Reviewerly/exHarmony huggingface dataset:
A risk prediction model for software project management based on similarity analysis of context histories

Risk event management has become strategic in Project Management, where uncertainties are inevitable. In this sense, the use of concepts of ubiquitous computing, such as contexts, context histories, and mobile computing can assist in proactive project management. This paper proposes a computational model for the reduction of the probability of project failure through the prediction of risks. The purpose of the study is to show a model to assist teams to identify and monitor risks at different points in the life cycle of projects. The work presents as scientific contribution to the use of context histories to infer the recommendation of risks to new projects. The research conducted a case study in a software development company. The study was applied in two scenarios. The first involved two teams that assessed the use of the prototype during the implementation of 5 projects. The second scenario considered 17 completed projects to assess the recommendations made by the Átropos model comparing the recommendations with the risks in the original projects. In this scenario, Átropos used 70% of each project's execution as learning for the recommendations of risks generated to the same projects. Thus, the scenario aimed to assess whether the recommended risks are contained in the remaining 30% of the executed projects. We used as context histories, a database with 153 software projects from a financial company. A project team with 18 professionals assessed the recommendations, obtaining a result of 73% acceptance and 83% accuracy when compared to projects already being executed. The results demonstrated a high percentage of acceptance of the recommendation of risks compared to the other models that do not use the characteristics and similarities of projects. The results show the applicability of the risk recommendation to new projects, based on the similarity analysis of context histories. This study applies inferences on context histories in the development and planning of projects, focusing on risk recommendation. Thus, with recommendations considering the characteristics of each new project, the manager starts with a larger set of information to make more assertive project planning.
Differentiable Hierarchical Graph Grouping for Multi-person Pose Estimation

Multi-person pose estimation is challenging because it localizes body keypoints for multiple persons simultaneously. Previous methods can be divided into two streams, i.e. top-down and bottom-up methods. The top-down methods localize keypoints after human detection, while the bottom-up methods localize keypoints directly and then cluster/group them for different persons, which are generally more efficient than top-down methods. However, in existing bottom-up methods, the keypoint grouping is usually solved independently from keypoint detection, making them not end-to-end trainable and have sub-optimal performance. In this paper, we investigate a new perspective of human part grouping and reformulate it as a graph clustering task. Especially, we propose a novel differentiable Hierarchical Graph Grouping (HGG) method to learn the graph grouping in bottom-up multi-person pose estimation task. Moreover, HGG is easily embedded into main-stream bottom-up methods. It takes human keypoint candidates as graph nodes and clusters keypoints in a multi-layer graph neural network model. The modules of HGG can be trained end-to-end with the keypoint detection network and is able to supervise the grouping process in a hierarchical manner. To improve the discrimination of the clustering, we add a set of edge discriminators and macro-node discriminators. Extensive experiments on both COCO and OCHuman datasets demonstrate that the proposed method improves the performance of bottom-up pose estimation methods.
Differential evolution with adaptive mutation strategy based on fitness landscape analysis

In recent years, many different differential evolution (DE) variants have been proposed to solve real-world optimization problems. However, the performance of them is largely determined by the selection of the mutation strategy, an approach to choose favorable mutation strategy when solving various optimization problems has attracted increasing attention recently. In this paper, we propose a DE with an adaptive mutation operator based on fitness landscape (FLDE). The application of fitness landscape to DE requires three stages. First, we analyzed the fitness landscape features of each benchmark training function, a total of 45 benchmark functions are taken from CEC2014 and 2015. Then, the relationship between three mutation strategies and fitness landscape features is trained by random forest (RF) offline. Finally, the trained RF is used to predict which mutation strategy should be utilized to perform mutation operator for each problem during the evolutionary process. Besides, a historical memory parameter adaption mechanism and population size linear reduction are applied to the FLDE. The CEC2017 benchmark set is utilized to perform the experiments, and five well-known DE variant algorithms are compared with the FLDE algorithm. The experimental results attest that the proposed FLDE algorithm is highly competitive with the other five DE algorithms.
Blockchain and smart contract for access control in healthcare: A survey, issues and challenges, and open issues

