Reviewerly/exHarmony huggingface dataset:
Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting

With well-selected data, homogeneous diffusion inpainting can reconstruct images from sparse data with high quality. While 4K colour images of size 3840 x 2160 can already be inpainted in real time, optimising the known data for applications like image compression remains challenging: Widely used stochastic strategies can take days for a single 4K image. Recently, a first neural approach for this so-called mask optimisation problem offered high speed and good quality for small images. It trains a mask generation network with the help of a neural inpainting surrogate. However, these mask networks can only output masks for the resolution and mask density they were trained for. We solve these problems and enable mask optimisation for high-resolution images through a neuroexplicit coarse-to-fine strategy. Additionally, we improve the training and interpretability of mask networks by including a numerical inpainting solver directly into the network. This allows to generate masks for 4K images in around 0.6 seconds while exceeding the quality of stochastic methods on practically relevant densities. Compared to popular existing approaches, this is an acceleration of up to four orders of magnitude.
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier

Data heterogeneity is an inherent challenge that hinders the performance of federated learning (FL). Recent studies have identified the biased classifiers of local models as the key bottleneck. Previous attempts have used classifier calibration after FL training, but this approach falls short in improving the poor feature representations caused by training-time classifier biases. Resolving the classifier bias dilemma in FL requires a full understanding of the mechanisms behind the classifier. Recent advances in neural collapse have shown that the classifiers and feature prototypes under perfect training scenarios collapse into an optimal structure called simplex equiangular tight frame (ETF). Building on this neural collapse insight, we propose a solution to the FL's classifier bias problem by utilizing a synthetic and fixed ETF classifier during training. The optimal classifier structure enables all clients to learn unified and optimal feature representations even under extremely heterogeneous data. We devise several effective modules to better adapt the ETF structure in FL, achieving both high generalization and personalization. Extensive experiments demonstrate that our method achieves state-of-the-art performances on CIFAR-10, CIFAR-100, and Tiny-ImageNet.
DialogPaint: A Dialog-based Image Editing Model

We introduce DialogPaint, a novel framework that bridges conversational interactions with image editing, enabling users to modify images through natural dialogue. By integrating a dialogue model with the Stable Diffusion image transformation technique, DialogPaint offers a more intuitive and interactive approach to image modifications. Our method stands out by effectively interpreting and executing both explicit and ambiguous instructions, handling tasks such as object replacement, style transfer, and color modification. Notably, DialogPaint supports iterative, multi-round editing, allowing users to refine image edits over successive interactions. Comprehensive evaluations highlight the robustness and versatility of our approach, marking a significant advancement in dialogue-driven image editing.
DS-MPEPC: Safe and Deadlock-Avoiding Robot Navigation in Cluttered Dynamic Scenes

We present an algorithm for safe robot navigation in complex dynamic environments using a variant of model predictive equilibrium point control. We use an optimization formulation to navigate robots gracefully in dynamic environments by optimizing over a trajectory cost function at each timestep. We present a novel trajectory cost formulation that significantly reduces the conservative and deadlock behaviors and generates smooth trajectories. In particular, we propose a new collision probability function that effectively captures the risk associated with a given configuration and the time to avoid collisions based on the velocity direction. Moreover, we propose a terminal state cost based on the expected time-to-goal and time-to-collision values that helps in avoiding trajectories that could result in deadlock. We evaluate our cost formulation in multiple simulated and real-world scenarios, including narrow corridors with dynamic obstacles, and observe significantly improved navigation behavior and reduced deadlocks as compared to prior methods.
Estimating Coherent Contributions to the Error Profile Using Cycle Error Reconstruction

Mitigation and calibration schemes are central to maximize the computational reach of today's Noisy Intermediate Scale Quantum (NISQ) hardware, but these schemes are often specialized to exclusively address either coherent or decoherent error sources. Quantifying the two types of errors hence constitutes a desirable feature when it comes to benchmarking error suppression tools. In this paper, we present a scalable and cycle-centric methodology for obtaining a detailed estimate of the coherent contribution to the error profile of a hard computing cycle. The protocol that we suggest is based on Cycle Error Reconstruction (CER), also known as K-body Noise Reconstruction (KNR). This protocol is similar to Cycle Benchmarking (CB) in that it provides a cycle-centric diagnostic based on Pauli fidelity estimation [1]. We introduce an additional hyper-parameter in CER by allowing the hard cycles to be folded multiple times before being subject to Pauli twirling. Performing CER for different values of our added hyper-parameter allows estimating the coherent error contributions through a generalization of the fidelity decay formula. We confirm the accuracy of our method through numerical simulations on a quantum simulator, and perform proof-of-concept experiments on three IBM chips, namely ibmq_guadalupe, ibmq_manila, and ibmq_montreal. In all three experiments, we measure substantial coherent errors biased in Z.
Generate, Transform, Answer: Question Specific Tool Synthesis for Tabular Data

