Reviewerly/exHarmony huggingface dataset:
Inovasi Fixture Sudut Pada Proses Welding Berdasarkan Design For Manufacture And Assembly (Dfma)

The angles fixture in the welding process is a supporting tool to holder the work-piece in the welding process. This tool is made in Do It Yourself (DIY) in micro-business. However, in its application there are problems. The solution provided to overcome these problems is the development of tools with the Design For Manufacture and Assembly (DFMA) method. The goal is to create a portable device design that is easy to carry and to calculate the time and cost of the manufacturing process and welding fixture assembly. The development of tools is carried out by looking for more complex problems through a consumer needs questionnaire and a product development questionnaire. Data obtained from the development questionnaire can be used as a concept design (sketch) for analysis based on DFMA. The DFA (Design For Assembly) is carried out to simplify the assembly design and facilitate the assembly process. The choice of material is the basis for the quality of the product. The DFM (Design For Manufacture) is done to determine the component manufacturing process design, calculate the cost of materials and manufacturing costs. From the DFMA process, an innovation design for the welding process consists of 22 components with 12 different types of components. The design of innovative angles aids in the welding process is called adjustable angles welding fixture. The cost used to manufacture this tool is Rp.115.082 and the assembly and manufacturing time is 133 minutes 40 seconds.Keywords : Design, fixutre, DFA, DFM, DFMA.
How to Disclose Selective Information from Permissioned DLT-Based Traceability Systems?

In order to exchange traceability information within supply chains, the use of Distributed Ledger Technology (DLT) has recently been discussed in research and industry. As part of the Design Science Research Methodology (DSRM) chosen for this work, a structured literature review is conducted to provide a knowledge base regarding this topic. By reviewing the proposed approaches, a summary of collected requirements is given and the identified system concepts and design patterns are presented and discussed. The lack of mechanisms to protect trade secrets is identified as the most important challenge for the real-world application of such systems. There is a particular need for solutions when the exchange of information with third parties or the public is required but business-relevant data or metadata could be disclosed. Therefore, the research question arises, how sensitive data could be provided from inside a DLTbased system to external third parties by considering the risk of metadata analysis. As result, we propose an approach using existing mechanisms and design patterns to reduce the risk of competitor analysis. The applicability in real-world scenarios is focused, which is why a proof of concept is implemented as software artifact using Hyperledger Fabric. An evaluation of this artifact regarding the key requirements concludes this paper.
Realistic and Relevant Role-Players for Experiential Learning

Providing experiential training in complex tasks on an any-time anywhere basis—whether for individual or team tasks—often requires simulating interaction with non-player characters (NPCs): co-workers, superiors, subordinates, opponents, subjects, stakeholders, consultants, tutors, peers etc. Simulating all aspects of human behavior is overwhelmingly complex. Pursuing full human simulation is also needlessly costly and distracts from the task at hand, which is providing a learner with prompts and reactions supporting experiences that promote mastery of learning objectives and appropriate transfer. The question then is, What techniques can be used to create relevantly realistic NPC agents to support desired learning outcomes? Rather than advance a one-size-fits-all silver bullet for instructional system NPC modeling, we advocate a flexibly configurable bag-of-tools approach. Using example systems that the authors have worked on, we discuss several different approaches to building NPCs for pedagogical effect. Choices of technologies to employ should be based on application requirements, considering issues such as: (1) content/authoring costs—both for achieving short term capability and for longer-term maintenance and scalability; (2) pedagogical approaches; and (3) relevant aspects of realism in behavior and interaction methods.
Play to Learn! Nurturing Fundamental Digital Skills of Romanian Preschoolers by Developing Edutainment Applications

From their early age, children are exposed to technology, mainly used for entertainment. It is considered that the current generations possess the so-called lifestyle digital skills (focused on entertainment goals), while their work-related skills are missing. In this paper we describe our approach in changing preschool children’s attitude towards technology by developing and integrating edutainment applications in their teaching-learning activities. The approach was used for preschoolers in the public formal educational system. We describe our experience in designing and implementing edutainment applications over a period of three years, the challenges encountered and the solutions we have used.
BLOKCAR: A Children Entertainment System to Enrich and Enhance Family Car Travel Experience

