Reviewerly/exHarmony huggingface dataset:
QA/QC protocols and documentation for ocean data – a case for standardization?

Since the earliest days of ocean sciences, the acquisition of observed data called for quality assurance and quality control procedures that had to be developed to ensure consistency of the collected information. The main goal is to exclude any grossly flawed data from the collected data stream either by preparing the used instrumentation in the best possible way and by eliminating any obviously wrong data within the data stream. However, only if one follows an agreed upon scheme or protocol and associated parametrization, it can be ensured that the information on the region of interest can be judged as traceable and repeatable. The issue that comes into the picture is that different observing configurations and environments will call for dedicated QA/QC procedures. This is what can be seen today, i.e., that every observing network like Ocean Networks Canada [1], OceanSITES [2], ARGO [3], etc. is following its own best practices. While these QA/QC practices are well documented at a high, procedural level, their individual application to e.g., time series may vary with respect to the selection and the sequence of individual tests. Further, the individual parametrization of these tests such as thresholds used or supporting data allows a large degree of freedom. As a result of these procedures, in most cases a verbose assessment of the data quality (good, bad) is determined and stored together with the raw data. However, the procedures used, the selection, sequence and parameterization of the tests remains undocumented, which makes verification impossible. For now, it is difficult to foresee whether a common standard for QA/QC can be developed and what any type of standard shall encompass. A new avenue maybe be opened by stripping down the QA/QC process into the essential functional steps taking into consideration the actual information flow. In analogy to the ISO 19115 series of standards one should consider to model QC processes and workflow models with UML models along with a common vocabulary or ontology on QC tests and their parameters. This would allow enough flexibility to integrate different workflows as necessary for the different observing scenarios and at the same time would allow for a rapid implementation of new or additional functional blocks into the basic QA/QC model. Based on this model we propose to develop a unified QA/QC documentation standard as well as a linked data-based replacement of the now used simple quality flags. The suggested method shall be exemplified by using the QA/QC routines developed as part of IOOS/QARTOD [4]. QARTOD has established authoritative QA/QC procedures for the US IOOS core variables which then enter the related manuals. The scope of QARTOD however, goes well beyond US observing efforts and includes the coordination with similar efforts of the Global Ocean Observing System (GOOS) and transatlantic programs like the EU funded project AtlantOS [5].
Requirement Analysis and Problem Finding Using Design Thinking Concepts in Students’ Information System Projects

Developing an information system from scratch involves finding the true problem of users and doing proper analysis of their requirements. However, it is very common for users not receiving an information system or management system that fully solves all their problems. We believe the requirement analysis stage is crucial and traditional system development lifecycles are not involving the users enough throughout the lifecycle, hence causing a subpar outcome especially in student’s final year project. In this paper, we present a way to do requirement analysis and problem finding by using design thinking concepts and apply it to students’ first phase of final year project. Two case studies of current students are presented in this paper and their deliverables of strategies used respective stages are shown. According to these students, by applying design thinking in their requirement analysis stage and involving their users in the loop, they have a better understanding on the foundation of their users’ problems and can come up with a proper proposed solution.
Design of Engineering Cost Database Software for Nuclear Power Plants

By collecting the cost data of various constructed nuclear power plants, this paper makes an exploratory research on the construction of nuclear power engineering cost database software based on the characteristics of the nuclear power industry cost data. Also, it proposes the overall framework and functional design of the database software, which can accomplish the storage and management of nuclear power cost data, and forms relevant cost indicators that enables quick cost evaluation for subsequent nuclear power projects. The design and development of engineering cost database software is the core method and powerful guarantee for realizing the information management of cost data. Moreover, it is the necessary condition for economic analysis and investment control of nuclear power projects.
Temporal Knowledge Graph Reasoning based on Graph Convolution Network

Temporal Knowledge Graphs Reasoning that predicts future facts has been widely explored. Previous works attempted to use linear encoding to get recurring facts that appears in temporal knowledge graphs or use graph neural network to aggregate each sequence in temporal knowledge graphs. However, these models cannot well obtain the sequence information and ignore time information for each fact. To get the time dependence when reasoning future facts, we propose a Dynamic Time-aware Graph Convolution Network (DT-GCN), which applies a relation aware GCN to get graph structure for each sequence and a time aware GCN to get time information for each fact, then uses a relation aware recurrent neural network to get sequence patterns in temporal knowledge graphs. Extensive experiments demonstrate that DT-GCN can get better time information and has better performance than baselines in the task of link prediction.
Pneumonia Prediction System Using Classification and Regression Trees Algorithm

