Reviewerly/exHarmony huggingface dataset:
$L_1$-Norm Regularization for Short-and-Sparse Blind Deconvolution: Point Source Separability and Region Selection

Blind deconvolution is about estimating both the convolution kernel and the latent signal from their convolution. Many blind deconvolution problems have a short-and-sparse (SaS) structure; i.e., the signal (or its gradient) is sparse and the kernel size is much smaller than the signal size. While $\ell_1$-norm relating regularizations have been widely used for solving SaS blind deconvolution problems, the so-called region/edge selection technique brings great empirical improvement to such $\ell_1$-norm relating regularizations in image deblurring. The essence of region/edge selection is during an alternative iterative scheme of SaS blind deconvolution: one estimates the kernel on an estimate of the latent image with well-separated image edges instead of the one with the least fitting error. In this paper, we first examine the validity and soundness of $\ell_1$-norm relating regularization in the setting of 1D SaS blind deconvolution. The analysis reveals the importance of the separation of nonzero signal entries toward the soundness of such a regularization. The studies laid out the foundation of region selection technique; i.e., during the iteration, an estimate of the latent image with well-separated edges is a better candidate for estimating the kernel than the one with the least fitting error. Based on the studies conducted in this paper, an alternating iterative scheme with region selection model is developed for SaS blind deconvolution, which is then applied to blind motion deblurring. The experiments show its effectiveness over many existing $\ell_1$-norm relating approaches.
EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model

Training deep graph neural networks (GNNs) poses a challenging task, as the performance of GNNs may suffer from the number of hidden message-passing layers. The literature has focused on the proposals of {over-smoothing} and {under-reaching} to explain the performance deterioration of deep GNNs. In this paper, we propose a new explanation for such deteriorated performance phenomenon, {mis-simplification}, that is, mistakenly simplifying graphs by preventing self-loops and forcing edges to be unweighted. We show that such simplifying can reduce the potential of message-passing layers to capture the structural information of graphs. In view of this, we propose a new framework, edge enhanced graph neural network (EEGNN). EEGNN uses the structural information extracted from the proposed Dirichlet mixture Poisson graph model (DMPGM), a Bayesian nonparametric model for graphs, to improve the performance of various deep message-passing GNNs. We propose a Markov chain Monte Carlo inference framework for DMPGM. Experiments over different datasets show that our method achieves considerable performance increase compared to baselines.
Communication-Efficient Federated Learning via Optimal Client Sampling

Federated learning (FL) ameliorates privacy concerns in settings where a central server coordinates learning from data distributed across many clients. The clients train locally and communicate the models they learn to the server; aggregation of local models requires frequent communication of large amounts of information between the clients and the central server. We propose a novel, simple and efficient way of updating the central model in communication-constrained settings based on collecting models from clients with informative updates and estimating local updates that were not communicated. In particular, modeling the progression of model's weights by an Ornstein-Uhlenbeck process allows us to derive an optimal sampling strategy for selecting a subset of clients with significant weight updates. The central server collects updated local models from only the selected clients and combines them with estimated model updates of the clients that were not selected for communication. We test this policy on a synthetic dataset for logistic regression and two FL benchmarks, namely, a classification task on EMNIST and a realistic language modeling task using the Shakespeare dataset. The results demonstrate that the proposed framework provides significant reduction in communication while maintaining competitive or achieving superior performance compared to a baseline. Our method represents a new line of strategies for communication-efficient FL that is orthogonal to the existing user-local methods such as quantization or sparsification, thus complementing rather than aiming to replace those existing methods.
Implementasi Algoritma C5.0 Untuk Memprediksi Kondisi Kelahiran Bayi

The condition of birth is the condition of the birth of a baby is one of the main problems in the health sector that is currently happening in Indonesia. In health, there are several indicators of birth conditions, namely normal and caesarean. Determining the degree of condition of the baby's birth is a reflection of the current status of the baby's birth. Children as the nation's successor are closely related to the growth and development of babies today. However, the implementation to improve infant growth and development is still not optimal. This study produces a decision tree of the cases raised showing that there are several attributes that influence the determination of birth conditions. A list of gain values for each attribute will be displayed. The position of the attribute that has the highest gain value displayed indicates the determination of which attribute is the determination of eligibility
Penerapan JST Backpropagation Untuk Memprediksi Data Penerimaan Mahasiswa Baru Pada Universitas Simalungun

