Reviewerly/exHarmony huggingface dataset:
Machine Learning based Missing Data Imputation Techniques

In the era of advanced machinery including sensor technology, massive amount of data than ever before is produced every day in a variety of different fields. In the process of handling huge data, some data values are missed due to various controllable or uncontrollable factors. The missing data problem is a real-life problem which found everywhere and that is why it has received huge attention in research in the past decades. It is a major hurdle in data analysis and research. Therefore, for better analysis of data there are various imputation methods to handle missing data varying from directly deleting the data which contains missing values or using traditional methods like mean, median and mode to fill the missing value to using complex deep learning and machine learning techniques to predict missing value. This paper first discusses different kinds of missing data and then analyzing various imputation techniques from traditional to modern approach for dealing with missing data. It also analyzes advantages and disadvantages of different imputation techniques to get a better perspective of these techniques.
GEOCHEMICAL CHARACTERISTICS OF THE MAIN ORE COMPONENTS IN THE GOSHGARCHAY PORPHYRY-EPITHERMAL SYSTEM (LESSER CAUCASUS, AZERBAIJAN)

The article deals with the determining characteristics of ore elements in the Goshgarchay porphyry-epithermal system. The genetic and geochemical characteristics of the main ore components were studied, and close positive and negative relationships between pairs of elements were determined by correlation analysis on the selected elements. According to the results of the cluster analysis, the geochemical associations that allow the localization of the development area of mineralization at different stages of formation of the porphyry-epithermal system were analyzed. The following series of stable elements, which are typical for hydrothermal deposits, are distinguished among the ore elements of the host and ore-bearing intrusive rocks: Cu, Mo, Pb, Zn, Ag, Ni, Co, Mn, Ti, Cr, V, Sr, etc. This group of elements is considered to be the indicator elements of the copper-porphyry deposit within the porphyry-epithermal system. It has been determined that correlations between elements in copper-porphyry mineralization are characterized by a wide range of element impurities. It includes both chalcophile (As, Sb, Cu, Bi, Cd, Ga, In, Ge, Au, Ag, Te) and siderophile (Co, Ni, Mo, Fe, Cr) elements. The range of values of vertical geochemical zonality on individual sections of the deposit allows to assess the level of the erosion truncation of occurrences in the porphyry-epithermal system.
Scalable Non‐Volatile Tuning of Photonic Computational Memories by Automated Silicon Ion Implantation

Photonic integrated circuits (PICs) are revolutionizing the realm of information technology, promising unprecedented speeds and efficiency in data processing and optical communication. However, the nanoscale precision required to fabricate these circuits at scale presents significant challenges, due to the need to maintain consistency across wavelength-selective components, which necessitates individualized adjustments after fabrication. Harnessing spectral alignment by automated silicon ion implantation, in this work scalable and non-volatile photonic computational memories are demonstrated in high-quality resonant devices. Precise spectral trimming of large-scale photonic ensembles from a few picometers to several nanometres is achieved with long-term stability and marginal loss penalty. Based on this approach, spectrally aligned photonic memory and computing systems for general matrix multiplication are demonstrated, enabling wavelength multiplexed integrated architectures at large scales.
Human-Guided Transfer Learning for Autonomous Robot

In recent years, neural networks have been successfully applied to many problems. However, prohibitively long learning time and vast training data are sometimes unavoidable. While the long learning time can be tolerated for many problems, it is crucial for autonomous robots learning in physical environments. One way to alleviate this problem is through transfer learning, which applies knowledge from one domain to another. In this study, we propose a method for transferring human common sense for guiding the subsequent reinforcement learning of a robot for real-time learning in a more complex environment. The efficacy of the transfer mechanism is analyzed to obtain new insights on the required prior knowledge to be transferred for training an autonomous robot. Different types of prior knowledge were analyzed and explained in this paper.
Subtle Sound Design: Designing for Experience Blend in a Historic House Museum