The research proposes an in-car entertainment system for children to relieve their in-car boredom and further enhance the travel experience. While more and more attention has already been paid on human-car interaction, there is still minimal research considering the interactions between back seat passengers and the car. Many parents reported the quality time they spent with their children in the car was invaluable [7, 23]. Due to the limited space of a vehicle, car traveling is a perfect opportunity to pull a family together and build the memory. However, the travel experience with children is usually not so pleasant for the parents. More than 60% of parents in the survey [6] admitted that traveling without children made them happier. Besides, driving with children also possibly compromise driving safety. We further executed user research to understand real users and their travel experience, especially with entertainment devices. As a result, we identified four significant pain points parents have been encountering. With the findings and insights, we generated the designs iteratively and finally proposed a system composed of three major components – 1. Mobile Application, 2. Interactive Block - BlokCar and 3. AR Interactive Window, to solve the problems, and more importantly, to enhance the riding experience for the children.
A Computer Model for the Analysis of Diversity and Coordination in Orchestration

We introduce a method for computer-assisted analysis of musical texture and orchestration.Our aim is to establish a formal understanding of these symbolic dimensions that could be on par with existing computational approaches to rhythm and pitch.We also propose to investigate the role that texture and orchestration control has in structuring musical form. Our research is based on the theoretical considerations made by Wallace Berry in the classicStructural Functions in Music and the subsequent numerical representation and combinatorial manipulation, grounded on the mathematical theory of integer partitions proposed by Pauxy Gentil-Nunes.To do this, we assumed that each local sonic configuration (an orchestral configuration in the case of symphonic music), or Local Sonic Setup, delineate a sound unit.The qualification of these setups according to the number of sonic resources used and the way in which they are distributed to create more or less polyphonic complexity is information that is added to the other dimensions that contribute to the development of a dynamic of the form through sound.
Voice-Based Bodyweight Training Support System Using Smartphone

Bodyweight training has grown in popularity; it is desirable to be fit and strong. However, training can be dangerous if performed incorrectly. Several systems are used to correct pose during training. However, most require wearable sensors that may interfere with training, or an expensive depth camera. We offer a new form of training support using a smartphone camera and a server. We use a verbal interface to help users to correct their pose and to encourage them. We describe our new system and experimentally evaluate it.
Towards a Predictive Framework for AR Receptivity

Given the sometimes disparate findings and the increasing application of AR in both training and operations, as well as increased affordability and availability, it is important for researchers, user interface and user experience (UI/UX) designers, and AR technology developers to understand the factors that impact the utility of AR. To increase the potential for realizing the full benefit of AR, adequately detailing the interrelated factors that drive outcomes of different AR usage schemes is imperative. A systematic approach to understanding influential factors, parameters, and the nature of the influence on performance provides the foundation for developing AR usage protocols and design principles, which currently are few. Toward this end, this work presents a theoretical model of factors impacting performance with AR systems. The framework of factors, including task, human, and environmental factors, conceptualizes the concept of “AR Receptivity”, which aims to characterize the degree to which the application of AR usage is receptive to the technology design and capabilities. The discussion begins with a brief overview of research efforts laying the foundation for the model’s development and moves to a review of receptivity as a concept of technology suitability. This work provides details on the model and factor components, concluding with implications for application of AR in both the training and operational settings.
Analysis and Feedback of Movement in Manual Assembly Process

A manual assembly has an important role in the manufacturing of products with small lot sizes and high variation. Becoming skilled manual labor requires knowledge transfers offered by human experts through a training process. To reduce the dependency of human experts, this paper introduces a framework called “Virtual Trainer” that incorporates the current state of the art marker-less RGB human pose estimation, activity detection for assembly step recognition, and training feedback through a multi-media presentation includes score evaluation and semantic description of trainee performance. Furthermore, the detailed transcript of each step and 3-D visualization compares to ideal movements also presented. The experimental design for evaluating the effectiveness and hypothesis is given.
Research of Automatic and Dynamic Readable Problem Generation for Electricity Knowledge Based on Forward Physical Rule

Intelligent Education Physics Platform (IEPP) based on visualized knowledge elements can better meet actual needs of teachers and students in the process of curriculum teaching for the acquisition, learning and exploration of subject knowledge. Visualized knowledge element expressions of subject domain knowledge and readable problem's automatic and dynamic generation for given scenario are cornerstone problems of ICAI intelligent education subject platform research. On the background of junior middle school IEPP, combined with structural characteristics of unit knowledge, the theory of automatic and dynamic readable physical problem generation with visualized knowledge element expression under given scenario has been expanded to (junior middle school) electricity unit. A solid foundation has been laid for ICAI electricity problem's readable solution and application with knowledge expression, reasoning, and automatic and dynamic readable problem generation method proposed in this paper. It will play an exemplary role when expanding research works of other unit of knowledge.
System of automatic video stream images evaluation and preprocessing for ADAS

