Reviewerly/exHarmony huggingface dataset:
CA2.5-Net Nuclei Segmentation Framework with a Microscopy Cell Benchmark Collection

Nuclei segmentation is an indispensable prerequisite for microscope image analyses. However, a successful instance segmentation result is still challenging attributable to the ubiquitous presence of clustered nuclei, as well as the morphological variation among dissimilar phenotype of cells. In this paper, a novel contour-aware 2.5-path decoder network (CA2.5-Net) is proposed for nuclei segmentation in microscope images. In contrast to the regular two-path decoders in many previous contour-aware networks, a shared decoder path is employed when the clustered-edge problem is severe. The range of recognizability difficulty generated by the extra half path also serves as a natural proxy to construct a curriculum-learning model, where training samples are sequenced for a better segmentation performance. Last, in this paper, we publicize two well-annotated privately-owned datasets covering a wide range of difficulty in the nuclei segmentation task, comprising 500 confocal microscopy image patches of deep-sea archaea and drosophila embryos obtained from 2013 to 2020. In the benchmark test of these two own datasets and one open-source set, our model outperforms the state-of-the-art nuclei segmentation approaches by a large margin, evaluated by the metrics of Average Jaccard Index and Dice score. Empirically, the proposed structure triples the training convergence speed in comparison with the competing CIA-net and BRP-net structures in nuclei segmentation.
Emoji Prediction from Twitter Data using Deep Learning Approach

Emojis are a small visual representation of emotions or objects that are usually used in text messages to enhance the communication experience between individuals. With the rise in the widespread use of social media platforms like Twitter and instant messaging, many users are using these emojis in their text messages to convey broad feelings efficiently, which sometimes cannot be expressed using just words. This combination of text and emojis to improve emotion has become an essential part of how people communicate in the 21st century. Thus, giving rise to a problem statement that is to identify the relationship between these text messages and the emojis used in them. In this paper, we propose an approach to predict multiple emojis for a given text-based tweet message. Our proposal contains three modules, where the first module preprocesses the given text data, the second module is the model on which the data is trained, and a multi-class classifier to predict the emojis evoked by the given text. The objective of this model is to understand the underlying semantics of the text sentence using natural language processing techniques to predict reasonable emojis.
Tangible Interfaces and Augmented Reality in a Nutrition Serious Game for Kids

In this paper we present and discuss the potentialities of using augmented reality and tangible interfaces to enhance kids' cooking skills within a serious nutrition hybrid board game, grounded in the research proj ect “FlavourGame”. The research project is developing a serious game for kids aged 10–12 years old to generate discussion and sense-making around nutrition, more specifically everyday choices of nourishment and food. The main aim is to develop a game design model for hybrid games, making use of digital and physical interactions with fictional worlds to create engaging experiences. In this article, we unravel the creative and technological processes inherent to combining the analog with the digital without disrupting the game's flow, as well as the benefits of simulating the recipes' culinary processes through augmented reality. Additionally, we describe the evaluation process of the prototype that connects the game board to the mobile application and presents the augmented reality challenge to the player. Lastly, we present an analysis of the data collected during the evaluation sessions, showing the good receptivity of participants regarding the use of augmented reality to simulate food and kitchen equipment, and learn and improve their culinary skills.
A semantically driven self-supervised algorithm for detecting anomalies in image sets

Humans can readily detect when an image does not belong in a set by comparing semantic information between images to derive meaning and relationships from the colors, shapes, sizes, locations, and textures within them. Current self-supervised anomaly detection algorithms in computer vision do not possess this ability. Most algorithms learn to detect anomalies through a training objective that only optimizes for machine-level features and do not evaluate semantic features. To fill this gap, we propose a novel self-supervised algorithm that detects anomalies by learning and modeling semantic information within a set of images. This is accomplished by first training our algorithm to be sensitive or invariant to targeted semantic information, and then modeling the semantic relationships learned so that we can detect anomalies by measuring how far images deviate from the model. Experimenting with our algorithm, we show that different datasets can have different semantic sensitivities, those sensitivities can fluctuate between and within sets depending on different aspects of the images, and directly targeting those sensitivities can improve anomaly detection performance. Using our algorithm, we were able to achieve a AUROC of 0.7146 on the CIFAR-10 dataset, which is an improvement of 0.0741 over another current leading self-supervised anomaly detection algorithm.
A virtual reality game to support visuospatial understanding of medical X-ray imaging

