Reviewerly/exHarmony huggingface dataset:
FACE MASK DETECTION

This project intends to develop a Face Mask Detection system using OpenCV, Keras/TensorFlow and Deep Learning. The system can be easily integrated to various embedded devices with limited computational capacity as it uses MobileNetV2 architecture. It will detect face masks in images as well as in real-time videos. In recent times, where Covid-19 has impacted a domino effect on manufacturing, travel, tourism, hospitality, crippling the global economy. In addition to it, is the growing curve of human deaths across the globe due to the pandemic, this project which relies on computer vision and deep learning, intends to make an impact and solve the real-world problem of safety measures at some significant level. This project can be used at airports, offices, hospitals and many more public places to ensure that the safety standards are maintained and people are abiding by the rules and regulations to wear protective masks at public places. If the detection system classifies as ‘No Mask’, reminders can be given as well as actions can be taken against such individuals.
Research on How to Develop Good Academic Questions

How to write a good academic paper has always been a topic of concern to people. Based on the investigation of 280 college students, this paper focuses on two aspects: college students' understanding of academic questions and the presentation of academic questions. So as to pave the way for the follow-up research on academic questions. The main research methods of this article are questionnaire survey and draw conclusions from comparative analysis of different studies. Through investigation and analysis, the characteristics, motivation, preparation, methods, media and influencing factors of academic questions in the minds of the investigated college students have been obtained. And how to raise a good academic questions has also been obtained. What’s more, the article still discusses the connection between academic questions and one’s thinking skills and creativity.
Intelligent Conversational Healthcare Chat-Bot System

Artificial Intelligence has core branches like, Machine Learning which takes in data, search patterns then improves itself using the data, and displays the outcome. To start a good day caring of our health is really important. In few village areas, it is quite hard to find a consultation with a doctor whenever needed in emergency. The proposal here is to build an intelligent conversational healthcare chat-bot using NLP which is a part of artificial intelligence that can diagnose and provide required details about the particular disease asked by the user before consulting a doctor. A chat-bot is an artificial intelligence based program which performs communication using text, audio and video system. Some chatbots acts as virtual assistants, which helps the patient know more about their disease and helps them with the consultation to the right doctor. A user can have the benefit of a chat-bot only when it can diagnose all kinds of diseases and provide all the needed information. A rule based text-to-text healthcare chat-bot involves patients in conversation which considers their health issues and provides a set of personalized diagnoses based on their provided inputs. The bot connects with patients who will visit the website and helps them to discover specialist doctors, book an appointment and guiding them for a right treatment. This Intelligent conversational healthcare chat-bot system will provide an all-day healthcare support to the users. Python scripting language is used in the implementation of such systems. Keywords: Artificial Intelligence, Natural Language Processing, Machine Learning, Text-to-Text, Intelligent, Healthcare, Chat-Bot, System.
A Study on PBL’s Application in New Standard College English Integrated Course 3

PBL (Project-Based Learning) is a student-centered approach in which learning is organized by means of projects. Students are required to explore problems with their teammates collaboratively and to display their output project. In this study, PBL has been conducted in three classes of North China Electric Power University (Baoding) for one semester to probe whether PBL serves as an effective way to combine explicit teaching content with implicit ideological and political education. Based on a questionnaire and in-depth interview, the results show that PBL enables value education to exert the impact on students’ development.
Difficulties Encountered By Students at a University in Vietnam in Speaking English

The research investigates into the speaking difficulties of fourth – year students at Thai Nguyen University. The goals of the research are to find out what linguistic difficulties and psychological difficulties do EFL learners at Thai Nguyen University face when speaking English. Quantitative method was used as the main method of this study. A total of 150 students were chosen as participants of the study. Due to the impact of Covid-19, data were collected through online survey in two weeks. The research discovered three linguistic difficulties (pronunciation, grammar, and vocabulary) and three psychological difficulties (attitude, anxiety and motivation). The findings of the study revealed that students tend to encounter linguistic difficulties than psychological difficulties. The most common problem is grammar incompetence, followed by vocabulary and anxiety. Furthermore, the outcome of the study indicated that EFL learners at Thai Nguyen University do not have pronunciation difficulty. The findings also show that students have positive attitudes, high levels of motivation toward speaking English. In addition, the researcher has proposed some recommendations for both teachers and students to reduce these speaking difficulties.
Optimal Covert Communication Techniques

