Reviewerly/exHarmony huggingface dataset:
Post Training Quantization after Neural Network

In recent years, neural network deployment to the target environment is considered a challenging task especially because of heavy burden of hardware requirements that DNN models lay on computation capabilities and power consumption. various methods based on post-training quantification are powerful tools to accelerate the optimization and perfection of deep neural networks. In case of low power edge devices, such as GNA - neural coprocessor, quantization becomes the only way to make the deployment possible. This article summarizes the research and improvement of neural network models and parameters of various methods in post-training quantization. Additionally, this article analyzes different methods and classifies them. Firstly, this article reviews the publications of post-training quantitative literature in recent years, analyzes the most effective cutting-edge algorithms in the field of deep neural networks. Secondly, advanced researches based on the AdaQuant algorithm, integer quantization analysis, and data-free quantization are discussed. It can reduce the model size and simplify the computation complexity. Compared to the state-of-the-art post-training quantization methods, experimental results show that our proposed method achieves superior performance on image classification, semantic segmentation, and object detection with minor overhead. Moreover, the model accuracy and development potential of post-training quantization in the field of deep neural networks are discussed further. The analysis shows the characteristics of different algorithms and the corresponding changed networks. The characteristics of the model affected by the parameters, and the future research directions and difficulties faced by the post-training quantization method and technology are covered in this article as well.
Evaluating Readiness for Technology Integration in the Classroom Post-Pandemic

Due to the COVID-19 pandemic, various aspects of life, such as work, school and social gatherings, have shifted to online platforms. Pervasive technologies exist to cater to this shift, and in the case of online learning, they can not only sustain but help enhance the overall experience for both students and teachers. Ubiquitous or pervasive learning occurs at any time and place of the learner’s choice whenever the need arises, using tools from everyday life such as mobile devices. This style of learning allows students and teachers to engage in the learning process at their convenience. Teachers usually prefer conducting their lessons in a face-to-face environment, but the pandemic caused them to learn how to convey concepts online using pervasive technologies. In the span of a year, teachers’ initial apprehensions and resistance to online teaching are expected to have subsided, and in this paper, a group of teachers has been surveyed to assess the extent to which their attitudes towards online teaching and ubiquitous learning have changed. In the survey, the teachers were asked which of the stated benefits of online teaching they have experienced, what issues they have faced, their willingness to adopt increasingly advanced solutions to these issues and their inclination towards online teaching as compared to face-to-face in-classroom teaching. The findings from the survey indicate that although teachers’ attitudes towards pervasive learning have changed for the better, but due to the lack of physical contact, they would still prefer in-person classes, and the paper suggests future research directions to improve this aspect of the online teaching and learning process.
A Decentralized Blockchain based system for Secure Health Record and Claims processing

Blockchain which emerged in the last decade has been considered as a very powerful and potential technology since it has been applied and gained popularity from various domains like supply chain, healthcare, energy, music, food, finance, insurance, government, etc. There are several use cases in the health care where we can apply blockchain for maintaining electronic medical records, remote patient monitoring records, drug supply chain, insurance claims, hospital information system, etc. Any intentionally modified data or data misrepresentation or deception in the insurance could intend to result in unauthorized benefits. These fraudulent claims keep increasing annually and because of this, the health insurance system is on a greater disadvantage. So by using blockchain we can overcome the various challenges and issues in the current system. Our focus is to build a secure system to securely manage the health care data and also to track the insurance activities so that we can prevent health insurance fraud. We design a blockchain based solution for health care data collection andinsurance claims by deploying blockchain since it is secure, tamper proof, immutable. Thus it also gives us added security since it is impossible for anyone to modify/claim the insurance falsely since only the authorized people will beable to do so. We have used ganache blockchain to experiment the same and used solidity language for the coding.
Function Analysis of Deep Learning in BP Neural Network Structure Design

It is pointed out that the selection of stealth neural heads is the focus of the research. In view of the lack of strict theoretical basis to solve this problem, designers mostly depend on experience, this paper introduces the feasible methods to optimize the design of hidden layer structure, summarizes various existing design research results, and discusses the new design direction. The emergence and theoretical basis of network hidden layer structure optimization problem are analyzed, various design methods are classified and summarized in detail, their advantages and disadvantages are discussed, and the research and development of neural network structure optimization problem are reviewed and prospected.
National approaches for citizen data management in response to COVID-19: An overview and implications of contact tracing apps in 21 countries