Tabular question answering (TQA) presents a challenging setting for neural systems by requiring joint reasoning of natural language with large amounts of semi-structured data. Unlike humans who use programmatic tools like filters to transform data before processing, language models in TQA process tables directly, resulting in information loss as table size increases. In this paper we propose ToolWriter to generate query specific programs and detect when to apply them to transform tables and align them with the TQA model's capabilities. Focusing ToolWriter to generate row-filtering tools improves the state-of-the-art for WikiTableQuestions and WikiSQL with the most performance gained on long tables. By investigating headroom, our work highlights the broader potential for programmatic tools combined with neural components to manipulate large amounts of structured data.
Generic axion Maxwell equations: path integral approach

Using the path integral approach, we derive the low energy interactions between axions and electromagnetic field that arise in models with heavy dyons charged under a spontaneously broken global axial $U(1)$ symmetry. Hence, we obtain generic axion-Maxwell equations relevant for experimental searches. We find that the structure of the axion Maxwell equations could be significantly different compared to what is normally assumed in the literature, as the derived equations feature new axion-dependent terms including CP-violating ones. The new terms can reconcile the Peccei-Quinn solution to the strong CP problem with astrophysical axion hints, as well as give unique signatures in light-shining-through-wall and haloscope experiments. Moreover, via the latter signatures, these experiments can indirectly probe the existence of heavy dyons.
PENGARUH KUALITAS LAYANAN WEBSITE SICEPAT EKSPRES DENGAN METODE WEBQEM TERHADAP KEPUASAN PENGGUNA

&#x0D; SiCepat Ekspres is a freight forwarding and logistics service company. SiCepat Ekspres has an official website at www.sicepat.com. The problem is that the increase in the number of visitors to SiCepat Ekspres is much lower than that of competing companies. To maintain and increase website users or visitors, it is necessary to know the relationship between website service quality and user satisfaction. Therefore, a research was conducted on the effect of website service quality with the WebQEM method on user satisfaction. The quality of website services with the WebQEM method has four variables, namely usability, functionality, reliability, and efficiency. With the distribution of questionnaires as many as 131 user respondents or visitors to the SiCepat Ekspres website, after being processed and analyzed by the multiple linear regression analysis method, it was obtained that 1) the reliability variable affected the user satisfaction variable by 32%, meaning that every increase in the reliability variable would increase user satisfaction by 32%. , 2) the efficiency variable affects the user satisfaction variable by 26%, meaning that every increase in the efficiency variable will increase user satisfaction by 26%, 3) the usability variable and the functionality variable have no effect on user satisfaction.&#x0D;
Semi-Private Computation of Data Similarity With Applications to Data Valuation and Pricing

Consider two data providers that want to contribute data to a certain learning model. Recent works have shown that the value of the data of one of the providers is dependent on the similarity with the data owned by the other provider. It would thus be beneficial if the two providers can calculate the similarity of their data, while keeping the actual data private. In this work, we devise multiparty computation-protocols to compute similarity of two data sets based on correlation, while offering controllable privacy guarantees. We consider a simple model with two participating providers and develop methods to compute exact and approximate correlation, respectively, with controlled information leakage. Both protocols have computational and communication complexities that are linear in the number of data samples. We also provide general bounds on the maximal error in the approximation case, and analyse the resulting errors for practical parameter choices.
Distributed Track-to-Track Fusion of Rfs Filters

In order to realize the distrbuted fusion on tracking of an unknown, time-varying number of targets with lower communication requirement, we propose two track fusion algorithms of Random Finite Set (RFS) filters. A time-varying number of local tracks with unknown relations are associated using travesal clustering or meanshift clustering, and the the cluster centers are utilized as global tracks. In track fusion, the local tracks as opposed to the multi-target densities are disseminated and fused, and thus track fusion, in contrast to the existing density fusion, escapes from heavy communication burden. Simulations demonstrate the excellent tracking accuracy and smaller communication volume of our algorithms.
Novel Geometric Calibration Method for Pan-Tilt Camera With Single Control Point

Pan-tilt (PT) camera is an indispensable part of the video surveillance systems due to its rotatable property and low cost.As the primitive output of the PT camera limits its practical applications, an accurate calibration method is required.Previous single point calibration method (SPCM) was presented to estimate angles Pan and Tilt via single control point.For the more intuitive geometric interpretation and more robust performance, we propose a novel single point calibration method (novel SPCM).In this scheme, a nonlinear PT camera function (PT function) is established via a normalization approach.With PT function, calibration problem is converted as the intersection situation of two circles formed by Pan and Tilt.Solutions can be regarded as the intersection points of two circles in 3D space.Theoretical analysis shows that novel SPCM is stable to measurement noise, for it still finds the least-square solutions even if two circles have no intersection.In the simulation experiments, reprojection error of novel SPCM is 32.4% smaller than SPCM for the large noise situation.It is 25.1% faster than SPCM.With the angle smooth strategy, novel SPCM achieves accurate and stable performance in the real data experiment.
رؤية الواقع من خلال الإبداع الأسطوري في الشرق القديم

