Reviewerly/exHarmony huggingface dataset:
Analisis Kinerja Algoritma K-Nearest Neighbor Imputation (KNNI) Untuk Missing Value Pada Klasifikasi Data Mining

At this time many researchers use datasets for research, in the dataset there is a lot of important information provided to make it easier for researchers to process the data, but there are obstacles when trying to process the data, namely some data values are lost, or even damaged (incompatible) with other values. The number of attributes and data samples in the dataset is unlimited which makes it difficult for researchers to find important information based on the goals of each researcher. The missing or damaged values are known as missing values and this often happens when researchers process data taken from datasets. When researchers classify a dataset, of course it is difficult to classify because if there is missing data, the classification process cannot be carried out completely. Of course this will greatly affect the results obtained and also the results of the accuracy of the process. The problem of missing data (missing value) must be resolved, one way to solve this problem is by using the K-Nearest Neighbor Imputation (K-NNI) algorithm. The KNNI algorithm is one of the data mining algorithms that can restore the missing value problem by performing pattern recognition of the nearest data from the missing data. The final result after applying KNNI in fixing the missing value is to obtain a weight value for each K closest observation of 51.6161 on K5. Datasets that experience missing values have been adjusted based on the weight values and the implementation of the KNNI algorithm with the rapidminer application. By returning data using the KNNI algorithm, it is hoped that it can help solve these problems so that the results obtained from the classification process are more accurate and the results of greater accuracy
Improved Calibration of Eye-in-Hand Robotic Vision System Based on Binocular Sensor

Eye-in-hand robotic binocular sensor systems are indispensable equipment in the modern manufacturing industry. However, because of the intrinsic deficiencies of the binocular sensor, such as the circle of confusion and observed error, the accuracy of the calibration matrix between the binocular sensor and the robot end is likely to decline. These deficiencies cause low accuracy of the matrix calibrated by the traditional method. In order to address this, an improved calibration method for the eye-in-hand robotic vision system based on the binocular sensor is proposed. First, to improve the accuracy of data used for solving the calibration matrix, a circle of confusion rectification method is proposed, which rectifies the position of the pixel in images in order to make the detected geometric feature close to the real situation. Subsequently, a transformation error correction method with the strong geometric constraint of a standard multi-target reference calibrator is developed, which introduces the observed error to the calibration matrix updating model. Finally, the effectiveness of the proposed method is validated by a series of experiments. The results show that the distance error is reduced to 0.080 mm from 0.192 mm compared with the traditional calibration method. Moreover, the measurement accuracy of local reference points with updated calibration results from the field is superior to 0.056 mm.
The Potential of Hyperspectral Data for Detection Lithium-Bearing Pegmatite: A Case Study at Uljin, South Korea

Lithium (Li) is one of the key minerals needed for low- carbon future industries such as electric vehicles. Recently, several remote sensing approaches (multispectral and hyperspectral) for lithium pegmatite are increasing. However, few studies have directly investigated Li pegmatite outcrops using ground-based hyperspectral data. In this study, we investigated the spectral properties of Li pegmatite samples from the Boam mine in Uljin and directly map Li-bearing minerals from the Boam mine outcrops to verify the potential of ground-based hyperspectral data for mineral exploration.
ARD, FAIR Earth Observation Principles, Data Fusion: Where are we and where do we need to go?

With artificial intelligence breakthroughs permeating the Earth Science domain, there is an immediate need to advance the data, tools, and resulting technologies to broader societal challenges. Different efforts are emerging with fragmented best practices for making Earth Observation (EO) data Artificial Intelligence (AI)-ready, availing computer vision and image analysis tools for broader reuse across the remote sensing community. This paper will revisit current best practices and outline a guideline for advancing EO data and derivative AI products for broader community use. We mainly discuss the Analysis Ready Data (ARD) essentials and aim to forge their evolution with Findable, Accessible, Interoperable, Reusable (FAIR) principles to support cross-modal/cross-sensor/cross-provider opportunities that appear to be central to solving complex EO challenges.
A Split Iterative Adaptive Approach for Super-Resolution Imaging of Sparse Scene

Recently, the iterative adaptive approach (IAA) has been widely applied to enhance the azimuth resolution of real beam mapping (RBM) imagery. However, the IAA suffers from extremely high computational complexity in practice. This paper proposes a Split IAA for sparse scene to reduce the complexity. First, the IAA cost function is decomposed. Then the echo data is split into blocks, and the iterative model is redefined according to the target block and its corresponding cost function. Consequently, the high-dimensional data inversion problem is decomposed into multiple low-dimensional sub-problems to achieve fast super-resolution imaging. The measured results show that the proposed Split IAA significantly reduces the computational complexity without affecting super-resolution performance.
Persepsi Siswa terhadap Pembelajaran Daring (Studi Kasus pada Siswa Kelas X SMA Negeri 2 Bondowoso)

