Reviewerly/exHarmony huggingface dataset:
On Solving the Rubik's Cube with Domain-Independent Planners Using Standard Representations

Rubik's Cube (RC) is a well-known and computationally challenging puzzle that has motivated AI researchers to explore efficient alternative representations and problem-solving methods. The ideal situation for planning here is that a problem be solved optimally and efficiently represented in a standard notation using a general-purpose solver and heuristics. The fastest solver today for RC is DeepCubeA with a custom representation, and another approach is with Scorpion planner with State-Action-Space+ (SAS+) representation. In this paper, we present the first RC representation in the popular PDDL language so that the domain becomes more accessible to PDDL planners, competitions, and knowledge engineering tools, and is more human-readable. We then bridge across existing approaches and compare performance. We find that in one comparable experiment, DeepCubeA (trained with 12 RC actions) solves all problems with varying complexities, albeit only 78.5% are optimal plans. For the same problem set, Scorpion with SAS+ representation and pattern database heuristics solves 61.50% problems optimally, while FastDownward with PDDL representation and FF heuristic solves 56.50% problems, out of which 79.64% of the plans generated were optimal. Our study provides valuable insights into the trade-offs between representational choice and plan optimality that can help researchers design future strategies for challenging domains combining general-purpose solving methods (planning, reinforcement learning), heuristics, and representations (standard or custom).
Node Injection Link Stealing Attack

In this paper, we present a stealthy and effective attack that exposes privacy vulnerabilities in Graph Neural Networks (GNNs) by inferring private links within graph-structured data. Focusing on the inductive setting where new nodes join the graph and an API is used to query predictions, we investigate the potential leakage of private edge information. We also propose methods to preserve privacy while maintaining model utility. Our attack demonstrates superior performance in inferring the links compared to the state of the art. Furthermore, we examine the application of differential privacy (DP) mechanisms to mitigate the impact of our proposed attack, we analyze the trade-off between privacy preservation and model utility. Our work highlights the privacy vulnerabilities inherent in GNNs, underscoring the importance of developing robust privacy-preserving mechanisms for their application.
A Planning Ontology to Represent and Exploit Planning Knowledge for Performance Efficiency

Ontologies are known for their ability to organize rich metadata, support the identification of novel insights via semantic queries, and promote reuse. In this paper, we consider the problem of automated planning, where the objective is to find a sequence of actions that will move an agent from an initial state of the world to a desired goal state. We hypothesize that given a large number of available planners and diverse planning domains; they carry essential information that can be leveraged to identify suitable planners and improve their performance for a domain. We use data on planning domains and planners from the International Planning Competition (IPC) to construct a planning ontology and demonstrate via experiments in two use cases that the ontology can lead to the selection of promising planners and improving their performance using macros - a form of action ordering constraints extracted from planning ontology. We also make the planning ontology and associated resources available to the community to promote further research.
Score-based Diffusion Models for Generating Liquid Argon Time Projection Chamber Images

For the first time, we show high-fidelity generation of LArTPC-like data using a generative neural network. This demonstrates that methods developed for natural images do transfer to LArTPC-produced images, which, in contrast to natural images, are globally sparse but locally dense. We present the score-based diffusion method employed. We evaluate the fidelity of the generated images using several quality metrics, including modified measures used to evaluate natural images, comparisons between high-dimensional distributions, and comparisons relevant to LArTPC experiments.
Noisy k-means++ Revisited

The $k$-means++ algorithm by Arthur and Vassilvitskii [SODA 2007] is a classical and time-tested algorithm for the $k$-means problem. While being very practical, the algorithm also has good theoretical guarantees: its solution is $O(\log k)$-approximate, in expectation. In a recent work, Bhattacharya, Eube, Roglin, and Schmidt [ESA 2020] considered the following question: does the algorithm retain its guarantees if we allow for a slight adversarial noise in the sampling probability distributions used by the algorithm? This is motivated e.g. by the fact that computations with real numbers in $k$-means++ implementations are inexact. Surprisingly, the analysis under this scenario gets substantially more difficult and the authors were able to prove only a weaker approximation guarantee of $O(\log^2 k)$. In this paper, we close the gap by providing a tight, $O(\log k)$-approximate guarantee for the $k$-means++ algorithm with noise.
ARB: Advanced Reasoning Benchmark for Large Language Models

