Reviewerly/exHarmony huggingface dataset:
Survey on Decentralized Democracy: Blockchain Voting System

Voting is a fundamental pillar of democracy, but traditional voting systems face numerous challenges, including concerns related to security, transparency, and accessibility. In recent years, blockchain technology has emerged as a promising solution to revolutionize the voting process. By leveraging the inherent characteristics of blockchain technology, a blockchain-based voting system can enhance the integrity and trustworthiness of the voting process. Blockchain-based voting systems offer several key features and benefits. One of the main advantages is the security and immutability of voter data, which makes it virtually impossible to tamper with or manipulate. Each vote is recorded as a transaction on the blockchain, creating a transparent and auditable ledger that can be accessed by all stakeholders. This eliminates the need for intermediaries such as government agencies or third-party voting providers, reducing the risk of interference and tampering. Another advantage of blockchain-based voting systems is increased accessibility. Remote and mobile voting can help improve voter turnout and participation, especially for individuals who may have difficulty accessing traditional polling stations. Moreover, blockchain technology can maintain the anonymity of voters while ensuring the verifiability of their choices, which guarantees the privacy of their decisions. Despite these benefits, challenges persist, including concerns about identity verification and cybersecurity. Addressing these issues is crucial for the widespread adoption of blockchain-based voting systems. In conclusion, a blockchain-based voting system has the potential to transform the way elections are conducted by providing a secure, efficient, and transparent platform that empowers citizens and strengthens the democratic process. As the technology continues to evolve, it is essential to address its limitations while harnessing its advantages to build a more robust and inclusive electoral system. Keywords: Blockchain, voting, election, decentralized, secure, transparency, immutability, privacy.
Automatic Intelligence Caption Generator

An Image Caption Generator is a sophisticated AI system that combines computer vision and natural language processing to automatically create descriptive textual captions for images. This technology utilizes deep learning, particularly Convolutional Neural Networks (CNNs), to analyze and extract meaningful visual features from the input image. These features capture details about the objects, scenes, and elements within the image. Subsequently, a natural language processing model, often built on Recurrent Neural Networks (RNNs) or Transformers, processes these visual features and generates coherent, contextually relevant captions. Post-processing steps may be applied to enhance the quality of the generated text. The primary aim of Image Caption Generators is to facilitate image understanding, improve accessibility, and enhance content search ability by providing human-readable descriptions for visual content. This technology is instrumental in various fields, including content tagging, accessibility tools for the visually impaired, and enhancing user experiences in multimedia content management systems, ultimately bridging the gap between visual and textual information for a more comprehensive and human-like interpretation of image. Key Words:Image Recognition, Internet, Image-To-Caption, Contextual Understanding,Image Captioning.
RECIPE GENERATION FROM FOOD IMAGES USING DEEP LEARNING

The research presented here is centered on the emergence of deep learning processes related to food, with a particular focus on map generation and cross-modal food retrieval. These tasks are aimed at enhancing their performance through unsupervised learning using map tree models. Creating recipes involves assembling food from images, which are obtained using a slicer. To enable this, different foods must be associated with images that resemble recipes based on queries. The challenge of translating directly from images to text led to the incorporation of visual objects in supporting these images, which ultimately improves performance. The Recipe1M dataset serves as the foundation of this research, featuring images of recipes along with ingredient descriptions and cooking instructions. However, there are significant differences from typical image-text cross-modal datasets like Flickr and MS-COCO, mainly due to multi-sentence cooking instructions and the lack of ingredient images. To address these challenges, a Structure-Aware Generative Network (SGN) is introduced, combining tree data with training methods to generate maps. This involves utilizing an RNN for map tree creation based on food images and integrating tree models into recipe generation with the help of GAT, ensuring detailed food descriptions. The study also highlights the efficacy of unsupervised learning using map tree models in cross-modal food retrieval. Various modules like 2tree, img2tree, and tree2recipe are employed to handle tree structures, contributing to the recipe generation process. The contributions of this research include the proposal of a 2-tree map, the introduction of img2tree and tree2recipe modules for creating and utilizing tree models, and the demonstration of performance improvements over existing methods. Additionally, experiments beyond the Recipe1M dataset are conducted to validate the effectiveness of the proposed approach. In summary, this study addresses the gap in knowledge related to long-cooked foods by employing unsupervised sentence-level tree learning. This approach integrates recipes and enhances cross-modal food retrieval, showcasing the potential to transform cooking-related tasks and achieving state- of-the-art performance on the Recipe1M dataset. The research includes imaging and ablation studies to assess its effectiveness thoroughly Key Words- Deep Learning, Unsupervised Learning, Cross-Modal Food Retrieval, Recipe Generation, Structure-Aware Generative Network, Map Tree Models, Recipe1M Dataset.
DEEPFAKE DETECTION USING DEEP LEARNING (CNN+LSTM)

