Reviewerly/exHarmony huggingface dataset:
Using structural equation modelling for understanding relationships influencing the middle school technology teacher’s attitudes toward STEAM education in Korea

Abstract The purpose of this study was to investigate the structural relationship between technology teachers’ attitudes toward STEAM education and the cooperation of instructional resources collaboration, school educational change receptivity, and instructional expertise and instructional efficacy. The population comprised about 3500 technology teachers working in middle schools in Korea, and the sample size was set at 650. Data were collected in parallel with postal surveys and online surveys. A total of 650 copies were distributed, of which 409 were recovered, and 283 copies were used for analysis after data cleaning. The Mplus 7 program was used for data analysis, and the significance level was set at 5%. First, we found that the suitability of the structural model established to clarify technology teachers’ attitudes toward STEAM education, instructional resource collaboration, school educational change receptivity, instructional expertise, and instructional efficacy was generally good, so the relationship between the variables was reasonably confirmed. Second, school educational change receptivity and instructional efficacy were found to have a direct effect on attitudes toward STEAM education. Third, the teachers’ instructional resources collaboration was confirmed to influence their attitudes toward STEAM education by mediating the school educational change receptivity and instructional efficacy, and their instructional expertise mediated their instructional efficacy. Fourth, school educational change receptivity was confirmed to influence attitudes toward STEAM education through the dual media of instructional expertise and instructional efficacy. Finally, instructional resource collaboration was confirmed to influence attitudes toward STEAM education through school educational change receptivity, instructional expertise, and instructional efficacy.
Identity Governance Framework for Privileged Users

Information technology companies have grown in size and recognized the need to protect their valuable assets. As a result, each IT application has its authentication mechanism, and an employee needs a username and password. As the number of applications increased, as a result, it became increasingly complex to manage all identities like the number of usernames and passwords of an employee. All identities had to be retrieved by users. Both the identities and the access rights associated with those identities had to be protected by an administrator. Management couldn’t even capture such access rights because they couldn’t verify things like privacy and security. Identity management can help solve this problem. The concept behind identity management is to centralize identity management and manage access identity centrally rather than multiple applications with their authentication and authorization mechanisms. In this research work, we develop governance and an identity management framework for information and technology infrastructures with privileged access management, consisting of cybersecurity policies and strategies. The results show the efficiency of the framework compared to the existing information security components. The integrated identity and access management and privileged access management enable organizations to respond to incidents and facilitate compliance. It can automate use cases that manage privileged accounts in the real world.
Eyewear 2021 The Forth Workshop on Eyewear Computing – Augmenting Social Situations and Democratizing Tools

Head-worn sensing, especially embedded in augmented and virtual reality (AR/VR) head-mounted displays and smart glasses is currently increasingly moving away from niche applications and small-scale research prototypes to large-scale consumer adoption (e.g. Oculus Quest 2, Hololens 2, J!NS MEME, Bose Frames). Significant progress in sensing technologies and modalities have lead to a constant increase of commercially available products and unobtrusive, affordable research prototypes. These recent advances allow us to extend the Eyewear Community to enable large scale in-situ studies, as one of the favored research methodologies in Ubiquitous Computing. One manifestation of this can already be observed in large scale dataset recording, eyewear student competitions and programming seminars.
Efficient Visual Pretraining With Contrastive Detection

Self-supervised pretraining has been shown to yield powerful representations for transfer learning. These performance gains come at a large computational cost however, with state-of-the-art methods requiring an order of magnitude more computation than supervised pretraining. We tackle this computational bottleneck by introducing a new self-supervised objective, contrastive detection, which tasks representations with identifying object-level features across augmentations. This objective extracts a rich learning signal per image, leading to state-of-the-art transfer accuracy on a variety of downstream tasks, while requiring up to 10x less pretraining. In particular, our strongest ImageNet-pretrained model performs on par with SEER, one of the largest self-supervised systems to date, which uses 1000x more pretraining data. Finally, our objective seamlessly handles pretraining on more complex images such as those in COCO, closing the gap with supervised transfer learning from COCO to PASCAL.
Preventing translation quality deterioration caused by beam search decoding in neural machine translation using statistical machine translation

