Reviewerly/exHarmony huggingface dataset:
Comparison of Stemming Test Results of Tala Algorithms with Nazief Adriani in Abstract Documents and National News

The existence of information is undeniably needed by many people. This statement describes the increasing importance of information and the corresponding increase in the need for access to relevant documents and literature. The contents of the information derived from these documents are then sorted to make their meaning more understandable. This sorting process is known as stemming. Stemming is a process that is widely applied in basic word searches. Separating meaningless words can make information clearer. It is necessary to pay attention to the appropriate stemming algorithm according to the language used. Many stemming algorithms can be used to perform this basic word search process. Some of them are the Tala and Nazief Adriani algorithms. The two algorithms have differences in their work processes. The Tala algorithm adopts a rule-based Porter algorithm, while the Nazief &amp; Adriani algorithm works based on a dictionary. The two algorithms have their respective advantages in terms of accuracy and speed. Therefore, in this study, an analysis will be carried out by comparing the performance of the two algorithms in the Indonesian language text-stemming process. The trial process uses several different data sources to measure the speed and accuracy of each algorithm. Data sources used in this study included abstracts of student thesis reports or final assignments of 30 students and information from online news as many as 200. From the results of the tests that have been carried out, it can be concluded that the Tala stemming algorithm has a lower accuracy level than Nazief Adriani. The Tala algorithm only has an average accuracy of 65.29%, while Nazief Adriani has an accuracy of 78.47%. Regarding speed, the Tala algorithm has a better speed than Nazief Adriani at 32.19 seconds and Nazief &amp; Adriani at 65.2 seconds.&#x0D;
Applying adversarial networks to increase the data efficiency and reliability of Self-Driving Cars

Convolutional Neural Networks (CNNs) are vulnerable to misclassifying images when small perturbations are present. With the increasing prevalence of CNNs in self-driving cars, it is vital to ensure these algorithms are robust to prevent collisions from occurring due to failure in recognizing a situation. In the Adversarial Self-Driving framework, a Generative Adversarial Network is implemented to generate realistic perturbations in an image that cause a classifier CNN to misclassify data. This perturbed data is then used to train the classifier CNN further. The Adversarial Self-driving framework is applied to an image classification algorithm to improve the classification accuracy on perturbed images and is later applied to train a self-driving car to drive in a simulation. A small-scale self-driving car is also built to drive around a track and classify signs. The Adversarial Self-driving framework produces perturbed images through learning a dataset, as a result removing the need to train on significant amounts of data. Experiments demonstrate that the Adversarial Self-Driving framework identifies situations where CNNs are vulnerable to perturbations and generates new examples of these situations for the CNN to train on. The additional data generated by the Adversarial Self-driving framework provides sufficient data for the CNN to generalize to the environment. Therefore, it is a viable tool to increase the resilience of CNNs to perturbations. Particularly, in the real-world self-driving car, the application of the Adversarial Self-Driving framework resulted in an 18 % increase in accuracy, and the simulated self-driving model had no collisions in 30 minutes of driving.
The Genetic Algorithm Implementation in Smart Contract for the Blockchain Technology

A blockchain (BC) is merely a decentralized ledger that records each transaction that takes place inside the network. A block is a unit of data that contains encrypted information about every transaction in the network's history. Decentralized technology implementation would necessitate increased security, enforce accountability, and may speed up the transaction from the current hierarchical structure to a decentralized, cooperative chain of command. By producing fresh offspring of those animals who are identifiable by their identification tags, a blockchain is used to conserve the data of a special species of endangered animals. This article presents a decentralized application (SONR DAPPs) by implemented genetic algorithm to forecast a brand-new kind of those animals. The implementation did by solidity framework. Even that the solidity has disadvantages of memory limitation, but the result shows a possibility of using it to implement complex algorithms for the smart contract. This experience gives a chance to develop the solidity for a more complex algorithm to increase the ability o-f choosing better user to be a new block in the chain of this technology.
Blockchain Technology: Revolutionizing Supply Chain Management

Supply chain management (SCM) is a complex process involving the flow of goods, information, and finances from suppliers to end consumers. In today's globalized economy, supply chains have become more intricate and vulnerable to various disruptions, increasing the need for efficient management practices. Among the numerous technological advancements, blockchain technology stands out as a promising solution that could revolutionize supply chain management. This article explores how blockchain technology can contribute to more transparent, efficient, and secure supply chains.
Development of PISA-Type Questions and Activities in a Smartphone Context

