Reviewerly/exHarmony huggingface dataset:
Information culture and recordkeeping: a case of Chinese enterprises

Purpose The purpose of this paper is to report findings from an investigation on the information culture and recordkeeping in two Chinese companies, exploring the interaction between information culture and recordkeeping. Design/methodology/approach On the basis of systematic literature review, this research investigates the information culture and recordkeeping in two Chinese companies by conducting in-depth interviews with the staff of the two companies. Findings The attitude of the leadership and the staff towards records and information is different in the result-oriented information culture and rule-following culture. If a company aims to stay innovative and competitive, an information culture that can facilitate the good governance of records and information should be developed, and information professionals can play a key role in working towards this. Originality/value As a qualitative study of information culture and recordkeeping in Chinese companies, this paper provides the insight into the interaction between information culture and recordkeeping, demonstrates the impact of information culture on information governance and identifies the factors influencing information culture in an organization.
Use of open data to analyze the publication of articles in scientific events

Objective. This article has analyzed the publications of articles in scientific events using open data from the Lattes Platform.&#x0D; Design/Methodology/Approach. The LattesDataXplorer tool was used to extract curricular data from the Lattes Platform. The selection stage consisted of verifying which curricula had works published in scientific events. In the treatment stage, a characterization of the articles was carried out, thus allowing the analysis of articles published in scientific events.&#x0D; Results/Discussion. It was possible to carry out some characterizations such as temporal analysis, analysis by a large area of expertise, and which articles use persistent identifiers. The temporal analysis made it possible to verify how many articles were published per year. Through the analysis by a large area of action, the individuals with the highest and lowest rate of publication in annals of events were identified. Approximately 3% of articles report the persistent identifier (DOI).&#x0D; Conclusions. The peak of publications is in 2011, afterwards, there was a significant drop. Individuals from Health Sciences have a higher rate of publication in event proceedings, while individuals from the Exact and Earth Sciences have a lower volume of publications. Only 30,936 of the articles have a persistent identifier, but individuals from the Exact and Earth Sciences (30.68%) are the ones who use persistent identifiers the most.&#x0D; Originality/Value. From data extracted from the Lattes Platform, the results of original research that seeks to characterize Brazilian scientific production utilizing events are presented.
Algorithm for the Comparison of Human Periodic Movements Using Wearable Devices

In the context of teaching-learning of motor skills in a virtual environment, videos are generally used. The person who wants to learn a certain movement watches a video and tries to perform the activity. In this sense, feedback is rarely thought of. This article proposes an algorithm in which two periodic movements are compared, the one carried out by an expert and the one carried out by the person who is learning, in order to determine how closely these two movements are performed and to provide feedback from them. The algorithm starts from the capture of data through a wearable device that yields data from an accelerometer; in this case, the data of the expert and the data of the person who is learning are captured in a dataset of salsa dance steps. Adjustments are made to the data in terms of Pearson iterations, synchronization, filtering, and normalization, and DTW, linear regression, and error analysis are used to make the corresponding comparison of the two datasets. With the above, it is possible to determine if the cycles of the two signals coincide and how closely the learner’s movements resemble those of the expert.
Image-Based 3D Ultrasound Reconstruction with Optical Flow via Pyramid Warping Network

3D Ultrasound (US) contains rich spatial information which is helpful for medical diagnosis. However, current reconstruction methods with tracking devices are not suitable for clinical application. The sensorless freehand methods reconstruct based on US images which is less accuracy. In this paper, we proposed a network which reconstructs the US volume based on US images features and optical flow features. We proposed the pyramid warping layer which merges the image features and optical flow features with warping operation. To fuse the warped features of different scales in different pyramid levels, we adopted the fusion module using the attention mechanism. Meanwhile, we adopted the channel attention and spatial attention to our network. Our method was evaluated in 100 freehand US sweeps of human forearms which exhibits the efficient performance on volume reconstruction compared with other methods.
Unsupervised Deep Background Matting Using Deep Matte Prior

Background matting is a recently developed image matting approach, with applications to image and video editing. It refers to estimating both the alpha matte and foreground from a pair of images with and without foreground objects. Recent work has applied deep learning to background matting, with very promising performance achieved. However, existing deep models are supervised which require a large dataset with ground truth alpha mattes for training. To avoid the cost of data collection and possible bias in training data, this paper proposes a dataset-free unsupervised deep learning-based approach for background matting. Observing that the local smoothness of alpha matte can be well characterized by the untrained network prior called deep matte prior, we model the foreground and alpha matte using the priors encoded by two generative convolutional neural networks. To avoid possible overfitting during unsupervised learning, a two-stage learning scheme is developed which contains projection-based training and Bayesian post refinement. An alpha-matte-driven initialization scheme is also developed for performance boost. Even without calling external training data, the proposed approach provides competitive performance to recent supervised learning-based methods in the experiments.
NTT Security: Global Threat Intelligence Report

