Reviewerly/exHarmony huggingface dataset:
Fumarolic degassing dynamics revealed by coupled seismo-acoustic observation (Pisciarelli, Campi Flegrei Caldera, Italy)&amp;#160;

&amp;lt;p&amp;gt;Hydrothermal activity is a natural manifestation of volcanic degassing at calderas, testified by the presence of fumarolic fields, boiling pools, steaming ground and soil diffuse degassing, which are of interest for volcano monitoring and surveillance as they can be related to the magma dynamics within the caldera reservoirs. Campi Flegrei (Italy) is a half submerged resurgent caldera with a nested structure located at the western edge of the bay of Naples. Since its last eruption in 1538, several episodes of ground uplift accompanied by seismic swarms and intense degassing have been reported. The last uplift phase started in 2005 and is still ongoing. The Pisciarelli fumarolic field is a key area of the Campi Flegrei caldera where a continuous and vigorous degassing of hydrothermal fluids, of magmatic origin, takes place. Such fumarolic degassing is associated with a persistent harmonic tremor showing within the last decade an increasing amplitude trend that correlates well with the geochemical and geodetic unrest indicators of the caldera.&amp;amp;#160;In the framework of the DPC-INGV 2012-2021 Agreement and the LOVE-CF Project, we investigated the seismo-acoustic wavefield produced by fumarolic degassing with the aim of characterizing the source process that produces the harmonic tremor, and to propose a potential seismo-acoustic based tool to estimate the fumarolic gas fluxes in real time.&amp;amp;#160;&amp;amp;#160;At this aim, we performed a series of temporary geophysical experiments with the deployment of 4-element small aperture seismo-acoustic arrays equipped, at each array element, by a short-period three-component seismometer and a broadband infrasonic pressure sensor.&amp;amp;#160;We show that the harmonic tremor source is located within the fumarolic field at shallow depth (&amp;lt;100m) and is strongly controlled by the dynamics of the water level within the fumarolic conduits.&amp;amp;#160;We detected for the first time the nearly continuous acoustic wavefield produced by Pisciarelli&amp;amp;#8217;s degassing activity. We recognize two distinct acoustic sources that are active at the same time and associated with 1) the intense bubbling from a water pool and with 2) the over-pressurized vapour degassing from the fumarolic vents.&amp;amp;#160;Integration between acoustic and seismic observation allowed us to propose a potential mechanism for tremor generation through a bubble collapse as soon as the volcanic gas approaches the earth surface while ascending through the conduit. Coupled acoustic and seismic observation has brought to a better understanding on the dynamics of fumarolic degassing at Campi Flegrei, paving the way to the design of an innovative tool for the real time monitoring of the fumarolic degassing. This will improve our capability to assess the volcanic risk for the Campi Flegrei Caldera, as any changes in fumarolic degassing may be related to a change in the on-going unrest dynamics.&amp;amp;#160;&amp;lt;/p&amp;gt;
‘I Feel Love’

This chapter proposes the concept of mutation as an alternative to notation to cover practices of music inscription in the twentieth century: the century when electronic technology changed the ways in which we feel, make, and compose music. The chapter zooms in on the song that catapulted pop music into dance music in the late 1970s: ‘I Feel Love’ (1977), co-written and performed by Donna Summer and produced by Giorgio Moroder and Pete Bellotte. While Moroder has always posited himself as the genius who invented dance, the chapter reveals that ‘I Feel Love’ is rather the result of a distributed form of creativity that is the hallmark of music mutation. The production process of ‘I Feel Love’ thus provides a unique insight into new forms of creativity and inscription emerging in the electronic age.
Ski-hill graph Pedagogy of the Meter and Psychoacoustics in Music Education Design

This thesis presents a psychoacoustically derived computational model of the perceived distance between any two major or minor triads, the degree of activity created by any given pair of triads, and the cadential effectiveness of three‐triad progressions. The model is tested against conventional music theory, and ratings given by thirty‐five participants for the “similarity” and “fit” of triads in a pair, and the “cadential effectiveness” of three‐triad progressions. Multiple regressions show that the model provides highly significant predictions of the experimentally obtained ratings. Finally, it is argued that because the model is based upon psychoacoustic axioms, it is likely the regression equations represent true causal models. As such, the computational model and its associated theory question the plausibility of theoretical approaches to tonality that use only long‐term memory and statistical features, as well as those approaches based upon symmetrical geometrical structures like the torus. It is hoped that the approach proposed here may herald not only the return of psychoacoustics to tonal music theory, but also the exploration of the tonal possibilities offered by non‐standard tunings and non‐harmonic timbres.
The Need for Responsive Environments: Bringing Flexibility to Clinic Spaces

