Reviewerly/exHarmony huggingface dataset:
Feature Selection Method Based on Hybrid Multi-Strategy Pelican Optimization Algorithm

Pelican Optimization Algorithm is a new stochastic natural heuristic optimization algorithm with good global development capability. However, the pelican algorithm has the problem of low convergence accuracy and easy to falls into local optimization in feature selection. In this paper, a hybrid multi-strategy pelican optimization algorithm (HMS-POA) is proposed. First, in the initialization phase, dynamic reverse learning and tent chaotic mapping are introduced to increase population diversity and avoid falling into local optimum. Secondly, in the first stage, a random movement strategy is selected, and in the second stage, the harris hawk algorithm is introduced. Compared with other optimization algorithms on 13 benchmark functions, the results show that HMS-POA performs better. The data classification research is conducted through 14 groups of classical UCI datasets. The experimental results demonstrate that the algorithm performs better and more competitively in delivering the optimum solution for optimization problems. It can analyze data more precisely to prevent redundant feature interference.
Optimisation of Multi-objective Rolling Stock Maintenance Scheduling with Harris’ Hawk Optimiser

In line with Industry 4.0, various advanced technologies such as sensors, automation, and artificial intelligence (AI) methods have been leveraged to enhance maintenance processes in the rolling stock industry. In particular, AI techniques are useful for optimising maintenance scheduling and planning tasks for rolling stocks. This study focuses on the use of a metaheuristic method, namely an enhanced multi-objective Harris’ Hawk optimiser (MO-HHO), for optimising competing objectives based on data obtained from a railway maintenance company. The results of MO-HHO are evaluated and compared with those from other competing models. The findings demonstrate the usefulness of MO-HHO in tackling multi-objective train maintenance scheduling tasks in practical environments.
A Zoom into Ecuadorian Politics: Manifesto Text Classification using NLP

Political science research on party manifestos helps to understand a candidate’s strategies and proposed actions during electoral campaigns. To achieve this, political scientists classify a manifesto’s sentences and quasi-sentences into seven main domains established by the Comparative Manifesto Project. However, manually coding is a time-consuming and labor-intensive task that can lead to biases. Automatically classifying manifestos has shown to produce good and reproducible annotations. In Ecuador, research on automatic manifesto analysis has been limited. Moreover, there is no large labeled Ecuadorian corpus available for training. Therefore, in this work we develop a Transformer network for automatically analyzing Ecuadorian manifestos using a cross-domain training approach. We implement a fractional factorial experimental design to determine which Transformer model, type of pre-processing operations, and Spanish text data should be used to maximize the accuracy of the classification model. The results show that the DistilBERT architecture trained with Mexico’s and Argentina´s manifestos increase the classification accuracy. Without using an Ecuadorian corpus for training, the implemented DistilBERT achieves a 44% accuracy on the Ecuadorian test set, which has a comparable performance to other models in literature trained with a Spanish corpus.
Design of Sports Equipment and Facilities Maintenance and Management System Based on PSO-SVM and DIALux

With the continuous progress and development of science and technology, people are paying more and more attention to sports. In modern society, sports are a very important component. Sports equipment and facilities are often referred to as hardware resources or software resources, and can also be understood as a technical means or tool. It consists of hardware products such as sports venues and fitness equipment, as well as software environment elements such as computer network systems, forming a physical system. This article mainly focuses on the research of a sports equipment and facility maintenance and management system developed based on PSO-SVM (Particle Swarm optimization Support Vector Machine) and Dialux language platform. The system is mainly composed of hardware layer and network components, utilizing servers, embedded operating systems, and data mining technology to manage these resources. Through the development of a framework, equipment maintenance and monitoring work is achieved, and relevant information is stored using a MySQL database. Afterwards, this article conducted functional testing on the system module, and the test results showed that the system’s availability could reach up to 83%.
IDTracker: Discovering Illicit Website Communities via Third-party Service IDs

Illicit websites are restricted by governments and application marketplaces due to their detrimental impact on society. Third-party web services play a crucial role in enabling illicit webmasters to establish websites rapidly and evade detection. In this paper, we discover that third-party services usually assign unique credentials to website developers as their identifications (IDs). Websites using the same services with identical IDs are likely to be hosted on shared infrastructures and have textually similar domain names. This observation sparks the idea of building a community of illicit websites by leveraging third-party service IDs. Therefore, we design IDTracker, a novel system for detecting illicit website communities based on domain name semantic and infrastructure relationship features, which empower classification algorithms to achieve a high F1 score of 0.8968. Furthermore, we deploy IDTracker on an Internet Service Provider's (ISP) environment for three months and identify 6,830 illicit communities containing 165,378 illicit websites. Many of these illicit websites can not be identified by the most sophisticated engines, such as Symantec and Baidu, because of the cloaking tactics. In addition, we conduct a large-scale and long-term measurement on the network infrastructures and third-party services of illicit communities, revealing new phenomena. Our findings can help security communities to thwart illicit websites more effectively.
A Credit Card Fraud Identification Technique Using Support Vector Machine

