Reviewerly/exHarmony huggingface dataset:
Between the Pen and the Line: Digital Drawing and the Feeling of Alienation and Distance

&#x0D; As an artist I work mainly with digital drawing tools, but I also have this feeling of alienation towards digital drawing: As an artist educated around the 80s/90s, the classic analog drawing is the basis for my understanding of both analogue anddigital drawing, and my experience with the digital drawing tools is filtered through this early training. &#x0D; &#x0D; &#x0D; In my research I have been testing various digital hardware and software to see how they work, and what kind of results they make. In my presentation I will look at the different quality of lines various hardware and software makes, show some of my line tests from different digital technology and reflect on my own drawing process. I will also reflect on my experience when working with digital tools and the experience I get from the finished works. The presentation will be illustrated with my own digital drawings made specially as part of my research.&#x0D; Drawing is an act traditionally done with a drawing tool held in the hand to make a line on a surface. Basically, the physical action - the gesture itself - is the same, whether I draw analog or digital: I hold the pen in the same way, move my hand and arm in the same way. But what exists between the hands gesture, the tip of the pen and the resulting line, is different in analog and digital drawing, and the line itself is also different. The digital drawing tools add their own qualities to the lines and the drawings. &#x0D; &#x0D; &#x0D; I will look at some of the differences I experience between digital and analog drawing: I see digital drawing both as a sort of drawing among many others, and at the same time as something completely different, it adds something other than traditional drawing tools. &#x0D;
Bayesian Network-Based Multi-objective Estimation of Distribution Algorithm for Feature Selection Tailored to Regression Problems

Feature selection is an essential pre-processing step in Machine Learning for improving the performance of models, reducing the time of predictions, and, more importantly, identifying the most significant features. Sometimes, this identification can reduce the time and cost of obtaining feature values because it could imply buying fewer sensors or spending less human time. This paper proposes an Estimation of Distribution Algorithm (EDA) for feature selection tailored to regression problems with a multi-objective approach. The objective is to maximize the performance of learning models and minimize the number of selected features. We use a Bayesian Network (BN) as the EDA distribution probability model. The main contribution of this work is the process used to create this BN structure. It aims to capture the redundancy and relevance among features. Also, the BN is used to create the initial EDA population. We test and compare the performance of our proposal with other multi-objective algorithms: an EDA with a Bernoulli distribution probability model, NSGA II, and AGEMOEA, using different datasets. The experimental results show that the proposed algorithm found solutions with a considerably fewer number of features. Additionally, the proposed algorithm achieves comparable results on models’ performance compared with the other algorithms. Our proposal generally expended less time and had fewer objective function evaluations.
Contribution of Landsat 8 OLI imagery to mapping of lithological series and lineaments: implications for Pb-Zn mineralization exploration in the Boudahar Massif, Eastern High Atlas, Morocco

The Boudahar massif is located in the southern part of the eastern belt of the Moroccan High Atlas and it corresponds to a reef mass of about ten kilometers in length. Jbel Boudahar is a district of lead-zinc-barite mineralization, known by a strong artisanal exploitation of its ores. This district is characterized by its topography with a contrast of altitude and an arid environment, which makes the information acquired more valuable. The processing of Landsat 8 OLI data allowed us to extract a lithological and structural map, aiming to make correlations to understand the distribution of lead-zinc mineralization. To this end, several tests were carried out, including the composition of color, principal component analysis and band ratio transformation. The location of the main deposits at Jbel Boudhar coincides well with the lineament derived from band 1 of principal component analysis and with the dolomitic pliensbachian formations mapped by MNF and BR from Landsat Oli8. A remote sensing geological prospecting model has been established for the Jbel Boudhar lead-zinc deposits, providing a basis for future prospecting of new deposits in a similar context.
Presenting an Effective Motivational Model on the Knowledge Acquisition Process Using Fuzzy Best-Worst Method (FBWM)

In the knowledge economy, knowledge-based organisations, in particular, open a special account for their employees. Knowledge acquisition is important for organisations, because it enables them to improve their skills and creates value, credibility and competitive advantage. This research has been made to identify the motivational factors effective for knowledge acquisition and prioritise these factors, as well as providing a framework for managers to enable knowledge sharing from knowledge workers and increase their desire to overcome current problems. The organisation has been paid. The statistical population of the research is 300 managers and experts in the automotive industry, and in this research, the opinions of 20 experts have been used to analyse the results. The results were analysed using the fuzzy technique to answer the research questions. The calculations obtained by applying the proposed method show that among the six factors affecting knowledge acquisition, Behavioural factors, with a weight of 0.296, have the most impact on knowledge acquisition compared to other factors. After that, the factor of Information Technology in the organisation, with a weight of 0.17, is in second place concerning the level of influence on knowledge acquisition. Also, the Organisational Learning Criteria are ranked third with a weight of 0.165. And the factors of Organisational Culture, Reward and Structure are placed in the next priorities with weights of 0.153, 0.094 and 0.121, respectively.
1D Quantum Convolutional Neural Network for Time Series Forecasting and Classification

