Reviewerly/exHarmony huggingface dataset:
Automatic Overheating Defect Diagnosis Based on Rotated Detector for Insulator in Infrared Image

Measuring the temperature of insulator in infrared image and diagnosing whether there is abnormal temperature rise are important for electric power inspection. Traditional object detection uses horizontal detection boxes. When they are applied to insulator temperature measurement, their rough positioning results in measurement deviation. Although some detectors with rotated box have been used to tackle this problem, their rotation angle prediction is still not precise enough, resulting in the positioning deviation of insulator core rod, which also leads to temperature measurement deviation. In this article, we propose a complete solution to address these problems. First, we design an adaptive balanced feature pyramid network (Adaptive BFPN) to fuse localization and semantic information more efficiently, which can improve the detection integrity of large-scale insulators. Second, we design a novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> -intersection over union ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> -IoU) loss to enhance the accuracy (Acc) of the detector in predicting rotation angle and insulator positioning. Third, we propose a method to locate and automatically correct the position of insulator core rod, measure the temperature, and diagnose defects. It enables faster temperature measurement and more automatic defect diagnosis. Finally, comparative experiments show that our method has a higher accuracy in diagnosing overheating defects than other advanced methods, reaching 97.07%. Our method provides a more intelligent and automated solution for inspecting insulator overheating defects.
Imputation of Missing Clinical Covariates for Downstream Classification Problems

Noticeable growth in the use of intelligent devices has resulted in the generation of vast amounts of data from sensor devices. When dealing with large amounts of data, it is common to observe databases with large amounts of missing values. This is a challenge for data miners because various methods for data analysis only work well on complete databases. A traditional approach to handling missing data is to discard instances of missing values and only use complete cases for analysis. However, research has shown that this approach is not practical especially when large amounts of data are missing. This led to an increased need to develop strategies for replacing missing values with plausible values through imputation. This study presents an imputation strategy called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">med.BFMVI</i> for recovering missing values before training downstream classification models. Experiments simulated missingness from 10% to 40% using MCAR and MAR mechanisms and the performance of the proposed technique was measured against state-of-the-art techniques. Overall, the proposed algorithm recorded the best imputation accuracy as opposed to benchmark techniques and showed significant improvements on downstream learning.
Retain and Recover: Delving Into Information Loss for Few-Shot Segmentation

Benefiting from advances in few-shot learning techniques, their application to dense prediction tasks (e.g., segmentation) has also made great strides in the past few years. However, most existing few-shot segmentation (FSS) approaches follow a similar pipeline to that of few-shot classification, where some core components are directly exploited regardless of various properties between tasks. We note that such an ill-conceived framework introduces unnecessary information loss, which is clearly unacceptable given the already very limited training sample. To this end, we delve into the typical types of information loss and provide a reasonably effective way, namely Retain And REcover (RARE). The main focus of this paper can be summarized as follows: (i) the loss of spatial information due to global pooling; (ii) the loss of boundary information due to mask interpolation; (iii) the degradation of representational power due to sample averaging. Accordingly, we propose a series of strategies to retain/recover the avoidable/unavoidable information, such as unidirectional pooling, error-prone region focusing, and adaptive integration. Extensive experiments on two popular benchmarks (i.e., PASCAL- 5i and COCO- 20i ) demonstrate the effectiveness of our scheme, which is not restricted to a particular baseline approach. The ultimate goal of our work is to address different information loss problems within a unified framework, and it also exhibits superior performance compared to other methods with similar motivations. The source code will be made available at https://github.com/chunbolang/RARE.
Communication-Efficient Federated Learning: A Variance-Reduced Stochastic Approach With Adaptive Sparsification

Federated learning (FL) is an emerging distributed machine learning paradigm that aims to realize model training without gathering the data from data sources to a central processing unit. A traditional FL framework consists of a central server as well as a number of computing devices (aka <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">workers</i> ). Training a model under the FL framework usually consumes a massive amount of communication resources because the server and devices should frequently communicate with each other. To alleviate the communication burden, we, in this paper, propose to adaptively sparsify the gradient vector which is transmitted to the server by each device, thus significantly reducing the amount of information that need to be sent to the central server. The proposed algorithm is built on the sparsified SAGA, a well-known variance-reduced stochastic algorithm. For the proposed algorithm, after the gradient vector is sparsified using conventional sparsification operators, an adaptive sparsification step is further added to identify the most informative elements in the sparsified gradient vector. Convergence analysis indicates that the proposed algorithm enjoys a linear convergence rate. Numerical results show that the adaptive sparsification mechanism can substantially improve the communication efficiency. Specifically, to achieve the same classification accuracy, the proposed method can reduce the communication overhead by at least 60% as compared with existing state-of-the-art sparsification-based methods.
Hardware–Software Co-Design for Real-Time Latency–Accuracy Navigation in Tiny Machine Learning Applications

