Reviewerly/exHarmony huggingface dataset:
Deep learning techniques for the fully automated detection and segmentation of brain MRI

Over the past decade, auto-segmentation for tumors has drawn a lot of attention due to its significant impact on cancer treatment. Auto-segmentation architectures have a significant role in alleviating the enormous workload on the medical staff. This has motivated us to explore the latest solutions in auto-segmentation to use it in auto-segmentation. It works on automatically contouring tumors to make radiology treatment more attainable since manual contouring is repetitive and subjective to human error. Auto-segmentation usually strives to achieve high accuracy to reduce the time the radiologists take to contour the tumor. Saving time is critical as instead of contouring all the tumors, the radiologist can spend the time editing on the segmented tumor thus more patients can be diagnosed in less amount of time. There have been a lot of auto-segmentation architectures created for general purposes like the Segnet which is sometimes used in medical segmentation, but such architectures fail to achieve high accuracy especially in the details of the tumor. The U-Net is an auto-segmentation architecture specifically created for auto-segmentation on medical images like MRI and CT. The U-Net architecture can achieve high accuracy of segmentation with fewer amounts of data. We improved U-Net performance by using residual blocks on each layer of the architecture itself usually referred to as Res-U-Net. Our final proposed fine-tuned Res-U-Net model has achieved 97.10&#x0025; on the used data which was the best of our 3 proposed fine-tuned models. The used data was Low-grade gliomas (LGGS) brain tumor dataset.
Maintain the Persistence of a Distributed Ledger for Future Generations

Blockchain or distributed ledger technology (DLT) guarantees revision safety of transaction logs independent from usage scenarios. Thus, trust is ensured by DLT in a long-term perspective. Hence, one can pose the question, whether DLT is an attractive technology for long-term archiving of data. In this paper we juxtapose distributed ledger and data preservation approaches as two candidates for continuous archival workings. We show that, although the DLT seems to be a viable solution for the long-term preservation of data from a naive perspective, long time effects and efficiency considerations prevent the effective use of DLTs. We discuss hidden costs of transactions, that are not reflected in current price structures. We conclude that data stored on the distributed ledger might not be as preserved for future generations as usually assumed. In fact, data preservation in an archivist's perspective resembles the trust preservation in DLTs.
RealityFlow: Open-Source Multi-User Immersive Authoring

Immersive Authoring remains primarily driven by user-generated content on social VR applications. This paper introduces and motivates an open-source tool, RealityFlow, implemented on top of the Unity engine, and which supports studying immersive authoring paradigms through visual programming languages. This paper describes the context of visual programming environments as well as the requirements, technical implementation, and overall motivations and goals for future iterations.
Conceptual Design of Emotional and Pain Expressions of a Virtual Patient in a Virtual Reality Training for Paramedics

Incorporating interactive elements into medical virtual reality (VR) training can increase presence and engagement of trainees. Based on this, we designed emotional and pain facial expressions for a virtual patient that interacts with trainees in a VR training scenario for paramedics. We follow a user-centered approach by considering the requirements of trainers. Furthermore, we validate our concept with an online study and we plan a user study with paramedic students as the next step.
An English Islamic Articles Dataset (EIAD) for developing an IslamBot Question Answering Chatbot

A chatbot is one of the most vastly recommended technologies to be used during these decades, especially through the digitization era. It could save much consumed time for both the users and the customer service employees. Chatbots could provide an answer to the asked questions instantly. IslamBot is an Islamic religion chatbot &#x201C;i.e.,&#x201D; responsible for answering any inquiries related to the Islamic religion. The aimed audience is non-Muslims people willing to join Islam or New-Muslims. Building such types of chatbots need to have an enormous amount of trusted data. Accordingly, in this paper The English Islamic Articles dataset (EIAD) is proposed as a benchmark reference for English Islamic question answering. So, this dataset contains about 10000 English Islamic articles. It is scrapped from authenticated and trusted websites like NewMuslims.com [1] IslamReligion.com [2], and IslamQA.com [3]. The dataset is about 275 articles from NewMuslims.com [1], 1550 articles from IslamReligion.com [2], and 8292 articles from IslamQA.com [3]. The EIAD dataset is a structured dataset &#x201C;i.e.,&#x201D; labeled and categorized. This dataset contains about 15 different categories. Each category is covering several different topics. This paper focuses on discussing how The English Islamic Articles dataset (EIAD) has been collected.
From Teachers’ Perspective: Can an Online Digital Competence Certification System Be Successfully Implemented in Schools?

