Reviewerly/exHarmony huggingface dataset:
Deepfake Face Provenance for Proactive Forensics

Malicious deepfake face not only violates the privacy of personal identities, but also confuses the public and causes huge social harm. The current deepfake detection only stays at the level of distinguishing between true and false, but cannot trace the original genuine face corresponding to the fake face, that is, it does not have the ability to trace the source of evidence. The deepfake countermeasure technology for judicial forensics urgently calls for deepfake inversion. This paper pioneers an interesting question about face deepfake, active forensics that "know what it is and how it happened". Given that deepfake faces do not completely discard the features of original faces, especially facial expressions and poses, we argue that original faces can be approximately speculated from their deepfake counterparts. Correspondingly, we design a disentangling reversing network that decouples latent space features of deepfake faces under the supervision of real-fake face pair samples to infer original faces in reverse.
Variational Deep Atmospheric Turbulence Correction for Video

This paper presents a novel variational deep-learning approach for video atmospheric turbulence correction. We modify and tailor a Nonlinear Activation Free Network to video restoration. By including it in a variational inference framework, we boost the model’s performance and stability. This is achieved through conditioning the model on features extracted by a variational autoencoder (VAE). Furthermore, we enhance these features by making the encoder of the VAE include information pertinent to the image formation via a new loss based on the prediction of parameters of the geometrical distortion and the spatially variant blur responsible for the video sequence degradation. Experiments on a comprehensive synthetic video dataset demonstrate the effectiveness and reliability of the proposed method and validate its superiority compared to existing state-of-the-art approaches.
Domain Adaptation of Digital Pathology Images using Joint Stain Color and Image Quality Constraints

Digital pathology diagnosis systems face significant domain shift problems that hinder their performance on new datasets. Existing methods for aligning digital pathology images from different domains mainly focus on stain color and overlook the potential domain shifts caused by variations in image quality. To address this issue, we propose a novel parametric model that incorporates both stain color and image quality constraints for domain adaptation of digital pathology images. We evaluate our approach on the domain adaptive mitosis detection task through extensive experiments and ablation studies, showing that our method outperforms other state-of-the-art methods.
Multi Task-Based Facial Expression Synthesis with Supervision Learning and Feature Disentanglement of Image Style

Image-to-Image synthesis paradigms have been widely used for facial expression synthesis. However, current generators are apt to either produce artifacts for largely posed and non-aligned faces or unduly change the identity information like AdaIN-based generator. In this work, we suggest to use image style feature to surrogate the expression cues in the generator, and propose a multi-task learning paradigm to explore this style information via the supervision learning and feature disentanglement. While the supervision learning can make the encoded style specifically represent the expression cues and enable the generator to produce correct expression, the feature disentanglement of content and style cues enables the generator to better preserve the identity information in expression synthesis. Experimental results show that the proposed algorithm can well reduce the artifacts for the synthesis of posed and non-aligned expressions, and achieves competitive performances in terms of FID, PNSR and classification accuracy, compared with four publicly available GANs. The code and pre-trained models are available at https://github.com/lumanxi236/MTSS.
A joint study of deep learning-based methods for identity document image binarization and its influence on attribute recognition

Text recognition has benefited considerably from deep learning research, as well as the preprocessing methods included in its workflow. Identity documents are critical in the field of document analysis and should be thoroughly researched in relation to this workflow. We propose to examine the link between deep learning-based binarization and recognition algorithms for this sort of documents on the MIDV-500 and MIDV-2020 datasets. We provide a series of experiments to illustrate the relation between the quality of the collected images with respect to the binarization results, as well as the influence of its output on final recognition performance. We show that deep learning-based binarization solutions are affected by the capture quality, which implies that they still need significant improvements. We also show that proper binarization results can improve the performance for many recognition methods. Our retrained U-Net-bin outperformed all other binarization methods, and the best result in recognition was obtained by Paddle Paddle OCR v2.
Improving The Students’ Ability On Listening Using Whisper Race Game At Mts Al Washliyah Batang Serangan