Decoding is an important part of machine translation systems, and the most popular inference algorithm used here is beam search. Beam search algorithm improves translation by allowing a larger search space to be traversed than greedy search. However, as the beam width increases, the translation performance declines after a certain point in neural machine translation (NMT). This problem is usually not observed in statistical machine translation (SMT) due to the decoding method. This paper proposes a hybrid system-based method that uses SMT predictions to prevent quality deterioration in the beam search algorithm used in NMT decoding. Our approach is based on the reranking n-best list of NMT according to the SMT system translation sentence. We propose two different algorithms for reranking NMT n-best lists. The first algorithm uses the length information of the SMT outputs. In contrast, the second uses a word-based similarity approach with the Jaccard Index, the Dice’s Coefficient, and the Overlap Coefficient. Experiments on three different language pairs show that the method we propose prevents the decrease in translation quality and produces a gain of 1.3 BLEU and 1.6 METEOR for different beam sizes and 1.8 BLEU and 2.1 METEOR average scores compared to the baseline results.
Proof-of-Work Cryptocurrencies: Does Mining Technology Undermine Decentralization?

Does the proof-of-work protocol serve its intended purpose of supporting decentralized cryptocurrency mining? To address this question, we develop a game-theoretical model where miners first invest in hardware to improve the efficiency of their operations, and then compete for mining rewards in a rent-seeking game. We argue that because of capacity constraints faced by miners, centralization in mining is lower than indicated by both public discourse and recent academic work. We show that advancements in hardware efficiency do not necessarily lead to larger miners increasing their advantage, investment in new hardware contributes to decentralization but rather allow smaller miners to expand and new miners to enter the competition. Our calibrated model illustrates that hardware efficiency has a small impact on the cost of attacking a network, while the mining reward has a significant impact. This highlights the vulnerability of smaller and emerging cryptocurrencies, as well as of established cryptocurrencies transitioning to a fee-based mining reward scheme.
Parallel Medical Imaging: An ACP-Based Approach for Intelligent Medical Image Recognition with Small Samples

The deep learning methods trained with large-scale manually annotated datasets have led to significant breakthroughs in medical image community. However, obtaining such datasets remains a challenging work in medical domain. In this paper, we propose an ACP-based approach named parallel medical imaging (PMI) for analyzing and addressing the small sample problem of medical image recognition. Firstly, we present the basic framework and key techniques of PMI, in which the Artificial imaging systems are constructed to effectively augment the “medical small sample”, the Computational experiments are carried out for designing and evaluating the visual models trained with small samples, and the Parallel execution is conducted to achieve closed-loop interaction and optimization in the domain-knowledge guidance. Furthermore, we illustrate a preliminary implementation and application of PMI approach through the case study of mammogram analysis.
Towards the Development of an Adaptive E-learning System with Chatbot Using Personalized E-learning Model

Although there is no distinctive header, this is the abstract. This submission template allows authors to submit their papers E-learning has become one of the most used electronic systems in the field of education. Although it is beneficial, there are still some lacking capabilities and considerations that can negatively affect the performance of the students. This leads to the innovation that makes e-learning systems adaptive to the users' personality, knowledge, behavior, interest, or preferences, the system is called personalized e-learning system. This survey paper aims to provide the general parameters in creating a personalized e-learning system based on the 150 research papers collected, and a timespan of 2016 to 2020 as a condition. Through a series of literature reviews of research papers published in the last five years, also related to personalized e-learning systems, this paper presents the common components, tools and algorithms, and learning model that are generally used in developing a personalized e-learning system to help as reference in developing more effective personalized e-learning systems. Moreover, considering the findings of this study, this paper has proposed developing a hybrid e-learning system with a chatbot.
Application of Artificial Intelligence Voice Technology in Radio and Television Media

Abstract With the application of artificial intelligence in various fields, more and more people see the contribution of artificial intelligence technology to the development of the industry, and put more energy in the research of artificial intelligence language, and strive to provide better technical support for the development of more industries. In radio and television media, artificial intelligence voice technology can play a very important value, it can effectively improve the efficiency and quality of traditional audio work, optimize the singing system, broadcasting system and retrieval system, so as to provide better service quality for the masses. This paper discusses the application and development of artificial intelligence technology based on the fusion media environment, analyzes the application and development of artificial intelligence technology in promoting production efficiency, intelligent robot writing, intelligent face recognition, intelligent speech semantic recognition, intelligent OCR recognition, automatic broadcast and other aspects. These AI applications provide efficient and secure support for services, greatly improving service efficiency.
S2 Reducer: High-Performance Sparse Communication to Accelerate Distributed Deep Learning

