Reviewerly/exHarmony huggingface dataset:
Integrating principal component analysis and U-statistics for mapping polluted areas in mining districts

Detecting metal contaminations and mapping the areas affected by pollutants surrounding the mining activities are essential issues in environmental geochemistry. Toxic metals can be broadly distributed around the mines owing to the effects of dust, water, and diverse anthropogenic activities. In this study, the effects of metal pollution from mining activities in Irankuh Pb-Zn mine, located in central Iran, on the surrounding environment have been investigated. Toxic metals, especially lead and zinc, dispersed in the agricultural area in the south of the Irankuh mine have been deemed environmental stressors. This study introduces a new combined methodology based on principal component analysis (PCA) and U-statistics modeling to recognize the zones afflicted by toxic metal pollution. The stepwise principal component analysis (SPCA) is a multi-stage dimension reduction technique applied to extract significant pollution sources of toxic metals around agricultural areas. After five stages of SPCA, the Pollution Principal Factor (PPF) was determined based on the rotated component matrix, and the toxic metals related to the pollution area were identified. For mapping the multivariate metal contamination, the U-statistics method was applied to the PPF for identification of toxic metal anomalies. The concentration-area fractal method was also utilized for separating and mapping different populations of Pb, Zn, As, Mn, Ba, and Cd. To apply this method, ordinary Kriging was used to grid and interpolate raw data. The U-values of PPF in the proposed method were delineated and compared to the results of the fractal method. The proposed approach displays more accurate results for pollution mapping. The methodology adopted here can be applied to other case studies for the source apportionment of toxic metals.
Solar Photovoltaic Power Prediction Using Big Data Tools

Solar photovoltaic (PV) installation has been continually growing to be utilized in a grid-connected or stand-alone network. However, since the generation of solar PV power is highly variable because of different factors, its accurate forecasting is critical for a reliable integration to the grid and for supplying the load in a stand-alone network. This paper presents a prediction model for calculating solar PV power based on historical data, such as solar PV data, solar irradiance, and weather data, which are stored, managed, and processed using big data tools. The considered variables in calculating the solar PV power include solar irradiance, efficiency of the PV system, and characteristics of the PV system. The solar PV power profiles for each day of January, which is a summer season, were presented to show the variability of the solar PV power in numerical examples. The simulation results show relatively accurate forecasting with 17.57 kW and 2.80% as the best root mean square error and mean relative error, respectively. Thus, the proposed solar PV power prediction model can help power system engineers in generation planning for a grid-connected or stand-alone solar PV system.
Personalized, Sequential, Attentive, Metric-Aware Product Search

The task of personalized product search aims at retrieving a ranked list of products given a user’s input query and his/her purchase history. To address this task, we propose the PSAM model, a Personalized, Sequential, Attentive and Metric-aware (PSAM) model, that learns the semantic representations of three different categories of entities, i.e., users, queries, and products, based on user sequential purchase historical data and the corresponding sequential queries. Specifically, a query-based attentive LSTM (QA-LSTM) model and an attention mechanism are designed to infer users dynamic embeddings, which is able to capture their short-term and long-term preferences. To obtain more fine-grained embeddings of the three categories of entities, a metric-aware objective is deployed in our model to force the inferred embeddings subject to the triangle inequality, which is a more realistic distance measurement for product search. Experiments conducted on four benchmark datasets show that our PSAM model significantly outperforms the state-of-the-art product search baselines in terms of effectiveness by up to 50.9% improvement under NDCG@20. Our visualization experiments further illustrate that the learned product embeddings are able to distinguish different types of products.
Model-Induced Generalization Error Bound for Information-Theoretic Representation Learning in Source-Data-Free Unsupervised Domain Adaptation

Many unsupervised domain adaptation (UDA) methods have been developed and have achieved promising results in various pattern recognition tasks. However, most existing methods assume that raw source data are available in the target domain when transferring knowledge from the source to the target domain. Due to the emerging regulations on data privacy, the availability of source data cannot be guaranteed when applying UDA methods in a new domain. The lack of source data makes UDA more challenging, and most existing methods are no longer applicable. To handle this issue, this paper analyzes the cross-domain representations in source-data-free unsupervised domain adaptation (SF-UDA). A new theorem is derived to bound the target-domain prediction error using the trained source model instead of the source data. On the basis of the proposed theorem, information bottleneck theory is introduced to minimize the generalization upper bound of the target-domain prediction error, thereby achieving domain adaptation. The minimization is implemented in a variational inference framework using a newly developed latent alignment variational autoencoder (LA-VAE). The experimental results show good performance of the proposed method in several cross-dataset classification tasks without using source data. Ablation studies and feature visualization also validate the effectiveness of our method in SF-UDA.
Building a Chatbot for Supporting the Admission of Universities

