Reviewerly/exHarmony huggingface dataset:
Analysis of Different Encryption Techniques used in Watermarking Algorithm for the Security of Medical Image

Today, in the computerized age, the development and use of internet technology in healthcare has been increasing rapidly. Quick improvement is going on in the telemedicine field with a clear goal to design integrated applications in diagnostics and treatment. Hence medical images are preserved in databases for some time so that post diagnosis treatment will become easier for the patient and for doctors as well. During the process of the treatment patients tend to take experts' advice and that time images of the reports will be shared from one hospital to another. With the use of sharing and transmission technique post treatment cost gets reduced whereas increases the issue concerning the security of patients medical reports. It is important to keep track of images shared between two medical centers to identify sources of image generation as well as protecting patient's sensitive information from intruders or hackers against unauthorized access and modification. Digital watermarking can be used to provide ownership and copyright protection. In the watermarking system image or text can be inserted in the cover image to get a watermarked image. The watermark image or secret image to be inserted can be protected with encryption technique. Hence this paper presents a digital watermarking system designed with encryption techniques to provide security to patients' medical records. The system is evaluated by implementing it with various encryption techniques like Advanced Encryption Standard (AES) with 256 bits, AES-GCM, Triple Data Encryption Standard (TDES), Blowfish etc. The performance of the system is measured with PSNR and SSIM and assessment of the evaluation parameters shows that AES encryption is better in comparison with TDES and Blowfish.
Functional Reference Generator for Dynamic Measurements in Electropuncture Diagnostics

In this paper, a functional reference current pulse generator of a given power for an electropuncture system is considered. The reference generator provides simultaneous registration of a study object section relative resistance value. The choice of the impact current magnitude to the study objects carried out by controlling a clock pulses frequency, leading to a change in their duty cycle. The power of the current pulse in this case remains constant, since the energy of the charged capacitor is determined. Controlling the magnitude of the impact current by changing the frequency of clock pulses is a distinctive feature of the functional generator proposed. Such approach makes it possible to easily switch to the reference generator mode of a given power at a clock frequency fixed value. Thanks to this solution, the functionality of the system under consideration is expanded. The results of the study are in good agreement with the available theoretical information.
Poster Paper: Towards a Comparison Framework for Blockchain Interoperability Implementations

Blockchain interoperability has gained importance in practice, is increasingly discussed in literature, and serves as basis for new use cases such as manufacturing and financial services. However, many of the blockchain interoperability solutions discussed in literature are still in the design phase, are unpopular or have a small developer community. Therefore, this study proposes a comparison framework for blockchain interoperability implementations, focusing on data from published GitHub repositories. Interim results show that these implementations vary significantly in terms of popularity, their developer communities as well as their source code, indicating differences in quality. The proposed framework facilitates the selection of an appropriate implementation to enable blockchain interoperability use cases.
Comprehensive reversible secret image sharing with palette cover images

The comprehensive reversible cover images make secret image sharing (SIS) has more applicable scenarios, such as remote-sensing images, medical diagnosis images and so on. The previous reversible secret image sharing (RSIS) methods use binary or grayscale images as cover images. In this paper, we design an effective reversible secret image sharing method using palette cover images (RSISP). Shadow pixel values are screened to keep their 4 higher bits identical with the corresponding cover image pixel value. In the recovery phase, the original cover images are easily recovered by a bit extraction operation according to the palettes. As the experiment results show, the shadow images are comprehensive and have no pixel expansion. In addition, both the secret image and cover images can be reconstructed losslessly.
Credit Card Fraud Detection using Logistic Regression and Decision Tree

Credit Card is used to make purchases such as AC, groceries, Television, gas, shopping and so on because of non-availability of money at that instance. Credit card have many features like points while using them for different types of transactions. Means of achieving money, services, goods and products by unauthorized means is said to be a fraud. Online fraud is basically the unauthorized use of an individual's personal and confidential information to buy products, services or to remove money from a user's account[1]. With e-commerce and cashless transactions increasing at a fast pace and with the world moving towards digitalization and cashless transactions the use of credit card has increased rapidly over the years and with that the number of frauds associated with credit card are also increasing[2]. The problem of credit card fraud threatens the credit card transactions. Fraud is an intentional deception with purpose of obtaining critical and confidential information for financial gain[3]. In fraud the confidential and private information of the user is disclosed which causes damage to the user. Fraud detection involves identifying fraud transactions among legitimate transactions as quickly as possible.
Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation

