Reviewerly/exHarmony huggingface dataset:
INTEGRATION OF BLOCKCHAIN TECHNOLOGY IN DIGITAL LIBRARIES: A SOFTWARE ENGINEERING DESIGN

Authors:
raw_author_name: Arie Gunawan
raw_affiliation_string: Universitas Pendidikan Indonesia Bandung, Universitas Nasional
raw_author_name: Munir Munir
raw_affiliation_string: Universitas Pendidikan Indonesia Bandung
raw_author_name: Chairul Furqon
raw_affiliation_string: Universitas Pendidikan Indonesia Bandung

Abstract: This research aims to design software engineering that integrates blockchain technology in digital libraries to improve system security and reliability. This integration is expected to overcome challenges related to data security, service reliability, and efficiency in digital library management. The research methodology involves collecting data through literature, expert interviews, and observations, on the implementation of blockchain technology in digital libraries, then analyzing data to support data design such as etherum, smart contracts, address, node.js, solidity, metamask, and sublime text, then using the Agile Extreme Programming (XP) method for software development. The research results include the design of a decentralized blockchain architecture, the use of smart contracts, and the application of cryptographic techniques to enhance security. Immutability testing in the context of blockchain involves verifying data consistency, validating the process of adding data, testing the ability to delete data, testing against attacks, and activities on immutable data. These tests were conducted using the Truffle framework. The results show that the system is able to maintain data integrity well.
Generation of Images from Text Using AI

Authors:
raw_author_name: Nimesh Yadav
raw_author_name: Aryan Sinha
raw_author_name: Mohit Jain
raw_author_name: Aman Agrawal

Abstract: Reading the words can be confusing, and it may be hard to picture what is happening. There are some circumstances where words can be misunderstood. It's much simpler to recognize text if it's displayed as an image. The use of visuals is proven to increase viewership and retention.Synthesizing realistic images automatically is a challenging undertaking, and even the most advanced artificial intelligence and machine learning algorithm has trouble meeting this standard. GANs (Generative Adversarial Networks) are just one example of a powerful neural network architecture that has shown promising results in recent years. Existing text-to-image methods can generate examples that generally reflect the meaning of the provided descriptions, but they often lack the necessary details and colorful object elements.The primary objective of our research was to explore diverse architectural methodologies with the intention of facilitating the generation of visual representations from textual descriptions. By delving into this investigation, we aimed to discover and examine various approaches that could effectively support the creation of visuals that accurately depict the content and context provided within written narratives. Our aim was to unlock new possibilities in the realm of visual storytelling by establishing a strong connection between language and imagery through innovative architectural techniques.
Multi-scale and multi-patch feature fusion network for person re-identification

Authors:
raw_author_name: Yanqiu Wu
raw_affiliation_string: Minnan University of Science and Technology, Quanzhou, Fujian, China
raw_author_name: Min Liu
raw_affiliation_string: Minnan University of Science and Technology, Quanzhou, Fujian, China
raw_author_name: Dehong Sun
raw_affiliation_string: Minnan University of Science and Technology, Quanzhou, Fujian, China

Abstract: Person re-identification relies on discriminative features. However, most researches focus on extracting features from the high-layer of network while ignoring the middle-layer features, some important details are overlooked frequently. To address this issue, we propose a Multi-Scale and Multi-Patch Feature Fusion Network(MSPF). We employ modified OSFA to extract, align, and fuse the feature maps in the middle-layer of network, which can compensate for the lack of detailed information in the high-level network features. To obtain richer detailed global features of pedestrian, we construct a multi-patch feature fusion module(MPF). We concatenate the global features extracted from modified OSFA and MPF to obtain global features with richer detailed representations. Cross-entropy loss, triplet loss and center loss are combined to constrain our model. We evaluate the performance of our model on Market-1501, CUHK03_labeled and DukeMTMC. The results prove that our method is superior to the state-of-the-art approaches.
Test of Good Governance in Nigeria using World Bank indicators

Authors:
raw_author_name: Ezekiel Elton Mike Micah
raw_affiliation_string: Department of Accounting, Air Force Institutes of Technology, Kaduna, Nigeria

