Reviewerly/exHarmony huggingface dataset:
Quality control test for unreliable meteorological and electrical photovoltaic measurements

&lt;p&gt;Meteorological and electrical measurements using predictive computational techniques have been used in the analysis of photovoltaic system operation and maintenance. International standards establish general and no standardized criteria on the quality control and validation of these measurements. In the present work, a methodology has been developed to correct erroneous photovoltaic experimental measurements: radiation, temperature, current, and voltage. We validated the proposed approach with 12 case studies with more than 5,000 meteorological and electric measurements from an experimental 3 kWp photovoltaic system. The approach is based on a set of non-intrusive criteria developed from the one diode model, the approach allowed to correct about 80% of the erroneous data, 30% more using polynomial regression. As for the regression methodology, we have shown that the proposed methodology includes 4 meteorologicalelectrical variables allowing a more rigorous analysis. For 75% of the cases evaluated, the proposed methodology achieves a better data correction.&lt;/p&gt;
Multiplicative processing in the modeling of cognitive activities in large neural networks

Abstract Explaining the foundation of cognitive abilities in the processing of information by neural systems has been in the beginnings of biophysics since McCulloch and Pitts pioneered work within the biophysics school of Chicago in the 1940s and the interdisciplinary cybernetists meetings in the 1950s, inseparable from the birth of computing and artificial intelligence. Since then, neural network models have traveled a long path, both in the biophysical and the computational disciplines. The biological, neurocomputational aspect reached its representational maturity with the Distributed Associative Memory models developed in the early 70 s. In this framework, the inclusion of signal-signal multiplication within neural network models was presented as a necessity to provide matrix associative memories with adaptive, context-sensitive associations, while greatly enhancing their computational capabilities. In this review, we show that several of the most successful neural network models use a form of multiplication of signals. We present several classical models that included such kind of multiplication and the computational reasons for the inclusion. We then turn to the different proposals about the possible biophysical implementation that underlies these computational capacities. We pinpoint the important ideas put forth by different theoretical models using a tensor product representation and show that these models endow memories with the context-dependent adaptive capabilities necessary to allow for evolutionary adaptation to changing and unpredictable environments. Finally, we show how the powerful abilities of contemporary computationally deep-learning models, inspired in neural networks, also depend on multiplications, and discuss some perspectives in view of the wide panorama unfolded. The computational relevance of multiplications calls for the development of new avenues of research that uncover the mechanisms our nervous system uses to achieve multiplication.
Object Tracking in SWIR Imaging Based on Both Correlation and Robust Kalman Filters

Short-wave infrared (SWIR) imaging has significant advantages in challenging propagation conditions where the effectiveness of visible-light and thermal imaging is limited. Object tracking in SWIR imaging is particularly difficult due to lack of color information, but also because of occlusions and maneuvers of the tracked object. This paper proposes a new algorithm for object tracking in SWIR imaging, using a kernelized correlation filter (KCF) as a basic tracker. To overcome occlusions, the paper proposes the use of the Kalman filter as a predictor and a method to expand the object search area. Expanding the object search area helps in better re-detection of the object after occlusion, but also leads to the occasional appearance of errors in measurement data that can lead to object loss. These errors can be treated as outliers. To cope with outliers, Huber’s M-robust approach is applied, so this paper proposes robustification of the Kalman filter by introducing a nonlinear Huber’s influence function in the Kalman filter estimation step. However, robustness to outliers comes at the cost of reduced estimator efficiency. To make a balance between desired estimator efficiency and resistance to outliers, a new adaptive M-robustified Kalman filter is proposed. This is achieved by adjusting the saturation threshold of the influence function using the detection confidence information from the basic KCF tracker. Experimental results on the created dataset of SWIR video sequences indicate that the proposed algorithm achieves a better performance than state-of-the-art trackers in tracking the maneuvering object in the presence of occlusions.
Development of an intelligent information resource model based on modern natural language processing methods

