Reviewerly/exHarmony huggingface dataset:
Live Streaming of Face Mask and Body Temperature Detection System using Transfer Learning and IoT

Abstract Since the outbreak of COVID-19 pandemic, enormous development has been done in computer vision systems in face masks and temperature detection. To enforce the mandate for wearing the mask in public places, the authors have suggested a solution using transfer learning (MobileNetV2) architecture as a foundation for image classification. The proposed solution is embedded devices with Raspberry Pi4 and python packages viz TensorFlow, OpenCV, and Keras. In this Work, a combination of transfer learning and IoT has been worked out using live streaming to detect an appropriate use of face masks along with body temperature. This technique includes the Single Shot Single box and Multi-box identification of the face mask. Further, a person can check the Temperature at the entrance. This temperature sensor is in noncontact mode, which senses a temperature from 2 cm to 5 cm and is displayed through LCD. It sends the data to the cloud for visualization and analysis, and an alert message is sent in a real-time environment based on that analysis.
Alpha-rooting and correlation method of image enhancement

This paper analyzes the known method of the Fourier transforms-based alpha-rooting in image enhancement and describes a new method of alpha-rooting, by using the autocorrelation function of the image. The alpha-rooting in the frequency-domain can be described by the Taylor series, as well as in the spatial domain, by using the inverse 2-D DFT. In such a series, the alpha-rooting is the convolution of the image with the series of the autocorrelation functions. The application of the Taylor series in alpha-rooting allows to use the parameterized filters even for parameter alpha in a much larger interval than [0,1]. Examples of application of these two filters for enhancement of the grayscale images are given.
Malayalam Question Answering System Using Deep Learning Approaches

Question Answering (QA) systems attempt to retrieve precise answers to questions posed in natural language by the users. It is a sophisticated form of Information Retrieval (IR) that uses a predefined collection of raw data in natural language. Malayalam is an official language in India, that is not only morphologically rich and agglutinative in nature but is also resource constrained. These aspects of the language make QA in Malayalam very challenging. This paper proposes a deep learning based QA system for Malayalam using techniques such as Long Short-Term Memory Networks (LSTM), Gated Recurrent Unit (GRU), and Memory Network models. Facebook bAbI dataset consisting of 20 tasks with the questions having multiple supporting facts, inductive and deductive reasoning, coreference, etc. have been used to train and test the system. It was observed that the Memory Network model achieved the best average accuracy (80%) among the three models implemented, in retrieving exact answers in Malayalam. This work is unique because all the reported work on Malayalam QA is rule-based, capable of extracting answers to factoid questions only. The proposed system which uses deep learning approaches is scalable and thus capable of enhancing the ongoing research in Malayalam QA along with the development of the Malayalam QA corpus.
Integrating User Experience into Agile : An Experience Report on Lean UX and Scrum

The integration of Agile development and user experience (UX) has received significant attention over the past decade. The literature contains several process models and wide-ranging discussion about the benefits and challenges of this integration. However, academia has given this integration short shrift. In fact, there are very few publications covering educational courses dealing with Agile development and UX. In this paper, we report on our experience of designing and running a graduate software engineering course that covers the integration of Lean UX into Scrum and employs gamification to improve student engagement. We identified six lessons learned that new point to important aspects to be considered when integrating Agile development and UX in academia. For example, we discuss the complexity of managing Lean UX activities in short sprints or how the design of a testable and tactical hypothesis can be one of the most challenging aspects of the Lean UX process.
Competence transfer in virtual realities: Can virtual reality bring products and services together?

BACKGROUND: The virtual and real worlds of work are increasingly merging through digital transformation. This also applies to products and services. Virtual Reality (VR) with all its learning opportunities is a promising technology to improve workflows and enable transparency between different departments and organizations. This transparency is particularly important when it comes to preventing potentially dangerous work situations. OBJECTIVE: We investigate weaknesses in competence transfer processes between computer-aided designers and service employees connected in a hybrid value chain. On the one hand, designers receive only little feedback, hence are missing necessary evaluation to adjust their designs to empirical specifications. On the other hand, service employees, therefore, work with sometimes impractical machine designs which makes their work on-site unergonomic, dangerous, and more difficult. METHODS: We present a design science-driven, empirical approach to provide enhanced competence transfer with the help of VR. Thereby, we evaluate a self-developed VR demonstrator with an iterative approach consisting of 60 qualitative interviews. RESULTS: The developed VR demonstrator supports interorganizational sharing of (tacit) knowledge by enabling designers to take the service perspective and ensuring collaboration across organizational boundaries. By intentionally using VR technology as an interruption to the work, the design can be viewed from a service perspective and evaluated for occupational safety and health issues. CONCLUSIONS: The work process improvements achieved by the VR demonstrator enable early consideration of design issues that are particularly relevant to safety, thus ensuring greater occupational safety and health protection in the processes for service employees.
DESIGN AND EXPERIMENTAL RESEARCH OF HAND–EYE COORDINATION CONTROL SYSTEM FOR ACUPUNCTURE ROBOT