Distributed stochastic gradient descent (SGD) approach has been widely used in large-scale deep learning, and the gradient collective method is vital to ensure the training scalability of the distributed deep learning system. Collective communication such as AllReduce has been widely adopted for the distributed SGD process to reduce the communication time. However, AllReduce incurs large bandwidth resources while most gradients are sparse in many cases since many gradient values are zeros and should be efficiently compressed for bandwidth saving. To reduce the sparse gradient communication overhead, we propose Sparse-Sketch Reducer (S2 Reducer), a novel sketch-based sparse gradient aggregation method with convergence guarantees. S2 Reducer reduces the communication cost by only compressing the non-zero gradients with count-sketch and bitmap, and enables the efficient AllReduce operators for parallel SGD training. We perform extensive evaluation against four state-of-the-art methods over five training models. Our results show that S2 Reducer converges to the same accuracy, reduces 81% sparse communication overhead, and achieves 1.8× distributed training speedup compared to state-of-the-art approaches.
Identification of Relevant Medical Parameter Values in Information Systems using Fuzzy Approach

Feature selection is an important process in machine learning. It aims to identify relevant features that describe the problem in the context of the categories distinguished in the problem domain. This paper presents the results of a study on the feasibility of using a discretization and value fuzzification process for numerical attributes to identify significant ranges of attribute values. In medicine it is applicable to identify significant values of parameters, medical indicators so that the size of medical examinations can be reduced and appropriate interpretation of symptoms can be created. The results obtained show that this is possible without losing the quality of classification while reducing the number of attributes. Moreover, by analyzing the obtained value intervals, it is possible to detail the feature selection process and detect irrelevant features not previously identified.
IoT based crowd estimation and stranger recognition in closed public areas

The population has been marginally increasing over the decade, thereby increasing the demand for resources and outgrowth of several diseases. One such significant year, 2020, has revolutionized the globe and put forth a different trend of living. Occasional public gathering around the world has been a sign of importance and inevitable circumstances. Managing the crowd and adhering to the ethics and norms of government is the reason behind effective diversion of resources and appropriate strategy in reducing human density in a particular location. In this year of 2020, every individual of this world is facing a problem in many issues. But the pandemic COVID19 made the situation of the individual even worse. The place where every individual can access without any restriction is public places. But gathering of peoples in the public places will lead to crowd formation. Government has advised not use the public places for certain period of time (lockdown) in order to avoid the crowd formation. This situation could still exist for few more days. Once the lockdown gets over, the crowd in the public places will get increased day by day. Peoples have waited for this situation for a long time, to get chill out, to breathe fresh air present outside, to have a meet up with their friends etc. Hence, the spreading of virus cannot be predicted and it would vanish after the lockdown comes to an end. This project has been proposed to avoid the crowd formation. Our ideology mainly focuses on closed public spaces like shopping complex, temples, sports arena and malls. The existing methodology of people count in public places doesn't possess the ideology of witnessing the count at any place and any instant of time. This project pays attention to this particular ideology of assisting people to choose a better public place based on their desire even before visiting the place with the help of the information provided to them via application. The process is achieved by implementing two IP cameras in the entry point and exit point of the closed public places. The person detected in the camera, is detected and recognized using Haar Cascade algorithm. The count value of people and the recognition of stranger(law breaker) is achieved using this algorithm. The count value and stranger recognition will be directed to the cloud storage as a real time value. Then the mobile webpage developed will access the data from cloud storage and gives the real time head count of people in particular public spaces. The stranger is recognized and notified to the authenticated user via mail. Henceforth, the crime rates in the city will be reduced.
Switchable K-class Hyperplanes for Noise-Robust Representation Learning

Optimizing the K-class hyperplanes in the latent space has become the standard paradigm for efficient representation learning. However, it’s almost impossible to find an optimal K-class hyperplane to accurately describe the latent space of massive noisy data. For this potential problem, we constructively propose a new method, named Switchable K-class Hyperplanes (SKH), to sufficiently describe the latent space by the mixture of K-class hyperplanes. It can directly replace the conventional single K-class hyperplane optimization as the new paradigm for noise-robust representation learning. When collaborated with the popular ArcFace on million-level data representation learning, we found that the switchable manner in SKH can effectively eliminate the gradient conflict generated by real-world label noise on a single K-class hyperplane. Moreover, combined with the margin-based loss functions (e.g. ArcFace), we propose a simple Posterior Data Clean strategy to reduce the model optimization deviation on clean dataset caused by the reduction of valid categories in each K-class hyperplane. Extensive experiments demonstrate that the proposed SKH easily achieves new state-of-the-art on IJB-B and IJB-C by encouraging noise-robust representation learning. Our code will be available at https://github.com/liubx07/SKH.git.
Web-Based Real-Time Gesture Recognition with Voice

