Reviewerly/exHarmony huggingface dataset:
Evidential belief functions modeling of geophysical and multi-element geochemical data for Pb-Zn mineral potential targeting

The Evidential Belief Functions model was used for investigating the geochemical and geophysical anomalies and delineating the Pb–Zn mineralization zones in the Chichakloo area, east of Takab, Iran. In addition, we evaluated the uncertainty of geochemical and geophysical results in the detection of mineralization zones. For this purpose, the individual belief, disbelief, plausibility, and uncertainty functions of geophysical and geochemical predictor maps were assigned and then integrated using belief function equations. The obtained belief map reveals a remarkable potential for Pb–Zn mineralization in the east part of the study area with a conservative estimation of more than 50% and uncertainty of less than 10%. An increase in conservative estimate and a decrease in the uncertainty rate in this zone, compared to any of the other individual exploration predictor maps, indicate remarkable overlapping anomalies. The results of drilled boreholes proved the existence of the Pb–Zn mineralization and confirmed the obtained mineral potential map. • The geochemical and geophysical data were integrated by Belief Functions. • The GMPI method was applied for geochemical mapping. • The GMPI map was weighted using the Ble, Dis, Unc, and Pls functions. • The resistivity and chargeability values were modeled.
Construction and Push of Financial Instructional Resource Bank Based on Rough Set Theory

A perfect resource bank for financial education that can foster resource exchange and charitable work must be established. The traditional recommendation algorithm is improved in this paper based on the rough set theory, avoiding the issue where the traditional similarity calculation does not match the actual similarity judgement. When used in the field of resource clustering for financial education, the improved algorithm has the concept of an approximate set for boundary problems. It can more accurately describe the boundary groups of resource classification. When the model is offline, a rough K-means user clustering algorithm is used; users are assigned to the upper and lower approximations of K classes based on how similar they are to cluster centers, creating the initial neighbor set of users. Find the target user’s nearest neighbor from the initial nearest neighbor set online, predict the item’s score, and offer suggestions to it. The evaluation’s findings indicate that this system can achieve a precision rate of 94.35 percent. Additionally, compared to the conventional method, the recommended recall rate is higher at 95.94 percent. This algorithm can effectively provide high-quality resources for financial teaching while overcoming the drawbacks of traditional recommendation algorithms, increasing recommendation accuracy.
A Novel Hybrid Medical Data Compression Using Huffman Coding and LZW in IoT

Today, telemedicine has become very essential, because it provides the possibility for the health centers, hospitals, and research centers to exchange medical and diagnostic data, under IoT. Since the volume of the generated medical information in IoT is very high, transmitting this data via channels of limited bandwidth is time-consuming; thus, the information should be compressed before transmission. Todays, some of the methods compress data significantly, but quality of the restored data in these methods is very low. Considering the importance of images and information for diagnostic and medical applications, desired quality is of great importance. Thus, this study tries to present a hybrid medical information compression technique such that quality of the restored information is desired and compression is efficient. This compression technique combines two lossless compression methods, including Huffman encoding and Lempel-Ziv-Welch (LZW) that rearranges information. In this combination, a binary information arrangement is used between Hoffman and LZW techniques so that the integration of binary information reaches an information mapping and, while simplifying the mapping, is completely different for each piece of information and makes this article stand out. The simulation results of the proposed method show that medical information including signal, text, and image is reduced by an average of 37.85%. Remarkably, quality of the restored image is not degraded.
Tagging Singapore English

Abstract It is well‐known that Outer Circle English has undergone extensive contact‐induced lexical and grammatical restructuring. Is it possible to use common NLP tools developed for Inner Circle English to process Outer Circle English texts? Here, we report our experience of using the Stanford PoS tagger to tag the Singaporean component of the International Corpus of English (ICE‐SIN). We isolate two major contact‐related causes of tagging errors: (1) lexical and grammatical loans directly borrowed from the local languages; and (2) English‐origin words with new grammatical meanings acquired from the local languages. While the first type may be easy to overcome, the latter type is intractable, creating an extra layer of morphosyntactic complexity. We achieved comparable accuracy rates in the more formal registers, and a lower but still decent 88% in the informal register of private conversations. A tagged ICE‐SIN allows us to investigate lexical and grammatical restructuring at unprecedented levels of detail.
Audiovisual gesture and spectromorphology: the <i>Invalid Data W.E.S.T.</i> project

