Reviewerly/exHarmony huggingface dataset:
Multi-agents as Data Mining Classifiers and Crawlers to Get Intelligent E-Advertisements

E-advertisement, also called online advertisement, is an advertisement type that utilizes the internet to convey its marketing to customers. Multi-agent systems are considered a subdomain of Artificial Intelligence that has witnessed fast development due to their flexibility to resolve complicated problems . This paper suggests a technique that utilizes intelligent software multi-agents as data mining classifiers and crawlers inside them, in order to classify the user query products keywords to correct the class number of the dataset inside every agent. The scientific value of this work is to improve marketing in such a way that the agent whose dataset confirms the best keywords for a user’s query will be chosen to retrieve ad URLs from their dataset using the crawler algorithm to satisfy the needs of customers . In addition, the agent crawler can crawl the web to get the advertisements that most match the user query. The proposed system classifies the dataset inside each agent with several machine-learning classifiers such as Decision Tree, KNN, Random Forest, and Naive Bayes, with evaluating performance metrics for them. The usage of several classifiers shows that the Random Forest is the best classifier with the highest evaluation metrics, and used to be an online real-time classifier inside each agent.
FILE SHARING USING BLOCKCHAIN

In today's world, data sharing is an important aspect of communication, but sharing sensitive data can be a challenge.Blockchain technology has emerged as a solution to this problem.In this project, a file-sharing system using blockchain technology.The system allows users to store files in a secure manner, ensuring that the data is tamper-proof and that access is only granted to authorized users.The file-sharing system is implemented as a decentralized application, where files are stored in a distributed network of nodes, and the ownership and access rights are maintained using blockchain technology.Users can upload files to the system, and access is granted based on predefined access permissions.Once a file is uploaded, it is verified, encrypted, and stored on the blockchain network, ensuring that the data remains tamper-proof and secure.To achieve this, Python and Flask as the primary programming languages, and integrated several modules, such as PyCryptodome, Bcrypt, and JWT.The system uses a Proof-of-Work consensus algorithm to maintain the integrity of the blockchain network.Overall, this project demonstrates the potential of blockchain technology to create secure and decentralized file-sharing systems.It provides an alternative to traditional data-sharing mechanisms, such as cloud-based storage systems, and ensures that the data is owned and controlled by the user.The system can be further enhanced by integrating additional features, such as decentralized identity management and smart contracts.
An Investigation Paper on Bogus Newscast Recognition

Abstract: With the approbation of portable proficiency and communal media growing, information is voluntarily accessible. The spread of deceptive broadcast has extensive consequences like the generating of prejudiced state of observance inducing biased race consequences to preserve certain aspirants. Furthermore, spammers exploit appealing news structures to return income relating notifications over click baits. In this article, we recommend to accomplish a parallel arrangement of divergent news sections accessible online with the support of judgments recognizing with Artificial Intelligence, Natural Language Processing, and Machine Learning. The outcome of the scheme controls the bogus news detection for social networks by machine learning and also determines the lawfulness of the dissemination newscast website
A patient information mining network for drug recommendation

As an important task of natural language processing, medication recommendation aims to recommend medication combinations according to the electronic health record, which can also be regarded as a multi-label classification task. But patients often have multiple diseases simultaneously, and the model must consider drug-drug interactions (DDI) of medication combinations when recommending medications, making medication recommendation more difficult. There is little existing work to explore the changes in patient conditions. However, these changes may point to future trends in patient conditions that are critical for reducing DDI rates in recommended drug combinations. In this paper, we proposed the Patient Information Mining Network (PIMNet), which models the current core medications of patient by mining the temporal and spatial changes of patient medication order and patient condition vector, and allocates some auxiliary medications as the currently recommended medication combination. The experimental results show that the proposed model greatly reduces the recommended DDI of medications while achieving results no lower than the state-of-the-art results.
An Empirical Study on How the Developers Discussed About Pandas Topics

