Reviewerly/exHarmony huggingface dataset:
SenseLite: A YOLO-Based Lightweight Model for Small Object Detection in Aerial Imagery

In the field of aerial remote sensing, detecting small objects in aerial images is challenging. Their subtle presence against broad backgrounds, combined with environmental complexities and low image resolution, complicates identification. While their detection is crucial for urban planning, traffic monitoring, and military reconnaissance, many deep learning approaches demand significant computational resources, hindering real-time applications. To elevate the accuracy of small object detection in aerial imagery and cater to real-time requirements, we introduce SenseLite, a lightweight and efficient model tailored for aerial image object detection. First, we innovatively structured the YOLOv5 model for a more streamlined structure. In the backbone, we replaced the original structure with cutting-edge lightweight neural operator Involution, enhancing contextual semantics and weight distribution. For the neck, we incorporated GSConv and slim-Neck, striking a balance between reduced computational complexity and performance, which is ideal for rapid predictions. Additionally, to enhance detection accuracy, we integrated a squeeze-and-excitation (SE) mechanism to amplify channel communication and improve detection accuracy. Finally, the Soft-NMS strategy was employed to manage overlapping targets, ensuring precise concurrent detections. Performance-wise, SenseLite reduces parameters by 30.5%, from 7.05 M to 4.9 M, as well as computational demands, with GFLOPs decreasing from 15.9 to 11.2. It surpasses the original YOLOv5, showing a 5.5% mAP0.5 improvement, 0.9% higher precision, and 1.4% better recall on the DOTA dataset. Compared to other leading methods, SenseLite stands out in terms of performance.
Incremental Learning for Online Data Using QR Factorization on Convolutional Neural Networks

Catastrophic forgetting, which means a rapid forgetting of learned representations while learning new data/samples, is one of the main problems of deep neural networks. In this paper, we propose a novel incremental learning framework that can address the forgetting problem by learning new incoming data in an online manner. We develop a new incremental learning framework that can learn extra data or new classes with less catastrophic forgetting. We adopt the hippocampal memory process to the deep neural networks by defining the effective maximum of neural activation and its boundary to represent a feature distribution. In addition, we incorporate incremental QR factorization into the deep neural networks to learn new data with both existing labels and new labels with less forgetting. The QR factorization can provide the accurate subspace prior, and incremental QR factorization can reasonably express the collaboration between new data with both existing classes and new class with less forgetting. In our framework, a set of appropriate features (i.e., nodes) provides improved representation for each class. We apply our method to the convolutional neural network (CNN) for learning Cifar-100 and Cifar-10 datasets. The experimental results show that the proposed method efficiently alleviates the stability and plasticity dilemma in the deep neural networks by providing the performance stability of a trained network while effectively learning unseen data and additional new classes.
Cybersecurity in Internet of Medical Vehicles: State-of-the-Art Analysis, Research Challenges and Future Perspectives

The "Internet-of-Medical-Vehicles (IOMV)" is one of the special applications of the Internet of Things resulting from combining connected healthcare and connected vehicles. As the IOMV communicates with a variety of networks along its travel path, it incurs various security risks due to sophisticated cyber-attacks. This can endanger the onboard patient's life. So, it is critical to understand subjects related to "cybersecurity" in the IOMV to develop robust cybersecurity measures. In this paper, the goal is to evaluate recent trends and state-of-the-art publications, gaps, and future outlooks related to this research area. With this aim, a variety of publications between 2016 and 2023 from "Web-of-Science" and "Scopus" databases were analysed. Our analysis revealed that the IOMV is a niche and unexplored research area with few defined standards and frameworks, and there is a great need to implement robust cybersecurity measures. This paper will help researchers to gain a comprehensive idea of this niche research topic, as it presents an analysis of top journals and highly cited papers, their challenges and limitations, the system model and architecture of the IOMV, related applicable standards, potential cyber-attacks, factors causing cybersecurity risks, various artificial intelligence techniques for developing potential countermeasures, the assessment and parameterisation of cybersecurity risks, constraints and challenges, and future outlooks for implementing cybersecurity measures in the IOMV.
GammaGAN: Gamma-Scaled Class Embeddings for Conditional Video Generation

