Reviewerly/exHarmony huggingface dataset:
Decoding Gender on Social Networks: An In-depth Analysis of Language in Online Discussions Using Natural Language Processing and Machine Learning

In today’s digital era, the internet is an indispensable platform for self-expression, facilitating communication, idea sharing, and community formation. Language, a pivotal tool in these online interactive spaces, is vital in reflecting personal identities, notably gender identification. This paper investigates gender identification on online discussion platforms, recognizing the crucial role of language in reflecting personal identities. The study employs Natural Language Processing techniques and machine learning algorithms to analyze data from a public discussion website. Beginning with a comprehensive literature review, the research explores the nexus between gender and language in online and offline contexts. The methodology involves data gathering, extensive preprocessing, and in-depth exploratory analysis, employing statistical methods and graphical representations. The study then rigorously evaluates their accuracy and effectiveness by applying diverse algorithms and models for gender-based text categorization. Results indicate the superior performance of transformer models, particularly distilBERT, in categorizing gender accurately. Additionally, the research underscores the challenges of gender-neutral analysis, emphasizing the need for inclusive methodologies in non-binary gender classification. The study contributes to the broader field of gender studies, providing valuable insights for future research and discussions on the interplay of gender and language in online spaces.
Artificial Bee Colony Algorithm with Population Reduction Strategy

Population size (NP) has always been a very important parameter in artificial bee colony (ABC) algorithm. It is found that large population size facilitates exploration and ensures population diversity, while small population size facilitates exploitation and saves computing resource. Therefore, dynamically reducing the population size during the search is helpful for the optimization process of the algorithm. This paper draws lessons from the population reduction strategies of other algorithms including linear reduction, half reduction and their combinations with K-means clustering, and applies them to the artificial bee colony algorithm. The experimental results on test set including continuous unimodal function, noise function and multimodal function about solution accuracy, stability and convergence speed proved the different effectiveness of the population reduction strategies for ABC algorithm. It is further found that the population reduction strategy with K-means clustering is better than that without K-means clustering.
A Prior-Guided Meta-Weight Network for Long-tailed Noisy Data in Item Categorization

Item categorization task aims to automatically infer the product category of an item based on its textual description. As a fundamental task in eCommerce domain, item categorization is widely adopted in many important applications such as product search, query understanding and product recommendation. However, this task faces several challenges. First, eCommerce data usually suffers from noisy facts since many key product values are self-reported by individual sellers and cannot be fully verified by experts. Second, eCommerce data usually follows the long-tail data distribution in which class distribution is highly imbalanced. To handle these challenges, some existing efforts simply combine approaches that are developed for noisy data and long-tail data separately. However, such a straightforward combination may not achieve satisfactory performance. In this paper, we propose a performance-driven Prior-Guided Meta-Weight Network (PGMWN) which handles the two challenges in a principled way. The proposed framework involves a meta re-weighting strategy to estimate the weights of samples mainly based on performance changes. Moreover, we leverage important data statistics to guide the meta reweighting mechanism towards distribution-aware weights. A self-supervised representation learning component is utilized to further improve the framework’s ability to address those two issues. To evaluate the effectiveness of the proposed PGMWN framework, comprehensive experiments are conducted on three public real-world datasets collected from Amazon. The proposed framework outperforms several state-of-the-art baselines in terms of various evaluation metrics. The experimental results show that the proposed model is able to handle the long tail data distribution and label noise issues and is effective in the item categorization task.
An Integrated Face Recognition based Attendance Monitoring System

Everything in the world today is mechanised and connected online. Where everything can be done by machine automatically. Such as it is difficult to deal with the presence of several students in a classroom under the conventional approach. As it is time consuming and there are chances of human errors taking attendance. Marking attendance is a day-to-day activity which is a real-world problem which can be solved using Face recognition. It is also efficient handling bulk of student's attendance than old traditional methods. Attendance systems come in a variety of forms, such as biometric, facial recognition, radio frequency card, and conventional paper-based ones. A facial identification-based attendance system is the safest and most efficient of them all. This paper presents an integrated system which automatically detects face and marks the attendance in the sheet. While building this system, we basically focused on two Machine Learning algorithms that are Haar Cascade and Local Binary pattern Histogram Algorithm (LBPH) also we used Python and OpenCV libraries which is used to make the model more efficient.
Workload Shifting Based on Low Carbon Intensity Periods: A Framework for Reducing Carbon Emissions in Cloud Computing