The aims of this research are to find out the effectiveness of whisper race games strategy in improving the students' ability on listening. This research is a classroom action research. The action research was conducted in two cycles, each of which involved planning, implementing, observing and reflecting. The subject of this research was the first year students of MTS Al-Washliyah Batang Serangan. The subject of this research was 37 students in class VII. The technique of collecting data were interview, document, observation and test. After analyzing the data, it was found out that the students' score increased from pretest until post test of cycle I and cycle II. It means there is an improvement on the students ability on listening through whisper race game strategy. It was shown from the improvement of the means of students' score namely the mean of post test of cycle I (66.75) was higher than the mean of the students' score in pretest (46.75) and the mean of cycle II (79.45) was higher than the mean of the students’ score in post test I. It can be seen that the score continuously improved from the pre-test until the post test of cycle 2. And for the presentation in post test I 29.73% was higher than pre test 5.40% and the post test II 62.16% it was higher than pre test and post test 1. From the hypothesis data computation that t-count =9.27 and t-table df-N-1= 37-1=36. It can be seen that coefficient of t-count = 9.27 with the fact level a = 0.05 which the real level of t –table =2,02 in the coefficient of t-count (9.27)&gt;t-count = (2.02). Thus, the hypothesis is acceptable. Based on the finding the statistics analysis stating that Whisper Race Game strategy can improve the students ability on listening Keywords : whisper race game , listening
Application of AI-Helped Image Classification of Fish Images: An iDigBio dataset example

Artificial Intelligence (AI) becomes more prevalent in data science as well as in areas of computational science. Commonly used classification methods in AI can also be used for unorganized databases, if a proper model is trained. Most of the classification work is done on image data for purposes such as object detection and face recognition. If an object is well detected from an image, the classification may be done to organize image data. In this work, we try to identify images from an Integrated Digitized Biocollections (iDigBio) dataset and to classify these images to generate metadata to use as an AI-ready dataset in the future. The main problem of the museum image datasets is the lack of metadata information on images, wrong categorization, or poor image quality. By using AI, it maybe possible to overcome these problems. Automatic tools can help find, eliminate or fix these problems. For our example, we trained a model for 10 classes (e.g., complete fish, photograph, notes/labels, X-ray, CT (computerized tomotography) scan, partial fish, fossil, skeleton) by using a manually tagged iDigBio image dataset. After training a model for each for class, we reclassified the dataset by using these trained models. Some of the results are given in Table 1. As can be seen in the table, even manually classified images can be identified as different classes, and some classes are very similar to each other visually such as CT scans and X-rays or fossils and skeletons. Those kind of similarities are very confusing for the human eye as well as AI results.
HCM: Online Action Detection With Hard Video Clip Mining

Online action detection plays a vital role in video action understanding and can be widely used in various video analysis applications. This task aims to detect actions at the current moment within long untrimmed video streams. However, accurately identifying action-background transitions that are ambiguous in terms of time during detection can be challenging due to the similarity between the action and background clips, adding to the difficulty in finding a suitable division between them. To address this issue, we propose a hard video clip mining method based on deep metric learning for online action detection named HCM. The HCM method first selects video clips that are hard to distinguish to determine the optimization objects. Then, a hard clip mining loss is adopted to push the features toward the centers of the categories to which they belong and away from others. Furthermore, we introduce an intra-class feature compaction loss to constrain the divergence of action features, ensuring the stability of their distribution. We evaluated the proposed method on two challenging online action detection datasets, THUMOS14 and TVSeries. The results show that HCM is effective and efficient in online action detection and action anticipation tasks.
An Investigation into Replacement of Washcare Labels with Cost Effective QR Code Labels

We have worked on project an investigation into replacement of wash care labels with cost effective QR code labels. In India the wash care label instructions are not mandatory but recommended by ISO 3758 standards. We need to give the wash care instruction on garment to every customer and it is essential and necessary too. We need follow the wash care instructions and it improves life cycle of garment. Normally in foreign countries gives much priority than India because they are having care instructions mandatory and it is under legal acts. India is a developing country and 2ND highest population country in the world and most of the people (around 65%) under below poverty line and lives in rural areas. But compared to India, developed country peoples are having well economic conditions and they are able to buy the washing machines and afford to clean the garments in washing factories. In India most of the peoples are wash the garments using detergent powders/bars in normal water
Teacher Digital Competence in the stages of Compulsory Education according to DigCompEdu: The impact of demographic predictors on its development

New educational trends mediated by technology require the development of Teacher Digital Competence (TDC). These have been regulated by competence frameworks, such as DigCompEdu. The aim of the study is to determine and compare the level of self-perception of digital competence among teachers of the different stages of compulsory education (Early Childhood, Primary and Secondary Education) as a function of gender, considering different age ranges, years of teaching experience and hours of permanent education. This study also analysed how different predictors influence each other in the acquisition of TDC. To this end, an ex post facto non-experimental design is used with 78,966 teachers from Andalusia (Spain). The results show that the diagnosed level is low-medium and that the analysed variables had significant impact and prediction levels. Consequently, the casuistry of this phenomenon is discussed, concluding that it is necessary to develop personalised training programmes for teachers.
EFL Students’ Intercultural Awareness in Multicultural Classroom: A Narrative Inquiry

