Reviewerly/exHarmony huggingface dataset:
Fringe projection profilometry without explicit projector calibration

Metric three-dimensional reconstruction by fringe projection profilometry requires calibrating the employed camera and projector. However, the calibration process is more difficult for projectors than for cameras. This work presents a reconstruction method where the projector parameters are not required explicitly. For this, we assume the projector follows the pinhole model and single-axis fringe projection is employed. The theoretical principles are explained, and the proposed method is validated experimentally by a metric three-dimensional reconstruction. The results provide a theoretical framework for further generalization, including implicit camera calibration and lens distortion, while keeping the metric reconstruction capability.
Performance evaluation of advanced correlation filters for printed character recognition

Correlation filters have been widely used in several pattern recognition applications. These filters can reliably detect and accurately locate a target with good tolerance to geometrical modifications and the presence of additive and nonoverlapping noise in the scene. This work presents an exhaustive performance evaluation of several advanced correlation filters for the task of printed character recognition. Several printed character strings in the English alphabet containing geometrical modifications and nonuniform illumination conditions are recognized using different advanced correlation filters. The performance of each tested filter is characterized in terms of efficiency of character recognition and accuracy of character location estimation.
Fingerprint Recognition Using SVM and CNN

Among other things, fingerprints are frequently used to open automobiles and residences, authorise payments, and unlock devices. Due to its accessibility, dependability, acceptability, and high level of security, digital fingerprinting has emerged as a popular biometric identification method. Unfortunately, a number of issues, including finger displacement during scanning, outside circumstances, and user behavior, have reduced the accuracy of fingerprint identification. SVM, CNN, image processing, image extraction, binarization, and thinning algorithms have all been used to address these issues. These methods reduce the misuse of fingerprint samples while providing creative solutions to fingerprint identification issues. Machine learning has been particularly beneficial in documenting student attendance in college residence halls, identifying victims at crime scenes, and accurately extracting images. Overall, it is a quick, dependable, accessible, and affordable approach to give everyone easy access.
Multispeaker and Multilingual Zero Shot Voice Cloning and Voice Conversion

This research study presents an integrated model for voice cloning and voice conversion, trained on English and French using the Open SLR Multilingual LibreSpeech Datasets. Voice cloning involves synthesizing the voice of a desired person from text, while voice conversion modifies the voice of a source speaker to resemble a target speaker. The proposed model combines spectrum-prosody-cycleGAN for voice conversion and utilizes speaker encoders, decoders, attention mechanisms, and a WaveNet-based WaveGAN vocoder for voice cloning. Additionally, the study focuses on zero-shot voice cloning and voice conversion, demonstrating promising results without explicit training on the target person's voice. This work opens avenues for efficient and versatile speech modification applications.
Neural Improvement Heuristics for Graph Combinatorial Optimization Problems

Recent advances in graph neural network (GNN) architectures and increased computation power have revolutionized the field of combinatorial optimization (CO). Among the proposed models for CO problems, neural improvement (NI) models have been particularly successful. However, the existing NI approaches are limited in their applicability to problems where crucial information is encoded in the edges, as they only consider node features and nodewise positional encodings (PEs). To overcome this limitation, we introduce a novel NI model capable of handling graph-based problems where information is encoded in the nodes, edges, or both. The presented model serves as a fundamental component for hill-climbing-based algorithms that guide the selection of neighborhood operations for each iteration. Conducted experiments demonstrate that the proposed model can recommend neighborhood operations that outperform conventional versions for the preference ranking problem (PRP) with a performance in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$99$</tex-math> </inline-formula> th percentile. We also extend the proposal to two well-known problems: the traveling salesman problem and the graph partitioning problem (GPP), recommending operations in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$98$</tex-math> </inline-formula> th and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$97$</tex-math> </inline-formula> th percentile, respectively.
Variational Counterfactual Prediction under Runtime Domain Corruption