In this paper, we propose a new model for conditional video generation (GammaGAN). Generally, it is challenging to generate a plausible video from a single image with a class label as a condition. Traditional methods based on conditional generative adversarial networks (cGANs) often encounter difficulties in effectively utilizing a class label, typically by concatenating a class label to the input or hidden layer. In contrast, the proposed GammaGAN adopts the projection method to effectively utilize a class label and proposes scaling class embeddings and normalizing outputs. Concretely, our proposed architecture consists of two streams: a class embedding stream and a data stream. In the class embedding stream, class embeddings are scaled to effectively emphasize class-specific differences. Meanwhile, the outputs in the data stream are normalized. Our normalization technique balances the outputs of both streams, ensuring a balance between the importance of feature vectors and class embeddings during training. This results in enhanced video quality. We evaluated the proposed method using the MUG facial expression dataset, which consists of six facial expressions. Compared with the prior conditional video generation model, ImaGINator, our model yielded relative improvements of 1.61%, 1.66%, and 0.36% in terms of PSNR, SSIM, and LPIPS, respectively. These results suggest potential for further advancements in conditional video generation.
Deep Learning Ensemble Approach to Age Group Classification Based On Fingerprint Pattern

The age distribution of a population is extremely valuable to any business or country. In order to make decisions with regard to facility allocations and other social economic developmental issues, determination of age group distribution information is essential. The attempt to deceive others about one's age is a significant problem in the sporting world, as well as in other organizations and electoral processes. Therefore, there is a requirement for an age detection system, which is required to authenticate individual claims. Fingerprint-based age estimate research is scarce due to paucity of dataset. However, there are indications that fingerprints can reveal age demographic. This study's objective is to live-scan fingerprint images in order to identify age groups. This study proposed novel Dynamic Horizontal Voting Ensemble (DHVE) with Hybrid of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) as the base learner. The method constructs a horizontal voting ensemble for prediction by dynamically determining proficient models based on the validation accuracy metric during base learner training on the training set. Accuracy, recall, precision, and the F1 score were employed as standard performance metrics to measures the model's performance analysis. According to this study, predicting individual age group was accurate to a degree of above 91%. The DHVE network performed well due to the design of the layers. Integration of dynamic selection approach to horizontal voting ensemble improved the average performance of the model output.
AoCStream: All-on-Chip CNN Accelerator with Stream-Based Line-Buffer Architecture and Accelerator-Aware Pruning

Convolutional neural networks (CNNs) play a crucial role in many EdgeAI and TinyML applications, but their implementation usually requires external memory, which degrades the feasibility of such resource-hungry environments. To solve this problem, this paper proposes memory-reduction methods at the algorithm and architecture level, implementing a reasonable-performance CNN with the on-chip memory of a practical device. At the algorithm level, accelerator-aware pruning is adopted to reduce the weight memory amount. For activation memory reduction, a stream-based line-buffer architecture is proposed. In the proposed architecture, each layer is implemented by a dedicated block, and the layer blocks operate in a pipelined way. Each block has a line buffer to store a few rows of input data instead of a frame buffer to store the whole feature map, reducing intermediate data-storage size. The experimental results show that the object-detection CNNs of MobileNetV1/V2 and an SSDLite variant, widely used in TinyML applications, can be implemented even on a low-end FPGA without external memory.
Contribution to the Characterization of the Kidney Ultrasound Image Using Singularity Levels

The aim to improve diagnosis decision systems of kidneys led to the concept of many methods of texture characterization of kidneys from ultrasound images. Here, as a first main contribution, we propose a texture characterization method based on the singularity defined by the Hölder exponent, which is multifractal local information. Indeed, the originality of our contribution here is to build a singularity-level matrix corresponding to different levels of regularities, from which we extract new texture features. Finally, as a second main contribution, we will evaluate the potential of our proposed multifractal features to characterize textured ultrasound images of the kidney. Having more reproducibility of the texture features first requires a good choice of Choquet capacity to calculate the irregularities and a selection of a more representative region of interest (ROIs) to analyze by carrying out an adapted virtual puncture in the kidney components. The results of the supervised classification, using three classes of images (young, healthy, and glomerulonephritis), are interesting and promising since the classification accuracy reaches about 80%. This encourages conducting further research to yield better results by overcoming the limitations and taking into account the recommendations made in this article.
Fairer swearers

Abstract This chapter considers how bias and influence affect offensive swearing. Swearing’s power to shock and offend means that whenever someone swears, people can hear the threat they are making, the anger they are expressing, or the contempt they are conveying. Instead of relying on gut feelings to compare the offensiveness of swearing, people should identify individual factors that make swearing more or less offensive. The chapter explains that the judgment should not infer the swearers’ race, gender, accent, or other irrelevant considerations to ensure it remains relevant to the situation. It considers the social intuitionist model of moral judgment to provide a framework that can help understand how to train judgments to be more accurate.
New Feature Selection Approach for Photovoltaïc Power Forecasting Using KCDE