Pandas is defined as a fast, easy open-source software library that is used for data analysis in Python programming language. It is rapidly used in different projects like software development, machine learning, computer vision, natural language processing, robotics, and others. Software developers show huge interest and discussions are becoming dominant in online developer forums, like Stack Overflow (SO) in pandas. Such discussions can help to understand the importance, prevalence, and difficulties of pandas topics. The aim of this work is to find the popularity and difficulty of pandas topics. In this regard, SO posts related to pandas are collected. Topic modeling is done on the textual contents of the posts. We found 26 topics which we further categorized into 5 board categories. We observed that developers discuss variety of pandas topics in SO related to error and excepting handling, visualization, External support, dataframe, and optimization. Also, a trend chart is generated according to the discussion of topics in a predefined time series. The finding of this paper provides a path to help developers, educators, and learners. For example, beginner developers can learn most important topics in pandas. Educators can understand the topics which seem hard to learners and make different tutorials that makes these topic understandable. From empirical study, it is possible to understand the preferences of developers in pandas topic by processing their SO posts.
RWD7 Reproduction of the Control Group in REMDACTA Trial Using the Millennial Medical Record, an Electric Health Record Database, in Japan

Developing a control group of a clinical trial using real world data is desirable and ethically sound despite challenges pertaining to internal validity. To examine the internal validity, we reproduced the control group in a Randomized Controlled Trial (RCT) using Electric Health Record (EHR) data and evaluated the difference between the outcome of the original trial and that of the reproduced analysis.
Geochronology of the Narenwula W polymetallic deposit in Inner Mongolia and its implications for ore prospecting

内蒙古那仁乌拉石英脉型钨多金属矿床是我国大兴安岭西南段近年来新发现的一个以钨为主,伴生有铋、银、锌、铜的大型矿床。然而,该区经历过多期次复杂的构造-岩浆活动,目前该矿床与区内不同期次岩浆活动的关系还不清楚,这不仅制约了对该矿床成因的认识,也阻碍了该地区钨矿找矿勘查工作的部署。本文选择区内粗粒二长花岗岩中的锆石、独居石以及石英脉型矿体中的黑钨矿、锡石开展精细的原位LA-HR-ICP-MS U-Pb年代学研究,进而构筑精细成岩成矿年代学框架。结果显示,花岗岩的锆石和独居石U-Pb年龄分别为146.2&#177;2.2Ma和144.2&#177;0.8Ma,二者在误差范围内一致,表明含矿花岗岩形成于晚侏罗世;主成矿期石英脉型矿石中的黑钨矿和锡石的U-Pb年龄分别为136.7&#177;1.0Ma和137.8&#177;1.9Ma,二者在误差范围内一致,表明该区钨锡矿化主要发生于早白垩世。由于含矿花岗岩的侵位年龄与成矿年龄之间存在超过10Myr的时差,考虑到同一岩浆-热液演化过程难以长达10Myr,表明含矿花岗岩仅仅为赋矿围岩,而与钨锡矿体无成因上的联系,并暗示区内存在与钨锡成矿有关的早白垩世隐伏的高分异花岗岩小岩体,深部具有钨锡矿找矿潜力。结合区域已有资料,本文提出那仁乌拉钨矿的形成主要受蒙古-鄂霍茨克洋闭合后的伸展过程控制,并可能受到古太平洋俯冲作用远程效应的影响。
MSR32 Validating a Machine-Learning Approach to Cancer Stage Identification Using Medicare Claims and SEER Data

Administrative claims data can provide information about real-world costs, treatments, and mortality for millions of patients with cancer, but claims’ diagnosis codes lack stage information, limiting research applications. Accurate assignment of cancer stage at diagnosis through claims would expand population research capabilities. This work aimed to build and validate a predictive machine-learning algorithm to assign patients’ cancer stage at diagnosis using claims data.
Subjective perception of emotional factors in CI users.

