Reviewerly/exHarmony huggingface dataset:
Blockchain Assisted Cloud Storage For Electronic Health Records

The Secure Health Framework is a progressive temporal blockchain approach that addresses the aforementioned problems (SeFra). Specifically, SeFra’s foundational components, the Context-based Merkle Tree (CBMT) and Enhancement of access control, confidentiality, and integrity checking procedures is the goal of context-based access control (CBAC). Additionally, the framework allows for Interoperability and handles scalability issues with the aid of temporal properties, HL7 specifications, and Interplanetary File System for data management (IPFS). The results of the study demonstrate that the proposed framework SeFra’s performance in securely managing the Personalised Micro Ledger (PML) is affected by the temporal shadow concepts and context-based properties. Additionally, it helps record health records as text with temporal properties and share them with colleagues according to the rules set out in a context-aware smart contract. Importantly, the framework may be able to solve the problems of data silos and supply unchangeable, secure transactions in the healthcare sector. Electronic health records (EHR) have been linked to medical inconsistencies and worries about privacy and security. Although it is true that lack of standardisation is not the only difficulty in electronic health records (EHR), it is arguably the most significant. To solve the problems of interoperability and privacy, blockchain-based decentralised online ledgers have already been proposed and implemented. Nevertheless, there are still a number of other obstacles to overcome. Main technical obstacles include scalability, usability, and accessibility. The first technical difficulty is the need to keep medical records in their various formats off-chain while providing secure access control to them on-chain. The second difficulty is providing an adequate description of who owns what data and how that data is shared during times of medical emergency. Accessibility remains a problem, however, as formal verification of smart contracts takes a long time relative to processing large batches of transactions.
Movie Recommendation System

Because of the advent of the Internet, now have access to an abundance of data across many disciplines. However, consumers frequently face situations where they have a plethora of options to consider and could use some guidance navigating those options. An effective method for closing this gap is the use of recommendation systems. There are many different methods being used to develop recommender systems, but they can be broken down into two broad categories: content-based and collaborative filtering. In order to address the limitations of traditional recommender systems, researchers are increasingly turning to hybrid approaches, wherein multiple recommendation methods are combined. In addition to these three tried-and-true methods, recommendation quality can also be enhanced by employing a context-based recommender system. Several methods exist for modelling contextual information in a recommendation system, including pre-and post-filtering, as well as contextual modelling. In this paper propose a hybrid system that combines the best features of both pre-and post-filtering contextualization techniques. In order to make better movie recommendations, the proposed method will make use of a database that is rich in context and contains information such as user data, item data, ratings, and contextual information. The proposed method as a whole is broken down into stages. To generate the first set of recommendations, it will be first applying a contextual pre-filtering approach to the entire database based on the most important contextual attribute for a user, thereby reducing the multi-dimensional data into a reduced dataset. The recommendations are then sent to a contextual post filter, where they undergo additional processing in the form of filtering and adjustment in light of the other two pertinent contextual attributes for that user.
Breast Tumor Detection Using Machine Learning and Deep Learning Algorithm

Breast tumour is the most prevalent frequent malignancy inside the world, and women are more likely to develop one. This is the world’s second greatest cause of mortality for women. It accounts for almost 22% of new cancer cases worldwide, with a 5-year survival rate of 61%. Breast cancer kills 450,000 people globally each year, according to the WHO. It is a mammography-based the most prevalent kind of cancer in women, breast cancer, has a software detection and diagnosis method. The system is built on the Numerous Learning paradigm, which has been used in the past by our team to create medical decision support systems. Wavelet sub bands are used to extract the GLCM features. Then, from each location, characteristics resulting from lesion identification (masses and micro-calcifications) as well as textural cues are retrieved and mammography tests into “normal” and “bad” categories. The areas that caused the automated diagnosis can be emphasized when an aberrant examination record is found. To define this anomaly detector, two methodologies are considered.
Analysis Of Performance, Scalability, Availability And Security In Different Cloud Environments For Cloud Computing

