Reviewerly/exHarmony huggingface dataset:
Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection

Stemming from the overlap of objects and undertraining due to few samples, road dense object detection is confronted with poor object identification performance and the inability to recognize edge objects. Based on this, one transfer learning-based YOLOv3 approach for identifying dense objects on the road has been proposed. Firstly, the Darknet-53 network structure is adopted to obtain a pre-trained YOLOv3 model. Then, the transfer training is introduced as the output layer for the special dataset of 2000 images containing vehicles. In the proposed model, one random function is adapted to initialize and optimize the weights of the transfer training model, which is separately designed from the pre-trained YOLOv3. The object detection classifier replaces the fully connected layer, which further improves the detection effect. The reduced size of the network model can further reduce the training and detection time. As a result, it can be better applied to actual scenarios. The experimental results demonstrate that the object detection accuracy of the presented approach is 87.75% for the Pascal VOC 2007 dataset, which is superior to the traditional YOLOv3 and the YOLOv5 by 4% and 0.59%, respectively. Additionally, the test was carried out using UA-DETRAC, a public road vehicle detection dataset. The object detection accuracy of the presented approach reaches 79.23% in detecting images, which is 4.13% better than the traditional YOLOv3, and compared with the existing relatively new object detection algorithm YOLOv5, the detection accuracy is 1.36% better. Moreover, the detection speed of the proposed YOLOv3 method reaches 31.2 Fps/s in detecting images, which is 7.6 Fps/s faster than the traditional YOLOv3, and compared with the existing new object detection algorithm YOLOv7, the speed is 1.5 Fps/s faster. The proposed YOLOv3 performs 67.36 Bn of floating point operations per second in detecting video, which is obviously less than the traditional YOLOv3 and the newer object detection algorithm YOLOv5.
Scientific Library Between Open and Corporate Science

The concept of Open science comes into conflict with the established practice of commercializing the results of research and development work. Commercialization of scientific achievements is both a way to attract funding and a deterrent to intensify the exchange of knowledge and the pace of innovation, especially in basic research. The principles of Open science, providing the ability to widely use and reuse the same data, comparing different approaches and accelerating the development of new research methods, make patenting applied developments much more difficult due to leveling the novelty of inventions in publications and open research data. The purpose of the article is to suggest the possibilities for scientific library to participate in the processes of production, exchange and dissemination of information and knowledge within the framework of the movement of science from the binary system “corporate science vs open science” to hybrid mechanisms of work. Such participation is based on the existing practices of the Centers for Legal and Technical Information and the efforts of scientific library to create an open scientific infrastructure. It involves the expansion of ongoing practices, taking into account the proposed by hybrid science differentiation of Open access to fundamental research and patenting applied developments. Thus, these opportunities are necessary for further modeling the place of scientific library in the ecosystem of open science.
IoT Sensors with Blockchain for Control Processes. Concept and Design Issues

We analyze the problem of IoT sensors in a blockchain system for control loops. We aim to determine the limits due to systems latencies and the bandwidth capabilities of IoT sensors control loops when the blockchain is involved. A robust and predictive control strategy and a communication method that is adapted to the control strategy are proposed.
AI-Based Open Logo Detection for Indian Scenario

Logo detection from images and videos has a wide range of applications. Most of the existing logo datasets does not contain the logos of Indian companies. Since there is no existing dataset with Indian companies’ logo, we created our own dataset covering three different industries with 4 different classes for each industry. In this work, we have trained a YOLOv5 model to detect Indian logos in closed and QMUL-Openlogo dataset settings, with pretrained weights and without pretrained weights. In addition to this, we also created a GUI using Python’s Tkinter library. The Python code for GUI can be accessed from our GitHub repository. ( https://github.com/Prasanth-S-n/IndianLogoDetection )
Automatic Detection and Classification of Melanoma Using the Combination of CNN and SVM

Accurate detection of melanoma in the skin is very essential for the exact analysis, interpretation, and right treatment. Skin cancer is due to the unrepaired deoxyribonucleic acid (DNA) in skin cells that generate defects or mutations which spreads gradually to other body parts. Skin melanoma which is detected and treated in the initial stage gives good long-term survival. So, it is vital to find the most effective skin melanoma detection method. Pre-processing is the initial step in the detection process which is vital in skin cancer analysis. It assists the automatic interpretation and helps to improve the image quality, by removing the noise and irrelevant structures present in the skin images. This work uses convolution neural network (CNN) to extract the features from various skin melanoma images, and support vector machine (SVM) is used for the classification. Experimental results demonstrated that the proposed method is working better in detecting various skin cancer tissues.
Performance Analysis of Hybridized Fuzzy Clustering Algorithms Using Metaheuristic Algorithms