Datacenter carbon emissions are rising, which poses a serious issue that must be addressed quickly. We may see differences in emissions when we look at the carbon intensity of the electrical system since various areas have different energy sources. We can time the execution of workloads to occur during periods when the carbon intensity is lower by using this temporal variability. This paper aims to address the challenge of reducing carbon emissions in cloud computing by proposing a framework for workload shifting based on low carbon intensity periods in the power grid. The study focuses on four countries and their carbon production in the year 2022, calculating the carbon intensity for each country. Additionally, the paper identifies different cloud computing workloads and integrates constraints such as power, SLA, carbon emissions, and routing into the framework. The crucial factors considered during workload shifting include geo-distributed load balancing and right-sizing the data center.A simulation is developed to evaluate the proposed framework, simulating scenarios with shiftable workloads. The results are compared and analyzed, assessing the framework’s effectiveness in reducing carbon emissions while meeting the specified constraints. The findings highlight the potential benefits of workload shifting in reducing carbon emissions and improving environmental sustainability. Overall, this research contributes to the advancement of green computing and offers insights into the development of sustainable cloud computing practices.
Enhanced Security Technique for Adhoc Transmission Using Hyper Elliptic Curve

The popular cryptosystem technology with a realistic way to a highly secure and a smaller encryption bit size is known as Hyper Elliptical Curve Cryptography (HECC). The key-agreement issue is a significant drawback in the crucial demand for an explanation. A secret key must be disseminated among the clients to protect group-based data transfer in a distributed architecture. A revolutionary dynamic group secret key control is present to establish effective key distribution and administration. This article suggests key-agreement architecture based on hyperelliptic curve cryptography. The concept of the strategy, in this case, is to split a large group into multiple segments, with each segment preserving its security code to regulate the segment and managing numerous segments using a HECC management algorithm based on the Key Agreement. The study introduces a novel approach to collective key generation with encryption. An architecture for mobile networks using the Key Agreement-based HECC for Encryption (KAHECCE) suggested algorithm were simulating in contrast to the traditional techniques currently used for MANETs. KAHECCS outperforms prior methods in terms of throughput efficiency while demonstrating sophisticated key exchange characteristics without sacrificing overhead or storage costs.
Automated Sentiment Classification of Amazon Product Reviews using LSTM and Bidirectional LSTM

Sentiment analysis is crucial for understanding opinions and sentiments expressed in online reviews, particularly on e-commerce platforms like Amazon. This study presents an automated sentiment classification model employing Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM) and Bidirectional LSTM models. The model accurately extracts sentiment polarity from a dataset of 50,000 Amazon product reviews, labeled with positive, negative, or neutral sentiment. Evaluation metrics including accuracy, precision, recall, and F1-score assess the model’s performance. The Bert model achieves 91% accuracy, LSTM model achieves 88% accuracy, while the bidirectional LSTM model outperforms with 90.7% accuracy. This research provides valuable insights into consumer sentiments, enhancing decision-making processes and the online shopping experience.
An Algorithmic Approach for the Detection and Classification of Lung Cancer from the Histopathological Images

Lung cancer, a prevalent malignancy affecting both genders, demands early detection to mitigate its significant human toll. Unfortunately, symptoms often manifest in advanced stages, necessitating reliance on medical imaging for early diagnosis. Researchers have devised algorithms geared towards identifying lung cancer in histopathological images. This study introduces a computer-aided detection method using Flower Pollination Optimization for feature extraction from histopathological images. The study employs standard histopathological images sourced from the Kaggle database. In this investigation, five classifiers, including the Gaussian Mixture Model (GMM), Non-Linear Regression (NLR), Naïve Bayes Classifier (NBC), Logistic Regression (LR), and Fisher Discriminant Classifier (FDC), are utilized to assess and categorize the Benign and Adenocarcinoma classes. Results show that the Naïve Bayes Classifier, combined with Flower Pollination, achieves the highest accuracy at 87.86%, according to established benchmark metrics.
H-MIS: A Hierarchical Multi-Identifier System Based on Blockchain

