Reviewerly/exHarmony huggingface dataset:
Evaluation Framework for Augmented Reality Smart Glasses as Assembly Operator Support: Case Study of Tool Implementation

Augmented reality smart glasses (ARSG) have been identified as relevant support tools for the Operator 4.0 paradigm. Although ARSG are starting to be used in industry, their use is not yet widespread. A previously developed online tool based on a framework for evaluating ARSG as assembly operator support is iteratively improved in this paper with expanded functionality. The added functionality consists of practical recommendations for implementing ARSG in production. These recommendations were produced with the help of five focus groups of industrial representatives working in production. The recommendations were evaluated using case studies at three different companies. The recommendations were found to be detailed and a good support for the process of considering ARSG integration into production. The companies overall found the tool and its recommendations to be relevant and correct for their cases.
Five questions to consider when conducting COVID-19 phone research

### Summary boxThe shift in research modality to phone surveys during the COVID-19 pandemic has highlighted tensions between the need for real-time data on the one hand and high-quality, generalisable data on the other. We present five questions for evaluating the trade-offs involved when navigating this shift (figure 1). We draw on prior research, our participation in the COVID-19 Research Network (CORE Net), and the range of phone research conducted by CORE Net from April to November 2020. The latter included four surveys across India on a range of topics and targeting diverse beneficiary populations. The largest of these was our survey from April-June 2020 to assess the efficacy of COVID-19-related governmententitlements across 47 000 low-income households in India. We also conducted asurvey of 17 000 women and men on the gendered impacts of Covid-19 fromOctober-November 2020, results of which are forthcoming. Additional CORE Netstudies referenced in this commentary include an ongoing study from April 2020by IDInsight on COVID-19 knowledge, behaviour change, and economic effects, aswell as a study from …
Word synonym relationships for text analysis: A graph-based approach

Keyword extraction refers to the process of detecting the most relevant terms and expressions in a given text in a timely manner. In the information explosion era, keyword extraction has attracted increasing attention. The importance of keyword extraction in text summarization, text comparisons, and document categorization has led to an emphasis on graph-based keyword extraction techniques because they can capture more structural information compared to other classic text analysis methods. In this paper, we propose a simple unsupervised text mining approach that aims to extract a set of keywords from a given text and analyze its topic diversity using graph analysis tools. Initially, the text is represented as a directed graph using synonym relationships. Then, community detection and other measures are used to identify keywords in the text. The set of extracted keywords is used to assess topic diversity within the text and analyze its sentiment. The proposed approach relies on grouping semantically similar candidate words. This approach ensures that the set of extracted keywords is comprehensive. Differing from other graph-based keyword extraction approaches, the proposed method does not require user parameters during graph construction and word scoring. The proposed approach achieved significant results compared to other keyword extraction techniques.
Harnessing the Potential of Function-Reuse in Multimedia Cloud Systems

Cloud-based computing systems can get oversubscribed due to the budget constraints of their users or limitations in certain resource types. The oversubscription can, in turn, degrade the users perceived Quality of Service (QoS). The approach we investigate to mitigate both the oversubscription and the incurred cost is based on smart reusing of the computation needed to process the service requests (i.e., tasks). We propose a reusing paradigm for the tasks that are waiting for execution. This paradigm can be particularly impactful in serverless platforms where multiple users can request similar services simultaneously. Our motivation is a multimedia streaming engine that processes the media segments in an on-demand manner. We propose a mechanism to identify various types of “mergeable” tasks and aggregate them to improve the QoS and mitigate the incurred cost. We develop novel approaches to determine when and how to perform task aggregation such that the QoS of other tasks is not affected. Evaluation results show that the proposed mechanism can improve the QoS by significantly reducing the percentage of tasks missing their deadlines and reduce the overall time (and subsequently the incurred cost) of utilizing cloud services by more than 9 percent.
A reinforcement learning approach to adaptive remediation in online training

