Reviewerly/exHarmony huggingface dataset:
Contrastive Learning Augmented Graph Auto-Encoder

Graph embedding aims to embed the information of graph data into low-dimensional representation space. Prior methods generally suffer from an imbalance of preserving structural information and node features due to their pre-defined inductive biases, leading to unsatisfactory generalization performance. In order to preserve the maximal information, graph contrastive learning (GCL) has become a prominent technique for learning discriminative embeddings. However, in contrast with graph-level embeddings, existing GCL methods generally learn less discriminative node embeddings in a self-supervised way. In this paper, we ascribe above problem to two challenges: 1) graph data augmentations, which are designed for generating contrastive representations, hurt the original semantic information for nodes. 2) the nodes within the same cluster are selected as negative samples. To alleviate these challenges, we propose Contrastive Variational Graph Auto-Encoder (CVGAE). Specifically, we first propose a distribution-dependent regularization to guide the paralleled encoders to generate contrastive representations following similar distributions. Then, we utilize truncated triplet loss, which only selects top-k nodes as negative samples, to avoid over-separate nodes affiliated to the same cluster. Experiments on several real-world datasets show that our model CVGAE advanced performance over all baselines in link prediction, node clustering tasks.
Enhancing LSTM and Fusing Articles of Law for Legal Text Summarization

The growing number of public legal documents has led to an increased demand for automatic summarization. Considering the well-organized structure of legal documents, extractive methods can be an efficient method for text summarization. Generic text summarisation models extract based on textual semantic information, ignoring the important role of topic information and articles of law in legal text summarization. In addition, the LSTM model fails to capture global topic information and suffers from long-distance information loss when dealing with legal texts that belong to long texts. In this paper, we propose a method for summarization extraction in the legal domain, which is based on enhanced LSTM and aggregated legal article information. The enhanced LSTM is an improvement of the LSTM model by fusing text topic vectors and introducing slot storage units. Topic information is applied to interact with sentences. The slot memory unit is applied to model the long-range relationship between sentences. The enhanced LSTM helps to improve the feature extraction of legal texts. The articles of law after being encoded is applied to the sentence classification to improve the performance of the model for summary extraction. We conduct experiments on the Chinese legal text summarization dataset, the experimental results demonstrate that our proposed method outperforms the baseline methods.
Efficient Text Data Cryptography using 2D Chaotic Tent Map

Many introduced methods for text data cryptography are suffering from some disadvantages such as: short and fixed length private key, fixed data block size, fixed and big number of rounds, sub keys generation and low throughput. In this research paper a new method of data cryptography process will be introduced, it will be shown that this method will suit cryptography of text messages with any length (short, medium and very long messages. The proposed method will raise the level of security providing a high degree of data protection by using a complicated variable length two dimensional matrix key, this key will be generated based on chaotic tenet map model, the block size and the number of rounds will be included in the chaotic tent map parameters allowing 1D and 2D keys generation. The proposed method will be tested using various text messages, the method throughput will be calculated, and also the messages quality during the encryption and decryption phases will be measured to insure that the proposed method achieves the required cryptography process enhancement. Several type of analysis will be performed to insure achieving the requirements of cryptography: increasing the quality of cryptography, increasing the throughput and increasing the level of security.
A quantum image encryption method for dual chaotic systems based on quantum logistic mapping

Abstract On the basis of using quantum NEQR (novel enhanced quantum representation of digital image) to display images, a dual chaos system based on quantum logistic mapping is proposed to encrypt quantum images to ensure the security of quantum image transmission. The encryption algorithm is based on quantum logistic mapping and Chen chaos system to generate chaotic sequences, and uses quantum rotation gate operations to rotate and transform each pixel of the quantum image to achieve the effect of image encryption. Traditional quantum image encryption usually uses classical randomly generated sequences to construct the encryption angle of the quantum rotating door. This method combines the randomness of measured quantum with the chaotic system to obtain a truly random sequence. Using this random sequence can better Keep images confidential. Experimental results show that this method has high security and sensitivity to keys. In the sensitivity analysis of the results of the simulation experiment, its NPCR (Number of Pixels Change Rate) values floated around 99.60%. In the field of image encryption, the reliability of image encryption is greatly enhanced.
Radian Scaling: A Novel Approach to Preventing Concept Drift in Electricity Load Prediction