There's one thing you can rely on with cyber criminals – they never let a good crisis go to waste. And so it has proved with the Covid-19 pandemic, with scammers and hackers taking full advantage of people's fears and confusion and the disruption of normal working practices. At the same time, cyber criminals are getting smarter and adopting ever-greater use of automation, according to the ‘2020 Global Threat Intelligence Report' (GTIR) from NTT Security.
Prediction Of Myers-Briggs Type Indicator Personality Using Long Short-Term Memory

Personality is defined as the mix of features and qualities that make up an individual's particular character, including thoughts, feelings, and behaviors. With the rapid development of technology, personality computing is becoming a popular research field by providing users with personalization. Many researchers have used social media data to automatically predict personality. This research uses a public dataset from Kaggle, namely the Myers-Briggs Personality Type Dataset. The purpose of this study is to predict the accuracy and F1-score values so that the performance for predicting and classifying Myers–Briggs Type Indicator (MBTI) personality can work optimally by using attributes from the MBTI dataset, namely posts and types. Predictive accuracy analysis was carried out using the Long Short-Term Memory (LSTM) algorithm with random oversampling technique with the Imblearn library for MBTI personality type prediction and comparing the performance of the method proposed in this study with other popular machine learning algorithms. Experiments show that the LSTM model using the RMSprop optimizer and learning speed of 10-3 provides higher performance in terms of accuracy while for the F1-score the LSTM model using the RMSprop Optimizer and learning speed of 10-2 gives a higher value than the proposed machine learning algorithm so that the model MBTI dataset using LSTM with random oversampling can help in identifying the MBTI personality type.
Deep learning model construction for a semi-supervised classification with feature learning

Abstract Several deep models were proposed in image processing, data interpretation, speech recognition, and video analysis. Most of these architectures need a massive proportion of training samples and use arbitrary configuration. This paper constructs a deep learning architecture with feature learning. Graph convolution networks (GCNs), semi-supervised learning and graph data representation, have become increasingly popular as cost-effective and efficient methods. Most existing merging node descriptions for node distribution on the graph use stabilised neighbourhood knowledge, typically requiring a significant amount of variables and a high degree of computational complexity. To address these concerns, this research presents DLM-SSC, a unique method semi-supervised node classification tasks that can combine knowledge from multiple neighbourhoods at the same time by integrating high-order convolution and feature learning. This paper employs two function learning techniques for reducing the number of parameters and hidden layers: modified marginal fisher analysis (MMFA) and kernel principal component analysis (KPCA). The MMFA and KPCA weight matrices are modified layer by layer when implementing the DLM, a supervised pretraining technique that doesn't require a lot of information. Free measuring on citation datasets (Citeseer, Pubmed, and Cora) and other data sets demonstrate that the suggested approaches outperform similar algorithms.
Comparison of Sentiment Analysis on Online Product Reviews Using Optimised RNN-LSTM with Support Vector Machine

Using sentiment analysis, opinion mining examines the emotional tone and polarity of text (positive, neutral, or negative), as well as the sentiment polarity of text. With the rise of online information, sentiment analysis of customer evaluations has become a hot topic among machine learning researchers. Review texts for products online express a wide range of feelings and thoughts. By using natural language processing tools on the Internet, it will be possible for natural language processors to extract useful information from online reviews by performing sentiment analysis. It assigns polarity to a positive or negative entity or item. From the product reviews collected on Amazon, we conduct a Sentiment Analysis. As a result of asymmetrical weighting, we feed our feature words to support vector machine classifiers as well as Recurrent Neural Networks-Long Short Term Memory (RNN-LSTM)-optimised methods for determining the sentiment direction of reviews.
Information Dissemination With Service-Oriented Incentive Mechanism in Industrial Internet of Things

As one of the essential paradigms of Industrial 4.0, the Industrial Internet of Things (IIoT) challenges existing data management and information services by supporting computational-intensive applications, in which devices share and receive information through interactions under resource constraints. When there exist diverse service requirements of IIoT applications, information dissemination will be more likely driven by service-oriented incentives. In this article, a novel information dissemination process with the service-oriented incentive mechanism is analyzed and modeled in IIoT, which depicts the dynamical evolution of IIoT devices’ interactions. In particular, the characteristics of service-oriented activating and dissemination degenerating are considered due to the unique capability of IIoT devices. Extensive theoretical and simulation results verify the dynamical behaviors of information dissemination, including the propagation threshold, equilibrium, and stability. In addition, comparative simulations have demonstrated the service-oriented incentive mechanism further expands information diffusion by driving the participation of IIoT devices.
“I” am more concrete than “we”: Linguistic abstraction and first-person pronoun usage.

