Reviewerly/exHarmony huggingface dataset:
Intelligent Word Embedding Methods to Support Project Proposal Grouping for Project Selection

Project proposal selection for allocating the fund is a critical decision-making process in government/private funding agencies, universities, and research institutes. Project proposal grouping according to their similarities is an essential procedure in the project selection process and is done to simplify the work that follows, such as reviewer assignment and evaluation of projects. Current approaches to grouping proposals are primarily based on manual matching of similar topics, discipline areas, and keywords declared by project applicants. When the number of proposals increases, this task becomes complex and takes too much time. Furthermore, because of their subjective viewpoints and potential misinterpretations, applicants frequently fail to select the correct research field or keywords for their proposals. Due to time constraints, a lack of understanding of the proposal's content, divergent perspectives, and incomplete information, proposals are misclassified, resulting in decreased evaluation quality. This article discusses how to effectively use rich information in the abstract and title of Turkish proposals by utilizing word embedding models. In the proposed method, texts are vectorized using the FastText, BERT and TF-IDF algorithms. The presented method is validated based on the proposals submitted to the Istanbul Development Agency. Experiments indicate that generated word embeddings can effectively represent proposal texts as vectors and be used as input for clustering or classification algorithms. In this way, proposal grouping can be conducted more efficiently, accurately, and without any loss of meaning.
Networks Clustering-Based Approach for Search of Reservoirs-Analogues

This article presents a new look at the problem of finding reservoirs-analogues, representing reservoirs as a network and solving the problem of finding reservoirs-analogues as a problem of finding communities in the network. The proposed network approach allows us to effectively search for a cluster of reservoirs-analogues and restore missing parameters in the target reservoir based on the found clusters of reservoirs-analogues. Also, the network approach was compared with the baseline approach and showed greater efficiency. Three approaches were also compared to restore gaps in the target reservoir using clusters of reservoirs-analogues.
Tensor Networks Across Physics

Researchers from Japan provide the first comprehensive review of the historical development of tensor networks from a statistical mechanics viewpoint, with a focus on its theoretical background.
Construction of the localized knowledge production model based on social design method in the post-epidemic era

During the period when the new crown epidemic spreads and coexists with human beings on a large scale around the world, the process of globalization has been impacted. The existing international order, geopolitical order, and social order have also been challenged to a certain extent. Globalization is not homogenization that eliminates differences. Tracing the roots of diverse cultures requires that the research object be transferred to the most basic unit, knowledge production in localized communities. This paper expounds on the connotation and opportunities of the post-epidemic era from the micro and macro levels, combined with the method of social design, analyzes and draws the principles for the construction of the localized knowledge production model, and constructs the model.
Comparative Study by Using a Greedy Approach and Advanced Bio-Inspired Strategies in the Context of the Traveling Thief Problem

Traveling Salesman Problem and Knapsack Problem are perhaps the best-known combinatorial optimization problems that researchers have been racking their brains over for many decades. But they can also be combined into a single multi-component optimization problem, the Traveling Thief Problem, where the optimal solution for each single component does not necessarily correspond to an optimal Traveling Thief Problem solution. The aim of this work is to compare two generic algorithms for solving a Traveling Thief Problem independently of the test instance.
A Directional Heuristics Pulse Algorithm for a Two Resources Constrained Shortest Path Problem with Reinitialization

In this paper, the path planning problem of UAV flight error correction is studied, and a mixed-integer linear programming (MILP) model is proposed to solve the resource constrained shortest path problem (RCSPP-R) with reinitialization, and its resource accumulation can be reinitialized to zero at the reset node. Since RCSPP-R is a NP-hard problem, Pulse algorithm is used to solve the proposed model. Aiming at the high computational cost of pulse algorithm, three pruning strategies are proposed to improve the effectiveness and efficiency of the algorithm, namely, preprocessing strategy based on direction constraint to reduce the search space, dynamic relaxation strategy to improve the quality of boundary, and heuristic search strategy to optimize the node expansion sequence. Then the time-consuming of the algorithm and the number of dominant solutions are used as evaluation indexes for comparison. Numerical results show that the proposed algorithm can deal with this problem more reliably. Specifically, compared with the Label Correction algorithm, the original Pulse algorithm and the Ant Colony algorithm, there are advantages in the number of superior solutions, and compared with the above three algorithms, the time consumption is reduced by 31.64%, 88.52% and 83.5% respectively.
Natural Computations and Artificial Intelligence

