Reviewerly/exHarmony huggingface dataset:
Dissipation and dephasing of interacting photons in transmon arrays

Transmon arrays are one of the most promising platforms for quantum information science. Despite being often considered simply as qubits, transmons are inherently quantum mechanical multilevel systems. Being experimentally controllable with high fidelity, the higher excited states beyond the qubit subspace provide an important resource for hardware-efficient many-body quantum simulations, quantum error correction, and quantum information protocols. Alas, dissipation and dephasing phenomena generated by couplings to various uncontrollable environments yield a practical limiting factor to their utilization. To quantify this in detail, we present here the primary consequences of single-transmon dissipation and dephasing to the many-body dynamics of transmon arrays. We use analytical methods from perturbation theory and quantum trajectory approach together with numerical simulations, and deliberately consider the full Hilbert space, including the higher excited states. The three main nonunitary processes are many-body decoherence, many-body dissipation, and heating/cooling transitions between different anharmonicity manifolds. Of these, the many-body decoherence---being proportional to the squared distance between the many-body Fock states---gives the strictest limit for observing effective unitary dynamics. Considering experimentally relevant parameters, including also the inevitable site-to-site disorder, our results show that the state-of-the-art transmon arrays should be ready for the task of demonstrating coherent many-body dynamics using the higher excited states. However, the wider utilization of transmons for ternary-and-beyond quantum computing calls for improving their coherence properties.
ANALISIS USER INTERFACE PADA WEBSITE BUKALAPAK DENGAN METODE HEURISTIC

Human and computer interaction is a science that focused on system planning and design a system so that users can interact and collaborate according to the needs of the users themselves in the most effective way at every step of their work. The role of the user interface has a significant impact on the usability of an application or system. Heuristic evaluation (HE) is considered to be the best usability evaluation method compared to other methods because it can find serious problems that cannot be detected by other methods. Therefore, researchers will analyze the user interface on the Bukalapak website using the HE method. User Interface Analysis on the Website with the HE Method through the distribution of online questionnaires, from the results of distributing questionnaires online, it can be concluded that the website has shortcomings in terms of helping and documentation features to assist users in using the system. It is declared lacking because it has the lowest rcount (Total) value of 0.587 compared to other rcount. Suggestions for research on user satisfaction analysis using the HE method, author s convey suggestions that need to be improved in future research, namely it is necessary to conduct research on Bukalapak user satisfaction using the HE method in a wider scope .
Transfer Learning in Natural Language Processing: A Survey

&#x0D; &#x0D; &#x0D; &#x0D; ABSTRACT&#x0D; Transfer learning is a discipline that is expanding quickly within the realm of natural language processing (NLP) and machine learning. It is the application of previously learned models to the solution of a variety of problems that are connected to one another. This paper presents a comprehensive survey of transfer learning techniques in NLP, focusing on five key classification algorithms: (1) BERT, (2) GPT, (3) ELMo, (4) RoBERTa, and (5) ALBERT. We discuss the fundamental concepts, methodologies, and performance benchmarks of each algorithm, highlighting the various approaches taken to leverage pre-existing knowledge for effective learning. Furthermore, we provide an overview of the latest advancements and challenges in transfer learning for NLP, along with promising directions for future research in this domain.&#x0D; &#x0D; &#x0D; &#x0D;
A systematic review of social classroom climate in online and technology-enhanced learning environments in primary and secondary school

Abstract Although the significance of a positive social classroom climate in face-to-face learning has been established, its role within online and technology-enhanced learning environments is unclear. The central aim of this systematic review was to synthesize the findings of empirical studies which have examined any aspect of the social classroom climate in online and technology-enhanced learning environments in primary and secondary schools. Appropriate search terms were entered into ACM Digital Library, Web of Science, Scopus, and ERIC in November 2021. Articles were included if they were relevant for the aim, reported primary data, sampled primary/secondary school students and/or teachers, and were published in journals, conference proceedings, or book chapters in English. Furthermore, articles were excluded if they focused on the development/testing of measurement tools. The thematic narrative synthesis includes 29 articles, comprising of qualitative, quantitative, and mixed-method studies. A quality assessment checklist was completed for all. The findings encompass examinations of the social classroom climate in online learning before and during the Covid-19 pandemic, in blended learning environments and a comparison between them. Furthermore, associations between the online social classroom climate and academic variables is explored, as is the fostering thereof through synchronous/asynchronous discussion groups and social media. We discuss the theoretical framing of the studies, the impact of a positive classroom climate in online and technology-enhanced learning environments on students, as well as practical approaches and new opportunities in leveraging technologies. Based on the findings and the studies’ limitations we outline implications and future research, such as the need to consider students’ voices and diversity, technology perspectives, a transdiciplinary approach and the reconceptualization of boundaries.
Commit or Not? H<scp>ow</scp> B<scp>lockchain</scp> C<scp>onsortia</scp> F<scp>orm and</scp> D<scp>evelop</scp>

