ABSTRACT
NTT Human Informatics Laboratories has been researching and developing technology to reproduce the appearance of an audience enjoying a live-streamed event online remotely from home (remote audience) at the venue where the event is being held (real venue) while harmonizing it with the situation at the real venue. At the 34th Mynavi TOKYO GIRLS COLLECTION 2022 SPRING/SUMMER held on March 21, 2022, a demonstration experiment was conducted to support the excitement of the event by using low-latency video communication and cross-modal sound search to reproduce pseudo cheers at the real venue for both real-venue and remote audience members who could not cheer due to the COVID-19 pandemic. This article introduces the activities of this demonstration experiment. © 2022 Nippon Telegraph and Telephone Corp.. All rights reserved.
ABSTRACT
The novel coronavirus (COVID-19) pandemic has forced people to change their lifestyles and become remote. To enable people to choose a remote lifestyle or combine remote and real-world lifestyles so as to enrich their lives, NTT Human Informatics Laboratories is aiming to enable the Remote World unique to the post-pandemic era in two ways: (i) identifying and analyzing issues specific to new lifestyles from a wide range of perspectives including not only technology but also social science and humanities and (ii) promoting research and development regarding such issues. © 2022 Nippon Telegraph and Telephone Corp.. All rights reserved.
ABSTRACT
During the Covid-19 pandemic Asian-Americans have been targets of prejudice and negative stereotyping. There has also been volumes of counter speech condemning this jaundiced attitude. Ironically, however, the dialogue on both sides is filled with offensive and abusive language. While abusive language directed at Asians encourages violence and hate crimes against this ethnic group, the use of derogatory language to insult alternative points of view showcases utter lack of respect and exploits people's fears to stir up social tensions. It is thus important to identify and demote both types of offensive content from anti-Asian social media conversations. The goal of this paper is to present a machine learning framework that can achieve the dual objective of detecting targeted anti-Asian bigotry as well as generalized offensive content. Tweets were collected using the hashtag #chinavirus. Each tweet was annotated in two ways;either it condemned or condoned anti-Asian bias, and whether it was offensive or non-offensive. A rich set of features both from the text and accompanying numerical data were extracted. These features were used to train conventional machine learning and deep learning models. Our results show that the Random Forest classifier can detect both generalized and targeted offensive content with around 0.88 accuracy and F1-score. Our results are promising from two perspectives. First, our approach outperforms contemporary efforts on detecting online abuse against Asian-Americans. Second, our unified approach detects both offensive speech targeted specifically at Asian-Americans and also identifies its generalized form which has the potential to mobilize a large number of people in socially challenging situations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In the wake of the rapid escalation of COVID-19 in the United States in 2020, Congress was charged with finding ways to effectively disseminate reliable information and encouraging compliance with public health measures in addition to designing policies that would be responsive to the unprecedented needs of those in their districts. Our team quickly transitioned and tailored the structure of our Deliberative Town Halls (DTHs) as to best facilitate the necessary conversations between members of Congress and their constituents: whereas pre-COVID-19 DTHs focused on a single issue with a single member of Congress, the COVID-19 events often featured a bipartisan pair of members, participating alongside subject matter experts. These two-way conversations allowed for members to gain the critical information pertaining to how to develop policies that were responsive to articulated constituents needs and allowed constituents to express their opinions and feelings on COVID-19 related policies, Congress's handling of the pandemic, and the personal struggles they had faced as the effects of the pandemic transpired. © 2022 Association for Computing Machinery.
ABSTRACT
The Covid-19 pandemic has changed all aspects of life, including the world of education globally. The learning methods provided are adapted to existing conditions (online learning). This condition has an impact on student learning outcomes. Besides, another factor that affects student learning outcomes is the gender of students, which impacts student behavior in learning. This study aims to see the effect of the learning methods provided (online and offline) and gender differences on the learning outcomes obtained and the interaction of the two factors. The research design used is a two-way multivariate analysis of variance, with the dependent variable (study exam score in Bahasa Indonesia, Mathematics, and natural science subjects). The results showed that the learning method and gender significantly affected test results in all subjects. However, there is no interaction between these two factors. © 2022 IEEE.
ABSTRACT
COVID-19 is an infectious disease caused by a newly discovered coronavirus called SARSCoV-2. There are two ways of contamination risk, namely spreading through droplets or aerosol-type spreading into the air with people's speech in crowded environments. The best way to prevent the spread of COVID-19 in a crowd public area is to follow social distance rules. Violation of the social distance is a common situation in areas where people frequently visit such as hospitals, schools and shopping centers. In this study, an artificial intelligence-based social distance determination study was developed in order to detect social distance violations in crowded areas. Within the scope of the study, a new dataset was proposed to determine social distance between pedestrians. The YOLOv3 algorithm, which is very successful in object detection, was compared with the SSD-MobileNET, which is considered to be a light weighted model, and the traditionally handcrafted methods Haar-like cascade and HOG methods. Inability to obtain depth information, which is one of the biggest problems encountered in monocular cameras, has been tried to be eliminated by perspective transformation. In this way, the social distance violation detected in specific area is notified by the system to the relevant people with a warning. © 2022 Lavoisier. All rights reserved.
