Reviewerly/exHarmony huggingface dataset:
Intrapreneurs: Essential to Building Healthy Academic Communities

Background: Intrapreneurialism is emerging as a powerful force for enhancing organizational transformation and viability in a post-pandemic world. It is the enfranchisement and empowerment of people, liberating them to engage their enterprising talent in the service of creating value for the benefit of the organization (Yashin-Shaw, 2018). Aim: For the purposes of this article, the notion of a healthy academic community refers to the long-term viability, robustness, and relevance of the institution. Being able to foster future success in the face of exponential change is a hallmark of a resilient organization (Duchek, 2020). In this sense, a healthy academic community is one where the institution and its stakeholders can thrive in the face of unprecedented disruptive change and unexpected conditions by adapting appropriately (Burnard, Bhamra, &amp; Tsinopoulos, 2018). The traditional form and function of higher education are being challenged as new technologies are democratizing access to knowledge, learning, and credentialing. A diversity of higher education options have emerged for students beyond the traditional university (Marshall, 2018). Such challenges can pose an existential threat to universities. Results: How can academic communities thrive in the face of these rapid changes? The answer is to liberate and engage the enterprising talent of staff, students, faculty, and the extended community associated with the institution, whether local or global, physical or virtual. Enterprising employees, who think and act like entrepreneurs, are called intrapreneurs. Conclusion: By embracing and supporting intrapreneurs at all levels, academic communities will be well placed to flourish in the new post-pandemic world.
A QUBO formulation for top-τ eigencentrality nodes

The efficient calculation of the centrality or “hierarchy” of nodes in a network has gained great relevance in recent years due to the generation of large amounts of data. The eigenvector centrality (aka eigencentrality) is quickly becoming a good metric for centrality due to both its simplicity and fidelity. In this work we lay the foundations for solving the eigencentrality problem of ranking the importance of the nodes of a network with scores from the eigenvector of the network, using quantum computational paradigms such as quantum annealing and gate-based quantum computing. The problem is reformulated as a quadratic unconstrained binary optimization (QUBO) that can be solved on both quantum architectures. The results focus on correctly identifying a given number of the most important nodes in numerous networks given by the sparse vector solution of our QUBO formulation of the problem of identifying the top- τ highest eigencentrality nodes in a network on both the D-Wave and IBM quantum computers.
Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning

Breast density, which is a measure of the relative amount of fibroglandular tissue within the breast area, is one of the most important breast cancer risk factors. Accurate segmentation of fibroglandular tissues and breast area is crucial for computing the breast density. Semiautomatic and fully automatic computer-aided design tools have been developed to estimate the percentage of breast density in mammograms. However, the available approaches are usually limited to specific mammogram views and are inadequate for complete delineation of the pectoral muscle. These tools also perform poorly in cases of data variability and often require an experienced radiologist to adjust the segmentation threshold for fibroglandular tissue within the breast area. This study proposes a new deep learning architecture that automatically estimates the area-based breast percentage density from mammograms using a weight-adaptive multitask learning approach. The proposed approach simultaneously segments the breast and dense tissues and further estimates the breast percentage density. We evaluate the performance of the proposed model in both segmentation and density estimation on an independent evaluation set of 7500 craniocaudal and mediolateral oblique-view mammograms from Kuopio University Hospital, Finland. The proposed multitask segmentation approach outperforms and achieves average relative improvements of 2.88% and 9.78% in terms of F-score compared to the multitask U-net and a fully convolutional neural network, respectively. The estimated breast density values using our approach strongly correlate with radiologists' assessments with a Pearson's correlation of [Formula: see text] (95% confidence interval [0.89, 0.91]). We conclude that our approach greatly improves the segmentation accuracy of the breast area and dense tissues; thus, it can play a vital role in accurately computing the breast density. Our density estimation model considerably reduces the time and effort needed to estimate density values from mammograms by radiologists and therefore, decreases inter- and intra-reader variability.
Reliability and Validity Study of an Augmented Reality Supported Mathematics Education Attitude Scale

Augmented reality provides an interactive learning medium for students to investigate abstract mathematical concepts in the real environment as well as contributes remarkable results in understanding concepts and supports enjoyment in the learning environment. Due to providing unique and novel opportunities in the learning environment and the sparseness of related studies about determining students’ attitudes regarding AR-based mathematics; in this study, an attitude scale was developed. Its validity and reliability studies were conducted. Six hundred and thirteen middle school students participated in the study. Students’ experiences and opinions regarding AR-supported mathematics education were investigated and an item pool for the scale was formed through content analysis of these opinions. To ensure construct validity, exploratory factor analysis was conducted. To determine the representative power of the factors formed, confirmatory factor analysis was performed. It is seen that the scale developed according to the results of the study is a valid and reliable measurement tool.
Deep Reinforcement Learning-based anomaly detection for Video Surveillance