Emerging technologies are playing a critical role in the evolution of healthcare systems by presenting eHealth to provide high-quality services and better health to wide-range of patients. Achieving the eHealth goals highly depends on employing modern information and communication technologies (ICTs) to securely and efficiently collect and transmit electronic health records (EHRs) and make them accessible to authorized users and healthcare providers. However, the adoption of EHRs in healthcare providers puts the patients’ privacy and their information security at risk of data breaches. The advent of smart contracts and blockchain technology paves a way for developing efficient EHR access control methods to support secure identification, authentication, and authorization of the clients. This paper delineates an extensive survey on the state-of-the-art blockchain-based access control methods in healthcare domain as a basis for categorizing the existing and future developments in access control area. A thematic taxonomy of the blockchain-based access control methods is also presented to recognize the security issues of the existing methods and highlight the fundamental security requirements to design a granular access control method. This paper also aims for examining the similarities and differences of the traditional access control methods and describes some substantial and outstanding issues and challenges as further directions.
Question Rewriting for Conversational Question Answering

Conversational question answering (QA) requires the ability to correctly interpret a question in the context of previous conversation turns. We address the conversational QA task by decomposing it into question rewriting and question answering subtasks. The question rewriting (QR) subtask is specifically designed to reformulate ambiguous questions, which depend on the conversational context, into unambiguous questions that can be correctly interpreted outside of the conversational context. We introduce a conversational QA architecture that sets the new state of the art on the TREC CAsT 2019 passage retrieval dataset. Moreover, we show that the same QR model improves QA performance on the QuAC dataset with respect to answer span extraction, which is the next step in QA after passage retrieval. Our evaluation results indicate that the QR model we proposed achieves near human-level performance on both datasets and the gap in performance on the end-to-end conversational QA task is attributed mostly to the errors in QA.
Self-Supervised Learning for Domain Adaptation on Point Clouds

Self-supervised learning (SSL) is a technique for learning useful representations from unlabeled data. It has been applied effectively to domain adaptation (DA) on images and videos. It is still unknown if and how it can be leveraged for domain adaptation in 3D perception problems. Here we describe the first study of SSL for DA on point clouds. We introduce a new family of pretext tasks, Deformation Reconstruction, inspired by the deformations encountered in sim-to-real transformations. In addition, we propose a novel training procedure for labeled point cloud data motivated by the MixUp method called Point cloud Mixup (PCM). Evaluations on domain adaptations datasets for classification and segmentation, demonstrate a large improvement over existing and baseline methods.
Remediating learning from non-immersive to immersive media: Using EEG to investigate the effects of environmental embeddedness on reading in Virtual Reality

Virtual Reality (VR) has the potential to enrich education but little is known about how unique affordances of immersive technology might influence leaning and cognition. This study investigates one particular affordance of VR, namely environmental embeddedness, which enables learners to be situated in simulated or imagined settings that contextualize their learning. A sample of 51 university students were administered written learning material in a between-subjects design study, wherein one group read text about sarcoma cancer on a physical pamphlet in the real world, and the other group read identical text on a virtual pamphlet embedded in an immersive VR environment which resembled a hospital room. The study combined advanced EEG measurement techniques, learning tests, and cognitive load measures to compare conditions. Results show that the VR group performed significantly better on a knowledge transfer post-test. However, reading in VR was found to be more cognitively effortful and less time-efficient. Findings suggest the significance of environmental embeddedness for learning, and provide important considerations for the design of educational VR environments, as we remediate learning content from non-immersive to immersive media.
What are bitcoin market reactions to its-related events?

Motivated by the risen linkage between events and Bitcoin return, this paper first defines Bitcoin-related events (BREs) based on the change points analysis and then divides these events into two categories. Furthermore, we model the impact of BREs on the Bitcoin market activities, using an event study methodology and a GARCH-X model. Empirical results show that the shock directions of Bitcoin-related events on Bitcoin price are heavily correlative with types of events. Additionally, there is a significant positive influence of domestic events on reaction volatility, whereas the foreign events impose their influences on both the expectations of market reactions and volatility.
Efficient Attention: Attention with Linear Complexities

