Reviewerly/exHarmony huggingface dataset:
A Comparative Performance Analysis of CBIR using Wavelet and Contourlet Transform

In the literature various algorithms for content based image retrieval (CBIR) were proposed on different variety of databases such as texture, face, and medical images. It is observed that the CBIR performance strongly depends on effective extraction of image features such as color, texture, and shape. In the proposed research work, contourlet transform based CBIR system is proposed for texture images. Similar to wavelets, texture images can be characterized via marginal distributions of subband coefficients of recently introduced contourlets. In addition, contourlets have two new properties namely directionality and anisotropy which makes them powerful in efficient representation of natural texture images. Hence, in the proposed research work, we have analyzed and compared the performance of both wavelet and contourlet schemes for content based texture image retrieval. It is observed that contourlet based method outperforms the wavelet method for highly texture images in terms of retrieval efficiency.
Automatic Classification and Feature Extraction of Bar Charts in Scanned Documents

With the introduction of high resolution cameras in mobile devices, users frequently scan document images using such devices. The presence of charts in such scanned documents are very common. Due to perceptual advantages, charts are used to represent meaningful information. Depending on the context, different chart representations are used. This paper presents a deep learning based technique for automatic classification of charts in scanned documents followed by their reconstruction using vector representation. In this paper, we presented a study of different convolution neural networks for chart classification. Based on the study, EfficientNet has been used that uniformly scales all dimensions of network depth, width and resolution using a compound coefficient for better performance. Once a chart is classified, dedicated image gradient based algorithms are used to extract chart cell, slice or bar information and these information is used for vector based reconstruction the charts. The algorithm has been applied on a number of scanned documents with charts in it. We have achieved a chart image classification accuracy of 91 % across six chart categories. Experimental results show the efficacy of the algorithm proposed.
Securing the Health Care Data Using Hybrid Rabin and Routing Algorithm

Chronic illnesses are becoming more common as a result of the current environmental conditions; most people prefer IoT to take care of their own health. The (IoT) plays a significant role in each and every industry that simplifies life. IoT also plays an important part in the medical field by routinely monitoring the health condition of patients in remote areas. While IoT methods for solving for a smarter living, it also has some problems while transmitting data, which is similar to the type of threat that occurs during transmitting data in WSN. When compared to other types of networks, the data transmitted in smart health monitoring is extremely sensitive. Various sorts of attacks occur during data transfer, such as sinkholes, worm holes, and grey holes, among others. To counteract these assaults, a hybrid Rabin algorithm and a modified routing method are proposed for data security. These algorithms provide 99.0% accuracy in data delivery while detecting attacks and increasing efficiency.
Application of bipolar fuzzy matrices in Flood Damage

Several industries are currently seeing significant growth in the study of bipolar fuzzy sets. Bipolar fuzzy matrices are also a highly recognised issue in several academic displines such as multi-criteria decision making, information science, engineering and quantum computing. In this article, define value matrix and score matrix of bipolar fuzzy matrix. Using the operation bounded sum with value of BFM, obtain an real life application which enables the land sliding, flood, cultivation, power cut and transport problem. Illustrate this idea with an example that helps in finding the most affected area in flood damage.
Penggunaan Design Thinking untuk Perancangan Brand Guideline, Content Pillar, dan Content Calendar

Delicatasty is a culinary-focused MSME that was founded in 2020. Along with the development of technology and social media platforms used as marketing media, conceptualization in the form of brand guidelines, content pillars, and content calendar is required to strengthen brands so that Delicatasty brands have more character, are more consistent, and can be distinguished from other MSMEs, particularly those in similar fields. Design thinking is used in this qualitative approach, which has six stages: Understand, Observe, Define, Ideate, Prototype, and Test. This study discovered that Delicatasty still has issues with its brand character, consistency, and trustworthiness. Therefore, suggestions for solutions are gathered and combined into a prototype in the form of a document. The findings of the research were then presented to the informants, who provided feedback in the form of what they liked, didn't like, questioned, and suggested ideas that could be implemented into the prototype to improve it even better.
A Novel Approach For Secured Decentralised Data Protection Vault

If you use a data manager (passwords, documents, card details, bank details, notes, etc.) or any other vault app, your data will be stored in a specific location. It's either local storage or private storage with extreme problems. One problem is that all data is centralized. This means that anyone in control of your storage can modify or hack your data. The controller can do whatever it wants with the data, like Modify data, sell data, delete data, or prevent access without the owner's knowledge. This leaves multiple users' data vulnerable to attack, abuse, and loss. Our basic idea is to provide our users with utility software that acts as a data wallet and manager, leveraging current developments in decentralized storage architectures to provide our users with a platform that stores all their data in an impenetrable way. Signing up and signing in the user with a unique way that is with only one field that is the password field, no username, no email-id, or any additional information is required from the user. Giving the user options to upload a series of image in a particular order and then verifying the same order as another level of security.
Improving Medical OCR Information Extraction with Integrated Bert and LayoutXLM Models

