Reviewerly/exHarmony huggingface dataset:
Perancangan Sistem Akuntansi Penyusutan Aset Inventaris Politeknik Jambi

Politeknik Jambi is the only vocational college in Jambi Province. The process of collecting inventory assets is not optimal when searching for data because there is no calculation of depreciation on inventory assets that are still manual. With that in order to be optimal, it is recommended to use an inventory asset depreciation accounting system. The purpose of this inventory asset depreciation accounting system is to overcome problems that arise in the current system at the Politeknik Jambi and to be a good solution for universities to create a faster and more accurate system in managing inventory assets. Methods of data collection is done by means of interviews, observation, documentation and analysis of the system used by the company.
Combining Think Pair Share Model with Digital Media to Enhance Students’ Basic Grammar Mastery: Is It Effective?

Grammar is an important aspect of learning English and any language. However, for some students, mastering English grammar is considered arduous as it requires students to understand plenty of rules, and this makes them desperate to learn. This study aims to find out whether Think Pair Share method combined with digital media may enhance the students’ grammar mastery at UPT SMPN 1 Binamu. This study was a pre-experimental study where the data was obtained from a basic grammar test. The population in this study were students of class VIII.I at UPT SMPN 1 Binamu for the academic year of 2022/2023. The results showed that the average score of students' grammar mastery consisting of pre-test and post-test, namely Pre-test was 18.75 which was categorized as "Very Poor and Poor" while post-test was 87,25 which is categorized as "Excellent and Very Good". This shows that the average value of the pre-test and post-test increased by 68.5 points. The t-test analysis showed that by applying a significant level of 0.05 with a degree of freedom of 23 and a p-value (sig .2-tailed) is 0.000 indicating that there is a significant difference between the pre-test and post-test results of the students. In other words, the result (sig.2-tailed) of the students' post-test is not greater than the significance level (0.000&lt; 0.05).
Ontology-Based APT Attack Detection and Defense Countermeasures

Cyberspace security has risen to the height of national strategies, represented by APT attacks, threatening important industries in various countries. In order to ensure cyberspace security, it is urgent to develop situational awareness of APT attacks. In view of the multi-step attack characteristics of APT, this paper proposes a series of questions that need to be answered in cyberspace threat situational awareness, and constructs the APT attack ontology. And through the representation and reasoning of a case, the consistency and effectiveness of the APT attack ontology are verified. It is proved that the ontology can realize the automatic correlation and reasoning of APT attack multi-source intelligence, find the weaknesses and loopholes exploited by each step of the attack, and deduce the corresponding mitigation measures. At the same time, we will find the causal relationship between various weaknesses and mitigation measures, and provide support for the auxiliary decision-making of country's cyberspace asset protection.
Exploring Explicit Coarse-Grained Structure in Artificial Neural Networks

We propose to employ a hierarchical coarse-grained structure in artificial neural networks explicitly to improve the interpretability without degrading performance. The idea has been applied in two situations. One is a neural network called TaylorNet, which aims to approximate the general mapping from input data to output result in terms of Taylor series directly, without resorting to any magic nonlinear activations. The other is a new setup for data distillation, which can perform multi-level abstraction of the input dataset and generate new data that possesses the relevant features of the original dataset and can be used as references for classification. In both the cases, the coarse-grained structure plays an important role in simplifying the network and improving both the interpretability and efficiency. The validity has been demonstrated on MNIST and CIFAR-10 datasets. Further improvement and some open questions related are also discussed.
A study on financial innovation and investment in India

The literature on financial innovation from a wide variety of disciplines financial economics, history, law, and artificial association. Fiscal invention - like invention away in business — is > an ongoing process whereby private parties trial to try to separatetheir products and services, re sponding to both unanticipated and regular changes in the frugality. Surely, invention ebbs and flows with some epochs flaunting bursts of exertionand others witnessing a slackening or indeed counterreaction. Still, when seen from a distance, the process of invention — in this case, fiscal invention — is a regular ongoingpart of a profit maximizing frugality. This paper will cover the recent trends in fiscal invention with reference to Indian script. Our attempt is to find out the Impact of GDP, Growth Rate(NIFTY) & Affectation on the fiscal invention revolution in India.
STUDY OF A QUANTUM COMPUTING SYSTEM AND IMPLEMENTATION OF A QUANTUM CORE ON FPGA

