Reviewerly/exHarmony huggingface dataset:
Assisted annotation in Deep LOGISMOS: Simultaneous multi‐compartment 3D MRI segmentation of calf muscles

Automated segmentation of individual calf muscle compartments in 3D MR images is gaining importance in diagnosing muscle disease, monitoring its progression, and prediction of the disease course. Although deep convolutional neural networks have ushered in a revolution in medical image segmentation, achieving clinically acceptable results is a challenging task and the availability of sufficiently large annotated datasets still limits their applicability.In this paper, we present a novel approach combing deep learning and graph optimization in the paradigm of assisted annotation for solving general segmentation problems in 3D, 4D, and generally n-D with limited annotation cost.Deep LOGISMOS combines deep-learning-based pre-segmentation of objects of interest provided by our convolutional neural network, FilterNet+, and our 3D multi-objects LOGISMOS framework (layered optimal graph image segmentation of multiple objects and surfaces) that uses newly designed trainable machine-learned cost functions. In the paradigm of assisted annotation, multi-object JEI for efficient editing of automated Deep LOGISMOS segmentation was employed to form a new larger training set with significant decrease of manual tracing effort.We have evaluated our method on 350 lower leg (left/right) T1-weighted MR images from 93 subjects (47 healthy, 46 patients with muscular morbidity) by fourfold cross-validation. Compared with the fully manual annotation approach, the annotation cost with assisted annotation is reduced by 95%, from 8 h to 25 min in this study. The experimental results showed average Dice similarity coefficient (DSC) of 96.56±0.26%$96.56\pm 0.26 \%$ and average absolute surface positioning error of 0.63 pixels (0.44 mm) for the five 3D muscle compartments for each leg. These results significantly improve our previously reported method and outperform the state-of-the-art nnUNet method.Our proposed approach can not only dramatically reduce the expert's annotation efforts but also significantly improve the segmentation performance compared to the state-of-the-art nnUNet method. The notable performance improvements suggest the clinical-use potential of our new fully automated simultaneous segmentation of calf muscle compartments.
Knowledge Distillation-enabled Multi-stage Incremental Learning for Online Process Monitoring in Advanced Manufacturing

In advanced manufacturing, the incorporation of online sensing technologies has enabled great potentials to achieve effective in-situ process monitoring via machine learning-based approaches. In manufacturing practice, the online sensor data are usually collected in a progressive manner, and the stream data collected at latter stages may also contain informative knowledge for process monitoring. Therefore, it is highly valuable to make the machine learning-based monitoring model learn incrementally in manufacturing. To achieve this goal, this paper develops a multi-stage incremental learning approach enabled by the knowledge distillation, which distills representative information from the machine learning model trained at early/offline stage and then enhances the monitoring performance at the latter stages. To validate its effectiveness, a real-world case study in additive manufacturing, which is an emerging advanced manufacturing technology, is conducted. The experimental results show that the developed knowledge distillation-enabled multi-stage incremental learning is very promising to improve the online monitoring performance in advanced manufacturing.
Linguistic Knowledge Application to Neuro-Symbolic Transformers in Sentiment Analysis

Neuro-symbolic approaches explore ways to com-bine neural networks with traditional symbolic knowledge. These methods are gaining attention due to their efficiency and the requirement of fewer data compared to currently used deep models. This work investigated several neuro-symbolic models for sentiment analysis focusing on a variety of ways to add linguistic knowledge to the transformer-based architecture. English and Polish WordNets were used as a knowledge source with their polarity extensions (SentiWordNet, plWordNet Emo). The neuro- symbolic methods using knowledge during fine-tuning were not better or worse than the baseline model. However, a statistically significant gain of about three percentage points in the Fl- macro was obtained for the SentiLARE model that applied domain data - word sentiment labels - already at the pretraining stage. It was the most visible for medium-sized training sets. Therefore, developing an effective neuro-symbolic model is not trivial. The conclusions drawn from this work indicate a further need for a detailed study of these approaches, especially in natural language processing. In the context of sentiment classification, it could help design more efficient AI systems that can be deployed in business or marketing.
Heterogeneous Drift Learning: Classification of Mix-Attribute Data with Concept Drifts

As many real data sets (e.g., social, financial, and medical data sets) are successively generated in evolution with the ever-changing environment, classification for data stream with concept drift attracts increasing attention in the fields of machine learning and data mining. However, to the best of our knowledge, existing works mainly consider the concept drift issue while ignoring another common characteristic of real data, i.e., existence of awkward heterogeneity caused by mixture of numerical and categorical attributes. It is worth noting that tackling both the concept drift and heterogeneity problems together is exponentially more challenging than dealing with only one of them. This paper, therefore, proposes an ensemble learning approach for the classification of numerical-and-categorical-attribute data (also called mixed data hereinafter) under concept drift. We first design a unified metric to appropriately address the heterogeneity of numerical and categorical attributes. Then a base classifier that can appropriately fuse the information provided by the heterogeneous attributes is formed accordingly. Furthermore, to make the classification adapt to the complex concept drifts demonstrated on the heterogeneous attributes, two types of base classifier ensembles are dynamically learned on the fly. Experimental results on various real mixed data sets with concept drifts demonstrate the efficacy of the proposed method.
Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks

Class imbalance occurs in many real-world applications, including image classification, where the number of images in each class differs significantly. With imbalanced data, the generative adversarial networks (GANs) leans to majority class samples. The two recent methods, Balancing GAN (BAGAN) and improved BAGAN (BAGAN-GP), are proposed as an augmentation tool to handle this problem and restore the balance to the data. The former pre-trains the autoencoder weights in an unsupervised manner. However, it is unstable when the images from different categories have similar features. The latter is improved based on BAGAN by facilitating supervised autoencoder training, but the pre-training is biased towards the majority classes. In this work, we propose a novel Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks (CAPGAN) as an augmentation tool to generate realistic synthetic images. In particular, we utilize a conditional convolutional variational autoencoder with supervised and balanced pre-training for the GAN initialization and training with gradient penalty. Our proposed method presents a superior performance of other state-of-the-art methods on the highly imbalanced version of MNIST, Fashion-MNIST, CIFAR-10, and two medical imaging datasets. Our method can synthesize high-quality minority samples in terms of Fréchet inception distance, structural similarity index measure and perceptual quality. The source code is available at https://github.com/alibraytee/CAPGAN.
Blockchain technology for sustainable development: a systematic literature review

Purpose Blockchain technology (BCT) is one of the latest disruptive innovations, brought along with-it immense scope of diverse applications contributing towards sustainable development (SD). The consistent increase in the publications reveal that the application of BCT for SD has become popular among researchers and practitioners in past few years, in turn, urged for a systematic literature review (SLR) to get an insight into the research journey travelled so far and setting directions for future research in this area. Therefore, this study aims to identify, map and synthesize the available literature on application of BCT for SD. Design/methodology/approach The automatic and manual search resulted into 1,277 studies from Scopus and Web of Science database. Further applying inclusion and exclusion criterion resulted in bringing out total of 157 studies, which were termed as primary studies. Based on the results of descriptive analysis, conducted through Bibliometric and VOSviewer software, the characteristics of BCT and its key capabilities, contributing towards shaping the recent SD literature, were critically examined. Identified research themes for clustering primary studies were aligned with United Nations Sustainability Development Goals (UNSDG). A mind-map was also prepared on the basis of thematic classification of primary studies. Findings The research themes “business practice and economic sustainability”, “agriculture and food security” and “business practice and environment sustainability” were found to be the focal points of scholarly attention. Synthesis and analysis of primary studies resulted into classification of research gaps under four categories – theoretical foundation, methodological limitation, research themes and technology implementation challenges. The study was concluded by sensitizing and sanitizing the concrete research questions for future research. Research limitations/implications The research findings shall be a roadmap for research scholars, academicians and practitioners to comprehend the present state of knowledge in the domain of “BCT application for SD” and decide upon adopting the future course of action to attain the UNSDGs by the year 2030. Originality/value To the best of the authors’ knowledge, the current study is the first attempt in its own sense to analyse and synthesize the available literature on “attaining SD through BCT” using SLR approach.
The neural machine translation models for the low-resource Kazakh–English language pair

The development of the machine translation field was driven by people's need to communicate with each other globally by automatically translating words, sentences, and texts from one language into another. The neural machine translation approach has become one of the most significant in recent years. This approach requires large parallel corpora not available for low-resource languages, such as the Kazakh language, which makes it difficult to achieve the high performance of the neural machine translation models. This article explores the existing methods for dealing with low-resource languages by artificially increasing the size of the corpora and improving the performance of the Kazakh-English machine translation models. These methods are called forward translation, backward translation, and transfer learning. Then the Sequence-to-Sequence (recurrent neural network and bidirectional recurrent neural network) and Transformer neural machine translation architectures with their features and specifications are concerned for conducting experiments in training models on parallel corpora. The experimental part focuses on building translation models for the high-quality translation of formal social, political, and scientific texts with the synthetic parallel sentences from existing monolingual data in the Kazakh language using the forward translation approach and combining them with the parallel corpora parsed from the official government websites. The total corpora of 380,000 parallel Kazakh-English sentences are trained on the recurrent neural network, bidirectional recurrent neural network, and Transformer models of the OpenNMT framework. The quality of the trained model is evaluated with the BLEU, WER, and TER metrics. Moreover, the sample translations were also analyzed. The RNN and BRNN models showed a more precise translation than the Transformer model. The Byte-Pair Encoding tokenization technique showed better metrics scores and translation than the word tokenization technique. The Bidirectional recurrent neural network with the Byte-Pair Encoding technique showed the best performance with 0.49 BLEU, 0.51 WER, and 0.45 TER.
Augmented Reality-Assisted Ultrasound Breast Biopsy