Advances in artificial intelligence (AI) and machine learning can be leveraged to tailor training based on the goals, learning needs, and preferences of learners. A key component of adaptive training systems is tutorial planning, which controls how scaffolding is structured and delivered to learners to create dynamically personalized learning experiences. The goal of this study was to induce data-driven policies for tutorial planning using reinforcement learning (RL) to provide adaptive scaffolding based on the Interactive, Constructive, Active, Passive framework for cognitive engagement. We describe a dataset that was collected to induce RL-based scaffolding policies, and we present the results of our policy analyses. Results showed that the best performing policies optimized learning gains by inducing an adaptive fading approach in which learners received less cognitively engaging forms of remediation as they advanced through the training course. This policy was consistent with preliminary analyses that showed constructive remediation became less effective as learners progressed through the training session. Results also showed that learners’ prior knowledge impacted the type of scaffold that was recommended, thus showing evidence of an aptitude–treatment interaction. We conclude with a discussion of how AI-based training can be leveraged to enhance training effectiveness as well as directions for future research.
Mining Process Data to Detect Aberrant Test Takers

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
A Survey of Human Face Recognition for Partial Face View

Face recognition technology is a modern approach with many applications. This paper reviews and compares the latest literature addressing the methods that have been adopted to recognize and identify faces using a partial face recognition view. The study covers different methods of face recognition for static images that can be classified according to three main approaches, namely the holistic, feature-based, and hybrid approaches. The different methods and approaches are examined and their limitations are identified.
Design of a novel chaotic neural network based encryption system for security applications

Cryptography is one of the vital domains in computer systems. It is continually evolving with a tremendous amount of active research. With the widespread use of technology, there is a constant requirement for faster, secure cryptosystems. The advance of neural networks has provided us with a wide range of opportunities to optimize cryptosystems. Chaotic neural networks (CNN) are sensitive to their initial conditions. This unique property allows us to improve the security of cryptosystems. In this paper, a chaotic neural network is used to design encryption and decryption algorithms. The algorithms are experimented on using a cipher block chaining mechanism. The cryptographic algorithm was optimized by thoroughly evaluating performance parameters such as autocorrelation, cross-correlation and throughput. This paper experiments with various chaotic maps such as 1-D Logistic map, complex squaring map and cubic map to identify the most efficient map for the outlined CNN in this paper.This paper provides a symmetrical chaotic neural network for a cryptographic application that works for a wide range of inputs. The inputs are not sensitive dependent on characters. Also, the network works seamlessly with UTF-8-based characters.
Cohort selection for construction of a clinical natural language processing corpus

In Electronic Health Record (EHR) systems, key patient information is often captured in the form of unstructured clinical notes. The information from these notes can be extracted using Clinical Natural Language Processing (NLP). Training corpus is a key factor in development of efficient clinical NLP models. Clinical NLP corpus construction is complex and multifaceted. There are several challenges in corpus construction, but one challenge often not researched well is cohort selection aspect. In this study, we present methods employed and challenges encountered in cohort selection for construction of a clinical NLP corpus. In specific we present our methods in selection of cancer pathology reports to construct a corpus for automatic deidentification. 2100 pathology reports were extracted from 1833 (518 male and 1313 female) cancer patients using Health Level-7 (HL7) message standard. In terms of the age group distribution, the age group 60–70 years was highest with 872 patients. Our findings suggest deciphering the segment information from HL7 messages that are collected from different hospitals is a challenging task. The quality of HL7 messages also varied significantly with inconsistent tags making it difficult to identify reports that meet criteria set a priori. One of key lessons learned is linking the HL7 reports data with additional EMR data such as admissions, would help in identifying high quality reports and resolve duplicates. Also, our findings suggest that, in general the EHR data quality is poor with varying clinical coding and metadata standards between different hospitals. It is vital to identify and address these challenges for development of a high-quality corpus.
Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation

Efficient, reliable and reproducible target volume delineation is a key step in the effective planning of breast radiotherapy. However, post-operative breast target delineation is challenging as the contrast between the tumor bed volume (TBV) and normal breast tissue is relatively low in CT images. In this study, we propose to mimic the marker-guidance procedure in manual target delineation. We developed a saliency-based deep learning segmentation (SDL-Seg) algorithm for accurate TBV segmentation in post-operative breast irradiation. The SDL-Seg algorithm incorporates saliency information in the form of markers' location cues into a U-Net model. The design forces the model to encode the location-related features, which underscores regions with high saliency levels and suppresses low saliency regions. The saliency maps were generated by identifying markers on CT images. Markers' location were then converted to probability maps using a distance transformation coupled with a Gaussian filter. Subsequently, the CT images and the corresponding saliency maps formed a multi-channel input for the SDL-Seg network. Our in-house dataset was comprised of 145 prone CT images from 29 post-operative breast cancer patients, who received 5-fraction partial breast irradiation (PBI) regimen on GammaPod. The 29 patients were randomly split into training (19), validation (5) and test (5) sets. The performance of the proposed method was compared against basic U-Net. Our model achieved mean (standard deviation) of 76.4(±2.7) %, 6.76(±1.83) mm, and 1.9(±0.66) mm for Dice similarity coefficient, 95 percentile Hausdorff distance, and average symmetric surface distance respectively on the test set with computation time of below 11 seconds per one CT volume. SDL-Seg showed superior performance relative to basic U-Net for all the evaluation metrics while preserving low computation cost. The findings demonstrate that SDL-Seg is a promising approach for improving the efficiency and accuracy of the on-line treatment planning procedure of PBI, such as GammaPod based PBI.
YouTube Sentiment Analysis on US Elections 2020