The quantum core method is one of the most important methods in quantum machine learning.However, the number of features used for quantum nuclei is limited to a few dozen features.The block product state structure is used as a quantum feature map and the implementation ofprogrammable gate matrices is demonstrated. The relevance of these studies lies in the mathematicaland software modeling and implementation of a quantum computing system as part of thedevelopment of the implementation of a quantum core on FPGA for solving classes of problems ofa classical nature. The scientific novelty of this research area is the development of a hybrid simulatorof the quantum cores of a central processing unit (CPU) and a programmable logic integratedcircuit (FPGA) several orders of magnitude faster than a conventional quantum computingsimulator. This joint development of the implemented quantum core and its efficient FPGA implementation allowed numerical simulation of the quantum core based on gates in terms of inputfeatures, up to 780-dimensional features using 4000 samples. We applied the quantum kernel toimage classification problems using the Fashion-MNIST dataset and showed that the quantumkernel is comparable to Gaussian kernels with optimized throughput. The analysis of the work inthis field has shown that a new qualitative level has now been reached, opening up promising opportunitiesfor the implementation of multi-qubit quantum computing. The prospects for implementationand development are connected not only with technological capabilities, but also with solvingthe issues of building effective quantum systems for solving actual mathematical problems,cryptography problems and control (optimization) problems.
SENTIMENT ANALYSIS OF TEXT REVIEWS USING TONE DICTIONARIES AND FUZZY SET CARDINALITY

Sentiment or opinion analysis aims to determine the polarity of people's opinions in relationto any product, service, event or any person. One of the most common methods used in sentimentanalysis of text content is natural language processing. Sentiment analysis of natural languagetext can be assessed using numerous methodologies such as machine learning algorithms andstatistical tools, while the application of fuzzy logic is not common. The use of fuzzy logic waschosen for the following reasons. First, fuzzy logic handles linguistic uncertainty well. This way ofdefining the problem leads to a reduction in bias, both positively and negatively. Secondly, learn ing approaches based on fuzzy rules are fundamentally different from those learning approachesthat are widely used in sentiment classification, such as support vector machines, naive Bayes,etc., as they relate to generative learning, i.e. i.e. the goal of learning is to assess the degree towhich an instance belongs to each individual class. The proposed model for sentiment analysis oftext reviews is based on the use of tone lexicons using fuzzy logic and consists of four main stages.The steps include tokenization, word bag model formulation, sentiment fuzzy score formulation,and polarity assignment. In the proposed model, the power of the fuzzy set is used as a measure ofthe evaluation of the indicators of the polarity of words. Word polarity values are obtained byapplying two sentiment lexicons: SentiWordNet and AFINN. Two versions of the model were createddepending on the type of vocabulary used: based on SentiWordNet and AFINN. Comparisonof the presented approach based on fuzzy logic with other dictionary-based methods demonstratesthe superiority of the developed models based on the application of fuzzy logic.
METHOD FOR MEASURING THE GAS-SENSITIVE CHARACTERISTICS OF SEMICONDUCTOR SENSORS BY DYNAMIC RESPONSE PARAMETERS

Detection of dangerous gases and vapors is relevant both at work and in everyday life. Sensorelements based on semiconductor structures are highly sensitive to gases of a very differentnature. To increase the accuracy of measurements, calibration and the speed of concentrationdetermination, special methods are required, such as signal processing in various ways. The studyuses gas sensors based on silicon–carbon films that are sensitive to a whole set of gases. In thefirst part of the article, general problems and a method for solving them are considered, whichmakes it possible to increase the selectivity of semiconductor gas sensors. The analysis of dynamicparameters, such as the first and second derivatives of the response curves, as well as the analysisof the Elovich equation is carried out. Such calibration dependences, constructed from the extremesof the derivatives and the slope coefficients of the Elovich equation, show high linearity.As a promising solution, it is proposed to use a set of calibration lines to determine the concentrationof the target gas. The developed method allows, using dynamic response parameters, to determinethe gas type and its concentration using a single sensor, also to increase the accuracy ofmeasurements, along with a reduction in detection time. The experimental results of data processingwith the determination of the gas type and its concentration using the described methodare presented. For a sensor based on silicon-carbon materials, the developed method and algorithmsmade it possible to carry out measurements using a single sensor element for a set of gases(NO2, CO, SO2) with different concentrations. At the same time, the smallest relative error did notexceed 3.6% for SO2, 2.7% for NO2, 2% for CO. The distinctive features of the developed methodare the use of several calibration lines in a multidimensional space, rather than one, as well as anoriginal algorithmic signal processing methods.
Research on the Impact of the Metaverse on the Future of Social Networking

As a technological concept that promises to seamlessly connect the real and virtual worlds, the concept of the Meta universe has been hotly debated by a large number of Internet users even before it was implemented. Considering the particularity of this concept, it is not difficult to imagine that this new industry, once launched, will have a near revolutionary impact on the entire social network. This paper will list case studies and questionnaires as survey methods. This article summarized the industry’s status and investigated regional opinions about the Meta Universe, which gives an industry profile to the relevant company or the company that intended to participate in the production of Meta universe. Thus, they can decide the direction of development and which strategy to utilize in the program. In addition, some suggestions for potential problems in the metaverse field in the future, the aspects that need to be paid attention to for the anti-addiction mechanism and legal regulation are all includ in this paper for the reference of relevant enterprises, so as to bring better user experience for the masses.
A compositional intersective account of Heterofunctional Coordination

