Reviewerly/exHarmony huggingface dataset:
Making Your Educational Data Visual

Graduate medical educators collect a vast amount of educational data about applicants, learners, faculty, programs, and systems. Using this data effectively can be difficult. Typically, we view our data in a table or Excel spreadsheet, draw a conclusion, and then take action. Yet optimizing the use of our data requires iterative, ongoing interaction with the stakeholders and data analysis skills. This iterative process helps us decide which features of the data are most relevant to represent visually and which visual presentation structure is best to communicate the data story.Data visualization involves translating information into a visual context such as a graph, chart, or map, to render the data easier to understand and to gain insights. Effective data visualization can quickly communicate large amounts of information and complex relationships, engage viewers, and facilitate opportunities to share insights and conclusions.1–3 In medical education, uses of data visualization range from assessment of resident competence in dashboards to communicating statistical findings from a research study.Often, data visualization is viewed as the last step in data analysis, used to present key findings rather than integrated as part of the data analysis process. Actively using data visualizations throughout the analysis process can prompt recognition of relationships between data points, additional questions, and new analyses. Data can be explored in many ways, such as descriptively (eg, plotting all points, mean, or median) and in more complex ways (eg, exploring the relationships between variables and reanalyzing data by subgroup), with the goal of identifying insights that meet the needs and objectives of the audience. Graphs, such as bar charts for categorical data and boxplots for continuous data, can inform preliminary visualizations.1,4,5 Once finalized, visual mockups are tested with the target audience(s) to ensure that the data addresses their needs and complements the compelling narrative within the written report. Interactive data representation, such as longitudinal competency dashboards, can meaningfully inform stakeholder objectives over time as well as the data analyst's understanding of the data.1,3
Building an Extended Reality Pedagogical Continuum Through 180° First-Person Point-of-View Video

The advancement of virtual reality (VR) technology for educational instruction and curricular (re)design have become highly attractive and newly demanding areas of both the technology and healthcare industries. However, the quickly evolving field is still learning about each of the associated VR technologies, whether they are evidence-based, and how they are validated to decrease cognitive load and in turn increase student/learner comprehension. Likewise, the instructional (re)design of the content that the student/learner is exposed to in VR, and whether it is immersive, and promotes memorable content and experiences can influence their learning outcomes. Here the Revinax® Handbook content library that is displayed in an immersive virtual reality application in first-person point-of-view (IVRA-FPV) is contrasted with third-person point-of-view (IVRA-TPV) through VR headsets to an individual, and computer displays to many individuals along with augmented reality (AR) are evaluated as emerging advancements in the field of VR and AR.
Visual Sentiment Prediction Using Few-shot Learning via Distribution Relations of Visual Features

A visual sentiment prediction method using few-shot learning via distribution relations of visual features is proposed in this paper. To train a model that can predict the sentiments of images with a small amount of labeled data, the proposed method focuses on distribution relations of feature embeddings of images. The relations are helpful for visual sentiment prediction since distances between different sentiments are not equal according to Mikels’ wheel. The proposed method incorporates distribution propagation in a graph network to take advantage of the distribution relations. The experiment shows that the prediction accuracy of the proposed method achieves an improvement compared to comparative methods. The main contribution of this paper is the introduction of a new method using few-shot learning via distribution relations of visual features for visual sentiment prediction.
Software-Hardware Co-Optimization for CNNs Based on Reconfigurable Devices

Convolutional Neural Network (CNN) is an efficient recognition algorithm used in pattern recognition, image processing, etc. The problem of how to build an efficient computational system for CNNs has become a pressing one, which is traditionally achieved by software optimization or hardware accelerator design. In contrast, this paper argues that software algorithms and hardware architectures affect each other in neural network applications with a high degree of coupling. In this paper, we propose a software and hardware-based co-optimization design to improve CNNs’ processing efficiency by co-optimization of software and hardware. First, a modular analysis and collaborative design are carried out for the ShuffleNetV2 model by implementing quantization and improving the computational unit. Second, the model is optimized based on reconfigurable computing devices for its characteristics. Third, 8 bit quantization is implemented, while the depth-separable convolution operation and channel selection module is redesigned to make the module perform operations in a hardware-friendly form. The experimental work was carried out on the FPGA platform Xilin xzynqxc-7Z045 using High Level Synthesis (HLS). The experimental results show that the optimized model has a significant improvement in terms of resource utilization and latency.
The Application of Data Mining and Fusion Technology in the Security Management of Sensitive Data in Data Center