In this article, we detail a project that seeks to expand the ways in which the concepts of gesture and spectromorphology can be understood and applied in audiovisual projects. We argue that understanding spectromorphology in visual domains requires consideration of the wavelengths of light that give us an image. In this project, the complex interaction between clouds and solar radiation becomes the medium for understanding this concept of visual spectra. Invalid Data W.E.S.T. is a collaboration between media artist Grayson Cooke, audio engineer Ian Stevenson, and the experimental improvising group Wilcox, Encarnacao, Swanton Trio (W.E.S.T.), comprising three Australian composer-performers: Felicity Wilcox (piano), John Encarnacao (guitar), and Lloyd Swanton (bass). In this project, a multi-camera recording of W.E.S.T. in studio performance is combined with Cooke’s exploration of creative uses of satellite data. Satellite images of clouds rendered using infrared light are intercut with in-depth, multi-camera footage of W.E.S.T. captured during improvisation. The resulting audiovisual meditation draws parallels between spontaneous artistic processes, collaborative creation, and environmental phenomena. In this article, we explore how concepts of gesture and spectromorphology shaped the conception, production and post-production phases of the project, and how these concepts can be used to enhance the production of audiovisual work.
Autonomy of experience: time, space, path and place as blended artistic methodology in <i>M</i> <i>A</i> <i>P</i>, an online, interactive poetry experience

This article will discuss M A P, an interactive poetic experience facilitated on the popular media sharing application Instagram, which was developed as a research-based, cross-course collaboration. The artistic methodology applied during M A P was developed during the global pandemic of COVID-19 and was created with, and inspired by the use of blended learning approaches. Acknowledging and addressing the social disconnect and technological hyper-connect during the pandemic, M A P can be understood as a practice-as-research experimentation that investigates how digital technologies might be repurposed in blended artistic practice to allow for a more reflective and embodied experience of ourselves and others in time and place. The makers of M A P advocate the facilitation of autonomous choice-making, particularly in relation to the use of time, space, path and pace, as well as the application of slow technology and slow design considerations to develop abstract story-telling and relational sense-making through the merging of online and real-world activities. M A P can be accessed via Instagram on @map_game_start or via https://www.themapexperience.com.
<i>Slow Down (You Move Too Fast)</i>: designing mechanics to encourage practices of ‘ecological perception’ through mobile digital performances

Slow Down (You Move Too Fast) is a mobile digital performance that the audience member experiences via headphones as they walk for fifty minutes outdoors. During the performance the audience member is encouraged to physically slow down the movement of their body as they walk, from a comfortable walking pace to extreme slow motion. As they slow down, they experience aural soundscapes that evoke progressively slower temporal rhythms in nature, from the circadian rhythm of a single day passing through to the millennial long cycle of rock eroding and moving. Drawing on Laura Sewall’s insight into the ‘skill of ecological perception’ the work sought to utilise the mechanics of slowing down to engage the audience member in an embodied reflection on the interconnection of their body with the natural world around them, and on the temporal practices that shape their awareness of this interconnection. This practice-as-research reflection explores the theoretical inspirations for the work and how these were translated into the goals and design of the work. It then examines participant responses to the work and offers insights into whether, and how, mechanics that evoke sensory engagement can allow participants to develop an expanded sense of an ‘ecological self’.
Discussion on Profession Development of Higher Vocational Education from The Perspective of Market Principles and Occupational Changes

Higher vocational education is a kind of vocational education, and the professional construction of higher vocational education should meet the market principle. This paper introduces the main points of market principles, analyzes the changes of professions caused by contemporary technological revolution and the requirements of new professions for the quality of talents. Suggestions are made for the professional construction of higher vocational education. The new round of scientific and technological revolution puts higher and more complex requirements on the structure of human knowledge and ability. Under the system of scientific knowledge and technology that integrates multiple disciplines and fields, composite talents with multiple skills are needed. The boundaries of academic talents, engineering talents, technical talents and skillful talents will be blurred more and more, and having system thinking and innovation ability will become the basic requirements for all kinds of talents.
C-E Translation of the Chinese Civil Code and Intellectual Property Law: A Systematic Functional Linguistic Approach

This thesis is based on the diverse C-E translation versions of the Chinese civil code and intellectual property law. Under the guidance of systematic functional linguistics, this thesis uses metafuction theory as the main perspective and takes the field, tenor and mode characteristics of intellectual property law into consideration to analyze the translation versions so as to find the most aptly worded translation version which reveals the legislation intention and original context. Also, efforts are made to seek the equivalence between the source language and the target language in terms of ideational metafunction, interpersonal metafunction and textual metafuncion, thereby ensuring the accuracy of declaration of will and avoiding gross misunderstanding. Based on the comparative study of different translated versions, the thesis concludes that the translation of intellectual property legal articles should pay attention to the usage of legal professional words and model verbs and ensure the formalness, conciseness and readability.
Perceptions and Discussions of Snus on Twitter: Observational Study