Feature selection helps improve the accuracy and computational time of solar forecasting. However, FS is often passed by or conducted with methods that do not suit the solar forecasting issue, such as filter or linear methods. In this study, we propose a wrapper method termed Sequential Forward Selection (SFS), with a Kernel Conditional Density Estimator (KCDE) named SFS-KCDE, as FS to forecast day-ahead regional PV power production in French Guiana. This method was compared to three other FS methods used in earlier studies: the Pearson correlation method, the RReliefF (RRF) method, and SFS using a linear regression. It has been shown that SFS-KCDE outperforms other FS methods, particularly for overcast sky conditions. Moreover, Wrapper methods show better forecasting performance than filter methods and should be used.
Postdigital Microcredentials

Since the start of the twenty-first century, perception of the four-year diploma’s value as a catalyst for personal, social, and economic mobility has rapidly declined. In response, public and private higher education institutions are increasingly partnering with private technology firms and public non-profit organizations in order to assist in the development, marketing, and sale of short courses and non-credentialed certificate programs termed ‘microcredentials’. The clients range from large corporate employers to struggling jobseekers in a broad range of trades and industries. The postdigital growth of microcredentials marks a significant development in the relationship between digital technology, educational theory, and pedagogical practice. The reception of this growing microcredentialing phenomenon among postdigital scholars has ranged from qualified endorsement to critical scrutiny.
The Postdigital School

Research into postdigital education has often been preoccupied with system level infrastructure and policy, rather than the messy realities of the postdigital school. Schools have been criticised for being both too digital and not digital enough. They are held simultaneously to be insufficiently digital to prepare students for the future and increasingly dominated by commercial providers and data mining algorithms. As a result, the postdigital school takes many forms (e.g., datafied, platformized) and the research that has examined them has tended to explore effects on students and teachers that are at best ambiguous.
Correction: Zhang, J.; Liu, K. Neural Information Squeezer for Causal Emergence. Entropy 2023, 25, 26

There was an error in the original publication [...]
Navigating gender dynamics: a male researcher's experiences on conducting feminist HCI research

In this perspective article, I invite readers to accompany me on a personal journey of self-discovery and transformation as a male researcher within the field of feminist Human-Computer Interaction (HCI). I will delve into the specific topic of gender-opposite research in socio-technical environments and reflect on my experiences, especially the associated challenges and opportunities. My journey exposed me to critical literature and engaged me in long-term fieldwork simultaneously, expanding my understanding of the dynamics that shape our society. My perspectives, sensitization, and awareness of gender-related issues evolved significantly over the course of more than 3 years of being dedicated to a feminist research project. By sharing this narrative, I hope to promote critical discussions about the significance of both, personal growth, and transformation as well as the need for reflexivity in the pursuit of feminist research (with a focus on the specific context of gender-opposite research in socio-technical environments). I will dive into the complexities that I encountered in the settings and compromises I felt obliged to make which were influenced by the embodied nature of my research. As I will furthermore show, there had been an impact on my research practices in terms of planning, observation, analysis, and writing.
Evaluating Surface Mesh Reconstruction Using Real Data

Surface reconstruction has been studied thoroughly, but very little work has been done to address its evaluation. In this article, we propose new visibility-based metrics to assess the completeness and accuracy of three-dimensional meshes based on a point cloud of higher accuracy than the one from which the reconstruction has been computed. We use the position from which each high-quality point has been acquired to compute the corresponding ray of free space. Based on the intersections between each ray and the reconstructed surface, our metrics allow evaluating both the global coherency of the reconstruction and the accuracy at close range. We validate this evaluation protocol by surveying several open-source algorithms as well as a piece of licensed software on three data sets. The results confirm the relevance of assessi ng local and global accuracy separately since algorithms sometimes fail at guaranteeing both simultaneously. In addition, algorithms making use of sensor positions perform better than the ones relying only on points and normals, indicating a potentially significant added value of this piece of information. Our implementation is available at https://github.com/umrlastig/SurfaceReconEval.
A Hybrid Deep Learning-Based Approach for Human Activity Recognition Using Wearable Sensors