Breast cancer is the most prevalent cancer in the world and the fifth-leading cause of cancer-related death. Treatment is effective in the early stages. Thus, a need to screen considerable portions of the population is crucial. When the screening procedure uncovers a suspect lesion, a biopsy is performed to assess its potential for malignancy. This procedure is usually performed using real-time Ultrasound (US) imaging. This work proposes a visualization system for US breast biopsy. It consists of an application running on AR glasses that interact with a computer application. The AR glasses track the position of QR codes mounted on an US probe and a biopsy needle. US images are shown in the user's field of view with enhanced lesion visualization and needle trajectory. To validate the system, latency of the transmission of US images was evaluated. Usability assessment compared our proposed prototype with a traditional approach with different users. It showed that needle alignment was more precise, with 92.67 ± 2.32° in our prototype versus 89.99 ± 37.49° in a traditional system. The users also reached the lesion more accurately. Overall, the proposed solution presents promising results, and the use of AR glasses as a tracking and visualization device exhibited good performance.
Rate-equation approach for multi-level quantum systems

Strong driving of quantum systems opens opportunities for both controlling and characterizing their states. For theoretical studying of these systems properties we use the rate-equation formalism. The advantage of such approach is its relative simplicity. We used the formalism for description of a two-level system (TLS) with further expanding it on a case of a multi-level system. Obtained theoretical results have good agreement with experiments. The presented approach can also be considered as one more way to explore properties of quantum systems and underlying physical processes such as for instance Landau-Zener-Stuckelberg-Majorana transitions and interference.
Using Deep Learning with Bayesian–Gaussian Inspired Convolutional Neural Architectural Search for Cancer Recognition and Classification from Histopathological Image Frames

We propose a neural architectural search model which examines histopathological images to detect the presence of cancer in both lung and colon tissues. In recent times, deep artificial neural networks have made tremendous impacts in healthcare. However, obtaining an optimal artificial neural network model that could yield excellent performance during training, evaluation, and inferencing has been a bottleneck for researchers. Our method uses a Bayesian convolutional neural architectural search algorithm in collaboration with Gaussian processes to provide an efficient neural network architecture for efficient colon and lung cancer classification and recognition. The proposed model learns by using the Gaussian process to estimate the required optimal architectural values by choosing a set of model parameters through the exploitation of the expected improvement (EI) values, thereby minimizing the number of sampled trials and suggesting the best model architecture. Several experiments were conducted, and a landmark performance was obtained in both validation and test data through the evaluation of the proposed model on a dataset consisting of 25,000 images of five different classes with convergence and F1-score matrices.
Characteristics of Blockchain and Its Relationship With Trust

Trust is a concept that lies amidst the rhetoric of empowering users by using blockchain. Bitcoin’s eminence hyped as “trust-free,” poignantly made trust an indispensable central element associated with the complexities and uncertainties of online transactions abound in the use and development of blockchain – leaving many users unacquainted with its use. Studies that focus on assessing the trustworthiness of trustees using various characteristics are scarce. By mapping the characteristics that constitute the basis of the formation of trust from the viewpoint of the trustor, this study explored the characteristics of blockchain. Semi-structured interviews were conducted with 14 individuals (aged 20-40 years, from technical and/or business prospects), using the blockchain. Three overlapping procedures; data reduction, data display, and conclusion drawing and verification, were used to identify categories and themes. The findings show that the characteristics of blockchain that influence trust are Reliability, Integrity, Tamper-proofing and Immutability, Versatility, Transparency, and Privacy. The Blockchain is a functionally versatile, tamper-proof, immutable, asset database on a decentralized network. Its reliable technical features, which ensure data integrity, and provide a transparent privacy protected environment, have meant that users are willing to trust it to generate value.
Role of Zoning in Facial Expression Using Deep Learning

Facial expression is an unspoken message essential to collaboration and effective discourse. An inner emotional state of a human is expressed using facial expressions and is very effective for communication with actual emotions. Anger, happiness, sadness, contempt, surprise, fear, disgust, and neutral are eight common expressions of humans. Scientific community proposed several face emotion recognition techniques. However, due to fewer face landmarks and their intensity for deep learning models, performance improvement for facial expression recognition still needs to be improved for accurately predicting facial emotion recognition. This study proposes a zoning-based face expression recognition (ZFER) to locate more face landmarks to perceive deep face emotions indemnity through zoning. After face extraction, landmarks from the face, such as the eyes, eyebrows, nose, forehead, and mouth, are extracted. The second step is zoning each landmark into four regions and zone-based face landmarks are passed to the VGG-16 model to generate a feature map. Finally, the feature map is given as input to fully connected neural network (FCNN) to classify facial emotions into multiple classes. Various experiments are performed on facial expression recognition (FER) 2013 and CK+ datasets to evaluate our proposed model with state-of-the-art facial expression recognition approaches using performance assessment metrics like accuracy. The accuracy of the proposed method with face features on CK+ and FER2013 are 98.4% and 65%, respectively. The experimental zoning results improve from 98.47% to 98.74% on the CK+ dataset.
WKN-OC: A New Deep Learning Method for Anomaly Detection in Intelligent Vehicles

