Reviewerly/exHarmony huggingface dataset:
AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure

The Internet of Things (IoT) is the most abundant technology in the fields of manufacturing, automation, transportation, robotics, and agriculture, utilizing the IoT's sensors-sensing capability. It plays a vital role in digital transformation and smart revolutions in critical infrastructure environments. However, handling heterogeneous data from different IoT devices is challenging from the perspective of security and privacy issues. The attacker targets the sensor communication between two IoT devices to jeopardize the regular operations of IoT-based critical infrastructure. In this paper, we propose an artificial intelligence (AI) and blockchain-driven secure data dissemination architecture to deal with critical infrastructure security and privacy issues. First, we reduced dimensionality using principal component analysis (PCA) and explainable AI (XAI) approaches. Furthermore, we applied different AI classifiers such as random forest (RF), decision tree (DT), support vector machine (SVM), perceptron, and Gaussian Naive Bayes (GaussianNB) that classify the data, i.e., malicious or non-malicious. Furthermore, we employ an interplanetary file system (IPFS)-driven blockchain network that offers security to the non-malicious data. In addition, to strengthen the security of AI classifiers, we analyze data poisoning attacks on the dataset that manipulate sensitive data and mislead the classifier, resulting in inaccurate results from the classifiers. To overcome this issue, we provide an anomaly detection approach that identifies malicious instances and removes the poisoned data from the dataset. The proposed architecture is evaluated using performance evaluation metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC curve). The findings show that the RF classifier transcends other AI classifiers in terms of accuracy, i.e., 98.46%.
A Systematic Literature Review of Waste Identification in Automatic Separation Systems

Proper waste separation is essential for recycling. However, it can be challenging to identify waste materials accurately, especially in real-world settings. In this study, a systematic literature review (SLR) was carried out to identify the physical enablers (sensors and computing devices), datasets, and machine learning (ML) algorithms used for waste identification in indirect separation systems. This review analyzed 55 studies, following the Kitchenham guidelines. The SLR identified three levels of autonomy in waste segregation systems: full, moderate, and low. Edge computing devices are the most widely used for data processing (9 of 17 studies). Five types of sensors are used for waste identification: inductive, capacitive, image-based, sound-based, and weight-based sensors. Visible-image-based sensors are the most common in the literature. Single classification is the most popular dataset type (65%), followed by bounding box detection (22.5%). Convolutional neural networks (CNNs) are the most commonly used ML technique for waste identification (24 out of 26 articles). One of the main conclusions is that waste identification faces challenges with real-world complexity, limited data in datasets, and a lack of detailed waste categorization. Future work in waste identification should focus on deployment and testing in non-controlled environments, expanding system functionalities, and exploring sensor fusion.
Explainable Deep Fuzzy Cognitive Map Diagnosis of Coronary Artery Disease: Integrating Myocardial Perfusion Imaging, Clinical Data, and Natural Language Insights

Myocardial Perfusion Imaging (MPI) has played a central role in the non-invasive identification of patients with Coronary Artery Disease (CAD). Clinical factors, such as recurrent diseases, predisposing factors, and diagnostic tests, also play a vital role. However, none of these factors offer a straightforward and reliable indication, making the diagnosis of CAD a non-trivial task for nuclear medicine experts. While Machine Learning (ML) and Deep Learning (DL) techniques have shown promise in this domain, their “black-box” nature remains a significant barrier to clinical adoption, a challenge that the existing literature has not yet fully addressed. This study introduces the Deep Fuzzy Cognitive Map (DeepFCM), a novel, transparent, and explainable model designed to diagnose CAD using imaging and clinical data. DeepFCM employs an inner Convolutional Neural Network (CNN) to classify MPI polar map images. The CNN’s prediction is combined with clinical data by the FCM-based classifier to reach an outcome regarding the presence of CAD. For the initialization of interconnections among DeepFCM concepts, expert knowledge is provided. Particle Swarm Optimization (PSO) is utilized to adjust the weight values to the correlated dataset and expert knowledge. The model’s key advantage lies in its explainability, provided through three main functionalities. First, DeepFCM integrates a Gradient Class Activation Mapping (Grad-CAM) algorithm to highlight significant regions on the polar maps. Second, DeepFCM discloses its internal weights and their impact on the diagnostic outcome. Third, the model employs the Generative Pre-trained Transformer (GPT) version 3.5 model to generate meaningful explanations for medical staff. Our dataset comprises 594 patients, who underwent invasive coronary angiography (ICA) at the department of Nuclear Medicine of the University Hospital of Patras in Greece. As far as the classification results are concerned, DeepFCM achieved an accuracy of 83.07%, a sensitivity of 86.21%, and a specificity of 79.99%. The explainability-enhancing methods were assessed by the medical experts on the authors’ team and are presented within. The proposed framework can have immediate application in daily routines and can also serve educational purposes.
Stabilized videos source identification method based on undecimated dual-tree complex wavelet transform and Bayesian adaptive direct search algorithm