Introduction Hearing impaired people describe different problems in everyday communication. So far, it is unclear how the perception of emotional parts of the speaker’s voice changes after CI fitting and how they affect the severity of the subjectively perceived handicap.
Your Day in Your Pocket: Complex Activity Recognition from Smartphone Accelerometers

Human Activity Recognition (HAR) enables context-aware user experiences where mobile apps can alter content and interactions depending on user activities. Hence, smartphones have become valuable for HAR as they allow large, and diversified data collection. Although previous work in HAR managed to detect simple activities (i.e., sitting, walking, running) with good accuracy using inertial sensors (i.e., accelerometer), the recognition of complex daily activities remains an open problem, specially in remote work/study settings when people are more sedentary. Moreover, understanding the everyday activities of a person can support the creation of applications that aim to support their well-being. This paper investigates the recognition of complex activities exclusively using smartphone accelerometer data. We used a large smartphone sensing dataset collected from over 600 users in five countries during the pandemic and showed that deep learning-based, binary classification of eight complex activities (sleeping, eating, watching videos, online communication, attending a lecture, sports, shopping, studying) can be achieved with AUROC scores up to 0.76 with partially personalized models. This shows encouraging signs toward assessing complex activities only using phone accelerometer data in the post-pandemic world.
Getting Quechua Closer to Final Users Through Knowledge Graphs

Quechua language and Quechua knowledge gather millions of people around the world, especially in several countries in South America. Unfortunately, there are only a few resources available to Quechua communities, and they are mainly stored in unstructured format. In this paper, the Quechua Knowledge Graph is envisioned and generated as an effort to get Quechua language and knowledge closer to the Quechua communities, researchers, and technology developers. The process model for building the Quechua Knowledge Graph involved its creation, hosting, curation, and deployment phases. Currently, there are 553,636 triples stored in the Quechua Knowledge Graph, which is accessible on the Web, retrievable by machines, and curated by users. To showcase the deployment of the Quechua Knowledge Graph, use cases and future work are described.
Aspect Based Sentiment Analysis of COVID-19 Tweets Using Blending Ensemble of Deep Learning Models

This paper takes into account the aspect-based sentiment analysis of COVID-19 tweets, in order to understand human emotions and provide decision support to policymakers. People these days use social media to share thoughts and feelings in critical situations like COVID-19. After the World Health Organization (WHO) declared COVID-19 a pandemic, a significant increase in the usage of the most influential Twitter platforms has been observed. Thus, it is impossible to manually track all the COVID-19-related tweets on the Twitter platform. Sentiment analysis is one of the solutions to this problem. In this work, we attempt to understand people’s feelings about certain aspects by analyzing the COVID-19 tweets to reduce the harmful consequences of the pandemic and to understand the crisis, humanitarian needs and measures. We, therefore, propose a framework for the aspect based sentiment analysis of COVID-19 tweets by extracting the top ten aspects and classifying positive, negative, or neutral tweets in each aspect using the blending ensemble of basic deep learning models. The experimental results show that the proposed framework achieves the highest accuracy of 85.65% compared to other benchmark deep learning models.
Neural Networks from Biological to Artificial and Vice Versa

In this paper, we examine how deep learning can be utilized to investigate neural health and the difficulties in interpreting neurological analyses within algorithmic models. The key contribution of this paper is the investigation of the impact of a dead neuron on the performance of artificial neural networks (ANNs). Therefore, we conduct several tests using different training algorithms and activation functions to identify the precise influence of the training process on neighboring neurons and the overall performance of the ANN in such cases. The aim is to assess the potential application of the findings in the biological domain, the expected results may have significant implications for the development of effective treatment strategies for neurological disorders. Successive training phases that incorporate visual and acoustic data derived from past social and familial experiences could be suggested to achieve this goal.Finally, we explore the conceptual analogy between the Adam optimizer and the learning process of the brain by delving into the specifics of both systems while acknowledging their fundamental differences.
Effects of prediction error and adaptation vary along hierarchical brain dimensions during deviance processing