To date, various neural methods have been proposed for causal effect estimation based on observational data, where a default assumption is the same distribution and availability of variables at both training and inference (i.e., runtime) stages. However, distribution shift (i.e., domain shift) could happen during runtime, and bigger challenges arise from the impaired accessibility of variables. This is commonly caused by increasing privacy and ethical concerns, which can make arbitrary variables unavailable in the entire runtime data and imputation impractical. We term the co-occurrence of domain shift and inaccessible variables <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">runtime domain corruption</i> , which seriously impairs the generalizability of a trained counterfactual predictor. To counter runtime domain corruption, we subsume counterfactual prediction under the notion of domain adaptation. Specifically, we upper-bound the error w.r.t. the target domain (i.e., runtime covariates) by the sum of source domain error and inter-domain distribution distance. In addition, we build an adversarially unified variational causal effect model, named VEGAN, with a novel two-stage adversarial domain adaptation scheme to reduce the latent distribution disparity between treated and control groups first, and between training and runtime variables afterwards. We demonstrate that VEGAN outperforms other state-of-the-art baselines on individual-level treatment effect estimation in the presence of runtime domain corruption on benchmark datasets.
Multimodality-aware Intra-dialytic Hypotension Classifier: A Bilingual NLP Approach to Classify Dialysis Records

Nursing records, rich in free-text data, encapsulate valuable insights into patients' physiological status and physicians' diagnoses, offering more holistic perspectives than mere tabular data. The scrutiny of these free-text descriptions often enables physicians to rapidly assimilate the patients' historical and current health states, thereby facilitating accurate diagnosis and treatment. One particularly pressing issue is the identification of intra-dialytic hypotension (IDH) symptoms within these records. IDH, a sporadic yet potentially fatal complication during hemodialysis sessions for end-stage kidney disease patients, is diagnosed by a decrease of 20 mmHg in systolic blood pressure during hemodialysis or a drop of 10 mmHg in mean arterial pressure coupled with hypo-perfusion-related symptoms. In response to this, we have meticulously developed a Multi-modality-aware IDH Classifier (MMIDHC) using a natural language processing (NLP) model aimed at the classification of these records. Primarily, this model focuses on the classification of English-Chinese bilingual dialysis records concerning IDH. Composed of three distinct modules, the model's intricate processes are delineated in Figure 1.
Initial interactions with the <scp>FDA</scp> on developing a validation dataset as a medical device development tool

Quantifying tumor-infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment. To reduce this burden, we are preparing a dataset for developers to validate their models and a proposal to the Medical Device Development Tool (MDDT) program in the Center for Devices and Radiological Health of the U.S. Food and Drug Administration (FDA). If the FDA qualifies the dataset for its submitted context of use, model developers can use it in a regulatory submission within the qualified context of use without additional documentation. Our dataset aims at reducing the regulatory burden placed on developers of models that estimate the density of TILs and will allow head-to-head comparison of multiple computational models on the same data. In this paper, we discuss the MDDT preparation and submission process, including the feedback we received from our initial interactions with the FDA and propose how a qualified MDDT validation dataset could be a mechanism for open, fair, and consistent measures of computational model performance. Our experiences will help the community understand what the FDA considers relevant and appropriate (from the perspective of the submitter), at the early stages of the MDDT submission process, for validating stromal TIL density estimation models and other potential computational models. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
Fault Detection in Transmission Lines: a Denial Constraint Approach

This paper introduces an approach for discovering denial constraints (DCs) to identify faults in transmission lines. However, the considerable volume of data in the studied scenario makes traditional DC discovery impractical due to lengthy execution times. We propose an alternative DC discovery approach that uses streaming windows to address this issue. Our experiments demonstrate that the DCs identified in pre-fault windows differ significantly from those in post-fault windows. This valuable insight enables us to detect faults autonomously, eliminating the need for human intervention (i.e., an unsupervised method). The experimental evaluation featuring diverse fault events reveals that our approach achieves fault detection with remarkable 100% accuracy.
Evaluation of methods of counterfactual explanation - A qualitative and quantitative analysis

There is currently a growing concern about the explainability of machine learning algorithms. Explainability refers to the ability to understand and interpret the decisions made by the models, that is, the process by which a model arrives at a given prediction or classification. The counterfactual explanation involves creating alternative examples where the models prediction differs from the original. This work aims to raise and discuss essential features in the context of counterfactual explanation methods. For this, the CSSE and LORE methods will be evaluated and applied to twelve public databases, considering different characteristics regarding the number of attributes and data types. In this way, we can better understand their strengths and weaknesses using standardized metrics for different methods. This facilitates the selection and development of more effective strategies and helps to identify cases where one approach may outperform another regarding the quality of explanations. The survey measured the metrics validity, prolixity, sparsity, similarity, and hit rate. In general terms, the CSSE performed better in these metrics, except for sparsity.
Digital Image Security Implementation With Uses Super Encryption Algorithm Myszkowski And The Algorithm Paillier Cryptosystem