Success and acclaim of cloud computing have been boosted by recent developments in the field and in technology more generally. Cloud computing allows for flexible, on-demand service delivery. Instead of using a server on a local area network (LAN) or a single computer, cloud computing takes advantage of distributed, centralized, and highly sophisticated remote network sites and servers connected to the internet for the purposes of storing, managing, and processing data. Statistics in cloud computing are now being carefully preserved with an emphasis on studying data in the cloud and its many connected security problems. Concerns about data loss in the cloud and the measures taken by different service providers to address those concerns are also addressed. Cloud storage makes data easily accessible for a wide variety of uses, but as the cloud grows in popularity and data distribution options expand to accommodate a diverse user base, the privacy of that data becomes a more pressing concern. It necessitates the covert transfer of data and the careful distribution of decryption keys between authorized parties. That’s why there are so many different approaches, and some of them centre on combining attribute-based cryptography (ABC) and AES. While it’s true that any given technology may have certain benefits, it’s also important to consider any drawbacks it may present, which may include things like increased demands on the system’s processing power and memory, as well as any potential bottlenecks in the network. Load balancing refers to the process of distributing the workload over multiple nodes in a distributed system in order to improve various aspects of the technology, such as the most efficient use of resources and the speed with which jobs may be completed. Load balancing is used to ensure that all of the mainframes in the building or all of the nodes in the network are doing nearly the same amount of work at any given moment, hence preventing the aforementioned issues. Achieving this goal is crucial to developing a practical and aesthetically pleasing loading algorithm. Now that we know the advantages and disadvantages of various load balancing algorithms. The idea of FCS is used to construct a newly developed load balancing algorithm.
Learning Hierarchically-Structured Concepts II: Overlapping Concepts, and Networks with Feedback

We continue our study from Lynch and Mallmann-Trenn (Neural Networks, 2021), of how concepts that have hierarchical structure might be represented in brain-like neural networks, how these representations might be used to recognize the concepts, and how these representations might be learned. In Lynch and Mallmann-Trenn (Neural Networks, 2021), we considered simple tree-structured concepts and feed-forward layered networks. Here we extend the model in two ways: we allow limited overlap between children of different concepts, and we allow networks to include feedback edges. For these more general cases, we describe and analyze algorithms for recognition and algorithms for learning.
NFTs in AR

Abstract: In the real world, we enjoy showing off our art collections through images and films to express our personalities and interact with others in the neighborhood. However, there aren’t many methods in the NFT community outside updating the profile image for us to show others how much we love our collections. Adding any NFT to your camera is quite simple with this project. This merely needs an OpenSea connection to access the associated material because of the compatibility with web3. And whether it is 3D or 2D, a superb AR experience will be created based on the NFT. The NFT can be used as a background if it’s a JPEG. However, if the 3D model is set up, you may control it by moving your body.
The quantum race

With all the hype and excitement surrounding quantum computers, James McKenzie wonders when they will become mainstream products and what they will be useful for.
Eye Tracking for Virtual Customer Requirements Evaluations

High-quality products can be achieved by developing product features in accordance with customer requirements. In the following approach eye tracking is used to accomplish virtual design reviews to validate the conformity of products with requirements during the product development process. Gaze data are thereby utilizing to clarify, prioritize and enrich customer statements during design reviews using gaze data. Thereby, the goal is to establish a method to complement qualitative methods with eye tracking to ultimately reach higher product quality and customer satisfaction. The research work thus lays a foundation for more objective and low-effort customer requirement analyses. In this way, it paves the way for increased customer co-creation and quality-oriented product development for consumer products as well.
Reanalyzing Effective Eye-related Information for Developing User’s Intent Detection Systems

Studies on gaze-based interactions have utilized natural eye-related information to detect user intent. Most use a machine learning-based approach to minimize the cost of choosing appropriate eye-related information. While those studies demonstrated the effectiveness of an intent detection system, understanding which eye-related information is useful for interactions is important. In this paper, we reanalyze how eye-related information affected the detection performance of a previous study to develop better intent detection systems in the future. Specifically, we analyzed two aspects of dimensionality reduction and adaptation to different tasks. The results showed that saccade and fixation are not always useful, and the direction of gaze movement could potentially cause overfitting.
Deriving Cognitive Strategies from Fixations during Visual Search in Scenes

Research has established that scene context improves performance in visual search affecting attentional guidance by providing predictions and expectations about where target objects are likely to be located. Here, the reliability of scene context is manipulated, by using an Exploitation/Exploration framework. The results showed that not only is context important, but that it depended on its reliability, showing a dynamic application over time. Eye movement patterns reflect this dynamic application of high-level information. This framework will allow us to define and detect different cognitive strategies as well as the switch between them, signaling a change in prioritization and a change in the influence of context.
EyeExplore: An Interactive Visualization Tool for Eye-Tracking Data for Novel Stimulus-based Analysis

