Reviewerly/exHarmony huggingface dataset:
IoT-Integrated Blockchain in the Drug Supply Chain

Lately, misusing and counterfeiting pharmaceuticals in the drug supply chain have grown evidently worldwide, especially in the developing world. A pharmaceutical product is counterfeit when having a fake identity (ID) or origin. A flawed drug supply chain is among the major causes of counterfeiting pharmaceuticals either for the complexity of tracing pharmaceuticals, the lack of transparency, or exchanging limited or irrelevant data between all the entities during the transactions. The counterfeit drugs adversely influence both patients’ health and the economic status of the actual producers. Transforming the existing drug supply chain by applying monitoring techniques between the manufacturer and the consumer would address such issues. The blockchain technology applies a digital signature where each block has a unique crypto ID, ensuring solid management over possession. The Internet of Things (IoT)-integrated blockchain is a competently distributed ledger (DLT), which preserves an entirely changeless register of the data on transactions avoiding faking and accessible by all parties for more transparency. This chapter explores counterfeiting pharmaceuticals and the innovative managing of the drug supply chain securely via the IoT-integrated blockchain framework. Adopting this framework would enhance managing pharmaceuticals in the drug supply chain, hence delivering better healthcare service.
Designing lessons aimed at teaching students to critically evaluate the personality of the heroes of the work of fiction

This article examines the conceivable outcomes of designing lessons aimed at teaching students to fundamentally evaluate the personalities of the protagonists of a fiction, at the side the formation of critical thinking and critical thinking skills of the heroes, the examination and translation of literary texts, as well as numerous positive qualities.
Introducing the Common Attack Process Framework for Incident Mapping

The paper presents a new framework that allows both educators and operational personnel to better overlay incidents into a simplified framework. While other attack frameworks exist, they either lack simplicity or are too focused on specific types of attacks. Therefore, the authors have attempted to define a framework that can be used broadly across both physical and cyber incidents. Furthermore, the paper provides several high-profile examples wherein it is shown how this new framework more accurately represents the adversary's actions. Lastly, the framework allows room for expansion in that, within each stage, a plethora of questions can be addressed, giving greater specificity into how that stage was carried out.
Video transaction algorithm considering <i>FISCO</i> alliance chain and improved trusted computing

With the advent of the era of self media, the demand for video trading is becoming more and more obvious. Alliance blockchain has the characteristics of traceable transaction records, tamper proof transaction records, decentralized transactions and faster transaction speed than public chains. These features make it a trading platform. Trusted computing can solve the problem of non Byzantine attack in the aspect of hardware. This paper proposes a video transaction algorithm considering FISCO alliance chain and improved trusted computing. First, an improved trusted computing algorithm is used to prepare a trusted transaction environment. Second, the video summary information extraction algorithm is used to extract the summary information that can uniquely identify the video. Finally, based on the video transactions algorithm of FISCO alliance chain, the video summary information is traded on the chain. Experimental results show that the proposed algorithm is efficient and robust for video transactions. At the same time, the algorithm has low computational power requirements and algorithm complexity, which can provide technical support for provincial and county financial media centers and relevant media departments.
Proposed Framework for Dealing COVID-19 Pandemic Using Blockchain Technology

The production and distribution of COVID-19 testing kits is an urgent and increasingly worldwide requirement, due to the ongoing pandemic. The accuracy of the kit is critically important and to save the world from the faulty kit becomes an issue. The kit before use has to be approved by an authorized medical research agency like US-FDA, ICMR, etc. In this paper, we proposed a framework that ensures that the testing kit is validated by various measures and gives the history of the supply chain of the testing kit. The parties that are used in the supply chain are Notary, Manufacturer, and Validating Party. A Consumer also plays an important role and can punch the batch number to check whether the kit is approved or not. The framework is developed using R3 Corda, a permissioned distributed ledger technology. A permissioned blockchain is used for data privacy and security so that only trusted parties can leave or join the system.
Evaluative Review of Streaming Analytics: Tools and Technologies in Real-Time Data Processing

Nowadays, big data processing systems are evolving to be more stream-oriented; where each data record is processed as it arrives by distributed and low latency computational frameworks [18]. Data streams have been extensively used in several fields of computational analytics such as data mining, business intelligence etc. [17]. In every field, the data stream can be considered as an ordered sequence of data items, as they continuously arrive over the period. Due to this characteristic, streaming data analytics is a challenging area of research [5, 11]. This paper aims to present data stream processing as a growing research field , along with streaming analytics frameworks as a rich focus area. The paper also contributes to evaluate the efficacy of available stream analytics frameworks. One of the Industry 4.0 use case - predictive maintenance rail transportation - has been illustrated here as a case study design mapped with streaming analytics framework.
An Effective Combination of Textures and Wavelet Features for Facial Expression Recognition

