Reviewerly/exHarmony huggingface dataset:
Extracting Interpretable Hierarchical Rules from Deep Neural Networks’ Latent Space

Deep neural networks, known for their superior learning capabilities, excel in identifying complex relationships between inputs and outputs, leveraging hierarchical, distributed data processing. Despite their impressive performance, these networks often resemble ’black boxes’ due to their highly intricate internal structure and representation, raising challenges in terms of safety, ethical standards, and social norms. Decompositional rule extraction techniques have sought to address these issues by delving into the latent space and retrieving a broad set of symbolic rules. However, the interpretability of these rules is often hampered by their size and complexity. In this paper, we introduce EDICT (Extracting Deep Interpretable Concepts using Trees), a novel approach for rule extraction which employs a hierarchy of decision trees to mine concepts learned in a neural network, thereby generating highly interpretable rules. Evaluations across multiple datasets reveal that our method extracts rules with greater speed and interpretability compared to existing decompositional rule extraction techniques. Simultaneously, our approach demonstrates competitive performance in classification accuracy and model fidelity.
Trade-Offs Around Production and Livelihood Decisions

Trade-offs are inherent to decision-making about resource allocation to different activities in agroecosystems, particularly when resources are scarce or such activities are mutually exclusive. Making design and management decisions implies finding compromise solutions between objectives, or strive for synergies. Mapping out trade-offs and possible synergies is essential to inform such decisions. This chapter presents concepts and methods to analyse trade-offs and synergies between resource allocation objectives in agroecosystems. It focuses on decision making at the scales of (i) cropping systems and animal herds, (ii) farms, and (iii) landscapes/territories. The methods presented range from simple ad-hoc calculations, through participatory and qualitative methods, to quantitative trade-offs assessment using simulation models and scenario analysis. Common trade-offs in agriculture are illustrated with real life examples.
Information Processing

How information is processed to produce meaning is, perhaps, even more rele﻿vant to the study of metacognition than understanding the underlying biological connections that make metacognition possible for the human mind. This chapter delves into the fascinating process of transforming raw information into meaningful insights. It examines various approaches to problem-solving, beginning with the intricate workings of computational neural networks and culminating in our understanding of how humans develop their problem-solving abilities. These abilities are found to be intricately linked to the fundamental steps of the scientific method, suggesting that humans possess a tremendously rich capacity for problem-solving that can be applied to metacognitive practices of self-investigation and development. This chapter is a journey of discovery, revealing the incredible potential of the human mind.
Predictive Maintenance for Array of Cherenkov Telescopes

A predictive maintenance (PdM) approach is considered among the best solution for the maintenance of equipment. The paper aims at presenting an approach to the realization of a predictive maintenance for the array of Cherenkov telescopes. The application of PdM is able to reduce array downtimes, to increase the longevity of telescope components, and to reduce costs due to unscheduled maintenance activities.
Software Compartmentalization Trade-Offs with Hardware Capabilities

Compartmentalization is a form of defensive software design in which an application is broken down into isolated but communicating components. Retrofitting compartmentalization into existing applications is often thought to be expensive from the engineering effort and performance overhead points of view. Still, recent years have seen proposals of compartmentalization methods with promises of low engineering efforts and reduced performance impact. ARM Morello combines a modern ARM processor with an implementation of Capability Hardware Enhanced RISC Instructions (CHERI) aiming to provide efficient and secure compartmentalization. Past works exploring CHERI-based compartmentalization were restricted to emulated/FPGA prototypes.
Dynamic Linkers Are the Narrow Waist of Operating Systems

While software applications, programming languages, and hardware have changed, operating systems have not. Widely-used commodity operating systems are still modelled after the ones designed in the seventies. The accumulated burden of backward compatibility with the large software ecosystems that run our workloads prevents systems from embracing more efficient and disruptive designs explored by the system research community.
Discrete-time scale-shift approach for wavelet construction and analysis

We provide a sufficient condition to construct a wavelet family from a purely discrete-time signal. The construction is based on an exact mathematical definition of the scale-shift operator in discrete-time and strongly motivated by a strict equivalence between voice transform and wavelet coefficients of a discrete-time signal. Particular classes of signals which satisfy this condition are given and furthers analysis are also considered.
A Ground-Risk-Map-Based Path-Planning Algorithm for UAVs in an Urban Environment with Beetle Swarm Optimization