The state-of-the-art visualization tools for multidimensional gaze or eye-tracking data focus on a few dimensions leading to incomplete analysis, e.g., fixations as time series data, scanpath trajectory, etc. We propose EyeExplore, an interactive visualization tool to explore eye-tracking data for a single static 2D stimulus (i.e., a selected image). It has multiple views, namely, single-user, user comparative, and cohort-summary views. We propose the use of ensemble clustering and visualization of co-association matrices for cohort analysis. We propose the use of semantics-aware areas of interest (AOI) through user interactivity leading to AOI transition matrix visualization. Our preliminary results show that EyeExplore provides a more complete data exploration.
Indirect gaze estimation from body movements based on relationship between gaze and body movements

This paper proposes indirect gaze estimation from body movements as an alternative to directly observing the eye regions, as done by conventional eye trackers. This is based on the fact that changes in gaze cause corresponding changes in the movements of other body parts. Experimental results show that the proposed method can limit estimation errors to about 10 degrees.
Comparative Analysis of Autoencoders and U-net-Based Image Steganography

In today’s fast paced and developing era, there is lot of information in different forms of data, pictures, audios, videos, documents, etc., which needs to be keep secured. Steganography is defined as a method of hiding data inside another form of data. Image steganography is a sub-branch where the data to be hidden is encoded inside an image. In this paper, two of the deep learning methods to hide data inside image (full RBG image inside another RGB image) have been studied and compared. The methods, namely autoencoder and U-net-based image steganography, have been compared using metrics like PSNR and SSIM. Experimental analysis is provided to prove that although for the encoding part, both U-net and autoencoder perform well, but while decoding the actual secret image, the performance of U-net is better than the autoencoder architecture.
Research on lightweight real-time semantic segmentation based on attention mechanism

Modern approaches to real-time semantic segmentation algorithms often sacrifice spatial resolution for real-time inference speed, which results in poor performance. Based on this, in this paper, we propose a lightweight real-time semantic segmentation network based on global attention mechanism by improving STDCNet. First, combined with asymmetric convolution, the short-term dense stitching module is light-weighted, and the global correlation of features is enhanced through global attention. Second, the edge branch can effectively filter out the boundary-independent information in the semantic features through the edge feature fusion module, and restore the lost detail information in the decoding stage. Finally, the improved loss function ensures that the network can update parameters in a direction that is conducive to small object segmentation. Tests and analysis on the Cityscapes dataset show that the lightweight contextual attention mechanism achieves 74.9% mIoU and 118.6FPS real-time semantic segmentation inference speed.
Bidirectional Semi-Supervised Dual-Branch CNN for Robust 3D Reconstruction of Stereo Endoscopic Images via Adaptive Cross and Parallel Supervisions

Semi-supervised learning via teacher-student network can train a model effectively on a few labeled samples. It enables a student model to distill knowledge from the teacher’s predictions of extra unlabeled data. However, such knowledge flow is typically unidirectional, having the accuracy vulnerable to the quality of teacher model. In this paper, we seek to robust 3D reconstruction of stereo endoscopic images by proposing a novel fashion of bidirectional learning between two learners, each of which can play both roles of teacher and student concurrently. Specifically, we introduce two self-supervisions, i.e., Adaptive Cross Supervision (ACS) and Adaptive Parallel Supervision (APS), to learn a dual-branch convolutional neural network. The two branches predict two different disparity probability distributions for the same position, and output their expectations as disparity values. The learned knowledge flows across branches along two directions: a cross direction (disparity guides distribution in ACS) and a parallel direction (disparity guides disparity in APS). Moreover, each branch also learns confidences to dynamically refine its provided supervisions. In ACS, the predicted disparity is softened into a unimodal distribution, and the lower the confidence, the smoother the distribution. In APS, the incorrect predictions are suppressed by lowering the weights of those with low confidence. With the adaptive bidirectional learning, the two branches enjoy well-tuned mutual supervisions, and eventually converge on a consistent and more accurate disparity estimation. The experimental results on four public datasets demonstrate our superior accuracy over other state-of-the-arts with a relative decrease of averaged disparity error by at least 9.76%.
Smart technologies in the personal digital educational environment of a teacher

The article is devoted to topical issues related to the digitalization of the system of higher professional education. The authors consider and clarify the concepts of “Smart technologies”, “digital educational environment”, and “personal digital educational environment of a teacher”. Based on the analysis of theoretical literature, the essence of the key concepts under consideration and their role in the organization and implementation of distance learning in higher education as a mechanism for ensuring the quality of education in digital format are determined. As a result of the intensive development of information technologies, smart education is gradually replacing “classical” e-learning, with which a number of concepts are associated, many of which do not have an unambiguous interpretation. Such concepts rightfully include “digitalization of education”, “smart technologies”, “digital educational environment”, and “personal digital educational environment of a teacher”. In the conditions of digitalization of education, the digital competencies of a modern teacher, which characterize his ability to use specific knowledge and modern ICT in the professional sphere, communication and software products in practice, become important for his further development as a specialist. This research is funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No. AP13068185).
Chinese grammatical error correction based on the BERT-BiLSTM-CRF model