With its wide range of applications, the Internet shows a future trend towards abundant and diverse data resources with multiple types of identifiers (multi-identifiers). However, the legacy Domain Name System (DNS) in the current TCP/IP network architecture has failed to manage these identifiers due to the centralized security issue. While some decentralized DNS alternatives have been proposed, they also face scalability issues. In this paper, we propose a blockchain-based Hierarchical Multi-Identifier System, named H-MIS, as a DNS alternative. Specially, it realizes optimal decentralization and scalability by introducing the Zero-Knowledge rollup (ZK-rollup) solution to synchronize the upper and lower on-chain identifier data, as well as off-chain associated resource data. Finally, we implement H-MIS on Ethereum and evaluate its performance. The experimental results indicate that compared to the original MIS and Ethereum Name Service (ENS), H-MIS has advantages in such aspects as efficiency, data consumption, and Gas fees.
Augmentation Applied with Transfer Learning Approach to Detect Real and Fake Faces

Face spoofing has been prevailing in the many ways such as in face biometric attacks, morphing of photos and so on. Morphing of the photos with the other photos will cause severe problem to the particular individual and it also effect the individuality of the particular individual. There are many approaches in machine learning and deep learning has been identified to prevent the morphing of the real photos. In this proposed system, we applied the augmentation to overcome under fitting and transfer learning approaches such as Mobile net and Resnet gives more accuracy over the other deep learning approaches. The fine tuning of the hyperparameters has been evaluated with performance metrics such as Accuracy, Precision and F1 score. The proposed method yields the more accuracy of nearly 75% and 78% than other methods
Text Extraction and Finetuning Transformers for Abstractive Summarisation

Data has always been an integral part of any industry that people refer to nowadays. And due to the advancement of technology more and more people have been able to access data and generate it effortlessly. This often leads to generation of large text documents which often times take a lot of energy. Natural Language Processing has been a constantly evolving field as it helps to summarize the long texts and allow people to get the main data in a short and concise manner thereby saving time and energy. In this paper an Abstractive Text Summarizer built on the concepts of NLP has been proposed. The proposed method has been implemented on two major and well known domains - News Articles and Legal documents. The system proposed in this paper consists of three major models developed and finetuned on the datasets gathered from the domains mentioned above.
Software Defect Prediction Using Deep Semantic Feature Learning

The software defect prediction (SDP) methodology has the potential to increase software reliability by foreseeing any ominous problems in its source code. Nevertheless, as recent research has demonstrated, creating defective prediction models is a challenging issue. Several research methods for predicting source code defects have been proposed over time. However, the majority of previous research has concentrated on traditional feature extraction and modelling. Traditional methods frequently fail to locate the contextual information needed to build reliable prediction deep learning models in source code files. On the other hand, defect prediction semantic feature techniques have only lately changed and advanced. Using such techniques, it would be possible to forecast suspicious problems by automatically extracting contextual information from source code files. We suggest employing deep learning, a potent representation-learning approach, to fill the gap between defect prediction data and semantics. In order to automatically extract semantic properties from word vectors generated by program abstract syntax trees (AST) for file-level defect prediction models and source code updates for change-level defect prediction models, we use a Long-Short Term Memory (LSTM) model. Two tasks—one for defect prediction at the file level (within-project defect prediction and cross-project defect prediction) and one for defect prediction at the change level (within-project defect prediction and cross-project defect prediction)—were used to assess our methodology. Our findings demonstrate that LSTM-AST-based semantic features can greatly enhance fault identification tasks. The findings demonstrate that the deep learning model LSTM-AST is a practical choice for medium to low sparsity ratio datasets and that, in comparison to other machine models, it performs typically better in high and extremely high sparsity ratio datasets.
Comparing Generative Chatbots Based on Process Requirements: A Case Study

Business processes are commonly represented by modelling languages, such as Event-driven Process Chain (EPC), Yet Another Workflow Language (YAWL), and the most popular standard notation for modelling business processes, the Business Process Model and Notation (BPMN). Most recently, chatbots, programs that allow users to interact with a machine using natural language, have been increasingly used for business process execution support. A recent category of chatbots worth mentioning is generative-based chatbots, powered by Large Language Models (LLMs) such as OpenAI’s Generative Pre-Trained Transformer (GPT) model and Google’s Pathways Language Model (PaLM), which are trained on billions of parameters and support conversational intelligence. However, it is not clear whether generative-based chatbots are able to understand and meet the requirements of constructs such as those provided by BPMN for process execution support. This paper presents a case study to compare the performance of prominent generative models, GPT and PaLM, in the context of process execution support. The research sheds light into the challenging problem of using conversational approaches supported by generative chatbots as a means to understand process-aware modelling notations and support users to execute their tasks.
USER: Towards High-Utility Sequential Rules with Repetitive Items

