Reviewerly/exHarmony huggingface dataset:
Extractive Search for Analysis of Biomedical Texts

Extractive search has been used to create datasets matching queries and syntactic patterns, but less attention has been paid on what to do with those datasets. We present a two-stage system targeted towards biomedical texts. First, it creates custom datasets using a powerful mix of keyword and syntactic matching. We then return lists of related words, provide semantic search, train a large language model, a synthetic data based QA model, a summarization model over those results, and so on. These are then used in downstream biomedical work.
Research on flexible resource access strategy based on block chain technology

In order to solve the problems of controllable load scheduling in the context of the development of distributed power trading mechanism, a decentralized transaction mode between controllable load users and load agents based on block chain technology was proposed. By integrating the operation characteristics of controllable load users and fully considering the purchasing and selling electricity demand information between different subclasses of users and load agents, two types of decision model functions of controllable load users and load agents are designed. Based on the basic principle of income optimization, a block chain node model is established considering operation and maintenance cost and operation efficiency. Based on the controllable load user-load agent blockchain transaction mode, the profit sharing incentive mechanism and the intelligent contract solving algorithm of the load agent are designed. At the same time, the change rate of the efficiency function of the load agent is proposed as the consensus algorithm, and the structure and operation process of the blockchain data are explained. The results of example analysis show that the proposed optimal scheduling strategy fully taps the potential of controllable load users and load agents for peak load cutting and valley filling of power grid, achieves economic optimization for both parties and guarantees the long-term effective operation of blockchain network, which can provide reference value for the application of blockchain technology in power demand response.
Multimodal Frame-Scoring Transformer for Video Summarization

As the number of video content has mushroomed in recent years, automatic video summarization has come useful when we want to just peek at the content of the video. However, there are two underlying limitations in generic video summarization task. First, most previous approaches read in just visual features as input, leaving other modality features behind. Second, existing datasets for generic video summarization are relatively insufficient to train a caption generator used for extracting text information from a video and to train the multimodal feature extractors. To address these two problems, this paper proposes the Multimodal Frame-Scoring Transformer (MFST), a framework exploiting visual, text, and audio features and scoring a video with respect to frames. Our MFST framework first extracts each modality features (audio-visual-text) using pretrained encoders. Then, MFST trains the multimodal frame-scoring transformer that uses multimodal representation based on extracted features as inputs and predicts frame-level scores. Our extensive experiments with previous models and ablation studies on TVSum and SumMe datasets demonstrate the effectiveness and superiority of our proposed method by a large margin in both F1 score and Rank-based evaluation.
Complementary Bi-directional Feature Compression for Indoor 360° Semantic Segmentation with Self-distillation

Recently, horizontal representation-based panoramic semantic segmentation approaches outperform projection-based solutions, because the distortions can be effectively removed by compressing the spherical data in the vertical direction. However, these methods ignore the distortion distribution prior and are limited to unbalanced receptive fields, e.g., the receptive fields are sufficient in the vertical direction and insufficient in the horizontal direction. Differently, a vertical representation compressed in another direction can offer implicit distortion prior and enlarge horizontal receptive fields. In this paper, we combine the two different representations and propose a novel 360{\deg} semantic segmentation solution from a complementary perspective. Our network comprises three modules: a feature extraction module, a bi-directional compression module, and an ensemble decoding module. First, we extract multi-scale features from a panorama. Then, a bi-directional compression module is designed to compress features into two complementary low-dimensional representations, which provide content perception and distortion prior. Furthermore, to facilitate the fusion of bi-directional features, we design a unique self distillation strategy in the ensemble decoding module to enhance the interaction of different features and further improve the performance. Experimental results show that our approach outperforms the state-of-the-art solutions with at least 10\% improvement on quantitative evaluations while displaying the best performance on visual appearance.
Respected Author, Your research paper has been published successfully. Please find the link below. http://ymerdigital.com/archives/?cpage=6&amp;issId=%202106 Please find the below DOI allocated to your article. 10.37896/YMER21.07/13 OR https://www.doi.org/10.37896/YMER21.07/13 Please find the below attached E- certificates Thanks.