The anomaly detection in automated video surveillance is considered as one of the most critical tasks to be solved, in which we aim to detect a variety of real-world abnormalities.This paper introduces a novel approach for anomaly detection based on deep reinforcement learning.In recent years, deep reinforcement learning has been achieving a significant success in various applications with data of a high degree of complexity such as robotics and games, by mimicking the way humans learn from experiences.Generally, the state-of-the-art methods classify a video as normal or abnormal without pinpointing the exact location of the anomaly in the input video due to the unlabeled clip-level data in training videos.We focus on adapting the prioritized Dueling deep Q-networks to the anomaly detection problem.This model learns to evaluate the anomaly in video clips by exploiting the video-level label to obtain a better detection accuracy.Extensive experiments on 13 cases class of real-word anomaly show that our DRL agent achieved a near optimal performance with a high accuracy in the real world video surveillance system compared to the state-of-the-art approaches.
Effects of game-based learning in comparison to traditional learning to provide an effective learning environment—A comparative review

With the advancement in technology, various new learning strategies have been developed according to the requirement and learning environments of the learners. The game-based learning strategy is an advanced learning strategy in which a learner is fully engaged in the learning process. This learning strategy completely replaces the traditional learning strategy. A lot of research studies have indicated the improvement in the learning process by game-based learning strategy. The purpose of the current study was to compare the students’ learning outcomes with game-based and traditional learning strategies by descriptive and statistical way. A total of 26 articles published from 2012 to 2021 were selected by following the inclusion and exclusion criteria. The literature review of previous studies indicated that game-based learning strategy proved to be a highly effective learning strategy in numerous disciplines under different learning environments. In the game-based learning strategy, the learners are highly engaged in the learning process. Keywords: Game-based learning, traditional learning strategy, effective, engagement;
An Adaptive Voting Mechanism Based on Relative Density for Filtering Label Noises

Label noise is an important problem in classification. As an efficient method to deal with label noise, the filtering method does not need to estimate the noise rate and does not depend on any loss function. In the relative density (RD) model, the relative density value can be used as a feature to detect noise, but the efficiency of filtering noise in the algorithm depends entirely on the setting of the threshold, which makes the algorithm not generalizable. In order to solve the threshold problem in the RD model, an adaptive voting mechanism based on relative density values is proposed in this paper. In this model, instead of setting a fixed threshold value for the model, the suspected noise is screened out by cross-validation, and then the noise is detected and filtered by the voting mechanism. And through a series of experiments, the effectiveness of our method is demonstrated.
Decoupled Knowledge Distillation

State-of-the-art distillation methods are mainly based on distilling deep features from intermediate layers, while the significance of logit distillation is greatly overlooked. To provide a novel viewpoint to study logit distillation, we reformulate the classical KD loss into two parts, i.e., target class knowledge distillation (TCKD) and non-target class knowledge distillation (NCKD). We empirically investigate and prove the effects of the two parts: TCKD transfers knowledge concerning the "difficulty" of training samples, while NCKD is the prominent reason why logit distillation works. More importantly, we reveal that the classical KD loss is a coupled formulation, which (1) suppresses the effectiveness of NCKD and (2) limits the flexibility to balance these two parts. To address these issues, we present Decoupled Knowledge Distillation (DKD), enabling TCKD and NCKD to play their roles more efficiently and flexibly. Compared with complex feature-based methods, our DKD achieves comparable or even better results and has better training efficiency on CIFAR-100, ImageNet, and MS-COCO datasets for image classification and object detection tasks. This paper proves the great potential of logit distillation, and we hope it will be helpful for future research. The code is available at https://github.com/megvii-research/mdistiller.
Fine-grained Activities of People Worldwide