The research program focused on the analysis of computational approaches to natural and artificial intelligence is one of four accepted for implementation at the Center for the Philosophy of Consciousness and Cognitive Sciences of the Institute of Philosophy, Russian Academy of Sciences. Presumably, it should become a direction of interdisciplinary research at the crossroad of philosophy, cognitive psychology, cognitive and social neuroscience, and artificial intelligence. The working hypothesis proposed for discussion attended by the relevant specialists is as follows: if an acceptable computational theory of mind appears, we will be able to restrict our research to a simple scientific ontology describing only parts of a physical implementation of computational algorithms, adding a relevant version of computational mathematics thereto. Another hypothesis proposed is that there is an essential ontological intersection between the mechanisms underlying human cognitive abilities and their social organization, both of which serving as an implementation medium for complex distributed cognitive computations. Particularly those which are associated with social organization are responsible for logical and verbal (“rational”) cognitive abilities. As a result of some previous research, an ontology of nested distributed computational systems was generally formulated, which, as expected, can demonstrate significant heuristic potential if supplemented with an adequate mathematical apparatus. Since only individuals with certain cognitive abilities can be social agents, a philosophical problem arises: are cognitive abilities necessary or sufficient to involve their carriers in stable social interactions? In the first case, we have a weak thesis about the cognitive determination of sociality, in the second — the strong one. The choice between these positions is, too, a subject of future research.
Intercultural Communication Processes in the Conditions of Digitalization of Higher Education of Russia

Purpose: The main content of this article is the consideration of modern approaches to the preparation and conduct of the educational process in the field of digital innovative technologies, which are aimed at increasing the general communicative culture, efficiency, professional competence, and activity of students of modern Russia. Methodology: In the process of work, the author of the article applied research methods such as empirical (observation, surveys, etc.); theoretical (study of various literature on the studied topic and its analysis); experiments on the study of pedagogical experience, analysis, and evaluation of its results. Results: The process of digitalization of education and sectors of intense living abilities reveals in a person the formation of a communicative culture, which allows competently taking advantage of the opening opportunities for organic entry into a certain environment of the information society. The novelty of digitalization lies in the mechanism for overcoming the situation of digital inequality in Russia in the formation of digital competencies among users of the network and in actualization in the social stratification of people (digital: gap, citizenship, socialization). The scientific novelty of the study is considered in light of the socio-cultural prerequisites of the phenomenon of the “digital gap.” The problem of modern intercultural communication (ICC = intercultural communication) is the exchange of information in the existing digital Internet computer society. Value: The system of existing education should first of all guarantee society an unmistakable transition to the modern digital era. The informational support of higher education has formed a new base for the transition to the next level of digitalization of the training of qualified specialists who own all types of mobile and Internet technologies after receiving e-learning.
Game On! Authors, Fans Face Off in Innovative Experiment

Authors have total recall of everything in their books, right? Or would an avid fan kick their butt in a trivia match? This ingenious concept—conceived in the early days of the pandemic by three-time National Book Award finalist Steve Sheinkin and Stacey Rattner, librarian at Castleton Elementary School in upstate New York—was only supposed to be a temporary virtual offering.
SELF-ORGANIZATION OF STUDENTS EXPERIENCE AS A PRACTICAL SIDE OF PROFESSIONAL DEVELOPMENT

The article is dedicated to the one of the most important education matters – students’ professional development. The problem of self-organization experience formation is discussed as the one of the aspects of developing the student as a future professional. The mechanisms of professional self-organization experience formation and the conditions providing for the effectiveness of this process are discussed.
Contents accessibility in archaeological museums and sites: a proposal for a neuropsychological approach

With specific reference to the issue of accessibility to cultural content and the inclusion of different audiences, the Authors point out an overview where museums usually tend to create educational activities and support assistive devices dedicated to specific audiences, rather than integrated solutions, that can “be usable by all people, to the greatest extent possible", as stated in the United Nations Convention on the Rights of Persons with Disabilities (2006).On the basis of previous studies on cultural accessibility and emotional appropriation, the Authors have recently carried out a survey focused in particular on archaeological museums audiences, considering their expectations, their reactions, and their prejudices. At the same time, they have conducted an extensive series of online interviews with Curators and Directors of many archaeological museums and sites in Europe and worldwide, including some in-depth site visits too. The investigations and surveys carried out have strengthened the awareness that museum spaces generate not only cognitive, but also physical and emotional reactions, and that the various publics react to cultural stimuli in very different ways. Therefore it is necessary, in designing museum communication, a disciplinary contamination involving in particular the field of neuropsychology. Such involvement would provide scientific support for the critical assessment of the effects that the environment and the cultural mediation trigger on the different publics.In particular, the Authors believe that the physical arrangement and the atmosphere are usually underestimated in the overall design of museums: on the contrary these two elements combined would allow each visitor to grasp the signals that best suit him or her, according to his/her senses and understanding. In other words, the atmosphere can play a key role in being an immersive communicative medium, in a truly inclusive way where everyone has the opportunity to "feel" and learn something.The Authors are developing a series of experiments that will be carried out first in the laboratory and then in some archaeological museums, in collaboration with a team of Neuropsychologists from the University of Turin and with the support of archaeological consultants and communication experts. By illustrating the current research and describing a series of examples (including best practices, problematic cases, ongoing projects), the paper aims at highlighting how the "design for all" in museums is a field in continuous development. Requiring an evolution in its approach, it also, and above all, represents a challenge in relation to the communication of particularly difficult cultural content, such as those related to the archaeological heritage.
End-to-End Deep Speaker Embedding Learning Using Multi-Scale Attentional Fusion and Graph Neural Networks