This study aims to assess the implementation effectiveness of the online platform for digital competence (DC) certification in schools. The testing platform was a prototype of a DC certification system developed and piloted during 2019 in primary and secondary schools in six European countries involving more than 800 teachers and 6000 students. The study resulted in positive proof that the effective integration and evaluation of the DC acquisition, evaluation, and certification within formal curricula in primary and secondary schools is possible. In addition, it was confirmed that information quality is a significant predictor of the impact on the platform end-users. In contrast, the quality of service is not a significant predictor of a successful implementation of the cloud-based platform with an intuitive user interface and proper online help, i.e., massive open online courses (MOOCs). Furthermore, the developed instrument can help schools implement and assess platforms for DC certification and help policymakers pursue and monitor the implementation of such platforms in schools.
Solitary Jogging with A Virtual Runner using Smartglasses

Group exercise is more effective for gaining motivation than exercising alone, but it can be difficult to always find such partners. In this paper, we explore the experiences that joggers have with a virtual partner instead of a human partner and report on the results of two controlled experiments evaluating our approach. In Study 1, we investigated how participants felt and how their behav-ior changed when they jogged indoors with a human partner or with a virtual partner compared to solitary jogging. The virtual partner was represented either as a full-body, limb-only, or a point-light avatar displayed on smartglasses. In Study 2, we investigated the differences between the three representations as virtual partners for casual joggers in an outdoor setting. Based on our results, we propose implications for the design of virtual runners as casual jogging partners and speculate on their relationship with human users.
When people do not want to talk anymore in online discussion boards: A corpus-based study of the multi-word expression <i>bù shuō le</i> ‘not talk anymore’ in Chinese

With Internet users constantly participating in online interactions, a wide range of novel usages have emerged, some of which involve multi-word expressions. The use of multi-word expressions in online discourses (e.g. their syntagmatic patterns and communicative functions) has not been fully explored. Therefore, this study sets out to investigate the Chinese word string bù shuō le ‘not talk anymore’, which occurs much more frequently in online discussion boards than in other written or spoken modes. In the corpus-based analysis, multiple contexts in which bù shuō le indicates a reluctance to further elaborate are identified. The most common context is that the writer recounts a face-threatening experience and uses bù shuō le to bring the post to the conclusion, express a casual attitude toward that experience, and exert a humorous effect. This study contributes additional evidence that demonstrates how multi-word expressions fulfill textual, expressive, and interpersonal functions in online discourses.
What Is Love?

Descriptions of love can be found in a wide range of literature. The meaning of love that a reader grasps from reading a literary work is mostly the result of self-understanding and is very likely different from the one that the author tried to express. Therefore, it is interesting to explore what love is from the authors' perspective to help readers have a deeper understanding of the meaning of love written by the author. The goal of this study is to build a text analysis framework to identify common words or phrases describing love in romance literature. The proposed analysis is divided into three types, namely 1) text classification and sentiment analysis, 2) key phrase extraction, and 3) topic modeling. The evaluation is performed on 10 romance books. The results of each analysis method are measured using performance metrics as well as presented using visuals like word cloud and histogram.
Head-Worn Markerless Augmented Reality Inside A Moving Vehicle

This paper describes a system that provides general head-worn outdoor augmented reality (AR) capability for the user inside a moving vehicle. Our system follows the concept of combining pose estimation from both vehicle navigation system and wearable sensors to address the failure of commercial AR devices inside a moving vehicle. We continuously match natural visual features from the camera against a prebuilt database of interior vehicle scenes. To improve the robustness in a moving vehicle with other passengers, a human detection module is adapted to filter out people from the camera scene. Experiments demonstrate the effectiveness of the proposed solution.
Analysis of English Writing Text Features Based on Random Forest and Logistic Regression Classification Algorithm

The characteristics of English writing text in natural scenes are characterized by low character detection rate, difficulty in small character detection, and various character detection categories. In order to improve the classification effect of English-written texts, solve the problem of feature loss and precision reduction of the prediction model based on dimensionality reduction neural network classifier in the analysis process. An improved stacking model combining random forest and logistic regression is proposed to analyze the characteristics of written English texts. The model uses multiple undersampling, trains multiple random forests as primary classifier, and uses logistic regression as secondary classifier. Experimental results show that this model can effectively improve the classification efficiency of unbalanced text classification. While ensuring the main features, the accuracy of prediction is substantially improved. It is proved that the model has high practicability in analyzing the features of English writing texts.
Logistic Regression Based on Halton Sequence Initialized Lion Swarm Optimization Algorithm

