Reviewerly/exHarmony huggingface dataset:
Despeckling Based Data Augmentation Approach in Deep Learning Based Radar Target Classification

Speckle noise in SAR images distorts the image of the target and its surroundings, making difficult the target recognition task. Therefore, decomposition process of the speckle noise from the SAR images is important for radar automatic target recognition applications. Besides since the succes of the deep networks depends on the amount of data used in the training stage data augmentation increases classification rates. In this study, a new data augmentation approach based on despeckling has been proposed rather than the classical data augmentation techniques used in the processing of natural images in order to increase the deep learning-based radar target classification performance. Edge Avoiding Wavelet filter is used for speckle reduction task. Classification performances for original, despeckled and despeckling based data augmented datasets are compared on two traditional and basic CNN models. The experimental results show that despeckling based data augmentation method can improve the deep learning based radar automatic target recognition classification performance.
ViTAWiN - Interprofessional Medical Mixed Reality Training for Paramedics and Emergency Nurses

Medical training can benefit from immersive technology such as multiuser virtual reality (VR) or mixed reality (MR). In this paper, we explore how such technology can be applied to interprofessional training settings, where students of two different professional groups, paramedics and emergency nurses, can train together on a shared case involving a burnt patient scenario. In order to support integration in existing real training environments, we further integrate a haptic simulation mannequin using a mixed reality approach. This paper presents a user study with 30 paramedic students and 14 nursing trainees. As a first, our evaluation involves the two user groups joining a mixed reality interprofessional medical training remotely from their respective training academies. While the results indicate good usability and presence effects at low simulator sickness, highlighting the potential of conjointly training multiple medical groups, we discuss the practical complexity of the setup and a potential currently limited in emotion fidelity, hardware comfort and stability of remote communication.
Modeling Large S-box in MILP and a (Related-Key) Differential Attack on Full Round PIPO-64/128

The differential characteristic search problem is converted into mixed integer linear programming (MILP) model to get the bound against differential attack. The difference distribution table is used to write the linear inequalities for MILP modeling of S-box. To construct a reduced set of such inequalities, we present the approaches based on Quine-McCluskey(QM) and Espresso algorithms that are used for active S-box minimization and probability optimization respectively. These approaches are used to search the differential characteristics for lightweight block cipher PIPO-64/128. There are 20621 inequalities in 23 variables corresponding to possible difference transitions in the DDT and these are minimized to 6035 inequalities. MILP model based on these inequalities is used to optimize the probability of differential and impossible differential characteristics for PIPO-64/128 reduced to 9 and 4 rounds respectively. We construct an iterative 2-round related-key differential characteristic with the probability of $$2^{-4}$$ and that is used to present a full round related-key differential distinguisher with the probability of $$2^{-24}$$ . We develop a key recovery attack using related keys on full round PIPO-64/128 with the data complexity of $$2^{32}$$ . We present a major collision in PIPO-64/128 which produces the same ciphertext (C) by encrypting the plaintext (P) under two different keys.
3D (c)GAN for Whole Body MR Synthesis

Synthesis of images has recently seen many works that produce high-quality real world images. In the domain of medical imaging the application of deep generative models especially Generative Adversarial Networks (GANs) can be applied to many different tasks. Under the premise of the generation of high-quality images that match the distribution of the original data, the synthesized data can be used to increase the size of small datasets, or in combination with conditioning on meta data, to increase the size of underrepresented classes in the dataset. In this work we propose a model that generates 3D medical images. The model can easily be conditioned on meta data, for example available patient information. We evaluate the quality of the generated images and compare our model against the 3D-StyleGAN model which is also designed for 3D medical image synthesis.
Canonical Narrowing for Variant-Based Conditional Rewrite Theories

Maude currently supports many symbolic reasoning features such as order-sorted equational unification and order-sorted narrowing-based symbolic reachability analysis. There have been many advances and new features added to improve narrowing in Maude but only at a theoretical level. In this paper, we provide a very elegant, transparent, and extremely pragmatic approach for conditional rewrite theories where the conditions are just equalities solved by equational unification. We show how two conditional theories, never executed before, are now executable with very good performance. We also show how real execution works better than the manually transformed version.
Ensembling Teaching-Learning-Based Optimization Algorithm with Analogy-Based Estimation to Predict Software Development Effort

