Reviewerly/exHarmony huggingface dataset:
Learning in Random Utility Models Via Online Decision Problems

This paper studies the Random Utility Model (RUM) in a repeated stochastic choice situation, in which the decision maker is imperfectly informed about the payoffs associated to each of the available alternatives. By embedding the RUM into an online decision problem, we develop a gradient-based learning algorithm and establish that a large class of RUMs are Hannan consistent (\citet{Hahn1957}); that is, the average difference between the expected payoffs generated by a RUM and that of the best fixed policy in hindsight goes to zero as the number of periods increase. We show that the class of Generalized Extreme Value (GEV) models can be implemented with our learning algorithm. Examples in the GEV class include the Generalized Nested Logit, Ordered GEV, and Product Differentiation models among many others. In addition, we show that our gradient-based algorithm is equivalent to the Follow the Regularized Leader (FTRL) algorithm, which is widely used in the Machine Learning literature to model learning in repeated stochastic choice problems. Thus the FTRL algorithm can be understood in an economically grounded optimization framework. Finally, we discuss how our approach can incorporate the recency bias effect and be used to implement prediction markets in general environments.
An example of "manufacturing" exhibition using experience and highly skilled products

"Manufacturing" is an important thing that supports modern society, but in modern times it is difficult to understand its importance. The Toyota Commemorative Museum of Industry and Technology aims to widely convey "manufacturing" to society and contribute to the development of society. Currently, the automobile production technology exhibition zone is being renovated into a new exhibition that adds "experience type" to "exhibition type". This time, in the first case of setting up the "painting training experience corner", we looked back on the purpose, the process leading up to the exhibition, and the reaction after the exhibition. As a result, we succeeded in understanding that "manufacturing", which is the purpose of setting up a new corner, is based on "human ability improvement" and that the current life is supported by human power. As a future task, it was pointed out that it was necessary to build a system to train guide members who could understand and explain the purpose of the exhibition.
Science and Knowledge Organization: An Editorial

KNOWLEDGE ORGANIZATION is a forum for all those interested in the organization of knowledge on a universal or a domain-specific scale, using concept-analytical or concept-synthetical approaches, as well as quantitative and qualitative methodologies. KNOWLEDGE ORGANIZATION also addresses the intellectual and automatic compilation and use of classification systems and thesauri in all fields of knowledge, with special attention being given to the problems of terminology.
About This Issue

April 04 2022 About This Issue Author and Article Information Online Issn: 1531-5169 Print Issn: 0148-9267 © 2022 Massachusetts Institute of Technology2022Massachusetts Institute of Technology Computer Music Journal (2022) 45 (1): 1. https://doi.org/10.1162/comj_e_00588 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation About This Issue. Computer Music Journal 2022; 45 (1): 1. doi: https://doi.org/10.1162/comj_e_00588 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll JournalsComputer Music Journal Search Advanced Search Computer Music Journal 37:2 included an article analyzing audio feedback systems in various musical compositions. One of the article's authors, Dario Sanfilippo, went on to write a Ph.D. thesis in this area. In the current issue, he presents a pair of articles based in part on his dissertation research. The first of these considers musical feedback using concepts and vocabulary from the field of complex adaptive systems. That field concerns nonlinear dynamical systems that are complex, in that their behavior may not be predictable from the behavior of individual components within the system, and adaptive, in that the behavior changes over time in response to events. Sanfilippo also draws upon the philosophical concept of radical constructivism, presenting as a case study his own composition Constructing Realities. His companion article elucidates technical aspects of the signal-processing algorithms he implemented for this work. These algorithms analyze the short-term or long-term information... You do not currently have access to this content.
An approach and view of "the engineer ethic seminar to be a leader"

The engineering ethics committee which is in Technology and Society divisiona of JSME has opened an engineer ethic seminar since 2008 every year. In this report, the theme of the past seminar is looked back on. Then the situation of the recent seminar is also arranged. From these results, the engineer ethic education of the near future is reviewed.
Vision Based Two-Dimensional Measurement of Machine Tool Motion Trajectory

