Reviewerly/exHarmony huggingface dataset:
Creating a Culture of Online Quality: The People, Policies, and Processes that Facilitate Institutional Change for Online Course Quality Assurance

The success of quality assurance initiatives in online education is dependent on the successful implementation of the processes, resources, and tools created to ensure quality. There is scant research, however, on the factors that most enable quality assurance implementation at higher education institutions. This research examined a specific population of institutions who were assessed as engaging in high levels of online course quality assurance efforts, as measured by their activity level of using the Quality Matters (QM) framework. The specific people, policies, and processes that enabled and supported institutional course design quality assurance are examined using a mixed-methods approach. Forty-three implementation coordinators from high-activity institutions completed a survey on institutional quality assurance efforts specific to course design and development. Seventeen of those coordinators also engaged in an individual interview to provide greater depth and insight into the institutional journey of quality assurance implementation for online course quality. Key takeaways showed an impact for focused, institutionally-supported goals for course design; choosing an experienced person who was passionate about quality assurance as the implementation lead; and utilizing an inclusive, flexible, collaborative implementation approach, rather than a top-down approach or mandates. The respondents also indicated that adapting the implementation to the institutional context was a key success factor, as was focusing on creating buy-in and shared goals for quality learning as a pathway toward creating an institutional culture shift.
Understanding and Leveraging Cluster Heterogeneity for Efficient Execution of Cloud Services

Cloud warehouses are becoming increasingly heterogeneous by introducing different types of processors of varying speed and energy-efficiency. Developing an optimal strategy for distributing latency-critical service (LC-service) requests across multiple instances in a heterogeneous cluster is non-trivial. In this paper, we present a detailed analysis of the impact of cluster heterogeneity on the achieved server utilization and energy footprint to meet the required service-level latency bound (SLO) of LC-services. We develop cluster-level control plane strategies to address two forms of cluster heterogeneity - capacity and energy-efficiency. First, we propose Maximum-SLO-Guaranteed Capacity (MSG-Capacity) proportional load balancing for LC-Services to address the capacity heterogeneity and show that it can achieve higher utilization than naive performance-based heterogeneity awareness. Then, we present Efficient-First (E-First) heuristic-based Instance Scaling to address the efficiency heterogeneity. Finally, to address the bi-dimensional (capacity and energy-efficiency) heterogeneity, we superimpose the two approaches to propose Energy-efficient and MSG-Capacity (E2MC) based control-plane strategy that maximizes utilization while minimizing the energy footprint.
A Method to Handle Unstable Time Series in Anomaly Detection Problem

Time series analysis is a well-known problem solved in many spheres of human activity. This analysis is most widely used in the tasks of tracking the state of any processes - checking stability of working machines in factories, tracking the rhythm of the heartbeat, and so on. One of the important tasks is decrease time to identify deviations in these processes, which reduces the cost of their elimination. A well known name for such tasks is anomaly detection and they belong to one-class classification problem. It is known that in order to effectively solve this type of problem, the spread of data within the training dataset should be taken into account. In this paper, we present a method that allows us to reduce intra-class data variation by automatically searching for and excluding unstable parts from the time series. The application of this method is considered as part of an algorithm that can be used to represent raw samples of time series as vectors suitable for further work with machine learning models. The algorithm has been tested on boundary-based machine learning methods such as One-class Support Vector Machine (OCSVM), Isolation Forest and Local Outlier Factor (LOF). Our dataset consists of time series from gas sensor, which are reflect sensor‘s sensitive surface reaction to several gases appearing in the environment.
Research Trends in Educational Technology

The researchers of this study selected five journals in the field of education and conducted a series of analyses regarding publications dating from 2010 to 2019 to investigate the research trends and characteristics in the field of Educational Technology. By using the analytic tool Content Analysis Toolkit for Academic Research (CATAR), the researchers in this study conducted bibliometric analysis and breakdown analyses to summarize major contributing countries, educational institutions, most productive authors, and most cited papers; moreover, they used co-word analysis to reveal the representative items within each cluster. The findings in this study can provide implications and references for educators and researchers in the field of Educational Technology when selecting variables for their studies and technologies for their students.
Segmentation with Speckle Reduction and Superresolution by Deep Leaning for Human Ultrasonic Echo Image

