Reviewerly/exHarmony huggingface dataset:
<i>The Aesthetics of Virtual Reality</i>.

In this book, Grant Tavinor, well known for his influential work on the aesthetics of videogames, offers the first focused study of the aesthetics of virtual reality media. When reading the first pages, one cannot help but notice Tavinor’s enthusiasm about virtual reality (VR) in the vivid descriptions of his explorations of virtual haunted houses, distant planets, and ancient Rome. These descriptions also reveal Tavinor’s refreshing aim to focus on present uses of VR media, instead of the so-called “perfect virtual realities” (virtual realities that are indistinguishable from the actual world) that are often the focus of philosophical discussions. When talking about VR, philosophers indeed often target hypothetical situations in which people are brains in vats, hooked up to experience machines, or living in a world that is, unbeknownst to them, merely a simulated one. Tavinor, however, intends to avoid metaphysical or ontological questions about hypothetical virtual realities, instead focusing on our aesthetic experiences of concrete VR media. With this, he generally refers to “immersive, interactive, and computable VR technologies” (p. 15), such as the HTC Vive, the PlayStation VR, and the Oculus, which depict virtual environments through head mounted displays. Tavinor’s aim is to show how these systems are media that allow for “a technologically fancy kind of picturing” (p. 12), and to contextualize them in the history of representational media alongside of painting, photography, and film.
A Cross-Cultural Pragmatics Study of Request Strategies and Politeness in Javanese and Sundanese

This study aims to examine and compare request strategies and politeness of two groups of native speakers from two different ethnic groups in Indonesia, i.e., Javanese and Sundanese. The data were collected by using Discourse Completion Task (DCT) with 60 participants from 30 Javanese (East Java) and 30 Sundanese (West Java) speakers. The data were analyzed by using the classification of request strategies by Blum-Kulka et al., 1989) and social contexts in terms of social power, social distance, and degree of imposition by Brown and Levinson (1987) to reveal the levels of directness and politeness of request employed by each group of speakers. The result of this study indicates that Direct Strategy in the form of mood derivable and Conventionally Indirect Strategy in the form of query preparatory are two request strategies that are mainly employed by both Javanese and Sundanese speakers. In particular, Javanese speakers employ a more Direct Strategy than Sundanese speakers. However, the patterns of request strategies employed by Javanese and Sundanese speakers in most social contexts are similar. In addition, both groups of speakers can also adapt to the situations and employ appropriate requests to the hearer. Therefore, politeness and appropriateness in requests employed by each group of speakers in their DCT responses can reflect the local wisdom of each culture.Keywords: requests; social contexts; politeness; Javanese; Sundanese
COMPARATIVE ANALYSIS OF THE K-NEAREST-NEIGHBOUR METHOD AND K-MEANS CLUSTER ANALYSIS FOR LITHOLOGICAL INTERPRETATION OF WELL LOGS OF THE SHUSHUFINDI OILFIELD, ECUADOR

The lithological interpretation of well logs is a fundamental task in Earth science that can be accomplished with the application of various machine learning algorithms. The current investigation attempts to evaluate the performance of the K-nearest-neighbour Density Estimate (KNN) and K-means cluster analysis methods for predicting lithology in a dataset of logs measured in the siliciclastic reservoir of the Shushufindi Oilfield of Ecuador. The comparison of lithological interpretation is assembled using classical methods, such as qualitative interpretation and density-neutron cross plot. The lithological interpretation results showed that the supervised method KNN has a higher fitting level with the comparison interpretation data (87.3%, 1145 m predicted of 1311.1 m interpreted) than the results of the K-means method (71.6%, 939.7 m predicted of 1311.1 m interpreted). The geological nature of the reservoir creates a level of a discrepancy because of the near geophysical responses between limestone and intermedia grain size rocks. The possibility of controlling this in the KNN algorithm makes it preferable for usage in these types of reservoir lithological interpretation.
An Explainable Knowledge-based AI Framework for Mobility as a Service

Mobility as a Service (MaaS) is a relatively new domain where new types of knowledge systems have recently emerged.It combines various modes of transportation and different kinds of data to present personalized services to travellers based on transport needs.A knowledge-based framework based on Artificial Intelligence (AI) is proposed in this paper to integrate, analyze, and process different types of mobility data.The framework includes a knowledge acquisition process to extract and structure data from various sources, including mobility experts and add new information to a knowledge base.The role of AI in this framework is to aid in automatically discovering knowledge from various data sets and recommend efficient and personalized mobility services with explanations.A scenario is also presented to demonstrate the interaction of the proposed framework's modules.
Mix-Nets from Re-randomizable and Replayable CCA-Secure Public-Key Encryption