In order to explore the accompanying examination goals for facial expression recognition, a proper combination of classification and adequate feature extraction is necessary. If inadequate features are used, even the best classifier could fail to achieve accurate recognition. In this paper, a new fusion technique for human facial expression recognition is used to accurately recognize human facial expressions. A combination of Discrete Wavelet Features (DWT), Local Binary Pattern (LBP), and Histogram of Gradients (HoG) feature extraction techniques was used to investigate six human emotions. K-Nearest Neighbors (KNN), Decision Tree (DT), Multi-Layer Perceptron (MLP), and Random Forest (RF) were chosen for classification. These algorithms were implemented and tested on the Static Facial Expression in Wild (SWEW) dataset which consists of facial expressions of high accuracy. The proposed algorithm exhibited 87% accuracy which is higher than the accuracy of the individual algorithms.
Detail 3D Face Reconstruction Based on 3DMM and Displacement Map

How to accurately reconstruct the 3D model human face is a challenge issue in the computer vision. Due to the complexity of face reconstruction and diversity of face features, most existing methods are aimed at reconstructing a smooth face model with ignoring face details. In this paper a novel deep learning-based face reconstruction method is proposed. It contains two modules: initial face reconstruction and face details synthesis. In the initial face reconstruction module, a neural network is used to detect the facial feature points and the angle of the pose face, and 3D Morphable Model (3DMM) is used to reconstruct the rough shape of the face model. In the face detail synthesis module, Conditional Generation Adversarial Network (CGAN) is used to synthesize the displacement map. The map provides texture features to render to the face surface reconstruction, so as to reflect the face details. Our proposal is evaluated by Facescape dataset in experiments and achieved better performance than other current methods.
An Approach to Target Reselection for Anti-Ship Missile Against Centroid Jamming with Accurate Tracking Information

By equaling the detection of centroid jamming to the “outlier” detection in the process of tracking with Kalman filter, an approach to target reselection for anti-ship missile against centroid jamming with accurate tracking information is proposed in this paper considering the mutation of the law of motion at the tracking point under centroid jamming. For this approach, an accurate target tracking model is built on the basis of extended Kalman filter (EKF). Using the information collected by radar seeker including distance, velocity and angle, it could achieve the accurate tracking of the target and determine the motion state of the target accurately. On this basis, the orthogonality of innovation in the process of Kalman filtering is utilized to detect any mutation of the motion state of the target, so that the existence of centroid jamming is detected when there is any mutation of the law of motion at the tracking point in the process of chaff centroid jamming. This offers a new solution for remote beyond-visual-range (BVR) anti-ship missile against centroid jamming.
A Novel Multi-label Feature Selection Based on Pareto Optimality

To enhance the generalization of the multi-label classifier, an approximately optimal feature subset can be obtained by leveraging multi-label feature selection approach to characterize the original space. In this paper, a multi-label feature selection with Pareto optimality without presetting threshold, called UN-MLFSPO. Firstly, it adopts the Hilbert-Schmidt Independence Criterion (HSIC) to map multi-label features to high-dimensional space to construct the correlation matrix between features and labels. And then, an approximation feature subset is obtained by combining Pareto optimization. The experimental results show the effectiveness of the proposed algorithm on multi-label tasks.
*-CFQ: Analyzing the Scalability of Machine Learning on a Compositional Task

We present *-CFQ (star-CFQ): a suite of large-scale datasets of varying scope based on the CFQ semantic parsing benchmark, designed for principled investigation of the scalability of machine learning systems in a realistic compositional task setting. Using this suite, we conduct a series of experiments investigating the ability of Transformers to benefit from increased training data size under conditions of fixed computational cost. We show that compositional generalization remains a challenge at all training sizes, and we show that increasing the scope of natural language leads to consistently higher error rates, which are only partially offset by increased training data. We further show that while additional training data from a related domain improves the accuracy in data-starved situations, this improvement is limited and diminishes as the distance from the related domain to the target domain increases.
Application of Active Learning in Carbonate Lithologic Identification