The COVID-19 pandemic brings the topic of citizen data management (CDAMA) into the public eye. This study is one of the first attempts to analyze the national approaches for CDAMA applied by governments of different countries and continents in public sectors. The study first conducts a systematic overview of the representative contact tracing apps in 21 countries of four continents, collecting information of the four aspects of the CDAMA system. It then summarizes and analyzes the various governments’ approaches to the CDAMA system applied by different countries and continents based on the app overview. We found that governments’ priority between national safety (i.e., public health in this study) and citizen privacy is different in terms of their national approaches for CDAMA. For example, governments of Asian countries are more intrusive and hold a stricter attitude in their national CDAMA approach than countries elsewhere. Our study has contributions both theoretically and practically. Theoretically, it fills the literature gap about data management by discussing the data management in governments; practically, the study provides the background information as well as implications for future debates and discussions on governments’ data management system and citizen data use.
A systematic approach to understanding MACB timestamps on Unix-like systems

File timestamps are used by forensics practitioners as a fundamental artifact. For example, the creation of user files can show traces of user activity, while system files, like configuration and log files, typically reveal when a program was run. Despite timestamps being ubiquitous, the understanding of their exact meaning is mostly overlooked in favor of fully-automated, correlation-based approaches. Existing work for practitioners aims at understanding Windows and is not directly applicable to Unix-like systems. In this paper, we review how each layer of the software stack (kernel, file system, libraries, application) influences MACB timestamps on Unix systems such as Linux, OpenBSD, FreeBSD and macOS. We examine how POSIX specifies the timestamp behavior and propose a framework for automatically profiling OS kernels, user mode libraries and applications, including compliance checks against POSIX. Our implementation covers four different operating systems, the GIO and Qt library, as well as several user mode applications and is released as open-source. Based on 187 compliance tests and automated profiling covering common file operations, we found multiple unexpected and non-compliant behaviors, both on common operations and in edge cases. Furthermore, we provide tables summarizing timestamp behavior aimed to be used by practitioners as a quick-reference.
A supervised learning‐based approach for focused web crawling for <scp>IoMT</scp> using global co‐occurrence matrix

Abstract Irrelevant search results for a given topic end up wasting search engine users' time. A learning focused web crawler downloads relevant URLs for a given topic using machine‐learning algorithms. The dynamic nature of the web is a challenge in related computation for focused web crawlers. Studies have shown that the learning focused crawler utilizes term frequency‐inverse document frequency (TF‐IDF) to compute the relevance between a web page and a given topic. The TF‐IDF detects similarity of the given topic to its co‐occurrence on the web page. The necessity of efficient mechanism to compute the relevance of URLs syntactically and semantically has led to the proposal of this paper with a word embedding approach to compute the relevance of the web page. The global vector representation cosine similarity is calculated between a topic and the web page contents. The calculated cosine similarity is provided as input to the trained random forest classifier to predict the relevancy of the web page. The evaluation results proved that the proposed crawler produced an average of 0.41 and of 0.59, which outperformed other learning‐focused crawlers on support vector machines, Naive Bayes and artificial neural networks.
Bounding Box Matching: A Sparse Object-centric Correspondence Method for Stereo Vision

In this work a simplified method for sparse, object-centric disparity estimation is proposed. It combines the state-of-the-art object detector YOLOv4 with image rectification to produce a disparity map with high speed suitable for real-time applications. Similarly, as other methods based on convolutional neural networks, this approach uses contextual and semantic image information and is robust to ill-posed image regions such as reflective, textureless and occluded regions, but requires less computational resources at the expense of detail and estimation accuracy. The method has been implemented on the Tensorflow platform and has been consequently deployed with the LabVIEW graphical programming language. It has been shown that the method works best at large distances, small object depth to distance ratios and moderate eccentricities. The source code can be found at: https://github.com/tsindel/bbox-matching.
Multi-Feature Fusion Based Deepfake Face Forgery Video Detection

With the rapid development of deep learning, generating realistic fake face videos is becoming easier. It is common to make fake news, network pornography, extortion and other related illegal events using deep forgery. In order to attenuate the harm of deep forgery face video, researchers proposed many detection methods based on the tampering traces introduced by deep forgery. However, these methods generally have poor cross-database detection performance. Therefore, this paper proposes a multi-feature fusion detection method to improve the generalization ability of the detector. This method combines feature information of face video in the spatial domain, frequency domain, Pattern of Local Gravitational Force (PLGF) and time domain and effectively reduces the average error rate of span detection while ensuring good detection effect in the library.
Designing and Experimentally Evaluating a Video-based American Sign Language Look-up System

