Reviewerly/exHarmony huggingface dataset:
TKUS: Mining top-k high utility sequential patterns

High-utility sequential pattern mining (HUSPM) has recently emerged as a focus of intense research interest. The main task of HUSPM is to find all subsequences, within a quantitative sequential database, that have high utility with respect to a user-defined minimum utility threshold. However, it is difficult to specify the minimum utility threshold, especially when database features, which are invisible in most cases, are not understood. To handle this problem, top-k HUSPM was proposed. Up to now, only very preliminary work has been conducted to capture top-k HUSPs, and existing strategies require improvement in terms of running time, memory consumption, unpromising candidate filtering, and scalability. Moreover, no systematic problem statement has been defined. In this paper, we formulate the problem of top-k HUSPM and propose a novel algorithm called TKUS. To improve efficiency, TKUS adopts a projection and local search mechanism and employs several schemes, including the Sequence Utility Raising, Terminate Descendants Early, and Eliminate Unpromising Items strategies, which allow it to greatly reduce the search space. Finally, experimental results demonstrate that TKUS can achieve sufficiently good top-k HUSPM performance compared to state-of-the-art algorithm TKHUS-Span.
Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks

Representation learning methods for heterogeneous networks produce a low-dimensional vector embedding (that is typically fixed for all tasks) for each node. Many of the existing methods focus on obtaining a static vector representation for a node in a way that is agnostic to the downstream application where it is being used. In practice, however, downstream tasks such as link prediction require specific contextual information that can be extracted from the subgraphs related to the nodes provided as input to the task. To tackle this challenge, we develop , a framework for bridging static representation learning methods using global information from the entire graph with localized attention driven mechanisms to learn contextual node representations. We first pre-train our model in a self-supervised manner by introducing higher-order semantic associations and masking nodes, and then fine-tune our model for a specific link prediction task. Instead of training node representations by aggregating information from all semantic neighbors connected via metapaths, we automatically learn the composition of different metapaths that characterize the context for a specific task without the need for any pre-defined metapaths. significantly outperforms both static and contextual embedding learning methods on several publicly available benchmark network datasets. We also demonstrate the interpretability, effectiveness of contextual learning, and the scalability of through extensive evaluation.
A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction

Joint entity and relation extraction framework constructs a unified model to perform entity recognition and relation extraction simultaneously, which can exploit the dependency between the two tasks to mitigate the error propagation problem suffered by the pipeline model. Current efforts on joint entity and relation extraction focus on enhancing the interaction between entity recognition and relation extraction through parameter sharing, joint decoding, or other ad-hoc tricks (e.g., modeled as a semi-Markov decision process, cast as a multi-round reading comprehension task). However, there are still two issues on the table. First, the interaction utilized by most methods is still weak and uni-directional, which is unable to model the mutual dependency between the two tasks. Second, relation triggers are ignored by most methods, which can help explain why humans would extract a relation in the sentence. They’re essential for relation extraction but overlooked. To this end, we present a Trigger-Sense Memory Flow Framework (TriMF) for joint entity and relation extraction. We build a memory module to remember category representations learned in entity recognition and relation extraction tasks. And based on it, we design a multi-level memory flow attention mechanism to enhance the bi-directional interaction between entity recognition and relation extraction. Moreover, without any human annotations, our model can enhance relation trigger information in a sentence through a trigger sensor module, which improves the model performance and makes model predictions with better interpretation. Experiment results show that our proposed framework achieves state-of-the-art results by improves the relation F1 to 52.44% (+3.2%) on SciERC, 66.49% (+4.9%) on ACE05, 72.35% (+0.6%) on CoNLL04 and 80.66% (+2.3%) on ADE.
In-service teachers' conceptions of mobile technology-integrated instruction: Tendency towards student-centered learning

