzing and symbolic execution techniques. Existing AEG generally targets executables for regular Linux and Windows platforms, but no AEG for vulnerable Internet of Things (IoT) devices. In response to this situation, we propose the exploit generation system IoTAEG, which automatically detects stack overflow vulnerabilities in the firmware of IoT devices, and automatically generates and exploits the generation system based on the stack overflow vulnerabilities. IoTAEG uses the mature fuzzing software AFL++ to detect vulnerabilities in IoT devices, uses the crashed input found by AFL++ to construct a symbolic state through symbolic execution, dynamically analyzes the constructed symbolic state, and detects whether there are exploitable vulnerabilities. If the above vulnerabilities exist, different exploit generation strategies will be adopted for different protection mechanisms, and some protection mechanisms such as Address space layout randomization (ASLR) and Non-eXecute (NX) will be bypassed. For some difficult-to-exploit cases, IoTAEG uses advanced stack overflow exploitation methods to generate exploits. Experiments show that IoTAEG can complete 20 MIPS/ARM binary files and 8 IoT devices’ firmware vulnerability detection and exploit generation. IoTAEG is the first publicly available vulnerability mining and exploit generation system for IoT devices.
High Definition Mapping for Inland Waterways: Techniques, Challenges and Prospects

Inland waterway transport (IWT) is an efficient way of mass good transportation with low energy consumption and reasonable ecological impact. As for other transport systems, there is a need for reliable advanced driver-assistance functions and increased autonomy. In this regard, High Definition (HD) Maps play a role by enhancing vessels' localisation and perception capabilities. Obtaining HD Maps is an important step towards the ongoing digitalisation of water-based transport and forms the basis for assistant systems to captains of inland vessels or even higher automated ships. This work discusses the workflow for HD map generation by using vessels as information platforms (i.e., by having precise localisation and environment-detecting perception capabilities). An architecture for HD map generation from vessels provided with geodetic equipment for precise localisation and environment-detecting perception is presented. An overview on standard techniques for HD mapping within the automobile and robotics domain are discussed, as well as the particular challenges and needs present for water-based applications. Finally, the conceptual use and appropriate data exchange of HD Maps are drafted.
Stability Analysis of Connected Automated Vehicle Platoon Controller Under Unreliable Communication Conditions

The paper investigates the longitudinal control problem of a connected automated vehicle (CAV) platoon operating under the multiple-predecessor following (MPF) topology, while considering unreliable communication circumstances characterized by random packet loss and heterogeneous time-varying delay. To model random packet loss, a Bernoulli probabilistic distribution is employed, where the packet loss rate is influenced by the communication distance. A linear feedback controller is then developed to handle such packet loss and delay. The sufficient conditions for the asymptotic stability of the platoon controller are derived using linear matrix inequalities and a Lyapunov-based approach. The delay margin condition is obtained, which is associated with the packet loss rate and the number of consecutive packet losses. The string stability of the platoon with delay and packet loss is also analyzed by the transfer function. Simulation experiments demonstrate that the proposed method approach effectively maintains the stability of the platoon in the presence of delay and packet loss.
Adaptive Multi-Sensor Integrated Navigation System Aided by Continuous Error Map from RSU for Autonomous Vehicles in Urban Areas

There has been a widespread study on multi-sensor integration to achieve precise and robust odometry for autonomous vehicles (AVs) in urban areas. LiDAR odometry and visual odometry can be affected by structureless scenarios and numerous dynamic objects. GNSS positioning can be degenerated due to the multipath and non-line-of-sight signals by buildings. Therefore, selecting appropriate weighing for heterogeneous sensors is a challenge for multi-sensor fusion. With the advancements in cellular vehicle-to-everything (C-V2X) and intelligent roadside units (RSUs), vehicles and the RSUs can collaborate to deliver reliable service. Inspired by this, this paper investigates continuous error maps for available sensors under different time conditions (noon, sunset, and night) to improve the positioning performance of surrounding AVs in complex urban environments. In particular, this paper presents an error-map-aided multi-sensor integrated system, which benefits from the error information collected by a sensor-rich AV. Then the error information is uploaded to the RSUs which is then distributed to the AVs. A smaller weight is assigned if a larger error is queried from the error map. To validate our approach, experiments were performed using the realistic CARLA simulator and our self-developed GNSS RUMS simulator. To benefit the research community, we open-sourced the implementation on our project page33https://sites.google. com! view/v2x-cooperative-navigation.
MLM-Net: Streamlined Multi-Lane Detection Network with Spatio-temporal Memory for Video Instance Lane Detection

