Reviewerly/exHarmony huggingface dataset:
Securing Medical Records Using Private Blockchain Technology

The term “electronic medical records” (EMRs) refers to information that is highly confidential and saved electronically. This information is associated with the diagnosis and treatment of a patient, and it must be routinely distributed among the concerned people such as doctors and nurses. The disclosure of private medical information or its alteration during an operation makes it extremely difficult for participants to exchange their medical files with one another. The most effective strategy for overcoming these obstacles is to implement an electronic medical records system that is based on blockchain technology. Within the scope of this study, we have addressed how the technology of blockchain might contribute to improved healthcare data management. Through the application of blockchain technology, the purpose of this research is to establish a connection between the health ministries and departments and both public and private hospitals in order to simplify the process of gaining access to one's medical records and transferring those records while maintaining patient confidentiality and improving safety.
Secure and Decentralized Generation of Secret Random Numbers on the Blockchain

We propose a trustless blockchain-based protocol for secure and decentralized generation of secret random numbers. In our protocol, which can be implemented as a smart contract, a client Cassie can ask for a secret random number <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> . The protocol will then allow anyone on the blockchain network to contribute to the process of generating <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> , but crucially, only Cassie would be able to see the final result. Additionally, our approach is auditable, i.e. if Cassie later wishes to announce her secret random number <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> , she can prove that the output of the process was indeed the claimed value. Finally, we ensure no one else is able to deduce <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> before Cassiediscloses it. A primary use-case of this approach is to enable auditable online gambling and casinos with verifiable and tamper-proof randomness. If Cassie is a casino, she can ask for a secret random number <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> at the beginning of the day, and then use it as a seed in a predefined pseudo-random number generator which is in turn used as the source of randomness for all bets during the day. At the end of the day, Cassie can prove that the seed was indeed generated by our decentralized protocol. Thus, she can prove that the casino had no control over the outcomes of the bets. Moreover, this is a cost- and time-efficient approach as it does not require us to generate a new random number for every bet.
A Distributed EMR Data Storage Scheme Based on LWE

In the traditional storage EMR storage scheme, user trustworthiness as well as data immutability cannot be effectively guaranteed, this paper proposes a solution for joint on-chain and off-chain storage of blockchain. The original data is stored in the off-chain server after encryption, and the index is stored in the blockchain. In order to ensure the efficiency as well as security of on-chain index retrieval, this paper proposes a searchable index encryption scheme combining Bloom filter and LWE. According to the experimental results, this scheme can improve the index retrieval efficiency by 27%-54% and has a faster construction and retrieval speed compared with similar methods.
Collaborative Autonomy for Underwater Surveys Using Decentralized Hungarian Assignment

This work outlines the methodology of a decentralized autonomy framework for scalable collaboration of unmanned underwater vehicles performing line survey operations. This framework utilizes the Hungarian method in order to efficiently allocate mission survey lines among the agents, as well as create routes for the vehicles to complete their assigned survey lines. Modifications for incorporating unbalanced assignment problems, integrating heterogeneous vehicle operations, improving the computational complexity of the algorithm, and overcoming single-agent failures are also discussed. Finally, the performance of the autonomy package is assessed with respect to standard methods involving greedy assignments, as well as optimal solutions through exhaustive search.
Interaction Design Strategy of Baitasi Historic Block from the Perspective of Urban Microrenewal

As a special area in cities, historic blocks are changing in time and space and conservators need special guidance, especially in the design theory that suggests parallel processes of conservation and regeneration. Based on the new perspective of urban microrenewal, this paper uses the five steps of the user experience model to map the process of interaction design in historic spaces. These designs accelerate the modernization of techniques for protection, renewal and dissemination in the new method of “regenerating vitality”. Specifically, this paper examines the results of interaction design of the Baitasi Regeneration Plan for Baitasi Historic Block. It further discusses the role of interaction design in the revitalization of historic blocks at multiple levels to provide a theoretical context and practical basis for the protection and renewal of historic districts during urban renewal.
Interoperable AI for Self-Organisation

