Reviewerly/exHarmony huggingface dataset:
A Study on the Relationship between Digital Citizenship and Information Literacy among Middle School Students

The paper aims to analyze the relationship between the three dimensions of digital citizenship (respect yourself/respect others; educate yourself/connect with others; protect yourself/protect others) and the three dimensions of information literacy (Students' use of ICT applications; Students' school-related IeT use; Students' KT self-efficacy, interest, and enjoyment). This article takes 205 middle school students as the research object, collects data through questionnaire surveys, and uses SPSS to conduct correlation analysis and regression analysis on the digital citizenship and information literacy of middle school students. According to the research results, the digital citizenship of middle school students is better on the whole, and their performance in respecting yourself /respecting others is relatively outstanding, while their information literacy is relatively neutral. the three dimensions of digital citizenship are significantly correlated with the three dimensions of information literacy among middle school students, their digital citizenship can significantly predict information literacy. Finally, various strategies are proposed to enhance the information literacy of middle school students.
Construction of the Mathematical Model for Teaching Classroom Evaluation in Colleges and Universities Using an Optimized Apriori Algorithm

In order to increase the effectiveness and teaching quality of numerous classroom activities in colleges and universities, this paper puts forward the effectiveness assessment technique of teaching activities in colleges, universities, and institutions of higher education based on the optimized Apriori algorithm. A mathematical model for assessing the efficacy and usefulness of classroom activities in colleges and universities is constructed. Teaching contents, teaching attitudes, teaching methods, teaching effects, test results, and students’ performance are introduced as comprehensive evaluation factors, and a decision-making model for assessing the success and efficiency of classroom activities in colleges and universities is established by adopting scientific, reasonable, and systematic teaching methods. Through the grey correlation analysis of classroom teaching quality, the delay characteristic analysis and the adaptive parameter adjustment method are adopted. This paper constructs the optimal Apriori algorithm model of college teaching classroom activities, constructs the optimal evaluation function model of college teaching classroom activities effectiveness evaluation by the method of support-confidence joint estimation, extracts the optimal quality parameter set of college teaching classroom activities by the optimization detection method and association rule mining, and realizes the effectiveness evaluation and multi-dimensional parameter estimation of college teaching classroom activities by the optimized Apriori algorithm. The test outcomes confirm that this technique is reliable in evaluating the effectiveness of classroom activities in colleges and universities, and the directional distribution of association rules of classroom quality in colleges and universities is significant, which meaningfully advances the classroom teaching levels and quantitative evaluation abilities.
Action Recognition of Industrial Workers using Detectron2 and AutoML Algorithms

In this work conducted by two partners called UFAM/CETELI and Envision (TPV Group) we propose to address the problem of recognizing product assembly actions effectively performed by workers on an industrial production line. In this scenario where we created a complete video database we propose a system which uses the Detectron2 and AutoML algorithms which is capable of performing training (to recognize assembly actions) and executing the trained algorithms. In terms of performance the fee was equal to approximately 92%
Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images

Abstract Purpose Training deep neural networks usually require a large number of human-annotated data. For organ segmentation from volumetric medical images, human annotation is tedious and inefficient. To save human labour and to accelerate the training process, the strategy of annotation by iterative deep learning recently becomes popular in the research community. However, due to the lack of domain knowledge or efficient human-interaction tools, the current AID methods still suffer from long training time and high annotation burden. Methods We develop a contour-based annotation by iterative deep learning (AID) algorithm which uses boundary representation instead of voxel labels to incorporate high-level organ shape knowledge. We propose a contour segmentation network with a multi-scale feature extraction backbone to improve the boundary detection accuracy. We also developed a contour-based human-intervention method to facilitate easy adjustments of organ boundaries. By combining the contour-based segmentation network and the contour-adjustment intervention method, our algorithm achieves fast few-shot learning and efficient human proofreading. Results For validation, two human operators independently annotated four abdominal organs in computed tomography (CT) images using our method and two compared methods, i.e. a traditional contour-interpolation method and a state-of-the-art (SOTA) convolutional network (CNN) method based on voxel label representation. Compared to these methods, our approach considerably saved annotation time and reduced inter-rater variabilities. Our contour detection network also outperforms the SOTA nnU-Net in producing anatomically plausible organ shape with only a small training set. Conclusion Taking advantage of the boundary shape prior and the contour representation, our method is more efficient, more accurate and less prone to inter-operator variability than the SOTA AID methods for organ segmentation from volumetric medical images. The good shape learning ability and flexible boundary adjustment function make it suitable for fast annotation of organ structures with regular shape.
Deep Feature Based Siamese Network for Visual Object Tracking