Pneumonia is an inflammation or chronic infection of lung tissue caused by various microorganisms, including parasites, viruses, and bacteria, as well as physical damage to the lungs and exposure to chemicals. The method used to calculate predictions uses the CART (Classification and Regression Trees) algorithm. The model is then implemented into a website-based prediction system. The purpose of this study was to determine the implementation of the CART algorithm to determine pneumonia and to determine the accuracy of the CART algorithm in predicting pneumonia. The average accuracy of the results of this study led to an accuracy value of 94%, r-square 87%, precision 95%, recall 94%, and f1 score 94% of the total dataset of 283. The results of this study get the best r-square on the 5th test with an accuracy of 85%.
Face ReID Method via Deep Learning

Recenlty, many face reid strategies have been developped. These strategies create vectorial spaces capable of representing data on reduced dimensions. Such representations are produced by deep learning models that learn to maximize the intra-class similarity and minimize the inter-class similarity. The method described in this article proposes a new face reid strategy based on the Facenet model. It consists of training support vector machines on the euclidean distances calculated between the embeddings of the Facenet model, notably reducing the false positive rate.
A Hybrid Method for Automatic Simplification of Japanese Sentence Patterns Based on Morphological Analysis and Grammatical Rules

In Japanese grammar, there are various Japanese sentence patterns with complicated usages. Learners of Japanese as a second language (JSL) feel that it is quite difficult to learn Japanese sentence patterns. To help JSL learners with their study of Japanese sentence patterns, this work introduces Japanese example sentence simplification which is a task of replacing difficult Japanese sentence patterns with simple Japanese sentence patterns or phrases. In this task, given an example sentence as an input, a new example sentence where the difficult Japanese sentence patterns have been replaced will be generated without changing the meaning of the original example sentence. For this purpose, we proposed a hybrid method combining morphological analysis and manual grammatical rules for identifying the difficult Japanese sentence patterns and simplifying the original example sentence. The final human evaluation results demonstrated the effectiveness of our proposed method. This proposed method can be applied to development of a Japanese/Simple Japanese parallel corpus for automatic Japanese grammar simplification and a computer-assisted language learning system (CALL) for Japanese reading comprehension.
Method of Metal Button Defect Detection Based on Extreme Learning Machine and Sparse Representation

We studied the algorithms of button defect detection, while metal button defect detection is seldom explored in the literature. In this paper, we propose to effectively improve the detecting speed, accuracy, and robustness of the model. We propose a new method of cascading Extreme Learning Machine (ELM) and Sparse Representation Classification (SRC). This method transforms the defect detection problem into a pattern classification problem. First, we preprocess the input button images via eliminating reflection, edges extraction, and dimensionality reduction. ELM has a faster learning speed and better detecting accuracy than the single layer perceptron and support layer machine. ELM was utilized to estimate the probability of the defective buttons, with parameters obtained from the five-fold cross-validation method. SRC was used to reclassify those button images with high noise. Our method improved the detection accuracy, as well as guaranteed the detecting speed. We showed state-of-the-art performance in comparison to other approaches.
Multi-attention mechanism news recommendation model with fused entity features

Existing news recommendation methods often do not sufficiently mine news fine-grained features, resulting in modeled news vectors that often do not accurately represent news. To address this problem, a multi-attention mechanism news recommendation model with fused entity features is proposed. The model is based on multi-head attention mechanism and additive attention mechanism, and uses news content entities to represent news content, deeply fusing and mining the intrinsic connection of fine-grained features such as news title, news categories, and news content. Additionally, in order to accurately model user preferences, activation units are introduced to adaptively learn vector representations of user interests from candidate news and users' recently viewed news. The experimental results of the dataset MIND show that the model has different degrees of improvement in the evaluation metrics AUC, MRR and nDCG compared with other news recommendation models.
Cross-validation Strategies for Balanced and Imbalanced Datasets

Cross-validation (CV) is a widely used technique in machine learning pipelines. However, some of its drawbacks have been recognized in the last decades. In particular, CV may generate folds unrepresentative of the whole dataset, which led some works to propose methods that attempt to produce more distribution-balanced folds. In this work, we propose an adaption of a cluster-based technique for cross-validation based on mini-batch k-means that is more computationally efficient. Furthermore, we compare our adaptation with other splitting strategies previously not compared and also analyze whether class imbalance may influence the quality of the estimators. Our results indicate that the more elaborate CV strategies show potential gains when a small number of folds is used, but stratified cross-validation is preferable for 10-fold CV or in imbalanced scenarios. Finally, our adaptation of the cluster-based splitter reduces its computational cost while retaining similar performance.
Structural Modeling of the Role of Attachment Styles on Students’ Academic Motivation Mediated by Academic Self-efficacy