Abstract In this article, the algorithm of preliminary filtering of images of a video stream based on assessment and the subsequent correction of such qualitative characteristics of images as contrast, noise level, sharpness and existence of illumination is considered.
A Multi-swarm Particle Swarm Optimization with an Adaptive Regrouping Strategy for Feature Selection

Feature selection is an important data pre-processing step which aims at removing irrelevant and redundant features. It can enhance the performance of the learning model and reduce the computational complexity. Particle swarm optimization (PSO) has been widely used in feature selection due to its rapid convergence, but it suffers from premature convergence when solving high-dimensional feature selection problem. Hence, this paper proposes a multi-swarm PSO to overcome the existing drawback. The multi-swarm is capable of maintaining good population diversity and shows strong global exploration ability. An adaptive regrouping strategy is proposed to promote the information exchange among the sub-swarms and speed up the convergence speed. Furthermore, a local search operator is hybridized to improve the local exploitation ability. The proposed MSPSO-A is compared with five PSO based wrappers on 11 UCI datasets. Experimental results demonstrate the proposed algorithm can effectively improve the classification accuracy and reduce the number of features. Furthermore, two statistical tests are performed to show the superiority of the proposed method over other methods is significant.
SPECIAL COURSE “MONITORING THE QUALITY OF EDUCATION IN THE CONTEXT OF INTERACTION WITH STAKEHOLDERS” AS A CONDITION FOR TRAINING COMPETENT PROFESSIONALS OF VOCATIONAL EDUCATION

The uniqueness of the special course program “Monitoring the quality of education in the context of interaction with stakeholders” is to take into account the sectoral context and the actual results of monitoring studies of quality training of vocational (professional and technical) education at the Bila Tserkva Institute of Continuing Professional Education. The article considers the purpose, objectives, andragogical principles of building a special course program. The content and logic of the special course, which reflects the movement from the analysis of the concepts of quality of education, quality of educational activities, educational monitoring and monitoring of the quality of professional development, to practice-oriented issues of monitoring research on the quality of education and educational services in the context of interaction with stakeholders. The generalized indicators on results of diagnostics of students of advanced training courses during approbation of a special course “Monitoring of quality of education in the context of interaction with stakeholders” are presented.
Testing Deep Segmentation of Computer Tomography Scans

Given the current relevance of deep learning approaches, their use in segmentation tasks and the fact that they can learn to segment from examples, it is important to assess the quality of the result. In this work we do an assessment of the quality of segmentation of Computer Tomography (CT) sequences of the upper abdomen, testing two popular deep learning networks on the liver. The shape of the liver evolves along the slices of the sequence, it is interesting to test how well the networks may be able to learn the varying shapes. We build two networks, evaluate segmenting the organ from CT scans and draw conclusions regarding merits and limitations. Future work involves more evaluation with larger datasets and considering how to improve the results further.
Bayesian Student Modeling in the AC&amp;NL Tutor

The reasoning process about the level of student’s knowledge can be challenging even for experienced human tutors. The Bayesian networks are a formalism for reasoning under uncertainty, which has been successfully used for various artificial intelligence applications, including student modeling. While Bayesian networks are a highly flexible graphical and probabilistic modeling framework, its main challenges are related to the structural design and the definition of “a priori” and conditional probabilities. Since the AC&NL Tutor’s authoring tool automatically generates tutoring elements of different linguistic complexity, the generated sentences and questions fall into three difficulty levels. Based on these levels, the probability-based Bayesian student model is proposed for mastery-based learning in intelligent tutoring system. The Bayesian network structure is defined by generated questions related to the node representing knowledge in a sentence. Also, there are relations between inverse questions at the same difficulty level. After the structure is defined, the process of assigning “a priori” and conditional probabilities is automated using several heuristic expert-based rules.
Towards Reduction of Software Maintenance Cost through Assignment of Critical Functionality Scores

Software maintenance incurs a significant cost during the life cycle of any software product. This is due to multiple factors such as addition of features demanded by new requirements, modification of existing features triggered by fixing of defects in the code and extending product support to newer platforms. These factors cause the testing phase to be repeated each time thereby increasing the maintenance cost. The quality of the code compounds to the maintenance cost since poorly written code are hard to comprehend and work with. This paper proposes a methodology to reduce the testing effort through successive releases by determining a set of most significant functionalities by computing scores based on the usage frequency, code maintainability and change vulnerability. The scores are recomputed as the software evolves over successive releases to keep the testing effort minimal.
Discovering Unknown Diseases with Explainable Automated Medical Imaging

