Reviewerly/exHarmony huggingface dataset:
Cellular Architecture on Whole Slide Images Allows the Prediction of Survival in Lung Adenocarcinoma

Pathology is the gold standard for cancer diagnosis. Numerous studies aim to automate the diagnosis based on digital slides, yet its prognostic utilities lack adequate investigation. Besides the inherent difficulties in predicting a patient’s prognosis, extracting informative features from gigapixel and heterogeneous whole slide images (WSI) remains an open challenge. We present a computational pipeline that can generate an embedded map to flexibly profile different cell populations’ local and global composition and architecture on WSIs. Our approach allows researchers to investigate tumor cells and tumor microenvironment based on these embedded maps of a reasonable size rather than dealing with gigantic WSIs. Here, we applied this pipeline to extract the texture patterns for tumor and immune cell types on the TCGA lung adenocarcinoma dataset. Based on extensive survival modeling, we have demonstrated that by pruning redundant and irrelevant features, the final prediction model has achieved an optimal C-index of 0.70 during testing. Our proof-of-concept study proves that the efficient local-global embedded maps bear valuable information with clinical correlations in lung cancer and potentially in other cancer types, warranting further investigations.
Sequential Digital Signatures for Cryptographic Software-Update Authentication

Consider a computer user who needs to update a piece of software installed on their computing device. To do so securely, a commonly accepted ad-hoc method stipulates that the old software version first retrieves the update information from the vendor’s public repository, then checks that a cryptographic signature embedded into it verifies with the vendor’s public key, and finally replaces itself with the new version. This updating method seems to be robust and lightweight, and to reliably ensure that no malicious third party (e.g., a distribution mirror) can inject harmful code into the update process. Unfortunately, recent prominent news reports (SolarWinds, Stuxnet, TikTok, Zoom, ...) suggest that nation state adversaries are broadening their efforts related to attacking software supply chains. This calls for a critical re-evaluation of the described signature based updating method with respect to the real-world security it provides against particularly powerful adversaries. We approach the setting by formalizing a cryptographic primitive that addresses specifically the secure software updating problem. We define strong, rigorous security models that capture forward security (stealing a vendor’s key today doesn’t allow modifying yesterday’s software version) as well as a form of self-enforcement that helps protecting vendors against coercion attacks in which they are forced, e.g. by nation state actors, to misuse or disclose their keys. We note that the common signature based software authentication method described above meets neither the one nor the other goal, and thus represents a suboptimal solution. Hence, after formalizing the syntax and security of the new primitive, we propose novel, efficient, and provably secure constructions.
Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images

Understanding the intensity characteristics of brain lesions is key for defining image-based biomarkers in neurological studies and for predicting disease burden and outcome. In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images. Furthermore, the proposed method can be used as a data augmentation module to generate synthetic images for training brain image segmentation networks. Experiments on multiple sclerosis (MS) brain images acquired on magnetic resonance imaging (MRI) demonstrate that the proposed method can generate highly realistic pseudo-healthy and pseudo-pathological brain images. Data augmentation using the synthetic images improves the brain image segmentation performance compared to traditional data augmentation methods as well as a recent lesion-aware data augmentation technique, CarveMix. The code will be released at https://github.com/dogabasaran/lesion-synthesis .
Puncturable Signature: A Generic Construction and Instantiations

Puncturable signature (PS), proposed by Bellare, Stepanovs and Waters at EUROCRYPT 2016, is a special kind of digital signature that supports a fine-grained revocation of signing capacity by updating the secret key with selective messages. Puncturable signature has many usages like asynchronous transaction data signing services and proof-of-stake blockchain protocols. Meanwhile, it is an essential building block in constructing disappearing signatures in the bounded storage model. In this paper, we propose the first generic construction of puncturable signature from identity-based signature by treating identities as prefixes. With the help of our generic framework, we present different puncturable signature instantiations over lattices, bilinear maps, and multivariate public key cryptography (MPKC). Specifically, the lattice-based instantiation is based on the short integer solution (SIS) assumption and is proven secure in the random oracle model. Besides, the pairing-based instantiation is based on the computational Diffie-Hellman (CDH) assumption and is proven secure in the standard model. In addition, we show that the instantiation over MPKC is secure under current attacks.
CVAD: An Anomaly Detector for Medical Images Based on Cascade VAE

