Token Model Research Articles

Upaya Meningkatkan Hasil Belajar Peserta Didik pada Mata Pelajaran PAI melalui Cooperative Learning Model Time Token

This research started from the problem of low PAI learning activity among class IV students at SDN 3 Sidawangi, Cirebon Regency. The aim of this research is to describe the process of implementing the Cooperative Learning Time Token learning model in PAI learning and to describe the increase in student learning outcomes in learning after implementing the model. The type of research used is classroom action research which consists of 2 cycles, where each cycle applies the Kurt Lewin model of action research design in the form of planning, implementation, observation and reflection. Data collection was carried out by observation and written tests. The research results show that the Cooperative Learning Time Token model can improve PAI learning outcomes for class IV students at SDN 3 Sidawangi, Cirebon Regency. This is proven by the value of learning outcomes which experienced an increase in completeness from 33.33% in the pre-cycle stage, to 74.07% in Cycle 1, and 81.48% in cycle 2. This shows that the Cooperative Learning Time Token model is a model appropriate for improving student learning outcomes in PAI learning for class IV students at SDN 3 Sidawangi, Cirebon Regency.

Ability to Write Poetry Texts for Class X Students at Al Washliyah Pasar Senen High School Using the Time Token Model

This research aims to determine the effect of the Time Token learning model on the ability to write poetry texts in class X of SMA Swasta Al Washliyah Pasar Senen for the 2023/2024 academic year. The population and sample in this study were all students of class X IPA IPS, totaling 24 students with the Time Token model. This research uses a one group pretest-posttest design experimental method using a quantitative approach. The instruments used in this research are the building blocks of writing poetry texts which consist of diction, theme, rhyme/rhythm, figurative language (figure of speech), and message. The data analysis techniques used are analysis requirements tests, normality tests, and hypothesis tests. The research results before using the Time Token model were 62.8 and classified as sufficient. Meanwhile, the research results after using the Time Token model were 73 and classified as good. Based on the results of the hypothesis test, it was found that tcount > ttable, namely 0.0107 > 0.337, so the hypothesis (Ho) was rejected and the alternative hypothesis (Ha) was accepted. This proves that there is a significant influence of using the Time Token model on the ability to write poetry in class X SMA Swasta Al Washliyah Pasar Senen Tahun Ajaran 2023/2024.

Time token model and wordwall interactive game in learning: effectiveness on communication and collaboration skills

The learning in Biology Education Study Program at Sembilanbelas November Kolaka University, which was specifically designed to hone students' communication and collaboration skills, was considered not optimal. Therefore, a study was conducted to determine the effectiveness of integrating the time token learning model and wordwall application-based interactive games on student communication and collaboration skills. This research is a pre-experimental type with a one-group treatment design. The population was 144 students’, and 24 samples were obtained through the purposive sampling technique. The research data was collected through observation techniques with communication and collaboration skills observation sheets as instruments. Data were analyzed descriptively and inferentially followed by the N-Gain test. The results showed that the average scores for communication and collaboration skills before and during treatment were respectively 45.31 and 78.53; 42.19 and 86.64. The results of the paired sample t-test all show the sig. 0.000 < 0.05. The results of the N-Gain test on communication skills were 61.93% (effective enough category) and collaboration skills were 78.75% (effective category). Thus, it can be concluded that integrating time token learning models and interactive games based on wordwall applications effectively improves students' communication and collaboration skills.

HuaBaseChain: An Extensible Blockchain With High Performance

Blockchain has been extensively used on the Internet of Things (IoT). However, several problems exist in current blockchain systems that limit their usability in IoT networks. Firstly, Proof of Work (PoW) consensus most used in current blockchain systems generates excessive energy consumption. Secondly, the single digital token system limits the development of blockchain in multiple IoT applications. Lastly, the large amount of data generated by IoT devices and stored in blockchains has huge storage requirements. To deal with these problems, we propose HuaBaseChain – an extensible blockchain with high performance. HuaBaseChain has three features. Firstly, HuaBaseChain uses a novel Proof of Participation (PoP) consensus to reduce energy consumption. Secondly, HuaBaseChain extends the single digital token model to a multi-dimensional digital tokens model, enabling more varied applications that can be tailored to the needs of IoT device managers. Thirdly, HuaBaseChain devises an underlying Merkle forest data structure to reduce storage requirements. HuaBaseChain is suited for IoT applications that contain a variety of IoT devices and generate massive amounts of data. We demonstrate the efficiency of HuaBaseChain on IoT datasets and the significantly larger Bitcoin dataset. Our experiments show that HuaBaseChain runs efficiently, with significantly reduced energy consumption, greater efficiency, lower storage requirements and higher query speed.

