Reviewerly/exHarmony huggingface dataset:
The role of social capital in addressing seed access constraints and adoption intensity: Evidence from Arsi Highland, Oromia Region, Ethiopia

In Ethiopia, limited empirical studies exist on how seed supply access constraints condition adoption intensity (demand). Hence, this research employs the augmented Double hurdle model to incorporate the effect of seed access (local supply) constraints in conditioning demand. Moreover, nine factors were constructed of twenty-eight indicators using Principal Components Analysis to resolve which cognitive and structural indicators drive social capital at the farm household level. The Double hurdle result reveals that social capital indeed determines wheat varieties access; besides, different forms of social capital have dissimilar effects on varieties demand. In addition to social capital variables (such as getting well with other farmers, generalized trust, and trust in agricultural institutions), information on seed access, training on varieties selection, and education have significant positive effects on relaxing seed access constraints and demand. Hence, the result suggests that agricultural policy and extension efforts should consider not only human, and physical capital, but also social capital in relaxing seed access constraints and demand. Furthermore, the government of Ethiopia should develop strong regulatory mechanisms to reduce corruption in the seed supply system.
QCNN-H: Single-Image Dehazing Using Quaternion Neural Networks

Single-image haze removal is challenging due to its ill-posed nature. The breadth of real-world scenarios makes it difficult to find an optimal dehazing approach that works well for various applications. This article addresses this challenge by utilizing a novel robust quaternion neural network architecture for single-image dehazing applications. The architecture’s performance to dehaze images and its impact on real applications, such as object detection, is presented. The proposed single-image dehazing network is based on an encoder-decoder architecture capable of taking advantage of quaternion image representation without interrupting the quaternion dataflow end-to-end. We achieve this by introducing a novel quaternion pixel-wise loss function and quaternion instance normalization layer. The performance of the proposed QCNN-H quaternion framework is evaluated on two synthetic datasets, two real-world datasets, and one real-world task-oriented benchmark. Extensive experiments confirm that the QCNN-H outperforms state-of-the-art haze removal procedures in visual quality and quantitative metrics. Furthermore, the evaluation shows increased accuracy and recall of state-of-the-art object detection in hazy scenes using the presented QCNN-H method. This is the first time the quaternion convolutional network has been applied to the haze removal task.
High threshold codes for neutral atom qubits with biased erasure errors

The requirements for fault-tolerant quantum error correction can be simplified by leveraging structure in the noise of the underlying hardware. In this work, we identify a new type of structured noise motivated by neutral atom qubits, biased erasure errors, which arises when qubit errors are dominated by detectable leakage from only one of the computational states of the qubit. We study the performance of this model using gate-level simulations of the XZZX surface code. Using the predicted erasure fraction and bias of metastable $^{171}$Yb qubits, we find a threshold of 8.2% for two-qubit gate errors, which is 1.9 times higher than the threshold for unbiased erasures, and 7.5 times higher than the threshold for depolarizing errors. Surprisingly, the improved threshold is achieved without bias-preserving controlled-not gates, and instead results from the lower noise entropy in this model. We also introduce an XZZX cluster state construction for measurement-based error correction, hybrid-fusion, that is optimized for this noise model. By combining fusion operations and deterministic entangling gates, this construction preserves the intrinsic symmetry of the XZZX code, leading to a higher threshold of 10.3% and enabling the use of rectangular codes with fewer qubits. We discuss a potential physical implementation using a single plane of atoms and moveable tweezers.
CYBER SECURITY FRAMEWORK SELECTION: COMPARISION OF NIST AND ISO27001

Plans for preventing and mitigating vulnerabilities in computer networks are known as cybersecurity frameworks. With the assistance of the Cybersecurity Framework, organisations are able to reduce their vulnerability to cyber attacks and improve the strength of their defences. The cybersecurity framework has made the decision to take part in trials so that it may improve its ability to handle long-term security frameworks. This gives rise to concerns about cost and time rather than safety at this point in time because of the situation that is occurring. A firm may utilise the similarities that are shared by many cyber security frameworks as a reference to assist it in selecting the framework that is the most suitable for the needs that are unique to the organisation. According to the findings of this study, the risk maturity level, cost, and certification are the three most significant components of CSF implementation for firms.
A Fuzzy Similarity Based Classification with Archimedean-Dombi Aggregation Operator