The admission process of universities in Vietnam is a labor-expensive task due to the involvement of humans. This paper introduces an intelligent system (a chatbot) that can support the admission process by automatically answering questions. Different from prior work that usually builds the bot from scratch, we develop the bot by using the Rasa platform. To do that, we investigate different combinations of components of natural language understanding to find the best pipeline. We also create and release a dataset in the admission domain to train the bot. Experimental results show that the pipeline using DIET with features from pre-trained language models is competitive. The introduction video of the system is also available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://youtu.be/gw7wvlADxnE
A Lightweight Underwater Object Detection Model: FL-YOLOV3-TINY

Due to the variety of underwater object species and small object, the traditional object detection model is difficult to adapt to underwater object detection in accuracy and real-time. In this paper, a lightweight detection model FL-YOLOV3-TINY is proposed, which improves the detection accuracy and real-time performance while shrinking the model size. In FL-YOLOV3-TINY, first, the model reduces the number of parameters by introducing deep separable convolutional module to replace traditional convolutional feature extraction module. Secondly, in order to improve the detection ability of small objects and obtain more delicate image features, FL-YOLOV3-TINY adds the feature size to the three-scale to improve the detection performance. Finally, the CIoU loss regression function is introduced to make the prediction box closer to the actual box. Experiments show that compared with other lightweight models YOLOV3-MobilenetV1 and YOLOV3-Tiny, FL-YOLOV3-TINY has better mAP performance (13.7% and 10.9% increase, respectively) and better real-time perfurmance(6% and 29% increase in FPS, respectively). Meanwhile, the model size is reduced by 43% compared to YOLOV3-Tiny.
Analyzing Competences in Software Testing: Combining Thematic Analysis with Natural Language Processing (NLP)

This Full Paper (Research) presents an analysis on the competences in software testing for the fresh graduates in computer science. Software Testing education (ST) is receiving increasing attention in literature, recent studies have evaluated instructional methods used in ST education. However, analysis of competences (skills, knowledge, and ability) required in ST education are lacking in literature. Competences play critical roles in curriculum development e.g., they inform the design of student learning outcomes, learning objectives and program outcomes. This full paper in the research category aims to analyze competences in ST education and then examine the gap between these competences and the current ST curriculum. Using natural language processing (NLP) techniques, we collect 2033 job descriptions from three popular job portals (indeed, monster, and career builder) in the USA and Canada. Also, we collected course syllabi from 20 universities offering ST courses and use these to assess the current curriculum in ST. We analyzed the data using thematic analysis and found that the current software testing curricula do not always teach or equip students with some of the soft skills they require to be successful in software testing career. For instance, our result shows that soft skills such as teamwork, communication, leadership, which are often required by software testing employers are not always taught in ST courses.
A hybrid approach based on deep learning and level set formulation for liver segmentation in CT images

Accurate liver segmentation is essential for radiation therapy planning of hepatocellular carcinoma and absorbed dose calculation. However, liver segmentation is a challenging task due to the anatomical variability in both shape and size and the low contrast between liver and its surrounding organs. Thus we propose a convolutional neural network (CNN) for automated liver segmentation. In our method, fractional differential enhancement is firstly applied for preprocessing. Subsequently, an initial liver segmentation is obtained by using a CNN. Finally, accurate liver segmentation is achieved by the evolution of an active contour model. Experimental results show that the proposed method outperforms existing methods. One hundred fifty CT scans are evaluated for the experiment. For liver segmentation, Dice of 95.8%, true positive rate of 95.1%, positive predictive value of 93.2%, and volume difference of 7% are calculated. In addition, the values of these evaluation measures show that the proposed method is able to provide a precise and robust segmentation estimate, which can also assist the manual liver segmentation task.
A Model-Driven Engineering Approach for Monitoring Machine Learning Models