Since ancient times, gold has been cherished for its value and worth. Back then, gold was primarily used for trading purposes and as a method of remuneration. But now, it is looked upon as an investment and is found to exhibit the wealth of a country. Expensive metals like gold, in critical times, are used to assure the reimbursement of money borrowed as well. Thereby, gold is not only found to behold the rich, but also the poor. During pandemic crises, like the Covid-19, investments on gold in early times, might have a beneficial impact. Hence, predicting gold rates with live data and investing on golds at the right time is quite useful. Various machine learning algorithms like the linear regression, decision tree and random forest have been used to predict the gold rates. By using different algorithms, we've come to a conclusion that the random forest method provides more accurate results. Keywords: Gold rates, prediction, live gold rates, random forest, decision tree, linear regression, predictive analytics
Transformers are Adaptable Task Planners

Every home is different, and every person likes things done in their particular way. Therefore, home robots of the future need to both reason about the sequential nature of day-to-day tasks and generalize to user's preferences. To this end, we propose a Transformer Task Planner(TTP) that learns high-level actions from demonstrations by leveraging object attribute-based representations. TTP can be pre-trained on multiple preferences and shows generalization to unseen preferences using a single demonstration as a prompt in a simulated dishwasher loading task. Further, we demonstrate real-world dish rearrangement using TTP with a Franka Panda robotic arm, prompted using a single human demonstration.
CULTURAL DETERMINANTS OF FOREIGN DIRECT INVESTMENT: NATIONALIST-XENOPHOBIC ATTITUDES

Cultural characteristics related to the values of national identity (patriotism, nationalism) and attitudes toward other peoples (xenophobia) represent important elements of cultural specificity that influence various dimensions of socioeconomic life, including economic processes. Unfortunately, these specific characteristics are not often investigated, especially in this context, probably because of the difficulty of quantifying them and the general sensitivity of the topic. This study presents an original approach to the study of nationalist-xenophobic attitudes and their relationship to foreign direct investment (FDI). The aim of this study is to identify theoretical benchmarks for approaching the topic of nationalism-xenophobia, to develop a synthetic indicator to quantify nationalism-xenophobic attitudes, and to highlight the mechanism of their influence on FDI attraction. The methodology used for conducting this research comprises the methods of analysis, synthesis, induction (for logical connections and theoretical argumentation), weighted average technique (for the elaboration of Nationalism-xenophobia Index), Cronbach’s Alpha coefficient of internal consistency (to test the reliability of the construct) and Pearson correlation coefficient (for the correlation analysis). The author's contributions include a study of the relationship between nationalist and xenophobic sentiments, the development of the Nationalism-xenophobia Index, an indicator of the intensity of these sentiments in society, an analysis of the relationship between nationalism-xenophobia relations and the amount of FDI attracted per capita, the identification and explanation of the cultural mechanism by which nationalist and xenophobic sentiments influence various phenomena, including FDI. Logical-theoretical assumptions and arguments are confirmed by the results of quantitative data analysis. The findings of the study confirm that the cultural characteristics associated with nationalism and xenophobia represent important elements of cultural specificity that influence economic processes, including FDI. Numerous studies show that the nationalist dimension of ethnocentrism is closely related to xenophobia. Analysis of countries' scores on the nationalism-xenophobia index depending on the dominant Christian denomination and the presence of a communist past shows a link between nationalist-xenophobic, Orthodox (and, to a lesser extent, Catholic) values and the values of communist ideology, which negatively affect FDI attraction. Furthermore, nationalist-xenophobic attitudes have a negative impact on FDI attraction, both directly and indirectly, showing a strong negative impact.
SciAuth: A Lightweight End-to-End Capability-Based Authorization Environment for Scientific Computing

We introduce a new end-to-end software environment that enables experimentation with using SciTokens for capability-based authorization in scientific computing. This set of interconnected Docker containers enables science projects to gain experience with the SciTokens model prior to adoption. It is a product of our SciAuth project, which supports the adoption of the SciTokens model through community engagement, support for coordinated adoption of community standards, assistance with software integration, security analysis and threat modeling, training, and workforce development.
Cytogenetics: a reliable tool for the diagnosis and prognosis of hematological malignancies