Myth played a principal role in old agricultural and pre-agricultural societies. The mythical thought has developed throughout the years but we got only the last versions of texts that have been recorded after being developed, though we approximately received their final version. The myth is a literary text which authors were the temples priests at that time. Its role is to determine the origins of the community’s vision towards the existence and the contribution of the society in this vision based upon a metaphysical belief scope. Thus, the text should express human feelings towards its problems on its cultural part. The problem of the present study is represented by the way the myth is moved to the lived reality and the intellectual or metaphysical expectancy.
A REVIEW OF KNOWLEDGE RISK CONCEPTION

Knowledge is regarded as a critical source of sustainable competitive advantage.However, knowledge has a negative aspect as well.Organizations face a variety of risks as a result of a lack of knowledge, its disappearance, incorrect application, or other consequences of knowledge-related use.Various knowledge risk dimensions were reported in the literature.The authors summarized and analyzed the theoretical perspectives on knowledge risks based on certain criteria such as comprehensiveness, clarity and flexibility.This research has provided an extended knowledge risk taxonomy of Durst and Zieba (2018).The taxonomy now includes a strategic knowledge risks group that embraces knowledge loss, knowledge leakage, and knowledge gaps risks.This type of knowledge risks has long-term severe impacts on organizational performance and must be managed by senior management/executives.Further research shall be conducted to empirically test and validate the proposed taxonomy.
Ordagrann återgivning eller klickbete?: Om citatteckenanvändning i tidningsrubriker

As a consequence of this, headlines on newspaper websites are often read without the reader considering the article they correspond to.Do these headlines faithfully reproduce quotes found in the corresponding article, or are they better thought of as clickbaits, containing sensational quotes that do not parallel those given in the article?In this paper, nearly 2,700 headlines from four of the largest Swedish newspapers are analysed to answer this question, as well as the question of how common quotes are in Swedish newspaper headlines.The analysis indicates that headlines with quotations are more common in Swedish newspapers than in previous studies of British, German and Spanish newspapers.Furthermore, a careful analysis of 312 headlines with quotes reveals that only one third of the headlines are verbatim in relation to the quote in the corresponding article.However, most changes are small and mainly concern the form of the quoted statement.When the meaning of the quote has been changed, it is often because hedging phrases have been removed, giving the statement a more drastic meaning than in the article.Headlines considered as clickbaits are nevertheless very few in the analysed material.Moreover, there are no major differences between the studied morning and evening papers in how accurately they reproduce quotes in headlines.
The Evolution of Cloud Computing Networking &amp; Principles

This is a review paper on the idea of cloud computing which is evolved from the era of internetworking. Subsequently the highlight includes classification of cloud computing, comparison of public versus private cloud infrastructure. We further the concept of SaaS and PaaS where we set in note for future work. We finally conclude with some of the salient characteristics of networks on the cloud.
KLASIFIKASI DIABETES PADA WANITA MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER

The report from Riskesdas shows that there is a 2x increase in diabetes every year in Indonesia. This is due to an increase in factors such as human population, age, obesity, irregular eating patterns and lack of physical activity. The increase in a factor that causes diabetes in Indonesia must be prevented. The first step in preventing diabetes is to detect the risk factors for diabetes that may occur. Influencing factors include behavioral factors and sociodemographic factors The increase in diabetes in a country is due to late identified factors. The number of factors that are collected in order to detect whether a person has diabetes or not requires a fairly large data processing system. The data used in this study are diabetes data obtained from the Pima Indian Diabetes Database with attributes of pregnant, glucose, diastolic, triceps, insulin, BMI, history of diabetes, age and 300 data output. The Naive Bayes Classifier method can be used to classify diabetes in women based on pregnant, glucose, diastolic, triceps, insulin, BMI, history of diabetes, age and output. The accuracy result of the Naive Bayes Classifier method in classifying diabetes in women is 84% ​​of 300 data which is divided into 2, namely 275 data as training data and 25 data as test data.
Pelatihan AutoCAD 3D Secara Online untuk Masyarakat Terdampak Covid-19