Aiming at the problems of the acupuncture robot’s difficulty in positioning acupoints and poor acupuncture accuracy, a hand–eye coordination control method is proposed in this paper. First, according to the specificity of acupuncture, a label-based acupoint positioning method was designed, and then the task of acupuncture acupoints was transformed into acupuncture with specific shapes of labels. Second, the vision subsystem was designed from detection, to label contour matching, to multi-label recognition based on tracking algorithm. Last, according to the control signal amount processed by the vision subsystem, the image Jacobian matrix, and the strategy of monocular hierarchical control, the main hand–eye coordination problem of the fixed-point needling in the acupuncture task is completed. The experimental results show that the method is used for acupuncture and acupuncture puncture with better puncture effect and higher positioning accuracy, which can meet the needs of acupuncture tasks.
Are there preferred viewing locations in Chinese reading? Evidence from eye-tracking and computer simulations.

The Chinese writing system is different from English in that individual words both comprise one to four characters and are not separated by clear word boundaries (e.g., interword spaces). These differences raise the question of how readers of Chinese know where to move their eyes to support efficient lexical processing? The widely accepted default-targeting hypothesis suggests that Chinese readers direct their eyes to a small number of preferred-viewing locations (PVLs), such as the beginning or middle of upcoming words. In this article, we report two eye-movement experiments testing this hypothesis. In both experiments, participants read sentences comprising entirely two-character words, but either without (Experiment 1) or with (Experiment 2) explicit knowledge of this structure prior to their participation. The results of both experiments indicate the absence of PVLs. Simulations using implemented versions of a simple oculomotor-based hypothesis, two variants of the default-targeting hypothesis, and the hypothesis that saccade lengths are modulated as a function of estimated parafoveal-processing difficulty (i.e., dynamic-adjustment hypothesis) suggest that the latter provides the best account of saccadic-targeting during Chinese reading. These results are discussed in relation to broader issues of eye-movement control during reading and how models of such must be modified to provide more accurate accounts of the reading of Chinese and other languages. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Temporal and Spatial Differences of Urban Ecological Environment and Economic Development Based on Graph Neural Network

The temporal and spatial difference between the urban ecological environment and economic development refers to the unbalanced and insufficient contradiction between the quality of urban ecological environment and the development of economic strength. Based on the relevant theories of urban ecological environment and economic development, this paper explores the development laws of urban ecology and economic development and uses graph neural network algorithm to model the spatial and temporal dependence of the city's ecological environment in a province. The quality data and economic development strength data are analyzed in detail. The analysis results show that the ecological benefit index and economic benefit index of each city in the province have reached above 0.6 after 5 years of development. The level of coordinated urban development has improved significantly compared with 2017. However, in the process of the development of the market economy, it is necessary to rationally adjust the proportion of the secondary industry and the tertiary industry in the urban production structure and continuously promote the balanced development of the economy and ecology.
A Case Study on the Demand Forecasting of the Navy Repair Parts using Machine Learning

The Naval Logistics Command forecasts demand for repair parts by compiling the time series techniques and qualitative judgment of repair parts item managers. However, the time series technique is difficult to forecast temporary and intermittent demand, and there is a problem in which it is difficult for an item manager dealing with more than 4,000 items to accurately grasp the demand information for each item. This paper proposes a demand forecasting method applied with machine learning techniques that have been actively utilized in many fields recently to improve those problems and increase demand forecasting rate. For the purpose of evaluating the proposed method of forecasting demand for repair parts, the accuracy was compared and analyzed by dividing the results of the forecast by item and quantity accuracy by applying the current procedure and method of forecasting demand for repair parts.
Mask Branch Network: Weakly Supervised Branch Network with a Template Mask for Classifying Masses in 3D Automated Breast Ultrasound

