Reviewerly/exHarmony huggingface dataset:
Multi‐Modal Face Stylization with a Generative Prior

Abstract In this work, we introduce a new approach for face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generating high‐quality artistic faces with diverse styles and accurate facial reconstruction. Our proposed framework, MMFS, supports multi‐modal face stylization by leveraging the strengths of StyleGAN and integrates it into an encoder‐decoder architecture. Specifically, we use the mid‐resolution and high‐resolution layers of StyleGAN as the decoder to generate high‐quality faces, while aligning its low‐resolution layer with the encoder to extract and preserve input facial details. We also introduce a two‐stage training strategy, where we train the encoder in the first stage to align the feature maps with StyleGAN and enable a faithful reconstruction of input faces. In the second stage, the entire network is fine‐tuned with artistic data for stylized face generation. To enable the fine‐tuned model to be applied in zero‐shot and one‐shot stylization tasks, we train an additional mapping network from the large‐scale Contrastive‐Language‐Image‐Pre‐training (CLIP) space to a latent w+ space of fine‐tuned StyleGAN. Qualitative and quantitative experiments show that our framework achieves superior performance in both one‐shot and zero‐shot face stylization tasks, outperforming state‐of‐the‐art methods by a large margin.
Multi‐Level Implicit Function for Detailed Human Reconstruction by Relaxing SMPL Constraints

Abstract Aiming at enhancing the rationality and robustness of the results of single‐view image‐based human reconstruction and acquiring richer surface details, we propose a multi‐level reconstruction framework based on implicit functions. This framework first utilizes the predicted SMPL model (Skinned Multi‐Person Linear Model) as a prior to further predict consistent 2.5D sketches (depth map and normal map), and then obtains a coarse reconstruction result through an Implicit Function fitting network (IF‐Net). Subsequently, with a pixel‐aligned feature extraction module and a fine IF‐Net, the strong constraints imposed by SMPL are relaxed to add more surface details to the reconstruction result and remove noise. Finally, to address the trade‐off between surface details and rationality under complex poses, we propose a novel fusion repair algorithm that reuses existing information. This algorithm compensates for the missing parts of the fine reconstruction results with the coarse reconstruction results, leading to a robust, rational, and richly detailed reconstruction. The final experiments prove the effectiveness of our method and demonstrate that it achieves the richest surface details while ensuring rationality. The project website can be found at https://github.com/MXKKK/2.5D‐MLIF .
Sharing Model Framework for Zero‐Shot Sketch‐Based Image Retrieval

Abstract Sketch‐based image retrieval (SBIR) is an emerging task in computer vision. Research interests have arisen in solving this problem under the realistic and challenging setting of zero‐shot learning. Given a sketch as a query, the search goal is to retrieve the corresponding photographs in a zero‐shot scenario. In this paper, we divide the aforementioned challenging work into three tasks and propose a sharing model framework that addresses these problems. First, the weights of the proposed sharing model effectively reduced the modality gap between sketches and photographs. Second, semantic information was used to handle different label spaces during the training and testing stages. The sketch and photograph domains share semantic information. Finally, a memory mechanism is used to reduce the intrinsic variety in sketches, even if they all belong to the same class. Sketches and photographs dominate the embeddings in turn. Because sketches are not limited by language, our ultimate goal is to find a method to replace text searches. We also designed a demonstration program to demonstrate the use of the proposed method in real‐world applications. Our results indicate that the proposed method exhibits considerably higher zero‐shot SBIR performance than do other state‐of‐the‐art methods on the challenging Sketchy, TU‐Berlin, and QuickDraw datasets.
Facial Image Shadow Removal via Graph‐based Feature Fusion

Abstract Despite natural image shadow removal methods have made significant progress, they often perform poorly for facial image due to the unique features of the face. Moreover, most learning‐based methods are designed based on pixel‐level strategies, ignoring the global contextual relationship in the image. In this paper, we propose a graph‐based feature fusion network (GraphFFNet) for facial image shadow removal. We apply a graph‐based convolution encoder (GCEncoder) to extract global contextual relationships between regions in the coarse shadow‐less image produced by an image flipper. Then, we introduce a feature modulation module to fuse the global topological relation onto the image features, enhancing the feature representation of the network. Finally, the fusion decoder integrates all the effective features to reconstruct the image features, producing a satisfactory shadow‐removal result. Experimental results demonstrate the superiority of the proposed GraphFFNet over the state‐of‐the‐art and validate the effectiveness of facial image shadow removal.
Dampak efikasi diri akademik terhadap keterlibatan siswa pada remaja