Logistic regression is a classical nonlinear binary classification algorithm with supervised learning in the field of artificial intelligence (AI). Its idea is to get a logical regression after the nonlinearization of linear regression through Sigmoid firstly, and then to get the optimization objective function - cross entropy loss function. Generally, it is solved by gradient descent (GD) or stochastic gradient descent (SGD) method, but these methods are easy to fall into the trap of local minimum; moreover, the closer it is to the optimal value, the sawtooth effect is easy to appear, resulting in the reduction of the operation efficiency of the algorithm. Therefore, in this paper, a new derivative-free optimization method - Halton sequence initialized lion swarm optimization algorithm has been proposed to replace the original GD method or SGD method, which makes the logistic regression algorithm more efficient, and via numerical experiment, the effectiveness of the proposed algorithm has been demonstrated.
Who do you look like? - Gaze-based authentication for workers in VR

Behavior-based authentication methods are actively being developed for XR. In particular, gaze-based methods promise continuous au-thentication of remote users. However, gaze behavior depends on the task being performed. Identification rate is typically highest when comparing data from the same task. In this study, we compared authentication performance using VR gaze data during random dot viewing, 360-degree image viewing, and a nuclear training simu-lation. We found that within-task authentication performed best for image viewing (72&#x0025;). The implication for practitioners is to integrate image viewing into a VR workflow to collect gaze data that is viable for authentication.
Improved LOF Algorithm Using Random Point

In the present study, Local outlier factor (LOF), the most popular and widely used density-based algorithm for outlier detection, is improved using a new idea. In the proposed idea, a random point in the neighborhood of all objects is used. This method improved the efficiency of the LOF algorithm to an acceptable level. Moreover, the results of the improved LOF indicated the competitiveness of the new algorithm in some real datasets. The comparisons are done based on two criteria: precision and AUC (Area Under the ROC Curve).
SivsFormer: Parallax-Aware Transformers for Single-image-based View Synthesis

Single-image-based view synthesis is significant for generating a 3D scene and gains increasing attention in recent years. However, this task is challenging as it requires inferring contents beyond what is immediately visible. Previous methods directly predict the unknown views using the convolutional neural networks, but the generated views suffer from visually unpleasant holes, deformations, and artifacts. In this paper, we propose a Single-image-based view synthesis transformer (named SivsFormer) for high-quality and realistic view synthesis. In particular, a warping and occlusion handing module is designed to reduce the influence of parallax on the network. Subsequently, a disparity alignment module captures the long-range information over the scene and ensures that pixels move in a geometrically correct manner with soft probabilistic disparity maps. Moreover, we present a parallax-aware loss function to improve the quality of the synthetic images, which explicitly quantifies the magnitude of parallaxes. We conduct extensive experiments on popular KITTI and Cityscapes datasets. Benefitting from the proposed parallax-aware transformer, our approach achieves superior performance in both quantitative and qualitative evaluations.
The Hierarchical Approach for Image Processing in Objects Recognition System

A multi-stage image processing scheme that improves the quality of object recognition in the multimedia stream is presented. The image processing pipeline optimization leads to objects recognition accuracy improvement with insignificant latency and computing resources usage increase. The system schema is assembled with several functional blocks with expected accuracy improvement impact for each block.
Modality-Invariant and -Specific Representations with Crossmodal Transformer for Multimodal Sentiment Analysis

Abstract Human emotion judgments usually receive information from multiple modalities such as language, audio, as well as facial expressions and gestures. Because different modalities are represented differently, multimodal data exhibit redundancy and complementarity, so a reasonable multimodal fusion approach is essential to improve the accuracy of sentiment analysis. Inspired by the Crossmodal Transformer for multimodal data fusion in the MulT (Multimodal Transformer) model, this paper adds the Crossmodal transformer for modal enhancement of different modal data in the fusion part of the MISA (Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis) model, and proposes three MISA-CT models. Tested on two publicly available multimodal sentiment analysis datasets MOSI and MOSEI, the experimental results of the models outperformed the original MISA model.
Uncertainty zone estimation of angles only tracking in undersea environment

Traditional underwater tracking is a method of estimating the state of a target based on measurements obtained from sensors. Observer uses measurements that are created by the radiation of the target for passive underwater target surveillance. A procedure for estimating the location of a target emitting acoustic signal is introduced. The issue is addressed in cases of known and unknown emitter location determination errors. Zones of uncertainty have been classified as error ellipses and confidence ellipses. Noise continuously corrupts the measurements, resulting a target position uncertainty zone. To improve the accuracy, to find uncertainty zone of target use bearing and elevation. Shifted Rayleigh Filter (SHRF) is a non-linear estimator that is effective for estimating the motion parameters of the target. It is important to know about the convergence of the solution in terms of the reduction of the uncertainty zone under a certain constraint in realistic situations. Utilizing the covariance matrix of SHRF in Monte-Carlo simulation, an attempt is made to reduce and discover the target's 3-dimensional range uncertainty ellipse zone (3DRUEZ). The solution is said to be converged when the 3DRUEZ is less than a certain threshold. In underwater surroundings, the simulation demonstrates that the proposed technique in this paper shows enhanced estimation accuracy and more stable performance than other filtering algorithms.
An improved algorithm of EfficientNet with Self Attention mechanism

