Reviewerly/exHarmony huggingface dataset:
Heterogeneous UAV fleet delivery route: a novel discrete sheep flock migrate optimization algorithm

Completing the delivery task of rescue supplies with the fastest delivery time during natural disasters is very important. For emergency logistics delivery scenarios in complex terrain environments, drone delivery has gradually become a hot issue. We introduce an optimization of heterogeneous UAV delivery path in complex task scenarios. The proposed model aims to minimize the total delivery time of multiple drones, taking into account not only the impact of different types of drones’ endurance and load capacity on path selection, but also the timeliness of delivery tasks. To solve the proposed problem, we propose a novel discrete sheep flock migrate optimization (DSFMO) algorithm. As a type of swarm intelligence optimization algorithm, DSFMO algorithm has the characteristics of high efficiency, independent of mathematical models, and not easily trapped in local optima. In the case validation section, the case was designed based on the rescue scenario of the Sichuan earthquake, we analyzed and solved the problem model using the DSFMO algorithm, verified the effectiveness of the model and algorithm, and discussed different task scenarios and drone storage quantity. The experimental results indicate that, while ensuring the completion of disaster relief tasks and considering the load constraints of heterogeneous, the DSFMO algorithm can optimize the timeliness of drone delivery. This study can provide theoretical basis for drone logistics distribution in complex environments, and thus provide support and reference for drone path planning research.
PRISM: A System of Evaluating Trustworthiness of IoT by Multi-Modal Surveillance

In this paper, we propose PRISM, a framework to evaluate trustworthiness of IoT system by multi-modal surveillance. Traditional surveillance methods can identify target people's trajectory and check objects' status in the IoT system, which mainly leverage video cameras to observe target people or objects by their appearance features through vision data. Nevertheless, such methods can easily be disturbed and fail in real scenes, such as occlusion, different illuminating conditions, or similar appearances of multiple targets. On the other hand, most people carry Wi-Fi enabled mobile phones with them, whose wireless signals can be sensed by Wi-Fi access points for positioning the person. The wireless signals are robust to vision disturbances, but are inevitable to introduce localization errors, nor can they provide the actual figures of the person. In this work, we combine videos and wireless signals and compliment one another for a novel multi-modal surveillance. We match videos and wireless signals of the same target and fuse for more representative features. Our method not only outperforms traditional surveillance method by over 45% in accuracy, but also improves overall matching rankings for all correct candidates.
Joint Sparsification and Quantization for Wireless Federated Learning under Communication Constraints

Communication efficiency is one of the key bottlenecks in Federated Learning (FL). Compression techniques, such as sparsification and quantization, are used to reduce communication overhead. However, joint designs of these techniques under communication constraints are not well-explored. This paper investigates the joint uplink compression problem in communication-constrained FL systems. We propose a Block-TopK sparsification scheme to reduce the proportion of bits used for locating entries of a sparsified vector. Considering the communication constraints, an optimization formulation is proposed to minimize the compression error. By solving the optimization problem, our joint compression method provides a better trade-off between sparsity budget and bit width. Numerical results demonstrate that our approach achieves 99.96% of baseline accuracy with only 1.56% of the baseline communication overhead when training ResNet-18 on CIFAR-10.
3D Depth Map Inpainting for Vietnamese Historical Printing Woodblocks: A Gated Convolution Approach

This research paper explores the application of inpainting techniques to reconstruct missing information in 3D depth maps of Vietnamese printing woodblock, a heritage object in need of preservation and reconstruction in Vietnam. In the paper, we provide a comprehensive review of related work encompassing various 3D formats, with a particular emphasis on depth map. The discussion delves into inpainting methods in the domain of depth images, notably including the deepfill with gated convolution approach [1] and Mask-Aware Transformer (MAT) inpainting [2] with transformer networks. Experimental results demonstrate the effectiveness of a gated convolution approach in restoring the missing data in woodblock images. We evaluate our technique using quantitative measures and visual comparisons, highlighting its ability to produce accurate and coherent results. We also discuss the strengths and limitations of our method, along with potential areas for future research. Our research contributes by evaluating the performance of popular 2D inpainting methods on 3D depth data. Moreover, our study contributes to the preservation and analysis of historical artifacts, specifically Vietnamese printing woodblock, by leveraging inpainting techniques to reconstruct their missing information.
FIW-GNN: A Heterogeneous Graph-Based Learning Model for Credit Card Fraud Detection