This research aims to determine the influence of academic self-efficacy on student engagement in adolescents. The method used is quantitative research with a correlational approach, and the research subjects are 84 students, consisting of 72 high school students and 12 junior high school students. The sampling technique employed is accidental sampling. The research instrument used in this study is the academic self-efficacy scale by Fredricks. The data analysis technique used is simple linear regression. The research results show that there is an influence of academic self-efficacy on student engagement with a significance value that meets the standards and a correlation coefficient value of 0.555. It can be concluded that the higher the level of academic self-efficacy an individual possesses, the higher their student engagement. Conversely, individuals with low academic self-efficacy tend to have low student engagement as well. Keywords: Academic self-efficacy, remaja, student engagement
A verifiable (t,n) threshold quantum secret sharing scheme based on asymmetric binary polynomial

Abstract Threshold quantum secret sharing is a typical method for quantum secret sharing (QSS) schemes. In this paper, we propose a verifiable <?CDATA $\left( {t,n} \right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mfenced close=")" open="("> <mml:mrow> <mml:mi>t</mml:mi> <mml:mo>,</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:mfenced> </mml:math> threshold QSS scheme based on the <?CDATA $d$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>d</mml:mi> </mml:math> -dimensional Bell state and asymmetric binary polynomial. In this scheme, the dealer encodes the secret using the asymmetric binary polynomial and generates the corresponding secret share for each participant. Then, the dealer prepares the <?CDATA $d$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>d</mml:mi> </mml:math> -dimensional Bell state, and the participants perform corresponding unitary operations on the particles in transmission to recover the secret. With the hash function and the session key pairs, not only the cheating behavior of the dishonest participants can be detected, but also the specific cheaters can be identified. Furthermore, we consider the case when no less than <?CDATA $t$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>t</mml:mi> </mml:math> participants cooperate to recover the secret, which makes the proposed scheme more practical. Analyses show that the scheme can resist forgery attack, collusion attack and other common attacks.
Virtual emotions and Criminal Law

This article examines the role that Criminal Law should play in regulating the non-therapeutic use of immersive Virtual Reality (VR), specifically its massive use by consumers. The starting point has been to consider VR as an intermediate risk scenario, for the purposes of Criminal Law, between the criminality entirely generated in the physical world and that developed in the 2D digital environments [cybercrimes and criminality linked to social networks and persuasive Artificial Intelligence (AI)]. Firstly, specialize literature has been analyzed to establish the nature of virtual reality. From a technical standpoint, virtual reality is a neurotechnology infused with high-risk artificial intelligence; an inseparable synthesis of non-invasive neurotechnology and a set of AI systems, considered high-risk for the fundamental rights of citizens. From the perspective of its functioning, VR is a "transformative" neurotechnology capable of altering what people perceive as reality. This is possible, because its realism lies in the emotional immersion of the user in the virtual experience, similarly to how our brain functions. Therefore, the key idea in the immersive functioning of virtual reality is its capacity to evoke and modify human emotions, which results its greater harmful potential compared to the 2D environment. From there, three central and specific areas of (legally unaddressed) risk arise: (1) the special comprehensive nature of the data collected and stored during its use; (2) its ability to mentally reproduce the "physical" experience of the avatar in the user; and (3) its significant capacity to manipulate individuals. Secondly, the paper examines both the reported cases and the foreseeable criminality in virtual worlds or "proto-metaverse," focusing on the three risk areas, and exemplifying them with attacks on mental privacy, sexual freedom, and consumer manipulation. Finally, it is proposed that Criminal Law should also intervene (as soon as possible) to define the "red lines" of massive virtual reality use by citizens. With a democratic and human-centered approach, a basic legal framework is outlined for the criminalization of specific harms and risks associated with virtual reality, adapting the existing legal framework as necessary.
Abstractive summarization with deep reinforcement learning using semantic similarity rewards

Abstract Abstractive summarization is an approach to document summarization that is not limited to selecting sentences from the document but can generate new sentences as well. We address the two main challenges in abstractive summarization: how to evaluate the performance of a summarization model and what is a good training objective. We first introduce new evaluation measures based on the semantic similarity of the input and corresponding summary. The similarity scores are obtained by the fine-tuned BERTurk model using either the cross-encoder or a bi-encoder architecture. The fine-tuning is done on the Turkish Natural Language Inference and Semantic Textual Similarity benchmark datasets. We show that these measures have better correlations with human evaluations compared to Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores and BERTScore. We then introduce a deep reinforcement learning algorithm that uses the proposed semantic similarity measures as rewards, together with a mixed training objective, in order to generate more natural summaries in terms of human readability. We show that training with a mixed training objective function compared to only the maximum-likelihood objective improves similarity scores.
The Impact of Knowledge Management Practices on Job Satisfaction of Moroccan Employees: Mediating Role of Organizational Commitment