Mix-nets are protocols that allow a set of senders to send messages anonymously. Faonio et al. (ASIACRYPT’19) showed how to instantiate mix-net protocols based on Public-Verifiable Re-randomizable Replayable CCA-secure (Rand-RCCA) PKE schemes. The bottleneck of their approach is that public-verifiable Rand-RCCA PKEs are less efficient than typical CPA-secure re-randomizable PKEs. In this paper, we revisit their mix-net protocol, showing how to get rid of the cumbersome public-verifiability property, and we give a more efficient instantiation for the mix-net protocol based on a (non publicly-verifiable) Rand-RCCA scheme. Additionally, we give a more careful security analysis of their mix-net protocol.
A Study on the Development of Consulting Analysis Tool for Non-face-to-face Education Operation of King Sejong Institute

This study aimed to develop an analysis tool for non-face- to-face Korean language education operation consulting at King Sejong Institute. For this purpose, analysis tools for related organizations such as King Sejong Institute operation consulting and cyber university evaluation tools were reviewed. The consulting analysis tool included the five areas of 'curriculum, class, teacher, learner, and teaching and learning environment' based on King Sejong Institute's non-face-to-face education operation. Through the discussion, the composition of analysis tools was systematized by describing the contents of sub-segments and indicators to be checked by area and exemplifying inspection data that could analyze the indicators. After that, an expert Delphi survey was conducted twice to determine the validity of the proposed analysis tool, which was then modified and supplemented by reflecting on the results of the survey. The analysis tool presented in this study can be used when consulting on the non-face-to-face education operation of King Sejong Institute in the future. Moreover, it can be flexibly adopted by domestic and foreign Korean language education institutions.
Towards Relational Multi-Agent Reinforcement Learning via Inductive Logic Programming

We present a relational multi-agent reinforcement learning algorithm in which two agents work together to achieve a goal in an environment represented by structured entities and relations. Our proposal takes a hybrid connectionist-symbolic approach, where a classical actor-critic method with an iterative weight update scheme is used to guide the derivation of an agent’s policy, which is purely expressed as first-order logic. A recent technique, differentiable inductive logic programming, is applied to integrate these two parts into a trainable system. We tailor the centralized training with decentralized execution framework to meet the symbolic-represented underlying structure. Agents are designed to communicate with one another in terms of logical predicates to alleviate the partially observable problem prevalent in the multi-agent setting. Empirical studies on the classical grid-world task demonstrate that the proposed method can learn close to optimal strategies and has better interpretability than traditional reinforcement learning approaches.
A Comparative Study on Evolutionary Algorithms and Mathematical Programming Methods for Continuous Optimization

Evolutionary algorithms and mathematical programming methods are currently the most popular optimizers for solving continuous optimization problems. Owing to the population based search strategies, evolutionary algorithms can find a set of promising solutions without using any problem-specific information. By contrast, with the assistance of gradient and other information of the functions, mathematical programming methods can quickly converge to a single optimum. While these two types of optimizers have their own advantages and disadvantages, the performance comparison between them is rarely touched. It is known that gradient descent methods generally converge faster than evolutionary algorithms, but when can evolutionary algorithms outperform gradient descent methods? How is the scalability of them? To answer these questions, this paper first gives a review of popular evolutionary algorithms and mathematical programming methods, then conducts several experiments to compare their performance from various aspects, and finally draws some conclusions.
Gesture Recognition using FastDTW and Deep Learning Methods in the MSRC-12 and the NTU RGB+D Databases

This work explores the use of three deep learning methods for gesture recognition: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) using Fast Dynamic Time Warping (FastDTW). The gestures were captured by Kinect sensors, two skeleton-based databases are used: Microsoft Research Cambridge-12 (MSRC-12) and NTU RGB+D. Also, the FastDTW technique was also employed to standardize the input size of the data. The MSRC-12 database achieved an accuracy rate of 82,36% in the test set with the CNN, the LSTM achieved an accuracy rate of 87,30% also in the test set, and in GRU the accuracy achieved in the test set was 89,34%. With the NTU RGB+D database, two evaluation methods were used: Cross-View and Cross-Subject. In the test set with Cross-View evaluation was obtained an accuracy rate of 63,53%, 55,14%, and 61,00%, with CNN, LSTM, and GRU respectively; and with the Cross-Subject evaluation method, it was achieved an accuracy rate of 66,19%, 64,43% and 60,17% in the test set on CNN, LSTM and GRU, respectively.
Strategy of Sustainable Development Paradigm for Society in Research Activities Sciences, Artificial Intelligence and Institutional Transformations of the Education System

