Reviewerly/exHarmony huggingface dataset:
Negations of Probability Distributions: A Survey

In recent years many papers have been devoted to the analysis and applications of negations of finite probability distributions (PD), first considered by Ronald Yager. This paper gives a brief overview of some formal results on the definition and properties of negations of PD. Negations of PD are generated by negators of probability values transforming element-by-element PD into a negation of PD. Negators are non-increasing functions of probability values. There are two types of negators: PD-independent and PD-dependent negators. Yager's negator is fundamental in the characterization of linear PD-independent negators as a convex combination of Yager's negator and uniform negator. Involutivity of negations is important in logic, and such involutive negator is considered in the paper. We propose a new simple definition of the class of linear negators generalizing Yager's negator. Different examples illustrate properties of negations of PD. Finally, we consider some open problems in the analysis of negations of probability distributions.
Leveraging Multi-Task Learning to Cope With Poor and Missing Labels of Mammograms

In breast cancer screening, binary classification of mammograms is a common task aiming to determine whether a case is malignant or benign. A Computer-Aided Diagnosis (CADx) system based on a trainable classifier requires clean data and labels coming from a confirmed diagnosis. Unfortunately, such labels are not easy to obtain in clinical practice, since the histopathological reports of biopsy may not be available alongside mammograms, while normal cases may not have an explicit follow-up confirmation. Such ambiguities result either in reducing the number of samples eligible for training or in a label uncertainty that may decrease the performances. In this work, we maximize the number of samples for training relying on multi-task learning. We design a deep-neural-network-based classifier yielding multiple outputs in one forward pass. The predicted classes include binary malignancy, cancer probability estimation, breast density, and image laterality. Since few samples have all classes available and confirmed, we propose to introduce the uncertainty related to the classes as a per-sample weight during training. Such weighting prevents updating the network's parameters when training on uncertain or missing labels. We evaluate our approach on the public INBreast and private datasets, showing statistically significant improvements compared to baseline and independent state-of-the-art approaches. Moreover, we use mammograms from Susan G. Komen Tissue Bank for fine-tuning, further demonstrating the ability to improve the performances in our multi-task learning setup from raw clinical data. We achieved the binary classification performance of AUC = 80.46 on our private dataset and AUC = 85.23 on the INBreast dataset.
A Few-Shot Learning Based Approach to IoT Traffic Classification

IoT traffic classification is an important step in network management. Efficient and accurate IoT traffic classification helps Internet Service Providers provide high-quality services to network users. At present, popular IoT traffic classification methods are using traditional machine learning or deep learning algorithm, which relies on a large amount of labeled traffic to construct the traffic-level fingerprinting. However, it is worth noting that some classes of IoT devices only generate limited labeled traffic when they are working, and this limited labeled traffic is insufficient for the aforementioned classification methods. In this letter, we propose Festic, a few-shot learning based approach to IoT traffic classification. Festic can accurately classify IoT traffic under conditions of insufficient labeled traffic. We evaluate Festic on two publicly available datasets, and the experimental results show that Festic has excellent classification accuracy and outperforms the state-of-the-art traffic classification methods.
Kalman filter-enabled parameter estimation for simultaneous quantum key distribution and classical communication scheme over a satellite-mediated link

An accurate estimation of system parameters is of significance for the practical implementation of the simultaneous quantum key distribution and classical communication (SQCC) over a satellite-mediated link when considering the finite-size effect. In this paper, we propose a Kalman filter (KF)-enabled parameter estimation method for the SQCC over a satellite-mediated link. The fast and slow phase drift can be both estimated by using the improved vector KF carrier phase estimation algorithm, and thus the phase estimation error can be tracked in real time and be almost approximate to the theoretical mean square error limit. Taking advantage of the achieved phase estimation and the dual modulation of the SQCC scheme, the excess noise can be estimated with not only a higher precise but also a lower sacrificing rate of raw keys. Numerical simulations demonstrate the feasibility of the SQCC in both the downlink and uplink in terms of the finite-size effect. As a comparison of the Mth-power algorithm, we find that the secret key rate and achievable zenith angle perform better by using the vector KF algorithm. It paves the way of practical implementations for the SQCC system.
Trust Management Systems in Cloud Services Environment: Taxonomy of Reputation Attacks and Defense Mechanisms