ABSTRACT
The study discusses the essence of digitalization of personnel selection by modern companies in the context of the restrictions associated with the covid-19 pandemic. Today, the development of digital platforms and services in the segment is a topical area, therefore it is recommended to introduce information and digital methods in recruiting. Purpose: Development of the implementation and operation of digital services by the heads of commercial organizations ensures the operational personnel management, reduces the costs and the expenses of contacting HR agencies, and also increases the company's competitiveness in the market. The emphasis in the work is made precisely on these aspects of the study. Methodology: The study used methods of analyzing the experience of Russian companies in the market in 2020, as well as a survey of the heads of 50 trading companies in Nizhny Novgorod. Findings: Based on the conducted two-way analysis, it was decided to use digital services and platforms for personnel selection, however, the respondents insist on a final face-to-face meeting with applicants in order to personally assess their soft-skills, as well as to assess personal qualities. Originality: The digitalization of recruiting in the company will allow attracting a larger number of applicants without territorial restrictions and forming effective teams by a professional. The problem of the study is the digitalization of personnel selection processes, but also the caution of company management to use them independently. The authors see the solution to this problem in the constant updating of digital skills. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The world has suffered a lot from the COVID-19 pandemic. Though vaccines have been developed, we still need to be ready for its variants and other possible pandemics in the future. To provide people with pandemic risk assessments without violating privacy, a Federated Learning (FL) framework is envisioned. However, most existing FL frameworks can only work for homogeneous models, i.e., models with the same configuration, ignoring the preferences of the users and the various properties of their devices. To this end, we propose a novel two-way knowledge distillation-based FL framework, Fed2KD. The knowledge exchange between the global and local models is achieved by distilling the information into or out from a tiny model with unified configuration. Nonetheless, the distillation cannot be conducted without a common dataset. To solve this bottleneck, we leverage the Conditional Variational Autoencoder (CVAE) to generate data that will be used as a proxy dataset for distillation. The proposed framework is firstly evaluated on benchmark datasets (MNIST and FashionMNIST) to test its performance against existing models such as Federated Averaging (FedAvg). The performance of Fed2KD improves by up to 30% on MNIST dataset, and 18% on FashionMNIST when data is non-independent and identically distributed (non-IID) as compared to FedAvg. Then, Fed2KD is evaluated on the pandemic risk assessment tasks through a mobile APP we developed, namely DP4coRUna, which provides indoor risk prediction. © 2022 IFIP.
ABSTRACT
Humans start their day by looking in the mirror at least once before leaving their homes every morning. In addition, they waste some considerable time of their busy workload in front of the mirror. To make this time more productive and useful, there ought to be a system that can be readily conducted, user-friendly, and smart according to the constant progress on the Internet of Things. The intelligent mirror is a new addition to the smart device family, which is a straightforward concept. There will be a screen placed behind a two-way mirror, and this Intelligent Mirror turns our room or bathroom mirror into a personal assistant with artificial intelligence. The purpose is to develop a smart mirror that can automate working humans' busy daily routines and manage their tasks when they spend their time in front of a mirror. To make the most of this moment, users can securely access all the relevant details of the day by looking in the mirror simultaneously. The intelligent mirror, which a single voice command can activate, will significantly help disabled persons and the general. Raspberry Pi has been used to build the proposed intelligent mirror, linked to the digital world via the Internet. The mirror can communicate with the user through voice commands and reply appropriately. The monitoring of emotions and health measuring function will provide a distinctive experience to the users. The mirror will reflect important elements such as weather, date & time, covid-19 situation reports, local news, To-do list, water reminder, home workouts, and meal plans. The mirror can also handle specialized functions such as automating and controlling home IoT devices. © 2021 IEEE.
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The spread of COVID-19 disease has reduced the visitors count to various public places like parks, libraries and museums. Even though Indian Government has relaxed the rules for the public to visit these places during the early 2021, people deny visiting those places due to the fear created by the impact of the disease. This leads to huge revenue loss due to lack of visitors. In order to solve this issue, a safer visiting procedure through a Mobile Application based Secured Smart Museum (MSSM) has been provided. This system strictly monitors the entry of the visitors through two way screening process which ensures the safety of the visitors at the museum. Two-way screening process involves the measuring the temperature of the visitors with the support of IR temperature sensor and monitoring the availability of the face mask with the help of Open Computer Vision. The system also facilitates the users to book the ticket through Mobile Application. This system also alerts the museum's housekeeping department to clean and sanitize the museum based upon the visitors count. In addition to this, our system facilitates the visitors to gain the knowledge about the contents available in the museum through mobile application itself in their own preferred language. © 2022 IEEE.
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Under the ravages of the COVID-19, online education platform business has achieved rapid development, and excessive platform integration and the spread of paid knowledge have also spawned security problems of data sharing between platforms and users. Meanwhile, the development of Internet technology has also increased the difficulty of knowledge safeguarding rights on education platforms and cracking down on piracy. Based on the application characteristics of blockchain technology, this paper proposes a dual authentication mode of node + platform based on hash detection. Through the smart contract provided by Ethereum, data encryption linking and two-way verification between different platforms are realized. After linking to blockchain, data transmitted over the network can be protected from hacking and tampering. The research in this paper has an enlightening effect on promoting the integration development of blockchain technology and emerging industries, and also makes a specific judgment on the next integration development direction. © 2021 ACM.
ABSTRACT
Extract summarization algorithms help identify significant information from the news by extracting meaningful sentences from the original text. The information background existing at the time of the news release often significantly affects its content. Such background can distort the text summarization algorithm working results. The study was conducted with the example of the theme “coronavirus” (COVID-19), which at the time of the study was one of the main topics in news feeds. Experiments were carried out on sports news articles, concerned football. This news area was selected because it is not related to medical topics. The TextRank algorithm for sport news extraction was applied in two ways. First, the key information from the source text of news was extracted. Then, a list of the COVID related words was created and the key information from news without considering words from this list was extracted. Our approach showed that mentioning a popular theme such as COVID that is not related to sports can have a negative impact on the text summarization algorithm. We suggest that to obtain accurate results of the algorithm operation, it is necessary to first compile a dictionary of terms related to the coronavirus theme and then exclude them when identifying the main content of news texts. © 2021, Springer Nature Switzerland AG.