Ultrasound (US) image diagnosis is widely used for detection and treatment of human malignant tissues. Physicians perform differentiation of tissues through interpreting ultrasound echo images morphologically. However, the ultrasound image always comes with speckles, which makes segmentation of a target tissue difficult. Recently, a deep learning (DL) approach becomes a new way for picture denoising instead of signal processing. In this report, we use the DL denoising to reduce the US speckles. Subsequently, we perform DL segmentation well known for other medical images. In order to further increase the segmentation accuracy, we also perform DL superresolution. The DL superresolution is also well known for a picture and however, not so for an echo image. The target segmentation tissue is a carotid artery, specifically a lumen. To verify the feasibilities of our approaches, simulations and in vivo experiments are performed.Clinical Relevance- Method effectiveness is confirmed for in vivo data.
Pervasive Security and Privacy—A Brief Reflection on Challenges and Opportunities

Developments in pervasive computing trigger a need to rethink security and privacy mechanisms. At the same time, security and privacy methods and systems can likewise benefit from the proliferation of pervasive computing technology, making novel approaches possible. In light of these exciting changes, beginning with this introductory column, IEEE Pervasive Computing introduces a new Security and Privacy Department in which I will invite columns on new developments and topics.
Behavioral Biometrics based Authentication System using MLP-NN and MVPA

In this age of information, mechanisms to ensure data and system security are of paramount importance. Password is one of the most widely used authentication mechanism, which also makes it prone to attacks. Methods to improve password authentication is a hot topic of research, in which biometric authentication offers new possibilities. Behavioral biometrics is a branch of science that studies the patterns of human activities that are uniquely recognisable and measurable. Behavioral biometrics which employs mechanisms like keystroke dynamics offers to improve password authentication without using additional hardware. This paper proposes an improvement in the existing authentication mechanism using Keystroke Dynamics (KD), Multilayer Perceptron Neural Network (MLP-NN) and Most Valuable Player Algorithm (MVPA). To overcome the drawbacks in the conventional training process, MLP-NN is trained using MVPA. Using MATLAB software, the proposed biometric authentication system is developed and validated on different users. Comparison with different authentication performance measures such FAR & FRR are also discussed.
Named Entity Recognizer for Konkani Text

A named entity recognizer (NER), an essential tool for natural language processing (NLP), is presented for the first time for the Konkani language. Gold data of 1000 NER-tagged Konkani sentences consisting of 1068 named entities is one of the linguistic resources generated through this work. A conditional random field (CRF) classifier built on the training data set of 794 named entities from 800 sentences of the corpus, demonstrated 96% accuracy and 72% f-score. On the test data set of 274 named entities from 200 sentences of the corpus, 86% accuracy and 66% f-score were obtained. When the training and test data were complemented with a lookup table consisting of a database of 12 months, 53 locations, 44 person-names and 23 numerals and their synonyms, the figures improved to 99% accuracy and 90% f-score for the training data set, and 89% accuracy and 73% f-score for the test data set. To place our research in perspective, a summary is presented of the NER literature for world languages as well as Indian languages, as also NER for Indian languages using CRF.
PERSPECTIVES ON KNOWLEDGE MANAGEMENT APPLICATION IN BPPT LIBRARY AS AN ORGANIZATIONAL KNOWLEDGE COLLECTION PLATFORM

This study evaluates the practice of a knowledge management system through the knowledge management application used by the Agency for the Assessment and Application of Technology (BPPT). The purpose of this study is to understand and get an overview from BPPT employees related to BPPT knowledge management (KM) application in its function as an organizational knowledge collection platform. This study is hopefully beneficial to see the effectiveness of the features of KM application in supporting the work activities of BPPT employees. The method used in this research is quantitative, using a questionnaire technique based on a survey to gather the data. The questionnaire was distributed to employees who are the administrator of the KM BPPT application from each BPPT working unit. The collected data were processed and analyzed using the AIDA model. This study indicates that BPPT employees have used the BPPT KM application, and some of the features also have been used, such as chats, tasks, drives, and events. Moreover, based on the results obtained, it is known that BPPT employees trust the app and consider the BPPT KM application reliable to store and manage organizational knowledge properly.
Kubeflow-based Automatic Data Processing Service for Data Center of State Grid Scenario

With the rapid development of machine learning and deep learning, more and more machine learning and deep learning have appeared in the power grid business. The data processing in the State Grid business is very complicated, the data processing is very cumbersome, and the reuse rate of the data processing code is also very low. In order to solve these problems, this paper proposes an efficient automated data processing service-EADP (Efficient Automated Data Processing). EADP service is built on Kubeflow. Kubeflow/Pipeline is Google’s open source workflow for building end-to-end services. Users can build the code as Pipeline/Component for use by Kubeflow/Pipeline. But Kubeflow's Component and Pipeline construction is extremely cumbersome and lacks management of Component and Pipeline. In order to solve these problems, EADP provides the function of automatically constructing Component and data processing DAG. There is a one-to-one correspondence between Component and Docker/Image. Docker/Image contains code blocks for data processing, which can be run after instantiation. The data processing flow can be constructed as a data processing DAG. The data processing DAG is composed of Component, and each node in the DAG corresponds to a Component. EADP uses a topological sorting algorithm to convert the data processing DAG into Kubeflow/Pipeline, thereby realizing automated data processing. On the surface of the experiment, EADP has high stability and convenience, which can greatly shorten the time-consuming data processing.
Optimized Quantum Circuit Generation with SPIRAL

