Reviewerly/exHarmony huggingface dataset:
Differential Evolution Optimal Parameters Tuning with Artificial Neural Network

Differential evolution (DE) is a simple and efficient population-based stochastic algorithm for solving global numerical optimization problems. DE largely depends on algorithm parameter values and search strategy. Knowledge on how to tune the best values of these parameters is scarce. This paper aims to present a consistent methodology for tuning optimal parameters. At the heart of the methodology is the use of an artificial neural network (ANN) that learns to draw links between the algorithm performance and parameter values. To do so, first, a data-set is generated and normalized, then the ANN approach is performed, and finally, the best parameter values are extracted. The proposed method is evaluated on a set of 24 test problems from the Black-Box Optimization Benchmarking (BBOB) benchmark. Experimental results show that three distinct cases may arise with the application of this method. For each case, specifications about the procedure to follow are given. Finally, a comparison with four tuning rules is performed in order to verify and validate the proposed method’s performance. This study provides a thorough insight into optimal parameter tuning, which may be of great use for users.
Automatic Detection of Invasive Ductal Carcinoma Based on the Fusion of Multi-Scale Residual Convolutional Neural Network and SVM

Invasive ductal carcinoma (IDC) is the most common type of breast cancer which is the leading cause of cancer-related deaths in middle-aged women.Pathological analysis of biopsy is the gold standard for diagnosis of breast cancer, and early detection, diagnosis, and treatment can significantly increase the survival rate.This paper proposes a method for the automatic detection of IDC based on the fusion of multi-scale residual convolutional neural network (MSRCNN) and SVM.First, the patches from whole slide images (WSI) were preprocessed by data enhancement and normalization and then input into the MSRCNN for features extraction.Second, the extracted features were input to the SVM and are classified into two categories: healthy and diseased patches.Finally, it is restored to the WSI according to the coordinate information of the patches, therefore the IDC and healthy tissue regions were built.Experimental results show that after 5-fold cross-validation, our method obtained an average accuracy of 87.45±0.81%,an average balance accuracy of 85.7±0.95%, and an average F1 score of 79.89±1.11%.Consequently, it has important practical value and scientific research significance.
Classification of Indoor Human Fall Events Using Deep Learning

Human fall identification can play a significant role in generating sensor based alarm systems, assisting physical therapists not only to reduce after fall effects but also to save human lives. Usually, elderly people suffer from various kinds of diseases and fall action is a very frequently occurring circumstance at this time for them. In this regard, this paper represents an architecture to classify fall events from others indoor natural activities of human beings. Video frame generator is applied to extract frame from video clips. Initially, a two dimensional convolutional neural network (2DCNN) model is proposed to extract features from video frames. Afterward, gated recurrent unit (GRU) network finds the temporal dependency of human movement. Binary cross-entropy loss function is calculated to update the attributes of the network like weights, learning rate to minimize the losses. Finally, sigmoid classifier is used for binary classification to detect human fall events. Experimental result shows that the proposed model obtains an accuracy of 99%, which outperforms other state-of-the-art models.
Decoding China’s COVID‐19 ‘virus exceptionalism’: Community‐based digital contact tracing in Wuhan

During the COVID‐19 pandemic, comprehensive, accurate, and timely digital contact tracing serves as a decisive measure in curbing viral transmission. Such a strategy integrates corporate innovation, government decision‐making, citizen participation, and community coordination with big data analytics. This article explores how key stakeholders in an open innovation ecosystem interact within the digital context to overcome challenges to public health and socio‐economic welfare imposed by the pandemic. To enhance the digital contact tracing effectiveness, communities are deployed to moderate the interactions between government, enterprises and citizens. As an example, we study the community‐based digital contact tracing in Wuhan, a representative case of China’s ‘virus exceptionalism’ in COVID‐19 mitigation. We discuss the effectiveness of this strategy and raise critical ethical concerns regarding decision‐making in R&amp;D management.
A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems

Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime.
A Comparison of Native and Cross-Platform Frameworks for Mobile Applications