Summary In this paper, we perform a sparse filtering recursion for efficient changepoint detection for discrete‐time observations. We attach auxiliary event times to the chronologically ordered observations and formulate multiple changepoint problems of discrete‐time observations into continuous‐time observations. Ideally, both the computational and memory costs of the proposed auxiliary uniformisation forward‐filtering backward‐sampling algorithm can be quadratically scaled down to the number of changepoints instead of the number of observations, which would otherwise be prohibitive for a long sequence of observations. To avoid model bias, a time‐varying changepoint recurrence rate across different segments is assumed to characterise diverse scales of run lengths of the changepoints. We demonstrate the methods through simulation studies and real data analysis.
SAR Open Set Recognition Based on Counterfactual Framework

Most SAR image target recognition is based on closed sets. However, in practical applications, the more extensive situations are open set recognition. In open set recognition, the categories of training data and testing data are not exactly the same, which requires the classifier to recognize the trained categories and mark the unseen categories as unknown. Existing open set recognition algorithms have a serious imbalance in the recognition rate of seen and unseen categories. Studies have shown that this imbalance situation comes from the distortion of imagination of unseen categories. Therefore, the counterfactual framework can be used to realize the fidelity of counterfactual generation based on sample features, which also avoids unrealistic imagination of unseen categories. The main part of the counterfactual generation framework is the counterfactual generation model, which generates images based on specific sample features instead of Gaussian noise. The loss function of the generation model is composed of reconstruction loss, contrastive loss, and KL divergence, which realizes the decoupling between sample features and category features. The contrastive loss function is used to measure the distance between images. A counterfactual image is generated by combining a certain sample feature with different categories. Therefore, the feature distance between a sample and its corresponding counterfactual image should be far. As a consequence, in the open set recognition, if the input sample is far away from the generated counterfactual sample, the sample is considered to belong to an unseen category, and it is marked as unknown. On the contrary, the classifier obtained by supervised learning is used to classify the seen sample and obtain its corresponding category. Experimental results show that the proposed method is better than other existing open set recognition algorithms, which can relieve the imbalance recognition rate of seen and unseen categories.
HRNet Encoder and Dual-Branch Decoder Framework-Based Scene Text Recognition Model

Scene text recognition (STR) is designed to automatically recognize the text content in natural scenes. Different from regular document text, text in natural scenes has the characteristics of irregular shapes, complex background, and distorted and blurred contents, which makes STR challenging. To solve the problems of STR for distorted, blurred, and low-resolution texts in natural scenes, this paper proposes a HRNet encoder and dual-branch decoder framework-based STR model. The model mainly consists of an encoder module and a dual-branch decoder module composed of a super-resolution branch and a recognition branch in parallel. In the encoder module, the HRNet is adopted to realize the cross-parallel aggregation representation with multiple resolutions during feature extraction and then outputs four kinds of feature maps with different resolutions. Moreover, the supervised attention module is used to strengthen the learning of the important feature information. In the decoder module, the dual-branch structure is adopted, in which the super-resolution branch takes the feature maps with the highest resolution obtained in the encoder module as input and restores images by upsampling through transposed convolution. The four kinds of feature maps with different resolutions are fused through independent transposed convolution layers for multiscale fusion in the recognition branch and then inputted into the attention-based decoder for text recognition. To improve the accuracy of text recognition, the feature extraction effect of the encoder module is together supervised by the super-resolution branch loss and the recognition branch loss. In addition, the super-resolution branch is only used for training and is abandoned during testing to reduce the complexity of the model. The proposed model is trained on Synth90K and SynthText datasets and tested on seven natural scene datasets. Compared with classical models such as ASTER, TextSR, and SCGAN, the recognition accuracy of the proposed model is improved and better recognition results can be achieved on irregular and blurred datasets such as IC15, SVTP, and CUTE80.
Real-Time Myanmar Sign Language Recognition Using Deep Learning

Language is the main communication tool between human beings. People who have suffered from deaf and dumb use sign language for communication. To communicate with deaf and dumb people, we need to know sign language well. Researchers are seeking to explicit the meaning of sign language and to develop communication among deaf people and normal people. The significance of sign language recognition is hand detection, sign classification, and language translation. The main focus of this work is to develop a vision-based sign language recognition system, to transfer a tiny YOLO object detection model, a CNN-based classification model, and to develop a graph-based recognition model. Experimental results show that our proposed system can recognize Myanmar sign language not only with an accuracy of over 98&#x0025; but also in real-time. In this paper, we have used 29 different hand signs for the proposed model in MATLAB 2020a.
Marathi Social Media Opinion Mining using XLM-R