In blockchain consortia, different companies band together to develop, govern, and operate a shared blockchain-based system. However, many blockchain-based systems are exposed to the risk of never going live without a proper understanding of the peculiar collaboration this technological architecture entails. To understand how blockchain consortia develop and advance collaborative relationships, this article reports on an extensive analysis of online material and interviews of key members. It draws from the literature on interorganizational collaboration and digital platforms to explain the staged progression of blockchain consortia and the key features of blockchain consortia.
Face Aging by Explainable Conditional Adversarial Autoencoders

This paper deals with Generative Adversarial Networks (GANs) applied to face aging. An explainable face aging framework is proposed that builds on a well-known face aging approach, namely the Conditional Adversarial Autoencoder (CAAE). The proposed framework, namely, xAI-CAAE, couples CAAE with explainable Artificial Intelligence (xAI) methods, such as Saliency maps or Shapley additive explanations, to provide corrective feedback from the discriminator to the generator. xAI-guided training aims to supplement this feedback with explanations that provide a “reason” for the discriminator’s decision. Moreover, Local Interpretable Model-agnostic Explanations (LIME) are leveraged to provide explanations for the face areas that most influence the decision of a pre-trained age classifier. To the best of our knowledge, xAI methods are utilized in the context of face aging for the first time. A thorough qualitative and quantitative evaluation demonstrates that the incorporation of the xAI systems contributed significantly to the generation of more realistic age-progressed and regressed images.
Evaluation of DeepMedic Neural Network for a Region of Interest Eextraction in Medical Image Watermarking

Medical image watermarking prevents data modification and falsification by third parties. For the reason of safety, the key part of the image used for diagnosis should be protected by watermarks that are stable to different attacks. However, watermarks produce little distortions that might influence automatic diagnostic procedures. The best way to overcome this is to divide the image into a region of interest (ROI) and a region of noninterest (RONI) where ROI is protected by a fragile watermark and RONI is served for storing correction coefficients required to restore ROI data precisely. To make the watermarking system fully automatic, ROI detection methods are needed. In this paper, it is proposed to use a DeepMedic neural network for ROI extraction against using state-of-the-art manual ROI extraction and binary thresholding. The manual ROI extraction is difficult because of additional human labor while thresholding usually leads to homogenous RONI where the correction coefficients cannot be effectively stored. In this paper, an evaluation of DeepMedic parameters is provided in the case of the ROI extraction for magnetic resonance imaging (MRI) data. The paper demonstrates that selected parameters give better semantic segmentation results than the original ones reported by the DeepMedic network authors.
A Systematic Literature Review on Cybersecurity Threats to Healthcare Data and Mitigation Strategies

The incidence of cyberattacks on healthcare institutions has seen a steady increase over the years, with a massive increase of 42% in 2020. More specifically, INTERPOL reported a substantial increase in ransomware attacks on healthcare institutions at the height of the Covid-19 pandemic. This increase can be attributed to the rise in the adoption of digital technologies, such as the Medical Internet of Things (MIoT), to support healthcare services. The sensitive nature of healthcare data and the volume of data handled by healthcare institutions make them attractive to cybercriminals. When compared to financial data, healthcare data has more value on the dark web. Because a cyberattack on healthcare data could have far-reaching consequences, healthcare institutions should have effective strategies to safeguard unauthorized access. This paper presents the results of a systematic literature review (SLR) that investigated the strategies that can be used to mitigate cybersecurity threats to healthcare data. Forty-one research papers retrieved from three databases were analyzed based on specific inclusion/exclusion criteria. Synthesis of the sources yielded three main themes, namely, (i) emerging technology trends that contribute to cybersecurity vulnerability in the healthcare sector, (ii) current cybersecurity challenges in the healthcare sector, and (iii) cybersecurity countermeasures and mitigation strategies. The research results showed that a holistic approach that incorporates people, technology and adherence to regulations is required to mitigate cybersecurity threats in the healthcare sector. The study has implications for policymakers, vendors/designers of healthcare technologies, and healthcare institution managers.
Reformulation Of Statistical Data Sources: Big Data New Data Sources Supporting Future Official Statistics?