Background: Academic motivation has always been considered by researchers and specialists in educational psychology as a critical and essential aspect of education. With motivation, students follow the necessary mobility to successfully finish an assignment and reach a goal by demonstrating a specific level of proficiency in their work to finally acquire the essential learning and academic success. This study aimed to evaluate the structural modeling of the role of attachment styles among students mediated by academic self-efficacy. Methods: This analytical cross-sectional research was conducted using structural equations. The research population included all high school students (n=3057) in the seventh, eighth, and ninth grades living in Gonabad City, Iran, in 2019-2020, of whom 1646 were girls and 1411 boys. A total of 250 students were selected by multi-stage cluster random sampling method based on principles of structural equations sampling. After obtaining informed consent, all participants completed the situational motivation scale, the Morgan-Jinks student attachment styles, the cognitive emotion regulation questionnaire, revised adult attachment scale, and the classroom environments questionnaire. Then, the data were collected and analyzed through the Pearson correlation method and structural equations in SPSS software version 19 and AMOS version 20. Results: Academic self-efficacy (0.20) and attachment styles (-0.15) directly and significantly affected academic motivation. In addition, attachment styles (-0.23) had a direct and significant effect on academic self-efficacy. There was an indirect relationship between emotional regulation (-0.07) and mediated academic self-efficacy on academic motivation (P&lt;0.01). Conclusion: The attachment styles model owes a good fit to students’ academic motivation with the mediating role of academic self-efficacy. Therefore, the awareness of teachers, parents, principals, and other educational experts can improve students’ academic motivation.
Deep learning - An efficient method for medical image analysis

Medical imaging provides the visual representation of internal organs of the body which facilitate in diagnosis, monitoring health etc. Previously, the automated diagnosis procedure was done using edge detection and tedious mathematical computations. With the advancement in artificial intelligence, medical imaging is now supporting the diagnosis of cancer, diabetic retinopathy, Detection of Alzheimer’s and Parkinson’s disease, brain injury etc. Diagnosis through medical imaging has reduced the mortality rate drastically especially in the field of cancer. In order to decrease the probability of human error machine learning came into existence. The commonly used machine learning algorithms are K-Nearest Neighbors, Supported Vector Machine (SVM), and Decision Trees etc. But machine learning method has its own limitation of high dependency to the features extracted which depends on many factors. In order to improve the efficiency by removing the dependency on feature deep learning method came into existence. This paper has made a detailed survey on the application of deep learning method in health care service.
Autonomous underwater vehicles (AUVs) path planning based on Deep Reinforcement Learning

In the path planning problem for autonomous underwater vehicles, good results can be achieved using deep reinforcement learning models, but the actual effectiveness of the algorithm is extremely dependent on the construction of the neural network, which can require tedious manual tuning. We used AUTORL to automate the search for a neural network architecture and trained a population of intelligences to search for an algorithm that max the final reward. We applied this to the DQN algorithm and trained it in a built simulation environment, demonstrating that the evolved underwater robot path planning algorithm improves both in terms of final reward and convergence speed.
Social Networking and Language Learning

This chapter gives a brief overview on the use of social network sites (SNSs) for language learning and presents an empirical study of the use of Instagram (IG), one of the most popular SNSs, to assess learners' oral communication skills in the foreign language classroom. Several studies have mainly revealed the perceptions and preferences of learners in regards to using SNSs for learning grammar, vocabulary, L2 writing, reading, and speaking. In the current study, the performance scores of participants on an oral communication speaking task delivered both on IG and in class, as well as their scores on the Big Five personality traits as measured by the Quick Big Five Personality Test (QBFPT), were examined statistically. The results demonstrate IG facilitated students' performance in oral communication skill significantly and that personality traits do not predict the performance on IG, but the extroverted and conscientious are highly likely to achieve high scores in classroom.
Resilience-Mechanism-Based Dynamic Resource Allocation in Dispersed Computing Network

When degradation occurs in a dispersed computing network, mitigating the persistent effects of failures and improving the disposal efficiency is an open problem. However, the occurrence of failed nodes in a dispersed computing network is a random and low-probability incident. Further, the resilient resources for emergency allocation are from devices with complex spatial locations in practice. These factors pose challenges to the resource allocation problem using resilience mechanisms. In this article, we explore and analyze a resilience mechanism for dispersed computing networks as an optimization model. We investigate a resilience-aware dynamic resource allocation model to cope with a degraded dispersed computing network and obtain better emergency response at a lower cost. The uncertainties of node failures are uniquely explored to capture failure nodes more precisely and initiate the resilience mechanism for such nodes. In addition, we propose a novel approach to deal with the dynamic and complex coupling characteristics of decision variables in the model. This approach incorporates the induced artificial fish swarm algorithm with dynamic system simulation to generate and improve the scheme for the allocation of resilient resources. Finally, numerical simulation results verify the improved performance of our model and the effectiveness of the algorithm.
CA-ConvNeXt: Coordinate Attention on ConvNeXt for Early Alzheimer’s Disease Classification