Aim: The current review aims to provide a holistic overview of the cytogenetic techniques used to diagnose and diagnose haematological malignancies.&#x0D; Background: Since the discovery of the Philadelphia chromosome as a sole abnormality for diagnosing chronic myeloid leukaemia, cytogenetics has been widely used to diagnose malignancies in blood. With the introduction of advanced cytogenetic techniques like Fluorescent In-Situ Hybridization, Spectral Karyotyping and Comparative Genomic Hybridization, identification of clones and monitoring the response to drug therapy has now become easy as these have overcome the challenges of conventional cytogenetics like culture failures, poor morphology of chromosomes and low mitotic index.&#x0D; Review results: Ample evidence suggests that chromosomal aberrations are critical early indicators of progressing to a hematopoietic disorder. Conventional cytogenetics augmented with molecular cytogenetic techniques, immunophenotyping and real-time PCR not only aids in identifying specific clones but also characterizes haematological malignancies, predicting and assessing the minimal residual disease status during their management.&#x0D; Conclusion: We conclude that in this era of array diagnostics, cytogenetic techniques are still reliable for accurate diagnoses, characterization and prognosis of haematological malignancies.&#x0D; Clinical Significance: The present review discusses the role of cytogenetic tools in the diagnoses and prognosis of various haematological malignancies. It also provides an insight into the technical challenges encountered while performing these studies and the considerations to overcome them.
Profiles of Potential Unconstitutionality of Legislation Restricting Personal Freedom for the Containment of COVID-19 on the Example of the Italian Republic

Abstract The Sars-CoV–2 pandemic is changing the main issues of Italian constitutional law. The phases of the Italian normative management of the crisis focused on important and extraordinary measures and brought to light some structural problems of the Italian constitutional legal system. More generally the ongoing health crisis is revealing the lack of an articulated emergency framework in the Italian Constitution and questioning whether existing legislative tools are suitable to face contemporary threats. This article aims to analyse the main issues raised by the Italian government’s reaction to the coronavirus: the notion of emergency in Italian constitutional law, the legal forms chosen to fight the virus, the choice of the Italian Government to regulate the emergency by decrees of the President of the Council of Ministers, the role of decree law (‘ decreto-legge ’), from the emergency and the compression and restriction of fundamental rights to the balance of the fundamental freedoms with the protection of right to health.
Hydraulic Servo System Fault Detection Based on Machine Learning

Servo systems are widely used in aerospace and other fields. To ensure the safety of the system operation, the fault detection of servo systems is very important. Servo system fault detection often uses comparative differential judgment method, which requires the establishment of a reliable model. On the one hand, servo system models are usually built using mathematical and physical methods by combining the transfer functions of the various components the actual servo system have into an algorithm that calculates the input–output relationship. On the other hand, machine learning algorithms can also be used to model the servo system. In this paper, we address this issue by developing a rocket data prediction model based on the LSTM algorithm using publicly available data provided by Shanghai Aerospace Control Technology Institute. After deriving the results, the results are evaluated by using R-squared metrics and some improvement outlooks are provided for the subsequent research.
Application of the Kaizen Method in Efforts to Maintain the Sustainability of Springs in Indonesia

Indonesia represents about 6% of the world's water sources. This shows that Indonesia has abundant water resources. However, the facts show that most areas of Indonesia experience a shortage of clean water supplies. This is because the spring can be lost within a certain period if there is no planning to maintain its availability. The solution offered is to fulfil clean water needs with the Kaizen method. Kaizen is a 5S philosophy that includes Seiri, Seinton, Seiso, Seiketsu, and Shitsuke. This study aims to review the application of Kaizen methods and their sustainability to be applied in Indonesia. The methods used in this study are descriptive observational are aimed at describing and describing phenomena of a scientific and human engineering nature. The results of this study are packaged in the House Model. The House Model is a tool used by an organization using a concept built to transform into action. Some activities in the house model are tailored to meet the needs of partners and further increase the efficiency and effectiveness of the programs being implemented. The program also has good sustainability in community aspects, regulations, and government.
Challenges and Lessons Learned of Formalizing the Partnership Between Libraries and Research Computing Groups to Support Research

At universities, collaboration between libraries and research computing personnel can enhance services for data-intensive research in a manner that encompasses the entire data lifecycle. The University of Colorado Boulder has undertaken such a libraries-research computing collaboration known as the Center for Research Data and Digital Scholarship (CRDDS). Here, the challenges, successes, and lessons learned during the first five years of CRDDS are shared. Differences in culture, nomenclature, tools, budgets, and operations can cause confusion and misunderstandings between the two groups and can hinder the goal of providing collaborative research support. CRDDS has mitigated these issues by implementing a coordinator role, developing and documenting standard procedures, and providing structured venues for members to share ideas and experiences. Some early successes include development of a comprehensive training program for data-oriented topics, implementation of support for "big data" publishing, and establishment of a graduate certificate program in Digital Humanities.
OPTIMAL TASK SCHEDULING FOR CLOUD COMPUTING USING RAO ALGORITHM

