Reviewerly/exHarmony huggingface dataset:
Smart Transportation and Challenges in Edge Computing with Blockchain

The typical centralized cloud computing system cannot meet the Quality of Service (QoS) prerequisites for some applications, notably those that require real-time, reliability, and security, since to the increment of connected gadgets to the Internet of Things (IoTs). Cloud computing has advanced with the emergence of edge computing. Its primary strength is its ability to move delicate information from the cloud to the edge while improving organization security, empowering high-recurrence correspondence, and empowering continuous information move. The edge computing network faces a number of serious security challenges as a result of the storage of confidential and sensitive data on edge servers. Since the information produced by IoT gadgets is parted into a few pieces and put away in different edge servers that are scattered across different areas, it is trying to guarantee information trustworthiness because of information misfortune and mistaken information capacity in edge servers. With the ascent of edge figuring, there are different security and information trustworthiness gives that blockchain innovation can address. We give an exhaustive assessment of edge processing in blend with blockchain innovation in this article. We start by presenting the models for blockchain and edge figuring. The objectives and plan of edge processing with combination of blockchain are then covered. The third concentrate on the blend of edge figuring and blockchain is advertised. The exploration issues are at last investigated.
Handwritten Text Recognition Using Word Beam Search

The automated location of and transcription of handwritten text continue to be difficult problems despite several attempts. The accuracy of the following recognition process is impacted by the frequent faults in text detection and segmentation algorithms. For applications requiring sequence recognition, such as handwritten text recognition, recurrent neural networks (RNNs) are employed. When RNN is trained with CTC decoding algorithm,every output probabilities is stored in a matrix.Final text is converted using Word Beam Search algorithm. One such strategy is token passing, which can restrict the recognised text to a set of dictionary words. Because of its foundation in the Image to Sequence architecture, it can accurately sequence text that has been extracted from an image without imposing any limitations on the orientation, layout, or size of text and non-text. The proposed method outperforms token passing, best path decoding, and standard beam search decoding on the IAM datasets. There is an open-source version included. There are a variety of applications where processing vast amounts of handwritten data is important, such as document analysis, envelope address and postcode recognition, bank check amount interpretation, document analysis, and signature verification, can benefit greatly from automatic handwritten text recognition.
Identify The Potential Instance of Copy Forgery Using Similar Algorithm

In a similar fashion one of the most popular types of digital art, counterfeit detection necessitates knowledge of picture structures and the ability to generate visual diversions for many purposes, including entertainment. The use of color is very crucial for creating city features. Previous studies have examined the interrelationships of color within homogenous metropolitan zones, looking for connections between neighboring areas and culturally comparable peoples. Color, acclaim, the dissemination of one's color, and satisfaction. When communicating with a foreign audience, it is common practice to have the message reviewed and edited to conform to the local language and cultural norms.
Dynamic Ranking of Personalized Recommendations using Cosine Similarity and User Ratings

Finding an experienced person to guide a youngster to support in building their career or getting help from a young mind for and experienced person to support them with ever-changing technologies is not an easy task. Hence, many are facing issues in getting the right support at the right time. A very experienced non-technical businessman may need support from a youngster with strong technical skills may be for cloud support to ensure the growth of the company is not hindered. The solution to this problem is to develop a mobile application that creates an interface between retired people and youngsters such that both domains of people get the recommendation of the most suitable list of people belonging to other domain based on cosine similarity between the profile vectors of two people and the ranking of the displayed list of people is dynamically updated based on the multi-criterion rating given by other users to people in the list. Here both the retired people and the youngsters who wish to provide support are termed as supporters and the people who desire to get support are termed as users. This reduces the work of users because they need not explicitly search for the people who are most suitable for providing support for them, they automatically get the recommendations, also the supporters can generate revenue. The application also allows the user to chat with supporters regarding any further issues by linking WhatsApp with the mobile application.
AutoML - Learning, Understanding and Applying Machine Learning to Datasets

Current scenario of the digital world is loaded with sample amounts of data and to smartly analyze this data requires researching an interesting machine learning model to suit a dataset which is a topic of research and is more focused upon. Various state of art machine learning models like supervised, unsupervised and reinforcement models along with deep learning models are available to analyze varied datasets and for a given machine learning task, selecting one model from a large pool of options is a surprisingly challenging problem, particularly if there is a lack of evaluation data to distinguish between models and this requires for a thorough understanding of the dataset which you are using to solve the task. Dataset plays an important role in the performance of any machine learning model and not always you will get a clean / well balanced dataset to directly apply the model on. The key contribution of this paper is focused on the challenge of picking a good model which can be reformulated by doing a proper analysis and cleaning of the dataset, with the objective of recommending a suitable model for a given task based on the performance of a small set of probe models under the domain of classification and regression.
No Key - Smart Door Unlock System Using Fingerprint from Bluetooth Device