Tiny machine learning (TinyML) applications increasingly operate in dynamically changing deployment scenarios, requiring optimization for both accuracy and latency. Existing methods mainly target a single point in the accuracy/latency tradeoff space, which is insufficient as no single static point can be optimal under variable conditions. We draw on a recently proposed weight-shared SuperNet mechanism to enable serving a stream of queries that activates different SubNets within a SuperNet. This creates an opportunity to exploit the inherent temporal locality of different queries that use the same SuperNet. We propose a hardware–software co-design called SUSHI that introduces a novel SubGraph Stationary optimization. SUSHI consists of a novel field-programmable gate array implementation and a software scheduler that controls which SubNets to serve and which SubGraph to cache in real time. SUSHI yields up to a 32% improvement in latency, 0.98% increase in served accuracy, and achieves up to 78.7% off-chip energy saved across several neural network architectures.
An Easy Data Augmentation Approach for Application Reviews Event Inference

Application review event inference aims to assess the effectiveness of application problems in response to user actions, which enables application developers to promptly discover and address potential issues in various applications, thereby improving their development and maintenance efficiency. Despite the development of event inference models for app reviews, which extract them as user action and app problem events and establish a relationship model between events and inference labels, the accuracy of these models is constrained due to limitations in labeling and characterizing noise and the lack of robustness and generalization. To address this challenge, we propose a model called Easy Data Augmentation for Application Reviews Event Inference (short for EDA-AREI), which comprises a denoising component, data augmentation component, and event inference prediction component. Specifically, the denoising component identifies labels and characterizes noisy data to enhance dataset quality, the data augmentation component replaces non-stop words with synonyms to increase textual diversity, and the event inference and prediction component reconstructs the classifier using denoised and augmented data. Experimental results on six datasets of one-star app reviews in the Apple App Store demonstrate that the EDA-AREI method achieves an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accuracy</i> of 71.19%, 79.14%, 69.05%, 69.02%, 68.24% and 68.48%, respectively, representing an improvement of 0.83%–2.09% compared to state-of-the-art models. Regarding the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1-score</i> , EDA-AREI achieves values of 71.30%, 69.93%, and 68.76% on the threshold_0.5, k-means_2, and random datasets, respectively, outperforming state-of-the-art models by 1.89%–4.02%. Furthermore, EDA-AREI achieves <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AUC</i> values of 75.66% and 73.37% on the threshold_0.5 and k-means_2 datasets, respectively. As a result, EDA-AREI demonstrates substantial improvements in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accuracy</i> , as well as enhanced <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1-score</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AUC</i> across most datasets, thereby enhancing the model's accuracy and robustness in identifying related action-problem pairs.
Quality Anomaly Detection Using Predictive Techniques: An Extensive Big Data Quality Framework for Reliable Data Analysis

The increasing reliance on Big Data analytics has highlighted the critical role of data quality in ensuring accurate and reliable results. Consequently, organizations aiming to leverage the power of Big Data recognize the crucial role of data quality as an integral component. One notable type of data quality anomaly observed in big datasets is the presence of outlier values. Detecting and addressing these outliers have become a subject of interest across diverse domains, leading to the development of numerous anomaly detection approaches. Although anomaly detection has witnessed a proliferation of practices in recent years, a significant gap remains in addressing anomalies related to the other aspects of data quality. Indeed, while most approaches focus on identifying anomalies that deviate from the expected patterns, they do not consider irregularities in data quality, such as missing, incorrect, or inconsistent data. Moreover, most of approaches are domain-correlated and lack the capability to detect anomalies in a generic manner. Thus, we aim through this paper to address this gap in the field and provide a holistic and effective solution for Big Data quality anomaly detection. To achieve this, we suggest a novel approach that allows a comprehensive detection of Big Data quality anomalies related to six quality dimensions: Accuracy, Consistency, Completeness, Conformity, Uniqueness, and Readability. Moreover, the framework allows for sophisticated detection of generic data quality anomalies through the implementation of an intelligent anomaly detection model without any correlation to a specific field. Furthermore, we introduce and measure a new metric called “Quality Anomaly Score,” which refers to the degree of anomalousness of the quality anomalies of each quality dimension and the entire dataset. Through the implementation and evaluation of our framework, the suggested framework has achieved an accuracy score of up to 99.91% and an F-score of 98.07%.
Anomaly Detection in Multi-Seasonal Time Series Data