Nowadays, network security problems are emerging one after another. In the face of these security problems, various security products have appeared. At the same time, various security products deployed in the network generate a large number of security events (log files) every day, and how to extract valuable information has become an urgent problem to be solved. The purpose of this article is to study the application of data mining and fusion technology in the security management of sensitive data in the data center. This article first summarizes the basic theories of data mining and fusion, and then extends the core technology of data mining and data fusion, and side-by-side combined with the current status of the security management of sensitive data in the data center, we use data mining and fusion technology to research and analyze the application of the sensitive data security management in the data center. This paper systematically explains the architecture and algorithm implementation of security management based on data mining and fusion technology, and the application of data mining and fusion technology in the security management of sensitive data in the data center is investigated and researched through the comparative method, field research and other research methods. The experimental research shows that the sensitive data security is compared with the traditional data center. Management, the data security management system using data mining and fusion technology has more excellent performance.
How Do Students Feel About Automated Security Static Analysis Exercises?

This Innovative Practice, work in progress (WIP) paper presents our experience related to two exercises that focus on automated security static analysis, a practice used to integrate security into development and operations (DevOps). The concept has gained popularity amongst information technology (IT) organizations. However, security-related concerns, such as security weaknesses in DevOps artifacts can cause serious consequences. Our preliminary findings indicate that (i) students positively perceive the introduced exercises; and (ii) the students perform well if they are provided necessary background on the exercises. Our WIP paper lays the groundwork to build course materials that will facilitate development, deployment, and dissemination of DevOps-related education materials that also incorporate cybersecurity concepts.
EASE Em: Hybrid Recommendation System for Item Cold Start Problem

Recommender Systems play a very important role in online services nowadays, with the huge repository of content being generated every day. While these systems are used on large scales, they still face a lot of problems. The item cold start is one of the most famous problems in recommender systems. While systems can develop an initial image of new user profiles through various methods, new items face a problem surfacing into user recommendations. This is because they have low to almost zero interactions, and most recommender systems use methods like collaborative filtering to share similar items to similar user profiles. This is an important problem to be dealt with. Otherwise, recommendation feeds get saturated with already popular content, which harms new creators on platforms that might be creating innovative content. We propose a hybrid recommender system: EASE’Em, that seeks to solve this problem by generating cold start items to users based on the item similarities and user profiles. Our model first uses an AutoEncoder recommendation system to fill a sparse user interaction matrix to develop a better understanding of user profiles. We then project item attributes and user profiles into a common low dimensional space and then use matrix factorization techniques to generate top-N recommendations for each user present in the set. We then compare our model generations with standard item-based recommender models on a subset of the Million Song Dataset and compare their results.
Asymmetric Three-dimensional Convolutions For Preterm Infants’ Pose Estimation

Computer-assisted tools for preterm infants' movement monitoring in neonatal intensive care unit (NICU) could support clinicians in highlighting preterm-birth complications. With such a view, in this work we propose a deep-learning framework for preterm infants' pose estimation from depth videos acquired in the actual clinical practice. The pipeline consists of two consecutive convolutional neural networks (CNNs). The first CNN (inherited from our previous work) acts to roughly predict joints and joint-connections position, while the second CNN (Asy-regression CNN) refines such predictions to trace the limb pose. Asy-regression relies on asymmetric convolutions to temporally optimize both the training and predictions phase. Compared to its counterpart without asymmetric convolutions, Asy-regression experiences a reduction in training and prediction time of 66% , while keeping the root mean square error, computed against manual pose annotation, merely unchanged. Research mostly works to develop highly accurate models, few efforts have been invested to make the training and deployment of such models time-effective. With a view to make these monitoring technologies sustainable, here we focused on the second aspect and addressed the problem of designing a framework as trade-off between reliability and efficiency.
Depth Static Hand Gesture Recognition