Sentiment Analysis is an important research field of data mining. It comes under the category of text mining. It is also known as opinion mining. Emotions are an important part of human life. It’s important for business companies, government etc. to note down opinion of public so that they can make their products and policies according to it. There are many social networking sites like Twitter, Facebook, YouTube, Instagram etc. On these social networking sites people give their opinions on various trending issues. YouTube is most accessed video sharing platform. Here the sentiments of people are in the form of comments. In this paper comments are mined to get public reaction on US Presidential Election 2020 using SentiWordNet. Research on sentiment analysis and frequently used words is done to know about emotions of people. Our analysis figured out that people have greeted Joe Biden on becoming new president of United States.
Smart training: Mask R-CNN oriented approach

This paper is aimed at the usage of an augmented reality assisted system set up on the smart-glasses for training activities. Literature review leads us to a comparison among related technologies, yielding that Mask Regions with Convolutional Neural Network (R-CNN) oriented approach fits the study needs. The proposed method including (1) pointing gesture capture, (2) finger-pointing analysis, and (3) virtual tool positioning and rotation angle are developed. Results show that the recognition of object detection is 95.5%, the Kappa value of recognition of gesture detection is 0.93, and the average time for detecting pointing gesture is 0.26 seconds. Furthermore, even under different lighting, such as indoor and outdoor, the pointing analysis accuracy is up to 79%. The error between the analysis angle and the actual angle is only 1.32 degrees. The results proved that the system is well suited to present the effect of augmented reality, making it applicable for real world usage.
Learning from the Web: Webly Supervised Meta-Learning for Masked Face Recognition

Mask wearing has been considered as an effective measure to prevent the spread of COVID-19 during the current pandemic. However, most advanced face recognition approaches are not adequate for masked face recognition, particularly in dealing with the issue of training through the datasets covering only a limited number of images with ground-truth labels. In this work, we propose to learn from the large scale of web images and corresponding tags without any manual annotations along with limited fully annotated datasets. In particular, inspired by the recent success of webly supervised learning in deep neural networks, we capitalize on readily-available web images with noisy annotations to learn a robust representation for masked faces. Besides, except for the conventional spatial representation learning, we propose to leverage the power of frequency domain to capture the local representative information of un-occluded facial parts. This approach learns robust feature embeddings derived from our feature fusion architecture to make joint and full use of information from both spatial and frequency domains. Experimental results on seven benchmarks show that the proposed approach significantly improves the performance compared with other state-of-the-art methods.
Effective action recognition with embedded key point shifts

Temporal feature extraction is an essential technique in video-based action recognition. Key points have been utilized in skeleton-based action recognition methods but they require costly key point annotation. In this paper, we propose a novel temporal feature extraction module, named Key Point Shifts Embedding Module (KPSEM), to adaptively extract channel-wise key point shifts across video frames without key point annotation. Key points are adaptively extracted as feature points with maximum feature values at split regions and key point shifts are the spatial displacements of corresponding key points. The key point shifts are encoded as the overall temporal features via linear embedding layers in a multi-set manner. Our method achieves competitive performance through embedding key point shifts with trivial computational cost, achieving the state-of-the-art performance of 78.81% on Mini-Kinetics and competitive performance on UCF101, Something-Something-v1 and HMDB51 datasets.
A Flexible Gimli Hardware Implementation in FPGA and Its Application to RFID Authentication Protocols

