Reviewerly/exHarmony huggingface dataset:
An optimised extreme learning machine (OELM) for simultaneous localisation and mapping in autonomous vehicles

Autonomous robots can navigate unexpected situations without human involvement. Multiple sensors for object recognition and mapping are used in autonomous driving, factory automation, and security service robots. Advances in motion planning allow autonomous cars to map their position and orientation. Vehicle position estimates may be off. SLAM algorithms map an unfamiliar environment and estimate a vehicle's position. We present a grey wolf optimised extreme learning machine (OELM) approach to leverage deep learning networks. It can build an environment map independently and help the system navigate autonomously. First, a CNN-GRU hybrid model for training Stereo and IMU sensor data is provided. ELM is then optimised for SLAM. The proposed method minimises posture estimate error for autonomous vehicles. Our method captures autonomous driving scenarios using KITTI data. IMU and stereo images collect data. Comparing the computed path to ground truth poses enhances accuracy and decreases error. Improved ELM has lower RMSE than prior SLAM. Data show that OELM-SLAM is more accurate than ELM.
PolarisX&lt;SUP align="right"&gt;2&lt;/SUP&gt;: auto-growing context-aware knowledge graph

Artificial intelligence requires advanced technologies in various fields. In particular, natural language processing consists of various tasks because computers need to understand and process human languages. Knowledge graphs represent common sense as a graph, making it easy to understand the relationships between entities. Various studies exist because knowledge graphs could play a crucial role in computers' understanding of natural language. PolarisX is an auto-growing knowledge graph that could especially cope with neologisms. However, existing studies have a limitation in that they rarely correspond to information containing numbers representing a cardinal, ordinal, or quantity and can extract only one relationship from one sentence. We propose the auto-growing context-aware knowledge graph, PolarisX2, an entity extraction model capable of responding to numeric information, and a relation extraction model considering type. It also enables multiple knowledge extraction from a single sentence by applying the candidate pair construction model.
Example Applications Beyond Node Classification

While our running example in this book was node classification, and also the benchmark datasets discussed above use mostly node classification as a target (with a few exceptions), RDF2vec is more versatile. In this chapter, we show examples of works that describe the use of RDF2vec for other purposes, such as recommender systems, relation extraction, ontology learning, or knowledge graph matching.
GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks

Label errors have been found to be prevalent in popular text, vision, and audio datasets, which heavily influence the safe development and evaluation of machine learning algorithms. Despite increasing efforts towards improving the quality of generic data types, such as images and texts, the problem of mislabel detection in graph data remains underexplored. To bridge the gap, we explore mislabelling issues in popular real-world graph datasets and propose GraphCleaner, a post-hoc method to detect and correct these mislabelled nodes in graph datasets. GraphCleaner combines the novel ideas of 1) Synthetic Mislabel Dataset Generation, which seeks to generate realistic mislabels; and 2) Neighborhood-Aware Mislabel Detection, where neighborhood dependency is exploited in both labels and base classifier predictions. Empirical evaluations on 6 datasets and 6 experimental settings demonstrate that GraphCleaner outperforms the closest baseline, with an average improvement of 0.14 in F1 score, and 0.16 in MCC. On real-data case studies, GraphCleaner detects real and previously unknown mislabels in popular graph benchmarks: PubMed, Cora, CiteSeer and OGB-arxiv; we find that at least 6.91% of PubMed data is mislabelled or ambiguous, and simply removing these mislabelled data can boost evaluation performance from 86.71% to 89.11%.
Entity Linking for KGQA Using AMR Graphs

Entity linking is an essential part of analytical systems for question answering on knowledge graphs (KGQA). The mentioned entity has to be spotted in the text and linked to the correct resource in the knowledge graph (KG). With this paper, we present our approach on entity linking using the abstract meaning representation (AMR) of the question to spot the surface forms of entities. We re-trained AMR models with automatically generated training data. Based on these models, we extract surface forms and map them to an entity dictionary of the desired KG. For the disambiguation process, we evaluated different options and configurations on QALD-9 and LC-QuaD 2.0. The results of the best performing configurations outperform existing entity linking approaches.
The use of fuzzy ontologies in the clustering of bibliographic information