Large Language Models (LLMs) have demonstrated remarkable performance on various quantitative reasoning and knowledge benchmarks. However, many of these benchmarks are losing utility as LLMs get increasingly high scores, despite not yet reaching expert performance in these domains. We introduce ARB, a novel benchmark composed of advanced reasoning problems in multiple fields. ARB presents a more challenging test than prior benchmarks, featuring problems in mathematics, physics, biology, chemistry, and law. As a subset of ARB, we introduce a challenging set of math and physics problems which require advanced symbolic reasoning and domain knowledge. We evaluate recent models such as GPT-4 and Claude on ARB and demonstrate that current models score well below 50% on more demanding tasks. In order to improve both automatic and assisted evaluation capabilities, we introduce a rubric-based evaluation approach, allowing GPT-4 to score its own intermediate reasoning steps. Further, we conduct a human evaluation of the symbolic subset of ARB, finding promising agreement between annotators and GPT-4 rubric evaluation scores.
CQNV: A combination of coarsely quantized bitstream and neural vocoder for low rate speech coding

Recently, speech codecs based on neural networks have proven to perform better than traditional methods. However, redundancy in traditional parameter quantization is visible within the codec architecture of combining the traditional codec with the neural vocoder. In this paper, we propose a novel framework named CQNV, which combines the coarsely quantized parameters of a traditional parametric codec to reduce the bitrate with a neural vocoder to improve the quality of the decoded speech. Furthermore, we introduce a parameters processing module into the neural vocoder to enhance the application of the bitstream of traditional speech coding parameters to the neural vocoder, further improving the reconstructed speech's quality. In the experiments, both subjective and objective evaluations demonstrate the effectiveness of the proposed CQNV framework. Specifically, our proposed method can achieve higher quality reconstructed speech at 1.1 kbps than Lyra and Encodec at 3 kbps.
Optical Flow boosts Unsupervised Localization and Segmentation

Unsupervised localization and segmentation are long-standing robot vision challenges that describe the critical ability for an autonomous robot to learn to decompose images into individual objects without labeled data. These tasks are important because of the limited availability of dense image manual annotation and the promising vision of adapting to an evolving set of object categories in lifelong learning. Most recent methods focus on using visual appearance continuity as object cues by spatially clustering features obtained from self-supervised vision transformers (ViT). In this work, we leverage motion cues, inspired by the common fate principle that pixels that share similar movements tend to belong to the same object. We propose a new loss term formulation that uses optical flow in unlabeled videos to encourage self-supervised ViT features to become closer to each other if their corresponding spatial locations share similar movements, and vice versa. We use the proposed loss function to finetune vision transformers that were originally trained on static images. Our fine-tuning procedure outperforms state-of-the-art techniques for unsupervised semantic segmentation through linear probing, without the use of any labeled data. This procedure also demonstrates increased performance over original ViT networks across unsupervised object localization and semantic segmentation benchmarks.
Convolutional Takagi–Sugeno–Kang-type Fuzzy Neural Network for Bearing Fault Diagnosis

Rotating machines are widely used in modern industry.In a mechanical system, rolling bearings are essential.Bearings must be able to operate in extreme environments, in which they are prone to various faults.To address the challenge related to accurately classify bearing fault types using vibration sensors, we propose a convolutional Takagi-Sugeno-Kang (TSK)-type fuzzy neural network classifier (CTFNNC) that comprises a convolutional layer and a TSK-type fuzzy neural network.In the CTFNNC, convolutional layers are used to extract the features of a vibration signal, and a TSK-type fuzzy neural network is used to classify bearing faults under various categories.In our experiment, the proposed CTFNNC was compared with other methods, such as a fuzzy neural network, an artificial neural network, and the LeNet-5 convolutional neural network.The experimental results indicate that the proposed CTFNNC has a bearing fault classification accuracy of 98.3% and requires half the number of parameters as LeNet-5.
Classifications of users on online platforms using machine learning techniques

Online platforms have become an integral part of our lives, and the number of users is increasing by the day. From social media platforms to e-commerce websites, these platforms are used by millions of people around the world. With such a large user base, it is essential for these platforms to classify their users based on their behavior, preferences, and interests. This paper explores how machine learning can be used to classify users on online platforms. When classifying users, they are divided into different categories based on their characteristics. By analyzing user behavior and preferences, online platforms can personalize their services and provide a better user experience. Machine learning techniques can help online platforms automate the classification process and reduce human effort. In this article, the behavioral classification of users on online platforms will be discussed in detail.
Research on types of self-actualization of future masters in pedagogy