Big Data is the new oil. The real issue of the development of information technology is the existence of Big Data. Big Data is very important, talking about Big Data not only touches the issue of how big data you have, but what can be done with the data. The high cost of Big Data access is inversely proportional to the collection of data through surveys or censuses for free. analysis of the application of efficiency theory in an economic approach in imposing access costs to statistical activities, knowing the impact of imposing Big Data access fee tariffs, knowing the potential of Big Data as a supporter of official statistics in the future, what are the obstacles and solutions in implementing it. The preparation of this paper is juridical-empirical research with the nature of descriptive research with an economic law approach. Big Data is managed for different purposes using different systems and methods and not necessarily using statistical rules. The implementation of Big Data as a new data source is carried out through a combination of data sources. The imposition of Big Data access fees as a source of data supporting official statistics causes low utilization of Big Data as a new data source. The information technology revolution makes Big Data has the potential to complement, replace, improve, add, and improve the composition of existing statistical data sources, and produce more timely outputs. Difficult access and high costs for data collection are major obstacles. Therefore, it needs to be supported by legal instruments that facilitate its implementation in Indonesia. One is the reformulation of existing regulations that make it easier for basic statistical organizations to obtain such data sources for free and use only in the national interest
Game-based learning - Teaching Artificial Intelligence to play Minecraft: a systematic literature review

Artificial Intelligence (AI) are machines designed to think and behave as humans would. Taking AI and placing it into a virtual world they become known as AI agents which uses the knowledge it gained from training to perform tasks in the world. AI agents in the virtual world has only been able to perform a narrow set of tasks with specialised models in environments with limited complexity and diversity. A rich world that requires an agent to continuously learn from and adapt to a wide variety of open-ended tasks and use previously gained knowledge to determine the next course of action will render the agent incapable. In order to investigate the AI teaching methods applied to instruct the agent to perform basic tasks in Minecraft in order to identify which AI teaching methods will yield the best results, a systematic literature review was conducted by extracting 57 papers and identifying themes and sub-themes that suited AI agent training methods and functions. This was to discover wat AI training methods can be implemented to enable an agent to perform tasks in a complex and rich world, contributing to game-based learning. The study found that a well-integrated Reinforcement Learning (RL) method with an effective reward system equipped the agent with the necessary knowledge to be able to perform tasks on a more complex level. A list of unique methods was integrated with RL such as Newtonian Action Advice (NAA), Behavioural Cloning (BC), VideoPreTraining (VPT), human demonstrations, and natural language commands to achieve a certain goal. This meant that AI agents can be taught to perform open ended tasks in a complex environment by setting up a well thought out framework on how to teach the agent in various areas leading to the possibility to incorporate those teachings into the real world through game- based learning.
Clustering Analysis Using K-Medoids on Poverty Level Problems in Central Java by District/City

Poverty describes the absence of property and poor income or the circumstance that the food, shelter, and clothing needs cannot be met. The performance of Central Java in poverty reduction has risen and declined during the last decade. This research aims to perform mapping analyses utilizing artificial intelligence techniques as clusters on the number of poverty levels in Central Java districts or cities. Since Central Java is after West Java and East Java is the third most populous province, this was necessary to achieve in the last few years through regional mapping of a macro-picture of the poverty level. The dataset used is from the statistics agency website on the number of poor people (millennia) in 2017–2019. The data used are from the Central Java Statistical Agency. The way to map the clusters is using the k-medoids method which is part of data clustering. The number of clusters utilized for mapping poverty levels is high and low. The results showed six provinces (17%) in the high and 29 (83%) in the low. In the high cluster (cluster 1) and in the low cluster (cluster 1) and {18.6, 19.4, 20.1} the final centroid values for each cluster were {293.2, 309.2, 343.5}. The results of mapping can help address the poor in places in which the high cluster (cluster 1), Cilacap District, Banyumas District, Kebumen District, Grobogan District, Pemalang District, and Brebes District are a priority of the government in Central Java province.&#x0D; Keywords: K-Medoids, clustering, poverty
The impact of Blockchain technique on trustworthy healthcare sector