Anomaly detection in medical imaging plays an important role to ensure AI generalization. However, existing out-of-distribution (OOD) detection approaches fail to account for OOD data granularity in medical images, where identifying both intra-class and inter-class OOD data is essential to the generalizability in the medical domain. We focus on the generalizability of outlier detection for medical images and propose a generic Cascade Variational autoencoder-based Anomaly Detector (CVAD). We use variational autoencoders’ cascade architecture, which combines latent representation at multiple scales, before being fed to a discriminator to distinguish the OOD data from the in-distribution data. Finally, both the reconstruction error and the OOD probability predicted by the binary discriminator are used to determine the anomalies. We compare the performance with the state-of-the-art deep learning models to demonstrate our model’s efficacy on various open-access natural and medical imaging datasets for intra- and inter-class OOD. Extensive experimental results on multiple datasets show our model’s effectiveness and generalizability. The code will be publicly available.
A Query Language for Language Analysis

Language analysis aims at establishing properties of languages, which provides strong guarantees on the behavior of every program written in such languages. Tools that automate language analysis often need to browse a language definition given as input and retrieve information from grammars, typing rules, reduction rules, and other components of the language. In this paper, we propose a languages-as-databases approach where language definitions are stored as database tables. Our main contribution is $$\textsc {Lang-Sql}$$ , a SQL-inspired query language that can express queries over languages. The key characteristic of $$\textsc {Lang-Sql}$$ is that it contains linguistic features that are specific to query operational semantics aspects. To demonstrate that $$\textsc {Lang-Sql}$$ can be used in practical applications, we have used $$\textsc {Lang-Sql}$$ queries to rewrite the majority of $$\textsc {Lang-n-Check}$$ , a tool that analyzes languages and establishes their type soundness. Our queries are declarative, and concisely express complicated operations.
Automatic Computer-Aided Histopathologic Segmentation for Nasopharyngeal Carcinoma Using Transformer Framework

The segmentation of the histopathological whole slide images (WSIs) of nasopharyngeal carcinoma (NPC) plays an essential role in the diagnosis, grading and even prognosis analysis. Due to the huge size of pathological images and the fact that NPC often occurs in the middle and advanced stages, it is still challenging to generate accurate segmentation results automatically. Although many convolutional neural network (CNN) methods had achieved good segmentation performance in many types of images, however, the encoding of global context is insufficient, and it is prone to misjudge the adjacent regions. Meanwhile, the area of NPC pathological image is dense, which means that the image with a tiny size may fall into one category. To overcome this limitation, we apply a transformer-based framework on NPC pathological images that is designed for extracting and encoding global context information. To validate and compare the transformer framework with various CNN-based methods, experiments have been conducted on the clinical dataset collection of NPC. The transformer framework outperformed the state-of-the-art pure CNN-based methods in AUC and recall. Especially, our framework achieved 2.5%–3.5% higher DSC in 5X images and 2.1%–3.2% higher DSC in 10X images than other methods.
Mood detection and prediction using conventional machine learning techniques on COVID19 data

Emotion detection is a promising field of research in multiple perspectives such as psychology, marketing, network analysis and so on. Multiple models have been suggested over the years for accurate and efficient mood detection. Identifying emotion, or mood, from text has progressed from a simple frequency distribution analysis to far more complicated learning approaches. The main aim of all these text mining and analysis is twofold. First is to categorise existing text into broad classes of emotions, such as happy, sad, angry, surprised and so on. The second aim is to accurately predict the moods of real-time streaming text. The novelty of the work lies in the extensive comparison of nine conventional learning methods with respect to performance metrics precision, recall, F1 and accuracy as well as studying the variance of mood over time using a wide array of moods (25). Using conventional classifiers allow near real-time predictions, can work on considerably less training data, and has the flexibility of feature engineering, as deep learning methods have feature engineering embedded in the model. Since a single line of text can be associated with multiple emotions, this article compares the performance of classifiers in predicting multiple moods for streaming text with likelihood-based ranking. An android application named Citizens' Sense was developed for text collection and analysis. The performance of mood classifiers are tested further using Twitter data related to COVID19. Based on the precision, recall, F1 and accuracy of the classifiers, it can be seen that Random Forest, Decision Tree and Complement Naive Bayes classifiers are marginally better than the other classifiers. The variance of mood over time, and predicted moods for text support this finding.
The Revenge of Password Crackers: Automated Training of Password Cracking Tools

