Reviewerly/exHarmony huggingface dataset:
Towards Designing Immersive Geovisualisations: Literature Review and Recommendations for Future Research

Geological fieldwork forms an integral part of science discovery, exploration, and learning in many geoscientific domains. Yet, there are barriers that can hinder its practice. To address this, prior research has investigated immersive geovisualisations, however, there is no consensus on the types of interaction tools and techniques that should be used. We have conducted a literature review of 31 papers and present the visualisation environments, interaction tools and techniques, and evaluation methods from this last decade. We found a lack of established taxonomy for visualisation environments; an absence of thorough reports on interaction tools and techniques; and a lack of use of relevant human-computer interaction (HCI) theories and user-centered approaches. This review contributes towards the development of a design framework as we propose a basic taxonomy; demonstrate the need for holistic records of user interactions; and highlight the need for HCI evaluation methods. Addressing these gaps will facilitate future innovation in the emerging field of immersive geovisualisations.
Co-designing a Technology Probe with Experienced Designers

Technology probes are low-fidelity devices that can be used to understand research participant's lived experiences, but they are not usually subject to iterative design. There are opportunities in human-computer interaction to develop technology probes through co-design, by including diverse perspectives during probe development. To explore this opportunity, five design researchers with different disciplinary and cultural backgrounds engaged with a technology probe to support daily reflections, discussed new directions in a co-design workshop and developed narratives to negotiate possibilities of the probe. This paper presents observations described by each of the researchers using the probe, and insights from the process we followed. We discuss how the the designers' postitionalities are reflected in the processes, and how they brought value by shaping narratives of the different roles a technology probe might take. We also discuss how we may use co-design of technology probes as a generative method, highlight the importance of open-endedness in the process, and reflect on lessons learned.
An investigation on strategies for remote interactions with cultural spaces

The global spread of Covid-19 has turned remote technologies into a basic requirement for people to participate in daily social life. Accordingly, the pandemic has forced cultural institutions to adapt and focus on remote experiences in order to sustain their service offerings and patronage. In this study, we analyse 15 recent case studies exploring novel interfaces for hybrid and virtual cultural venues, offering progressive approaches from physical to virtual interactions. In particular, we posit sociability and physical situatedness as core features that should be considered when designing remote interactions with cultural spaces. Based on that, we propose a preliminary method to rank those two features (the Local-Social Index), and utilise it to compare the different emerging implementations against each other, as well as against traditional in situ visits. Finally, we discuss potential ways forward for designing hybrid social visits where local and remote visitors can jointly partake in cultural appreciation.
A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models

Translating the use of modern machine learning algorithms into clinical applications requires settling challenges related to explain-ability and management of nuanced confounding factors. To suitably interpret the results, removing or explaining the effect of confounding variables (or metadata) is essential. Confounding variables affect the relationship between input training data and target outputs. Accordingly, when we train a model on such data, confounding variables will bias the distribution of the learned features. A recent promising solution, Meta-Data Normalization (MDN), estimates the linear relationship between the metadata and each feature based on a non-trainable closed-form solution. However, this estimation is confined by the sample size of a mini-batch and thereby may result in an oscillating performance. In this paper, we extend the MDN method by applying a Penalty approach (referred to as PDMN). We cast the problem into a bi-level nested optimization problem. We then approximate that objective using a penalty method so that the linear parameters within the MDN layer are trainable and learned on all samples. This enables PMDN to be plugged into any architectures, even those unfit to run batch-level operations such as transformers and recurrent models. We show improvement in model accuracy and independence from the confounders using PMDN over MDN in a synthetic experiment and a multi-label, multi-site classification of magnetic resonance images.
A Self-Evaluating Architecture for Describing Data

This paper introduces Linguoplotter, a workspace-based architecture for generating short natural language descriptions. All processes within Linguoplotter are carried out by codelets, small pieces of code each responsible for making incremental changes to the program’s state, the idea of which is borrowed from Hofstadter et al. [6]. Codelets in Linguoplotter gradually transform a representation of temperatures on a map into a description which can be output. Many processes emerge in the program out of the actions of many codelets, including language generation, self-evaluation, and higher-level decisions such as when to stop a given process, and when to end all processing and publish a final text. The program outputs a piece of text along with a satisfaction score indicating how good the program judges the text to be. The iteration of the program described in this paper is capable of linguistically more diverse outputs than a previous version; human judges rate the outputs of this version more highly than those of the last; and there is some correlation between rankings by human judges and the program’s own satisfaction score. But, the program still publishes disappointingly short and simple texts (despite being capable of longer, more complete descriptions). This paper describes: the workings of the program; a recent evaluation of its performance; and possible improvements for a future iteration.
Joint Region-Attention and Multi-scale Transformer for Microsatellite Instability Detection from Whole Slide Images in Gastrointestinal Cancer