Artificial neural networks often suffer from concept drift when predicting the electricity load in smart cities due to seasonal trends. The most commonly used methods to solve this issue are automatically updating the model, using feature engineering to capture the seasonality trends, and using ensemble methods, making the model complex for someone to replicate. Furthermore, as the issue is mostly due to the shifted minimum and maximum values, we need a method to normalize without being bound by the training data's scale, mean, and median. We proposed radian scaling to normalize the angles between the two consecutive values for the artificial neural networks to process. Our prediction result shows high accuracy with a root-mean-square error score of 0.0021 and 0.0036 for many-to-one and many-to-many forecasting scenarios, surpassing existing methods without model modification.
An Efficient Blockchain and Smart Contracts Based Approach for Document Verification

Many users upload documents to the cloud to be shared and verified by third-party people before they get access to digital services, e.g. opening a bank account, healthcare service, and making payments online. The documents include everything from a UID (unique identifying digit), Know your customer (KYC), company data, and files are uploaded and verified at a significant rate by an authorized third party. This process involves the transfer of sensitive data over the internet, and tracking the usage of the data by the users is almost impossible. This poses a significant disadvantage for the user as he/she cannot know if the data is being used for the intended purpose or not. The proposed approach in this paper provides secure data transfer to the intended party as well as a way to verify that the intended third party is only accessing the data for the intended purpose. With the use of smart contracts in this approach, the document can be masked with some random content when the intended task is completed and can provide a means to check if the data is being used for the intended purpose or not. It is found from the results that the proposed approach performs better than the existing approaches in terms of computational cost and monetary cost. Also, the overall execution time of the document verification process is improved, and would also allow the user to track the status of the document in the verification process with the help of the status tab.
Improving Flow Control in Electro-pneumatic Lung Ventilators: PID versus Fuzzy Logic Control Systems. Partial Results

This study aims to evaluate the implementation of artificial intelligence, specifically fuzzy logic, in the flow control system of a mechanical pulmonary ventilator. The research focuses on comparing the performance of the fuzzy logic control system with the conventional PED control system. A test bench was constructed to test and analyze the control algorithms on an electro-pneumatic ventilator, with the PID control algorithm initially applied and the parameters of a test lung adjusted under various conditions. The results demonstrated that the PID controller algorithm is effective and precise within the target flow range set by the user. However, it is anticipated that the fuzzy logic algorithm will significantly enhance these results due to its sensitivity to small changes, reliability, and improved accuracy. This research contributes to the understanding and improvement of flow control systems in electro-pneumatic lung ventilators, paving the way for enhanced patient care and treatment outcomes.
Educational Tool for the Use and Control of Atta-Bot

This article presents the details of a final graduation project aimed at developing an application to modify the behavior of a mobile robot named Atta-Bot, with the purpose of using it for educational purposes. The process of identifying the needs of end users regarding robot configuration is shown, as well as the creation of an intuitive application that does not require advanced programming knowledge for its use. The general context of the problem that led to the project, the theoretical foundations, the methodology used, the results obtained, and the final conclusions or considerations are detailed.
Near-Term Digital Health Predictions: A Glimpse into Tomorrow’s AI-driven Healthcare

Healthcare is rapidly evolving, particularly in the realm of digital health. When we consider the future of digital healthcare, it is impossible to ignore the vast potential of artificial intelligence (AI) and the profound impact it will have on the healthcare industry. This momentum of change has accelerated, particularly since the onset of the COVID-19 pandemic, and is largely attributable to workforce shortages and an increased demand for healthcare services. These circumstances have given rise to a unique scenario, compelling health-care to harness AI for various applications.The integration of AI in healthcare necessitates a comprehensive and rigorous approach to ensure accuracy and safety, acknowledging the inherent risks to patient care and safety when used improperly. When imple-menting care models that rely on AI for decision-making, it is imperative to establish meticulous workflows that emphasize human guidance in model development and allow models to adapt and learn from input data. In addition to prioritizing accuracy and safety, equal emphasis should be placed on the implementation of robust measures to protect patients from potential cybersecurity threats posed by data breaches. AI’s advantages extend beyond healthcare institutions, as patients will also experience a transformation in the way they receive care. Harnessing AI will empower patients to establish stronger connections with their health data and gain access to unique insights that are not readily available in traditional care models. These enhanced connections will enable patients to collaborate more effectively with their healthcare teams and receive care that is tailored to their specific needs.
ANALYSIS OF COMMUNITY-BASED PEATLAND FIRE PREVENTION STRATEGIES