Sentiment Analysis is one of the most important tasks for any language and a very important domain in Natural Language Processing which has shown remarkable progress recently. Popular and widely used languages like English, Russian and Spanish have a great availability of language models for these tasks and widely available datasets too. But the research in Low Resource Languages like Hindi and Marathi is far behind. The Marathi language is one of the languages mentioned in the 8 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> schedule of the constitution of India and is the third most spoken language in India which is mainly used in the Deccan region which includes Maharashtra and Goa. There has been low research for sentiment analysis approaches based on the Marathi text. Therefore, this project proposes use of XLM-RoBERTa (XLM-R) models that can be used for the opinion mining of the social media Marathi texts without using any translations. Not using translations will not only get better results but also an error free model trained over the target language only. The multilingual model XLM-R and its versions will be put under training over the Marathi tweets dataset after tokenizing using RoBERTa tokenizer for the purpose of opinion mining and classification. Authors aim at presenting the results of multiple XLM-R models over the Marathi tweets dataset for the task of opinion mining.
Automated Framework for Web Content Security Through Content Management System

Content Management Technical users can develop various web applications or websites according to their requirements. The Content Management Systems (CMS) plays a significant role for technical or non-technical users who want to enable custom web applications according to their needs. CMS has many attractive and valuable features like a set of plugins, web templates, a database, and an extended set of tools. The Internet is a source of information, and it also provides a platform for various functional web application development for Worldwide users. This paper presented a new integrated process of an Automated framework for CMS. The framework introduced multiple sets of process descriptions and guidelines. The proposed framework is making it more secure.
From maintenance in industry to bibliographic data

In this paper, we report on a software demonstrator that utilizes a spatial hypertext UI to support knowledge management in the context of maintenance in industry. To demonstrate the flexibility of that approach, we re-use the software to visualize bibliographic data of the Hypertext conference series.
Co-Mart - A Daily Necessity Price Comparison Application

Co-Mart is a daily necessity price comparison application where users can compare the prices of groceries on various E-commerce websites. With the advancement in technology and the growing E-commerce business, the number of E-commerce websites has increased but at the same time, it has become difficult for people to choose the best deals from these websites. This paper focuses on an web application called Co-Mart, where one can compare prices of various products on different E-commerce websites and thus save one's Time, Efforts and Money. The methods that will be used for identifying the best deals will be web crawling and web scraping. The web scraping scripts will be written using python libraries and web crawling works on HTML labels. The framework will be designed using HTML (Hypertext markup language), CSS (Cascading style sheet), and JavaScript as front-end and Python and Django will be used for back-end support with SQLite as the Database Management System.
Communicating Measurement Outcomes with (Better) Graphics

Abstract Data visualization is a core tenet of communicating measurement research and outcomes. Measurement professionals utilize data visualization in various phases of research, including exploration and communication. However, data visualization has not received enough attention in the measurement field. While it is true that many measurement graphics are relatively standard, many others are not and there is a wide variety of visualization quality and effectiveness seen in measurement journals. This article provides an overview of the current data visualization trends in measurement and provides some general tips for effective data visualization, with examples. This article is not a comprehensive treatise on data visualization. Therefore, we provide some resources for additional reading. Finally, we call on the measurement community to pay greater attention to the details of data visualization. We also call on measurement training programs to emphasize statistical reasoning through data visualization.
African agricultural development: How are we contributing?

Abstract A database of journal articles and conference papers that cover the economics of agricultural development in Africa during the period 2016–2020 was compiled. These papers are first described in terms of demographic and bibliometric criteria, followed by a network analysis of the main centers of origin and of influence of this research. Finally, three different processes were used to test the policy relevance of the work. These include a simple enumeration of the policy recommendations in a sample of the papers, a megatrend analysis, and a comparison of their focus with the standard narrative of the role of agriculture in economic development. The results show that a large proportion of the literature is not forward‐looking, and is weak on policy relevance. We ignore a number of issues that engage the attention of those concerned with implementing structural change in agriculture across the many very different but very real natural resource, political, social, economic, and technological environments around the continent.
LITE

There is a strong need for GPU trusted execution environments (TEEs) as GPU is increasingly used in the cloud environment. However, current proposals either ignore memory security (i.e., not encrypting memory) or impose a separate memory encryption domain from the host TEE, causing a very substantial slowdown for communicating data from/to the host.
A Proficient Approach to Detect Osteosarcoma Through Deep Learning