The global economic losses caused by credit card fraud are enormous and continuously increasing. Effective and accurate fraud detection has become a crucial task in recent years. Prior approaches can achieve good detection performance under certain conditions. However, these existing methods lack the robustness and scalability to deal with real-world credit card transactional datasets containing a large number of missing values. In this paper, we propose a Feature Importance-based Weighted Graph Neural Network (FIW-GNN) as an effective, stable, and practical solution for credit card fraud detection. First, we propose a method to construct a heterogeneous graph designed for credit card transactional datasets. Next, based on the architecture of the relational graph convolutional network, a feature importance-based method is employed to assign edge weights. Finally, we evaluate the effectiveness of FIW-GNN on two benchmark datasets. The experimental results demonstrate that FIW-GNN outperforms the state-of-the-art baselines in all selected evaluation metrics.
VehiDE Dataset: New dataset for Automatic vehicle damage detection in Car insurance

In the world of auto insurance, automatic car damage identification has garnered a lot of interest. However, it is difficult for us to develop a workable model for car damage identification due to the absence of high-quality datasets that are accessible to the general public. In order to achieve this, the Vehicle Damage Detection (VehiDE) dataset, the large-scale dataset made available to the public for the purpose of segmenting and detecting visual automotive damage. This dataset comprises 13,945 high-resolution photos of damaged cars together with more than 32,000 occurrences of each damage category with detailed annotations. Statistical dataset analysis is provided together with a description of the image collecting, selection, and annotation procedures. In order to emphasize the expertise of automotive damage identification, extensive experiments on the VehiDE dataset are conducted using cutting-edge deep approaches for a variety of jobs and provide thorough analysis.
Control Plane Isolation of Network Security Protocols using FPGA-SoC Trusted Execution Environment

Current and next-generation communication infrastructures are enabling the realization of critical applications. These applications rely on operating systems such as Linux and have strict requirements for performance and security. Due to the performance limitation of general-purpose CPUs hosting the operating systems, the data plane of security protocols is offloaded to dedicated hardware, such as FPGAs and ASICs, with the control plane kept in software. The resulting system architecture introduces a new attack surface on the security protocol. Several threats have been identified that could risk the confidentiality, integrity, and availability of the control plane. A Trusted Execution Environment (TEE) is an isolated environment for executing code securely, thereby, discarding the impact of threats from the rest of a device. A TEE is achieved by partitioning the hardware and software resources of a processor. This paper analyzes the use of TEE in FPGA-SoCs for isolating the control plane of security protocols from the operating system. The data link layer security protocol MACsec was chosen as a reference use case, however, the assessment framework can be applied to other security protocols as they share a similar system architecture. This paper proposes three types of isolation and presents the architecture requirements for its implementation using OP-TEE, an open-source TEE implementation for Arm TrustZone technology inside Arm processors. The isolation can provide a significant reduction in the impact of threat events in the system with 10 out of 12 threat events being fully prevented.
An AI Contribution to African Economic Development Through Mother Tongue Education - A Linguistic Evaluation of an African-Based AI Mathematics Tutoring Bot

In recent years, Africa has been economically battered by the lockdowns imposed by governments around the world due to the COVID pandemic. Then, as countries started to recover from the lockdowns, the European war between Ukraine and Russia has caused additional economic pain. This paper looks at how artificial intelligence can be used to help with economic development in Africa. Recent advances in artificial intelligence make it possible to create mobile tutoring bots which pupils and students can access from their mobile devices and receive help in whichever topic has been configured. This paper specifically looks at an artificially intelligent mathematics tutoring bot and investigates how well this bot communicates with pupils and students using the mother tongue of the pupil or student. In this particular study, Afrikaans was the language under research.
Lens Flare Attenuation Accelerator Design with Deep Learning and High-Level Synthesis

Lens flare artifacts are undesired visual distortions caused by stray light, which can negatively impact the integrity and quality of an image. These artifacts pose a significant challenge in industrial applications like automotive and surveillance, where the quality and reliability of input images from cameras are crucial. Artificial intelligence, particularly deep learning neural networks, have shown promising results in attenuating lens flare. In this work, a synthetic flare dataset is generated, and an iterative training process that includes evaluation of transfer learning is employed to develop FlareNet, the first compact and lightweight U-Net based model for lens flare reduction. The FlareNet architecture, with less than 150,000 parameters comprising convolutional layers, demonstrates improvement in image quality by reducing flare artifacts on synthetic test images and real-life images, indicating its potential for achieving visually satisfactory results despite having less than 0.5% of the weights of the state-of-the-art neural architecture used for this same application. To demonstrate the viability of using a model such as FlareNet as a hardware accelerator, the neural network is implemented in C++ using Vitis HLS. Synthesis and validation are performed using the Vitis tool, and reports are analyzed while experimenting with HLS optimization directives. Resource utilization of less than 20% on a Zeus Zynq UltraScale FPGA is shown but further work is needed to optimize the design for real-time applications and effectively deploy the solution on an FPGA.
Selective Attention UNet for Segmenting Liver Tumors