The final goal of this study is to develop a vision-based method for measuring machine 3D-dynamic motion errors. In this report, a measurement method is proposed, and its resolution and measurement error in plane motion measurement are discussed. The influence of the defocus condition on the detection resolution is analyzed. The displacement of the marker installed on the spindle of the machine tool is obtained through the video captured by the high-speed camera. In order to obtain the displacement of the marker, the center position of the marker is identified by fitting a mathematical function to obtain the position of the intensity peak of the marker. The measurement resolution and accuracy of the proposed method are investigated by experiments. In addition to the model fitting method, edge detection method and threshold detection method are also used for marker position detection for comparison. The model fitting method reach the accuracy of 40 μm and resolution of 7 μm compared with the grid encoder as the reference. The standard deviation of the detected static position is less than 7 μm when the defocus length is less than 190 mm.
THE METHOD OF DATA FLOW PROCESSING INCOMING FROM AN IP-CAMERA

The article discusses methods of video stream analysis incoming from an IP-camera using the methods of image analysis, machine learning and knowledge engineering. For efficient data storage, a motion identification method was developed to determine the frame rate using a neural network (NN) classification of the block representation of the frame difference. In addition, the model of domain knowledge base was developed to analyze the events taking place in the image. The article proposes the method of event analysis based on logical conclusion where the YOLO neural network (NN) results are used as input data. The YOLO NN pops up entries in the image, such as a human being, table, TV set, etc. To confirm the module effectiveness a software system was designed comprising two modules. The first module is intended to test the effectiveness of the motion identification method and save the smallest number of frames necessary to determine the events taking place. The second module is developed to test the effectiveness of the method of analyzing occurring events resulted in the information on the state of controlled territories where video surveillance is performed. The input data for the second module are the saved frames obtained because of the operation of the first system module.
Electric vehicle utilizing PMV auxiliary parts (in-wheel motor)

In manufacturing education, it is desirable to have an education that guides students to think what they want to do, seek a way to realize it, and realize it. However, 1st graders do not have enough knowledge to think about what they want to do. And even if they come up with what they want to do, their skills often cannot catch up. Therefore, the authors conducted a lesson in which students introduced a simple electric vehicle that can be manufactured within a limited class period as a teaching material. In this paper, we report the outline.
Software connector as software process

In component-based architectures, interest has often been focused on the component. Recent applications are becoming more and more communicative, often reaching equivalent proportions between the interactional and functional parts. This study is interested in explicit connectors by proposing a rich semantic connector to model and implement complex communications in heterogeneous environments. We present a new structure of the connector and model its behaviour through a software process. We no longer consider it as a software product or artefact but rather as a communication process. The connector is treated as a first-class entity with the same degree of citizenship as a component. This research concentrated on the motivations and benefits of first-class connectors and some connector-centric works to arrive at the proposed solution through a Meta-model illustrated by a case study using SPEM profile.
Ethics

Any project analyzing human-generated texts needs to consider its ethical implications. The relevant considerations depend on the source and the nature of the texts, including any reasonable expectations to privacy for the text authors. There is no consensus on ethical uses of social media data (Samuel, Derrick, and van Leeuwen, 2019) so it is important to consider a variety of perspectives. This chapter makes the case for analyzing public social web texts not requiring explicit consent from post authors, although not all researchers agree (Golder et al., 2017).
Deep Learning Models: An Understandable Interpretable Approach

In many of applications which are mainly related to image, text, audio, video, graphics etc. mainly consist of task like classification and prediction. Classification and prediction tasks are to be done in different manner. The best level accuracy can be achieved by deep neural networks. The networks mainly works likes black box function approximations that make a good prediction of output for a given value of input. In various applications like medical diagnosis, planning and control where a high level of trust needed on machine output, deep neural networks proved to be best. In order to evaluate uncertainty of particular output value statistical matrices can be used. However concept of trust on a particular output also rely on visibility that a human is also en route towards working of machine. In the same context, neural networks provides well founded concepts which are human understandable for an output which describes perception about inner working. Interpretability of deep learning models is an immovable concept. As human understanding exists at different levels, there also exist a multitude of different dimensions that collectively define the concept of interpretability of deep learning models. Interpretability can be described either in terms of network parameters or in term of input attributes. This chapter mainly describes the dimensions which are convenient for interpretability of a model. Based on these dimensions, classification of prior work can be done. By means of describing the interpretability, analysis of gap can be done that must be filled up for gaining more interpretability of model. Interpretability of a model can be easily described by understanding model transparency. Model transparency can be further achieved by calculating the model simulatability, computability and transparency of algorithm used. Similarly, model functionality can be expressed in terms of visualization of parameters used in deep learning model, textual description in terms of input/output values and explanation of mapping of input values to the trusted output values. This chapter also describes the various challenges that come across while federation of models or on the time of using different approaches for attaining interpretability of models. Major challenges may be in form of fairness of models and accountability of models which are to be describes in detail in this chapter. Interpretability is more generous term to be used in deep learning model than that of explain ability. Interpretability of model can be related to Bayesian approach as the concept can be extended to Bayesian model. Both Bayesian framework and deep learning frame work are complement to each other. Model transparency and model functionality are two main terms used for interpretability.
Online Engineering Courses and Student Evaluation