The 1D Convolutional Neural Network (1D CNN) is a kind of Artificial Neural Network (ANN) that has been shown to obtain state-of-the-art performance levels on several applications with minimal computational complexity and whose advantages are well established. In this article, we propose a 1D quantum convolution, which extracts local features by means of a quantum circuit in a way similar to the classical convolution. In this work, we test the performance of the proposed 1D quantum convolutional layer building a 1D Quantum Convolutional Neural Network (1D QCNN) that consists of the 1D quantum convolution followed by classical layers. The proposed model is compared with classical models for both time series forecasting and classification tasks including benchmark and real-world datasets. The obtained results show that the 1D QCNN can successfully extract features from temporal data, and in certain cases outperform classical models in terms of accuracy and convergence.
EKSTRA KURIKULER AUGMENTED REALITY UNTUK SMA SINAR DHARMA HIGH SCHOOL

ABSTRACT&#x0D; The advancement of information and communication technology has become an important factor that is useful in various aspects. Everything that is done online also encourages progress in the field of information and communication technology. One form of product from information and communication technology is games. With the rapid development and popularity of games, it supports many game majors in college. At the high school level, especially upper secondary, game learning can be carried out through extracurricular activities. With extracurricular activities, high school students can develop their potential earlier and make the learning they get as a provision for entering the world of college later. This extracurricular activity also becomes one of the ways for students to support the improvement of non-academic achievements. Similar community service programs have been carried out previously for extracurricular teaching and have received a positive response. Sinar Dharma High School, which previously won a competition held through community service programs, now opens an extracurricular for game programming with Unity. The community service program is divided into several stages, namely needs research, material creation, teaching implementation, and final assessment.
Acceptance: VR Use in an Interior Design Studio Course

A pilot study examined undergraduate interior design juniors’ technology acceptance of virtual reality (VR) technology, correlating it to their spatial presence capability. The pilot study pre-tested with a sample size of 13. It tested for the students’ perception of spatial presence capabilities. Also, it tested the validity of the Measurement, Effects, Conditions- Spatial Presence Questionnaire (MEC-SPQ) survey. The pilot study tested for a correlation between spatial presence capabilities and technology acceptance. A bivariate correlation analysis was performed. The variables were found to be highly correlated. The data derived from the analysis showed that technology acceptance was statistically significant and positively correlated with spatial presence capabilities, r(13) = .74, p < .004. For each incremental increase in the VR technology’s spatial presence capabilities, there was a .740 increase in student technology acceptance.
Classifying Rhythm Game Chart Difficulty Level Using Hidden Markov Models

Difficulty levels in rhythm game charts are normally labeled with the corresponding chart author’s relative opinion, and there exists no real quantifiable measure to provide a specific difficulty level for charts within these games. This paper proposes using Hidden Markov Models to estimate the difficulty levels of charts existing within the rhythm game entitled Dance Dance Revolution. Dance Dance Revolution’s chart consists of a series of steps, which in turn consists of a series of arrows, with each possible step being considered as an observed symbol in the game’s chart. For this study, the HMM’s hidden states will correspond to the estimated difficulty level charts are classified as either “Basic” difficulty, “Difficult” difficulty, or “Expert” difficulty. The process to obtain the parameters of the Hidden Markov Model as well as the conducted experiments leading to the constructed model amounting to having a 70.37% accuracy of classifying a chart’s difficulty level with a TP rate of 69.74% of classifying segments according to difficulty levels are discussed in this paper.
Stylistic features of the Chinese-language artistic and scientific Internet discourse

The article examines stylistic features of Chinese-language fiction and scientific Internet discourse. It considers the concept of "internet discourse", as well as the concepts of "fiction discourse" and "scientific discourse", gives an explanation of certain lexical and grammatical features of the Chinese language. Their manifestation in the considered types of Internet discourse is analyzed. It is noted that the vocabulary of fiction Internet discourse tends to simplification, abbreviations, the use of idioms and catchphrases for greater concentration of information. The authors also use onomatopoeia to imitate the sounds of the surrounding reality by phonetic means of speech. It is shown on examples how, in contrast to popular science texts, the authors of fiction works use reduplication much more often, thereby creating a language more favorable for communication. It is explained why there are quite a lot of slang words in the artistic works of online discourses, which decorate and distinguish the work of the authors. This phenomenon reflects the existence of many dialects and changes in the language, the use of slang words depends on the origin of the author, as well as the emotional color that the author wants to reproduce. The use of linguistic stylistic means is highlighted on the examples of popular scientific and fiction texts. The lexical and grammatical means of expressiveness of the Chinese language of the texts published on the Internet are analyzed. It is indicated that an important feature of Internet works is the use of emoticons and various signs in the texts (smiley of tears, emoticon of shyness, etc.). The functioning of linguistic and stylistic means in the scientific and artistic type of Internet discourse is shown in the comparison. At the grammatical level, fiction and popular science texts are almost the same, there is the use of exclamation points for greater unity between the author and the reader, but in popular science texts they are more restrained and occur only when the author quotes his clients. There is a significant difference between popular science text and fiction. It is noted that the authors of the fiction discourse try to have more contact with the readers, conditionally involve them in the creation of the finale of the fiction work.
Detection of Radar Pulse Signals Based on Deep Learning