Finger-spelling recognition is the popular way to realize Human-Computer Interaction (HCI). The sign language datasets for finger-spelling recognition are mostly collected by the color camera and the depth camera. The depth camera directly captures the 3D information between the performer and lens, which can extract the precise hand gesture texture information without being influenced by the complex background and illumination. This work develops a hand gesture recognition system using depth image. This system employs a VGGNet to learn the feature extraction and classification. The experimental results show that the proposed hand gesture recognition system can correctly recognize the hand gesture using only depth image. This may reduce computational cost of the hand gesture recognition system.
Construction of Course Achievement Evaluation System Based on Big Data Analysis

The advent of the era of big data has brought a new development opportunity for higher education. Along with the increasingly mature analysis technology, it has further promoted the evaluation and standardization process of education and teaching. In this paper, from the perspective of designing three kinds of supporting relations of curriculum achievement evaluation, the construction scheme of achievement evaluation system is described by taking information courses as an example, and suggestions for continuous improvement of teaching content and methods are put forward.
Recommender System of Final Project Topic Using Rule-based and Machine Learning Techniques

The final project is a mandatory graduation requirement for bachelor's degree students. However, students often having problems in determining topic that is suitable with their interests and competencies. As a result, some students might have to change their topics halfway, which can affect their study period. Ironically, the abundant volume of previous final project documents available in the university library only add more confusion and difficulty for the students in finding relevant references for their research topic. Therefore, the focus of this research is to implement a machine learning approach to analyze and model an algorithm to recommend final project topics, based on student's interest, competencies, and their respective supervisor. This research also aims to establish a framework to map academic attributes, as part of feature selection. As the result, we develop a recommender system based on cosine similarity algorithm to recommend topics based on similarity between student's profile and topics represented by lists of keywords. Performance is measured by comparing the recommendations given by the proposed system against the actual topic chosen by students, with a very satisfying result of 71.43% precision.
Optimized Edge Aggregation for Hierarchical Federated Learning

In this paper, we consider a hierarchical federated learning system and formulate a joint problem of edge aggregation interval control and time allocation to minimize the weighted sum of training loss and training latency. To quantify the learning performance, an upper bound of the average global gradient deviation, in terms of the edge aggregation interval, the time allocated for training, and the number of successfully participating devices, is derived. Then an alternative problem is formulated, which can be decoupled into two sub-problems and solved with two steps. In the first step, given the time allocation strategy, a relaxation and rounding method is proposed to optimize the edge aggregation interval. In the second step, with the results of the obtained edge aggregation interval and based on the convex optimization theory, an optimal time allocation can be evaluated. Simulation results show that the proposed scheme, compared to the benchmarks, can achieve higher learning performance with lower training latency.
Commodity Image Retrieval Method Based on CNN and Score Fusion

Content-based image retrieval (CBIR) is an important research direction of computer vision and has been well studied for a long period. The important aim in CBIR is to reduce the semantic gap issue that improves the performance of image retrieval. It plays an important role in the field of e-commerce. This paper proposes a CBIR method based on ResNet and EfficientNet, and conducts experiments on the dataset of eProduct Visual Search Challenge 2021. First, because the dataset lacks direct query-index tags and the categories are not balanced, transfer learning is used to fine-tune the model with a balanced sub-dataset constructed by data augmentation. Second, the semantic features are extracted by the ResNet and EfficientNet and similarity comparison is calculated between the query and index image. Finally, the similarity result is decided through fusing the scores obtained by the two CNNs through a strategy. Experiments on the eProduct dataset demonstrate our algorithm can achieve a good performance.
SMS-Builder: An adaptive software tool for building systematic mapping studies

A Systematic Mapping Study is an instrument frequently used to carry out a search process, identification, and classification of studies in different fields. Researchers in front of this type of process have a challenge while managing the data about these studies. This paper presents a software tool that has been created to help those who need to build a systematic mapping study. In addition, this work follows the evidence-based software engineering approach and extends it through a software tool by including different ways of adapting this process.
Intelligent Algorithm Big Data Analysis for the Construction of Smart Campus

China has entered the era of big data, and certain changes have taken place in the field of education and teaching. This paper will focus on the application of intelligent algorithm big data analysis in the construction of smart campus, so as to ensure the scientificity and effectiveness of the construction of smart campus. This way can combine the intelligent algorithm big data with the construction of smart campus. On the basis of the construction of smart campus, it can provide good teaching conditions and teaching environment for students, and then cultivate high-quality comprehensive talents for the society.
Part of speech tagging for Arabic text based radial basis function

