Effectiveness of Train-the-Trainer Workshops in Intelligent Industrial Robotics

In this work we investigate the effectiveness of two train-the-trainer workshops on intelligent industrial robotics. The two workshops, which took place in summer 2021 in Tennessee and Alabama, were the first of a series of six workshops. A total of 32 persons applied to the two summer workshops from 10 states, of whom 15 attended and successfully completed the workshops. Evaluation results show that the participants' knowledge on industrial robotics significantly improved after the workshops, and the vast majority indicated that the training will be used in their home institutions. The major challenge faced during the workshops was the spread of the delta variant of CoVid-19 at the time the workshops were scheduled to take place, and the wide diversity of the educational background of participants. © 2023 IEEE.

Tune Down the Misinformation, Please: Generating Corrective Messages for COVID-19 Misinformation

The ongoing COVID-19 pandemic drastically changed our lives in multiple aspects, one of which is the reliance on social media during quarantine, both for social interaction and information-seeking purposes. However, the wide dissemination of misinformation on social media has impacted public health negatively. Previous studies on COVID-19 misinformation mainly focused on exploration of impacts and explanation of motivations, with few exceptions. In this study, we propose an analytical pipeline that generates corrective messages toward COVID-19 misinformation in a semiautomatic fashion, and then evaluate it against a large amount of data. Both the automated and manual evaluation results suggest the efficiency of the proposed pipeline, which can be used in combination with human intelligence by individuals and public health organizations in fighting COVID-19 misinformation. © 2022 IEEE Computer Society. All rights reserved.

Encouraging Crowd Avoidance Behavior using Dynamic Pricing Framework Towards Preventing the Spread of COVID-19

In the COVID-19 epidemic, balancing a trade-off between preventing the spread of infection and maintaining economic activity is a global challenge. Based on the idea that avoiding crowds leads to the prevention of the spread of infection, we propose to leverage a dynamic pricing method to level out congestion with an aim to balance the trade-off between preventing the spread of infection and economic activity. In our method, reward points are provided according to the degree of congestion in stores to encourage customers to visit stores at less crowded times to avoid crowds. Since store congestion is greatly affected by movement restrictions such as a state of emergency, we propose a demand prediction model that takes into account the biases of the data acquisition circumstances. In an offline evaluation, we validated the effectiveness of the proposed unbiased demand prediction model based on the data from an actual campaign conducted for more than 7 months in Kyushu University. The evaluation results showed that our unbiased model reduced the prediction error by up to relatively 25.0% compared with the model that does not consider biases. Our system has been deployed in our closed service since December, 2021. Online evaluation result showed that our application improved conversion rate by 12.0% and reduced cost per acquisition by up to 11.6%. © 2022 IEEE.

Research on Distribution Capacity of Fresh Food E-commerce Company Based on Fuzzy Synthetic Evaluation

With the introduction of the new retail model and the explosion of COVID-19, more and more community residents are using fresh food e-commerce companies to buy the fresh produce they need on a daily basis. In this paper, three fresh produce e-commerce companies with a high market share were selected as research subjects and their company financial reports were used as raw data, and then the intra-city distribution capability of fresh food e-commerce companies was studied based on the raw data. Firstly, the weights of the primary and secondary indicators were calculated using the hierarchical analysis and entropy methods respectively, and the weights were fused. After that, a fuzzy synthetic evaluation of each of the three fresh food e-commerce companies was conducted, which in turn quantified the evaluation results. Finally, the quantified evaluation results are compared and appropriate recommendations are given for each fresh food e-commerce company. © 2022 IEEE.

On-line Teaching Appraisal System of Vocational Education Based on Convolutional Neural Network and Big Data Platform

Covid-19 has undeniably accelerated the process of world education informatization. How to evaluate result of On-line Teaching through scientific and effective means is an important issue in the development of on-line education. On the "JZ on-line"platform, this paper constructs an On-line Teaching Appraisal system of vocational education based on convolutional neural network algorithm, which mainly includes five parts: evaluation personnel management, evaluation index management, On-line Teaching evaluation, evaluation content and evaluation result analysis. Based on the content of an indicator Appraisal system of On-line Teaching, a convolution neural network On-line Teaching evaluation model is constructed. Through the analysis of the data results of the system test, the system can obtain scientific and objective On-line Teaching evaluation results, and can put forward teaching problems and suggestions. It can also further analyze the distribution of different disciplines, different majors and different courses in the overall On-line Teaching evaluation. The system error is small and the precision is high, which is helpful to improve the quality of on-line course construction and the professional quality of teachers' educational technology. © 2022 ACM.

Transitioning Sustainable Manufacturing Undergraduate Research Experiences from an In-Person to a Virtual Format

The Summer Academy in Sustainable Manufacturing is an NSF Research Experience for Undergraduates (REU) Site that offers unique summer undergraduate research experiences in the challenging field of sustainable manufacturing to national undergraduate students from two and four-year institutions. The objective of the REU Site is to introduce undergraduate students to the forefront of sustainable manufacturing research and to provide participants with the skills and pathways to pursue advanced degrees or careers in sustainable manufacturing. The intensive ten-week summer research and professional development experience hosts ten students per summer and addresses National Science Foundation priority areas such as advanced manufacturing and sustainability. Undergraduate research projects in the REU site address manufacturing process, manufacturing system, and fundamental sustainable manufacturing principles within continuous (e.g. chemical manufacturing) and discrete (e.g. automotive manufacturing) manufacturing systems. Projects are further associated with topics that cross cut the aforementioned thrust areas such as, emerging and environmentally benign materials manufacturing, sustainable process design and control, and life-cycle engineering and value recovery. Traditionally, this REU Site hosts in-person undergraduate researchers to undertake their research projects in a faculty mentor laboratory during the program. However, the COVID-19 pandemic necessitated that the REU Site program be held virtually during the summer of 2021. This poster and summary paper detail the steps taken to transition REU Site program activities to a virtual environment and post-program evaluation results of participant experiences. Evaluation results of the virtual program are compared to evaluation results of prior in-person Summer Academy in Sustainable Manufacturing programs. © American Society for Engineering Education, 2022.

