Reviewerly/exHarmony huggingface dataset:
Optimization Techniques for Focused Web Crawlers

Applications for data mining and information retrieval heavily rely on web crawling. In particular, focused web crawling seeks to extract pertinent and targeted data from the massive and ever-changing internet. The optimisation of focussed web crawlers is examined in this work with an emphasis on methods to increase coverage, accuracy, and efficiency. We go through a number of techniques and algorithms that improve the crawling process, including content relevance analysis, crawling frontier management, prioritisation, and seed selection. We also provide relevant articles on the subject for further reading.
A Long-algorithm Based Variational Quantum Scheme for Pattern Matching Problem

Grover algorithm is a popular model in quantum computation field and has the advantage of outperforming its classical counterpart in terms of searching speed, hence it has been applied in various domains such as pattern matching. Unfortunately, there are two central issue for this quantum search algorithm, i.e., Grover algorithm, in practical application that firstly it can only achieve a probability nearing 100% rather than exactly, secondly it tends to require a deep quantum circuit. In this work, we propose a Long-algorithm based quantum scheme for pattern matching problem, which can effectively enhance the success probability of quantum pattern matching and mitigate the needs for quantum computing resources. The performance of this scheme is demonstrated numerically, validating our design.
Variable search domain improved A-star algorithm for AMR in warehouse environments

In order to reduce autonomous mobile robot path planning time and improve efficiency in the warehouse environment, the variable search domain improved A-star algorithm is proposed. Firstly, a raster map matrix is created based on the actual map, then inflection point features and path information are extracted to set up a search template, and finally, different search steps and search directions are adaptively obtained from the search template according to the location information of the nodes to be expanded, simplifying the search space and improving the computational efficiency of the A-star algorithm. The traditional A-star algorithm and the improved A-star algorithm are compared and tested respectively. The simulation test results show that the variable search domain A-star algorithm proposed in this paper has fewer traversing nodes, shorter search time, and better performance.
Resources, Events &amp; Agents (REA) Sebagai Solusi Pada Siklus Pendapatan &amp; Pengeluaran Kas : Tinjauan Empiris

Purpose: This writing aims to solve problems in one of the cycles in the accounting information system with the REA data model so that the management of the income and cash disbursement cycle is more effective and efficient.&#x0D; Method: Using qualitative methods with a case approach that occurred in the company. Primary data by conducting interviews with the object under study. As well as secondary data by documenting matters related to the income and expenditure cycle. Result: The results of the research are that the implementation of the income cycle has been running according to the specified function. However, the expenditure cycle is not running adequately. It is recommended for the application of cycles, companies can make plans for company goods and adjust cash records with established accounting standards. The cycle of income and cash disbursements in the documentation of cash receipts and cash outflows is not sequential, in which case it is recommended that the company plan a list of company goods and record cash in accordance with operating standards and procedures (SOP) and accounting standards and is equipped with regulations regarding punishment for office facilities misused by employees.&#x0D; Contribution: This research contributes to understanding the implementation and design of an accounting information system for the income and cash disbursement cycle using real data models in organizations.&#x0D; Limitation: The implementation of the Accounting Information System only covers the Revenue and Cash Expenditure cycles of the company that have been running so far.&#x0D;
Impact of the Application of Artificial Intelligence on Human Resource Management in the Tourism Companies Sector.

Digital transformation and artificial intelligence (AI) technologies have become some of the fastest-growing and most widespread concepts around the world today, especially in light of the rapid developments in the field of information and communication technology, which has become an active and influential part of the tourism and hospitality industry through the reliance on electronic platforms and virtual tourism. Therefore, the study aimed to identify the impact of AI technologies on human resource management in tourism companies. To achieve the research objectives, the researchers used the descriptive analytical approach, and the study sample was selected from human resource managers in tourism companies in Assiut Governorate. The sample size was 255 employees who were selected using a random sampling method. The data was analyzed using the SPSS statistical program to test the study hypotheses. The most important finding of the study was the existence of a statistically significant relationship between the application of AI technologies and their positive role in human resource management. The study also presented several recommendations, including the dissemination of technology culture, especially digital transformation and AI technologies, to maximize the benefits of these applications, especially in the tourism and hospitality sectors. Keywords: Digital transformation, artificial intelligence, tourism companies, human resource management, virtual tourism, virtual reality.
Implementation of Metacognitive Strategies on Students' Ability to Read English Texts