Peatland is a wetland ecosystem characterized by high organic matter accumulation and low decomposition rates. Rasau Jaya Tiga Village is one of the villages in the Rasau Jaya District with a soil type dominated by peat soil. Hence, the majority of its people are active in agriculture. As a form of effort to reduce the occurrence of land fires, it is necessary to conduct research on community-based peatland fire prevention strategies in the village of Rasau Jaya Tiga with a descriptive quantitative research type. The sample used was farmers in the village of Rasau Jaya Tiga with a data collection technique in the form of a community-based peatland fire prevention strategy questionnaire. The results showed that the most widely used strategy for preventing peatland fires was canal blocking or blocking canals, with 45 respondents. In comparison, the strategy for preventing land fires by pointing as many as 39 respondents, and the number of respondents with a strategy for preventing land fires using wood vinegar was 28, and as many as five respondents still use the strategy of burning the land in agricultural activities.
Securing Health Data in Mobile Cloud Computing by a Modular Encryption Approach

Abstract: In the dynamic field of healthcare informatics, this research review explores health data security in mobile cloud computing, emphasizing modular encryption standards. In this paper, an exploration of advanced security methodologies within the Mobile Cloud Computing landscape is presented. The study examines three distinct encryption strategies – Symmetric Key Encryption, Public Key Infrastructure, and Homomorphic Encryption – each strategically employing the Modular Encryption Standard. The findings underscore the versatility of these methods in fortifying health information security within mobile environments, providing an understanding of their implementation nuances and comparative advantages. The literature critically assesses protocols, authentication methods, and vulnerabilities in healthcare contexts, extending its analysis to incorporate emerging technologies.
Future AI and Robotics: Visual and Spatial Perception Enhancement and Reasoning

Over the past several decades, artificial intelligence (AI) has been tremendously boosted by new algorithm designs, exponentially increased computing power, and an immense volume of calculation materials (i [...]
Leveraging Blockchain Technology in Adopting Digital Tokenization of Green Bonds

In February 2022, the Finance Minister of India in the Union Budget 2022 announced that the government proposed to issue sovereign green bonds to mobilize assets for green infrastructure. These bonds are a sort of fixed-income instrument where the money raised from investors is used exclusively to finance projects having a positive environmental impact. The announcement was in sync with India's commitment to achieving net-zero carbon emissions by 2070. However, many issues come with it such as the complexity of green data, and the lack of uniform standards to measure the impact of green investments leading to allegations of “greenwashing,” among others. Its solution lies in the digital tokenization of green bonds using blockchain technology. Foreign investors scout for green bonds issued by growing markets like India, which have attractive valuations and good growth prospects. Marketing and issuing green bonds properly would have a far greater potential to bring investment to the security markets and the much-needed advancement in the sustainable sector. It is much more likely that green bonds will bring investment to the security markets and much-needed advancement to the sustainable sector if they are marketed and issued through digital tokenization. Financial regulators and policymakers can create a global framework for the application of blockchain technology in sustainable finance. This might entail tokenizing eco-friendly assets, issuing eco-friendly bonds, trading renewable energy and 2-2 carbon credits in a decentralized ecosystem, and decentralizing crowdfunding for eco-friendly enterprises.This chapter seeks to demonstrate how blockchain technology can help issue green bonds and increase the overall efficiency of green finance in the economy. It also aims to scrutinize how such digital tokenization of green bonds would affect the security market and increase the standards of environmental, social, and governance (ESG) worldwide. While discussing how this process is shaping up and impacting the economies of various countries, it also seeks to provide suggestions to be taken into consideration while adopting the digital tokenization of green bonds.
An Investigation of CNN-CARU for Image Captioning

The goal of an image description is to extract essential information and a description of the content of a media feature from an image. This description can be obtained directly from a human-understandable description of an interesting image (retrieval-based image with object(s) and their action description) or encoded by an encoder–decoder neural network. The challenge of the learning model is that it tries to project the media feature into a neutral language, which also produces the description in another feature domain. It may suffer from misidentification of scene or semantic elements. In this chapter, we attempt to address these challenges by introducing a novel image captioning framework that combines generation and retrieval. A CNN-CARU model is introduced, where the image is first encoded by a CNN-based network, and multiple captions are generated/created for a target image by an RNN network of CARU.
Empowering Multilingual AI: Cross-Lingual Transfer Learning