Knowledge or information sharing is one of the foundational principles shared by the Web and any well functioning decentralised multi-agent system (MAS). Another is interop-erability. Engineers of MAS follow design strategies for ensuring interoperability that range from using a common ontology to using translation mechanisms. But what kind of sustainable communication strategy would AI agents themselves use if given the chance to self-organise? In this short paper we summarise our previous findings from [6], where we used an evolutionary agent-based modelling approach to answer this question.
Towards Blockchain-Based Remote Management Systems for Patients with Movement Disorders

Secure storage and sharing of patients' medical data over the Internet are part of the challenges for emerging healthcare systems. The use of blockchain technology in medical Internet of things systems can be considered a safe and novel solution to overcome such challenges. Patients with movement disorders require multi-disciplinary management and must be continuously receive medical care by a specialist. Due to the increasing costs of face-to-face treatment, especially during the pandemic, patients would highly benefit from remote monitoring and management. The proposed work presents a model for blockchain-based remote management systems for patients with movement disorders, especially those with Parkinson's disease. The model ensures a high level of integrity and decreases the security risks of medical data sharing.
A Neural Network Recommendation Model for Final Project Topic Selection

A student of a three-year vocational program (Diploma III), must work on their final project in the third year. The student's final project must correspond to the output profile of informatics engineering, including Junior Web Programmer, Junior Network Administrator and Junior Programmer. However, the most difficult aspect of the annual graduating design final project is topic selection by students or supervisors. This work intends to use the best recommendation model of neural network (NN) to classify the student into three graduate profiles based on the profile of graduates in the study program with data support in the form of personal abilities, obtained from an IQ test, recap of academic grades and student interest, simultaneously, Chat-GPT algorithm is utilized to generate recommendations for the final project topic. Furthermore, researchers can utilize the data to determine which themes are more famous and to prevent duplicate ones. The primary objectives of the paper are as follows: 1. Supervisors can easily direct students toward topics that are relevant for the graduate profiles. This is mostly utilized as reference by institutions to improve their curriculum. 2. Students study and work on the final project based on the fundamental skills they possess and will master.
Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI

The importance of detecting and preventing ovarian cancer is of utmost significance for women’s overall health and wellness. Referred to as the “silent killer,” ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models.
Embracing emojis: Bridging the gap in workplace technology adoption and elevating communication effectiveness

An emoji is a small digital picture or icon that is used in electronic messaging to show a feeling, thought, or idea. Emoticons are frequently employed in written communication, such as text messaging, social media platforms and various digital mediums, with the purpose of expressing emotions or providing more meaning to a statement. Adopting emojis in the professional environment has become a modern method to convey emotions, offer feedback, and express appreciation amongst peers. This exploration underlines the intersection of technology – specifically the use of emojis – and its influence on interpersonal interactions and dynamics within the professional setting. This draws attention to the need for effective communication, setting clear expectations, and understanding the implications of technology on workers' attitudes and behaviours. For deeper analysis, this study employs two pivotal frameworks: the theory of operant conditioning and the Technology Acceptance Model (TAM). The former probes into the effects of outcomes, such as the usage of emojis, on employees' behaviours in the office context. The latter, on the other hand, explores the acceptance and perceptions employees have towards such technological innovations. Emphasizing effective communication becomes paramount when we consider the role technology plays in shaping our interactions and viewpoints. While emojis can potentially enhance the clarity and effectiveness of communication, they come with their set of challenges. It's vital to approach their use with discretion, particularly in sensitive scenarios like performance evaluations. In such contexts, the margin for misunderstanding is high, and incorrect interpretations can lead to unintended consequences.
Relationship between Remittances and Inflation in Nepal

Empirical evidence highlights the significant impact of remittances on a country’s macroeconomic indicators. This holds particular significance for import-dependent developing nations like Nepal, where remittances serve as a crucial source of foreign currency earnings and facilitate the financing of imports. However, there remains a limited understanding of their implications for inflation, as their effects on inflation are contingent upon whether the demand or supply side is more influenced by remittances. This study leverages time series data and employs an error correction model (ECM) to explore the influence of remittances on the domestic price level. The findings indicate that the inflow of remittances has a dampening effect on the domestic inflation rate. This suggests that remittances have played a pivotal role in enabling the import of relatively more affordable goods from abroad, notwithstanding their impact on the overall expenditure of the economy. Furthermore, our research reveals that conventional macroeconomic indicators such as GDP, narrow money supply, and Indian inflation appear to exert pressure on domestic inflation in Nepal. These findings offer valuable insights into the complex relationship between remittances and inflation dynamics in the Nepalese context.
Optimizing the Use of Indonesian in Learning in the Digital Era