The number of outpatient care facilities (e.g., primary care clinics, pediatric care centers) in the US has increased 51% in the last decade, with significant technological implementations (e.g., electronic health records, telemedicine). These trends are compelling outpatient practices to improve their flexibility in providing care to meet this rising demand, including tailoring building spaces to changing needs of users (e.g., physicians, patients). Responsive environments can perceive their users’ needs and interactions in spaces, the technologies they use, and provide mechanisms for prompt transformations of spatial features (e.g., size of space, furniture layout, and temperature). Previous research studies have developed innovative responsive environment applications such as context aware lighting systems and architectural robotics but have yet to address situations unique to outpatient clinic settings. This paper presents research findings from investigations with healthcare professionals that map out the needs for and challenges in transforming existing spaces for increased demand and technology assisted practice. This paper will also discuss the challenges and opportunities for implementing responsive environments, specifically highlighting major events of spatial transformations relevant to outpatient settings, such as detected user activities, relevant sensor and software systems, and transformed architectural elements and spatial features (e.g., walls, furniture, and lighting). Findings from this paper serve as a basis for frameworks for designing new outpatient clinic spaces and capturing requirements for responsive environments.
Competent Triple Identification for Knowledge Graph Completion under the Open-World Assumption

The usefulness and usability of existing knowledge graphs (KGs) are mostly limited because of the incompleteness of knowledge compared to the growing number of facts about the real world. Most existing ontology-based KG completion methods are based on the closed-world assumption, where KGs are fixed. In these methods, entities and relations are defined, and new entity information cannot be easily added. In contrast, in open-world assumptions, entities and relations are not previously defined. Thus there is a vast scope to find new entity information. Despite this, knowledge acquisition under the open-world assumption is challenging because most available knowledge is in a noisy unstructured text format. Nevertheless, Open Information Extraction (OpenIE) systems can extract triples, namely (head text; relation text; tail text), from raw text without any prespecified vocabulary. Such triples contain noisy information that is not essential for KGs. Therefore, to use such triples for the KG completion task, it is necessary to identify competent triples for KGs from the extracted triple set. Here, competent triples are the triples that can contribute to add new information to the existing KGs. In this paper, we propose the Competent Triple Identification (CTID) model for KGs. We also propose two types of feature, namely syntax- and semantic-based features, to identify competent triples from a triple set extracted by a state-of-the-art OpenIE system. We investigate both types of feature and test their effectiveness. It is found that the performance of the proposed features is about 20% better compared to that of the RᴇVᴇʀʙ system in identifying competent triples.
The GR1 Algorithm for Subgraph Isomorphism. A Study from Parallelism to Quantum Computing

In this paper was described the GR1 algorithm that provides feasible execution times for the subgraph isomorphism problem. It is a parallel algorithm that uses a variant of the producer–consumer pattern. It was designed to easily accept and interchange different pruning techniques. The results obtained are occurrences of different query graphs in a RI human protein-to-protein interaction data graph (Ferro et al., [18], Szklarczyk et al., Nucleic Acids Res 39, 2011 [20]). This is the graph in which the algorithm will execute the search. The execution times are feasible for increasingly larger query graphs (from three up to twenty nodes) and with the included quantum computing approach were obtained superior results. The work consists of implementing and testing the algorithm in an original way starting from a simple multiprocessing example (Python multiprocessing producer consumer pattern, [28]) and then writing and adapting it for use with multiple consumer processes, undirected graphs and motif finding. There are also two tables containing the average execution times. The two tables represent two series of test cases.
Lessons learned from high-school students contribute to exposure database

