Reviewerly/exHarmony huggingface dataset:
Low-Cost Training of Image-to-Image Diffusion Models with Incremental Learning and Task/Domain Adaptation

Diffusion models specialized in image-to-image translation tasks, like inpainting and colorization, have outperformed the state of the art, yet their computational requirements are exceptionally demanding. This study analyzes different strategies to train image-to-image diffusion models in a low-resource setting. The studied strategies include incremental learning and task/domain transfer learning. First, a base model for human face inpainting is trained from scratch with an incremental learning strategy. The resulting model achieves an FID score almost equivalent to that of its batch learning equivalent while significantly reducing the training time. Second, the base model is fine-tuned to perform a different task, image colorization, and, in a different domain, landscape images. The resulting colorization models showcase exceptional performances with a minimal number of training epochs. We examine the impact of different configurations and provide insights into the ability of image-to-image diffusion models for transfer learning across tasks and domains.
ContextualHomeLLM: A Context-Aware Large Language Model for Smart Homes

ContextualHomeLLM is a state-of-the-art Large Language Model (LLM) specifically designed to enhance user interactions and smart home management. Leveraging the power of Transformer architecture, this model is trained to understand, interpret, and respond to user queries, commands, and contextual cues within the smart home ecosystem. By integrating advanced contextual understanding, personalized recommendations, and real-time control, ContextualHomeLLM offers an innovative approach to seamless smart home control and assistance.
Sentiment Analysis of Product Reviews Based on Bi-LSTM and Max Pooling

Product reviews are discussions about the value of the product, for which customers will have different emotional expressions. It is necessary to analyze whether customers have negative, positive, or other emotions about the value of the product, to facilitate manufacturers and sales manufacturers to upgrade and improve the product and analyze customers’ demand for the product. A text sentiment analysis model is constructed through sentiment dictionary and machine learning, a benchmark model is set up, and a method combining Bi-LSTM (Bi-directional Long Short-Term Memory), and Max pooling is proposed to construct a deep learning model of text sentiment analysis. Through experiments, the accuracy of the model in the sentiment classification of product reviews reaches 0.9332, which is higher than the result of SVM (Support Vector Machine) (0.9214) in machine learning and the benchmark model (Gradient Boosting) (0.8972).
EnigmaScan: Streamlining Matrix Puzzle Solving through Image Recognition

In an era increasingly shaped by technology, the enduring allure of word puzzles, such as word searches, crosswords, and Sudoku, remains steadfast. Yet, manually tackling these puzzles can prove to be a time-intensive and frequently exasperating endeavor. To meet this challenge, we suggest employing image recognition-based algorithms designed for puzzle-solving, specifically targeting Sudoku, word searches, and crossword puzzles. EnigmaScan is publicly accessible at https://github.com/theNewtonCode/EnigmaScan, allowing users to promptly solve their puzzles in real-time by uploading snapshots of unsolved Sudoku, word searches, or crosswords. This ground-breaking tool is designed to streamline and hasten the puzzle-solving process for users of all proficiency levels, thereby promoting a more enjoyable and accessible interaction with these timeless pastimes. The proposed algorithms exhibit remarkable efficiency, solving these puzzles within seconds and achieving an accuracy of 100%.
Research on Chinese Spam SMS Classification Based on ECA-TextCNN Model

In response to the issues of traditional Chinese spam SMS classification models, which fail to comprehensively extract text features and obtain representative text features, we propose a text classification model based on the ECA-TextCNN model, combining an efficient channel attention mechanism and TextCNN classification model. Firstly, the model utilizes a skip-gram pre-training model for text representation. Secondly, it employs convolutional kernels of different sizes for feature extraction. Furthermore, ECA-Net is used to assign corresponding weights to each channel feature. Finally, the feature vectors are inputted into the softmax layer to obtain the classification results. The experiments are conducted on the publicly available Message80W Chinese SMS dataset. The results show that compared to the baseline model, the text classification model based on ECA-TextCNN exhibits varying degrees of improvement in classification accuracy, precision, recall, and F1 value.
Positive Solution of Moving Platform Position Based on Bat-Iterative Algorithm