Reading is one of the crucial language skills for learners, especially when they are learning English. English learners can usually read texts well, but many of them cannot understand the content of the text. This study aims to determine how to implement metacognitive strategies to learn reading skills. Results showed that students had a good awareness of metacognitive reading strategies. However, it did not affect students' reading comprehension test results much. This is because several other factors, such as interest, background knowledge of students, and teachers' role, also significantly influence students' reading comprehension. This study used a case study approach to its analysis. This research demonstrates the efficacy of self-planning, self-monitoring, and self-evaluation as three phases of metacognitive strategy execution. These steps are accomplished via friendly conversation with the pupils. Five pupils in particular shown exceptional levels of motivation, success, self-awareness, and independence in their reading comprehension and other academic endeavours.
Deep Neural Networks for Traffic Sign Recognition Systems

The purpose of this research is to offer a deep learning-based traffic sign recognition approach that focuses on the identification and categorization of traffic signs. In this study, we used ResNet-50, VGG16, RegNetX002 and, DenseNet121 to implement transfer learning, which entails leveraging pre-trained models to extract features from images and then training a new model on top of those features for the detection task. Finally, on the Traffic Signs (GTSRB plus 162 special classes) dataset, we computed the accuracy rate obtained by each transfer learning model.
A Real Time Hand Gesture Recognition for Indian Sign Language using Advanced Neural Networks

One of the most important means of survival in a group is verbal exchange. In addition, according to the World Health Organization, over 5% of people worldwide have speech impairments as of 2021. This means that they lack a fundamental requirement for the survival of the human species. The “Sign Language“ that these people use in their communities contains meanings, grammar, and vocabulary that everyone else may not understand. Our suggested approach focuses on developing a frame-based system that leverages deep learning, an advanced machine learning technology, to translate sign language into legible speech or text on an embedded device. Our algorithm processes the gathered samples by using an integrated webcam to record hand gestures in real-time. For improved performance, the model uses a dataset including one million images for each move. A 9:1 ratio has been established between the training and test sets of data.
Portable Engine Health Monitoring System based on Vehicle Emissions

Pollution is a more concern in a revolutionized world. Air pollution has majorly caused by the vehicle transportation. Vehicle is a major and significant producer of various hazardous making gases. The vehicle health is a most important to secure the nature health in terms of air. So every passenger vehicle has surpassed and enquired its health with the aid of various sensor and controller units. The proposed system has used to elucidate the various parameters to prevent different factors affecting the engine health. The aim has reached with the aid of smart sensors and controller units. The compact integration of microcontroller and various gas detecting sensors are used to alert the critical nature. Internet of things plays a major role in huge data processing sectors and decision making units. Internet of Things impacts to reduce the amount of carbon-di-oxide and various oxides in a globe scale. This system has efficiently evolved the entire greenhouse gases level and improve the engine health in an effective manner.
Anatomy-Guided Synthesis of Novel CT Images at Full Hounsfield Range

In this work, we present an approach that can synthesize novel CT images across the full Hounsfield range using a very small annotated dataset of around thirty patients and a large non-annotated dataset with high resolution medical images. Our method uses these two datasets in a sequence of steps involving texture learning via StyleGAN and semi-supervised learning via CycleGAN to generate a large annotated medical dataset suitable for use in deep learning algorithms for medical applications. Using an anatomy exploration interface we can then generate CT images with anatomies that were non-existent within either of the datasets, without compromising accuracy and quality. We show that our approach works for all Hounsfield windows with minimal depreciation in anatomical plausibility.
Feature Importance Estimation Using Gradient Based Method for Multimodal Fused Neural Networks

The combination of multimodal data such as phenotype, clinical, and imaging data has been shown to improve performance in many medical machine learning applications. While intuition suggests that more information should help in many applications, there has been limited study interpreting the relative importance of combining multimodal features in deep neural networks. This work proposes a novel gradient based feature importance method for multimodal neural networks to help improve interpretability and to guide architecture selection. To demonstrate our method, we develop a validation platform to establish performance baselines; this platform simulates test cases and compares the performance of our feature importance method with ground truth. As a use case, we employ a hybrid fusion neural network to solve a binary classification problem with multiple image and non-image inputs. The gradient based method for feature importance is used in the post-fusion section of the architecture to obtain feature importances for deep features. These importances are then aggregated for each modality to obtain importance for each multimodal input. In our example application, we use 58830 abdominal CT scans from medMNIST with simulated clinical data for training. The ground truth labels and ground truth importances are obtained using a synthetic decision functions. All of our eight test cases show strong agreement with the ground truth ranks with an average rank biased overlap of 0.93 and a root mean squared error of around 0.13 across all modalities. This work represents among the first effort to estimate feature importance in multimodal machine learning models, paving the way for improved interpretability of deep learning methods.
Syntactical Analysis of Student's Descriptive Answer Sheets and Predict Student's Performance Using NLP and Tensor flow Decision Forests

