Reviewerly/exHarmony huggingface dataset:
SQL and NoSQL Databases for Cyber Physical Production Systems in Internet of Things for Manufacturing (IoTfM)

Abstract In this paper, the design and performance differences between Relational Database Management Systems (RDBMS) and NoSQL Database Systems are examined, with attention to their applicability for real-world Internet of Things for manufacturing (IoTfM) data. While previous work has extensively compared SQL and NoSQL for both generalized and IoT uses, this work specifically examines the tradeoffs and performance differences for manufacturing applications by using a high-fidelity data set collected from a large US manufacturing firm. Growing an IoT system beyond the pilot stage requires scalable data storage; this work seeks to determine the impact of selected database systems on data write performance at scale. Payload size and message frequency were used as the primary characteristics to maintain model fidelity in simulated clients. As the number of simulated asset clients grow, the data write latency was calculated to determine how both database systems’ performance were affected. To isolate the RDBMS and NoSQL differences, a cloud environment was created using Amazon Web Services (AWS) with two identical data ingestion pipelines: writing data to an RDMBS (1) using AWS Aurora MySQL, and (2) using AWS DynamoDB NoSQL. The findings may provide guidance for further experimentation in large-scale manufacturing IoT implementations.
Uncertainty-based Self-training for Biomedical Keyphrase Extraction

To keep pace with the increased generation and digitization of documents, automated methods that can improve search, discovery and mining of the vast body of literature are essential. Keyphrases provide a concise representation by identifying salient concepts in a document. Various supervised approaches model keyphrase extraction using local context to predict the label for each token and perform much better than the unsupervised counterparts. However, existing supervised datasets have limited annotated examples to train better deep learning models. In contrast, many domains have large amount of un-annotated data that can be leveraged to improve model performance in keyphrase extraction. We introduce a self- learning based model that incorporates uncertainty estimates to select instances from large-scale unlabeled data to augment the small labeled training set. Performance evaluation on a publicly available biomedical dataset demonstrates that our method improves performance of keyphrase extraction over state of the art models.
The Test on the Design and Effect of Practical Training for “Project Planning and Control” -Using SPSS Software for Paired Sample Test and Correlation Analysis

The development of society requires high-quality talents, which propels colleges and universities to cultivate graduates with both theoretical and practical abilities. As an applied undergraduate university, Beijing City University has always attached importance to cultivating students' practical ability based on theoretical teaching. In “Project Planning and Control”, a module for the major of project management, the task-driven method is used for practical training. The college aims to improve students' ability to apply the theory through practical training scenarios, real-life training tasks, assessments and other key steps. In order to test the effect of practical training, SPSS statistical software is used to perform paired sample T test and correlation analysis on the usual scores and final scores. The analysis results show that the design of practical training is reasonable, achieving good results.
Average-Reward Reinforcement Learning with Trust Region Methods

Most of reinforcement learning algorithms optimize the discounted criterion which is beneficial to accelerate the convergence and reduce the variance of estimates. Although the discounted criterion is appropriate for certain tasks such as financial related problems, many engineering problems treat future rewards equally and prefer a long-run average criterion. In this paper, we study the reinforcement learning problem with the long-run average criterion. Firstly, we develop a unified trust region theory with discounted and average criteria. With the average criterion, a novel performance bound within the trust region is derived with the Perturbation Analysis (PA) theory. Secondly, we propose a practical algorithm named Average Policy Optimization (APO), which improves the value estimation with a novel technique named Average Value Constraint. To the best of our knowledge, our work is the first one to study the trust region approach with the average criterion and it complements the framework of reinforcement learning beyond the discounted criterion. Finally, experiments are conducted in the continuous control environment MuJoCo. In most tasks, APO performs better than the discounted PPO, which demonstrates the effectiveness of our approach.
Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation

In object re-identification (ReID), the development of deep learning techniques often involves model updates and deployment. It is unbearable to re-embedding and re-index with the system suspended when deploying new models. Therefore, backward-compatible representation is proposed to enable ``new'' features to be compared with ``old'' features directly, which means that the database is active when there are both ``new'' and ``old'' features in it. Thus we can scroll-refresh the database or even do nothing on the database to update. The existing backward-compatible methods either require a strong overlap between old and new training data or simply conduct constraints at the instance level. Thus they are difficult in handling complicated cluster structures and are limited in eliminating the impact of outliers in old embeddings, resulting in a risk of damaging the discriminative capability of new features. In this work, we propose a Neighborhood Consensus Contrastive Learning (NCCL) method. With no assumptions about the new training data, we estimate the sub-cluster structures of old embeddings. A new embedding is constrained with multiple old embeddings in both embedding space and discrimination space at the sub-class level. The effect of outliers diminished, as the multiple samples serve as ``mean teachers''. Besides, we propose a scheme to filter the old embeddings with low credibility, further improving the compatibility robustness. Our method ensures the compatibility without impairing the accuracy of the new model. It can even improve the new model's accuracy in most scenarios.
Set Partitioning in Hierarchical Trees for Point Cloud Attribute Compression