The article examines the potential for enhancing the world's educational system in light of ongoing advancements in artificial intelligence and their implementation in the educational process. The main emphasis is placed on how well potential strategies for integrating AI into educational environments adhere to the sustainable development paradigm standards set by the UN for the coming ten years. Both beneficial and harmful scenarios of human interaction with AI are considered in the context of the study. This task is a priority at this point in civilization's evolution. Furthermore, limitations on AI development and application are required to prevent the unpredictable effects of a "technological singularity". The environmental issues are quite often misevaluated as relevant only to natural sciences, social and humanitarian tasks of sustainable development are underestimated. This paper reveals the theoretical and conceptual foundations of the issues of global institutional transformations, transformative strategies of higher education, quality issues, personality-oriented strategies, multiculturalism, communication strategies of higher education. It presents the level and quality of internationalization of higher education, the formation of a worldview basis to stimulate and ways to ensure sustainable development of society, to increase the social responsibility of higher education and to strengthen its functional efficiency for society. In higher education now it is lack of basic systemic knowledge about sustainable development, its aims and ways.
TEXT MINING MENGGUNAKAN GENERATE ASSOCIATION RULE WITH WEIGHT (GARW) ALGORITHM UNTUK ANALISIS TEKS WEB CRAWLER

Text mining is widely used to find hidden patterns and information in a large number of semi and unstructured texts. Text mining extracts interesting patterns to explore knowledge from textual data sources. Association rule extraction GARW (Generating Association Rule using Weighting Scheme) can be used to find knowledge from a collection of web content without having to read all the web content manually from the many search results of crawlers. The GARW algorithm is a development of a priori to produce relevant association rules. From the results of this knowledge discovery can facilitate netizens users in finding relevant information from search keywords without having to review one by one web content generated from search engine searches.
Review for the Different Portfolio Methods Such as Mean-variance Analysis and Fama Factor Model

Portfolio Theory contains two important contents: the mean-variance analysis method and the portfolio effective boundary model.When people invest, they are essentially choosing between uncertain returns and risks.They maximize the expected return under a given level of expected risk or minimize the expected risk under a given level of expected return.Two important models will be used, Markowitz's portfolio theory and the Fama-french model.We would explain the difference in the returns of different stocks.And found that these models have certain shortcomings, it analyzes a practical example that gives an annotation for the proposed model and method.The proposed investment scheme will bring the corporation the greatest profits in terms of theory.
Channel Inverse Design Using Tandem Neural Network

A tandem neural network (NN) with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score-based loss function is proposed in this paper for channel inverse design. Tandem NN consists of an inverse neural network from target performance to design parameters and a pre-trained forward neural network from design parameters to design targets. The training of the actual INN uses the fixed pre-trained forward model to evaluate the inverse design output. A channel inverse design example for target impedance and attenuation at multiple frequency points is applied in this paper to evaluate the performance of tandem NN. Numerical results show that tandem NN achieves a good design result compared with target performance and regular NN.
A new imperialist competitive algorithm with spiral rising mechanism for solving path optimization problems

Intelligent optimization algorithms have now become important means for solving global optimization problems. The imperialist competitive algorithm (ICA) is a nature-inspired meta-heuristic algorithm that imitates social behavior. ICA has been widely used in optimization problems, however, ICA tends to fall into a local optimal solution because of its fast convergence speed, which may lead to premature convergence when solving optimization problems. To solve these problems, a new improved ICA algorithm is proposed. Based on the original ICA algorithm, the theory of spiral rising is introduced to enlarge the search space and enhance the global search ability of the algorithm based on ensuring the necessary speed of convergence. In this paper, the improved optimization algorithm is applied to 19 classical benchmark functions, and the improved ICA is applied to the robot path optimization problems to solve the optimal path. The improved ICA algorithm improves the optimization ability and algorithm stability.
Blockchain for Industrial Internet of Things

Abstract: Sensory data are used in industrial processes to gather the accurate results. In addition toasystem in order to draw useful inference from gathered data. That can guarantee that only reliable data is transferred. Physical data needs to be reliable. It must be model-free and use many overlapping field-of-view sensor origins. Events that take place during the lifespan of the product and are given process monitoring, recognition, and greatest control once trustworthy data is captured on the blockchain. As a result, the method of employing Blockchain to identify data mistakes and derive inferences from them includes recommendations for preventative actions to take before severe occurrences. We present thorough evaluation results of innovative research for blockchain. Throughout this study, we have emphasised the considerable benefits of employing on blockchain. In light of recent project, we propose dependable works built on the blockchain architecture. We also talk about present and upcoming blockchain implementation difficulties that call for more research.
Dependency Parsing with Backtracking using Deep Reinforcement Learning

