Reviewerly/exHarmony huggingface dataset:
A Deep Learning Model for Breast Cancer Diagnosis Using Mammography Images Classification

Medical image classification is a technique for categorizing images to the appropriate pathological stage. It is an essential step in computer-aided diagnosis, which was developed to assist the radiologist in the reading and analysis of medical images and to help in the early diagnosis of tumors and other diseases. Deep convolutional neural network (DCNN) models have proven to be widely used in the medical field in recent years and achieve great feats in image classification, especially in terms of high performance and robustness. In this paper, we suggest two approaches using the Transfer Learning (TL) technique to classify Breast Cancer (BC) using mammography images from the Mini-MIAS dataset. The first approach adopts the pre-trained models as Feature Extractors then the classification is performed by two classifiers: SVM (Support Vector Machine) and RF (Random Forest), the second approach performs the classification process using the model fine tuning technique by adjusting the hyperparameters. The DCNNs models chosen for this study are: DenseNet121, MobileNetV2 and InceptionResNetV2. The data augmentation technique performed during preprocessing is of great importance for the improvement of our small dataset. The experimental results shows that MobileNetV2 in the second approach achieves an accuracy of 97% to differentiate abnormal cases from normal ones, and therefore proves to be the most accurate classifier.
Performance Analysis of Breast Cancer Classification from Mammogram Images Using Vision Transformer

Accurate identification and diagnosis of Breast Cancer (BC) is one of the most critical and laborious challenges in the field of medical image processing. Mammography is presently the imaging modality of choice for the detection and early identification of BC in the vast majority of instances. Mammogram mass classifications (malignant or benign) remain a significant problem for radiologists and are essential to their ability to make correct diagnoses. In order to keep the tumour in check, early identification and diagnosis of BC are the most convenient and efficient ways to prevent it from becoming malignant. Manual segmentation is prone to errors due to the fact that it might be performed differently by different people. Vision transformers (ViT) have shown enormous promise in a number of computer vision applications because of their extraordinary ability to replicate long-range dependence via the self-attention process. ViT-based automation has been proposed to tackle this problem. Numerous experiments on the widely used benchmark dataset for classifying BC in mammogram images (INbreast database) confirmed the effectiveness of this architecture in comparison to current approaches, achieving a 96.48% accuracy rate with real-time performance and taking very little training time, an important condition for medical image analysis. A Graphical User Interface (GUI) is constructed for the suggested model, which can aid doctors and radiologists in making better decisions and detecting BC more quickly.
Artificial Intelligence Meets Tactical Autonomy: Challenges and Perspectives

Artificial Intelligence (AI) enabled systems have shown tremendous impact in our national defense and in our society due to recent advances in artificial neural networks, deep learning, machine learning, and Internet of Things, big data, computing and communications. New AI capabilities can improve efficiency, trust, and efficacy for mission critical applications for tactical autonomy with minimal supervision from human operators in multi-domain battlefield (MDB) environments that are complex, contested and unpredictable. Although AI-enabled tools have been responsive to people and complementary to human capabilities, in order to realize its full potential in tactical applications, there are several challenges to be addressed for making trustworthy, ethical, fair, real-time explainable AI-enabled autonomous systems. Collaborations between platforms/systems as well as joint human-machine learning/teaming could address many of these issues to provide trusted and shared understanding and delivering cost-effective and adaptive systems to assist operations across military domains (space, air, land, maritime, and cyber) at combat speed using a shared set of resources. In this paper, we present some challenges and perspectives for AI enabled tactical autonomy.
An approach to dealing with incremental concept drift in personalized learning systems

In recent years, personalized learning systems have garnered significant academic research attention in the field of education. In a personalized learning system, learners receive a customized learning style that is tailored to their unique needs, goals, and abilities. Thus, students can achieve their objectives faster than with the traditional method of learning. Rapid advancements in artificial intelligence technologies enable tracking and influencing each student’s learning process. Machine learning algorithms facilitate the determination of students’ learning styles, abilities, and progress throughout the learning process. One of the major challenges to effective personalization is the resistance of machine learning models to adapt to non-stationary data streams. Machine learning models for personalized learning systems are susceptible to the concept drift phenomenon, in which the models’ performance deteriorates over time due to changes in data distribution. For successful personalization, it is critical for the underlying predictive and classification models to adapt to data distribution changes. In this paper, we propose an approach to address concept drifts in personalized learning systems, and evaluate the approach on the OULAD dataset infused with concept drift. The proposed method comprises training utilizing sequential features extracted automatically.
Evaluating the Privacy Exposure of Interpretable Global Explainers

