Reviewerly/exHarmony huggingface dataset:
Situated data analysis: a new method for analysing encoded power relationships in social media platforms and apps

Abstract This paper proposes situated data analysis as a new method for analysing social media platforms and digital apps. An analysis of the fitness tracking app Strava is used as a case study to develop and illustrate the method. Building upon Haraway’s concept of situated knowledge and recent research on algorithmic bias, situated data analysis allows researchers to analyse how data is constructed, framed and processed for different audiences and purposes. Situated data analysis recognises that data is always partial and situated, and it gives scholars tools to analyse how it is situated, and what effects this may have. Situated data analysis examines representations of data , like data visualisations, which are meant for humans, and operations with data , which occur when personal or aggregate data is processed algorithmically by machines, for instance to predict behaviour patterns, adjust services or recommend content. The continuum between representational and operational uses of data is connected to different power relationships between platforms, users and society, ranging from normative disciplinary power and technologies of the self to environmental power , a concept that has begun to be developed in analyses of digital media as a power that is embedded in the environment, making certain actions easier or more difficult, and thus remaining external to the subject, in contrast to disciplinary power which is internalised. Situated data analysis can be applied to the aggregation, representation and operationalization of personal data in social media platforms like Facebook or YouTube, or by companies like Google or Amazon, and gives researchers more nuanced tools for analysing power relationships between companies, platforms and users.
Dynamic graph convolutional network for multi-video summarization

Multi-video summarization is an effective tool for users to browse multiple videos. In this paper, multi-video summarization is formulated as a graph analysis problem and a dynamic graph convolutional network is proposed to measure the importance and relevance of each video shot in its own video as well as in the whole video collection. Two strategies are proposed to solve the inherent class imbalance problem of video summarization task. Moreover, we propose a diversity regularization to encourage the model to generate a diverse summary. Extensive experiments are conducted, and the comparisons are carried out with the state-of-the-art video summarization methods, the traditional and novel graph models. Our method achieves state-of-the-art performances on two standard video summarization datasets. The results demonstrate the effectiveness of our proposed model in generating a representative summary for multiple videos with good diversity.
Competent Ultra Data Compression By Enhanced Features Excerption Using Deep Learning Techniques

The objective of data compression is to extract the main features of the data and to restore the decompressed data from latent space i.e., compressed data without any quality or noise. In this paper, a Convolutional LSTM model is proposed to reduce the redundancy data and unnecessary information in the data set. The proposed methodology normalizes the picture to reduce blur and noises, with a compression ratio of 50%. The Convolutional LSTM model is compared with other models such as autoencoder, denoising autoencoder, convolutional neural network and our present work shows better RMSE compared to the other models. Datasets like MNIST and other datasets are used for testing and training the images.
Quantum spectral clustering

Spectral clustering is a powerful unsupervised machine learning algorithm for clustering data with nonconvex or nested structures [A. Y. Ng, M. I. Jordan, and Y. Weiss, On spectral clustering: Analysis and an algorithm, in Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference (MIT Press, Cambridge, MA, 2002), pp. 849--856]. With roots in graph theory, it uses the spectral properties of the Laplacian matrix to project the data in a low-dimensional space where clustering is more efficient. Despite its success in clustering tasks, spectral clustering suffers in practice from a fast-growing running time of $O({n}^{3})$, where $n$ is the number of points in the data set. In this work we propose an end-to-end quantum algorithm performing spectral clustering, extending a number of works in quantum machine learning. The quantum algorithm is composed of two parts: the first is the efficient creation of the quantum state corresponding to the projected Laplacian matrix, and the second consists of applying the existing quantum analog of the $k$-means algorithm [I. Kerenidis, J. Landman, A. Luongo, and A. Prakash, $q$-means: A quantum algorithm for unsupervised machine learning, in Advances in Neural Information Processing Systems 32: Proceedings of the 2019 Conference (Curran Associates, Red Hook, NY, 2020), pp. 4136--4146]. Both steps depend polynomially on the number of clusters, as well as precision and data parameters arising from quantum procedures, and polylogarithmically on the dimension of the input vectors. Our numerical simulations show an asymptotic linear growth with $n$ when all terms are taken into account, significantly better than the classical cubic growth. This work opens the path to other graph-based quantum machine learning algorithms, as it provides routines for efficient computation and quantum access to the incidence, adjacency, and projected Laplacian matrices of a graph.
Multi-label Classifier Performance Evaluation with Confusion Matrix

