Reviewerly/exHarmony huggingface dataset:
GPSR: Gradient-Prior-Based Network for Image Super-Resolution

Recent deep learning has shown great potential in super-resolution (SR) tasks. However, most deep learning-based SR networks are optimized via pixel-level loss (i.e., L1, L2, and MSE), which forces the networks to output the average of all possible predictions, leading to blurred details. Especially in SR tasks with large scaling factors (i.e., ×4, ×8), the limitation is further aggravated. To alleviate this limitation, we propose a Gradient-Prior-based Super-Resolution network (GPSR). Specifically, a detail-preserving Gradient Guidance Strategy is proposed to fully exploit the gradient prior to guide the SR process from two aspects. On the one hand, an additional gradient branch is introduced into GPSR to provide the critical structural information. On the other hand, a compact gradient-guided loss is proposed to strengthen the constraints on the spatial structure and to prevent the blind restoration of high-frequency details. Moreover, two residual spatial attention adaptive aggregation modules are proposed and incorporated into the SR branch and the gradient branch, respectively, to fully exploit the crucial intermediate features to enhance the feature representation ability. Comprehensive experimental results demonstrate that the proposed GPSR outperforms state-of-the-art methods regarding both subjective visual quality and objective quantitative metrics in SR tasks with large scaling factors (i.e., ×4 and ×8).
Locally Differentially Private Heterogeneous Graph Aggregation with Utility Optimization

Graph data are widely collected and exploited by organizations, providing convenient services from policy formation and market decisions to medical care and social interactions. Yet, recent exposures of private data abuses have caused huge financial and reputational costs to both organizations and their users, enabling designing efficient privacy protection mechanisms a top priority. Local differential privacy (LDP) is an emerging privacy preservation standard and has been studied in various fields, including graph data aggregation. However, existing research studies of graph aggregation with LDP mainly provide single edge privacy for pure graph, leaving heterogeneous graph data aggregation with stronger privacy as an open challenge. In this paper, we take a step toward simultaneously collecting mixed attributed graph data while retaining intrinsic associations, with stronger local differential privacy protecting more than single edge. Specifically, we first propose a moderate granularity attributewise local differential privacy (ALDP) and formulate the problem of aggregating mixed attributed graph data as collecting two statistics under ALDP. Then we provide mechanisms to privately collect these statistics. For the categorical-attributed graph, we devise a utility-improved PrivAG mechanism, which randomizes and aggregates subsets of attribute and degree vectors. For heterogeneous graph, we present an adaptive binning scheme (ABS) to dynamically segment and simultaneously collect mixed attributed data, and extend the prior mechanism to a generalized PrivHG mechanism based on it. Finally, we practically optimize the utility of the mechanisms by reducing the computation costs and estimation errors. The effectiveness and efficiency of the mechanisms are validated through extensive experiments, and better performance is shown compared with the state-of-the-art mechanisms.
Understanding Digital Identity during the Pandemic: An Investigation of Two Chinese Spanish Teachers

During combatting the COVID-19 pandemic, the most widespread change in Spanish as a foreign language instruction is imperative online teaching. It demands that language teachers move all teaching activities to virtual platforms, facilitating the construction of their digital identities. However, there is scarce attention on Spanish teachers’ professional development, given the necessity of understanding the evolvement of their identities across virtual learning platforms. Through the lens of a case study, this research explores the digital identities of Spanish as a foreign language teachers during the school lockdown in 2022. The data includes semi-structured interviews, virtual classroom discourse, lesson plans, and reflective writing. The results show that Spanish teachers formed multiple digital identities, including curriculum innovators, vulnerable actors, involuntary team workers, overseas returnees, and academic researchers. Among them, the first three are core identities, while overseas returnees and academic researchers are peripheral identities. Regardless, they were formed and negotiated under the influence of teachers’ past experiences, the exercise of agency, emotional vulnerability, and social context. In addition, a contradictory belief in teaching was also identified during the formation of Chinese Spanish teachers’ digital identities.
Cryptanalysis of Reversible Data Hiding in Encrypted Images Based on the VQ Attack