&amp;lt;p&amp;gt;The National Institute of Oceanography and Applied Geophysics &amp;amp;#8211; OGS is engaged in a wide range of educational and training activities for more than a decade, &amp;lt;span&amp;gt;mainly focused on seismic risk mitigation through data acquisition in the field and practical stages&amp;lt;/span&amp;gt;. To by-pass the restrictions imposed by the COVID-19 pandemic, in 2021 OGS started a new project, deployed in fully remote mode, to involve high-school students and increase their risk awareness and preparedness. The Project, named &amp;amp;#8220;CEDAS: building CEnsus for seismic Damage Assessment&amp;amp;#8221;, consists in the collection and elaboration of data which is included in the exposure database. Knowing the type and distribution of exposed assets, in particular buildings, is thus paramount for effective mitigation of disasters. The students classified and georeferenced the main residential buildings typologies of northeastern Italy, a seismically active area which suffered consequences from strong past earthquakes. During the project, 170 high school students collected reports on more than 3200 buildings, performing a statistical analysis of their results.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The project activities consisted of two main phases: plenary virtual training meetings and individual data collection and processing. Tutors held intermediate meetings with students for verification and discussion and brainstorming sessions with their professors to discuss the efficacy of their actions. All the meetings, including those for direct training of students, were delivered in virtual mode. The training material (including the meeting recordings) was then shared remotely with students and teachers through cloud solutions, and remained available during the whole project. In this way the students were able to both collect basic data for exposure assessment, and carry out their preliminary analysis using standard statistical tools available online. The comparative data analysis, performed for selected sub-areas and for the overall dataset, allowed them for some basic interpretation and a better understanding of the exposed assets situation in sub-areas. The CEDAS project, though motivated by the need to provide a practical solution to the COVID-19-related restrictions, went well beyond the emergency situation, as it demonstrated that training activities can contribute to both enhancing the available exposure dataset and to increasing risk awareness among young students in the region.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;
Development of Instructional Media on Mathematics Assisted Mobile Learning-Based with I-SPRING ON Algebraic Materials

This study aims to develop mobile learning-based mathematics learning media that can be accessed offline, as a solution to the problems faced by students during the learning process from home. The test subjects of this study were 25 students of SMP Taman Sari. The research method uses the ADDIE development model which consists of 5 stages, namely: Analysis, Design, Development, Implementation, and Evaluation. From the research process carried out, the results obtained are media that are developed and practical to use.&#x0D; Keyword : Mathematics Learning Media, Applications, Mobile Learning.
Economic and non-economic determinants of economic growth in Bangladesh: multivariate regression analysis

This study focuses on the scenario of the economic development of Bangladesh. The major objective of the study is to examine the economic and non-economic determinants of the economic growth of the country. This study employs the Multivariate OLS regression and GLM technique to explore the influences of those variables to the economic growth and development of the country. The empirical results show that agriculture, industry, and service sector contribution to real GDP are positive where industry and service sectors are statistically significant. The results of the economic determinant model illustrate that the capital, labor forces, imports, and total reserve of the country positively influence the economic development of the country which are significant also. The OLS estimation shows that coefficients of non-economic determinants such as control of corruption and bureaucratic quality are positively significant to influence the economy. The results also present that internal conflict, democratic accountability, and rule and order situation negatively affect the economic growth in Bangladesh. The GLM estimation shows the control of corruption, rule, and order also a positive effect on economic growth. These empirical findings are consistent with the exploratory analysis and practices.
The high variety of image wafer inspection field

This paper contains three different problems and solutions for detecting defects in very large-scale integration of wafer images. The goal is to explain the typical categories of wafer inspection problems: topology, device inspection, wafer analysis. Unusual and particular cases have been selected to show the high problem variety into the same wafer inspection field. This aspect implies solutions very different and specific variations of classic image analysis techniques and computer vision methods.
Influence of parameterization strategy for parent material effects in predictive mapping of topsoil geochemistry&amp;#160;

&amp;lt;p&amp;gt;The distribution of trace elements in soils is complex and reflects the geochemistry of the original geological substrates modified by variety of environmental and human-induced changes of soil environment. An effective use of geological information within digital soil mapping and geochemical mapping over large require a degree of class aggregation into several broad nominal (or ordinal) classes. Nevertheless, there are several potential weaknesses of the reclassification of lithological information - lithological variation within geological units, variation in composition of individual lithological types, and inadequate description of lithology in geological map. Hence, we tested how the predictive geochemical mapping using environmental correlation will be sensitive under various complement scenarios using aggregated geological substrates and additional numeric covariates that partially represent parent material such as subsoil texture, land gravity data (gravity survey Bouguer anomaly) and other geophysical spatial data (airborne magnetic and gamma radiometric surveys). To compare various scenarios, we have used lithological classification in combinations with other numerical substrate-wise covariates in pragmatic predictive geochemical models using quantile regression forest over contrast area (approximately 11 000 km&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;) in the Czech Republic. Thee independent geochemical datasets for soil trace elements after the acid digestion procedure were used to train and validate the predictive models. Lithology-wise covariates were iteratively combined with the joint set of other readily available covariates representing topography, land use, remotely sensed surface characterisation (using a cloudless bare soil composite assembled from Sentinel 2) and depositional inputs of trace elements into soil to compare the prediction of topsoil concentrations of trace elements under various research strategies for parametrisation of lithological information. The results enabled to select optimal covariates suite for lithology parametrisation for the complex nation-wide model for topsoil contents of trace elements.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The research has been supported by the Technology Agency of the Czech Republic under the research project No. SS03010364.&amp;lt;/p&amp;gt;
Innovating under Uncertainty: The Patent-Eligibility of Artificial Intelligence after Alice Corp. v. CLS Bank International