The author has tried to improve the lesson by using an interactive lesson method. Under the circumstances of the corona, we conducted a full online class, and conducted a flipped classroom and a class that adopted the sink pair sharing method. In this report, we will consider whether the lessons conducted were effective based on the confirmation of comprehension level by the minute paper filled out by the students for each lesson and the lesson evaluation by the student questionnaire.
MPLEMENTATION OF EDUCATIONAL WORK IN THE EDUCATIONAL PROCESS OF THE MILITARY TRAINING CENTER

The article presents an analysis of the implementation of educational work in the educational process during the training of students at the Military Training Center of the RGAU-MSHA named after K. A. Timiryazev, and also gives some recommendations to teachers. A hypothesis is put forward about the influence of scientific work (not included in the standard curriculum) of students on the level of their upbringing and, as a result, academic performance.
Use of virtual social networks and social skills in university students from northern Peru.

The use of online social networks is giving rise to a line of research focused on the phenomenon of addiction that occurs due to the intense use of technologies and the Internet. The objective of the study was to determine the re-lationship between the use of virtual social networks and social skills in universi-ty students from northern Peru. The design was non-experimental, cross-sectional, correlational. A survey was applied as a technique and the instruments were two questionnaires, the Test of addiction to the use of social networks-ARS and a Test scale of social skills-EHS. It was found that 71.7% of students showed a low level of obsession with the use of virtual social networks, 27.2% medium level, while 1.1% high level. Concluding that addiction to social net-works has a significant relationship with social skills; that is, the greater the use of social networks, the greater the development of social skills. Keywords: Addiction, college students, social skills, social media
Smart cities and potential legal issues in kingdom of bahrain

The Kingdom of Bahrain has taken the initiative to enhance urban living and see how smart solutions will fit into the existing system. Blockchain technology, recognized as the fourth industrial revolution has the potential of changing the game and making cities more secure and is used as the foundation of plans to enhance urban living. Nevertheless, the current use of this technology raises important legal issues including protection of personal data and consumers. Hence, it is the aim of this paper to examine the position of Blockchain technology in relation to the Kingdom&apos;s smart cities initiative. To achieve that, the paper will provide an overview of the pillars of smart cities and Blockchain technology and will examine the potential legal challenges in relation to protection of personal data and smart contracts.
CURRENT AND CONTINUING ERRORS OF PSEUDOGRADIENT CYLINDRICAL IMAGE FILTERING

When solving many practical problems, such as the Earth and Space monitoring, medical diagnostics, navigation, robotics, etc., the challenges of image and other multidimensional data processing arise. To solve these problems successfully, their mathematical formulation is required, including a mathematical description that is an image model. Most studies in image processing methods, consider the images as a system of random variables specified on a rectangular grid with dimensions of two and higher. There are much less studies in images defined on curved surfaces. However, many real images are given on cylindrical surfaces, for example, the surface of a pipeline, a tank, a rotation shaft, and etc. Therefore, for a formal description of such images, their corresponding models are required. The 2D models are unsuitable for that it is not possible to represent the correlation properties of a cylindrical image with a rectangular image. The article describes the autoregressive models defined on a spiral-shaped cylindrical grid, that is on a helical line. This model can also be used to represent quasiperiodic stochastic processes. One of the main tasks of image processing is the problem of filtering them against the background of noise considered in this article. In this case the parameters of real images are usually unknown a priori. In addition the images often have significant and random heterogeneity. In this paper the authors use the adaptive pseudogradient algorithms based on cylindrical models to filter homogeneous and inhomogeneous images. These algorithms do not require setting the values of image parameters. The values of the root-mean-square filtering error are obtained based on the parameters of images and noise.
Proposal of Teaching Materials to Touch and Understand the Mechanism of Gears

The authors have developed a teaching material to understand the structure and principle of gears by using CG animation materials. However, it is difficult for younger children who have low cognitive ability to understand only visual information to understand the structure of gears with CG contents. In this study, we developed a teaching material of gears to promote the understanding of the structure by touching the object, which cannot be supplemented by visual information alone. In this teaching material, the gears can be attached to any place on the board by using Velcro or strong magnets on the shaft of the gears, so children can learn the principle of gears while playing.
Design of a low-cost digital spirometer for remote monitoring of COPD and COVID patients