Discovering interesting sequential rules in the sequence database is quite important for a variety of fields, ranging from customer behavior analysis to intrusion detection. High utility sequential rule mining (HUSRM) was proposed to obtain more informative rules. Its goal is to find those sequential rules with high utility values and high confidence, i.e., HUSRs. As far as we know, a few algorithms are proposed to discover HUSRs. However, these algorithms do not fully consider the existence of repetitive items in the sequences of the database. In this paper, we propose an algorithm named USER to discover HUSRs in multi-sequences with the existence of repetitive items. A data structure called an occurrence information (OI)-list is designed to distinguish the different occurrences of items in a sequence. Moreover, the change in the upper bound value after the rule expansion is discussed in detail, which is complicated by the repetitive items. We also introduce two pruning strategies (ROOR and REIO-I) to optimize mining efficiency when there are too many repetitive items in the sequence. Finally, we conduct experiments on several datasets, and the results show that USER is able to discover HUSRs with more accurate utility values in an acceptable amount of time and memory consumption.
Correlation and Unintended Biases on Univariate and Multivariate Decision Trees

Decision Trees are accessible, interpretable, and well-performing classification models. A plethora of variants with increasing expressiveness has been proposed in the last forty years. We contrast the two families of univariate DTs, whose split functions partition data through axis-parallel hyperplanes, and multivariate DTs, whose splits instead partition data through oblique hyperplanes. The latter include the former, hence multivariate DTs are in principle more powerful. Surprisingly enough, however, univariate DTs consistently show comparable performances in the literature. We analyze the reasons behind this, both with synthetic and real-world benchmark datasets. Our research questions test whether the pre-processing phase of removing correlation among features in datasets has an impact on the relative performances of univariate vs multivariate DTs. We find that existing benchmark datasets are likely biased towards favoring univariate DTs.
GAME: Improving Efficiency and Effectiveness of Knowledge-Graph Rule Mining via Data Reduction

Knowledge graphs have been growing in popularity in recent years, due to their successful use in many data-analytics and knowledge-discovery tasks. As the sizes of domain graphs continue to grow, they can become too large to be processed efficiently by downstream applications. It is thus indispensable in many cases to aim to address the efficiency issue by reducing knowledge-graph sizes via summarization. At the same time, to provide quality results, that is, to be effective, on the graph summaries, many downstream analytics and knowledge-discovery tasks require the summaries to retain as much information from the original graph as possible. It turns out that state-of-the-art data-reduction approaches may generate task-specific or lossy summaries, which may present challenges for the effectiveness of downstream analytics in some cases. As a result, it is important to study trade-offs between the performance and accuracy of graph summarizations for analytics and knowledge discovery.In this paper, in the scope of knowledge-graph size reduction via summarization, we consider the trade-off between the performance and accuracy of one type of knowledge-graph analytics, inference-rule mining, on the summarized graphs as compared with the original graphs. Toward addressing the tradeoff challenge, we introduce a domain- and task-independent knowledge-graph summarization approach called GAME for generating reduced abstract knowledge graphs from a given graph. Our preliminary experimental results suggest that our GAME approach can preserve as much information from the given (original) graph as possible for the purpose of rule mining, while potentially significantly improving the rule-mining efficiency. We anticipate that the knowledge-graph reduction provided by the proposed GAME approach can enable efficient, yet effective, results for diverse applications and use cases.
Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects

Data science projects often involve various machine learning (ML) methods that depend on data, code, and models. One of the key activities in these projects is the selection of a model or algorithm that is appropriate for the data analysis at hand. ML model selection depends on several factors, which include data-related attributes such as sample size, functional requirements such as the prediction algorithm type, and nonfunctional requirements such as performance and bias. However, the factors that influence such selection are often not well understood and explicitly represented. This paper describes ongoing work on extending an adaptive variability-aware model selection method with bias detection in ML projects. The method involves: (i) modeling the variability of the factors that affect model selection using feature models based on heuristics proposed in the literature; (ii) instantiating our variability model with added features related to bias (e.g., bias-related metrics); and (iii) conducting experiments that illustrate the method in a specific case study to illustrate our approach based on a heart failure prediction project. The proposed approach aims to advance the state of the art by making explicit factors that influence model selection, particularly those related to bias, as well as their interactions. The provided representations can transform model selection in ML projects into a non ad hoc, adaptive, and explainable process.