There needs to be special efforts to increase the use of Indonesian in digital learning. This includes content development, delivery techniques, and utilizing the latest technology that supports the language. Using the literature study method, in this research the author obtained data from books, scientific works, documents and other relevant sources. This research shows how important it is to use Indonesian as best as possible when studying in the digital era. Even though English is considered an international language, especially in the digital world, Indonesian is still a good tool for communicating in learning in Indonesia. Limited Indonesian language resources are one of the main obstacles in this optimization process. The development of Indonesian language educational resources can be a strategic step for parties interested in the world of education in Indonesia. So far, positive student responses show that digital learning can still optimize Indonesian. Therefore, educators, platform developers and policy makers must work together to ensure that Indonesian gets its rightful place in the digital era.
PoWVDF: Progressive Real-Time Block Rate Targeting

In conventional PoW mining, miners iterate a nonce until the block header's hash falls below a predefined target. The mining time for a single block approximately follows an exponential distribution, resulting in a large variance in block generation time. Consequently, the generation time of a single block cannot accurately reflect mining power. Therefore, conventional PoW blockchains use the generation time of numerous blocks to evaluate mining power, leading to lengthy difficulty adjustment periods that cannot adapt to the fast-paced market. To seek better responsiveness to changing hashrate, an innovative consensus algorithm called Real-Time Block Rate Targeting (RTT) has been proposed. This consensus algorithm features shorter difficulty adjustment periods and improves the stability of block generation. In this paper, we demonstrate that the RTT consensus algorithm make chains more vulnerable to double-spending attacks, enabling attackers to conduct successful attacks at lower costs. To address this vulnerability, we propose the PoWVDF protocol, which combines Proof-of-Work and verifiable delay functions. This protocol maintains the advantages of RTT's shorter diffi-culty adjustment periods and stability of block generation while enhancing the security against double-spending attacks.
Exploring Arbitrary Real-Life Problems in Proof-of-Useful-Work: Myth Busting?

Proof-of-Useful-Work (PoUW) consensus protocols (CPs) are commonly used to improve the Blockchain (BC) efficiency and security. Numerous papers propose various PoUW concepts, majority of them being based on solving the hard real-life optimization problems. A recently proposed Combinato-rial Optimization Consensus Protocol (COCP), tried to address exploration of an arbitrary real-life combinatorial optimization problem. Although the ideas are quite straightforward, the actual implementation of such a BC system is still challenging. In this paper we reflect upon the main components of PoUW-based BC system with an emphasis on implementation challenges that arise from the necessity to define and solve an arbitrary real-life CO problem with the appropriate CO algorithm,
In-plane Strain Analysis by Correlating Geometry and Visual Data Through a Gradient-Based Surface Reconstruction

Abnormalities in tissue can be detected and analyzed by evaluating mechanical properties, such as strain and stiffness. While current sensor systems are effective in measuring longitudinal properties perpendicular to the measurement sensor, identifying in-plane deformation remains a significant challenge. To address this issue, this paper presents a novel method for reconstructing in-plane deformation of observed tissue surfaces using a fringe projection sensor specifically designed for measuring tissue deformations. The method employs the latest techniques from computer vision, such as differentiable rendering, to formulate the in-plane reconstruction as a differentiable optimization problem. This enables the use of gradient-based solvers for an efficient and effective optimization of the problem optimum. Depth information and image information are combined using landmark correspondences between the respective image observations of the undeformed and deformed scenes. By comparing the reconstructed pre- and post-deformation geometry, the in-plane deformation can be revealed through the analysis of relative variations between the corresponding models’ geometries. The proposed reconstruction pipeline is validated on an experimental setup, and the potential for intraoperative applications is discussed.
Blockchain-based Electronic Medical Record Security Sharing Scheme