Human Activity Recognition (HAR) is a branch of computer science that uses raw time-series data information from embedded smartphone sensors and wearable devices to infer human actions. It has aroused considerable interest in various smart home contexts, particularly for constantly monitoring human behavior in an ecologically friendly atmosphere for elderly people and rehabilitation. Data collection, feature extraction from noise and distortion, feature selection, and pre-processing and categorization are among the operating components of a typical HAR system. Extraction of feature and selection strategies have recently been developed using cutting-edge approaches and traditional machine learning classifiers. The majority of the solutions, on the other hand, rely on simple feature extraction algorithms that are unable to detect complex behaviors. Deep learning techniques are often utilized in different HAR approaches to recover features and classification swiftly because of the introduction and development of vast computing resources. The vast majority of solutions, on the other hand, depend on simplistic feature extraction algorithms incapable of recognizing complicated behaviors. Due to advancements in high computational capabilities, deep learning algorithms are now often utilized in HAR methods to efficiently extract meaningful features which can successfully categorize sensor data. In this chapter, we present a hybrid deep learning-based classification model comprising of Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM), which is named CNN-LSTM. The proposed hybrid deep learning model has been tested over three benchmark HAR datasets: MHEALTH, OPPORTUNITY, and HARTH. On the aforementioned datasets, the proposed hybrid model obtained 99.07%, 95.2%, and 94.68% classification accuracies, respectively, which is quite impressive. The source code of the proposed work can be accessed by using the following link: https://github.com/DSharma05/Human-Activity-Recognition-using-hybrid-Deep-learning-approach .
Convolutional Neural Network-Based Cancer Detection Using Histopathologic Images

Cancer is one of the major health issues currently. Amongst different types of cancer in women, breast cancer stands in the second-highest place. Compared to other kinds of cancer, breast cancer mortality is high. The histopathological analysis is still considered the typical method of identifying cancer, even with the swift developments in medical disciplines. Analysis of histopathological images is time-consuming due to its complexity, and additionally, the results pertain to pathologist subjectivity. Therefore, developing robust and precise cancer detection using histopathological image analysis methods is essential. In this chapter, we discuss the problem of Cancer Detection using a deep learning-based approach. We provide details of the convolutional neural network (CNN), a neural network used for image datasets. Next, we describe various hyper-optimization techniques to optimize the training of the CNN network. Furthermore, we implement Cancer Detection by proposing a learning-based framework on the open-source Histopathologic Cancer Detection dataset. This dataset is available on Kaggle. The proposed framework uses CNN to detect cancer using advanced deep learning frameworks like TensorFlow. This approach can be extended to classify histopathologic images in other biomedical areas.
A lightweight bus passenger detection model based on YOLOv5

Abstract The bus passenger detection algorithm is a key component of a public transportation bus management system. The detection techniques based on the convolutional neural network have been widely used in bus passenger detection. However, they require high memory and computational requirements, which hinder the deployment of bus passenger detectors in the bus system. In this paper, a lightweight bus passenger detection model based on YOLOv5 is introduced. To make the model more lightweight, the inner and outer cross‐stage bottleneck modules, called ICB and OCB, respectively, are proposed. The proposed module reduces the quantity of parameter and floating point operations and increases the detection speed. In addition, the neighbour feature attention pooling is adopted to improve detection accuracy. The performance of the lightweight model on the bus passenger dataset is empirically demonstrated. The experiment results demonstrate that the proposed model is lightweight and efficient. Compared lightweight YOLOv5n with the original algorithm, the model weight is reduced by 31% to 2.6M, and the detection speed is increased by 6% to 40FPS without an accuracy drop.
Cross-modality representation learning from transformer for hashtag prediction

Abstract Hashtags are the keywords that describe the theme of social media content and have become very popular in influence marketing and trending topics. In recent years, hashtag prediction has become a hot topic in AI research to help users with automatic hashtag recommendations by capturing the theme of the post. Most of the previous work mainly focused only on textual information, but many microblog posts contain not only text but also the corresponding images. This work explores both image-text features of the microblog post. Inspired by the self-attention mechanism of the transformer in natural language processing, the visual-linguistics pre-train model with transfer learning also outperforms many downstream tasks that require image and text inputs. However, most of the existing models for multimodal hashtag recommendation are based on the traditional co-attention mechanism. This paper investigates the cross-modality transformer LXMERT for multimodal hashtag prediction for developing LXMERT4Hashtag, a cross-modality representation learning transformer model for hashtag prediction. It is a large-scale transformer model that consists of three encoders: a language encoder, an object encoder, and a cross-modality encoder. We evaluate the presented approach on dataset InstaNY100K. Experimental results show that our model is competitive and achieves impressive results, including precision of 50.5% vs 46.12%, recall of 44.02% vs 38.93%, and F1-score of 47.04% vs 42.22% compared to the existing state-of-the-art baseline model.