Dot-product attention has wide applications in computer vision and natural language processing. However, its memory and computational costs grow quadratically with the input size. Such growth prohibits its application on high- resolution inputs. To remedy this drawback, this paper proposes a novel efficient attention mechanism equivalent to dot-product attention but with substantially less memory and computational costs. Its resource efficiency allows more widespread and flexible integration of attention modules into a network, which leads to better accuracies. Empirical evaluations demonstrated the effectiveness of its advantages. Efficient attention modules brought significant performance boosts to object detectors and instance segmenters on MS-COCO 2017. Further, the resource efficiency democratizes attention to complex models, where high costs prohibit the use of dot-product attention. As an exemplar, a model with efficient attention achieved state-of- the-art accuracies for stereo depth estimation on the Scene Flow dataset. Code is available at https://github.com/cmsflash/efficient-attention.
Anonymous and Privacy-Preserving Federated Learning With Industrial Big Data

Many artificial intelligence technologies have been applied for extracting useful information from massive industrial big data. However, the privacy issues are usually overlooked in many existing methods. In this article, we propose an anonymous and privacy-preserving federated learning scheme for the mining of industrial big data. We explored the effect of the proportion of shared parameters on the accuracy through experiments, and found that sharing partial parameters can almost achieve the accuracy of sharing all the parameters. On this basis, our proposed federated learning scheme reduces the privacy leakage by sharing fewer parameters between the server and each participant. Specifically, we leverage differential privacy on shared parameters with Gaussian mechanism to provide strict privacy preservation; the effect of different ε and δ on accuracy is tested; and we keep track of δ-when it reaches a certain threshold, training shall be stopped. What's more, we employ a proxy server as the middle layer between the server and all the participants to achieve anonymity of participants; it is worth noting that this can also reduce the communication burden on the federated learning server. Finally, we provide the security analysis and performance evaluations by comparing with other schemes.
Face Mask Detection Using MobileNetV2 in The Era of COVID-19 Pandemic

Corona Virus Disease (COVID-19) pandemic is causing a health crisis.One of the effective methods against the virus is wearing a face mask.This paper introduces face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID-19.The face mask recognition in this study is developed with a machine learning algorithm through the image classification method: MobileNetV2.The steps for building the model are collecting the data, pre-processing, split the data, testing the model, and implement the model.The built model can detect people who are wearing a face mask and not wearing it at an accuracy of 96,85 percent.After the model implemented in 25 cities from various source of image, the percentage of people wearing face mask in the cities has a strong correlation to the vigilance index of COVID-19 which is 0,62.
Statistical stability indices for LIME: Obtaining reliable explanations for machine learning models

Nowadays we are witnessing a transformation of the business processes towards a more computation driven approach. The ever increasing usage of Machine Learning techniques is the clearest example of such trend. This sort of revolution is often providing advantages, such as an increase in prediction accuracy and a reduced time to obtain the results. However, these methods present a major drawback: it is very difficult to understand on what grounds the algorithm took the decision. To address this issue we consider the LIME method. We give a general background on LIME then, we focus on the stability issue: employing the method repeated times, under the same conditions, may yield to different explanations. Two complementary indices are proposed, to measure LIME stability. It is important for the practitioner to be aware of the issue, as well as to have a tool for spotting it. Stability guarantees LIME explanations to be reliable, therefore a stability assessment, made through the proposed indices, is crucial. As a case study, we apply both Machine Learning and classical statistical techniques to Credit Risk data. We test LIME on the Machine Learning algorithm and check its stability. Eventually, we examine the goodness of the explanations returned.
Modeling the effect of exposure notification and non-pharmaceutical interventions on COVID-19 transmission in Washington state

Contact tracing is increasingly used to combat COVID-19, and digital implementations are now being deployed, many based on Apple and Google's Exposure Notification System. These systems utilize non-traditional smartphone-based technology, presenting challenges in understanding possible outcomes. In this work, we create individual-based models of three Washington state counties to explore how digital exposure notifications combined with other non-pharmaceutical interventions influence COVID-19 disease spread under various adoption, compliance, and mobility scenarios. In a model with 15% participation, we found that exposure notification could reduce infections and deaths by approximately 8% and 6% and could effectively complement traditional contact tracing. We believe this can provide health authorities in Washington state and beyond with guidance on how exposure notification can complement traditional interventions to suppress the spread of COVID-19.
Generalized framework for non-sinusoidal fringe analysis using deep learning