This paper presents a path-planning strategy for unmanned aerial vehicles (UAVs) in urban environments with a ground risk map. The aim is to generate a UAV path that minimizes the ground risk as well as the flying cost, enforcing safety and efficiency over inhabited areas. A quantitative model is proposed to evaluate the ground risk, which is then used as a risk constraint for UAV path optimization. Subsequently, beetle swarm optimization (BSO) is proposed based on a beetle antennae search (BAS) that considers turning angles and path length. In this proposed BSO, an adaptive step size for every beetle and a random proportionality coefficient mechanism are designed to improve the deficiencies of the local optimum and slow convergence. Furthermore, a global optimum attraction operator is established to share the social information in a swarm to lead to the global best position in the search space. Experiments were performed and compared with particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FA), and BAS. This case study shows that the proposed BSO works well with different swarm sizes, beetle dimensions, and iterations. It outperforms the aforementioned methods not only in terms of efficiency but also in terms of accuracy. The simulation results confirm the suitability of the proposed BSO approach.
A Domain-Free Semi-supervised Method for Myocardium Segmentation in 2D Echocardiography Sequences

Many deep learning methods have been applied in myocardium segmentation, however, the robustness of these algorithms is relatively low, especially when dealing with datasets from different domains, such as machines. In this paper, we propose a domain-free semi-supervised deep learning algorithm to improve the model robustness between different machines. Two domain-free factors (the shape of the myocardium and the motion tendency between adjacent frames) are adopted. Specifically, an optical flow field-based segmentation network is proposed for enhancing the performance by combining the motion tendency of myocardium between adjacent frames. Moreover, a shape-based semi-supervised adversarial network is presented to utilize the shape of the myocardium for the purposes of improving the segmentation robustness. Experiments on our private and public datasets show that the proposed method not only improves the segmentation performance, but also decreases the performance gap when applied to different machines, thus demonstrating the effectiveness of the proposed method.
Consistent and Accurate Segmentation for Serial Infant Brain MR Images with Registration Assistance

The infant brain develops dramatically during the first two years of life. Accurate segmentation of brain tissues is essential to understand the early development of both normal and disease changes. However, the segmentation results of the same subject could demonstrate unexpectedly large variations across different time points, which may even lead to inaccurate and inconsistent results in charting infant brain development. In this paper, we propose a deep learning framework, which simultaneously exploits registration and segmentation for guaranteeing the longitudinal consistency among the segmentation results. Firstly, a manual label-guided registration model is designed to fast and accurately obtain the warped images from other time points. Secondly, a segmentation network with a longitudinal consistency constraint is developed to effectively obtain the temporal segmentation results. Thus, our proposed segmentation network could exploit the tissue information of warped intensity images from other time points to aid in segmenting the isointense phase (approximately 6–8 months) data, which is the most difficult case due to the low intensity contrast of tissues. Extensive experiments on infant brain images have shown improved performance achieved by our proposed method, compared with the existing state-of-the-art methods.
A Study on the Formation of Literary Field and Discourse in Children's Magazines in the Liberation Period: Focusing on Children's Magazines in North and South Korea

The purpose of this paper is to discuss the plan pursued by contemporary children's literature through various discourses of children's magazines in South and North Korea during the liberation period. The children's magazines of South and North Korea during the liberation period reveal education, society, literature, and politics as a literary space, and each children's magazine develops various theories and contents of the work.The children's magazines of South and North Korea during the liberation period allowed children of the time to expand their consciousness through 'education', 'culture', 'Hangeul', 'children's writing', and 'propaganda'. This is an important point to consider as a stepping stone for the formation of a new nation. This study presents an important point of insight into social problems, culture, and literature through various discourses of children's magazines in South and North Korea during the liberation period.
CLUSTERING BERITA MENGGUNAKAN ALGORITMA TF-IDF DAN K-MEANS DENGAN MEMANFAATKAN SUMBER DATA CRAWLING PADA SITUS DETIK.COM

News clustering aims to identify each news group that is formed from the implementation of the K-Means method which is based on the word weighting process using the TF-IDF (Term Frequency Inverse Document Frequency) Algorithm. The clustering process uses news crawled from the detik.com site for a period of one year (2018), totaling 124,509 news stories and stored in the form of a CSV (Comma Seperated Value) file. Before carrying out the clustering process, the previous dataset must go through a text-processing stage in the form of: case folding, tokenizing, stopword removal, and stemming. The TF-IDF and K-Means methods are used for the clustering process. The TF-IDF method assigns weights to each keyword in each category to find the similarity of keywords to the available categories, then continues with the K-Means Method for the grouping process based on similar characteristics / similarities between documents. In the process, there are two implementations of the K-Means method, each using 16 centroids and 12 centroids. This is because in the first process, there are groups / clusters that cannot be identified because they contain common words, so a second implementation is needed. Based on the results of testing on 124,509 news stories, there are 27 news groups that have been successfully identified with adequate application capabilities in processing large data.
Analisis Sentimen Berdasarkan Opini dari Media Sosial Twitter terhadap “Figure Pemimpin” Menggunakan Python

