Reviewerly/exHarmony huggingface dataset:
TRANSFORMASI PUBLIKASI STMKG DIGITAL: PENINGKATKAN SUMBER DAYA MANUSIA UNGGUL DAN PERCEPATAN AKREDITASI INSTITUSI

Improving excellent human resources is an initiative that aims to develop and strengthen individual and collective potential within the BMKG (Meteorology, Climatology and Geophysics Agency) organization to make it a leading global player in the fields of meteorology, climatology, geophysics and instrumentation. Excellent human resources in higher education institutions have the potential to conduct quality research and produce quality scientific publications. In this action of change, the integration of the STMKG Digital publication service program, both updating the E-Journal and building the STMKG PRESS publishing media as a single digital-based and indexed account, has been successfully carried out and is the key to the realization of a comprehensive publication information system. The integration of publication services is aimed at improving efficiency and effectiveness in both indexing and digital documents. This transformation will encourage teams involved in this change action plan to collaborate more, effective communication, and writing literacy. The results of this change action are expected to be useful for STMKG's internal interests, namely to facilitate the accreditation preparation process, academic data collection, and the accreditation assessment simulation process. The benefits for BMKG are the implementation of the BMKG 2022-2024 strategic plan and the improvement of superior human resources towards 500 Doctorates and BMKG Global Player.
Izrada OWL ontologije za prikaz, povezivanje i pretraživanje SemAF diskursnih oznaka

Linguistic Linked Open Data (LLOD) are technologies that provide a powerful instrument for representing and interpreting language phenomena on a web-scale. The main objective of this paper is to demonstrate how LLOD technologies can be applied to represent and annotate a corpus composed of multiword discourse markers, and what the effects of this are. In particular, it is our aim to apply semantic web standards such as RDF and OWL for publishing and integrating data. We present a novel scheme for discourse annotation that combines ISO standards describing discourse relations and dialogue acts – ISO DR-Core (ISO 24617-8) and ISO-Dialogue Acts (ISO 24617-2) in 9 languages (cf. Silvano and Damova 2022; Silvano, et al. 2022). We develop an OWL ontology to formalize that scheme, provide a newly annotated dataset and link its RDF edition with the ontology. Consequently, we describe the conjoint querying of the ontology and the annotations by means of SPARQL, the standard query language for the web of data. The ultimate result is that we are able to perform queries over multiple, interlinked datasets with complex internal structure. This is a first, but essential step, in developing novel, powerful, and groundbreaking means for the corpus-based study of multilingual discourse, communication analysis, or attitudes discovery.
CVR-LSE: Compact Vectorized Representation of Local Static Environments for Reliable Obstacle Detection

This article proposes a compact vectorized representation approach to realize reliable obstacle detection for autonomous ground vehicles. Bridging the gap between the advantage of occupancy grid map in general obstacle detection and its shortcomings in memory-consuming and computational cost, a novel obstacle representation by multiple convex polygons is first proposed. Specially, to overcome the computational challenge brought by many 3-D point cloud, based on ground plane extraction and nearest obstacle selection, 3-D obstacle points are transformed into 2-D obstacle points. In addition, the maintenance process of grid map with a fixed range is proposed to further improve efficiency. More importantly, the compact obstacle representation is realized via multiple convex polygons through double threshold-based boundary simplification and convex polygon segmentation. The compact vectorized representation is the main contribution, which achieves the goal of compactness and accuracy on the premise of ensuring effective and reliable obstacle detection. The proposed approach has been applied in a practical autonomous driving project because of its superior performance on general obstacles detection. In addition, the quantitative evaluation shows the superior performance on making use of fewer number of points (decreased by about 50%) to represent local static environments.
A Transparent Scalable E-Voting Protocol Based on Open Vote Network Protocol and Zk-STARKs

Electronic voting (e-voting) has become increasingly popular as an approach to achieve novel security requirements compared to paper voting. Among the technologies used to implement e-voting, blockchain has gained significant attention due to its distinctive properties. This paper will assess the feasibility of applying cutting-edge technologies such as smart contracts, zero-knowledge rollup, etc., to tackle some widely concerned limitations of blockchain-based electoral systems, namely scalability and transparency.
\({\ell_0}\) Optimization with Robust Non-Oracular Quantum Search

In this article, we introduce an innovative hybrid quantum search algorithm, the Robust Non-oracle Quantum Search (RNQS), which is specifically designed to efficiently identify the minimum value within a large set of random numbers. Distinct from the Grover’s algorithm, the proposed RNQS algorithm circumvents the need for an oracle function that describes the true solution state, a feature often impractical for data science applications. Building on existing non-oracular quantum search algorithms, RNQS enhances robustness while substantially reducing running time. The superior properties of RNQS have been demonstrated through careful analysis and extensive empirical experiments. Our findings underscore the potential of the RNQS algorithm as an effective and efficient solution to combinatorial optimization problems in the realm of quantum computing.
Pressure Oxidation Modelling for Design and Optimisation

