Reviewerly/exHarmony huggingface dataset:
Bi-Objective Optimization of Bandwidth Resources and Energy Consumption for Efficient Virtual Machine Placement in Cloud Computing

Cloud computing transformed IT organizations with its on-demand model, notably Infrastructure as a Service (IaaS). Cloud providers manage extensive physical devices, consuming substantial energy and bandwidth for data traffic during virtual machine deployment. Balancing energy and bandwidth are critical. This thesis presents BOHGOA integrated with ACO to optimize virtual machine placement in clouds, simultaneously considering bandwidth and energy. It yields Pareto-optimal solutions for this trade-off. Using CloudSim, BOHGOA outperformed GA, ACO, and FFD algorithms, reducing bandwidth consumption by 54.14%, 32.11%, 57.47% (240 VMs) and 38.12%, 22.76%, 47.05% (500 VMs) respectively. Additionally, it decreased physical machine energy usage by 37.70%, 34.01%, 40.14% (240 VMs) and 27.50%, 22.28%, 30.52% (500 VMs) respectively. These results underscore BOHGOA's effectiveness in optimizing cloud VM placement.
COACT: Coronary artery centerline tracker

Abstract Background The curved planar reformation (CPR) technique is one of the most commonly used methods in clinical practice to locate coronary arteries in medical images. Purpose The artery centerline is the cornerstone for the generation of the CPR image. Here, we describe the development of a new fully automatic artery centerline tracker with the aim of increasing the efficiency and accuracy of the process. Methods We propose a COronary artery Centerline Tracker (COACT) framework which consists of an ostium point finder (OPFinder) model, an intersection point detector (IPDetector) model and a set of centerline tracking strategies. The output of OPFinder is the ostium points. The function of the IPDetector is to predict the intersections of a sample sphere and the centerlines. The centerline tracking process starts from two ostium points detected by the OPFinder, and combines the results of the IPDetector with a series of strategies to gradually reconstruct the coronary artery centerline tree. Results Two coronary CT angiography (CCTA) datasets were used to validate the models. Dataset1 contains 160 cases (32 for test and 128 for training) and dataset2 contains 70 cases (20 for test and 50 for training). The results show that the average distance between the ostium points predicted by the OPFinder and the manually annotated ostium points was 0.88 mm, which is similar to the differences between the results obtained by two observers (0.85 mm). For the IPDetector, the average overlap of the predicted and ground truth intersection points was 97.82% and this is also close to the inter‐observer agreement of 98.50%. For the entire coronary centerline tree, the overlap between the results obtained by COACT and the gold standard was 94.33%, which is slightly lower than the inter‐observer agreement, 98.39%. Conclusions We have developed a fully automatic centerline tracking method for CCTA scans and achieved a satisfactory result. The proposed algorithms are also incorporated in the medical image analysis platform TIMESlice ( https://slice‐doc.netlify.app ) for further studies.
Learning Dynamic Prototypes for Visual Pattern Debiasing

Abstract Deep learning has achieved great success in academic benchmarks but fails to work effectively in the real world due to the potential dataset bias. The current learning methods are prone to inheriting or even amplifying the bias present in a training dataset and under-represent specific demographic groups. More recently, some dataset debiasing methods have been developed to address the above challenges based on the awareness of protected or sensitive attribute labels. However, the number of protected or sensitive attributes may be considerably large, making it laborious and costly to acquire sufficient manual annotation. To this end, we propose a prototype-based network to dynamically balance the learning of different subgroups for a given dataset. First, an object pattern embedding mechanism is presented to make the network focus on the foreground region. Then we design a prototype learning method to discover and extract the visual patterns from the training data in an unsupervised way. The number of prototypes is dynamic depending on the pattern structure of the feature space. We evaluate the proposed prototype-based network on three widely used polyp segmentation datasets with abundant qualitative and quantitative experiments. Experimental results show that our proposed method outperforms the CNN-based and transformer-based state-of-the-art methods in terms of both effectiveness and fairness metrics. Moreover, extensive ablation studies are conducted to show the effectiveness of each proposed component and various parameter values. Lastly, we analyze how the number of prototypes grows during the training process and visualize the associated subgroups for each learned prototype. The code and data will be released at https://github.com/zijinY/dynamic-prototype-debiasing .
Statistical Methods for Visualizing Healthcare Big Data