Reversible data hiding in encrypted images (RDHEI) is commonly used for privacy protection in images stored on cloud storage. Currently, block permutation and co-modulation (BPCM) encryption is commonly utilized in most existing RDHEI schemes to generate encrypted images. In this paper, we analyze the vulnerabilities of RDHEI based on BPCM encryption and then propose a cryptanalysis method based on the vector quantization (VQ) attack. Unlike the existing cryptanalysis method, our method does not require the help of a plaintext image instead of adopting the symmetric property between the original cover image and the encrypted cover image. To obtain the pixel-changing pattern of a block before and after co-modulation, the concept of a pixel difference block (PDB) is first defined. Then, the VQ technique is used to estimate the content of the ciphertext block. Finally, we propose a sequence recovery method to help obtain the final recovered image based on the premise that the generator is compromised. The experimental results demonstrate that when the block size is 4 × 4, our proposed cryptanalysis method can decrypt the contents of the ciphertext image well. The average similarity can exceed 75% when comparing the edge information of the estimated image and the original image. It is concluded from our study that the BPCM encryption algorithm is not robust enough.
Dynamic Signature Verification Technique for the Online and Offline Representation of Electronic Signatures in Biometric Systems

Biometric systems input physical or personal human characteristics for identification, authentication, and security purposes. With the advancement in communication and intelligent security systems, biometrics are programmed to validate electronic signatures (E-signatures) for online and offline authentication. This article introduces a dynamic signature verification technique (DSVT) using mutual compliance (MC) between the security system and the biometric device. The security system is responsible for online and offline signature approval using personal inputs from humans. This personal verification is related to the stored online/offline signatures using certificates provided for authentication. The certificate-based authentication is valid within a session for online representation. Contrarily, this authentication is valid for persons under offline conditions. In this mode of segregation, application-level authentication verification is performed. A conventional tree classifier for dynamic signature verification is used for differentiating online and offline signatures. Moreover, the security metrics—such as signing bit, key, and size—are verified for both modes using classifier learning. For the segregated mode, the validation of the above is required to be unanimous to accelerate the dynamicity. The proposed technique’s performance is analyzed using the authentication success rate, verification failing ratio, verification time, and complexity.
Improved Method Based on Retinex and Gabor for the Surface Defect Enhancement of Aluminum Strips

Aiming at the problems of the blurred image defect contour and the surface texture of the aluminum strip suppressing defect feature extraction when collecting photos online in the air cushion furnace production line, we propose an algorithm for the surface defect enhancement and detection of aluminum strips based on the Retinex theory and Gobar filter. The Retinex algorithm can enhance the information and detail part of the image, while the Gobar algorithm can maintain the integrity of the defect edges well. The method first improves the high-frequency information of the image using a multi-scale Retinex based on a Laplacian filter, scales the original image and the enhanced image, and enhances the contrast of the image by adaptive histogram equalization. Then, the image is denoised, and texture suppressed using median filtering and morphological operations. Finally, Gobar edge detection is performed on the obtained sample images by convolving the sinusoidal plane wave and the Gaussian kernel function in the null domain and performing double-threshold segmentation to extract and refine the edges. The algorithm in this paper is compared with histogram equalization and the Gaussian filter-based MSR algorithm, and the surface defects of aluminum strips are significantly enhanced for the background. The experimental results show that the information entropy of the aluminum strip material defect image is improved from 5.03 to 7.85 in the original image, the average gradient of the image is improved from 3.51 to 9.51 in the original image, the contrast between the foreground and background is improved from 16.66 to 117.53 in the original image, the peak signal-to-noise ratio index is improved to 24.50 dB, and the integrity of the edges is well maintained while denoising. This paper’s algorithm effectively enhances and detects the surface defects of aluminum strips, and the edges of defect contours are clearer and more complete.
Like-attracts-like optimizer-based video robotics clustering control design for power device monitoring and inspection