Passwords are stored in the form of salted one-way hashes so that attacks on servers cannot leak them in the clear. However, humans tend to select passwords that are easy to remember, and a motivated attacker may attempt to hash quite large sets of easy passwords until a match is found with the target hash. Password cracking tools such as hashcat and john the ripper do this job very efficiently, using different forms of attacks that, for example, try passwords with a certain syntactic structure or passwords taken from a dictionary and mangled through appropriate rules. Recent work on password guessing has shown that machine learning can, in principle, outperform existing cracking tools in terms of success rate, by generating sophisticated password models. In this paper, we give password cracking tools a second chance, by exploring automated training techniques that aim to effectively improve the success rate. To achieve this ambitious goal, we carry out a systematic and in-depth analysis of various cracking strategies, and we propose a new combination of techniques that we train and test on a dataset of more than 700M real passwords. Our results show that, with this new approach, we can almost double the success rate, returning the primacy to password cracking tools. The techniques are general, repeatable and publicly available up to ethical constraints, providing a new benchmark for future research on password guessing.
Continuous label distribution learning

Label distribution learning (LDL) is a suitable paradigm to deal with label ambiguity through learning the correlations among different labels. Most existing label distribution learning methods consider the labels to be discrete and directly establish the mapping from features to labels. However, in many real-world applications, labels naturally form a continuous distribution, which is ignored by the existing methods. As a result, the distribution information of labels can not be accurately described and finally affects the whole learning system. The goal of this paper is to propose a novel approach which can capture the continuous distribution of different labels explicitly and effectively. Specifically, we propose Continuous Label Distribution Learning (CLDL) which describes labels as a continuous density function and learns the distribution information of the labels in the latent space. In this way, the high-order correlations among different labels can be effectively extracted and only a few parameters for describing the continuous distribution need to be learned. Extensive description degree prediction experiments on real-world datasets validate the superiority of CLDL over the existing approaches.
Inequality convergence: A world-systems theory approach

This article explores whether there is inequality convergence among countries. The analysis adopts a framework of dependency theory and its later development through world-systems theory. The analysis draws on Gini indices and Palma ratios from 103 countries for the period 1990–2018. The OLS and GMM analyses reveal horizontal convergence between semi-periphery and core countries as classified by Chase-Dunn et al. (2000). This classification also explains vertical convergence examined with clustering analysis between these countries.
Towards Parameter-Free Clustering for Real-World Data

While many clustering algorithms have been published, existing algorithms are often afflicted by some problems in processing real-world data. We present an algorithm to deal with two of these problems in this paper. First, the majority of clustering algorithms depend on one or more parameters. Second, some algorithms are not suitable for clusters of Gaussian distribution, whereas clusters of many real datasets are of Gaussian distribution approximately. Our algorithm generates clusters sequentially, and each cluster is obtained by expanding an initial cluster. The initial cluster is extracted with the dominant set algorithm, and we study the correlation between the pairwise data similarity matrix and clustering result to determine the involved scaling parameter adaptively. In expanding the initial cluster, we improve the density peak algorithm so that the expansion will not cross the boundary between two clusters, and the involved density parameter has little influence on clustering results. In our algorithm, the cluster expansion enables our algorithm to work well with clusters of Gaussian distribution, and two involved parameters can be fixed or determined adaptively. Our algorithm goes a step forward in parameter-free clustering for real-world data, and it is shown to perform better than or comparably to some commonly used algorithms with parameters in experiments with synthetic datasets composed of Gaussian clusters and real datasets.
Efficient two-party SM2 signing protocol based on secret sharing

