Reviewerly/exHarmony huggingface dataset:
STUDENT MOTIVES TO WATCH SPORTS NEWS PROGRAM "GALERI SEPAK BOLA INDONESIA" ON TRANS7

This study aims to determine students' motives for watching the sports news program “Galeri Sepak Bola Indonesia” on Trans7. This research employs the theories of uses and gratification, as well as individual differences. In this study the dimensions used include information motives, personal identity motives, integrity and social interaction motives, and entertainment motives. This study uses a quantitative approach, with research methods through surveys, the population of respondents is Stikom Interstudi students majoring in broadcasting class of 2016-2018, totaling 141 people. Using a technique of purposive sampling, we asked respondents whether they had watched the program "Galeri Sepak Bola Indonesia" on Trans7 at least twice in the previous month before they were allowed to complete out our questionnaire, thus we obtained 80 students as respondents. The results showed that the motive for watching the “Galeri Sepak Bola Indonesia” program for Stikom Interstudi students was mostly information motives, from the 4 dimensions of the existing research results.
Supplemental Material: Tracking isotopic sources of immiscible melts at the enigmatic magnetite-(apatite) deposit at El Laco, Chile, using Pb isotopes

&lt;p&gt;Supplemental Text: The Pb-Pb age calculation using the two-stage model. Table S1: Sample location (WGS-84). Table S2: Mean values of NBS 981 measurements used for mass fractionation of Pb isotopic ratios obtained using TIMS. Table S3: Uncorrected Pb isotopic ratios of the primary standard GOR-132-G. Table S4: Corrected Pb isotopic ratios of the secondary standard BCR-2G. Table S5: Common Pb calculations of the whole-rock magnetite, El Laco. Table S6: Weighted mean values and uncertainties of plagioclase, melt inclusions, and clinopyroxene analyses analyzed by SIMS. Table S7: Pb isotope composition from Neogene-Quaternary volcanoes from the Central Volcanic Zone in the Andes—literature review. Table S8: Pb isotopic composition from Ordovician–Devonian sedimentary rocks—literature review. Table S9: The slope of isochrons m and the corresponding model ages for the two-stage model. Figure S1: The cathodoluminescence (CL) and crossed-polar (CPL) photomicrographs of plagioclase phenocryst from EL Laco andesite. Figure S2: MLA analyses of LAC-AND sample. Figure S3: MLA analyses of LCO-1 sample. Figure S4: Clinopyroxene weighted mean error ellipses plotted on Figs. 5 and 6 from the main manuscript. Figure S5: Cross plots of SIMS analyses—LAC-AND sample. Figure S6: Cross plots of SIMS analyses—LCO-1 sample. Figure S7: Cross plots of SIMS analyses—LCO-9 sample.&lt;/p&gt;
LSTM Based Forecasting of PV Power for a Second Order Lever Principle Single Axis Solar Tracker

Nowadays solar power generation has significantly improved all over the world. Therefore, the power estimation of photovoltaic (PV) using weather parameters presents the future management of energy utilization in power system planning. This article presents the power forecast of the Second Order Lever Principle Single Axis Solar Tracker (SOLPSAST) system. A deep neural network is developed using Long Short Term Memory (LSTM) and is validated on sunny, cloudy and partially cloudy days. The performance of the proposed LSTM in comparison with Support Vector Machine (SVM) has improved the Mean Absolute Proportion Error (MAPE) forecasts accuracy to 4.29%, 5.16%, and 4.82% for sunny, cloudy and partially cloudy days, respectively. Also, the estimated Mean Relative Error (MRE) value of the LSTM model for sunny, cloudy and partially cloudy days is 3.19%, 4.10%, and 4.02%, respectively. Finally, the forecasted power generation of the SOLPSAST system’s monthly average and annual generation is found to be 2.45 Wh, and 29.44 kWh, respectively.
Special Issue: 2022 Research Data Access and Preservation (RDAP) Summit

