Reviewerly/exHarmony huggingface dataset:
Codensity Games for Bisimilarity

Bisimilarity as an equivalence notion of systems has been central to process theory. Due to the recent rise of interest in quantitative systems (probabilistic, weighted, hybrid, etc.), bisimilarity has been extended in various ways, such as bisimulation metric between probabilistic systems. An important feature of bisimilarity is its game-theoretic characterization, where Spoiler and Duplicator play against each other; extension of bisimilarity games to quantitative settings has been actively pursued too. In this paper, we present a general framework that uniformly describes game characterizations of bisimilarity-like notions. Our framework is formalized categorically using fibrations and coalgebras. In particular, our characterization of bisimilarity in terms of fibrational predicate transformers allows us to derive what we call codensity bisimilarity games: a general categorical game characterization of bisimilarity. Our framework covers known bisimilarity-like notions (such as bisimulation metric and bisimulation seminorm) as well as new ones (including what we call bisimulation topology).
An AI-Augmented Lesion Detection Framework For Liver Metastases With Model Interpretability

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer-related deaths worldwide. Most CRC deaths are the result of progression of metastases. The assessment of metastases is done using the RECIST criterion, which is time consuming and subjective, as clinicians need to manually measure anatomical tumor sizes. AI has many successes in image object detection, but often suffers because the models used are not interpretable, leading to issues in trust and implementation in the clinical setting. We propose a framework for an AI-augmented system in which an interactive AI system assists clinicians in the metastasis assessment. We include model interpretability to give explanations of the reasoning of the underlying models.
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English

Sentiment analysis has a range of corpora available across multiple languages. For emotion analysis, the situation is more limited, which hinders potential research on cross-lingual modeling and the development of predictive models for other languages. In this paper, we fill this gap for German by constructing deISEAR, a corpus designed in analogy to the well-established English ISEAR emotion dataset. Motivated by Scherer's appraisal theory, we implement a crowdsourcing experiment which consists of two steps. In step 1, participants create descriptions of emotional events for a given emotion. In step 2, five annotators assess the emotion expressed by the texts. We show that transferring an emotion classification model from the original English ISEAR to the German crowdsourced deISEAR via machine translation does not, on average, cause a performance drop.
PAC-Bayes Un-Expected Bernstein Inequality

We present a new PAC-Bayesian generalization bound. Standard bounds contain a $\sqrt{L_n \cdot \KL/n}$ complexity term which dominates unless $L_n$, the empirical error of the learning algorithm's randomized predictions, vanishes. We manage to replace $L_n$ by a term which vanishes in many more situations, essentially whenever the employed learning algorithm is sufficiently stable on the dataset at hand. Our new bound consistently beats state-of-the-art bounds both on a toy example and on UCI datasets (with large enough $n$). Theoretically, unlike existing bounds, our new bound can be expected to converge to $0$ faster whenever a Bernstein/Tsybakov condition holds, thus connecting PAC-Bayesian generalization and {\em excess risk\/} bounds---for the latter it has long been known that faster convergence can be obtained under Bernstein conditions. Our main technical tool is a new concentration inequality which is like Bernstein's but with $X^2$ taken outside its expectation.
SECURED ONTOLOGY MAPPING

Todays market evolution and high volatility of business requirements put an increasing emphasis on the ability for systems to accommodate the changes required by new organizational needs while maintaining security objectives satisfiability. This is all the more true in case of collaboration and interoperability between different organizations and thus between their information systems. Ontology mapping has been used for interoperability and several mapping systems have evolved to support the same. Usual solutions do not take care of security. That is almost all systems do a mapping of ontologies which are unsecured.We have developed a system for mapping secured ontologies using graph similarity concept. Here we give no importance to the strings that describe ontology concepts, properties etc. Because these strings may be encrypted in the secured ontology. Instead we use the pure graphical structure to determine mapping between various concepts of given two secured ontologies. The paper also gives the measure of accuracy of experiment in a tabular form in terms of precision, recall and F-measure.
Orthogonal Range Reporting and Rectangle Stabbing for Fat Rectangles