Purpose: The study aims to investigate how knowledge management (KM) practices impact JS among Moroccan employees, while also exploring the mediating role of organizational commitment (OC) in this relationship.&#x0D; &#x0D; Theoretical framework: The study draws upon established theories in KM, job satisfaction (JS), and OC. It suggests that effective KM practices can enhance JS and that OC acts as a mediator in this relationship.&#x0D; &#x0D; Design/Methodology/Approach: This study employs a deductive approach and quantitative research design to explore the relationship between KM practices and JS in Moroccan SMEs. Data from 175 respondents were analyzed using linear regression with the assistance of the SPSS statistical software.&#x0D; &#x0D; Findings: Effective KM practices positively impact job satisfaction (JS) in Moroccan SMEs. Additionally, organizational commitment (OC) acts as a mediator between KM practices and JS. Implementing successful KM strategies and fostering employee commitment can enhance JS in Moroccan SMEs.&#x0D; &#x0D; Research, practical &amp; social implications: The study's implications highlight that implementing effective KM practices in Moroccan SMEs can have far-reaching benefits, including increased JS, enhanced productivity, improved talent attraction, and positive contributions to socio-economic development, emphasizing the significance of KM for organizational success and broader societal advancement.&#x0D; Originality/Value: The originality of this study lie in its examination of KM practices specifically within Moroccan SMEs. By focusing on this context, the study provides unique insights into the challenges and opportunities faced by these organizations, offering practical implications and recommendations that can contribute to the development of tailored KM strategies for similar SMEs in Morocco and beyond.
Supply Chain Management of Drug Products in Blockchain Using Reinforcement Learning

Supply chain management (SCM) is a complex system that consists of two parts: a management system for the medication supply chain and a recommendation system. The first part of the system is the management system powered by reinforcement learning. The reinforcement Learning model is trained to recommend the most suitable medications for a diverse set of customers. The model will be fed customer ratings and reviews via the proposed framework client application, where it will learn over time to make the most informed drug recommendations possible. The proposed system will provide patients with recommendations for successful drug therapies.
Program Intervensi Narimo Ing Pandum dalam Meningkatkan Penerimaan Diri ADHA di Kota Yogyakarta

According to the Ministry of Health, around 1.7 million children under the age of 14 globally are living with HIV. The condition of physical hardship and social rejection of ADHA makes ADHA have a negative perception that then affects his acceptance. This study aimed to evaluate the effect of the Narimo Ing Pandum intervention program in increasing self-acceptance among children with HIV. In this study, a total of 9 children with HIV aged 7 to 15 years were recruited. The study method used is experimental research design of one group pretest-posttest, and data analysis techniques using t-test statistical analysis techniques. The results showed that the levels of self-acceptance weren’t significantly different before and after the intervention.
Pelatihan Penulisan Esai Opini dan Mentoring Studi Lanjut bagi Pendidik Perempuan Indonesia

This Community Service aims to help enhance the writing skills of female educators and help them prepare the documents needed for further studies. For this reason, female educators such as lecturers and teachers from various regions in Indonesia were invited to attend virtual tutoring through the Zoom platform. This training and mentoring activities were carried out in several meetings and delivered by several resource persons regarding the techniques of writing opinion essays and supporting documents such as study objective and personal statement, needed to continue studies, especially at foreign universities. Nevertheless, despite having received a positive response from the participants, this training was still limited only to the introduction of opinion essays and structure in essay writing and only concerned with writing study objective and personal statement. Therefore, the recommendation for the following programs is providing training that discusses other types of essays on the IELTS test and other skills needed to improve the English competence of Indonesian female educators. In addition, other writing workshops for female educators are also necessary for postgraduate studies in the future.&#x0D; &#x0D; Keywords: Postgraduate Studies Mentoring, Female Educators, Opinion Essay Writing
Workshop Penggunaan Aplikasi Presensi Berbasis Mobile dengan Validasi Radius Lokasi

Employee attendance is data that shows the daily attendance of employees in a company. Data that can be generated from an employee's absence is the time of arrival and departure of employees as proof of attendance at work in the office. There are several problems that arise from this finger print presence equipment. The first problem is the sensitivity of the tool, this presence tool sometimes takes a long time to recognize employee fingerprints, secondly the location of the finger print tool is too far from the employee room. The use of a Mobile-Based Presence application with Location Radius Validation is a solution to the presence problem. To facilitate the use of this presence application, it is necessary to conduct a workshop on the use of the presence application by employeesKeyword : fingerprint, presence, location
Dynamic financial and monetary security risk assessment based on information service security assessment model and blockchain