Quantum computers have been at the bleeding edge of computing technology for nearly 40 years and promise to make several classically-difficult problems tractable. However, despite many hardware and algorithm breakthroughs, the software infrastructure that compiles programs for these devices requires further development in order to produce sufficiently scalable code. In this work we present a novel approach to compiling more efficient quantum programs. Rather than mapping individual programs onto quantum hardware, we capture the input as a high-level mathematical transform and generate a multitude of architecture-compliant programs directly from that specification, allowing us to search over a much larger space of implementations to select the best output. Specifically, this is achieved by casting circuit generation as a sparse matrix factorization task and recursively searching over a host of divide-and-conquer decomposition rules. By generating programs from the top-down, we retain high-level information about the program we are compiling and can explore algorithm-specific rewrites that are nearly impossible to recognize given only a program stream. We implement the proposed framework [33] [34] with SPIRAL [18], a code generation platform founded on the GAP [42] computer algebra system; we ultimately demonstrate that SPIRAL is a promising supplemental tool for future quantum frameworks.
A Novel Approach to Cyber Situational Awareness in Embedded Systems

Impacts from cyberattacks on mission capability of an embedded system and their mitigations vary according to the system’s application and constituents. To effectively respond to a cyber attack, the operator must be given proper context of how that attack impacts mission capability and performance. In this paper, we describe a novel approach toward developing and visualizing cyber situational awareness on embedded systems. The approach uses a mission decomposition model to derive component-level dependencies on mission functionality. This allows us to calculate a mission capability metric which indicates the relative ability to perform mission functionality while undergoing cyber duress in operation. Our approach provides the operator with traceable, concise information about system components, how they are affected by cyberattacks, and the impact on mission capability – allowing the operator to make informed decisions and take timely courses of action.
Indices of Favourable and Unfavourable Emotions in the Inter-Actional Context of the Classroom: Constructions from the Chilean Case

The system for measuring the quality of education (SIMCE) is a standardised evaluation that provides results on the academic achievement of Chilean students, while also including indicators of personal and social development. Through a mixed analysis of variables extracted from these indicators, the purpose of this research study is to build a measurement system to assess the favourable and unfavourable emotions of students who took the test in 2018. To contextualise this work, a systematic literature review was carried out synthetising scientific evidence concerning emotions and the interactional context of the classroom. Through a methodological transposition, a qualitative theoretical model epistemologically grounded on radical constructivism was validated quantitatively. This transposition resulted in the construction of three indices of favourable and unfavourable emotions: motivation arising from interaction, exclusionary interaction and interactional context. The results show favourable and unfavourable emotions for learning are a variable in the SIMCE indicators that can be used to understand student academic achievement, confirming existing empirical evidence regarding the explanatory and predictive value of emotions in students’ performance. These results highlight the potential benefits of expanding on this type of research to improve the quality of Chilean education based on the resources already available in the current evaluation system.
Improving Tiny YOLO with Fewer Model Parameters

With the rapid development of convolutional neural networks (CNNs), there are a variety of techniques that can improve existing CNN models, including attention mechanisms, activation functions, and data augmentation. However, integrating these techniques can lead to a significant increase in the number of parameters and FLOPs. Here, we integrated Efficient Channel Attention Net(ECA-Net), Mish activation function, All Convolutional Net (ALL-CNN), and a twin detection head architecture into YOLOv4-tiny, yielding an AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> of 44.2% on the MS COCO 2017 dataset. The proposed Attention ALL-CNN Twin Head YOLO (A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -YOLO) outperforms the original YOLOv4-tiny on the same dataset by 3.3% and reduces the model parameters by 7.26%. Source code is at https://github.com/e96031413/AA-YOLO
CANDIDATE: A tool for generating anonymous participant-linking IDs in multi-session studies