Abstract: The concern for good governance and its role in development process has increased since 1980s. Nigeria has experienced uninterrupted democracy since 1999. This study carryout a test of good governance in Nigeria using the six indicators of good governance used by World Bank. Specifically, the study determine the effect of voice and accountability, political stability and absence of violence and terrorism, government effectiveness, regulatory quality, rule of law and control of corruption on economic development in Nigeria using per capita GDP as a proxy for economic development. Inflation rate and oil price were included in the model in order to produce a robust model. Annual data covering 1999 to 2022 were collected from World Bank website. Descriptive statistics were used to summarize data while Autoregressive Distributed Lag Model and Bound tests were used to estimate relationship. Findings revealed that the performance of governance in Nigeria in all the six indicators during the period of study was weak. This consequently affect per capita income negatively both in the short-run and long-run. It was concluded that governance has negative effect on economic development in Nigeria. It was recommended that the government should use worldwide indicators to conduct self-evaluation and carry out reforms that will help to improve governance in Nigeria for the purpose of achieving development or improving the wellbeing of Nigerians.
Efficient quantum digital signatures over long distances with likely bit strings

Authors:
raw_author_name: Ji-Qian Qin
raw_affiliation_string: State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China
raw_author_name: Zong-Wen Yu
raw_author_name: Xiang-Bin Wang
raw_affiliation_string: Frontier Science Center for Quantum Information, Beijing 100084, China; Jinan Institute of Quantum Technology, SAICT, Jinan 250101, China; Shanghai Branch, CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics, University of Science and Technology of China, Shanghai 201315, China; Shenzhen Institute for Quantum Science and Engineering, and Physics Department, Southern University of Science and Technology, Shenzhen 518055, China; State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China

Abstract: Quantum digital signatures (QDSs) can provide information-theoretic security of messages against forgery and repudiation. Compared with previous QDS protocols that focus on signing one-bit messages, hash function--based QDS protocols can save quantum resources and are able to sign messages of arbitrary length. Using the idea of likely bit strings, we propose an efficient QDS protocol with hash functions over long distances. Our method of likely bit strings can be applied to any quantum key distribution--based QDS protocol to significantly improve the signature rate and dramatically increase the secure signature distance of QDS protocols. In order to save computing resources, we propose an improved method where Alice participates in the verification process of Bob and Charlie. This eliminates the computational complexity relating to the huge number of all likely strings. We demonstrate the advantages of our method and our improved method with the example of sending-or-not-sending QDS. Under typical parameters, both our method and our improved method can improve the signature rate by more than $100$ times and increase the signature distance by about $150\phantom{\rule{0.2em}{0ex}}\mathrm{km}$ compared with hash function--based QDS protocols without likely bit strings.
At the frontier of the metaverse: NFTs, artistic expression, and digital immersions

Authors:
raw_author_name: João Teixeira
raw_affiliation_string: Department of Electronics and Systems, Federal University of Pernambuco

Abstract: &lt;strong&gt;&lt;strong&gt;&lt;/strong&gt;&lt;/strong&gt;&lt;p&gt;This paper explores the multifaceted landscape of Non-Fungible Tokens beyond their role in digitizing artworks. It delves into the intersection of art, utility, and cultural preservation within the NFT realm. The discussion revolves around the duality of “art for art’s sake” and functional utility, sparking ongoing debates about the essence of art in both the analog and digital realms. The convergence of contemporary aesthetics with practical token utility challenges conventional definitions, offering an alternative to narrative retrieval and contributing to cultural preservation in the virtual sphere. As one enters the NFT ecosystem, a utopian quest for autonomy in healthy production becomes evident, albeit not without formidable obstacles. The paper examines the promise of NFTs to liberate creators from intermediary control and the entwined complexities accompanying their evolution. It sheds light on the cautious and ethical approach required for the mass adoption of these technologies, contrasting this with the current global context characterized by rampant consumerism, individualism, and the relentless pursuit of easy financial gains.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
Dementia Research on Facebook and Twitter: Current Practice and Challenges

Authors:
raw_author_name: Zijian An
raw_affiliation_string: Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA
raw_author_name: Kenneth Joseph
raw_affiliation_string: Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA

Abstract: Background: Social media is a powerful tool for engaging diverse audiences in dementia research. However, there is little data summarizing current content exchange in this context. Objective: To inform ethical dementia research engagement on social media, we characterized current practices by analyzing public social media posts. Methods: We retrieved Facebook (2-year period, N = 7,896) and Twitter (1-year period, N = 9,323) posts containing dementia research-related keywords using manual and machine learning-based search strategies. We performed qualitative and quantitative content and sentiment analyses on random samples (10%) of the posts. Results: Top Facebook users were advocacy (45%) and health organizations (25%). On Twitter, academics/researchers were the largest user group. Prevention was the most frequently coded theme (Facebook 30%; Twitter 26%), followed by treatment (Facebook 15%; Twitter 18%). Diagnostics had the highest Facebook engagement. Sharing knowledge was the primary form of content exchange (Facebook 63%; Twitter 80%). Most shared journal articles were peer-reviewed and open access. Emotional tone was overall more positive on Facebook. Justice was a prominent ethics topic regarding inequalities related to identity and intersecting modes of marginalization in dementia research. Conclusion: The findings indicate the importance of social media as an engagement tool of current topics in health research and reveal areas of potential for increased engagement. These data can inform consensus-based best practices for ethical social media application in dementia research.
Habits, Motivation and Performance: An Integrative Model for Organisational Improvement Grounded in Brain Science