Transactions using credit card are increasing rapidly due to the advancements in electronic commerce. The clients as well as merchants are seriously affected by frauds in modern days and that causes high commercial losses. To minimize this loss, financial institution and banks requires a fraud detection system. Random Forests (RF) approach is method to developed for detecting fraud’s in credit card. In this method, training about abnormal and normal transactions is done using CART-based Random Forest (CARTRF) and Random-Tree-Based Random Forest (RTBRF). Complexity of this system made difficulties in the usage. This system requires large computational resources and they are harder and lea intuitive. To improve the accuracy with high true positive rate, Support Vector Machine (SVM) is proposed. From Taiwan and German dataset, default data of credit card clients are collected and preprocessed. Attributes are normalized using min-max normalization. Attribute selection based on Information Gain (IG) is used for reducing feature set. Frequent attribute selection and pruning are performed using Aprori algorithm. Candidate’s itemset size is reduced by this and improved performance gain is produced. Support Vector Machine (SVM) performs detection of frauds in credit card using frequent attribute set. The parameters like recall, precision, detection rate and pruning time are used to compare performance of proposed method against existing methods like CART-based random forest approaches and Random-tree-based random forest approach. Experimental results show better performance of proposed technique.
ResUnet-GAN with Dynamic Memory for Mura Defect Detection

“Mura” is a phenomenon in which panels have uneven display defects, irregular shapes, and different sizes. It is impossible to produce perfect panels on production lines, so panel inspection is necessary to differentiate between “light Mura” and “serious Mura” manually. The performance of conventional defect detection models for Mura detection is worse since they only differentiate between “normal” and “abnormal” samples. To reduce human cost and increase the accuracy of Mura detection, we propose a “ResUnet-GAN with Dynamic Memory Model an unsupervised anomaly detection method based on a Generative Adversarial Network (GAN) with a memory module to distinguish panel defects. In the dynamic memory, we designed a dynamic feature filtering (DFF) method to choose important features of images, enhancing the ability to recognize light Mura features of the ResUnet-GAN. The proposed model can achieve an Area Under Curve (AUC) of approximately 0.8 for accurate Mura detection. The mechanism of this paper is novel, and the result contributes to practical application.
Security Management Strategy of Financial Data Based on Intelligent Assignment of Role Permission

The financial secondary data center needs to fully benefit from the data platform, integrate the management system, and fully integrate the business data, realize the automation and intelligence of financial audit through the deeply integrated data sharing platform, broaden the traditional financial supervision function, and mine and utilize enterprise data. Based on WRPTAC and NCV60 permission model, this paper describes the roles and permissions required by workflow, and constructs the application process structure of this model. Make the data of the system safer, easier to maintain and distribute to all users. The conclusion of this study is to use WRPTAC and NCV60 permission model, and use adaptive function items to deal with the given operation permission of the system, thus creating permission management for our department. Permission management makes the data of the system more secure, easier to maintain and distribute to all users.
Multilingual Coreference Resolution in Multiparty Dialogue

Abstract Existing multiparty dialogue datasets for entity coreference resolution are nascent, and many challenges are still unaddressed. We create a large-scale dataset, Multilingual Multiparty Coref (MMC), for this task based on TV transcripts. Due to the availability of gold-quality subtitles in multiple languages, we propose reusing the annotations to create silver coreference resolution data in other languages (Chinese and Farsi) via annotation projection. On the gold (English) data, off-the-shelf models perform relatively poorly on MMC, suggesting that MMC has broader coverage of multiparty coreference than prior datasets. On the silver data, we find success both using it for data augmentation and training from scratch, which effectively simulates the zero-shot cross-lingual setting.
Lung Cancer Stage Prediction Using Multi-Layer Perceptron and Deep Learning Classifier

Providing accurate diagnosis for cancer is a challenging problem in precision oncology. Multiomics Data about cancer cells comprises of details about cancer cells like their radius, texture, perimeter, area, smoothness, compactness, and concavity. Analysis of this data is very much useful in the prediction of the stage of cancer in Lung cancer patients. This project aims at analyzing the lung cancer cell data set and making a prediction for the given data using ada boost classifier and multi-layer perceptron deep learning classifier.
Learn to be Fair without Labels: A Distribution-based Learning Framework for Fair Ranking

