Reviewerly/exHarmony huggingface dataset:
Employing serious game for assessing knowledge of blood transfusion procedure among nurses: A qualitative evaluation and feedback improvement study

Employing serious game for assessing nurses' blood transfusion knowledge and skill competency has gained interest among nurse educators worldwide. However, its acceptance by nurses has not been fully explored. To explore the registered nurses' experiences using serious game for nursing skills competency and gather feedback to improve the quality of learning using serious game. Acute tertiary hospital in Singapore. Registered nurses in inpatient settings. This qualitative evaluation and feedback improvement study was conducted in an acute tertiary hospital in Singapore between September 2019 and November 2019. Registered nurses who had completed the blood transfusion knowledge and skills competency via the serious game platform were invited to participate in this study. Individual face-to-face interviews using a semi-structured questionnaire were conducted. All interviews were audio-recorded and transcribed verbatim. Data analysis was performed inductively using the Braun and Clarke's six-step of thematic analysis. A total of 11 registered nurses were interviewed from a variety of clinical areas including medical and surgical wards, and intensive care units. The mean age of the participants was 28 years old. The majority of participants were females (81.8%), with their years of experience ranging from 1 to 13 years. Each interview took between 20 and 30 min. Three themes were identified: (1) novel learning experience; (2) varying levels of appreciation towards the serious game; and (3) navigating the interface and technical issues. Employing serious game for nursing skills competency is considered acceptable by the nurses in this sample population. Compared to the traditional modes of competency assessment, the serious game is innovative and stimulates learning. Nevertheless, more rigorous efforts are needed to improve the interface and technical issues to enhance the user learning experience. Future versions of the serious game will need to be more accessible and intuitive for all levels of nursing staffs.
Developing Facebook Chatbot Based on Deep Learning Using RASA Framework for University Enquiries

Abstract Smart systems for universities powered by artificial intelligence have been massively developed to help humans in various tasks. The chatbot concept is not something new in today’s society which is developing with recent technology. College students or candidates of college students often need actual information like asking for something to customer service, especially during this pandemic, when it is difficult to have an immediate face-to-face meeting. Chatbots are functionally helping in several things such as curriculum information, admission for new students, schedule info for any lecture courses, students grade information, and some adding features for Muslim worships schedule, also weather forecast information. This Chatbot is developed by deep learning models, which was adopted by an artificial intelligence model that replicates human intelligence with some specific training schemes. This kind of deep learning is based on RNN which has some specific memory savings scheme for the deep learning model, specifically this chatbot using LSTM which already integrates by RASA framework. LSTM is also known as Long Short Term Memory which efficiently saves some required memory but will remove some memory that is not needed. This Chatbot uses the Facebook platform because the Facebook users have already reached up to 60.8% of its entire population in Indonesia.
Improving Skip-Gram Embeddings Using BERT

Contextualized embeddings such as BERT and GPT have been shown to give significant improvement in NLP tasks. On the other hand, static embeddings such as skip-gram and GloVe still have desirable characteristics such as low computational cost, easy deployment and freedom from severe contextualized variation in representation. There has been some recent attempt enhancing the skip-gram model by adding syntactic information of context using GCN. We investigate the use of BERT embeddings instead for stronger context representation, which contains not only syntactic and surface features, but also rich knowledge from large-scale pre-training. Results show that BERT-enhanced skip-gram embeddings outperform GCN-enhanced embeddings on a range of tasks. Such embeddings also outperform recent effort distilling BERT embeddings into context-independent vectors.
Explicit Constructions of Two-Dimensional Reed-Solomon Codes in High Insertion and Deletion Noise Regime

Insertion and deletion (insdel for short) errors are synchronization errors in communication systems caused by the loss of positional information in the message. Reed-Solomon codes have gained a lot of interest due to its encoding simplicity, well structuredness and list-decoding capability in the classical setting. This interest also translates to the insdel metric setting, as the Guruswami-Sudan decoding algorithm can be utilized to provide a deletion correcting algorithm in the insdel metric. Nevertheless, there have been few studies on the insdel error-correcting capability of Reed-Solomon codes. Our main contributions in this article are explicit constructions of two families of 2-dimensional Reed-Solomon codes with insdel error-correcting capabilities asymptotically reaching those provided by the Singleton bound. The first construction gives a family of Reed-Solomon codes with insdel error-correcting capability asymptotic to its length. The second construction provides a family of Reed-Solomon codes with an exact insdel error-correcting capability up to its length. Both our constructions improve the previously known construction of 2-dimensional Reed-Solomon codes whose insdel error-correcting capability is only logarithmic on the code length.
A UWB-Driven Self-Actuated Projector Platform for Interactive Augmented Reality Applications