To address the forward position problem of a 6-DOF moving platform, a Bat-iterative method leveraging the Bat algorithm was proposed as a solution. By combining the Bat algorithm with the traditional Newton-Raphson iterative algorithm, the convergence speed and accuracy of the algorithm can be improved while the convergence and global search ability are guaranteed. The concrete implementation process of the algorithm is given, and the corresponding positive solution problem of 6-DOF parallel mechanism is solved by using the algorithm, and the corresponding operation example is given. At the same time, the inverse solution method is used to obtain the data set for error analysis and comparison. In summary, the comparison results demonstrate that the Bat-iterative algorithm offers notable benefits such as fast computation speed and high precision. As a result, it proves to be well-suited for practical engineering applications, meeting the necessary requirements effectively.
Predictive Modelling for Wordle Game: Analysing Word Attributes and Future Trends

Wordle, an engaging online crossword puzzle, challenges players to decipher a hidden five-letter word. In this manuscript, our primary focus lies in predicting the distribution of reported results in the Wordle game. To accomplish this, we devised and compared multiple predictive models, seeking the most accurate and stable solution. The foundation of our research revolves around training 31 distinct word attributes through five diverse multi-output regression data processing models. Then we choose a model whose average error of the test set error is very small, indicating that the prediction accuracy of our model is better, and also more stable than other models. Finally, to illustrate the practical application of our model, we provide a concrete example of predicting the associated percentages of (1, 2, 3, 4, 5, 6, X) for the word “EERIE” in future game instances.
Citation Analysis of Post Graduate Theses Submitted to Department of Renewable Energy Sources in College of Agricultural Engineering and Technology, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli, Maharashtra

Citation analysis helps to understand the different ways that users look for material. Citation analysis can be used to calculate the obsolescence rate. Finding the rate of obsolescence of publications in the field of renewable energy sources is the aim of this study. Citation analysis is being done on M.Tech. (Agril.Engg.) PG theses that were submitted to the College of Agricultural Engineering and Technology's department of Renewable Energy Sources. The study uses theses that were submitted between 2017 and 2021. There are 14 theses in total, with 707 citations. Analysis is also conducted to determine the Authorship pattern and Types of Information Sources that the researchers mentioned in their theses.
Unary Quantum Finite State Automata with Control Language

We study quantum finite automata with control language (QFCs), a theoretical model for finite memory hybrid systems coupling a classical computational framework with a quantum component. We constructively show how to simulate measure-once, measure-many, reversible, and Latvian QFAs by QFCs, emphasizing the size cost of such simulations. Next, we prove the decidability of testing the periodicity of the stochastic events induced by a given QFC. Thanks to our QFA simulations, we can extend such a decidability result to measure-once, measure-many, reversible, and Latvian QFAs as well. Finally, we focus on comparing the size efficiency of quantum and classical finite state automata on unary regular language recognition. We show that unary regular languages can be recognized by isolated cut point QFCs for which the size is generally quadratically smaller than the size of equivalent DFAs.
Asymmetric VR Game Subgenres: Implications for Analysis and Design

This paper identifies subgenres of asymmetric virtual reality (AVR) games and proposes the AVR Game Genre (AVRGG) framework for developing AVR games. We examined 66 games “in the wild” to develop the AVRGG and used it to identify 5 subgenres of AVR games including David(s) vs. Goliath, Hide and Seek, Perspective Puzzle, Order Simulation, and Lifeline. We describe these genres, which account for nearly half of the 66 games reviewed, in terms of the AVRGG framework that highlights salient asymmetries in the mechanics, dynamics, and aesthetics categories. To evaluate the usefulness of the AVRGG framework, we conducted four workshops (two with the AVRGG framework and two without) with novice game designers who generated 16 original AVR game concepts. Comparisons between the workshop groups, observations of the design sessions, focus groups, and surveys showed the promise and limitations of the AVRGG framework as a design tool. We found that novice designers were able to understand and apply the AVRGG framework after only a brief introduction. The observations indicated two primary challenges that AVR designers face: balancing the game between VR and non-VR player(s) and generating original game concepts. The AVRGG framework helped overcome the balancing concerns due to its ability to inspire novice game designers with example subgenres and draw attention to the asymmetric mechanics and competitive/cooperative nature of games. While half of those who used the AVRGG framework to design with created games that fit directly into existing subgenres, the other half viewed the subgenres as “creative constraints” useful in jumpstarting novel game designs that combined, modified, or purposefully avoided existing subgenres. Additional benefits and limitations of the AVRGG framework are outlined in the paper.
Sustainable Optimizing Performance and Energy Efficiency in Proof of Work Blockchain: A Multilinear Regression Approach