Due to advancements in hacking and reverse engineering tools, threat against transfer of sensitive data or highly classified information is always at risk of being intercepted by an attacker. Covert communication outwits this malicious breach of privacy act better than cryptography as it camouflages secret information inside another innocent looking information, while cryptography shows scrambled information that might arouse attention of an attacker. However, the challenges in Steganography are the modification of carrier that causes some abnormalities, which are detectable and often the methods are not optimized. This paper presents an approach in Covert communication Chanel, which utilizes mathematical concept of combination to optimize time of transmission using sets of multiple transmitters, and receivers addresses where each abstractly represents a set of bits or characters combination without modifying the address. To minimize the number of physical address for use, a combination and permutation concept of virtual address generation from physical address is introduce. The paper in addition presents some technique like relationship and their application in both re-enforcing resistivity against Steganalysis and generating combinations. Furthermore, a concept of dynamical clockwise and anti-clockwise rotation of combination over addresses after every transmission is introduced to improve on resistivity against Steganalysis. A simple test was performed for demonstrating relay address, combination and permutation concepts. Based on test results and analysis, the method is effective as expected and it is quite easy to use as it can be implemented in different platforms without much difficulties.
Study on Data-Driven Approaches for the Automated Assembly of Board-to-Board Connectors

The mating of the board-to-board (BtB) connector is rugged because of its design complexity, small pitch (0.4 mm), and susceptibility to damage. Currently, the assembly task of BtB connectors is performed manually because of these factors. A high chance of damage to the connectors can also occur during the mating process. Thus, it is essential to automate the assembly process to ensure its safety and reliability during the mating process. Commonly, the mating procedure adopts a model-based approach, including error recovery methods, owing to less design complexity and fewer pins with a high pitch. However, we propose a data-driven approach prediction for the mating process of the fine pitch 0.4 mm board-to-board connector utilizing a manipulator arm and force sensor. The data-driven approach uses force data for time series encoding methods such as recurrence plot (RP), Gramian matrix, k-nearest neighbor dynamic time warping (kNN-DTW), Markov transition field (MTF), and long short-term memory (LSTM) to compare each of the model prediction performances. The proposed method combines the RP model with the convolutional neural network (RP-CNN) to predict the force data. In the experiment, the proposed RP-CNN model used two final layers, SoftMax and L2-SVM, to compare with the other prediction models mentioned above. The output of the data-driven prediction is the coordinate alignment of the female board-to-board connector with the male board-to-board connector based on the value of force. Based on the experiment, the encoding approach, especially RP-CNN (L2-SVM), outperformed all prediction models as mentioned above with an accuracy of 86%.
An extensive appraisal of weight-sharing on the NAS-Bench-101 benchmark

Weight-sharing (WS) has recently emerged as a paradigm to accelerate the automated search for efficient neural architectures, a process dubbed Neural Architecture Search (NAS). By using and training the same set of weights for the whole search space, WS allows for the quick evaluation of millions of architectures, where classical NAS approaches require lengthy individual trainings. Although very appealing, WS is not without drawbacks and several works have started to question its capabilities on small hand-crafted benchmarks. In this paper, we take advantage of the NAS-Bench-101 dataset to challenge the efficiency of a uniform-sampling based WS variant on several representative search spaces. After reviewing previous studies on WS and highlighting several of their shortcomings, we introduce our own experimental setup, from which we extract several good practices that one should keep in mind when evaluating WS. With our experiments we first establish that, given the correct evaluation procedure, WS is able to produce accuracy scores decently correlated with standalone ones. We then provide evidence that on some search spaces, this WS variant is able to rapidly find better than random architectures, whilst it is equivalent or sometimes even worse than a baseline random search on others, as we find that given the same budget, the probability of superiority of an architecture found using WS over an architecture found through random search can vary between 7% and 78% depending on the search space. We present evidence that the search space itself has an intricate effect on the capabilities of WS and can bias weight-sharing towards certain architectural patterns with no clear accuracy advantage. We conclude that the impact of WS is heavily search-space dependent and difficult to anticipate for a given problem.
On The Digital Singularity Recognising Virtual Property Through The Eyes of New Jurisprudence Over The Conflicts of Digital Goods