Phase retrieval from fringe images is essential to many optical metrology applications. In the field of fringe projection profilometry, the phase is often obtained with systematic errors if the fringe pattern is not a perfect sinusoid. Several factors can account for non-sinusoidal fringe patterns, such as the non-linear input–output response (e.g., the gamma effect) of digital projectors, the residual harmonics in binary defocusing projection, and the image saturation due to intense reflection. Traditionally, these problems are handled separately with different well-designed methods, which can be seen as “one-to-one” strategies. Inspired by recent successful artificial intelligence-based optical imaging applications, we propose a “one-to-many” deep learning technique that can analyze non-sinusoidal fringe images resulting from different non-sinusoidal factors and even the coupling of these factors. We show for the first time, to the best of our knowledge, a trained deep neural network can effectively suppress the phase errors due to various kinds of non-sinusoidal patterns. Our work paves the way to robust and powerful learning-based fringe analysis approaches.
Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR

Early diagnosis of breast lesions and differentiation of malignant lesions from benign lesions are important for the prognosis of breast cancer. In the diagnosis of this disease ultrasound is an extremely important radiological imaging method because it enables biopsy as well as lesion characterization. Since ultrasonographic diagnosis depends on the expert, the knowledge level and experience of the user is very important. In addition, the contribution of computer aided systems is quite high, as these systems can reduce the workload of radiologists and reinforce their knowledge and experience when considered together with a dense patient population in hospital conditions. In this paper, a hybrid based CNN system is developed for diagnosing breast cancer lesions with respect to benign, malignant and normal. Alexnet, MobilenetV2, and Resnet50 models are used as the base for the Hybrid structure. The features of these models used are obtained and concatenated separately. Thus, the number of features used are increased. Later, the most valuable of these features are selected by the mRMR (Minimum Redundancy Maximum Relevance) feature selection method and classified with machine learning classifiers such as SVM, KNN. The highest rate is obtained in the SVM classifier with 95.6% in accuracy.
FaasCache: keeping serverless computing alive with greedy-dual caching

Functions as a Service (also called serverless computing) promises to revolutionize how applications use cloud resources. However, functions suffer from cold-start problems due to the overhead of initializing their code and data dependencies before they can start executing. Keeping functions alive and warm after they have finished execution can alleviate the cold-start overhead. Keep-alive policies must keep functions alive based on their resource and usage characteristics, which is challenging due to the diversity in FaaS workloads.
A Lightweight Chaos-Based Medical Image Encryption Scheme Using Random Shuffling and XOR Operations

Abstract Medical images possess significant importance in diagnostics when it comes to healthcare systems. These images contain confidential and sensitive information such as patients’ X-rays, ultrasounds, computed tomography scans, brain images, and magnetic resonance imaging. However, the low security of communication channels and the loopholes in storage systems of hospitals or medical centres put these images at risk of being accessed by unauthorized users who illegally exploit them for non-diagnostic purposes. In addition to improving the security of communication channels and storage systems, image encryption is a popular strategy adopted to ensure the safety of medical images against unauthorized access. In this work, we propose a lightweight cryptosystem based on Henon chaotic map, Brownian motion, and Chen’s chaotic system to encrypt medical images with elevated security. The efficiency of the proposed system is proved in terms of histogram analysis, adjacent pixels correlation analysis, contrast analysis, homogeneity analysis, energy analysis, NIST analysis, mean square error, information entropy, number of pixels changing rate, unified average changing intensity, peak to signal noise ratio and time complexity. The experimental results show that the proposed cryptosystem is a lightweight approach that can achieve the desired security level for encrypting confidential image-based patients’ information.
Emformer: Efficient Memory Transformer Based Acoustic Model for Low Latency Streaming Speech Recognition

This paper proposes an efficient memory transformer Emformer for low latency streaming speech recognition. In Emformer, the long-range history context is distilled into an augmented memory bank to reduce self-attention’s computation complexity. A cache mechanism saves the computation for the key and value in self-attention for the left context. Emformer applies a parallelized block processing in training to support low latency models. We carry out experiments on benchmark LibriSpeech data. Under average latency of 960 ms, Emformer gets WER 2.50% on test-clean and 5.62% on test-other. Comparing with a strong baseline augmented memory transformer (AM-TRF), Emformer gets 4.6 folds training speedup and 18% relative real-time factor (RTF) reduction in decoding with relative WER reduction 17% on test-clean and 9% on test-other. For a low latency scenario with an average latency of 80 ms, Emformer achieves WER 3.01% on test-clean and 7.09% on test-other. Comparing with the LSTM baseline with the same latency and model size, Emformer gets relative WER reduction 9% and 16% on test-clean and test-other, respectively.