Reviewerly/exHarmony huggingface dataset:
RPLNet: Object-Object Affordance Recognition via Relational Phrase Learning

Object-object affordance recognition aims to recognize the interactive relations between an object and other objects, which plays a crucial role in task decision-making and object selection of industrial robots. To address the problem of interference from complex interaction relations, we propose to recognize object-object affordances via relational phrase learning. The relational phrases are used as knowledge prior to improve the affordance expression. In addition, we propose a multi-scale feature pooling and aggregation module to enhance the visual feature representation of images. We redesign the initial block of transformer decoder to model the visual and phrase semantic features, improving the recognition performance. We collect and annotate object-object interaction images from the robot’s view, and train and test our model on them. The experimental results show that our approach achieves the best performance compared to the state-of-the-art methods.
Front-End web development project architecture design

In the realm of modern software development, user interfaces play a pivotal role in shaping user experiences and interactions. Effective front-end project architecture design is crucial for creating maintainable, scalable, and responsive web applications. This article delves into the fundamental principles and methodologies that guide the architecture design process, offering insights into best practices contributing to the success of front-end projects.
Residual domain expert architecture for few-shot learning classification task

Significant progress has been made in the deep learning image classification area during last decades. Image classifiers evolved from few-classes datasets to large-scale benchmarks. The size of the models was dramatically increased both in wide and depth. This increase was mandatory in order to maintain model capacity, which is so important to handle all the knowledge inside the model. The challenge is here in increasing such a capacity while maintaining an overfit rate, which leads to overcomplicated neural network architectures. Few-shot learning techniques were developed as an alternative to models which keep all the knowledge inside themselves. Such approaches are mostly based on external databases and exploit models as a relation mapper between query and support inputs. This turns out models to be something that knows almost nothing about domain knowledge but operates only in some sort of similarity. In this paper we research ways to implement a few-shot learning model which relies upon neither external databases nor a support set. We built a novel architecture called an RDE (residual domain experts) which consists of a set of experts. Each of such experts is responsible for certain class probability scores. Our experiments showed that RDE architecture achieves nearly the same performance level while allowing a flexible configuration of experts.
Applications of Machine Learning in Software Defect Prediction: A Literature Review

Software Defect Prediction (SDP) employs resources to identify defects and improve quality. This study investigates Machine Learning (ML) techniques in SDP and associated performance measures. Three research questions guide the exploration, covering popular techniques, common performance metrics, and dataset sources. The review highlights a shift towards leveraging rich source code information, particularly Abstract Syntax Trees. Notably, Artificial Neural Networks dominate ML techniques. AUC and G-mean gain prominence for performance evaluation, addressing class imbalance. Dataset scarcity remains a challenge, leading to reliance on external repositories. This study enriches SDP insights and informs future research directions in the evolving landscape of software defect prediction.
Enhancing Global Model Accuracy: Federated Learning for Imbalanced Medical Image Datasets

Federated learning (FL) is a deep-learning framework developed for cases where data privacy protection is essential due to security and privacy guidelines. Collaboration between independent institutions is critical to achieving better deep-learning model accuracy due to a single institution's shortage of information acquisition. This study focuses on medical image analysis of brain tumor dataset, using a federated learning framework. One of the drawbacks of FL is the global model accuracy impact due to the imbalanced distribution of dataset's samples and classes, called non-independent and identically distributed (non-IID). FL global model accuracy degradation can be optimized by various approaches, either by manipulating the dataset using augmentation techniques and data sharing or optimizing the global model aggregation algorithm. Our method uses two augmentation techniques: Generative adversarial network (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE) to balance non-IID with the typical FedAvg aggregation algorithm to optimize global model accuracy. Global model accuracy using SMOTE and GAN outperforms IID and non-IID with a few numbers of global model iterations using local imbalance or globally imbalanced datasets.
BERT Transformers Multitask Learning Sarcasm and Sentiment Classification (BMSS)

Numerous researches were reported based on analyzing the sentiment of texts, which are becoming considerably vital to discover the sentiment in various domains of economic, marketing, politic, and etc. However, one of the most challenging aspects of Natural Language Processing (NLP) is discovery of the sarcasm. The sarcasm and the sentiment as similar subjects have been explored in various articles. In this research, two significant achievements were employed. Firstly, the pre-trained BERT transformer model was used to transfer its knowledge to our model. Then, fine tuned BERT model was combined with a machine learning approach, specifically Multitask learning (MTL). By establishing a connection between sarcasm and sentiment, we observed a simultaneous enhancement in both sarcasm detection and sentiment analysis performance. The achieved accuracy results (average of 0.97) were deemed satisfactory and commendable for both tasks.
On DDoS Attacks as an Expression of Digital Protest in the Russo-Ukrainian War 2022