With the advancement and acceleration of digital technology, the demand and supply of healthcare big data are increasing. In Korea, the government and companies have made various efforts to utilize healthcare big data, such as deregulation data-related legal regulations and data linkage between different institutions. As a result, many researchers have been able to access a variety of healthcare big data. Although healthcare big data has a vast amount and high value, many researchers are unable to fully access healthcare big data because there are difficulties in processing, analysis, and interpretation for data. The data visualization is recognized as an important tool that can solve these limitations. Using data visualization, researchers can intuitively understand complex data and receive support for decision-making. Additionally, these visualizations promote effective communication between experts in different fields and between experts and non-experts. Visualization is used in a variety of research fields and processes, including data summarization, data exploration, and evaluation and interpretation of predictive models. Various types of visualization have different meanings depending on how they are expressed. Therefore, it is important to express the meaning of visualization appropriately. This study provides representative examples of visual representations for data summarization, data exploration, and predictive model evaluation. This study aimed to improve easier access and utilization of healthcare big data by providing R code and visualization results.
Gaming Expertise Metrics: A Sociological Examination of Online Knowledge Creation Platforms

Abstract Online environments have the potential to disrupt traditional orderings of expertise and allow a wider audience to engage as experts in the process of knowledge creation. Many online environments use crowdsourced metric-based systems, such as upvotes and reputation scores, to help identify experts. While these online systems provide opportunities to recognize expertise in new ways, they continue to reproduce and reconfigure existing issues around what is considered expertise and who is considered an expert. To explore these issues, we conduct an ethnographic engagement with Stack Overflow, a community knowledge sharing platform for programmers and coders. Our empirical material comprises 14 interviews with Stack Overflow users and documentary material collected from the platform. We find that while platform owners often present their systems as arbiters of expertise, crowdsourced metric-based systems like Stack Overflow may not locate subject matter experts, but rather may measure community trust and engagement. Reward mechanics may also tempt users to play the game of increasing their metrics rather than developing expertise. Furthermore, our findings suggest that certain conditions in online environments may actively prevent those from under-represented groups from being recognized as experts. This presents an ethical challenge to the premise that online platforms may democratize the sharing of knowledge and recognition of expertise. Our findings suggest that while users may be aware of such issues, metrics are so intertwined with the functioning of the platforms they use that they may accept the premise that those metrics are relevant measures of expertise regardless.
Explainable empirical risk minimization

Abstract The successful application of machine learning (ML) methods increasingly depends on their interpretability or explainability. Designing explainable ML (XML) systems is instrumental for ensuring transparency of automated decision-making that targets humans. The explainability of ML methods is also an essential ingredient for trustworthy artificial intelligence. A key challenge in ensuring explainability is its dependence on the specific human end user of an ML system. The users of ML methods might have vastly different background knowledge about ML principles, with some having formal training in the specific field and others having none. We use information-theoretic concepts to develop a novel measure for the subjective explainability of predictions delivered by a ML method. We construct this measure via the conditional entropy of predictions, given the user signal. Our approach allows for a wide range of user signals, ranging from responses to surveys to biophysical measurements. We use this measure of subjective explainability as a regularizer for model training. The resulting explainable empirical risk minimization (EERM) principle strives to balance subjective explainability and risk. The EERM principle is flexible and can be combined with arbitrary ML models. We present several practical implementations of EERM for linear models and decision trees. Numerical experiments demonstrate the application of EERM to weather prediction and detecting inappropriate language in social media.
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-Based Active Learning

We show that uncertainty sampling is sufficient to achieve exploration versus exploitation in graph-based active learning, as long as the measure of uncertainty properly aligns with the underlying model and the model properly reflects uncertainty in unexplored regions. In particular, we use a recently developed algorithm, Poisson ReWeighted Laplace Learning (PWLL), for the classifier and we introduce an acquisition function designed to measure uncertainty in this graph-based classifier that identifies unexplored regions of the data. We introduce a diagonal perturbation in PWLL which produces exponential localization of solutions, and controls the exploration versus exploitation tradeoff in active learning. We use the well-posed continuum limit of PWLL to rigorously analyze our method and present experimental results on a number of graph-based image classification problems.
Independently convolutional gated recurrent neural unit for space-based ADS-B signal separation with single antenna

