Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Apache Kafka – A Modern Platform for Real Time Data Streaming

Current generation businesses become more demanding on timely availability of data. Many real-time data streaming tools and technologies are capable to meet business expectations. Apache Kafka is one of the capable open-source distributed scalable technology that enables real-time data streaming with good throughput and latency. In traditional batch processing, data is getting processed in groups or batches but in streaming services, data records are handled separately and there is a flow of data processing that is continuous and real-time. Once Data is available at the source, Kafka can detect and stream it in real-time to the target application. After doing the literature survey it was observed that there are insufficient experiments have been done till now with a variety of volumes and with different values of the number of partitions and polling intervals. The purpose of this study is to elaborate on Apache Kafka implementation and evaluate its performance. This study will analyse key performance indicators for the streaming platform and will provide useful insights from it. These insights will help to design optimized applications in Apache Kafka. Based on gaps identified after the literature survey, multiple experiments have been conducted for the producer and consumer API (Application Programming interface). Configuration of Kafka with Apache Zookeeper helped to drive the results which are captured in tabular form for different values of polling intervals, volumes, and partitions. Data for all test runs have been analysed further to drive the conclusions as mentioned in the results section. This study provides valuable insights about the utilization of CPU (Central Processing Unit) and memory for Apache Kafka streaming on changing volumes, also elaborates the impacts on streaming performance when key configurations are getting changed.
Insurtech Fostering Automated Insurance Process using Deep Learning Approach

The COVID-19 pandemic comes up with an opportunity for many industries to remake themselves strategically financially and operationally as per the changing needs of the markets. Application of various technologies is helping industries in moving forward and embrace the fifth Industrial Revolution. Technologies like big data analysis, artificial intelligence, fiber optics, image recognition, drones, are playing an important in the growth of industrial &#x0026; service sectors. Technology has transformed every industry of the countries and needless to say, the Insurance industry is no exception here. The term &#x201C;Insurtech&#x201D; has been around since at least 2010, but its reach and influence have accelerated in the past few years and it will continue to with the advancement in big data, AI, IoT, wearable devices and nanotechnologies. In most basic term, Insurtech is applications of various technologies in the insurance industry. From both product and process perspectives, Insurtech is transforming the insurance industry. It is helping the Insurance Industry to become more customer-centric and create operational excellence. Now with Insurtech applications great results were seen in fraud detection rates, effective access to insurance services, low-cost to the customers and the company, increase in operational efficiencies and enhancement in customer experience. This paper discusses about digital growth and development in insurance sector and the ways the Insurtech is transforming the Insurance industry to a greater extent. Also focus of paper is on how insurtech is fostering the automation of insurance process using deep learning and other technologies.
Handling information loss of graph convolutional networks in collaborative filtering

Collaborative filtering (CF) methods based on graph convolutional network (GCN) and autoencoder (AE) achieve outstanding performance. But the GCN-based CF methods suffer from information loss problems, which are caused by information lossy initialization and using low-order Chebyshev Polynomial to fit the graph convolution kernel. And the AE-based CF methods obtain the prediction results by reconstructing the user-item interaction matrix, which does not conduct deep excavation of the behavior patterns, resulting in the limited-expression ability. To solve the above problems, we propose Variational AutoEncoder-Enhanced Graph Convolutional Network (VE-GCN) for CF. Specifically, we use a variational autoencoder (VAE) to compress the interactive behavior patterns as the prior information of GCN to achieve sufficient learning, thus alleviating the information lossy initialization problem. And then the generalized graph Laplacian convolution kernel is proposed in GCN to handle the high-frequency information loss problem caused by Chebyshev Polynomial fitting in the GCN-based CF. To the best of our knowledge, VE-GCN is a feasible method to handle the information loss problems mentioned above in GCN-based CF for the first time. Meanwhile, the structure of GCN is optimized by removing redundant feature transformation and nonlinear activation function, and using DenseGCN to complete multi-level information interaction. Experiments on four real-world datasets show that the VE-GCN achieves state-of-the-art performance.
Real-Time Learning from an Expert in Deep Recommendation Systems with Application to mHealth for Physical Exercises