Confusion matrix is a useful and comprehensive presentation of the classifier performance.It is commonly used in the evaluation of multi-class, single-label classification models, where each data instance can belong to just one class at any given point in time.However, the real world is rarely unambiguous and hard classification of data instance to a single class, i.e. defining its properties with single distinctive feature, is not always possible.For example, an image can contain multiple objects and regions which makes multi-class classification inappropriate to describe its content.Proposed solutions to this set of problems are based on multi-label classification model where each data instance is assigned one or more labels describing its features.While most of the evaluation measures used to evaluate single-label classifier can be adapted to a multi-label classification model, presentation and evaluation of the obtained results using standard confusion matrices cannot be expanded to this case.In this paper we propose a novel method for the computation of a confusion matrix for multi-label classification.The proposed algorithm overcomes the limitations of the existing approaches in modeling relations between the classifier output and the Ground Truth (i.e.hand-labeled) classification, and due to its versatility can be used in many different research fields.
Integrity Verification Mechanism of Sensor Data Based on Bilinear Map Accumulator

With the explosive growth in the number of IoT devices, ensuring the integrity of the massive data generated by these devices has become an important issue. Due to the limitation of hardware, most past data integrity verification schemes randomly select partial data blocks and then perform integrity validation on those blocks instead of examining the entire dataset. This will result in that unsampled data blocks cannot be detected even if they are tampered with. To solve this problem, we propose a new and effective integrity auditing mechanism of sensor data based on a bilinear map accumulator. Using the proposed approach will examine all the data blocks in the dataset, not just some of the data blocks, thus, eliminating the possibility of any cloud manipulation. Compared with other schemes, our proposed solution has been proved to be highly secure for all necessary security requirements, including tag forgery, data deletion, replacement, replay, and data leakage attacks. The solution reduces the computational and storage costs of cloud storage providers and verifiers, and also supports dynamic operations for data owners to insert, delete, and update data by using a tag index table (TIT). Compared with existing schemes based on RSA accumulator, our scheme has the advantages of fast verification and witness generation and no need to map data blocks to prime numbers. The new solution supports all the characteristics of a data integrity verification scheme.
Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers

With the tremendous development of artificial intelligence, many machine learning algorithms have been applied to the diagnosis of human cancers. Recently, rather than predicting categorical variables (e.g., stages and subtypes) as in cancer diagnosis, several prognosis prediction models basing on patients’ survival information have been adopted to estimate the clinical outcome of cancer patients. However, most existing studies treat the diagnosis and prognosis tasks separately. In fact, the diagnosis information (e.g., TNM Stages) indicates the extent of the disease severity that is highly correlated with the patients’ survival. While the diagnosis is largely made based on histopathological images, recent studies have also demonstrated that integrative analysis of histopathological images and genomic data can hold great promise for improving the diagnosis and prognosis of cancers. However, direct combination of these two types of data may bring redundant features that will negatively affect the prediction performance. Therefore, it is necessary to select informative features from the derived multi-modal data. Based on the above considerations, we propose a multi-task multi-modal feature selection method for joint diagnosis and prognosis of cancers. Specifically, we make use of the task relationship learning framework to automatically discover the relationships between the diagnosis and prognosis tasks, through which we can identify important image and genomics features for both tasks. In addition, we add a regularization term to ensure that the correlation within the multi-modal data can be captured. We evaluate our method on three cancer datasets from The Cancer Genome Atlas project, and the experimental results verify that our method can achieve better performance on both diagnosis and prognosis tasks than the related methods.
JAKET: Joint Pre-training of Knowledge Graph and Language Understanding

