Reviewerly/exHarmony huggingface dataset:
Optimisation of multi-objective problems using an efficient Levy flight grasshopper algorithm

This paper is concerned in improving the performance of the nature-inspired multi-objective grasshopper optimisation algorithm (MOGOA). The Levy flight technique is used for the first time to boost MOGOA algorithm by providing better solution's diversity. The proposed Levy flight trajectory-based multi-objective grasshopper optimisation algorithm (LMOGOA) benefits from fast convergence speed to the true Pareto-optimal front and thus it overcomes MOGOA drawbacks of premature convergence and local optima stagnation. Then, LMOGOA algorithm is tested on a set of standard multi-objective problems (MOPs) and results proved significantly its out performance compared to MOGOA algorithm.
OpenAPI QL: Searching in OpenAPI Service Catalogs

An OpenAPI description details the actions exposed by a REST API. Existing query languages (e.g. JSONpath or N1QL for JSON) are not designed for OpenAPI and suffer from several drawbacks the most important of them being that, queries are complicated expressions and users must be familiar with the underlying OpenAPI representation. We introduce OpenPI QL, a query language for OpenAPI service descriptions. OpenAPI QL relies on a model that identifies the features that can be used to query a service description. OpenAPI QL employees SQL syntax and maintains the necessary simplicity of expression (i.e. using properties of REST services in simple SQL statements) regardless of service complexity and ignoring the nested structure of OpenAPI. Although independent from OpenAPI, queries can address the most important features of a service description in a database. The run-time performance of SOWL QL has been assessed experimentally in a database with OpenAPI descriptions of real services. A critical analysis of its performance is also presented along with several query examples.
Effect of Two Post Space Depths on the Accuracy of the Scan

Objective: This study aims to measure the accuracy of post space scans using 2 post space depths 1.7millimeters 2.10millimeters.Materials and Methods: A total of 14 human mandibular premolar teeth with similar root form and root canal shape averaging cervico-occlusal length of the crown about 8+/-0.5 mm and 14.5+/-0.5 mm for root length were collected for this study , endodontic treatment was performed for all teeth followed by coronal decapitation 2mm above CEJ and post space preparation , After the preparation of each root, the corresponding teeth were allocated randomly to two groups (n=7/ group) according to the depth of the preparation.Group A: 7 mm and Group B: 10 mm.All teeth were scanned using the (inEos X5 Sirona-Germany) to produce a reference scan, then scanning of post space directly using intraoral scanner for all teeth.Results: Samples with 10 mm post space (106.19±38.94)had a significantly higher RMS value than samples with 7 mm post space (70.39±28.63)(p=0.005).Conclusion: On the basis of the results and conditions of this study, the following conclusions can be drawn, diverse depths of the post space preparation showed varying degrees of trueness compared to the reference scan.Samples with 10 mm post space had a lower trueness value than samples with 7 mm post space.
Algorithms for the classification of text documents, taking into account proximity in the attribute space

Text classification is one of the key issues in text development research, where documents are classified based on information under supervision. Since there is a considerable number of text classification algorithms, it is currently necessary to compile an overview list of them in order to simplify the orientation in the classification tools that are available at the moment. Many text representation schemes and classification/learning algorithms used to classify text documents into predefined categories can be found in the literature, but some of them require detailed analysis and unleashed potential. The purpose of this study is to provide an overview of different text presentation schemes and a comparison of different classifiers that are used to classify text documents into predefined categories. During the study, a comparison method was used as part of the methodology – modern classification approaches based on criteria, algorithms used and time complexity were compared, as well as methods of analysis, modelling and combination. As a result of the study, several algorithms or combinations of algorithms have been proposed for automatic classification of documents as hybrid approaches. The SVM (Support Vector Machine) classifier was recognised as one of the most effective text classification methods when comparing guided machine learning algorithms. It was concluded that SVM captures the inherent characteristics of the data and embeds the structural risk minimisation (SRM) principle, which minimises the upper bound of the generalisation error better than the empirical risk minimisation principle.
Cyborganic Living

Abstract Transdisciplinary artist and researcher Maria Kuptsova works in the interstices between organic and digital systems. As she seeks new ways to design landscapes that are a rich mix of biological processes and computational algorithms and tools, she generates what she calls ‘cyborganic’ or ‘bio‐machinic’ architectural languages.
Automatic Detection of Levels of Intimate Partner Violence Against Women with Natural Language Processing Using Machine Learning and Deep Learning Techniques