Process modelling is used as a simulation tool for the purposes of design and optimisation. This paper discusses the application of modelling in hydrometallurgical project development and the value it creates by allowing for rapid high-level flowsheet option evaluation, testwork planning and analysis, provision of data for equipment design, and operating plant optimisation. This paper explains the development process for a pressure oxidation model from input data such as throughput, feed composition, and testwork results. Case studies demonstrate the real-world application of modelling for process development.
HRRP Radar Angular Scintillation Suppression Algorithm Based on the Adaptive Weighting of Position Information

Angular scintillation error is the main error source of millimeter-wave guidance heads seeking guidance. The monopulse angular algorithm based on high-resolution radar can effectively improve the angular scintillation phenomenon. Most of the existing studies are based on the idea of amplitude weighting, using the amplitude information of the distance unit for weighting. In this paper, based on the Minimum Mean Square Error (MMSE) criterion, combined with the High Range Resolution Profile (HRRP) goniometric algorithm, an adaptive weighting method is proposed to fuse and analyze the position and amplitude information [1], and finally, an Extended Kalman Filter (EKF) is used for angle smoothing. Simulation experiments show that the algorithm has a good suppression effect on angular flicker.
Using Principal Component Analysis (PCA) to Map Alteration Types Related to Rare Earth Elements (REE) Occurrence in the Palapye Group Rocks

The global demand for Rare Earth Elements (REE) due to population increase, and demand for green technology has increased, hence the need for more REE as they are used in various electronic devices, [1] . To meet this demand for REE Botswana has joined the global community to search and explore REE that have been previously reported. The REE occurrence had been reported around Kodibeleng area, in the Central part of Botswana. The geology of area is made up of the middle Precambrian Palapye group which consists of feldspathic grits, quartzites, shales and conglomerates overlain by locally cherty haematitic ironstone, [2] . The purpose of the investigation was to check if the previously reported REE occurrence coincides with any alteration zone or types mapped using principal component analysis from Advanced Space Borne Thermal Emission and Reflection (ASTER) data. The REE deposit that was reported is related to the iron oxide copper gold (IOCG) mineral system according to [3] , due to its nature hydrothermal alteration is envisaged.
Vibrational Spectral Line Detection in GIS Environment for Mineral Mapping Applications of Hyperspectral Data of the PRISMA Satellite

We develop a mathematical algorithm that calculates discrete spectral derivative of the hyperspectral data of the satellite mission PRISMA in a Geographic Information System (GIS) environment. Vibrational fingerprits of minerals appear in the reflectance spectra as local "dips" centered at a resonance wavelength that characterizes some classes of minerals. The algorithm is then applied twice to each spectrum in each image datacube: once on the descending slope of a known vibrational feature, and once on the ascending slope. The sum of the absolute value of the two derivatives, obtained from a total of several tens of hyperspectral channels, is then combined in a vibrational line intensity score, which is then used to build mineral abundance maps. We apply the method to a mountain area, mapping freshly exposed gneiss under molten glaciers (vibrational resonance at 2222 nm), and to volcanic islands mapping rhyolite deposits on shores (resonance at 2104 nm).
Avaliação de Transparência de Portais de Ecossistemas de Software Utilizando Ferramentas de Análise de Experiência de Desenvolvedor

Context: Software Ecosystems (SECO) portals are web interfaces that allow a developer to access an ecosystem. They allow the consumption of information and communication by actors. Motivation: Improving the Developer Experience (DX) is an important concern for developers to be engaged with a SECO portal. Transparency helps in the understanding of the information made available and in the communication between the actors. Problem: If the DX is unsatisfactory, developers can abandon the portal and consequently the SECO. Objective: The objective of this work is to use methods and tools to capture multimodal and interaction information in SECO portals, aiming to promote the improvement of transparency aspects that can lead to the improvement of DX in such portals. Research Method: Studies were carried out with developers and methods were used for quantitative and qualitative data analysis. Results: Factors that influence DX in the software development process and factors related to transparency that influence DX were identified based on reports from the study participants. Contributions: The main contribution of this work is to support the engagement of developers in SECO portals and encourage the improvement of transparency about the information and processes made available.
A high-throughput DAG-based blockchain protocol