Fuzzy clustering effectively handles the problem of mystically separable data through fuzzy partitioning. Popularly known as soft clustering, fuzzy clustering is based on membership degree of each data point. Various fuzzy clustering algorithms have been proposed in the literature. These algorithms work well in lower dimensions but are unable to find the global optimum in higher dimension. This problem has been solved by hybridizing the fuzzy clustering algorithms with various optimization algorithms. In this paper, comparative analysis of three hybridized fuzzy clustering algorithms: Fuzzy C-Means, Intuitionistic Fuzzy C-Means, and Kernel Intuitionistic Fuzzy C-Means with four metaheuristic optimization algorithms: Particle Swarm Optimization, Firefly Algorithm, Artificial Bee Colony, and Genetic Algorithm has been done using three synthetic data sets. It was observed that Kernel Intuitionistic Fuzzy C-Means outperformed all other algorithms when hybridized with Particle Swarm Optimization, Firefly Algorithm, Artificial Bee Colony, and Genetic Algorithm.
Wasserstein GANs-Enabled Spectral Normalization on Credit Card Fraud Detection

Credit card fraud detection has become quite a crucial task to increase reliability of online financial transactions. The generative models have been investigated for the augmentation task, but they need careful parameter tuning and are susceptible to mode collapse. Recent works have shown that the Wasserstein distance is more stable for model training than the J-S divergence. In this work, Wasserstein GAN has been used for detecting credit card frauds. This model is compared against more conventional methods like Undersampling and Oversampling. Additionally, WGAN has been improved by adding Spectral Normalization to enforce the 1-Lipschitz constraint instead of weight clipping. L1 loss has been added to the Generator loss as well. These methods have been shown to outperform more conventional methods.
Multimodal Classification via Visual and Lingual Feature Layer Fusion

Multimedia content has been widely available over social media and web services. Recently, the growth of multimedia data has presented new obstacles and great prospects for the multimedia community. Mostly, the content through the web comes in a multimodal manner, and images are generally accompanied by text. This paper presents a new multimodal classification framework involving images and their related texts. More precisely, it presents the fusion of textual and visual features with a batch normalisation technique to handle multimodal data in an efficient way. The proposed work is validated using the large-scale food dataset as the standard benchmark for multimodal food classification. The results show that the proposed model outperforms the text-only, image-only models and the state-of-the-art results.
A Feature Representation Technique for Angular Margin Loss

The most crucial task in deep learning is feature extraction and representation. How features are extracted and represented can lead to a variety of possible solutions to the difficult issue of computer vision. Many studies have been conducted to extract features in an efficient manner; however, few studies have been conducted on feature representation. This work proposes a straightforward yet effective feature representation approach for the angular margin loss (Arc) feature. The representation approach makes it possible to detect similarities between distinct characteristics, which is particularly beneficial for many computer vision problems such as face recognition and object tracking. We offer what is perhaps the most comprehensive experimental evaluation of the most recent state-of-the-art face recognition algorithms, ArcFace, using three face recognition benchmarks. In most situations, our technique outperforms the previous state-of-the-art model in terms of accuracy and true positive rate. The code is available at https://github.com/asfakali/FRAML.git
Applying the Goal, Question, Metric method to derive tailored dynamic cyber risk metrics

Purpose This paper aims to propose a new method to derive custom dynamic cyber risk metrics based on the well-known Goal, Question, Metric (GQM) approach. A framework that complements it and makes it much easier to use has been proposed too. Both, the method and the framework, have been validated within two challenging application domains: continuous risk assessment within a smart farm and risk-based adaptive security to reconfigure a Web application firewall. Design/methodology/approach The authors have identified a problem and provided motivation. They have developed their theory and engineered a new method and a framework to complement it. They have demonstrated the proposed method and framework work, validating them in two real use cases. Findings The GQM method, often applied within the software quality field, is a good basis for proposing a method to define new tailored cyber risk metrics that meet the requirements of current application domains. A comprehensive framework that formalises possible goals and questions translated to potential measurements can greatly facilitate the use of this method. Originality/value The proposed method enables the application of the GQM approach to cyber risk measurement. The proposed framework allows new cyber risk metrics to be inferred by choosing between suggested goals and questions and measuring the relevant elements of probability and impact. The authors’ approach demonstrates to be generic and flexible enough to allow very different organisations with heterogeneous requirements to derive tailored metrics useful for their particular risk management processes.
Exploring technology acceptance of head‐mounted device‐based augmented reality surgical navigation in orthopaedic surgery