Currently, medical records in most hospitals are paper-based and rely on manual input, but with the advancements in OCR and NLP technologies, it is now possible to convert such records into electronic and structured formats. In this paper, we explore the CHIP2022 evaluation task 4 and compare the performance of two pre-training models: Bert without additional coordinate information and LayoutXLM with additional coordinate information. We apply a selection and regularization process to refine the results and evaluate our framework’s accuracy through a list in Ali Cloud Tianchi. Our results demonstrate that our framework achieved good performance.
Comprehensive Experimental Evaluation of Open Source Deep Learning Framework for Single Image Deraining Applications

The presence of rain patterns degrades the visual quality of the outdoor captured image for human perception and affects the performance of computer vision systems. Recently, deep learning algorithms have given more attention to single image/video deraining applications. Several open-source Deep Learning(DL) frameworks are available to assess the performance of existing Deraining deep network algorithms. In general, increasing popularity of more open source DL frameworks, it is necessary to identify the best platform for single image deraining applications in terms of prediction accuracy, CPU optimization, convergence factor and, Memory usage. In this work, a comprehensive survey has been presented and summarized to identify the best open-source tool for deraining applications. In this paper, we conduct numerous experimental analyses of four popular DL frameworks, namely Tensorflow, Pytorch, Caffe, Keras using JORDER and Derain SRCNN deraining architecture. Various experiments have been conducted on two CPU platforms using a different rainy database. The Experimental results show that Tensorflow and Pytorch offer a better draining performance for DerainSRCNN and JORDER Deraining Network.
A Multi-span-Based Conditional Information Extraction Model

Conditional information extraction plays an important role in medical information extraction applications, such as medical information retrieval, medical knowledge graph construction, intelligent diagnosis and medical question-answering. Based on the evaluation task of China Conference on Health Information Processing 2022 (CHIP 2022), we propose a Multi-span-based Conditional Information Extraction model (MSCIE), which can well solve the conditional information extraction by extracting multiple span and the relations between each span. Moreover, the model provide a solution to conditional information extraction in complex scenes such as discontinuous entities, entity overlap, and entity nesting. Finally, our model, with the fusion of two pretrained models, has obtained the performance of the 1st in list A and the 2nd in list B, which also proves the effectiveness of the model.
Rule-Enhanced Disease Coding Method Based on Roberta

Disease coding is crucial in medical informatics, but its complexity and sheer volume of disease codes make traditional methods ineffective. To address this challenge, this paper formulates disease coding as a classification problem, leveraging pre-trained language models and integrating domain knowledge to propose a rule-enhanced clinical coding approach. Experimental results demonstrate the effectiveness of this method, which achieved the second-best performance in the China Health Information Processing Conference (CHIP) 2022 competition.
New Approaches in The Formation of Modern Digital Competencies in Higher Education

The digital world and related technologies are developing at an accelerated pace. It is extremely difficult for specialists, university teachers, especially students to keep up with the latest innovations. An important feature of the last decades has been the use of ready-made technological solutions in the construction of new tools and new information systems. This is due to the active use of various frameworks, information technologies platforms and various digital services. This leads to the need for everyone to constantly improve their skills, lifelong learning becomes an urgent necessity. In addition, it is necessary to change the formats of training sessions, the use of new technologies and approaches in education. The paper also draws attention to the risks associated with the introduction of new digital solutions and services.
Soundness of Stablecoins

Stablecoin regulation is an important topic in crypto assets. As some assumed there were no regulations for crypto assets before, it can be painful for the crypto industry to be compliant with financial regulations. Traditional finance (tradfi) has experienced numerous crises over the centuries, and thus is strictly regulated. Regulators, having seen the impact of new financial technologies in the recent financial crisis, should not and cannot neglect the impact of new financial products, whether they are tradfi or defi. We can learn from history to find appropriate regulatory standards. In particular, we discuss monitoring items which can show the soundness of stablecoins. Regulators should require regular disclosure of assets backing stablecoins, sufficient overcollateralization, appropriate asset quality requirements to assure safety and liquidity, auditing for operational risk, AML/KYC compliance, and a dispute resolution process.
Broken Proofs of Solvency in Blockchain Custodial Wallets and Exchanges