To ensure the privacy of participants is an ethical and legal obligation for researchers. Yet, achieving anonymity can be technically difficult. When observing participants over time one needs mechanisms to link the data from the different sessions. Also, it is often necessary to expand the sample of participants during a project.To help researchers simplify the administration of such studies the CANDIDATE tool is proposed. This tool allows simple, unique, and anonymous participant IDs to be generated on the fly.Simulations were used to validate the uniqueness of the IDs as well as their anonymity.The tool can successfully generate IDs with a low collision rate while maintaining high anonymity. A practical compromise between integrity and anonymity was achieved when the ID space is about ten times the number of participants.The tool holds potential for making it easier to collect more comprehensive empirical evidence over time that in turn will provide a more solid basis for drawing reliable conclusions based on research data. An open-source implementation of the tool that runs locally in a web-browser is made available.
A Novel Curved Gaussian Mixture Model and Its Application in Motion Skill Encoding

The purpose of this paper is to present a novel curved Gaussian Mixture Model (CGMM) and to study the application of it in motion skill encoding. Primarily, Gaussian mixture model (GMM) has been widely applied on many occasions when a probability density function is needed to approximate a complex probability distribution. However, GMM cannot efficiently approach highly non-linear distributions. Thus, the proposed novel CGMM, as a weighted mixture of curved Gaussian models (CGM), is structured with non-linear transfers, which reshapes the flat GMM into a geo-metrically curved one. As a consequence, CGMM has more freedoms and flexibilities than the flat GMM so a CGMM requires fewer number of components in fitting highly non-linear motion trajectories. Moreover, we derive a dedicated iterative parameter estimation algorithm for the CGMM based on maximum likelihood estimation (MLE) theory. To evaluate the performance of the CGMM and its parameter estimation algorithm, a series of quantitative experiments are carried out. We first test the model performance in the data fitting task with the generated synthetic data. Then a motion skill encoding test is carried out on a human motion trajectory dataset built by a Virtual Reality (VR) based motion tracking system. The empirical results support that CGMM outperforms state-of-the-arts in the model performance test. Meanwhile, CGMM has a significant improvement in encoding high dimensional non-linear trajectory data compared to the GMM in motion skill encoding test with its dedicated parameter estimation algorithm.
Multiple Intelligence Theory for Gamification of Online STEM Modules

Due to the Corona crisis teaching in higher education was shifted to online learning and has changed the lives of learners around the world. Students study in online courses from home and sit in front of technical devices all day. To get the students’ attention teachers need to find new ways of teaching. Smart gamification allows the students to participate in many ways in the lectures. As Howard Gardener states in the Multiple Intelligence Theory people have different intelligence types and need to be activated individually. Lecturers from different universities in Europe have come together to develop and exchange new methods to keep the students active during the STEM lectures and increase their motivation to study (STEM=science, technology, engineering, mathematics). Furthermore, students have the chance to collaborate in virtual mobility with students from Germany, Poland, Portugal, and Spain. A tool is needed that gets the students’ attention with addressing different intelligence types Gardener classified, e.g. musical, visual, verbal, logical-mathematical, bodily-kinesthetic, interpersonal, intrapersonal, naturalistic, existential. With the WBS LearnSpace 3D®, which is a tool for online working academia, students and lecturers from different countries are brought together on a virtual campus which takes virtual mobility to a next level.
Learning Heterogeneous Strategies via Graph-based Multi-agent Reinforcement Learning

In a mixed cooperative-competitive environment, each agent needs to learn heterogeneous strategies. The complex game relationship between heterogeneous agents causes difficulties for strategy learning. In this paper, we propose a graph-based multi-agent reinforcement learning method to simplify the learning process, named hierarchical heterogeneous graph multi-agent actor-critic (H2G-MAAC). The method first uses a hierarchical heterogeneous graph to model the hierarchical relationships among multiple heterogeneous agents. Then, it conducts representation learning for multiple agents with hierarchical graph attention networks. Finally, it learns heterogeneous strategies with multi-agent actor-critic. We conduct experiments in Predator-Prey games. The results indicate that the proposed method can simplify the learning process and outperforms existing state-of-the-art methods.
A Mobile Platform-Oriented English Vocabulary Deep Search Learning Algorithm

Mobile learning is a new learning model that emerged under the background of the integration of teaching and information technology. It has the characteristics of ubiquity, individualization, and interaction, which promotes various vocabulary mobile learning apps to become the main form of English vocabulary learning by about 6%. Based on the analysis of the feasibility of mobile learning to enhance college English learning from the four dimensions of social needs, social possibilities, personal needs, and personal possibilities, this paper analyzes the design ideas and system characteristics of the mobile learning system for college English vocabulary based on the deep learning, and the result shows that the algorithm improve the efficiency to 8.2%