The widely adopted video stabilization techniques in imaging devices have become a bottleneck that limits the performance of source camera identification (SCI) based on photo-response non-uniform (PRNU) noise. Affine transformations in the stabilized video, including scaling, translation, and rotation, introduce varying degrees of distortion to the PRNU pattern across frames, which lead to a degradation of accuracy and prolonged matching time in SCI methods. To address these issues, we propose a stabilized video source identification method that combines the undecimated dual-tree complex wavelet transform (UDTCWT) with the Bayesian adaptive direct search (BADS) algorithm. First, decoded frames are obtained by skipping the loop filter in the video codec to preserve more PRNU. Then, a PRNU extraction algorithm based on the UDTCWT is utilized to filter out more reliable PRNU from the decoded frames. Finally, the distorted PRNU is optimized and recovered by the affine transformation model with the BADS. Experiments on public datasets show that the proposed method outperforms state-of-the-art methods in terms of matching speed and accuracy.
A survey of model compression strategies for object detection

Abstract Deep neural networks (DNNs) have achieved great success in many object detection tasks. However, such DNNS-based large object detection models are generally computationally expensive and memory intensive. It is difficult to deploy them to devices with low memory resources or scenarios with high real-time requirements, which greatly limits their application and promotion. In recent years, many researchers have focused on compressing large object detection models without significantly degrading their performance, and have made great progress. Therefore, this paper presents a survey of object detection model compression techniques in recent years. Firstly, these compression techniques were divided into six categories: network pruning, lightweight network design, neural architecture search (NAS), low-rank decomposition, network quantization, and Knowledge distillation (KD) methods. For each category, we select some representative state-of-the-art methods and compare and analyze their performance on public datasets. After that, we discuss the application scenarios and future directions of model compression techniques. Finally, this paper is further concluded by analyzing the advantages and disadvantages of six types of model compression techniques.
The effect of online ad campaigns on DDoS‐attacks: A cross‐national difference‐in‐differences quasi‐experiment

Abstract Research Summary European law enforcement agencies have begun to use targeted online ad campaigns to raise cybercrime awareness among at‐risk populations. Despite their rapid proliferation, there is little research to support their efficacy and effectiveness. This study uses a quasi‐experimental difference‐in‐differences design to evaluate the effect of seven campaigns deployed in 2021 and 2022 on the volume of (distributed) denial‐of‐service or (D)DoS‐attacks recorded in six European countries: Denmark, Finland, the Netherlands, Norway, Sweden, and Portugal. The results show mixed effects, suggesting that the campaigns are not clearly effective in reducing DDoS‐attacks in the short term. Policy Implications Law enforcement has partly justified the use of targeted online ad campaigns on the premise that they reduce DDoS‐attacks. However, this study shows that the evidence in this regard is inconclusive. If public support for the use of such campaigns is to be secured in the long term, law enforcement will likely need to rely on stronger arguments. The effect of this type of campaigns needs to be further investigated.
Semantic Map Guided Identity Transfer GAN for Person Re-identification

Generative adversarial networks (GANs)-based person re-identification (re-id) schemes provide potential ways to augment data in practical applications. However, existing solutions perform poorly because of the separation of data generation and re-id training and a lack of diverse data in real-world scenarios. In this paper, a person re-id model (IDGAN) based on semantic map guided identity transfer GAN is proposed to improve the person re-id performance. With the aid of the semantic map, IDGAN generates pedestrian images with varying poses, perspectives, and backgrounds efficiently and accurately, improving the diversity of training data. To increase the visual realism, IDGAN utilizes a gradient augmentation method based on local quality attention to refine the generated image locally. Then, a two-stage joint training framework is employed to allow the GAN and the person re-id network to learn from each other to better use the generated data. Detailed experimental results demonstrate that, compared with the existing state-of-the-art methods, IDGAN is capable of producing high-quality images and significantly enhancing re-id performance, with the FID of generated images on the Market-1501 dataset being reduced by 1.15, and mAP on the Market-1501 and DukeMTMC-reID datasets being increased by 3.3% and 2.6%, respectively.
Fintech Key-Phrase: A New Chinese Financial High-Tech Dataset Accelerating Expression-Level Information Retrieval

