Reviewerly/exHarmony huggingface dataset:
A Fast and Memory-Efficient Brain MRI Segmentation Framework for Clinical Applications

Current segmentation tools of brain MRI provide quantitative structural information for diagnosing neurological disorders. However, their clinical application is generally limited due to high memory usage and time consumption. Although 3D CNN-based segmentation methods have recently achieved the state-of-the-art and come up with timely available results, they heavily require high memory GPUs. In this paper, we customize a memory-efficient (GPU) brain structure segmentation framework, named FLBS, based on nnU-nets which enables our framework to adapt its architecture based on memory constraints dynamically. To further reduce the need for memory, we also reduce multi-label brain segmentation to the fusion of sequential single-label segmentations. In the first step, single label patches are extracted from the T1w and segmentation maps by locating the approximate area of each structure on the MNI305 template, including the safety margin. These considerations not only decrease the hardware usage but also maintains comparable computational time. Moreover, the target brain structures are customizable based on the specific clinical applications. We evaluate the performance in terms of Dice coefficient, runtime, and GPU requirement on OASIS-3 and CoRR-BNU1 datasets. The validation results show our comparable accuracies with state-of-the-arts and confirm the generalizability on unseen datasets while significantly reducing GPU requirements and maintaining runtime duration. Our framework is also executable on a budget GPU with a minimum requirement of 4G RAM. Clinical Relevance- We develop a memory-efficient deep Brain MRI segmentation tool that significantly reduces the hardware requirement of MRI segmentation while maintaining comparable accuracy and time. These advantages make FLBS suitable for widespread use in clinical applications especially for clinics with a limited budget. We plan to release the framework as a part of a free clinical brain imaging analysis tool. The code for this framework is publicly available.
Exploring human activity recognition using feature level fusion of inertial and electromyography data

Wearables are objective tools for human activity recognition (HAR). Advances in wearables enable synchronized multi-sensing within a single device. This has resulted in studies investigating the use of single or multiple wearable sensor modalities for HAR. Some studies use inertial data, others use surface electromyography (sEMG) from multiple muscles and different post-processing approaches. Yet, questions remain about accuracies relating to e.g., multi-modal approaches, and sEMG post-processing. Here, we explored how inertial and sEMG could be efficiently combined with machine learning and used with post-processing methods for better HAR. This study aims recognition of four basic daily life activities; walking, standing, stair ascent and descent. Firstly, we created a new feature vector based on the domain knowledge gained from previous mobility studies. Then, a feature level data fusion approach was used to combine inertial and sEMG data. Finally, two supervised learning classifiers (Support Vector Machine, SVM, and the k-Nearest Neighbors, kNN) were tested with 5-fold cross-validation. Results show the use of inertial data with sEMG increased overall accuracy by 3.5% (SVM) and 6.3% (kNN). Extracting features from linear envelopes instead of bandpass filtered sEMG improves overall HAR accuracy in both classifiers. Clinical Relevance- Post-processing on sEMG signals can improve the performance of multimodal HAR.
Design of Integrated Management System of IPE Instructional Resources Information Based on Artificial Intelligence Environment

With the development of modern networks and their fast and convenient characteristics, the construction of instructional resources has become the focus of information construction in universities. Based on AI (Artificial intelligence) technology, this paper discusses the software development technology used in the comprehensive management system of IPE instructional resources. According to the application environment and application objects of the system, a comprehensive information management system of IPE instructional resources based on AI technology is constructed. In this paper, a concept similarity algorithm based on semantic similarity and semantic correlation is proposed to calculate the distance between words and establish concept mapping pairs. The semantic analysis of the information input by students is realized, and the proposed concept similarity algorithm is used in the domain ontology knowledge base to search for similar courses and recommend them to students for their choice. The test results show that the stability of this system can reach about 96%, which is about 10% higher than other systems. The comprehensive management system of instructional resources information proposed in this paper will promote educators and learners to find high-quality IPE instructional resources.
Local Shape Preserving Deformations for Augmented Reality Assisted Laparoscopic Surgery