Traditional convolutional neural networks usually consider both resource budget and operation cost. However, if there is a better operating environment and more advanced resources available, a more accurate and faster algorithm to solve some problems of existing networks can be provided. In this paper, the author thoroughly studied the Self Attention mechanism and EfficientNet network that Google has released and combined the advantages of the two networks. Based on this result, the author proposes an improved algorithm of EfficientNet with Self Attention mechanism. Before introducing EfficientNet pre-training model to start training, EfficientNet uses simple but efficient composite coefficients to uniformly scale all dimensions of depth, width and resolution. A simple Self Attention network structure is trained in advance to process data images. This will enable the pre-training network to focus as much as possible on the data and image features with a large amount of information and large gap before entering EfficientNet training. After that, the pre-training network will train the model. Through experiments, it is found that the model has higher quality. The improved algorithm showed extremely high accuracy and fast convergence speed on CIFAR-100, OxFlowers and ImageNet, and three other transfer learning datasets. The accuracy of the improved algorithm network with efficientnet-b4 as the pre-training model on the three data sets is greater than 95%. Compared with the original EfficientNet, the comprehensive improvement is more than 10%, and the running speed is increased by about 30%. Compared with the classic convnet, it can be increased by 20% and the running speed is increased by more than 50%.
Privacy Preservation Using Block chain for Credit Card Data

Credit data has a lot of personal information that needs to be kept private. It is easy to leak or get the wrong information while collecting and querying credit data. As a result, the leakage of credit data will have a big impact on the person who had their information. This includes things like a bad reputation and a threat to their home. Dual blockchains can be used to store and query credit data, which can help prevent user privacy leaks, information asymmetry, easy tampering, forgery, and over centralization when credit data is collected. The system has two chains. One chain is used to store real-time credit data from a lot of people, and the other chain is used to store personal credit reports from people who live near each other. People who want to get a credit report can use a solution that includes two blockchains. This means that credit agencies will be able to keep records of real-time credit data and quickly and automatically make personal credit reports, avoiding the risk of privacy leaks and tampering and forging confidential credit data in the process of getting a credit report, because of the chain structure, privacy protection, data integrity, and trusted centre. The advantages of this model can be seen in seven different ways, such as how timely it is, how many copies of the same information there are, and how easy it is to store and search for credit data.
Identifying plagiarised programming assignments based on source code similarity scores

Background and Context Source code plagiarism is a common occurrence in undergraduate computer science education. Many source code plagiarism detection tools have been proposed to address this problem. However, such tools do not identify plagiarism, nor suggest what assignment submissions are suspicious of plagiarism. Source code plagiarism detection tools simply evaluate and report the similarity of assignment submissions. Detecting plagiarism always requires additional human intervention.Objective This work presents an approach that enables the automated identification of suspicious assignment submissions by analysing similarity scores as reported by source code plagiarism detection tools.Method Density-based clustering is applied to a set of reported similarity scores. Clusters of scores are used to incrementally build an association graph. The process stops when there is an oversized component found in the association graph, representing a larger than expected number of students plagiarising. Thus, the constructed association graph represents groups of colluding students.Findings The approach was evaluated on data sets of real and simulated cases of plagiarism. Results indicate that the presented approach can accurately identify groups of suspicious assignment submissions, with a low error rate.Implications The approach has the potential to aid instructors in the identification of source code plagiarism, thus reducing the workload of manual reviewing.
Synthetic-Aperture Radar Observation Data Amount Compression Technique Using Orthogonal Binary Sequences

Modern satellite and aircraft synthetic-aperture radars (SAR) require high pulse rate frequency as well as high data sample rate to obtain better range and azimuth resolution images without distortion. These properties lead to significant data streams transferring from a data sampling device to a storage device or to a remote ground station. Thus, two main characteristics have to be considered: the data storage device volume and the data write/transfer rate. The capacity of the storage devices develops rapidly, although it may still be a problem for space devices due to the lack of radiation hardened ICs. However, the data transfer rate is also a problem and it is restrained by some physical limitations. The paper proposes an approach to compress the amount of data by combining responses from several probing pulses with orthogonal binary sequences. Presented simulation results and compressed radar images show the solid application potential of the proposed technique.