Despite some prior research and commercial systems, if someone sees an unfamiliar American Sign Language (ASL) word and wishes to look up its meaning in a dictionary, this remains a difficult task. There is no standard label a user can type to search for a sign, and formulating a query based on linguistic properties is challenging for students learning ASL. Advances in sign-language recognition technology will soon enable the design of a search system for ASL word look-up in dictionaries, by allowing users to generate a query by submitting a video of themselves performing the word they believe they encountered somewhere. Users would then view a results list of video clips or animations, to seek the desired word. In this research, we are investigating the usability of such a proposed system, a webcam-based ASL dictionary system, using a Wizard-of-Oz prototype and enhanced the design so that it can support sign language word look-up even when the performance of the underlying sign-recognition technology is low. We have also investigated the requirements of students learning ASL in regard to how results should be displayed and how a system could enable them to filter the results of the initial query, to aid in their search for a desired word. We compared users’ satisfaction when using a system with or without post-query filtering capabilities. We discuss our upcoming study to investigate users’ experience with a working prototype based on actual sign-recognition technology that is being designed. Finally, we discuss extensions of this work to the context of users searching datasets of videos of other human movements, e.g. dance moves, or when searching for words in other languages.
A Case Study of Applying ‘Notion’ when Reading Classics of Great Literature

The purpose of smart learning and self-directed learning is to make learners realize that they can cultivate the ability to process various types of informative knowledge and then create new knowledge in a singular fashion. This is also the purpose of learner-centered lesson design. This study aims to provide a case that will determine the effects of the ‘Notion’ application when used as a smart-learning tool and when applying question-based learning in a program which makes use of smart learning and self-directed learning. Namely, the Reading Classics of Great Literature course. ‘Notion’ application is a new tool that has not been dealt with in previous studies in the field of smart learning and learner-centered lesson design. The reason why I introduced ‘Notion’ into the course is because I consider it to be the best application to date for the implementation of learner-centered lesson design. In fact, ‘Notion’ has a more diverse user interface than other smart learning online applications. It helps learners to better understand problematic phenomena structurally and to solve problems creatively. This in turn allows them to go beyond simply memorizing or utilizing information. Furthermore, this study identifies the utility of ‘Notion’ through three cases of 1) the access of data 2) interaction 3) self-designing question-based learning, and then concentrates on the analysis of these use cases in regards to its to educational implications. This analysis is crucial to analyze the characteristics and components of the program on Reading Classics of Great Literature. Therefore, this case study will explore effective ways to implement smart learning and self-directed learning in the field General Education.
Protocol

Protocol describes a cascade of formalised standards or agreements to be implemented as control regimes for flexible material and/or semiotic organisation.It predictably structures in an often layered, sometimes hierarchical way the behaviours of data and objects to participate in infrastructural networks.While 'protocol' may refer specifically to Internet protocols, it also describes a mode of organisation evident in a variety of technical and non-technical settings.
People First, Data Second: A Humanitarian Research Framework for Fieldwork with Refugees by War Zones

War begets crises that are among the most urgent areas requiring help from the international HCI/CSCW community; yet too few scientists address it using context-based, participatory field methods and by engaging in country and regionally based, longitudinal partnerships. Drawing on the author's ongoing eight-year engagement as a design ethnographer with UNHCR Jordan and region for the Syrian War, this paper discusses Humanitarian Research as a framework for guiding HCI/CSCW research in conflict zones with displaced persons. Based on the principle of "People First, Data Second," Humanitarian Research is explained with illustrative examples along with the nature of war and UNHCR's protection mandate, the research challenges and ethical roles of HCI scientists in conflict zones and how these roles entwine with refugee stakeholders, NGOs, country actors, and university IRBs.
3D Face Geometry Optimization Using Artificial Intelligence and Computer Graphics