Purpose is to identify types of self-actualization of future Masters in Pedagogy in the dimensions of educational and professional activity. The psycho-diagnostic instruments applied are valid and reliable. Three levels of statistical processing were used: 1) descriptive frequency characteristics; 2) coefficients for establishing statistical significance; 3) k-means clustering for identifying types of self-actualization of the research participants. The main concepts of А. Maslow’s theory of self-actualization (1970) comprise methodological foundations of the empirical research. The study shows that in descriptive frequency characteristics there were no statistically significant differences with similar dimensions. Spearman’s correlation analysis (rs) and correlation matrix allowed comprehending the variety of statistically significant content correlations of the examined phenomenon. K-means clustering made it possible to identify four types of the respondents’ self-actualization: “Creative self-actualization” (n=32; 14.34%), “Hyperactive self-actualization” (n=60; 26.91%), “Egoistic self-actualization” (n=72; 32.29%) and “Life self-actualization” (n=59; 26.46%). Appropriateness of implementing the research results in organization of educational and scientific training for Masters in Pedagogy was empirically established and theoretically substantiated.
Innovative development of the education industry in the European higher education area

The article identifies positive changes and suggests trends in the innovative development of the educational sector in the European area of higher education. The goal is determined, and the functions of the European area of higher education are considered. It has been proven that two trends, which are analyzed in the article, prevail in the structure of world higher education: unitary (single), a system manifested in higher education and provided by universities; binary (dual), a system with a traditional education sector based on the Humboldt University concept and a separate non-university sector of higher education. The main principles regulating aspects of educational activity are revealed. Considered is a very important and necessary distance form in the educational space, related to the introduction of information and communication technologies into the educational process, which is being updated and developed to improve an open and widely accessible education system, aimed at training a qualified competitive specialist.
Implementation of teaching technology in the process of future specialists' professional development

The technology of mentoring and the possibilities of its implementation in the process of professional development of future specialists are considered. The functions of mentors (advisory, socio-pedagogical, spiritual) and criteria of mentoring (democratic, social orientation, originality, research, dialogue, leadership, cooperation) were analyzed. Assertiveness, fascination, attraction of the mentor's personality, spiritual constants, and moral and ethical maxims of behavior are considered. The task of the mentor is written. The factors that influence the effectiveness of mentoring and the characteristic features of the mentors' style are singled out. To increase the effectiveness of pedagogical mentoring, the need for principles is substantiated. The types of mentoring (religious, based, industrial, professional) are given. The importance of a remote form of support for young professionals - telementoring - is shown, which is necessary with the rapid development of Internet communications.
TANGEDAL, Ross K. 2021. The Preface: American Authorship in the Twentieth Century.

Review
The The Effect of Nutrition Education Using Videos and E-booklets on Increasing Knowledge of Balanced Nutrition in High School Students in Jakarta

Abstract: The adolescent counts as a productive age group who needs an adequate nutritional intake to ensure their productivity in their studies and work. A long-term period of unbalanced nutrition consumption might impact one's health and productivity. One of the interventions that can be done is through education to increase their knowledge of balanced nutrition. The media used in this research are e-booklets and videos. Objectives: This research aims to understand the difference between balanced nutrition knowledge before and after nutritional education using E-booklets and videos among high school students in Jakarta and also to find the difference in the effectiveness of both media. Methods: The sampling technique used in this research was random sampling with a total of 95 respondents. The method used is a quasi-experiment with a Wilcoxon test to find the difference in knowledge before and after the intervention and a Mann-Whitney test to find the difference between media effectiveness in increasing knowledge. Results: The results show that both e-booklets (p-value = 0.000) and videos (p-value = 0.000) give a significant effect in increasing balanced nutrition knowledge levels. There is also no significant difference in the effectiveness of both media (p-value = 0.350). Conclusion: Balanced nutrition education using e-booklets and video proven to be effective in increasing nutrition knowledge in adolescents. Further research could be done by expanding the population and increase sample number.
A Method for Extracting Multifaceted Information from Free Descriptions in Questionnaires