The methods used for developing applications for mobile devices have become an important field of research. In this article, we analyze the two most popular approaches, native and cross platform, and compare identical applications developed using various tools.
Instant Edit Propagation on Images Based on Bilateral Grid

The ability to quickly and intuitively edit digital content has become increasingly important in our everyday life.However, existing edit propagation methods for editing digital images are typically based on optimization with high computational cost for large inputs.Moreover, existing edit propagation methods are generally inefficient and highly time-consuming.Accordingly, to improve edit efficiency, this paper proposes a novel edit propagation method using a bilateral grid, which can achieve instant propagation of sparse image edits.Firstly, given an input image with user interactions, we resample each of its pixels into a regularly sampled bilateral grid, which facilitates efficient mapping from an image to the bilateral space.As a result, all pixels with the same feature information (color, coordinates) are clustered to the same grid, which can achieve the goal of reducing both the amount of image data processing and the cost of calculation.We then reformulate the propagation as a function of the interpolation problem in bilateral space, which is solved very efficiently using radial basis functions.Experimental results show that our method improves the efficiency of color editing, making it faster than existing edit approaches, and results in excellent edited images with high quality.
Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning

In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning models require a lot of labeled data. In fields such as HAR, it is difficult to collect data and there are high costs and efforts involved in manual labeling. The existing methods rely heavily on manual data collection and proper labeling of the data, which is done by human administrators. This often results in the data gathering process often being slow and prone to human-biased labeling. To address these problems, we proposed a new solution for the existing data gathering methods by reducing the labeling tasks conducted on new data based by using the data learned through the semi-supervised active transfer learning method. This method achieved 95.9% performance while also reducing labeling compared to the random sampling or active transfer learning methods.
A Predictive Priority-Based Dynamic Resource Provisioning Scheme With Load Balancing in Heterogeneous Cloud Computing

In cloud computing, resource provisioning is a key challenging task due to dynamic resource provisioning for the applications.As per the workload requirements of the application's resources should be dynamically allocated for the application.Disparities in resource provisioning produce energy, cost wastages, and additionally, it affects Quality of Service (QoS) and increases Service Level Agreement (SLA) violations.So, applications allocated resources quantity should match with the applications required resources quantity.Load balancing in cloud computing can be addressed through optimal scheduling techniques, whereas this solution belongs to the NP-Complete optimization problem category.However, the cloud providers always face resource management issues for variable cloud workloads in the heterogeneous system environment.This issue has been solved by the proposed Predictive Priority-based Modified Heterogeneous Earliest Finish Time (PMHEFT) algorithm, which can estimate the application's upcoming resource demands.This research contributes towards developing the prediction-based model for efficient and dynamic resource provisioning in a heterogamous system environment to fulfill the end user's requirements.Existing algorithms fail to meet the user's Quality of Service (QoS) requirements such as makespan minimization and budget constraints satisfaction, or to incorporate cloud computing principles, i.e., elasticity and heterogeneity of computing resources.In this paper, we proposed a PMHEFT algorithm to minimize the makespan of a given workflow application by improving the load balancing across all the virtual machines.Experimental results show that our proposed algorithm's makespan, efficiency, and power consumption are better than other algorithms.
Better Feature Integration for Named Entity Recognition

It has been shown that named entity recognition (NER) could benefit from incorporating the long-distance structured information captured by dependency trees.We believe this is because both types of features -the contextual information captured by the linear sequences and the structured information captured by the dependency trees may complement each other.However, existing approaches largely focused on stacking the LSTM and graph neural networks such as graph convolutional networks (GCNs) for building improved NER models, where the exact interaction mechanism between the two different types of features is not very clear, and the performance gain does not appear to be significant.In this work, we propose a simple and robust solution to incorporate both types of features with our Synergized-LSTM (Syn-LSTM), which clearly captures how the two types of features interact.We conduct extensive experiments on several standard datasets across four languages.The results demonstrate that the proposed model achieves better performance than previous approaches while requiring fewer parameters.Our further analysis demonstrates that our model can capture longer dependencies compared with strong baselines. 1
Confidence Estimation via Auxiliary Models