Sometimes our smartphone's internal storage is complete, and we must download an application that is very necessary at that time; this is where numeracy plays a role in solving this problem, namely the way we download the desired application with a complete smartphone's internal storage. This study aims to produce valid and practical PISA-type questions and activities using a smartphone context and potentially effective on students' numeracy skills. This design research-type development study comprised a preliminary and formative evaluation phase consisting of the design stage, self-evaluation stage, expert review stage, one-to-one stage, and small group stage. This research used question grids, question cards, assessment rubrics, lesson plans, PISA-type questions, and student activity sheets as its tools. This study was initiated owing to the insufficient numeracy skills of Indonesian students. This study focuses on seventh-grade students at secondary school number 59 Palembang, Indonesia. The qualitative findings of this research are based on field data collected through walkthroughs, observations, tests, interviews, and document reviews. This research yielded PISA-type questions and activities, namely four activity questions and six evaluation questions with smartphone context that seventh graders may utilize to develop their numeracy abilities. In conclusion, PISA-type questions and activities in the context of smartphones can be used in classroom learning to strengthen students' numeracy skills.
Financial Risk Analysis using Machine Learning

A financial risk analyst distinguishes and examinations potential dangers undermining the monetary place of business and modern partnerships and public/confidential associations. Monetary gamble experts by and large work in one of four gambling classes: credit, market, administrative, or functional. In Financial risk analysts Monetary gamble examiners, by and large, spend significant time in one of four gambling classes: credit, market, administrative, or functional. Risk examiners should monitor improvements in the economy close by monetary appraisals to distinguish any repercussions or suggestions for their clients. Whenever they have distinguished possible dangers, they prescribe procedures to stay away from or limit them. Clients require day-to-day observation of dangers to their resources, credit, and business status. Ordinary bosses are monetary establishments, however independent work is additionally conceivable.
NLP based AI Voice Assistant

All electronic devices will operate in the very near future using a remote helper, which is easily accessible but requires a certain level of weakness.This setup allowed users to access the system via voice commands. The user can ask the assistant to do anything that the framework is capable of doing, such as play music, open a certain application, open tabs, open websites, and so forth. Programming experts known as voice associates are able to understand human speech and respond with carefully crafted voices. Our voice assistant's primary goal is to educate individuals and provide quick, calculated results. The voice help receives voice input from our microphones (wired and Bluetooth) and turns it into computer- understandable English before providing the user with the necessary solutions and replies. This assistant makes a connection to the Internet to deliver answers to the user's queries. A natural language processing algorithm enables computer systems to communicate using many forms of natural human language. Keywords: NLP (Natural language processing), TTS (Text-to-Speech), AI (Artificial Intelligence), Voice Assistant, NLTK(Natural Language Toolkit),Python.
Complex network of eye movements during rapid automatized naming

Introduction Although the method of visualizing eye-tracking data as a time-series might enhance performance in the understanding of gaze behavior, it has not yet been thoroughly examined in the context of rapid automated naming (RAN). Methods This study attempted, for the first time, to measure gaze behavior during RAN from the perspective of network-domain, which constructed a complex network [referred to as gaze-time-series-based complex network (GCN)] from gaze time-series. Hence, without designating regions of interest, the features of gaze behavior during RAN were extracted by computing topological parameters of GCN. A sample of 98 children (52 males, aged 11.50 ± 0.28 years) was studied. Nine topological parameters (i.e., average degree, network diameter, characteristic path length, clustering coefficient, global efficiency, assortativity coefficient, modularity, community number, and small-worldness) were computed. Results Findings showed that GCN in each RAN task was assortative and possessed “small-world” and community architecture. Additionally, observations regarding the influence of RAN task types included that: (i) five topological parameters (i.e., average degree, clustering coefficient, assortativity coefficient, modularity, and community number) could reflect the difference between tasks N-num (i.e., naming of numbers) and N-cha (i.e., naming of Chinese characters); (ii) there was only one topological parameter (i.e., network diameter) which could reflect the difference between tasks N-obj (i.e., naming of objects) and N-col (i.e., naming of colors); and (iii) when compared to GCN in alphanumeric RAN, GCN in non-alphanumeric RAN may have higher average degree, global efficiency, and small-worldness, but lower network diameter, characteristic path length, clustering coefficient, and modularity. Findings also illustrated that most of these topological parameters were largely independent of traditional eye-movement metrics. Discussion This article revealed the architecture and topological parameters of GCN as well as the influence of task types on them, and thus brought some new insights into the understanding of RAN from the perspective of complex network.
Implementasi Chatbot untuk Layanan Frequently Asked Question Akademik dengan Penggunaan Dialogflow