In this paper we study two geometric data structure problems in the special case when input objects or queries are fat rectangles. We show that in this case a significant improvement compared to the general case can be achieved. We describe data structures that answer two- and three-dimensional orthogonal range reporting queries in the case when the query range is a \emph{fat} rectangle. Our two-dimensional data structure uses $O(n)$ words and supports queries in $O(\log\log U +k)$ time, where $n$ is the number of points in the data structure, $U$ is the size of the universe and $k$ is the number of points in the query range. Our three-dimensional data structure needs $O(n\log^{\varepsilon}U)$ words of space and answers queries in $O(\log \log U + k)$ time. We also consider the rectangle stabbing problem on a set of three-dimensional fat rectangles. Our data structure uses $O(n)$ space and answers stabbing queries in $O(\log U\log\log U +k)$ time.
Polynomial Invariant Theory and Shape Enumerator of Self-Dual Codes in the NRT-Metric

In this paper we consider self-dual NRT-codes, that is, self-dual codes in the metric space endowed with the Niederreiter-Rosenbloom-Tsfasman (NRT-metric). We use polynomial invariant theory to describe the shape enumerator of a binary self-dual, doubly even self-dual, and doubly-doubly even self dual NRT-code $C\subseteq M_{n,2}(\mathbb{F}_{2})$. Motivated by these results we describe the number of invariant polinomials that we must find to describe the shape enumerator of a self-dual NRT-code of $M_{n,s}(\mathbb{F}_{2})$. We define the ordered flip of a matrix $A\in M_{k,ns}(\mathbb{F}_{q})$ and present some constructions of self-dual NRT-codes over $\mathbb{F}_{q}$. We further give an application of ordered flip to the classification of bidimensional self-dual NRT-codes.
Fast Weakly Supervised Action Segmentation Using Mutual Consistency

Action segmentation is the task of predicting the actions for each frame of a video. As obtaining the full annotation of videos for action segmentation is expensive, weakly supervised approaches that can learn only from transcripts are appealing. In this paper, we propose a novel end-to-end approach for weakly supervised action segmentation based on a two-branch neural network. The two branches of our network predict two redundant but different representations for action segmentation and we propose a novel mutual consistency (MuCon) loss that enforces the consistency of the two redundant representations. Using the MuCon loss together with a loss for transcript prediction, our proposed approach achieves the accuracy of state-of-the-art approaches while being $14$ times faster to train and $20$ times faster during inference. The MuCon loss proves beneficial even in the fully supervised setting.
Unsupervised Image Matching and Object Discovery as Optimization

Learning with complete or partial supervision is powerful but relies on ever-growing human annotation efforts. As a way to mitigate this serious problem, as well as to serve specific applications, unsupervised learning has emerged as an important field of research. In computer vision, unsupervised learning comes in various guises. We focus here on the unsupervised discovery and matching of object categories among images in a collection, following the work of Cho et al. 2015. We show that the original approach can be reformulated and solved as a proper optimization problem. Experiments on several benchmarks establish the merit of our approach.
To Stir or Not to Stir: Online Estimation of Liquid Properties for  Pouring Actions

Our brains are able to exploit coarse physical models of fluids to solve everyday manipulation tasks. There has been considerable interest in developing such a capability in robots so that they can autonomously manipulate fluids adapting to different conditions. In this paper, we investigate the problem of adaptation to liquids with different characteristics. We develop a simple calibration task (stirring with a stick) that enables rapid inference of the parameters of the liquid from RBG data. We perform the inference in the space of simulation parameters rather than on physically accurate parameters. This facilitates prediction and optimization tasks since the inferred parameters may be fed directly to the simulator. We demonstrate that our "stirring" learner performs better than when the robot is calibrated with pouring actions. We show that our method is able to infer properties of three different liquids -- water, glycerin and gel -- and present experimental results by executing stirring and pouring actions on a UR10. We believe that decoupling of the training actions from the goal task is an important step towards simple, autonomous learning of the behavior of different fluids in unstructured environments.
Cyberthreat Detection from Twitter using Deep Neural Networks

To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platforms, namely social media networks such as Twitter, are capable of aggregating a vast amount of cybersecurity-related sources. To process such information streams, we require scalable and efficient tools capable of identifying and summarizing relevant information for specified assets. This paper presents the processing pipeline of a novel tool that uses deep neural networks to process cybersecurity information received from Twitter. A convolutional neural network identifies tweets containing security-related information relevant to assets in an IT infrastructure. Then, a bidirectional long short-term memory network extracts named entities from these tweets to form a security alert or to fill an indicator of compromise. The proposed pipeline achieves an average 94% true positive rate and 91% true negative rate for the classification task and an average F1-score of 92% for the named entity recognition task, across three case study infrastructures.
A Exploratory Study of the Educational View of Jesus