Abstract Automatic Dependent Surveillance-Broadcast (ADS-B) is a critical technology to transform aircraft navigation by improving safety and overall effectiveness in the aviation industry. However, overlapping of ADS-B signals is a large challenge, especially for space-based ADS-B systems. Existing traditional methods are not effective when dealing with cases that overlapped signals with small difference (such as power difference and carrier frequency difference) require to be separated. In order to generate an effective separation performance of the ADS-B signals by exploring its temporal relationship, Independently Convolutional Gated Recurrent Neural Unit (Ind-CGRU) is presented for encoder–decoder network construction. Experimental results on the dataset SR-ADSB demonstrate that the proposed Ind-CGRU achieves good performance.
LPI Radar Signal Recognition Based on Feature Enhancement with Deep Metric Learning

Low probability of intercept (LPI) radar signals are widely used in electronic countermeasures due to their low power and large bandwidth. However, they are susceptible to interference from noise, posing challenges for accurate identification. To address this issue, we propose an LPI radar signal recognition method based on feature enhancement with deep metric learning. Specifically, time-domain LPI signals are first transformed into time–frequency images via the Choi–Williams distribution. Then, we propose a feature enhancement network with attention-based dynamic feature extraction blocks to fully extract the fine-grained features in time–frequency images. Meanwhile, we introduce deep metric learning to reduce noise interference and enhance the time–frequency features. Finally, we construct an end-to-end classification network to achieve the signal recognition task. Experimental results demonstrate that our method obtains significantly higher recognition accuracy under a low signal-to-noise ratio compared with other baseline methods. When the signal-to-noise ratio is −10 dB, the successful recognition rate for twelve typical LPI signals reaches 94.38%.
The Use of Sense of Presence in Studies on Human Behavior in Virtual Environments: A Systematic Review

Sense of presence is a key element of the user experience in the study of virtual environments. Understanding it is essential for disciplines, such as architecture and environmental psychology, that study human responses using simulated environments. More evidence is needed on how to optimize spatial presence in simulations of built environments. A systematic review was conducted to define the use of sense of presence in research on human behavior in virtual spaces. Conceptualized dimensions, measurement methodologies, simulation technologies and associated factors were identified. The study identified a diversity of approaches and the predominance of subjective measures over sense of presence indicators. Several studies noted that environmental variables and spatial typologies had significant effects on presence. The results showed that different user profiles responded to stimuli in different ways. The results emphasized the importance of conceiving the construct in interrelation with the built context. A more comprehensive and multidisciplinary orientation is required to identify principles that optimize the spatial experience in virtual environments. This will be important for disciplines that research the human experience using virtual environments.
Deep Learning Misconduct and How Conscious Learning Avoids it

“Deep learning” uses Post-Selection—selection of a model after training multiple models using data. The performance data of “Seep Learning” have been deceptively inflated due to two misconducts: 1: cheating in the absence of a test; 2: hiding bad-looking data. Through the same misconducts, a simple method Pure-Guess Nearest Neighbor (PGNN) gives no errors on any validation dataset V, as long as V is in the possession of the authors and both the amount of storage space and the time of training are finite but unbounded. The misconducts are fatal, because “Deep Learning” is not generalizable, by overfitting a sample set V. The charges here are applicable to all learning modes. This chapter proposes new AI metrics, called developmental errors for all networks trained, under four Learning Conditions: (1) a body including sensors and effectors, (2) an incremental learning architecture (due to the “big data” flaw), (3) a training experience, and (4) a limited amount of computational resources. Developmental Networks avoid Deep Learning misconduct because they train a sole system, which automatically discovers context rules on the fly by generating emergent Turing machines that are optimal in the sense of maximum likelihood across a lifetime, conditioned on the four Learning Conditions.
Dictionary-based matching graph network for biomedical named entity recognition

Biomedical named entity recognition (BioNER) is an essential task in biomedical information analysis. Recently, deep neural approaches have become widely utilized for BioNER. Biomedical dictionaries, implemented through a masked manner, are frequently employed in these methods to enhance entity recognition. However, their performance remains limited. In this work, we propose a dictionary-based matching graph network for BioNER. This approach utilizes the matching graph method to project all possible dictionary-based entity combinations in the text onto a directional graph. The network is implemented coherently with a bi-directional graph convolutional network (BiGCN) that incorporates the matching graph information. Our proposed approach fully leverages the dictionary-based matching graph instead of a simple masked manner. We have conducted numerous experiments on five typical Bio-NER datasets. The proposed model shows significant improvements in F1 score compared to the state-of-the-art (SOTA) models: 2.8% on BC2GM, 1.3% on BC4CHEMD, 1.1% on BC5CDR, 1.6% on NCBI-disease, and 0.5% on JNLPBA. The results show that our model, which is superior to other models, can effectively recognize natural biomedical named entities.
Exploring Cybersecurity Education and Training Techniques: A Comprehensive Review of Traditional, Virtual Reality, and Augmented Reality Approaches