A new meta-heuristic algorithm called like-attracts-like optimizer (LALO) is proposed in this article. It is inspired by the fact that an excellent person (i.e., a high-quality solution) easily attracts like-minded people to approach him or her. This LALO algorithm is an important inspiration for video robotics cluster control. First, the searching individuals are dynamically divided into multiple clusters by a growing neural gas network according to their positions, in which the topological relations between different clusters can also be determined. Second, each individual will approach a better individual from its superordinate cluster and the adjacent clusters. The performance of LALO is evaluated based on unimodal benchmark functions compared with various well-known meta-heuristic algorithms, which reveals that it is competitive for some optimizations.
A Qualitative Case Study on the Development of Competency Formation Class Model in Small Middle Schools

이 연구의 목적은 전북지역에 소재한 소규모 중학교의 수업에 대한 질적 사례연구를 통하여 소규모 중학교의 수업실태 파악 및 수업 실천 사례 분석을 실시하고, 그 결과를 토대로 소규모 중학교의 역량형성 수업모형을 개발하는 데 있다. 중학교 교사들을 대상으로 한 심층면담과 수업실천 사례의 분석 결과, 소규모 중학교의 수업은 학생 맞춤형 수업을 위한 교육과정 재구성과 수업방식의 유연화라는 장점과 기초학력 부족, 가정연계 학습곤란, 사회성 발달 교육의 어려움이라는 단점을 엿볼 수 있었다. 또한, 수업실천 사례의 공통된 핵심은‘연계’와 ‘프로젝트 학습’이었다. 즉, 소규모 중학교의 수업실천에서 연계는 교과 교육과정 간은 물론 교과와 비교과 간의 접합, 학교 안과 밖 교육활동 간의 연결을 의미하고, 프로젝트 학습은 이러한 연결을 구체화하는 유목적적 활동의 과정과 결과를 견인하는 실행 구조이자 원리로써 작동함을 알 수 있었다. 이 연구는 이러한 ‘연계’와 ‘프로젝트 학습’을 바탕으로 교과 간, 교과-비교과 간, 그리고 교과와 비교과 및 학교 밖 활동을 연계하는 융합프로젝트 활동을 기반으로 하는 역량형성 수업모형을 수업 기본 디자인하기 차원에서 제시하였다. 이 연구의 결과는 융합프로젝트 활동을 중심으로 하는 수업모형 개발을 통해 중학생의 기초학력, 기본학력, 교과 역량을 신장하고, 소규모 중학교의 교실수업을 개선하는 데 기초자료가 되기를 기대한다.The purpose of this study is to understand the class status of small middle schools and analyze the class practice cases through qualitative case studies of small middle schools located in Jeollabuk-do, and to develop a competency-building class model of small middle schools based on the results. As a result of in-depth interviews with middle school teachers and analysis of class practice cases, small middle school classes showed the advantages of reorganizing the curriculum for students' customized classes, lack of basic education, difficulty in family-linked learning, and difficulty in social development education. In addition, the core of the common request for class practice cases was 'linked' and 'project learning'. In other words, in the classroom practice of small middle schools, connection means connection between curriculums, as well as between subjects and comparative subjects, and project learning works as an implementation structure and principle that drives the process and results of purposeful activities that embody this connection. Based on these 'links' and 'project learning', this study presented a competency-forming class model based on convergence project activities linking subjects, subject-non-subjects, and subject-comparison and out-of-school activities to design basic classes. The results of this study are expected to serve as basic data for improving the basic education, basic education, and curriculum capabilities of middle school students and improving classroom classes in small middle schools through the development of a class model centered on convergence project activities.
Technoparticipation: Scrapbooks, stories and secrets