Ranking algorithms as an essential component of retrieval systems have been constantly improved in previous studies, especially regarding relevance-based utilities. In recent years, more and more research attempts have been proposed regarding fairness in rankings due to increasing concerns about potential discrimination and the issue of echo chamber. These attempts include traditional score-based methods that allocate exposure resources to different groups using pre-defined scoring functions or selection strategies and learning-based methods that learn the scoring functions based on data samples. Learning-based models are more flexible and achieve better performance than traditional methods. However, most of the learning-based models were trained and tested on outdated datasets where fairness labels are barely available. State-of-art models utilize relevance-based utility scores as a substitute for the fairness labels to train their fairness-aware loss, where plugging in the substitution does not guarantee the minimum loss. This inconsistency challenges the model's accuracy and performance, especially when learning is achieved by gradient descent. Hence, we propose a distribution-based fair learning framework (DLF) that does not require labels by replacing the unavailable fairness labels with target fairness exposure distributions. Experimental studies on TREC fair ranking track dataset confirm that our proposed framework achieves better fairness performance while maintaining better control over the fairness-relevance trade-off than state-of-art fair ranking frameworks.
Optimization of CTR Prediction in Recommendation with Rating-Time Decay

The Click-Through-Rate (CTR) prediction is a significant concern in the advertising industry, and this research aims to address the three open aspects of learning, feature, and bias that require attention in CTR. The research conducted a literature review and identified appropriate methods from previous research to tackle these aspects. The proposed model optimizes learning time, prevents over-fitting using an early stopping strategy, and handles bias recommendations using Rating-Time decay. Testing on user interest in new and unpopular items provides promising results, indicating that the user’s latest preferences align with the latest event for CTR. The study’s findings demonstrate that the proposed model resolves CTR and over-fitting issues and optimizes the learning aspect of CTR models.
TSPSO: Enhanced Task Scheduling using Optimized Particle Swarm Algorithm in Cloud Computing Environment

The most significant constraint in cloud computing infrastructure is job/task scheduling which affords the vital role of efficiency of the entire cloud computing services and offerings. Job/ task scheduling in cloud infrastructure means that to assign the best appropriate cloud resources for the given job/task by considering different factors: execution time and cost, infrastructure scalability and reliability, platform availability and throughput, resource utilization, and make span. The proposed enhanced task scheduling algorithm using particle swarm optimization considers the optimization of makes pan and scheduling time. We propose the proposed model by using dynamic adjustment of parameters with discrete positioning (DAPDP) based algorithm to schedule and allocate cloud jobs/tasks that ensure optimized makes pan and scheduling time. DAPDP can witness a substantial role in attaining reliability by seeing the available, scheduled, and allocated cloud resources. Our approach DAPDP compared with other existing particle swarm and optimization job/task scheduling algorithms to prove that DAPDP can save in makes pan, scheduling, and execution time.
Solar radiation estimation for photovoltaic power generation using Geostationary and Low-Earth Orbit Satellite Data

Solar radiation measurement data is used to predict photovoltaic power generation. Ground measurement is highly accurate, but there is a problem the measurement location is limited. On the other hand, satellite measurement is available anywhere on the earth, but the accuracy is not sufficient. Therefore, this paper has proposed to model the relationship between ground measurement data and satellite measurement data using machine learning. Focusing on the recently developed constellation technology that links multiple low earth orbit satellite to observe data with high temporal resolution, this paper has proposed a solar radiation estimation model that combines geostationary orbit satellite data and low earth orbit satellite data as input data. This paper aims to estimate the solar radiation on the ground using satellite measurement data and improve the estimation accuracy.
A Node Role Embedding Method Based on Neighborhood Clustering Coefficient

Node role embedding is one of the main research contents of graph network embedding, and it is of great significance in network communication. The main method of existing node role embedding is mining based on subgraph structure. Although this type of method can well describe the connection mode between nodes and other nodes, the computational cost is relatively high. To address the computational cost issue, a new role embedding method based on the clustering coefficient, termed as RENCC, is proposed in this paper. In RENCC, we propose to use the neighborhood clustering coefficient to describe the connection mode between a node and its neighbors. Then, the role similarity can be calculated based on the neighborhood clustering coefficient. Finally, according to the role similarity matrix of nodes, the eigenvalue of the objective function is used as the embedding vector of the node. We verified the effectiveness of RENCC on a barbell network, a taekwondo network and a real airport network. The experimental results reveal that RENCC outperforms the comparison methods and can well mine the role of nodes.
Data-centric AI to Improve Early Detection of Mental Illness