This article presents an approach to clustering short texts using a fuzzy ontology. We propose a modification of the TF-IDF model for vectorization of short texts using a fuzzy ontology. A fuzzy ontology determines the degree of membership between the terms of the subject area. The paper presents a comparison of the efficiency of 4 types of clustering (K-means, MiniBatchKMeans, DBSCAN, Agglomerative) and 3 types of short text vectorization (Bag of Words, Word2Vec and modified TF-IDF). The most effective was the use of K-means and modified TF-IDF for short texts from the news portal. The second set of experiments consisted in clustering texts of abstracts of scientific articles from the elibrary portal. The results of the experiments will be used to create new scientific groups and expand existing scientific groups on topics.
NLE-DM: Natural-Language Explanations for Decision Making of Autonomous Driving Based on Semantic Scene Understanding

In recent years, the advancement of deep-learning technologies has greatly promoted the research progress of autonomous driving. However, deep neural network is like a black box. Given a specific input, it is difficult to explain the output of the network. Without explainable results, it would be unsafe to deploy deep networks in unseen environments or environments with potential unexpected situations. Especially for decision-making networks, inappropriate outputs could lead to severe traffic accidents. To provide a solution to this problem, we propose a deep neural network that jointly predicts the decision-making actions and corresponding natural-language explanations based on semantic scene understanding. Two types of explanations, the reasons of driving actions and the surrounding environment descriptions of the ego-vehicle, are designed. Both the reasons and descriptions are in the form of natural language. The decision-making actions could be explained by the corresponding reasons or the environment descriptions. We also release a large-scale dataset with hand-labelled ground truth including driving actions and environment descriptions. The superiority of our network over other methods is demonstrated on both our dataset and a public dataset.
Metvaerse’de İyilik ve Kötülüğün Ontolojik Değeri

The Metaverse is a world created with virtual reality technologies, providing an unlimited freedom space where people can perform all the actions they can imagine. In our study, the Metaverse is scientifically explained within the framework of how the human brain works. Considering that the human brain perceives both virtual and real-world experiences and emotions, emphasis is placed on the lack of a sharp distinction between the virtual and real worlds in the Metaverse. Additionally, the disappearance of the boundary between the virtual and real worlds demonstrates that human actions and behaviors have an ontological value from the perspective of human nature. Our study also draws attention to the important role that the Metaverse will play in religion, morality, and humanity. With the influence of advancing technological developments, the Metaverse will have an even more significant and dynamic place in human life. Therefore, the importance of establishing a moral and meaningful structure in the Metaverse is emphasized. The study emphasizes the crucial role that religion should play in this field, arguing that a virtual world devoid of religion and ethical rules could become a chaotic place full of pain for people.
An Approach to the Implementation of Nonparametric Algorithms for Controlling Multidimensional Processes in a Production Problem

In paper, consider nonparametric algorithms for the identification and control of multidimensional discrete-continuous processes that are typical for many practical problems. The main feature of the considered multidimensional systems is the presence in them of stochastic dependences of both input and output variables. Under such conditions, the mathematical description of such objects leads to a system of implicit equations. Nonparametric algorithms for modeling and control for multidimensional systems are proposed, as well as the subsequent implementation of software-based on these algorithms in production. Attention should be paid to the fact that the modeling of such processes is reduced to the search for the predicted values of the output variables for the known input. Moreover, for implicit equations, it is only known that one or another input variable can depend on other input and output variables that determine the state of a multidimensional system. The subsequent control of multidimensional systems is considered in conditions of insufficient a priori information about the object of research. The main component in the control problem is the determination of the reference influences, for the search of which a nonparametric algorithm is used. The essence of this algorithm is to find a common area of intersection of the values of the output variables, which will satisfy all components of the output vector. When implementing software, it is necessary to take into account information security issues, especially for three key tasks: process control, archiving (saving history) of technological parameters and working with alarms. This article provides some guidelines for implementing safeguards for archiving and alarms.
What's the Stitch?

A digital reference guide to sutures and needles (adapted from Twitter Thread) As a student and junior resident, I didn’t understand how surgeons decided when to use each suture/needle. I tried to pick up on patterns but the info was never in one place.
Analisis pengaruh desain kerja terhadap knowledge productivity arsiparis di Arsip Nasional Republik Indonesia

Introduction. This study discusses how a job design influences knowledge productivity among archivists at the Indonesian National Archives. A job design is examined with knowledge productivity and the relationship between them. The three job design was used to measure knowledge productivity characteristics (motivation, social, and work context).&#x0D; Data Collection Methods. This research used a quantitative approach with qestionnaires distributed in March-April 2021 to archivists at The National Archives of Indonesia&#x0D; Data Analysis. The validity and reliability of the instrument were processed using the IBM SPSS v26 program. &#x0D; Results and Discussion. The results showed that the job design had a relationship with knowledge productivity of 0.859 with a significance value (Sig.) of 0.000 and a coefficient of determination of 0.738. The results of the regression analysis showed Y = 37.707 + 0.189X means that job design has a positive effect on knowledge productivity.&#x0D; Conclusion. Based on data analysis, there is a positive influence between job design and knowledge productivity. It was found that knowledge productivity variable that the work context indicator did not have a significant effect on knowledge productivity.
An emotion speech sequence alignment algorithm based on ShapeDTW++