Violence against women continues to claim thousands of lives worldwide each year. The COVID-19 pandemic only aggravated the problem by confining many victims together with their aggressors. When a woman experiences this situation, she usually falls into denial, justifies the aggressive behavior of her partner, or even blames herself for provoking it. The sooner she realizes that she is experiencing intimate partner violence, she can act and prevent her advance in the violence cycle (from psychological violence to physical violence, which could lead to femicide). The work proposes a classifier artificial intelligence model to detect levels of psychological violence against women in written virtual expressions (messages ‘from him to her’ in a couple) to ‘alert her’ about the risk that she runs in that relationship. 5250 records in Spanish were extracted with 4 techniques from 6 different sources. Definition of 5 intimate partner psychological violence levels (0-Low Risk, 1-Emotional Blackmail, 2-Jealousy/Justification, 3-Insults/Humiliations, and 4-Threats/Possessiveness) and the data labeling were supervised by a psychologist expert on the problem. Techniques TF-IDF and Word2Vec were used to get the vectors and were tested five Machine Learning algorithms (SVM, MLP, Random Forest, Logistic Regression, and Naive Bayes) with various combinations of parameters. Too were tested pad sequences with LSTM and Bidirectional LSTM. The best result was 93.45% accuracy and 0.2476 categorical cross-entropy loss, obtained with extensive preprocessing, pad sequences, and LSTM.
Influence-Based Deep Network for Next POIs Prediction

The dramatic proliferation of location-based social networks have resulted in a significant amount of data. This has led to the development of location-based recommendation tools that assist users in discovering attractive Points-of-Interest (POIs). Next POIs recommendation is of great importance for not only individuals but also group of users since group activities have become an integral part of our daily life. However, most existing methods make recommendations through aggregating individual predictive results rather than considering the collective features that govern user preferences made within a group. This insufficiency can directly affect the completeness and semantic accuracy of group features. For this reason, we propose a novel approach which accommodates both individual preferences and group decisions in a joint model. More specifically, based on influencing users in a group, we device a hybrid deep architecture model built with graph convolution networks and attention mechanism to extract connections between group and personal preferences and then capture the impact of each user on the group decision-making, respectively. We conduct extensive experiments to evaluate the performance of our model on two well-known real large-scale datasets, namely, Gowalla and Foursquare. The experimental results show its superiority over the state-of-the-art methods.
Fully Utilizing Neighbors for Session-Based Recommendation with Graph Neural Networks

Session-based recommendation (SBR) has attracted many researchers due to its highly practical value in many online services. Recently, graph neural networks (GNN) are widely applied to SBR due to their superiority on learning better item and session embeddings. However, existing GNN-based SBR models mainly leverage direct neighbors, lacking efficient utilization of multi-hop neighbors information. To address this issue, we propose a multi-head graph attention diffusion layer to utilize multi-hop neighbors information. Furthermore, we spot the information loss of local contextual aggregation in existing GNN-based models. To handle this problem, we propose positional graph attention aggregation layer to exploit direct neighbors information. Combining these two designs, we propose a novel model named FUN-GNN (Fully Utilizing Neighbors with Graph Neural Networks) for session-based recommendation. Experiments on three real datasets demonstrate its superiority over existing state-of-the-art baselines in terms of Precision and Mean Reciprocal Rank metrics.
CoVoMe: New Methodology for Building Controlled Vocabulary

The use of methodologies in knowledge management and engineering is deeply comprehensive due to their important advantages. In this paper, we propose CoVoMe that is a methodology for building controlled vocabularies. This methodology covers almost all variants of that vocabularies, and it is designed to be close to the currently available languages for creating thesauri, subject headings, taxonomies, authority files, synonym rings, and glossaries.
Comparative Study of Machine Learning Algorithms for Breast Cancer Classification

Breast cancer is one of the diseases with a high number of deaths every year and has become very prominent among Indian women in recent times. Cancer must be detected at the early stages of its formation to improve the mortality rates. This paper focuses on a comparison of multiple machine learning models with the use of different filters for the detection of breast cancer. Models used in this experiment include logistic regression, random forest, extreme gradient boosting, Gaussian Naive Bayes, K-nearest neighbors, and it is observed that the performance of these models significantly improve with the implementation of various data manipulation techniques. The best model for classification is found to be random forest (Accuracy = 98.04% and F1-Score = 96.27%) and XGBoost classifier (Accuracy = 98.32% and F1-Score = 98.31%).
A Task Planning Simulation Method Based On Hungarian Algorithm