The growing era of globalization makes people continue their education to a higher level. The greater the interest of prospective students, each year the number of prospective students will increase. The increase has made the university can manage and know the estimated number of prospective students each year. So we need a method to help the university so that the number of prospective students can be predicted quickly and accurately. In predicting the data of prospective new students at Simalungun University the backpropagation method is used, this study is expected to predict the number of new students with smaller error results. The data used was obtained from the Simalungun university administration from 2015 to 2018. Backpropagation is one method that is often used in solving complex problems. Its application researchers conducted the test using the Matlab application. This study uses 5 architectural models: 2-5-1, 2-10-1.2-15-1, 2-25-1, 2-3-5-1, the best accuracy is obtained from architectural models 2-3-5-1 with values accuracy of 86%, epoch 8406 iteration, and MSE namely 0, 0778011336
Penerapan Data Mining dengan Algoritma C.45 Dalam Memprediksi Penjualan Tempe

Many people talk about business, benefits of business and the many different types of businesses that definitely have one purpose, financial profit or profit. With so many types of business as well as the benefits that can be taken from business, often we definitely feel we feel quickly to pave in the world of business to get profit or more specifically is income. One example of business is selling tempe. Sales is the activity of selling a product or service that needs business authorities or business acknowledgments of a kind of business selling tempe at mandiri ac. In selling of course we have a very many collection of sales information why we should be able to digest the sales information to become new data. In this issue the c4.5 algorithm is a procedure that can help digest or predicate the value of sales at the time to arrive. This research was tested in order to help sellers to predict the sales of their merchantability so that they can prepare or stock materials which is predicted to face an increase in their sales at the time of getting the information between supplied information. From this research can be conclusioned if using the c4.5 algorithm the sales of tempe can be predicted with a quite high accuracy
Implementasi Algoritma Set Partitioning in Hierarchical Trees (SPIHT) dan Algoritma Huffman Untuk Kompresi File Citra

As technology develops, the process of sending files no longer needs to be done manually. Data or files can be sent electronically via email. The image file size is sometimes very large, where the better the quality of the resulting image, the bigger the pixel size needed to record the image. With a very large image file size, the transfer process may fail, because the media storage space exceeds its limit. The solution to this problem is how the image file can be compressed in order to speed up the transfer and storage of image files. Image compression is done by reducing the size of the image file by reducing the bits in the image file, but not eliminating the information data in it. By compressing, large data will be reduced in size so as to save storage space. In this study, the algorithm used is the Swiss army knife data compression, using this method, the compression results from the value of k have different compression results for each value, and the compression results will be beneficial in sending and moving image files more easily
Akurasi Algoritma Fletcher-Reeves untuk Prediksi Ekspor Karet Remah Berdasarkan Negara Tujuan Utama

Crumb rubber is a natural rubber specially designed to ensure its technical quality. Rubber is produced mainly in Southeast Asia, where Indonesia is the second largest producer in the world after Thailand. This study aims to predict the export of powdered rubber in Indonesia. The prediction method used is FletcherReeves which is one of the artificial neural network methods commonly used to predict data. The research data used is crumb rubber export data by main destination country for the period 2012-2020 which was obtained from the website of the Indonesian Central Statistics Agency. Based on this data, network architecture models will be trained and defined, including 7-10-1, 7-15-1, 7-20-1, 7-25-1, 7-30-1 (trancgf). Of the five models, after training and testing, the best data architecture model is 7-15-1 (trancegf) 7 is the input layer, 15 is the number of neurons in the hidden layer and 1 is the exit layer. The level of accuracy of the architectural model with the MSE value is 0.00482054.
Analisis Kepuasan Pelanggan Menggunakan Algoritma C5.0

Water is one of the necessities that are needed by living things, especially humans. In everyday life, humans cannot be separated from the need for air. PDAM is a regional-owned business unit, which is engaged in the distribution of clean water for the general public. However, along the way, along with receiving complaints from the public or customers, public complaints about the increasing difficulty of getting clean water are still an obstacle that can be completely overcome, for example in the North Sumatra area, precisely in Gunung Malela District. On the one hand, public demand will increase, but the quality of services provided is not comparable to meeting the demands of the community. Therefore, this study was conducted to determine customer satisfaction with PDAM Tirta Lihou in Gunung Malela District. Many branches of computer science study various algorithms, one of which is data mining methods, DSS, ANN, and others. This study uses a data mining method with the C5.0 algorithm to solve the problem of factors that affect customer satisfaction with PDAM Tirta Lihou, Gunung Malela District
Rancang Bangun Aplikasi Duplicate Docx Scanner Menerapkan Metode SHA-256