Considering the alarming increase in cyberattacks and their potential financial implications, the importance of cybersecurity education and training cannot be overstated. This paper presents a systematic literature review that examines different cybersecurity education and training techniques with a focus on symmetry. It primarily focuses on traditional cybersecurity education techniques and emerging technologies, such as virtual reality (VR) and augmented reality (AR), through the lens of symmetry. The main objective of this study is to explore the existing cybersecurity training techniques, identify the challenges involved, and assess the effectiveness of cybersecurity training based on VR and AR while emphasizing the concept of symmetry. Through careful selection criteria, 66 primary studies were selected from a total of 150 pertinent research studies. This article offers valuable insights into the pros and cons of conventional training approaches, explores the use of VR and AR in cybersecurity education concerning symmetry, and thoroughly discusses the challenges associated with these technologies. The findings of this review contribute significantly to the continuing efforts in cybersecurity education by offering recommendations for improving employees’ knowledge, engagement, and motivation in cybersecurity training programs while maintaining symmetry in the learning process.
Modeling of Fuzzy Systems Based on the Competitive Neural Network

This paper presents a method to dynamically model Type-1 fuzzy inference systems using a Competitive Neural Network. The aim is to exploit the potential of Competitive Neural Networks and fuzzy logic systems to generate an intelligent hybrid model with the ability to group and classify any dataset. The approach uses the Competitive Neural Network to cluster the dataset and the fuzzy model to perform the classification. It is important to note that the fuzzy inference system is generated automatically from the classes and centroids obtained with the Competitive Neural Network, namely, all the parameters of the membership functions are adapted according to the values of the input data. In the approach, two fuzzy inference systems, Sugeno and Mamdani, are proposed. Additionally, variations of these models are presented using three types of membership functions, including Trapezoidal, Triangular, and Gaussian functions. The proposed models are applied to three classification datasets: Wine, Iris, and Wisconsin Breast Cancer (WDBC). The simulations and results present higher classification accuracy when implementing the Sugeno fuzzy inference system compared to the Mamdani system, and in both models (Mamdani and Sugeno), better results are obtained when the Gaussian membership function is used.
Random Shortening of Linear Codes and Applications

Random linear codes (RLCs) are well known to have nice combinatorial properties and near-optimal parameters in many different settings. However, getting explicit constructions matching the parameters of RLCs is challenging, and RLCs are hard to decode efficiently. This motivated several previous works to study the problem of partially derandomizing RLCs, by applying certain operations to an explicit mother code. Among them, one of the most well studied operations is random puncturing, where a series of works culminated in the work of Guruswami and Mosheiff (FOCS’ 22), which showed that a random puncturing of a low-biased code is likely to possess almost all interesting local properties of RLCs. In this work, we provide an in-depth study of another, dual operation of random puncturing, known as random shortening, which can be viewed equivalently as random puncturing on the dual code. Our main results show that for any small $$\varepsilon $$ , by starting from a mother code with certain weaker conditions (e.g., having a large distance) and performing a random (or even pseudorandom) shortening, the new code is $$\varepsilon $$ -biased with high probability. Our results hold for any field size and yield a shortened code with constant rate. This can be viewed as a complement to random puncturing, and together, we can obtain codes with properties like RLCs from weaker initial conditions. Our proofs involve several non-trivial methods of estimating the weight distribution of codewords, which may be of independent interest.
A multitemporal snow-covered remote sensing image matching method considering global and contextual features

Multitemporal snow remote sensing image matching is an important data processing step for snow monitoring and environmental change analysis. The extensive snow coverage in snow remote sensing images weakens local feature saliency, resulting in significant feature differences between two images and making it difficult to obtain consistency in local features. This poses a great challenge to image matching tasks. To address this issue, we propose a multitemporal snow remote sensing image matching method that considers global and contextual features. This method can extract consistent features between two images and perform matching tasks even in cases of extensive snow coverage. Specifically, our method enhances the ability to aggregate global information by extracting global positional information and contextual features of the images at different scales and convolutional fields, obtaining robust matching descriptors with nonlocal information. We design corresponding loss functions, incorporating average precision loss before extracting contextual features, and combining it with description loss and keypoints detection loss for training. Extensive experiments demonstrate that our method achieves good results in the task of multitemporal snow remote sensing image matching, which improves the match precision and Recall by 11.5% and 7.5%, respectively, compared with the next best results in the experiments.