In the process of oil and gas reservoir exploration, the high cost of lithology information obtained by core drilling makes it difficult to collect a large number of lithology samples. It is the key to use a small number of labeled samples with lithology information to train lithologic identification models. This paper proposes to apply active learning(AL) to lithologic identification. Active learning can select high-value samples from unlabeled samples, but does not consider the problem of excessive similarity between high-value samples. By introducing Euclidean distance into active learning, active learning with Euclidean metric (ALWED) can judge the similarity between samples, thereby ensuring the diversity of high-value samples. Taking the carbonate reservoir in the east area of the Sulige gas field as an example, Compared with random selection and AL, The accuracy of the machine learning model trained with samples selected by ALWED increased the lithologic identification increased by 4.89% and 2.70%, respectively. It provides a new idea for lithology identification using few-shot learning.
An Analysis of the Trends in Studies Related to Contactless and Remote Class Operations for the College Students of Nursing and Health Related Major Programs

Purpose : The purpose of this study is to provide basic data for the future research and direction of non-face-to-face remote class operation research for nursing and health college students by analyzing and confirming trends on non-face-to-face remote class operation research targeting domestic nursing and health college students. Method : Literature searches were conducted through the databases such as KERIS, K-eArticles, DBpia, KISS, the Academy of Academic Education, and others, covering those published from 2011 to April 2021. The main terms of search were ‘online class,’ ‘LMS class,’ ‘corona class operation,’ ‘Nursing and health college students,’ and ‘online class satisfaction.’ The final analysis of the 50 films which meet the criteria for selection was performed. Result : A total of 50 research papers on the operation of the contactless, remote classes for college students in the department of public health have been analyzed. Among the 87 key words, the most frequent were “online classes,” “LMS classes,” “corona class management,” “Nursing and health college students,” “online class satisfaction,” and “online classes.” Of the 50 papers, 31 were quantitative research studies, 11 was a experimental research, and 8 was an qualitative research Conclusion : An analysis of the contactless, remote classes identified in this study shows that this study can lay a foundation for developing the programs needed to establish a systematic contactless, remote class system for the Nursing and health college students.
Towards a New generation of Personalized Intelligent Conversational Agents

The Personalized Intelligent Conversational Agents workshop focuses on both long-term engaging spoken dialogue systems and text-based chatbots, as well as conversational recommender systems. The goal of the workshop is to stimulate discussion around problems, challenges, possible solutions and research directions regarding the exploitation of natural language processing and machine learning techniques to learn user features and to use them to personalize the dialogue in the next generation of intelligent conversational agents.
Picking Pearl from Seabed: Extracting Artefacts from Noisy Issue Triaging Collaborative Conversations for Hybrid Cloud Services

Site Reliability Engineers (SREs) play a key role in identifying the cause of an issue and preforming remediation steps to resolve it. After an issue is reported, SREs come together in a virtual room (collaboration platform) to triage the issue. While doing so, they leave behind a wealth of information, in the form of conversations, which can be used later for triaging similar issues. However, usability of these conversations offer challenges due to them being and scarcity of conversation utterance label. This paper presents a novel approach for issue artefact extraction from noisy conversations with minimal labelled data. We propose a combination of unsupervised and supervised models with minimal human intervention that leverages domain knowledge to predict artefacts for a small amount of conversation data and use that for fine-tuning an already pre-trained language model for artefact prediction on a large amount of conversation data. Experimental results on our dataset show that the proposed ensemble of the unsupervised and supervised models is better than using either one of them individually. We also present a deployment case study of the proposed artefact prediction.
Attempts to develop students’ generic skills by raising their awareness in experimental practice

To develop the generic skills of students, colleges and universities are required to improve the curriculum to encourage students to engage proactively in learning and strengthening their competency to utilize acquired skills effectively in the society. However, no teaching methods as such have been established, especially, those of enhancing students' competency. Competency is difficult to improve in a short period, therefore, continuous, effective approaches are needed. In this paper, based on the results of the continuous surveys, we present skills and competency of students which are required in the real world but show only low growth and need to be enhanced by the time they graduate from Kosen (five-year technical colleges in Japan), namely, planning, plan implementation and collaboration. Then we propose curriculum supplements and report on their effects. There are two points we attempted. First, to make students aware of the skills and competency to be improved, self-evaluation was performed before and after the course. Second, teachers tried to accelerate the progress of students' skills by changing the way of conducting experiment classes. We report on the effects of these two points based on the evaluation by the students themselves.
Advancing 1+1=1 Fully Integrated Designs using a Five Formative Figures Approach