Using cloud data storage, large amounts of data can be stored by data owners in a flexible way and at low cost.Hence, there has been a major increase in online cloud service providers and their users.The privacy and security of data in the cloud computing environment is a major issue.Data privacy can be ensured by using a cryptographic access control method, so that data can only be accessed by authorized customers while keeping it inaccessible to unauthorized users.However, this type of cryptographic approach does not address the issue of trust.An integration between several trust models and cryptographic access control models has been presented in many research papers, the aim of which is to make data stored in cloud storage systems more secure.The objective of this study is to determine a solution that can suitably handle trust issues in access control models to decrease the risk as greater security to cloud storage systems, and the quality of decisions being made by data owners and cloud operators is improved.In this paper, we have presented a taxonomy for trust criteria and reputation attacks in cloud computing.Also, some of the fundamental concepts regarding trust management of services within cloud environments have been presented in this paper, along with the latest technologies.There are three layers in the model, and a series of dimensions are further determined for every layer (which are the assessment criteria), which serve as the benchmark to evaluate many research prototypes of trust models in a cloud computing environment by comparing these criteria when evaluating several trust models in a cloud computing environment.In this paper, a comparison of fifteen representative trust management research samples in cloud computing and the appropriate research domains were also carried out by employing this analytical model.
Band Regrouping and Response-Level Fusion for End-to-End Hyperspectral Object Tracking

Visual object tracking plays a fundamental role in computer vision. Extracting the unique spectral and spatial features of hyperspectral images (HSIs) can significantly improve tracking performance in complex scenarios, especially in hyperspectral object tracking. However, due to the limited training samples, handcrafted features are employed in most current hyperspectral trackers, although they cannot sufficiently describe the intrinsic nature of the object. This letter proposes a band regrouping and response-level fusion network (BRRF-Net) for hyperspectral object tracking based on deep transfer learning, employing a deep model trained on color videos to represent features to solve this problem. Specifically, a new band regrouping subnetwork that generates the band weights using hyperspectral feature information is proposed. The bands are divided into several groups using band weights and imported into the Siamese network. Finally, the response-level fusion strategy is adopted to integrate the tracker results for the precise location of objects. Experiments on hyperspectral video reveal that the accuracy of the BRRF-Net is up to 0.689, which is the state-of-the-art performance compared with the current hyperspectral object trackers and proves the effectiveness and superiority of the BRRF-Net.
Transformer based Contextual Model for Sentiment Analysis of Customer Reviews: A Fine-tuned BERT

The Bidirectional Encoder Representations from Transformers (BERT) is a state-of-the-art language model used for multiple natural language processing tasks and sequential modeling applications.The accuracy of predictions from contextbased sentiment and analysis of customer review data from various social media platforms are challenging and timeconsuming tasks due to the high volumes of unstructured data.In recent years, more research has been conducted based on the recurrent neural network algorithm, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) as well as hybrid, neutral, and traditional text classification algorithms.This paper presents our experimental research work to overcome these known challenges of the sentiment analysis models, such as its performance, accuracy, and context-based predictions.We've proposed a fine-tuned BERT model to predict customer sentiments through the utilization of customer reviews from Twitter, IMDB Movie Reviews, Yelp, Amazon.In addition, we compared the results of the proposed model with our custom Linear Support Vector Machine (LSVM), fastText, BiLSTM and hybrid fastText-BiLSTM models, as well as presented a comparative analysis dashboard report.This experiment result shows that the proposed model performs better than other models with respect to various performance measures.
Strong recovery of geometric planted matchings

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Strong recovery of geometric planted matchingsDmitriy Kunisky and Jonathan Niles-WeedDmitriy Kunisky and Jonathan Niles-Weedpp.834 - 876Chapter DOI:https://doi.org/10.1137/1.9781611977073.36PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We study the problem of efficiently recovering the matching between an unlabelled collection of n points in ℝd and a small random perturbation of those points. We consider a model where the initial points are i.i.d. standard Gaussian vectors, perturbed by adding i.i.d. Gaussian vectors with covariance σ2Id. In this setting, the maximum likelihood estimator (MLE) can be found in polynomial time as the solution of a linear assignment problem. We establish thresholds on σ2 for the MLE to perfectly recover the planted matching (making no errors) and to strongly recover the planted matching (making o(n) errors) both for d constant and d = d(n) growing arbitrarily. Between these two thresholds, we show that the MLE makes nδ+o(1) errors for an explicit δ ∊ (0, 1). These results extend a recent line of work on recovering matchings planted in random graphs with independently-weighted edges to the geometric setting. Our proof techniques rely on careful analysis of the combinatorial structure of partial matchings in large, weakly dependent random graphs using the first and second moment methods. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Kidney Tumor Segmentation Using Two-Stage Bottleneck Block Architecture