Some previous research has illustrated teachers' conceptions and responsibilities in student-centered classes for assisting potent implementation of novel pedagogies. On the other hand, mobile learning has been widely adopted in education in the past decades, and a great deal of previous research has revealed the effects of mobile learning on students' learning. However, few studies have probed teachers' conceptions of mobile technology-integrated instruction. In this study, a total of 25 elementary school teachers in Taiwan were interviewed, and the phenomenographic method was adopted to explore the teachers' conceptions of mobile learning. As a result of the data analysis, seven qualitatively and hierarchically different categories (7-T) of mobile learning were revealed, namely a “Tool,” “Travel,” “Timeliness/Tutor,” a “Toy,” “Tracking,” “Teamwork,” and a “Tutee.” These hierarchical categories represent a spectrum ranging from “teacher-centered” to “student/teacher interaction,” and “student-centered” instructional strategies. Among the 7 categories, 4 (i.e., Toy, Tracking, Teamwork, and Tutee) are considered as student-centered instructional strategies, and the tracking category had the maximum frequency of teachers’ main conceptions of mobile learning. Thus, mobile technology offers opportunities to facilitate student-centered learning. The results provide educators and teachers with deep comprehension of advanced technology-enhanced learning for designing student-centered learning activities. • A phenomenographic method was adopted to explore the teachers' conceptions. • 7 hierarchically different categories (7-T) of mobile learning were revealed. • 7 categories show a spectrum form teacher-centered to student-centered instruction. • Among the 7 categories, 4 are considered as student-centered instruction. • Mobile technology offers opportunities to facilitate student-centered learning.
Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

Patient monitoring is vital in all stages of care. We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm.
A Reversible Watermarking System for Medical Color Images: Balancing Capacity, Imperceptibility, and Robustness

The authenticity and integrity of medical images in telemedicine has to be protected. Robust reversible watermarking (RRW) algorithms provide copyright protection and the original images can be recovered at the receiver’s end. However, the existing algorithms have limitations in their ability to balance the tradeoff among robustness, imperceptibility, and embedded capacity. Some of them are even not completely reversible. Besides, most medical image watermarking algorithms are not designed for color images. To improve their performance in protecting medical color image information, we propose a novel RRW scheme based on the discrete wavelet transform (DWT). First, the DWT provides a robust solution. Second, the modification of the wavelet domain coefficient guarantees the changes of integer values in the spatial domain and ensures the reversibility of the watermarking scheme. Third, the embedding scheme makes full use of the characteristics of the original image and watermarking. This reduces the modification of the original image and ensures better imperceptibility. Lastly, the selection of the Zernike moments order for geometric correction is optimized to predict attack parameters more accurately by using less information. This enhances the robustness of the proposed scheme against geometric attacks such as rotation and scaling. The proposed scheme is robust against common and geometric attacks and has a high embedding capacity without obvious distortion of the image. The paper contributes towards improving the security of medical images in remote healthcare.
A comprehensive data‐level investigation of cancer diagnosis on imbalanced data

Abstract Cancer is one of the leading causes of death in the world. Cancer research is vital as the prognosis of cancer enables clinical applications for patients. In this study, we have proposed the Stacked Ensemble Model (Stacking of bagged and boosted learners) for the automatic disease diagnosis. The experimental results prove the superiority of the proposed method to conventional machine learning techniques. In the empirical study, the performance of eight data handling methods and 14 classification methods is compared to obtain prediction results. The performance of the model has been evaluated on five benchmark datasets. The appreciable Area under the Curve scores achieved by the proposed methodology on Cervical Cancer (0.98), Mesothelioma (0.93), Breast Cancer (0.99), Prostate Cancer (0.97), and Hepatitis‐C Virus (0.998) datasets make this work more significant than the previously published works. The experimental results show that our proposed method is superior to conventional machine learning techniques and the proposed model contributes in the form of an efficient computational model.
Application of complex systems topologies in artificial neural networks optimization: An overview

Through the success of artificial neural networks (ANNs) in different domains, intense research has been recently centered on changing the networks architecture to optimize the performance. Due to the broad connectivity and complex structure of artificial neural networks (ANNs), designing diluted ANNs with less time, wiring costs and space while obtaining high performance have attracted much attention. Complex systems theory, in which the influence of structure on the overall behavior of the network is mainly considered, have been applied in ANNs to have more efficient and less complex structures. It has been shown that complex random topologies outperform the fully-connected ANNs with less connectivity. But according to neurobiological investigations, highly clustered neurons with short characteristic path length and scale-free distributions are more favored while spending less connectivity costs. Therefore, applying small-world and scale-free topologies on ANNs has been explored in recent years. In this paper, the methodology and results of recent studies are summarized and discussed in which the authors investigated the efficacy of small-world, scale-free and hybrid complex networks on the performance of Hopfield associative memory and layered ANNs compared with conventional fully-connected and random structures.
Demonstration of high-speed and low-complexity continuous variable quantum key distribution system with local local oscillator

