Reviewerly/exHarmony huggingface dataset:
Experiencing as Doing and Undergoing

In this chapter I aim to draw some initial alignments between Dewey’s work and design research by focusing in on the theme of experience. To begin, I examine Dewey’s approach to experience in both its general and aesthetic forms. This then leads in to a consideration of how the theme of experience is currently approached within the field of design. Here, some existent points of Deweyan inspiration are identified in user experience and experience-centered design literature. Thereafter, I seek to extend the discussion further by offering an in-depth exploration of Dewey’s theoretical interlinking of experience and nature within his ‘naturalistic metaphysics’—an often-overlooked aspect of his philosophy. The chapter then concludes with a reflection on how, by drawing on Dewey’s work, a more expansive understanding of experience might be established within design.
Construction-Based Secret Image Sharing

Most secret image sharing (SIS) schemes output noise-like shadows and are fragile to any noise due to their restoring methods are based on mathematical operations. A noise-like shadow increases the suspicion of an attacker and the fragileness leads to the secret pixel is wrongly restored even if a bit error occurs. In this paper, we propose a construction-based SIS (CSIS) scheme for a (k, n)-threshold based on quick response (QR) code and the principle of polynomial-based SIS. In the proposed CSIS, each output shadow is a valid QR code, which is thus comprehensible and robust to typical noises. The secret image is losslessly restored by barcode scanning operation and Lagrange interpolation with any k or more shadows. The comprehensible shadow not only reduces the suspicion of an attacker but also improves the management efficiency of shadows. The robustness makes the proposed scheme applicable to noisy channel when transmitting shadows. We provide experiments to validate the proposed scheme.
Can Phones Build Relationships?

Wildlife conservancies across the globe are increasingly recognizing their need to support their surrounding communities to sustainably operate. Rapidly shifting environmental and sociopolitical climates increasingly stress existing resource and service provisions, forcing wildlife conservancies to co-manage with local communities shared resources like water, wildlife, soil, pollinators, and security. This work presents a case study in Laikipia, Kenya on Ol Pejeta Conservancy's use of text-based technologies to provide services and build relationships with the many widely-dispersed communities on its borders. Through technology deployments, staff interviews, and community focus groups, we investigate a potential role for basic mobile phone services, like SMS and USSD, to help conservancy personnel disseminate accurate and timely information, gather community feedback, address grievances, and improve accountability. Our findings show that communication with locals requires intense and ongoing effort from conservancy staff. Partially successful deployments of phone services provide a proof-of-concept for their utility in community relations but highlight particular design challenges for wildlife conservancies; having critical needs for broad inclusive engagement; clear, deliberate communication; and careful trust-building.
Application of K-Nearest Neighbor Algorithm for Classification of Mental Disabilities Patients Based on Age

Abstract People with mental disabilities have the same position, rights and obligations as the non-disabled community. As part of Indonesian citizens, it is not appropriate for men and women with mental disabilities to be treated bad and experience discrimination. In Sukamulia Subdistrict, East Lombok, it is noted that the number of people with disabilities at the end of 2018 recorded 358 sufferers where people with disabilities are dominated by people over 18 years who are categorized as persons with disabilities. To find out the number of patients categorized as persons with disabilities, classification is needed. The purpose of this study was to classify people with disabilities based on age using the classification of KNN (K-Nearest Neighbor) Algorithm based on Age. Validity test is used to determine the best and most accurate level of accuracy. In this study the level of accuracy obtained is that in testing using K-Fold Validation 3 with a value of k = 3, the accuracy result is 97.49%.
Knowledge Exploration from Tables on the Web

Tables in Web pages are the main source of knowledge graphs because they contain summarised information. Since there are various ways to represent information in web tables, it is not easy to extract information efficiently. This paper proposes a method for extracting information from structured and unstructured web pages and a method for converting the extracted information into a knowledge graph. In particular, in order to construct a Korean knowledge graph, this paper introduces a method for extracting large table data from web pages that are written in Korean and expressing it in a graph.
Approach for Development of Engineering Tools Based on Knowledge Graphs and Context Separation

During the development of complex multidisciplinary systems, engineers often have problems associated with the complexity of a target system and interactions of different groups of engineers and subcontractors. In potential, besides engineers and subcontractors, the new agents can participate in such interactions, like AGI. This paper presents ideas on how to develop engineering tools based on knowledge graphs to manage this complexity. This paper proposes the approach to make possible to agents that have different cognition contexts understand each other and a simple data format of context separation. At the end of the article, we have tried to show the example of how this approach can be applied to make a tool that uses engineering data in the proposed format.
Analysing Petri Nets in a Calculus of Context-Aware Ambients