2022 marked the Research Data Access and Preservation (RDAP) Summit’s second fully virtual conference, which focused on the theme of Envisioning an Inclusive Data Future. Presenters shared perspectives on new and emerging services, as well as observations of existing and prior practices, and strategies for re-envisioning these activities. The Summit built upon last year’s theme of Radical Change and Data, which encouraged presenters and attendees to consider the intersections between data and social change.
Collecting VS Sharing Personal Data: Examining the Viability of the Concepts

The proposition of a data-sharing concept deals with governing the processes of personal biometrical data interchange. The data sharing concept proposes a set of mechanisms enabling individuals (data producers) to govern the data they produce. Data sharing concepts open a series of research questions: exploring the interactions between the society stakeholders and the sharing effect on their relations; invoking distributed data storing and analysis concepts; redefining the data governance processes; introducing AI-supported data negotiating mechanisms, to name a few. Data sharing can result in a new level of social dynamics, in which individuals and organisations negotiate on each data sharing interaction and thus dynamically redefine the relationship between society’s transparency and individuals’ privacy. The proposed data sharing concept posts multiple implications for all stakeholders. Amongst them are individuals who provide new means for data self-governance, organisations and data researchers by adapting to the distributed data gathering strategies, and regulators for redesigning social frameworks and policy design processes. As the proposal induces significant disruption to the fabric of society, it poses several challenges. For the data researchers and practitioners, the insights into distributed data and analysis software design and development are provided; for social researchers, the AI-supported transdisciplinary participative research; for the managers, insight on how to organise data negotiation processes into business processes.
Application of Deep Learning Techniques to Modulation and Detection for 5G and Beyond Wireless Systems

In modern society, wireless communication systems play a very crucial role in entertainment, business, commercial, health, and safety applications. These systems keep evolving from one generation to next generation, and currently we are seeing the deployment of the fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems, which will be sixth generation (6G) of the evolution. One of the key components of 6G systems will be the use of artificial intelligence (AI) and machine learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network, and application layers, will involve AI/ML techniques. Sixth generation (6G) is a new wireless technology that many academics and researchers are embarking on. The main promises of 6G are to extend AI and ML benefits in the wireless networks and to the users. The 6G technology also will provide advances in technical metrics such as high throughput, supporting new high-demand applications, improved usage of radio-frequency bands, and many more using AI and ML techniques. One of the major ML technologies envisioned as a key technology for 6G will be deep learning (DL) because of its strong applications in achieving learning from scenarios that are more close to human. This chapter presents an up-to-date review of future wireless system concepts such as 6G and role of ML techniques in the modulation and detection of signals.
The Multi-State Inclusion-Exclusion Principle: Conventional and Improved Versions

This chapter considers an emergent variant of the classical problem of calculating the probability of the union of n events, or, more precisely, the expectation of the disjunction (ORing) of n indicator variables for these events, i.e., the probability of this disjunction being equal to one. The variant under consideration herein works with multi-valued variables, with the required probability representing the dependability of a multi-state delivery network (MSDN), whose binary system success is a two-valued function defined in terms of multi-valued component successes. The chapter discusses a simple method for dealing with the aforementioned problem in terms of a standard example MSDN, where success is defined as the disjunction of prime implicants or minimal paths of the relevant network. This method employs the multi-state inclusion-exclusion (MS-IE) principle, which is associated with a multi-state generalization of the ANDing operation's idempotency property. The method is demonstrated with a detailed symbolic example of a real-world case study, and it yields a more precise version of the same numerical value obtained previously. The example demonstrates the MS-IE method's well-known flaws and extreme inefficiency, but it also reveals the path to alternative methods in which such a flaw is (partially) mitigated. The construction of a multi-state probability-ready expression(MS-PRE)&nbsp; is a well-known example of these methods.&nbsp; Applying the MS-IE principle to the union of fewer (factored or composite) pathways that are converted (at a low cost) to PRE form is another potential approach. An innovative technique for fusing the MS-PRE and MS-IE principles together is used as a third candidate method in this article. The use of MS-PRE is limited to 'shellable' disjointing of ORed terms, and the resulting partially orthogonalized disjunctive form is subsequently subjected to MS-IE. This new approach avoids the issues brought on by either MS-PRE or MS-IE and makes the most of both of them. The identical real-world issue that was encountered with the traditional MS-IE is effectively exploited to illustrate the method.
Data Mining Approaches on EHR System