With the rapid development of interactive technology, creating systems that allow users to define their interactive envelope freely and provide multi-interactive modalities is important to build up an intuitive interactive space. We present an indoor interactive system where a human can customize and interact through a projected screen utilizing the surrounding surfaces. An ultra-wideband (UWB) wireless sensor network was used to assist human-centered interaction design and navigate the self-actuated projector platform. We developed a UWB-based calibration algorithm to facilitate the interaction with the customized projected screens, where a hand-held input device was designed to perform mid-air interactive functions. Sixteen participants were recruited to evaluate the system performance. A prototype level implementation was tested inside a simulated museum environment, where a self-actuated projector provides interactive explanatory content for the on-display artifacts under the user’s command. Our results depict the applicability to designate the interactive screen efficiently indoors and interact with the augmented content with reasonable accuracy and relatively low workload. Our findings also provide valuable user experience information regarding the design of mobile and projection-based augmented reality systems, with the ability to overcome the limitations of other conventional techniques.
Hardware Architecture for Supersingular Isogeny Diffie-Hellman and Key Encapsulation Using a Fast Montgomery Multiplier

Public key cryptography lies among the most important bases of security protocols. The classic instances of these cryptosystems are no longer secure when a large-scale quantum computer emerges. These cryptosystems must be replaced by post-quantum ones, such as isogeny-based cryptographic schemes. Supersingular isogeny Diffie-Hellman (SIDH) and key encapsulation (SIKE) are two of the most important such schemes. To improve the performance of these protocols, we have designed several modular multipliers. These multipliers have been implemented for all the prime fields used in SIKE round 3, on a Virtex-7 FPGA, showing a time and area-time product improvement of up to 60.1% and 64.5%, respectively. These multipliers are also suitable for applications such as RSA, as shown by implementations for 512-bit, 1024-bit, and 2048-bit generic moduli on a Virtex-7 FPGA. Our fastest multiplier has been used in the implementation of SIDH and SIKE round 3. Employing six instances of this multiplier, SIDH completes after 7.33, 8.93, 13.39, and 18.67 milliseconds and the encapsulation and the decapsulation of SIKE is performed in 7.13, 8.68, 13.08, and 18.16 milliseconds over p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">434</sub> , p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">503</sub> , p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">610</sub> , p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">751</sub> , respectively, which yields a least improvement factor of 1.23.
Uncertainty-Aware Contact-Safe Model-Based Reinforcement Learning

This letter presents contact-safe Model-based Reinforcement Learning (MBRL) for robot applications that achieves contact-safe behaviors in the learning process. In typical MBRL, we cannot expect the data-driven model to generate accurate and reliable policies to the intended robotic tasks during the learning process due to sample scarcity. Operating these unreliable policies in a contact-rich environment could cause damage to the robot and its surroundings. To alleviate the risk of causing damage through unexpected intensive physical contacts, we present the contact-safe MBRL that associates the probabilistic Model Predictive Control's (pMPC) control limits with the model uncertainty so that the allowed acceleration of controlled behavior is adjusted according to learning progress. Control planning with such uncertainty-aware control limits is formulated as a deterministic MPC problem using a computation-efficient approximated GP dynamics and an approximated inference technique. Our approach's effectiveness is evaluated through bowl mixing tasks with simulated and real robots, scooping tasks with a real robot as examples of contact-rich manipulation skills.
Compressed MobileNet V3:A Light Weight Variant for Resource-Constrained Platforms

Convolutional Neural Networks (CNNs) are ubiquitous in computer vision applications. This is attributed to their excellent performance in image classification which forms the foundation for many complex tasks such as object localization, object tracking, etc. Despite their huge success, the intensive computation, memory bandwidth, and energy requirements have made it difficult to deploy them in low power and resource-constrained platforms. To overcome this, many researchers have designed compact models achieving a tradeoff between model size and accuracy. MobileNet V3, the latest variant of MobileNets is one of the CNN models complying with this trend [1]. It has a model size of 15.3 MB with a validation accuracy of 88.93% on the CIFAR-10 dataset[2]. In this paper, we have modified the baseline architecture to further reduce its size to 2.3 MB while achieving an accuracy of 89.13%.
Has the Internet increased FDI, economic growth, and trade? Evidence from Asian economies

