Reviewerly/exHarmony huggingface dataset:
DEEP HOLOENTROPY-CORRELATIVE BLOOD CELL SEGMENTATION APPROACH WITH ESCO-BASED DCNN FOR BREAST CANCER CLASSIFICATION

Breast cancer is the leading cause of cancer death among women. Early identification of breast cancer allows patients to receive appropriate therapy, increasing their chances of survival. However, the early and precise detection of breast cancer is more challenging for researchers. Besides, histopathological image is the most effective tool for precise and early detection of breast cancer. Although it has restricted efficiency, breast cancer detection is the main challenge in medical image analysis. This study develops an Enhanced Cat Swarm Optimization-based Deep Convolutional Neural Network (ECSO-based DCNN) for the classification of breast cancer. Pre-processing is also more crucial in image processing since it improves image quality by removing noise from an input image. The segmentation process is used through a designed deep holoentropy-correlative segmentation method, where significant blood cells are extracted. The breast cancer detection and classification are performed using DCNN, which is trained by devised ECSO algorithm. The execution of the introduced deep holoentropy-correlative blood cell segmentation model with optimized DCNN for breast cancer categorization is performed using BreakHis and Breast Cancer Histopathological Annotation and Diagnosis (BreCaHAD) datasets. The proposed ECSO-based DCNN model obtained better performance with accuracy, sensitivity, and specificity of 96.26%, 97.6%, and 93.57%.
Customized Information Extraction and Processing Pipeline for Commercial Invoices

Extracting information from scanned invoices and other commercial documents, a critical component of corporate function, typically requires significant manual processing. Much research has been conducted in the field of automated information extraction and document processing to alleviate the manual resources used for document analysis, but resultant literature and commercially available products have demonstrated limitations in customizability for identifying specific information. In this paper, we propose a customized machine learning-based pipeline for extracting and tabulating relevant key–value pairs from commercial invoice documents. Specifically, the pipeline combines general document understanding, OCR extraction, and key–value matching with custom rules pertaining to a provided invoice dataset. Then, we demonstrate that the pipeline greatly outperforms a commercially available product and can significantly reduce the amount of manual labor required to process invoice documents. Future work will focus on generalizing the pipeline, so as to apply it on more varied datasets.
Bug Localization with Features Crossing and Structured Semantic Information Matching

Bug localization techniques aim to locate the relevant buggy source files according to the bug described by the given bug report, so as to improve the localization efficiency of developers and reduce the cost of software maintenance. The traditional bug localization techniques based on Information Retrieval (IR) usually use the classical text retrieval model and combines the specific domain knowledge features in software engineering to locate the bugs. However, there exists the vocabulary mismatch problem between the bug report and the source file, which may affect the performance of bug localization. Therefore, the relevant deep learning model was introduced later to compute the similarity between the bug report and the source file from the perspective of semantic features. Bug localization approaches based on IR and deep learning have their own advantages and disadvantages, so this paper proposes a model named LocFront which combines IR and deep learning. On the one hand, the Features Crossing module in LocFront carries out the deep crossing operation on the extracted software-specific features to fully acquire the linear and nonlinear relationships. On the other hand, the Structured Semantic Information Matching module in LocFront performs semantic matching on the structured information between the bug report and the source file. Then the Fusion module in LocFront fuses the results of the two above modules to obtain the final localization score. The experimental results on five benchmark datasets show that LocFront outperforms the state-of-the-art bug localization approaches.
Keydrop: Dynamic Keyboard Layout for Faster Typing and Fewer Typos

The keyboard, in a physical or virtual form, is one of the most popular and available communication mediums for computers and mobile devices. In this paper, we introduce the Keydrop, a novel virtual keyboard layout for a touchscreen that dynamically changes the keys’ labels. The results collected from 54 participants showed that Keydrop outperforms the usual QWERTY virtual keyboard in terms of typing speed and the number of typos. The solution gained positive feedback from participants, and the empirical results showed that users type 6% faster, making 33% fewer typos. The proposed solution achieves a speed of 25.17 words per minute (WPM) and 1.20 keystrokes per character (KSPC).
Copy-Move Forgery Detection and Localization Using Deep Learning