The progress of peoples and their advancement linked to the extent of their keenness to spread their civilization and scientific progress in their language and tongue. Language is the pot of civilization without a doubt. The Arabic language is one of the most spoken languages in the world, with more than 467 million people speaking it. The Arabic language has succeeded in playing a unique civilized role, making it the leader of civilization and knowledge at the level of science for centuries in a row. In the current era, because of the proliferation of modern technologies, advanced technology and software that take the foreign languages its own platform, the role of the Arabic language has declined in influence, and it has become imprisoned in universities, schools and the role of science that only its people can learn. However, at the present, it is time to gather its forces to meet the requirements of the present and the future in the technical, civilizational, and epistemological field. The aim of this work is to detect the Part of Speech (POS), in which to simplify the rules of the Arabic language to ensure its vitality by keeping pace with the language of technical developments. In addition, to spread Arabic language sciences through the application resulting from this reality via the Internet, which makes it the greatest influence in simplifying its own rules for non-native speakers and the desire to study and learn its arts. The proposed model consists of three phases, that starting by the phase of reading the text and segments it into sentences and words, followed by the phase of analyzing and tagging each word inside the text. Finally, the syntax analyzer (Parser) that used to determine the (POS) will built by using the Radial Basis Function network (RBF) which is a type of Artificial Neural Network (ANN). RBF simply contains three layers (layer that is responsible for entering data, the hidden layer in which processing is done, and adjusting the network weights to perform their role in matching the inputs and outputs, and the output layer).
Facial Micro-Expression Recognition in Video using Squeezed Landmark Feature Maps

Micro-Expression is the muscle movements of face that appear unconsciously. It is difficult to observe the Micro-Expression because of its short duration and low intensity. In recent work, a two-dimensional (2D) Landmark Feature Map (LFM) that is able to represent spatiotemporal feature of Micro-Expression was proposed. The LFM handles the duplicate values since there are 68 Landmark points in LFM and each point measures the distance from all remaining points. These values raise the problem of increasing processing time, cost complexity, and CNN network size. we propose a Squeezed LFM and a Squeezed CLFM that solves the problems. We also evaluate the performance of the proposed method using a public micro-expression dataset SMIC. The proposed method achieves the reduction of the data size and CNN network parameter amount without any recognition accuracy degradation of Micro-Expression.
Serving Machine Learning Inference Using Heterogeneous Hardware

The growing popularity of machine learning algorithms and the wide availability of hardware accelerators have brought up new challenges on inference serving. This paper explores the opportunity to serve inference queries with a heterogeneous system. The system has a central optimizer that allocates heterogeneous hardware resources to cooperatively serve queries. The optimizer supports both energy minimization and throughput maximization while satisfying a latency target. The optimized heterogeneous serving system is evaluated against a homogeneous system, on two representative real-world applications of radar nowcasting and object detection. Our evaluation results show that the power-optimized heterogeneous system can achieve up to 36% of power saving, and the throughput-optimized heterogeneous system can increase query throughput by up to 53%.
Traditional or Agile Contracting for Software Development

As the use of software is present in so many activities today, it is important for business in particular to be aware of challenges that may seem different today than before the prevalence of software in our lives. Agile project management is one example: this more recent and nimble approach to software development presents its own challenges. Fortunately, the guiding legal principles related to traditional contract formation and execution are based in principles of fairness and equity, making the customization of legal principles to Agile contracting a reasonable endeavor. This chapter presents basic contract law and such law as it more specifically relates to contracts dealing with Agile software development.
RoboCup 2021 Worldwide: A Successful Robotics Competition During a Pandemic [Competitions]

The COVID-19 global pandemic has challenged the entire academic community (and the whole world) to adopt new and creative ways of making progress. Most conferences have now organized at least one fully virtual event, often including keynote lectures, paper presentation sessions, poster sessions, and even social events. However, when the core of one’s event is a collection of competitions among physical robots, what can be done when researchers are not able to travel? This article answers that question by recounting RoboCup 2021 Worldwide, a successful robotics competitions held during the summer of 2021.