One of the most important and challenging research subjects in computer vision is visual object tracking. The information obtained from the first frame consists of limited and insufficient information to represent an object. If prior information about robust representation that can represent an object well is not sufficient, object tracking fails when not robustly responding to changes in features of the target object according to various factors, namely shape, illumination variation, and scene distortion. In this paper, a real-time single object tracking algorithm is proposed based on a Siamese network to solve this problem. For the object feature extraction, we designed a fully convolutional neural network that removes a fully connected layer and configured a convolution block consisting of a bottleneck structure that preserves the information in a previous layer. This network was designed as a Siamese network, while a regional proposal network was combined at the end of the network for object tracking. The ImageNet Large-Scale Visual Recognition Challenge 2017 dataset was used to train the network in the pre-training phase. Then, in the experimental phase, the object tracking benchmark dataset was used to quantitatively evaluate the network. The experimental results revealed that the proposed tracking algorithm produced more competitive results compared to other tracking algorithms.
DnSwin: Toward real-world denoising via a continuous Wavelet Sliding Transformer

Real-world image denoising is a practical image restoration problem that aims to obtain clean images from in-the-wild noisy inputs. Recently, the Vision Transformer (ViT) has exhibited a strong ability to capture long-range dependencies, and many researchers have attempted to apply the ViT to image denoising tasks. However, a real-world image is an isolated frame that makes the ViT build long-range dependencies based on the internal patches, which divides images into patches, disarranges noise patterns and damages gradient continuity. In this article, we propose to resolve this issue by using a continuous Wavelet Sliding-Transformer that builds frequency correspondences under real-world scenes, called DnSwin. Specifically, we first extract the bottom features from noisy input images by using a convolutional neural network (CNN) encoder. The key to DnSwin is to extract high-frequency and low-frequency information from the observed features and build frequency dependencies. To this end, we propose a Wavelet Sliding-Window Transformer (WSWT) that utilizes the discrete wavelet transform (DWT), self-attention and the inverse DWT (IDWT) to extract deep features. Finally, we reconstruct the deep features into denoised images using a CNN decoder. Both quantitative and qualitative evaluations conducted on real-world denoising benchmarks demonstrate that the proposed DnSwin performs favorably against the state-of-the-art methods.
Cyberbullying Detection in social media Using Supervised ML &amp; NLP Techniques

Abstract: From the day internet came into existence, the era of social networking sprouted. In the beginning, no one may have thought the internet would be a host of numerous amazing services the social networking. Today we can say that online applications and social networking websites have become a non-separable part of one’s life. Many people from diverse age groups spend hours daily on such websites. Despite thoughtlet is emotionally connected through media, these facilities bring along big threats with them such as cyber-attacks, which includes include lying. As social networking sites are increasing, cyberbullying is increasing day by day. To identify word similarities in the tweets made by bullies and make use of machine learning and can develop an ML model that automatically detects social media bullying actions. However, many social media bullying detection techniques have been implemented, but many of them were textual based. Under this background and motivation, it can help to prevent the happen of cyberbullying if we can develop relevant techniques to discover cyberbullying in social media. A machine learning model is proposed to detect and prevent bullying on Twitter. Naïve Bayes is used for training and testing social media bullying content.
Walkable auralizations for experiential learning in an immersive classroom

This paper proposes an experiential method for learning acoustics and consequences of room design through the rapid creation of audio-visual congruent walkable auralizations. An efficient method produces auralizations of acoustical landmarks using a two-dimensional ray-tracing algorithm and publicly available floor plans for a 128-channel wave-field synthesis system. Late reverberation parameters are calculated using additional volumetric data. Congruent visuals are produced using a web-based interface accessible via personal devices, which automatically formats for and transmits to the immersive display. Massive user-contributed online databases are harnessed through application programming interfaces, such as those offered by the Google Maps Platform, to provide near-instant access to innumerable locations. The approach allows the rapid sonic recreation of historical concert venues with adequate sound sources. Listeners can walk through these recreations over an extended user area (12 m × 10 m).
Overview of Face Recognition Algorithms for Person Identification