Following the COVID epidemic, the healthcare sector faced numerous issues as telehealth became more prevalent and the necessity for a safe and efficient healthcare record system became critical. Many issues plague the healthcare industry today, including security, trust, data availability, and drug traceability. Blockchain technology is a relatively new technology that has demonstrated its effectiveness in a variety of industries, including finance, banking, bitcoin, and healthcare. This research investigates the impact of blockchain on the trustworthiness of healthcare. The authors adopt a descriptive-analytical methodology. The trustworthiness of the healthcare sector is considered a dependent variable. However, blockchain technology is considered an independent variable. Three dimensions of dependent variables were explored: integrity, confidence, and reliability. For studying this impact, a questionnaire of 25 items was created and distributed to the stakeholders in the healthcare sector in the north of Lebanon. The collected answers were analyzed using the SPSS application and statistical tools. The results verify that the use of blockchain technology has a great impact on healthcare trustworthiness with its three dimensions (integrity, confidence, and reliability).
Design and Implementation of Secure Data Retrieval Method in Medical Cyber Physical Network

Medical cyber-physical networks (MCPNs) have the potential to totally alter the healthcare business since they make it feasible to remotely monitor, diagnose, and treat patients. Despite this, MCPN usage is increasing at a startling rate, which raises concerns about security and privacy. Safe data retrieval is critical for maintaining the privacy and accuracy of patients' medical information. The challenges of securing data retrieval in MCPNs, as well as the existing solutions, are the major emphasis of this paper, which provides a review of the relevant literature. Some of the procedures and strategies discussed include access control, encryption, authentication, data masking or pseudonymization, and safe data storage. The challenges of implementing these safety measures and minimising the effect of human variables are also explored. The healthcare sector must maintain its investment in robust security measures to ensure the safety and privacy of patients and the confidentiality of their medical information. If proper safeguards are in place, the usage of MCPNs has the potential to dramatically alter healthcare and lead to improved results for patients.
SAM C-GAN: a method for removal of face masks from masked faces

The past years of COVID-19 have attracted researchers to carry out benchmark work in face mask detection. However, the existing work does not focus on the problem of reconstructing the face area behind the mask and completing the face that can be used for face recognition. In order to address this problem, in this work we have proposed a spatial attention module-based conditional generative adversarial network method that can generate plausible images of faces without masks by removing the face masks from the face region. The method proposed in this work utilizes a self-created dataset consisting of faces with three types of face masks for training and testing purposes. With the proposed method, an SSIM value of 0.91231 which is 3.89% higher and a PSNR value of 30.9879 which is 3.17% higher has been obtained as compared to the vanilla C-GAN method.
Affect as a proxy for literary mood

We propose to use affect as a proxy for mood in literary texts. In this study, we explore the differences in computationally detecting tone versus detecting mood. Methodologically we utilize affective word embeddings to look at the affective distribution in different text segments. We also present a simple yet efficient and effective method of enhancing emotion lexicons to take both semantic shift and the domain of the text into account producing real-world congruent results closely matching both contemporary and modern qualitative analyses.
Unlocking Temporal Question Answering for Large Language Models Using Code Execution

Large language models (LLMs) have made significant progress in natural language processing (NLP), and are utilized extensively in various applications. Recent works, such as chain-of-thought (CoT), have shown that intermediate reasoning steps can improve the performance of LLMs for complex reasoning tasks, such as math problems and symbolic question-answering tasks. However, we notice the challenge that LLMs face when it comes to temporal reasoning. Our preliminary experiments show that generating intermediate reasoning steps does not always boost the performance of complex temporal question-answering tasks. Therefore, we propose a novel framework that combines the extraction capability of LLMs and the logical reasoning capability of a Python solver to tackle this issue. Extensive experiments and analysis demonstrate the effectiveness of our framework in handling intricate time-bound reasoning tasks.
PrivyKG: Security and Privacy Preservation of Knowledge Graphs Using BlockChain Technology

Recently, knowledge graph has proved its benefits in AI applications and especially in knowledge representation and reasoning. Therefore, with the huge amount of published data, knowledge graph privacy, trust and integrity take an important role to protect it from illegal access and modification. In this paper, we propose PrivyKG, a new framework which uses the blockchain technology along with an access control based on it to preserve the knowledge graph privacy, integrity, and ensure security and trust. The proposed work has two essential parts to build a decentralized knowledge graph, where the first one uses off-chain storage for ensuring the blockchain scalability and the second one integrates a permissioned Hyperledger Fabric powered with GO smart contracts. The proposed framework is evaluated with a DBpedia dataset that illustrates the efficiency and the feasibility of our proposed against potential threats.