This paper proposes an approach to analysing and verifying Petri nets using a Calculus of Context-aware Ambients (CCA). We propose an algorithm that transforms a Petri net into a CCA process, which can then be analysed and verified using the CCA verification tools. Besides, this demonstrates that CCA is at least as expressive as Petri nets. We illustrate the practicality of our approach using a case study of the dining cryptographers problem.
The Practice of TBL+ Flipped Classroom in the Music Aesthetics Course Under the Music Gesture Theory

As a new comprehensive analysis method in the 21st century, music gesture is instructive to the course of “Aesthetics of Music”. Under the premise of understanding the music gesture theory, this paper combines the team-based TBL teaching mode with the flipped classroom, and uses specific music examples to demonstrate the method and practice of using music gesture theory to reform the teaching of Music Aesthetics.
A Lean Tactic for Normalising Ring Expressions with Exponents (Short Paper)

This paper describes the design of the normalising tactic ring_exp for the Lean prover. This tactic improves on existing tactics by extending commutative rings with a binary exponent operator. An inductive family of types represents the normal form, enforcing various invariants. The design can also be extended with more operators.
How to Train A-to-B and B-to-A Neural Networks So That the Resulting Transformations Are (Almost) Exact Inverses

In many practical situations, there exist several representations, each of which is convenient for some operations, and many data processing algorithms involve transforming back and forth between these representations. Many such transformations are computationally time-consuming when performed exactly. So, taking into account that input data is usually only 1–10% accurate anyway, it makes sense to replace time-consuming exact transformations with faster approximate ones. One of the natural ways to get a fast-computing approximation to a transformation is to train the corresponding neural network. The problem is that if we train A-to-B and B-to-A networks separately, the resulting approximate transformations are only approximately inverse to each other. As a result, each time we transform back and forth, we add new approximation error—and the accumulated error may become significant. In this paper, we show how we can avoid this accumulation. Specifically, we show how to train A-to-B and B-to-A neural networks so that the resulting transformations are (almost) exact inverses.
Information Security Management System

Security of information is always a challenging domain for any computer network organization. An organization always sets different types of policies with the course of time so that no information can be leaked. Some external or some internal factors of an organization play important roles in revealing the information. An organization mainly depends on its employees. An employee manages the data and information and there exists some chance among employees to reveal the data. There is need to study and set policies for employees so that no full information can be revealed. Information security management system (ISMS) has collection of different types of policies and procedures for systematically managing organizationally sensitive data. ISMSs have to deal with management of employees of an organization to minimize the risk of revealing information. This chapter studies employee management so that an organization can continue its business securely.
Data mining for identifying trends in markets

Data Mining is the process of analyzing and gathering proper information from giant data warehouses by the use of different algorithms, patterns, and tools. This knowledge can be then classified using specific rules or patterns by translating raw data into useful information. Furthermore, the collective data benefits any user/organization such that they can predict trends and user behavior by processing and analyzing this information. Web Data Mining is a subdivision of Data Mining concerning publicly available information on the internet. The concept of Web Data Mining is that of extracting relevant data on different web pages and shaping it into consistent information. Moreover, the common use case of Web Data Mining is to acquire information about what users of search engines are interested in such that statistics of tendencies can be devised to serve different purposes. The paper presents a solution that allows, based on the mining of public data existing within different online car sales platforms, the identification of trends in the Romanian car market. Moreover, this application allows the user to calculate the correct price of a car, taking into account all the vehicles whose specifications correspond to those chosen by the user.
Comparison of Various Scale Invariant Crowd Count Analysis Methods

A comparison of various scale invariant crowd count analysis methods is conducted. The methods are chosen such that they have the ability to accurately predict crowd count from an arbitrary perspective. An investigation is conducted where their comparison is based on MAE (Mean Absolute Error) and MSE (Mean Squared Error) which are the chosen evaluation metrics and their architecture is discussed. Models are to be assessed based on their performance and subsequent results on the ShanghaiTech Dataset and the UCF-CC-50 Dataset. This is enough to give a thorough comparison of these models. The approach compares a variety of models that utilize Dilated Convolutional Neural Networks, Fully Convolutional Neural Networks and Multi-Scale Convolutional Neural Networks.
Reinforcement Learning and Genetic Algorithms