Once a machine learning (ML) model is produced and used for commercial purposes, it is desirable to continuously monitor it for any potential performance degradation. Domain experts in the area of ML, commonly lack the required expertise in the area of software engineering, needed to implement a robust and scalable monitoring solution. This paper presents an approach based on model-driven engineering (MDE) principles, for detecting and responding to events that can affect a ML model’s performance. The proposed solution allows ML experts to schedule the execution of drift detecting algorithms on a computing cluster and receive email notifications of the outcome without requiring extensive software engineering knowledge.
Bias and Fairness in Face Detection

Processing of face images is used in many areas, for example: commercial applications such as video-games; facial biometrics; facial expression recognition, etc. Face detection is a crucial step for any system that processes face images. Therefore, if there is bias or unfairness in this first step, all the processing steps that follow may be compromised. Errors in automatic face detection may be harmful to people as, for instance, in situations where a decision may limit or restrict their freedom to come and go. Therefore, it is crucial to investigate the existence of these errors caused due to bias or unfairness. In this paper, an analysis of five well-known top accuracy face detectors is performed to investigate the presence of bias and unfairness in their results. Some of the metrics used to identify the existence of bias and unfairness involved the verification of demographic parity, verification of existence of false positives and/or false negatives, rate of positive prediction, and verification of equalized odds. Data from about 365 different individuals were randomly selected from the Facebook Casual Conversations Dataset, resulting in approximately 5,500 videos, providing 550,000 frames used for face detection in the performed experiments. The obtained results show that all five face detectors presented a high risk of not detecting faces from the female gender and from people between 46 and 85 years old. Furthermore, the skin tone groups related with dark skin are the groups pointed out with highest risk of faces not being detected for four of the five evaluated face detectors. This paper points out the necessity of the research community to engage in breaking the perpetuation of injustice that may be present in datasets or machine learning models.
Combining Implicit and Explicit Feature Extraction for Eye Tracking: Attention Classification Using a Heterogeneous Input

Statistical measurements of eye movement-specific properties, such as fixations, saccades, blinks, or pupil dilation, are frequently utilized as input features for machine learning algorithms applied to eye tracking recordings. These characteristics are intended to be interpretable aspects of eye gazing behavior. However, prior research has demonstrated that when trained on implicit representations of raw eye tracking data, neural networks outperform these traditional techniques. To leverage the strengths and information of both feature sets, we integrated implicit and explicit eye tracking features in one classification approach in this work. A neural network was adapted to process the heterogeneous input and predict the internally and externally directed attention of 154 participants. We compared the accuracies reached by the implicit and combined features for different window lengths and evaluated the approaches in terms of person- and task-independence. The results indicate that combining implicit and explicit feature extraction techniques for eye tracking data improves classification results for attentional state detection significantly. The attentional state was correctly classified during new tasks with an accuracy better than chance, and person-independent classification even outperformed person-dependently trained classifiers for some settings. For future experiments and applications that require eye tracking data classification, we suggest to consider implicit data representation in addition to interpretable explicit features.
Strategy for dynamic blockchain construction and transmission in novel edge computing networks

The distributed transmission of a dynamic blockchain in a cloud computing platform may cause network overload. To overcome this challenge, a novel idea for a network architecture that is suitable for blockchain transmission and storage is proposed. Emerging edge computing technology has been proven to improve the efficiency of blockchain construction and operation. In this paper, we focus on solving the problem of unbalanced resource allocation for blockchain construction and transmission in a novel edge computing network architecture. We propose the edge bandwidth and storage optimization (EBSO) algorithm to balance effective bandwidth allocation and storage space selection during dynamic blockchain processing. In addition, we build a dynamic blockchain based on a link bandwidth allocation analysis in an edge computing network environment. Subsequently, the reasonable value range of the tradeoff parameter γ between the link bandwidth and storage space during the transmission of the constructed dynamic blockchain is determined. Finally, we use version 2 of the network simulator (ns-2) to simulate the performance of the EBSO algorithm and compare it with other excellent algorithms in terms of transmission bandwidth efficiency (TBE), storage space efficiency (SSE), blockchain construction efficiency (BCE), average throughput, and average delay in the same network environment.
The Single Page Application architecture when developing secure Web services