Image registration is a commonly required task in computer assisted surgical procedures. Existing registration methods in laparoscopic navigation systems suffer from several constraints, such as lack of deformation compensation. The proposed algorithm aims to provide the surgeons with updated navigational information about the deep-seated anatomy, which considers the continuous deformations in the operating environment. We extended an initial rigid registration to a shape-preserving deformable registration pathway by incorporating user interaction and an iterative mesh editing scheme which preserves local details. The proposed deformable registration workflow was tested with phantom and animal trial datasets. A qualitative evaluation based on expert feedback demonstrated satisfactory outcome, and an commensurate execution efficiency was achieved. The improvements offered by the method, couples with its relatively easy implementation, makes it an attractive method for adoption in future pre-clinical and clinical applications of augmented reality assisted surgeries.
3D endoscope system with AR display superimposing dense and wide-angle-of-view 3D points obtained by using micro pattern projector

In recent years, augmented reality (AR) technologies have been widespread for supporting various kinds of tasks, by superimposing useful information on the users' view of the real environments. In endoscopic diagnosis, AR systems can be helpful as an aid in presenting information to endoscopists who have their hands full. In this paper, we propose a system that can superimpose shapes, which are reconstructed from an endoscope image, onto the field of view. The feature of the proposed system is that it reconstructs 3D shapes from the images captured by the endoscope and superimposes them onto the real views. As a result, the superimposed view allows the doctor to keep operating the endoscope while observing the patient's internal body with additional information. The proposed system is composed of the reconstruction module and the display module. The reconstruction module is for acquiring 3D shapes based on an active stereo method. In particular, we propose a novel projection pattern that can reconstruct wide areas of the endoscopic view. The display module shows the 3D shape obtained by the reconstructed module, superimposing on the field of view. In the experiments, we show that it is possible to perform a wide range of dense 3D reconstructions using the new projection patterns. In addition, we confirmed the usefulness of the AR system by interviewing medical doctors.
Metaphoric Function and Emotional Cognition of English Loanwords in the Internet Environment

With the rapid development of society, the emergence and innovation of network buzzwords continue to emerge. On the rapidly changing social network platform, previous sentiment analysis tasks cannot fully meet the needs of users. This paper aims to study the metaphorical function and affective cognition in Internet English loanwords. This paper proposes a neural network algorithm and conducts a comprehensive analysis of the metaphorical function and emotional cognition of Internet English loanwords. The neural network algorithm has a powerful sentiment analysis function, so the article chooses this algorithm. The experimental results of this paper show that with the popularity of the Internet, more and more people go online. In 2013, the proportion of Internet users was the highest at 23.2%. In 2015, the proportion of Internet users was 38.3%, an increase of 15.1% in just one year. The percentage of people online will reach 68.3% by 2021, indicating that almost half of the people have learned to surf the Internet. This also means that Internet English loanwords have also been developed. The rapid development of loanwords in Internet English is because they have metaphorical functions and express people's emotions.
Multi-parametric Magnetic Resonance Imaging Fusion for Automatic Classification of Prostate Cancer

Computer-aided diagnosis (CAD) of prostate cancer (PCa) using multi-parametric magnetic resonance imaging (mp-MRI) has recently gained great research interest. In this work, a fully automatic CAD pipeline of PCa using mp-MRI data is presented. In order to fully explore the mp-MRI data, we systematically investigate three multi-modal medical image fusion strategies in convolutional neural networks, namely input-level fusion, feature-level fusion, and decision-level fusion. Extensive experiments are conducted on two datasets with different PCa-related diagnostic tasks. We identify a pipeline that works relatively the best for both diagnostic tasks, two important components of which are stacking three adjacent slices as the input and performing decision-level fusion with specific loss weights. Clinical relevance- This work provides a practical method for automated diagnosis of PCa based on multi-parametric MRI.
Are Gyroscopes an Added Value in Leave-One-Subject-Out Activity Recognition with IMUs?

Inertial sensors have played a key role in the development of Human Activity Recognition (HAR) systems. Adding gyroscopes in HAR systems leads to increased battery and processing resources. Therefore, it is important to explore their added value compared with using accelerometers only. This study evaluates the added value of gyroscopes in activity recognition. Two public available datasets recorded by accelerometers and gyroscopes were studied. These datasets focus on multiple types of activities: UCI HAR dataset includes walking, walking upstairs, walking downstairs, sitting, standing, laying and WISDM dataset includes 18 hand-oriented and non-hand-oriented activities. Several machine learning models were applied to both datasets for activity recognition. Leave-one-subject-out cross-validation (LOSO) was applied to evaluate the models, where the training set and test set were from different subjects. For UCI HAR dataset, the multilayer perceptron (MLP) model obtained the highest f1-scores. Adding a gyroscope on the waist significantly improved the f1-scores of sitting and laying (both ). For WISDM dataset, the support vector machines (SVM) model obtained the highest f1-scores. The gyroscope on the wrist improved hand-oriented activities while the gyroscope in the pockets improved non-hand-oriented activities (all . The results showed the improvement for recognition performance by adding gyroscopes. However, the improvement was dependent on the type of activity and the mounting place of the gyroscope. Clinical relevance- Gyroscopes are common sensors for activity recognition in wearable healthcare systems. This study proves the added value by adding gyroscopes on different mounting places for recognition performance.
Emotion Analysis of Literary Works Based on Attentional Mechanisms and the Fusion of Two-Channel Features