Microsatellite instability (MSI) is a crucial biomarker to clinical immunotherapy in gastrointestinal cancer, while additional immunohistochemical or genetic tests for MSI are generally missing due to lack of medical resources. Deep learning has achieved promising performance in detecting MSI from hematoxylin and eosin (H &E) stained histopathology slides. However, these methods are primarily based on patch-supervised slide-label models and then aggregate patch-level results into the slides-level result, resulting unstable prediction due to noisy patches and aggregation ways. In this paper, we propose a joint region-attention and multi-scale transformer (RAMST) network for microsatellite instability detection from whole slide images in gastrointestinal cancer. Specifically, we present a region-attention mechanism and a feature weight uniform sampling (FWUS) method to learn a representative subset of image patches from whole slide images. Moreover, we introduce the transformer architecture to fuse the multi-scale histopathology features consisting of patch-level features with region-level features to characterize the whole slide images for slide-level MSI detection. Compared to the existing MSI detection methods, the proposed RAMST shows the best performances on the colorectal and stomach cancer dataset from The Cancer Genome Atlas (TCGA) and provides an effective features representation learning method for WSI-label tasks.
Automatic Grammar Correction of Commas in Czech Written Texts: Comparative Study

The task of grammatical error correction is a widely studied field of natural language processing where the traditional rule-based approaches compete with the machine learning methods. The rule-based approach benefits mainly from a wide knowledge base available for a given language. On the contrary, the transfer learning methods and especially the use of pre-trained Transformers have the ability to be trained from a huge number of texts in a given language. In this paper, we focus on the task of automatic correction of missing commas in Czech written texts and we compare the rule-based approach with the Transformer-based model trained for this task.
Self-supervised Pre-training for Nuclei Segmentation

The accurate segmentation of nuclei is crucial for cancer diagnosis and further clinical treatments. For semantic segmentation of nuclei, Vision Transformers (VT) have the potentiality to outperform Convolutional Neural Network (CNN) based models due to their ability to model long-range dependencies (i.e., global context). Usually, VT and CNN models are pre-trained with large-scale natural image dataset (i.e., ImageNet) in fully-supervised manner. However, pre-training nuclei segmentation models with ImageNet is not much helpful because of morphological and textural differences between natural image domain and medical image domain. Also, ImageNet-like large-scale annotated histology dataset rarely exists in medical image domain. In this paper, we propose a novel region-level Self-Supervised Learning (SSL) approach and corresponding triplet loss for pre-training semantic nuclei segmentation model with unannotated histology images extracted from Whole Slide Images (WSI). Our proposed region-level SSL is based on the observation that, non-background (i.e., nuclei) patches of an input image are difficult to predict from surrounding neighbor patches, and vice versa. We empirically demonstrate the superiority of our proposed SSL incorporated VT model on two public nuclei segmentation datasets.
Federated Learning in Heterogeneous Data Settings for Virtual Assistants – A Case Study

Due to recent increased interest in data privacy, it is important to consider how personal virtual assistants (VA) handle data. The established design of VAs makes data sharing mandatory. Federated learning (FL) appears to be the most optimal way of increasing data privacy of data processed by VAs, as in FL, models are trained directly on users’ devices, without sending them to a centralized server. However, VAs operate in a heterogeneous environment – they are installed on various devices and acquire various quantities of data. In our work, we check how FL performs in such heterogeneous settings. We compare the performance of several optimizers for data of various levels of heterogeneity and various percentages of stragglers. As a FL algorithm, we used FedProx, proposed by Sahu et al. in 2018. For a test database, we use a publicly available Leyzer corpus, dedicated to VA-related experiments. We show that skewed quantity and label distributions affect the quality of VA models trained to solve intent classification problems. We conclude by showing that a carefully selected local optimizer can successfully mitigate this effect, yielding 99% accuracy for the ADAM and RMSProp optimizers even for highly skewed distributions and a high share of stragglers.
Context-Aware Voxel-Wise Contrastive Learning for Label Efficient Multi-organ Segmentation