This study examines the causal relationship between the Internet and economic factors in Asian economies between 1997 and 2017. The economic factors consist of gross domestic product (GDP), foreign direct investment (FDI), imports, and exports. A comparative analysis of East, South, and Western Asia was conducted using a panel vector autoregressive model. The findings show bidirectional causality between FDI and Internet use in South Asia, unidirectional causality from Internet use to FDI in East Asia, and unidirectional causality from FDI to Internet use in Western Asia. Moreover, the findings indicate unidirectional causality from exports to Internet use in East Asia and unidirectional causality from Internet use to exports in South Asia, but no impact in Western Asia. Finally, the results show unidirectional causality from Internet use to GDP in Western Asia. As these results suggest that Internet use has boosted economic performance in Asia, policy makers in the region should improve Internet use with a focus on economic growth, improving transaction efficiency, and facilitating foreign investment.
A privacy-preserving resource trading scheme for Cloud Manufacturing with edge-PLCs in IIoT

With the development of industrial Internet of things (IIoT), Cloud Manufacturing has been increasingly popular to the manufacturing industry. It can provide resource-sharing and on-demand manufacturing services as well as automatic collaborative manufacturing with the help of edge Programmable Logic Controllers (edge-PLCs). In such a system, there is a high risk of exposing user privacy and trading secret, due to exposure of sensitive transaction data to public servers. We propose a new privacy-preserving resource-trading scheme (PRTS), which leverages the concept of homomorphic cryptography and asymmetric searchable encryption, to simultaneously protect the privacy of the equipment factory and parts factories, while supporting best matching results in terms of parts parameters and price. Furthermore, a random forest-based method is applied to identify abnormal participants. The experimental results and security analysis show that the proposed scheme is accurate, effective, and secure, even under Off-line Keyword Guessing Attacks. Finally, encrypted data can resist analysis from mainstream machine learning techniques.
A Deep Learning–based Approach for Emotions Classification in Big Corpus of Imbalanced Tweets

Emotions detection in natural languages is very effective in analyzing the user's mood about a concerned product, news, topic, and so on. However, it is really a challenging task to extract important features from a burst of raw social text, as emotions are subjective with limited fuzzy boundaries. These subjective features can be conveyed in various perceptions and terminologies. In this article, we proposed an IoT-based framework for emotions classification of tweets using a hybrid approach of Term Frequency Inverse Document Frequency (TFIDF) and deep learning model. First, the raw tweets are filtered using the tokenization method for capturing useful features without noisy information. Second, the TFIDF statistical technique is applied to estimate the importance of features locally as well as globally. Third, the Adaptive Synthetic (ADASYN) class balancing technique is applied to solve the imbalance class issue among different classes of emotions. Finally, a deep learning model is designed to predict the emotions with dynamic epoch curves. The proposed methodology is analyzed on two different Twitter emotions datasets. The dynamic epoch curves are shown to show the behavior of test and train data points. It is proved that this methodology outperformed the popular state-of-the-art methods.
Smart Eye Testing

At present, most of the persons are suffering from eyesight. Their food habits and their genes will create eyesight. To test eyesight, there is a manual approach that consumes time as well as money. The noted disadvantages such as the need to take more care personally while interacting with an unknown environment and need to collaborate with other organizations for the charity of testing the eyesights. To avoid these inconveniences, the proposed approach is required which automatically tests the eyesight for right and left eyes using IoT. This smart vision approach using IoT will test eyesight for numerous users and will generate reports separately. That report sent to the user mobile. The virtual software developed uses IoT which enables checking of the sight functionality and the report is sent to the user mobile. In this, the virtual environment is created where IoT devices and networked computer vision devices are connected to test the eyesight. The proposed approach is demanded and is required in this modern and future culture too. Many benefits like consultancy price-cutting transport cost-cutting, time is reduced, speed up in generating reports are achieved. This is a future demanded revolutionary approach which directs many online spectacle shopping sites. This is considered a virtual doctor and serves lots of people since there is no tiredness because it is an automatic approach.
A Fully Residual Convolutional Neural Network for Background Subtraction

Background subtraction is an important step involved in solving computer vision problems. This paper proposes a novel background subtraction method with fully residual convolutional neural network (FR-CNN). This fully residual connection helps to fuse the fine scale and coarse scale feature information efficiently. The extracted non-handcrafted features are robust and promisingly efficient compared to the handcrafted features. Furthermore, the method uses temporal and spatial information for the background subtraction process. The optical flow image is used for extracting the temporal information. Additionally, a new background modeling technique is also proposed for the efficient background subtraction. The model is trained using the randomly selected 50 frames from each video sequence of the CDnet-2014 dataset and the FR-CNN model is evaluated by CDnet-2014 dataset. The results shown from the qualitative and quantitative analyses reveal that the FR-CNN model outperforms the state-of-the-art methods.
Inclined Quadrotor Landing using Deep Reinforcement Learning