IMPROVING THE FIFTH-GRADE ELEMENTARY STUDENTS’ LEARNING OUTCOMES IN INTEGRATED THEMATIC LEARNING ON THEME 6 USING TIME TOKEN MODEL

This paper is based on research about learning in which the students still obtain learning from the teachers only and the students did not contribute during the learning process mostly. It is because the teacher did not implement innovative, creative, and diverse models. The research aims to describe how Integrated Thematic learning assisted by Time Token model in Class V at SDN 16 Enam Lingkung in Padang Pariaman can improve students’ learning outcomes. The research approach is Classroom Action Research. The research subjects involved 18 teachers and students in class V. Data collection was carried out through observations, documents, tests, and non-tests. The research was conducted in two phases, Cycle I and II. In Cycle I, according to the lesson plan's findings, the average percentage was 83.33% (excellent), conversely, in Cycle II, the average percentage was 94.44%. (excellent). In Cycle I, according to the teacher, the average percentage was 82.81% (good), conversely, in Cycle II, the average percentage was 93.75%. (excellent). Students' average learning outcomes in Cycle I were 75.9% and the student’s average learning outcomes in Cycle II were 89.13%. Thus, the Time Token technique in integrated thematic learning indicates students' learning outcomes produce an improvement.

Indonesian Hate Speech Detection Using Bidirectional Long Short-Term Memory (Bi-LSTM)

Abstract 
 Social media is a platform that allows users to express themselves freely including spreading hate speech content. The government has issued the regulation in the UU ITE to handle and prevent hate speech on social media. The research was also conducted using the Bi-LSTM to classify the text into hate speech or not. Another research was purposed to detect hate speech and its categories using Bi-GRU. However, the performance of the model Bi-GRU is still lower than Bi-LSTM with an accuracy of 86.44% and 96.44%. Therefore, this study aims to build a model that can detect hate speech and its categories. The research offers Bi-LSTM as a classification model and IndoBERT as a tokenization model. The dataset used is a public dataset containing 13 thousand tweets. As a result, the best model obtained is using 20 epochs, 192 batch sizes, 1 layer Bi-LSTM with 40 nodes, and applying class weighing in the optimization process. The pre-train model from IndoBERT that is used to support the performance of the model in classifying is "indobenchmark/indobert-large-p2". The performance given by the purposed model is very good with an average accuracy, precision, and recall of 97.66%, 96.50%, and 85.25%.

Language-Independent Text Tokenization Using Unsupervised Deep Learning

Languages–independent text tokenization can aid in classification of languages with few sources. There is a global research effort to generate text classification for any language. Human text classification is a slow procedure. Consequently, the text summary generation of different languages, using machine text classification, has been considered in recent years. There is no research on the machine text classification for many languages such as Czech, Rome, Urdu. This research proposes a cross-language text tokenization model using a Transformer technique. The proposed Transformer employs an encoder that has ten layers with self-attention encoding and a feedforward sublayer. This model improves the efficiency of text classification by providing a draft text classification for a number of documents. We also propose a novel Sub-Word tokenization model with frequent vocabulary usage in the documents. The Sub-Word Byte-Pair Tokenization technique (SBPT) utilizes the sharing of the vocabulary of one sentence with other sentences. The Sub-Word tokenization model enhances the performance of other Sub-Word tokenization models such pair encoding model by +10% using precision metric.

Legal Insights on Token Model

Legal Insights on Token Model

Application Possibilities of Tokens on an E-Learning Platform

The goal was to develop a concept for the useful application of tokens in the learning platform of the Blockchain Academy Mittweida (BCAM). Thereby, imparted knowledge about the blockchain should become tangible for users. Based on the research of existing concepts, different ideas were developed and their use was tested. In the process, the specifics of the e-learning sector as well as those of blockchain technology are considered. Combining tokenization, e-learning, and gamification, a token model was finally developed, which is technically based on a smart contract of the Ethereum Blockchain. The model includes tokens that users can collect and exchange for badges. The badges are in turn represented as NFTs.

Large-Scale Distributed Training of Transformers for Chemical Fingerprinting.

