Reviewerly/exHarmony huggingface dataset:
Discussion on online and offline mixed teaching mode of hydraulics course

With the continuous development of information technology, the teaching mode is constantly updated.In recent years, online and offline hybrid teaching mode has received extensive attention.Taking the hydraulics course of water conservancy and hydropower engineering specialty in ordinary undergraduate colleges and universities with the cultivation of applied talents as the main direction as an example, this paper analyzes the problems existing in the single teaching mode, discusses the online and offline mixed teaching mode of hydraulics course, and analyzes the implementation effect, in order to further improve the teaching quality of hydraulics course and provide reference for the construction and reform of related professional courses.
Penggunaan Ayat Al-Quran dalam Produk Pakaian Muslimah

The change to the digital era is an extraordinary evolution of human civilization, which involves technological resources. This can be proven by the ease with which anyone can access anything through digital media. At the same time, it gave birth to various activities. One of them is the emergence of a variety of selling Muslim clothing products with modern and classic concepts which later became a market field. From 1980-1990, syar'i clothing was discriminated against. The change in the political system in 1998 had an impact on freedom of dress, especially expressing religious symbols in the use of the hijab. Muslim clothing products are also growing rapidly and become the lifestyle of the Indonesian Muslim middle-class community. In popular culture, the hijab is not merely a cover for their genitals, but also a commodity and "fashion" for them. This in the end eliminates the value of spirituality, as well as presents a new meaning of piety in religious symbols.
The Fractality of Sentiment Arcs for Literary Quality Assessment: the Case of Nobel Laureates

In the few works that have used NLP to study literary quality, sentiment and emotion analysis have often been considered valuable sources of information. At the same time, the idea that the nature and polarity of the sentiments expressed by a novel might have something to do with its perceived quality seems limited at best. In this paper, we argue that the fractality of narratives, specifically the longterm memory of their sentiment arcs, rather than their simple shape or average valence, might play an important role in the perception of literary quality by a human audience. In particular, we argue that such measure can help distinguish Nobel-winning writers from control groups in a recent corpus of English language novels. To test this hypothesis, we present the results from two studies: (i) a probability distribution test, where we compute the probability of seeing a title from a Nobel laureate at different levels of arc fractality; (ii) a classification test, where we use several machine learning algorithms to measure the predictive power of both sentiment arcs and their fractality measure. Lastly, we perform another experiment to examine whether arc fractality may be used to distinguish more or less popular works within the Nobel canon itself, looking at the probability of higher GoodReads’ ratings at different levels of arc fractality. Our findings seem to indicate that despite the competitive and complex nature of the task, the populations of Nobel and non-Nobel laureates seem to behave differently and can to some extent be told apart by a classifier. Moreover, the probability of Nobel titles having better ratings appears higher at different levels of arc fractality.
Property of Tensor Satisfying Binary Law 4

I have already reported &amp;quot;Property of Tensor Satisfying Binary Law 3&amp;quot;. This article is the article that I revise contents of &amp;quot;Property of Tensor Satisfying Binary Law 3&amp;quot;, and increased the report about new characteristics. I did not touch it about a contraction in the tensor which satisfied Binary Law in &amp;quot;Property of Tensor Satisfying Binary Law 3&amp;quot;. I report a contraction in the tensor satisfying Binary Law in this article.
Design of a Web-Based Vehicle Asset Information System at The Islamic Shariah Office in Banda Aceh City

This study describes the design of a web-based vehicle information system for Islamic Shariah services in Banda Aceh City. An issue under discussion is the use of computers in the design of vehicle asset information systems. The purpose of this study is to examine the use of vehicle asset information systems. Data collection was conducted through literature and field surveys by interviews and direct observation of the objects in question. Extreme programming techniques are used in system development. The following can be concluded from the created application program: 1) Vehicle Asset Information System of Islamic Shariah Service can quickly and efficiently view the vehicle asset data system according to existing data, so you can get timely vehicle asset data reports. 2) Implementation of asset vehicle information system in Islamic Shariah office will make data processing faster, more accurate and more efficient. 3) Applying asset vehicle information system in Islamic Shariah office facilitates data retrieval and greatly reduces human and financial resources.
Leakage-Resilient Anonymous Multi-Receiver Outsourced Revocable Certificateless Encryption