In recent years we are witnessing the diffusion of AI systems based on powerful Machine Learning models which find application in many critical contexts such as medicine, financial market and credit scoring. In such a context it is particularly important to design Trustworthy AI systems while guaranteeing transparency, with respect to their decision reasoning and privacy protection. Although many works in the literature addressed the lack of transparency and the risk of privacy exposure of Machine Learning models, the privacy risks of explainers have not been appropriately studied. This paper presents a methodology for evaluating the privacy exposure raised by interpretable global explainers able to imitate the original black-box classifier. Our methodology exploits the well-known Membership Inference Attack. The experimental results highlight that global explainers based on interpretable trees lead to an increase in privacy exposure.
MT-ONet: Mixed Transformer O-Net for Medical Image Segmentation

In the past few years, the deep learning is widely used in the medical industry due to its advantage. Constructed using Convolutional Neural Networks (CNN), the U-Net framework has become the industry standard for solving medical image segmentation tasks. Nonetheless, this framework is incapable of entirely learning all global and remote semantic information. It has been demonstrated that the transformer structure collects more global information than U-Net but less local information than CNN. To improve the performance of segmentation and classification in medical images while maximizing global and local data, we integrate O-Net with Mixed Transformer [1], this fuses the advantages of CNN and Transformer. This enables us to maximize both types of data. We combine CNN, Mixed Transformer, and Local-Global Gaussian-Weighted Self-Attention (LGG-SA) in the encoder component of our proposed O-Net architecture to obtain more global and local background information. The decoder part combines the Mixed Transformer and CNN blocks to obtain the results. The segmentation capability of the proposed network is evaluated by the multi-organ CT dataset containing synaptic information. The results of our trials demonstrate that the proposed MT-ONet can deliver superior segmentation performance relative to cutting-edge methods, resulting in improved classification precision.
A Robust Strategy for Roadside Cooperative Perception Based on Multi-Sensor Fusion

Roadside perception is a fundamental task for vehicle-to-road cooperative perception and traffic scheduling. However, most existing roadside perception strategies prefer to deploy sensors in a single perspective or test in a simulation environment. Due to the limited field of view covered by a single sensor, such methods usually cannot continuously detect the same object from different viewpoints or provide a wide sensing range in complex scenarios. To address these issues, a robust strategy for roadside cooperative perception based on multi-sensor fusion (RCP-MSF) is proposed in this paper. A 2D object detector is improved based on the NanoDet model to handle multiple images simultaneously. In addition, an ultra-fast 3D object detection strategy is suggested based on point cloud processing rather than relying on existing high-cost deep-learning models. Moreover, to match the 2D and 3D bounding boxes, a data association module for multi-modal sensor information fusion is presented. Any 2D and 3D object detector can follow this module. Furthermore, a roadside perception dataset named SCUT-V2R is constructed to verify the performance of the proposed method. Experiments on the dataset demonstrate that the RCP-MSF outperforms the camera-only and lidar-only strategies in object detection precision while maintaining real-time performance.
Assessing cyber-security compliance level in paperless hospitals: An ethnographic approach

Digitalisation in healthcare can be a double-edged sword. While it improves the efficiency of healthcare operations through IT infrastructure, its security and privacy concerns can be life-threatening, posing danger to the very lives that healthcare exists to safeguard. Motivated by this, we combined focus-group discussion and observational methods to assess the cyber security situations in two hospitals that have migrated to paperless systems in Ghana. Paperless systems involve the adoption of the Electronic Health Record (EHR) system by hospitals and are usually hosted within healthcare IT infrastructure. This is one of the few studies that has used the ethnographic investigation method to reveal the nature of security and privacy practice in hospitals, with the aim to enhance security practices in health-care. The findings revealed that the participants understood various areas of security practice knowledge; however, this did not translate into their security behaviour, as various security gaps were identified. System misuse and security and privacy violations, characterised by password sharing, authentication sharing, unauthorised access to patient information and physical security issues, were associated with the security practice of the healthcare staff. The causes of these security malpractices were attributed to factors such as peer pressure, bad moral conduct (e.g. personal gain), work factors and security development issues. Based on the assessment, practical implications were suggested to the hospitals. In addition, hospitals that are migrating to paperless systems were advised to establish fundamental security structures, such as security policy establishment and the right security personnel, to averse such security menace.
On Persisting EMF Data Using Blockchains

In recent years, blockchain technology in synergy with smart contracts has opened new horizons within almost any field from entertainment to healthcare. However, in order to enable innovative usage scenarios, significant efforts are needed to adapt the existing systems and solutions, so the full potential of blockchain-based tools can be leveraged. In this paper, we propose a model-driven framework which provides automated persistence of domain-specific data within Ethereum blockchain platform, starting from Ecore model instances. Moreover, the corresponding Solidity smart contracts are generated relying on model-to-model and model-to-text transformations using Acceleo. The proposed approach is evaluated on persons-movies dataset inside the Ganache environment. According to the obtained results, our solution successfully automatized the persistence of the evaluated dataset.
Quick decentralized estimation of earthquake epicenter with low-cost IoT network