In this article, we present and discuss a user-study prototype, developed for Bakkehuset historic house museum in Copenhagen. We examine how the prototype - a digital sound installation - can expand visitors’ experiences of the house and offer encounters with immaterial cultural heritage. Historic house museums often hold back on utilizing digital communication tools inside the houses, since a central purpose of this type of museum is to preserve an original environment. Digital communication tools however hold great potential for facilitating rich encounters with cultural heritage and in particular with the immaterial aspects of museum collections and their histories. In this article we present our design steps and choices, aiming at subtly and seamlessly adding a digital dimension to a historic house. Based on qualitative interviews, we evaluate how the sound installation at Bakkehuset is sensed, interpreted, and used by visitors as part of their museum experience. In turn, we shed light on the historic house museum as a distinct design context for designing hybrid visitor experiences and point to the potentials of digital communication tools in this context.
PERANCANGAN SISTEM INVENTORI BARANG BERBASIS WEB PADA RAPHAEL’S DIVAN

Raphael's Divan is a company engaged in the furniture sector. In addition to selling furniture, Raphael's Divan also produces furniture, namely divans. Currently, data collection and report generationat Raphael's Divan is still done manually, so data information still has many shortcomings such asinappropriate stock of goods, lack of time efficiency, and data redundancy. Looking at the problems inRaphael's Divan, an inventory system application was made using the Prototype development method. Thisweb-based inventory system application in its design uses HTML, PHP, Java Script and for the database ituses MySQL. The creation of a web-based inventory information system application aims to increase theeffectiveness in data collection, reporting and information availability of goods, specifically raw materialsin Raphael's Divan.
Կիբեռոլորտում ներխուժումների հայտնաբերման համակարգերում մեքենայական ուսուցման կիրառման որոշ խնդիրների վերլուծություն/Analysis of some tasks of machine learning applications in the intrusion detection systems in the cyber domain

One of the indicators of the fourth industrial revolution is the “digital block”, which presents a bridge between the physical and digital realities. The entire digital reality is a series of devices connected with data networks. In this regard, threats to the network infrastructure were investigated, in particular, taking into account some peculiarities of network-centric wars. Software and hardware solutions, based on different principles and operation algorithms, are used in order to neutralize various threats to the network infrastructure. Tasks of both general and particular type, arising in the study of intrusion detection systems (IDS) and operating via machine learning were identified. In addition, the article formulated those tasks, the comprehensive solution of which will ensure the minimization of the “attack surface” on network infrastructures. As a discrete object of study, the article discusses research problems of measuring the effectiveness (efficiency) of applying machine learning in intrusion detection systems for both mobile and embedded systems. The article dwells on the enhancements made to the research work on the application of machine learning in IDS. The solution of such problems will increase the degree of stability of the network infrastructure in case a possible or probable network-centric conflict occurs.
Data Insights by Using Data Visualization and Exploration

Any effort to make data more understandable by presenting it visually falls under the wide definition of data visualization. The graphic depiction of quantitative information is called data visualization. In other words, data visualizations turn big and small data sets into images that the brain can process more quickly. Users using data visualization can gain insight into vast volumes of data. They can use it to find new patterns and data mistakes. Users can concentrate on areas that show progress or warning signs by making sense of these patterns. This procedure then advances the business. Surprisingly frequently, data representations assume the well-known shape of charts and graphs in our daily lives. It can be used to uncover unknown facts and trends. Good data visualizations result when communication, data science, and design work together. When done properly, data visualizations provide important insights into huge, complex data sets in simple, understandable ways. Data visualization is the graphic depiction of information and data. Trends, outliers, and patterns in data are easy to spot and comprehend with the use of data visualization tools, which employ visual components like charts, graphs, and maps. Furthermore, it enables employees or business owners to convey information to non-technical audiences without misunderstanding them. In the world of big data, it is essential to have access to tools and technology for data visualization to analyze vast volumes of data and make data-driven decisions. We will discuss data visualization, its significance, data visualization technologies, and other topics in this article.
Developing Snake and Ladder Game to Teach Vocabulary on Recount Text for Eighth Grade Students at MTs Syekh Subakir 2 Sumberasri

The purpose of this research is to create a snake and ladder game to educate eighth grade students at Mts Syekh Subakir 2 Sumberasri vocabulary on recount texts. The R&amp;D Method was used by the researcher to conduct this study. So research and development methods are methods that produce, develop and test the effectiveness of a product. The design and creation of snake and ladder media resulted in a media validation questionnaire from experts as proof that snake and ladder media in this study is suitable for use as a medium for teaching recount text vocabulary to eighth grade students of Syekh Subakir 2 Sumberasri. This research was conducted to find problems experienced by eighth grade students in English lessons and provide solutions to these problems. In eighth grade Syekh Subakir 2 Sumberasri, the researcher found a problem with students, namely a lack of vocabulary knowledge, which hindered the learning process. Therefore, the researcher created snake and ladder game media as an English learning media so that students were motivated to learn vocabulary.
Building a Knowledge-Based Dialogue System with Text Infilling