Artificial intelligence advancements have led to the development of deepfake technology, which seriously jeopardises the integrity of visual media material. Robust detection algorithms are becoming more and more necessary as deepfake creation techniques become more complex. This study combines Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNNs) to present a novel method for deepfake identification. The suggested CNN+LSTM architecture makes use of LSTMs' temporal modelling capabilities and CNNs' spatial feature extraction capabilities. While the LSTM component analyses the temporal connections between frames to identify patterns suggestive of deepfake manipulation, the CNN component concentrates on capturing local features and patterns in individual frames. The combination of these two networks improves the model's capacity to identify minute anomalies and inconsistencies that are indicative of deepfake content. To extract frame-level characteristics, we use Res-Next Convolutional Neural Networks. These attributes are then used to train a Recurrent Neural Network (RNN) based on Long Short-Term Memory (LSTM) to determine whether a video has been manipulated, i.e., whether it is a deepfake or a genuine video. We intend to train our deepfake detection model on a varied set of public datasets in order to improve its real-time performance. We improve the model's adaptability by learning features from different photos. Face-Forensic++, Deepfake Detection Challenge, and Celeb-DF datasets are used to extract videos. Furthermore, to assure competitive performance in real-world scenarios, our model will be assessed against a large amount of real-time data, including the YouTube dataset. Key Words: Temporal modelling, Deepfake technology , Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs)
Language Model for Translating the English Language to Garhwali Language

The main aim of this paper is to develop a language model for translating the English language to Garhwali, a language spoken in the Garhwal region of Uttarakhand, India. The model will use state-of-the-art natural language processing techniques, including machine learning and neural networks, to enable accurate and efficient translation of English sentences into Garhwali and vice versa. The model will be trained on a large dataset of parallel English-Garhwali text to ensure high accuracy and fluency of the translations. The successful development of this language model will help bridge the language barrier between English speakers and Garhwali speakers, facilitate communication and exchange of ideas, and promote cultural exchange and understanding. Local language conversion system can help to improve the user experience by providing content in the language that the user is most comfortable with. It can help to reduce confusion and frustration, and make the user experience more enjoyable. Keywords: Statistical Machine Translation, Mathematical model, English-Garhwali, Evaluation.
A STUDY ON “MOTIVATION OF JUNIOR WORKERS TO ENHANCE THE PERFORMANCE” AT BOMBAY RAYON FASHIONS LIMITED, DODDABALLPUR.

This study explores the factors influencing junior workers' motivation and their impact on performance improvement. Using a mixed-methods approach, it interviews and surveys junior employees to understand their personal motivators, job satisfaction levels, and perceived barriers to optimal performance. The findings suggest a positive correlation between effective motivation strategies and performance improvement. A supportive work environment, skill development opportunities, and transparent communication channels are expected to be critical contributors. The findings can help organizations tailor motivational initiatives to junior workers' unique needs, fostering a more engaged and productive workforce. The study contributes to existing knowledge on motivation and performance.
The influence of intangible assets disclosures on businesses’ performance

The purpose of this research is to determine how intangible asset declarations affected manufacturing companies listed on the Palestine Exchange in terms of performance throughout the years [2017-2021]. The study sample consisted of 13 industrial companies listed on the Palestinian Stock Exchange, and in order to verify the study hypotheses, a descriptive analytical approach was followed. Several statistical methods were used, such as random effect analysis. After conducting the required tests, the results of the tests showed that the intangible assets do not have an effective impact on the performance of the companies that were studied, as the relationship was negative and it was not proven that the intangibility of assets had any positive results. However, it was found that leverage has a positive effect on the performance of these companies. The study recommended that the approaches for service and industrial companies should be improved and developed to enhance the level of performance in accordance with developments that occurred in the financial market.
Inter-rater Reliability and Cost in Pricing for Creating Dataset Focused on Mediolateral Oblique View in Mammography