In February 2022, a new escalation of the Russo-Ukrainian war began in Europe. While certain events were foreshadowing an escalation, the Russian troops moved to the territory of Ukraine on February 24th. Later on the same day, the hacktivism collective “Anonymous” stated to be in a cyber war with the Russian government and called for support by overloading specific Russian web servers. Since the beginning of this escalation, we observe these servers and measure their availability. In this paper, we investigate whether the downtimes of these servers correlate to the calls of “Anonymous” and what changes were made to these servers. It is inevitable to discuss whether such actions – usually seen as attacks on service availability – should be seen as an expression of digital protest.
Development of an Interface for the Rapid Construction of Formal Context-Logical Queries for Searching for Information in Large Arrays of Information

The article is devoted to the problem of constructing accurate queries to a large volume of textual information in order to obtain concretized information for the desired query. Many modern systems have extensive capabilities for configuring search, but using these capabilities requires special knowledge. The article presents the interface of a visual, simple, intuitive system for searching for information on a given topic. The system is intended primarily for use by experts in professional activities. When performing work tasks, many requests can be template or of the same type. Therefore, working with the system begins with the use of a library of standard queries. The principles of encapsulation, modularity, ergonomics are used. Queries are connected using visual and simple commands. Dictionaries are used to search for derived words.
Ontological Analysis of the Digital Crime Conceptual Model

A conceptual ontological model of Digital Crime has been developed, consisting of five non-empty classes. Identification and classification were carried out according to the experience of domestic and foreign experts. The central place of the model is occupied by objects of the Digital Crime class, which have their own attributes and are connected by various links with other objects. Objects of some classes are filled in by users, others are formed by knowledge engineers. Based on the ontological analysis, SWRL-rules are constructed that automatically form links between objects of the custom classes Digital crime and Digital evidence with objects of the Type of crime and Criminal Liability classes. The last class contains articles of the Criminal Code of Ukraine on criminal liability for digital crimes. The ontological model is developed taking into account possible requests to it. Such queries can be formed on the basis of direct relationships between human and automatic concepts. And also contain logical chains to concepts that do not contain an explicit link in the ontology. It is assumed that the model can be expanded and supplemented. The ontological model of Digital Crime can be used to share information, develop new systems and applications, improve information retrieval and ensure its accordance.
Standardized ReACT Logits: An Effective Approach for Anomaly Segmentation in Self-driving Cars

The identification of unexpected road objects is a crucial aspect in the field of autonomous driving. Various methods have been proposed for anomaly segmentation, which can be categorized into three important categories: the use of auxiliary datasets, the utilization of uncertainty maps, and the reconstruction networks. In this study, the DeepLabv3+ network serves as the primary semantic segmentation model. We calculate the energy function, a type of uncertainty, before the output of the upsampling layer and employ it as an anomaly score. Unlike samples within the distribution, samples outside the distribution do not exhibit deviations from the standard criteria in terms of the distribution of these values. To address this issue, we use the ReAcT operator, which replaces values exceeding a threshold with the threshold value, leading to improved performance.The proposed method enhances the performance of anomaly segmentation by incorporating a single step of standardizing anomaly scores and considering the semantic dependencies of pixels in each region. This is achieved through two steps: Iterative Boundary Suppression and Dilated Smoothing. Evaluation on three common datasets in the field, namely Fishyscapes Lost & Found, Fishyscapes Static, and Road Anomaly, demonstrates the method’s robustness in anomaly segmentation without the need for auxiliary datasets or network retraining.
Sentiment Analysis of PLN Mobile Review Data Using Lexicon Vader and Naive Bayes Classification