Expression-level information extraction is a challenging task in natural language processing (NLP), which aims to retrieve crucial semantic information from linguistic documents. However, there is a lack of up-to-date data resources for accelerating expression-level information extraction, particularly in the Chinese financial high technology field. To address this gap, we introduce Fintech Key-Phrase: a human-annotated key-phrase dataset for the Chinese financial high technology domain. This dataset comprises over 12K paragraphs along with annotated domain-specific key-phrases. We extract the publicly released reports, Chinese management’s discussion and analysis (CMD&amp;A), from the renowned Chinese research data services platform (CNRDS) and then filter the reports related to high technology. The high technology key-phrases are annotated following pre-defined philosophy guidelines to ensure annotation quality. In order to better understand the limitations and challenges in the purposed dataset, we conducted comprehensive noise evaluation experiments for the Fintech Key-Phrase, including annotation consistency assessment and absolute annotation quality evaluation. To demonstrate the usefulness of our released Fintech Key-Phrase in retrieving valuable information in the Chinese financial high technology field, we evaluate its significance using several superior information retrieval systems as representative baselines and report corresponding performance statistics. Additionally, we further applied ChatGPT to the text augmentation approach of the Fintech Key-Phrase dataset. Extensive comparative experiments demonstrate that the augmented Fintech Key-Phrase dataset significantly improved the coverage and accuracy of extracting key phrases in the finance and high-tech domains. We believe that this dataset can facilitate scientific research and exploration in the Chinese financial high technology field. We have made the Fintech Key-Phrase dataset and the experimental code of the adopted baselines accessible on Github: https://github.com/albert-jin/Fintech-Key-Phrase. To encourage newcomers to participate in the financial high-tech domain information retrieval research, we have developed a series of tools, including an open website 1 and corresponding real-time information retrieval APIs. 2
Multi-stage Factorized Spatio-Temporal Representation for RGB-D Action and Gesture Recognition

RGB-D action and gesture recognition remain an interesting topic in human-centered scene understanding, primarily due to the multiple granularities and large variation in human motion. Although many RGB-D based action and gesture recognition approaches have demonstrated remarkable results by utilizing highly integrated spatio-temporal representations across multiple modalities (i.e., RGB and depth data), they still encounter several challenges. Firstly, vanilla 3D convolution makes it hard to capture fine-grained motion differences between local clips under different modalities. Secondly, the intricate nature of highly integrated spatio-temporal modeling can lead to optimization difficulties. Thirdly, duplicate and unnecessary information can add complexity and complicate entangled spatio-temporal modeling. To address the above issues, we propose an innovative heuristic architecture called Multi-stage Factorized Spatio-Temporal (MFST) for RGB-D action and gesture recognition. The proposed MFST model comprises a 3D Central Difference Convolution Stem (CDC-Stem) module and multiple factorized spatio-temporal stages. The CDC-Stem enriches fine-grained temporal perception, and the multiple hierarchical spatio-temporal stages construct dimension-independent higher-order semantic primitives. Specifically, the CDC-Stem module captures bottom-level spatio-temporal features and passes them successively to the following spatio-temporal factored stages to capture the hierarchical spatial and temporal features through the Multi-Scale Convolution and Transformer (MSC-Trans) hybrid block and Weight-shared Multi-Scale Transformer (WMS-Trans) block. The seamless integration of these innovative designs results in a robust spatio-temporal representation that outperforms state-of-the-art approaches on RGB-D action and gesture recognition datasets.
Query Evaluation under Differential Privacy

Differential privacy has garnered significant attention in recent years due to its potential in offering robust privacy protection for individual data during analysis. With the increasing volume of sensitive information being collected by organizations and analyzed through SQL queries, the development of a general-purpose query engine that is capable of supporting a broad range of queries while maintaining differential privacy has become the holy grail in privacypreserving query release. Towards this goal, this article surveys recent advances in query evaluation under differential privacy.
A survey of real-time rendering on Web3D application