This study used the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate the acceptance of HMD-based AR surgical navigation.An experiment was conducted in which participants drilled 12 predefined holes using freehand drilling, proprioceptive control, and AR assistance. Technology acceptance was assessed through a survey and non-participant observations.Participants' intention to use AR correlated (p < 0.05) with social influence (Spearman's rho (rs) = 0.599), perceived performance improvement (rs = 0.592) and attitude towards AR (rs = 0.542).While most participants acknowledged the potential of AR, they also highlighted persistent barriers to adoption, such as issues related to user-friendliness, time efficiency and device discomfort. To overcome these challenges, future AR surgical navigation systems should focus on enhancing surgical performance while minimising disruptions to workflows and operating times. Engaging orthopaedic surgeons in the development process can facilitate the creation of tailored solutions and accelerate adoption.
Character Shifts in Harry Potter Fanfiction

Abstract In fanfictions, authors can explore the fictitious universe of a fandom without restricting themselves to its canonical rules and orders. These explorations are often linked to diverging character conceptualizations based on readers’ response to omissions and indeterminacies in the source material. Following a three part structure, we examine these character shifts in German Harry Potter fanfictions by (1) comparing basic frequency distributions of character names, (2) analyzing character co-occurrences in order to identify central character pairings as well as collocations surrounding these characters, and (3) using a word embedding-based approach to sentiment analysis to quantify the emotional charging of characters. We identified certain character types predestined for re-conceptualization, such as minor characters or figures from pre- or sequels who generate interest among fanfiction writers, as well as villainous characters whose redesign follows typical fanfiction tropes such as »Enemies to Lovers«.
Sravnitel'nyi analiz kolichestvennykh i kachestvennykh pokazatelei publikatsionnoi aktivnosti kafedr "DGPU"

The chapter presents the results of a comparative analysis of the publication activity of the teaching staff of the departments of the Faculty of Physical Education of Dagestan State Pedagogical University. The obtained research results are based on the scientometric database of the RSCI. According to a set of hetero-geneous scientometric indicators: the number of publications, the citation index, the Hirsch index, the impact factor and the categories of journals in the List of the Higher Attestation Commission in which articles are published, the authors have compiled ratings of departments, an integrated assessment of their publication productivity is given. The importance of research related to the publication activity of higher school teachers is emphasized: the study of the publication activity of teachers and structural units of the university allows not only to assess the current situation of the effectiveness of research activities, but also assumes its informational value in solving the tasks of improving the efficiency of planning and organization of scientific work of departments (faculties), in order to increase the competitiveness of the university.
Natural geochemical hazard as a pseudo-component of negative environmental impact of mining complexes

The paper presents the authors’ research findings that enable to avoid unreasonable charges for negative environmental impact by applying mathematical statistics and GIS technology, modern methods of geochemical sample data processing, as well as by analysis of the dynamics of engineering-geological phenomena in the mining complexes areas. The authors’ data on the manifestation of high concentrations of toxic elements in some landscape components on the territory of mining complexes are provided. The latter, according to the authors, are associated with the outcrop of molybdenum ore occurrences, which were previously blocked by thin Pleistocene deposits. Then, due to sloughing of these deposits, molybdenum compounds began penetrating to the environment components.
Exact Short Products From Truncated Multipliers

Abstract We sometimes need to compute the most significant digits of the product of small integers with a multiplier requiring much storage, e.g. a large integer (e.g. $5^{100}$) or an irrational number ($\pi $). We only need to access the most significant digits of the multiplier—as long as the integers are sufficiently small. We provide an efficient algorithm to compute the range of integers given a truncated multiplier and a desired number of digits.
A new clustering method based on multipartite networks

The clustering problem is one of the most studied and challenging in machine learning, as it attempts to identify similarities within data without any prior knowledge. Among modern clustering algorithms, the network-based ones are some of the most popular. Most of them convert the data into a graph in which instances of the data represent the nodes and a similarity measure is used to add edges. This article proposes a novel approach that uses a multipartite network in which layers correspond to attributes of the data and nodes represent intervals for the data. Clusters are intuitively constructed based on the information provided by the paths in the network. Numerical experiments performed on synthetic and real-world benchmarks are used to illustrate the performance of the approach. As a real application, the method is used to group countries based on health, nutrition, and population information from the World Bank database. The results indicate that the proposed method is comparable in performance with some of the state-of-the-art clustering methods, outperforming them for some data sets.
Self-prompting Large Vision Models for Few-Shot Medical Image Segmentation

