Reviewerly/exHarmony huggingface dataset:
Aspect-Category based Sentiment Analysis with Unified Sequence-To-Sequence Transfer Transformers

In recent years, Aspect-based sentiment analysis (ABSA) has received increasing attention from the scientific community for Vietnamese language. However, most previous studies solved various subtasks in ABSA based on machine learning, deep learning and transformer-based architectures in a classification way. Recently, the release of pre-trained sequence-to-sequence brings a new approach to address the ABSA as a text generation problem for Vietnamese ABSA tasks. In this paper, we formulate the Aspect-category based sentiment analysis task as the conditional text generation task and investigate different unified generative transformer-based models. To represent the labels in a natural sentence, we apply a simple statistical method and observation of the commenting style. We conduct experiments on two benchmark datasets. As a result, our model achieved new state-of-the-art results with the micro F1-score of 75.53% and 86.60% for the two datasets with different levels for the restaurant domain. In addition, our experimental results achieved the best score for the smartphone domain with the macro F1-score of 81.10%.
The Impact of Developmental Leadership on Adaptive Performance- Analytical Research General Directorates of Education in Baghdad province

The research aims to reveal the impact of developmental leadership in its dimensions (ideal model, interest in employees, inspiration and motivation) as an independent variable in achieving adaptive performance in its dimensions (Creative problem solving, learning and motivation, Training efforts, adaptability, work stress management, dealing with crises and emergencies) as a dependent variable, and the research aims to enhance the quality of developmental leadership in the research sample through this leadership style, which is a modern style and is still under research and study with a focus on the benefit of adaptive performance From a psychological and organizational research sample consisting of (106) respondents from the middle administrative leaders in the general directorates of education in Baghdad governorate, adopting the questionnaire as a main tool in data collection, and using a number of statistical methods, including (standard deviation, Soble test, and Pearson correlation coefficient). There is a positive and direct significant relationship between developmental leadership and adaptive performance in general, but at the level of dimensions, it was strong with the dimension of (learning and training efforts) and from medium to strong with (the dimension of personal adaptation and management of work pressures and emergencies.&#x0D; Paper type: Research paper.
An Ensemble Clustering Approach for Modeling Hidden Categorization Perspectives for Cloud Workloads

Abstract Effectively managing cloud resources is a complex task due to the interdependencies of various cloud-hosted services and applications. This task is integral to workload categorization, which groups similar cloud workloads to inform workload scheduling and resource management procedures. Although traditional clustering algorithms can categorize workloads into single data grouping patterns, the multifaceted nature of cloud workloads may conceal multiple valid categorization perspectives, indicating a need for a more adaptable clustering approach. This paper presents a novel ensemble clustering approach to enhance the scheduling workload categorization process in cloud computing. Our approach combines various normalization and transformation techniques, including principal component analysis, to form multiple data preprocessing pipelines. The data derived from these pipelines then serve as input for multiple base clustering learners. A novel combined score based on the Silhouette score, Calinski-Harabasz index, and Davies-Bouldin index is then employed to select the optimal models and preprocessing setups. The clustering outcomes of these models are encoded and inputted into a meta-clustering algorithm, effectively capturing complex categorization perspectives. Evaluation of this approach using real-world workload trace data from Microsoft Azure significantly enhances workload segmentation efficacy, thereby improving resource management and quality of service in cloud data centers. This method offers a promising pathway toward improved workload clustering in cloud computing, demonstrating the practical utility of advanced ensemble clustering techniques.
Higher-Order Thinking Skills (HOTS)-based literacy media: An innovative learning strategy to promote the secondary students’ social awareness

In this high-technology era, students are increasingly inclined to use digital services such as Facebook, Instagram, and YouTube. Unsurprisingly, most of them are becoming more addicted to using their digital devices. Unfortunately, the accessed contents are not always suitable and reliable for their needs and ages. Therefore, they absorb all information directly without filtering the messages. This study scrutinizes the implementation of Higher-Order Thinking Skills (HOTS)-based media literacy to promote the critical social awareness of junior high school students in an EFL classroom. A case study was employed as a research design involving one eighth-grader class and one EFL teacher as participants. The data were gathered via classroom observations, interviews with the teacher and the students, and document analysis. The data were then analyzed qualitatively by referring to the principles of teaching media literacy, Higher-Order Thinking Skills, and social awareness. The findings of this study revealed that the teacher applied some principles in teaching media literacy, namely introducing general concepts, making generalizations, analyzing information, evaluating the content of information, and drawing a conclusion from the students’ actions. These principles consequently promoted the students’ critical social awareness, including emotional awareness, self-regulation, empathy, active listening, cooperation, and respect. Considering such benefits, HOTS-based media literacy can be used as an alternative learning strategy to promote the students’ critical social awareness in EFL classrooms.
Discourse Competence as an Essential Variable in Developing Grade 11 English First Additional Language Learners’ Writing Skills