Reliably quantifying the confidence of deep neural classifiers is a challenging yet fundamental requirement for deploying such models in safety-critical applications. In this paper, we introduce a novel target criterion for model confidence, namely the true class probability (TCP). We show that TCP offers better properties for confidence estimation than standard maximum class probability (MCP). Since the true class is by essence unknown at test time, we propose to learn TCP criterion from data with an auxiliary model, introducing a specific learning scheme adapted to this context. We evaluate our approach on the task of failure prediction and of self-training with pseudo-labels for domain adaptation, which both necessitate effective confidence estimates. Extensive experiments are conducted for validating the relevance of the proposed approach in each task. We study various network architectures and experiment with small and large datasets for image classification and semantic segmentation. In every tested benchmark, our approach outperforms strong baselines.
Towards BERT-based Automatic ICD Coding: Limitations and Opportunities

Automatic ICD coding is the task of assigning codes from the International Classification of Diseases (ICD) to medical notes.These codes describe the state of the patient and have multiple applications, e.g., computer-assisted diagnosis or epidemiological studies.ICD coding is a challenging task due to the complexity and length of medical notes.Unlike the general trend in language processing, no transformer model has been reported to reach high performance on this task.Here, we investigate in detail ICD coding using PubMedBERT, a stateof-the-art transformer model for biomedical language understanding.We find that the difficulty of fine-tuning the model on long pieces of text is the main limitation for BERT-based models on ICD coding.We run extensive experiments and show that despite the gap with current state-of-the-art, pretrained transformers can reach competitive performance using relatively small portions of text.We point at better methods to aggregate information from long texts as the main need for improving BERT-based ICD coding.
Counterfactual Data Augmentation for Neural Machine Translation

We propose a data augmentation method for neural machine translation.It works by interpreting language models and phrasal alignment causally.Specifically, it creates augmented parallel translation corpora by generating (path-specific) counterfactual aligned phrases.We generate these by sampling new source phrases from a masked language model, then sampling an aligned counterfactual target phrase by noting that a translation language model can be interpreted as a Gumbel-Max Structural Causal Model (Oberst and Sontag, 2019).Compared to previous work, our method takes both context and alignment into account to maintain the symmetry between source and target sequences.Experiments on IWSLT'15 English → Vietnamese, WMT'17 English → German, WMT'18 English → Turkish, and WMT'19 robust English → French show that the method can improve the performance of translation, backtranslation and translation robustness.
An Intelligent Question Answering System based on Power Knowledge Graph

The intelligent question answering (IQA) system can accurately capture users' search intention by understanding the natural language questions, searching relevant content efficiently from a massive knowledge-base, and returning the answer directly to the user. Since the IQA system can save inestimable time and workforce in data search and reasoning, it has received more and more attention in data science and artificial intelligence. This article introduced a domain knowledge graph using the graph database and graph computing technologies from massive heterogeneous data in electric power. It then proposed an IQA system based on the electrical power knowledge graph to extract the intent and constraints of natural interrogation based on the natural language processing (NLP) method, to construct graph data query statements via knowledge reasoning, and to complete the accurate knowledge search and analysis to provide users with an intuitive visualization. This method thoroughly combined knowledge graph and graph computing characteristics, realized high-speed multi-hop knowledge correlation reasoning analysis in tremendous knowledge. The proposed work can also provide a basis for the context-aware intelligent question and answer.
An Effective Cost-Sensitive XGBoost Method for Malicious URLs Detection in Imbalanced Dataset

Imbalanced class has been a common problem encountered in the modeling process, and has attracted more and more attention from scholars. Biased classifiers, which limit the classifiers' performance for minority classes, will be produced if the imbalanced ratio between the number of positive labels and negative labels is ignored. The synthetic minority over-sampling technique (SMOTE) is a very classic and popular over-sampling method, which is widely used to address this problem. However, SMOTE increases label noise and the training time during the over-sampling process. To improve the detection rate of minority classes while ensuring efficiency, we propose a cost-sensitive XGBoost (CS-XGB) for the imbalanced data problem. The CS-XGB method can reduce the classifiers' preference for most classes without changing the distribution of the original data. 600000 Uniform Resource Locators (URLs) were collected to validate the CS-XGB method. We compare XGBoost (XGB), SMOTE+XGB and CS-XGB, and the experimental results confirm that the CS-XGB is robust and efficient for imbalanced cases.
DropLoss for Long-Tail Instance Segmentation