When an earthquake occurs, it is crucial to quickly estimate its epicenter coordinates and the most affected areas to inform rescue decision-making. Current seismological services perform this estimation relying on centralized processing. On the other hand, we propose a fully decentralized architecture that uses edge computation on a network of sensing and communication low-cost IoT nodes. Nodes exploit M2M messages with neighbors to exchange early warning alerts and to grasp relevant information to estimate the epicenter in real time with a lightweight three-step algorithm. Following an iterative process, the pipeline produces a good estimation with an average error as low as 3km. We describe the system architecture and the proposed strategy and analyze the performances of each estimation step.
KBN-YOLOv5: Improved YOLOv5 for Detecting Bird’s Nest in High-Voltage Tower

The problem of birds nesting in high-voltage towers has laid a major hidden danger to the safe operation of long-distance transmission lines. In the power line inspection, the image background of the bird’s nest is complex. Some nests are small and locally occluded, and it is difficult for existing object detection algorithms to detect with low computational effort under high accuracy. To solve the above problems, we recommend using KBN-YOLOv5. Based on YOLOv5, we use K-means algorithm to cluster the size of bird nest image to set the size of anchor frame. In the Backbone network, we replace the Focus and SPP modules with Conv and SPPF modules, respectively, and adjust the number of BottleneckCSP modules as well as their positions. Finally, in the Neck network, we improve the BottleneckCSP module combined with the Efficient Channel Attention (ECA) module for a more effective weight information distribution to accomplish a more detailed detection capability. The experimental results show that KBN-YOLOv5 demonstrates better performance with other mainstream algorithms in terms of combined performance of detection accuracy and model computation. Compared to baseline model (YOLOv5), the Recall and mAP values of KBN-YOLOv5 reach 92.3% and 96.0%, which improve 6.7% and 5.4%, respectively. In addition, KBN-YOLOv5 has good robustness, and the model computation is reduced by 6.7% while detection accuracy of the model is improved.
Adaptive interactive multiple model target tracking algorithm based on Markov matrix with acceleration correction factor

In the field of target tracking, the interactive multimodel (IMM) algorithm is a very effective method. To improve the tracking performance, This paper proposes an improved current statistical model (CS) combined with the IMM algorithm and an improved model set to implement the model set adaptive AIMM-CS algorithm. At the same time, to address the problem of slow model switching and reduced tracking accuracy caused by the Markov matrix given by a priori knowledge and always constant, this paper obtains a correction factor from the estimated value of acceleration and corrects the Markov matrix. Experiments show that the algorithm in this paper can effectively improve the tracking accuracy and increase the model switching speed.
A target tracking system based on multi-camera information fusion

In order to promote the development of the public security sky-eyes system and the Internet of things, our system completes the integrated multi-camera cooperative tracking of multi-targets. The system uses the self-calibration method to map the camera detection results to the global coordinates through perspective transformation. The system uses YOLO-X network for target recognition and classification; The improved SORT algorithm is used to track and improve the ID switch problem. At the same time, 3D-box method is used to regress the attitude angle information of the object, so as to judge the state of the object and correct the deviation. On the premise of ensuring stability, the system achieves a faster running speed. It uses an RTX3080 GPU to accelerate, the overall speed reaches 60Hz, the classification accuracy is close to 100%, and stable target tracking is achieved. The system algorithm fully considers the occlusion, the change of target feature state, the disappearance of the target, etc., and can still ensure good results.
Deep Federated Anomaly Detection for Multivariate Time Series Data

Although many anomaly detection approaches have been developed for multivariate time series data, limited effort has been made in federated settings in which multivariate time series data are heterogeneously distributed among different edge devices while data sharing is prohibited. In this paper, we investigate the problem of federated unsupervised anomaly detection and present a Federated Exemplar-based Deep Neural Network (Fed-ExDNN) to conduct anomaly detection for multivariate time series data on different edge devices. Specifically, we first design an Exemplar-based Deep Neural network (ExDNN) for learning local time series representations based on their compatibility with an exemplar module which consists of hidden parameters learned to capture varieties of normal patterns on each edge device. Next, a constrained clustering mechanism (FedCC) is employed on the centralized server to align and aggregate the parameters of different local exemplar modules to obtain a unified global exemplar module. Finally, the global exemplar module is deployed together with a shared feature encoder to each edge device, and anomaly detection is conducted by examining the compatibility of testing data to the exemplar module. Fed-ExDNN captures local normal time series patterns with ExDNN and aggregates these patterns by FedCC, and thus can handle the heterogeneous data distributed over different edge devices simultaneously. Thoroughly empirical studies on six public datasets show that ExDNN and Fed-ExDNN can outperform state-of-the-art anomaly detection algorithms and federated learning techniques, respectively.
Moving target detection of elevating fire truck based on D-vibe algorithm in vehicular environment