The energy-intensive characteristics of the computations performed by graphics processing units (GPUs) in proof-of-work (PoW) blockchain technology are readily apparent. The optimization of GPU feature configuration is a complex subject that significantly impacts a system’s energy consumption and performance efficiency. The primary objectives of this study are to examine and improve the energy consumption characteristics of GPUs, which play a crucial role in the functioning of blockchains and the mining of cryptocurrencies. This study examines the complex relationship between GPU configurations and system architecture components and their effects on energy efficiency and sustainability. The methodology of this study conducts experiments involving various GPU models and mining software, evaluating their effectiveness across various configurations and environments. Multilinear regression analysis is used to study the complex relationships between critical performance indicators like power consumption, thermal dynamics, core speed, and hash rate and their effects on energy efficiency and performance. The results reveal that strategically adjusting GPU hardware, software, and configuration can preserve substantial energy while preserving computational efficiency. GPU core speed, temperature, core memory speed, ETASH algorithms, fan speed, and energy usage significantly affected the dependent computational-efficiency variable (p = 0.000 and R2 = 0.962) using multilinear regression analysis. GPU core speed, temperature, core memory speed, fan speed, and energy usage significantly affected efficient energy usage (p = 0.000 and R2 = 0.989). The contributions of this study offer practical recommendations for optimizing the feature configurations of GPUs to reduce energy consumption, mitigate the environmental impacts of blockchain operations, and contribute to the current research on performance in PoW blockchain applications.
Not Simply the Surface: Manifesting Meaning in What Lies Above.

A recommendation of: Sara Daffara, Carlo Giraudi, Gabriele L.F. Berruti, Sandro Caracausi, Francesca Garanzini First evidence of a Palaeolithic occupation of the Po plain in Piedmont: the case of Trino (north-western Italy) https://doi.org/10.31219/osf.io/pz4uf
An In-Depth Analysis of Collision Avoidance Path Planning Algorithms in Autonomous Vehicles

Abstract: Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding an optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment.
ENHANCING SENTIMENT ANALYSIS ACCURACY IN DELIVERY SERVICES THROUGH SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE

Paxel is one of the delivery services that use the application. On Google Play, there are more than 10 thousand users leaving reviews. From this review data, a sentiment analysis was then carried out to determine the level of user satisfaction with Paxel's services. The methods used in this study are Random Forest (RF) and Support Vector Machine (SVM), as well as applying Synthetic Minority Oversampling Technique (SMOTE) to overcome data imbalance. The results showed that the method testing by dividing the data into two, namely training data and testing data by 80:20, stated that by applying the SMOTE, a higher accuracy value was obtained, where the accuracy of the RF method reached 91%, and the SVM method reached 87%. The level of user satisfaction with Paxel services tends to be neutral. This can be seen in the classification of the RF method with F1-Score values for the Positive class 89%, Neutral class 93%, and Negative class 92%.
Gerakan literasi berbasis inklusi sosial di Taman Bacaan Masyarakat Sehati Kabupaten Bandung

The Sehati Community Reading Park (Taman Bacaan Masyarakat, TBM) in Bandung Regency is an initiative aimed at enhancing the reading interest of the community, especially in the Cimaung District of Bandung Regency. The literacy movement, organized by TBM Sehati, is designed for all ages, from children to adults. This research aims to understand the literacy movement at the Sehati TBM in Bandung Regency by analyzing the types of activities conducted and the benefits of the literacy movement. A qualitative approach through a case study research method was used in this study. The results indicate that the activities within the literacy movement at TBM Sehati are divided into two categories: routine and non-routine activities. Routine activities include reading and writing training, religious study groups, training for managers and volunteers, as well as drawing and painting workshops. Non-routine activities include baking training, waste management programs (fabric and cattle waste), and writing training (village journalism). The community literacy movement aims to improve the local economy, provide free access to knowledge, offer skills in arts and culture, and ensure an adequate learning environment. Volunteers gain knowledge, experience, a sense of joy, and gratitude. TBM managers also receive community support and trust, establish relationships and cooperation with various parties, benefit financially, and gain unexpected experiences and opportunities. The research concludes that the activities within the literacy movement at TBM Sehati have positively impacted the surrounding community, volunteers, and the TBM managers themselves.
Implementasi Model Atik dalam Mengembangkan Kecerdasan Kinestetik di Pos PAUD Sedap Malam 16