Lane detection is a crucial task in autonomous driving as it helps vehicles maintain stable and efficient driving in complex traffic environments. Image-based and video-based lane detection are two common methods, with video-based lane detection being more effective in handling complex scenarios such as lane occlusion and wear. However, most existing video-based lane detection methods suffer from information redundancy, where the same key frames of the same lane are repeatedly calculated during the detection of multiple lanes. Additionally, the spatio-temporal sequence information of video-based lanes is not fully utilized. To address these challenges, MLM-Net is proposed as a streamlined multilane network that facilitates the sharing of spatiotemporal information. The Global Memory Feature (GMF) module is redesigned to mitigate information redundancy across multiple lanes, while the Local Spatio-Temporal Feature (LSTF) module is introduced to enhance the utilization of spatiotemporal consistency information. Additionally, the Enhanced Global Memory Feature (EGMF) module is introduced to extract enhanced features and handle challenging detection scenarios. Experimental results demonstrate that our method achieves state-of-the-art performance for video instance lane detection on the VIL-100 dataset.
Translating the unspoken Deep learning approaches to Indian Sign Language recognition using CNN and LSTM networks

In India, there are over 5 million deaf and mute people, with many cases going unreported. Communication can be difficult due to the lack of standardized sign language across the country. The Indian Sign Language (ISL) Detection project aims to improve communication by developing a system that can recognize and interpret ISL gestures. The system uses a type of artificial intelligence called machine learning, specifically Convo-lutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. These techniques help the system detect and classify ISL gestures from video input with high accuracy. The project has the potential to provide a more accessible means of communication for millions of deaf individuals in India.
New Product Launching and Hedonism: Augmented Reality as A Moderator of Impulsive Buying Behavior on Motor Show Events

This paper aims to investigate the effects of new product launching, hedonism, and Augmented Reality as a moderator on visitor intention at Motor Show Events. Online survey was used to collect data. Data were analysed using Partial least squares structural equation modelling (PLS-SEM) techniques. The result shows that new product launching, and hedonism significantly and positively affect visitor intention. Furthermore, augmented Reality also significantly moderates between new product launching and intention to visit, as well as hedonism and intention to visit.
Hard Example Mining-based Training Strategy for Pointly-Supervised Instance Segmentation

The time-consuming task of generating mask-level annotations has impeded the extensive adoption of instance segmentation. To address this challenge, weakly-supervised instance segmentation has emerged, leveraging more available annotation types to achieve performance comparable to fully-supervised approaches. Recent advancements in pointly-supervised instance segmentation demonstrate promising outcomes by employing a mere 20 labeled points within bounding box per object. This approach substantially decreases the annotation burden while preserving competitive performance levels. However, its indiscriminate training strategy for all labeled points severely limits the segmentation performance of the model. In this paper, we propose a novel Hard Example Mining-based Training Strategy for pointly-supervised instance segmentation, effectively utilizing limited point-based supervision and focusing on challenging labeled points. Our method addresses the need for efficient supervision by leveraging sparse point annotations while maintaining competitive performance. On COCO, we show that models trained with our strategy yield significantly gain compared against normal training strategy.
Mobility Assistance for the Visually Impaired in Unstructured Environment