As an attractive research in biometric authentication, Text Independent Speaker Verification (TI-SV) problem aims to specify whether two given unconstrained utterances come from the same speaker or not. As state-of-the-art solutions, end-to-end approaches using deep neural networks seek to learn a highly discriminative speaker embedding space.In this paper, we propose a novel end-to-end approach for speaker embedding learning by focusing on two crucial factors: speaker embedder architecture and objective function. The proposed module in the speaker embedder is composed of an E fficient M ulti-resolution feature R epresentation (EMR) block followed by a M ulti-scale C hannel A ttention F usion (MCAF) block. The EMR effectively addresses the issue of fixed resolution convolutional kernels which commonly used in most embedder architectures. Moreover, the MCAF significantly improves the simple summation-based feature fusion used in residual embedder networks. Regarding the objective function, we conduct the speaker embedding space towards learning the embedding-to-embedding relations, in addition to only embedding-to-training class relations employed by most previous methods. So, we propose to employ a dynamic graph attention network, on top of the proposed embedder to learn all informative relations between embeddings, and then learn both embedder and graph-based networks in an end-to-end manner.We conduct various experiments on a large-scale benchmark dataset called VoxCeleb1&2. The effectiveness of all proposed components is verified through an ablation study. We show the superior performances of the proposed approach compared to four well-known embedding architectures and 25 SV systems, regarding two evaluation metrics, EER and minDCF, as well as the number of embedder parameters.
Modale Klangstrukturen in Madrigalen Giovanni Pierluigi da Palestrinas und ihre Analyse durch die Software PALESTRiNIZER

The software PALESTRiNIZER enables the statistical analysis and visualization of modal music data and shows the differences between the genres madrigal and motet and, randomly, between individual modes within tonal-compositional parameters.
The Issue of Borrowing in Science and Art of the Digital Era: Communicative Aspect

Purpose Today, modern scientists have an inherent understanding of the unity (term of A.F. Losev) of “science” and “art” phenomenon, which presuppose the presence of holistic thinking (rational-intuition, rational-emotional-intuition). The purpose of this work is to consider the conceptual problems of authorship in the context of the fundamental unity of types of thinking (mythological/non-mythological), as well as the significance of borrowings in the modern system of communication between people and between a human and AI. Design/methodology/approach The study is based on numerous examples of the treatise tradition, the work of outstanding composers, artists, poets, works of modern scientists in the field of natural science and humanitarian. In accordance with the selected material, the methodological basis of the research includes a complex of natural science, philosophical-aesthetic, art-criticism approaches, supplemented by certain provisions of the phenomenological and hermeneutic methods. Originality/value For the first time, the article proposes to structure the stated problems of borrowing in creative and scientific communication processes as follows: borrowing by “a human from another human” (“H–H”), borrowing by “a human from a computer” (“H–AI”), borrowing by “a computer from a human” (“AI–H”). Findings The relevance and prospects of this approach are obvious since it allows to arrive at conclusions on the relative difference in the functions that are inherent in AI, depending on the nature of communication processes—from the function of an “expert control system” (non-mythological thinking) to the function of a “universal reference book of eternal meanings” (mythological thinking). At the same time, without remaining within the framework of a “technical means” that helps to improve the paths of cognition, AI, in its turn (which is very important and requires further special consideration), has a significant impact on culture and on a person (scientist/creator).
Object-Oriented Programming

In this chapter, we look at R’s flavor of object-oriented programming. Actually, R has three different systems for object-oriented programming: S3, S4, and RC. We will only look at S3, which is the simplest and (is my impression) the most widely used.
Natural 3D Object Manipulation for Interactive Laparoscopic Augmented Reality Registration