In recent years, generation-based dialogue systems using state-of-the-art (SoTA) transformer-based models have demonstrated impressive performance in simulating human-like conversations. To improve the coherence and knowledge utilization capabilities of dialogue systems, knowledge-based dialogue systems integrate retrieved graph knowledge into transformer-based models. However, knowledge-based dialog systems sometimes generate responses without using the retrieved knowledge. In this work, we propose a method in which the knowledge-based dialogue system can constantly utilize the retrieved knowledge using text infilling . Text infilling is the task of predicting missing spans of a sentence or paragraph. We utilize this text infilling to enable dialog systems to fill incomplete responses with the retrieved knowledge. Our proposed dialogue system has been proven to generate significantly more correct responses than baseline dialogue systems.
Developing an argument annotation scheme based on a semantic classification of arguments

Corpora of argumentative discourse are commonly analyzed in terms of argumentative units, consisting of claims and premises. Both argument detection and classification are complex discourse processing tasks. Our paper introduces a semantic classification of arguments that can help to facilitate argument detection. We report on our experiences with corpus annotations using a function-based classification of arguments and a procedure for operationalizing the scheme by using semantic templates.
Generating clickbait spoilers with an ensemble of large language models

Clickbait posts are a widespread problem in the webspace. The generation of spoilers, i.e. short texts that neutralize clickbait by providing information that makes it uninteresting, is one of the proposed solutions to the problem. Current state-of-the-art methods are based on passage retrieval or question answering approaches and are limited to generating spoilers only in the form of a phrase or a passage. In this work, we propose an ensemble of fine-tuned large language models for clickbait spoiler generation. Our approach is not limited to phrase or passage spoilers, but is also able to generate multipart spoilers that refer to several non-consecutive parts of text. Experimental evaluation demonstrates that the proposed ensemble model outperforms the baselines in terms of BLEU, METEOR and BERTScore metrics.
Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System

Lingbo Mo, Shijie Chen, Ziru Chen, Xiang Deng, Ashley Lewis, Sunit Singh, Samuel Stevens, Chang-You Tai, Zhen Wang, Xiang Yue, Tianshu Zhang, Yu Su, Huan Sun. Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue. 2023.
Advancing Geophysics: IRIS and UNAVCO Merge to Form EarthScope Consortium

Abstract A new organization, EarthScope Consortium, was formed 1 January 2023 following the merger of the Incorporated Research Institutions for Seismology (IRIS) and UNAVCO. EarthScope Consortium is a consortium dedicated to supporting transformative global geophysical research and education.
Microlens array wavefront measurement based on transport of intensity equation and hybrid Gerchberg and Saxton iteration

Microlens arrays are widely used in various industrial cameras and detectors, and their wavefront consistency directly affects the performance of optical systems. The current non-interferometry computational imaging methods for measuring the wavefront of microlens arrays typically produce boundary artifacts and partially dope noise of high-frequency information. In order to accurately study and measure the consistency, relative position and wavefront aberration of each element in the microlens array. This paper presents a computational imaging method based on the intensity transfer equation (TIE) of discrete cosine transform (DCT) analysis and a novel hybrid Gerchberg-Saxton iteration (HGS-TIE) combining error reduction algorithm and input/output algorithm. This algorithm improves the accuracy of high-frequency phase measurement at the transitions between microlens and substrate. At the same time, boundary artifacts are eliminated, improving the overall accuracy of the measurement.
Human Behavior Recognition of Video Surveillance System Based on Neural Network

Video surveillance system is widely used in modern society. Human behavior recognition based on neural network uses computer vision and image processing technology to analyze and judge the behavior of different objects in surveillance video. In order to improve the recognition accuracy of human behavior, this paper makes an in-depth study on the design of video surveillance system and target tracking using the relevant algorithms of neural network. In this paper, the application of relevant neural network algorithms and the technology of character behavior recognition are analyzed by means of experimental method comparison and algorithm comparison. The experimental data shows that in the detection of abnormal behavior, the highest accuracy rate of human behavior recognition is 90% by using the improved hu invariant moment model.
Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges

Artificial intelligence (AI) and machine learning (ML) are revolutionizing human activities in various fields, with medicine and infectious diseases being not exempt from their rapid and exponential growth. Furthermore, the field of explainable AI and ML has gained particular relevance and is attracting increasing interest. Infectious diseases have already started to benefit from explainable AI/ML models. For example, they have been employed or proposed to better understand complex models aimed at improving the diagnosis and management of coronavirus disease 2019, in the field of antimicrobial resistance prediction and in quantum vaccine algorithms. Although some issues concerning the dichotomy between explainability and interpretability still require careful attention, an in-depth understanding of how complex AI/ML models arrive at their predictions or recommendations is becoming increasingly essential to properly face the growing challenges of infectious diseases in the present century.
Review of research progress and development trend of digital image correlation

Purpose The purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC). Design/methodology/approach The approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research objectives. Findings The findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research endeavors. Originality/value This review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.
On Significance of Subword Tokenization for Low-Resource and Efficient Named Entity Recognition: A Case Study in Marathi

Named Entity Recognition (NER) systems play a vital role in NLP applications such as machine translation, summarization, and question-answering. These systems identify named entities, which encompass real-world concepts like locations, persons, and organizations. Despite extensive research on NER systems for the English language, they have not received adequate attention in the context of low resource languages. In this work, we focus on NER for low-resource language and present our case study in the context of the Indian language Marathi. The advancement of NLP research revolves around the utilization of pre-trained transformer models such as BERT for the development of NER models. However, we focus on improving the performance of shallow models based on CNN, and LSTM by combining the best of both worlds. In the era of transformers, these traditional deep learning models are still relevant because of their high computational efficiency. We propose a hybrid approach for efficient NER by integrating a BERT-based subword tokenizer into vanilla CNN/LSTM models. We show that this simple approach of replacing a traditional word-based tokenizer with a BERT-tokenizer brings the accuracy of vanilla single-layer models closer to that of deep pre-trained models like BERT. We show the importance of using sub-word tokenization for NER and present our study toward building efficient NLP systems. The evaluation is performed on L3Cube-MahaNER dataset using tokenizers from MahaBERT, MahaGPT, IndicBERT, and mBERT.
AUTOMATE DEPLOYMENT APLIKASI WEB MENGGUNAKAN METODE GITOPS PADA KUBERNETES CLUSTER (STUDI KASUS: WEB RISET INFORMATIKA UNIVERSITAS MUHAMMADIYAH MALANG)

The increase in internet users requires web services to have High Availability, which is the ability of web services to serve users and reduce downtime in the shortest possible time. One of the factors that can increase downtime is during the deployment process. Repetitive and manual deployment processes will be very vulnerable to human errors which will have an impact on downtime. On Informatics research website of the Muhammadiyah University of Malang is still implementing web services without using automation in deployment and also not using web containerization. In this study, the researcher designed a server environment that supports the two previously mentioned, by implementing Jenkins server as a CI/CD tool, cluster kubernetes as an orchestration container, and Github as a repository that supports the GitOps method, as a source of truth. This study tested 4 Informatics research website of the Muhammadiyah University of Malangg in automate deployment and got an average time of 126.74 seconds to build, push, and deploy
PLU‐Net: Extraction of multiscale feature fusion

Abstract In recent years, deep learning algorithms have achieved remarkable results in medical image segmentation. These networks with an enormous number of parameters often encounter challenges in handling image boundaries and details, which may result in suboptimal segmentation results. To solve the problem, we develop atrous spatial pyramid pooling (ASPP) and combine it with the squeeze‐and‐excitation block (SE block), as well as present the PS module, which employs a broader and multiscale receptive field at the network's bottom to obtain more detailed semantic information. We also propose the local guided block (LG block) and also its combination with the SE block to form the LS block, which can obtain more abundant local features in the feature map, so that more edge information can be retained in each down sampling process, thereby improving the performance of boundary segmentation. We propose PLU‐Net and integrate our PS module and LS block into U‐Net. We put our PLU‐Net to the test on three benchmark datasets, and the results show that by fewer parameters and FLOPs, it outperforms on medical semantic segmentation tasks.