Transformer models have become a popular choice for various machine learning tasks due to their often outstanding performance. Recently, transformers have been used in chemistry for classifying reactions, reaction prediction, physiochemical property prediction, and more. These models require huge amounts of data and localized compute to train effectively. In this work, we demonstrate that these models can successfully be trained for chemical problems in a distributed manner across many computers—a more common scenario for chemistry institutions. We introduce MFBERT: Molecular Fingerprints through Bidirectional Encoder Representations from Transformers. We use distributed computing to pre-train a transformer model on one of the largest aggregate datasets in chemical literature and achieve state-of-the-art scores on a virtual screening benchmark for molecular fingerprints. We then fine-tune our model on smaller, more specific datasets to generate more targeted fingerprints and assess their quality. We utilize a SentencePiece tokenization model, where the whole procedure from raw molecular representation to molecular fingerprints becomes data-driven, with no explicit tokenization rules.

Everlasting UC Commitments from Fully Malicious PUFs

Everlasting security models the setting where hardness assumptions hold during the execution of a protocol but may get broken in the future. Due to the strength of this adversarial model, achieving any meaningful security guarantees for composable protocols is impossible without relying on hardware assumptions (Müller-Quade and Unruh, JoC’10). For this reason, a rich line of research has tried to leverage physical assumptions to construct well-known everlasting cryptographic primitives, such as commitment schemes. The only known everlastingly UC secure commitment scheme, due to Müller-Quade and Unruh (JoC’10), assumes honestly generated hardware tokens. The authors leave the possibility of constructing everlastingly UC secure commitments from malicious hardware tokens as an open problem. Goyal et al. (Crypto’10) constructs unconditionally UC-secure commitments and secure computation from malicious hardware tokens, with the caveat that the honest tokens must encapsulate other tokens. This extra restriction rules out interesting classes of hardware tokens, such as physically uncloneable functions (PUFs). In this work, we present the first construction of an everlastingly UC-secure commitment scheme in the fully malicious token model without requiring honest token encapsulation. Our scheme assumes the existence of PUFs and is secure in the common reference string model. We also show that our results are tight by giving an impossibility proof for everlasting UC-secure computation from non-erasable tokens (such as PUFs), even with trusted setup.

Redeemable Platform Currencies

AbstractCan massive online retailers such as Amazon and Alibaba issue digital tokens that potentially compete with bank debit accounts? There is a long history of trading stamps and loyalty points, but new technologies are poised to sharply raise the significance of redeemable assets as a store of value. Here, we develop a simple stylized model of redeemable tokens that can be used to study sales and pricing strategies for issuing tokens, including ICOs. Our central finding is that platforms can potentially earn higher revenues by making tokens non-tradable unless they can generate a sufficiently high outside-platform convenience yield.

Blockchain Token Model for Supply Chain Financing of SMMEs

Financing difficulties are common among the small-, medium- and micro-enterprises (SMMEs). Although supply chain financing alleviates the problems of SMMEs, such as narrow financing channels, intractable financing and expensive financing, however, due to the centralised storage and management of data, the authenticity of data cannot be guaranteed. The credit of the core enterprises in the supply chain cannot penetrate the SMMEs in upstream and downstream. This paper establishes a blockchain pass-through model for supply chain financing by improving the PBFT consensus algorithm based on blockchain’s decentralised and tamper-evident characteristics and the pass-through of SMMEs’ assets in the supply chain. The model improves the circulation efficiency of the supply chain; moreover, it enables the credit of core enterprises to the upstream and downstream, solving the financing dilemma of SMMEs.

Redeemable Platform Currencies

Can massive online retailers such as Amazon and Alibaba issue digital tokens that potentially compete with bank debit accounts? There is a long history of trading stamps and loyalty points, but new technologies are poised to sharply raise the significance of redeemable assets as a store of value. Here we develop a simple stylized model of redeemable tokens that can be used to study sales and pricing strategies for issuing tokens, including ICOs. Our central finding is that platforms can generally earn higher revenues by making tokens non-tradable unless they can generate a sufficiently high outside-platform convenience yield.

Modelling the simultaneous encoding/serial experience theory of the perceptual moment: a blink of meta-experience.

One way to understand a system is to explore how its behaviour degrades when it is overloaded. This approach can be applied to understanding conscious perception by presenting stimuli in rapid succession in the ‘same’ perceptual event/moment. In previous work, we have identified a striking dissociation during the perceptual moment, between what is encoded into working memory [Lag-1 sparing in the attentional blink (AB)] and what is consciously perceived (Lag-1 impairing in the experiential blink). This paper links this dissociation to what, taking inspiration from the metacognition literature, could be called meta-experience; i.e. how the ability to track and comment on one’s visual experience with subjectivity ratings reflects objective performance. Specifically, we provide evidence that the information (in bits) associated with an encoding into working memory decouples from the experiential reflection upon that perceptual/encoding event and that this decoupling is largest when there is the greatest perceptual overload. This is the meta-experiential blink. Meta-experiential self-observation is common to many computational models, including connectionist interpretations of consciousness, Bayesian observers and the readout-enhanced simultaneous type/serial token (reSTST) model. We assess how our meta-experiential blink data could be modelled using the concept of self-observation, providing model fits to behavioural and electroencephalogram responses in the reSTST model. We discuss the implications of our computational modelling of parallel encoding but serial experience for theories of conscious perception. Specifically, we (i) inform theories of Lag-1 sparing during the AB and (ii) consider the implications for the global workspace theory of conscious perception and higher-order theories of consciousness.