Discourse competence, which entails the interrelatedness of concepts in sentences in spoken and written language, is essential in the development of learners' receptive and productive English skills. Learners with excellent discourse competency skills can better grasp spoken and written texts on a local and global level. The main objective of this paper was to investigate the impact of discourse competence in grade 11 English First Additional Language (EFAL) learners’ writing skills. Halliday and Hassan’s Model of Evaluation framework, which advocates that the primary means of linking texts in discourse is through lexical cohesion, underpinned this study. This paper adopted an interpretivist paradigm. A qualitative approach was employed and a case study design was used to gather data from 40 purposely selected grade 11 learners. Document analysis was used. Findings indicated that (i) restricted knowledge of lexicon, (ii) inadequate knowledge about reiteration and collocation, and (iii) insufficient knowledge about appropriate use of cohesive and coherent devices, were among the established reasons for learners’ writing deficiencies. This paper recommends that essay writing skills can best be achieved through the implementation of the proposed recent language teaching methods such as the Text-based Approach, which uses texts to teach language structures and writing skills. The Department of Education should monitor the development of writing skills from the learners’ earliest years of schooling.
Optimal encoding of oscillators into more oscillators

Bosonic encoding of quantum information into harmonic oscillators is a hardware efficient approach to battle noise. In this regard, oscillator-to-oscillator codes not only provide an additional opportunity in bosonic encoding, but also extend the applicability of error correction to continuous-variable states ubiquitous in quantum sensing and communication. In this work, we derive the optimal oscillator-to-oscillator codes among the general family of Gottesman-Kitaev-Preskill (GKP)-stablizer codes for homogeneous noise. We prove that an arbitrary GKP-stabilizer code can be reduced to a generalized GKP two-mode-squeezing (TMS) code. The optimal encoding to minimize the geometric mean error can be constructed from GKP-TMS codes with an optimized GKP lattice and TMS gains. For single-mode data and ancilla, this optimal code design problem can be efficiently solved, and we further provide numerical evidence that a hexagonal GKP lattice is optimal and strictly better than the previously adopted square lattice. For the multimode case, general GKP lattice optimization is challenging. In the two-mode data and ancilla case, we identify the D4 lattice—a 4-dimensional dense-packing lattice—to be superior to a product of lower dimensional lattices. As a by-product, the code reduction allows us to prove a universal no-threshold-theorem for arbitrary oscillators-to-oscillators codes based on Gaussian encoding, even when the ancilla are not GKP states.
K-Fold Cross-Validation through Identification of the Opinion Classification Algorithm for the Satisfaction of University Students

When using machine-learning techniques to determine algorithms or ranking models that identify student satisfaction, algorithms are often trained and tested on a single data set, leading to bias in their performance metrics. This article aims to identify the best algorithm to classify the satisfaction of university students applying the K-fold cross-validation technique, comparing the error rates of the performance metrics before and after its application. The method used began with the collection of student opinions on the teaching performance of the social network Twitter during an academic semester. Then, sentiment analysis was used for data processing, through which it was possible to categorize the opinions of the students into “satisfied” or “dissatisfied.” The results showed that the algorithm with the lowest error rate in its performance metric was the support vector machine (SVM). In addition, it was identified that its classification probability reached an accuracy of 91.76%. It is concluded that SVM classification using K-fold cross-validation will contribute to determining which factors associated with the teacher’s didactic strategies should be improved in each class session, since traditional surveying techniques have shortcomings.
KNN Based Under Sampling: A Cognitive Centred Solution for Imbalanced Dataset Problem in Anaphora Resolution

Background: Like many other real world applications, the machine learning system of anaphora resolution also struggles with skewed data.The problem of imbalanced classes occurs with classification task where there a huge difference exists in the number of instances among the involved classes.Objectives: The proposed framework intends to remove the imbalance first between positive and negative class instances before classifying them by KBUS that makes use of cognitive knowledge about the language and analysis is done at attribute level.Method: Nine pruning rules are crafted by KBUS(KNN Based Under Sampling) for TDIL dataset.Findings: During experimentation, number of positive instances are increased from 5.32% to 43.95%, whereas the number of negative instances are decreased from 94.68% to 56.05%.Loss ratio of positive and negative instance is 1:112.Finally the pruned dataset is classified by a list of classifiers namelyNaïve Bayes, SVM, Random forest, decision tree and k-NN.Novelty: Classifier results are discussed in two perspectives: Firstly the number of input instances and secondly the performance improvement achieved after pruning.It is adduced that pruning shows a remarkable improvement for all the classifiers.The proposed system produced an encouraging result as 78% of f-measure for k-NN and 77% for decision tree.Performance is presented in a comparative manner before and after pruning and the improvement of fmeasureranges from 13% (k-NN) to 41% (Random Forest).Thus this work has come up with a machine learning model to resolve Tamil anaphoric situations effectively in an imbalanced classification environment.
Exploring the Landscape of Web Data Mining: An In-depth Research Analysis