Authors:
raw_author_name: Abubaker Qutieshat
raw_affiliation_string: University of Zambia

Abstract: Purpose: Organizations aim to cultivate motivated and productive workforces, yet often struggle to understand the complex neurological factors shaping employee habits, engagement, and performance. This paper presents an integrative model linking psychological needs, motivation systems, and contextual supports to optimize collective habits and outcomes.&#x0D; Methodology: A mixed methods study of 400 employees in Zambia’s public health sector combined surveys, interviews, statistical analysis, and prior organizational research.&#x0D; Findings: Results indicated the limitations of existing habit frameworks in capturing neurological dynamics. Just 31% felt current models reliably predict long-term impacts on performance. Key findings revealed that the top motivators were financial rewards, career growth, and training. On-the-job skill-building is also rated highly. Clear expectations, collaboration, and resources further enabled productivity.&#x0D; Unique Contribution to Theory, Policy and Practice: Based on the data, a Motivation-Performance Model is proposed, encompassing elements like empowerment, open communication, and flexibility to fulfil psychological needs. Individual, team, and organizational factors intersect to form collective habits and effectiveness. Implementing diagnostic and enhancement strategies based on the model offers an actionable approach to leveraging habits for competitive advantage.
The digital economy brings new opportunities for arts and culture

Authors:
raw_author_name: Tianshu Zhang
raw_affiliation_string: Faculty of Foreign Languages, Southwestern University of Finance and Economics
raw_author_name: Yuanyuan Jiang
raw_affiliation_string: School of Finance, Southwestern University of Finance and Economics
raw_author_name: Yingying Jiang
raw_affiliation_string: School of Business, East China University of Science and Technology

Abstract: We are currently in the digital era of the 21st century, and the rapid advancement of artificial intelligence has brought new energy to the evolution of museums. Museums must inevitably advance towards digitisation. Therefore, the variety of applications for artificial intelligence and other related technologies should be broadened. At the same time, numerous arising issues need to be addressed. The focus of this paper is the objective of increasing young visitors’ satisfaction. Herein, we leverage an assortment of digital technology tools and continuously evaluate their commercial feasibility. It is hoped that a rise in satisfaction can be accomplished through the exemplar business plan provided.
Workshop Media Pembelajaran Berbasis Artificial Intelegence

Authors:
raw_author_name: Dirga Ayu Lestari
raw_affiliation_string: Sekolah Tinggi Agama Islam KH. Abdul Kabier Serang, Banten

Abstract: The aim of this community service is to enhance teachers' creativity in utilizing Artificial Intelligence (AI) technology in teaching activities. The service method was conducted in-person, utilizing LCD Projectors and sound systems to support workshop activities. The results of the implementation show that teachers have gained an understanding of creating instructional media using Canva and CapCup. This is evident as teachers can operate both AI applications and produce instructional media that are sufficiently good and diverse. In conclusion, the workshop proceeded smoothly as scheduled, providing a positive impact on teachers.
Benchmarking hybrid digitized-counterdiabatic quantum optimization

Authors:
raw_author_name: Ruoqian Xu
raw_affiliation_string: Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain
raw_author_name: Jialiang Tang
raw_affiliation_string: Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain
raw_author_name: Pranav Chandarana
raw_affiliation_string: EHU Quantum Center, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940 Leioa, Spain
raw_author_name: Koushik Paul
raw_affiliation_string: EHU Quantum Center, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940 Leioa, Spain
raw_author_name: Xusheng Xu
raw_affiliation_string: Department of Physics, State Key Laboratory of Low-Dimensional Quantum Physics, Tsinghua University, Beijing 100084, People's Republic of China
raw_author_name: Manhong Yung
raw_affiliation_string: Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, People's Republic of China
raw_author_name: Xi Chen
raw_affiliation_string: EHU Quantum Center, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940 Leioa, Spain