Abstract As information technology continues to evolve, the need for accuracy and efficiency in task planning in computer simulation continues to strengthen. Task planning is a key part of computer-generated resource planning. To improve the effectiveness of task planning, this paper tries to design a task planning method. Firstly, a task planning model is designed by decomposing the requirements of the task according to the task capability of the platform, describing the resources quantitatively, and extracting the constraints and objective functions. Then the Hungarian algorithm is used to solve the problem. Finally, a typical case is used to test the proposed method, which verifies the correctness and feasibility of the method.
Intersection between environmental data science and the R community in Latin America

Abstract Environmental data science is used to solve environmental problems, and to help answer complex questions about biodiversity loss, climate change, and water scarcity, among others thereby allowing sustainable decision‐making using data‐driven approaches. In this opinion piece, I describe some recent Latin American experiences in the data science community, and what is needed for environmental data scientists to take advantage of those experiences. Specifically, I show the importance of being part of a diverse and local data science community.
Tumor Detection Based on 3D Segmentation Using Region of Interest

The chapter addressed two complex issues segmenting 2D slices of CT or MRI and view segmented 2D slices in 3D viewer. For segmenting 2D slices, we used the watershed algorithm. The 3D viewer is used for better visualization, which gives more information about segmented tumors. For better segmentation, included Gradient technique with the watershed algorithm. Segmented series of 2D images are viewed in 3D viewer. Experimental result shows viewing segmented 2D slices in 3D viewer. In our proposed methodology, we have shown the essence and feasibility of an automated tumor segmentation method for both CT and MRI images, and a simple model consists of the watershed algorithm is used to segment the tumor. The method segments a series of slices that consists of tumors and can be viewed using the 3D viewer technique and has been validated on five clinical MRI datasets, which consists of a minimum of 20–25 slices. The end results show a promising result.
Giving entangled photons new colors

A fiber-based modulator shifts photon frequency while preserving quantum correlations.
Anticipation-Free Training for Simultaneous Machine Translation

Simultaneous machine translation (SimulMT) speeds up the translation process by starting to translate before the source sentence is completely available.It is difficult due to limited context and word order difference between languages.Existing methods increase latency or introduce adaptive read-write policies for SimulMT models to handle local reordering and improve translation quality.However, the long-distance reordering would make the SimulMT models learn translation mistakenly.Specifically, the model may be forced to predict target tokens when the corresponding source tokens have not been read.This leads to aggressive anticipation during inference, resulting in the hallucination phenomenon.To mitigate this problem, we propose a new framework that decompose the translation process into the monotonic translation step and the reordering step, and we model the latter by the auxiliary sorting network (ASN).The ASN rearranges the hidden states to match the order in the target language, so that the SimulMT model could learn to translate more reasonably.The entire model is optimized end-to-end and does not rely on external aligners or data.During inference, ASN is removed to achieve streaming.Experiments show the proposed framework could outperform previous methods with less latency.
Background Driving Distribution Functions and Series Representations for Log-Gamma Self-Decomposable Random Variables

We identify the background driving distribution functions (BDDF) for self-decomposable distributions (random variables). For log-gamma variables and their background driving variables, we find their series representations. An innovation variable for Bessel-K distribution is given as a compound Poisson variable.
Snigiryova, T. and Podchinyonov, A., eds. (2021). The incomplete. The phenomenon of creative practice. St. Petersburg: Aleteya; Ekaterinburg: Uralskiy un-t. (In Russ.)

The second edition of the book, much enlarged, is focused on the practical use of the idea of the incomplete as the important feature in the contemporary artistic mind. The book is made of four parts, each one divided into sections or chapters written by different authors whose names are mentioned only on the reverse of the title-page. This principle (not very convenient for the reader) insists on the fact the genre of the book is a monograph and not a random collection. With this claim in view one could expect more order and thoughtful organisation when every article would be complimentary to others, whereas now they are more independent and occasional in the treatment of the incomplete. The typology proposed in the theoretical chapters and the major idea brought forth by the theoreticians are not much on the demand in creative practice. As the ruling principle for the incomplete/ unfinished in the artistic mind, the idea of wholeness was advanced, not violated by the fact of incompleteness, but taking a new shape worth a collective research.
The Current State of Digital Musical Materials in Japan

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Research on agricultural data mining model based on knowledge graph

With the development of artificial intelligence technology, knowledge graph has been applied in more and more fields. In this paper, agricultural data is constructed as graph structure data set, and the graph structure is mapped to low dimensional vector space through the first and second order similarity of the graph. The attention mechanism is used to constrain the information propagation in the process of graph convolution, and finally the classification of each data node is obtained by SVM. In the process of information transmission, the accuracy of the model is improved to some extent by effectively utilizing the higher-order information of the graph. Compared with GCN and GAT models, the accuracy of the experiment in this paper is increased by eight percent and four percent.
A linear-algebraic and lattice-theoretical look at the Cleaning Lemma of quantum coding theory