In recent years, blockchain has developed rapidly and achieved great success in a variety of industries. However, the current shortcomings of blockchain in terms of low throughput and slow transaction confirmation have led to the difficulty of implementing and using blockchain in some highly concurrent scenarios. In this regard, blockchain technology based on DAG was born. DAG is characterized by high concurrency and high throughput due to its unique data structure, and its combination with blockchain can break the blockchain throughput bottleneck. In this regard, a DAG-based blockchain consensus algorithm is proposed to fully order all blocks, provide the total order and effectively improve the throughput of the blockchain using the division of subgraphs. The new proposed consensus algorithm is experimentally demonstrated to have significant improvement in data throughput, and it is found that SgDAG has 79 times higher throughput than Bitcoin, 36.9 times higher than Ethereum, and 5.5 times higher than IOTA, with high throughput and can use smart contracts
Application of the InterCriteria Analysis Aproach to a Burnout Syndrome Data

In this study a statistical data related to burnout syndrome among the medical employees was investigated. The data were collected through a survey among the staff in 5 medical centers in Bulgaria (2 University General Hospitals for Active Treatment, 1 General hospital for active treatment (municipal), and 1 Specialized rehabilitation hospital). The dependencies between the various parameters describing the studied objects are studied by InterCriteria approach (ICA).
The mediating effect of professor-student interaction on the relationship between major selection motivation and college life satisfaction of adult learners in LiFE project college

Objectives The purpose of this study is to examine the relationship between adult learners motivations for major selection, professor-student interactions, and university life satisfaction.&#x0D; Methods The study was conducted through an online survey from May 1st to June 10th, 2023, targeting a total of 260 adult learners from K University and D University. Seven copies with inappropriate responses for the pur-pose of the survey were excluded, and the final analysis was based on 253 completed surveys. To examine the relationship between motivations for major selection, professor-student interactions, and university life satisfaction. we conducted analyses including means, correlations, and multiple regression analysis.&#x0D; Results The results of this study are as follows. first, It was found that there was a statistically significant correlation between adult learners' major selection motivation, professor-student interaction, and university life satisfaction. Second, In the relationship between major selection motivation and university life satisfaction, professor-student interaction was found to have a partial mediating effect.&#x0D; Conclusions These results revealed the partial mediating effect of professor-student interaction in the relation-ship between major selection motivation and university life satisfaction of adult learners who participated in the LiFE. Through this, In order to improve adult learners' satisfaction with college life, it is necessary to pay attention to the intrinsic and extrinsic motivations of major selection, and various measures, support policies, and programs were required to promote professor-student interaction identified as parameters.
Research on proof of stake consensus algorithm based on dynamic trust

Blockchain is a decentralized and tamper-proof distributed ledger technology. The consensus algorithm is one of the key technologies underlying blockchain. The Proof of Stake (PoS) consensus mechanism determines the next block producer on the blockchain based on the user's staked equity. Compared to Proof of Work (PoW), the Proof of Stake consensus mechanism solves the problem of wasted resources. However, the PoS consensus mechanism faces serious issues such as the coin age accumulation attack and the zero-cost benefit problem. Therefore, this paper proposes a Trust-Based Proof of Stake (TPoS) mechanism based on dynamic trustworthiness. TPoS divides nodes in the network into miner nodes and basic equity representative (shareholder) nodes, and assigns corresponding trustworthiness to nodes based on their participation in creating blocks. Shareholder nodes sign blocks and assign them trustworthiness, and finally compete for the weight of trustworthiness obtained by blocks to go on the chain. In addition, this paper analyzes the attack cost and system response to bribery attacks and common equity accumulation attacks. The results of simulation experiments show that the TPoS mechanism has significant advantages over traditional Proof of Stake mechanisms in dealing with bribery attacks and equity accumulation attacks.
Research on sentiment analysis methods based on aspect word embedding graph convolutional networks

Aspect-based sentiment analysis is a fine-grained task in the field of sentiment analysis. Various GCN approaches have recently emerged to work on this, but many approaches ignored the critical role of aspectual word information and the effect of noise. In view of this situation, we propose an aspect-based word embedding graph convolutional network (AWEGCN) model. In order to make good use of the aspect information and distinguish the contextual information that is more important for a particular aspect, the aspect information is embedded in the output of the hidden layer. To reduce the noise effect when multiple aspect words appear in a sentence, after going through the bidirectional graph convolutional network, the aspect information is embedded. A specific contextual representation is computed through an attention mechanism, which is used as the final classification feature. Experiments show that our model achieves impressive performance on five public datasets, and we also apply BERT and XLNet pre-trained models to this task and obtain advanced results that validate the effectiveness of our model.
1234P HistoMate: Automated preprocessing software for digital histopathology image to enhance deep learning