Forgery detection is one of the challenging subjects in computer vision. Forgery is performed using image manipulation with editor tools. Image manipulation tries to change the concept of the image but preserves the integrity of the texture and structure of the image as much as possible. Images are used as evidence in some applications, so if the images are manipulated, they will not be reliable. The copy-move forgery is one of the simplest image manipulation methods. This method removes or inserts information into the image with the least clue by copying a part of the image and pasting it into other places of the same image. Recently, traditional (block-based and keypoint-based) and deep learning methods have been proposed to detect forgery images. Traditional methods include two main steps, feature extraction, and feature matching. Unlike the traditional methods, the deep learning method performs the forgery detection automatically by extracting hierarchical features directly from the data. This paper presents a deep learning method for forgery detection at both image and pixel levels. In this method, we used a pre-trained deep model with a global average pooling (GAP) layer instead of default fully connected layers to detect forgery. The GAP layer creates a good dependency between the feature maps and the classes. In pixel forgery detection, a visualization technique called heatmap activation is used in forgery images. This technique identifies parts of the image that are candidates for forgery. Then, the best candidate is selected and the location of the forgery is determined. The proposed method is performed on the CoMoFod and MICC datasets. The extensive experiments showed the satisfactory performance of the proposed method.
Outsourcing teenage language: a participatory approach for exploring speech and text messaging

Abstract This paper presents a remote method used for engaging teenagers as citizen sociolinguists within the research project Teen Speak in Estonia. The project, launched in January 2020, aims to investigate young people’s language by creating the first systematic dual corpus of Estonian teenagers’ spoken language and text messaging. Previously, youth language in Estonia has not been the subject of much research. Yet scholarly attention to teenage language usage has been on the rise internationally, with corpora compiled in English and several other languages. The article describes the process of recruiting and training participants, as well as adjustments made to data collection in connection with the outbreak of COVID-19 and the restrictions which followed. While the pandemic did modestly interfere with the participatory approach, the data collection was successfully carried out, thanks primarily to well-networked and dedicated 9- to 18-year-old participants. Notwithstanding a gender imbalance, the corpus of 97 h of speech and nearly 60,000 words of text messages will facilitate research into Estonian teenagers’ Estonian–English codeswitching and other linguistic features, across two registers. The remote method is recommended for use elsewhere, especially in places where mutual trust prevails, and teenagers claim a fair amount of independence, responsibility and facility with online applications.
Efficient probability intervals for classification using inductive venn predictors

Learning enabled components are frequently used by autonomous systems and it is common for deep neural networks to be integrated in such systems for their ability to learn complex, non-linear data patterns and make accurate predictions in dynamic environments. However, their large number of parameters and their use as black boxes introduce risks as the confidence in each prediction is unknown and output values like softmax scores are not usually well-calibrated. Different frameworks have been proposed to compute accurate confidence measures along with the predictions but at the same time introduce a number of limitations like execution time overhead or inability to be used with high-dimensional data. In this paper, we use the Inductive Venn Predictors framework for computing probability intervals regarding the correctness of each prediction in real-time. We propose taxonomies based on distance metric learning to compute informative probability intervals in applications involving high-dimensional inputs. By assigning pseudo-labels to unlabeled input data during system deployment we further improve the efficiency of the computed probability intervals. Empirical evaluation on image classification and botnet attacks detection in Internet-of-Things (IoT) applications demonstrates improved accuracy and calibration. The proposed method is computationally efficient, and therefore, can be used in real-time. The code is available at https://github.com/dboursinos/Efficient-Probability-Intervals-Classification-Inductive-Venn-Predictors.
Application of intelligent recommendation system based on deep learning algorithm in university library talent training

Intelligent recommendation system can greatly save users' time and energy by asking for relevant information and providing powerful guidance and help to users. Deep learning algorithm has become the most popular research direction in the past decade due to its powerful expression ability, whether it is the ability to fit data patterns or the ability to mine data feature combinations. This paper takes the university library as the main research data. The data come from the original data of a university library for five years, including the collection of book information, the reader information of borrowing books, the book-borrowing records, and the book-related information with research, and experimental data are selected. At the same time, in order to alleviate the start-up problem of new users, college library, as a new form of traditional library, combined with the resources of traditional library, analyzes the role of college library in college talent training in the intelligent recommendation system of college library, thinks about the driving force of college library service innovation, and discusses some measures of library service innovation under the background of talent training. Through the study of the existing book recommendation system, the shortcomings of the existing recommendation service are found, and a new service architecture of the recommendation system is proposed. The artificial intelligence recommendation algorithm is applied to achieve the purpose of intelligent book recommendation and provide the best service for teachers and students.
Personalising prosthetics: digital interventions in disability and dance