The Covid-19 pandemic has had an impact on society, especially in the field of education. Various educational activities are carried out online, so the Service Team holds online AutoCAD 3D training activities to add insight into education and technical design for communities affected by Covid-19. The stages of the program that have been implemented are, the first stage of pre-training which consists of an online survey to communities affected by COVID-19, the design of the AutoCAD 3D training module, and preparation of making online media such as websites, Instagram, and YouTube. The second stage is training which consists of registration of affected communities, online media publications, and online training using online media meetings that are integrated with websites, Instagram, and YouTube. The third stage is post-training in the form of an evaluation to analyze the satisfaction of people who have attended AutoCAD 3D Online training. The results of the analysis of community satisfaction with the facilitators, facilities, presenters, and materials stated that they were 100% very satisfied and satisfied. In addition, the training was also 100% running as expected based on the results of the participant's evaluation.
Pelatihan Dasar Aplikasi Editing Video Sebagai Pendukung Pembelajaran Bagi Guru Smp Islam Al-Azhar 22 Sentra Primer Jakarta

BASIC TRAINING OF VIDEO EDITING APPLICATIONS AS LEARNING SUPPORT FOR TEACHERS OF SMP ISLAM AL-AZHAR 22 CENTRA PRIMER JAKARTA. The purpose of this training is to provide knowledge and skills to the teachers of SMP Islam Al-Azhar 22 Centra Primer Jakarta in editing videos using the Filmora application. This training activity is carried out using a procedure consisting of three stages, namely 1) initial activities, in the form of exploration through initial tests, 2) core activities in the form of modeling, constructing together and constructing independently, 3) Final activities, namely in the form of confirmation. through knowledge and skills tests. The training that has been carried out has been able to improve the knowledge and skills of the teachers of SMP Al-Azhar 22 Centra Primer Jakarta regarding the use of the Filmora application for video editing. This training activity needs to be carried out further so that the knowledge and skills of teachers in editing videos as learning media can continue to increase.
A Workshop on How to Find Current Topics For Research

Conducted in a 2-hour online workshop session format in November 2021, this community service program aims to share my practical experiences as an Indonesian researcher on finding current research topics, specifically in English as a Foreign Language (EFL) teaching and learning. The participants of this online workshop were seventeen (17) lecturers in the English Language Education program at Universitas Muhammadiyah Yogyakarta, Indonesia. Conducted synchronously in a Zoom platform, the workshop started with my presentation on three main points: (1) “my ways” in finding research topics, (2) my published articles that might provide writing ideas for the participants, and (3) reasons why the participants need to write and publish their work in an academic journal. This presentation session was ended by a question and answer session from the participants. Some reflective questions for Indonesian researchers and ideas for future community service programs were also presented.
Analisis Sentimen Dengan Klasifikasi Naïve Bayes pada Review Hotel Tripadvisor

In this era of rapidly growing data digitization, people are facilitated by the existing reviews to decide something. Reviews about something positive, of course, will also have a positive impact on those being reviewed. In this study, the tripadvisor hotel reviews dataset is taken from the Kaggle dataset. The classification method used is nave Bayes. The first thing to do after dataset retrieval is data preprocessing such as cleaning data by changing text to lowercase, eliminating numbers, eliminating double spaces, tokenization, eliminating stopwords and lemmatization. From the results of the classification with nave Bayes, the results obtained with an accuracy of 95.6%, which means that the results of the predictions are very good. For future research, other classification methods will be used to compare and find the best accuracy results.
Efficiently Guided Active Contours for Image Segmentation

Segmenting natural and outdoor images are challenging for most of the latest variational segmentation models. For this purpose we employ derived image data (DID) and propose a robust variational model. The DID rely on three images by utilizing image local and global statistics as well as filter image which is obtained through our design high pass filtering techniques. Then these derived image data are incorporated into our proposed energy functional which can robustly segment images having inhomogeneity, mix backgrounds and multi-regions. Furthermore, the results of DID are compared with other well known methods with finding Jaccard similarity index to proof the efficient and qualitative performance of proposed model over the traditional methods. Finally, the proposed DID based model is tested on real world 3D images to ensure that it also preserve its performance in vector valued images as well.
Improved Ability to Recognize Letters Through Flashcard Media With Maximization Memory for Children With Autistic Special Needs

Generally, the difficulties experienced by autistic children are basic academic abilities, namely reading, writing, and arithmetic. Autistic children often experience attention disorders so that in the learning process children are easily bored to learn. Therefore, learning media is needed that can make children excited about learning. The Flashcard media program is carried out by focusing on strengthening memory at the beginning, according to Rasto's theory, the initial stage of beginner reading is to use Visual Memory. Muses single-subject research with the form of A-B-A program design. Basedon the results of the program, the use of Flashcards with memory maximization methods can help children recognize letters as a basic step to meet the first stage in beginner reading With the pretest that originally could only spell 2 letters, after intervening against the increase to 9 letters at the time of posttest.