Osteosarcoma is a life-threatening bone cancer that usually attacks young adults and children, independent of age. It habitually starts in quick-growing bone areas close to the ends of the arm or leg bones, such as the distal femur, proximal tibia, and proximal humerus. However, it can still be revealed in any bone, including the pelvis, jaw, and shoulder. The starting and the preeminent conclusion of any cancer are to identify the tumor as before long as conceivable, and it's moreover pertinent for Osteosarcoma. Osteosarcoma has a few arrange in its life cycle. The need of categorizing cancer patients into tall or short risk categories has prompted several research organizations in the biomedical and bioinformatics fields to consider using Profound Learning (Deep Learning) methodologies. Fast.ai, a Deep Learning Framework for enhancing the efficiency and accu-racy of osteosarcoma tumor categorization into tumor classes, is presented in this study (tumor vs non-tumor). At the conclusion of the study, we found that employing neural networks may provide excellent precision and capability in osteosarcoma classification and model comparison.
Chaotic whale-atom search optimization-based deep stacked auto encoder for crowd behaviour recognition

The activity recognition gained immense popularity due to increasing number of surveillance cameras. The purpose of activity recognition is to detect the actions from the series of examination by varying the environmental condition. In this paper, Chaotic Whale Atom Search Optimisation (CWASO)-based Deep stacked autoencoder (CWASO-Deep SAE) is proposed for crowd behaviour recognition. The key frames are subjected to the descriptor of feature to extort the features, which bring out the classifier input vector. In this model, the statistical features, optical flow features and visual features are conducted to extract important features. Furthermore, the significant features are shown in the deep stacked auto-encoder (Deep SAE) for activity recognition, as the guidance of deep SAE is performed byCWASO, that is planned is designed by adjoining Atom search optimisation (ASO) algorithm and Chaotic Whale optimisation algorithm (CWOA). The proposed systems’ performance is analysed using two datasets. By considering the training data, the projected method attains performance that is high for dataset-1 with maximum precision, sensitivity, and with specific value of 96.826%, 96.790%, and 99.395%, respectively. Similarly, by considering the K-Fold, this method attains the maximum precision of 96.897%, sensitivity of 96.885%, and with specific values of 97.245% for the dataset-1.
Sidechain: A Scalable Blockchain

In today's world, the revolutionary concept of blockchain and its applications is attracting an increasing amount of attention. The digital currency Platforms have been at the forefront of implementing new technologies based on blockchain. The capabilities of blockchains are used for more than just monetary transactions.The introduction of smart contracts enabled the development of a variety of distributed applications.As the demand for blockchains grows, so does the demand for larger, more scalable, and more versatile “multipurpose” networks. Traditional blockchains (including those that support smart contracts) are unable to meet all of these needs.The concept of sidechains was proposed to overcome these concerns.Scaling the blockchain network can be accomplished in a variety of ways. A sidechain is a convenient way for scaling a block chain, removing the shortcomings of the current system and enhancing the current blockchain system's benifits.
An intelligent optimization algorithm with a deep learning‐enabled block‐based motion estimation model

Abstract The motion estimation process has gained significant attention in several video applications such as video compression, global motion estimation, and so on. The deep learning (DL) models that perform effectively in computer vision tasks can be employed for motion estimation. Yet, they have high computational complexity and model parameters since motion estimation in the pixel domain is not effective in practical applications. Instead, a block‐based motion estimation process has been developed where the blocks of sequential images are compared. To that end, this paper presents the Anas platyrhynchos optimizer with deep learning‐enabled block‐based motion estimation (APODL‐BBME) model. The proposed model estimates motion using block‐based concepts and DL approaches. To accomplish this, the training and testing frames are separated into non‐overlapping block categories, and the blocks are filtered via the bilateral filtering (BF) approach. In addition, a histogram of gradients (HOG) and densely connected network (DenseNet) model are employed to extract features, which are then fed into the bidirectional long short‐term memory (BiLSTM) model to classify the input features. Finally, the APO algorithm is applied to optimally tune the hyperparameters of the BiLSTM model, which helps to improve the overall motion estimation efficacy, showing the novelty of the work. To demonstrate the enhanced performance of the APODL‐BBME model, a comprehensive analysis is carried out, and when we compare the results, the APODL‐BBME model outperforms recent motion estimation approaches.