Abstract Greedy algorithms for NLP such as transition-based parsing are prone to error propagation. One way to overcome this problem is to allow the algorithm to backtrack and explore an alternative solution in cases where new evidence contradicts the solution explored so far. In order to implement such a behavior, we use reinforcement learning and let the algorithm backtrack in cases where such an action gets a better reward than continuing to explore the current solution. We test this idea on both POS tagging and dependency parsing and show that backtracking is an effective means to fight against error propagation.
3D Cardiac Substructures Segmentation from CMRI using Generative Adversarial Network (GAN)

Cardiac magnetic resonance imaging (CMRI) improves the diagnosis of cardiovascular diseases by providing images at high spatio-temporal resolution helping physicians in providing correct treatment plans. Segmentation and identification of various substructures of the heart at different cardiac phases of end-systole and end-diastole helps in the extraction of ventricular function information such as stroke volume, ejection fraction, myocardium thickness, etc. Manual delineation of the substructures is tedious, time-consuming, and error-prone. We have implemented a 3D GAN that includes 3D contextual information capable of segmenting and identifying the substructures at different cardiac phases with improved accuracy. Our method is evaluated on the ACDC dataset (4 pathologies, 1 healthy group) to show that the proposed out-performs other methods in literature with less amount of data. Also, the proposed provided a better Dice score in segmentation surpassing other methods on a blind-tested M&Ms dataset.
Automatic classification of specification of ERP modifications documents

Producing ERP software modifications during implementation is one of the longest lasting and most expensive stages of implementation.Although methodologies for conducting implementation projects are constantly being refined due to changing customer demands or technology, there are no tools to support the automation of implementation work. The paper presents the results of the InfoConsulting research and development project related to development of an NLP solution for implementation documentation in order to significantly shorten work and costs by automatically searching for similar solutions in previous implementations.
Fourteen modal theorems consistent with the theory of mental models

Very important concepts in the theory of mental models are those of ‘possibility’ and ‘necessity’. Based on the definitions this psychology theory gives for those concepts, it has been shown that it complies with the requirement Fitting and Mendelsonh provides in general for modal logic. That requirement consists of being coherent with the modal interpretation of the square of opposition presented by Aristotle. This paper tries to move forward in this direction. It analyzes fourteen modal theorems indicated by Carnap under the approach of the theory of mental models. The results reveal that the theory is also compatible with those theorems.
Graph Game Embedding

Graph embedding aims to encode nodes/edges into low-dimensional continuous features, and has become a crucial tool for graph analysis including graph/node classification, link prediction, etc. In this paper we propose a novel graph learning framework, named graph game embedding, to learn discriminative node representation as well as encode graph structures. Inspired by the spirit of game learning, node embedding is converted to the selection/searching process of player strategies, where each node corresponds to one player and each edge corresponds to the interaction of two players. Then, a utility function, which theoretically satisfies the Nash Equilibrium, is defined to measure the benefit/loss of players during graph evolution. Furthermore, a collaboration and competition mechanism is introduced to increase the discriminant learning ability. Under this graph game embedding framework, considering different interaction manners of nodes, we propose two specific models, named paired game embedding for paired nodes and group game embedding for group interaction. Comparing with existing graph embedding methods, our algorithm possesses two advantages: (1) the designed utility function ensures the stable graph evolution with theoretical convergence and Nash Equilibrium satisfaction; (2) the introduced collaboration and competition mechanism endows the graph game embedding framework with discriminative feature leaning ability by guiding each node to learn an optimal strategy distinguished from others. We test the proposed method on three public datasets about citation networks, and the experimental results verify the effectiveness of our method.
Shape–Texture Debiased Training for Robust Template Matching

Finding a template in a search image is an important task underlying many computer vision applications. This is typically solved by calculating a similarity map using features extracted from the separate images. Recent approaches perform template matching in a deep feature space, produced by a convolutional neural network (CNN), which is found to provide more tolerance to changes in appearance. Inspired by these findings, in this article we investigate whether enhancing the CNN’s encoding of shape information can produce more distinguishable features that improve the performance of template matching. By comparing features from the same CNN trained using different shape–texture training methods, we determined a feature space which improves the performance of most template matching algorithms. When combining the proposed method with the Divisive Input Modulation (DIM) template matching algorithm, its performance is greatly improved, and the resulting method produces state-of-the-art results on a standard benchmark. To confirm these results, we create a new benchmark and show that the proposed method outperforms existing techniques on this new dataset.