Visuospatial aspects of medical X-ray imaging can be difficult to understand. xRay World is a serious game built as a virtual reality application that is designed to introduce players to the X-ray imaging process. xRay World is equally suitable for beginners and advanced players thanks to different levels of difficulty and different game modes. In various minigames., users have to solve tasks by X-raying sealed boxes that contain nonanatomical objects. In addition., a virtual practice lab is included which allows users to freely experiment with X-ray imaging without a given task. The usability and game design of xRay World were rated positively by test users. The application can be considered a safe and cost-effective solution to teach relevant basics of X-ray imaging.
Blockchain architecture and its applications in a bank risk mitigation framework

This study proposes a simple two-period model to consider consumers’ borrowing behaviour in a decentralised consensus and information distribution platform. Based on this model, we develop a bank risk mitigation framework and find that decentralised digital identity and encryption technology are the most important factors for attaining market equilibrium between decentralised consensus and information distribution. Specifically, the greater the scope of digital identity construction and the more blockchain consensus records there are, the less likely the borrower will default. Our study provides meaningful practical implications for bankers and policy regulators to help them better understand consumers’ borrowing behaviour and decisions to default.
A Two-Phase Deep Learning-Based Recommender System: Enhanced by a Data Quality Inspector

Research regarding collaborative filtering recommenders has grown fast lately. However, little attention has been paid to discuss how the input data quality impacts the result. Indeed, some review-rating pairs that a user gave to an item are inconsistent and express a different opinion, making the recommendation result biased. To solve the above drawback, this study proposes a two-phase deep learning-based recommender system. Firstly, a sentiment predictor of textual reviews is created, serving as the quality inspector that cleans and improves the input for a recommender. To build accurate predictors, this phase tries and compares a set of deep learning-based algorithms. Secondly, besides only exploiting the consistent review-rating pairs generated by the quality inspector, this phase builds deep learning-based recommender engines. The experiments on a real-world dataset showed the proposed data quality inspector, based on textual reviews, improves the overall performance of recommenders. On average, applying deep learning-based quality inspectors result in an above 6% improvement in RMSE, and more than a 2% boost in F1 score, and accuracy. This is robust evidence to prove the importance of the input data cleaning process in this field. Moreover, empirical evidence indicates the deep learning approach is suitable for modeling the sentiment predictor, and the core recommendation process, clearly outperforming the traditional machine learning methods.
Is home-based webcam eye-tracking with older adults living with and without Alzheimer's disease feasible?

Home-based eye tracking studies using built-in webcams are typically conducted with younger people and incur long set-up times and a large number of calibration failures. We investigated the set-up time, number of calibration failures and issues faced by twelve older adults living with and without Alzheimer's disease during home-based eye tracking. We found that home-based eye tracking is feasible with set-up support and we provide recommendations for future studies of this nature.
Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network

In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications. The advantage of the proposed structure is the absence of multiple fully connected layers, which is used to increase the classification accuracy but raises the computational cost. The combination of low-cost separable convolutional layers and a convolutional layer is proposed to gain high accuracy and, simultaneously, to reduce the complexity of neural classifiers. Advantages are demonstrated at binary and multiclass classifications of written texts by means of the proposed networks under the sigmoid and Softmax activation functions in convolutional layer. At binary and multiclass classifications, the accuracy obtained by separable convolutional neural networks is higher in comparison with some investigated types of recurrent neural networks and fully connected networks.
Enhancing Differential Privacy for Federated Learning at Scale

Federated learning (FL) is an emerging technique that trains machine learning models across multiple de-centralized systems.It enables local devices to collaboratively learn a model by aggregating locally computed updates via a server.Privacy is a core aspect of FL, and recent works in this area are advancing the privacy guarantee of an FL network.To ensure rigorous privacy guarantee for FL, prior works have focused on methods to securely aggregate local updates and provide differential privacy (DP).In this paper, we investigate a new privacy risk for FL.Specifically, FL may frequently encounter unexpected user dropouts because it is implemented over a large-scale network.We first observe that user dropouts of an FL network may lead to failure in achieving the desired level of privacy protection, i.e., over-consumption of the privacy budget.Subsequently, we develop a DP mechanism robust to user dropouts by dynamically calibrating noise with account of the dropout rate.We evaluate the proposed technique to train convolutional neural network models on MNIST and FEMNIST datasets over a simulated FL network.Our results show that our approach significantly improves privacy guarantee for user dropouts compared to existing DP algorithms on FL networks.
Analyzing and Repairing Concept Drift Adaptation in Data Stream Classification

