Reviewerly/exHarmony huggingface dataset:
Image Caption Generation Based on Image-Text Matching Schema in Deep Reinforcement Learning

Image captioning applications require prompt and precise caption generation, which can improve the accessibility and understanding capabilities of images. We utilize an actor-critic approach based on deep reinforcement learning and propose a two-fold approach to enhance the performance of the actor-critic approach. First, we propose a novel image-text matching module to compute the reward in image-matching, for the actor-critic model. This module enables more accurate and meaningful evaluations, contributing to improved caption generation. Second, we apply various training scenarios in reinforcement learning, strategically updating both the policy and value networks. The scenarios ensure more effective learning dynamics and lead to enhanced overall performance. To assess the efficiency of our approach, we employ the Microsoft COCO dataset. The experiments demonstrate the superiority of our method in terms of both speed and precision compared to the existing techniques.
Time-Scheduled End-to-End Entanglement Establishment in Memory-Cells-Limited Quantum Networks

A routing and entangled pairs allocation algorithm was proposed to support remote entangled pairs sharing with finite storage capacity and limited storage time, upon satisfying the time constraint, and the proposed algorithm is further verified via simulation.
A Dataset for Object Detection in Night Rainy Scenes

Object detection is a fundamental task in computer vision. As the datasets for object detection are constructed, deep learning approaches are leveraged to show promising results. However, most of the existing datasets are aimed at normal weather scenarios, which limits the use of the trained neural networks in normal conditions. Recently, some datasets cover the night samples or rainy samples though, rare samples are utilized to focus on the night rainy scenarios. As we evaluate, the night effects and rainy effects are not decoupled. They result in a more complicated problem, thus a need for a night rainy dataset is raised to boost the deep learning tools in the data aspect. Besides, we collect the rainfall rate with a rain gauge to measure the rain intensity, splitting the dataset according to the rain intensity so that the networks can be trained according to different rainfall rates. Our whole dataset contains more than 3000 samples, with 4865 objects labeled. Specifically, the dataset contains 5 classes labeled as pedestrian, car, bus, truck, and motor cyclist. To show the effectiveness of the dataset, we use the latest detection algorithm trained on the open dataset and our dataset for comparison. The significant improvement reveals that our dataset are unique and strongly pushes the networks to the practical useful end.
A Study for Securing IoV Using Blockchain

New and advanced technologies have introduced amazing areas like the Internet of Vehicles (IoV) and Unmanned Aerial Vehicles (UAV) etc. With these new emerging technologies and the open nature of these technologies security and privacy are always a serious concern. The enhancement of IoV brought unmanned cars where data security, privacy, physical attack, and getting stolen are always prime concerns. As reported in the literature, we had lost billions of dollars because of stolen cars and cyber-attacks. To avoid these attacks, we have suggested the Blockchain concept to secure IoV. In this era of data and trust where the internet is flooded with information and where we don't even know how our information can be used, data security and confidentiality should be a must. To solve this problem Blockchain comes into the picture with the concept of a decentralized, distributed ledger that stores the records of ownership of digital assets. Any data stored on the blockchain cannot be modified. It is client-client-based technology where data is decentralized in a way that changes in one node will need the permissions of another node. It uses a hash algorithm where the next node has a record of the previous node's state in the hash function. Using this decentralized, distributed ledger or client-client model of blockchain, we will secure our IoV, where we will see how we can secure our confidential data from any cyberattack and physical attack which will try to breach our security levels. We will use different levels of security modes if there are any security breaches or accidents, vehicles will soon send alert warning messages with the location and current state of vehicles to each of its stakeholders and police station.
Design and Control of a Robotic Hand for Generating Gestures Based on Indian Sign Language

The gestures involving hand movements are an important aspect of communication, particularly for speech and hearing-impaired persons. There are many travellers worldwide that use gestures involving hand movements as a source of communication when traveling in countries where they need to communicate with persons having speech and hearing disability. Sign language is a type of language that is used by persons who are unable to hear or speak. The paper mainly focuses on gesture generation based on Indian Sign Language which is used in the deaf community all over India. To assist persons having speech and hearing disabilities there is a requirement for the design of a Robotic hand model that could generate various gestures corresponding to the speech delivered in the form of alphabets by the different persons based on the Indian Sign Language system. Considering the experimental results achieved the proposed model of the design of the robotic hand interpreter system generates the gesture corresponding to the different alphabets used in the Indian Sign Language system accurately. The overall accuracy of the robotic hand interpreter system model based on the experimental results is 94 percent based on the signs generated by it.
Improved Knowledge Distillation via Teacher Assistants for Sentiment Analysis