Recommendation systems play an important role in today's digital world. They have found applications in various areas such as music platforms, e.g., Spotify, and movie streaming services, e.g., Netflix. Less research effort has been devoted to physical exercise recommendation systems. Sedentary lifestyles have become the major driver of several diseases as well as healthcare costs. In this paper, we develop a recommendation system to recommend daily exercise activities to users based on their history, profiles and similar users. The developed recommendation system uses a deep recurrent neural network with user-profile attention and temporal attention mechanisms. Moreover, exercise recommendation systems are significantly different from streaming recommendation systems in that we are not able to collect click feedback from the participants in exercise recommendation systems. Thus, we propose a real-time, expert-in-the-loop active learning procedure. The active learner calculates the uncertainty of the recommendation system at each time step for each user and asks an expert for recommendation when the certainty is low. In this paper, we derive the probability distribution function of marginal distance, and use it to determine when to ask experts for feedback. Our experimental results on a mHealth and MovieLens datasets show improved accuracy after incorporating the real-time active learner with the recommendation system.
A novel insulator defect detection scheme based on Deep Convolutional Auto‐Encoder for small negative samples

This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto-Encoder (DCAE) for small negative samples. The proposed DCAE scheme combines the advantages of supervised learning and unsupervised learning. In order to reduce the high cost of training Deep Neural Networks, this paper pre-trained the Convolutional Neural Networks (CNN) through open labelled datasets. Through transferring learning, the encoder part of the traditional Convolutional Auto-Encoder was replaced by the first three layers of the CNN, and a small number of defect samples were used to fine-tune the parameters. A threshold discrimination scheme was designed to evaluate the model detection, realising the self-explosion detection of insulator by judging the residual result and abnormal score. The experimental results show that compared with the existing insulator self-explosion detection schemes, the proposed scheme can reduce the model training time by up to 40%, and the recognition accuracy can reach 97%. Moreover, this model does not need a large number of insulator labelled data and is especially suitable for small negative sample application.
Significance of activation functions in developing an online classifier for semiconductor defect detection

In anomaly detection problems for advanced semiconductor devices, non-visual defects occur frequently. Machine learning (ML) algorithms have the advantage of identifying such defects. However, in this real-world problem, data comes sequentially in a streaming fashion, thus, we may not have sufficient data to train an ML model in batch mode. In such a scenario, online ML models are useful to detect defects immediately since they work in a single-pass mode. Besides, when data is collected from more realistic non-stationary monitoring environments, online ML models with evolving architecture are more practical. Thus, evolving and online ML models are developed in this work to detect defects in technology computer-aided design (TCAD)-based digital twin model of advanced nano-scaled semiconductor devices such as a fin field-effect transistor (FinFET) and a gate-all-around field-effect transistor (GAA-FET). Activation functions (AFs) in deep neural networks (DNNs) and membership functions (MFs) in neuro-fuzzy systems (NFSs) play an important role in the performance of those ML models. This work focuses on analyzing the effects of various AFs/MFs in our developed online ML models while detecting defects in real-world nano-scaled semiconductor devices, where significant training samples are not available. From various semiconductor datasets having fewer samples, it has been observed that the proposed evolving neuro-fuzzy system (ENFS) with Leaky-ReLU MF performs better (improvement in the range of 1.9% to 30.8% considering overall classification accuracy) than the other DNN or ENFS-based online ML models. Having an evolving architecture and online learning mechanism, besides anomaly detection, the proposed model’s performance has also been evaluated for handling large data streams problems with concept drift. The performance of the proposed method has been compared with some recently developed baselines under the prequential test-then-train protocol. The classification rates of the proposed method has an improvement in the range of 1.1% to 65.9% than the existing methods. The code of this work has been made publicly available at https://github.com/MdFerdaus/LREC.
NEKO: Proposal of the first super-agile methodology to improve work efficiency

Agile methodologies are the preferred strategy for work teams around the world thanks to their rapid response to changes and adaptability. However, this prioritization of efficiency and speed has neglected the well-being of the most important element of any project: the teams. This article presents NEKO as a super-agile methodology focused on people and that aims to prioritize the mental health of workers without neglecting effectiveness, relying on new technologies such as Artificial Intelligence to enhance its processes. During the research stage, using the methodology proposed by Kitchenham, fifteen articles were analyzed and allowed us to rescue the strengths and identify the problems presented by the agile methodologies most used today. Then, the proposal was developed dividing it into three phases; each including events, artifacts, and tools to achieve your goal. Later, during the discussion, the proposal was contrasted with the methodologies initially analyzed and it was concluded that taking the points in favor of these and adding state-of-the-art technology focused on the well-being of the teams is synonymous with efficiency, meaning a possible change in the paradigm of the current world of work.
360 Depth Estimation in the Wild - the Depth360 Dataset and the SegFuse Network