With the increasing popularity of snus, it is essential to understand the public perception of this oral tobacco product. Twitter-a popular social media platform that is being used to share personal experiences and opinions-provides an ideal data source for studying the public perception of snus.This study aims to examine public perceptions and discussions of snus on Twitter.Twitter posts (tweets) about snus were collected through the Twitter streaming application programming interface from March 11, 2021, to February 26, 2022. A temporal analysis was conducted to examine the change in number of snus-related tweets over time. A sentiment analysis was conducted to examine the sentiments of snus-related tweets. Topic modeling was applied to tweets to determine popular topics. Finally, a keyword search and hand-coding were used to understand the health symptoms mentioned in snus-related tweets.The sentiment analysis showed that the proportion of snus-related tweets with a positive sentiment was significantly higher than the proportion of negative sentiment tweets (4341/11,631, 37.32% vs 3094/11,631, 26.60%; P<.001). The topic modeling analysis revealed that positive tweets focused on snus's harm reduction and snus use being an alternative to smoking, while negative tweets focused on health concerns related to snus. Mouth and respiratory symptoms were the most mentioned health symptoms in snus-related tweets.This study examined the public perception of snus and popular snus-related topics discussed on Twitter, thus providing a guide for policy makers with regard to the future formulation and adjustment of tobacco regulation policies.
Utilizing Test Item Analysis to Portray the Quality of English Final Test

This study aims at describing the quality of the item test by analyzing the difficulty level, discriminating index, and distractor effectiveness of English final test item. The researchers use a descriptive quantitative as the research design. The population and sample of the study is the student’s answer sheets of the tenth grade of SMK PGRI Wonoasri. There are 22 student’s answer sheet as the primary data. The researchers use documentation technique and test kits of the English final test as the instrument. The data is analyzed by using classical theory measurement and item analysis’ formula. The result shows: (1) the level of difficulty which categorized as difficult is 35% (40 items), medium is 47% (19 items), and easy is 18% (7 items); (2) discrimination index that recommended for being rejected is 15% (6 items), accepted 38% (15 items), and not accepted is 47% (19 items); (3) distractor effectiveness which has functioning distractors as very good is 20% (8 items), good is 43% (17 items), fair 27% (11 items), and less good is 10% (4 items). In short, the quality and acceptance of test items of English final is medium or fair. So, there are still a lot of items need to be revised.
THE USE OF KWL (KNOW, WANT, LEARNED) STRATEGY ON STUDENTS’ READING COMPREHENSION

This study aims at investigating the effectiveness of KWL (Know, Want, and Learned) strategy on the students’ reading comprehension. Quasi-experimental design was used in the study. The population&lt;br /&gt;taken was the seventh grade students of SMP PGRI 6 Malang. There were 54 students belonging to class VII C as the experimental group and VII A as the control group. Both pre and post tests were executed to obtain the data. The primary data were gained by comparing the result of post-test from the experimental group and that of the control group. The result of post-test exhibits that the sig. (2 tailed) or p- value is 0.000 lower than 0.05 (the level of significance). It means that the students who are taught by using KWL strategy have better reading comprehension than those who are not. The results of questionnaire show that the students give very good responses related to the implementation of KWL strategy. Overall, the results imply that the students’ reading comprehension can be effectively increased by using KWL strategy.
Safety Production Detection Algorithm of Smart Factory Based on PC Control Technology

Abstract In order to obtain the best scheme of safety production and operation decision-making of a smart factory and improve the rapid response ability of product information of a smart factory, the safety production detection algorithm of a smart factory is designed by using programmable computer (PC) control technology. The main interface, login interface, function interface, and navigation interface are designed, respectively, to realize the front-end design of the smart factory safety production detection platform. Based on PC control technology, the hardware equipment of perception layer, network layer, and application layer are installed, and on this basis, the database of the smart factory safety production detection platform is established. Once the operation data information is collected and detected, it is stored to the equipment fault experience knowledge base, and the fault diagnosis method of the minimum cut set of the fault tree is used to implement the on-site equipment diagnosis of the underlying Internet of Things of the smart factory, and finally the information sharing function of safety production detection through the information transmission of the safety production links of each smart factory is realized. Experiments show that the throughput data of the designed algorithm run stably, improve the control accuracy of safety production, and effectively improve the work efficiency.
A commonality-based enhancement for sentence modeling with supervision