Social media is a medium used to socialize and exchange information by its users using the internet. There are many benefits of social media, several uses of social media such as meeting new friends, knowing sports, economics, tourism information and also for political matters. One of them is the figure of the leader of the presidential candidate for 2024, so the writer wants to know what information can be taken from public opinion on Twitter social media about the figure of the leader of the presidential candidate for 2024. This problem can be overcome by conducting research in the field of Sentiment Analysis, which is a a field of research that focuses on the computational study of opinions, behavior, and emotions towards an entity as outlined in the form of text. This study was conducted to find out how the results of sentiment analysis related to public response to the news of the 2024 presidential candidate and classify them into three classes, namely positive, negative, and neutral using Python. The results of this study are positive class sentiments of 21.6% with a total of 108, neutral by 78% with a total of 390 and negative by 0.2% with a number of 2. It can be concluded that this study tends to be neutral.
KLASIFIKASI RAMBU LALU LINTAS MENGGUNAKAN DECISION TREE J48 DAN LOCAL BINARY PATTERN

Automatic steering technology or autopilot in cars is developing rapidly. This feature makes it easier for the driver because the car can run according to the program directions. The driver of course can still take control of the car manually as desired, so it is possible for the driver to violate traffic signs, whether intentionally or not. This study seeks to create a traffic sign recognition system that can help reduce violations committed by drivers knowingly. The test is carried out using a combination of the Local Binary Pattern algorithm as feature extraction and Decision Tree J48 algorithm as a classification system to recognize traffic signs. It is hoped that this research can provide an overview of the combined ability of these two algorithms in classifying types of traffic signs.
The “Turnkey School” project model for teacher team training

This study aims to present a new model of professional training for teachers in educational institutions within the framework of the "Turnkey School" project within the Master of Education degree program at the Humanitarian and Pedagogical Institute of Sevastopol State University (SevSU). This study used a controlled research design method to validate the team concept through a proposed model based on the socio-constructivist and contextual paradigms in education. The model involves training in a master's program as part of team building for a new educational institution in which cohort groups are formed based on the specific needs of assigned schools, which are then evaluated by an independent community of experts, as well as with the writing of a final thesis as a result of cohort team collaboration. This study confirms that the cohort group formed through this model shows a better level of maturity and functioning than randomly assigned teachers. These findings support the effectiveness of a Master of Education degree in improving the training and performance of future teachers. This model emphasizes not only the creation of updated educational products but also the self-implementation of those products, which contributes to the overall improvement of educational institutions.
Enhancing Ethical Values in Language Pedagogy: Lessons from Gowe Tugalaoyo (Judgment Stone) in Siwawo Village

In the era of the ancestors of the Nias people, the majority adhered to ritual beliefs by facing and prostrating to ask for guidance on the truth through menhirs or the like that are believed and agreed upon by a group of people. The purpose of this research is to describe the genealogy and reveal the value of honesty contained in the Tugala Oyo Gowe (judgment stone) located in Siwawӧ Village, Tugala Oyo Sub-district, North Nias Regency. This research method is descriptive research by applying the observation method through an ethnographic approach. To obtain information about the meaning of rituals contained in gowe (judgment stone) through figures and residents. Data collection techniques are carried out through interviews, observations, field notes and documentation while data analysis techniques are formulated by paying attention to the stages of activity, namely qualitative structuring of raw data, data selection. Furthermore, in collecting data this research used the original Nias language of the informants and then translated into bilingualism. The qualitative approach in question is to explain the data related to community views on the value of honesty towards the object being observed, namely gowe Tugala Oyo. The character value of respecting one's own culture is very important to instill in children so that cultural resistance can be maintained for generations. Cultural values such as religion, morals, cooperation, trust, and responsibility should be used as a philosophical foundation in people's lives. The character of upholding ancestral dynasties is a characteristic of someone who remains steadfast
Evolutionary measures and their correlations with the performance of cross‐version defect prediction for object‐oriented projects