Cases of kidney cancer have shown a rapid increase in recent years.Advanced technology has allowed bettering the existing treatment methods.Research on the subject is still continuing.Medical segmentation is also of increasing importance.In particular, deep learning-based studies are of great importance for accurate segmentation.Tumor detection is a relatively difficult procedure for soft tissue organs such as kidneys and the prostate.Kidney tumors, specifically, are a type of cancer with a higher incidence in older people.As age progresses, the importance of having diagnostic tests increases.In some cases, patients with kidney tumors may not show any serious symptoms until the last stage.Therefore, early diagnosis of the tumor is important.This study aimed to develop support systems that could help physicians in the segmentation of kidney tumors.In the study, improvements were made on the encoder and decoder phases of the V-Net model.With the double-stage bottleneck block structure, the architecture was transformed into a unique one, which achieved an 86.9% kidney tumor Dice similarity coefficient.The results show that the model gives applicable and accurate results for kidney tumor segmentation.
Crowd Density Estimation from Autonomous Drones Using Deep Learning: Challenges and Applications

Crowd flow estimation from Drones or normally referred as Unmanned Aerial Vehicle (UAV ) for crowd management and monitoring is an essential research problem for adaptive monitoring and controlling dynamic crowd gatherings. Various challenges exist in this context, i.e. variation in density, scale, brightness, height from UAV platform, occlusion and inefficient pose estimation. Currently, gathering of crowd is mostly monitored by Close Circuit Television (CCTV) cameras where various problems exist, i.e. coverage in little area and constant involvement of human to monitor crowd which encourage researchers to move towards deep learning and computer vision techniques to minimize the need of human operator and thus develop intelligent crowd counting techniques. Deep learning frameworks are promising for intelligent crowd analysis from frames of video despite the fact of various challenges for detecting humans from unstable UAV camera platforms. This research presents rigorous investigation and analysis in existing methods with their applications for crowd flow estimation from UAV. Besides, comprehensive performance evaluation for existing methods using recent deep learning frameworks is illustrated for crowd counting purposes. In addition, strong foundation for future direction is given by elaborating observations on existing research frameworks.
A Comparison between Named Entity Recognition Models in the Biomedical Domain

The domain-specialised application of Named Entity Recognition (NER) is known as Biomedical NER (BioNER), which aims to identify and classify biomedical concepts that are of interest to researchers, such as genes, proteins, chemical compounds, drugs, mutations, diseases, and so on.The BioNER task is very similar to general NER but recognising Biomedical Named Entities (BNEs) is more challenging than recognising proper names from newspapers due to the characteristics of biomedical nomenclature.In order to address the challenges posed by BioNER, seven machine learning models were implemented comparing a transfer learning approach based on fine-tuned BERT with Bi-LSTM based neural models and a CRF model used as baseline.Precision, Recall and F1-score were used as performance scores evaluating the models on two well-known biomedical corpora: JNLPBA and BIOCREATIVE IV (BC-IV).Strict and partial matching were considered as evaluation criteria.The reported results show that a transfer learning approach based on fine-tuned BERT outperforms all others methods achieving the highest scores for all metrics on both corpora.
Public Security Video Image Detection System Construction Platform in Cloud Computing Environment

The public security image detection system is an important way to assist the police in the investigation. In today's cloud computing environment, the processing power of cloud computing is gradually improving. In order to explore its application in the investigation system, this paper constructs a public security video image investigation system based on cloud computing environment. This paper uses cloud computing technology to improve the processing capacity of the system. It then combines the storage capabilities of the backend server implementation technology and the working principle of Hadoop to construct a basic model. Then combined with the storage capability of the backend server implementation technology and the working principle of Hadoop and CP-ABE encryption, decryption, and reconstruction, a basic model is constructed. This paper also designs public security video surveillance system vehicle detection and test experiments, cloud storage encryption algorithm experiments, and computational storage requirements analysis experiments. It optimizes the system based on the results of the experiment and finally compares it with the traditional investigation system. The experimental results show that the public security video image detection system based on cloud computing can improve the accuracy by 5%-25% compared with the traditional detection system. And the public security video image detection system based on cloud computing can increase the efficiency by 2%-17% compared with the traditional detection system.
Kabuki as a Synthetic Narrative