The exponential growth of Web services and Web-based applications has led to an enormous volume of data, providing a rich source for mining valuable insights. Web mining differs from traditional data mining due to the unique nature of the data it handles. Web data exists in diverse forms, including web server logs, news pages, and hyperlinks. As the usage of the internet continues to surge, web mining has become essential to extract meaningful information and patterns from these varied data sources. Traditional data mining methods may not be directly applicable to web data due to its unstructured and heterogeneous nature. Web server logs contain valuable information about user interactions, click-streams, and user preferences, which can be mined to understand user behavior and improve website performance. News pages and other forms of web content are valuable sources for sentiment analysis, topic modeling, and information retrieval, helping businesses and researchers gain insights into public opinions and trends. Additionally, web structure mining deals with the analysis of hyperlinks, enabling the discovery of relationships between web pages and identifying authoritative sources. The continuous growth of web-based data necessitates the use of specialized methods in web mining to effectively extract knowledge and valuable patterns. Researchers and practitioners in this field are constantly exploring innovative techniques to make sense of the vast amount of data available on the World Wide Web. The paper provides web mining techniques on web data and presenting the latest advancements, researchers and practitioners can gain insights into the state of the field and identify potential areas for further exploration. This paper also reports the comparisons and summary of various methods of web data mining with applications, which gives the overview of development in research and some importantresearch issues.
An Efficient Outlier Detection Using Isolation Forest Based on Robust Scaling and Principal Component Analysis for the Prediction of Anxiety Disorder

Objectives: To develop a model for the prediction of anxiety disorder.Presence of outliers may affect the model performance.Hence, here the main consideration is the removal of outliers.Methods: This study proposed an outlier detection method IF-RSPCA in which outliers are handled in two phases.In the first phase, reduce the impact of outliers by using Inter Quartile Range (IQR), then dimensionality reduction is done using principal component analysis.Training time can be reduced by Principal Component Analysis.In the second phase of IF-RSPCA, check the outliers for still exist and removed.Findings: For the performance evaluation two datasets are generated; one using conventional isolation forest and the other using IF-RSPCA.Performance of both datasets are tested using K Neighbors, Decision Tree and Naïve Bayes classifiers, highest accuracies are obtained as 96.10 %,93.80% and 90.2%, respectively.It is found that the dataset generated using proposed method performed well.As compared to previous works on survey datasets, the proposed model is capable of identifying the anxiety, more accurately.Novelty: Conventional isolation forest algorithm is modified using Inter Quartile Range there by improve the performance measures.It helps to removes all the outliers completely.The previous models for the predictions of anxiety disorder are developed without performing the outlier detection; hence chances of misclassification exist.In several previous works, isolation forest combined with clustering, but it is not capable of removing all the outliers.In these cases, outlier handling is performed in a single phase only.
A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning

This paper presents a depth-based hybrid method to generate safe flight corridors for a memoryless local navigation planner. It is first proposed to use raw depth images as inputs in the learning-based object-detection engine with no requirement for map fusion. We then employ an object-detection network to directly predict the base of polyhedral safe corridors in a new raw depth image. Furthermore, we apply a verification procedure to eliminate any false predictions so that the resulting collision-free corridors are guaranteed. More importantly, the proposed mechanism helps produce separate safe corridors with minimal overlap that are suitable to be used as space boundaries for path planning. The average intersection of union (IoU) of corridors obtained by the proposed algorithm is less than 2%. To evaluate the effectiveness of our method, we incorporated it into a memoryless planner with a straight-line path-planning algorithm. We then tested the entire system in both synthetic and real-world obstacle-dense environments. The obtained results with very high success rates demonstrate that the proposed approach is highly capable of producing safe corridors for memoryless local planning.
Cultivating a Secure Digital Future: The Nexus of Innovation and Data Protection in Modern Organizations