Radio Frequency Identification (RFID) systems have bestowed numerous conveniences in a multitude of applications, but the underlying wireless communications architecture makes it vulnerable to several security threats.To mitigate these issues, various authentication protocols have been proposed.The literature accommodates comprehensive proposals and analysis of authentication protocols, but not many of them provide hardware implementations.In addition, there is diverse demand for hardware area and throughput (TP) requirements from RFID system components (tags, readers, database servers), which demand a flexible implementation strategy.This paper proposes a flexible implementation strategy for the lightweight authenticated encryption (AE) and hash function called Gimli, and applies it to a state-of-theart authentication protocol.This allows the authentication protocol to be implemented efficiently, wherein the area and TP can be adjusted flexibly according to the RFID system requirements.This implementation strategy is generic; it can be used to implement any other AE and hash functions.This strategy can also be applied to other authentication protocols that heavily use AE and hash functions.To provide a detailed analysis, the hardware optimization techniques in each component of the RFID system for a state-ofthe-art authentication protocol are analyzed.When implemented with the most area-optimized versions, we achieve TP of 740 Mbps and 420 Mbps for Gimli hash and Gimli AE, respectively, and for throughputoriented implementation, the results are 3.08 Gbps and 1.43 Gbps, respectively.This shows that the proposed implementation strategies allow us to implement authentication protocols in a flexible manner to meet the differing requirements in TP and area for RFID applications.
A Paired Learner-Based Approach for Concept Drift Detection and Adaptation in Software Defect Prediction

The early and accurate prediction of defects helps in testing software and therefore leads to an overall higher-quality product. Due to drift in software defect data, prediction model performances may degrade over time. Very few earlier works have investigated the significance of concept drift (CD) in software-defect prediction (SDP). Their results have shown that CD is present in software defect data and tha it has a significant impact on the performance of defect prediction. Motivated from this observation, this paper presents a paired learner-based drift detection and adaptation approach in SDP that dynamically adapts the varying concepts by updating one of the learners in pair. For a given defect dataset, a subset of data modules is analyzed at a time by both learners based on their learning experience from the past. A difference in accuracies of the two is used to detect drift in the data. We perform an evaluation of the presented study using defect datasets collected from the SEACraft and PROMISE data repositories. The experimentation results show that the presented approach successfully detects the concept drift points and performs better compared to existing methods, as is evident from the comparative analysis performed using various performance parameters such as number of drift points, ROC-AUC score, accuracy, and statistical analysis using Wilcoxon signed rank test.
Effects of learner-centred education on academic achievement: a meta-analysis

Given the information age, an important educational objective is cultivating innovators who can propose unique ideas. Learner-centred education is a way to achieve this objective; however, what is the current effectiveness of learner-centred education? A meta-analysis was conducted to analyse 72 effect sizes from 42 quantitative studies to determine the effect of learner-centred education on students' academic achievement across a 10-year period (2010–2020). The overall effect size was 0.5446 (95% CI [0.3754, 0.7138]), which was medium, positive, and significant (P < 0.001). Thus, learner-centred education can significantly improve students' academic achievement. There were significant differences (all P < 0.05) in the influence of learner-centred education on academic achievement among six independent variables (subject, region, education phase, learning environment, teaching strategies, and autonomous learning form). Future research should focus on the positive impact of learner-centred education and choosing teaching strategies and learning forms to the discipline to promote it.
Bias-variance decomposition of absolute errors for diagnosing regression models of continuous data

<h2>Summary</h2> Bias-variance decomposition (BVD) is a powerful tool for understanding and improving data-driven models. It reveals sources of estimation errors. Existing literature has defined BVD for squared error but not absolute error, while absolute error is the more natural error metric and has shown advantages over squared error in many scientific fields. Here, I analytically derive the absolute-error BVD, empirically investigate its behaviors, and compare that with other error metrics. Different error metrics offer distinctly different perspectives. I find the commonly believed bias/variance trade-off under squared error is often absent under absolute error, and ensembles—a never hurt technique under squared error—could harm performance under absolute error. Compared with squared error, absolute-error BVD better promotes model traits reducing estimation residuals and better illustrates relative importance of different error sources. As data scientists pay increasing attention to uncertainty issues, the technique introduced here can be a useful addition to a data-driven modeler's toolset.
A Vision for Improving Business Continuity through Cyber-resilience Mechanisms and Frameworks

Nowadays, business organizations support daily operations using Information and Communication Technologies. They serve as a basis to have a controlled management of resources, services and business goals, aligned with the mission of the organization. In this paper, we review standards and frameworks for achieving cyber-resilience in organizations, such as the NIST framework, ENISA, or international standards as the ISO/IEC 27032. We then envision the need of a new cyber-resilience framework, that leveraging machine learning techniques contributes to improve business continuity.