Landing a quadrotor on an inclined surface is a challenging maneuver. The final state of any inclined landing trajectory is not an equilibrium, which precludes the use of most conventional control methods. We propose a deep reinforcement learning approach to design an autonomous landing controller for inclined surfaces. Using the proximal policy optimization (PPO) algorithm with sparse rewards and a tailored curriculum learning approach, an inclined landing policy can be trained in simulation in less than 90 minutes on a standard laptop. The policy then directly runs on a real Crazyflie 2.1 quadrotor and successfully performs real inclined landings in a flying arena. A single policy evaluation takes approximately 2.5\,ms, which makes it suitable for a future embedded implementation on the quadrotor.
A Study on Machine Learning Applied to Software Bug Priority Prediction

Bugs are among the top problems faced by software developers. As the size and complexity of software projects increase so does the number of bugs and their complexity. Bug priority prediction helps software developers focus their efforts on the most critical bugs that affect the core functionality of a software. By automating the process of priority prediction, it is possible to reduce the time spent analyzing new bug reports. In this paper, we extract bug reports from the bug tracking software of six popular open-source projects Hadoop, HBase, HDFS, Mesos, Spark, and MapReduce and apply five machine learning classifiers Multinomial Naive Bayes, Decision Tree, Logistic Regression, Random Forest, AdaBoost to automatically predict bug priority using the title, description, and summary of the bug report. We use tf-idf to extract useful features from the bug reports and employ precision, recall, and F1-score for measuring the performance of the classifiers. A stratified 10-fold cross-validation technique is used for model evaluation and the results are averaged over all 10 folds. We find that machine learning applied to bug priority prediction provides excellent results and can be used to significantly reduce the time involved in the bug prioritization process. From our experiments, we observe that no single classifier consistently performs best on all priority levels and metrics across all datasets. However, trends from results show that Multinomial Naive Bayes gives well balanced performance and is also fast to train and test. Logistic Regression and AdaBoost also performed well and are potential alternatives.
A Systematic Review on Spam Filtering Techniques based on Natural Language Processing Framework

Humans are referred to be as the sharpest species on the Earth. The capability to impart and share knowledge makes individual most sharp of all. They turned out to be brilliant to the point that they created up certain computer languages. The online networking stages so forth avails in sharing data, conveying yet accompanies downside additionally. The paramount downside is Spamming and Digital tormenting. Spams are the undesirable messages that entice the clients, goes through our data transmission and compromise our privacy. Spams are immerged as obstruction for email administrations. Around 70% of business mails are Spam. The Principle point is to identify the Spams and remove them which incorporates offensiveness, deceives to other sites, inappropriate content, vulgarity and those not specific with content by means of Natural Language Processing. Natural language processing is abridged as NLP, is an application of Artificial Intelligence. Subsequently, different methodologies have been proposed to manage undesirable Spams and Spam filtering is one of them. Various methods have been proposed by the researchers to deal with Spams. The implementation text classification techniques like tokenizing, stemming, POS-tagging and chunking took part in it.
Second-order encoding networks for semantic segmentation

Recently most of the state-of-the-art semantic segmentation methods have focused on context modeling for more accurate prediction. As real-world images often contain multiple objects and stuff, image features may have complex and multi-modal distributions. However, existing methods do not fully consider sch complex distributions, having limited capability for context modeling. Towards addressing this problem, this paper proposes a second-order encoding network (SoENet) trainable end-to-end for harvesting complex contextual knowledge. At the core of SoENet is an encoding module which can capture second-order statistics in individual feature subspaces. Specifically, we divide the entire feature space into a set of subspaces (clusters) represented by codewords, in each of which a covariance matrix is computed for second-order statistical modeling. The covariance matrices of all subspaces are concatenated to form a 3D tensor, which is then subject to convolutions and nonlinear activations and finally used for scaling of input features. In this way, we can encode the context which involves the complex distribution into learning process in an end-to-end manner. The proposed SoENet is evaluated on four commonly used challenging benchmarks, i.e., PASCAL Context, PASCAL VOC 2012, ADE20K and Cityscapes. The experiments show that our network significantly outperforms its counterparts and is competitive compared to state-of-the-art methods.