Reviewerly/exHarmony huggingface dataset:
Bigram semantic distance as an index of continuous semantic flow in natural language: Theory, tools, and applications.

Much of our understanding of word meaning has been informed through studies of single words. High-dimensional semantic space models have recently proven instrumental in elucidating connections between words. Here we show how bigram semantic distance can yield novel insights into conceptual cohesion and topic flow when computed over continuous language samples. For example, "Cats drink milk" is comprised of an ordered vector of bigrams (cat-drink, drink-milk). Each of these bigrams has a unique semantic distance. These distances in turn may provide a metric of dispersion or the flow of concepts as language unfolds. We offer an R-package ("semdistflow") that transforms any user-specified language transcript into a vector of ordered bigrams, appending two metrics of semantic distance to each pair. We validated these distance metrics on a continuous stream of simulated verbal fluency data assigning predicted switch markers between alternating semantic clusters (animals, musical instruments, fruit). We then generated bigram distance norms on a large sample of text and demonstrated applications of the technique to a classic work of short fiction, To Build a Fire (London, 1908). In one application, we showed that bigrams spanning sentence boundaries are punctuated by jumps in the semantic distance. We discuss the promise of this technique for characterizing semantic processing in real-world narratives and for bridging findings at the single word level with macroscale discourse analyses. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks

We present a new abstract interpretation framework for the precise over-approximation of numerical fixpoint iterators. Our key observation is that unlike in standard abstract interpretation (AI), typically used to over-approximate all reachable program states, in this setting, one only needs to abstract the concrete fixpoints, i.e., the final program states. Our framework targets numerical fixpoint iterators with convergence and uniqueness guarantees in the concrete and is based on two major technical contributions: (i) theoretical insights which allow us to compute sound and precise fixpoint abstractions without using joins, and (ii) a new abstract domain, CH-Zonotope, which admits efficient propagation and inclusion checks while retaining high precision. We implement our framework in a tool called CRAFT and evaluate it on a novel fixpoint-based neural network architecture (monDEQ) that is particularly challenging to verify. Our extensive evaluation demonstrates that CRAFT exceeds the state-of-the-art performance in terms of speed (two orders of magnitude), scalability (one order of magnitude), and precision (25% higher certified accuracies).
Local exchangeability

Exchangeability—in which the distribution of an infinite sequence is invariant to reorderings of its elements—implies the existence of a simple conditional independence structure that may be leveraged in the design of statistical models and inference procedures. In this work, we study a relaxation of exchangeability in which this invariance need not hold precisely. We introduce the notion of local exchangeability—where swapping data associated with nearby covariates causes a bounded change in the distribution. We prove that locally exchangeable processes correspond to independent observations from an underlying measure-valued stochastic process. Using this main probabilistic result, we show that the local empirical measure of a finite collection of observations provides an approximation of the underlying measure-valued process and Bayesian posterior predictive distributions. The paper concludes with applications of the main theoretical results to a model from Bayesian nonparametrics and covariate-dependent permutation tests.
Personalized Recommender System for Children's Book Recommendation with A Realtime Interactive Robot

In this paper we study the personalized book recommender system in a child-robot interactive environment. Firstly, we propose a novel text search algorithm using an inverse filtering mechanism that improves the efficiency. Secondly, we propose a user interest prediction method based on the Bayesian network and a novel feedback mechanism. According to children's fuzzy language input, the proposed method gives the predicted interests. Thirdly, the domain specific synonym association is proposed based on word vectorization, in order to improve the understanding of user intention. Experimental results show that the proposed recommender system has an improved performance and it can operate on embedded consumer devices with limited computational resources.
Analyzing drop coalescence in microfluidic devices with a deep learning generative model

Predicting drop coalescence based on process parameters is crucial for experimental design in chemical engineering. However, predictive models can suffer from the lack of training data and more importantly, the label imbalance problem. In this study, we propose the use of deep learning generative models to tackle this bottleneck by training the predictive models using generated synthetic data. A novel generative model, named double space conditional variational autoencoder (DSCVAE) is developed for labelled tabular data. By introducing label constraints in both the latent and the original space, DSCVAE is capable of generating consistent and realistic samples compared to the standard conditional variational autoencoder (CVAE). Two predictive models, namely random forest and gradient boosting classifiers, are enhanced on synthetic data and their performances are evaluated based on real experimental data. Numerical results show that a considerable improvement in prediction accuracy can be achieved by using synthetic data and the proposed DSCVAE clearly outperforms the standard CVAE. This research clearly provides more insights into handling imbalanced data for classification problems, especially in chemical engineering.
Structural components of the competence of self-expression of the future teacher of music art in the context of the competence approach