Human face plays an indispensable role in emotional expression and information exchange, which attracts large number of researchers to study face recognition. Nowadays, with the rapid development of computer graphics, artificial intelligence, and other technologies, the ability of the human vision system to recognize facial expressions and facial organs is enhanced. More and more experts think about how to make the computer vision system have this capability. By combining artificial intelligence and computer graphics, this paper studies how to optimize the 3D face network model and extract geometric features for 3D face recognition. We propose a 3D face network model based on Poisson equation to realize face hole recognition and boundary preprocessing. Besides, we also establish the 3D face surface equation and equal face value extraction and enhance the face feature based on facial semantic information. We study the hole repair of 3D face model based on Poisson equation integrating semantic information and achieve the purpose of optimizing the 3D face model. After the optimization, the method is compared with mesh repair and Poisson repair, which demonstrates that our Poisson-based 3D face hole repairing model obtains the best results among compared methods.
Renal Cancer Detection: Fusing Deep and Texture Features from Histopathology Images

Histopathological images contain morphological markers of disease progression that have diagnostic and predictive values, with many computer-aided diagnosis systems using common deep learning methods that have been proposed to save time and labour. Even though deep learning methods are an end-to-end method, they perform exceptionally well given a large dataset and often show relatively inferior results for a small dataset. In contrast, traditional feature extraction methods have greater robustness and perform well with a small/medium dataset. Moreover, a texture representation-based global approach is commonly used to classify histological tissue images expect in explicit segmentation to extract the structure properties. Considering the scarcity of medical datasets and the usefulness of texture representation, we would like to integrate both the advantages of deep learning and traditional machine learning, i.e., texture representation. To accomplish this task, we proposed a classification model to detect renal cancer using a histopathology dataset by fusing the features from a deep learning model with the extracted texture feature descriptors. Here, five texture feature descriptors from three texture feature families were applied to complement Alex-Net for the extensive validation of the fusion between the deep features and texture features. The texture features are from (1) statistic feature family: histogram of gradient, gray-level cooccurrence matrix, and local binary pattern; (2) transform-based texture feature family: Gabor filters; and (3) model-based texture feature family: Markov random field. The final experimental results for classification outperformed both Alex-Net and a singular texture descriptor, showing the effectiveness of combining the deep features and texture features in renal cancer detection.
Will computers blow up the school: or is our digital wisdom evolving?

Following the unexpected lockdown of education in March 2020 and again in February 2021, this research looks at the subsequent online learning that took place during the second lockdown at a vocational further education college located in the south of England. These online sessions used Google Meet as a forum and teachers at the college suddenly had to adapt their planned teaching to suit this new environment, Observation data was collected using an assessment tool consisting of 11 criteria with which to assess these sessions. This was followed up with teacher self-assessment using the same criteria and students surveyed for their perspective after the lockdown had finished in March. The data shows some discrepancy between what the observer 'saw' and what the teachers thought about their own TPACK levels in terms of outcomes and the student data indicates that online learning was 'unengaging' in terms of activities. The conclusion looks to the concept of digital wisdom as a means of using technology as a meaningful learning tool rather than as simply a different way to cover content in the light of the changes to curriculum delivery.
Library Personalized Recommendation System Based on Collaborative Filtering Recommendation Algorithm

The personalized recommendation service originated in the commercial field. It builds the user interest model by collecting user personal information, and recommends products or services of interest to users based on certain algorithms. The library personalized recommendation system is an automated and intelligent reader service system that finds the personalized needs of different users by targeted analyzing borrowing data and recommends books to users. First, the constructed smart campus architecture analyzes the advantages, disadvantages and application of various recommendation algorithms; then, it focuses on the user-based collaborative filtering book personalized recommendation algorithm, which effectively solves the problems of cold start and data loss; finally, the similarity calculation of book personalized recommendation is given, which solves the key problem in the collaborative filtering recommendation algorithm.
Supervision of Video Game Car Steering Implementing HORCNN Network

This research aims to make an algorithm that can autonomously drive a car in the perspective of a 3<sup>rd</sup> person in a video game using the Convolutional Neural Network (CNN) approach. The Hybrid Object Recognition CNN (HORCNN) model has been programmed, combining two different networks. The environment and its parameters are extracted from an 800&#x002A;600 windowed mode of the game. One frame is sent to a Neural Network Pipeline, first consisting of image processing, then feeding it into a Conv Net, the infamous Alex-Net. The output of that would be a vector containing probabilities of which direction to steer and whether to go forward or not. Parallelly, the same image is fed into the singleshot detector (SSD) network to get the objects detected in that particular video frame, and this information is used to label objects in that frame. The proposed model gets the absolute accuracy at the speed of 113 km/h, which can also simulate an actual autonomous vehicle.