In 1993, aged 15, I discovered&amp;nbsp;Dymaxion Chronofile,&amp;nbsp;architect Buckminster Fuller's very large scrapbook&amp;nbsp;which he documented his life every 15 minutes from 1920 to 1983. Around the same time, my art teacher at secondary school suggested that I keep a scrapbook of images,&amp;nbsp;This&amp;nbsp;reflective account explores how I have worked with&amp;nbsp;imagery of pages from&amp;nbsp;that&amp;nbsp;very large scrapbook&amp;nbsp;&amp;nbsp;I kept when I was a teenager in the 1990s for just over five years – my personal private archived of images that in many ways helped to shape my understanding of (gay) male desire at a time when I felt too uncomfortable to come out as gay. The account begins with a poem I have written reflecting upon my motivations around the scrapbook and about the sociocultural context and time which it was produced in; 1990s homophobic suburban Britain. Fast forwarding from the early 1990s to 2021, the account then explains how I returned to my scrapbook as a source of inspiration for creating a poetry film as part of a body of work excavating my personal archive as an artist of over 25 years. The account then discusses how I made use of my scrapbook poetry film as part of&amp;nbsp;Technoparticipation -&amp;nbsp;an ongoing practice-as-research project that explores how technology becomes an additional performer/participant. In other words, how technology participates.
Sine Wave in Music and Sound Art: A typology of artistic approaches

Despite the sine wave’s close links to the birth of electronic music in the mid-twentieth century, it has been little studied aesthetically, and no systematic review of its artistic usages exists. This article presents a brief literature review, followed by the results of a survey on the principles guiding sine wave-based works. This allows to put forward a typological framework contributing to an understanding of the application of the sine wave in music.
Toward Code Generation for Process-oriented, Role-based Dashboards : An Example of Digital Advertising in Vietnam

With the rise of data visualization techniques, dashboard engineering has attracted a great deal of attention in the last decade, but has been hampered by a lack of automation tools. In today’s enterprise systems, business users expect dashboards to be more process-oriented in addition to being context-sensitive. While many commercial business intelligence tools offer role-based dashboard, the idea of constructing a process-oriented dashboard has drawn the attention of scholars in recent years. The paper reports on our preliminary work in the generation of such a dashboard by iterating through all the pools and data objects visually depicted in a business process and generating the computer-interpretable source code that represents the corresponding visualization charts. To make this process-oriented dashboard lively visible, we load a real dataset together with the generated source code into a dashboard-rendering library. To demonstrate the relevance of our work, we walk through a real-life digital advertising project and illustrate how a dashboard could be generated and rendered for a given business process.
Secure Recommender System based on Neural Collaborative Filtering and Federated Learning

A recommender system aims to suggest the most relevant items to users based on their personal data. However, data privacy is a growing concern for anyone. Secure recommender system is a research direction to preserve user privacy while maintaining as high performance as possible. The most recent strategy is to use Federated Learning, a machine learning technique for privacy-preserving distributed training. In Federated Learning, a subset of users will be selected for training model using data at local systems, the server will securely aggregate the computing result from local models to generate a global model, finally that model will give recommendations to users. In this paper, we present a novel algorithm to train Collaborative Filtering recommender system specialized for the ranking task in Federated Learning setting, where the goal is to protect user interaction information (i.e., implicit feedback). Specifically, with the help of the algorithm, the recommender system will be trained by Neural Collaborative Filtering, one of the state-of-the-art matrix factorization methods and Bayesian Personalized Ranking, the most common pairwise approach. In contrast to existing approaches which protect user privacy by requiring users to download/upload the information associated with all interactions that they can possibly interact with in order to perform training, the algorithm can protect user privacy at low communication cost, where users only need to obtain/transfer the information related to a small number of interactions per training iteration. Above all, through extensive experiments, the algorithm has demonstrated to utilize user data more efficient than the most recent research called FedeRank, while ensuring that user privacy is still preserved.
TEACHER DIDACTICS AND CHILDREN’S COGNITIVE SKILLS IN EARLY SCHOOL AGE