Artificial intelligence-related inventions raise complex questions of how to define the boundaries around patentable subject matter. In the United States, many claim that the recent doctrinal developments by the Supreme Court have led to incoherence and excessive uncertainty within the innovation community. In response, policymakers and stakeholders have suggested legislative amendments to address these concerns. We first review these developments, and subsequently use the patent examination record to empirically test the claims of increased uncertainty. We find that, although uncertainty did spike following the Supreme Court's holding in Alice, it quickly returned to levels comparable to its historic norm. This has implications both for those advocating for legislative changes to the law of eligible subject matter, as well as other jurisdictions considering adopting a test similar to that applied in Alice.
Extending HoloGAN by Embedding Image Content into Latent Vectors for Novel View Synthesis

This study aims to further develop the task of novel view synthesis by generative adversarial networks (GAN). The goal of novel view synthesis is to, given one or more input images, synthesize images of the same target content but from different viewpoints. Previous research showed that the unsupervised learning model HoloGAN achieved high performance in generating images from different viewpoints. However, HoloGAN is less capable of specifying the target content to generate and is difficult to train due to high data requirements. Therefore, this study proposes two approaches to improve the current limitations of HoloGAN and make it suitable for the task of novel view synthesis. The first approach reuses the encoder network of HoloGAN to get the corresponding latent vectors of the image contents to specify the target content of the generated images. The second approach introduces an auto-encoder architecture to HoloGAN so that more viewpoints can be generated correctly. The experiment results indicate that the first approach is efficient in specifying a target content. Meanwhile, the second approach method helps HoloGAN to learn a richer range of viewpoints but is not compatible with the first approach. The combination of these two approaches and their application to service robotics are discussed in conclusion.
Test Technology and Application of Big Data Analysis System

The analysis of data information plays a crucial role in the development of today’s social economy. Especially, with the advent of the Internet and the era of big data, it provides powerful technical support for the analysis and application of data information. In recent years, with the development of Science and technology in China and the development and application of big data, Big data analysis technology has good market advantages and development potential. However, in the process of development, we are still faced with problems such as weak industrial foundation of big data, solid top-level design and narrow innovative application fields, which shows that the application of big data analysis technology still needs support from many aspects. This paper starts with the characteristics of big data testing, and focuses on the test technology of big data analysis system. On this basis, combined with the 4V characteristics of big data, it mainly introduces the test content, application quality test, test process, test analysis and test design of big data analysis system.
Speaker Authentication Method using Reservoir Computing for Security System

In recent years, we have been using biometric authentication systems in various places such as daily life and businesses. However, it&#x0027;s insufficient in hospital and food factory to introduction of the security system of the room access control. This is because they wear gloves, masks and hats in hospitals and factories, so they cannot authenticate faces or fingerprints. To solve this problem, I turned my attention to voice authentication. In this study, I propose a speaker authentication system based on Reservoir Computing. Reservoir computing is a new type of recursive neural network. In this study, we conducted classification experiments on 3, 5, and 10 speakers. The results show that the F-measure is above 0.9 for all the number of speakers.
Pengaruh Strategi Pembelajaran Takaku terhadap Penguasaan Konsep Mata Pelajaran Matematika Sekolah Dasar Kelas VI

Learning strategy is one of the most important parts in the learning process so it is necessary for an education to determine the right strategy before the implementation of learning especially in mathematics learning which is believed to be difficult to understand makasimal The purpose of this study to find out the influence of the use of learning strategies takaku on the mastery of the concept of elementary school mathematics class VI B SDN Cakra Negara Mataram. Based on the graph, the average pretest values of 61 and postest 80 mean that there is a significant increase from before treatment and after treatment. Likewise, on the percentage there was a significant increase from before which was 45% and after being given treatment rose to 75%. This shows that there is an influence on the use of Takaku learning strategies on the mastering of concepts in mathematics subjects. In addition, the results of the calculation of research data that have been done are known sig values. 0,000 &lt; 0.005 then Ho and Ha are accepted. This means that the use of Takaku startegi has an effect in increasing the mastery of the concept of mathematics subjects SDN Class B Cakra Negara in 2021/2022.