Automated breast ultrasound (ABUS) is being rapidly utilized for screening and diagnosing breast cancer. Breast masses, including cancers shown in ABUS scans, often appear as irregular hypoechoic areas that are hard to distinguish from background shadings. We propose a novel branch network architecture incorporating segmentation information of masses in the training process. The branch network is integrated into neural network, providing the spatial attention effect. The branch network boosts the performance of existing classifiers, helping to learn meaningful features around the target breast mass. For the segmentation information, we leverage the existing radiology reports without additional labeling efforts. The reports, which is generated in medical image reading process, should include the characteristics of breast masses, such as shape and orientation, and a template mask can be created in a rule-based manner. Experimental results show that the proposed branch network with a template mask significantly improves the performance of existing classifiers. We also provide qualitative interpretation of the proposed method by visualizing the attention effect on target objects.
HiruXR: a Web library for Collaborative and Interactive Data Visualizations in XR and 2D

HiruXR is a Javascript library aimed at creating collaborative and interactive data visualizations in eXtended Reality (XR). Developers can use it to create environments where multiple users can communicate and collaborate around one or many visualizations. We want to open these environments to users with 2D displaying and interaction capabilities, e.g. allowing them to collaborate from their laptops and phones with others using Head Mounted Displays (HMD). But displaying the same interface is not optimal, users should see an interface according to their device capabilities. Therefore, we are steering the design of HiruXR towards supporting responsiveness without putting all the burden in application developers. The library defines visualization, interaction and collaboration components that adapt to the user device capabilities, i.e. 2D or XR. In this paper, we share the design philosophy, initial implementation examples and lessons learned so far building some of these components for collaboration between VR and 2D devices.
Global Context Vision Transformers

We propose global context vision transformer (GC ViT), a novel architecture that enhances parameter and compute utilization for computer vision. Our method leverages global context self-attention modules, joint with standard local self-attention, to effectively and efficiently model both long and short-range spatial interactions, without the need for expensive operations such as computing attention masks or shifting local windows. In addition, we address the lack of the inductive bias in ViTs, and propose to leverage a modified fused inverted residual blocks in our architecture. Our proposed GC ViT achieves state-of-the-art results across image classification, object detection and semantic segmentation tasks. On ImageNet-1K dataset for classification, the variants of GC ViT with 51M, 90M and 201M parameters achieve 84.3%, 85.0% and 85.7% Top-1 accuracy, respectively, at 224 image resolution and without any pre-training, hence surpassing comparably-sized prior art such as CNN-based ConvNeXt and ViT-based MaxViT and Swin Transformer by a large margin. Pre-trained GC ViT backbones in downstream tasks of object detection, instance segmentation, and semantic segmentation using MS COCO and ADE20K datasets outperform prior work consistently. Specifically, GC ViT with a 4-scale DINO detection head achieves a box AP of 58.3 on MS COCO dataset.
The Effects of COVID 19 on Job Satisfaction among the Library Professionals in Delhi NCR, India

The main purpose of this study examines the Covid 19 situation effect on job Satisfaction among the library professional in Delhi and NCR. The some are many variables use in this study like a gender, age, designation, LIS professional Institutional affiliation, job satisfaction, Job security in organization, agreement with the dissatisfaction of job with their present employers, prime consideration while going for a job change etc. The total response generated 80 43 are male and 37 female, from the data analysis it emerged that very third professional in Delhi NCR enters in to LIS profession by chance 54% and 27% by choice; more than 24 respondents have expressed their willingness to change their present employer, if given better opportunity. More than 30 % professional either fully or partly agree 24% that most of the LIS professionals Delhi and NCR are Current work station is good but, I am expecting library priority in organizations 8% dissatisfied with their employers. All these and many more areas have been explored, which reflect the aspects of job satisfaction and dissatisfaction among LIS professional in Delhi and NCR region.
Interference and integration in hierarchical task learning.

A key feature of human task learning is shared task representation: Simple, subordinate tasks can be learned and then shared by multiple complex superordinate tasks as building blocks to facilitate task learning. An important yet unanswered question is how superordinate tasks sharing the same subordinate task affects the learning and memory of each other. Leveraging theories of associative memory, we hypothesize that shared subordinate tasks can cause both interference and facilitation between superordinate tasks. These hypotheses are tested using a novel experimental task which trains participants to perform superordinate tasks consisting of shared, trained subordinate tasks. Across 3 experiments, we demonstrate that sharing a subordinate task can (a) impair the memory of previously learned superordinate tasks and (b) integrate learned superordinate tasks to facilitate new superordinate task learning without direct experience. These findings shed light on the organizational principles of task knowledge and their consequences on task learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Human Rights Implications of the Digital Revolution in Health Care in India.