Medical image segmentation is a prerequisite for many clinical applications including disease diagnosis, surgical planning and computer assisted interventions. Due to the challenges in obtaining expert-level accurate, densely annotated multi-organ dataset, existing datasets for multi-organ segmentation either have small number of samples, or only have annotations of a few organs instead of all organs, which are termed as partially labeled data. There exist previous attempts to develop label efficient segmentation method to make use of these partially labeled dataset for improving the performance of multi-organ segmentation. However, most of these methods suffer from the limitation that they only use the labeled information in the dataset without taking advantage of the large amount of unlabeled data. To this end, we propose a context-aware voxel-wise contrastive learning method to take full advantage of both labeled and unlabeled data in partially labeled dataset for an improvement of multi-organ segmentation performance. Experimental Results demonstrated that our proposed method achieved superior performance than other state-of-the-art methods.
Combining Code Context and Fine-grained Code Difference for Commit Message Generation

Generating natural language messages for source code changes is an essential task in software development and maintenance. Existing solutions mainly treat a piece of code difference as natural language, and adopt seq2seq learning to translate it into a commit message. The basic assumption of such solutions lies in the naturalness hypothesis, i.e., source code written by programming languages is to some extent similar to natural language text. However, compared with natural language, source code also bears syntactic regularities. In this paper, we propose to simultaneously model the naturalness and syntactic regularities of source code changes for commit message generation. Specifically, to model syntactic regularities, we first enlarge the input with additional context information, i.e., the code statements that have dependency with the variables in the code difference, and then extract the paths in the corresponding ASTs. Moreover, to better model code difference, we align the two versions of code before and after the committed code change at token level, and annotate their differences with fine-grained edit operations. The context and difference are simultaneously encoded in a learning framework to generate the commit messages. We collected from GitHub a large dataset containing 480 Java projects with over 160k commits, and the experimental results demonstrate the effectiveness of the proposed approach.
Securing Distributed Data Mechanism Based On Blockchain Technology

Providing consistent security in certain network of any organisation is most important and difficult too. One of the important objective components of operations for storage and acquiring healthcare data is hospital data management. The main challenging issues in the medical sector today is protecting data against hackers. Because the data acquired from people in healthcare field is so sensitive, security precautions in today’s market are required. Existing research have been implemented to share data wirelessly, like this one, which uses Patient ID to store healthcare data. Data authentication and data secrecy are two major data security challenges discussed in this paper. Data secrecy is protected by the implementation of the Blockchain system, which enable ethical people to use required data. This study focuses on using blockchain technology to build encryption methods such as triple DES and Blowfish, as well as a reliable method to successfully transfer data. This algorithms are used one after the other for encryption purpose to make more finetune and then it is made to several blocks which can be stored in cloud. Finally, the data is encrypted and saved in cloud infrastructure. The medical reports are stored within the network using cloud computing technology, which allows for quick approach to data. The data can be decrypted and processed with concern person by an authenticated user. Then processed data is stored on the cloud server using blockchain after processing. As a result it is useful to make patient data more secure by using combination of both algorithms and blockchain technology where intruders cant breakout this security easily.
Calibration and Classifier Design for IoT Healthcare Applications using Federated Learning

Recent trends in health care informatics rely on federated learning to preserve privacy and improve communication efficiency. Applications, for example, remote patient monitoring and fitness tracking, use IoT devices for communication and on-device computations. Often these devices use low-cost sensors for signal acquisition, leading to calibration errors and loss in classification accuracy. In this work, we propose an algorithm to jointly calibrate and design classifiers for a finite number of IoT devices using the federated learning architecture. We correct the gain and offset deviations using a linear calibration model, thus preparing the data for device-aware classifier design. By employing on-device training, we learn individual classifiers and communicate the compressed classifier parameters to learn a global classifier model. Further, the global classifier is recalibrated to minimize the joint mean square error of the designed classifiers. We validate the calibration and classifier design algorithms on synthetic data and an available fitness tracker dataset with several users.
Computational Modelling of World Leaders’ Covid -19 Opinions: A Sentiment Analysis Approach