&lt;span lang="EN-US"&gt;Currently, there is an avalanche-like increase in the need for automatic text processing, respectively, new effective methods and tools for processing texts in natural language are emerging. Although these methods, tools and resources are mostly presented on the internet, many of them remain inaccessible to developers, since they are not systematized, distributed in various directories or on separate sites of both humanitarian and technical orientation. All this greatly complicates their search and practical use in conducting research in computational linguistics and developing applied systems for natural text processing. This paper is aimed at solving the need described above. The paper goal is to develop model of an intelligent information resource based on modern methods of natural language processing (IIR NLP). The main goal of IIR NLP is to render convenient valuable access for specialists in the field of computational linguistics. The originality of our proposed approach is that the developed ontology of the subject area “NLP” will be used to systematize all the above knowledge, data, information resources and organize meaningful access to them, and semantic web standards and technology tools will be used as a software basis.&lt;/span&gt;
Query expansion using novel use case scenario relationship for finding feature location

&lt;span lang="EN-US"&gt;Feature location is a technique for determining source code that implements specific features in software. It developed to help minimize effort on program comprehension. The main challenge of feature location research is how to bridge the gap between abstract keywords in use cases and detail in source code. The use case scenarios are software requirements artifacts that state the input, logic, rules, actor, and output of a function in the software. The sentence on use case scenario is sometimes described another sentence in other use case scenario. This study contributes to creating expansion queries in feature locations by finding the relationship between use case scenarios. The relationships include inner association, outer association and intratoken association. The research employs latent Dirichlet allocation (LDA) to create model topics on source code. Query expansion using inner, outer and intratoken was tested for finding feature locations on a Java-based open-source project. The best precision rate was 50%. The best recall was 100%, which was found in several use case scenarios implemented in a few files. The best average precision rate was 16.7%, which was found in inner association experiments. The best average recall rate was 68.3%, which was found in all compound association experiments.&lt;/span&gt;
Noise-robustness test for ultrasound breast nodule neural network models as medical devices

Deep learning technology has been widely applied to medical image analysis. But due to the limitations of its own imaging principle, ultrasound image has the disadvantages of low resolution and high Speckle Noise density, which not only hinder the diagnosis of patients' conditions but also affect the extraction of ultrasound image features by computer technology.In this study, we investigate the robustness of deep convolutional neural network (CNN) for classification, segmentation, and target detection of breast ultrasound image through random Salt & Pepper Noise and Gaussian Noise.We trained and validated 9 CNN architectures in 8617 breast ultrasound images, but tested the models with noisy test set. Then, we trained and validated 9 CNN architectures with different levels of noise in these breast ultrasound images, and tested the models with noisy test set. Diseases of each breast ultrasound image in our dataset were annotated and voted by three sonographers based on their malignancy suspiciousness. we use evaluation indexes to evaluate the robustness of the neural network algorithm respectively.There is a moderate to high impact (The accuracy of the model decreased by about 5%-40%) on model accuracy when Salt and Pepper Noise, Speckle Noise, or Gaussian Noise is introduced to the images respectively. Consequently, DenseNet, UNet++ and Yolov5 were selected as the most robust model based on the selected index. When any two of these three kinds of noise are introduced into the image at the same time, the accuracy of the model will be greatly affected.Our experimental results reveal new insights: The variation trend of accuracy with the noise level in Each network used for classification tasks and object detection tasks has some unique characteristics. This finding provides us with a method to reveal the black-box architecture of computer-aided diagnosis (CAD) systems. On the other hand, the purpose of this study is to explore the impact of adding noise directly to the image on the performance of neural networks, which is different from the existing articles on robustness in the field of medical image processing. Consequently, it provides a new way to evaluate the robustness of CAD systems in the future.
Don’t You Agree with My Ethics? Let’s Negotiate!

The rapid growth in autonomous technology has made it possible to develop intelligent systems that can think and act like humans and can self-govern. Such intelligent systems can make ethical decisions on behalf of humans by learning their ethical preferences. When considering ethics in the decision-making process of autonomous systems that represent humans for ethical decision-making, the main challenge is agreement on ethical principles, as each human has its own ethical beliefs. To address this challenge, we propose a hybrid approach that combines human ethical principles with automated negotiation to resolve conflicts between autonomous systems and reach an agreement that satisfies the ethical beliefs of all parties involved.
APPLYING K-MEANS ALGORITHM FOR CLUSTERING ANALYSIS EARTHQUAKES DATA IN WEST NUSA TENGGARA PROVINCE