We propose an embedded attribute encoding method for point clouds based on set partitioning in hierarchical trees (SPIHT). The encoder is used with the region-adaptive hierarchical transform which has been a popular transform for point cloud coding, even included in the standard geometry-based point cloud coder (G-PCC). The result is an encoder that is efficient, scalable, and embedded. That is, higher compression is achieved by trimming the full bit-stream. G-PCCs RAHT coefficient prediction prevents the straightforward incorporation of SPIHT into G-PCC. However, our results over other RAHT-based coders are promising, improving over the original, nonpredictive RAHT encoder, while providing the key functionality of being embedded.
Digital and data-driven sports journalism

Jana Wiske and Thomas Horky argue that due to the transformation of sport into “media sport”, contemporary sports journalism is not only operating digitally but is also partially driven by data. Thus, they present four new challenges for digital and data-driven sports journalism. (1) Interaction: the inclusion of material produced by the public and social media agenda setting; (2) data-driven content: the growing impact of data and the attendant problems of sampling and explaining this information; (3) loss of the gatekeeping function; the manner in which legacy media are being bypassed by different new media players such as athletes, clubs and governing bodies, and; (4) new players in sports journalism: the new competition provided to traditional media by livestreaming outlets like Amazon or YouTube and sports reporting on social media such as Twitter or Facebook. Wiske and Horky then go on to suggest important future developments for the field of sports journalism.
Motion Phase-based 2-Dimensional Displacement Measurements under Various Illumination Conditions with Maker and Filter Design

The advanced displacement measurement technique using computer vision has shown several advantages, such as a high spatial resolution and no mass loading effect, compared to the conventional contact-sensing techniques. However, the accuracy and robustness of vision-based techniques are subjected to various illumination conditions, including uneven illumination and insufficient lighting. This study introduced an accurate displacement measurement technique, with high robustness with respect to the illumination changes, that used two complex Gabor filters and a specially designed marker with orthogonal features. The parameters of the two complex Gabor filters, including the center frequency, bandwidth, and direction, were selected based on the marker size and attitude to extract the accurate phase motion information on two directions with dense features. The noise caused by insufficient light and/or uneven illumination could also be reduced by emphasizing the orthogonal features using the proper filter parameters. Further, motion phase-based optical flow was applied to calculate the 2D displacement signal of the marker. The excellent performance of displacement measurement was confirmed by comparing to that by a laser doppler vibrometer(LDV) under various lighting conditions. Tracking of multiple markers on a plate was performed to demonstrate the capability of measuring the out-plane motion using the proposed technique.
Timely and accurate detection of model deviation in self-adaptive software-intensive systems

Control-based approaches to self-adaptive software-intensive systems (SASs) are hailed for their optimal performance and theoretical guarantees on the reliability of adaptation behavior. However, in practice the guarantees are often threatened by model deviations occurred at runtime. In this paper, we propose a Model-guided Deviation Detector (MoD2) for timely and accurate detection of model deviations. To ensure reliability, a SAS can switch a control-based optimal controller for a mandatory controller once an unsafe model deviation is detected. MoD2 achieves both high timeliness and high accuracy through a deliberate fusion of parameter deviation estimation, uncertainty compensation, and safe region quantification. Empirical evaluation with three exemplar systems validated the efficacy of MoD2 (93.3% shorter detection delay, 39.4% lower FN rate, and 25.2% lower FP rate), as well as the benefits of the adaptation-switching mechanism (abnormal rate dropped by 29.2%).
Web Application Authentication Using Visual Cryptography and Cued Clicked Point Recall-based Graphical Password