The term "classification" refers to a supervised learning technique in which samples are given class labels based on predetermined classes.Fuzzy classifiers are renowned for their ability to address the issue of outliers and deliver the performance resilience that is much needed.The major goal of this study is to provide a classification algorithm that is effective and accurate.In this work, we address Archimedean-Dombi aggregation operator by extending the similarity classifier.Earlier, Dombi operators were used to study the similarity classifier.We focus on the application of Archimedean-Dombi operators during the classifier's aggregate similarity calculation.Since Archimedean and Dombi operators are well-known for offering appropriate generalization and flexibility respectively in aggregating data, so a different version of the similarity classifier is created.One realworld medical dataset, namely Parkinson disease data set is used to test the proposed approaches.When compared to older existing operators, the new classifiers have better classification accuracy.
Flexible, Programable Sensing System with poly(AAm-HEMA-SA) for Human Motion Detection

Conductive hydrogels are powerful material candidates for artificial skin and muscle, flexible and implantable bioelectronics, and tissue engineering.However, the preparation of highly conductive hydrogels without sacrificing their mechanical and electrical properties remains challenging.Herein, composites hydrogels (CAHSx) were prepared by in-situ polymerization with carbon nanotube (CNT) as conductive filler and poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (PEDOT:PSS) as the conductive liquid uniformly dispersed in AAm-HEMA-SA (sodium alginate) hydrogel system.The elongation at break of CAHS6 (0.6 wt% CNT) reached 550%, which was much higher than pure PEDOT:PSS hydrogel.The developed hydrogel can withstand 6000 cycles of cyclic loading at 30% tensile strain without crack propagation.Practically, the conductive hydrogel was employed as a flexible strain sensor with excellent sensitivity, good durability, and repeatability, which meets the requirements of human joint motion detection.Additionally, our composites conductive hydrogels can also be synthesized by the direct ink writing (DIW) method to enrich the application range.
Evaluation and Implementation of IT Governance Using the 2019 COBIT Framework at the Department of Food Security, Agriculture and Fisheries of Balangan Regency

The food security, fisheries and agriculture services work with local government to help the community. In carrying out its work, the government applies information technology to achieve the goals. Therefore, it is necessary to have an IT governance design to ensure the alignment of the government’s objectives and the use of IT. Governance is an important asset to measure the effectiveness and efficiency of work process improvement using IT. This research is conducted because there has never been an IT governance design assessment. The analysis and design of IT governance at government agencies are carried out using the COBIT 2019 framework to measure the level of IT capability. The research method used is descriptive qualitative by conducting interviews with influential government parties. This study obtained an IT governance design and detected important processes for the domains used, namely EDM04, APO04, APO07, APO08, APO11, BAI03, BAI08, BAI10.
Implementation Naïve Bayes Classification for Sentiment Analysis on Internet Movie Database

A film review is a subjective opinion of someone who has different feelings about each film. As a result, film enthusiasts will struggle to assess whether the film meets their requirements. Based on these issues, sentiment analysis is the best way to fix them. Sentiment analysis, also known as opinion mining, is the study of assigning views or emotional labels to texts in order to determine if the text contains positive or negative thoughts. The Nave Bayes method was chosen because it can classify data based on the computation of each class's probability against objects in a given data sample. The best model was created utilizing data without lemmatization, 500 vector sizes, and Nave Bayes classification, with an accuracy of 78.96 percent and a f1-score of 78.81 percent. Changes in vector size affect the system's capacity to foresee positive and negative sentiments. The difference in accuracy and recall values shows that when vector size 300 is utilized, the precision and recall outcomes are lower than when vector size 500 is used.
A performed knapsack problem on the fuzzy chaos cryptosystem with cosine Lozi chaotic map

Based on a Knapsack problem approach, a fuzzy chaos based cryptography method for a cosine chaotic map is proposed, where the system work through designing the Takagi-Sugeno(TS) fuzzy model on a chaotic map as a seed map in cosine map. The basic idea of the method is to apply chaotification method to use a cosine chaotic map approached on the out puts of TS fuzzy model to compute a real super increasing sequence applied in Knapsack equation. The proposed method only requires the application on Lozi chaotic map covered by cosine map in generating the secure key at the real super increasing sequence and in encryption, decryptions models. Algorithmic performance of the method is also derived. Also some numerical simulations are presented to demonstrate the effectiveness of the proposed method.
Secure Distributed Cloud Storage based on the Blockchain Technology and Smart Contracts