【目的】マンモグラフィのポジショニングを評価する深層学習モデルの生成を目指している．精度良い学習用データセットの生成のため，ラベリングにおける評価者間信頼性とその作業負荷を評価した．【方法】107枚の画像について，検診マンモグラフィ撮影認定技師2名が画像評価基準に基づいて判定し，カッパ係数にて一致度を評価した．更に，1枚当たりの作業コストを作業時間と月収から算出した．【結果】カッパ係数は，乳房下部は0.71, 乳頭側面性は0.43, 大胸筋は0.45, 左右対称性は0.10, 乳腺後隙は0.61であった．乳線の広がりは有意差が認められなかった．作業コストは1枚11.0円となった．【結語】評価者間信頼性は，乳房下部，乳頭側面性，大胸筋，乳腺後隙は「中程度の一致」から「かなりの一致」となった．左右対称性は「わずかな一致」で，ラベル付与には評価者間の十分な合意形成が必要と考えられた．ラベル付与のコストは既存サービスと同等もしくは高い値となった．
Metodologia para Projeto de Sistemas Fuzzy no Domínio Hipercomplexo Aplicada em Filtragem Adaptativa

In this paper, a fuzzy descent method applied to adaptive filtering algorithms in the hypercomplex domain is proposed. The adopted methodology consists of Mamdani fuzzy inference system design, in the hypercomplex domain, applied to the computation of adaptation step size to be adopted for the convergence of the adaptive filtering algorithm. The partitioning of the universe of discourse regarding to the components of input and output hypercomplex variables of fuzzy inference system, to define the rule base, is performed by the expert’s knowledge about the application. For validation purposes, the proposed methodology was applied to a Normalized Least Mean Square (NLMS) filter variation, which is based on the minimization of cost function of the Recursive Least Square (RLS) algorithm. Computational results and comparative analysis illustrates the efficiency and applicability of the proposed methodology, providing lower computational cost and lower final misfit for estimating the optimal weights of the adaptive filter.
Projeto de Filtro de Kalman Fuzzy Evolutivo Tipo-2 Intervalar a Partir de Dados Experimentais

This paper presents a computational approach for interval type-2 fuzzy evolving Kalman filters desingnig from experimental data. First, an initial parametric estimation of interval type-2 evolving fuzzy Kalman filter model is obtained considering an initial window of the experimental data. From this initial estimation, the parameters defined in the antecedent and consequent propositions of interval type-2 evolving fuzzy Kalman filter are updated recursively from each new sample of the dataset through a type-2 fuzzy version of evolving Takagi-Sugeno (eTS) clustering algorithm and a type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm, respectively. Computational results and comparative analysis demonstrate the efficiency and applicability of the proposed methodology when applied to Mackey–Glass chaotic time series modeling.
A Systematic Exploration of Bug Bounty Platforms

Abstract: This review explores the intersection of Bug Bounty Programs and Blockchain Security, aiming to address the evolving challenges and advancements in this critical domain. The rationale for this review is rooted in the escalating importance of securing blockchain networks, and the role bug bounty programs play in fortifying these digital ecosystems. Focusing on many seminal studies, including investigations into decentralized security bounty management, gas usage reduction in Ethereum smart contracts, and predictive models for the effectiveness of bug bounty programs, this paper systematically evaluates diverse methodologies and their implications. The conclusions drawn from these analyses provide valuable insights into the dynamics of bug bounty platforms, bug hunters' perspectives, and the potential scalability solutions offered by emerging blockchain technologies. This abstract encapsulates the essence of the comprehensive review, offering a glimpse into the multifaceted landscape of bug bounty programs in blockchain security. The findings presented underscore the critical need for adaptive security measures in blockchain environments, positioning bug bounty programs as instrumental tools in fortifying these digital infrastructures. As we navigate through the key studies, we uncover not only the current state of the field but also identify avenues for future research, thereby contributing to the ongoing discourse on securing the ever-expanding realm of blockchain technology.
Principles of environmental and social psychology in UI/UX design for metaverse social games: A case study of Horizon Worlds