The COVID-19 pandemic has ushered in rapidly evolving developments in digital health, and governments around the world are experimenting with different ways of introducing technological tools in the management and delivery of health care services. India, among the countries that faced one of the most serious outbreaks in the second wave of the pandemic, recently rolled out the National Digital Health Mission, which promises an integrated but federated digital architecture and a digital health ecosystem that will solve the information asymmetries of the health care sector in India. While the promises of the National Digital Health Mission are many, India's experience with using another digital tool during the pandemic-the CoWIN portal for vaccine management-alerts us to the human rights concerns of rapid introductions of digital tools to address infrastructural and governance challenges in health care. This paper attempts to take a closer look at these two digital tools and the potential human rights implications of the National Digital Health Mission, particularly for the right to health.
The Management of Educational Talents in Vocational Colleges Based on Wireless Network in the Artificial Intelligence Era

Faced with the AI (artificial intelligence) era, it is both theoretically and practically important to examine the challenges and opportunities that vocational college education in China faces, as well as to actively explore how vocational college education can overcome the challenges and achieve a realistic path. This paper proposes and implements a wireless network-based vocational college talent management system. The main personnel management system primarily completes business operations related to daily personnel file management, while the data mining subsystem mines talent data using the DT (decision tree) classification algorithm to aid talent selection. At the same time, a new topology optimization algorithm based on the principle of minimum rigidity graph is proposed for talent management system wireless network optimization. The maximum-minimum balance criterion and the user-by-user optimization mechanism are designed to obtain the optimal relay selection and subchannel allocation strategy, ensuring the system's reliability and fairness. The optimized algorithm has a user security satisfaction of 0.93 in the range of 250 m, which is higher than other algorithms. It demonstrates that this algorithm's communication link is short, and it has good network connectivity and structural stability.
Towards Generalization in Deepfake Detection

In recent years there have been astonishing advances in AI-based synthetic media generation. Thanks to deep learning-based approaches it is now possible to generate data with a high level of realism. While this opens up new opportunities for the entertainment industry, it simultaneously undermines the reliability of multimedia content and supports the spread of false or manipulated information on the Internet. This is especially true for human faces, allowing to easily create new identities or change only some specific attributes of a real face in a video, so-called deepfakes. In this context, it is important to develop automated tools to detect manipulated media in a reliable and timely manner. This talk will describe the most reliable deep learning-based approaches for detecting deepfakes, with a focus on those that enable domain generalization [1]. The results will be presented on challenging datasets [2,3] with reference to realistic scenarios, such as the dissemination of manipulated images and videos on social networks. Finally, new possible directions will be outlined.
Understanding the Role of Technology-mediated Solutions for Women’s Safety in Urban India

Women’s safety in public spaces has become a prime concern in India. Much of the academic and non-academic efforts have focused on developing technology-mediated solutions such as mobile apps and helplines for combating harassment experiences. However, there is an acute understanding of women’s perceptions and desires around these technological solutions. In this work, we investigate women’s perceptions around technological interventions dedicated to safety, the nature of support they seek from technology, and the characteristics they desire technology to embody to offer support. We draw on interviews with women across urban areas of India, demonstrating how they currently navigate unsafe experiences and also unpack their desires around technological solutions encompassing precautionary, reactionary, and healing support for navigating public spaces in the future. Finally, we conclude with implications focusing on the need for embedded technological solutions and going beyond victim-centric approaches to design technology for safety.
Using Blockchain and Distributed Machine Learning to Evaluate Performance Management and Its Effect on the Construction Industry

Blockchain and machine learning technology are the most important two technologies that help to develop organizational performance in the marketplace. The purpose of this study is evaluation of the performance management and effect of this technology in the construction industry. In the present time, most of the sector has used machine learning and blockchain technology to grow their business. Among these industries, the construction industry is one of the most important sectors that have increased performance through blockchain and machine learning technology. In the construction industry, supply chain management is the most effective aspect. Machine learning and blockchain technology have helped to improve supply chain management. Moreover, the construction industry has improved its performance management through the implementation of 2.0 technology. This research study has selected the secondary qualitative data collection method to get realistic and genuine data for the research study. The teachers have collected the secondary qualitative data from online articles, books, and journals. In this regard, from the secondary qualitative data, the teachers have conducted the thematic analysis to find the authentic result of this study. In this research study, the researcher has analyzed the importance of blockchain and machine learning to develop performance management in an organization. After all analysis, this research study concludes that machine learning and blockchain technology have affected the construction industry positively to manage the performance.