ABSTRACT The study analyzed the relationship between teacher didactics and cognitive skills of attention, memory, speed and verbal fluidity in first-year students of a public school in Cúcuta-Colombia. An exploratory script was used, together with qualitative analysis and mixed-source data collection, with an intentional sample of 22 students and 4 teachers. Cognitive abilities were evaluated through neuropsychological tests standardized for Colombia, and didactics were explored through semi-structured interviews following categories of teaching, learning and techniques. Cognitive skills were analyzed using descriptive statistics and interviews by categorical triangulation analysis, concluding in important children memory-speed and reduced attention and verbal fluidity, remarkable findings linked to didactics based on the use of learning sheets, repetition, mechanization, while lower results were associated with the use of video and hobbies.
An Object Aware Hybrid U-Net for Breast Tumour Annotation

Introduction: During digital examination of histopathological slides, the pathologist annotates the slides by marking the rough polygonal boundary around the suspected tumour region. The polygonal boundary covers the extent of the tumour in the slide. However, this kind of annotation is time consuming and tedious when there are large datasets. Objective: In this proposed work, we have tried to imitate pathologist like annotation by segmenting tumour extents by polygonal boundaries. Methods: For polygon like annotation, we used Active Contours. To penalize the Active Contour we used modified U-Net architecture for learning penalization values. The model is tested against both semantic segmentation and hybrid models for performance evaluation. Results: The IoU obtained from the proposed network (76.45%) is better than the base DSAC model and the modifications suggested in the paper improves the overall performance of the breast tumour dataset. Conclusion: The results obtained show that the pathologist like annotation could be achieved by developing such hybrid models that integrate the domain knowledge through classical segmentation methods like Active Contours and global knowledge through semantic segmentation models.
Intelligent Image Segmentation Methods Using Deep Convolutional Neural Network

Image segmentation is conceded as one of the significant procedures in the digital image processing and computer vision tasks involving object detection, object recognition, anatomical structure analysis, etc. The majority of these computer-aided analyses require an intelligent segmentation approach for easy understanding of each image component separately, automatically, and spontaneously. Consequently, AI becomes the primary guiding medium towards achieving image segmentation intelligently. Several AI-based algorithms such as Deep Convolutional Neural Network (DCNN) and adversarial networks can successfully provide solutions to these intelligent segmentation approaches. However, the proficiency of these recent approaches is primarily data-dependent and problem domain-specific. Thus, it is essential to understand the existing model architecture to solve any specific task or develop customized segmentation models. Therefore, the main objective of this chapter is to discuss the concepts related to the image segmentation elaborately. As a result, in this chapter, different available dataset domains such as natural scenes, aerial imaging, autonomous driving and the purpose of image segmentation are discussed. The underlying general mathematical operations combined with the currently used handy performance metrics are reviewed. Besides, different segmentation types along with intelligent segmentation algorithms under each category are explained. Finally, the prevailing research challenges that still demand researchers’ attention are highlighted.
KEUSANGAN LITERATUR PADA ARTIKEL JURNAL KAJIAN INFORMASI &amp; PERPUSTAKAAN (JKIP) UNIVERSITAS PADJADJARAN TAHUN 2015-2019

Science’s development occurs rapidly, so that the literature with the latest information has been created and causes the old literature to become obsolete. This is one of the impacts of the development of science known as literature obsolescence. This article analyzes the obsolescence of literature from Jurnal Kajian Informasi &amp; Perpustakaan (JKIP) Universitas Padjadjaran in 2015-2019. The method used is bibliometrics with citation analysis. Literature obsolescence can be determined by analyzing the half-life of the article. The data was obtained from the JKIP website, then the citations from each article were analyzed to obtain the half-life of the article. The results of the citation analysis on 14 articles with total of 1.254 citations obtained that the half-life of JKIP in 2015-2019 was 7,012 years. From the half-life of JKIP articles, the obsolescence of JKIP literature in 2015-2019 is 63% with 789 new citations and 464 obsolete citations.