At present, fire trucks are widely used in various fire fighting and emergency tasks. In order to deal with different situation, fire trucks are equipped with several different rescue tools. It is very important for quickly replace tools of fire trucks for different rescue purpose. In this paper machine vision is utilized for Intelligent recognition and positioning the tools on the long mechanical arm in the process of replacing the tools when the mechanical arm moving in vehicle environment. We acquired the RGB-D image including the pixel mapping relationship between the color image and the depth image. Then filtering algorithms are used to process the depth image. A D-vibe algorithm based on depth image is proposed to get the characteristics of depth image. The spatiotemporal depth information is introduced to construct the depth model for Intelligent recognition and positioning. Experimental results show that our method can effectively eliminate the "Edge Ghost" caused by shaking video and improve the target detection effect.
A Deep Mutual Learning Based Strategy for Cloud-edge Collaborative Anomaly Detection in Cigarette Packaging

In order to ensure the delivery quality of products, anomaly detection technology is an important part of the production of cigarette packages. Recently, with the development of federated learning, it is possible for each production line in each factory to obtain better models by collaborative training without leaking data. However, due to the problem of data heterogeneity, the Global Shared Model (GSM) obtained by federated co-training can be worse than the local model which is only trained on private data of the edge. In this work, a deep mutual learning based strategy is proposed for cloud-edge collaborative anomaly detection, dealing with the Non-IIDness of data for each edge. Firstly, each edge side is trained collaboratively using FedAvg [1] algorithm to obtain the GSM. Then, the model personalization is performed at each edge side inspired by the idea of mutual learning. Specifically, a lightweight AE is initialized at edge side, called Lightweight Private Model (LPM), which has the same structure as GSM. The Feature extracted by the encoder of GSM and LPM are constrained by the smooth l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> loss, and both GSM and LPM are guided by MSE loss to reconstruct normal samples like most previous unsupervised anomaly detection tasks. As a result, the encoder has stronger feature representation abilities through all edge co-training, and the entire LPM can be more adapted to the edge side tasks. Finally, we demonstrate the superiority of this method through anomaly detection experiments on our cigarette package dataset.
A Vision-based Taillight Intention Detection Method for Intelligent and Connected Vehicles

Vehicle taillight intention detection is crucial to the perception of intelligent and connected vehicle (ICV). In this paper, a vision-based method of vehicle taillight intention detection for ICVs is proposed. Based on the YOLOv4 network structure, an improved method is conducted for the problem that the vehicle taillight object is susceptible to background effects such as traffic lights. The proposed method firstly detects the vehicle object, and then classifies the taillight’s intention information within the coordinate range of the detected vehicle object, thereby reducing the interference of the taillight information caused by the traffic background. The experimental results on real scene dataset show that the proposed detection method has a 4.16% higher accuracy than the original YOLOv4 model, proving that the improved method can detect vehicle taillight intention information more effectively. In addition, the potential of the method for practical application is tested on the NVIDIA TX2 mobile terminal.
Design an Efficient DNN Inference Framework with PS-PL Synergies in FPGA for Edge Computing

in recent years, Deep Neural Network (DNN) based methods have achieved great success for changing inflexible machine to intelligent and live system. Internet of Things (IoT) applications equipped with DNN in domains such as self-driving, speech processing and video surveillance particular challenges. Since DNNs are memory-/computing- intensive applications, FPGAs are customized to accelerate DNN on edge system due to their low latency and high energy efficiency. However, embedded CPU is usually essential for IoT system and single accelerator of FPGA is hard to gain maximized performance of the whole IoT system. Therefore, we design an efficient DNN inference framework with programming system (PS, e.g., ARM CPU) and programming logic (PL, e.g. FPGA) synergies for intelligent IoT system. Compared with the common IoT system only separately leverages FPGA for DNN accelerating and ARM for other tasks, our framework can make ARM and FPGA jointly accelerate DNN inference from the beginning. Our PS-PL co-design approach can take full advantage of the merits of ARM (control operations) and FPGA (parallel computing operations) for various DNN model accelerating. We implemented our co-design framework on Zynq SoCs. The experimental results show that our framework can achieve maximal 205.9 GOPs/s DNN inference accelerating under 6.2W while it would not affect other CPU tasks running on IoT system.