There has been much discussion around when people use "I" versus "we" pronouns, and abstract versus concrete communications, as well as how each of these can shape communication effectiveness. In the current research we bring together these separate research streams. Drawing on research arguing that abstract and concrete language are linked with communicative scope, we argue for an association between linguistic abstractness and personal pronoun usage. Across three archival data sets and two experiments, we find support for this association: Speakers who use more concrete language also use more first person singular (vs. plural) pronouns. In two follow-up studies we further find that this association can impact message effectiveness, such that using more first person singular than plural pronouns is increasingly ineffective when using abstract rather than concrete language, and using more concrete language is increasingly effective when using first-person singular rather than plural pronouns. By illustrating the link between linguistic abstraction and pronoun use, we offer insights into previously documented phenomena and suggest a key way of enhancing communication effectiveness. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
PROFESSIONAL AND PEDAGOGICAL DEFICITS OF TEACHERS IN THE SYSTEM OF SECONDARY VOCATIONAL EDUCATION: RESEARCH RESULTS

The article presents the results of a study conducted in order to reveal the priority directions of professional and pedagogical activity of pedagogical specialists in secondary vocational education institutions in relation to the value orientation. The problems arising in the implementation of these directions are identified. The main goals of the professional activity of vocational teachers and trainers and the existing professional deficits in the professional and pedagogical sphere are determined. The results of the study allow to develop the most important directions for improving the process of vocational teacher training, including individual methodological assistance.
SAR Image Despeckling Based on Block-Matching and Noise-Referenced Deep Learning Method

The noise2noise-based despeckling method, capable of training the despeckling deep neural network with only noisy synthetic aperture radar (SAR) image, has presented very good performance in recent research. This method requires a fine-registered multi-temporal dataset with minor time variance and uses similarity estimation to compensate for the time variance. However, constructing such a training dataset is very time-consuming and may not be viable for a certain practitioner. In this article, we propose a novel single-image-capable speckling method that combines the similarity-based block-matching and noise referenced deep learning network. The denoising network designed for this method is an encoder–decoder convolutional neural network and is accommodated to small image patches. This method firstly constructs a large number of noisy pairs as training input by similarity-based block-matching in either one noisy SAR image or multiple images. Then, the method trains the network in a Siamese manner with two parameter-sharing branches. The proposed method demonstrates favorable despeckling performance with both simulated and real SAR data with respect to other state-of-the-art reference filters. It also presents satisfying generalization capability as the trained network can despeckle well the unseen image of the same sensor. The main advantage of the proposed method is its application flexibility. It could be trained with either one noisy image or multiple images. Furthermore, the despeckling could be inferred by either the ad hoc trained network or a pre-trained one of the same sensor.
Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models

This work addresses the challenge of building an accurate and generalizable periocular recognition model with a small number of learnable parameters. Deeper (larger) models are typically more capable of learning complex information. For this reason, knowledge distillation (kd) was previously proposed to carry this knowledge from a large model (teacher) into a small model (student). Conventional KD optimizes the student output to be similar to the teacher output (commonly classification output). In biometrics, comparison (verification) and storage operations are conducted on biometric templates, extracted from pre-classification layers. In this work, we propose a novel template-driven KD approach that optimizes the distillation process so that the student model learns to produce templates similar to those produced by the teacher model. We demonstrate our approach on intra- and cross-device periocular verification. Our results demonstrate the superiority of our proposed approach over a network trained without KD and networks trained with conventional (vanilla) KD. For example, the targeted small model achieved an equal error rate (EER) value of 22.2% on cross-device verification without KD. The same model achieved an EER of 21.9% with the conventional KD, and only 14.7% EER when using our proposed template-driven KD.
The codevelopment of reading and attention from middle childhood to early adolescence: A multivariate latent growth curve study.