&lt;p&gt;Radar is widely used in aviation, meteorology and military fields, and radar pulse signal detection has become an indispensable and essential function of cognitive radio systems as well as electronic warfare systems. In this paper, we propose a deep learning based radar signal detection method. Firstly, we propose a detection method based on raw in-phase and quadrature (IQ) input, which utilizes a convolutional neural network (CNN) to automatically learn the features of radar pulse signals and noises, so as to accomplish the detection task. To further reduce the computational complexity, we also propose a hybrid detection method that combines compressed sensing (CS) and deep learning, which reduces the length of signal by compressed downsampling, and then feeds the compressed signal to the CNN for detection. Extensive simulation results show that our proposed IQ-based method outperforms the traditional short-time Fourier transform method as well as three existing deep learning-based detection methods in terms of probability of detection. Furthermore, our proposed IQ-CS-based method is able to achieve satisfactory detection performance with significantly reduced computational complexity.&lt;/p&gt;
Tad: A Domain-Aware Framework Learning to Adapt Target Shifts of Hate Speech

&lt;p&gt;Hate speech targets different social groups such as race and gender, and poses a significant threat to social harmony. Researchers are increasingly motivated to devise efficient techniques to improve automatic hate speech detection on social media platforms. However, current models are usually evaluated without considering hate speech targets and fail when the targets are unseen in the training data.&lt;/p&gt; &lt;p&gt;In this study, we examine target (domain) shifts of hate speech and propose, \emph{Tad}, an adaptation framework for neural models that adopts domain-aware networks to improve cross-domain hate speech detection. Tad features a hate knowledge lexicon infusion network, a domain-specific network, and a weighting network. We demonstrate that incorporating Tad improves the performance of leading neural models in hate speech detection when tested on unseen domains. Specifically, Tad yields improvements of up to 8$.1\%$ and an average of $2.4\%$ in macro F1-scores. Moreover, we identify data quality and quantity as vital factors to address performance gaps between models tested on seen and unseen domains. Our results reveal that excessive knowledge infusion may result in a decrease in performance such as for \textsl{Religion}. &lt;/p&gt; &lt;p&gt;In addition, we find trade-offs in cross-domain hate speech detection. For example, weighted loss for heavily imbalanced data generally improves performance.&lt;/p&gt;
Leverage Your Website as a Strategic Asset

The Membership Management ReportVolume 19, Issue 12 p. 8-8 Website Management Leverage Your Website as a Strategic Asset First published: 08 November 2023 https://doi.org/10.1002/mmr.32250Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume19, Issue12December 2023Pages 8-8 RelatedInformation
COPPER SHALES OF THE PRIKOLYMSKY TERRANE (NORTH-EAST OF RUSSIA): MINERALOGICAL AND GEOCHEMICAL FEATURES AND ORE-FORMING CONDITIONS

Copper mineralization in the Magadan region (North-East of Russia) has been established in quartz-chloritoid and chlorite shales of the middle and upper Riphean of the Prikolymsky terrane. Compared to the upper crust, copper shales of the Oroek deposit are noticeably enriched in a narrow range of trace elements: Cu, Ag, Au, Rh, and Se, with enrichment factors ranging from 10-fold (Se, Rh) to 100-fold (Ag, Au) and even 1000-fold (Cu) suggesting, apparently, their synchronous involvement in ore formation. In addition, the studied samples are slightly enriched in Cd, Li, Co, Zn, V, U, Sc, Y, and REE. Copper shales show relatively flat rare earth element (REE) patterns similar to the chondrite-normalized pattern having no distinct positive or negative Eu anomalies and dominated by light lanthanides. The Ce/Ce* and Eu/Eu* values indicate that ore formation proceeded under oxidizing conditions. The obtained geochemical data show that the Riphean host rocks could serve as a source of trace elements and REE ore-forming fluids. In the ores, covellin and chalcocite are the main copper minerals; less common are roxbyite, idaite, nukundamite, spioncopite, chalcopyrite, silver-bearing (Ag about 1 wt. %) bornite and yarrovite, as well as native gold. Fluid inclusions in quartz suggest that copper mineralization was formed at temperatures of 212–190 °C and a pressure of 1 kbar, from hydrothermal solutions of medium salinity (from 13.8 to 12.4 wt. % eq. NaCl) and high density (0.95–0.98 g/cm3) saturated with chlorides of Ca, Mg, and K. The copper shales of the Prikolymsky terrane differ from sedimentary copper and copper shales of the African Belt by low Co and Ni contents and from the Kupfershifer by low concentrations of Pb and Zn. The obtained results can be used for prognosticating new deposits.
EXPERIENCE OF MINDFULNESS ONLINE GROUPS FOR ANXIETY IN ADOLESCENTS DURING COVID-19