Hard drives are one of the core components of a computer in various types and sizes. Large hard disks can become full, even though they don't feel like storing large files. The possibility of saving the same file in the directory is very difficult and will take a long time. The Duplicate Docx Scanner application is able to solve this problem. The Duplicate Docx Scanner application is able to find out the same file is located in a different directory by determining the directory on the hard disk and other storage media. This application can calculate the hash value so that it can find the same file. The Duplicate Docx Scanner application uses the SHA-256 hash method. SHA-256 is used to perform file integrity checks in a variety of situations. This Duplicate Docx Scanner application is designed to use the Microsoft Visual Basic.Net 2008 programming language
Implementasi Metode Regionprops Untuk Mendeteksi Objek Image Fraktur Tulang

Bone is one of the most important parts of the human body. The function of the bone itself is as a framework, a support for the human body and a place for muscles to attach, so that the body can move optimally. Not only that, some parts of the bones also have a function to protect other organs in the body. Such as the skull that serves to protect the brain from various kinds of impact from the outside, the arrangement of the ribs which serves to protect the lungs and so on. Regionprops method is a method used to measure a set of properties - properties of each region that has been labeled in the label matrix. A positive integer that is an element of the matrix corresponding to the corresponding region. This study uses the Regionprops method to detect an object of damage that is on a bone invoice. The application for detection of bone invoice object image results from Regionprops shows that the resolution produced by the detected image is different. The speed of the application to perform the image detection process depends on the pixel resolution and also depends on the specifications of the computer running the image detection application
Analisa Kualitas Layanan Pada E-learning di Sekolah Menggunakan Metode Servqual

Service quality is the main thing that users of e-learning applications always hope to receive, and these virtues are used to meet the expectations of users. The problem that occurs is that the quality of service is still lacking in the service order, causing the e-learning application to be unable to be reopened. Students who are late in submitting assignments must enter manually and the answers cannot be tracked. The purpose of this study was to measure the quality of service that affects the level of user satisfaction with the servqual method. Measurement of satisfaction for users in measuring the level of satisfaction with 5 dimensions of service quality, namely Tangible, Reliability, Responsiviness, Assurance, and Empathy. The result of the gap between perceptions and expectations of users is -0.08. The gaps are grouped based on attributes using an IPA (Importance Performance Analysis) diagram with the Tangible dimension, the value of the gap is -0.09, the attributes of e-learning application services must be maintained by users who always wear clear student ID cards so that they can be known. Reliability with a gap of -0.07, covering website context related to school e-learning, and guidebooks according to website content. Responsiveness with a gap value of -0.10, namely the attendance menu on the website is functioning properly, notifications appear when there is new information. Assurance with a gap of -0.10, guaranteed website security, and easy to file a complaint if there are website problems. And Empathy with a gap value of -0.08 for convenience in using the website, easy contact, and good communication with supervisors.
Perancangan Aplikasi Kompresi File Teks Menggunakan Algoritma Context Tree Weighting

Currently the use of applications as information is increasingly being used. but there is a problem that is often encountered, namely the need for a large space as a storage medium. Data compression is the process of converting a set of data into a coded form to save the need for data storage. Context Tree Weighting algorithm is a data compression algorithm. To determine the results of the compression process, compression ratio and space saving media are used. The results will be known, the compression ratio and space saving. In accordance with the results of the trials conducted, it can be seen that the data that was originally large in size can be compressed very well with the implementation of compression text files.
The super-recogniser advantage extends to the detection of hyper-realistic face masks

Hyper-realistic silicone masks provide a viable route to identity fraud. Over the last decade, more than 40 known criminal acts have been committed by perpetrators using this type of disguise. With the increasing availability and bespoke sophistication of these masks, research must now focus on ways to enhance their detection. In this study, we investigate whether super-recognisers (SRs), people who excel at identity recognition, are more likely to detect this type of fraud, in comparison to typical-recogniser controls. Across three tasks, we examined mask detection rates in the absence of a pre-task prompt (covert task), and again after making participants aware of their use in criminal settings (explicit task). Finally, participants were asked to indicate which aspects of the masks could support their detection (regions of interest task). The findings show an SR advantage for the detection of hyper-realistic masks across the covert and explicit mask detection tasks. In addition, the eye, mouth, and nose regions appear to be particularly indicative of the presence of a mask. The lack of natural skin texture, proportional features, expressiveness, and asymmetry are also salient cues. The theoretical and applied implications of these findings are discussed.
Maps for Health Metrics: An Epidemiology Resource Webliography

Maps have a long history of being used as sources to track disease outbreaks, link causes and effects of disease, combat misinformation, present ideas and improve patient care. This webliography is a compendium of thematic maps, including health metrics, risk factors, infectious diseases, cancers, chronic diseases, and psychiatric disorders. Maps were gathered after evaluating data for reliability and currency. These selective epidemiology resources may aid public health professionals, medical practitioners, researchers, and librarians, who serve an information-seeking clientele interested in health-related quality of life.
Learning to unlearn: The role of negative evidence in morphophonological learning