Abstract We present an experimental demonstration of the feasibility of the first 20 + Mb/s Gaussian modulated coherent state continuous variable quantum key distribution system with a locally generated local oscillator at the receiver (LLO-CVQKD). To increase the signal repetition rate, and hence the potential secure key rate, we equip our system with high-performance, wideband devices and design the components to support high repetition rate operation. We have successfully trialed the signal repetition rate as high as 500 MHz. To reduce the system complexity and correct for any phase shift during transmission, reference pulses are interleaved with quantum signals at Alice. Customized monitoring software has been developed, allowing all parameters to be controlled in real-time without any physical setup modification. We introduce a system-level noise model analysis at high bandwidth and propose a new ‘combined-optimization’ technique to optimize system parameters simultaneously to high precision. We use the measured excess noise, to predict that the system is capable of realizing a record 26.9 Mb/s key generation in the asymptotic regime over a 15 km signal mode fibre. We further demonstrate the potential for an even faster implementation.
A Novel Deep Learning based Sentiment Analysis of Twitter Data for US Airline Service

The airline industry has evolved quite dynamically over the last two decades. Airline firms use traditional customer feedback types that are very routine and time-intensive. Sentiment analysis may be a crucial approach to the analysis of input in order to minimize the problem. Twitter data acts as a valuable method for gathering user tweets and viewpoint analyzes. This paper proposed a novel deep learning model that effectively combines different word embedding with deep learning methods to evaluate a dataset made up of tweets for six major US Airlines and multi-class sentiment analysis. System selections integrate these features with different deep-learning approaches for term embedding and classify sentimental documents. This methodology starts with raw DNN data extraction and tweet-cleaning pre-processing methods for CNN. The test set product is a positive/negative/neutral tweet interpretation with a 3-class data set and data set precision assessment. Finally, we understand the findings obtained from the models presented by various researchers and prove that our model is more reliable than the previous frameworks.
“OK, Google, why do I use you?” Motivations, post-consumption evaluations, and perceptions of voice AI assistants

Voice-activated, artificial intelligence–based assistants (voice AI assistants) have become an integral part of everyday life because they can be easily activated to complete numerous tasks. However, little is known about what motivates people to use them or how these motivations influence users’ post-consumption evaluations and perceptions. This study develops measures to capture uses and gratifications related to voice AI assistants. It identifies five primary motivations for use—social interaction, personal identity, conformity, life efficiency, and information. Results show that the utilitarian motivations of life efficiency and information influence all post-consumption evaluations and behavioral intentions positively (i.e., users’ attitudes, satisfaction, and intentions to continue using voice AI assistants). However, social motivations are also important. Social interaction and conformity motivations also influence user satisfaction, and the conformity motivation shapes individuals’ intentions to continue using voice AI assistants. The findings further demonstrate that users’ motivations influence perceptions of their voice AI assistants’ roles. In keeping with the “Computers Are Social Actors” (CASA) paradigm, users motivated by social interaction are likely to perceive a voice AI assistant as socially attractive and as a friend, whereas users motivated by life efficiency are likely to perceive it as an assistant. Users motivated by information are more likely to perceive a voice AI assistant as technology, while those motivated by social interaction are less likely to do so. The implications of these findings are discussed, and recommendations for future research are provided.
Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noise

In industrial vision, the anomaly detection problem can be addressed with an autoencoder trained to map an arbitrary image, i.e. with or without any defect, to a clean image, i.e. without any defect. In this approach, anomaly detection relies conventionally on the reconstruction residual or, alternatively, on the reconstruction uncertainty. To improve the sharpness of the reconstruction, we consider an autoencoder architecture with skip connections. In the common scenario where only clean images are available for training, we propose to corrupt them with a synthetic noise model to prevent the convergence of the network towards the identity mapping, and introduce an original Stain noise model for that purpose. We show that this model favors the reconstruction of clean images from arbitrary real-world images, regardless of the actual defects appearance. In addition to demonstrating the relevance of our approach, our validation provides the first consistent assessment of reconstruction-based methods, by comparing their performance over the MVTec AD dataset [1], both for pixel- and image-wise anomaly detection. Our implementation is available at https://github.com/anncollin/AnomalyDetection-Keras.
The nurse’s work in the context of COVID-19 pandemic