Objectives The purpose of this study is to provide information for teachers to use when reflecting on their educational views by exploring Jesus and his educational views taught through personal relationships after sharing with his disciples for three years.&#x0D; Methods This study was done through a literature analysis method. First, After producing an initial analysis frame on Jesus' view of education through the analysis of previous studies, the Bible text was reviewed based on this to reveal the specific meaning of the intermediate topic and sub-topic of the analysis frame. Second, the intermediate topic and sub-topic of the revised analysis frame were confirmed as the final analysis frame after expert review, and based on this, the Bible text that recorded Jesus' teachings was carefully reviewed. Third, based on the analysis results with fianl analysis frame, I discussed the implications of Jesus' view of education.&#x0D; Results The view of Jesus' education revealed through analysis is as follows. The educational purpose of Jesus is the formation of faith, the growth of faith, and the spread of faith. The educational content of Jesus is the Jesus himself, the kingdom of God, and the role of the people of God's kingdom. The educational methods of Jesus are showing example, figures of speech, questioning, practice, use of circumstances, and level education. The characteristics of educational evaluation of Jesus are the use of criteria, feedback. The implications discussed from the results are as follows. From the educational purpose of Jesus, I derive implications for education for one life, expression of expectations for students, and the spread of self-educational view. From Jesus' educational content, I derive implications for expressing the teacher himself to the student, managing the class as a small society, and raising questions about what was taken for granted. From Jesus' educational methods, I derive implications for standing up once again, communicating in a language that students can understand, becoming aware of the student's real problems, and waiting for the best time. From Jesus' educational evaluation I derive implications for evaluating in principle, evaluating students' lives outside the classroom, parents' participation in education, and enduring unwanted educational results.&#x0D; Conclusions The conclusions reached through this study are as follows. Teachers in Korean schools already show some characteristics of Jesus' education. It would be very helpful if teachers could use Jesus as a role model and apply his education view collectively to themselves.
Research on automatic detection of text-oriented counterfactual statements

Counter-fact is the highest form of causal reasoning. Focusing on counter-fact will provide new ideas for higher order causal reasoning. For counterfactual text detection, two detection methods are proposed. One is the automatic detection method based on classification model, that is, counterfactual detection is carried out on the given annotated text data. In this way, the data imbalance problem is solved first, then word vector is generated by doc2vec, and finally the binary SVM training model is adopted.&#84;he second is automatic detection method based on sequence annotation model, namely the counterfactual statements of reason and result orientation, to this sequence labeling method is adopted, the individual words in the text annotation, after processing the data to be included in the BiLSTM after training, and through a layer of conditions with the airport, the resulting text labeling information. Experiments show that the proposed method can accurately detect and locate the cause and result in the counterfactual text.
The role of blockchain in financial applications

Blockchain is developing rapidly as a popular area of research these days. The blockchain is a technology employed to run bitcoin. It is distributed database having a list of records growing continuously called blocks in order to certify the security of individuals blocks from adjustment and tampering. Every block is connected to other blocks by keeping the hash of the previous block in the chain. This chapter discusses the technical aspects of blockchain and the banking sector. One of the greatest threats to the banking sector today is technology. Whether it is coming from large technology firms, or from new financial technology, traditional banks are beginning to take notice. One potential disrupter for the financial business today comes from applications involving blockchain technology. The system of distributed ledgers that underlie cryptocurrencies such as Bitcoin. Large financial institutions from investment banks to stock exchanges to central banks are all beginning to work on their own blockchain-based solutions to stay on top of this innovation.
Reassessing Rigvedic Strata

&#x0D; &#x0D; &#x0D; It is commonly assumed that linguistically the Rigveda is a heterogeneous collection of texts. Since the early days of Vedic scholarship, researchers have tried to correlate grammatical properties with individual parts of the Rigveda, aiming at identifying layers and strata. In this article we review stratifications proposed in previous literature and assess the support for these strata by performing statistical analyses with allomorphic linguistic features that have been claimed to bear signals of stratification. In addition, we run a cluster analysis exploring how the books are grouped given the same allomorphic linguistic data. Our results show that once we control for metrical positioning, prosodic structure, and content, the allomorph distributions do not lend significant support for any of the proposed stratifications. Moreover, the cluster analysis favors the assumption that book 10 occupies a special position, but overall differences across the ten books of the Rigveda are not substantial.&#x0D; &#x0D; &#x0D;
Effect Analysis of Liberal Arts Lectures Developed to Cultivate Creativity Competencies of University Students