Being an intercultural speaker has become a necessity in global communication. Multicultural classes have a positive impact on intercultural learning because students from various cultural backgrounds come together to learn English. Therefore, this study aims to explore and narrate the development of intercultural awareness achieved by the students based on their experiences in a multicultural class setting. This study is a qualitative research with a narrative inquiry method. The participants in this study are three Indonesian students and two Thai students who are majoring in English Education and are in the same class at one of the universities in Indonesia. Data was collected through in-depth questionnaires with open-ended questions. The results of the study show that the students in the multicultural class have a good level of intercultural awareness. They have mastered all levels of intercultural awareness, from basic cultural awareness, advanced cultural awareness to intercultural awareness. Although they faced obstacles such as language barriers and limited exposure to the target language's culture in the class, they are able to practice tolerance and respect for different cultures. In addition, they have been able to adapt and integrate their cultural knowledge to be implemented in communication with speakers from different cultures
Social integral GPA (SSCI) as a factor in increasing the competitiveness of future specialists

In today's rapidly developing age, not a single person can be isolated from society. From early childhood to old age, we are in close contact with the people around us, establishing interpersonal relationships, uniting people with each other in whole groups, thus becoming an integral part of countless and diverse types of relationships. Students who are full members of society are also considered as individuals with high social activity.The article discusses the meaning of the concept of social integral GPA. In addition, the components that ensure the implementation of social activity are identified. The Social Integral GPA includes intellectual development, communication skills, social and volunteer activities, creative activity and sports achievements. Also, in the context of globalization, the social integral GPA provides the labor market with practice-oriented human capital.Participation in student organizations, volunteering and sports activities have a positive effect on the formation and development of teamwork skills, leadership qualities, individual and collective responsibility. Rowlin and Bangerter's research [1] showsthat leadership in a student organization contributes to the strengthening of leadership skills. There is also a high correlation between student participation in social activities and the development of critical thinking.Social activity develops many competencies and skills: communication skills, playing sports and a healthy lifestyle, emotional intelligence (EI), volunteering, teamwork, etc.The relevance of the research topic is determined by the importance of the goal of the higher education system -the formation of a personality involved in the creation of a knowledge-intensive economy and having a high social responsibility to society.
A Comparative Study of Object Detection using YOLO and SSD Algorithms

One of the most significant and difficult areas of computer vision is object detection, which is used in many aspects of daily life, including security monitoring, autonomous vehicle operation, and so on, to find instances of semantic objects belonging to a particular class. In this survey, we first look at the current approaches of typical detection models and discuss the benchmark datasets in order to comprehensively and deeply comprehend the major development status of the object detection pipeline. This project explains how convolutional neural network-based deep learning techniques are used for object detection. In this study, deep learning methods for cutting-edge object identification systems are evaluated. Any kind of visual medium is represented by a computer as a collection of numerical numbers. In order to inspect the contents of images, they need image processing techniques. In order to determine which is the quickest and most effective object detection algorithm, this research analyzes two popular algorithms: Single Shot Detection (SSD) and You Only Look Once (YOLO). The COCO (Common Object in Context) dataset is used in this comparative research to assess the effectiveness of the two algorithms and to analyze their strengths and weaknesses in terms of factors like accuracy, precision, and speed. Based on the findings analysis, it can be said that YOLO-v3 outperforms SSD in the same testing scenario. Keywords- Object Detection, Deep learning, CNN, COCO, YOLO, SSD
Pengembangan E-Modul Menggunakan Aplikasi 3D Pageflip Professional Materi Teks Biografi di Kelas X SMK Negeri 2 Alasa

The observation results obtained were that students tend to get bored studying using existing printed books. The purpose of this research is (1) to develop e-modules. (2) to determine the feasibility of the e-module (3) to determine the practicality of the e-module, (4) to determine the effectiveness of the e-module. The method used is ADDIE which has 5 stages consisting of analysis, design, development, implementation, and evaluation. Based on the results of research by validation by material experts, the first revision was 56%, the second revision was 75.8% and the third revision was 94.8%. The results of validation by linguists in the first revision were 71%, the second revision was 78% and the third revision was 100%. The results of validation by media experts in the first revision were 52%, the second revision was 73% and the third revision was 96%. The results of individual trials were 86.6%, small group trials were 90%, and field trials were 97%. The completeness results obtained at the final evaluation reached 98.5% completeness. So, it can be concluded that e-modules can be said to be feasible, practical, and effective as learning references. Researchers suggest that through e-module media students will be motivated to learn
Point of Interest recommendation for social network using the Internet of Things and deep reinforcement learning