Long-tailed class distributions are prevalent among the practical applications of object detection and instance segmentation. Prior work in long-tail instance segmentation addresses the imbalance of losses between rare and frequent categories by reducing the penalty for a model incorrectly predicting a rare class label. We demonstrate that the rare categories are heavily suppressed by correct background predictions, which reduces the probability for all foreground categories with equal weight. Due to the relative infrequency of rare categories, this leads to an imbalance that biases towards predicting more frequent categories. Based on this insight, we develop DropLoss -- a novel adaptive loss to compensate for this imbalance without a trade-off between rare and frequent categories. With this loss, we show state-of-the-art mAP across rare, common, and frequent categories on the LVIS dataset. Codes are available at https://github.com/timy90022/DropLoss.
Controllable Abstractive Dialogue Summarization with Sketch Supervision

In this paper, we aim to improve abstractive dialogue summarization quality and, at the same time, enable granularity control.Our model has two primary components and stages: 1) a two-stage generation strategy that generates a preliminary summary sketch serving as the basis for the final summary.This summary sketch provides a weakly supervised signal in the form of pseudo-labeled interrogative pronoun categories and key phrases extracted using a constituency parser.2) A simple strategy to control the granularity of the final summary, in that our model can automatically determine or control the number of generated summary sentences for a given dialogue by predicting and highlighting different text spans from the source text.Our model achieves state-of-theart performance on the largest dialogue summarization corpus SAMSum, with as high as 50.79 in ROUGE-L score.In addition, we conduct a case study and show competitive human evaluation results and controllability to humanannotated summaries.
Robots in service experiences: negotiating food tourism in pandemic futures

Purpose This research discusses the role of service robots in gastronomic and dining experiences in a post–coronavirus disease (COVID) world. Design/methodology/approach The research note draws on secondary data to analyse the use of robots as an important mechanism to address the impacts of global disease outbreaks on the food industry, as related to hospitality and tourism. Findings Hospitality and tourism services are highly affected by the current travel bans, lockdowns and health-related rules that have emerged to manage the current pandemic (COVID-19). This research showcases the role of service robots in food encounters in hospitality and tourism as agents that contribute to safe and innovative experiences. Originality/value This research note explores how the use of robots in tourism futures will not only transform food experiences, but it is also critical to sustainable development of communities and destinations in a future that will continuously have to deal with disease outbreaks and pandemics with continued travel mobilities.
AI and Data Democratisation for Intelligent Energy Management

Despite the large number of technology-intensive organisations, their corporate know-how and underlying workforce skill are not mature enough for a successful rollout of Artificial Intelligence (AI) services in the near-term. However, things have started to change, owing to the increased adoption of data democratisation processes, and the capability offered by emerging technologies for data sharing while respecting privacy, protection, and security, as well as appropriate learning-based modelling capabilities for non-expert end-users. This is particularly evident in the energy sector. In this context, the aim of this paper is to analyse AI and data democratisation, in order to explore the strengths and challenges in terms of data access problems and data sharing, algorithmic bias, AI transparency, privacy and other regulatory constraints for AI-based decisions, as well as novel applications in different domains, giving particular emphasis on the energy sector. A data democratisation framework for intelligent energy management is presented. In doing so, it highlights the need for the democratisation of data and analytics in the energy sector, toward making data available for the right people at the right time, allowing them to make the right decisions, and eventually facilitating the adoption of decentralised, decarbonised, and democratised energy business models.
Applied Directional Statistics: Modern Methods and Case Studies

Applied Directional Statistics: Modern Methods and Case Studies is a great book on directional data analysis. It contains several interesting case studies and a number of useful statistical methods...