Flipped Classroom to increase the Student Success in Manufacturing Courses

Delivering hands-on design and manufacturing courses is challenging in several lecture and laboratory settings. This type of instruction is even harder lately due to higher education institutions' strict COVID-19 policies and procedures, since offering the courses in on-ground settings is not a possibility. One method practiced by a high number of educators to meet course learning outcomes and ABET student outcomes is to implement the Flipped Classroom technique. In a Flipped Classroom, course lectures and laboratories are provided to students earlier than the class time. Then, class times are used to provide more practice and content so that students can learn more in their regular lectures and lab hours. This paper reports the structure of a few Flipped Classroom courses from a diverse group of institutions and the evaluation results received from these courses. © American Society for Engineering Education, 2022

PowerQoPE: A Personal Quality of Internet Protection and Experience Configurator

Security configuration remains obscure for many Internet users, especially those with limited computing skills. This obscurity exposes such users to various Internet attacks. Recently, there has been an increase in cyberattacks targeted at individuals due to the remote workforce imposed by the COVID 19 pandemic. These attacks have exposed the inefficiencies of the non-human-centric implementation of Internet security mechanisms and protocols. Security research usually positions users as the weakest link in the security ecosystem, making system and protocol developers exclude the users in the development process. This stereotypical approach has negatively affected users’ security uptake. Mostly, security systems are not comprehensible for an average user, negatively affecting performance and Quality of Experience. This causes the users to shun using security mechanisms. Building on human-centric cybersecurity research, we present a tool that aids in configuring Internet Quality of protection and Experience (referred to as PowerQoPE in this paper). We describe its architecture and design methodology and finally present evaluation results. Preliminary evaluation results show that user-centric and data-driven approaches in the design of Internet security systems improves users’ Quality of Experience. The controlled experiment results show that users are not really stupid;they know what they want and that given proper security configuration platforms with proper framing of components and information, they can make optimal security decisions. © 2022, IFIP International Federation for Information Processing.

A Physician Assisting COVID-19 Diagnosis System to Categorize Positive Cases using Radiology Images

The objective of the proposed work deals with assisting the doctors by providing the required pre-diagnosis data of COVID-19 patients using radiology images of the targeted patients. A machine learning approach is utilized to evaluate the radiology images of COVID-19 patients which performs preprocessing, segmentation, feature extraction and classification. The proposed work also deals with predetermined evaluation results of COVID-19 patients which give the stages of the COVID-19 patients stating from STAGE 1 through STAGE 4. It will help the physicians for the easy diagnosis of the COVID-19 in patients. A conventional machine learning approach based classification is performed in first pass which discriminates the patients as normal patients or COVID-19 positive patients. Initially, the pre-processing of the input radiology images is carried out to enhance the quality of the images. Second order statistical textural features are extracted using Gabor filter bank Finally, the extracted features are used to classify the COVID-19 positive and negative patients using a simple decision tree classifier. During second pass, the affected portion of the lung is segmented, and amount of infection is estimated through the evaluation of length and width of the affected lung portions. Now, the COVID-19 positive cases will be given higher priority to undergo second level of diagnosis and treatment processes by the physicians whereas the COVID-19 negative cases will undergo for continuous observation. Thus, the proposed diagnosis system will help the physician to speed up their service towards COVID-19 positive patients. © 2021 IEEE.

Hybrid learning during the COVID-19 pandemic of engineering students at KMITL, Thailand

This article presents the hybrid learning model during the COVID-19 for students of Agro-Industrial Systems Engineering who enrolled in the course embedded system, course code 01386304, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang for 29 students by selecting a specific sample group. The researcher presented a hybrid learning model during the COVID-19, where students were orientated for the same understanding and followed by teaching as active learning formed project-based learning. Finally, we assessed five aspects of hybrid learning satisfaction during the COVID-19. The evaluation results revealed that the students' satisfaction was at a good level and their arithmetic mean was 4.206 and the standard deviation is equal to 0.74 (X = 4.206, S.D. = 0.74) © 2021 IEEE.

Ixamed's submission description for WMT20 Biomedical shared task: Benefits and limitations of using terminologies for domain adaptation

In this paper we describe the systems developed at Ixa for our participation in WMT20 Biomedical shared task in three language pairs, en-eu, en-es and es-en. When defining our approach, we have put the focus on making an efficient use of corpora recently compiled for training Machine Translation (MT) systems to translate Covid-19 related text, as well as reusing previously compiled corpora and developed systems for biomedical or clinical domain. Regarding the techniques used, we base on the findings from our previous works for translating clinical texts into Basque, making use of clinical terminology for adapting the MT systems to the clinical domain. However, after manually inspecting some of the outputs generated by our systems, for most of the submissions we end up using the system trained only with the basic corpus, since the systems including the clinical terminologies generated outputs shorter in length than the corresponding references. Thus, we present simple baselines for translating s between English and Spanish (en/es);while for translating s and terms from English into Basque (en-eu), we concatenate the best en-es system for each kind of text with our es-eu system. We present automatic evaluation results in terms of BLEU scores, and analyse the effect of including clinical terminology on the average sentence length of the generated outputs. Following the recent recommendations for a responsible use of GPUs for NLP research, we include an estimation of the generated CO2 emissions, based on the power consumed for training the MT systems. © 2020 Association for Computational Linguistics