This research presents the proposal for a low-cost digital spirometer called “Phukuy” that includes the following sensors: an oximeter that measures the per-centage of oxygenation of the blood (SpO2) and the heart rate (bpm), a body and environmental thermometer, and a barometer. This spirometer will serve for the diagnosis and remote monitoring of people with Chronic Obstructive Pulmonary Diseases (COPD) including COVID; benefiting people who do not have a near-by health center; This will help decongest hospitals and prevent them from be-coming an infectious source. In addition, all the information obtained from the measurements will be saved on a web platform, which can be accessed by a des-ignated doctor. The proposed spirometer was validated with a 3-liter calibration syringe, obtaining an average error of 0.033 L (1.1%) and a maximum error of 0.070 L (2.33%), which is within the range recommended by SEPAR (± 3 %).
Are You AI's Favorite? EU Legal Implications of Biased AI Systems in Clinical Genetics and Genomics

The article provides a legal overview of biased AI systems in clinical genetics and genomics. For the overview, two perspectives to look at bias are taken into consideration: societal and statistical. The paper explores how biases can be defined in these two perspectives and how generally they can be classified. Based on two perspectives, the paper explores three negative consequences of biases in AI systems: discrimination and stigmatization (as the more societal concepts) and inaccuracy of AI’s decisions (more related to the statistical perception of bias). Each of these consequences is analyzed within the frameworks they correspond to. Recognizing inaccuracy as harm caused by biased AI systems is one of the most important contributions of the article. It is argued that once identified, bias in an AI system indicates possible inaccuracy in its outcomes. The article demonstrates it through the analysis of the medical devices framework: if it is applicable to AI applications used in genomics and genetics, how it defines bias, and what are the requirements to prevent them. The paper also looks at how this framework can work together with anti-discrimination and stigmatization rules, especially in the light of the upcoming general legal framework on AI. The authors conclude that all the frameworks shall be considered for fighting against bias in AI systems because they reflect different approaches to the nature of bias and thus provide a broader range of mechanisms to prevent or minimize them.
MODERN TRENDS OF THE USE OF INFORMATION TECHNOLOGIES IN EDUCATIONAL INSTITUTIONS

The article examines the development trends of information and communication technologies in education as the basis for the formation of an information society. The stages of development of information technologies from manual information to network technology are described. The purpose of the article is to analyze the current trends of the use of information technologies in educational institutions. General scientific methods were used during the research, in particular: analysis and synthesis, comparison, analysis of time series, graphical method, etc. The meaning of information and communication technologies is revealed, as well as the regulatory and legal framework for their development and implementation in the educational process in Ukraine is indicated. Comparison of government forms of statistical reporting is carried out in terms of the use of modern information technologies, namely, the expansion of the ZNZ-1 form with the following indicators: about the technology and speed of the Internet, the availability of Wi-Fi technology, the availability of modern printing devices (including 3D printers by type), educational software used in the educational process and are on the balance sheet of the institution, etc. The current state of the provision of general secondary education institutions with an Internet connection was investigated (less than 100 institutions do not have such a connection, which is less than 1% of the total number). The level of provision of schools' connection to a high-quality Internet network (with a data transfer rate of more than 30+ Mbit/s), as well as the number of students per one stationary or laptop computer by regions, are described.
Hybrid bad smell detection approaches based on historical changes of LOC

Code smells are indicators of problems in software. They harm software quality and usage. Now a day it is critical to building any system in a correct and controlled manner, even if the systems are different and they have different objectives, and the developer needs to categorize the required functions to implement. Furthermore, any functional or non-functional requirements of the system need to be free of errors as it is critical for the organization. Usually, the developer uses codes for designing the system and he is seeking a correct and simple LOC to build the system, to get this it is important to implement bad smell detection for different LOC. However, this paper concentrates on two types of bad smell detection methods, these two methods will use the same codes and get the average and ensure the accuracy of the code before using it in the entire system. Hybrid bad smell detection method is using LOC as a historical information for the accuracy of the codes. In contrast, bad smell detection methods used here are DECOR which focuses on describe, constitute, process, detect, and validate. The other bad smell detection method is LongMethod which uses a formula to estimate the detection of bad smell. The most accurate step is the average of the two numbers. Developer will feel safe while using the code for the systems without any errors.