In the digital era, the interconnectivity between producers and consumers has evolved, presenting both opportunities and challenges. This article delves into the role of fostering innovation behavior in organizations, emphasizing its significance in data protection and the cultivation of a technology security culture. Introducing the "Data Tradeoff Framework", the article provides a comprehensive perspective on big data application scenarios, highlighting the importance of understanding data's intrinsic characteristics. With the rise of Fintech in regions like China and Southeast Asia, questions about data ownership, privacy, and competitive dynamics have become paramount. The article argues that while data has the potential to influence market dynamics, its responsible use can lead to a vibrant competitive landscape, promoting innovation and safeguarding consumer interests. The overarching message is the necessity for organizations to adopt a holistic approach to data, ensuring protection, transparency, and innovation in tandem.
Towards a Common Standard Framework for Blockchain Interoperability - A Position Paper

&lt;p&gt;Decentralized ledger technology (DLT) is becoming ubiquitous in today's society. After an initial grassroots adoption, enterprises embrace this technology, following the opportunity to expand to new businesses - the technology is maturing. However, organizations need to connect their existing systems and processes to blockchains (centralized - decentralized) securely and reliably, sometimes also implying to connect blockchains (decentralized - decentralized). This challenge is known as blockchain interoperability.&lt;/p&gt; &lt;p&gt;Blockchain interoperability comes in three modes: data transfers, asset transfers, and asset exchanges. As blockchain interoperability is still maturing, there are many unsolved challenges across the different interoperability modes. In this \emph{position paper}, we illustrate the remaining challenges of interoperability, focusing on the systematic evaluation of interoperability mechanisms, based on the state of the art and our own experience. We first introduce the notion of cross-chain interoperability and cross-chain rules. Then, we present our survey and results. &lt;/p&gt; &lt;p&gt;We ran an online survey comprised of 17 items targeting blockchain specialists. Our quantitative analysis shows that several interesting metrics can show promising directions to evaluate integration solutions systematically. Finally, building upon our study, we propose the blockchain interoperability evaluation framework, the first step to evaluate blockchain interoperability solutions systematically.&lt;/p&gt;
DAG Blockchain-based Personalized Federated Mutual learning in Internet of Vehicles

&lt;p&gt; Federated learning (FL) emerges as a distributed training method in the Internet of Vehicles (IoVs), allowing connected and automated vehicles (CAVs) to train a global model by exchanging models instead of raw data, protecting data privacy. Due to the limitation of model accuracy and communication overhead in FL, in this paper, we propose a directed acyclic graph (DAG) blockchain-based IoV that comprises a DAG layer and a CAV layer for model sharing and training, respectively. Furthermore, a DAG blockchain-based asynchronous federated mutual learning (DAFML) algorithm is introduced to improve the model performance, which utilizes a student-teacher model in the local training and a mutual distillation method that enables models to be partially updated using mutual transferred knowledge. Moreover, to further improve the model accuracy, the personalized weight is designed to adjust the mutual distillation in the model updating. Simulation results demonstrate that the proposed DAFML algorithm outperforms other benchmarks in terms of the model accuracy and distillation ratio.&lt;/p&gt;
Application of Innovative Systems for Achieving Compliance in Countering Hybrid Threats

The main goal of this article is to draw attention to and contribute to the discussion on the new challenges facing the countering of the financing of hybrid threats. The research problem cuts across three study and policy areas: national security, legal codification and Artificial Intelligence. A serious theoretical conflict regarding the concept of hybrid warfare is identified and an attempt is made to demonstrate the seriousness of the risk of funding hybrid threats by global actors. The focus of the study is the economic aspects of hybrid warfare, related to the threat of covert financing of hybrid operations, and accordingly a possible model with innovative financial technologies for risk management. A short but comprehensive overview of the current legal regulations providing possible guidelines for legislative changes and technological solutions in this area has been made. On the basis of an interdisciplinary systematic analysis, including a presentation of technical parameters of innovation systems with Artificial Intelligence elements (AIE) and an overview of European and American practices and regulations, gaps in the current regulations in Bulgaria are identified and some conclusions are made about this aspect of the country’s security.
Semantic Segmentation for Pet Detection

This research project encompasses an in-depth exploration of semantic segmentation methods for pet detection using the Oxford Pets Dataset. The primary objective involves the developmentof a convolutional neural network model rooted in deep-learning principles, designed to achieve precise segmentation and detection of pets within images. The approach integrates advanced image processing techniques, leveraging deep learning methodologies, and dataset augmentation strategies to enhance pet detection accuracy substantially. The outcomes underscore the considerable potential of semantic segmentation in elevating the effectiveness of pet detection applications. This study offers promising avenues for practical integration in real-world contexts such as pet care and surveillance systems. The achieved advancements underscore the proposed technique's viability and contribute to the broader discourse on enhancing object detection through sophisticated segmentation strategies.