In Japanese universities, student questionnaires completed in class are used to collect various types of information with two main objectives: students’ impressions of classes and finding areas of improvement. The advantage of a class evaluation questionnaire with numerous questions is that detailed information can be collected; however, contrarily, the number of questionnaires collected and the rate of collection can decrease. This study proposes and tests university students a method to extract relevant information from a simple questionnaire. The proposed questionnaire comprises only five items on students’ impressions of the classes and free descriptions. Text mining techniques were used to analyze the free descriptions and extract the positive or negative feelings expressed by the students. The results were classified according to the questionnaire score. This analysis method efficiently extracts information to identify the factors affecting students’ comprehension, achievement, and satisfaction, which can improve classes. Text mining techniques were used to analyze the free descriptions and extract the positive or negative feelings the students expressed.
The Interpretation of Male Power Relation in Elementary School Text Book in Indonesia

The purpose of this research is to analyze the elements of the discourse of men's power relations in the text. This study used data in integrated subject Indonesian textbooks of 2nd grade elementary school. In addition, textbooks are a form of research. This study uses data analysis methods along with field methods through observations and interviews. Data collection techniques are checklists, interviews and documentation of language forms in textbooks. The theory used in this research is discourse analysis theory (Dijk 1993). The results of this study show that there are elements of discourse that refer to male power relations in texts, such as microstructure, macrostructure and superstructure. Microstructure contains grammatical elements that include references, deletions and conjunctions. Context, situation and contact culture are the macro elements in the integrated subject textbooks of Indonesian primary school. Character values are implicitly described in text characters. The microstructure, macrostructure and superstructure of the data show the dominance of male power relations.
Compounding with a Polymorphic Deverbaliser in Turkish

This paper is concerned with compounds headed by nouns built by the deverbaliser -Im in Turkish. Our basic observation is that they polymorphically correspond to compounds such as ‘home-made’ as well as compounds such as ‘cake-baking’. We argue that the theme has a distinguished status in these compounds, suggesting that the base for -Im cannot be a plain event predicate, but has an argument structure. Thus, as we provide a compositional semantics for compounds built by -Im, we also hope to contribute to the debate on whether the base of a deverbal noun may have argument structure or not.
The Meaning of Nominalisation

Nominalisation is a morphological process producing a noun on the basis of an input that may belong to various categories. As noun is a syntactic category, whether something is a noun can only be decided on the basis of syntactic evidence, not on the basis of its meaning or morphological behaviour.&#x0D; As a theoretical framework, I use Jackendoff’s Parallel Architecture (PA) as a basis, but I argue for a separate word formation component. The central difference between word formation rules and regular lexical entries is that the latter contribute to the production of the representation of interpreted performance, whereas the former produce new lexical entries, thus changing competence. As a consequence, an expression in interpreted performance needs the identification of a reference in the communicative context, whereas word formation needs the identification of a concept in a speaker’s knowledge, which involves onomasiological coercion.&#x0D; A distinction can be made between two types of nominalisation, which I illustrate with Dutch examples. In one type, the meaning is changed, e.g. jager ‘hunter’ from jagen ‘huntv’, in the other it is not, e.g. telling ‘countn’ from tellen ‘countv’. Nominalisations of the latter type are transpositions. This distinction can be made both for deverbal and for deadjectival nouns.&#x0D; Rules changing representations in PA can be classified in seven classes according to which of the structures they modify. Only those that change conceptual structure qualify for being part of the word formation component. This excludes transpositions. Many nouns can be interpreted as either a transposition or a result of word formation. An example is vertaling ‘translation’, which can refer to the process or the result of translation. I argue that there is a word formation rule that produces the second reading on the basis of the first, and show that this rule belongs to a type that is predicted by the typology of rules for modifying representations in PA.
Morphosemantic Features of Mengambil ‘Take’ in the Light Verb Constructions of Indonesian

Light verb constructions (LVCs) are the compositional form of a verb and a noun. The construction is usually denoting an idiomatic meaning. This study aimed at identifying and describing the morphosemantic feature of the verb mengambil ‘take’ in the Indonesian LVCs. This study has been done by utilizing semantics and morphology as the theoretical framework. The study data has been collected from three reputable corpora, namely LCCI, SEAlang, and KBBI. It has been done by using the UDPipe for tokenization, tagging, lemmatization, and dependency. This study's results show at least three main features of the verb mengambil, ‘take’ in the Indonesian LVCs based on the morphosemantics analysis. The three attributes are: (i) the first is regarding the verb type of ACTION, (ii) the second is about the semantic role of argument, namely AGENT of ARGUMENT-S, and (iii) the last is regarding the grammatical meaning of RESULTATIVE. It has been suggested to conduct an analysis on the other possibility of sense in Indonesian LVCs.