Spacecraft telemetry data are real-time data as the only basis for ground operation station and management system to judge the working performance and health status of spacecrafts in orbit. Telemetry data are high dimension, strong-dependent, and pseudo-periodic series, which bring great challenges to traditional anomaly detection methods of multivariate parameters. In this case, with the advantages of strong feature extraction and space injection ability, Mahalanobis distance (MD)-based approach has been a strong foundation for industrial system health monitoring. However, the typical MD-based method performs anomaly detection with a fixed threshold for MD series without capturing temporal evolution which cause high false alarms or missing alarms for complex abnormal modes. In this work, the temporal dependence Mahalanobis distance (TDMD) is realized based on multi-factors prediction which can effectively detect contextual and collective anomalies in multivariate telemetry series. Upper and lower limits with time series correlation and dynamic characteristics for the MD of each arriving multivariate point are constructed for online testing. Adequate experiments on simulated and real telemetry series verify the effectiveness and applicability of the proposed method.
The locus coeruleus in aging

Kernel logistic regression is a widely used method in machine learning applications. However, the common kernels are not flexible or representative enough due to their simplistic parametric formulation. Non-parametric learning on kernels can enlarge the representation capability of the kernel, but the low-rank property is required to suppress the model complexity. To improve the flexibility of kernels, we perform low-rank decomposition on an adjust matrix, which can explicitly control the model complexity without using complicated rank penalty terms. In this paper, we also extend our method to learn an indefinite kernel by a different decomposition. Experimental results demonstrate that the proposed learning method for non-parametric kernels outperforms other representative algorithms.
Evaluation of viewer’s interest in video clips using eye-fixation-related brain potentials

Temperature and precipitation changes are among the vital climatic driving forces of global vegetation change. However, the strategy to separate the relative contributions of these two critical climatic factors is still lacking. Here, we propose an index CRTP (contribution ratio of temperature and precipitation) to quantify their impacts on vegetation and then construct the CRTP classification prediction models based on climatic, geographic, and environmental factors using the Random Forest classifier. We find that precipitation predominates more than 70% of the significant vegetation change, mainly located in the low and middle latitudes during 2000–2021. Precipitation will remain the dominant climatic factor affecting global vegetation change in the coming six decades, whereas areas with temperature-dominated vegetation change will expand under higher radiative forcings. Hopefully, the promising index CRTP will be applied in the research about climatic attribution for regional vegetation degradation, monitoring drought-type conversion, and alarming the potential ecological risk.
Interpreting oscillations as travelling waves: the role of alpha-band oscillations in cognition

In a multi-objective setting, a portfolio manager’s highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement. The present investigation proposes a new approach to identify a set of non-dominated neural network models for further selection by the decision-maker. A new co-evolution approach is proposed to simultaneously select the features and topology of neural networks (collectively referred to as neural architecture), where the features are viewed from a topological perspective as input neurons. Further, the co-evolution is posed as a multi-criteria problem to evolve sparse and efficacious neural architectures. The well-known dominance and decomposition based multi-objective evolutionary algorithms are augmented with a non-geometric crossover operator to diversify and balance the search for neural architectures across conflicting criteria. Moreover, the co-evolution is augmented to accommodate the data-based implications of distinct market behaviors prior to and during the ongoing COVID-19 pandemic. A detailed comparative evaluation is carried out with the conventional sequential approach of feature selection followed by neural topology design, as well as a scalarized co-evolution approach. The results on three market indices (NASDAQ, NYSE, and S&P500) in pre- and peri-COVID time windows convincingly demonstrate that the proposed co-evolution approach can evolve a set of non-dominated neural forecasting models with better generalization capabilities.
Spatial recognition: A temporal tale of multiple window spatial registration