This study aims to determine student perceptions of online learning in class X SMA Negeri 2 Bondowoso. This research is a descriptive quantitative research with data collection methods used, namely questionnaires, interviews, and documents. The subjects in this study were class X students of SMA Negeri 2 Bondowoso. The results of the research show that students' perceptions of online learning vary in terms of learning processes indicators and support. Students are less interested in learning online because they cannot interact smoothly with the teacher. In addition, students find it difficult to understand the material and do assignments because the assignments given are disproportionate. However, students can work on assignments independently. On the support indicator, some students receive support from related parties, including schools, government, and student guardians.
Hannotate: Flexible Annotation for Text Analytics from Anywhere

Data annotation is a critical but the most expensive step in any text analytics project. There have been several frameworks built for enabling and easing this step. Most of these frameworks are however either not easy to be configured to specific users’ needs, have no functionalities for annotating text pairs, or lack of efficient mechanism for data management and progress monitoring. Moreover, they have mostly no graphical user interfaces that are specifically designed for mobile devices. In this paper, we introduce Hannotate, a highly flexible, lightweight web-based framework that provides functionalities for a wide range of text annotation from both desktop and mobile devices. Our framework inherits the advantages of the typical existing ones while allowing users to easily customize the annotation work according to their demand and budget. The framework also supports users in managing data, monitoring the progress, and giving feedback to annotators.
TRIC: A Triples Corrupter for Knowledge Graphs

We study the problem of corrupting triples in Knowledge Graphs (KG) for the purpose of assisting anomaly detection and error detection techniques developed for KG quality enhancement. Our goal is to provide users with the highest possible level of control over the triples corruption process, and simultaneously develop a solution that scales to large KGs. Hence, we introduce TRIC, an approach for corrupting triples considering both semantic and type information to generate errors in a KG. In this paper, we discuss how the problem of triples corruption is challenging, and different from existing negative sampling techniques used in link prediction. To the best of our knowledge, there is no approach in the literature dedicated for generating abnormal triples in KGs to support anomaly detection and error detection tasks.
Cognitive Performance and Use of Digital Tools as Predictors of Academic Success in University Students

Quality education is one of the sustainable development goals (SDG-4), which in the context of the pandemic has undergone several transformations; for this reason, this study aims to assess the cognitive profile and digital tools that enhance learning in the classroom, as facilitators of academic success in university students. A qualitative-quantitative methodology was used, with a non-experimental design, with a descriptive and correlational scope. Cognitive tests, a comprehensive academic evaluation, observation records, and surveys were applied to 100 students from Israel University, from 18 to 45 years of age, in online, blended, and hybrid modalities. The results showed that there is a significant relationship between the scores of the attentional tests and the cognitive tasks that included visuospatial rationing (p = 0.016), resistance to fatigue (0.002), and the academic average of the university students (p = 0.000). A high positive correlation (0.724) was also found between the TOT and the academic average, and a moderate positive correlation (0.598) between the CON and the academic average. No significant differences were observed between gender and age groups. The majority of students (43.2%) reported that the digital tools that enhance their learning that contributed to their academic achievement achieved were mainly H5P and flipped classroom.
Learning the Co-evolution Process on Live Stream Platforms with Dual Self-attention for Next-topic Recommendations

Live stream platforms have gained popularity in light of emerging social media platforms. Unlike traditional on-demand video platforms, viewers and streamers on the live stream platforms are able to interact in real-time, and this makes viewer interests and live stream topics mutually affect each other on the fly, which is the unique co-evolution phenomenon on live stream platforms. In this paper, we make the first attempt to introduce a novel next-topic recommendation problem for the streamers, LSNR, which incorporates the co-evolution phenomenon. A novel framework CENTR introducing the Co-evolutionary Sequence Embedding Structure that captures the temporal relations of viewer interests and live stream topic sequences with two stacks of self-attention layers is proposed. Instead of learning the sequences individually, a novel dual self-attention mechanism is designed to model interactions between the sequences. The dual self-attention includes two modules, LCA and LVA, to leverage viewer loyalty to improve efficiency and flexibility. Finally, to facilitate cold-start recommendations for new streamers, a collaborative diffusion mechanism is implemented to improve a meta learner. Through the experiments in real datasets, CENTR outperforms state-of-the-art recommender systems in both regular and cold-start scenarios.
Prod2Vec-Var: A Session Based Recommendation System with Enhanced Diversity