The analysis presented in this paper extends the uniform intersective (“boolean”) treatment of conjunctive coordinators to Heterofunctional Coordination (HC), i.e., coordination of different grammatical functions. A compositional account of HC based on mainstream derivational syntax is proposed, one that makes Champollion’s (2015) “quantificational event semantics” compatible with derivational syntax. The analysis is based on the assumption, common in Minimalism, that traces of moved quantifiers denote domain restrictions rather than just variables.
Extracting Statistical Properties of Solar and Photovoltaic Power Production for the Scope of Building a Sophisticated Forecasting Framework

Building a sophisticated forecasting framework for solar and photovoltaic power production in geographic zones with severe meteorological conditions is very challenging. This difficulty is linked to the high variability of the global solar radiation on which the energy production depends. A suitable forecasting framework might take into account this high variability and could be able to adjust/re-adjust model parameters to reduce sensitivity to estimation errors. The framework should also be able to re-adapt the model parameters whenever the atmospheric conditions change drastically or suddenly—this changes according to microscopic variations. This work presents a new methodology to analyze carefully the meaningful features of global solar radiation variability and extract some relevant information about the probabilistic laws which governs its dynamic evolution. The work establishes a framework able to identify the macroscopic variations from the solar irradiance. The different categories of variability correspond to different levels of meteorological conditions and events and can occur in different time intervals. Thereafter, the tool will be able to extract the abrupt changes, corresponding to microscopic variations, inside each level of variability. The methodology is based on a combination of probability and possibility theory. An unsupervised clustering technique based on a Gaussian mixture model is proposed to identify, first, the categories of variability and, using a hidden Markov model, we study the temporal dependency of the process to identify the dynamic evolution of the solar irradiance as different temporal states. Finally, by means of some transformations of probabilities to possibilities, we identify the abrupt changes in the solar radiation. The study is performed in Guadeloupe, where we have a long record of global solar radiation data recorded at 1 Hertz.
ANALISIS DAN PERANCANGAN SISTEM PRESENSI MAHASISWA BERBASIS TEKNOLOGI PENGENALAN WAJAH DI FAKULTAS ILMU KOMPUTER UPN VETERAN JAWA TIMUR

Recording student attendance in classes at the National Development University "Veteran" East Java is currently still done manually. This is considered less efficient. With the rapid development of today's technology, attendance systems that utilize biometrics should be created and developed. In this research paper, the researcher presents the proposed design of a facial recognition-based biometric presence system. The research method used is literature study, current system analysis, system requirements analysis, and system design. The learning method used is Convolutional Neural Network (CNN) by utilizing the OpenCV and tensorflow libraries. The implementation of the system will utilize CCTV surveillance cameras that are in the class of the Computer Science Faculty of UPN "Veteran" Jawa Timur.
Sentiment Analysis and Opinion Mining Applied to Scientific Paper Reviews

Abstract: Sentiment analysis and opinion mining is an area that has experienced considerable growth over the last decade.Todo this, natural language techniques and machine learning algorithms are used. This research attempts to determine the feelings, opinions, emotions, among other things, of people on something. A hybrid approach that combines an unsupervised machine learning algorithm with techniques from natural lan- guage processing is proposed to analyze reviews. The first aimof this analysis is to automatically determine the orientation of a review.This article discusses the problem of extracting sentiment and opinions from a collection of reviews on scientific articles conducted under an international conference on computing in northern Chile.
Arabic Location Named Entity Recognition for Tweets using a Deep Learning Approach

Social media sites like Twitter have emerged in recent years as a major data source utilized in a variety of disciplines, including economics, politics, and scientific study.To extract pertinent data for decision-making and behavioral analysis, one can use Twitter data.To extract event location names and entities from colloquial Arabic texts using deep learning techniques, this study proposed Named Entity Recognition (NER) and Linking (NEL) models.Google Maps was also used to obtain up-to-date details for each extracted site and link them to the geographical coordination.Our method was able to predict 40% and 48% of the locations of tweets at the regional and city levels, respectively, while the F-measure was able to reliably identify and detect 63% of the locations of tweets at a single Point of Interest.
Research on Key Technologies of Smart City Building Interior Decoration Construction based on In-Depth Learning