To solve the difficulty of medical data sharing in traditional medical information systems, we proposed an electronic medical record secure-sharing scheme based on the Blockchain technique. The encrypted text of the patient’s electronic medical record is stored in the cloud server while the metadata of the medical record and access strategy is stored in the blockchain system. We employed smart contracts in the blockchain system to achieve user rights management. We used the decentralized, tamper-proof, and traceable features of the blockchain to realize the safe sharing of electronic medical records. The experimental results of security analysis show that the method can defend against potential network attacks while satisfying patient privacy protection and confidentiality. This study verifies the feasibility and great operating efficiency of the blockchain-based electronic medical record security sharing scheme.Clinical relevance— Our proposed blockchain-based electronic medical record-sharing scheme has great potential for the safe access of third-party users to patient data.
Penggunaan Artificial Intelligence, Software Akuntansi, dan Peneyebaran Berita di Sosial Media terhadap Jumlah Peminat Jurusan Akuntansi

Accounting is one of the targeted study programs for prospective new students who want to continue their education to the university level. With technology that continues to develop from year to year, it makes some people think that graduates from accounting majors will later be replaced by the presence of advanced technology, such as Artificial Intelligence and Software that have been widely used by workers in accounting field. In addition, with many news on social media about threats to accounting graduates, many prospective students are rethinking entering and studying this department more deeply in the world of lectures
MAX-STABLE PROCESS WITH GEOMETRIC GAUSSIAN MODEL ON RAINFALL DATA IN SEMARANG CITY

Spatial extreme value (SEV) is a statistical technique for modeling extreme events at multiple locations with spatial dependencies between locations. High intensity rainfall can cause disasters such as floods and landslides. Rainfall modelling is needed as an early detection step. SEV was developed from the univariate Extreme Value Theory (EVT) method to become multivariate. This work uses the SEV approach, namely the Max-stable process, which is an extension of the multivariate EVT into infinite dimensions. There are 4 Max-stable process models, namely Smith, Schlater, Brown Resnik, and Geometric Gaussian, which have the Generalized Extreme Value (GEV) distribution. This study models extreme rainfall, using rainfall data in the city of Semarang. This research was carried out by modeling data using the Geometric Gaussian model. This method is developed from the Smith and Schlater model, so this model can get better modeling results than the previous model. The maximum extreme rainfall prediction results for the next two periods are Semarang climatology station 129.30 mm3, Ahmad Yani 121.40 mm3, and Tanjung Mas 111.00 mm3. The result from this study can be used as an alternative for the government for early detection of the possibility of extreme rainfall.
Choosing Data Points to Label for Semi-supervised Learning Based on Neighborhood

Semi-supervised fuzzy clustering is an extension of fuzzy clustering, it uses additional information to guide and monitor the clustering process. The additional information can be seed form (labeled data), constraint form (must-link/cannot-link), or predefined membership form. In fact, with based clustering, the clustering result depends on the selected data point, so with different selected data points will produce different clustering results. In some cases, the performance of clustering can be reduced if the data points are not selected properly. This is a really problem with semi-supervised clustering algorithms. In addition, the selection of the right data points helps to reduce the number of queries from experts. For this purpose, we propose a new learning algorithm (called WHFMN) for the seed's collection activity, this activity identifies candidates for additional information using fuzzy min max neural network. Four of the most interesting characteristics of WHFMN are that (1) it automatically calculates data points to add additional information, (2) it automatically adds new clusters without retraining, (3) it automatically finds the noise objects according to the density of the natural clusters and (4) it can find natural cluster structure even if density and shape are different between clusters. Experimental results prove that the proposed algorithm can improve the efficiency of the label selection method more than the previously published methods.
Application of Smartphone-based imaging devices for studying biological specimen

Smartphone-based imaging devices have been shown to be an alternative to the high-end microscope. The present study deals with the possible use and study of different samples including histopathological samples.
Strong-Field Homodyne Detection with Superconducting Nanowire Single-Photon Detectors

Using continuous-wave homodyne detection, we investigate the count rate linearity of two SNSPDs. With the demonstrated broad linear regime and a shot-noise clearance of 47 dB, they are versatile detectors for DV and CV experiments.