Abstract: Hybrid digitized-counterdiabatic quantum computing (DCQC) is a promising approach for leveraging the capabilities of nearterm quantum computers, utilizing parameterized quantum circuits designed with counterdiabatic protocols. However, the classical aspect of this approach has received limited attention. In this study, we systematically analyze the convergence behavior and solution quality of various classical optimizers when used in conjunction with the digitized-counterdiabatic approach. We demonstrate the effectiveness of this hybrid algorithm by comparing its performance to the traditional QAOA on systems containing up to 28 qubits. Furthermore, we employ principal component analysis to investigate the cost landscape and explore the crucial influence of parametrization on the performance of the counterdiabatic ansatz. Our findings indicate that fewer iterations are required when local cost landscape minima are present, and the SPSA-based BFGS optimizer emerges as a standout choice for the hybrid DCQC paradigm.
Separability criteria based on the correlation tensor moments for arbitrary dimensional states

Authors:
raw_author_name: Xiaofen Huang
raw_affiliation_string: School of Mathematics and Statistics, Hainan Normal University, Haikou, 571158, China
raw_author_name: Naihuan Jing
raw_affiliation_string: Department of Mathematics, North Carolina State University, Raleigh, NC, 27695, USA

Abstract: As one of the most profound features of quantum mechanics, entanglement is a vital resource for quantum information processing. Inspired by the recent work on PT-moments and separability [Phys. Rev. Lett. 127, 060504 (2021)], we propose two sets of separability criteria using moments of the correlation tensor for bipartite and multipartite quantum states, which are shown to be stronger in some aspects in detecting entanglement.
3D tracking using a multi-spectral 4th-generation phase plate

Authors:
raw_author_name: Sudhakar Prasad
raw_affiliation_string: University of Minnesota, Minneapolis, MN, USA

Abstract: 3D imaging and object tracking is critical for medical and biological research and can be achieved by multifocal imaging with diffractive optical elements (DOEs) converting depth (z) information into a modification of the 2D image. Recently, we introduced a simple system for achieving precise localization and tracking of point sources emitting fluorescent light at 525 nm using a spiral phase plate in a standard wide-field optical microscope with minor modification. We achieved a bead localization precision of <25 nm in the transverse dimensions and ≤70 nm along the axial dimension over an axial range of 6 μm. 3D diffusion constants of chromatin labelled with LacR-GFP arrays matched expected values. We are now pursuing chromatin tracking experiments to better understand how DNA damage affects chromatin motions and how chromatin dynamics impacts the DNA damage response. We are extending our chromatin tracking capabilities to simultaneously track chromatin and a DNA repair factor (53BP1) which has been fluorescently labelled at 610 nm with mCherry. To do this, we are testing a new 4th-generation waveplate that will enable 3D tracking of cellular objects labeled with two different fluorophores. Unlike 3rd-generation DOEs that are limited to tracking objects that fluoresce within a narrow wavelength range centered on their design wavelength, 4th-generation optics have the potential to perform 3D tracking with high efficiency over a broad wavelength range that can span the whole visible spectrum. We will present preliminary data on the performance of a microscope fitted with a multiwave vector vortex waveplate that can track particles fluorescing in a wide range of narrow wavelength channels, making this optical system ideal for single particle tracking in multispectral microscope systems.
Small Sample Fiber Full State Diagnosis Based on Fuzzy Clustering and Improved ResNet Network

Authors:
raw_author_name: Xiangqun Li
raw_affiliation_string: Gannan Power Supply Company of State Grid Gansu Electric Power Supply Company, Hezuo 747000, China
raw_author_name: Jiawen Liang
raw_affiliation_string: Gannan Power Supply Company of State Grid Gansu Electric Power Supply Company, Hezuo 747000, China

Abstract: The optical time domain reflectometer (OTDR) curve features of communication fibers exhibit subtle differences among their normal, subhealthy, and faulty operating states, making it challenging for existing machine learning-based fault diagnosis algorithms to extract these minute features. In addition, the OTDR curve field fault data are scarce, and data-driven deep neural network that needs a lot of data training cannot meet the requirements. In response to this issue, this paper proposes a communication fiber state diagnosis model based on fuzzy clustering and an improved ResNet. First, the pretrained residual network (ResNet) is modified by removing the classification layer and retaining the feature extraction layers. A global average pooling (GAP) layer is designed as a replacement for the fully connected layer. Second, fuzzy clustering, instead of the softmax classification layer, is employed in ResNet for its characteristic of requiring no subsequent data training. The improved model requires only a small amount of sample training to optimize the parameters of the GAP layer, thereby accommodating state diagnosis in scenarios with limited data availability. During the diagnosis process, the OTDR curves are input into the network, resulting in 512 features outputted in the GAP layer. These features are used to construct a feature vector matrix, and a dynamic clustering graph is formed using fuzzy clustering to realize the fiber state diagnosis. Through on-site data detection and validation, it has been demonstrated that the improved ResNet can effectively identify the full cycle of fiber states.