In the medicinal field, the granules of information on electronic health record (EHR) frameworks give chances to increase the care of victim by utilizing informatics recovery strategies. In any case, it is notable that numerous methodological obstructions exist in getting information inside EHRs. Specifically, clinical notes routinely put away in EHR are formed from history, profoundly unstructured, and heterogeneous biomedical content. This natural multifaceted nature blocks the capacity to perform automatic substantial restorative learning extraction undertakings without the utilization of computational semantics strategies. This is the place where data mining plays a vital role in developing procedures that best help the business knowledge and operational control essential for patients’ well-being frameworks. We showed this article to explain a systematized overview of mining systems for complete EHR modeling. We, at first, give a point-by-point perception of the genuine application regions to which EHR mining has been associated and, after that, look at the possibility of EHR data and its going with troubles. Next, we portray critical approaches used for EHR mining, the estimations identified with EHRs, and the distinctive examination plans.
A support vector machine training scheme based on quantum circuits

In order to improve the training efficiency of the support vector machine, a quantum circuit training scheme based on the inner product of the quantum state for the support vector machine is proposed in this work. Firstly, on the basis of the full analysis of the computational complexity of the classical support vector machine, the kernel function which is the main factor affecting the computational complexity of the algorithm is primarily analyzed. Based on quantum mechanics and quantum computing theory, the training sample elements in the kernel function are quantized to generate the corresponding quantum states. Secondly, according to the quantum states of the training sample elements, the types and quantities of the required quantum logic gates are derived and calculated, and the quantum circuit that can generate the corresponding quantum states of the training sample elements through the evolution of the quantum initial ground states and the quantum logic gates is designed. Then, in the light of the relationship between the inner product of the quantum state and the quantum logic gate SWAP, the quantum circuit is designed to complete the exchange operation of the corresponding quantum state amplitude. The inner product of the quantum state is realized by exchanging and evolving the amplitude of the quantum state in the quantum circuit. Finally, by measuring the quantum state of the controlling qubit, the inner product solution of the kernel function is obtained, and the acceleration effect of training support vector machine is realized. The verification results show that the scheme enables the support vector machine not only to complete the correct classification, but also to operate the quantum part of the scheme on the real quantum computer . Compared with the classical algorithm, the scheme reduces the time complexity of the algorithm for the polynomial degree, greatly shortens the training time of the model, and improves the efficiency of the algorithm. The scheme has certain feasibility, effectiveness and innovation, and expands the training idea of the support vector machine.
UAV multispectral multi-domain feature optimization for the air-to-ground recognition of outdoor injured human targets under cross-scene environment