Objectives: This paper addresses the problem of secure data storage and sharing over cloud storage infrastructures. A secure, distributed cloud storage structure incorporating the blockchain structure is proposed that supports confidentiality, integrity, and availability. Methods/Analysis: The proposed structure combines two well-known technologies: one of them is the Ethereum Blockchain and its Smart Contracts and the other is the RSA encryption and authentication scheme. The Ethereum Blockchain is used as a data structure, which ensures data availability and integrity while RSA provides sensitive data confidentiality and source authentication. Findings: As a result, users of the proposed structure can trust it and be certain that they can securely exchange information through a publicly accessible and shared cloud storage. The application can be used either through a user interface (UI) or a command-line interface (CLI). Novelty /Improvement:The novelty of this work is that the system that is proposed could be used for secure data storage on the cloud as well as for file sharing and authentication verification. Also, secure data storage and file sharing are already offered by the proposed system. Doi: 10.28991/ESJ-2023-07-02-012 Full Text: PDF
The Impact of AI on Developer Productivity: Evidence from GitHub Copilot

Generative AI tools hold promise to increase human productivity. This paper presents results from a controlled experiment with GitHub Copilot, an AI pair programmer. Recruited software developers were asked to implement an HTTP server in JavaScript as quickly as possible. The treatment group, with access to the AI pair programmer, completed the task 55.8% faster than the control group. Observed heterogenous effects show promise for AI pair programmers to help people transition into software development careers.
A quantum algorithm for heat conduction with symmetrization

Heat conduction, driven by thermal non-equilibrium, is the transfer of internal thermal energy through physical contacts, and it exists widely in various engineering problems, such as spacecraft and state-of-the-art dilution refrigerators. The mathematical equation for heat conduction is a prototypical partial differential equation. Here we report a quantum algorithm for heat conduction (QHC) that significantly outperforms classical algorithms. We represent the original heat conduction system by a symmetric system with an ancilla qubit so that the quantum circuit complexity is polylogarithmic in the number of discretized grid points. Compared with the existing algorithms based on solving linear equations via the Harrow-Hassidim-Lloyd (HHL) algorithm, our method evolves the linear process directly without phase estimation, which involves complex quantum operations and large output error. Therefore, this algorithm is experimental-friendly and without output error after the discretization procedure. We experimentally implemented the algorithm for a one-dimensional thermal conduction process with two-edge constant temperatures and adiabatic conditions on a nuclear spin quantum processor. The spatial and temporal distributions of the temperature are accurately determined from the experimental results. Our work can be naturally applied to any physical processes that can be reduced to the heat equation.
Coupling Quantum Random Walks with Long- and Short-Term Memory for High Pixel Image Encryption Schemes

This paper proposes an encryption scheme for high pixel density images. Based on the application of the quantum random walk algorithm, the long short-term memory (LSTM) can effectively solve the problem of low efficiency of the quantum random walk algorithm in generating large-scale pseudorandom matrices, and further improve the statistical properties of the pseudorandom matrices required for encryption. The LSTM is then divided into columns and fed into the LSTM in order for training. Due to the randomness of the input matrix, the LSTM cannot be trained effectively, so the output matrix is predicted to be highly random. The LSTM prediction matrix of the same size as the key matrix is generated based on the pixel density of the image to be encrypted, which can effectively complete the encryption of the image. In the statistical performance test, the proposed encryption scheme achieves an average information entropy of 7.9992, an average number of pixels changed rate (NPCR) of 99.6231%, an average uniform average change intensity (UACI) of 33.6029%, and an average correlation of 0.0032. Finally, various noise simulation tests are also conducted to verify its robustness in real-world applications where common noise and attack interference are encountered.
Cross-corpora spoken language identification with domain diversification and generalization

This work addresses the cross-corpora generalization issue for the low-resourced spoken language identification (LID) problem. We have conducted the experiments in the context of Indian LID and identified strikingly poor cross-corpora generalization due to corpora-dependent non-lingual biases. Our contribution to this work is twofold. First, we propose domain diversification, which diversifies the limited training data using different audio data augmentation methods. We then propose the concept of maximally diversity-aware cascaded augmentations and optimize the augmentation fold-factor for effective diversification of the training data. Second, we introduce the idea of domain generalization considering the augmentation methods as pseudo-domains. Towards this, we investigate both domain-invariant and domain-aware approaches. Our LID system is based on the state-of-the-art emphasized channel attention, propagation, and aggregation based time delay neural network (ECAPA-TDNN) architecture. We have conducted extensive experiments with three widely used corpora for Indian LID research. In addition, we conduct a final blind evaluation of our proposed methods on the Indian subset of VoxLingua107 corpus collected in the wild. Our experiments demonstrate that the proposed domain diversification is more promising over commonly used simple augmentation methods. The study also reveals that domain generalization is a more effective solution than domain diversification. We also notice that domain-aware learning performs better for same-corpora LID, whereas domain-invariant learning is more suitable for cross-corpora generalization. Compared to basic ECAPA-TDNN, its proposed domain-invariant extensions improve the cross-corpora EER up to 5.23%. In contrast, the proposed domain-aware extensions also improve performance for same-corpora test scenarios.
Data storage mechanism of industrial IoT based on LRC sharding blockchain

