Reviewerly/exHarmony huggingface dataset:
Data Localization and Privacy-Preserving Healthcare for Big Data Applications: Architecture and Future Directions

Verma, Ashwin Bhattacharya, Pronaya Patel, Yogesh Shah, Komal Tanwar, Sudeep Khan, BaseemWith the exponential rise of generated big data in Healthcare 4.0 ecosystems, the data is shared among multiple stakeholders for analysis and data collection process. During the data collection process, a public data set is released from privately collected data through anonymizing critical and sensitive attribute fields. However, existing privacy preservation techniques are often susceptible to linkage and statistical-based attacks that releases sensitive attributes of the patient. Thus, researchers worldwide have shifted attention towards privacy preservation of healthcare data. Owing to the risk of release of critical attributes, well-defined privacy-preservation techniques and ethical data collection process are much-sought issues. Thus, in this paper, we present a systematic survey of data localization issues coupled with privacy preservation mechanisms, specifically in the context of the healthcare industry. In privacy preservation, we present key techniques like K-anonymity, I-diversity, t-closeness, aggregation, removal of linkage-based records, homomorphic encryption, and noise-based schemes. The survey intends to help existing healthcare practitioners, stakeholders, and researchers design effective data localization and privacy preservation mechanisms to safeguard critical and sensitive patient data.
Revisiting, Benchmarking and Exploring API Recommendation: How Far Are We?

Application Programming Interfaces (APIs), which encapsulate the implementation of specific functions as interfaces, greatly improve the efficiency of modern software development. As the number of APIs grows up fast nowadays, developers can hardly be familiar with all the APIs and usually need to search for appropriate APIs for usage. So lots of efforts have been devoted to improving the API recommendation task. However, it has been increasingly difficult to gauge the performance of new models due to the lack of a uniform definition of the task and a standardized benchmark. For example, some studies regard the task as a code completion problem, while others recommend relative APIs given natural language queries. To reduce the challenges and better facilitate future research, in this paper, we revisit the API recommendation task and aim at benchmarking the approaches. Specifically, the paper groups the approaches into two categories according to the task definition, i.e., query-based API recommendation and code-based API recommendation. We study 11 recently-proposed approaches along with 4 widely-used IDEs. One benchmark named APIBench is then built for the two respective categories of approaches. Based on APIBench, we distill some actionable insights and challenges for API recommendation. We also achieve some implications and directions for improving the performance of recommending APIs, including appropriate query reformulation, data source selection, low resource setting, user-defined APIs, and query-based API recommendation with usage patterns.
Graph Augmentation Learning

Graph Augmentation Learning (GAL) provides outstanding solutions for graph learning in handling incomplete data, noise data, etc. Numerous GAL methods have been proposed for graph-based applications such as social network analysis and traffic flow forecasting. However, the underlying reasons for the effectiveness of these GAL methods are still unclear. As a consequence, how to choose optimal graph augmentation strategy for a certain application scenario is still in black box. There is a lack of systematic, comprehensive, and experimentally validated guideline of GAL for scholars. Therefore, in this survey, we in-depth review GAL techniques from macro (graph), meso (subgraph), and micro (node/edge) levels. We further detailedly illustrate how GAL enhance the data quality and the model performance. The aggregation mechanism of augmentation strategies and graph learning models are also discussed by different application scenarios, i.e., data-specific, model-specific, and hybrid scenarios. To better show the outperformance of GAL, we experimentally validate the effectiveness and adaptability of different GAL strategies in different downstream tasks. Finally, we share our insights on several open issues of GAL, including heterogeneity, spatio-temporal dynamics, scalability, and generalization.
Adversarial Multi-task Model for Emotion, Sentiment, and Sarcasm Aided Complaint Detection

Automatic identification of consumer complaints about products or services purchased can be crucial for businesses and online merchants since they can utilize this knowledge to address the needs of their clients, including handling and resolving complaints. Previous studies on complaint detection do not consider sarcasm, which is often used to express a breach of expectation without directly stating the complaint. Furthermore, since every speech act is influenced by emotions, the customer's emotional state has a considerable impact on the complaint expression. In this paper, we hypothesize that sarcasm, along with two closely related tasks of sentiment and emotion, could aid the process of complaint identification and thereby propose a deep multi-task framework to solve the four problems jointly. We manually annotate the recently released Complaints dataset with the emotion, sentiment, and sarcasm classes. We present an attention-based adversarial multi-task deep neural network model for complaint detection. Experimental results on the extended version of the Complaints dataset show the effectiveness of our proposed approach for complaint detection over the existing state-of-the-art system. The evaluation also demonstrates that the proposed multi-task system improves performance for the primary task, i.e., complaint detection, with the assistance of the three auxiliary tasks, emotion recognition, sentiment analysis, and sarcasm detection.
Domain Adaptive Box-Supervised Instance Segmentation Network for Mitosis Detection