ABSTRACTABSTRACTThis paper emerges from an interdisciplinary collaboration between prosthesis-using disabled dance artists, computer scientists, dance researchers and engineers to explore the transformative potential of digital technologies to co-create aesthetically personalised prosthetics from dance movements. Beginning with the dancers performing improvised movement sequences in motion capture suits, which drove a computational design algorithm, an 'aesthetic seed' for each dancer was generated: a kind of personal signature from their movement. These seeds were then algorithmically mapped onto the shapes of prosthetic limb covers that could be 3D printed in a variety of materials. The paper will share some of the reflections from the dancers on how the process generated questions about agency, appropriation, ownership and the political implications of disability as a site of resistance. It will suggest some ways in which digital methods can offer disabled artists different routes towards making and sharing work, whilst foregrounding the importance of inclusion to challenge normative thinking around what 'connection' and 'access' means in the context of digital innovation.KEYWORDS: Danceprostheticscomputing sciencegenerative design3D printing Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSarah WhatleySarah Whatley is Director, Centre for Dance Research, Coventry University. Her research includes exploring the interface between dance, disability and digital technologies.Steve BenfordSteve Benford is Dunford Professor of Computer Science where he co-founded the Mixed Reality Laboratory. His research lies in HCI with a focus on creative applications of emerging technologies.Kate MarshKate Marsh is Assistant Professor at the Centre for Dance Research, Coventry University, and has a practice as a disabled dancer/choreographer. Her research is broadly focused on leadership in the field of dance and disability.Feng ZhouFeng Zhou recently completed his PhD on the topic of using motion capture and generative algorithms to enable disabled dancers to aesthetically personalise their prostheses.Ian AshcroftIan Ashcroft is member of the Additive Manufacturing and 3D Printing Research Group and Head of Department of Mechanical Materials and Manufacturing Engineering at Nottingham University.Paul TennentPaul Tennent in Assistant Professor in Computer Science at Nottingham University specialising in the design of immersive experiences, including applications of motion capture technologies.Welly O'BrienWelly O'Brien is an independent disabled dance artist. She performs and teaches nationally and internationally with a number of major dance companies and collaborators.Tanja ErhartTanja Erhart is a crip dance artist and cultural anthropologist. She creates her own work, teaches and choreographs, and has performed in pieces by artists and companies worldwide.
Philosophical Horizonts of Virtual Reality. Review of: Chalmers, D. (2022). Reality +: Virtual Words and the Problems of Philosophy. New York: W. W. Norton &amp; Company. pp. 503

The review is devoted to the new monograph "Reality +: virtual worlds and problems of philosophy" by the famous philosopher David Chalmers. As can be seen from the title of the book, in it the author examines the philosophical potential of virtual reality for the possible solution of philosophical problems, namely: from ontology (problems of reality and hypotheses about simulation) to values (a good and happy life in virtual reality).
Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices

Physics-based covariance models provide a systematic way to construct covariance models that are consistent with the underlying physical laws in Gaussian process analysis. The unknown parameters in the covariance models can be estimated using maximum likelihood estimation, but direct construction of the covariance matrix and classical strategies of computing with it require physical model runs, storage complexity, and computational complexity. To address such challenges, we propose to approximate the discretized covariance function using hierarchical matrices. By utilizing randomized range sketching for individual off-diagonal blocks, the construction process of the hierarchical covariance approximation requires physical model applications and the maximum likelihood computations require effort per iteration. We propose a new approach to compute exactly the trace of products of hierarchical matrices which results in the expected Fisher information matrix being computable in as well. The construction is totally matrix-free and the derivatives of the covariance matrix can then be approximated in the same hierarchical structure by differentiating the whole process. Numerical results are provided to demonstrate the effectiveness, accuracy, and efficiency of the proposed method for parameter estimations and uncertainty quantification.
Continuous-time graph directed information maximization for temporal network representation

Given a temporal network that is composed of a stream of events that evolves continuously over time, like social posts on Reddit, how can we spot interesting events like anomalous posts? In general, it is critical to learn useful information from event time, attributes, and network structure, which means that the anomalous post can be detected by over-frequent posts, impolite comments, and incorrect choices of subreddits. In this paper, we propose a new learning objective, named Continuous-time Graph Directed Information Maximization (CGDIM), to learn informative node presentations for temporal networks. The proposed CGDIM is based on the popular mutual information maximization (InfoMax) method to learn node features that are shared across temporal neighborhoods. Specifically, to capture the time causal relations among edges with continuous time, we utilize directed information rather than mutual information as the measure. The designed learning objective is to maximize the directed information with the direction from the inputs of temporal neighborhoods to the node representations. By setting a source variable that only contains useful information on each node, we prove that directed information is more effective than mutual information for preserving information of the source variables. To show the effectiveness of the CGDIM, we train several popular backbone models for temporal network representation with the CGDIM and test the performance on temporal node/edge classification. Experiment results on four real-world temporal network datasets show that the proposed CGDIM can improve the Average-Precision scores of the backbone models by 3%-8%.
A Demonstration of GeoTorchAI: A Spatiotemporal Deep Learning Framework