Objectives In this study, the effectiveness of the liberal arts course developed to cultivate creativity, one of the core competencies of future talent, was verified for college students.&#x0D; Methods The liberal arts course for nurturing the creativity competency of college students aims to cultivate self-actualizing creativity, while continuously experiencing daily creativity, develop meta-cognition of creativity, and acquire integrated creativity by acquiring complex knowledge and thinking skills about creativity. Classes were conducted in a variety of ways, including lectures by professors, individual practice, conversation with peers, group games, team discussions, team projects, and presentations. The team consisted of 5-6 members per team considering the diversity of sex, grade, major, and problem-solving ability. The duration of the course was 13 weeks per semester (excluding midterm and final exams), and was conducted twice a week, 75 minutes at a time, over a semester. The questionnaire was conducted during the first week of class (1st week) and the week before the final exam (14th week), and the collected creativity and social efficacy questionnaire responses were analyzed through descriptive statistics and paired-sample t-test.&#x0D; Results It was found that the level of integrated creativity and social self-efficacy of the students who took the course increased significantly after taking the course. In addition, the perception of creativity was recognized as an abstract and single component before taking it, but as a concrete and complex component after taking it.&#x0D; Conclusions This study shows that the liberal arts course developed for the purpose of cultivating the creative competency of college students was effective. This provides meaningful implications for the direction and method of creativity education in universities.
An Analysis of Achievement Standards for Environmental Education in The 2015 Special Education Curriculum

Objectives The purpose of this study is to analyze the achievement standards related to environmental education in the 2015 Special education curriculum to understand the current status of achievement standards related to environmental education and to understand implications for future curriculum revisions and follow-up studies.&#x0D; Methods For this purpose a framework for analyzing environmental education related content elements was selected based on previous studies, and integrated subjects, social studies, science, practical studies, career and occupations related to the environment were selected and analyzed in the 2015 special education curriculum.&#x0D; Results As a result of the analysis, when identified based on the subject, the integrated subject included the most achievement standards, and elementary schools included the most achievement standards by school level. Finally, when analyzed focusing on the environmental education area, environmental knowledge had the most achievement standards and when analyzed focusing on the sub-area of environmental education, the global ecosystem of environmental knowledge included the most achievement standards.&#x0D; Conclusions Based on the results of the study, it was found that in the 2015 Special Education Curriculum, achievement standards related to environmental education were not evenly implemented in various subjects and school levels, and the area of environmental education was also biased toward environmental knowledge. Based on these results, implications for future curriculum revisions and follow-up studies were derived.
The Effect of Satisfaction with Real-Time Non-face-to-face Education for Nursery Teachers in Childcare Support Center on Educational Effects through Teacher Effectiveness

Objectives The purpose of this study is to examine the influence on education effect by satisfaction level of untact real-time education for childcare teacher-executed by the Support Center for Childcare- with mediacy of teacher’s efficacy and to provide basic data required in managing education program by the Support Center for Childcare&#x0D; Methods The objects of this study were 204 childcare teachers working at daycare centers in Gyeonggi-do. The survey for satisfaction level of real-time untact education, education effects, teacher’s efficacy was performed from 11 March to 24 March in 2022. Excluding some outlying data, 202 samples was analyzed for this study, and data analyzing tool was SPSS WIN 22.0 program for frequency analysis, reliability analysis, descriptive statistics, difference analysis, correlation analysis, and mediating effect analysis.&#x0D; Results First, satisfaction level of real-time untact education, education effect and teacher’s efficacy shows high position. Second, among several variables of childcare teacher, number of real-time untact education increases their efficacy for education. Third, between satisfaction of real-time untact education, education effect, and teacher’s efficacy, statical correlation was verified. Fourth, relation between real-time untact education satisfaction and education effect, teacher’s efficacy functions as a mediating parameter. So, plan or alternative for increasing teacher’s efficacy is required.&#x0D; Conclusions Through this study, we were able to confirm the high satisfaction and educational effects of real-time untact education at the Support Center Childcarere and the mediating effects of teacher efficacy. This can be used as a basic data for the efficient operation of childcare teachers' education at the Support Center Childcarere, and there is an empirical implication in the need to improve the teacher's sense of efficacy.
You Code It! Answers: What’s Wrong With These Claims?

The answer to this month’s You Code It! scenario is presented here.