There are a number of intelligences within children those if developed can become special potentials and talents, one of which is kinesthetic intelligence. One of the efforts to improve kinesthetic intelligence in children can be done with fun activities, one of which is with a well-known early childhood learning model, namely the ATIK model (Observe, Imitate and Do). This study aims to develop an ATIK model in improving children's ability to observe, imitate, and be able to perform movements with the hope and purpose of improving kinesthetic intelligence. The study used descriptive qualitative methods. The data source came from group B students of PAUD Sedap Malam 16 Post Students totaling 15 students. Data collection through observation, interviews and documentation. The results showed that the implementation of the ATIK model was effective in developing children's kinesthetic intelligence through foot pattern motoric play activities. The ATIK model in the game imitating foot patterns provides an increase in children's curiosity by observing, imitating and working so that children's kinesthetic development activities are carried out happily, purposefully and meaningfully.
Blockchain in healthcare: A comprehensive review of applications and security concerns

Blockchain technology presents a transformative opportunity for the healthcare industry, offering enhanced data security, integrity, transparency, and operational efficiency. This paper comprehensively examines blockchain's applications, advantages, limitations, and challenges in healthcare. Blockchain ensures the privacy of patient data while complying with regulations such as HIPAA and GDPR, and its immutability guarantees data integrity. Moreover, blockchain streamlines operations, reducing fraud and errors in billing and claims processing. Despite its potential, blockchain faces technical complexities, regulatory hurdles, and scalability issues. Interoperability between diverse healthcare systems remains challenging, and energy consumption is a concern in some blockchain implementations. Addressing these challenges and fostering education and adoption are crucial steps toward realizing the full potential of blockchain in revolutionizing healthcare delivery.
EXPERT SYSTEM FOR DISEASE IDENTIFICATION BASED ON HEMATOCHEZIA SYMPTOMS WITH NAÏVE BAYES METHOD

Hematochezia is a common clinical symptom in various gastrointestinal diseases, requiring accurate diagnosis for effective treatment. This study aims to develop an expert system for the rapid and precise identification of hematochezia-causing diseases. The expert system is designed to assist patients in efficiently recognizing diseases, minimizing treatment failure risks. It employs the Naïve Bayes method, a data calculation approach involving summing combinations and frequencies of each dataset. The expert system methodology begins with training using a dataset comprising hematochezia symptoms and corresponding disease diagnoses. The dataset is input into a database as training data. Subsequently, it undergoes classification and training stages. Symptom data can then be processed using the Naïve Bayes method. The system's end result displays probability values for each disease based on provided symptoms. This analysis relies on specific symptoms selected by the user, such as Rectal Pain, Hematochezia, Constipation, Fatigue, and Abdominal Cramps. It yields a Hemorrhoids diagnosis with a posterior probability of 0.514738. In testing with 35 sample cases, the expert system exhibited a remarkable accuracy rate of 94.29%. This expert system efficiently supports disease diagnosis based on hematochezia symptoms, aiding in swift and accurate identification.
Usulan Strategi Bisnis untuk Meningkatkan Keunggulan Kompetitif Usaha Pada Startup Prove Fragrance dengan Menggunakan Metode SWOT

This study delves into understanding the factors contributing to the sales decline experienced by the rebranded cosmetics startup "Prove." Employing qualitative research and fishbone diagram techniques, four primary root causes emerged: insufficient product information dissemination, limited product variety, underutilized digital marketing, and a lack of robust business development strategies. The proposed solution involves crafting a more effective business development strategy, recognizing that Prove lacked a well-defined approach to enhance its competitive advantage. The research emphasizes a SWOT analysis of Prove, focusing on its internal and external environment. The study, centered around Prove's Bandung office, involves the CEO and several employees as the unit of analysis. Collected through in-depth interviews, field observations, and documentation. The results highlight Prove's strengths in innovative product design and creative opportunities for partnerships and business expansion.
Perubahan Teknologi Pembelajaran

The purpose of this study is to examine the impact of the development of information and communication technology on individual awareness of their identity, focusing on shifts in the way they think, behave, the language used, and the values held. The spread of the Covid-19 virus has changed life in the Land of Papua, including education. Although evenly distributed, this virus forced a paradigm shift in learning from face-to-face to online. While this leap has been easier for generations growing up in the digital age, older teachers have had to adjust. Primary education, especially in rural areas, faces extra challenges with the operational needs of networks and the lack of teaching online pedagogy. Parents at the elementary level helped, but not all were able to provide good support, some even made poems so that the pandemic would soon pass. Middle and high school teachers find it difficult to transform affective aspects through online learning, pointing to intergenerational challenges and significant changes in the education system. This research uses qualitative methods with phenomenological research types. Phenomenon study is a data collection technique by collecting and analyzing phenomena that occur. Phenomena are observed, interviewed then analyzed, compared and combined (synthesis) to form a systematic, integrated and complete study.