This study aims to cluster and visualize the earthquake data on a geographical map to determine earthquakes' characteristics using the k-means algorithm. Cluster analysis using the k-means algorithm was carried out on the earthquake data. K-means algorithm is familiar and is one of the well-known techniques to have been applied in cluster analysis. One of Its advantages in cluster analysis is scaling large datasets, for example, earthquake data. The data used in this study is earthquake data in the West Nusa Tenggara from 1991 to 2021. Applying the proposed k-means algorithm, the optimal number of clusters (k) used in this clustering is 2, based on the highest silhouette score of 0.749. The cluster analysis on the geographical map showed that the epicenters of the earthquakes were pretty spread out before 2018, and the number of earthquakes in the eastern region of West Nusa Tenggara is more than in the western area. However, in 2018, the clusters were all bunched in the northern Lombok region. There were a few earthquakes in the west region in 2018, but they happened before August 5. Even after 2019, most earthquakes continue to occur, with the epicenters clustered close to the northern Lombok region
PENERAPAN METODE WAWANCARA NARASUMBER UNTUK MENINGKATKAN KEMAMPUAN MENULIS TEKS TANGGAPAN

The purpose of this study was to improve students' ability to write response texts at MTs N 2 Sragen by conducting Classroom Action Research (CAR) using interviews with informants. Data collection was carried out through documents, informants, and student test results which were analyzed using descriptive comparative and critical analysis techniques. The techniques used to check the validity of the data are triangulation and informant review. The result of the research is that the students' ability to write response texts has increased in each cycle. The resource interview method was carried out in the following stages: (1) students formed groups consisting of two people; (2) the teacher conveys the problems to be addressed; (3) students compile a list of interview questions; (4) students determine informants (5) students conduct interviews with informants; (6) students write response texts based on the results of interviews with informants. In the pre-action the ability to write student response texts was only 42.9% and then experienced an increase to 71.4% in cycle I. Then in cycle II there was a significant increase, namely 88.6%. The application of the resource interview method can improve the skills of writing response texts for class IX G MTs N 2 Sragen in 2021/2022. ABSTRAKTujuan penelitian ini untuk meningkatkan kemampuan menulis teks tanggapan siswa MTs N 2 Sragen dilakukan Penelitian Tindakan Kelas (PTK) dengan menggunakan metode wawancara narasumber. Perolehan data dilakukan melalui dokumen, narasumber, serta hasil tes siswa yang dianalisis dengan deskriptif komparatif dan teknik analisis kritis. Teknik yang digunakan untuk memeriksa validitas data yakni triangulasi dan review informan. Hasil penelitian adalah kemampuan menulis teks tanggapan siswa mengalami peningkatan pada tiap siklusnya. Metode wawancara narasumber dilaksanakan dengan tahap : (1) siswa membuat kelompok yang terdiri dari dua orang; (2) guru menyampaikan permasalahan yang akan ditanggapi; (3) siswa menyusun daftar pertanyaan wawancara ; (4) siswa menentukan narasumber (5) siswa melakukan wawancara dengan narasumber; (6) siswa menulis teks tanggapan berdasarkan hasil wawancara dengan narasumber. Pada pratindakan kemampuan menulis teks tanggapan siswa hanya sebesar 42,9% lalu mengalami peningkatan menjadi 71,4% pada siklus I. Selanjutnya pada siklus II terjadi peningkatan cukup signifikan yakni 88,6%. Penerapan metode wawancara narasumber dapat meningkatkan keterampilan menulis teks tanggapan siswa kelas IX G MTs N 2 Sragen tahun 2021/2022.
FedNRM: A Federal Personalized News Recommendation Model Achieving User Privacy Protection

In recent years, the type and quantity of news are growing rapidly, and it is not easy for users to find the news they are interested in the massive amount of news. A news recommendation system can score and predict the candidate news, and finally recommend the news with high scores to users. However, existing user models usually only consider users’ long-term interests and ignore users’ recent interests, which affects users’ usage experience. Therefore, this paper introduces gated recurrent unit (GRU) sequence network to capture users’ short-term interests and combines users’ short-term interests and long-term interests to characterize users. While existing models often only use the user’s browsing history and ignore the variability of different users’ interest in the same news, we introduce additional user’s ID information and apply the personalized attention mechanism for user representation. Thus, we achieve a more accurate user representation. We also consider the risk of compromising user privacy if the user model training is placed on the server side. To solve this problem, we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side. We further employ secure multi-party computation to request news representations from the server side, which protects privacy to some extent. Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics. Compared with the current state-of-the-art federated news recommendation models, our model has increased by 0.54% in AUC, 1.97% in MRR, 2.59% in nDCG@5%, and 1.89% in nDCG@10. At the same time, because we use a federated learning framework, compared with other centralized news recommendation methods, we achieve privacy protection for users.
The application and prospection of augmented reality in hepato-pancreato-biliary surgery