Data collected over time often exhibit changes in distribution, or concept drift, caused by changes in hidden context relevant to the classification task, e.g. weather conditions. Adaptive learning methods are able to retain performance in changing conditions by explicitly detecting concept drift and changing the classifier used to make predictions. However, in realworld conditions, existing methods often select classifiers which poorly represent current data due to adaptation errors, where change in context is misidentified. We propose the AiRStream system, which uses a novel repair algorithm to identify and correct adaptation errors. We identify errors by periodically testing the performance of inactive classifiers. If an error is identified, a backtracking procedure repairs training done under the misidentified context. AiRStream achieves higher accuracy compared to baseline methods and selects classifiers which better match changes in context. A case study on a real-world air quality inference task shows that AiRStream is able to build a robust model of environmental conditions, allowing the adaptions made to concept drift to be analysed and related to changes in weather.
Combating COVID-19Fake News on Social Media through Fact Checking: Antecedents and Consequences

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An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

Cloud computing systems are rapidly evolving toward multicloud architectures supported on heterogeneous hardware. Cloud service providers are widely offering different types of storage infrastructures and multi-NUMA architecture servers. Existing cloud resource allocation solutions do not comprehensively consider this heterogeneous infrastructure. In this study, we present a novel approach comprised of a hierarchical framework based on genetic programming to solve problems related to data placement and virtual machine allocation for analytics applications running on heterogeneous hardware with a variety of storage types and nonuniform memory access. Our approach optimizes data placement using the Hadoop File System on heterogeneous storage devices on multicloud systems. It guarantees the efficient allocation of virtual machines on physical machines with multiple NUMA (nonuniform memory access) domains by minimizing contention between workloads. We prove that our solutions for data placement and virtual machine allocation outperform other state-of-the-art approaches.
Practical dynamic group signature with efficient concurrent joins and batch verifications

Dynamic group signatures (DGS) enable a user to generate a signature on behalf of a group of users, allowing a prospective user to join via an appropriate join protocol. A natural security requirement in the dynamic setting is to permit an adversary to concurrently perform join protocol executions. To date, most of DGS schemes do not provide the efficient concurrent join protocols in their security analysis, because of the need to use knowledge extractors. Also, DGS schemes have to provide efficient batch verifications for practical applications such as Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, where a large number of group signatures should be verified in a very short time. In this paper, we propose a new practical DGS scheme that supports not only efficient concurrent joins but also batch verifications. The concurrent security is proven by showing that our join protocols are simulated without any knowledge extractor in security analysis. To do this, we introduce a modified Pointcheval–Sanders (PS) problem that can guarantee efficiently checking equality of discrete logarithms. In terms of efficiency, when considering a type-3 pairing, our DGS scheme has the advantages that the signature generation and verification are faster and especially our batch verification is at least 7 times faster in case of verifying 100 signatures, compared to other comparable pairing-based DGS schemes in the literature.
Managing the tension between opposing effects of explainability of artificial intelligence

Research into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote the benefits of explainability or criticize it due to its counterproductive effects. This study addresses this polarized space and aims to identify opposing effects of the explainability of AI and the tensions between them and propose how to manage this tension to optimize AI system performance and trustworthiness.
Online Competition “VIVA Mathematics with Computer”

The characteristic features of the online competition “VIVA Mathematics with Computer”, organized by the Institute of Mathematics and Informatics of the Bulgarian Academy of Sciences, the Union of Mathematicians in Bulgaria and the telecommunications company VIVACOM are described. The tasks are classified according to the type of their answers. The GeoGebra help-files accompanying some of the tasks are discussed as tools for simultaneous development of students‘ digital and mathematical competence. An analysis of the results of the competition, held on April 25, 2020, is presented.
Smart Voting System