Trends in computer vision and pattern recognition and capabilities of modern computers contributed to a considerable amount of research of these areas application in facial recognition systems. The purpose of this paper is to investigate the most significant methods of face recognition. In the first two sections of current paper, the methods of face recognition and identification are presented. The analysis of these methods covers the most important features of the pattern recognition area. An application of groups of methods is considered for different purposes. This paper contains comments for capabilities of algorithms under observation. The third section reveals result of the algorithms testing using real-world datasets and examples.
Circulating genuine multiparty entanglement in a quantum network

We propose a deterministic scheme of generating genuine multiparty entangled states in quantum networks of arbitrary size having various geometric structures---we refer to it as entanglement circulation. The procedure involves optimization over a set of two-qubit arbitrary unitary operators and the entanglement of the initial resource state. We report that the set of unitary operators that maximize the genuine multipartite entanglement quantified via generalized geometric measure (GGM) is not unique. We prove that the GGM of the resulting state of arbitrary qubits coincides with the minimum GGM of the initial resource states. By fixing the output state as the six-qubit one, we find the optimal way to create such states according to the available resource. Moreover, we show that the method proposed here can be implemented by using logic gates, or by using the time dynamics of realizable spin Hamiltonians. In case of an ordered system, GGM varies periodically with time while the evolution via disordered models lead to a low but constant multipartite entanglement in outputs at a critical time, which decreases exponentially with the increase of the strength of the disorder.
Comparison of Reinforcement and Imitation Learning algorithms in autonomous sailboat Digital Twins

This project aims to study the performance of two reinforcement machine learning algorithms, namely the Proximal Policy Optimization and Soft Actor Critic, in the simulation of autonomous sailboats and their response to different wind directions while avoiding obstacles detected by image analysis and following defined target check-points. Also, the effect of the imitation learning algorithms Behavioral Cloning and Generative Adversarial Imitation Learning combined with the first mentioned algorithms is studied. The proposed scenarios consist of areas filled with random static or moving obstacles and with the presence of favorable or crosswinds. The motivation for the project comes from the lack of studies of the mentioned algorithms in autonomous sailboats, issue which the current study tries to address. The Unity platform and ML-Agents machine learning toolkit are used for development and the methodology that guides the project can be similarly applied to other reinforcement learning problems. Through agent training, it is possible to compare the results and observe that the Proximal Policy Optimization obtains better performance within the proposed scenarios, both with and without the support of imitation learning algorithms.
D-TRACE: Deep Triply-Aligned Clustering

Deep clustering has recently emerged as a promising direction in clustering analysis, which aims to leverage the representation learning power of deep neural networks to enhance the clustering of highly-complex data. However, most of the existing deep clustering algorithms tend to utilize a single layer (typically the last fully-connected layer) of representation to build the clustering, yet cannot well exploit the rich and diverse information hidden in multiple layers. In view of this, this paper proposes a deep triply-aligned clustering (D-TRACE) approach, which is able to jointly explore three types of representations from multiple layers in the neural network. Specifically, we incorporate the contrastive learning into the first-stage network training, where three modules (i.e., the backbone network, the instance contrastive head, and the cluster contrastive head) are simultaneously optimized. By fusing the three types of representations from the three modules, we further propose the concept of the triply-aligned representation, based on which a unified neural network with a reconstruction loss and a Kullback-Leibler (KL) divergence based clustering loss is trained and thus the final clustering can be achieved in an unsupervised manner. Experiments on multiple image datasets demonstrate the superiority of our D-TRACE approach over the state-of-the-art deep clustering approaches.
Multi-DS Strategy for Source Camera Identification in Few-Shot Sample Data Sets

Source camera identification (SCI) is an intriguing problem in digital forensics, which identifies the source device of given images. However, most existing works require sufficient training samples to ensure performance. In this work, we propose a method based on semi-supervised ensemble learning (multi-DS) strategy, which extends labeled data set by multi-distance-based clustering strategy and then calibrate the pseudo-labels through a self-correction mechanism. Next, we iteratively perform the calibration-appending-training process to improve our model. We design comprehensive experiments, and our model achieves satisfactory performance on benchmark public databases (i.e., Dresden, VISION, and SOCRatES).
Small target detection method based on feature fusion for deep learning in state grid environment evaluation