In recent years, with the rapid development of mobile Internet and Web3D technologies, a large number of web-based online 3D visualization applications have emerged. Web3D applications, including Web3D online tourism, Web3D online architecture, Web3D online education environment, Web3D online medical care, and Web3D online shopping are examples of these applications that leverage 3D rendering on the web. These applications have pushed the boundaries of traditional web applications that use text, sound, image, video, and 2D animation as their main communication media, and resorted to 3D virtual scenes as the main interaction object, enabling a user experience that delivers a strong sense of immersion. This paper approached the emerging Web3D applications that generate stronger impacts on people's lives through "real-time rendering technology", which is the core technology of Web3D. This paper discusses all the major 3D graphics APIs of Web3D and the well-known Web3D engines at home and abroad and classify the real-time rendering frameworks of Web3D applications into different categories. Finally, this study analyzed the specific demand posed by different fields to Web3D applications by referring to the representative Web3D applications in each particular field. Our survey results show that Web3D applications based on real-time rendering have in-depth sectors of society and even family, which is a trend that has influence on every line of industry.
Supervised Manifold Learning via Random Forest Geometry-Preserving Proximities*

Manifold learning approaches seek the intrinsic, low-dimensional data structure within a high-dimensional space. Mainstream manifold learning algorithms, such as Isomap, UMAP, t-SNE, Diffusion Map, and Laplacian Eigenmaps do not use data labels and are thus considered unsupervised. Existing supervised extensions of these methods are limited to classification problems and fall short of uncovering meaningful embeddings due to their construction using order non-preserving, class-conditional distances. In this paper, we show the weaknesses of class-conditional manifold learning quantitatively and visually and propose an alternate choice of kernel for supervised dimensionality reduction using a data-geometry-preserving variant of random forest proximities as an initialization for manifold learning methods. We show that local structure preservation using these proximities is near universal across manifold learning approaches and global structure is properly maintained using diffusion-based algorithms.
On the Optimal Recovery of Graph Signals

Learning a smooth graph signal from partially observed data is a well-studied task in graph-based machine learning. We consider this task from the perspective of optimal recovery, a mathematical framework for learning a function from observational data that adopts a worst-case perspective tied to model assumptions on the function to be learned. Earlier work in the optimal recovery literature has shown that minimizing a regularized objective produces optimal solutions for a general class of problems, but did not fully identify the regularization parameter. Our main contribution provides a way to compute regularization parameters that are optimal or near-optimal (depending on the setting), specifically for graph signal processing problems. Our results offer a new interpretation for classical optimization techniques in graph-based learning and also come with new insights for hyperparameter selection. We illustrate the potential of our methods in numerical experiments on several semi-synthetic graph signal processing datasets.
Real-Time Military Tank Detection Using YOLOv5 Implemented on Raspberry Pi

Military target detection is an essential step to improve battlefield situation generation, surveillance, and command decision-making. In this paper, we utilized YOLOv5 as a popular deep learning model for tank detection in automated war operations. Due to a limited availability of domain-specific datasets, we collected a real dataset and then customized it with some open source images from different military tanks, light and weather conditions. Tank instances were manually annotated under the guidance of military experts. We evaluated the out-of-the-box performance of three detectors of YOLO (YOLOv5s, YOLOv5m and YOLOv5l) on our dataset and compared them in terms of accuracy and run-time. In addition, transfer learning is applied to overcome the inadequate data problem in military object detection. We also investigated the effect of data augmentation on the detection accuracy. The model YOLO5vl can got the maximum average precision (98.5%) compared with two other detectors. Run time of the detectors was compared with an implementation of Raspberry Pi which was connected to camera for data collection. YOLO5vs can recognize the military tanks in a captured RGB image with size 614×614 pixels at 7.9 ms per image. This results show that the applicability of YOLO5vs for the real-time military tank detection.
Lightweight Joint Source-Channel Coding for Semantic Communications

Semantic communications, which aim to effectively convey the meaning of messages (such as text and images) rather than transmitting the exact messages themselves, have garnered widespread attention from industry and academia. A suitable joint source-channel coding (JSCC) scheme is crucial for semantic communication systems, as it can significantly improve system performance, such as communication reliability. Current research efforts primarily focus on employing various deep neural network (DNN) models, particularly the Transformer model, to design JSCC schemes. However, existing Transformer-based JSCC schemes usually exhibit a considerable number of model parameters and computational demands, limiting their real-world applicability. To address this challenge, we propose a novel DNN model based on DeLighT, a deep and lightweight variant of the standard Transformer, using a text semantic communication system (TSC) as an example. This proposed model enables a lightweight JSCC scheme for the TSC system. Through simulation results, we demonstrate that the proposed JSCC scheme achieves comparable or better communication reliability than the Transformer-based JSCC scheme while requiring significantly fewer parameters and smaller runtime.