Academic Frequently Asked Questions (FAQ) service is a service designed to answer academic questions that are often asked by students. A survey conducted on April 12, 2022, to 33 students of the Ahmad Dahlan University Informatics study program showed that 39.4% of students rarely open the FAQ menu on the portal application. Students more often ask questions to administrators and lecturers, but 30.3% of students receive answers that last more than 10 minutes. Chatbot is an artificial intelligence system used to interact with users in real-time to provide information. The use of dialog flow in the system helps manage information and can provide an answer quickly and precisely to users. This research uses the waterfall method, which is a simple classic model with a linear flow system. The User Experience Questionnaire (UEQ) survey results get a score of 1,536 for attractiveness, 1,714 for clarity, 1,375 for efficiency, 1,357 for fixity, 1,536 for stimulation, and 0.964 for novelty. Based on these results, it shows that the application level is above average on the UEQ scale.
Improving the Accuracy of the Deep Energy Method

The deep energy method (DEM), a type of physics-informed neural network, is evolving as an alternative to finite element analysis. This method employs the principle of minimum potential energy to predict deformations under static loading conditions. However, the model&amp;rsquo;s accuracy is contingent upon choosing the appropriate architecture for the model, which can be challenging due to the high interactions between hyperparameters, large search space, difficulty in identifying objective functions, and non-convex relationships with the objective functions. To improve DEM&amp;rsquo;s accuracy, we first introduce random Fourier feature (RFF) mapping. RFF mapping helps with the training of the model by reducing bias towards high frequencies. The effects of six hyperparameters are then studied under compression, tension, and bending loads in planar linear elasticity. Based on this study, a systematic automated hyperparameter optimization approach is proposed. Due to the high interaction between hyperparameters and the non-convex nature of the optimization problem, Bayesian optimization algorithms are used. The models trained using optimized hyperparameters and having Fourier feature mapping can accurately predict deflections compared to finite element analysis. Additionally, the deflections obtained for tension and compression load cases are more sensitive to variations in hyperparameters than bending.
Boosting the Impact of Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe Graph-Massivizer: A Horizon Europe Project

We explore the potential of the Graph-Massivizer project funded by the Horizon Europe research and innovation program of the European Union to boost the impact of extreme and sustainable graph processing for mitigating existing urgent societal challenges. Current graph processing platforms do not support diverse workloads, models, languages, and algebraic frameworks. Existing specialized platforms are difficult to use by non-experts and suffer from limited portability and interoperability, leading to redundant efforts and inefficient resource and energy consumption due to vendor and even platform lock-in. While synthetic data emerged as an invaluable resource overshadowing actual data for developing robust artificial intelligence analytics, graph generation remains a challenge due to extreme dimensionality and complexity. On the European scale, this practice is unsustainable and, thus, threatens the possibility of creating a climate-neutral and sustainable economy based on graph data. Making graph processing sustainable is essential but needs credible evidence. The grand vision of the Graph-Massivizer project is a technological solution, coupled with field experiments and experience-sharing, for a high-performance and sustainable graph processing of extreme data with a proper response for any need and organizational size by 2030.
Real-Time Retrieving Vedic Sanskrit Text into Multi-Lingual Text and Audio for Cultural Tourism Motivation

There are many uses for handwritten writing that has been digitally transformed. helping both those working in the field of education and visitors better understand the local culture. The Vedas are the best informational source for understanding Indian culture; the only drawback is that it exists in physical form. The ability to access cultural knowledge and historic heritage is facilitated by the real-time retrieval and translation of Vedic Sanskrit text. The tourist will benefit from this in understanding Indian culture and traditions and its significance. The manual Sanskrit text is translated into an easily editable format using the suggested methods in this study. The language translation and the audio generation in a specific selected language are the features of the methodology used. The proposed method has a four-step process i) image processing, ii) text extraction, iii) text-to-speech conversion, and iv) multi language translation. The OTSUs threshold method is employed in the suggested method to differentiate between the image’s foreground and background. Tesseract OCR is used for image processing. The LSTM is utilised in the real-time text extraction process. The outcome demonstrates that the suggested technique was effective in rendering the Sanskrit text into a variety of languages for the purpose of promoting cultural tourism.
IMPLEMENTASI PROGRAM GERAKAN LITERASI SEKOLAH DI SMA