A Chinese grammatical error correction (CGEC) method based on the BERT-BiLSTM-CRF model is proposed to address CGEC problems. Firstly, the BERT pre-training model is implemented to generate a deep bi-directional linguistic representation vector that incorporates contextual information. Secondly, the effective text information is mined by using BiLSTM to establish text dependencies. Finally, considering that the text correction process includes substitution, insertion, deletion, and permutation operations, the conditional random field (CRF) algorithm is added to constrain the output variables so they conform to certain grammatical rules in order to obtain the global optimal text sequence. In addition, considering that most characters in the input text do not need modifications and are easily predicted, the focus should be on the wrong position. Therefore, focal loss is introduced to solve this problem. Experiments on the SIGHAN15 and HybirdSet datasets indicate that our approach demonstrates its superiority in error detection and correction tasks regarding their sentence level accuracy, precision, recall, and F1 value metrics.
3D object detection model based on VoteNet

By using point clouds as the only input, 3D object detection has made significant progress. However, point clouds often suffer from incomplete geometry and lack of semantic information, which makes it difficult for detectors to accurately classify and locate detected objects, in order to effectively use the rich semantic information in images to improve the performance of point-based 3D detectors, we propose a three-dimensional object detection method based on VoteNet multimodal self-attention mechanism. Firstly, the feature extraction of the data of the two modes of point cloud and image is carried out. Sencondly the three-dimensional point cloud features are mapped to the image, pseudo-3D votes are generated on the image, and then stitched with the point cloud features. Finally the stitched features are deeply fused through the self-attention mechanism. We validated our method on a challenging SUN RGB-D dataset. The results show that our model provides a significant gain (+4.8mAP@0.25) over VoteNet.
Experimental study of spam classifier based on naive Bayesian model

These days, we are bombarded with endless spam e-mails. A survey showed that spam accounts for over 70% of all emails. The harm of spam to our daily life includes: spam usually has fraud and unhealthy contents, which requires a vast waste of network bandwidth to transfer spam, and vast waste of space to store spam. As spam is usually embedded in normal e-mails, it is difficult to identify them. In this paper, main technologies to identify and block spam are analyzed including information filtering, blacklist and white list, and intention analysis. Then two experiments of different settings are conducted and analyzed to show how different settings affects the <i>accuracy, precision, recall, </i>and <i>f1_score</i> of the model: the first experiment shows how different thresholds are set to determine whether an e-mail is a spam or not effects the <i>accuracy, precision, recall,</i> and <i>f1_score</i> of the model, the second experiment shows the effects on <i>accuracy, precision, recall, </i>and <i>f1_score</i> of the model when training set is bigger than test set.
Electronic Health Record System Using Blockchain Technology

Abstract: Patient’s Health Records (PHRs) are valuable assets to individuals because they enable them to integrate and manage their medical data. Due to the rapid growth of HealthCare management, it is crucialto maintain the Electronic Health Record of patients. Electronic Health Record systems face problems interms of security, integrity, and management since a large volume of patient’s sensitive data is shared between anonymous bodies. To overcome these issues, this project aims to explain how Blockchain technology can be a solution for Electronic Health Record systems. These records for not only useful for the consultation but also for creation of historic family health information tree that keeps track of genetichealth issues and diseases it can also be used for any health service with the authorization from both the patient and medical organization. Hence, proposed a system consists blockchain technology for ElectronicHealth Record and to provide secure storage of electronic records by defining granular access to the usersby interpreting an Electronic Health Record web application.
Differentiating Systematic Cyber Attacks from Random Attacks in Space Communication

Cyberattacks can be conducted while a transmission is in progress between the sender and receiver. Prevention for each attack requires a different set of approaches and techniques. However, one area that is not explored enough is to automatically differentiate space network attacks as systematic or random. The Lempel-Ziv (LZ) algorithm is known as one of the eminent methods to detect errors depending on the randomness of their distribution in the data. In this work, we generated the binary sequence, where 1's indicate the non-transmitted segments (attacked segments) and 0's indicate the successfully transmitted segments in space communication. Unique substrings from the binary sequence are generated via the execution of an updated version of LZ complexity. The normalized LZ complexity proved to be a differentiator in whether the non-receiving packages in space communication are caused by systematic or random attacks.