Deep neural network (DNN) classifiers have attained remarkable performance in diagnosing known diseases when the models are trained on a large amount of data from known diseases. However, DNN classifiers trained on known diseases usually fail when they confront new diseases such as COVID-19. In this paper, we propose a new deep learning framework and pipeline for explainable medical imaging that can classify known diseases as well as detect new/unknown diseases when the models are only trained on known disease images. We first provide in-depth mathematical analysis to explain the overconfidence phenomena and present the calibrated confidence that can mitigate the overconfidence. Using calibrated confidence, we design a decision engine to determine if a medical image belongs to some known diseases or a new disease. At last, we introduce a new visual explanation to further reveal the suspected region inside each image. Using both Skin Lesion and Chest X-Ray datasets, we validate that our framework significantly improves the accuracy of new disease discovery, i.e., distinguish COVID-19 from pneumonia without seeing any COVID-19 data during training. We also qualitatively show that our visual explanations are highly consistent with doctors' ground truth. While our work was not designed to target COVID-19, our experimental validation using the real world COVID-19 cases/data demonstrates the general applicability of our pipeline for different diseases based on medical imaging.
Application of Hermite Functions in Image Reconstruction: Student paper

We present a mathematical technique for using Hermite functions to reconstruct various types of images. Even though the functions are not orthogonal, they can be used in image processing. The functions permit us to get good localization of the signals and it is shown that we get better reconstructed image quality for a larger number of Hermite functions used. Finally, we use a Hermit function frame to decompose and reconstruct a few types of images and compare the results for various number of Hermit functions.
Extended User Interface: NFC-Enabled Product Packaging for Enhanced User Experience

User-centered physical-digital systems let designers create interactive interfaces filled with special moments and experiences, giving brand owners the chance to have profound communication with their consumers. In fact, product’s packaging has recently begun to investigate as one of such interfaces to form a strong link between manufacturers and their end-users. Microprocessors, sensors, actuators and wireless data-exchange supporting chips can be embedded, into packaging design creating an extended user interface – a touchpoint for a visual, tactile and digital encounter with consumers. Near Field Communication (NFC) is one of the rapidly increasing technologies that researchers begin to investigate as a potential tool for enhanced consumer-brand communication. However, although NFC is available in the market since late 2010, the technology is still not widely applied to the packaging industry. As a result, the main purpose of this research is to investigate the current state-of-the-art and potentials of NFC system. The results of this study provide a systematic review of NFC characteristics, including technological capabilities, consumer- and brand-oriented benefits, and technology- and user-centered potential barriers for NFC to become widely accepted. The findings of this study are expected to contribute to a better understanding of the effectiveness of NFC-enabled packaging, allowing brands to dynamically adapt to emerging consumer needs by improving their products and services.
Quantum Computing Future-Proofing What Lies Beyond SuperComputing

The race is on as more computing power is required to solve some of the most complex, dataintensive problems in existence today.How will quantum computing overcome its inherent challenges -cost, footprint, power consumption, temperature requirements, instability -to leapfrog super computers as the platform for the future?See how quantum simulators are helping to overcome those challenges and enabling researchers and end users alike the ability to develop quantum algorithms that will solve problems faster, leading to new pharmaceuticals and medical treatments, improved financial modelling and weather forecasting, cheaper energy production and much more.
A two-stream network with joint spatial-temporal distance for video-based person re-identification

Video-based person re-identification aims to match videos of pedestrians captured by non-overlapping cameras. Video provides spatial information and temporal information. However, most existing methods do not combine these two types of information well and ignore that they are of different importance in most cases. To address the above issues, we propose a two-stream network with a joint distance metric for measuring the similarity of two videos. The proposed two-stream network has several appealing properties. First, the spatial stream focuses on multiple parts of a person and outputs robust local spatial features. Second, a lightweight and effective temporal information extraction block is introduced in video-based person re-identification. In the inference stage, the distance of two videos is measured by the weighted sum of spatial distance and temporal distance. We conduct extensive experiments on four public datasets, i.e., MARS, PRID2011, iLIDS-VID and DukeMTMC-VideoReID to show that our proposed approach outperforms existing methods in video-based person re-ID.