Bidirectional Encoder Representations from Transformers (BERT) has achieved state-of-the-art results on various NLP tasks. However, the size of BERT makes application in time-sensitive scenarios challenging. There are lines of research compressing BERT through different techniques and Knowledge Distillation (KD) is the most popular. Nevertheless, more recent studies challenge the effectiveness of KD from an arbitrarily large teacher model. So far, research on the negative impact of the teacher-student gap on the effectiveness of knowledge transfer has been confined mainly to computer vision. Additionally, those researches were limited to distillations between teachers and students with similar model architectures. To fill the gap in the literature, we implemented a teacher assistant (TA) model lying between a fine-tuned BERT model and non-transformer-based machine learning models, including CNN and Bi-LSTM, for sentiment analysis. We have shown that teaching-assistant-facilitated KD outperformed traditional KD while maintaining a competitive inference efficiency. In particular, a well-designed CNN model could retain 97% of BERT's performance while being 1410x smaller for sentiment analysis. We have also found that BERT is not necessarily a better teacher model than non-transformer-based neural networks.
Hardware Designing and Modelling of Joystick based Electric Wheelchair Drive

This paper discusses a wheelchair that is joystick controlled and can just on just only be controlled by pointing the joystick in the direction. This wheelchair consists of a BLDC motor that is the main motor for controlling the forward and backward direction of the wheelchair. It consists of a brushed DC motor that’s main function is to give direction to the wheelchair. It is connected via a gear mechanism just above the main motor, i.e., the BLDC motor. The relay controls both the BLDC and brushed motor, and the relay is operated by the Arduino that instruct according to the instruction provided by the joystick.
An Innovative Method to Predict Breast Cancer at Earlier Stage based on RAUNET Approach

Recent increases in healthcare spending have made early disease detection a pressing concern. The growth of populations has a direct correlation with the rising death toll from breast cancer. Breast cancer ranks second among the deadliest cancers the proposed approach knows about. An autonomous disease detection system minimizes the likelihood of fatalities by providing a rapid response from medical personnel who can effectively treat the condition. The proposed approach entails three steps: preprocessing, segmentation, and model training. The proposed approach employed a combination of Contrast Limited Histogram Equalization and Fuzzy Histogram Equalization as a preprocessing technique. More and more comprehensive segmentation regions are produced when numerous segmentation regions are connected. After accumulating the attributes, the models are trained with RA-UNet. The proposed technique outperforms its counterparts, including RLM and U-Net. The probability of this strategy succeeding is 97.36 percent.
Text Extraction from Document Images using CNN and LSTM

This research study proposes an advanced system that combines image processing techniques with deep learning models to achieve precise text extraction from images. Our approach in this paper involves several key steps. First, we apply preprocessing techniques to enhance the image quality and clarity of text regions. Next, the proposed method employs image processing algorithms to identify and isolate text regions within the image. To recognize and transcribe the extracted text, the proposed method utilizes deep learning models, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs). To train the deep learning model in the proposed method, we generate a labeled dataset by annotating text regions in images. The proposed model highlights the effectiveness of integrating image processing and deep learning techniques in achieving accurate and efficient text extraction from images. The system's performance and flexibility make it a valuable tool for applications that require reliable text extraction capabilities.
Unraveling the Landscape of Bitcoin Research Using Machine Learning: A Topic Modelling Approach

This study provides a comprehensive analysis of the evolving field of Bitcoin research through the application of bibliometric analysis and structural topic modeling techniques. A dataset of 1,937 articles from the Scopus database, spanning the period between 2013 and 2023, was examined, with a specific focus on the prominent digital currency, Bitcoin. The analysis encompasses publication trends, influential journals, authors, institutions, and impactful articles in the field. Through the application of structural topic modeling, six distinct thematic clusters in Bitcoin research are identified, encompassing topics such as blockchain-based digital currency, volatility modeling, portfolio diversification, Bitcoin futures trading, return forecasting, and cryptocurrency regulations. Furthermore, this study outlines future research directions in the domains of finance, economics, and management pertaining to Bitcoin. By bridging the gap in existing literature and providing valuable insights, this study aims to support the development of effective risk management strategies, regulatory frameworks, and business approaches. The findings serve as a valuable resource for industry practitioners, academics, regulators, and policymakers navigating the evolving landscape of cryptocurrencies.
Sistem Pendukung Keputusan Penentuan Anggaran Keuangan menggunakan Metode Wp (Weighted Product) Pada Panti Wreda Griya Nugraha Bandar Lampung