Although the author has previously written several papers on kabuki addressing the narrative generation of kabuki or kabuki as narrative generation, this chapter is a kind of synthesis. At first, the chapter provides a focused summary of what kabuki is, which has already been summarized in the author's previous papers, and presents the synthetic characteristics of kabuki as narrative generation. The next section describes the main 15 narrative elements or techniques and several cross-sectional elements or techniques beyond conceptual description. Through two types of systems, including an integrated narrative generation system (INGS) and geinō information system (GIS), the author presents an entire image and design of narrative generation mechanisms in kabuki. Dependent on the above, the last section explains in detail several elements and techniques of kabuki and presents ideas of concrete design and implementation of kabuki-related narrative generation systems by the author.
Applications of Game Theory in a Blockchain-Based Healthcare Information System

Healthcare service providers are dependent on accurate information systems. Some crucial challenges are (1) improvement of patient caring services, (2) containing the deployment cost of information systems, and (3) avoiding any disturbances in its business processes at the time of data gathering and processing activities. Technological advancements are a significant driver of efficient healthcare information systems and services. By creating a rich healthcare-related data foundation and integrating technologies like the internet of things (IoT), blockchain technology, artificial intelligence (AI) techniques, and big data analytics, the digital transformation in healthcare is recognized as a pivotal component to tackle these challenges. For example, it can improve diagnostics, prevention, and patient therapy, ultimately empowering caregivers to use an evidence-based method to enhance clinical decision-making. Real-time interactions permit a physician to monitor a patient 'live' instead of interaction regularly (e.g., weekly, monthly). This way, healthcare operation can provide better services. However, the IoT system creates the risk of a sensitive data breach without a highly secure infrastructure. Blockchain technology improves the reliance on a centralized authority to certify information integrity and ownership and mediate transactions and exchange of digital assets. As a result, the mining process in the blockchain is very resource-intensive; hence, miners create coalition groups to cross-check each block of transactions in return for a reward. In addition, it creates enormous competition among miners, and consequently, dishonest mining strategies (e.g., block withholding attack, selfish mining, eclipse attack) need to be controlled. Consequently, it is necessary to regulate the mining process to make miners accountable for any dishonest mining behaviours; game theory can help regulate it. Finally, this chapter presents a survey of game theory used to address the common issues in the blockchain network.
Compound computer vision workflow for efficient and automated immunohistochemical analysis of whole slide images

Aims Immunohistochemistry (IHC) assessment of tissue is a central component of the modern pathology workflow, but quantification is challenged by subjective estimates by pathologists or manual steps in semi-automated digital tools. This study integrates various computer vision tools to develop a fully automated workflow for quantifying Ki-67, a standard IHC test used to assess cell proliferation on digital whole slide images (WSIs). Methods We create an automated nuclear segmentation strategy by deploying a Mask R-CNN classifier to recognise and count 3,3′-diaminobenzidine positive and negative nuclei. To further improve automation, we replaced manual selection of regions of interest (ROIs) by aligning Ki-67 WSIs with corresponding H&amp;E-stained sections, using scale-invariant feature transform (SIFT) and a conventional histomorphological convolutional neural networks to define tumour-rich areas for quantification. Results The Mask R-CNN was tested on 147 images generated from 34 brain tumour Ki-67 WSIs and showed a high concordance with aggregate pathologists’ estimates ( <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" overflow="scroll"><m:mstyle displaystyle="true" scriptlevel="0"><m:mrow><m:mi>n</m:mi><m:mo>=</m:mo><m:mn>3</m:mn></m:mrow></m:mstyle></m:math> assessors; <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" overflow="scroll"><m:mstyle displaystyle="true" scriptlevel="0"><m:mrow><m:mi>y</m:mi><m:mo>=</m:mo><m:mn>0.9712</m:mn><m:mi>x</m:mi><m:mo>−</m:mo><m:mn>1.945</m:mn><m:mo>,</m:mo></m:mrow></m:mstyle></m:math> r=0.9750). Concordance of each assessor’s Ki-67 estimates was higher when compared with the Mask R-CNN than between individual assessors (r avg =0.9322 vs 0.8703; p=0.0213). Coupling the Mask R-CNN with SIFT-CNN workflow demonstrated ROIs can be automatically chosen and partially sampled to improve automation and dramatically decrease computational time (average: 88.55–19.28 min; p&lt;0.0001). Conclusions We show how innovations in computer vision can be serially compounded to automate and improve implementation in clinical workflows. Generalisation of this approach to other ancillary studies has significant implications for computational pathology.