Phonological forms often express many different morphological functions. Learning these functions is quite challenging. How do learners accomplish this? Linguistic studies often assume that learning involves co-occurrence of cues and outcomes –contiguity–, but research shows that mere contiguity cannot explain learning effects (Nixon, 2020, Ramscar et al., 2010). Error-driven learning theories (Rescorla and Wagner, 1972) instead assume learning to be contingency-based: Cues predict outcomes, and learning is viewed as a continuous process of adjusting predictions on the basis of errors. Cues compete with each other to predict outcomes. This cue competition drives learning. One effect of cue competition is unlearning – a cue losing its association to an outcome –, which has not yet been investigated empirically in morphophonological learning. This paper reports an artificial language learning experiment, as well as computational simulations, in which we investigated unlearning in morphophonology: We taught participants a morphophonological pattern, in which word forms contained two grammatical functions simultaneously. In a subsequent step they were shown word forms which contained only one of those functions. It turned out that the cue that was predictive of this single function in the second part of the experiment was less strongly associated with the other function – an effect that we explain as an effect of unlearning. Additionally, we also observed that the strength of unlearning depended on the salience of the cues.
Automated assessment of immunofixations with deep neural networks

Abstract Objectives The reliable evaluation of immunofixation electrophoresis is part of the laboratory diagnosis of multiple myeloma. Until now, this has been done routinely by the subjective assessment of a qualified laboratory staff member. The possibility of subjective errors and relatively high costs with long staff retention are the challenges of this approach commonly used today. Methods Deep Convolutional Neural Networks are applied to the assessment of immunofixation images. In addition to standard monoclonal gammopathies (IgA-Kappa, IgA-Lambda, IgG-Kappa, IgG-Lambda, IgM-Kappa, and IgM-Lambda), also bi- or oligoclonal gammopathies, free chain gammopathies, non-pathological cases, and cases with no clear finding are detected. The assignment to one of these 10 classes comes with a confidence value. Results On a test data set with over 4,000 images, approximately 25% of the cases are sorted out as inconclusive or due to low confidence for subsequent manual evaluation. On the remaining 75%, about 3,000 cases, not even one is classified as falsely positive, and only one as falsely negative. The remaining few deviations of the automated assessment from the classifications assigned manually by experts are borderline cases or can be explained otherwise. As a software running on a standard desktop computer, the Deep Convolutional Neural Network needs less than a second for the assessment of an immunofixation image. Conclusions Assisting the laboratory expert in the assessment of immunofixation images can be a useful addition to laboratory diagnostics. However, the decision-making authority should always remain with the physician responsible for the findings.
The Students’ ICT Skills in Producing Infographic Media and Video: Guidance and Counselling E-Project Tasks

The study sees, there is still a lack of student ICT skills in producing a multimedia platform to support guidance and counselling services. This mixed method aimed to analyze the ICT skills of students from the department dept. of Guidance and Counselling in producing interesting, effective, and efficient infographic media and video as well as evaluation materials for lecturers in correcting the shortcomings of the lecture process. Using saturated total sampling technique of 76 students’ from class of 2020 this present study focuses on Infographic Design, Video, and Material. Data collection were based in the form of documentation include e-infographic project tasks, video tasks, infographic assessment sheets, and video assessment sheets which analyzed using descriptive statistics in the form of average values and percentages, which are then converted to qualitative data. The results of the assessment obtained for infographic design (3.53 ), materials in the appropriate category (3.78), and for video (4.00). Media that has been created by students in the form of Infographics and videos provides one of the solutions in providing guidance and counselling services during the Covid-19 pandemic or in the new normal situation. Therefore this study highlighted the excellence in-making skills of students or candidates for Guidance and counseling of Mulawarman University in creating infographic media and videos are very important in supporting and providing guidance and counselling services as their career path in the future.
Traffic Density Estimation Based On Video Processing

In this paper, the fundamental work is to control traffic density in significant urban communities which has been a mainstream research subject among researchers and modern experts.The current framework depends on a fixed time idea where the LOI is utilized which had a couple of hindrances in it.The proposed framework enjoys numerous benefits where the camera catches the video and the Haar course calculation is utilized which tallies the quantity of vehicles and perceives the vehicles out and about in the intersection.The video preparing changes the video over to a picture where the calculation checks the quantity of vehicles and remember it.With this, the density is determined and the traffic signal is changed naturally dependent on the density computation.