To reduce the complexity of managing certificates in public-key infrastructure (PKI)-based public key systems (PKI-PKS) and solve the key escrow issue in identity (ID)-based public-key systems (ID-PKS), certificateless public-key systems (CL-PKS) are proposed and play an important role in public-key cryptography. But, a CL-PKS setting should consider the revocation issue to revoke intruded members so that a revocable certificateless public-key setting (RCL-PKS) is used to resolve this issue. Recently, side-channel attacks have been realized and endangered the existing cryptographic schemes/protocols. Fortunately, the research of leakage-resilient cryptographic schemes/protocols resisting such attacks have attracted the attention of cryptologists. Indeed, several leakage-resilient certificateless encryption (LR-CLE) schemes have been proposed. However, these LR-CLE schemes have some deficiencies. In the paper, the first leakage-resilient anonymous multi-receiver outsourced revocable certificateless encryption (LRAMR-ORCLE) scheme is proposed. As compared with several previous schemes, the proposed scheme possesses several merits: (1) The revocation functionality is performed by an outsourced revocation authority; (2) It guards against side-channel attacks and possesses leakage-resilient property; (3) An authorized receiver's identity is anonymous for other receivers; (4) It is a secure one-to-many encryption scheme.
GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates

Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, annotating large datasets to develop supervised systems for remote sensing (RS) semantic segmentation is costly and time-consuming. Unsupervised Domain Adaption (UDA) could tackle these issues by adapting a model trained on a source domain, where labels are available, to a target domain, without annotations. UDA, while gaining importance in computer vision, is still under-investigated in RS. Thus, we propose a new lightweight model, GeoMultiTaskNet, based on two contributions: a GeoMultiTask module (GeoMT), which utilizes geographical coordinates to align the source and target domains, and a Dynamic Class Sampling (DCS) strategy, to adapt the semantic segmentation loss to the frequency of classes. This approach is the first to use geographical metadata for UDA in semantic segmentation. It reaches state-of-the-art performances (47,22% mIoU), reducing at the same time the number of parameters (33M), on a subset of the FLAIR dataset, a recently proposed dataset properly shaped for RS UDA, used for the first time ever for research scopes here.
Advancing Comprehension of Quantum Application Outputs: A Visualization Technique

Noise in quantum computers presents a challenge for the users of quantum computing despite the rapid progress we have seen in the past few years in building quantum computers. Existing works have addressed the noise in quantum computers using a variety of mitigation techniques since error correction requires a large number of qubits which is infeasible at present. One of the consequences of quantum computing noise is that users are unable to reproduce similar output from the same quantum computer at different times, let alone from various quantum computers. In this work, we have made initial attempts to visualize quantum basis states for all the circuits that were used in quantum machine learning from various quantum computers and noise-free quantum simulators. We have opened up a pathway for further research into this field where we will be able to isolate noisy states from non-noisy states leading to efficient error mitigation. This is where our work provides an important step in the direction of efficient error mitigation. Our work also provides a ground for quantum noise visualization in the case of large numbers of qubits.
IR Reasoner: Real-time Infrared Object Detection by Visual Reasoning

Thermal Infrared (IR) imagery is utilized in several applications due to their unique properties. However, there are a number of challenges, such as small target objects, image noise, lack of textural information, and background clutter, negatively affecting detection of objects in IR images. Current real-time object detection methods treat each image region separately and, in face of these challenges, this sole dependency on feature maps extracted by convolutional layers is not ideal. In this paper, we introduce a new architecture for real-time object detection in IR images by reasoning the relations between image regions by using self-attention. The proposed method, IR Reasoner, takes the spatial and semantic coherency between image regions into account to enhance the feature maps. We integrated this approach into the current state-of-the-art one-stage object detectors YOLOv4, YOLOR, and YOLOv7, and trained them from scratch on the FLIR ADAS dataset. Experimental evaluations show that the Reasoner variants perform better than the baseline models while still running in real-time. Our best performing Reasoner model YOLOv7-W6-Reasoner achieves 40.5% AP at 32.7 FPS. The code is publicly available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
ANALISIS LAYANAN PERPUSTAKAAN MENGGUNAKAN APLIKASI ICILEGON

Library services are activities to meet the information needs of library users, in a fast, precise, and accurate manner. Based on the results of this study, it was stated that this research was descriptive with a quantitative approach. Data collection techniques were carried out by observing, documenting, interviewing, questionnaires, and studying the literature. This sampling technique is used if the total population is above 1000, thus taking a sample of at least 10% or equal to 10% so that the sample in the study is 117 out of 1172 populations. The results of the data analysis show that the information system at the library using the iCilegon application can run well, this is proven by obtaining 81.5% on system quality, 79.2% on information quality, 81.4% on service quality, 80.6% on usage, user satisfaction which has a total score of 79.6%, and 81.7% in net benefits. Thus this study argues that the implementation of using the iCilegon application can improve the quality of library services at the Cilegon City Library and Archives Service.
End-to-end Wiener–CNN optimization for lensless extended depth of field imaging with phase mask