The PLN Mobile application was introduced by PLN as a superior digital platform to suit all client needs, provide convenience, and give a unique power service experience. The PLN Mobile application offers simplicity and speed of service to PLN customers with around 24.8 million users and more than 35 million registered customer IDs (https://web.pln.co.id/media/press-release/2022/07, July 2, 2020). The customer ratings for the Google Play Store app range from 1 to 5. The quality of the program is not accurately portrayed when users provide ratings that do not correspond to their reviews. It will take some time to read all of the reviews on the PLN Mobile app because there are so many of them. Classification is used to measure public sentiment. The sentiment research used 1,000 review sample data points from 67950 population data points obtained from the PLN Mobile application between January and June 2022. Initially, online scraping, machine translation, data labelling, text pre-processing, TF-IDF, text classification, and model evaluation approaches were used to acquire review data. The labelling results for the Lexicon-based text classification approach with the Vader Lexicon dictionary-based approach are 489 positive sentiments, 145 negative sentiments, and 366 neutral. Positive classes get a rating of 67%, neutral classes get a rating of 6%, and negative classes get a rating of 27%. Based on the comparison of positive, neutral, and negative classes from the data sample, PLN Mobile application users give ratings that do not match the reviews given. The Naive Bayes approach is also utilized in the categorization process. For the distribution of test and training data, the author employs a split data ratio of 90:10. The confusion matrix evaluation technique yields 70% accuracy with the F1 score value for each class, namely for the positive class (62%), neutral class (57%), and negative class (77%).
Development of Labeling and Prediction System for Breast Cancer Detection

This study uses a variety of machine learning (ML) approaches to offer a labeling and prediction system for breast cancer detection. The system uses a large dataset and preprocessing methods for feature extraction and data quality. To determine which strategy is most successful, ML algorithms are compared. Both a labeling component to aid in correct diagnosis and a prediction component to forecast the existence and severity of cancer are included in the system. Experiments in evaluation show great specificity, sensitivity, and accuracy. Technology gives medical practitioners an important tool and advances machine learning applications in healthcare, transforming cancer detection and diagnosis.
HiCAP: Hierarchical Clustering-based Attention Pooling for Graph Representation Learning

Graph representation learning has gained increasing importance in the analysis of complex relational data. The utilization of Graph Neural Networks (GNNs) has demonstrated significant potential for capturing graph structures and node features in this domain. However, the challenge of graph pooling, which involves summarizing graphs into concise representations, remains a persistent obstacle. In this study, a novel method called HiCAP (Hierarchical Cluster-Based Attention Pooling) is proposed to address the limitations of existing graph pooling approaches. By combining the strengths of node clustering and node dropping techniques, HiCAP establishes a hierarchical framework. Initially, a soft cluster assignment matrix is learned through the application of a GNN. Subsequently, the matrix undergoes a transformation into a hard assignment by incorporating structural considerations. Attention-based scoring is subsequently employed to select representative nodes within each cluster. Extensive experimental evaluations on benchmark datasets showcase the effectiveness of HiCAP in graph classification tasks, outperforming state-of-the-art baselines. This method introduces an innovative approach to graph representation learning, thereby advancing graph pooling techniques.
Sentiment Analysis of Social Media Comments Using Machine Learning Algorithms

Sentiment Analysis is a highly relevant and popular area in modern technology. It involves examining web-based data to identify and classify opinions in text. This paper provides an overview of sentiment analysis, its applications, methodologies, and challenges. Our study focuses on sentiment analysis in the Azerbaijani language, which has not been extensively studied before. We collected 52,000 comments from social media and used supervised machine learning algorithms to classify them into positive, negative, and neutral categories. Our methodology included data feature extraction, model training, and evaluation. Our findings contribute to the field and offer insights for future research and practical applications.
Research on trusted traceability method of large-scale manufacturing industry based on blockchain

With the development of large-scale manufacturing industry, product traceability and quality management become more and more important. This paper proposes a blockchain-based trusted traceability method, which aims to build a multi-party collaborative trusted traceability system and realize trusted traceability in large-scale manufacturing industries with complex identification systems and multi-source heterogeneous data. This paper first introduces the background of blockchain technology and industrial product traceability, and then proposes a multi-chain collaborative industrial product traceability architecture for managing traceability data of industrial products. Secondly, this paper proposes a method for industrial product traceability data management, including data uplink, storage, query and authorization. Furthermore, this paper proposes a traceability method for industrial product data, which displays product traceability information based on the DAG graph. This article also introduces typical application scenarios of industrial product traceability, including consumer complaints, regulatory acceptance and manufacturer recalls. Finally, this paper conducts performance verification experiments to verify the feasibility and effectiveness of the method proposed in this paper in large-scale industrial product traceability, and can meet the needs of high concurrency, multi-user and large-scale industrial product traceability.
Incorporating the Regional Dominance Strategy into Multi-Swarm PSO