Connected and automated vehicles (CAVs) play a vital role in transforming human mobility, tackling road congestion and road safety. However, CAVs rely heavily on the security, accuracy, and stability of sensor readings and network data. Abnormal sensor readings caused by malicious cyberattacks or faulty car sensors can have devastating consequences, possibly even lead to a fatal crash. In order to avoid the CAVs data anomalies caused by network attacks or data failures, we propose a Wavelet Kernel Network with Omni-Scale Convolutional (WKN-OC) for anomaly detection in intelligent transportation systems (ITS), which can select the optimal scale adaptively and processes high-frequency signals better. The proposed method pays more attention to the high-frequency components of input data, and fully extracts valuable features through the feature extraction framework, so that the model has strong anomaly detection performance. We verify the reliability of the WKN-OC method on the Safe Pilot Model Deployment (SPMD) data set. It is shown that the proposed WKN-OC model has good detection performance for various anomaly data, especially in the mixed anomaly experiment. We have achieved 96.78% average accuracy in mixed anomaly experiments and 96.13% accuracy in multi anomaly experiments. The results show that the model has strong generalization performance for the anomaly detection problem faced by the Internet of Vehicles (IoVs) and can identify the unknown anomalies in reality well.
New Finding and Unified Framework for Fake Image Detection

Recently, fake face images generated by generative adversarial network (GAN) have been widely spread in social networks, raising serious social concerns and security risks. To identify the fake images, the top priority is to find what properties make the fake images different from the real images. In this letter, we reveal an important observation about real/fake images, i.e., the GAN generated fake images contain stronger non-local self-similarity than the real images. Motivated by this observation, we propose a simple yet effective non-local attention based fake image detection network, namely NAFID, to distinguish GAN generated fake images from real images. Specifically, we develop a non-local feature extraction (NFE) module to extract the non-local features of the real/fake images, followed by a multi-stage classification module to distinguish the images with the extracted non-local features. Experimental results on various datasets demonstrate the superiority of our NAFID over state-of-the-art (SOTA) face forgery detection methods. More importantly, since the NFE module is independent from classification, we can plug it into any other forgery detection models. The results show that the NFE module can consistently improve the detection accuracy of other models, which verifies the universality of the proposed method.
A Novel Fuzzy Graph Connectivity Measure to Perform Word Sense Disambiguation Using Fuzzy Hindi WordNet

With the advent of the WordNet (a lexical database), the field of linguistics ushered into the new realm where this structured database, based on ontology can be used for a variety of operations such as Word Sense Disambiguation (WSD), checking Semantic Similarity, performing Text Based Summarization etc. The development of various knowledge bases/lexicons like WordNet, with graphically structured information allowed many graph based approaches to be used for choosing the best sense in the Word Sense Disambiguation process. In these graph based approaches, local graph connectivity measures like Fuzzy Degree Centrality ($\mathrm{V}_{\mathrm{F}\mathrm{D}\mathrm{C}}$), Fuzzy Closeness Centrality ($\mathrm{V}_{\mathrm{F}\mathrm{C}\mathrm{C}}$), Fuzzy Betweenness Centrality ($\mathrm{V}_{\mathrm{F}\mathrm{B}\mathrm{C}}$), Fuzzy PageRank Centrality ($\mathrm{V}_{\mathrm{F}\mathrm{P}\mathrm{C}}$) and global graph connectivity measures like Fuzzy Compactness ($\mathrm{V}_{\mathrm{F}\mathrm{C}}$) and Fuzzy Graph Entropy ($\mathrm{V}_{\mathrm{F}\mathrm{G}\mathrm{E}}$) are widely used to perform WSD.Proposed and implemented here is a novel fuzzy graph connectivity measure named as Fuzzy Expected Force (FExF) to choose the correct word sense, by taking into consideration the influential as well as less influential nodes for the node under consideration in the graph formed using Fuzzy Hindi WordNet. FExF performs less computations than $\mathrm{V}_{\mathrm{F}\mathrm{G}\mathrm{E}}$ to give accurate results; thus becoming a reliable local graph connectivity measure, as overall structure of graph is not necessary for finding correct sense of the word.