There are a wide variety of empirical settings that do not easily fit within an optimization framework and for which results that are “good,” but not necessarily the “best,” are the only practical option. When business firms compete, for instance, a single firm can dominate a market by “just” being better than its competitors. Over the past decade, reinforcement learning has built on this perspective with considerable success. Although several features of reinforcement learning are some distance from our full regression approach, its promise motivates a brief discussion. Reinforcement learning also is sometimes included as a component of deep learning.
AR-Based Assembly Assistance System with Efficient Evaluation of Misalignment Between Virtual and Real Objects

Good assembly instructions improve the effectiveness of assembly operations and training. The development of augmented reality (AR) has enhanced the experience of assembly operations by showing computer-aided design (CAD) models at installation locations to an operator wearing an AR head-mounted display (HMD). In such operations, it is important to detect whether misassembly occurs. This research proposes two methods to construct an AR assembly assistance system using only an HMD of Microsoft HoloLens. One method is coordinate calibration to make the CAD models display correctly at desired locations by aligning a reference CAD model to the corresponding real object’s position. The other evaluates in real time whether misalignment has occurred by comparing the depth images of the CAD model and the real object for efficiency. With the preliminary development, we can use only an HMD to achieve the AR-based assembly instruction and verification to some extent.
Demographic Prediction Based on User's Movie-Watching Behavior

Demographic attributes play an important role in adjusting the market strategy of film companies and customizing recommendation for their user. However, it's difficult to obtain full information of demographic attributes of online user and previous work only adapt single-source data (user historical record) for prediction, which is not enough for user representation. In this paper, we proposed a novel approach named CNN-ETNA to predict user demographic attributes from their movie-watching behavior. Our model CNN-ETNA is further improved by extracting semantic feature from movie to learn informative user representations. Experiments conducted on Movielens dataset demonstrate that our approach effectively improves the performance of demographic prediction compared to baseline algorithms.
Evaluation of Embedded Vectors for Lexemes and Synsets Toward Expansion of Japanese WordNet

In this paper, we discuss the possibility to expand Japanese WordNet using AutoExtend that can produce embedded vectors based on dictionary structure. Recently several kinds of NLP tasks showed that the distributed representations for words are effective, however, the word-embedded vectors constructed based on contexts of surrounded words would be difficult to discriminate meanings of a word because every vector is produced for a word. On the other hand, AutoExtend that can produce embedded vectors for meanings and concepts as well as words taking into account thesaurus structure of dictionary, has been proposed and applied into English WordNet. Thus, in this paper, we apply AutoExtend into a Japanese dictionary i.e., Japanese WordNet to construct embedded vectors for lexems and synsets as well as words taking into account thesaurus structure of Japanese WordNet. The experimental results show that embedded vectors constructed by AutoExtend can be helpful to find corresponding meanings for unregistered words in the dictionary.
Foam Sheets as Material for Fabricating Large and Functional Soft Objects

We report on our experiments to fabricate soft, functional and furniture size objects from PE foam sheets. This material is desirable because it is flexible, lightweight, widely available in a variety of thicknesses, and could be made out of sugar cane. We present a set of unique joining mechanisms that exploit the flexibility and do not use adhesive or fasteners. We show how to program softness in meta structures to avoid the use of multiple materials. Finally, we demonstrate a number of examples and discuss the applicability for prototyping as well as customization in deploying the design.
Website as Foundation Information Media under the auspices of Nahdlatul Wathan

Abstract The Nahdlatul Wathan organization is one of the largest and most widespread organizations in West Nusa Tenggara, especially in East Lombok. The number of foundations under the Nahdlatul Wathan organization has increased rapidly from time to time. But parties from the organization’s Nahdaltul Wathan were still having difficulty processing data and getting the latest information about the number and conditions of each foundation under the auspices of Nahdlatul Wathan. This is because the management of the Nahdalatul Wathan organization does not yet have a system that can manage the data as mentioned above. So the Nahdlatul Wathan organization needs the media that was used to overcome it which is called a web-based information system foundation that uses the PHP programming language and MySQL as its database data. With this system, it can simplify and streamline the performance of the Nahdlatul Wathan management, as well as the community, to get the latest information on the development and number of foundations under the Nahdalatul Wathan, which can be accessed quickly and precisely, so it does not take a long time.
Claim Consistency Checking Using Soft Logic