Decision Support Systems are part of computer-based information systems that are used to support decision making in an organization or company. One type of decision making method is WP (Weighted Product). This research aims to implement a Decision Making System using the WP (Weighted Product) method on the financial budget in one of the Nursing Homes, which is beneficial for stakeholders in the nursing home so that there are no errors in decision making in determining the financial budget at the Griya Nugraha Nursing Home. The assessment criteria include the number of needs, number of elderly people, time period, and savings funds. The information system was built on a desktop basis with a system management database using SQL. From the test results of the decision support system for determining the financial budget which was tried using 10 samples of nursing home residents which produced the best score of 0.1035 where the assessment was between 0-1, and the maximum score was 1. This research can help in representing decisions that are usually issued by the Chairman. In general, every time you do a financial budget at a nursing home.
Hierarchical Reinforcement Learning for Non-Stationary Environments

What indications are there when the environment changes and the learned policy is no longer optimal? Is it possible to predict when a non-stationary environment changes in some way? In this paper we propose a method that helps agents know when to retrain their policies via reinforcement learning. The agents detect changes based on the temporal difference. A hierarchical learning model is used to aid in these non- stationary environments. The hierarchical learning model has two levels, the higher-level policy, which we call the learning switch, and the lower-level policy, which tells the agents their suitable action to play the game. The higher-level policy determines when reinforcement learning should be turned on or off based on the temporal differences calculated within a game or episode. Two multi-agent differential games are used as examples. The first two examples tackle the problem in cooperative games, while the last example addresses the competitive game. The results show that the agents can maintain suitable performance by switching on the learning process for a few iterations after environment changes occurs. In this paper we consider the change in environment to be long term occurrence within the dynamics of games; for example the mass of an agent may become heavier.
Pushing Boundaries: Exploring Zero Shot Object Classification with Large Multimodal Models

The synergy of language and vision models has given rise to Large Language and Vision Assistant models (LLVAs), designed to engage users in rich conversational experiences intertwined with image-based queries. These comprehensive multimodal models seamlessly integrate vision encoders with Large Language Models (LLMs), expanding their applications in general-purpose language and visual comprehension. The advent of Large Multimodal Models (LMMs) heralds a new era in Artificial Intelligence (AI) assistance, extending the horizons of AI utilization. This paper takes a unique perspective on LMMs, exploring their efficacy in performing image classification tasks using tailored prompts designed for specific datasets. We also investigate the LLVAs zero-shot learning capabilities. Our study includes a benchmarking analysis across four diverse datasets: MNIST, Cats Vs. Dogs, Hymnoptera (Ants Vs. Bees), and an unconventional dataset comprising Pox Vs. Non-Pox skin images. The results of our experiments demonstrate the model's remarkable performance, achieving classification accuracies of 85%, 100%, 77%, and 79% for the respective datasets without any fine-tuning. To bolster our analysis, we assess the model's performance post fine-tuning for specific tasks. In one instance, fine-tuning is conducted over a dataset comprising images of faces of children with and without autism. Prior to fine-tuning, the model demonstrated a test accuracy of 55%, which significantly improved to 83% post fine-tuning. These results, coupled with our prior findings, underscore the transformative potential of LLVAs and their versatile applications in real-world scenarios.
Recognition of Browsing a Book Using Smartwatches Toward Inferring Interest

For physical stores, it would be useful if they can know to what degree customers are interested in each item placed in the store before purchasing. There have been many studies on relationship between a person's behavior and interest to targets mainly focusing on their gazes. However, considering cases in which persons move around a store, keeping track of gazes of them using multiple camera devices and so on will be costly. This paper focuses on cases of browsing several books in a bookstore and presents a novel scheme to recognize a motion of browsing a book using one of popular wearable devices, a smartwatch. Time series of gyro sensor and acceleration sensors are analyzed to recognize time length of browsing and the number of times a page turned during browsing. Results of experiments show it can achieve F value over 0.8 regarding recognition of browsing time sections, and also show that the number of a page turned is recognized with high accuracy for a motion of turning a single page at a time.
Pasolini a Stoccolma

The article is an introduction to the monographic issue of Moderna Språk dedicated to Pasolini. It explains the reasons why the special issue was created and summarizes the articles content.
The Policy Decision Process of Converting Firefighters to National Position Explained Through Kingdon's Multiple Streams Model

This paper explored the Policy Decision Process of the firefighter national service by applying Kingdon's Multiple Stream Model. Methodologically, this analysis was used along with a literature analysis. For conducting the analysis, research from news, articles, reports, and theses was collected. In previous studies conducted using Kingdon’s model, the political stream and policy entrepreneurs play important roles in the process of policy making. However, in the case of the firefighter national service, the large forest fire in 2019 played a leading role, combining the three streams to establish an agenda setting, with the process rapidly promoted. The firefighter national service policy was irrationally formed by focusing on an event.
Evaluasi Program Pelayanan Publik Mobil Perpustakaan Keliling dalam Meningkatkan Budaya Literasi Siswa-Siswi Kabupaten Bintan