For aligning non-uniform time series, the traditional DTW algorithm is commonly used. However, in emotional voice conversion, traditional DTW algorithms tend to over-align and have poor local matching for aligning non-uniform parallel emotional speech sequences. This results in the emotional speech alignment data obtained affecting the quality and naturalness of the final emotional conversion. To address this issue, we propose an emotion speech sequence alignment algorithm based on ShapeDTW++. Our new algorithm improves on the DTW algorithm by incorporating shape descriptors from the ShapeDTW algorithm, which is a significant breakthrough in the popular DTW algorithm and can increase local matching. We also add cumulative distance loss weight and relaxation endpoints to reduce over-alignment of emotional speech sequences. Finally, the ShapeDTW++ algorithm provides performance similar to the ShapeDTW algorithm while increasing local matching and reducing over-alignment when aligning emotional speech sequences. This improvement in the objective evaluation of the ShapeDTW++ algorithm for aligning emotional speech sequences compared to traditional DTW algorithms is significant.
A Short-term PV Power Forecasting Method based on NWP Correction Considering Meteorological Coupling Correlation

Accurate photovoltaic (PV) power forecasting has become one of the key basic technologies to improve the operation quality of power system and reduce the reserve capacity. Accurate Numerical Weather Prediction (NWP) is the important factor for short-term PV power forecasting. However, the accuracy of NWP is sometimes unsatisfactory. Meanwhile, due to various reasons, there were many kinds of anomalies in the measured data of photovoltaic stations, which will lead to the difficulty in obtaining accurate enough forecasting results. In this paper, a NWP correction method considering the coupling correlation of meteorological factors and a short-term photovoltaic power forecasting model based on the correction results are proposed to further improve the prediction accuracy. Firstly, the anomalies are detected and eliminated. Secondly, the optimal forecasting model is trained with the measured meteorological data and power data, meanwhile the optimal combination of meteorological factors is determined. Finally, a short-term PV power forecasting model based on convolutional long-short term memory network (CNN-LSTM) is established. The simulation results show that, compared with the method without optimal meteorological factors determination and NWP correction, the accuracy can be significantly improved by proposed method in this paper. The necessity and effectiveness of NWP correction are also verified.
A method for intelligent segmentation and acknowledge of musical notes based on artificial brilliance

In the era of big data with explosive growth of data, how to efficiently classify, retrieve and manage massive music has become a difficult problem. The traditional manual labeling method is far from meeting the needs. Therefore, content-based music element analysis has become a research hotspot. Content-based music analysis is an vital branch in the field of computer music brilliance processing, in which the segmentation and acknowledge of musical notes is one of the key technologies. The segmentation process of the traditional music note segmentation acknowledge method is relatively complicated, and the acknowledge accuracy is not high. In the music retrieval system through the user's humming input, the segmentation and identification of musical notes is one of the key issues. Content-based music analysis is an vital branch in the field of computer music brilliance processing, in which the segmentation and acknowledge of musical notes is one of the key technologies. The method adopted in this paper is artificial brilliance, which can enhance human decision-making ability in certain fields, and even replace human decision-making in some aspects. Artificial brilliance is influencing the social and political preferences of audience groups, and subtly influencing and manipulating the behavior patterns of audiences.
Ways of development of modern music therapy

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
The Implementation of Clustering Method With K-Means Algorithm In Grouping Data of Students’ Course Scores at Universitas Muhammadiyah Yogyakarta

Student grades can be a reference. A large number of student grade data in a university causes data accumulation; thus, data are grouped with data mining. This study aims to classify student grade data in the second semester. Grouping student grade data was performed using the clustering method with the K-means algorithm. The research data were derived from the database of Universitas Muhammadiyah Yogyakarta. The data were students’ grades in the academic years of 2010/2011, 2011/2012, 2012/2013, 2013/2014, and 2014/2015. The analysis process was carried out using WEKA software, SQL Server 2014 Management Studio and Microsoft Excel. The clustering method could be applied to group student grade data. Clustering with K-means formed three clusters, with cluster 0 comprising 72 students, cluster 1 consisting of 190 students, and cluster 2 totaling 133 students. A cluster with the lowest average score could be used as a consideration in updating the learning methods to optimize students’ score acquisition.
We are on the Same Side. Alternative Sieving Strategies for the Number Field Sieve