Single-view depth estimation from omnidirectional images has gained popularity with its wide range of applications such as autonomous driving and scene reconstruction. Although data-driven learning-based methods demonstrate significant potential in this field, scarce training data and ineffective 360 estimation algorithms are still two key limitations hindering accurate estimation across diverse domains. In this work, we first establish a large-scale dataset with varied settings called Depth360 to tackle the training data problem. This is achieved by exploring the use of a plenteous source of data, 360 videos from the internet, using a test-time training method that leverages unique information in each omnidirectional sequence. With novel geometric and temporal constraints, our method generates consistent and convincing depth samples to facilitate single-view estimation. We then propose an end-to-end two-branch multi-task learning network, SegFuse, that mimics the human eye to effectively learn from the dataset and estimate high-quality depth maps from diverse monocular RGB images. With a peripheral branch that uses equirectangular projection for depth estimation and a foveal branch that uses cubemap projection for semantic segmentation, our method predicts consistent global depth while maintaining sharp details at local regions. Experimental results show favorable performance against the state-of-the-art methods.
Association Rule Mining Algorithm in College Students’ Quality Evaluation System

An association rule mining algorithm is an algorithm that mines the association between things and is often used to mine the association knowledge between things. Association rule mining algorithms can find potential connections between different qualities of college students from the data of college students’ life and learning, which can help teachers discover the problems and their own strengths of different students and achieve teaching according to their aptitude. The purpose of this paper is to solve some problems related to the associative rule extraction algorithm and to investigate the impact of applying the associative rule extraction algorithm in a college student quality assessment system. Based on the algorithm, a quality assessment system for college students has been developed. A modified script-based associative rule extraction algorithm is used to find the correlation between the quality and the ability of college students. The quality assessment data of college students are analyzed and studied. The results show that the use of associative rule extraction algorithms to assess the quality and ability of college students can improve the efficiency of the test by 24% and the accuracy of the test score by 33% and reduce the probability of outliers in the scoring process by 27%. It can be seen that the association rule extraction algorithm can be applied to college students’ quality assessment system and also reduces the probability of encountering obstacles in accuracy and performance assessment. At the same time, this experiment also proves the robustness and feasibility of the algorithm in this paper.
Chaotic Fruit Fly Algorithm for Solving Engineering Design Problems

The aim of this article is to present a chaotic fruit fly algorithm (CFFA) as an optimization approach for solving engineering design problems (EDPs). In CFFA, the fruit fly algorithm (FFA), which is recognized for its durability and efficiency in addressing optimization problems, was paired with the chaotic local search (CLS) method, which allows for local exploitation. CFFA will be set up to work in two phases: in the first, FFA will be used to discover an approximate solution, and in the second, chaotic local search (CLS) will be used to locate the optimal solution. As a result, CFFA can address difficulties associated with the basic FFA such as falling into local optima, an imbalance between exploitation and exploration, and a lack of optimum solution acquisition (i.e., overcoming the drawback of premature convergence and increasing the local exploitation capability). The chaotic logistic map is employed in the CLS because it has been demonstrated to be effective in improving the quality of solutions and giving the best performance by many studies. The proposed algorithm is tested by the set of CEC’2005 special sessions on real parameter optimization and many EDPs from the most recent test suite CEC’2020. The results have demonstrated the superiority of the proposed approach to finding the global optimal solution. Finally, CFFA′s results were compared to those of earlier research, and statistical analysis using Friedman and Wilcoxon's tests revealed its superiority and capacity to tackle this type of problem.
From Novelty Detection to a Genetic Algorithm Optimized Classification for the Diagnosis of a SCADA-Equipped Complex Machine

In the field of Diagnostics, the fundamental task of detecting damage is basically a binary classification problem, which is addressed in many cases via Novelty Detection (ND): an observation is classified as novel if it differs significantly from reference, healthy data. ND is practically implemented summarizing a multivariate dataset with univariate distance information called Novelty Index. As many different approaches are possible to produce NIs, in this analysis, the possibility of implementing a simple classifier in a reduced-dimensionality space of NIs is studied. In addition to a simple decision-tree-like classification method, the process for obtaining the NIs can result as a dimension reduction method and, in turn, the NIs can be used for other classification algorithms. In addition, a case study will be analyzed thanks to the data published by the Prognostics and Health Management Europe (PHME) society, on the occasion of the Data Challenge 2021.
Moving Object Tracking Based on Sparse Optical Flow with Moving Window and Target Estimator