With the rapid development of Industry 4.0, the data security of Industrial Internet of Things in the Industry 4.0 environment has received widespread attention. Blockchain has the characteristics of decentralization and tamper-proof. Therefore, it has a natural advantage in solving the data security problem of Industrial Internet of Things. However, current blockchain technologies face challenges in providing consistency, scalability and data security at the same time in Industrial Internet of Things. To address the scalability problem and data security problem of Industrial Internet of Things, this paper constructs a highly scalable data storage mechanism for Industrial Internet of Things based on coded sharding blockchain. The mechanism uses coded sharding technology for data processing to improve the fault tolerance and storage load of the blockchain to solve the scalability problem. Then a cryptographic accumulator-based data storage scheme is designed which connects the cryptographic accumulator with the sharding nodes to save storage overhead and solve the security problem of data storage and verification. Finally, the scheme is proved to be security and the performance of the scheme is evaluated.
Purifying teleportation

Coupling to the environment typically suppresses quantum properties of physical systems via decoherence mechanisms. This is one of the main obstacles in practical implementations of quantum protocols. In this work we show how decoherence effects can be reversed/suppressed during quantum teleportation in a network scenario. Treating the environment quantumly, we show that under a general pure dephasing coupling, performing a second teleportation step can probabilistically reverse the decoherence effects if certain commutativity conditions hold. This effect is purely quantum and most pronounced for qubit systems, where in 25 % of instances the decoherence can be reversed completely. As an example, we show the effect in a physical model of a qubit register coupled to a bosonic bath. We also analyze general $d$-dimensional systems, identifying all instances of decoherence suppression. Our results are proof-of-concept but we believe will be relevant for the emerging field of quantum networks as teleportation is the key building block of network protocols.
Technical Dimensions of Programming Systems

Programming requires much more than just writing code in a programming language. It is usually done in the context of a stateful environment, by interacting with a system through a graphical user interface. Yet, this wide space of possibilities lacks a common structure for navigation. Work on programming systems fails to form a coherent body of research, making it hard to improve on past work and advance the state of the art. In computer science, much has been said and done to allow comparison of **programming languages**, yet no similar theory exists for *programming systems*; we believe that programming systems deserve a theory too. We present a framework of *technical dimensions* which capture the underlying characteristics of programming systems and provide a means for conceptualizing and comparing them. We identify technical dimensions by examining past influential programming systems and reviewing their design principles, technical capabilities, and styles of user interaction. Technical dimensions capture characteristics that may be studied, compared and advanced independently. This makes it possible to talk about programming systems in a way that can be shared and constructively debated rather than relying solely on personal impressions. Our framework is derived using a qualitative analysis of past programming systems. We outline two concrete ways of using our framework. First, we show how it can analyze a recently developed novel programming system. Then, we use it to identify an interesting unexplored point in the design space of programming systems. Much research effort focuses on building programming systems that are easier to use, accessible to non-experts, moldable and/or powerful, but such efforts are disconnected. They are informal, guided by the personal vision of their authors and thus are only evaluable and comparable on the basis of individual experience using them. By providing foundations for more systematic research, we can help programming systems researchers to stand, at last, on the shoulders of giants.
Application of artificial intelligence in predicting lymph node metastasis in breast cancer

Breast cancer is a leading cause of death for women globally. A characteristic of breast cancer includes its ability to metastasize to distant regions of the body, and the disease achieves this through first spreading to the axillary lymph nodes. Traditional diagnosis of axillary lymph node metastasis includes an invasive technique that leads to potential clinical complications for breast cancer patients. The rise of artificial intelligence in the medical imaging field has led to the creation of innovative deep learning models that can predict the metastatic status of axillary lymph nodes noninvasively, which would result in no unnecessary biopsies and dissections for patients. In this review, we discuss the success of various deep learning artificial intelligence models across multiple imaging modalities in their performance of predicting axillary lymph node metastasis.