UAV-based multispectral detection and identification technology for ground injured human targets, is a novel and promising unmanned technology for public health and safety IoT applications, such as outdoor lost injured searching and battlefield casualty searching, and our previous research has demonstrated its feasibility. However, in practical applications, the searched human target always exhibits low target-background contrast relative to the vast and diverse surrounding environment, and the ground environment also shifts randomly during the UAV cruise process. These two key factors make it difficult to achieve highly robust, stable, and accurate recognition performance under the cross-scene situation.This paper proposes a cross-scene multi-domain feature joint optimization (CMFJO) for cross-scene outdoor static human target recognition.In the experiments, we first investigated the impact severity of the cross-scene problem and the necessity to solve it by designing 3 typical single-scene experiments. Experimental results show that although a single-scene model holds good recognition capability for its scenes (96.35% in desert scenes, 99.81% in woodland scenes, and 97.39% in urban scenes), its recognition performance for other scenes deteriorates sharply (below 75% overall) after scene changes. On the other hand, the proposed CMFJO method was also validated using the same cross-scene feature dataset. The recognition results for both individual scene and composite scene show that this method could achieve an average classification accuracy of 92.55% under cross-scene situation.This study first tried to construct an excellent cross-scene recognition model for the human target recognition, named CMFJO method, which is based on multispectral multi-domain feature vectors with scenario-independent, stable and efficient target recognition capability. It will significantly improve the accuracy and usability of UAV-based multispectral technology method for outdoor injured human target search in practical applications and provide a powerful supporting technology for public safety and health.
A Study on the Crime of Unmanned Stores Using Big Data

The purpose of this study is to provide basic data and derive meaningful implications for future studies related to unmanned store crimes. For this purpose, text data was collected and refined from representative portal sites in Korea, and visualized after analysis. As a result of using various analysis techniques, effective crime prevention measures should be prepared as the role of the police is very important to prevent and respond to the increasing number of crimes against unmanned stores. In order to prevent crimes in unmanned stores, it is necessary to provide systematic education for store owners and all citizens, especially juveniles, establish a system that can only enter through self-certification even if it is inconvenient and expensive, and have a system that alerts and broadcasts crimes through artificial intelligence sensors or CCTV. In addition, a plan was proposed to actively utilize elements that can conduct patrol and defense activities by supplementing the police, such as private security companies and autonomous crime prevention units.
A Novel Approach for Enhancing QoS for Visualization of High Dynamic Range Imaging

High dynamic range (HDR) imaging has gained a lot of interest as a technology to mimic human visual features as a result of advancements in display and camera technology. Prior schemes have explored the issues of low accuracy of local image data. When lighting is poor, then the HDR images appear vague that lead to poor image analysis. Similar to it, the image distortion could deprive the image information. Hence, a novel image fusion framework is designed by combining the dense SIFT descriptor and improved guided filter. The guided filter has improvised the tone mapping process so as to preserve the edges of an image. Initially, the dense SIFT descriptor extracts the local image data from the guided images. In the case of dynamic scenes, the formation of histogram equalization, median filtering and Gaussian filtering are employed to estimate the color dissimilarity feature. The initial weights used to hybrid the Gaussian and median filters are done by image data such as contrast, brightness and the dissimilarity features. Henceforth, the adoption of improved guided filters is employed to eliminate the noise and the discontinuity of the initial weights estimation. Experimental results of the proposed techniques have shown the better results with respect to different evaluation metrics.
Localizing and Analyzing the Infographics in Document Using Deep Learning

In explaining complicated concepts, infographics are a far more effective medium of communication than regular prose. In recent years, deep learning has seen a lot of success in a range of applications requiring pattern identification and artificial intelligence. One of these applications is image recognition. There are many kinds of infographics that may be used in resumes and CVs to demonstrate the degree of competence. The objective was to identify those infographics that were already existing in the CV and to determine the word that corresponded to each infographic before attempting to measure each infographic using numeric characters. The YOLO algorithm was used to identify infographics, while OCR was utilized in order to identify associated words. The filled component of the infographic was distinguished from the unfilled area by using the image intensity histogram analysis, thresholding and contours.
PERANCANGAN PEMBANGKIT LISTRIK TENAGA SURYA ATAP DI HOTEL NOVOTEL UBUD RESORT &amp; SUITE BALI