Recently, the field of digital pathology has seen an increase in research utilizing deep learning techniques. This allows for predictions such as cancer diagnosis, prognosis, and treatment response, which previously required additional analysis, such as genetic analysis that was both time-consuming and costly, to be made based on pathological imaging. However, digital pathology images often contain noise, such as inconsistent staining and inconsistent annotations. To address this issue, image preprocessing is essential in deep learning analysis of digital pathology images, and various preprocessing techniques have been proposed to address issues such as bias, overfitting, and robust deep learning model development. However, automated preprocessing methods for digital pathology images have not yet been fully developed. To address this, we have developed a user-friendly tool for image preprocessing analysis called HistoMate. HistoMate provides GUI-based image segmentation, image tiling, color normalization, and deep learning-based data augmentation to automate the preprocessing process. It also provides functionality to evaluate image quality and select appropriate patches. In conclusion, HistoMate provides an automated preprocessing tool for pathological image-based research, accelerating digital pathology-based research.
Explore, Compare, and Predict Investment Opportunities through What-If Analysis: US Housing Market Investigation

A key challenge in data analysis tools for domain-specific applications with high-dimensional time series data is to provide an intuitive way for users to explore their datasets, analyze trends and understand the models developed for these applications through human-computer interaction. To address this challenge, we propose a three-stage workflow that allows domain experts to explore their data, compare the different entities’ features, and predict the variable’s long-term trend using what-if analyses. Based on this workflow, we created a data visualization workspace for real estate investment using data from the US housing market at state and city level. The underlying machine learning model ARIMAX uses house price data together with socio-economic data from 2000 to 2021 to learn the dependencies of the house prices on the socio-economic factors and make informative and robust predictions for future years.
Using SHAP to Measure Interpretability of Neuronal Feature Visualization

Neuronal feature visualization is widely used in Explainable Artificial Intelligence (XAI). It can provide an intuitive visualization to depict the feature extraction of an individual neuron in a Convolutional Neural Network (CNN). However, it is extremely exhaustive for human users to identify highly interpretable visualizations by manually browsing massive neurons contained in a CNN. Inspired by the Shapley Value Method in coalitional game theory, this paper proposes a metric to quantitatively measure the interpretability of a neuronal feature visualization by calculating the similarity between the SHAP (SHapley Additive exPlanation) image and the visualization. This metric can help human users quickly find highly interpretable neuronal feature visualizations for understanding the classification results of a CNN.
The Importance of Memory for Language-Capable Robots

Robots need to be able to communicate with people through natural language. But how should their memory systems be designed to facilitate this communication?
CycleGAN-Based Cloud Removal from a Feature Enhancement Perspective by Transformer

Cloud removal has attracted significant research attention in various remote sensing applications, such as object detection and semantic segmentation. In this study, the classical cycle-consistent generative adversarial network (CycleGAN) is adopted for suppressing clouds from a feature enhancement perspective by capitalizing on the transformer architecture. Specifically, a transformer-based feature enhancement (TFE) module is proposed to extract high-level cloud-clear features by leveraging the Swin transformer’s capability of building long-range dependencies. As a result, the proposed TFE module can eliminate clouds in remote sensing images while retaining cloud-free regions unchanged. Extensive simulation experiments on the RICE dataset are conducted to substantiate the impressive performance of the proposed TFE model as compared to several existing cloud-removal methods.
Task Agnostic Cost Prediction Module for Semantic Labeling in Active Learning

We consider the problem of cost effective active learning for semantic segmentation, which aims at reducing the efforts of semantically annotating images. Current studies have ignored the inclusion of cost of labeling into their active learning frameworks. To this end, we first present a novel cost prediction module based on what we call the M-Net. M-Net combines the power of unsupervised W-Net and supervised U-Net to compute a refined segmentation map. The refined segmentation map is used to estimate the cost of annotations. The cost of annotation is estimated by the number of clicks required to annotate an image. To solve this task, we make use of the harris corner detector algorithm to estimate the location of the clicks required to annotate an image. Finally, we employ a multi armed bandit setting to minimize the cost of annotations while maximizing the performance of the semantic segmentation task. The M-Net outperforms fully supervised U-Net with +4.37 Acc and +3.75 mIoU. The proposed active learning framework also outperforms the existing baselines to prove the relevance of the approach in the current paradigm.
Improving Few-Shot Object Detection with Object Part Proposals

Few-Shot Object Detection (FSOD) allows fast adaptation of an object detection model to new classes of objects using few examples per class. This has many applications, in particular in satellite and aerial observation, as it allows learning from experts who can only annotate a few examples for new classes and helps migrate models across tasks. In this work, we present a technique to improve the performance of FSOD in remote sensing by defining a contrastive loss that utilizes parts of objects. For this, we generate, what we call, Object Parts Proposals (OPPs) on the fly for each novel class, and use them to learn more robust features with an additional contrastive objective. We observe that training with OPPs brings a consistent improvement over the state-of-the-art when evaluating on the DIOR dataset.The code is available at https://github.com/arthurchevalley/Improving-FSOD-on-RSI-using-Sub-Parts.