This study is a study of the evaluation of gaps in the implementation of the School Literacy Movement program in two high school in the South Jakarta area. As an intervention program launched by the government to assist learners in developing critical, creative, innovative, entrepreneurial attitudes, social empathy behavior, and love for knowledge, it is necessary to know the extent to which the program has an impact in improving reading skills understanding in learners and what gaps in running GLS programs through the gap evaluation model (The Discrepancy Evaluation Model). The model developed by Malcolm Provus with this qualitative approach generally aims to obtain information about gaps in the implementation of the GLS program. Research data is obtained through observations, interviews, and document studies. Data analysis is carried out with three flow of activities that occur simultaneously, namely: data reduction, data presentation, data withdrawal or verification. results showed that the positive impact of gls program implementation can be monitored even though academic measurement has not been done. One of the related to the success of KBM is the increased readiness of students in attending KBM, increased confidence when speaking in public and writing. Gaps in program implementation in both high schools appear in the design of the target and installation of the program implementation, although both boil to the development of language skills and literacy of learners. In addition, there needs to be a firmer emphasis in the master design that the establishment of a literate environment is the responsibility of all school residents. Despite the gaps that occur in the implementation of school conditions have allowed the formation of a literate environment.
Parallel processing of E-Atheer algorithm using pthread paradigm

The development in the field of computer technology, and the increase in the growth rate of database, alongside the extraction of certain data from a huge pool of database involve intricate and complex processes. The processes comprise text mining, pattern recognition, retrieval of information and text processing. Thus, the need for enhancing the performance of string matching algorithms is required, which is considered as one of the challenges to the researchers. Consequently, one of the resolution to address this problem is the parallelization for exact string matching algorithms. In this study, we implemented the parallel exact string matching algorithm termed as E-Atheer with multi-core processing utilizing Pthread (POSIX) for the reduction of time consumption. The Pitch, XML, Protein, and DNA database types are utilized to test the impact of the proposed parallel algorithm. The parallelization algorithm obtained positive results in the parallel execution time, and a more superior expediting capabilities, in comparison to the sequential result. The Pitch database indicated optimal results in parallel execution time, and when utilizing long and short pattern lengths. The DNA database indicated optimal speedup performance when utilizing short and long pattern length, meanwhile the XML and Protein on the other hand indicated the worst results.
Enhanced feature clustering method based on ant colony optimization for feature selection

The popular modified graph clustering ant colony optimization (ACO) algorithm (MGCACO) performs feature selection (FS) by grouping highly correlated features. However, the MGCACO has problems in local search, thus limiting the search for optimal feature subset. Hence, an enhanced feature clustering with ant colony optimization (ECACO) algorithm is proposed. The improvement constructs an ACO feature clustering method to obtain clusters of highly correlated features. The ACO feature clustering method utilizes the ability of various mechanisms, such as local and global search to provide highly correlated features. The performance of ECACO was evaluated on six benchmark datasets from the University California Irvine (UCI) repository and two deoxyribonucleic acid microarray datasets, and its performance was compared against that of five benchmark metaheuristic algorithms. The classifiers used are random forest, k-nearest neighbors, decision tree, and support vector machine. Experimental results on the UCI dataset show the superior performance of ECACO compared with other algorithms in all classifiers in terms of classification accuracy. Experiments on the microarray datasets, in general, showed that the ECACO algorithm outperforms other algorithms in terms of average classification accuracy. ECACO can be utilized for FS in classification tasks for high-dimensionality datasets in various application domains such as medical diagnosis, biological classification, and health care systems.
Recognition Of Chicken Species Through Sound Using Weierstrass Transform

Voice recognition is an applied technique in the field of digital signal processing that has been widely used, such as technology in the field of telecommunications which is now able to provide data transmission services not only text but can also serve data transmission using voice. Speech recognition studies to date have only focused on human speech recognition, so it is important to develop research on speech recognition in animals. In this research a speech recognition system will be developed for balenggek, pelung and bekisar ornamental chickens using the Delphi 7 programming language and the weiertrass transform method. The performance evaluation measurement of the speech recognition system in this study uses two parameters, namely the detection rate and the false positive rate. The results showed that the speech recognition system for ornamental chickens using the weierstrass transform method had an average detection rate of 83.00% and an average false positive rate of 16%.