The number of mitotic cells present in histopathological slides is an important predictor of tumor proliferation in the diagnosis of breast cancer. However, the current approaches can hardly perform precise pixel-level prediction for mitosis datasets with only weak labels (i.e., only provide the centroid location of mitotic cells), and take no account of the large domain gap across histopathological slides from different pathology laboratories. In this work, we propose a Domain adaptive Box-supervised Instance segmentation Network (DBIN) to address the above issues. In DBIN, we propose a high-performance Box-supervised Instance-Aware (BIA) head with the core idea of redesigning three box-supervised mask loss terms. Furthermore, we add a Pseudo-Mask-supervised Semantic (PMS) head for enriching characteristics extracted from underlying feature maps. Besides, we align the pixel-level feature distributions between source and target domains by a Cross-Domain Adaptive Module (CDAM), so as to adapt the detector learned from one lab can work well on unlabeled data from another lab. The proposed method achieves state-of-the-art performance across four mainstream datasets. A series of analysis and experiments show that our proposed BIA and PMS head can accomplish mitosis pixel-wise localization under weak supervision, and we can boost the generalization ability of our model by CDAM.
Achieving Differential Privacy in Vertically Partitioned Multiparty Learning

Preserving differential privacy has been well studied under the centralized setting. However, it’s very challenging to preserve differential privacy under multiparty setting, especially for the vertically partitioned case. In this work, we propose a new framework for differential privacy preserving multiparty learning in the vertically partitioned setting. Our core idea is based on the functional mechanism that achieves differential privacy of the released model by adding noise to the objective function. We show the server can simply dissect the objective function into single-party and cross-party sub-functionsa, and allocate computation and perturbation of their polynomial coefficients to local parties. Our method needs only one round of noise addition and secure aggregation. The released model in our framework achieves the same utility as applying the functional mechanism in the centralized setting. Evaluation on real-world and synthetic datasets for linear and logistic regressions shows the effectiveness of our proposed method.
A Multi Moving Target Recognition Algorithm Based on Remote Sensing Video

The Earth observation remote sensing images can display ground activities and status intuitively, which plays an important role in civil and military fields.However, the information obtained from the research only from the perspective of images is limited, so in this paper we conduct research from the perspective of video.At present, the main problems faced when using a computer to identify remote sensing images are: They are difficult to build a fixed regular model of the target due to their weak moving regularity.Additionally, the number of pixels occupied by the target is not enough for accurate detection.However, the number of moving targets is large at the same time.In this case, the main targets cannot be recognized completely.This paper studies from the perspective of Gestalt vision, transforms the problem of moving target detection into the problem of salient region probability, and forms a Saliency map algorithm to extract moving targets.On this basis, a convolutional neural network with global information is constructed to identify and label the target.And the experimental results show that the algorithm can extract moving targets and realize moving target recognition under many complex conditions such as target's long-term stay and small-amplitude movement.
DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers

Prior research in the area of Natural Language Processing (NLP) has shown that including the syntactic structure of a sentence using a dependency parse tree while training a representation learning model improves the performance on downstream tasks. However, most of these modeling approaches make use of the dependency parse tree of sentences for learning task-specific word representations rather than considering that for learning generic representations. In this paper, we propose a new model named DIBERT which stands for Dependency Injected Bidirectional Encoder Representations from Transformers. DIBERT is a variation of the BERT, that apart from Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) also incorporates an additional third objective called Parent Prediction (PP). PP injects the syntactic structure of a dependency tree while pre-training the DIBERT, which generates syntax-aware generic representations. We use the WikiText-103 benchmark dataset to pre-train both the original BERT (BERT-Base) and the proposed DIBERT models. After fine-tuning, we observe that DIBERT performs better than BERT-Base on various NLP downstream tasks including Semantic Similarity, Natural Language Inference and Sentiment Analysis hinting at the fact that incorporating dependency information when learning textual representations can improve the quality of the learned representations.
Emergency Bot in Healthcare Using Industry 4.0