This study aims to implement digital image security by applying two encryption algorithms, namely the Myszkowski algorithm and the Paillier Cryptosystem algorithm. Digital images are a very important form of data and are used frequently in a variety of applications, so protecting their security is a major concern. The encryption method proposed in this study uses a combination of the Myszkowski algorithm to randomize image pixels and the Paillier Cryptosystem algorithm to perform symmetric key encryption. At the experimental stage, qualitative and quantitative analysis was carried out on the performance of the encryption implemented on digital images. Testing is carried out by comparing the level of security and encryption speed of the two algorithms used. In addition, size analysis of encrypted images was also performed to evaluate the efficiency of the proposed system. The results of the study show that the use of a combination of the Myszkowski algorithm and the Paillier Cryptosystem algorithm provides a high level of security for digital images. In addition, the efficiency of this system has also been proven in producing efficient encryption image sizes, so that it can be implemented in image-based applications that require a higher level of security.
Design and Build a System for Turning on an IoT-Based Motorcycle Starter Using Voice

Motorcycles require maintenance, especially on the engine, by always heating the motorcycle before use or when not in use. However, sometimes someone is lazy to warm up the motorcycle engine. The aim is to make it easier for users to warm up the motorcycle engine to avoid damage to the engine and make it easier for motorcycle users if the electric starter on the motorbike is damaged. Research using the IoT-based google assistant uses NodeMCU 8266 connected to an Android smartphone to start motorbikes automatically. Based on the research results this tool can function properly. The conclusion of the study is that the motorcycle starter system with a smartphone control system can avoid damage to the motorcycle engine, and makes it easier for users to start the motorcycle if the electric starter is damaged.
Linguo-stylistic features of communication in social networks.

The Internet is becoming one of the main elements of human development in almost all spheres of life. The Internet space creates a special language that is actively used in the everyday speech of the members of society. Its influence is clearly visible everywhere, not only in virtual communication process. Its future is unpredictable due to the changes, novelties, innovations that internet is discovering for us.
Dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning for optimization problems

Abstract The introduction of a multi-population structure in differential evolution (DE) algorithm has been proven to be an effective way to achieve algorithm adaptation and multi-strategy integration. However, in existing studies, the mutation strategy selection of each subpopulation during execution is fixed, resulting in poor self-adaptation of subpopulations. To solve this problem, a dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning (RLDMDE) is proposed in this paper. By employing reinforcement learning, each subpopulation can adaptively select the mutation strategy according to the current environmental state (population diversity). Based on the population state, this paper proposes an individual dynamic migration strategy to “reward” or “punish” the population to avoid wasting individual computing resources. Furthermore, this paper applies two methods of good point set and random opposition-based learning (ROBL) in the population initialization stage to improve the quality of the initial solutions. Finally, to evaluate the performance of the RLDMDE algorithm, this paper selects two benchmark function sets, CEC2013 and CEC2017, and six engineering design problems for testing. The results demonstrate that the RLDMDE algorithm has good performance and strong competitiveness in solving optimization problems.
Diagnosis of Baby Blues Syndrome sing the Certainty Factor Method (Case Study: FULL BETHESDA Hospital)

Baby blues syndrome is a psychological disorder experienced by women after giving birth, such as feeling excessively upset and sad, and tired for no apparent reason. About 80% of women who have just given birth will experience Baby blues Syndrome, if this continues and is prolonged it will be very dangerous for the health of the mother and baby. From the problems above, the hospital needs to have an additional system that can help make it easier for the medical team to speed up handlers in analyzing and diagnosing Baby blues Syndrome suffered by patients using the certainty factor method. The purpose of this research is to build an expert system for diagnosing the symptoms of Baby blues syndrome using the certainty factor method. Based on the results of the CF calculation, the highest score is the type of baby blues syndrome with a value of 0.9602 or 96.02%. From the results obtained, the system identified that the patient had a type of baby blues syndrome.
Grouping Number of Library Members For Determining the Location of Socialization Using Clustering Method

The high use of smartphones at this time led to a decline in public interest in reading books in the library directly. Especially students and students. This is certainly a problem for the Langkat Regency Archives and Libraries Office. Socialization is needed to increase efforts to read interest in the community. The right socialization location must have several criteria so that the socialization carried out is right on target. The existence of a database for each member of the library will facilitate the location selection process. Data mining techniques can classify the number of library members based on the results of large data analysis into information in the form of patterns. The clustering method is a method in data mining that can analyze data with the aim of grouping data based on the same characteristics. The K-Means algorithm is a simple algorithm for classifying a large number of objects with certain attributes into clusters which are usually used in data mining.
Application Of Super Encryption Using Rot 13 Algorithm Method and Algorithm Beaufort Cipher For Image Security Digital