The article considers general issues of determining the competence of cultural self-expression in the context of training future teachers of music through the prism of various foreign scholars and researchers. The analysis of the structural elements of the competence of self-expression was based on the development of key competences for life for the citizens of the European Union. The research methods were determined by the analysis of open sources and research of organizations and individual scientists, systematization of the obtained results and their interpretation. The study proves that the competence of self-expression is the next stage in the constant evolution of competences in the field of culture and interpersonal interaction of people. Data on various definitions of competence in the field of cultural awareness and self-expression are given. The author analyzed the framework of descriptors that are inherent in music teachers in the European Union, the main of which are: personal self-development; development of musical abilities; management of the process of musical (self) development; management of non-formal music (self) education. The results obtained can be a valuable basis for scientists from China who will develop educational standards and competency frameworks for future music teachers. The author emphasizes the need to form the competence of self-expression as the highest stage of human development on the basis of creativity, intercultural understanding and awareness of their place in the world.
Diamonds of Sadness: A Story of High-Tech Greed, Power, and Hypocrisy

column Share on Diamonds of Sadness: A Story of High-Tech Greed, Power, and Hypocrisy Author: Daria Loi Fishtail FishtailView Profile Authors Info & Claims InteractionsVolume 30Issue 3May + June 2023pp 26–28https://doi.org/10.1145/3587941Published:01 May 2023Publication History 0citation302DownloadsMetricsTotal Citations0Total Downloads302Last 12 Months302Last 6 weeks302 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Estimation of Gaussian directed acyclic graphs using partial ordering information with applications to DREAM3 networks and dairy cattle data

Estimating a directed acyclic graph (DAG) from observational data represents a canonical learning problem and has generated a lot of interest in recent years. Research has focused mostly on the following two cases: when no information regarding the ordering of the nodes in the DAG is available and when a domain-specific complete ordering of the nodes is available. In this paper, motivated by a recent application in dairy science, we develop a method for DAG estimation for the middle scenario, where partition-based partial ordering of the nodes is known based on domain-specific knowledge. We develop an efficient algorithm that solves the posited problem, coined Partition-DAG. Through extensive simulations, using the DREAM3 Yeast networks, we illustrate that Partition-DAG effectively incorporates the partial ordering information to improve both speed and accuracy. We then illustrate the usefulness of Partition-DAG by applying it to recently collected dairy cattle data, and inferring relationships between various variables involved in dairy agroecosystems.
Competition for roadside camera monocular 3D object detection

This perspective highlights the significance and challenges of roadside 3D monocular detection competition, and introduces the champion algorithm: a dual-stream network based on a depth map.
Can Consumer Citizenship Behavior be Created Using Service Robots in Luxury Hospitality Services?

Purpose : This study sought to increase an understanding of consumer citizenship behavior through the service quality of AI-mediated service robots (SR) in the luxury hotel context. Since the expectations and demands from luxury customers are different, this paper studied the effect of service quality provided by robots on customer citizenship behavior. Methodology : In the initial phase, qualitative research was conducted by interviewing people who had experienced SR in luxury hospitality settings. The second phase of the study was conducted based on the respondents’ themes. In the second phase, video clips of robots performing tasks like concierge and front-desk employees were shown to understand the customers’ perceptions. Findings : The study’s findings showcased the importance of communication quality between the inorganic service agent and humans. Simultaneously, the factors like problem-solving and trendiness did not have a positive impact on service quality. However, an interesting finding was that the perception of robot service quality positively affected consumer citizenship behavior in the luxury hotel context. Practical Implications : Luxury hotels have deployed SR in their operations in developed economies. Luxury hotels are renowned for their service quality, and by deploying SR, they are entering a new service industry domain. The results of this study showcased that communication ability, competence, and quality are significant factors in perceiving high-service quality by luxury consumers. Originality/Value : The research explored the impact of service robots in the luxury hospitality industry on consumer citizenship behavior toward the hospitality organization. The study empirically validated the essential factors for creating citizenship behavior among luxury patrons.
Media Competence in the Curriculum from Latin American Countries: A Systematic Review