Internet-based provision of computer resources is known as cloud computing. It is possible to utilise data that is controlled by a third party or some other individual at a distant place through cloud computing. Service Level Agreements (SLAs) are used by the majority of Cloud providers to define the services they provide. As part of the SLA, the service provider promises a certain level of quality of the service. Computing and data clouds are two sorts of clouds in a cloud-based system. In cloud technology, task scheduling is critical to ensuring service quality and SLA. One of the most important aspects of cloud computing is a well-organized work schedule. In this article, we have designed an optimal task scheduling method using RAO approach. The RAO algorithm is simple, required number of parameters, and required no tuning of parameters as compared to the other algorithm. Further, a multi-objective function is designed based on RAO algorithm performs the optimal scheduling. The performance evaluation is done by considering number of tasks such as 50,100,200, 300,400, and 500. Further, number of performance metrics are determined for it. The outcomes represents that the presented technique provides lesser values of AWT, ATT, and make span over the existing method.
Enhancing interaction of people with quadriplegia

In this work we address the expansion of multimodal interaction for people with quadriplegia through the design and development of a multimodal integration component, a pervasive device with quadriplegia and it was possible for her to perform new tasks using the same hardware platform. That platform was mirrored in a simulation environment and real usage scenarios were tested using different configurations. Results showed that integration of that sort of device to ACAT has little impact on CPU usage and its standalone use requires a fraction of CPU resources.
A Security Situation Assessment Method based on Support Vector Regression for Power Monitoring System

At present, most of the security situation assessment methods for the power monitoring system only consider the network factors, ignore the security factors inside the system, and the weight distribution of assessment indicators is subjective. This paper analyzes the security factors affecting the power monitoring system, and gives the security situation assessment indicator system of the power monitoring system. On this basis, the security situation of each equipment is assessed based on Support Vector Regression, and the weight is calculated according to the importance of the security area division of the power monitoring system where different equipment is located. Finally, the overall security situation of the power monitoring system is comprehensively evaluated through weighted summation. On the one hand, it can improve the accuracy, the flexibility and the expansibility of the security situation assessment for the power monitoring system. On the other hand, it can also trace the source of security problems in the power monitoring system, which is convenient for managers to isolate risks quickly and efficiently or take defense strategies.
Comparing Static and Dynamic Weighted Software Coupling Metrics

Coupling metrics are an established way to measure software architecture quality with respect to modularity. Static coupling metrics are obtained from the source or compiled code of a program, while dynamic metrics use runtime data gathered e.g., by monitoring a system in production. We study \emph{weighted} dynamic coupling that takes into account how often a connection is executed during a system's run. We investigate the correlation between dynamic weighted metrics and their static counterparts. We use data collected from four different experiments, each monitoring production use of a commercial software system over a period of four weeks. We observe an unexpected level of correlation between the static and the weighted dynamic case as well as revealing differences between class- and package-level analyses.
A Bibliometric Analysis of Intelligent Voice Interaction Based on VOSviewer

VOSviewer was used as an analysis tool to sort out and analyze intelligent voice interaction. The time span was set as five years, and 12,558 results were searched in total. The database was screened by the types of papers in WOS core journal sets, and 2687 records were refined. Word frequency statistics and keyword clustering method based on author and country relationship were used to construct the atlas, and the clustering results were displayed intuitively by VOSviewer. The results show that the current studies of voice interaction mostly take behavioral experiments. Keywords of voice interaction include voice, recognition, perception, health, knowledge education, social participation, information retrieval, human-computer interface. Based on human-computer interaction research, artificial intelligence, behavior, gender keywords becomes new research trend in recent years. In the study of author distribution, Munteanu, Cosmin Clark, Leigh, Torre and Ilaria are the authors as the core, showing scattering pattern, and the correlation among authors is not very close. From the perspective of national branches, European and American regions led by the United States are the first to initiate research. In recent years, China, Spain, Australia and other countries have also emerged in the field of research.
Automatically Computing Connotative Shifts of Lexical Items

Connotation is a dimension of lexical meaning at the semantic-pragmatic interface. Connotations can be used to express point of views, perspectives, and implied emotional associations. Variations in connotations of the same lexical item can occur at different level of analysis: from individuals, to community of speech, specific domains, and even time. In this paper, we present a simple yet effective method to assign connotative values to selected target items and to quantify connotation shifts. We test our method via a set of experiments using different social media data (Reddit and Twitter) and languages (English and Italian). While we kept the connotative axis (i.e., the polarity associated to a lexical item) fixed, we investigated connotation shifts along two dimensions: the first target shifts across communities of speech and domain while the second targets shifts in time. Our results indicate the validity of the proposed method and its potential application for the identification of connotation shifts and application to automatically induce specific connotation lexica.