The term "Cleaning Lemma" refers to a family of similar propositions that have been used in Quantum Coding Theory to estimate the minimum distance of a code in terms of its length and dimension. We show that the mathematical core is a simple fact of linear algebra of inner product spaces; moreover, it admits a further reduction to a combinatorial, lattice-theoretical level. Several concrete variants of the Cleaning Lemma and some additional propositions are derived as corollaries of the proposed approach.
Bias disparity in graph-based collaborative filtering recommenders

In collaborative filtering (CF) recommendations, user preferences may be reinforced resulting in the under-representation of specific categories of items in the recommendation lists received by users belonging to specific groups. We call this phenomenon bias disparity. In this paper, we focus on CF recommenders that exploit the bipartite user-item interaction graph and present algorithms that modify the random walks on this graph to address bias disparity. We evaluate experimentally both the conditions under which bias disparity appears and the effectiveness of our algorithms.
Feasibility of breast lesion localisation using Sirius Pintuition; A new magnetic seed localisation system - Report of the first dozen cases in the UK

Introduction: Impalpable breast cancers can be localised using different techniques. Recently, magnetic seed localisation systems have been developed and shown to offer benefits over traditional wire guided techniques. This study aims to assess the feasibility of a new seed localisation device, Sirius Pintuition, at St Helen's and Knowsley NHS Trust.
An extensive agent-based simulation study of sycomore <sup>++</sup> , a DAG-based permissionless ledger

A recent evolution of the blockchain structure is emerging to address the performance issue of permissionless chain-based ledgers, in particular the small number of transactions confirmed per second. To address such an issue, new designs have been brought forward, including Bitcoin-NG, which favors an off-chain mechanism in which blocks refer to a leader in charge of validating transactions batched in micro-blocks out of the chain [6]; Lightning [10], which follows the same principle but only publishes the outcome of repeated transactions among a set of parties. Others propositions such as HashGraph [2], ByteBall [5], and Iota [4] leverage the presence of well known institutions to get rid of blocks, while Ghost [12] and Spectre [11] protocols family modifies the blockchain data structure from a totally ordered sequence of blocks to a directed graph of blocks. Blocks are built so that they commit the state of the directed graph at the time blocks were created which decreases the opportunity for powerful attackers to create blocks in advance. Regarding the graph-based approach, the absence of mechanisms to prevent the presence of conflicting records (i.e., blocks with conflicting transactions) or the presence of cycles in the directed graph (Spectre [11] organises blocks in a directed, but not acyclic, graph of blocks) require that participants execute a complex algorithm to extract from the graph the set of accepted (i.e., valid) transactions [11]. Sycomore1 is an immutable permissionless distributed ledger whose structure is a particular directed acyclic graph of blocks, called SYC-DAG [1]. Its design differs from existing distributed ledgers in that its graph structure dynamically adapts to fluctuations in transaction submission rates: When the leaf block of a chain (more precisely the last blocks appended to a chain) of the graph exceeds a maximal loading threshold (the load is measured in Bytes), subsequent blocks are partitioned over two sibling chains, and these blocks are mined in parallel (as will be described shortly, even if blocks are appended in parallel to the SYC-DAG they cannot be conflicting, i.e., each valid transaction cannot appear in more than one block). Conversely, when the leaf blocks of two sibling chains (again the last blocks of two sibling chains) fall short of a minimal loading threshold, subsequent blocks will belong to a unique chain. The decision to split a leaf chain of the SYC-DAG or to merge two sibling ones is locally taken by each miner, and soundness of this decision is verifiable by everyone at any time [1]. Actually, Sycomore has been designed to meet the following properties [1]:
A Study on the Current Status of Childcare Teacher Policy and Policy Evaluation in Korea

This study attempted to suggest the future direction of policy for childcare teachers in Korea by examining and evaluating the legal status of childcare teachers, the flow of childcare policies, and policies related to improving the treatment of childcare teachers by period. For this, the related laws, press releases, and basic mid-to-long-term childcare plans were analyzed by examining the respective home pages (websites) of the 「Child Care Act」 and the 「Early Childhood Education Act」, as well as those of the Ministry of Education, the Ministry of Health and Welfare, the Ministry of Gender Equality and Family, the Presidential Committee on Aging Society and Population Policy, and Central Childcare. In conclusion, the childcare policies up to now have the strong characteristic of being marginal efforts to improve the poor working environment and compensation of childcare teachers rather than being a fundamental solution for the long term. Accordingly, it is necessary to prepare a fundamental and long-term policy that can more realistically reflect the childcare teachers’ policy demands.