This paper demonstrates GeoTorchAI, a spatiotemporal deep learning framework. In recent years, many neural network models have been proposed focusing on the applications of raster imagery and spatiotemporal non-imagery datasets. Implementing these models using existing deep learning frameworks, such as PyTorch and TensorFlow, requires nontrivial coding efforts from the developers because these models differ extensively from state-of-the-art models supported by existing deep learning frameworks. Moreover, existing deep learning frameworks lack the support for scalable data preprocessing, a mandatory step for converting spatiotemporal datasets into trainable tensors. GeoTorchAI enables machine learning practitioners to implement spatiotemporal deep learning models with minimum coding efforts on top of PyTorch. It provides state-of-the-art neural network models, ready-to-use benchmark datasets, and transformation operations for raster imagery and spatiotemporal non-imagery datasets. Besides deep learning, GeoTorchAI contains a data preprocessing module that allows preparing trainable spatiotemporal vector datasets and the transformation of raster images in a cluster computing setting.
Optimizing Tensor Computations: From Applications to Compilation and Runtime Techniques

Machine learning (ML) training and scoring fundamentally relies on linear algebra programs and more general tensor computations. Most ML systems utilize distributed parameter servers and similar distribution strategies for mini-batch stochastic gradient descent training. However, many more tasks in the data science and engineering lifecycle can benefit from efficient tensor computations. Examples include primitives for data cleaning, data and model debugging, data augmentation, query processing, numerical simulations, as well as a wide variety of training and scoring algorithms. In this survey tutorial, we first make a case for the importance of optimizing more general tensor computations, and then provide an in-depth survey of existing applications, optimizing compilation techniques, and underlying runtime strategies. Interestingly, there are close connections to data-intensive applications, query rewriting and optimization, as well as query processing and physical design. Our goal for the tutorial is to structure existing work, create common terminology, and identify open research challenges.
Demonstrating NaturalMiner: Searching Large Data Sets for Abstract Patterns Described in Natural Language

The NaturalMiner system seeks to extract facts from large relational data sets that match abstract patterns defined in natural language. For instance, this enables users to search, with regards to a specific airline, for evidence that "the airline underperforms" or "the airline outperforms'' within a data set containing flight statistics, hinting at areas for improvements or strengths to advertise.
Third International Workshop on Big Data in Emergent Distributed Environments (BiDEDE)

The Third International Workshop on Big Data in Emergent Distributed Environments (BiDEDE) is centered around addressing scalable data management issues in emerging computing environments such as (post) cloud and fog/edge/dew computing. These environments aim to incorporate efficient data management and processing into distributed systems to minimize communication and computational costs, while simultaneously enhancing application throughput, reducing latencies, and prolonging battery life for nodes. Despite over a decade of research in this area, there are still numerous challenges that remain unsolved due to technological advancements such as lightweight virtualization, greater node capabilities, and increased parallelization. This year we also welcome contributions on data management for or solved by quantum computing. The workshop provides a platform for active discussions in these and related topics.
Link Local Differential Privacy in GNNs via Bayesian Estimation

Recent years have witnessed the emergence of graph neural networks (GNNs) and an increasing amount of attention on GNNs from the data management community. Yet, training GNNs may raise privacy concerns as they may reveal sensitive information that must be kept private according to laws. In this paper, we study GNNs with link local differential privacy over decentralized nodes, where an untrusted server collaborates with node clients to train a GNN model without revealing the existence of any link. We find that by spending the privacy budget independently on links and degrees of the graph, the server can use Bayesian estimation to better denoise the graph topology. Unlike existing approaches, our mechanism does not aim to preserve graph density, but allows the server to estimate fewer links under lower privacy budget and higher uncertainty. Hence, the server makes fewer false positive link estimations and trains better models. Finally, we conduct extensive experiments to demonstrate that our method achieves considerably better performance with higher accuracy under same privacy budget compared to existing approaches.