Augmented Reality (AR) is one of the main forms of Extended Reality (XR) application in surgery. hepato-pancreato-biliary (HPB) surgeons could benefit from AR as an efficient tool for making surgical plans, providing intraoperative navigation, and enhancing surgical skills. The introduction of AR to HPB surgery is less than 30 years but brings profound influence. From the early days of projecting liver models on patients' surfaces for locating a better puncture point to today's assisting surgeons to perform live donor liver transplantation, a series of successful clinical practices have proved that AR can play a constructive role in HPB surgery and has great potential. Thus far, AR has been shown to increase efficiency and safety in surgical resection, and, at the same time, can improve oncological outcomes and reduce surgical risk. Although AR has presented admitted advantages in surgery, AR's application is still immature as an emerging technique and needs more exploration. In this paper, we reviewed the principles of AR and its developing history in HPB surgery, describing its significant practical applications over the past 30 years. Reviewing the past attempts of AR in HPB surgery could make HPB surgeons a better understanding of future surgery and the digital trends in medicine. The routine uses of AR in HPB surgery, as an indication of the operating room entering the new era, is coming soon.
Multilingual NLP

The subject area of multilingual natural language processing (NLP) is concerned with the processing of natural language data in several languages. NLP systems that can translate between languages are becoming more and more necessary as the globe gets more interconnected in order to promote understanding and communication among speakers of various languages. To be effective, communication must overcome a number of obstacles presented by multilingual NLP. Lack of language standardization, which results in major variations in the grammatical constructions, vocabulary, and writing systems used in many languages, is one of the fundamental problems. The requirement for substantial amounts of annotated data for machine learning model training presents another difficulty. The creation of high-quality annotated datasets in numerous languages is time- and money-consuming, which restricts the supply of multilingual NLP resources. The problem of creating NLP systems that can handle several languages at once is the last one. This necessitates the deployment of sophisticated algorithms that can handle and evaluate data in numerous languages while producing precise findings. Researchers and developers are working on a variety of methods to address these issues. Creating standardized formats for multilingual data representation, like Universal Dependencies, which offers a unified framework for annotating linguistic data in several languages, is one strategy. Using transfer learning techniques to transfer knowledge from high-resource languages to low-resource languages is an alternative strategy. The amount of annotated data required for training NLP models in low-resource languages can bede creased with the use of this method. Last but not least, researchers are working to create multilingual NLP models that can manage numerous languages at once. To deliver precise results across numerous languages, these models employ cutting-edge methodologies like neural machine translation and multilingual word embedding’s. Despite the fact that multilingual NLP presents a number of difficult issues, with continuing study and development, it is possible to create NLP systems that are capable of processing natural language data from several languages.
MACHINE LEARNING FOR THE DOCUMENTATION, PREDICTION, AND AUGMENTATION OF HERITAGE STRUCTURE DATA

Abstract. The paper presents an effort to develop learning models based on the massive amounts of data that has been accumulated over the past decades during the process of digital documentation of heritage structures around the globe especially those in disaster zones.The development of an ontology is proposed that describes heritage buildings, their sites, and major hazard events that may cause damage to them. This ontology can serve as a repository for documenting heritage structures and provide highly structured data for developing machine learning systems that can identify patterns of damage from recorded image data. For heritage structures in seismic zones, the first step in ontology development is analyzing available earthquake information about the event and the damage information. The resulting model will create links between information items, for example relating the extent of the damage of an element to the earthquake magnitude and its distance from the epicenter. The ontology may also include collected images from previous earthquake events, with links to the objects in each image. Special tools will focus on selecting sub-models to be included in a machine learning model. For example, if the learning objective is to identify the damage and its extent from an image, then the rules will select the features in the model that relate to structural damage and identify each type of damage. It is hoped that this work will help develop learning systems that speed up processing of large volumes of image damage data collected from heritage sites.