With the rapid development of blockchain technology, the financial and monetary (FM) blockchain fields also began to collide. Therefore, this study aims to explore the relationship between the two fields and efficiently evaluate the security of financial information. Firstly, this study introduces the theoretical basis of blockchain in the dynamic linkage mechanism of FM and gives the overall framework of digital currency based on blockchain. Meanwhile, the relationship between blockchain and digital finance is empirically analyzed and designed. Secondly, the framework of financial information service security assessment (ISSA) is created using blockchain technology, and the frame of security risk evaluation is verified by taking electronic invoice information as the research object. Finally, the results show that: (1) foreign exchange (forex) events and major stock index decline events have a significant impact on the return of Bitcoin (BTC) in the short term. Moreover, more than 70% of uncertainty events will make BTC's abnormal return (AR) significantly positive; (2) Under the influence of forex uncertainty events, only one BTC's AR is remarkably negative in short order, while the other seven times are markedly positive. In the uncertainty events of major stock indexes, only two times are significantly negative, and the other six times are positive. This indicates that uncertain events in the short run make prominent AR of BTC; the proposed blockchain-based ISSA model and assessment index are scientific, feasible, and operable.
Cloud Platform

The Google Cloud Platform (GCP) global infrastructure is presented in this chapter. Google’s global infrastructure presently includes 35 regions that support over 100 services with more of each being planned. Customers can deploy their virtual infrastructure in GCP zones, data centers, and edge locations. Major GCP service categories of management tools, identity and security, networking, compute, storage, and databases services related to each category are covered. As was previously accomplished for AWS and Azure, in the lab you will create a fully operational three-tier web architecture that resides and is fully operational on the GCP infrastructure.
Cloud Industry Trends

Cloud technology will continue to evolve at a rapid pace. This chapter evaluates major trends driving the future of the cloud industry. In cloud services management, for example, managed, unmanaged, and serverless services are emerging. Application development is evolving from monolithic platforms to Service-Oriented Architectures (SOAs), to microservices. Use of containers based on Docker, Kubernetes, and other technologies continues to accelerate. Networking technologies including edge computing and Software Defined Networks will also continue to expand. This chapter prompts cloud engineers to become network security, automation, and programmability skilled because to remain relevant in the industry cloud professionals must continually learn and apply new technologies.
Cloud Computing Concepts

This chapter examines the fundamentals of cloud computing and its applications. Beginning with the National Institute of Standards and Technology (NIST) definition of cloud computing, it covers the five essential characteristics, three service models, and four deployment models the NIST has defined. It introduces compute, storage, database, and network as components that work together to create a modern data center. It presents an overview of virtualization, a disruptive technology that paved the way to the cloud. Business drivers for cloud adoption are presented. This chapter concludes by comparing a legacy to a virtualized three-tier web architecture.
Well-Architected Framework

This chapter presents guidelines for creating an AWS cloud infrastructure called the Well-Architected Framework (WAF). The WAF provides recommendations to AWS clients and partners for developing secure, high-performance, resilient, and efficient cloud infrastructures. Although AWS-based in origin, the WAF practices have been adopted by Microsoft Azure and the Google Cloud Platform with minimal variation of content. The AWS WAF is described as having six pillars: operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability. Each pillar then defines high-level design principles, which are supported by recommended best practices.
Effect of Digital Wordless Picture Books to Improve Students Ability in Writing Narrative Text at SMP Islam As-Shofa Pekanbaru

&#x0D; &#x0D; &#x0D; &#x0D; This research aims to introduce teaching media based on technology using digital wordless picture books to improve students’ narrative writing abilities. This teaching media is presented in the form of PDF and e-books. The research design in this research used quasi-experimental. The instrument in this research used writing test. The subject in this research are the eighth-grade students of SMP Islam As-Shofa Pekanbaru. Purposive sampling technique was employed to select the sample. The result of normality test indicate the data is normal distribution and the homogeneity test is homogeneous. The calculation of the Independent Sample T-test shows that the p-value for the posttest data of the experimental group and the control group is p = 0,002 &lt; 0,05. The result of the Independent Sample T-test indicate a that the Digital Wordless Picture Books media is effectively used in improving students’ ability in writing narrative text to the eighth-grade students of SMP Islam As-Shofa Pekanbaru.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D;