WebPredict.ai is a model web developed for converting image into text, followed by the conversion of the predicted text to speech. Images used in the proposed application are taken in the form of hand gestural inputs with the help of webcam. This application also helps to interact with google home or smart home of google. As the gestures captured by the webcam can be given for functionality which works for google home and the application can speak out the gesture inputs with different OS voices. These different OS voices are supported by user’s browser and Device. This application was developed on HTML, JavaScript and with the help of Tensorflow.js. Tensorflow.js is an open-source library which is used to train, define, and run machine learning models entirely in the browser, with the help of JavaScript and some high-level API’s. The runtime environment for the proposed model is provided by Node.js which helps in the development of web application and is written in JavaScript. In the proposed application, ML algorithm is running in the web browser, hence there is no need to install any driver or library. Users just need to open the webpage and the program is ready to run. Users can also open the webpage from a mobile device.
EmoChannel-SA: exploring emotional dependency towards classification task with self-attention mechanism

Abstract Exploiting hand-crafted lexicon knowledge to enhance emotional or sentimental features at word-level has become a widely adopted method in emotion-relevant classification studies. However, few attempts have been made to explore the emotion construction in the classification task, which provides insights to how a sentence’s emotion is constructed. The major challenge of exploring emotion construction is that the current studies assume the dataset labels as relatively independent emotions, which overlooks the connections among different emotions. This work aims to understand the coarse-grained emotion construction and their dependency by incorporating fine-grained emotions from domain knowledge. Incorporating domain knowledge and dimensional sentiment lexicons, our previous work proposes a novel method named EmoChannel to capture the intensity variation of a particular emotion in time series. We utilize the resultant knowledge of 151 available fine-grained emotions to comprise the representation of sentence-level emotion construction. Furthermore, this work explicitly employs a self-attention module to extract the dependency relationship within all emotions and propose EmoChannel-SA Network to enhance emotion classification performance. We conducted experiments to demonstrate that the proposed method produces competitive performances against the state-of-the-art baselines on both multi-class datasets and sentiment analysis datasets.
Transferability Analysis of an Adversarial Attack on Gender Classification to Face Recognition

Modern biometric systems establish their decision based on the outcome of machine learning (ML) classifiers trained to make accurate predictions. Such classifiers are vulnerable to diverse adversarial attacks, altering the classifiers’ predictions by adding a crafted perturbation. According to ML literature, those attacks are transferable among models that perform the same task. However, models performing different tasks, but sharing the same input space and the same model architecture, were never included in transferability scenarios. In this paper, we analyze this phenomenon for the special case of VGG16-based biometric classifiers. Concretely, we study the effect of the white-box FGSM attack, on a gender classifier and compare several defense methods as countermeasures. Then, in a black-box manner, we attack a pre-trained face recognition classifier using adversarial images generated by the FGSM. Our experiments show that this attack is transferable from a gender classifier to a face recognition classifier where both were independently trained.
Homomorphism on bipolar anti fuzzy d-Ideals of d-algebra

In this paper, we study the concept of homomorphism on bipolar anti fuzzy d-ideals of d-algebra and we study the properties of anti homomorphic image and anti pre-image of bipolar anti fuzzy d-ideals. Further, we establish that the anti homomorphic image and anti pre-image of bipolar anti fuzzy d-ideals is a bipolar anti fuzzy d-ideal.
Enhanced Breast Lesion Classification via Knowledge Guided Cross-Modal and Semantic Data Augmentation

Ultrasound (US) imaging is a fundamental modality for detecting and diagnosing breast lesions, while shear-wave elastography (SWE) serves as a crucial complementary counterpart. Although an automated breast lesion classification system is desired, training of such a system is constrained by data scarcity and modality imbalance problems due to the lack of SWE devices in rural hospitals. To enhance the diagnosis with only US available, in this work, we propose a knowledge-guided data augmentation framework, which consists of a modal translater and a semantic inverter, achieving cross-modal and semantic data augmentation simultaneously. Extensive experiments show a significant improvement of AUC from 84.36% to 86.71% using the same ResNet18 classifier after leveraging our augmentation framework, further improving classification performance based on conventional data augmentation methods and GAN-based data augmentation methods without knowledge guidance.