Sentiment analysis uses the processing of Natural language Processing (NLP) together with analysis of text and linguistic patterns to help in identifying the state of information which has been mined from reliable sources. The polarization of a textual pattern, whether a linguistic pattern is positive, neutral or negative can be evaluated with the help of computational modelling of the pattern. Performing computational modelling on sentiment analysis can bring out useful patterns which can be employed for monitoring purposes in social media. Sentiment analysis also finds its vast applications in the management and social aspects of sciences due to its varied importance to commercial applications and society. The growing prominence of sentiment analysis matches with the exponential progress of social media such as Facebook, Twitter, and other social networks. This research paper recommends a standalone platform which allows data handlers to execute sentiment analysis which employs Twitter API to collect information about COVID- 19 related tweets of world leaders. The proposed model is used to analyse whether the sentiment in a tweet is positive, neutral or negative. The pre-processed data passes through the sentiment generation phase where sentiment classes are demarcated for polarity. In this research paper, tweets of various world leaders are labelled with sentiments, and the overall sentiment of their tweets are analysed.
A P2P Digital Voting System for Elections in India

Elections are necessary for a democratic government to function. The people elect their country’s rulers through an election process. India, being one of the world’s largest democracies, organizes elections as a mega-event which extends over many days. Over 900 million people are eligible to vote, there are over 1.2 million electronic voting devices in use, and over 38 billion dollars has been spent. In India, elections are more complicated than most people realize. This paper analyses the flaws in the current EVM-based voting system and suggests a method of voting that uses a peer-to-peer network that is less vulnerable to hacking and manipulation while also being significantly more efficient. The proposed solution is divided into 3 phases and the entire process is transparent and tamper proof. The implementation costs are low, and the votes can be counted in real time. Another benefit is that, the results can be released the same day as the voting. We perform the above-mentioned digital elections using peer-to-peer technology, which makes the entire voting process more secure and reliable. This paper explains why electronic voting machines (EVMs) are not suited for conducting a tamper-proof, corruption-free election and proposes a new method of voting based on peer-to-peer technology and cryptography.
Regarding the quality of disparity estimation from distorted light fields

Recent developments in the field of plenoptic imaging have directly coincided with standardisation efforts. Such efforts are in no small part reliant on quality evaluations. For traditional imaging modalities, such as static images and videos, subjective evaluations have played a very important part. The goal of most objective quality evaluations has been the correlation performance with subjective results. However, plenoptic modalities enable applications that rely more on the objective rather than subjective quality. As such, it is necessary to examine the performance of currently used objective quality metrics. The paper provides a study on the predictive abilities of objective quality metrics on the quality of light field disparity map estimation. The study measures the influence of distortions on light field disparity estimations; furthermore, the study examines how well can objective quality metrics predict the quality of the resulting disparity maps. None of the tested metrics showed satisfactory prediction performance. Thus, two metric fusion algorithms were also examined to see if either can further improve prediction accuracy. The metric fusion algorithms showed that the prediction can be significantly improved. A novel objective quality metric that would correlate well with both subjective and objective results could thus be introduced. • Impact of distorted light fields on disparity estimation examined. • Relation between LF quality metrics and quality of disparity estimation investigated. • Metric fusion to improve performance of objective quality evaluation.
The Next level of Security Scalable Solution Blockchain (SSSB) in Satellite Communication System

Abstract Data security is one of the topmost priorities for government organisations and multinational companies. In particular, extremely sensitive data is regularly transferred between different branches of the government and the military. Hacking and interruption of data transmission of such high-level data transfers can pose a huge risk to the security of a country and to its integrity as well. The method that the article proposes has been implemented on a smaller scale for cryptocurrency transactions by the organisation “Blockstream”. This study serves the purpose of exploring a new use case of blockchain technology using satellite communication in order to establish an extremely secure and very high-speed data transmission connection between two parties. In this idea, the required sensitive data that is to be transferred to another party, sends the data through blockchain method of data transfer. This data is sent to the satellite where the satellite behaves as a node of data transmission in this blockchain connection and the private key is also encoded into the satellite software making it impossible in attempting to hack the encryption of the private key. When the satellite receives this data, it verifies the authenticity of the sender and analyses the encryption of the data. Then this data will be transmitted to its designated receiver. Once the data is received, it will be converted from blockchain to the regular data packets and the information can be shared within its local network. In this article, we explore further about the technology that’s being used to implement the connection from about the technology that’s being used to implement the connection from the ground station to the satellite via blockchain in the present day. The results produced by this article will help in attaining uncompromising security and speed of data transmission which will put an end to the tensions faced by higher authorities while sharing highly classified data.