The Number Field Sieve (NFS) is the state-of-the art algorithm for integer factoring, and sieving is a crucial step in the NFS. It is a very time-consuming operation, whose goal is to collect many relations. The ultimate goal is to generate random smooth integers mod N with their prime decomposition, where smooth is defined on the rational and algebraic sides according to two prime factor bases. In modern factorization tool, such as Cado-NFS, sieving is split into different stages depending on the size of the primes, but defining good parameters for all stages is based on heuristic and practical arguments. At the beginning, candidates are sieved by small primes on both sides, and if they pass the test, they continue to the next stages with bigger primes, up to the final one where we factor the remaining part using the ECM algorithm. On the one hand, first stages are fast but many false relations pass them, and we spend a lot of time with useless relations. On the other hand final stages are more time demanding but outputs less relations. It is not easy to evaluate the performance of the best strategy on the overall sieving step since it depends on the distribution of numbers that results at each stage. In this article, we try to examine different sieving strategies to speed up this step since many improvements have been done on all other steps of the NFS. Based on the relations collected during the RSA-250 factorization and all parameters, we try to study different strategies to better understand this step. Many strategies have been defined since the discovery of NFS, and we provide here an experimental evaluation.
RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems

Large Language Models (LLMs) have greatly advanced code auto-completion systems, with a potential for substantial productivity enhancements for developers. However, current benchmarks mainly focus on single-file tasks, leaving an assessment gap for more complex, real-world, multi-file programming scenarios. To fill this gap, we introduce RepoBench, a new benchmark specifically designed for evaluating repository-level code auto-completion systems. RepoBench supports both Python and Java and consists of three interconnected evaluation tasks: RepoBench-R (Retrieval), RepoBench-C (Code Completion), and RepoBench-P (Pipeline). Each task respectively measures the system's ability to retrieve the most relevant code snippets from other files as cross-file context, predict the next line of code with cross-file and in-file context, and handle complex tasks that require a combination of both retrieval and next-line prediction. RepoBench aims to facilitate a more complete comparison of performance and encouraging continuous improvement in auto-completion systems. RepoBench is publicly available at https://github.com/Leolty/repobench.
LGTRL-DE: Local and Global Temporal Representation Learning with Demographic Embedding for in-hospital mortality prediction

Predicting the patient's in-hospital mortality from the historical Electronic Medical Records (EMRs) can assist physicians to make clinical decisions and assign medical resources. In recent years, researchers proposed many deep learning methods to predict in-hospital mortality by learning patient representations. However, most of these methods fail to comprehensively learn the temporal representations and do not sufficiently mine the contextual knowledge of demographic information. We propose a novel end-to-end approach based on Local and Global Temporal Representation Learning with Demographic Embedding (LGTRL-DE) to address the current issues for in-hospital mortality prediction. LGTRL-DE is enabled by (1) a local temporal representation learning module that captures the temporal information and analyzes the health status from a local perspective through a recurrent neural network with the demographic initialization and the local attention mechanism; (2) a Transformer-based global temporal representation learning module that extracts the interaction dependencies among clinical events; (3) a multi-view representation fusion module that fuses temporal and static information and generates the final patient's health representations. We evaluate our proposed LGTRL-DE on two public real-world clinical datasets (MIMIC-III and e-ICU). Experimental results show that LGTRL-DE achieves area under receiver operating characteristic curve of 0.8685 and 0.8733 on the MIMIC-III and e-ICU datasets, respectively, outperforming several state-of-the-art approaches.
PBFT consensus algorithm based on reward and punishment mechanism

The Practical Byzantine Fault Tolerant (PBFT) algorithm is a common consensus algorithm for coalition chain, but it has some problems such as low efficiency, poor scalability and high communication complexity. To solve these problems, this paper proposes a PBFT consensus algorithm based on reward and punishment mechanism (RP-PBFT). Based on the PBFT algorithm, the algorithm rewards and penalises the performance of each node in the consensus process, and divides the nodes into three categories according to their reputation value. The two categories of nodes with high reputation value can participate in the consensus, reduce the scale of nodes participating in the consensus, improve the security and simplify the three-stage consensus to improve the efficiency. The experimental results show that the communication complexity of RP-PBFT algorithm is significantly reduced, the throughput is increased, the delay is reduced, and the system efficiency is improved.