Most of today’s time series data contain anomalies and multiple seasonalities, and accurate anomaly detection in these data is critical to almost any type of business. However, most mainstream forecasting models used for anomaly detection can only incorporate one or no seasonal component into their forecasts and cannot capture every known seasonal pattern in time series data. In this paper, we propose a new multi-seasonal forecasting model for anomaly detection in time series data that extends the popular Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Our model, named multi-SARIMA, utilizes a time series dataset’s multiple pre-determined seasonal trends to increase anomaly detection accuracy even more than the original SARIMA model. Our experimental results demonstrate the higher accuracy of multi-SARIMA when multiple seasonalities are present than most models with one or no seasonal component, although with more processing time.
Resume Recommendation using RNN Classification and Cosine Similarity

One way to save time and resources in the human recruitment and hiring process is to post open job positions on the Internet, but the overload of applications creates challenges for hiring managers and companies to select an adequate candidate. One of the solutions is to apply intelligent tools such as Deep Learning and recommender system algorithms to speed up the hiring process and identify the right candidates. In this paper, we propose a two-fold algorithmic approach to 1) building an RNN classifier for resume classification; 2) using cosine similarity for resume recommendation to find a candidate that fits job requirements best after selecting all the resumes that belong to the right category. The performance of the proposed RNN classifier is evaluated in terms of accuracy, precision, recall, F1-score, and confusion matrix criteria. The experiment results have shown that the RNN classifier performs better than the other classifiers such as GNB, Linear SVM, RF, and BERT on the same dataset.
Automated Image Captioning Using Sparrow Search Algorithm With Improved Deep Learning Model

Image captioning is a deep learning technique that intends to create and generate textual descriptions or captions for images. It integrates computer vision and natural language processing (NLP) to comprehend the visual content of an image and generate human-like descriptions. Deep learning (DL) based image captioning models can be trained on large-scale datasets, allowing them to generalize various types of images and generate captions that apply to a wide range of visual scenarios. By combining computer vision and natural language processing, DL-enabled image captioning models can understand both visual and textual information, which enables them to generate captions that not only describe the visual content but also incorporate contextual and semantic information. This study develops an Automated Image Captioning using Sparrow Search Algorithm with Improved Deep Learning (AIC-SSAIDL) technique. The major intention of the AIC-SSAIDL technique lies in the automated generation of textual captions for the input images. To accomplish this, the AIC-SSAIDL technique utilizes the MobileNetv2 model to generate feature descriptors of the input images and its hyperparameter tuning process takes place using SSA. For the image captioning process, the AIC-SSAIDL technique utilizes an attention mechanism with long short-term memory (AM-LSTM) network. Finally, the hyperparameter selection of the AM-LSTM model is performed by the fruit fly optimization (FFO) algorithm. A wide range of experiments has been conducted on benchmark data to depict the better performance of the AIC-SSAIDL method. The comprehensive result analysis highlighted the enhanced captioning results of the AIC-SSAIDL method with maximum CIDEr of 46.12, 61.89, and 137.45 on Flickr8k, Flickr30k, and MSCOCO datasets, respectively.
Characterizing Mobile Money Phishing Using Reinforcement Learning