In order to segregate liver tumours in medical imaging applications, a novel architecture called Selective Attention UNet is proposed in this article. The suggested architecture, which is based on the well-known UNet architecture, has a selective attention module that enables the network to concentrate on crucial tasks while suppressing unnecessary ones. Link skipping between the encoder and decoder routes is another element of the design that enables the network to effectively segment data using both low-level and high-level attributes. On the publicly accessible LiTS dataset, we assessed the performance of the suggested architecture and contrasted it with four fundamental models: FCN, UNet, UNet++, and SegNet. The Dice Similarity Coefficient (DSC) of 0.89 a mean IOU of 0.76 obtained in our experiments demonstrates that the suggested architecture beats all baseline models in terms of accuracy and robustness criteria. The project is accessible at: https://github.com/darshan8850/Liver-tumor-Segmentation
Test Case Generation from Quality Attribute Scenarios Using Machine Learning Approach

Identifying testable quality attribute scenarios (QASs) and generating test cases is challenging due to subjectivity, complexity, interdependency, and lack of well-defined metrics in the quality attributes. In this study, we address the problems by developing a ML model that classifies QASs and generates test cases from testable QASs. The proposed model first classifies QASs into testable or non-testable and then it generates test cases from testable QASs. We collected 1967 QAS dataset from literature, textbooks, and publicly available software specification documents. The Machine Learning algorithms used to build the model are SVM, MNB and DTree for the classification of QASs and RF, AdaBoost and GBM for the generation of test cases. This study employed TF-IDF and word2vec techniques for feature extraction. Grid search techniques are used to tune the optimal value of hyperparameters from the predefined possible values. The QAS classification model prediction accuracies are 89%, 88%, and 82% DTree, SVM, and MNB with TF-IDF respectively and 70%, 79%, and 60% DTree, SVM, and MNB with word2vec respectively. For the generation of the test case, the proposed model performs 87 %, 78 %, and 86 % RF, AdaBoost, and GBM with TF-IDF respectively, and 54%, 56%, and 62% RF, AdaBoost, and GBM with word2vec respectively. DTree with TF-IDF outperforms the others for the QAS classification and RF with TF-IDF outperforms for the test case generation.
The Curse of Performative User Studies

Computer graphics research frequently evaluates research outputs with user studies, often through online crowdworking platforms. When performed carefully and thoughtfully, studies on human behavior and preferences provide valuable insights, useful for both developing and evaluating new tools. Yet, I argue that many of the current studies are performative: they result from reviewers' expectation that "papers should have some evaluation," not from careful thought about the value and usefulness of the studies themselves. These casually done studies are often uninformative or misleading, while putting undue burden on authors and reviewers. The expectation of positive user evaluation results can also inhibit creative new work. I call for reviewers to be more thoughtful about asking for user studies, for authors to be more thoughtful when they perform studies, and for our field to conduct new research and create new guidelines on when and how user studies are genuinely useful.
A New Multinetwork Mean Distillation Loss Function for Open-World Domain Incremental Object Detection

The development of object detection networks has reached a high point, and there have been significant improvements in accuracy and detection speed. Object detection is widely used in intelligent robots, self-driving cars, and other edge-intelligent terminals. Unfortunately, when a detector is allowed to learn new objects in an unfamiliar environment, it can catastrophically forget the objects it has already learned. In particular, reliable and stable knowledge cannot be extracted from old models. Based on this, a new multinetwork mean distillation loss function for open-world domain incremental object detection is presented. To better extract reliable and stable knowledge from old models, we enhanced the distillation output of the detector with a ResNet50 backbone and an output RoI head. The distillation output of the intermediate RPN is softened by adaptive distillation. To obtain more stable results, the ResNet50 backbone and RPN on the channel are zero-averaged. Various incremental steps and stability experiments are performed on two benchmark datasets, PASCAL VOC and MS COCO. The experimental results show the excellent performance of our method in different experimental scenarios, and it is superior to the most advanced methods. For example, in the setting of the batch task, incremental object detection on the PASCAL VOC and MS COCO datasets is improved by 3.4% and 2.1%, respectively.
Research on Ballistic Planning Method Based on Improved DDPG Algorithm