Abstract Cross‐version defect prediction (CVDP) for evolutionary projects has attracted much attention from researchers in recent years. For multiple versions of an object‐oriented project, the degree of evolution (e.g., the degree of class change) between successive versions can reflect the differences between versions, which could affect the performance of CVDP. Therefore, how to measure the degree of evolution between successive versions and explore the correlations with the performance of CVDP are very important for software defect prediction. Based on the successive versions of evolutionary projects, this paper proposes six evolutionary measures from three aspects of class change, metric change, and label change, including the Ratio of New Classes ( RNC ), the Ratio of Deleted Classes ( RDC ), the Average Ratio of Metric Change ( ARMC ), the Ratio of Label Changed Classes ( RLCC ), the Ratio of Unchanged Classes ( RUC ), and the Ratio of Interference Classes ( RIC ). An empirical study was conducted on 40 versions of 11 object‐oriented projects from the PROMISE repository. Precision, Recall, F‐measure, and AUC were used as the performance indicators. Three correlation approaches (Pearson, Spearman, and Kendall) are applied to show the correlations between evolutionary measures and the performance of CVDP. The statistical results show that RNC , RDC , and RUC show no correlation with four performance indicators. ARMC shows weak or medium positive correlations with Recall and F‐measure. RLCC and RIC show very strong or strong negative correlations with Recall and F‐measure. The results indicate that the correlations between the proposed evolutionary measures and the performance of CVDP are different, which can guide the training set selection of CVDP.
Improving quantum annealing by engineering the coupling to the environment

Abstract A large class of optimisation problems can be mapped to the Ising model where all details are encoded in the coupling of spins. The task of the original mathematical optimisation is then equivalent to finding the ground state of the corresponding spin system which can be achieved via quantum annealing relying on the adiabatic theorem. Some of the inherent disadvantages of this procedure can be alleviated or resolved using a stochastic approach, and by coupling to the external environment. We show that careful engineering of the system-bath coupling at an individual spin level can further improve annealing.
Relation-balanced graph convolutional network for 3D human pose estimation

Graph convolutional networks (GCNs) have been applied to 2D-to-3D human pose estimation (HPE) and have shown encouraging performance. However, existing GCNs model the relations between joints via individual kernels, which can be overly flexible and fail to capture common relational patterns due to the symmetric nature of the human body. Although some GCNs share kernels to capture common relations, the unified way for all neighbors limits relational diversity to some extent. In order to balance the diversity and commonality of relations, we conduct a comprehensive study of existing kernel-sharing strategies and propose a Relation-balanced Graph Convolutional Network (RbGC-Net). RbGC-Net introduces the Part-Specific Kernel-Sharing strategy (PSKS) that assigns kernels based on the semantic meanings of neighbors to establish specific relational patterns for different types of neighborhoods. Furthermore, RbGC-Net incorporates a Local–Global Feature Fusion module (LGFF) that extracts the local relations among joints and balances them with the final global relations to improve the interactions between joints. Compared with state-of-the-art methods for 3D HPE, our RbGC-Net achieves the optimal balance between model size and estimation errors. Results on two benchmark Human3.6 M and MPI-INF-3DHP datasets demonstrate the excellent performance and strong generalization ability of our pure GCN-based method.
Library and Information Week at SCHN: the source of our success

During our Library planning day in January, the team decided to plan a comprehensive program for LIW 2023 and to track the outcomes. Our aims were to provide engaging training sessions, share key library resources and promote the library generally.
Novel solar forecasting scheme modelled by mixer dual path network and based on sky images

The prediction of global horizontal irradiance has become an effective technique to address the intermittence issue of photovoltaic (PV) power generation. This article proposes a novel deep neural network(DNN), named Mixer Dual Path Network (Mixer-DPN), for promising solar forecasting. It shares common features of cloud images and maintains the flexibility to explore new features through dual-path architecture by combining the Mixer layer and Dual Path Network. Therefore, the proposed model can provide more accurate prediction results compared to the classical DNN-based predictors. Moreover, the proposed model shows a faster convergence speed and smaller model size, which makes it suitable for a practical global horizontal irradiance. The merits of the proposed model are verified by testing it with the data from National Renewable Energy Laboratory comparing it with other DNN-based prediction models. Studies have shown that the new model has achieved excellent results in MSE, MAE and other indicators, and the R2 prediction accuracy rate has increased by 14% compared with the baseline model.