Technology has improved, and smart locking systems have become more sophisticated. In this case, the android-based Smart System is primarily intended for multimode operations. Such a system is necessary in banks and businesses since it provides f u n c t i o n s that let users control locks. The implementation’s efficiency the system is incredibly helpful because of its functionality and user-friendly interface. Some homeowners aim to connect their home’s numerous home automation devices. Those connected to a Windows-based PC are the most popular home controllers. In our study, we introduced a form of smart technology that utilized Bluetooth while using a mobile smartphone. Consequently, using it will be simpler and more effective. Additionally, it supported the free and open-source Android and Arduino platforms. This paper proposes a door lock automation system that uses an Android smartphone with Bluetooth as the first piece of hardware. Following a description of the design and software development process, a Bluetooth-based Smartphone application for locking and unlocking doors is demonstrated. The task module acts as the agent in the hardware design for the door-lock system, the Arduino microcontroller serves as the controller and dataprocessing hub, and the solenoid acts as the door lock output. The results of each test show that it is compatible with the original plan for this study.
Semantic Modelling of Multivariate Time-Series Data in Cognitive IoT

With the use of sensors and intelligent devices to assist interactions with the physical environment, we observe a significant rise in the amount of sensory input, which is continual, volatile, sometimes actual, and diverse in structure. The Internet of Things (IoT) encapsulates this phenomenon. In addition, when IoT integrates cognition into system design, it is called Cognitive IoT (CIoT). CIoT requires techniques to autonomously extract semantic patterns from plain sensory data for efficient service delivery. Only a little scientific attention has been paid to automatically identifying semantic patterns from a massive time-series data set. This research presents a scaffold for real-time automatic semantic pattern extraction in the Cognitive Internet of Things. It utilizes the Symbolic Aggregate Approximation (SAX) technique for sensory data with a multivariate time series. Further, the output of SAX is used to create a symbol table with their frequency. This frequency table is transformed into its corresponding probabilistic value. Finally, this value is used to generate the semantic pattern, which can be further processed using a rule-based method to create a machine-interpretable pattern. Experimental evaluation of this research study reveals that the proposed method can extract semantic patterns from heterogeneous multivariate sensory data.
Hand Sign Recognition To Structured Sentences

Computer vision is not just a concept of deep learning; it has wide applications such as motion recognition, object recognition, video indexing, video media understanding, and recognition-based intelligence. -However, vision-based systems are a challenging field for research and accurate results. Recent areas of interest are human action recognition or human hands gesture recognition techniques using video data set, still, an image data set, spatiotemporal methods, features in RGB, deep learning methods. Hand action recognition has applications such as communication systems to shorten the bridge gap for people with speech disabilities by using a vision-based system to recognize hand sign language and convert it to text, forming structured sentences which will be easy to understand and communicate.
Deepcheque: a Large Language Model Approach to Automated Cheque Collection Framework and Information Retrival Using Multiple Loss Functions

The field of automated cheque collection has received a lot of attention recently due to the complexity and time-consuming nature of the manual process, which requires significant investment in technology, infrastructure, and staff training. However, some key areas such as Customer Experience and Interoperability with Image Processing and machine learning systems have not been explored. The challenge lies in the fact that handwritten characters are unique, and there is a lack of open-sourced foundation models. Furthermore, cheques and bank receipts contain both handwritten and non-handwritten text, making it difficult to extract information. The research proposes a novel framework that takes curvature into account when learning and employs CNN LSTM and Yolov5-OCR to extract information from printed and handwritten documents. The CTC-loss-function has limited application to processing bank cheques we had identified this gap and further improved overfitting which results due to CTC loss by combining CTC, curvature loss, and embedding loss, This research has further contributed to the development of an automated cheque collection framework that can extract information from both printed and handwritten texts, improving the overall efficiency of the cheque collection process and interoperability within various systems which is laborious in nature.
Joint Intent Classification and Slot Tagging on Agricultural Dataset for Indic Languages