In this paper, we improve the exploitation capability of Multi-Swarm Particle Swarm Optimization (MSPSO) by integrating a dominance policy implemented between competing sub-swarms in some randomly generated regions defined by hyperspheres. Furthermore, we modify a stage of the classical MSPSO using Mesh Adaptive Direct Search in the place of a line search optimization method to enhance the exploration ability. The proposed method has been experimented on some well-known benchmark functions for the continuous optimization problems.
Motion Path Planning of Agent Based on Proximal Policy Optimization Algorithm

Aiming at the problems of insufficient intelligence level, computational complexity, and lack of autonomous learning capability of traditional path planning algorithms, this paper proposes a solution for implementing the motion path planning of agent using proximal policy optimization algorithm based on deep reinforcement learning. The solution can provide a universal method for the path planning of agent moving in a two-dimensional plane, which splits the task into two subtasks: path generation and path tracking, where the agent generates a feasible path based on proximal policy optimization algorithm, and the path tracking task can be implemented by a traditional control algorithm. Finally, the proposed algorithm is simulated and verified in training and testing environments. The results show that the proposed algorithm can effectively complete the path generation task, and the proximal policy optimization algorithm has advantages over the A* algorithm in terms of intelligence level and environmental adaptability. Furthermore, the algorithm is generalizable to all movable objects.
DIPT: Diversified Personalized Transformer for QAC systems

Today, with the explosion of information on the web, search engines play a more prominent role in serving users' information needs. Query auto-completion (QAC) is one of the most crucial aspects of search engines that helps users formulate relevant and precise queries based on their information needs. A QAC system generates a list of query candidates according to the user's provided prefix and then updates it with each new keystroke. The existing methods mostly focus on personalizing query candidates to make them revolve around the user's interests and past interactions. Due to the limited number of suggested queries, mere personalization can result in pushing redundant suggestions into the list as well as the exclusion of effective ones. In this paper, we address the diversification task in QAC systems, presenting a novel method called DIPT, a Diversified Personalized Transformer for QAC systems. The proposed method diversifies the suggested queries to include the potential future interests of users in addition to their past interests and interactions. Experimental results on the AOL standard dataset demonstrate the advantage DIPT over state-of-the-art personalized QAC systems in terms of the MRR(Mean Reciprocal Rank) criterion.
Prototype Pseudo Denoising Domain Adaptation Network for Image Classification

In the field of unsupervised domain adaptation, most existing methods rely on global features that may not be able to capture fine-grained features, resulting in suboptimal transfer learning performance. Although some approaches have incorporated inter-subdomain relationships across different domains within the same category, the forecasting accuracy of the model within the target domain remains unsatisfactory. In response to the challenge mentioned earlier, a solution has been proposed by us: a self-training approach that integrates pseudo labels. However, it is important to acknowledge that the pseudo labels generated in this iterative process are prone to noise, which can result in the potential dispersion of the target features due to the underlying domain differences. This paper presents Prototype Pseudo-Denoising Adaptation (PPDA), a novel method for addressing challenges in unsupervised domain adaptation. By utilizing feature centroids, known as prototypes, our approach effectively tackles two critical issues in the adaptation process. We leverage the feature distances derived from prototypes to enhance the informational content provided by pseudo labels. This integration allows us to refine the estimation of pseudo label probabilities, facilitating online correction during training. Experimental evaluation on object recognition tasks showcases the significant performance improvement achieved by our proposed method.
Improving Clinical Decision-Making with Polynomial Regression-Based Real Patient State Estimation

As science and technology continue to evolve, clinical decision-making has become increasingly important in the field of medicine. This process not only aids experienced clinicians in making critical decisions but also provides guidance for those who lack experience. However, in the past, clinicians had to rely solely on their own expertise and medical reports to analyze patients, resulting in a time-consuming process. To address this issue, a scoring model was developed that analyzes patient conditions based on each parameter's value using data collected by the hospital. By utilizing computer analysis, evaluations, predictions, and optimizations, a suitable model for clinicians and patients can be created. This paper proposes a nonlinear polynomial regression approach as a model for predicting patient health scores, which has been validated through computer simulations and fits multiple researches and clinical examinations. The predicted results have been consistent with actual results when using the model, making it easier for clinicians to make clinical decisions. In summary, this model not only analyzes patient conditions but also predicts patient health scores with the support of appropriate parameters, making it a valuable tool for clinicians in their clinical decision-making process in the near future.
Challenges and Specificities of Adopting Continuous Integration within Scalable Cloud Environments

This paper delves into the convergence of Continuous Integration processes and Scalable Cloud Environments, highlighting the intricacies of their integration. It offers an analytical evaluation of challenges introduced by adoption of Cloud Technologies within Continuous Integration workflows.