The service quality of a hotel is supported by a sustainable supply of electrical energy.Electrical energy at the Novotel Ubud Resort &amp; Suite Bali hotel is supplied from PT. PLN.Source Power plant PT. PLN predominantly uses fossil fuels that are less environmentallyfriendly. The solution to this problem is to use renewable fuels to reduce environmental pollutionand as a source of electrical energy at the Novotel Ubud Resort &amp; Suite Bali hotel. This studyaims to design a rooftop solar power plant at the Novotel Hotel Ubud Resort &amp; Suite Bali. Theresults of the simulation of PLTS electrical energy production using the HOMER application.The feasibility of rooftop solar power plants using the NPV, PI and DPP methods. Based on thedata obtained, the Novotel Ubud Resort &amp; Suite Bali Hotel consists of five flat and pyramidroofedbuildings with a total roof area of 2723 m2. This study uses 576 solar modules with atotal power output of 236.16 kWp and uses 3 inverters with a capacity of 80 kW. The results ofthe HOMER simulation show that the annual electrical energy production from PLTS is 377,083kWh. From the feasibility of PLTS has an NPV value of Rp. 633,125,923, the PI value is 1.25and the DPP value is 13.8 years. The results of this design can be said to be feasible becausethe NPV value is positive and is able to reverse the initial cost of PLTS for 13.8 years from theassumption for 25 years.
THE VIRTUAL LEARNING DURING COVID 19 PANDEMIC: WHAT ARE STUDENTS BELIEF ON IT?

There were exceedingly rare researches in analysing students’ gender differences of internet beliefs for Indonesia context. Therefore, this study was aimed at describing students’ beliefs of the internet usage, and at gauging male and female students’ differences on the internet beliefs. This study employed 27 students in East Java (6 male and 21 female). The study data was via questionnaire and gauged in MS Excel and SPSS 25. Hence, this study used quantitative descriptive to explain students beliefs on the internet usage in learning. While measuring differences based on the gender, the researcher used Mann Whitney analysis. Results diagnosed that most of students gained positive beliefs for the internet usage in their learning. However, there were no significant differences because of significance two tailed value upper than .05. It could be assumed both gender students have in similar line beliefs on the internet usage for their learning. Fruitful suggestions are recommended, due to the pedagogic and future research aspect. For pedagogical aspect, the internet should be elaborated and integrated extensively in the learning instruments as well as learning materials to develop better process and outputs. For future research, those findings can be used to explore more for other study fields.
Pemodelan Aplikasi Pemesanan E-Tiket pendakian Gunung Berbasis Android

Ordering climbing tickets, which are getting more and more, requires a system to make it easier for climbers. Climbing activities are a hobby for climbers who like nature activities. Evidenced by the increasing number of enthusiasts of climbing activities today, booking climbing tickets is an important problem. Some climbers are willing to queue for 30 minutes and they even have to be willing to spend the night at the ticket reservation to get a climbing ticket. The solution to overcome this problem is to make an online ticket booking application. The method used in designing this application is the waterfall method. This design is made with an Android-based program so that it can be accessed anytime and anywhere. Purchasing tickets is easier because the ordering system is online. Hikers can easily see information about climbing so that it is more efficient.
Threat assessment and higher education public safety

While many of us can link the historical discussions about behavioral threat and risk assessments on college campuses to the tragic events at Virginia Tech in 2007, recent events elsewhere have surfaced the issue yet again.
Assisted discovery of optimal solution for logistics problems using ontology modelling

The logistics processes conjointly execute various physical and logical tasks. The physical tasks may concern the elements such as goods to be transported, vehicles, and human resources. Whereas, the logical tasks are generally accomplished by software units that can be fully automatic or interactive such as optimisation methods or event management, etc. The logistics problems are often complex np-hard combinatorial optimisation problems. In this paper, we define the shared conceptual vocabulary concerning the logistics problems and inherent optimisation solutions. The result of the work is a knowledge base system, which is formalised and implemented as a set of ontologies. These have been used as key components of a tool that may assist the logistics expert to identify the related optimisation methods to solve the concerned problem and eventually list the available corresponding web-services implementing them.