Extended depth of field (EDoF) has been widely studied in recent years and has many applications in various imaging tasks. Currently, the EDoF imaging system is independently based on either traditional reconstruction algorithms or deep learning. Traditional algorithms, on the other hand, cannot always achieve high image quality or large depth-of-field (DoF) due to their limited model fitting ability, whereas deep learning networks always suffer from low robustness to fabrication errors. In this work, we propose a compact lensless system for EDoF that combines these two methods. First, we build an end-to-end framework based on Wiener deconvolution to optimize the imaging model. On this basis, we develop an end-to-end denoising network based on a convolutional neural network (CNN) and train it to improve the quality of the reconstructed images. The proposed system achieves superior EDoF imaging performance in image quality compared to other lensless systems. Additionally, the proposed system is demonstrated to have high robustness against sensor noise, fabrication errors and quantization errors.
Robustness Against Gradient based Attacks through Cost Effective Network Fine-Tuning

Adversarial perturbations aim to modify the image pixels in an imperceptible manner such that the CNN classifier misclassifies an image, whereas humans can predict the original class. Several defense algorithms against adversarial attacks are proposed in the literature, such as binary classification which aims to detect adversarial examples, and network retraining using perturbed images. The challenge with the adversarial detection approach is that once the perturbed samples are detected, they are discarded, and the system requires fresh input. On the other hand, adversarial training requires the generation of adversarial images for data augmentation and hence is computationally demanding. It is well known that training a deep CNN architecture is resource-intensive, and therefore retraining again from scratch is not feasible in resource-constrained scenarios. We propose computationally efficient fine-tuning of pre-trained networks to increase their robustness against the prevalent gradient-based attacks. The proposed finetuning is performed in a complete black-box fashion, where we do not know the training setting such as optimizer, batch size, and learning rate used in the training of the network. Extensive experiments using multiple CNN architectures such as VGG and ResNet show that the proposed fine-tuning provides significant robustness against various widespread gradient attacks.
Research on Dense Enhanced Document Retrieval Based on G-mixup

The dense document retrieval model based on Mixup regards words as independent individuals, splits the connection between words, ignores the semantic information of the text, and also has the problem of insufficient labeled training data.In view of the above problems, this paper proposes a G-mixup graph based data intensive enhanced document retrieval model GDAR (Graph Data Augment Retrieval).The model first uses the graph convolutional neural network to convert queries and documents into graph data; then, uses the same type of document graph to construct a graph generator Graphon; finally, mixes the graph generator Graphon in the Euclidean space to obtain The new graph generator Graphons performs linear interpolation and perturbation operations on Graphons to obtain new training data with soft labels, solving the problem of lack of labeled data in dense document retrieval models.Experiments on the Natural Questions and TriviaQA datasets show that the method improves the accuracy index of T-1 by 4.12% and 4.88% respectively compared with the best baseline method.
Robust Asynchronous Federated Learning with Time-weighted and Stale Model Aggregation

Federated Learning (FL) ensures collaborative learning among multiple clients while maintaining data locally. However, the traditional synchronous FL solutions have lower accuracy and require more communication time in scenarios where most devices drop out during learning. Therefore, we propose an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Asy</u> nchronous <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> ederated <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning (AsyFL) scheme using time-weighted and stale model aggregation, which effectively solves the problem of poor model performance due to the heterogeneity of devices. Then, we integrate Symmetric Homomorphic Encryption (SHE) into AsyFL to propose <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Asy</u> nchronous <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rivacy- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> reserving <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> ederated <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning (Asy-PPFL), which protects the privacy of clients and achieves lightweight computing. Privacy analysis shows that Asy-PPFL is indistinguishable under Known Plaintext Attack (KPA) and convergence analysis proves the effectiveness of our schemes. A large number of experiments show that AsyFL and Asy-PPFL can achieve the highest accuracy of 58.40% and 58.26% on Cifar-10 dataset when most clients (i.e., 80%) are offline or delayed, respectively.
Inovasi Sosial untuk Pemberdayaan Perpustakaan Taman Pamekar dalam Program Comdev Star Energy Geothermal Salak, Ltd