Every day, humans perform many closely related activities that involve subtle discriminative motions, such as putting on a shirt vs. putting on a jacket, or shaking hands vs. giving a high five. Activity recognition by ethical visual AI could provide insights into our patterns of daily life, however existing activity recognition datasets do not capture the massive diversity of these human activities around the world. To address this limitation, we introduce Collector, a free mobile app to record video while simultaneously annotating objects and activities of consented subjects. This new data collection platform was used to curate the Consented Activities of People (CAP) dataset, the first large-scale, fine-grained activity dataset of people worldwide. The CAP dataset contains 1.45M video clips of 512 fine grained activity labels of daily life, collected by 780 subjects in 33 countries. We provide activity classification and activity detection benchmarks for this dataset, and analyze baseline results to gain insight into how people around with world perform common activities. The dataset, benchmarks, evaluation tools, public leaderboards and mobile apps are available for use at visym.github.io/cap.
Research on Real-time Data Transmission between IoT Gateway and Cloud Platform based on Two-way Communication Technology

To realize the real-time two-way communication between the IoT gateway and the cloud platform and improve the transmission efficiency, this paper adopts the designed open IoT platform, uses the Node.js server as the operating platform, and uses the Redis database fast access technology. The design is based on Modularized processing of real-time two-way communication system of Internet of Things gateway and resource platform based on Socket.IO communication protocol. Compared with the traditional Internet of Things communicationprotocol, the advantage of Socket.IO is that only a handshake between the client and the server is needed to quickly establish a Socket two-way channel, which can realize the real-time twoway transmission of data and effectively save broadband resources.Thereby improving the system's real-time update efficiency of the data model, and ensuring the stability and reliability of the system.For the system function and performance test, the experimental results show that the use of Socket.IO two-way communication technology can realize the simultaneous update of the physical model of the IoT gateway and the virtual model of the IoT cloud platform, and effectively improve the data transmission efficiency of the system.
E-ADMINISTRATION SYSTEM FOR EFFECTIVITY SCHOOL ADMINISTRATION IN PANDEMIC COVID-19 ERA

The existence of internet in modern era makes society easy to do their activities. All aspect of human life is based on online or connected to internet. The development of e-administration in school administration is one of them. This study was descriptive qualitative which aims to describe and to explain school administration concept based online to ease school management in digital era. Source of data divided into two, including document and informants referring to education expert, education implementer unit and society as education user. Technique of collecting the data were content analysis and in-depth interview. Triangulation of source of data technique were used to validate the data. The result shows that there are some school administration system based online to ease school administration process. They are school registration based online (e-registration), school payment based online integrated with certain bank account (e-school payment), teaching journal based online (e-journal), and student achievement report based online (e-raport). Besides, school is also facilitated by school administrator which can be contacted via whatsapp and telegram applications to ease education user on asking for information about school and education
SSDT: Distance Tracking Model Based on Deep Learning

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and population vulnerability increased all over the world due to lack of effective remedial measures. Nowadays vaccines are available; but in India, only 18.8% population has been fully vaccinated till now. Therefore, social distancing is only precautionary norm to avoid the spreading of this deadly virus. The risk of virus spread can be avoided by adhering to this norm. The main objective of this work is to provide a framework for tracking social distancing violations among people. This paper proposes a deep learning platform-based Smart Social Distancing Tracker (SSDT) model which is trained on MOT (Multiple Object Tracking) datasets. The proposed model is a hybrid approach that is a combination of YOLOv4 as object detection model merged with MF-SORT, Kalman Filter and brute force feature matching technique to distinguish people from background and provide a bounding box around these. Further, the results are also compared with another model, namely, Faster- RCNN in terms of FPS (frames per second), mAP(mean Average Precision) and training time over the dataset. The results show that the proposed model provides better and more balanced results. The experiment has been carried out in challenging conditions including, occlusion and under lighting variations with mAP of 97% and a real-time speed of 24 fps. The datasets provide numerous classes and from all the classes of objects, only people class has been used for identifying people in a closet. The ultimate goal of the model is to provide a tracking solution that will be helpful for different authorities to redesigning the layout of public places and reducing the risk. This model is also helpful in computing the distance between two people in an image and the results confirm that the proposed model successfully distinguishes between individuals who walk too close or breach the social distancing norms.
Research on Intelligent Video Detection of Small Targets Based on Deep Learning Intelligent Algorithm

Compared with the traditional object detection algorithm, the object detection algorithm based on deep learning has stronger robustness to complex scenarios, which is the hot direction of current research. According to the process characteristics of the object detection algorithm based on deep learning, it is divided into two-stage object detection algorithm and single-stage object detection algorithm, focusing on the problems solved by some classical algorithms and their advantages and disadvantages. In view of the problem of object detection, especially small object detection, the commonly used data sets and performance evaluation indicators are summarized; the characteristics, advantages, and detection difficulties of various common data sets are compared; the challenges faced by commonly used object detection methods and small object detection are systematically summarized; the latest work of small object detection methods based on deep learning is sorted out; and the small object detection methods based on multiscale and small object detection methods based on super-resolution are introduced. At the same time, the lightweight strategy for target detection methods and the performance of some lightweight models are introduced; the characteristics, advantages, and limitations of various methods are summarized; and the future development direction of small object detection methods based on deep learning is prospected.
Application of Improved VMD-LSTM Model in Sports Artificial Intelligence