The performance ability of position logic circuitry of gamma-camera to superimpose the X-Y gains obtained from different pulse height analyzers is multiple window spatial registration (MWSR). This study was conducted to evaluate the feasibility of dual-tracer method using 99mTc and 131I to assess MWSR.The procedure was carried out on a dual-head gamma-camera with rectangular NaI (Tl) crystal using a point source containing 99mTc pertechnetate and 131I-NaI. Pixel coordinates, full width at half-maximum of point spread function at 364 keV and 140 keV, and pixels with maximum count of images for dual detectors were analyzed and compared.The experiment demonstrated an excellent correlation between the X and Y coordinates identified on 364 keV and 140 keV images (Spearman's coefficient of rank correlation: 0.9, P < 0.0001). Intraclass correlation of studies performed on two separate occasions showed a good correlation (k = 0.87) demonstrating the reproducibility of the procedure.Dual-tracer method for evaluation of MWSR is a reproducible, accurate, and logical alternative to the routine National Electrical Manufacturers Association procedure.
Open Science in the Context of the Integration of Ukrainian Education into the European Research Area: a collection of materials of the 1st scientific and practical conference with international participation

On April 27, 2023, the 1st scientific-practical conference with international participation "Open Science in the Context of the Integration of Ukrainian Education into the European Research Area" (OS-UA-ERA-2023) was held. The format of participation was combined: face-to-face presence and remote access via Zoom. Organizers of the conference: the Institute for Digitalisation of Education of the NAES of Ukraine, the State Scientific Institution “Encyclopedic Publishing House”, the project of the Visegrad Foundation "Educational Academic Portal of IT Integration into Education" EDUPORT, the National University of Bioresources and Nature Management of Ukraine. The topics of the conference included the development of theoretical foundations and the exchange of practical experience in the application of open science technologies in educational and research institutions, the use of research digital services and e-infrastructures, ways of integration into the European research space, and covered such a scope: 1. Philosophy and practice of using open data and open access. 2. Digitisation of educational and research activities in the context of open sources. 3. Encyclopedics as a tool for the integration of open science terminologies. 4. Technologies of open science in educational institutions. 5. Method and models of using open science services in educational process. 6. Ethical principles of open scientific research, copyright and license law. 7. Modern practices of evaluating the results of scientific research. 8. Integration into the European Research Area (ERA). 9. Problems and prospects of the development of open science in Ukraine during the war and in the post-war period.
Classification and Analysis of Human Body Movement Characteristics Associated with Acrophobia Induced by Virtual Reality Scenes of Heights

Acrophobia (fear of heights), a prevalent psychological disorder, elicits profound fear and evokes a range of adverse physiological responses in individuals when exposed to heights, which will lead to a very dangerous state for people in actual heights. In this paper, we explore the behavioral influences in terms of movements in people confronted with virtual reality scenes of extreme heights and develop an acrophobia classification model based on human movement characteristics. To this end, we used wireless miniaturized inertial navigation sensors (WMINS) network to obtain the information of limb movements in the virtual environment. Based on these data, we constructed a series of data feature processing processes, proposed a system model for the classification of acrophobia and non-acrophobia based on human motion feature analysis, and realized the classification recognition of acrophobia and non-acrophobia through the designed integrated learning model. The final accuracy of acrophobia dichotomous classification based on limb motion information reached 94.64%, which has higher accuracy and efficiency compared with other existing research models. Overall, our study demonstrates a strong correlation between people’s mental state during fear of heights and their limb movements at that time.
Application of software engineering methods in computer software development

With the advent of the era of big data, accelerating economic and social development has become a new demand to match it, and it is imperative to carry out corresponding research on software engineering.Software engineering involves many specialized technical areas.To improve the efficiency of software, it is necessary to establish under the guidance of scientific theory and technology, especially the theory and technology of software engineering, in order to better solve the problems related to it and ensure its efficiency.Using the idea of "software engineering" can effectively improve the practicality of software.