Reflect on the work experienced by the nurse in coping with the COVID-19 pandemic in a public hospital of the State of Rio Grande do Norte.Reflective essay based in the professional experience in a public reference hospital for the care of patients affected by COVID-19 in the State of Rio Grande do Norte. Results were organized in two empirical categories, which emphasize potentialities and barriers in the nurse's work in the face of the COVID-19, presented by means of Ishikawa diagram.Two categories emerged from the experiences: Nursing leadership in organizing health services to face COVID-19; and the performance of nursing care management in the COVID-19 pandemic.It is necessary to value the nurse's work in all its attributes, as well as strengthen the interdisciplinary work processes, which collaborate to overcome the crisis caused by the pandemic.
Secure Data Sharing Through Untrusted Clouds with Blockchain-enhanced Key Management

With the rapid development of cloud technology and the huge amount of big data generation, outsourcing data storage to a cloud service provider is an efficient solution. The challenges on the trustworthiness of cloud providers are urgent to be solved due to many security and privacy violation incidents in recent years. Some enhanced data protection methods such as All-Or-Nothing Transformation (AONT) are proposed to provide additional protection on data security. However, the key management and access revocation operations will become a difficult task and the key manager will be vulnerable. In this paper, we propose to use the scalable consortium blockchain system to solve the key management for the AON-based data protection and outsourcing in the multi-clouds scenario. We propose a model that can provide secure data sharing through untrusted clouds with the key management provided by the consortium blockchain system as a service. We also provide practical case studies for using the novel AON data approaches to protect data security and tamper-proofing on key management.
Condition-CNN: A hierarchical multi-label fashion image classification model

Current state of the art image classifiers predict a single class label of an image. However, in many industry settings such as online shopping, images belong to a class hierarchy where the first level represents the coarse grained or the most abstract class with subsequent levels representing the more specific classes. We propose a novel hierarchical image classification model, Condition-CNN, which addresses some of the shortcomings of the branching convolutional neural network in terms of training time and fine-grained accuracy. It applies the Teacher Forcing training algorithm, where the actual class labels of the higher level classes rather than the predicted labels are used to train the lower level branches. The technique also prevents error propagation, and thereby, reduces the training time. Besides learning the image features for each level of classes, Condition-CNN also learns the relationship between different levels of classes as conditional probabilities, which is used to estimate class predictions during scoring. By feeding the estimated higher-level class predictions as priors to the lower-level class prediction, Condition-CNN achieves a superior prediction accuracy while requiring fewer trainable parameters compared to the baseline CNN models. The validation results of Condition-CNN using the Kaggle Fashion Product Images data set demonstrate a prediction accuracy of 99.8%, 98.1%, and 91.0% for Level 1, 2 and 3 classes respectively, which are greater than that of B-CNN and other baseline CNN models. Moreover, Condition-CNN used only 77.58% of the total number of trainable parameters as that of B-CNN.
A Multichannel CNN-LSTM Network for Daily Activity Recognition using Smartwatch Sensor Data

Recognition of human behavior is recently an active and stimulating study. The HAR can provide valuable information on human movement and the behavior of everyday life activities. In the last decade, a large range of HAR-based applications have been implemented, such as healthcare tracking, biometric user authentication, and so on. Previously, several deep learning approaches have been introduced to focus on the issue of conventional machine learning approaches with handcrafted features. So, a novel deep learning architecture to solve the HAR problem is proposed in this study. The introduced architecture is a hybrid model called a multichannel CNN-LSTM network. The model is evaluated by state-of-the-art evaluation metrics; accuracy, precision, recall and F1-score, with a public dataset of smartwatch's accelerometer data called DHA dataset. The proposed multichannel CNN-LSTM outperforms other deep learning methods in terms of accuracy, with a score of 96.87%.