Digital image security is becoming increasingly critical in today's digital era, where sensitive information and data are often stored in image form. Therefore, an effective and secure encryption method is needed to protect the integrity and confidentiality of digital images. This study aims to implement a stronger security approach by combining classic encryption methods, namely the ROT13 algorithm and the Beaufort Cipher algorithm which produces an encryption called "Super Encryption". In this study, first of all, the ROT13 encryption method will be applied to randomize digital image text by shifting characters as far as 13 positions in the alphabet. Then, the Beaufort Cipher algorithm will be used to apply additional encryption to the digital image, which involves using the key as input in the encryption process. The results of this study indicate that the Super Encryption method which combines the ROT13 and Beaufort Cipher algorithms provides a higher level of security compared to using each method separately. Security testing and vulnerability analysis show that the combination of these two algorithms produces digital images that are more difficult to decrypt by commonly used decryption attacks.
ADVANCING GASIFICATION-COMBINED UP AND DOWN DRAFT GASIFIER-BASED TREATMENT OF TEXTILE WASTE: ASSESSING FEASIBILITY, ENVIRONMENTAL IMPACTS AND ENERGY RECOVERY POTENTIAL

Speech enhancement, a critical component in various applications including voice assistants and telecommunication systems, aims to improve the quality and intelligibility of spoken content. This paper introduces a novel approach leveraging Natural Language Processing (NLP) techniques for speech enhancement. By harnessing the power of NLP models, we propose a methodology that not only considers the acoustic features but also incorporates linguistic context in the enhancement process. The study encompasses data collection, preprocessing, and the application of advanced NLP algorithms to extract relevant linguistic information. The experimental results demonstrate a significant improvement in speech quality, as evidenced by a substantial increase in Signal-to-Noise Ratio (SNR) and Mean Opinion Score (MOS) compared to conventional methods. Furthermore, a comparative analysis with existing techniques showcases the superiority of our approach. This research not only contributes to the advancement of speech enhancement technology but also underscores the pivotal role of NLP in this domain. The findings pave the way for future research directions, suggesting potential applications in areas such as voice recognition systems and real-time communication platforms. Keywords:Acoustic Features, Linguistic Context, Mean Opinion Score (MOS), Natural Language Processing, NLP Algorithms, Signal-to-Noise Ratio (SNR), Speech Enhancement, Speech Quality, Telecommunication Systems, Voice Assistants.
Analisa Mengenai Kependidikan pada MAS PAB 1 Sampali

MAS PAB 1 Sampali is located at Jalan Pasar Hitam No. 69 Sampali, Sampali Village, Sampali Village, Percut SEI Tuan sub-district, Sampali Village, Deli Serdang district, North Sumatra Province. MAS PAB Sampali is a private school, this school is incorporated in one area or one entrance gate with several schools, so the PAB Educational Institution organizes formal education. MAS PAB 1 SAMPALI School is an educational facility located at JL. Black Market No. 69 Sampali, Sampali Village, Percut SEI Tuan sub-district, Sampali Sub-district, Deli Serdang district, North Sumatra Province was established and operational in 1988 and is still operating today. Apart from being a place of education, this school also has educators and administrators as well as school administrators with the best quality and competence in their fields, learning support activities such as extracurriculars, student organizations, learning communities, sports teams, and libraries so that students can learn optimally. MAS PAB 1 SAMPALI also guides students to have good morals (noble character), and is also a place to hone talents in various fields such as: sports in the arts and also other extracurricular activities such as Osis, Scouts and Paskibra. The facilities at the Madrasah MAS PAB 1 Sampali school can also be said to be adequate, even though the BK room is still attached to the principal's room, administration room, and UKS but there is already a special room for BK, if there are students who experience problems, they need guidance, or if you need counseling, you can go to the counseling room, the counseling room there is not too big, and there is a bathroom in it.&#x0D; Keywords: Madrasah, Education, Students.
Keterampilan Dasar Mengajar Guru dalam Menyediakan Media Pembelajaran AUD

Education is one important aspect in the development of a country. The teacher as a teacher has a very vital role in creating an effective and enjoyable learning environment for students. One of the factors that can increase the effectiveness of learning is the use of appropriate learning media. Good learning media can help teachers convey learning material more clearly, interestingly, and easily understood by students. This research is qualitative in nature, where we do not collect primary data through surveys or interviews.&#x0D; Keywords: Teaching skills, AUD learning media, Teachers