Various surveys indicate that the literacy rate among the Indonesian population is quite low. According to this issued, the presence of Taman Pamekar Village Library amidst various challenges, there is significant potential for development. Through the Corporate Social Responsibility and Environmental Responsibility program, Star Energy Geothermal Salak, Ltd, located in Kabandungan Village, Sukabumi Regency, empowers Taman Pamekar Library. This program is aimed at enhancing the community's capabilities in literacy, education, social aspects, and even the economy. This empowerment program utilizes the Participatory Rural Appraisal method, which treats the community not only as the object but also as the subject of change. The activities conducted during the mentoring process focus on infrastructure development, improvement of facilities and infrastructure, enhancement of human resources quality, and the provision of various innovations in library services. These innovations are divided into social innovation and digital technology innovation. The results of this program are analyzed descriptively and qualitatively based on three aspects of library empowerment previously conducted: connectivity, content, and human resources.
Image Denoising with Self Operational and Convolutional Cycle-GANs

In this paper, we propose self operational and convolutional cycle-consistent generative adversarial networks (SOC-Cycle GANs), which are low learning models for removing noise in medical images. We focus on operational Cycle-GANs that combine Cycle-GANs and self operational neural networks (Self-ONNs), and by incorporating convolutional neural networks (CNNs) into the operational Cycle-GANs to reduce the load on the operational Cycle-GANs model. In addition, we eliminate noise from medical images using the SOC-Cycle GANs. In this research, we train and experiment with the SOC-Cycle GANs on a dataset of X-ray images, where the SOC-Cycle GANs are trained on 500 X-ray images with noise and 500 clean X-ray images without noise. In the simulations, we conduct a quantitative evaluation in terms of PSNR and SSIM value, number of parameters, and training time. We show that the our proposed method is model that do not require much training time and are effective methods for removing noise from medical images.
Platform for the recognition of people and their emotions for SMEs and Startups

Industry 4.0 refers to profound changes driven by advances in technologies such as robotics, artificial intelligence, nanotechnology, biotechnology, and others, which will be experienced by industries and society. Facial recognition has allowed for advancements with increasingly more applications in the fields of security, finance, education, and social security. This technology opens opportunities for SMEs (Small and medium-sized enterprises) and startups to develop new products and services with a high technological component. SMEs and startups require high scientific and technical knowledge in implementing this type of technology.We propose a hardware-software multiservice platform in the cloud for facial recognition of people and their emotions for SMEs and startups. This platform will allow SMEs and startups to identify the main algorithms for facial recognition. In addition, they will have the opportunity to evaluate facial recognition models. They could also train such models with different available data sets. Preliminary results are shown of the configuration, training, and validation of two emotion recognition architectures (Resnet and VGG).
Evaluasi Implementasi Program Ekstrakurikuler Pramuka di SDN Poris Pelawad 3 Kota Tangerang Banten

This thesis aims to identify, analyze and evaluate the Scout extra-curricular program at SDN Poris Pelawad 3, Cipondoh District, Tangerang City, Banten. The method used is a qualitative method. Collecting data using observation, interviews and documentation. Interviews were conducted with school principals, teachers, school committees, parents and students. Evaluation analysis uses the CIPP (Context Input Process and Product) evaluation model. The research in this thesis is motivated by the lack of efficiency in the mandatory Scout extracurricular activities, and the role of teachers in motivating activities in the implementation of activities that take place every week is felt to be less than optimal and efficient. It can be seen that very few students are interested in joining scout exercises. The results obtained from this research are because students do not yet have values or norms of noble character. The input varies because students come from economic, social, cultural and educational backgrounds and the facilities and infrastructure are still inadequate. The process has been good, because all scout activities or work programs have been implemented properly, as evidenced by the several group achievements achieved by SD Negeri PorisPelawad 3 Tangerang. The product of students at SD Negeri Poris Pelawad 3 Tangerang got achievements but that was in groups, if as an individual, SD students still lack achievement. The real results that are felt by students, teachers and parents are the development of good character values of students after participating in extracurricular activities.
Marketing Campaign Insight in Open and Distance Learning Higher Education from Internet Analytic

This study aims to find marketing campaigns that match the interests of the online audience at the Universitas Terbuka (UT) as an Open and Distance Learning (ODL) institution in Indonesia. Keyword research is used as a research method with the help of the keyword everywhere tool to get insights from keywords. Universitas Terbuka has high internet searches, indicating a high level of audience interest. Area and academic themes became the highest audience interest in the June-August range. Universitas Terbuka can create and schedule content based on keyword research findings. This research provides insight into a practical marketing plan that can be made based on keyword research so that it is effective and in the interests of the audience on the internet.&#x0D;