Accurate evaluation of software development effort estimation is a significant challenge for project planning and management. The analogy-based estimation (ABE) model is an effective method to estimate the effort required for software development. In this study, we proposed a teaching-learning-based optimization (TLBO) guided analogy-based estimation (TABE) model in which TLBO is ensembled with ABE to estimate effort in a reliable way. TLBO generates optimized feature weights during training, and those optimized feature weights are used during testing to estimate development effort. Five datasets, including Kemerer, Desharnais, Albrecht, China and Maxwell, were used to validate the proposed TABE algorithm. The experimental findings demonstrate that TABE can provide a better PRED while minimizing the MMRE value for a few of the studied datasets.
A Binary Water Flow Optimizer Applied to Feature Selection

In this work, a binary version of the Water Flow Optimizer (WFO) algorithm, called Binary Water Flow Optimizer (BWFO), is introduced addressing the feature selection problem. WFO is an evolutionary algorithm inspired by the way water flows in nature. In this new approach, the BWFO uses the laminar flow and turbulent flow operators in a binary version, using the Optimum-Path Forest (OPF) classifier as a fitness function. The proposed approach is evaluated through a comparative analysis made with classical methods of dimensionality reduction, more specifically with the Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) and with the metaheuristics Binary Water Wave Optimization (BWWO), Binary Bat Algorithm (BBA) and Binary Cuckoo Search (BCS). The computational results demonstrate that the approach is a valid and effective alternative to the feature selection problem.
Online Task-free Continual Learning with Dynamic Sparse Distributed Memory

This paper addresses the very challenging problem of online task-free continual learning in which a sequence of new tasks is learned from non-stationary data using each sample only once for training and without knowledge of task boundaries. We propose in this paper an efficient semi-distributed associative memory algorithm called Dynamic Sparse Distributed Memory (DSDM) where learning and evaluating can be carried out at any point of time. DSDM evolves dynamically and continually modeling the distribution of any non-stationary data stream. DSDM relies on locally distributed, but only partially overlapping clusters of representations to effectively eliminate catastrophic forgetting, while at the same time, maintaining the generalization capacities of distributed networks. In addition, a local density-based pruning technique is used to control the network’s memory footprint. DSDM significantly outperforms state-of-the-art continual learning methods on different image classification baselines, even in a low data regime. Code is publicly available: https://github.com/Julien-pour/Dynamic-Sparse-Distributed-Memory .
Fusion of Federated Learning and 6G in Internet-of-Medical-Things: Architecture, Case Study and Emerging Directions

Smart health care has transitioned Internet-of-Medical-Things (IoMT) to share data via sensor nodes among a large number of users. Earlier, IoMT data was mainly stored on centralized cloud servers for analytics. Cloud server induces challenges of high-end computational latency, network bottlenecks and privacy and security issues against adversarial attacks. Thus, the paradigm has shifted towards decentralized learning through the deployment of tiny models with local data via federated learning (FL) schemes. Coupled with sixth-generation (6G) network services, humongous sensor data exchange is possible in real time. Thus, 6G-assisted FL addresses the open challenges of responsive network orchestration and secured user privacy. Thus, the paper proposes a survey article on the fusion of FL and 6G at the backdrop of IoMT ecosystems. A 6G-assisted FL reference architecture for IoMT is proposed, and an $$(\epsilon , \delta )$$ differential privacy preserving 6G-assisted FL case study is proposed for IoMT. We also discuss the potential open issues and future research directions on the integration of FL and 6G in IoMT. The survey intends to assist researchers, and healthcare stakeholders to design secure FL schemes for IoMT ecosystems assisted with 6G networks.
Extracting the Main Content of Web Pages Using the First Impression Area

Extracting the main content from a web page is essential in various applications such as web crawlers and browser reader modes. Existing extraction methods using text-based algorithms and features for English text can be ineffective for non-English web pages. This study proposes a main content extraction method that obtains visual and structural features from the rendered web page. Our method uses the first impression area (FIA), a part of a web page that users initially view. In this area, websites have applied many techniques that enable users to find the main content easily. Using the non-textual properties in the FIA, our method selects three points with high content area density and expands the area from each point until it meets several structural and visual-based conditions. We evaluated our method, browsers’ (Mozilla Firefox and Google Chrome) reader modes, and existing main content extraction methods on multilingual datasets using two measures: Longest Common Subsequences and matched text blocks. The results showed that our method performed better than other methods in both English (up to 46%, matched text blocks F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</sub> ) and non-English (up to 42%, matched text blocks F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</sub> ) web pages.
FPRNet: End-to-End Full-Page Recognition Model for Handwritten Chinese Essay