Based on the objectives of the mobile library car public service program in improving the literacy culture of Bintan Regency students. The Regional Library and Archives Office conducts a mobile library car tour by visiting various schools. This study aims to determine the evaluation of the mobile library car public service program in improving the literacy culture of Bintan Regency students. This research uses qualitative research methods with a descriptive approach. The location of this research is at the Regional Library and Archives Office of Bintan Regency. The results and discussion show that to achieve the goal of increasing the literacy culture of Bintan Regency students, the District Library and Archives Office has implemented the public service program to schools and the reciprocity obtained results in positive things and hopes to come back to provide a forum for students.
Analysis of Challenges Faced by PGMI Students at STAI Miftahul Ula Nganjuk in the Development of Digital Learning Media

Students in the Elementary School Education program are future prospective teachers. With the advancement of time, prospective teachers are required to adapt their teaching methods to the progress of information technology, one of which is digital-based learning media. In light of this issue, this research aims to describe PGMI students at STAI Miftahul Ula Nganjuk in developing digital learning media. This study employs a descriptive quantitative research method. The subjects of this study are PGMI students at STAI Miftahul Ula Nganjuk, selected using simple random sampling. The research instrument consists of five questions presented on the Google Form platform, answered by 53 respondents. Subsequently, the data is analyzed using descriptive statistics. The findings of this research indicate that PGMI students at STAI Miftahul Ula Nganjuk face difficulties in developing digital learning media. The factors contributing to these difficulties include: 1) the low understanding of PGMI students at STAI Miftahul Ula Nganjuk in developing digital learning media due to a lack of related training, 2) difficulties in summarizing materials used for the development of digital learning media, and 3) time constraints for developing digital-based learning media.
Offline Handwritten Text Extraction and Recognition Using CNN-BLSTM-CTC Network

Offline handwriting recognition is a significant research area that aims at tackling problems encountered with handwritten forms in college application and registration processes. The objective of this study is to address the problems of English language offline handwriting recognition via CNN-BLSTM-CTC neural network applied for an NCE Admission form. The system uses OpenCV for image processing, TensorFlow for neural network training and handwritten text recognition, and trains and tests it on the IAM database using image segmentation-based handwriting recognition. With the help of proper image verification, the system allows the users to upload images of the NCE Admission form provided that they strictly comply with the specified format; it denies access to images not conforming to the set standards. Following the successful delivery of a valid image, the form goes through extensive processing that includes text extraction from specific regions of interest (ROIs). The extracted texts are then passed to text recognition block. The recognized texts are then recorded in a CSV file under respective fields. The text recognition model has a CER of approximately 9.33%. The study performed with 15 NCE Admission forms found that the average Character Error Rate (CER) was approximately 12.2% for scanned images and 19.3% for camera-captured images. The results show that accuracy depends on aspects such as the quality and orientation of the image; thus, scanned images are preferred for better performance.
Providing English Village Training for Students and Boat Rowers at Rammang-Rammang World Geopark Tourism

This study aims to provide English Village training for high school students and boat rowers at Rammang-Rammang World Geopark Tourism. This activity is expected to attract the enthusiasm of students and boat rowers to learn English as a foreign language. The researchers conducted a study in Salenrang Rammang-Rammang Maros Village. The population of this study amounted to 98 people, consisting of 20 high school students and 78 boat rowers. The cluster sampling technique was used in selecting samples with prior knowledge of English so that only 33 were selected as respondents, namely 20 high school students and 13 boat rowers. The instrument used by researchers in this study is a questionnaire. Questionnaires are used to collect information about current conditions, identify respondents' needs in learning English in English Village and find out the problems encountered in speaking. The results reported that 33 respondents expressed enthusiasm to welcome the program, and they were well motivated to join the training. They were also happy to empower English language training because English is essential to their work as boat rowers. The same goes for the students because they can increase their knowledge of English in addition to what they get from school.
Load Time Optimization on React Website using Incremental Static Regeneration with NextJS

There’s a lot of tools that can be used to build or develop a website. Starting from basic HTML CSS and JavaScript to the use of UI Framework such as React, Angular, Vue JS or Svelte But, the use of UI Framework doesn’t come with no cons. UI Framework like React, use virtual DOM, instead of modifying the DOM directly, so when the first time application load, the framework needs to load library to modify the virtual DOM, before the page can load. This can leads to slow first loading time. This paper research performance improvement when using ISR ( Incremental Static Regeneration ) in NextJS