In order to analyze sentiment data of foreign literary works, this paper proposes an algorithm for sentiment classification of literary works. We do this by fusing different features of literary works, which in turn captures more feature information for the classifier. As traditional word embedding models are difficult to achieve fusion with the sentiment features of literary works, we consider a multifeature fusion approach of word embedding features and lexical features of literary works. A two-channel and single-channel comparison is also used to analyze the classification accuracy based on the two feature fusion methods, and a parallel CNN and BiLSTM-attention two-channel neural network model proposed. Finally, the proposed model was evaluated using a real dataset of sentiment reviews of literary works and compared with different classification algorithms in the experiments. The experimental results show that the new hybrid approach has better classification accuracy, recall, and F1 metrics. The proposed methodology is an important guide for the creation of literary works and their screenplays, as it can be used to judge whether a work appeals to readers and, importantly, can also be considered as one of the criteria for the success of a film adaptation of a literary work.
A subsampling approach for Bayesian model selection

It is common practice to use Laplace approximations to decrease the computational burden when computing the marginal likelihoods in Bayesian versions of generalised linear models (GLM). Marginal likelihoods combined with model priors are then used in different search algorithms to compute the posterior marginal probabilities of models and individual covariates. This allows performing Bayesian model selection and model averaging. For large sample sizes, even the Laplace approximation becomes computationally challenging because the optimisation routine involved needs to evaluate the likelihood on the full dataset in multiple iterations. As a consequence, the algorithm is not scalable for large datasets. To address this problem, we suggest using stochastic optimisation approaches, which only use a subsample of the data for each iteration. We combine stochastic optimisation with Markov chain Monte Carlo (MCMC) based methods for Bayesian model selection and provide some theoretical results on the convergence of the estimates for the resulting time-inhomogeneous MCMC. Finally, we report results from experiments illustrating the performance of the proposed algorithm.
A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters

With the increasing application of dual-PTZ (Pan-Tilt-Zoom) cameras in intelligent unmanned systems, research regarding their calibration methods is becoming more and more important. The intrinsic and extrinsic parameters of dual-PTZ cameras continuously change during rotation and zoom, resulting in difficulties in obtaining precise calibration. Here, we propose a general calibration method for dual-PTZ cameras with variable focal length and posture under the following conditions: the optical center of the camera does not coincide with the horizontal and pitch rotation axes, and the horizontal and pitch rotation axes are not perpendicular to each other. We establish a relationship between the intrinsic and extrinsic parameters and the feedback parameters (pan, tilt, zoom value) of dual-PTZ cameras by fitting and calculating previous calibration results acquired at specific angles and zoom values using Zhang’s calibration method. Subsequently, we derive the intrinsic and extrinsic parameter calculation formula at arbitrary focal length and posture based on the camera’s feedback parameters. The experimental results show that intrinsic and extrinsic parameters computed using the proposed method can better meet precision requirements compared with the ground truth calibrated using Zhang’s method. The average focal length error is less than 4%, the cosine similarity of the rotation matrix between the left and right cameras is more than 99.8%, the translation vector error is less than 1%, and the recalculated Euler angle errors are less than 1 degree. Our work can quickly and accurately obtain intrinsic and extrinsic parameters during the use of the dual-PTZ camera.
Telegram Messenger for Supporting Educational Process Under the Conditions of Quarantine Restrictions

The article is devoted to the problem of using Telegram messenger in the educational process in supporting the higher school under the conditions of forced quarantine restrictions due to the Covid-19 pandemic. Modern messengers (Telegram, Viber, Facebook Messenger, WhatsApp) are analyzed via pre-defined criteria (commerciality, functionality, architecture, security) and the range of indicators, the benefits of using Telegram messenger to support the educational process are outlined. The essence, benefits, and possibilities of using Telegram ChatBot are characterized. The main steps for creating Telegram ChatBot are described. Recommendations are provided to improve the pedagogical effect of using Telegram ChatBots. The analysis of results of educational interaction via Telegram messenger is presented (112 students’ survey, evaluation of learning outcomes in experiment (112 students) and control (110 students) groups). The empirical research has shown that Telegram messenger allowed supporting the educational process in the conditions of unexpected and forced quarantine restrictions, with no loss of qualitative indicators, achieving the pedagogical goals.
E-Voting System Using Blockchain