The digitalization of healthcare is underway. With populations aging, chronic diseases rising and medical expenses skyrocketing, the healthcare universe is in dire need of the improvements that digitalization will bring in terms of saving costs, improved diagnostics and more effective care. Moreover, the global lack of specialists, clinical chaperons and trained professionals and experts requests for improved effectiveness and the requirement for technology to help bridge the demand-supply crevice in services. With the combination of artificial intelligence (AI), Internet of things (IoT) and ever improving imaging techniques, great leaps forward in symptomatic precision and more finely targeted therapies are also being made. Data from the healthcare sector of the digital universe is growing at a 48% annual rate, and the challenge is to decipher that surge of information into significant data. That is how AI, with its capacity to link it to data sources within IoT, can make a huge difference. Our implementation of IoT in the field of healthcare is development of an app that makes it easier for first responders of medical emergencies. Since emergencies will occur, preplanning is fundamental. At the onset of an emergency, a lot of decisions ought to be made in a brief period of time. It offers assistance the vigilante with that by providing them with resources that will help them respond to the situation in a better way.
Toward Accountable and Explainable Artificial Intelligence Part Two: The Framework Implementation

This paper builds upon the theoretical foundations of the Accountable eXplainable Artificial Intelligence (AXAI) capability framework presented in part one of this paper. We demonstrate incorporation of the AXAI capability in the real time Affective State Assessment Module (ASAM) of a robotic system. We show that adhering to the eXtreme Programming (XP) practices would help in understanding user behavior and systematic incorporation of the AXAI capability in Machine Learning (ML) systems. We further show that a collaborative software design and development process (SDDP) would facilitate identification of ethical, technical, functional, and domain-specific system requirements. Meeting these requirements would increase user confidence in ML and AI systems. Our results show that the ASAM can synthesize discrete and continuous models of affective state expressions for classifying them in real-time. The ASAM continuously shares important inputs, processed data and the output information with users via a graphical user interface (GUI). Thus, the GUI presents reasons behind system decisions and disseminates information about local reasoning, data handling and decision-making. Through this demonstrated work, we expect to move toward enhancing AI systems’ acceptability, utility and establishing a chain of responsibility if a system fails. We hope this work will initiate further investigations on developing the AXAI capability and use of a suitable SDDP for incorporating them in AI systems.
Relaxed virtual memory in Armv8-A

Abstract Virtual memory is an essential mechanism for enforcing security boundaries, but its relaxed-memory concurrency semantics has not previously been investigated in detail. The concurrent systems code managing virtual memory has been left on an entirely informal basis, and OS and hypervisor verification has had to make major simplifying assumptions. We explore the design space for relaxed virtual memory semantics in the Armv8-A architecture, to support future system-software verification. We identify many design questions, in discussion with Arm; develop a test suite, including use cases from the pKVM production hypervisor under development by Google; delimit the design space with axiomatic-style concurrency models; prove that under simple stable configurations our architectural model collapses to previous “user” models; develop tooling to compute allowed behaviours in the model integrated with the full Armv8-A ISA semantics; and develop a hardware test harness. This lays out some of the main issues in relaxed virtual memory bringing these security-critical systems phenomena into the domain of programming-language semantics and verification with foundational architecture semantics.
Feature matching based on spatial clustering for aerial image registration with large view differences

Over the past few years, drones have been widely used in many fields, while the registration of aerial images is the basis of applications. In this paper, the RFM-SCAN algorithm is applied to the registration task of aerial images with large view differences, used to remove the mismatches in the coarse matching results of feature points. We make some improvements to address the problems in practice. To address the issue of data redundancy in the feature vectors used for clustering, our algorithm uses principal component analysis to lower the dimensionality of the feature vectors, which reduces the data redundancy and improves the clustering performance. To address the problem that the clustering results are sensitive to spatial location and easily classify isolated positive matches as mismatches, our algorithm recalls isolated positive matches by motion consistency constraint. We respectively conduct qualitative and quantitative experiments, which show that our improved algorithm has excellent performance compared to existing state-of-the-art algorithms.
A Unified Framework for Automatic Distributed Active Learning