Handwritten Chinese Essay Recognition (HCER) is a special branch of handwritten Chinese text recognition with great interest. In a naive way, it can be firstly segmented into text lines or even characters, followed by a text line or character recognition step. Instead, we propose an end-to-end recognition model named FPRNet which directly runs on full-page images in light of the segmentation-free strategy. Our well-designed model can extract text from a full-page image only supervised with text labels and adapt better to authentic noisy images. Besides, we propose an effective dimensionality reduction mechanism based on reshape operation to bridge features between 2D and 1D without information loss. Moreover, we propose an order-align strategy to mitigate the decoding confusion caused by skewness. Experiments are conducted on real-world essay images. Our model achieves a 5.83% character error rate (CER), which is comparable with the state-of-the-art approaches.
Short Leakage Resilient and Non-malleable Secret Sharing Schemes

Leakage resilient secret sharing (LRSS) allows a dealer to share a secret amongst n parties such that any authorized subset of the parties can recover the secret from their shares, while an adversary that obtains shares of any unauthorized subset of parties along with bounded leakage from the other shares learns no information about the secret. Non-malleable secret sharing (NMSS) provides a guarantee that even shares that are tampered by an adversary will reconstruct to either the original message or something independent of it. The most important parameter of LRSS and NMSS schemes is the size of each share. For LRSS, in the local leakage model (i.e., when the leakage functions on each share are independent of each other and bounded), Srinivasan and Vasudevan (CRYPTO 2019), gave a scheme for threshold access structures with share size of approximately $$(3\cdot \mathsf {message \ length} \ + \mu )$$ , where $$\mu $$ is the number of bits of leakage tolerated from every share. For the case of NMSS, the best known result (again due to the above work) has share size of $$(11\cdot \mathsf {message \ length})$$ . In this work, we build LRSS and NMSS schemes with much improved share size. Additionally, our LRSS scheme obtains optimal share and leakage size. In particular, we get the following results:
Multi-modal Volumetric Concept Activation to Explain Detection and Classification of Metastatic Prostate Cancer on PSMA-PET/CT

Explainable artificial intelligence (XAI) is increasingly used to analyze the behavior of neural networks. Concept activation uses human-interpretable concepts to explain neural network behavior. This study aimed at assessing the feasibility of regression concept activation to explain detection and classification of multi-modal volumetric data. Proof-of-concept was demonstrated in metastatic prostate cancer patients imaged with positron emission tomography/computed tomography (PET/CT). Multi-modal volumetric concept activation was used to provide global and local explanations. Sensitivity was 80% at 1.78 false positive per patient. Global explanations showed that detection focused on CT for anatomical location and on PET for its confidence in the detection. Local explanations showed promise to aid in distinguishing true positives from false positives. Hence, this study demonstrated feasibility to explain detection and classification of multi-modal volumetric data using regression concept activation.
Multiscale Object Contrastive Learning-Derived Few-Shot Object Detection in VHR Imagery

The feature representation capability of the object detection model is considerably reduced if the available training samples are few-shot. The challenges of arbitrary orientation and complex background of ground objects are universal in very high spatial resolution remote sensing imageries, resulting in massive difficulty on few-shot object detection task. However, existing methods for few-shot object detection are not explored in terms of the capabilities of feature representation in remote sensing images. To solve these issues, we propose a few-shot object detection method incorporating a multiscale object contrastive learning. First, our method performs contrastive learning to represent the object feature fully by adopting Siamese network structure in the few-shot training. On the one hand, the Siamese structure’s lower branch is embedded in the contrastive learning process to cope with the challenge of the complexity of images; on the other hand, a multiscale instance feature module is designed to obtain multiscale contrastive information. Second, we leverage the proposed contrastive multiscale proposal loss (CMSP Loss) to make full use of multiscale information. It can promote our method to fit the data better. The experimental results show that the proposed method has good feature representation capabilities in the few-shot object detection. Moreover, the performance of the proposed method is better than that of related methods.
Character Recognition System Using CNN for Sanskrit Text