Agriculture makes up for about 50% of the total workforce in India while contributing to approximately 20% of the Gross Domestic Product (GDP) as of 2021. Due to the lack of resources in regional languages for farmers, there is a dire need for a corpus that spans multiple topics relating to agriculture. The corpus can be developed by collecting data via web scraping. The existence of such a corpus must be accompanied by a classification system that could direct the farmers along the database. For this objective, the proposed work concentrates on designing an intent classification dataset, Query-based Agricultural Data System (QuADS) along with a high accuracy model that could be implemented for identifying what the input intent and slots are, and further be expanded to provide output using voice recognition. This paper mainly focuses on comparing the Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) models for intent recognition, which returned an accuracy of 93.89% and 98.32% on QuADS respectively. It further demonstrates the application of intent classification in the domain of a query-based voice assistant using google interfaced with python APIs for ease of farmer interaction.
Deep Dive On Various Security Challenges, Threats And Attacks Over The Cloud Security

Cloud computing has received more attention in recent years due to the obvious, ever-increasing demand. With cloud storage, there seem to be a number of benefits that businesses can reap. Cloud computing provides a simplified IT infrastructure and strategic planning, as well as the ability to access your data from all over the universe with a reliable Internet connection. More research is needed into cloud computing’s security and privacy issues. In existing studies, academic, industrial, and standards organisation researchers offered possible solutions to such problems. Cloud security issues and specifications, identified threats, as well as vulnerabilities are all discussed in this systematic review. In reality, the goal of this work is to investigate the various aspects of cloud computing, such as the present-day privacy and security issues that the systems face. Moreover, this study aims to introduce classification of security related mitigation strategies in this work. It also addressed unresolved issues and future directions of various kinds of security threats that threaten cloud computing. A deep understanding of the security concerns confronting various entities such as cloud users, data owners and cloud service providers will be the emphasis of this paper, which will be explored in great length.
Machine Learning Applied in Emotion Classification: A Survey on Dataset, Techniques, and Trends for Text Based Documents

One of the most explored yet challenging topics in text mining is emotion detection, which is an extension of sentiment analysis. Recent research on emotion recognition has mostly concentrated on gestures, speech, audio, and facial expressions. However, as most web material is text-based, it is challenging to accurately depict human emotions when there are not any audio or face features to the data. In this survey, the focus is on emotion detection from text-based sources using machine learning and deep learning approaches. Recent trends and opportunities for researchers in the said field are investigated, which can be used for coordinating the upcoming research to overcome shortcomings and use in applications accordingly.
Cyber Physical Systems: Security Technologies, Application and Defense

New security threats have surfaced in response to the meteoric rise in the prevalence of Cyber-Physical Systems (CPS). The latest iteration of CPS has a wide range of flaws, dangers, attacks, and safeguards. However, there hasn't been any comprehensive research done on CPS security problems. In particular, it has been challenging to examine the issue using a single generalized model due to the variability of CPS elements and the diversity of CPS systems. In this study, we use a holistic security model to examine CPS safety. We examine the technologies at the perceptual, network, and application levels. Smart grids, transportation, healthcare, and residential applications, along with the most common problems and solutions. Since many studies did not focus on a particular use case, we categorize them into broad categories, such as ICSs, cloud servers, and embedded devices.
Spot Brain Tumors Using MRI Scans

The vast majority of cells in the human body develop and divide in a well-regulated manner to generate new cells, which are necessary for the body to maintain its healthy and functional state. However, cells may undergo uncontrolled and rapid proliferation when they lose the ability to self-regulate their growth, leading to the formation of abnormal growths or tumors in the body's tissues. Brain tumors, for example, arise due to the excessive proliferation of cells in the brain. This paper intends to evaluate the present status of segmentation techniques utilized in biomedical image processing and suggest means to enhance them. The primary objective is to analyze the current status of semiautomatic and automatic methods for segmenting anatomical medical images, including their pros and cons. Through this evaluation, we hope to provide insight into techniques that can improve the accuracy and efficiency of segmentation methods. To achieve this goal, we employ database testing and training to detect brain cancers and classify them into different stages. Segmentation in the context of testing often involves the use of spatial fuzzy c-means (FCM), a clustering algorithm that partitions an image into different regions based on both intensity and spatial information. Our study will explore various ways to enhance the performance of segmentation methods, including the use of prior knowledge, combining multiple methods, and incorporating expert feedback. By improving the accuracy and efficiency of segmentation techniques, we can potentially provide better diagnosis, treatment, and monitoring of brain tumors and other medical conditions.
Record Suppression with Personalized Security System