Since the Mt. Gox Bitcoin exchange collapse in 2014, a number of custodial cryptocurrency wallets offer a form of financial solvency proofs to bolster their users’ confidence. We identified that despite recent academic works that highlight potential security and privacy vulnerabilities in popular auditability protocols, a number of high-profile exchanges implement these proofs incorrectly, thus defeating their initial purpose. In this paper we provide an overview of broken liability proof systems used in production today and suggest fixes, in the hope of closing the gap between theory and practice. Surprisingly, many of these exploitable attacks are due to a) weak cryptographic operations, for instance SHA1 hashing or hash-output truncation to 8 bytes, b) lack of data binding, such as wrong Merkle tree inputs and misuse of public bulletin boards, and c) lack of user-ID uniqueness guarantees.
Optimal orientation of the gable roofs covered with photovoltaic panels: Case study Kragujevac

In this paper, a study was conducted to determine the optimal orientation of the gable roofs covered with photovoltaic panels (PVP) for the area of the city of Kragujevac. Numerical simulations using Google SketchUp and EnergyPlus software are performed for two different PVP placement cases. The first case analyzes the installation of PVP only on one side of the gable roof. The second case analyzes the installation of PVP on both sides of the gable roof. The weather conditions for the city of Kragujevac were taken into account during the simulations. In the case of one-sided gable roof covering, the annual electricity production is the highest when the PVPs are facing south (223.26 kWh/m 2 /a). In the case of bilateral (two-sided) covering, the annual electricity production can be up to 181.3 kWh/m 2 /a, if the gable roof is oriented in the East-West direction. By comparing both cases, south-oriented PVPs from the first case annually generate 18.79% more electricity than East-West-oriented PVPs from the second case.
Batch Normalized Siamese Network Deep Learning Based Image Similarity Estimation

The assessment of how two distinct images are equal are indeed called image similarity and consistency. In other words, it measures how much the intensity patterns in two images are comparable to one another. In order to achieve this, researchers examine the image descriptors recursively in order to identify descriptor pairs that are comparable. The two images are deemed comparable if the number of related descriptors exceeds a predetermined threshold and both images exhibit the very same entity. The computation of image similarity is used for various applications which graves to be the mandatory process for production of the application. With this intent, the Fashion MNIST dataset from KAGGLE is used for implementing the image similarity estimation. This paper proposes Batch Normalized Siamese Network (BNSN) deep learning based model for computing the image similarity. The BNSN model is designed with two subnetworks that generates feature vectors of two input images. The lambda batch normalization is performed with single dense layer to predict the image similarity with label 0 indicating the identical images and label 1 denoting the different images. The 30,000 training images were fitted with BNSN and tested with 30,000 images. Python was implemented on a Geforce Tesla V100 NVidia Graphics card webserver with a batch size of 64 and 30 training epochs. The training images are also tested with traditional image similarity method and implementation of proposed BNSN shows the accuracy of 91.91%, Precision of 92.93%, Recall of 90.72% and FScore of 91.81%.
Biomedical Named Entity Recognition Under Low-Resource Situation

Biomedical named entity recognition is Key technology for automatic processing of health medical information. However, it is labor-expensive for get enough labeled data and always facing low-resource situation. We propose a method for biomedical named entity recognition that under low-resource situation. Our work is based on the 8th China Health Information Processing Conference task-1 and ranked third among all the teams. In the final results of the test set, the precision is $$38.07\%$$ , the recall is $$51.04\%$$ and the F1-score is 43.61.
Geoecological evaluation of natural potentials of Šavnik (Montenegro) for the purposes of recreational tourism using V-Wert method in GIS environment

The geoecological assessment of the natural potentials of the Montenegrin municipality of Šavnik for the purposes of recreational tourism is carried out in this work using the quantitative diversity method (V-Wert method). The main criteria used in this evaluation method are natural elements (forests, water areas, relief, climate) and land cover. For the evaluation, the whole procedure was implemented in the environment GIS (Geographic Information Systems) using the software QGIS 3.14. The evaluation results show that unfavorable areas occupy 247 km 2 (39%), conditionally favorable 185 km 2 (30%), favorable 141 km 2 (22%) and very favorable 54 km 2 (9%). Despite the significant natural potential, which is partially recognized by this method with certain advantages and disadvantages, recreational tourism and all forms of tourism are underdeveloped in the municipality.
German Voters’ Attitudes Towards Voting Online with a Verifiable System

A representative study came to the conclusion that more than 63% of German voters would have like to cast their vote for the federal election in 2021 online. In this paper, we aimed to investigate why Germans might be in favour or against online voting, conducting a online survey. We furthermore aimed to study the reactions of people being in favor of online voting if confronted with a verifiable remote voting system, as well as with interventions aimed at communicating that it is important to follow all the steps to verify. Our findings show that the majority of our participants were generally willing to vote online. Convenience emerged as the most popular reason for voting online. The reaction to the verifiable remote voting system was diverse, from our participants being irritated from the complexity, to very positive reactions due to high security level. Nonetheless, the majority of the participants did not change their willingness to vote online after seeing the proposed system. The different interventions had no effect. Furthermore, the majority agreed on the importance of verifiability being in place.
A system architecture for preventing social engineering attacks via e-mail