Aiming at the ballistic planning problem given the target position indication under the condition of satisfying the missile capability boundary and process constraints, an improved deep deterministic policy gradient (DDPG) algorithm is proposed. This method solves the problems of the traditional trajectory planning method, such as strong dependence on model, low adaptability to environment and lack of real-time performance. The agent is firstly trained by this algorithm at the initial stage in a simple environment through a guided training based on the slow training speed of deep neural network, and then the generated strategy is adopted as an initial strategy for retraining. In the process, the sampling strategy of experience-first playback is applied to the gradient algorithm of deep deterministic strategy, and the Ornstein-Uhlenbeck (OU) noise model is employed for increasing the exploration ability of the agent. Then, this work develops a comprehensive simulation model according to an actual environment, where an improved DDPG method is thus introduced into the ballistic planning field by combining with performance indicators for planning and the trajectory can also meet the constraints. In order to verify the effectiveness of this method, the Gaussian pseudo-spectrum method is also used to compare with the proposed method in this paper. The simulation results show that the ballistic planning time of this improved algorithm is only within 10ms, which is about 8 times shorter than that of Gaussian pseudo-spectrum method. The corresponding ballistics that satisfy the constraints can be quickly generated, which has a good convergence and robustness, and is also suitable for solving online ballistic planning problems.
Research and implementation of deep network-based breast tumor classification

With the development of computer technology, computer-aided diagnosis technology has become one of the important research directions in the field of medicine. Among them, considerable progress has been made in the application of artificial intelligence technology in medical data processing, especially in medical imaging. Currently, breast cancer is one of the most common malignant tumors in women worldwide, accounting for about one-fourth of all malignant tumors, and its incidence has been on the rise in the past decades. If breast cancer can be successfully diagnosed at an early stage, the survival rate of patients can be greatly improved, so early diagnosis and treatment of breast cancer is especially important. However, most of the current diagnostic methods are application-oriented and have not been explored in depth on how to improve the recognition accuracy, so the purpose of this paper is to explore how to improve the accuracy of breast cancer recognition.
Machine Learning-Based Categorization of Racism in Amharic Text Using Multi-Class Classification

The study focuses on combating racism on social media, especially in the Amharic language, a critical issue in Ethiopia due to its widespread online user base. The data collection process utilized tools such as Face Pager and Beautiful Soup. Feature extraction was carried out using word2vec. We develop a classification model to automatically identify and categorize racist content on platforms like Facebook and Telegram. This study aims to categorize the extent of racist content into individual racism, regional racism, country-level racism, and Non-racism. We employed a range of machine learning models, including SVM, Random Forest, and Naive Bayes, alongside deep learning models like RNN, LSTM, and CNN for Multi-Class Classification. These models are rigorously evaluated using standard classification metrics. In comparison to the other models, the Bi-directional Long Short-Term Memory (BI-LSTM) model demonstrated significantly higher accuracy, achieving an impressive 99.68% accuracy rate. This implies that the BI-LSTM model outperformed the remaining models in terms of its ability to accurately classify the data.
Incorporating Path Constraints into Bayesian network under the Sparse Parent Graph

When a Bayesian network (BN) is used to model a practical problem, sufficient prior knowledge is required, which is in the form of path constraints. Dynamic programming (DP) approach is one of the exact algorithms that is used for structure learning of BNs by optimizing a decomposable score function. In this study, we attempt to provide an efficient and compact method for incorporating the path constraints in DP with the guidance of sparse parent graph (SPG) and drive algorithms, which is used to obtain the optimal BN satisfying the given constraints. We use a rigorous deduction theory to provide useful insights on the implementation of path constraints in the DP algorithm. Our experiments demonstrate that the proposed approach reduces the time and space complexities significantly.
FALLNET: A Deep Learning Model for Fall Detection using improved CNN-Attention-LSTM

As the aging population continues to grow, fall detection has become a key issue in public health and healthcare. To address the problem of low accuracy and poor real-time performance of fall detection algorithms in real scenarios, an improved fall detection model, FALLNET, is proposed. First, an improved YOLOv7-X-pose algorithm is used to quickly extract multiple human body keypoints in the multi-person keypoint extraction module. Second, the classic CNN_Attention_LSTM model is improved in the dangerous action recognition module by adding an LSTM layer to better capture the important features of the input sequence and improve the model’s prediction accuracy. In addition, a Kalman filter target tracking algorithm is also added to the system to ensure the extraction algorithm’s extraction order and accuracy. The improved model performs well in all tasks and can efficiently and accurately identify falling actions in practical scenarios. These results demonstrate that our model has good practicality and application prospects and is expected to provide reliable technical support for fall warning and elderly health management.