The electoral system is flawed in all parts of the world. Even large democracies like United States of America and India have an inefficient voting system. The popular technology which seems to be emerging, distributed and decentralized is the blockchain technology. The transparency and trust are the two main factors of blockchain technology which makes it to be applied to election process. A secure e-voting system can be built using blockchain while even protecting the privacy of the clients, i.e., voters. Using smart contract logic, the Ethereum network is the most suitable ones. In this project implementation and testing, an e-voting system application as a smart contract for the Ethereum network is done using Ethereum cryptocurrency and solidity language (Raja Rajeswari et al., 6th International Conference on Communication and Electronics Systems (ICCES) (2021)).
Ukrainian News Corpus as Text Classification Benchmark

One of the crucial problems of natural language processing for languages such as Ukrainian is lack of datasets both unlabeled (for pretraining of word embeddings or large deep learning models) and labeled (for benchmarking existing approaches). In this paper we describe a framework for simple classification dataset creation with minimal labeling effort. We create a dataset for Ukrainian news classification and compare several pretrained models for Ukrainian language in different training settings. We show that ukr-RoBERTa, ukr-ELECTRA and XLM-R tend to show the highest performance, although XLM-R tends to perform better on longer texts, while ukr-RoBERTa performs substantially better on shorter sequences. We publish this dataset on Kaggle ( https://www.kaggle.com/c/ukrainian-news-classification/ ) and suggest to use it for further comparison of approaches for Ukrainian text classification.
Featured games

Feature-based analysis of software product lines and family-based model checking have seen rapid development. Many model checking problems can be reduced to two-player games on finite graphs. A prominent example is mu-calculus model checking, which is generally done by translating to parity games, but also many quantitative model-checking problems can be reduced to (quantitative) games. As part of a program to make game-based model checking available for software product lines, we introduce featured reachability games, featured minimum reachability games, featured discounted games, featured energy games, and featured parity games. We show that all these admit optimal featured strategies, which project to optimal strategies for any product, and how to compute winners and values of such games in a family-based manner.
Investigating Proactive Digital Forensics Leveraging Adversary Emulation

Traditional digital forensics techniques are becoming obsolete due to rapid technological change. Proactive digital forensic investigations (PDFI) solve the challenges of cloud computing forensics such as evidence identification, collection, preservation, and timelining from heterogeneous cumulative data. Cumulative data heterogeneity poses significant challenges to the sound collection of electronically stored information (ESI) or digital evidence across cloud endpoints and/or networked systems. In addition, the distribution of networked systems and/or cloud environments makes it impossible for forensics investigators to be present at several premises to perform the investigation. Hence, it is important to have PDFI in place to ensure continuous operation in the event of a cyberattack, because it does not require the presence of an investigator at the target location. In this study, researchers put the idea of proactive digital forensics to the test and concluded that it is an indispensable tool for networked systems and cloud computing environments in response to modern-day digital forensics challenges. This research was based on an experimental computer science and engineering approach using a virtualised environment simulating an information communication infrastructure. To generate evidence (digital artefacts), and validate the proof-of-concept, adversary emulation was used by adapting the MITRE ATT&amp;CK framework. Research results have shown that PDFI improves digital forensics activities in terms of speed and accuracy, thereby providing credible and timely comprehensive digital evidence. Enhanced Incident detection capabilities enable an analyst to focus much more on forensic investigation functions and thus perform their tasks effectively. However, the legality of live and/or remote forensics is still of great concern in several jurisdictions, thereby affecting the credibility of digital artefacts obtained in this manner. Nevertheless, where possible, the law component should also be kept up to date with modern-day technologies to solve any inconveniences caused by the ever-growing technology demands.
Improved image noise level estimation based on segmentation and block processing

Accurate estimation of noise parameters in digital images is of great significance to improve the quality of image processing.Principal component analysis is an important means of image denoising, and the traditional method is to estimate the whole image.Due to the complexity of image content, this paper proposes a preprocessing method based on superpixel segmentation to process the largest possible smooth block in the image.Compared with contrast method, the estimated value of the proposed method is closer to the true value.