Understanding user behavior and leveraging this information in recommendation systems pose challenges for websites lacking a login system or with limited logged-in users. This study introduces the Prod2Vec-Var recommendation system, a modified version of a session-based recommendation algorithm aimed at enhancing the performance of product recommendation systems with a cold-start extension. The proposed model builds upon the original prod2vec algorithm, incorporating an additional step to improve the diversity of product recommendations. The project entails a well-designed data pipeline, effectively processing user actions to align them with the model, and implementing unique functions that expand the range of products capturing users' attention. Moreover, a straightforward yet effective cold-start model is developed to address newly added products that have not been viewed by users. The outcome of our project, namely product suggestions, is presented to users of cimri.com, one of iLab's affiliated companies, which attracts millions of daily visits, thereby enabling seamless access to desired products. Experimental results demonstrated the superior performance of our model compared to the other two different strategies in running recommendation on popular products, as evidenced by favorable R@1, R@5, R@10, and R@15 metrics. Concerning less popular products, we observed an improvement in our model's performance as the value of K increased, ultimately achieving optimal results in terms of R@15. Additionally, our cold-start model for new products substantiated the efficacy of our methodology, yielding the highest scores across R@5, R@10, and R@15 metrics.
Multi-Order Relations Hyperbolic Fusion for Heterogeneous Graphs

Heterogeneous graphs with multiple node and edge types are prevalent in real-world scenarios. However, most methods use meta-paths on the original graph structure to learn information in heterogeneous graphs, and these methods only consider pairwise relations and rely on meta-paths. In this paper, we use simplicial complexes to extract higher-order relations containing multiple nodes from heterogeneous graphs. We also discover power-law structures in both the heterogeneous graph and the extracted simplicial complex. Thus, we propose the Simplicial Hyperbolic Attention Network (SHAN), a graph neural network for heterogeneous graphs. SHAN extracts simplicial complexes and the original graph structure from the heterogeneous graph to represent multi-order relations between nodes. Next, SHAN uses hyperbolic multi-perspective attention to learn the importance of different neighbors and relations in hyperbolic space. Finally, SHAN integrates multi-order relations to obtain a more comprehensive node representation. We conducted extensive experiments to verify the effectiveness of SHAN and the results of node classification experiments on three publicly available heterogeneous graph datasets demonstrate that SHAN outperforms representative baseline models.
Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training

Existing recommender systems face difficulties with zero-shot items, i.e. items that have no historical interactions with users during the training stage. Though recent works extract universal item representation via pre-trained language models (PLMs), they ignore the crucial item relationships. This paper presents a novel paradigm for the Zero-Shot Item-based Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph (PKG) to refine the item features from PLMs. We identify three challenges for pre-training PKG, which are multi-type relations in PKG, semantic divergence between item generic information and relations and domain discrepancy from PKG to downstream ZSIR task. We address the challenges by proposing four pre-training tasks and novel task-oriented adaptation (ToA) layers. Moreover, this paper discusses how to fine-tune the model on new recommendation task such that the ToA layers are adapted to ZSIR task. Comprehensive experiments on 18 markets dataset are conducted to verify the effectiveness of the proposed MPKG model.
Optimizing CNN hyperparameters with genetic algorithms for face mask usage classification

Convolutional Neural Networks (CNNs) have gained significant traction in the field of image categorization, particularly in the domains of health and safety. This study aims to categorize the utilization of face masks, which is a vital determinant of respiratory health. Convolutional neural networks (CNNs) possess a high level of complexity, making it crucial to execute hyperparameter adjustment in order to optimize the performance of the model. The conventional approach of trial-and-error hyperparameter configuration often yields suboptimal outcomes and is time-consuming. Genetic Algorithms (GA), an optimization technique grounded in the principles of natural selection, were employed to identify the optimal hyperparameters for Convolutional Neural Networks (CNNs). The objective was to enhance the performance of the model, namely in the classification of photographs into two categories: those with face masks and those without face masks. The convolutional neural network (CNN) model, which was enhanced by the utilization of hyperparameters adjusted by a genetic algorithm (GA), demonstrated a commendable accuracy rate of 94.82% following rigorous testing and validation procedures. The observed outcome exhibited a 2.04% improvement compared to models that employed a trial and error approach for hyperparameter tuning. Our research exhibits exceptional quality in the domain of investigations utilizing Convolutional Neural Networks (CNNs). Our research integrates the resilience of Genetic Algorithms (GA), in contrast to previous studies that employed Convolutional Neural Networks (CNN) or conventional machine learning models without adjusting hyperparameters. This unique approach enhances the accuracy and methodology of hyperparameter tuning in Convolutional Neural Networks (CNNs).
Improving Conversational Recommender Systems via Knowledge-Enhanced Temporal Embedding