Knowledge graphs (KGs) contain rich information about world knowledge, entities, and relations. Thus, they can be great supplements to existing pre-trained language models. However, it remains a challenge to efficiently integrate information from KG into language modeling. And the understanding of a knowledge graph requires related context. We propose a novel joint pre-training framework, JAKET, to model both the knowledge graph and language. The knowledge module and language module provide essential information to mutually assist each other: the knowledge module produces embeddings for entities in text while the language module generates context-aware initial embeddings for entities and relations in the graph. Our design enables the pre-trained model to easily adapt to unseen knowledge graphs in new domains. Experiment results on several knowledge-aware NLP tasks show that our proposed framework achieves superior performance by effectively leveraging knowledge in language understanding.
Learning on Attribute-Missing Graphs

Graphs with complete node attributes have been widely explored recently. While in practice, there is a graph where attributes of only partial nodes could be available and those of the others might be entirely missing. This attribute-missing graph is related to numerous real-world applications and there are limited studies investigating the corresponding learning problems. Existing graph learning methods including the popular GNN cannot provide satisfied learning performance since they are not specified for attribute-missing graphs. Thereby, designing a new GNN for these graphs is a burning issue to the graph learning community. In this article, we make a shared-latent space assumption on graphs and develop a novel distribution matching-based GNN called structure-attribute transformer (SAT) for attribute-missing graphs. SAT leverages structures and attributes in a decoupled scheme and achieves the joint distribution modeling of structures and attributes by distribution matching techniques. It could not only perform the link prediction task but also the newly introduced node attribute completion task. Furthermore, practical measures are introduced to quantify the performance of node attribute completion. Extensive experiments on seven real-world datasets indicate SAT shows better performance than other methods on both link prediction and node attribute completion tasks.
Effects of augmented reality technology in a mobile touring system on university students’ learning performance and interest

In this study, we proposed an augmented reality- (AR-) based mobile touring system for an authentic learning activity, which enables students to observe and learn key information regarding cultural heritage sites. To evaluate the effectiveness of the proposed system, we conducted an experiment in a cultural heritage course at a Taiwanese university. A total of 72 students were divided into two groups and randomly assigned to an experimental group who learned with the AR-based mobile touring system and a control group who learned with a conventional mobile touring system. The experimental results show that the AR-based mobile touring system significantly improved the students’ memorising dimension of learning achievement, as compared with a conventional mobile touring system. Moreover, the positive impact of the proposed system on students’ actualised interest and task-based interest factors provide a particularly encouraging result for outdoor experiences. These experimental results also suggest the potential of AR technology to increase students’ learning interest and improve their learning achievements in outdoor settings. Therefore, we suggest that, when applying an AR-based mobile touring system in authentic learning activities, AR virtual objects should be designed appropriately, in order that the content recall performance and learning interest are enhanced.&#x0D; Implications for practice or policy:&#x0D; &#x0D; Teachers may need to consider using an AR-based mobile touring system for outdoor learning activities.&#x0D; First-year student learning achievements and interest can be improved by an AR-based mobile touring system.&#x0D; First-year student learning performance and interest can be improved by AR technology.&#x0D; The usefulness of AR technology can be proved through a comparison of experimental designs.&#x0D;
From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI

This paper reviews the field of Game AI, which not only deals with creating agents that can play a certain game, but also with areas as diverse as creating game content automatically, game analytics, or player modelling. While Game AI was for a long time not very well recognized by the larger scientific community, it has established itself as a research area for developing and testing the most advanced forms of AI algorithms and articles covering advances in mastering video games such as StarCraft 2 and Quake III appear in the most prestigious journals. Because of the growth of the field, a single review cannot cover it completely. Therefore, we put a focus on important recent developments, including that advances in Game AI are starting to be extended to areas outside of games, such as robotics or the synthesis of chemicals. In this article, we review the algorithms and methods that have paved the way for these breakthroughs, report on the other important areas of Game AI research, and also point out exciting directions for the future of Game AI.
BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition

With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text.Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages.Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER.We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese.To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models.Our in-domain model outperformed the baseline model in F1-score by 2.72%, achieving higher performance in 11 out of 13 assessed entities.We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks.The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.
A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient’s age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.
Early Exiting BERT for Efficient Document Ranking

Pre-trained language models such as BERT have shown their effectiveness in various tasks.Despite their power, they are known to be computationally intensive, which hinders realworld applications.In this paper, we introduce early exiting BERT for document ranking.With a slight modification, BERT becomes a model with multiple output paths, and each inference sample can exit early from these paths.In this way, computation can be effectively allocated among samples, and overall system latency is significantly reduced while the original quality is maintained.Our experiments on two document ranking datasets demonstrate up to 2.5× inference speedup with minimal quality degradation.The source code of our implementation can be found at https://github.com/castorini/earlyexiting-monobert.
Comparative Review of the Blockchain Consensus Algorithm Between Proof of Stake (POS) and Delegated Proof of Stake (DPOS)

Blockchain is a public ledger technology to which everyone has access without a central authority having control. This technology typically gets to use registration and smart contract. To make sure there are security and authenticity in the transaction information in blockchain, many researchers are study on consensus algorithm. There is a lot of consensus algorithm are using in blockchain. Determining leading research area and finding the best key of consensus algorithm is the motivation of this study. Thus, this study will investigate how consensus algorithm areas of research can be determined by study on the Proof of Stake (PoS) and Delegated Proof of Stake (DPos). Besides that, this research study about the key parameters that being using in these two algorithms. In addition, after getting the parameters we measure the comparison in terms of their transaction per second, nodes, and block sizes. Furthermore, we conclude the efficiency of these two algorithms in terms of their scalability. Although DPoS performance is expected to be much better than PoS, the impact of parameters can be assessed and analyzed to see and prove results. We hope after this study, can solved the performance and security affected by the application of consensus algorithm used in the blockchain.
Convolutional neural network for automated mass segmentation in mammography

Abstract Background Automatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as deep learning (DL) methods. We developed a new model based on the architecture of the semantic segmentation U-Net model to precisely segment mass lesions in MG images. The proposed end-to-end convolutional neural network (CNN) based model extracts contextual information by combining low-level and high-level features. We trained the proposed model using huge publicly available databases, (CBIS-DDSM, BCDR-01, and INbreast), and a private database from the University of Connecticut Health Center (UCHC). Results We compared the performance of the proposed model with those of the state-of-the-art DL models including the fully convolutional network (FCN), SegNet, Dilated-Net, original U-Net, and Faster R-CNN models and the conventional region growing (RG) method. The proposed Vanilla U-Net model outperforms the Faster R-CNN model significantly in terms of the runtime and the Intersection over Union metric (IOU). Training with digitized film-based and fully digitized MG images, the proposed Vanilla U-Net model achieves a mean test accuracy of 92.6%. The proposed model achieves a mean Dice coefficient index (DI) of 0.951 and a mean IOU of 0.909 that show how close the output segments are to the corresponding lesions in the ground truth maps. Data augmentation has been very effective in our experiments resulting in an increase in the mean DI and the mean IOU from 0.922 to 0.951 and 0.856 to 0.909, respectively. Conclusions The proposed Vanilla U-Net based model can be used for precise segmentation of masses in MG images. This is because the segmentation process incorporates more multi-scale spatial context, and captures more local and global context to predict a precise pixel-wise segmentation map of an input full MG image. These detected maps can help radiologists in differentiating benign and malignant lesions depend on the lesion shapes. We show that using transfer learning, introducing augmentation, and modifying the architecture of the original model results in better performance in terms of the mean accuracy, the mean DI, and the mean IOU in detecting mass lesion compared to the other DL and the conventional models.