Point of Interest (POI) recommendation is one of the important means for businesses to fully understand user preferences and meet their personalized needs, laying a solid foundation for the development of e-commerce and social networks. However, traditional social network POI recommendation algorithms suffer from various problems such as low accuracy and low recall. Therefore, a social network POI recommendation algorithm using the Internet of Things (IoT) and deep reinforcement learning (DRL) is proposed. First, the overall framework of the POI recommendation algorithm is designed by integrating IoT technology and DRL algorithm. Second, under the support of this framework, IoT technology is utilized to deeply explore users' personalized preferences for POI recommendation, analyze the internal rules of user check-in behavior and integrate multiple data sources. Finally, a DRL algorithm is used to construct the recommendation model. Multiple data sources are used as input to the model, based on which the check-in probability is calculated to generate the POI recommendation list and complete the design of the social network POI recommendation algorithm. Experimental results show that the accuracy of the proposed algorithm for social network POI recommendation has a maximum value of 98%, the maximum recall is 97% and the root mean square error is low. The recommendation time is short, and the maximum recommendation quality is 0.92, indicating that the recommendation effect of the proposed algorithm is better. By applying this method to the e-commerce field, businesses can fully utilize POI recommendation to recommend products and services that are suitable for users, thus promoting the development of the social economy.
Synthesis of Tungsten Trioxide/Hydroxyapatite (WO3/HAp) Photocatalyst For Methylene Blue Degradation

Abstract: Dyes have toxic properties and complex chemical structures which become a threat to the ecosystem if it exposes to the environment. A photocatalyst is a semiconductor material that can run a photocatalytic process under UV or visible light to degrade most organic molecules, ideally to H2O and CO2. Tungsten trioxide (WO3) is a visible photocatalyst which could use sunlight irradiation to treat the wastewater pollutant. In this study, WO3 material was hybridized with hydroxyapatite (HAp) through the sol-gel method to degrade methylene blue (MB) synthetic pollutants. The FTIR and SEM-EDX characterization confirmed the presence element of the WO3/HAp hybrid material. The original WO3 and modified WO3/HAp photocatalysts with concentrations of 10, 15, and 20 mg were tested to degrade 15 ppm of 50 ml MB solution under visible light for 4 hours. The highest pollutant degradation was obtained using a 20 mg photocatalyst, which reached the photodegradation efficiency of 73.072% for WO3 and 88.689% for WO3/HAp. The result proved that WO3/HAp hybrid photocatalyst showed higher degradation performance than the unmodified WO3 photocatalyst. The results of this research can be developed for industrial wastewater treatment, including the papermaking, printing and dyeing industries.Abstrak: Zat warna memiliki sifat beracun dan memiliki struktur kimia kompleks yang dapat menjadi ancaman bagi ekosistem apabila terpapar ke lingkungan. Fotokatalis heterogen menggunakan semikonduktor yang memicu proses fotokatalitik dengan bantuan sinar UV atau sinar tampak dalam mendegradasi kebanyakan limbah organik, menjadi H2O dan CO2. Tungsten trioksida (WO3) merupakan fotokatalis yang dapat menggunakan cahaya matahari untuk mendegradasi polutan pada air limbah. Pada penelitian ini, WO3 dihibridisasi dengan hidroksiapatit (HAp) melalui metode sol-gel untuk mendegradasi polutan sintetik Methylene Blue (MB). Hasil karakterisasi FTIR dan SEM-EDX mengkonfirmasi unsur-unsur penyusun material WO3/HAp. Fotokatalis WO3 munri dan WO3/HAp yang dimodifikasi diuji untuk mendegradasi 15 ppm larutan MB 50 ml di bawah cahaya tampak selama 4 jam dengan konsentrasi 10, 15 dan 20 mg. Degradasi polutan tertinggi diperoleh dengan menggunakan fotokatalis 20 mg yang mencapai efisiensi 73,072% untuk WO3 dan 88,689% untuk WO3/HAp. Hasil tersebut membuktikan bahwa fotokatalis hybrid WO3/HAp menunjukkan kinerja degradasi yang lebih baik daripada fotokatalis WO3 tanpa modifikasi.
Preparation of Standard Operating Procedures (SOPs) for Procurement of Goods, Especially Shopping for Consumables of Practical Materials at SMK Negeri 6 Batam

This final project discusses the mechanism of procurement of goods, especially the expenditure of consumables for practice at SMK Negeri 6 Batam, starting from submitting a budget plan to the process of spending on goods. The purpose of this study was to determine the causes of wasted spending, to find out the causes of incomplete supporting documents and to make Standard Operating Procedures (SOP) for procurement of goods. In this study, the authors used the method of data collection interviews, documentation and observation. For the data analysis method using descriptive data analysis method supported by qualitative data. Given this research requires exposure and analysis of events that occurred during the procurement process.