Increasing concerns about the prevalence of false information and fake news has led to calls for automated fact-checking systems that are capable of verifying the truthfulness of statements, especially on the internet. Most previous automated fact-checking systems have focused on the use of grammar rules only for determining the properties of the language used in statements. Here, we demonstrate a novel approach to the fact-checking of natural language text, which uses a combination of all the following techniques: knowledge extraction to establish a knowledge base, logical inference for fact-checking of claims not explicitly mentioned in the text through the verification of the consistency of a set of beliefs with established trusted knowledge, and a re-querying approach that enables continuous learning. The approach that is presented here addresses the limitations of existing automated fact-checking systems via this novel procedure. This procedure is as follows: the approach investigates the consistency of presented facts or claims while using probabilistic soft logic and a Knowledge Base, which is continuously updated through continuous learning strategies. We demonstrate this approach by focusing on the task of checking facts about family-tree relationships against a corpus of web resources concerned with the UK Royal Family.
Exact probability of fixed patterns occurring in a random sequence

We derive a procedure to obtain the exact probability that a specific pattern of letters occurs in a longer random sequence of letters. The procedure is generalized to find the exact probability of a fixed (specific) single pattern, and a union or intersection of multiple fixed (specific) patterns within a random sequence perfectly for any distributions of a cell in the random sequence, and can handle patterns with uncertain letters (including missing, blank, unclear, ambiguous, transposition, etc.). The procedure also finds the probability that a pattern that is randomly picked will appear in a separate longer random sequence of letters. These methods are of particular applicability in genetic sequence analysis, diagnostics, anthropology, clinical medicine, data mining, computational molecular biology, and pattern analysis and recognition.
Evaluating the impact of comprehensive human resourcequality management on Individual performance: a guidefrom pharmaceutical companies in Jordan

This study aims to identify the Evaluation of the impact of comprehensive human resource qualitymanagement on firm performance: Evidence from pharmaceutical companies in Jordan as it is an ongoing processto detect and minimize defects in the Jordanian pharmaceutical industry and increase customer satisfaction,comprehensive human resource quality management focused on improving the quality of pharmaceuticalcompanies' production in Jordan through the development and innovation of operations and internal control fromall parties involved in the production process. The present research investigates the impact of comprehensivehuman resource quality management practices on firms performance in selected pharmaceutical companies ofJordan. Data has been collected through a field survey and interviews conducted in the year 2018. A total of 400questionnaires designed on five point likert scale were distributed among the employees wherein 112 were rejectedand 388 were accepted for analysis. The sample size of the study has been 388 employees. Linear regression hasbeen used as the statistical tool for analysis. The findings highlighted that there is a significant impact ofcomprehensive human resource quality management practices on firm performance in companies under study.
State-of-the-Art in Silicon Quantum Computer Development

State-of-the-art in silicon quantum computer development is introduced.
Designing Towards Inward-Oriented Fashion Technology

Fashion technology designs typically combine sensing technology and actuators to register and respond to information about the environment and/or the human body. The ways in which designers use and integrate these data into garments, however, varies on a scale from highly theatrical and outward-oriented designs to subtle and inward-oriented applications. This pictorial presents five garment designs created between 2013 and 2020, that occupy the more utilitarian and inward-oriented end of the fashion technology spectrum. We demonstrate five designs that combine sensing and actuation, highlighting the benefits of direct biofeedback and of keeping the personal data within the garment. The selection of projects aims to search the right balance between sensing and actuation.
Rethinking Our Assumptions About Language Model Evaluation

Many applications of pre-trained language models use their learned internal representations, also known as word- or sentence embeddings, as input features for other language-based tasks. Over recent years, this has led to the implicit assumption that the quality of such embeddings is determined solely by their ability to facilitate transfer learning. In this position paper we argue that pre-trained linguistic embeddings have value above and beyond their utility as input features for downstream tasks. We adopt a paradigm in which they are instead treated as implicit knowledge repositories that can be used to solve common-sense reasoning problems via linear operations on embedded text. To validate this paradigm, we apply our methodology to tasks such as threat detection, emotional classification, and sentiment analysis, and demonstrate that linguistic embeddings show strong potential at solving such tasks directly, without the need for additional training. Motivated by these results, we advocate for empirical evaluations of language models that include vector-based reasoning tasks in addition to more traditional benchmarks, with the ultimate goal of facilitating language-based reasoning, or ‘reasoning in the linguistic domain’. We conclude by analyzing the structure of currently available embedding models and identifying several shortcomings which must be overcome in order to realize the full potential of this approach.