An epidemic of such magnitude as the one caused by the COVID  forced us to rethink the scenarios of acting within Psychology itself. The confi nement and cessation of the usual social activities of Cuban adolescents caused the appearance of anxiety in a group of them, which led them to receive treatment through an online Mindfulness meditation group, an experience that constitutes the relevance of this research. With the aim of providing treatment for the manifestations of generalized anxiety disorder in this target population, two groups were developed virtually from social networks that, based on the therapist’s guidance, implement Mindfulness meditation procedures. Thus, constituting the present qualitative research of the action research type supported by historical-logical, analytical-synthetic and modeling methods among others. The sample was formed of  adolescents from Avila diagnosed with the pathology who sought help during confi nement in mental health services. As a result, the therapeutic procedure was executed as follows: two weekly group sessions as a frequency, with each session lasting  minutes (therapeutic hour) and an extension of  weeks, according to the therapeutic contract. The particularity in this case was that as an alternative means due to the need for physical distancing and confi nement due to the pandemic, it was developed through two WhatsApp groups and Zoom platform. By way of conclusions, it can be refl ected that at the end of the therapeutic procedure, a reduction in the initial symptoms was achieved in % of those involved, who, when subjected to reevaluation, demonstrated indicators of having gone through the pathology towards its compensation. It is important in this regard to clarify that in none of the cases medication was necessary at any time.
Multi-Camera Enhanced Real-Time Content- Aware Vehicle Detection: An Economical and Robust Solution

This paper introduces an open-source solution for real-time object awareness in the context of intelligent vehicles, designed to seamlessly adapt to simulation environments. The system transcends traditional vehicle detection, offering a comprehensive framework that includes object classification, precise location, and rotation estimation. Leveraging a multi-camera setup, our approach provides a comprehensive understanding of the surrounding environment, extending beyond vehicle recognition to include various other objects. What sets our solution apart is its capability to efficiently reformat and transition to simulation environments like Blender, enabling integration and testing within virtual contexts. The core components of our system encompass data collection, preprocessing, deep learning model selection, Use of Look up table, model training, efficient inference, and post- processing techniques. Designed for open-source collaboration, this solution is positioned for continuous improvement, adaptation to evolving needs, and addressing emerging challenges in the field of intelligent transportation and related domains. This paper represents a foundational step towards establishing an accessible and adaptable real-time object awareness system, encouraging innovation and research in the realm of intelligent vehicles and their applications, both in the real world and in simulated environments
<i>Review of Machine Learning for Social and Behavioral Research (Methodology in the Social Sciences)</i> <i>Review of Machine Learning for Social and Behavioral Research (Methodology in the Social Sciences)</i> . By Ross Jacobucci, Kevin J. Grimm, Zhiyong Zhang. New York, NY: The Guilford Press, (2023), 416 pp. $93.00 (Hardback), ISBN: 9781462552931. $62.00 (Paperback), ISBN: 9781462552924. $62.00 (PDF).

Click to increase image sizeClick to decrease image size AcknowledgmentsThe authors express their gratitude to the Indonesian Ministry of Finance’s Indonesia Endowment Fund for Education (LPDP) for providing financial support for the publication of this article and for the authors’ pursuit of postgraduate education.Disclosure StatementThe authors reported no potential conflicts of interest.Additional informationFundingThis study was supported by the Lembaga Pengelola Dana Pendidikan.
Performance comparison of Gilbert’s algorithm and machine learning in classifying Bell-diagonal two-qutrit entanglement

Abstract While classifying states as entangled or separable is one of the fundamental tasks in quantum information theory, it is also extremely challenging. This task is highly nontrivial even for relatively simple cases, such as two-qutrit Bell-diagonal states, i.e., mixtures of nine mutually orthogonal maximally entangled states. In this article we apply Gilbert’s algorithm to revise previously obtained results for this class. In particular we use “entanglement cartography” to argue that most states left in [Hiesmayr, B. C. Scientific Reports 11 , 19739 (2021)] as unknown to be entangled or separable are most likely indeed separable, or very weakly entangled, beyond any practical relevance. The presented technique can find endless applications in more general cases.