Multilingual Natural Language Processing (NLP) and Cross-Lingual Transfer Learning have emerged as pivotal fields in the realm of language technology. This abstract explores the essential concepts and methodologies behind these areas, shedding light on their significance in a world characterized by linguistic diversity. Multilingual NLP enables machines to process and generate text in multiple languages, breaking down communication barriers and fostering global collaboration. Cross-lingual transfer learning, on the other hand, leverages knowledge from one language to enhance NLP tasks in another, facilitating efficient resource utilization and improved model performance. The abstract highlights the growing relevance of these approaches in a multilingual and interconnected world, underscoring their potential to reshape the future of natural language understanding and communication.
A Programmable Crypto-Processor for National Institute of Standards and Technology Post-Quantum Cryptography Standardization Based on the RISC-V Architecture

The advancement of quantum computing threatens the security of conventional public-key cryptosystems. Post-quantum cryptography (PQC) was introduced to ensure data confidentiality in communication channels, and various algorithms are being developed. The National Institute of Standards and Technology (NIST) has initiated PQC standardization, and the selected algorithms for standardization and round 4 candidates were announced in 2022. Due to the large memory footprint and highly repetitive operations, there have been numerous attempts to accelerate PQC on both hardware and software. This paper introduces the RISC-V instruction set extension for NIST PQC standard algorithms and round 4 candidates. The proposed programmable crypto-processor can support a wide range of PQC algorithms with the extended RISC-V instruction set and demonstrates significant reductions in code size, the number of executed instructions, and execution cycle counts of target operations in PQC algorithms of up to 79%, 92%, and 87%, respectively, compared to RV64IM with optimization level 3 (-O3) in the GNU toolchain.
Multinucleon structure and dynamics via quantum computing

We propose a framework for computing the structure and dynamics for second-quantized many-nucleon Hamiltonians on quantum computers. We develop an oracle-based Hamiltonian input model that computes the many-nucleon states and nonzero Hamiltonian matrix elements of the many-nucleon system. With our Fock-state based input model, we show how to implement the sparse matrix simulation algorithms to calculate the dynamics of the second-quantized many-nucleon Hamiltonian. Based on the dynamics simulation methods, we also present the methodology for structure calculations of the many-nucleon system. In this work, we provide an explicit circuit design of our input model of the second-quantized Hamiltonian within a direct encoding scheme that maps the occupation of each available single-particle state in the many-nucleon state to the state of specific qubit in a quantum register. We analyze our method and provide the asymptotic cost in computing resources for structure and dynamics calculations of many-nucleon systems. For pedagogical purposes, we demonstrate our input model with two model problems in restricted model spaces.
[A preliminary prediction model of depression based on whole blood cell count by machine learning method].

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.通过机器学习算法挖掘常规检验的血细胞分析参数，构建抑郁症诊断及与焦虑症鉴别诊断的预测模型。利用2020—2021年北京朝阳医院和河北医科大学第一医院血细胞分析参数进行研究，收集研究对象22项血细胞分析检验项目及年龄、性别参数，探索采用支持向量机、决策树、朴素贝叶斯、随机森林和多层感知器多种机器学习技术构建抑郁症预测模型。结果显示，基于健康人群和抑郁症组血细胞分析参数，构建的随机森林模型预测抑郁症发生准确率高达0.99，F1为0.975，受试者工作特征曲线下面积和平均精准度分别为0.985和0.967。血小板参数变化是影响抑郁症发生的重要因素。而基于抑郁症组和焦虑症组的血细胞分析数据，构建的随机森林鉴别诊断模型显示出最高的预测准确性（0.68）和AUC（0.622）。年龄和血细胞参数及红细胞平均体积对该模型贡献最大。综上，本研究通过挖掘血细胞分析项目，初步建立了抑郁症预测及与焦虑症的鉴别诊断模型，显示了机器学习模型在精神疾病中更为客观的评估价值。.
NoSQL Database Modeling and Management: A Systematic Literature Review

The NoSQL databases that emerged this century were created to solve the limitations of relational database systems due to the different types of data that have appeared for information processing. In this paper, we present the results of a secondary study carried out to find and synthesize the research made up to now on modeling processes, characteristics of the used types of data, and management tools for NoSQL Databases. Currently, four types are recognized and classified according to the data model they use: key-value, document-oriented, column-based, and graph-based. With this study, it was possible to identify that the most frequently type of NoSQL database model is that of documents because it offers greater flexibility and versatility compared to the other three models. Although it offers more complex search methods, in terms of data, column and document schemas are the ones that usually describe their characteristics. It was also possible to observe a trend in the use of the column-oriented model and the document-oriented model in the management tools, and, although they all comply with the basic functionalities, the differences lie in the way in which the information is stored and the way they can be accessed.