The present paper focuses on the analysis of bibliography, jurisprudence and case studies internationally such as Bragg v Linden Labs, forming an analysis of the obstacles for the recognition of virtual property as well as providing arguments for it’s acknowledgment on a multitude of legal systems internationally, all the while incentivizing the debate for its implementation with the use of a set of doctrines and directives. For this purpose, we will make comparisons of the different concepts of property on an international scale through the analysis of a host of different doctrines and jurisprudence from the United States, Europe, Russia and Brazil, aiming to demonstrate the resilience or acceptance of this concept of property. Finally, we will present at the end of this article the aforementioned directives that will serve to guide future discussions and implementations of virtual property.
Integration and Deployment of Cloud-Based Assistance System in Pharaon Large Scale Pilots—Experiences and Lessons Learned

The EU project Pharaon aims to support older European adults by integrating digital services, tools, interoperable open platforms, and devices. One of the objectives is to validate the integrated solutions in large-scale pilots. The integration of mature solutions and existing systems is one of the preconditions for the successful realization of the different aims of the pilots. One such solution is an intelligent, privacy-aware home-care assistance system, SmartHabits. After briefly introducing the Pharaon and SmartHabits, the authors propose different Pharaon models in the Ambient/Active Assisted Living (AAL) domain, namely the Pharaon conceptual model, Pharaon reference logical architecture view, AAL ecosystem model, meta AAL ecosystem model, and Pharaon ecosystem and governance models. Building on the proposed models, the authors provide details of the holistic integration and deployment process of the SmartHabits system into the Pharaon ecosystem. Both technical and supporting integration challenges and activities are discussed. Technical activities, including syntactic and semantic integration and securing the transfer of the Pharaon sensitive data, are among the priorities. Supporting activities include achieving legal and regulatory compliance, device procurement, and use-case co-designing in COVID-19 conditions.
Machine Learning Applied to Routinely Collected Health Administrative Data

There has been considerable growth in the development of machine learning algorithms for clinical applications. The authors survey recent machine learning models developed with the use of large health administrative databases at ICES and highlight three areas of ongoing development that are particularly important for health system applications.
Micro-Knowledge Embedding for Zero-shot Classification

Zero-shot learning is one of the most challenging machine learning tasks, in which learning stable and transferable knowledge from seen classes plays a pivotal role. To improve the currently unsatisfactory performance of zero-shot object recognition, this paper proposes a novel image representation method, namely, micro-knowledge. In our method, the segmentation of micro-regions and the consequent learning of micro-knowledge are unified by the introduction of a self-attention mechanism. A zero-shot classification framework is carefully designed based on micro-knowledge of images. Under this framework, multiple micro-region descriptions are first obtained by embedding micro-knowledge and then merged to carry out the final classification of unseen objects. Finally, a capsule-unified framework is employed as a graphical programming tool to accomplish the aforementioned tasks. Experiments on public datasets show that the proposed framework can generally achieve competitive results for the classification of unseen objects. Specifically, these results verify that the micro-knowledge learned from one dataset can be directly applied to others without complicated adjustments and demonstrate that using visual features instead of semantic features can result in a decrease in classification error. This research will bring new ideas into the field of zero-shot learning and will serve as an appealing option when addressing the problem of domain shift.
[Design and implementation for portable ultrasound-aided breast cancer screening system].

Early screening is an important means to reduce breast cancer mortality. In order to solve the problem of low breast cancer screening rates caused by limited medical resources in remote and impoverished areas, this paper designs a breast cancer screening system aided with portable ultrasound Clarius. The system automatically segments the tumor area of the B-ultrasound image on the mobile terminal and uses the ultrasound radio frequency data on the cloud server to automatically classify the benign and malignant tumors. Experimental results in this study show that the accuracy of breast tumor segmentation reaches 98%, and the accuracy of benign and malignant classification reaches 82%, and the system is accurate and reliable. The system is easy to set up and operate, which is convenient for patients in remote and poor areas to carry out early breast cancer screening. It is beneficial to objectively diagnose disease, and it is the first time for the domestic breast cancer auxiliary screening system on the mobile terminal.早期筛查是降低乳腺癌死亡率的重要手段。为了解决偏远、贫困地区医疗资源有限导致乳腺癌筛查率较低的问题，本文设计了一种基于便携式超声Clarius的乳腺癌辅助筛查系统。该系统在移动终端上对B超图像的肿瘤区域进行自动分割，在云服务器上利用超声射频数据进行肿瘤的良、恶性自动分类。本文研究实验结果表明，该系统对乳腺肿瘤分割准确率达到98%，良、恶性分类准确率达到82%，系统准确可靠。该系统架设方便、操作简单，便于偏远、贫困地区的患者进行乳腺癌早期筛查，有利于客观地对病情做出判断，是国内乳腺癌辅助筛查系统在移动终端上的首次实现。.
Future Teacher Training for Work in the Inclusive Education Framework