Mobile money helps people accumulate, send and receive money using their mobile phones without having a bank account (i.e., in some African countries). Such technology is heavily and efficiently used in many areas where bank services are unavailable and/or in crisis (i.e., during the Covid-19 pandemic) when transportation and services are limited. However, malicious users such as scammers have leveraged social engineering techniques to abuse mobile money services through scams, and frauds, among others. Existing countermeasures, which are specific to mobile money security, mostly ignore the dynamic aspect of interactions between the malicious party and the victim. Considering the above insufficiency, this paper proposes a new approach to characterize mobile money phishing attacks based on reinforcement learning (RL) through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning and Markov decision processes (MDP) and on deep reinforcement learning (DRL) through DRL algorithms, namely, Deep sarsa, Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), and Deep Q-learning (DQN). In fact, the proposed approach models the optimal sequences of attacker actions to achieve their goals through reinforcement learning and deep reinforcement methods. We experiment on real attack scenarios that have been encountered at Orange and MTN telecoms. Furthermore, we compared reinforcement learning and deep reinforcement learning algorithms to each other and thereby demonstrated the difference between them. This analysis showed a better performance in learning with RL. We also deduced that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> - learning takes less execution time than CDM and therefore its learning quality is better for characterizing mobile money phishing attacks. Finally, we found that some deep reinforcement algorithms, such as Deep Sarsa and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A2C</i> , can improve the characterization of scammer-victim interactions during mobile payments.
Comparative Analysis of Data Synthesis Methods for Prognostic Models Development in Cardiology

In recent years, machine learning (ML) methods have been widely used in various subject areas. Depending on which area we interfere, the cost of collecting big data and preparing it for further analysis can vary greatly. Medicine is one of those costly areas for data collection, which is often represented by unbalanced classes, combined with their total number limitation. The model accuracy largely depends on the initial data amount, which was used for the model training. The final predictive value of the model could be worsening in case of datasets imbalance and insufficient volume of the minority class. Oversampling methods are used to solve this problem, with the leading role of the SMOTE algorithm and its varieties. Previous researches have shown that oversampling algorithms have varying efficiency degrees, depending on the applying ML method. This paper represents the results of hypothesis testing about possibility of synthesized data usage in term of predicting the atrial fibrillation development and in-hospital mortality for patients with coronary heart disease after coronary artery bypass grafting. For model’s development the following ML methods were used: multivariate logistic regression, stochastic gradient boosting and random forest. Data generation was performed by several varieties of the SMOTE algorithm. The analysis has shown that their usage for dataset extension in order to predict fatal and non-fatal cardiovascular events does not guarantee forecast quality improvement and in most cases leads to retraining of the models.
Engineering Management Humanistic Theory

Engineering comes along with the origin of humankind. At the same time, it develops along with the evolution of humankind. Whether to meet people’s material needs or spiritual sustenance, any project is dominated and built by people. Engineering activities should not only create artificial nature, nor be limited to the satisfaction of functions, but also become a creative practice for human beings to take care of their survival and design their future, and at the same time, shape and enhance human nature. Therefore, the entire process of engineering activities is technology integration and cultural activities. The artificial nature as a product of engineering necessarily embodies a strong humanistic connotation, carrying culture and recording history.
Creating of a General Purpose Language for the Construction of Dynamic Reports

In digital environment, generating reports is an integral part of any business, and therefore there is a huge need for a tool for creating complex adaptive reports in investment, marketing activities, financial projects etc. The purpose of the paper is to formulate the requirements for GPL for the creation of custom reports in different business areas and build a software product that will use it. We have researched the possibilities of creating a universal language for building complex reports that is flexible enough to be used in any business domain. We have also identified the main requirements for such a language and the software product that would utilize it. We have paid particular attention to the peculiarities and problems associated with the creation and use of such a language, and have proposed ways to address them. As an experiment, we have created a prototype software module using a language based on mathematical formulas. The developed module can be used both for reports and for any calculations of companies engaged in product and business analytics.
A Motion Refinement Network With Local Compensation for Video Frame Interpolation

Video frame interpolation (VFI) is a challenging yet promising task that involves synthesizing intermediate frames from two given frames. State-of-the-art approaches have made significant progress by directly synthesizing images using forward optical flow. However, these methods often encounter issues such as occlusion and pixel blurring when handling large motion scenes. To address these challenges, this paper proposes a novel approach based on the concept of local compensation. By adopting this approach, more refined optical flow estimation can be obtained, leading to higher-quality video frame interpolation results. Specifically, we introduce two modules, namely the Comprehensive Contextual Feature Extraction (CCFE) module and Motion-Guided Feature Fusion (MGFF) module, to enable local compensation of optical flow estimation. The CCFE module is designed to be embedded in each layer of the image pyramid structure. It aims to encourage the model to extract clean and sufficiently rich contextual information from the input images. On the other hand, the MGFF can guide the multi-source features fusion based on motion features, making the feature fusion of moving objects more precise, thus providing local compensation for optical flow estimation. Extensive experimental results demonstrate that incorporating our proposed modules into the baseline network significantly enhances the performance of video frame interpolation.