Attention skills are strong cross-sectional predictors of reading comprehension from childhood through to adolescence. However, less is known about the developmental relations between these two domains across this period. This study examined the codevelopment of reading and attention in a community sample of 614 Australian school students (50% female). Reading and attention were assessed at ages 8, 10, 12, and 14. Results of univariate latent growth models demonstrated, on average, curvilinear trajectories for reading in which rapid growth across younger age spans decelerates as children reach adolescence. By contrast, attention skills remained relatively stable on average. Significant negative correlations were observed between the intercept and slope factors in separate reading (r = -.62) and attention models (r = -.39) suggesting compensatory growth patterns in which poorer performing students in both domains at age 8 have steeper trajectories than their higher performing peers. A comparison of a multivariate latent growth model and an autoregressive latent trajectory model with structured residuals (ALT-SR) examined the interrelatedness of development in reading and attention. Both between-individual and within-individual cross-domain parameters showed reading and attention to be positively related at Grade 3, indicating an association between better attention and higher reading achievement at age 8. However, there was little evidence for interrelated growth across domains in this sample. The results contribute to theories which explain whether and how multiple cognitive domains codevelop over a substantial period of childhood and early adolescence. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
CDeRSNet: Towards High Performance Object Detection in Vietnamese Document Images

In recent years, document image understanding (DIU) has received much attention from the research community. Localizing page objects (tables, figures, equations) in document images is an important problem in DIU, which is the foundation for extracting information from document images. However, it has remained many challenges due to the high degree of intra-class variability in page document. Especially, object detection in Vietnamese image documents has still limited. In this paper, we propose CDeRSNet: a novel end-to-end trainable deep learning network to solve object detection in Vietnamese documents. The proposed network consists of Cascade R-CNN with the deformable convolution backbone and Rank & Sort (RS) Loss. CDeRSNet detects objects varying in scale with high detection accuracy at a higher IoU threshold to localize objects that differ in scale with detection accuracy at high quality. We empirically evaluate CDeRSNet on the Vietnamese image document dataset - UIT-DODV with four classes of objects: table, figure, caption, and formula. We achieved the best performance on the UIT-DODV dataset with 79.9% in terms of mAP, which is higher 5.4% than current results. In addition, we also provide a comprehensive evaluation and insightful analysis of CDeRSNet. Finally, we demonstrate CDeRSNet outperformance over state-of-the-arts models in object detection such as GFocal, GFocalV2, VFNet, DetectoRS on the UIT-DODV dataset. Code can be available at: https://github.com/trongthuan205/CDeRSNet.git .
Digital Transformation of Schools and Student’s Information and Communication Literacy

Information and communication literacy is one of the main meta-subject competencies that graduates of the secondary school should possess. The full-fledged formation of this competence is considered as one of the tasks of preparing students for life in the information society and the digital economy. The article discusses the results of a monitoring study of the information and communication literacy of 9th grade students, which was conducted in 21 regions of the Russian Federation in 2020. About half (45.4%) demonstrated a level of competence that corresponds to the readiness for life in the digital economy. Was considered the influence of the equipment of schools and the peculiarities of the organization of educational activities on the students’ level of information and communication literacy (ICL). The results of the study indicate a strong connection between the students’ competence and their out-of-school environment and a weak — with their work in the school. Research findings allow to determine the reasons for the insufficient level of ICL in a significant part of graduates and used to propose recommendations for its increase.
Learning Quality-Aware Representation for Multi-Person Pose Regression

Off-the-shelf single-stage multi-person pose regression methods generally leverage the instance score (i.e., confidence of the instance localization) to indicate the pose quality for selecting the pose candidates. We consider that there are two gaps involved in existing paradigm: 1) The instance score is not well interrelated with the pose regression quality. 2) The instance feature representation, which is used for predicting the instance score, does not explicitly encode the structural pose information to predict the reasonable score that represents pose regression quality. To address the aforementioned issues, we propose to learn the pose regression quality-aware representation. Concretely, for the first gap, instead of using the previous instance confidence label (e.g., discrete {1,0} or Gaussian representation) to denote the position and confidence for person instance, we firstly introduce the Consistent Instance Representation (CIR) that unifies the pose regression quality score of instance and the confidence of background into a pixel-wise score map to calibrates the inconsistency between instance score and pose regression quality. To fill the second gap, we further present the Query Encoding Module (QEM) including the Keypoint Query Encoding (KQE) to encode the positional and semantic information for each keypoint and the Pose Query Encoding (PQE) which explicitly encodes the predicted structural pose information to better fit the Consistent Instance Representation (CIR). By using the proposed components, we significantly alleviate the above gaps. Our method outperforms previous single-stage regression-based even bottom-up methods and achieves the state-of-the-art result of 71.7 AP on MS COCO test-dev set.