Aiming at the problem that small and medium-sized targets cannot be detected in real time in high-resolution images, a new target detection network model is proposed. Firstly, the residual network RESNET is used as the basic network structure, an additional pyramid network model is added, and the pool layer is used to increase the number of hierarchical feature mapping. Then, the feature map is deconvoluted, and the high-level semantic feature map information and shallow feature map information are fused. Finally, the target is detected. Based on the analysis of the experimental results, compared with the existing target detection network model, the deep learning network model using feature fusion techniques has a detection accuracy of 80.2% on the standard dataset Pascal voc2007, and the detection speed reaches 27 frames per second, which meet the requirements of high-resolution image real-time monitoring and small target detection.
Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs

The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified and interpreted. We propose to use the rich variety of knowledge present in search histories to represent cliques modeling the main interpretable practices of information retrieval issued from the same “cognitive community”, identified by their use of keywords and by the search experience of the users sharing the same research question. Modeling typical cliques belonging to the same cognitive community is achieved through a new conceptual framework, based on user profiles, namely a bipartite geometric scientific knowledge graph, SKG GRAPHYP. Further studies of interpretation will test differences of documentary profiles and their meaning in various possible contexts which studies on “disagreements in scientific literature” have outlined. This final adjusted version of GRAPHYP optimizes the modeling of “Manifold Subnetworks of Cliques in Cognitive Communities” (MSCCC), captured from previous user experience in the same search domain. Cliques are built from graph grids of three parameters outlining the manifold of search experiences: mass of users; intensity of uses of items; and attention, identified as a ratio of “feature augmentation” by literature on information retrieval, its mean value allows calculation of an observed “steady” value of the user/item ratio or, conversely, a documentary behavior “deviating” from this mean value. An illustration of our approach is supplied in a positive first test, which stimulates further work on modeling subnetworks of users in search experience, that could help identify the varied alternative documentary sources of information retrieval, and in particular the scientific controversies and scholarly disputes.
SALES LEVEL ANALYSIS USING THE ASSOCIATION METHOD WITH THE APRIORI ALGORITHM

The company does not yet know the pattern of consumer purchases because so far, the sales transaction data has not been used correctly and does not have a unique method to determine consumer buying patterns. To overcome the problems on the company, this research was done to reprocess sales transaction data for 2018-2019 using data mining techniques with association methods and apriori algorithms. RapidMiner is a supporting application used to find association rules derived from transaction data. Processed transaction data using the Knowledge Discovery in Database (KDD) approach. Thus, the company can determine consumer habits in buying goods derived from sales transaction data for 2018-2019. The results of this study are that in 2018, nine association rules were obtained, of which the best were CT G-246 ⇒ CT G-250 and CT G-250 ⇒ CT G-246. In 2019, nineteen association rules were obtained, of which the best were PN 0441, SK 0175 ⇒ SK 0530 and SK 0175, SK 0283 ⇒ SK 0530. From the best association rules, the goods in the Coat (imported), Pants, and Skirt categories are categories that are often bought together.
Developing Pretrained Language Models for Turkish Biomedical Domain

Pretrained language models elevated with in-domain corpora show impressive results in biomedicine and clinical NLP tasks in English. However, there is minimal work in low-resource languages. This work introduces the BioBERTurk family, three pretrained models in Turkish for biomedicine. To evaluate models, we also introduce a labeled dataset to classify radiology reports of CT exams. Our first model was initialized from BERTurk and pretrained with biomedical corpus. The second model again continues to pretrain the general BERT model with a corpus of Ph.D. theses on radiology to test the effect of the task-related text. The final model combines radiology and biomedicine corpora with the corpus of BERTurk and pretrained a BERT model from scratch. F-scores of our models in the radiology resort classification are 92.99, 92.75, and 89.49 respectively. As far as we know, this is the first model that evaluates the effect of small size in-domain corpus in pretraining from scratch.
DIGITAL CULTURE: CONCEPTUALIZATION WAYS

The concept of digital culture determines a set of values, practices and expectations regarding the format of human interaction in the modern Internet society. Predictions of digital culture describe not only the specifics of the Internet environment but also the general context of social life. The range of interpretations of digital culture varies between two poles: from the recognition of digital technologies as a way of presenting libraries, museums, historical monuments, etc. to the concepts of digital culture as a new socio-anthropological reality, the content of which is not limited to ICT. Culture as a phenomenon means the semantic unity of human activity, the desire to adjust social life to ideals and values, the movement from existing to mandatory, from realistic to potential, and digital culture is an adequate response to the demands and challenges of time.