Conversational recommender systems are becoming increasingly popular due to their potential to facilitate personalized interactions between users. However, one major challenge lies in accurately representing the semantic meaning of the conversational history to make relevant recommendations. In this paper, we propose a knowledge-enhanced model KMTE to enhance conversational recommender systems. To achieve a more nuanced understanding of users’ evolving interests and behaviors over time, a knowledge-enhanced temporal embedding is integrated into KMTE to facilitate the encoding of temporal aspects into the representation of user dialogues. Our proposal is extensively evaluated on a real conversational dataset, and the experimental results demonstrate the effectiveness and superiority of our proposals in improving the accuracy and relevance of conversational recommender systems. Our work sheds light on the potential of leveraging advanced language models to enhance the performance of conversational recommender systems.
Fast Text Comparison Based on ElasticSearch and Dynamic Programming

Text comparison is a process of comparing and matching two or more texts to determine their similarities or differences. By calculating the similarity between two texts, tasks such as classification, clustering, retrieval, and comparison can be performed on texts. In this work, we have improved existing text matching methods based on ElasticSearch and dynamic programming. Leveraging the powerful indexing and search capabilities of ElasticSearch, our method enables fast retrieval and comparison of relevant documents. During the text comparison process, we utilize an improved LCS (Longest Common Subsequence) algorithm to calculate the matches between the texts. We conduct extensive experiments on real-world datasets to evaluate the performance and effectiveness of our method. The results demonstrate that our approach can accomplish text comparison tasks more efficiently while handling various types of text noise.
Literature review on the relationship between corporate culture and performance in academic research

Corporate culture is an important factor that improves performance and determines the future success of a business. In today's competitive business environment, differentiation is shifting from products and services to corporate culture and corporate resources. With research data was collected and analyzed from the Google Scholar database on VOSviewer 1.16.19 software with 389 articles for the keywords "corporate culture" and "operational efficiency" to systematize previous research on the influence of corporate culture on performance. The results showed the most influential authors according to the number of citations and number of articles, the keywords that have received the most attention in researching the relationship between corporate culture and performance. At the same time, keyword analysis identifies many contents about corporate culture and performance, thereby guiding important research for future research.
Defining a Preprocessing Pipeline for the MULTI-SITA Project and General Medical Italian Natural Language Data

The application of Natural Language Processing (NLP) to medical data has revolutionized different aspects of health care. The benefits obtained from the implementation of this technique spill over into several areas, including in the implementation of chatbots, which can provide medical assistance remotely. Every possible application of NLP depends on one first main step: the pre-processing of the corpus retrieved. The raw data must be prepared with the aim to be used efficiently for further analysis. Considerable progress has been made in this direction for the English language but for other languages, such as Italian, the state of the art is not equivalently advanced, especially for texts containing technical medical terms. The aim of this work is to identify and develop a preprocessing pipeline suitable for medical data written in Italian. The pipeline has been developed in Python environment, employing Enchant, ntlk modules and Hugging Face's BERT and BART-based models. Then, it has been tested on real conversations typed between patients and physicians regarding medical questions. The algorithm has been developed within the MULTI-SITA project of the Italian Society of Anti-Infective Therapy (SITA), but shows a flexible structure that can adapt to a large variety of data.
Certified Private Inference on Neural Networks via Lipschitz-Guided Abstraction Refinement

Private inference on neural networks requires running all the computation on encrypted data. Unfortunately, neural networks contain a large number of non-arithmetic operations, such as ReLU activation functions and max pooling layers, which incur a high latency cost in their encrypted form. To address this issue, the majority of private inference methods re- place some or all of the non-arithmetic operations with a polynomial approximation. This step introduces approximation errors that can substantially alter the output of the neural network and decrease its predictive performance. In this paper, we propose a Lipschitz- Guided Abstraction Refinement method (LiGAR), which provides strong guarantees on the global approximation error. Our method is iterative, and leverages state-of-the-art Lipschitz constant estimation techniques to produce increasingly tighter bounds on the worst-case error. At each iteration, LiGAR designs the least expensive polynomial approx- imation by solving the dual of the corresponding optimization problem. Our preliminary experiments show that LiGAR can easily converge to the optimum on medium-sized neural networks.