The employment of computer-based assessment methods has become increasingly significant on a global scale for various types of academic evaluations. This approach exhibits a relatively higher speed compared to our conventional manual assessment procedure. The present study introduces a novel approach for assessing students' concise responses, including descriptive answers, through the utilization of Natural Language Processing (NLP) techniques. Within this framework, an educator generates an answer sheet and a dataset of keywords to facilitate the examination procedure. The dataset is stored in designated data storage, while the students input their responses on the examination page. This research endeavour involves conducting syntactical analysis to compare standard responses and recording the results in a database. Additionally, an evaluation of the same response is performed using a Natural Language Processing (NLP) algorithm to identify grammatical errors, and the results are analysed using Tensorflow in the database. Through the implementation of these techniques, we can achieve optimal outcomes. The outcomes produced by the system are contrasted with the assessment conducted by the academic staff.
Algorithm for polyp segmentation with local encoding and decoding fusion and multi-scale attention

In recent years, the application of medical image semantic segmentation tasks in medical diagnosis and treatment planning has received widespread attention from the research community. The High-Resolution Network (HRNet) has good adaptability to high-resolution and high-scale medical images. In this paper, a novel high-resolution serial feature fusion encoding and decoding structure is proposed, and a CBAM attention mechanism is fused to construct a module that can jointly focus on spatial, channel, and multi-scale hierarchical information, which can improve the feature representation ability of the model and effectively reduce parameter complexity. We use the HRNet architecture to construct our model. Experimental results show that our method achieves MIoU coefficient of 98.44% on the Kvasir-SEG dataset, which is 1.43 percentage points higher than the original HR-Net model, validating the effectiveness and reliability of our method.
Multi-feature extraction network cross-resolution person re-identification based on SR technology

In the real world, the resolution of the image that is collected can vary depending on the camera's quality or the change in the distance from the pedestrian. Important information is lost from the low-resolution image. It can be difficult to match Low Resolution (LR) input photographs with High Resolution (HR) gallery images. Thus, we suggest that the super-resolution module and the multi-feature extraction module be improved in order to address the aforementioned issues. To be more precise, the resolution of the low-resolution query image is restored in the first step using an upgraded Super Resolution (SR) model (VDSR-NAM). A two-stream feature extraction network extracts and fuses the features of the LR and SR images in the second stage. The potential of our model has been shown in numerous tests on cross-resolution person re-id datasets. The efficacy of the loss function on our model is concurrently confirmed by ablation experiments on the dataset MLR-VIPER.
Genre based learning model integrated literacy activities and character strengthening

Genre based learning or Text-based Instruction is a learning approach used in Indonesian language learning since 2013 until now. However, learning will be more engaging if the learning process is constantly adapted to students’ needs and innovations. This study aimed at building a Genre-based Learning paradigm that incorporates literacy activities and character development. The guidelines of the National Literacy Movement and Strengthening Character Education in secondary schools prompted this action. Based on this, the two things are integrated in learning to help teachers implement literacy and strengthen character education in the learning process. This research employed Plomp’s Development model (Preliminary Research, Prototyping Phase Assessment Phase). In the Assessment Phase, researchers tested products that had been developed to determine the practicality and effectiveness of learning Indonesian using the developed model. The research instruments are in the form of questionnaires, observation sheets and tests. The research subjects were students of one of the high schools in the city of Padang. The results demonstrated that the Genre-based Learning model, which integrates literacy activities with character development, is useful and time-efficient for language learning. Additionally, this concept is applicable for enhancing student learning activities. This learning strategy can be utilized as an option to combine literacy activities and character development into secondary school Indonesian language instruction.
Analysis of Valid Strategies According to Selection Probability Function Adjustment in an Extended Prisoner’s Dilemma Game Using Genetic Algorithm