Recent advancements in large foundation models have shown promising potential in the medical industry due to their flexible prompting capability. One such model, the Segment Anything Model (SAM), a prompt-driven segmentation model, has shown remarkable performance improvements, surpassing state-of-the-art approaches in medical image segmentation. However, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available. In this paper, we propose a novel perspective on self-prompting in medical vision applications. Specifically, we harness the embedding space of SAM to prompt itself through a simple yet effective linear pixel-wise classifier. By preserving the encoding capabilities of the large model, the contextual information from its decoder, and leveraging its interactive promptability, we achieve competitive results on multiple datasets (i.e. improvement of more than 15% compared to fine-tuning the mask decoder using a few images). Our code is available at https://github.com/PeterYYZhang/few-shot-self-prompt-SAM
SEDA: Self-ensembling ViT with Defensive Distillation and Adversarial Training for Robust Chest X-Rays Classification

Deep Learning methods have recently seen increased adoption in medical imaging applications. However, elevated vulnerabilities have been explored in recent Deep Learning solutions, which can hinder future adoption. Particularly, the vulnerability of Vision Transformer (ViT) to adversarial, privacy, and confidentiality attacks raise serious concerns about their reliability in medical settings. This work aims to enhance the robustness of self-ensembling ViTs for the tuberculosis chest x-ray classification task. We propose Self-Ensembling ViT with defensive Distillation and Adversarial training (SEDA). SEDA utilizes efficient CNN blocks to learn spatial features with various levels of abstraction from feature representations extracted from intermediate ViT blocks, that are largely unaffected by adversarial perturbations. Furthermore, SEDA leverages adversarial training in combination with defensive distillation for improved robustness against adversaries. Training using adversarial examples leads to better model generalizability and improves its ability to handle perturbations. Distillation using soft probabilities introduces uncertainty and variation into the output probabilities, making it more difficult for adversarial and privacy attacks. Extensive experiments performed with the proposed architecture and training paradigm on publicly available Tuberculosis x-ray dataset shows SOTA efficacy of SEDA compared to SEViT in terms of computational efficiency with $$70\times $$ times lighter framework and enhanced robustness of +9%. Code: Github .
Boosting Knowledge Distillation via Random Fourier Features for Prostate Cancer Grading in Histopathology Images

There has been a growing number of pathology image datasets, in particular for cancer diagnosis. Although these datasets permit easy access and development of computational pathology tools, the current computational models still struggle to handle unseen datasets due to various reasons. Transfer learning and fine-tuning are standard techniques to adapt an existing model that was trained on one dataset to another. However, this approach does not fully exploit the existing model and the target dataset. Inspired by knowledge distillation, we propose a student-teacher strategy that distills knowledge from a well-trained teacher model, generally trained on a larger dataset, to a student model to be tested on a small dataset. To facilitate efficient and effective knowledge distillation and transfer, we employ contrastive learning and non-parameterized random Fourier features for compressed feature mapping into a lower-dimensional space. We evaluated our proposed method using three prostate cancer datasets, including a teacher dataset, a target student dataset, and an independent test dataset. The experimental results demonstrate that the proposed approach outperforms other transfer learning and state-of-the-art knowledge distillation methods. Code is available at: https://github.com/trinhvg/KD_CoRFF .
A Continual Learning Approach for Cross-Domain White Blood Cell Classification

Accurate classification of white blood cells in peripheral blood is essential for diagnosing hematological diseases. Due to constantly evolving clinical settings, data sources, and disease classifications, it is necessary to update machine learning classification models regularly for practical real-world use. Such models significantly benefit from sequentially learning from incoming data streams without forgetting previously acquired knowledge. However, models can suffer from catastrophic forgetting, causing a drop in performance on previous tasks when fine-tuned on new data. Here, we propose a rehearsal-based continual learning approach for class incremental and domain incremental scenarios in white blood cell classification. To choose representative samples from previous tasks, we employ exemplar set selection based on the model's predictions. This involves selecting the most confident samples and the most challenging samples identified through uncertainty estimation of the model. We thoroughly evaluated our proposed approach on three white blood cell classification datasets that differ in color, resolution, and class composition, including scenarios where new domains or new classes are introduced to the model with every task. We also test a long class incremental experiment with both new domains and new classes. Our results demonstrate that our approach outperforms established baselines in continual learning, including existing iCaRL and EWC methods for classifying white blood cells in cross-domain environments.