In this paper a novel certification method in online voting system using facial detection of the voter. In India, at present there are two types of voting scheme in put into practice. They are top secret Ballet paper and Electronic Voting Machines (EVM), but both of the procedure has a few limits. Indian online voting is a face up to put into practice. The present voting system is not in safe hands. The voters require going dispersed places like polling booths and standing in a lengthy line up to cast their vote, for the reason that most of the people miss their possibility of voting. The voter who is not eligible can also cast their vote by false that means which may lead to a lot of troubles. So in this paper, we have to recommend a scheme for voting which is extremely effectual in voting system. In this process, we have 3 stage of security in voting procedure. The initial level is the authentication of Aadhar number, second stage is the authentication of Voter ID and third stage is facing matching. The protection level of our scheme is really enhanced by the novel application technique for every voter. The user authentication procedure of the scheme is enhanced by addition face detection using by application which will recognize whether the user is authenticated user or not.
ASA President’s Task Force Statement on Statistical Significance and Replicability

The value of hypothesis testing, and the frequent misinterpretation of p-values as a cornerstone of statistical methodology, continues to be debated. In 2019, the President of the American Statistical Association (ASA) convened a Task Force to write a succinct statement about the use of statistical methods in scientific studies, specifically hypothesis tests and p-values, and their connection to replicability.
Enabling Fast Public Auditing and Data Dynamics in Cloud Services

Hosting data in the cloud minimizes maintenance requirements, allowing users to easily access their data on cloud servers. However, cloud servers have full control over outsourced data, which raises security concerns about data integrity. The cloud, for example, might have the financial incentive to discard rarely accessed data, freeing up valuable storage space to, say, host other data-centric applications. Therefore, users need to confirm periodically that their data is intact but this has become increasingly onerous due to the ever-growing volume of data being outsourced.
Autonomous Trajectory Planning Method for Multi-UAV Collaborative Search

Unmanned Aerial Vehicles (UAVs), in multi-UAV collaborative search systems, need to realize adaptive autonomous trajectory planning based on their own state and surrounding environmental conditions. To allow for a fast trajectory planning of multiple UAVs from the current state to the target state, this paper proposes a trajectory planning method for multi-UAV collaborative search tasks based on genetic algorithm (GA). And using genetic algorithm, the UAV's trajectory can be optimized online based on the current mission. Through the analysis of the tasks of the multi-UAV collaborative decision-making part and the autonomous trajectory planning part, the genetic algorithm is used to model and optimize the trajectory planning problem to obtain a feasible and efficient optimal search trajectory from the current state to the decision-making target state. Finally, through specific simulation experiments, the optimal trajectory from the current state to the target state of the UAV i in the multi-UAV system is obtained, which verifies the effectiveness of the algorithm.
Dynamic Object Detection Algorithm Based on Lightweight Shared Feature Pyramid

Current object detection algorithms perform inference on all samples at a fixed computational cost in the inference stage, which wastes computing resources and is not flexible. To solve this problem, a dynamic object detection algorithm based on a lightweight shared feature pyramid is proposed, which performs adaptive inference according to computing resources and the difficulty of samples, greatly improving the efficiency of inference. Specifically, a lightweight shared feature pyramid network and lightweight detection head is proposed to reduce the amount of computation and parameters in the feature fusion part and detection head of the dynamic object detection model. On the PASCAL VOC dataset, under the two conditions of “anytime prediction” and “budgeted batch object detection”, the performance, computation amount and parameter amount are better than the dynamic object detection models constructed by networks such as ResNet, DenseNet and MSDNet.
EEPSA: Energy Efficiency Priority Scheduling Algorithm for Cloud Computing

Cloud computing technology is a prominent technique among available techniques for deploying and allocating computing resources across applications. Due to digitalization the size of data is now rapidly increasing and necessitating the establishment of additional computing facilities and capabilities. To handle enormous, volumes of relevant data most cloud providers are using a fairly high commuting technology that utilizes a massive amount of energy to give the finest capabilities and activities to attract customers, this is also possible to maintain system resources. This paper describes the "EEPSA" method, which is a new energy efficiency priority scheduling system for cloud computing that can help minimize overall energy utilization. The proposed approach mainly emphasizes the pre-emptive aspect and calculates the energy usage for all the workloads scheduling on virtual machines. To reduce the particular energy usage all the requests have been routed to processing servers, not only based on an optimum fit value but also system accessibility. Our proposed EEPSA technique focuses mostly on the proactive elements and estimates the energy consumption amongst all operations scheduled on all of the VMs (virtual machines). An experimental analysis compares the effectiveness of the proposed EEPSA approach to existing methods in based on energy usage, number of tasks migrated, as well as processing time.