Design psychology and environmental and social psychology are two independent yet interconnected fields. This paper aims to integrate these fields and explore their application in user interface/user experience (UI/UX) design for metaverse social games. The principles of design psychology help understand users’ cognitive and emotional needs, enabling the creation of user-friendly interfaces and interaction designs. On the other hand, principles of environmental and social psychology provide insights into how environmental and social factors influence user behavior and psychological states. Building upon the importance and application of design psychology principles in UI/UX design, we delve into the specific environmental and social psychology factors that impact UI/UX design in metaverse social games. As a virtual global social platform, the metaverse necessitates designers to consider users’ social needs, identity expression, and personal interactions, in order to create an immersive and authentic virtual social experience. Through an in-depth analysis of Zuckerberg’s “Horizon Worlds” and its UI/UX design, we explore the comprehensive application of design psychology and environmental and social psychology principles in metaverse social games. We examine aspects such as usability, personalization, social interaction, and emotional connection in UI/UX design and discuss how visual elements, sound effects, and interactive feedback can facilitate the establishment of emotional connections and social networks within the virtual world. This research aims to provide designers with deeper insights and guidance in creating metaverse social game interfaces that align with user needs and societal adaptability. It will further expand the theoretical foundation of UI/UX design for metaverse social games and serve as a case study and reference for empirical research on the application of environmental and social psychology in game interface design.
Active learning for data streams: a survey

Abstract Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in real time. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research.
Learning Single Image Rain Streak Removal Based on Deep Attention Mechanism

Bad weather conditions (e.g., rain or hazy) may significantly degrade the visual quality of captured images/videos and the performances of related applications (e.g., outdoor visual surveillance). To solve this problem, this paper presents to learn rain steak removal from a single image. By using the ECNet (Embedding Consistency Network, by Li et al., 2022) as our basis network architecture, a deep encoder-decoder-based network with channel attention and the proposed multi-scale pixel attention module (MSPAM) is presented to single image rain streak removal, i.e., deraining. Together with the "Rain Embedding Consistency" mechanism used in the ECNet, we have shown that the channel attention can be used to enhance the extracted features before being fed into the encoder, and our MSPAM can be embedded into the skip connection between the encoder and the decoder for further boosting the features to achieve better image reconstruction. Experimental results have demonstrated that the proposed framework outperforms the ECNet quantitatively and qualitatively.
Privacy-Preserving Image Transformation Method for Person Detection and Re-ID

In recent years, the number of surveillance cameras is on the rise, and the limitation on image recognition including person re-identification (Re-ID) is lifted to ensure a safer and more convenient world. However, this has led to increasing privacy concerns. To address this issue, numerous privacy-preserving technologies have been proposed but they focused primarily on detection or classification of people. In this study, we propose a novel image transformation method to preserve privacy in detecting and re-identifying people. Privacy-preserving images visually protect images of people, but a machine can detect and classify them without any additional information. Our results show that the privacy-preserving images, transformed by our method, maintained the accuracy of person detection and Re-ID compared to that of the images with gaussian noise, blur, and pixelation.
Accent-Preserving Voice Conversion between Native-Nonnative Speakers for Second Language Learning

The use of generated corrected accent speech with a learner’s own voice holds potential for second language learners to enhance their pronunciation through self-imitation exercises. However, synthesizing speech with a corrected accent poses significant challenges, particularly due to the unavailability of data from the same speaker exhibiting both native and non-native accents. To address this, voice conversion (VC) and foreign accent correction (FAC) approaches have been explored. VC aims to transform the voice of a native speaker source while preserving linguistic content, whereas FAC targets accent conversion of a non-native source while retaining the speaker’s voice. This study investigates the application of the MaskCycleGAN-VC model as an approach for generating corrected accent speech from native sources. The model is evaluated as it was originally proposed and with the adoption of speaker embeddings, speaker native language ID, conditioned discriminator, and accent loss. Objective and subjective metrics are assessed on English and Japanese corpora, and including speaker embedding, speaker native language ID, and conditioned discriminator showed improvements.
Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks

Diffusion models have shown great potential for vision-related tasks, particularly for image generation. However, their training is typically conducted in a centralized manner, relying on data collected from publicly available sources. This approach may not be feasible or practical in many domains, such as the medical field, which involves privacy concerns over data collection. Despite the challenges associated with privacysensitive data, such domains could still benefit from valuable vision services provided by diffusion models. Federated learning (FL) plays a crucial role in enabling decentralized model training without compromising data privacy. Instead of collecting data, an FL system gathers model parameters, effectively safeguarding the private data of different parties involved. This makes FL systems vital for managing decentralized learning tasks, especially in scenarios where privacy-sensitive data is distributed across a network of clients. Nonetheless, FL presents its own set of challenges due to its distributed nature and privacy-preserving properties. Therefore, in this study, we explore the FL strategy to train diffusion models, paving the way for the development of federated diffusion models. We conduct experiments on various FL scenarios, and our findings demonstrate that federated diffusion models have great potential to deliver vision services to privacy-sensitive domains.