We propose a novel unified frameork for automated distributed active learning (AutoDAL) to address multiple challenging problems in active learning such as limited labeled data, imbalanced datasets, automatic hyperparameter selection as well as scalability to big data. First, automated graph-based semi-supervised learning is conducted by aggregating the proposed cost functions from different compute nodes and jointly optimizing hyperparameters in both the classification and query selection stages. For dense datasets, clustering-based uncertainty sampling with maximum entropy (CME) loss is applied in the optimization. For sparse and imbalanced datasets, shrinkage optimized KL-divergence regularization and local selection based active learning (SOAR) loss are further naturally adapted in AutoDAL. The optimization is efficiently resolved by iteratively executing a genetic algorithm (GA) refined with a local generating set search (GSS) and solving an integer linear programming (ILP) problem. Moreover, we propose an efficient distributed active learning algorithm which is scalable for big data. The proposed AutoDAL algorithm is applied to multiple benchmark datasets and two real-world datasets including an electrocardiogram (ECG) dataset and a credit fraud detection dataset for classification. We demonstrate that the proposed AutoDAL algorithm is capable of achieving significantly better performance compared to several state-of-the-art AutoML approaches and active learning algorithms.
Explainable machine learning in identifying credit card defaulters

Machine learning is fast becoming one of the central solutions to various real-world problems. Thanks to powerful hardware and large datasets, training a machine learning model has become easier and more rewarding. However, an inherent problem in various machine learning models is a lack of understanding of what goes on ’under the hood’. A lack of explainability and interpretability leads to lower levels of trust in the model's predictions, which means it can't be used in sensitive applications like diagnosing medical ailments and detecting terrorism. This has led to various advances in making machine learning explainable. In this paper various black-box models are used to classify credit card defaulters. These models are compared using different performance metrics, and explanations of these models are provided using a model-agnostic explainer. Finally, the best model-explainer combo is proposed with potential areas of future exploration.
Dual-Branch Convolutional Neural Network Based on Ultrasound Imaging in the Early Prediction of Neoadjuvant Chemotherapy Response in Patients With Locally Advanced Breast Cancer

The early prediction of a patient's response to neoadjuvant chemotherapy (NAC) in breast cancer treatment is crucial for guiding therapy decisions. We aimed to develop a novel approach, named the dual-branch convolutional neural network (DBNN), based on deep learning that uses ultrasound (US) images for the early prediction of NAC response in patients with locally advanced breast cancer (LABC). This retrospective study included 114 women who were monitored with US during pretreatment (NAC pre) and after one cycle of NAC (NAC1). Pathologic complete response (pCR) was defined as no residual invasive carcinoma in the breast. For predicting pCR, the data were randomly split into a training set and test set (4:1). DBNN with US images was proposed to predict pCR early in breast cancer patients who received NAC. The connection between pretreatment data and data obtained after the first cycle of NAC was considered through the feature sharing of different branches. Moreover, the importance of data in various stages was emphasized by changing the weight of the two paths to classify those with pCR. The optimal model architecture of DBNN was determined by two ablation experiments. The diagnostic performance of DBNN for predicting pCR was compared with that of four methods from the latest research. To further validate the potential of DBNN in the early prediction of NAC response, the data from NAC pre and NAC1 were separately assessed. In the prediction of pCR, the highest diagnostic performance was obtained when combining the US image information of NAC pre and NAC1 (area under the receiver operating characteristic curve (AUC): 0.939; 95% confidence interval (CI): 0.907, 0.972; F1-score: 0.850; overall accuracy: 87.5%; sensitivity: 90.67%; and specificity: 85.67%), and the diagnostic performance with the combined data was superior to the performance when only NAC pre (AUC: 0.730; 95% CI: 0.657, 0.802; F1-score: 0.675; sensitivity: 76.00%; and specificity: 68.38%) or NAC1 (AUC: 0.739; 95% CI: 0.664, 0.813; F1-score: 0.611; sensitivity: 53.33%; and specificity: 86.32%) (p<0.01) was used. As a noninvasive prediction tool, DBNN can achieve outstanding results in the early prediction of NAC response in patients with LABC when combining the US data of NAC pre and NAC1.
Understanding the Role of Context in Creating Enjoyable Co-Located Interactions

In recent years, public discourse has blamed digital technologies for making people feel "alone together,'' distracting us from engaging with one another, even when we are interacting in-person. We argue that in order to design technologies that foster and augment co-located interactions, we need to first understand the context in which enjoyable co-located socialization takes place. We address this gap by surveying and interviewing over 1,000 U.S.-based participants to understand what, where, with whom, how, and why people enjoy spending time in-person. Our findings suggest that people enjoy engaging in everyday activities with individuals with whom they have strong social ties because it helps enable nonverbal cues, facilitate spontaneity, support authenticity, encourage undivided attention, and leverage the physicality of their bodies and the environment. We conclude by providing a set of recommendations for designers interested in creating co-located technologies that encourage social engagement and relationship building.