Abstract The article is devoted to the development of a prototype of a secure single page-application (SPA) web service for automating user information accounting. The relevance of this study is very high due to the use of web services. The article provides a classification of web applications and shows the features of the architecture of a single page application used in the development of the service. Based on a comparative analysis of the architectural styles of the API, the most appropriate style was selected. Considered and taken into account the key points of the development of a secure application programming interface (API), the requirements that must be met by RESTful API services. The work used popular authentication schemes (methods). A comparative characteristic of web frameworks of the Python programming language is given, on the basis of which a tool for implementing a web service is selected. Shows the main advantages of using Python when developing paged web services and the security tools included in the standard package of the Flask web services development framework. Shows how to securely prototype a Python RESTful SPA Web Service API using Flask. An example of using the Swagger tool to describe the specifications of the developed API is given. The process of setting up the application is considered in detail. The main recommendations for securing a web application, setting up a database and a web server are listed. The key points of ensuring the protection of the developed web application are considered. Conclusions are made regarding the choice of the architectural style of the application API, the most suitable tools and technologies for the software implementation of the service.
Multi-membrane search algorithm

This research proposes a new multi-membrane search algorithm (MSA) based on cell biological behavior. Cell secretion protein behavior and cell division and fusion strategy are the main inspirations for the algorithm. In order to verify the performance of the algorithm, we used 19 benchmark functions to compare the MSA test results with MVO, GWO, MFO and ALO. The number of iterations of each algorithm on each benchmark function is 100, the population number is 10, and the running is repeated 50 times, and the average and standard deviation of the results are recorded. Tests show that the MSA is competitive in unimodal benchmark functions and multi-modal benchmark functions, and the results in composite benchmark functions are all superior to MVO, MFO, ALO, and GWO algorithms. This paper also uses MSA to solve two classic engineering problems: welded beam design and pressure vessel design. The result of welded beam design is 1.7252, and the result of pressure vessel design is 5887.7052, which is better than other comparison algorithms. Statistical experiments show that MSA is a high-performance algorithm that is competitive in unimodal and multimodal functions, and its performance in compound functions is significantly better than MVO, MFO, ALO, and GWO algorithms.
Enhanced Collaborative Filtering for Personalized E-Government Recommendation

The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborative filtering (CF) has shown its superiority by characterizing both items and users by the latent features inferred from the user–item interaction matrix. A fundamental challenge is to enhance the expression of the user or/and item embedding latent features from the implicit feedback. This problem negatively affected the performance of the recommendation system in e-government. In this paper, we firstly propose to learn positive items’ latent features by leveraging both the negative item information and the original embedding features. We present the negative items mixed collaborative filtering (NMCF) method to enhance the CF-based recommender system. Such mixing information is beneficial for extending the expressiveness of the latent features. Comprehensive experimentation on a real-world e-government dataset showed that our approach improved the performance significantly compared with the state-of-the-art baseline algorithms.
Incremental Fisher linear discriminant based on data denoising

In this article we consider Incremental Fisher linear discriminant (IFLD) based on data denoising. The data denoising is completed by Markov sampling such that the generated non-noise sample sequence is an uniformly ergodic Markov chain (u.e.M.c.). We first establish the generalization bounds of IFLD with u.e.M.c. samples, and prove that the IFLD algorithm with u.e.M.c. samples is consistent. We also present two new IFLD classification algorithms based on Markov sampling, IFLD based on Markov sampling (IFLD-MS) and improved IFLD based on Markov sampling (IIFLD-MS). Experimental results of benchmark repository suggest that IFLD-MS and IIFLD-MS have better performance than the classical IFLD, the incremental support vector machine (ISVM) and other IFLD algorithms.
An Overview of Multi-sensor Information Fusion

Multi-sensor information fusion technology has been widely used in target recognition, home appliances, robotics, health care, image processing, pattern recognition and other fields. With the development of multi-sensor information fusion technology, the application field of sensors is expanding rapidly. This paper introduces the development history, concept and fusion model of multi-sensor information fusion technology. An overview of multi-sensor information fusion algorithms and their features in recent years is introduced. In this paper, the development trend of multi-sensor information fusion technology and fusion algorithm is also predicted.