Alphanumerical usernames and passwords are the most used computer authentication technique. This approach has been found to have a number of disadvantages. Users, for example, frequently choose passwords that are simple to guess. On the other side, if a password is difficult to guess, it is also difficult to remember. Graphical passwords have been proposed in the literature as a potential alternative to alphanumerical passwords, based on the fact that people remember pictures better than text. Existing graphical passwords, on the other hand, are vulnerable to a shoulder surfing assault. To address this shoulder surfing vulnerability, this study proposes an authentication system for web-applications based on visual cryptography and cued click point recall-based graphical password. The efficiency of the proposed system was validated using unit, system and usability testing measures. The results of the system and unit testing showed that the proposed system accomplished its objectives and requirements. The results of the usability test showed that the proposed system is easy to use, friendly and highly secured.
Information and communication technologies in distance education for improving competence of students in the university

The objective of the study was to develop and implement modern information and communication technologies in the online learning process of university students. The subject of the study is the modernization of the educational process based on communicative competence in distance education. The sociological survey carried out showed the high effectiveness of the proposed methodology. The article presents the practical foundations of the application of information and communication technology in the implementation of distance education in the context of the Covid-19 pandemic. The results of the study made it possible to develop modern means of information and communication, including video and remote-controlled complexes.
An Efficient Machine Learning Model for Prediction of Dyslexia from Eye Fixation Events

Dyslexia is a type of learning disability in which a person has trouble spelling and reading words fluently. Dyslexia is not curable, but with the correct remedial help, dyslexics can achieve great success in school and in life. Eye movement patterns during the reading process can provide a deeper knowledge of dyslexia-related reading difficulties. Eye movements can be recorded with an eye-tracker, and the relationship between how eyes move in proportion to the words they read can be deduced. Based on statistical measurements, a collection of binocular fixation and saccade properties were derived from raw eye tracking data in this study. Based on statistical measurements, a collection of binocular fixation and saccade properties were derived from raw eye tracking data in this study. Machine learning algorithms such as the Random Forest Classifier (RF), the Support Vector Machine (SVM) for classification, and the K-Nearest Neighbor (KNN) for prediction of dyslexia were investigated to provide classification models for dyslexia prediction. In comparison to SVM and RF, KNN provided 95 percent accuracy over a small feature set associated to fixations and saccades. These characteristics of the eyes can be exploited to design screening tools for dyslexia prediction. Early detection of dyslexia can assist children in receiving treatment, allowing them to achieve academic success.
Improvement of Legitimate Mail Server Detection Method using Sender Authentication

Anti-spam measures include methods for determining unsolicited email from the email content and methods for using sender information. If it can be determined from the sender's IP address of sender information and the sender's domain name whether the email should be received, it is possible to reduce the processing of the spam filter by the email content that has a high processing load for the determination. This study uses sender authentication technology to identify the sender of forwarded email. We consider that the sender of this forwarded email is the legitimate email sender to receive, and propose to use these as an allow list. In this paper, we propose a method to further improve the method we proposed and reduce misjudgment of the allow list. We verified that this new method is effective by using the log information of the emails actually received.
Web Service Discovery Approaches for Dynamic Mobile Environment

Mobile web service (MWS) discovery is taking a new direction due to the explosion of users accessing mobile services using diverse mobile devices, coupled with the persistent changes in a dynamic mobile environment (DME). This leads to renewed adoption of lightweight solutions for the identification of the most suitable web services that correspond with the service requests. Contemporary mobile web service discovery approaches are plagued with performance and accuracy problems and are rarely compatible with the DME. The objective of this systematic literature review is to develop a more rigorous understanding and identify recent research trends in mobile web service discovery techniques in a dynamic mobile environment. This review followed the systematic literature review (SLR) guidelines. Essential information was extracted from the 76 relevant articles in line with the formulated questions and finally reported after in-depth analysis. The results of this study discuss the critical contributions and limitations of the proposed approaches.
Introduction

This paper considers the emergence of a virtual reality (VR) rock-art platform called PleitoVR, charting the creation of new potentialities cutting across archaeological, computing, and indigenous ontologies. The increasing use of VR to create immersive environments in cultural heritage and archaeological sectors calls into question how differing ontologies—understood through differing relationalities across human and nonhuman kinds—interplay within such newly created experiential platforms. We argue that the immersive platforms are not just simulacra of the archaeological sites, but are novel and new entities in and of themselves. This occurs through a recombination and reappraisal of divergent ontologies; these new entities emerge in the process of questioning the analytics of animacy, vitality, and agency as experienced through new spatial and, with the diffusion of such technology and social relations. We consider tensions between philosophical and indigenous ontologies with ontologies developed for information representation in computer science which are problematic due to the abstract nature of computing ontologies. However, the concepts of class, attributes, and instances present in object-oriented programming could provide a template for comparing and evaluating emergent entities and their ontological relationship with pre-existing archaeology. Here, we outline this process through exploring a newly created VR environment of a magnificent hunter-gatherer rock art site in Southern California. We then move to consider how new immersive spaces create novel entities through which new ontological relations can be actualized.
Medical image quality metrics for foveated model observers