In recent years, with the rapid development of a new generation of artificial intelligence technology, how to deeply apply artificial intelligence technology to physical education and break through the limitations of time-space scenarios and knowledge transfer methods in traditional models has become a key issue in intelligent physical education in the era of artificial intelligence. In order to realize the online monitoring of wearable devices with artificial intelligence in sports and overcome the problem of low recognition accuracy of electrocardiogram, blood oxygen, and respiratory signals in many cases, this paper proposes a combination of variational modal decomposition based on the maximum envelope kurtosis method. Long-short-term neural network (VMD-LSTM) monitoring method for wearable sports equipment. Through experimental analysis and verification, the current signal of the VMD model shows a trend of fluctuating from large to stable and then to large with motion, while the training accuracy of LSTM after the 150th iteration is 94.09%, which shows that the coupling model VMD LSTM can better predict the direction of sports artificial intelligence. In addition, although the training time of the BP neural network is shorter than that of the LSTM model, there is a large gap between the recognition effect and the LSTM, and there are also large differences between different neural network structures. This shows that the VMD-LSTM model has broad application prospects in such models.
PENDETEKSIAN PENGGUNAAN MASKER WAJAH DENGAN METODE CONVOLUTIONAL NEURAL NETWORK

“Face Mask Detection Using the Convolutional Neural Network” is a PC based program that aims to detect and classify human beings whether a person is using a mask or not with access through a webcam camera. This program is created using the Python language with several libraries. The classification of face masks uses the Convolutional Neural Network method with the MobileNetV2 architecture. Meanwhile, human face detection uses the Haarcascade Classifier. How the program works is by accessing the connected camera and if the person detected is wearing a mask, the person will be labeled "using a mask" and given a green box to mark the detection along with the analysis value, whereas if not, it will be labeled "not using a mask" and a red box with also the predicted value. From the test results, it can be proven that the accuracy program is good enough to detect the use of face masks with an average object detection accuracy of 88.53% and the classifier for the use of mask an average of 84.45%.
Modelling of a GSA-based framework of an agent based system within parameter boundaries: applications to robotics and automatic voltage regulator systems

The enormous potential of the agent-based nature-inspired algorithm named the gravitational search algorithm (GSA) remains untapped, so it is the subject of this work. Existing GSA has provided ways to select the super-agents or prune the poor-performing agents. Here, a holistic framework is proposed by introducing a survival boost which re-establishes self-significance of a poor-performing agent and liberates all agents from an infinity trap for acceleration. It restricts the agents within the search space whose violation produces infeasible solutions. The analysis is extended to solve a number of benchmark test functions, an inverse kinematic problem of a 7 DOF robot manipulator, and a voltage regulation problem of an automatic voltage regulator system. The results and comparative studies show better performances in than some of the popular methods from existing literature (e.g., existing GSA, chaotic GSA, different variants of particle swarm optimisation, artificial bee colony, differential evolution, Jaya algorithm).
Design and Development of Interactive Systems for Integration of Comparative Visual Analytics in Design Workflow

In architectural design, data-driven processes are increasingly utilized in creating and selecting design alternatives.Multiple design-aid systems that support such processes exist.Still, these systems dominantly support parametric modelling only or lack sufficient support for organizing, scanning and comparing multiple alternatives in the process of their creation while considering both their forms and performance data.In this paper, we argue that (a) evaluating and selecting potential alternatives must take place in the same context they are created and explored, (b) interactive data visualizations can provide real-time feedback about various aspects of design alternatives, and they should be incorporated as early in the design process as possible, and (c) design environment must enable comparing design alternatives as an integral part of the design workflow.We call our approach "comparative design analytics," which aims to identify, develop, and validate practical key features of visualization tools for assisting designers in analyzing and comparing multiple solutions with their data.We present D-CAT as a visualization prototype tool integrated with an existing CAD application.D-CAT acts as a platform for generating knowledge about using interactive data visualization for comparing design alternatives.Our goal is to transfer the findings from evaluating this interface to developing practical applications for real-world use.