Record suppression works best when several client’s re-appropriate comparable data to dispersed storage, but it presents security and ownership concerns. Each owner of similar information can prove to the distributed storage server that he has full ownership of the information by providing evidence of ownership schemes. Many clients would most likely encrypt their data before re-appropriating it to distributed storage to save security. The overwhelming majority of designs have security issues because they do not consider important shifts in information responsibility. We provide a sophisticated server-side deduplication method for encoded data in this work. Even when ownership radically changes, it enables the cloud server to restrict access to updated data. While the added computational cost is negligible, this paper plot is virtually as successful as the earlier versions. The recommended system defends against attacks based on label irregularities and information trustworthiness. This approach increases the security of the proposed application to guarantee data integrity and privacy.
Analyzing Data Compression Techniques for Biomedical Signals and Images Using Downsampling and Upsampling

In recent years, due to the enormous data utility everywhere, the need for data compression grows drastically in almost all fields of science and engineering. A few examples of data compression include such as mobile phones, CD players, DVD players, digital television, etc. Compression can be achieved with lossy or lossless; obviously, the algorithms become tough for the lossless compression for better efficiency. This article focuses mainly on the utility behind the downsampling and upsampling, and hence this would lead to the sub-band coding, which is a widely used data compression technique. During the process of sub-band coding, both the orthogonal and bi-orthogonal filters are used for the averaging and differencing tasks. Simulation results reveal that, as expected, the most energy compaction appears at the low-low sub-images. The Matlab 9.4 tool is used for experimental simulation with the retinal and asthma test images for data compression.
Improved CycleGAN for natural scenery images style transfer

Natural scenery images style transfer is a technique using computer technology to change the stylization effects of images by processing the high-level features which are extracted by images in neural networks, and is used to improve the diversities and aesthetics of images. Since existing neural network models cannot achieve a good effect when dealing with the style transfer tasks of natural photos, this paper proposes an improved CycleGAN method that has the advantage of changing two unpaired image datasets in style. In order to save more image content and solve the model overfitting problem, we added a channel attention mechanism to the generator and optimized the cycle consistency loss. We defined the developed loss function as MS-SSIM+SmoothL1 in this paper. The method can alleviate the overfitting phenomenon of the model as the epoch increases. The images generated by our proposed method have better performance in detail. Experiments demonstrate that the images generated by our proposed improved network are more correspond with human perception in visual. In the FID score, our proposed method was 42.24% lower in the Summer2winter datasets and 23.76% lower in the Monet2photo datasets than CycleGAN.
The Influence of Emoticons on New Product Evaluation

Emoticons are widely used to compensate for the lack of nonverbal elements in the communication process. Extant literature focuses on the benefits of using emoticons in a computer-mediated communication (CMC) context, whereas this research aimed to investigate the influence of emoticons on a new product evaluation and explore a potential boundary condition of emoticon use. A user guide for a new product was designed to examine the effect of emoticons in the knowledge acquisition context. The results showed that emoticons influence product attitude mediated by fluency elicited by emoticons, which in turn affects a product’ s perceived competency. Furthermore, the need for cognition (NFC) moderated the sequential multiple mediations; as NFC increased, perceived fluency decreased, implying that the goal of the information search is a potential boundary condition. Specifically, when the purpose of the information search was to acquire knowledge, using emoticons reduced fluency experience, resulting in low perceived product competency and a less favorable product attitude.
Analisis Kemampuan Menulis Puisi Bebas pada Siswa Kelas V SD Negeri 79 Palembang

This study aims to determine the ability to write free poetry in fifth grade students of SD Negeri 79Palembang. The method used in this research is a descriptive qualitative method. The total number of students studied in this study amounted to 15 students. The collection technique in this research is Observation, Interview and Documentation in the form of writing free poetry for fifth grade elementary school students. The data analysis technique used is data reduction, data presentation, conclusion drawing and verification. Aspects assessed in this study include indicators of poetry writing, namely the suitability of the content with the theme, diction and imagery. The results of the study showed that the ability to write free poetry in fifth grade students was categorized as sufficient. This can be seen from the aspect of the assessment. Conformity of the content with the title theme, totaling 6 students who get a score of 3, 6 students who get a score of 2, and 3 students who get a score of 1. For the aspect of Diction assessment, there are 2 students who get a score of 3, 6 students who get a score of 2, and 7 students who get a score of 1. For the Citraan assessment aspect, 3 students who get a score of 3, 1 student who gets a score of 2, and 11 students who get a score of 1.