Moving object detection and tracking are technologies applied to wide research fields including traffic monitoring and recognition of workers in surrounding heavy equipment environments. However, the conventional moving object detection methods have faced many problems such as much computing time, image noises, and disappearance of targets due to obstacles. In this paper, we introduce a new moving object detection and tracking algorithm based on the sparse optical flow for reducing computing time, removing noises and estimating the target efficiently. The developed algorithm maintains a variety of corner features with refreshed corner features, and the moving window detector is proposed to determine the feature points for tracking, based on the location history of the points. The performance of detecting moving objects is greatly improved through the moving window detector and the continuous target estimation. The memory-based estimator provides the capability to recall the location of corner features for a period of time, and it has an effect of tracking targets obscured by obstacles. The suggested approach was applied to real environments including various illumination (indoor and outdoor) conditions, a number of moving objects and obstacles, and the performance was evaluated on an embedded board (Raspberry pi4). The experimental results show that the proposed method maintains a high FPS (frame per seconds) and improves the accuracy performance, compared with the conventional optical flow methods and vision approaches such as Haar-like and Hog methods.
Improving Semantic Dependency Parsing with Higher-Order Information Encoded by Graph Neural Networks

Higher-order information brings significant accuracy gains in semantic dependency parsing. However, modeling higher-order information is non-trivial. Graph neural networks (GNNs) have been demonstrated to be an effective tool for encoding higher-order information in many graph learning tasks. Inspired by the success of GNNs, we investigate improving semantic dependency parsing with higher-order information encoded by multi-layer GNNs. Experiments are conducted on the SemEval 2015 Task 18 dataset in three languages (Chinese, English, and Czech). Compared to the previous state-of-the-art parser, our parser yields 0.3% and 2.2% improvement in average labeled F1-score on English in-domain (ID) and out-of-domain (OOD) test sets, 2.6% improvement on Chinese ID test set, and 2.0% and 1.8% improvement on Czech ID and OOD test sets. Experimental results show that our parser outperforms the previous best one on the SemEval 2015 Task 18 dataset in three languages. The outstanding performance of our parser demonstrates that the higher-order information encoded by GNNs is exceedingly beneficial for improving SDP.
HYPER2: Hyperbolic embedding for hyper-relational link prediction

Knowledge graphs (KGs) Embedding has been broadly studied in recent years. However, less light is shed on the ubiquitous hyper-relational KGs. Most existing hyper-relational KG embedding methods decompose n-ary facts into smaller tuples, undermining the structure of n-ary facts. Moreover, these models always suffer from low expressiveness and high complexity. In this work, to tackle the indecomposability issue, we represent n-ary fact as a hyperedge, keeping the integrity of fact and maintaining the vital role that primary triple plays. To address the expressiveness and complexity issue, we propose HYPER2 where we generalize hyperbolic Poincaré embedding from binary to arbitrary arity data, and we design an information aggregation module to capture the interaction between entities within and beyond triple. Extensive experiments demonstrate HYPER2 is superior to its translational and deep analogues, improving MRR and other metrics by a large margin with relatively few dimensions. Moreover, we study the side effect of literals, and we theoretically and experimentally compare the computational complexity of HYPER2 against several best-performing baselines. HYPER2 is much quicker than its counterparts.
A unified hierarchical attention framework for sequential recommendation by fusing long and short-term preferences

Sequential recommendation becomes a critical task in many application scenarios, since people’s online activities are increasing. In order to predict the next item that users may be interested, it is necessary to take both general and dynamic preferences of the user into account. Existing approaches typically integrate the user–item or item–item feature interactions directly without considering the dynamic changes of the user’s long-term and short-term preferences, which also limits the capability of the model. To address these issues, we propose a novel unified framework for sequential recommendation task, modeling users’ long and short-term sequential behaviors at each time step and capturing item-to-item dependencies in higher-order by hierarchical attention mechanism. The proposed model considers the dynamic long and short-term user preferences simultaneously, and a joint learning mechanism is introduced to fuse them for better recommendation. We extensively evaluate our model with several state-of-the-art methods by different validation metrics on three real-world datasets. The experimental results demonstrate the significant improvement of our approach over other compared models. • A novel unified framework for sequential recommendation is proposed. • Multi-head self-attention captures both the general and dynamic user preferences. • Hierarchical attention obtains the item–item interaction features in higher order. • A joint learning mechanism fuses the long and short-term features effectively. • The proposed model achieves significant improvement over other related method.