Sentence pair modeling is a fundamental yet challenging issue for feature mining in natural language processing (NLP) tasks. Recently, most works have generated feature and sentence representation based on the interactive attention mechanism. However, these models have two limitations: (1) they only consider global information through attention coefficient weighting, which makes insufficient utilization of critical features; (2) they only conduct internal training by fine-tuning network parameters, in which attention results are poorly explained. In this paper, inspired by human reasoning, we propose a Commonality Aggregated approach (CA) to enhance the lightweight interaction model by considering phrase features and contextual words. Specifically, we first fuse positional encoding and employ supervised training to extract critical phrase information from the text as the commonality of sentence pairs. Then, we deploy transfer learning and utilize interaction network to combine crucial phrase features, core word features, and positional encoding to enhance sentence pair modeling. Compared with the original network, extensive experiments on multiple benchmark datasets demonstrate the effectiveness of the proposed commonality aggregated method with stronger competitiveness. Further visual analysisanalysies validated the more explicit interpretability of attention, and extended experimental results indicate the excellent generalization of our approach.
Building a Rule-Based Generalisation Service for Geovisualisation of Business Data

Effective geovisualisation is characterised by the fact that the application context, the output medium and the visual-cognitive abilities of the targeted audience are carefully considered when it comes to an informed (carto)graphic representation of a spatial data set. In this way, an optimal visual communication and analysis of the spatial facts is made possible. An essential key to this is the absence of graphic conflicts that tend to impede proper visual perception. At the level of (carto)graphic modelling, this is achieved by application-specific generalisation measures that are bundled in a toolbox. In the automated generation of effective map graphics, these generalisation measures are used in a rule-based manner. To avoid specific graphical conflicts, interactive components are integrated into the processing chain that support expert generalisation solutions. To date, such tools, some of which work automatically, exist for specific visualisation systems and GIS only. A professional generalisation solution that is automated and can be implemented across systems and platforms is currently missing. Against this background this paper discusses a modular, web-based concept for the rule-based generalisation service of quantitative business data.
Feedback Control of Medication Delivery Device Using Machine Learning-Based Control Co-Design

Although the opioid crisis is a problem worldwide, recent emerging technology has the potential of curtailing the epidemic.By administering microdoses of medication as needed, a feedback-driven medicine pump could lessen the highs and lows associated with the formation of an addiction.The focus of this study was to develop a feedback control loop for this pump that optimizes drug concentration in the bloodstream based on set criteria.In the process of optimization of the system, the mathematical model representing the system was analyzed to find an open loop transfer function.Using this function, a PID tuner was applied to set feedback control.Both machine learning (ML) and deep learning (DL) techniques are explored to act as a classifier that aids the pump in administering doses.The setpoint concentration of medication in the patient's bloodstream was calculated to be 7.55 mg/ml this setpoint was the basis for steady state concentration of the transfer function.When a PID tuner was added to the feedback system, the plot was optimized to satisfy the design criteria of a rise time less than 25-minutes and no more than a 5% overshoot of the setpoint concentration.Naïve Bayesian (NB), Tree and support-vector machines (SVM) classifiers achieved the best classification accuracy of 100%.A DL network was successfully developed to predict patient class.This work is the theoretical basis for developing a feedbackdriven medicine pump and an algorithm that can classify patients based on their body's metabolism that will aid the doctor in formatting the medicine pump so that the patient is receiving the proper amount of medication.
A Survey of Literature Analysis Methods Based on Representation Learning

The analysis of massive scientific literature collections can help researchers capture potential knowledge, understand disciplinary insights, and discover opportunities for academic collaboration. However, the huge volume of literature data becomes a major problem for human analysis and computer processing, which prevents scholars from rapidly obtaining disciplinary knowledge. Representation learning methods can effectively handle large-scale text and graph data, which have excelled in literature analysis tasks. In this study, we introduce the types of literature data and several typical academic networks. Then, we review the representation learning models and classify these methods into (a) word representation learning-based, including word2vec, GloVe, ELMo, Bert, etc.; (b) graph representation learning-based, including matrix factorization-based, random walk-based and deep learning-based methods, such as DeepWalk, node2vec, GCN, etc. Finally, we discuss the opportunities and present three challenges of representation learning-based approaches for literature analysis.