The digital signature is widely used in practice to verify a user’s identity and ensure data integrity for applications such as e-commerce and data exchange. The safe storage and reasonable use of the private key ensure the robustness of the cryptographic system. However, the private key stored in a single device faces the risk of invalidation due to theft or abuse. Moreover, the concentration of signature power inhibits their potential application in multi-user settings. Cooperative signature with the deployment of distributed key is a practical way to reduce the damage caused by key exposures. However, existing schemes of SM2 generally suffer from high computing overhead and exponential extension. In this paper, we present a new two-party SM2 protocol leveraging the Beaver’s multiplication that requires lower computational cost than previous two-party SM2 signing protocols. The proposed protocol is proven secure, as long as the standard SM2 signature scheme is secure. We implement our protocol in C to demonstrate that the proposed protocol has practical application prospects due to its efficiency advantage.
A Novel Action Transformer Network for Hybrid Multimodal Sign Language Recognition

Sign language fills the communication gap for people with hearing and speaking ailments.It includes both visual modalities, manual gestures consisting of movements of hands, and non-manual gestures incorporating body movements including head, facial expressions, eyes, shoulder shrugging, etc. Previously both gestures have been detected; identifying separately may have better accuracy, but much communicational information is lost.A proper sign language mechanism is needed to detect manual and non-manual gestures to convey the appropriate detailed message to others.Our novel proposed system contributes as Sign Language Action Transformer Network (SLATN), localizing hand, body, and facial gestures in video sequences.Here we are expending a Transformer-style structural design as a "base network" to extract features from a spatiotemporal domain.The model impulsively learns to track individual persons and their action context in multiple frames.Furthermore, a "head network" emphasizes hand movement and facial expression simultaneously, which is often crucial to understanding sign language, using its attention mechanism for creating tight bounding boxes around classified gestures.The model's work is later compared with the traditional identification methods of activity recognition.It not only works faster but achieves better accuracy as well.The model achieves overall 82.66% testing accuracy with a very considerable performance of computation with 94.13 Giga-Floating Point Operations per Second (G-FLOPS).Another contribution is a newly created dataset of Pakistan Sign Language for Manual and Non-Manual (PkSLMNM) gestures.
Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

Solar energy represents one of the most important renewable energy sources contributing to the energy transition process.Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it.Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory.Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF), Locally Weighted Linear Regression (LWLR), Random Subspace (RS), and M5P.The base algorithms of the developed models are scarcely applied in previous studies to predict solar radiation.The testing phase outcomes demonstrated that the AR-RF models outperform all other hybrid models.The provided models were validated by statistical metrics, such as the correlation coefficient (R) and root mean square error (RMSE).The results proved that Scenario #6, utilizing extraterrestrial solar radiation, relative humidity, wind speed, and mean, maximum, and minimum ambient air temperatures as the model inputs, leads to the most accurate predictions among all scenarios (R = 0.968-0.988and RMSE = 1.274-1.403MJ/m 2 •d).Also, Scenario #3 stood in the next rank of accuracy for predicting the solar radiation in both validating stations.The AD-RF model was the best predictive, followed by AD-RS and AD-LWLR.CMC, 2023, vol.74, no.1 Hence, this study recommends new effective methods to predict GSR in semiarid regions.
Real Objects Understanding Using 3D Haptic Virtual Reality for E-Learning Education

In the past two decades, there has been a lot of work on computer vision technology that incorporates many tasks which implement basic filtering to image classification.The major research areas of this field include object detection and object recognition.Moreover, wireless communication technologies are presently adopted and they have impacted the way of education that has been changed.There are different phases of changes in the traditional system.Perception of three-dimensional (3D) from two-dimensional (2D) image is one of the demanding tasks.Because human can easily perceive but making 3D using software will take time manually.Firstly, the blackboard has been replaced by projectors and other digital screens so such that people can understand the concept better through visualization.Secondly, the computer labs in schools are now more common than ever.Thirdly, online classes have become a reality.However, transferring to online education or e-learning is not without challenges.Therefore, we propose a method for improving the efficiency of e-learning.Our proposed system consists of twoand-a-half dimensional (2.5D) features extraction using machine learning and image processing.Then, these features are utilized to generate 3D mesh using ellipsoidal deformation method.After that, 3D bounding box estimation is applied.Our results show that there is a need to move to 3D virtual reality (VR) with haptic sensors in the field of e-learning for a better understanding of real-world objects.Thus, people will have more information as compared to the traditional or simple online education tools.We compare our result with the ShapeNet dataset to check the accuracy of our proposed method.Our proposed system achieved an accuracy of 90.77% on plane class, 85.72% on chair class, and car class have 72.14%.Mean accuracy of our method is 70.89%.