A non-fungible token model for tracking emissions in the fuel value chain

A non-fungible token model for tracking emissions in the fuel value chain

IMPROVING STUDENT'S SPEAKING SKILLS THROUGH THE TIME TOKEN CLASS IV MODEL IN SDN 11 BONGOMEME, GORONTALO DISTRICT

The purpose of this study was to improve students' speaking skills through the Time Token model in the fourth grade of SDN 11 Bongomeme. This type of research is classroom action research. This study uses 2 cycles. Data collection techniques include tests, observations and documentation. The results showed that in the first cycle of the 17 students who were subjected to the action, there were 9 students or 53% who were able to speak. In the second cycle it increased to 15 students or 88% who were able to speak and there were still 2 students or 12% who were not able to speak. Based on the results of the study, it was concluded that through the time token model, the fourth grade students' speaking ability at SDN 11 Bongomeme, Gorontalo Regency increased.

SMILES Pair Encoding: A Data-Driven Substructure Tokenization Algorithm for Deep Learning.

Simplified molecular input line entry system (SMILES)-based deep learning models are slowly emerging as an important research topic in cheminformatics. In this study, we introduce SMILES pair encoding (SPE), a data-driven tokenization algorithm. SPE first learns a vocabulary of high-frequency SMILES substrings from a large chemical dataset (e.g., ChEMBL) and then tokenizes SMILES based on the learned vocabulary for the actual training of deep learning models. SPE augments the widely used atom-level tokenization by adding human-readable and chemically explainable SMILES substrings as tokens. Case studies show that SPE can achieve superior performances on both molecular generation and quantitative structure-activity relationship (QSAR) prediction tasks. In particular, the SPE-based generative models outperformed the atom-level tokenization model in the aspects of novelty, diversity, and ability to resemble the training set distribution. The performance of SPE-based QSAR prediction models were evaluated using 24 benchmark datasets where SPE consistently either did match or outperform atom-level and k-mer tokenization. Therefore, SPE could be a promising tokenization method for SMILES-based deep learning models. An open-source Python package SmilesPE was developed to implement this algorithm and is now freely available at https://github.com/XinhaoLi74/SmilesPE.

Utility Token Design

This paper analyzes a dynamic general equilibrium model of cryptographic utility tokens. The tokens are native to a proof-of-stake blockchain and serve a two-fold role. On the one hand, tokens are paid out to validators for consensus provision. On the other hand, tokens are required by blockchain users to access the services offered on the secured chain. Validators and users repeatedly interact in a spot market. There is an inefficiency at the extensive margin because users need to be given some rent in equilibrium, yet there no inefficiency at the intensive margin: given operation, the blockchain platform always chooses the socially optimal value of services. Further, I formulate a utility token trilemma. Any equilibrium features at most two of the following: (i) bonding of tokens used for service consumption, (ii) price stability, (iii) constant value of the provided services. The results have implications both for antitrust and for practical token market design.

Effect of Time Token Model Containing Science Literacy on Students’ Learning Competencies at Senior High School in Batusangkar

Research on the problems that exist in Batusangkar 1 Senior High School, student learning competencies are still low. The efforts that can be done are to implement a time learning model that contains scientific literacy. The purpose of this study was to determine the effect of the time token model with scientific literacy on the learning competency of students' in Batusangkar 1 Senior High School. This type of research was experimental research with a randomized control group posttest only design. The population in this study were all class X MIPA Batusangkar 1 senior high school registered in the 2018/2019. Sampling was done using Purposive Sampling techniques. X MIPA 2 as the experimental class and X MIPA 1 as the control class. The instruments used were in the form of posttest questions for knowledge competencies, observation sheets for attitude and skills competencies. Based on the results of the study using the t-test it can be concluded that the knowledge competencies of students t count 3.41> t table 1.67, the attitudes competency t count 5.95> t table 1.67, and the skills competency tcount 2.34 > t table 1.67. it shows that the hypothesis was accepted. So, the application of the time token model with scientific literacy can improve the competency of knowledge, attitudes, and skills of class X students of senior high school 1 Batusangkar