The intelligentization of building interior decoration construction is of great significance to the construction of smart city, and robot automation has brought an opportunity for this.Robot self-decoration is the development trend in the future.One of the key issues involved, is the self-planning of mobile path.In this regard, the research adopts the proximal policy optimization algorithms (PPO) to improve the self-planning path ability of the decoration robot.For the information of lidar and robot status, the Full Connect Neural Network (FCNN) is used to process it.In addition, the reward function and the corresponding Credit Assignment Problem (CAP) model are designed, to accelerate the learning process of path planning.Aiming at the dynamic uncertainty in the actual environment, the adaptive loss function is used to build an auxiliary model to predict the environmental change.The simulation results show that the research and design strategy significantly improves the learning efficiency and path planning success rate of the decoration robot, and shows good adaptability to the dynamic environment, which has important reference significance for the practical application of the decoration robot.
Probabilistic resumable quantum teleportation for an arbitrary two-qubit entangled state with different quantum channels

Abstract We propose two probabilistic resumable quantum teleportation schemes for an arbitrary two-qubit entangled state with different quantum channels. The characteristic of our schemes is that the initial state in the sender’s hand can be recovered instead of being destroyed when teleportation fails, so we can improve the success probabilities by repeating the teleportation process more times. The analysis results show that the number of repetitions required for successful teleportation depends on the entanglement strength of the quantum channels, and even weak entanglement enables a high probability of success.
Joint transformer progressive self‐calibration network for low light enhancement

Abstract When the lighting conditions are poor and the environmental light is weak, the image captured by the imaging device often has lower brightness and is accompanied by a lot of noise. The paper designs a progressive self‐calibration network model (PSCNet) for recovering high‐quality low‐light‐enhanced images. First, shallow features in low‐light images can be better focused and extracted with the help of attention mechanism. Next, the feature mapping is passed to the encoder and decoder modules, where the transformer and encoder‐decoder jump connection structures can be better combined with the semantic information of the context to learn rich deep feature information. Finally, the self‐calibration module can adaptively cascade the features decoded by the decoder and input them into the residual attention module quickly and accurately. Meanwhile, the LBP features of the image are also fused into the feature information of the residual attention module to enhance the detailed texture information of the image. Qualitative analysis and quantitative comparison of a large number of experimental results show that this method outperforms existing methods.
Pajjappi dalam Pengobatan Tradisional Bugis di Desa Mattaropurae Kabupaten Bone

This study aims to examine the meaning of Pajjappi which is used in Bugis traditional medicine in Mattaropurae Village, Bone Regency by using heuristic and hermeneutic theories. The type and design of the research is descriptive qualitative research. The data in this study is Pajjappi which is used in Bugis traditional medicine in Mattaropurae Village. Riffaterre's Semiotic Theory is used to dissect the Pajjappi text to describe the meaning contained in Pajjappi. The results of this study found eight Pajjappi or healing mantras, namely, pajjappi peddi eppong, pajjappi olong, pajjappi polo, pajjappi nanre api, pajjappi bitong, pajjappi semmeng, pajjappi kasuwiang and pajjappi peddi eppong based on heuristic reading, there are several different meanings of Pajjappi texts if they have different meanings. interpreted word and if interpreted sentence. The process of reading the first level is done with sentence refinement to find out the heuristic meaning of the Pajjappi text. Hermeneutic reading on Pajjappi helps to find meaning in the text. Sanro made a communication or request to Allah SWT. via pajjappi. The results of data analysis showed that eight pajjappi or healing mantras were found to describe a request for healing and protection to Allah SWT.&#x0D;
Uniswap v2

Uniswap v2 is a decentralized exchange based on a system of smart contracts on the Ethereum blockchain and other networks (Polygon, Optimism, Arbitrum). It is formed by liquidity pools that enable automated market making; that is, they enable traders to buy and sell assets against the protocol without the need for a third party. Each Uniswap v2 liquidity pool consists of reserves of two ERC-20 tokens deposited by liquidity providers, who benefit from the fees that the protocol charges to traders. The collected fees are shared proportionally among all liquidity providers.
Цифровая грамотность как средство от инфодемии Covid-19 в Индонезии (Digital Literacy as an Antidote to Covid-19 Infodemic in Indonesi)

Since the beginning of the pandemic, incorrect information or fake news related to the Covid-19 spread out uncontrollably and caused panic and unrest in the community. This is in line with the report from Ministry of Communication and Information of Republic Indonesia that shown as February 2022, there is 2121 fake news related to Covid-19 or infodemic circulated in social media in Indonesia. For this reason, the need for the role of digital literacy in preventing and anticipating infodemic discussed in this paper. Using qualitative research approach with library research methods, it was found that the need of digital literacy to raise awareness of selecting and checking the sources of information, training critical thinking to every information, the importance of developing skills to identify incorrect information, and correcting infodemic in the society.
Improving the methodology of teaching the topic of nonmetal of the 8th grade in the e-learning environment

The use of pedagogical technologies in the process of teaching chemistry has a positive effect on the effectiveness of teaching. Pedagogical technologies can be applied to the process of teaching these subjects in order to increase the level of students’ knowledge and increase their interest in the lesson in the process of teaching halogens in inorganic chemistry.