Early diagnosis of Alzheimer’s disease allows patients to receive early and effective treatment as a way to increase their chances of survival. We propose CA-ConvNeXt for Early Alzheimer’s disease classification to solve the common MCI, AD, and NC classification problems. We employ the latest ConvNeXt network, which has a simpler topology and greater performance than ResNet and Swin Transformer. We effectively increase the model performance and reach 96 $$\%$$ accuracy on the public ADNI dataset by adding Coordinate Attention to the ConvNeXt network.
Towards Speculative Praxis: Finding the Politics in Speculation with Deleuze and Design

The philosophy of A. N. Whitehead has become the central source for understanding and theorizing the purposes and potentials of speculation. Whitehead’s philosophy, however, lacks a substantive political dimension which is of vital importance in our contemporary times of political, social, and ecological crisis. In this chapter, I argue that by considering an alternate foundation for speculation, we can fully explore its political potentials. I put Deleuze and Guattari’s concept of deterritorialization into conversation with the design tradition of speculative design to detail how we can create and use a speculative praxis. By using speculative praxis in research and design processes, we can prefigure the futures that we hope to work towards and enact a world-building praxis through our speculation.
Virtual Try-On Using Style Transfer

Achieving Clothing Try-On with 2D images is always complicated because retrieving all the characteristics of a person and the clothing can be difficult. The absence of an ideal dataset and the presence of considerable variance in the problem poses challenges. Despite this, there has been a growing interest in this task, thanks to the advancements in generative modelling. Various approaches have been developed and implemented, but due to the data and computation expensive nature of the task, they may not be widely applicable. To overcome this limitation, we present two different models: an Encoder-Decoder-based model and a Generative Adversarial Network-based model, both of which can effectively achieve virtual try-on with minimal architectural complexity through style transfer. Without excessive data, we aim to put the desired garment onto an intended target image. The network has been trained to learn the clothing and posture details of the image and perform the desired style transfer. The proposed method gives competitive results despite a considerable difference in model complexity.
Influence of lens and perspective distortion on optical surface metrology instrumentation

For any surface metrology system that obtains measurement with the aid of an imaging system, distortion must be carefully scrutinized. Both intrinsic lens distortion and perspective distortion embed surface error distributions that skew the interpretation of resultant surface maps. Either displaces acquired information due to the imaging process. Here, we quantify the origins of distortion, its modeling philosophy, and the effects of its digital correction procedure. This study includes simulation for lens-distorted systems such as interferometers and perspective-distorted systems such as monoscopic fringe projection profilometry and deflectometry. Summarily, this study hopes to clarify differences in low-order shape between surface metrology instrument measurements in which surface maps were not rectified for distortion.
Convenient Speed Monitoring Design of Air-actuated System based on S7-300 Controller

Air-actuated System is a key element of modern quality inspection. SIEMENS S7-300 PLC is an important product that has been widely used. In this paper, we propose a convenient speed monitoring design based on S7-300 controller. The proposed system design achieved high accuracy on a more complex system. With the design, the whole system could achieve error within 3% during extended sessions and an error with 2% during retract sessions. Both achieved an ideal accuracy, and it has certain practical value and application prospect.
GCISG: Guided Causal Invariant Learning for Improved Syn-to-Real Generalization

Training a deep learning model with artificially generated data can be an alternative when training data are scarce, yet it suffers from poor generalization performance due to a large domain gap. In this paper, we characterize the domain gap by using a causal framework for data generation. We assume that the real and synthetic data have common content variables but different style variables. Thus, a model trained on synthetic dataset might have poor generalization as the model learns the nuisance style variables. To that end, we propose causal invariance learning which encourages the model to learn a style-invariant representation that enhances the syn-to-real generalization. Furthermore, we propose a simple yet effective feature distillation method that prevents catastrophic forgetting of semantic knowledge of the real domain. In sum, we refer to our method as Guided Causal Invariant Syn-to-real Generalization that effectively improves the performance of syn-to-real generalization. We empirically verify the validity of proposed methods, and especially, our method achieves state-of-the-art on visual syn-to-real domain generalization tasks such as image classification and semantic segmentation.