The relevance of the chosen topic is determined by the need for improvement of the inclusive education system in the current conditions as well as by the necessity to properly train and prepare future teachers for professional activity in the inclusive learning environment. In the given article, we have analyzed the developments on the topic, identified models of integration of children with special educational needs in the educational process in inclusive education. We also articulated the concept and essence of the professional readiness of future teachers. The ways to form the readiness of teachers for professional activity in the conditions of inclusive education are presented. The article analyzes the understanding of the concept of inclusive education and provides a brief overview of the studies by prominent researchers on this issue, taking into account their proposed educational values. Based on the analysis, the authors conclud that it is necessary to critically review traditional approaches to the understanding of inclusive education, and to the formation of readiness of future teachers for professional activities in inclusive education to eventually benefit modern education. The authors of the article note that in the context of professional training for teachers' activity, four main components will be effective, which will promote the integration of children with special educational needs into the educational process.
BioTouch: Reliable Re-Authentication via Finger Bio-Capacitance and Touching Behavior

Re-authentication continuously checks to see if a user is authorized during a whole usage session, enhancing secrecy capabilities for computational devices, especially against insider attacks. However, it is challenging to design a reliable re-authentication scheme with accuracy, transparency and robustness. Specifically, the approaches of using biometric features (e.g., fingerprint, iris) are often accurate in identifying users but not transparent to them due to the need for user cooperation. On the other hand, while the approaches exploiting behavior features (e.g., touch-screen gesture, movement) are often transparent in use, their applications suffer from low accuracy and robustness as behavior information collected is subjective and may change frequently over different use situations and even user's motion. In this paper, we propose BioTouch, a reliable re-authentication scheme that satisfies all the above requirements. First, BioTouch utilizes multiple features (finger capacitance and touching behavior) to identify the user for better accuracy. Second, BioTouch automatically works during user operation on capacitive-touch devices, achieving transparency without the need for manual assistance. Finally, by applying finger bio-capacitance, BioTouch is also robust to various conditions, as this feature is determined by the user's physical characteristics and will not change by different user positions and motions. We implement BioTouch for proof-of-concept and conduct comprehensive evaluations. The results show that BioTouch can flag 98% of anomalous behaviors within ten touching operations and achieve up to 99.84% accuracy during usage.
RETRACTED: A secured biomedical image processing scheme to detect pneumonia disease using dynamic learning principles

Now-a-days, the medical industry is growing a lot with the adaptation of latest technologies as well as the logical evaluation and security norms provides a robust platform to enhance the effectiveness of the industry at a drastic level. In this paper, a digital bio-medical image processing based Pneumonia disease identification system is introduced with enhanced security features. Due to improving the efficiency of the application, a well-known watermarking based security constraint is included to provide the protection to the respective hospital environment and patients as well. To avoid these issues, some sort of security aspects need to be followed so that this paper included watermarking based security to provide a rich level of protection to the images going to be tested. The main intention of this paper is to introduce a novel security enabled digital image processing scheme to identify the Pneumonic disease in earlier stages with respect to the proper classification principles. In this paper, a novel deep learning algorithm is introduced called enhanced Dynamic Learning Neural Network in which it is a hybrid algorithm with the combinations of conventional DLNN algorithm and the Support Vector Classification algorithm. This proposed approach effectively identifies the Pneumonia disease in earlier stages but the security inspection on the testing stage is so important to analyze the disease. The respective testing image is properly watermarked with the logo of the corresponding hospital; the image is processed otherwise the proposed approach skips the image to process. These kinds of security features emphasize the medical industry and boost up the levels more as well as the patients can get an appropriate error free care with the help of such technology. A proper Chest X-Ray based Kaggle dataset is considered to process the system as well as which contains 5856 Chest X-Ray images under two different categories such as Pneumonia and Normal. With respect to processing these images and identifying the Pneumonia disease effectively as well as the proposed watermarking enabled security features provide a good impact in the medical field protection system. The resulting section provides the proper proof to the effectiveness of the proposed approach and its prediction efficiency.