For designs aiming to be fully integrated, lacking concrete tools to facilitate connections between methods and inferences stymie efforts toward advancing solutions to the “1+1=1 integration challenge.” Therefore, this article highlights formative joint displays as a mixing strategy to signpost explicit integration points in a mixed methods research design. Specifically, we present a practical moniker of Five Formative Figures to encapsulate a framework for using formative joint displays at each stage of the design. We situate these formative joint displays in fully integrated mixed methods designs, mixing across all phases of the research process. This article draws from a 4-stage, fully integrated study on the organization of and language use in transfer student webpages on institutional websites, highlighting how the methods are applicable beyond the original context. We provide example displays for the planning/research questions, data collection, sampling, analysis, and inference stages of the study. Although the moniker suggests using five joint displays, formative joint displays could be used several times to connect different aspects of the design and to form tightly integrated meta-inferences. This work demonstrates the capacity of formative joint displays to ensure mixing in the design formulation and to connect inferences across strands in a single design.
Task Scheduling in a Cloud Computing Environment Using a Whale Optimization Algorithm

AbstractIn cloud computing, task scheduling is the procedure of mapping the tasks onto the available resources while minimizing the execution cost and makespan. Scheduling of cloud tasks in a computing environment is a well-known NP-hard optimization problem and thus inflexible with accurate solutions. It turns out to be even more interesting in the cloud computing environment because of its heterogeneous and dynamic nature. Multiple scheduling techniques have been suggested by multiple researchers to execute the independent tasks in a heterogeneous environment of the cloud. This paper proposes the multi-objective model-based task scheduling algorithm known as the whale optimization algorithm (WOA) for optimum scheduling of independent tasks onto the available computing resources. Firstly, it uses the integer linear programming (ILP) model to calculate the fitness value by computing the makespan and execution cost function. The proposed scheduling algorithm can generate the optimum scheduler that mapped the independent tasks onto the available virtual machines (VIs) while reducing the cost and makespan. To evaluate the optimality and execution speed of the proposed scheduling algorithm, it is implemented in a CloudSim environment and the simulation results show that the proposed WOA can reduce the cost and execution time in contrast to the PSO algorithm.KeywordsTask schedulingCloud computingWhale optimization algorithm (WOA)
Chinese Character Decomposition for Neural MT with Multi-Word Expressions.

Chinese character decomposition has been used as a feature to enhance Machine Translation (MT) models, combining radicals into character and word level models. Recent work has investigated ideograph or stroke level embedding. However, questions remain about different decomposition levels of Chinese character representations, radical and strokes, best suited for MT. To investigate the impact of Chinese decomposition embedding in detail, i.e., radical, stroke, and intermediate levels, and how well these decompositions represent the meaning of the original character sequences, we carry out analysis with both automated and human evaluation of MT. Furthermore, we investigate if the combination of decomposed Multiword Expressions (MWEs) can enhance the model learning. MWE integration into MT has seen more than a decade of exploration. However, decomposed MWEs has not previously been explored.
Vision Based Human Activity Recognition: A Comprehensive Review of Methods &amp; Techniques

Human Activity Recognition (HAR) plays an important role in various domains. There are four major phases of HAR: Data Collection, Pre-Processing, Feature Extraction and Training. Out of these, Pre-processing and Feature Extraction requires massive attention, as these are the building block for the training phase. In this paper, an extensive and thorough review of the state-of-the-art methods is given along with identification of challenging areas of HAR ecosystem. Primarily, trends in research outflowing in the area of Vision based approach is effectively studied in the present review. Further, a consideration with state-of-the-art applications and the importance of handcrafted and learning based features is emphasized. The review presented in this paper, gives the details of available training datasets of HAR and also discusses the popular publicly available datasets. The readers will be benefitted with a comprehensive review of the extensive work done in the field of HAR and its Vision based approaches.
Implementation of Gamification into Management of Education for Motivating Learners

As we know now, Indonesia has begun to enter the era of revolution 4.0 which in that era many changes occurs in all fields. Included in the field of education, the changes that happen in the world of education today is so significant with the start of abandonment of teaching methods that still use conventional way. Keeping track of tasks with books, face-to- face communication, collecting tasks in hardcopy, which of course will cause many losses in a certain period of time, such as many long-buried tasks and hard to find when needed. The learning method is considered a dull way, where students can not explore in the learning search for the number of files that have been collected. So from now on the application of learning  methods is changed with the concept of gamification. Gamification learning method was made to keep up with the times when the students prefer to play games rather than learn, therefore gamification methods can be applied to educational management in higher education. It is expected that the method can increase students' interest in learning so that it motivates to explore the ability in the learning process.