Due to the growing focus on minimally invasive surgery, there is increasing interest in intraoperative software support. For example, augmented reality can be used to provide additional information. Accurate registration is required for effective support. In this work, we present a manual registration method that aims at mimicking natural manipulation of 3D objects using tracked surgical instruments. This method is compared to a point-based registration method in a simulated laparoscopic environment. Both registration methods serve as an initial alignment step prior to surface-based registration refinement. For the evaluation, we conducted a user study with 12 participants. The registration methods were compared in terms of registration accuracy, registration duration, and subjective usability feedback. No significant differences could be found with respect to the previously mentioned criteria between the manual and the point-based registration methods. Thus, the manual registration did not outperform the reference method. However, we found that our method offers qualitative advantages, which may make it more suitable for some application scenarios. Furthermore we identified possible approaches for improvement, which should be investigated in the future to strengthen possible advantages of our registration method.
Lhasa Dialect Recognition of Different Phonemes Based on TDNN Method

Time-delay neural networks (TDNN) acoustic models have significantly advanced forward in recent years, outperforming the traditional Gaussian Mixture Hidden Markov Model (GMM-HMM) in large vocabulary continuous speech recognition tasks. We try to develop a practical Lhasa Tibetan ASR system. For higher speech recognition accuracy, in this paper, we consider to investigate the performances of Tibetan acoustic modeling using TDNN method based on several different phoneme sets, which are defined based on linguistic and phonological knowledge of Tibetan Lhasa dialect. Experiments are conducted on a Tibetan corpus recorded by 20 persons, using a bigram language model over phones. The phone error rate (PER) results show that the acoustic model with CTL set performs best, which is relatively 10.43% higher accuracy than the basic phoneme set. Moreover, our results confirm the fact that for Lhasa Tibetan acoustic model, the paradigm TDNN-HMM outperforms the conventional GMM-HMM.
Towards an Ontology-Based Decision Support System to Support Car-Reconfiguration for Novice Wheelchair Users

The ability to drive a vehicle is fundamental to fostering people’s independence, especially people characterized by physical disability. European regulations foresee that these drivers’ cars need to undergo a process of adaptations that may involve modifying the vehicle’s mechanics and adding specific driving aids depending on the particular driver’s health condition. Since drivers can have access to different configurations for their vehicles, they may not be familiar with driving aids and how they modify the driving activity. To help drivers understand the possible car configurations and their aids, this work proposes an ontology-based decision support system (DSS) to identify the possible configurations and explicit the list of driving aids and mechanical modifications that the vehicle must equip. The DSS leverages ontology-based knowledge representation within the Rip@rto research project to determine and illustrate the possible car configurations. Also, taking advantage of a driving simulator and cognitive assessment of drivers, the DSS may support clinical personnel in defining restrictions and limitations to the use of vehicles for some drivers. This paper presents the ontology engineering process, delving into the domain analysis, conceptualization, and development phase of the DSS prototype, and sketches the future research directions.
Artificial Intelligence and Machine Learning for Diabetes Decision Support

Artificially intelligent decision support systems are proving instrumental in the quest of enabling diabetes patients to lead a normal life. These systems provide suggestions to the patients to enhance their judgments regarding their glycemic profile. A variant of such systems, the clinical decision support systems, helps clinicians and healthcare professionals make clinical decisions. For a diabetes patient, it is imperative to keep their blood glucose level inside a lower bound of 70 mg/dL and an upper bound of 140 mg/dL. This range is known as the normoglycemic range. Such systems aim to utilize artificial intelligence and machine learning techniques to estimate relationships between patient-related data and the glycemic outcomes and then propose preventive/protective measures to keep the glycemic profile of the patient in the specified range. Apart from tracking and correcting the glycemic profile of a diabetes patient, the decision support systems are also responsible for detecting/predicting adverse glycemic events like hypoglycemia and suggest proactive measures to be taken by the patient so that adversity is avoided. The recommendations given by such systems to patients with diabetes may consist of information about meal intake in the form of carbohydrates, insulin delivery, medicine/drug consumption and other lifestyle-related advice such as physical activity and sleep routine, etc. On the other hand, the clinical decision support systems aid healthcare professionals in diagnosing diabetes and its comorbidities. Such systems may also assist the doctors by issuing prognosis of the illness as well as help them in drug prescriptions. This chapter discusses the latest trends in artificial intelligence and machine learning-based decision support in diabetes healthcare. Moreover, it also weighs up the challenges designers face in this domain. This chapter could be a thorough guide to the researchers planning to work in diabetes decision support.