The growth of information technology and advancements in artificial intelligence (AI) have made data creation and usage more prevalent. AI research can be grouped into two categories: model-centric and data-centric. Model-centric AI focuses on using the same data and making changes to model hyper-parameters, architectures, and other configurations. Data-centric AI, on the other hand, prioritizes improving existing data or incorporating new data to improve the performance of machine learning (ML) algorithms. Data-centric AI can greatly improve the performance of machine learning models by improving data quality, increasing data diversity, and using advanced data augmentation methods. The use of ML for early detection of mental health issues is vital due to its ability to identify issues early, provide personalized treatments, detect patterns, and increase accessibility to mental health services. While there have been numerous mental illness detection studies using model-centric approaches, there is a lack of research from a data-centric AI perspective. This study aims to address this gap by comparing established tabular data synthesis methods to explore the impact of synthetic data and data-centric AI on the early detection of mental health issues.
An Analysis of the Significant Role Played by Mobile Cloud Forensics and its Key Obstacles

Nowadays, everyone has a mobile device on which they save a wealth of personal information. Information about people is a prime target for hackers looking to steal sensitive data. Small CPUs, short battery life, and insufficient storage space constrain mobile devices. However, thanks to cloud computing, customers can access an infinite computer power supply. With SMDs, mobile users gain access to the cloud services offered by an MCC service provider. These users are then charged only for the number of cloud resources consumed (pay-as-you-go). In distributed resource networks, mobile cloud computing (MCC) is quickly emerging as a top research area. Offloading mobile applications to the cloud is simple, and users can access them whenever and wherever they choose. Given how accessible mobile networks are, hackers often target them to steal credentials for use in other apps, both in and out of the cloud. A digital investigator's first order of business while looking into hacker assaults is determining the attack's first point of entry. Virtualization, data dispersion, multi-tenancy, interoperability, and mobility make it challenging to investigate MCC architecture. In this paper, we provide a high-level introduction to digital forensics, emphasizing the critical components of the former and the major forensic issues faced by digital investigators in the latter. This work aims to help researchers better grasp the difficulties of forensic research and refocus their efforts in productive directions. Insights into mobile are provided, as well as a discussion of the many difficulties encountered by the researchers.
Energy-efficient DC Motor Speed Control Using the Genetic Algorithm

The aim of this paper is to synthesize energy-efficient speed control of a DC motor. The genetic algorithm is applied to determine the parameters of a PID controller, a fuzzy PID controller and an LQ regulator. The objective function is the minimum of the mean square error and the minimum of the energy, determining the conflicting requirements for the speed of the transient process and the magnitude of the control action, respectively. The results of the synthesis show that the system with a PID controller and the system with a fuzzy PID controller tuned using a genetic algorithm meet the set quality requirements for the transient process. The system with an LQ regulator tuned with a genetic algorithm meets the set quality requirements for the transient process with 8.32% less power consumption compared to the other two systems.
VoiceGuard: An Effective and Practical Approach for Detecting and Blocking Unauthorized Voice Commands to Smart Speakers

Smart speakers bring convenience to people's daily lives. However, various attacks can be launched against smart speakers to execute malicious commands, which may cause serious safety or security issues. The existing solutions against sophisticated attacks such as voice replay attacks and voice synthesis attacks require intrusive modifications of the smart speaker hardware and/or software, which are impractical for general users. In this work, we present a novel security scheme- VoiceGuard that can effectively detect and block unauthorized voice commands to smart speakers. VoiceGuard does not require any modification to smart speakers' hardware or software. We implement a prototype of VoiceGuard on two popular smart speakers: Amazon Echo Dot and Google Home Mini, and evaluate the scheme in three real-world testbeds, which include both single-user and multi-user scenarios. The experimental results show that VoiceGuard achieves an accuracy of 97% in blocking malicious voice commands issued by illegitimate sources while having a negligible impact on the user experience.
System for String Monitoring in small Experimental PV Plants

This paper presents a system for real-time measurement of electrical and physical parameters of a small (12kWp) photovoltaic power plant. This experimental photovoltaic installation is located in the urban area of Ruse, on the territory of the University of Ruse. A system was designed that measures the energy produced by each solar array in shaded locations commonly found in urban environments, as well as environment parameters, solar radiation, PV cell temperature and even infrared images of the facility. The collected data will be used to model and forecast energy production from solar panels under urban conditions.
Learning Aligned Cross-Modal and Cross-Product Embeddings for Generating the Topics of Shopping Needs

The paper addresses the issue of generating keywords to describe the topic of a shopping need based on the titles and photos of products being browsed or compared. We extend to learn cross-modal and cross-product embeddings to capture the relationships between textual and visual semantics and the shared features between comparable products. Experiments conducted on 3 real-world datasets have shown that the keywords decoded from such embeddings gain significant improvement compared to state-of-the-art cross-modal embeddings.