In recognizing the handwritten Sanskrit characters, several procedures have to be performed; no single machine or single process can perform in Sanskrit character recognition. The issues of handwritten character recognition are still an active area of research, with ever-improving the need for automation of office work, it is vital to give effective and practical solutions. Initially, the scanned documents are preprocessed using the balanced histogram process for binarization and non-local median filter for denoizing process. Next multiscale segmentation process is carried out to extract the features. A comparison of the character recognition classifiers Naive Bayesian classifiers, SVM classifier and CNN classifier is done to analyze the efficiency of the classifiers. The accuracy of the CNN classifier is 93.45% to recognize the given Sanskrit text.
Security Ontologies: A Systematic Literature Review

Security ontologies have been developed to facilitate the organization and management of security knowledge. A comparison and evaluation of how these ontologies relate to one another is challenging due to their structure, size, complexity, and level of expressiveness. Differences between ontologies can be found on both the ontological and linguistic levels, resulting in errors and inconsistencies (i.e., different concept hierarchies, types of concepts, definitions) when comparing and aligning them. Moreover, many concepts related to security ontologies have not been thoroughly explored and do not fully meet security standards. By using standards, we can ensure that concepts and definitions are unified and coherent. In this study, we address these deficiencies by reviewing existing security ontologies to identify core concepts and relationships. The primary objective of the systematic literature review is to identify core concepts and relationships that are used to describe security issues. We further analyse and map these core concepts and relationships to five security standards (i.e., NIST SP 800-160, NIST SP 800-30 rev.1, NIST SP 800-27 rev.A, ISO/IEC 27001 and NISTIR 8053). As a contribution, this paper provides a set of core concepts and relationships that comply with the standards mentioned above and allow for a new security ontology to be developed.
A Study on Named Entity Recognition with Different Word Embeddings on GMB Dataset using Deep Learning Pipelines

Many natural language applications, such as QA tools, information retrieval, text summarization, and machine translation, are built on top of Named Entity Recognition (NER). The primary task of Named Entity Recognition is to classify all objects (Named Entities) in a given text sample into specified classes such as person, location, and organization names. This work aims to create a NER model for the popular Groningen Meaning Bank (GMB) dataset, comprising tens of thousands of unprocessed and tokenized texts. The dataset has been published by the University of Groningen and updated by developers at IBM in the year 2020. The proposed system is designed into 7 different models – passing the embedding vectors created by keras embedding (Python), BERT, Glove, Word2Vec, fastText, and other two involving Character Embedding along with the Attention layer into a BiLSTM-CRF network. These techniques were employed to compare and analyze the model performances against each other for a better understanding.
COUNTERSAVIOR: AIoMT and IIoT-Enabled Adaptive Virus Outbreak Discovery Framework for Healthcare Informatics

In the current pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes global positioning system (GPS) spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject’s cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behavior, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behavior patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3-D tracker movements of individuals, 3-D contact analysis of COVID-19 and suspected individuals for 24 h, forecasting and risk classification of COVID-19, suspected and safe individuals.
Literature review on dynamic hand gesture recognition

Sign language is a method for the dumb and deaf society to connect with the exterior world. The dumb and deaf people find it very difficult to express their feelings to the normal people, since the normal people have the insufficiency knowledge of the sign language used by the mute and deaf people. Thus the development of a system to fill the communication gap in between mute/deaf and the normal people who are well able to speak and hear would be of great utility to the society. A sign language recognition model involves of accurate and effective tool to translate sign language into text/speech. Gesture recognition identifies a significant expressions of a man-made gesture by hands. Gesture identification from video sequences has significant deviations in the field of behavioral understanding and computer vision. This will provides a platform for conversion of predictable gestures in sentences that are cumulative of words used in an approved sequence. With the advance of deep learning, artificial intelligence has completed significant achievement in modern growths in gesture recognition technology for videos based on deep learning. As compare to others gesture recognition methods, dynamic hand gesture recognition using deep learning to progress the recognition accuracy. This paper presents a research gap survey on various hand gesture recognition methods. Hand gestures are categorized in two types-1) Static hand gestures and 2) Dynamic hand gestures. The first type as static hand gesture recognition is the fingerspelling level detection of alphabets, words and numbers. The second type as dynamic hand gesture recognition are used for identification of sign language sentences. Sign language recognition systems using gesture play a significant part in Human-computer interaction (HCI) to establish communication between dumb/deaf societies and normal people. In this paper, a literature review on hand gesture recognition methods are presented.