The objective of this systematic review is to characterize scientific production on media competence (MC) in the curriculum of 33 countries in Latin America, starting with the analysis of four dimensions: geographical-temporal distribution, conceptual configuration, methodology, and the main curricular experiences and conclusions.After the identification and selection of the literature following the PRISMA protocol, 32 studies conducted between January, 2012, and December, 2021, were identified in the Web of Science (WoS), Scopus, ERIC (ProQuest), Dialnet (Plus) and Redalyc databases, after the application of the inclusion criteria.Among the findings, the following can be underlined: 1) there was a predominance of studies whose objective was to determine the presence of MC in the curriculum; 2) none of the countries had a course whose main objective was MC, nevertheless, the widespread presence of its dimensions suggests its inclusion as a cross-curricular component; 3) the region lacks studies centered on initial education, adult education, and the curricular basis of education for indigenous people.For this, opportunities must be offered to address future studies, projects, and curricular proposals that guarantee the development of critical, operational, and social competences, to face the socio-cultural demands and phenomena of the new media system.
How to Conquer the Liar and Enthrone the Logical Concept of Truth

This article informally presents a solution to the paradoxes of truth and shows how the solution solves classical paradoxes (such as the original Liar) as well as the paradoxes that were invented as counterarguments for various proposed solutions (“the revenge of the Liar”). This solution complements the classical procedure of determining the truth values of sentences by its own failure and, when the procedure fails, through an appropriate semantic shift allows us to express the failure in a classical two-valued language. Formally speaking, the solution is a language with one meaning of symbols and two valuations of the truth values of sentences. The primary valuation is a classical valuation that is partial in the presence of the truth predicate. It enables us to determine the classical truth value of a sentence or leads to the failure of that determination. The language with the primary valuation is precisely the largest intrinsic fixed point of the strong Kleene three-valued semantics (LIFPSK3). The semantic shift that allows us to express the failure of the primary valuation is precisely the classical closure of LIFPSK3: it extends LIFPSK3 to a classical language in parts where LIFPSK3 is undetermined. Thus, this article provides an argumentation, which has not been present in contemporary debates so far, for the choice of LIFPSK3 and its classical closure as the right model for the truth predicate. In the end, an erroneous critique of Kripke-Feferman axiomatic theory of truth, which is present in contemporary literature, is pointed out.
An Overview of Context Capturing Techniques in NLP

In the NLP context identification has become a prominent way to overcome syntactic and semantic ambiguities. Ambiguities are unsolved problems but can be reduced to a certain level. This ambiguity reduction helps to improve the quality of several NLP processes, such as text translation, text simplification, text retrieval, word sense disambiguation, etc. Context identification, also known as contextualization, takes place in the preprocessing phase of NLP processes. The essence of this identification is to uniquely represent a word or a phrase to improve the decision-making during the transfer phase of the NLP processes. The improved decision-making helps to improve the quality of the output. This paper tries to provide an overview of different context-capturing mechanisms used in NLP.
Aspects of Integrity in Pre-school and Primary School Education in Mathematics – Aspects Related to Some Elements of Geometry

Contemporary society is characterized by swift advances in science and technologies which leads to major changes in people’s lives. The significant evolution of science, mathematics, technologies, and engineering over the recent decades has got obvious consequences for people and society as a whole. One of the educational models in most countries aims at teaching through the use of certain interconnections between natural sciences, mathematics, technologies, and engineering in primary, secondary, and highest education. In recent years this model has got an effect on the process of education in the Republic of Bulgaria and has an important role in applying innovative pedagogy practices, forming 21stcentury skills in children, and facilitating their creative activities. The purpose of its application in kindergarten and primary school is to create conditions for the children of preschool and primary school age to actively participate in the process of education in mathematics. Within the frame of this vision, it is important that the process of teaching mathematics engages them not only with solving mathematical tasks thus developing students’ thinking but also in the process of their reasoning to form problem-solving skills and to help the students to realize the applicability of mathematics. The article systematized theoretical concepts related to aspects of integrity in education and presents a methodology system of work with some elements from geometry in the course of mathematics in pre-school and primary school education.
A Comparative Study of Different Pre-Trained Deep Learning Models and Custom CNN for Pancreatic Tumor Detection

Artificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging is briefly making the internal structure of the human body visible with various methods. With deep learning models, cancer detection, which is one of the most lethal diseases in the world, can be made possible with high accuracy. Pancreatic Tumor detection, which is one of the cancer types with the highest fatality rate, is one of the main targets of this project, together with the data set of Computed Tomography images, which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. In the field of image classification, which is a computer vision task, the transfer learning technique, which has gained popularity in recent years, has been applied quite frequently. Using pre-trained models were previously trained on a fairly large dataset and using them on medical images is common nowadays. The main objective of this article is to use this method, which is very popular in the medical imaging field, in the detection of PDAC, one of the deadliest types of pancreatic cancer, and to investigate how it per- forms compared to the custom model created and trained from scratch. The pre-trained models which are used in this project are VGG-16 and ResNet, which are popular Convolutional Neutral Network models, for Pancreatic Tumor Detection task. With the use of these models, early diagnosis of pancreatic cancer, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started, may be possible. Due to the abundance of medical images reviewed by medical professionals, which is one of the main causes for heavy workload of healthcare systems, this application can assist radiologists and other specialists in Pancreatic Tumor detection by providing faster and more accurate method
Routing UAVs in landslides Monitoring: A neural network heuristic for team orienteering with mandatory visits

Unmanned aerial vehicles (UAVs) are widely used for surveillance in both civilian and military scenarios. The utilization of UAVs provides an opportunity for monitoring landslide-prone areas by automatically collecting geological information, thereby reducing the risks and the time required to be working in harsh environments. Due to the maximum travel time limit of UAVs and the geographical dispersion of landslide-prone areas, multiple UAVs are dispatched for surveillance tasks, and landslide-prone areas with high emergency priorities require mandatory visits. Here, we investigate a team orienteering problem with mandatory visits (TOPMV) for routing multi-UAVs to monitor scattered landslide-prone areas, with mandatory visits on those in poorly stable states. The proposed TOPMV aims to plan the optimal multi-UAV paths for maximizing the total amount of collected geological information. To solve the TOPMV with a realistic scale, we develop a large neighborhood search (LNS) algorithm embedding a neural network heuristic (NNH), in which the embedded NNH learns to perform adaptive destroy operators through a hierarchical recurrent graph convolutional network (HRGCN). We consider a real-world case study for monitoring of landslide-prone areas in three counties in southern Shaanxi Province, China. Finally, we test the proposed NNH on both small- and large-scale benchmark instances of the team orienteering problem. The experimental results demonstrate that our proposed NNH exhibits higher efficiency and provides better solution quality than state-of-the-art methods, especially in large-scale settings.
Utilization of ASTER data in lithological and lineament mapping of the southern flank of the Central High Atlas in Morocco

Geological mapping undoubtedly plays an important role in several studies and remote sensing data are of great significance in geological mapping, particularly in poorly mapped areas situated in inaccessible regions. In the present study, Principal Component Analysis (PCA), Band Rationing (BR) and Minimum Noise Fraction (MNF) algorithms are applied to map lithological units and extract lineaments in the Amezri-Amassine area, by using multispectral ASTER image and global digital elevation model (GDEM) data for the first time. Following preprocessing of ASTER images, advanced image algorithms such as PCA, BR and MNF analyses are applied to the 9ASTER bands. Validation of the resultant maps has relied on matching lithological boundaries and faults in the study area and on the basis of pre-existing geological maps. In addition to the PCA image, a new band-ratio image, 4/6–5/8–4/5, as adopted in the present work, provides high accuracy in discriminating lithological units. The MNF transformation reveals improvement over previous enhancement techniques, in detailing most rock units in the area. Hence, results derived from the enhancement techniques show a good correlation with the existing litho-structural map of the study area. In addition, the present results have allowed to update this map by identifying new lithological units and structural lineaments. Consequently, the methodology followed here has provided satisfactory results and has demonstrated the high potential of multispectral ASTER data for improving lithological discrimination and lineament extraction.
Food Recommendation System

In the past, people obtained suggestions for restaurants from friends or other conventional sources or sites. Although this method is straight forward and user friendly, it has some severe limitations. First, the recommendations from friends or other common people are limited to those places they have visited before. Thus, the user is not able to gain information about places less visited by their friends. Besides that, there is a chance of users not liking the place recommended by their friends. Second, the information provided by the site can often be biased; thus, the information provided cannot always consider being accurate. Recommender systems are widely deployed to predict the preferences of users to items. They are popular in helping users find restaurants and products in general. The primary aim of the Food Recommendation System is to suggest the best restaurant nearby along with food using the given user preferences. This application takes the food preferences and ratings into consideration to recommend the food to the users.