Purpose: A recently proposed model observer mimics the foveated nature of the human visual system by processing the entire image with varying spatial detail, executing eye movements, and scrolling through slices. The model can predict how human search performance changes with signal type and modality (2D versus 3D), yet its implementation is computationally expensive and time-consuming. Here, we evaluate various image quality metrics using extensions of the classic index of detectability expression and assess foveated model observers for search tasks. Approach: We evaluated foveated extensions of a channelized Hotelling and nonprewhitening matched filter model with an eye filter. The proposed methods involve calculating a model index of detectability (d ′ ) for each retinal eccentricity and combining these with a weighting function into a single detectability metric. We assessed different versions of the weighting function that varied in the required measurements of the human observers’ search (no measurements, eye movement patterns, size of the image, and median search times). Results: We show that the index of detectability across eccentricities weighted using the eye movement patterns of observers best predicted human performance in 2D versus 3D search performance for a small microcalcification-like signal and a larger mass-like. The metric with a weighting function based on median search times was the second best predicting human results. Conclusions: The findings provide a set of model observer tools to evaluate image quality in the early stages of imaging system evaluation or design without implementing the more computationally complex foveated search model.
Adaptive Label Smoothing To Regularize Large-Scale Graph Training

Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many domains. To handle large-scale graphs, most of the existing methods partition the input graph into multiple sub-graphs (e.g., through node clustering) and apply batch training to save memory cost. However, such batch training will lead to label bias within each batch, and then result in over-confidence in model predictions. Since the connected nodes with positively related labels tend to be assigned together, the traditional cross-entropy minimization process will attend on the predictions of biased classes in the batch, and may intensify the overfitting issue. To overcome the label bias problem, we propose the adaptive label smoothing (ALS) method to replace the one-hot hard labels with smoothed ones, which learns to allocate label confidences from the biased classes to the others. Specifically, ALS propagates node labels to aggregate the neighborhood label distribution in a pre-processing step, and then updates the optimal smoothed labels online to adapt to specific graph structure. Experiments on the real-world datasets demonstrate that ALS can be generally applied to the main scalable learning frameworks to calibrate the biased labels and improve generalization performances.
Handbook of Automated Scoring: Theory into Practice

Recent advances in computer technology, artificial intelligence, and natural language processing have prompted significant progress in the construction and application of automated scoring (AS) sys...
Terminating Calculi and Countermodels for Constructive Modal Logics

We investigate terminating sequent calculi for constructive modal logics \(\mathsf {CK}\) and \(\mathsf {CCDL}\) in the style of Dyckhoff’s calculi for intuitionistic logic. We first present strictly terminating calculi for these logics. Our calculi provide immediately a decision procedure for the respective logics and have good proof-theoretical properties, namely they allow for a syntactic proof of cut admissibility. We then present refutation calculi for non-provability in both logics. Their main feature is that they support direct countermodel extraction: each refutation directly defines a finite countermodel of the refuted formula in a natural neighbourhood semantics for these logics.
News on Brazilian Nursing in the COVID-19 pandemic

Abstract Objective: To analyze the impact and visibility that journalistic news brought to Brazilian Nursing during the COVID-19 pandemic. Methods: Documentary study developed based on information collected from news sites, news programs, radio programs and written press through clipping; as well as the monitoring of demands performed by the Communication Office of the Federal Nursing Council (Portuguese acronym: COFEN), based on articles of national and international scope. After selection, these were systematized based on the content analysis framework. Results: One [...]
Linking Discourse Marker Inventories.

The paper describes the first comprehensive edition of machine-readable discourse marker lexicons. Discourse markers such as and, because, but, though or thereafter are essential communicative signals in human conversation, as they indicate how an utterance relates to its communicative context. As much of this information is implicit or expressed differently in different languages, discourse parsing, context-adequate natural language generation and machine translation are considered particularly challenging aspects of Natural Language Processing. Providing this data in machine-readable, standard-compliant form will thus facilitate such technical tasks, and moreover, allow to explore techniques for translation inference to be applied to this particular group of lexical resources that was previously largely neglected in the context of Linguistic Linked (Open) Data.