With the rapid upsurge in deep learning technologies and their implementation in various areas of computer vision and image processing, a substantial amount of research work has been focused on obstacle detection and mobility assistance for the visually impaired, the accuracy and performance of which are subject to the availability of reliable datasets. However, these works are mostly based on accessible environments that have a standard structure, which is considerably easier to design and implement. These technologies are prone to failure in congested and unstructured environments, and cannot be utilized by the visually impaired people of regions where pathways and road systems are not consistently constructed keeping in mind the mobility of people with disabilities. The aim of this work is to develop a camera-based mobility assistance system that functions in unstructured environments. In this paper, we propose a novel dataset of obstacles commonly encountered by pedestrians on unstructured roads, that we have collected from various unstructured regions and annotated according to appropriate labels. In the proposed system, we have built a computer vision and deep learning based object detection framework which is trained and tested on the curated dataset. We have also created a separate dataset with the help of visually impaired people, to test our model and determine its robustness in real-world scenarios. The model achieves good overall accuracies on the proposed dataset and shows promising scope for safe and reliable mobility assistance for the visually impaired upon large-scale user-testing.
Interplay between Cost and Energy Consumption of 5G C-RAN With Flexible Fronthaul Split

In recent times, a centralized radio access network (C-RAN) has been suggested to expand network capacity, lower energy use, and enhance scalability. Due to the rapid expansion of the telecom sector towards 5G and beyond (5GB) and the emergence of data-driven and time-sensitive applications, mobile network operators (MNOs) are facing a massive problem in fulfilling these new expectations. The CRAN is an affordable architecture that improves 5GB speed. CRAN’s fronthaul portion demands a link with high capacity and low latency. In this research, we suggested a method for employing the CRAN architecture to optimize the total cost for 5G under various splitting situations. As fronthaul links with three splitting conditions, we took into consideration TWDM-PON, EPON, AON, and mm-Wave.
Trajectory Planning for Formation Variation of UGVs in Cluttered Environment

The problem of trajectory planning for formation variation of unmanned ground vehicles (UGVs) is challenging due to the coupled non-collision constraints that apply to multiple vehicles. To address this issue, an innovative frame-work based on the alternating direction method of multipliers (ADMM) is proposed in this paper. The framework decouples complex constraints into two categories: individual constraints and mutual constraints. The former category involves dynamic, formation, and static obstacle constraints, which can be solved in parallel by each vehicle. Convex feasible set (CFS) algorithm is employed in this paper to simplify the static obstacle constraints. The latter category deals with collision avoidance among vehicles during the variation process and can be solved by nonlinear solvers. The convergence of the ADMM for the optimization problem is proved in this paper. Compared with solving the entire problem directly, the proposed framework reduces computation time and improves the quality of solution.
Classification of Geometric Shape Drawings by Using SVM and CNN Models

The landscape of education in Indonesia has been significantly shaped by globalization, with technology emerging as a pivotal influencer. This study addresses this intersection by leveraging technology to enhance teaching, focusing on preschool children’s engagement with digital tools. Researchers have developed a straightforward detection application using a convolutional neural network (CNN) as a learning tool, capitalizing on the allure of gadgets for young learners. Alternative algorithmic methods, including Support Vector Machine (SVM), were employed to assess and compare classification processes. The CNN algorithm normalizes output values and generates probabilities for each class, facilitating a comprehensive performance evaluation. This research uses 1000 images of hand-drawn simple plane figures to determine the optimal outcome, comparing accuracy values, classification reports, and confusion matrix values. The analysis reveals that the CNN model significantly outperforms the SVM model in the classification task, achieving a remarkable 100% precision, recall, and F1-score. In contrast, the SVM model attained an average precision, recall, and F1-score of 88%. This study underscores the efficacy of CNN in enhancing learning experiences for preschoolers, offering valuable insights into integrating technology into education.
The Implementation of A* Algorithm for Global Path Planning in Autonomous Vehicles

Path planning is utilized to design trajectory planning systems for autonomous vehicles. The A* algorithm has been proven successful in creating optimal, efficient, and safe navigation paths for autonomous vehicles. The resulting vehicle navigation path is an optimal trajectory considers heuristic values. The parameters employed in this research include the heuristic value, G-value (current cost), F-value (total estimated cost), Openlist, and Closelist. An average total estimated cost value of 2475 was obtained, with an average distance traveled over 10 seconds amounting to 139 meters. The results indicate that calculating lateral position from the path planning using the A* algorithm is reliable.