Modern business and the expansion of internet technology have caused a great growth in communication via electronic mail. Bearing in mind that the weakest link in any system is the human, the greatest danger of unauthorized access to ICT resources is recognized in this section of the system [6]. For this reason, the greatest attention regarding the protection of ICT systems should be focused on the users and preventive response to phishing campaigns as the most common form of cyber attack. This paper will present the system architecture for preventive response to phishing campaigns. The architecture itself, which will be explained in the continuation of the text, consists of several different modules integrated into a whole. First, a sender analysis module, which would be based on the blacklist principle; next, an email attachment analysis module, which would perform the functions of static and dynamic analysis of potentially malicious attachments; a link analysis module, which would include the application of Cortex, an open source intelligence service; and finally, a text analysis module, based on statistical models.
Mineral raw materials of Serbia on the list of critical minerals of the European Commission (CRM EU-2020) during the Covid-19 pandemic and harmonized in the UNFC-2009 system and the PERC standard

The existence of mineralization, mining and deposits of non-ferrous and precious and other metals (Pb-Zn, Ag; Cu, Au, as well as B-Li, Mg, Sb, etc.) in the territory of Serbia is conditioned by the structural-geological-metallogenetic structure and processes formation of deposits of mineral raw materials in a certain period of geohistorical development. Mining activity in the territory of Serbia, i.e. extraction of non-ferrous and precious metal ores, goes back thousands of years, which resulted in the "production" of significant accumulations of mining and industrial waste. These facts indicate the existence of a significant mineral potential of Serbia, and the possibility of recovering metals through technological processes of waste material recycling. Mineral deposits are the primary sources, and accumulated mining and industrial waste are secondary sources of providing mineral resources such as Cu, Pb-Zn, Co, Ni, Al, Li, W, Bi, In, Te, Ga, Ge, REE and others, many of which are on the list of CRM EU-2020 or 2023. Critical minerals as a professional-formal term is not defined in Serbia, and in due to the specific way and extent of occurrence in nature, exhaustibility and non-renewable reserves, as well as the provision of certain countries or regions with solid mineral resources in certain socio-economic conditions (world crises, wars, embargoes, pandemics, etc.) become deficient, i.e. missing (on the market), i.e. in terms of supply chain. During the Covid 19 pandemic, publicly available data on critical raw materials in Serbia were analyzed, when it was assessed that e.g. for the supply chain of the health sector, only ten of them are available (Sb, Ba, B, Ge, In, Mg, F, ...). The solid mineral raw materials of Serbia classified into categories and classes of geological reserves (classes of mineral resources and reserves) can, for certain reporting purposes, be harmonized with the Mineral Resources/Reserves from international standards (PERC, JORC, Ni-43 101 and codes of UNFC).
Matrix permanent and determinant from a spin system

In contrast to the determinant, no algorithm is known for the exact determination of the permanent of a square matrix that runs in time polynomial in its dimension. Consequently, noninteracting fermions are classically efficiently simulatable while noninteracting bosons are not, underpinning quantum supremacy arguments for sampling the output distribution of photon interferometer arrays. This work introduces a graph-theoretic framework that bridges both the determinant and permanent. The only nonzero eigenvalues of a sparse non-Hermitian operator $\stackrel{\ifmmode \u\else \textasciibreve \fi{}}{M}$ for $n$ spin-$1/2$ particles are the $n\mathrm{th}$ roots of the permanent or determinant of an $n\ifmmode\times\else\texttimes\fi{}n$ matrix $M$, interpreting basis states as bosonic or fermionic occupation states, respectively. This operator can be used to design a simple and straightforward method for the classical determination of the permanent that matches the efficiency of the best-known algorithm. Gauss-Jordan elimination for the determinant of $M$ is then equivalent to the successive removal of the generalized zero eigenspace of the fermionic $\stackrel{\ifmmode \u\else \textasciibreve \fi{}}{M}$, equivalent to the deletion of some nodes and reweighting of the remaining edges in the graph such that only $n$ nodes survive after the last step. In the bosonic case, the successive removal of generalized zero eigenspaces for $\stackrel{\ifmmode \u\else \textasciibreve \fi{}}{M}$ is also equivalent to node deletion, but new edges are added during this process, which gives rise to the higher complexity of computing the permanent. Our analysis may point the way to new strategies for classical and quantum evaluation of the permanent.