The Prisoner’s Dilemma, a representative example of game theory, is a classic game in which two players receive rewards based on two action strategies(cooperation or defection). However, many variations have been made depending onchanges in conditions(number of participants, behavioral strategies, reward values, number of games, etc.). In this study, an extended Prisoner’s Dilemma game was used in which a genetic algorithm was applied to the iterative Prisoner’sDilemma game to improve the selection probability function to determine which action strategy should be selected according to changes in reward value. A possible SPS(selection probability strategy) was developed. As a result of theprogram simulation, this SPS ranked high in the early stages, showed good performance, and was generally a dominant strategy that survived until the final stage. This study is significant in that it suggests the possibility of developing a new strategy in the Prisoner’s Dilemma game and reaffirms existing strategies that are generally effective.
Digitization and Secure Process of Forensic Data Management Using Blockchain

Cyber threats and data manipulation are the main concerns in this technological society. The investigator finds it challenging to submit evidence to the court because of the information breach as continuity documentation of records is very critical for safeguarding against criminal events. Blockchain-based digital forensic data management systems have been developed to deal with this issue of Privacy preservation for evidence items. Blockchain technology provides authentication, immutability, traceability, resilience, and distributed trust for forensic data. In order to ensure security and ease of use, the system utilizes onion architecture. Data can be stored and analysed whenever required. They provide trust between the examiners and the system. The system is a multi-platform system that provides effective usage where it can be accessed and used from anywhere. The honest information is make sure to follow the crime investigator of the steps. if we use the propose system of block chain, it is can't be altered the evidence during the submission of court. The storing data of each block is verify and added to the blockchian. The creation of safe database was implemented by a decentralised network with bitcoin and networks of cryptocurrency. In proposed system, the blockchain technology helps in can't able to access and altered the data during the investigation periods. This system is used to implements the easily accessed and used in anywhere.
Blockchain based Smart Energy Trading in a Sustainable Community: A Comparative Technological Analysis

The significance of blockchain enabled smart con- tracts for the energy market is widely recognized, given the continuous shift towards a more decentralized and flexible energy grid. Blockchain Technology (BCT) is viewed as a critical technology for facilitating the shift to a more economical, transparent, and transactive sustainable energy market because of its autonomous, configurable, and tamper-proof character. This paper explores the significance and scope of blockchain technology in various stages of smart energy trading in sustainable communities. Our investigation analyzes the existing processes, difficulties, and potential of energy trading mechanisms in renewable energy markets, providing an adequate basis for the research. This paper also proposes a blockchain based Peer to Peer Energy Trading (P2P-ET) architecture using Hyperledger Fabric, notably on the concepts of organizational focus, private channels, and chain code, as well as the flow of transactions in the Hyperledger Fabric network. These components must be successfully developed and implemented in order to establish a strong energy trading network.
Revolutionizing Tourism and Hospitality Services: Integrating AI in the Metaverse

The purpose of the study is to examine the integration of AI into the metaverse for exploring the potential benefits and issues in the hospitality and tourism industry. The study highlights the structure of integration through a conceptual model and fragmenting each parameter included separately and covers a comprehensive analysis of the structural formulation of a metaverse in the industry. Additionally, elaborates on security issues and countermeasures. The paper provides a framework for businesses in exploring and revolutionizing the industry with innovative ideas. However, the analysis is constrained to a conceptual model, creating a real-time virtual environment will be the future agenda.
Socialization of Groundwater Treatment with Modified IPA Prototype in Doyomulyo, Lamongan District

Doyomulyo is a village located in Lamongan residents , most of the population's livelihood as a farmer. The village is also known have a source of groundwater. Even during a drought these sources remain abundant. Doyomulyo Village is indeed included in the drought-prone map during the rainy season like now. However, Donomulyo Village still has a source of raw water that is used for daily needs. One of them is near the Donomulyo Village hall mosque, but the water source is adjacent to the mosque's bathroom septic tank. So the laboratory results show the results of DO, TDS, COD, BOD, Total Coliform and E. Coli which do not meet the standards. And there is the desire of the people of Doyomulyo Village, Kembangbahu District, Lamongan Regency to produce groundwater into bottled drinking water (AMDK). So that Unisla through community service activities wants to improve the quality of Doyomulya village water sources so that they become drinking water that is ready for consumption. In Community Service (PKM) activities, the team used a groundwater treatment method with a modified IPAM prototype. Based on the results of groundwater characteristics obtained, the design of equipment for the drinking water treatment unit in Doyomulyo, Lamongan Regency requires that the filter media for the filter tube are activated carbon, silica sand and manganese zeolite used to remove dissolved particles above 50 µ in size and can also reduce concentration of iron and organic matter contained. Community Service Program (PKM) activities by FIKES UNISLA in Doyomulya Village have resulted in a planning program for drinking water treatment to increase the demand for drinking water for residents of Doyomulya Village and its surroundings.