Reviewerly/exHarmony huggingface dataset:
Activity Strategies of Public Libraries of Podillia in Conditions of Martial Law in Ukraine: Realities and Challenges of Nowadays

The aim of the article is to study the optimal activity strategies of public libraries of Podillia in conditions of martial law in Ukraine.&#x0D; In this article, such research methods as analysis, synthesis, information method, comparison method, structural-functional method, generalization method are used; innovative, institutional, activity and socio-communication approaches are applied; principles of objectivity and comprehensiveness of the study are embraced.&#x0D; Scientific novelty. For the first time, the optimal activity strategies for public libraries of Podillia in conditions of martial law in Ukraine are highlighted; this is caused by the change in priorities of the library practice. It is argued that in conditions of the Russian-Ukrainian war, the library sphere of Ukraine continues to function successfully. The library itself, as a unique social institution, has become a center of consolidation of citizens, and an important source of reliable information and knowledge for all categories of users.&#x0D; Main conclusions. The activity strategy of the public library is an integrated development direction of a library institution, which is aimed to ensure its long-term success, and is focused on the provision of library and information services. It is based on the effective use of its own resources, as well as on the involvement of electronic information resources, including the global network, with applying modern information and communication technologies for all categories of users in order to enrich their spiritual and intellectual potential. In conditions of social upheavals, current strategies of public libraries in Podillia ground on the principles of client orientation, people-centeredness, multiperspectivity, multiaspectness, universality and infosymbiosis. This results in accumulation of the reputational capital, positively affects the image of the Ukrainian library institute and its competitiveness among information establishments. The reliability and efficiency of the library sphere functioning in conditions of martial law in Ukraine directly depend on the openness of the accumulated documentary funds and their availability 24/7, regardless of the location of the user, as well as his knowledge and access to information.
Modernization of the System of Library Functions in Hybrid Threats Conditions

The aim of the article is to represent the modernization of the system of library functions as a basic concept of activity transformation in conditions of information society and evolution of all its functioning areas in the digitalization epoch.&#x0D; Research methods are determined by the goal of the article, and are based on the application of general scientific (cognitive, system analysis, generalization) and special (library science and source science search) methods, including structural and functional, institutional, social and communication approaches.&#x0D; Scientific novelty of the study consists in grounding the social and communication approach regarding the research of the library as a social and communication infrastructure, as an integral element of the system of active social communication, and nowadays, as a digital communication in the electronic environment on websites, blogs, social networks, etc.; in modernization of the system of library functions as a basic concept of its activity transformation in conditions of information society and evolution of all its functioning areas in the digitalization epoch.&#x0D; Main conclusions. Under the influence of such determining factors as digitalization, globalization and the Russian-Ukrainian war, the transformation of the system of library functions is determined by the socio-political and socio-economic level of development of Ukrainian society. It was the digital era that in conditions of constantly growing information arrays and documentation sources allowed libraries not to lose their position as key institutions in the information and communication structure of society, but to open new opportunities for the national revival of culture, education and science as the basis of the state economic development.&#x0D; During the dynamic growth of document creation and document exchange, libraries of Ukraine effectively enrich traditional functions subsystem with new content (educational, general cultural, informational, compensatory, communication, memorial, organizational, educational, cognitive, relaxation, social, value orientation), and successfully realize innovative functions subsystem (educational and advocacy, ensuring free access to the world information space, guidance and counseling, overcoming digital inequality, forming a sustainable interest in reading, collecting documentary evidence of crimes committed by the Russian Federation in relation to objects of cultural heritage of Ukraine), which proves the optimal adaptation of the functioning of Ukrainian librarianship in hybrid threats conditions.
Agile Method in Developing Electronic Local Government Food Reserve Distribution Services (E-CPPD) in Sukabumi City

Indonesia is a country with a region that has disasters here. As a Regional Apparatus Organization which must distribute regional government food reserves to the community when natural disasters strike, Dinas Ketahanan Pangan, Peternakan dan Perikanan Kota Sukabumi took the initiative to develop an application that can speed up the distribution of aid to the community. This national food reserve policy can support national defense in emergency conditions. The hope is that the development of this application can speed up the administration of official correspondence, where the administration of this correspondence is an element that slows down actions in almost every department, resulting in the length of time that citizens receive assistance. There are many discussions and interviews with various users who need to adapt an environment that requires flexibility in changes to system development, so this system development uses the spiral method. As a result, based on the user requirement list, 100% of user needs can be completed on time. The result, almost nine (9) tons of rice have been distributed to residents spread across 22 of the 33 sub-districts in Sukabumi City.
Research Paper on Green Cloud Computing

Abstract: Nowadays Cloud Computing is most important part of IT Industries. Cloud Computing Which is based on cloud (internet) but Internet Play Most Important Role for Cloud Computing this is the bunch of software, hardware and internet. It has lot of benefits in computing. Cloud computing provide shared file of computing resources that can fastly and easily provisioned and released based on user’s demand to service for wide use and it is constantly spreading in amount of range of information in processing needs. First time cloud computing has come in 2006 but Cloud Computing has come of age later Amazons introduce the first of its kind of cloud services. It was suitable for unbelievable amount of data. That are being processed here daily in several sectors, and there are signs that subscription to cloud services by the local IT companies will soon be on a crash course, despite a slow start in beginning years. As in research field.
How to avoid the “infrastructural blues”? Studying‐while‐caring for data stewardship

Abstract When it comes to climate crisis research, current debates are increasingly thematizing the needs but also the challenges of collaborative, transdisciplinary work. Geophysical characterizations of climate change are increasingly deemed insufficient to respond to the challenges that vulnerable communities face worldwide. In this paper, I describe the work of studying‐while‐caring for an environmental data infrastructure in order to address this issue. I suggest framing “data management” anthropologically as a question of collective stewardship that is better conceived as a “knowledge infrastructure” (Edwards 2010) instead of a formal approach to automated data curation. To examine the sociotechnical blindspots of data management, I elaborate on the anthropological concept of “infrastructural blues” based on the data engineering work I conducted. For the conclusion, I discuss the concept of “common” as a substitute for “open” technologies and address the broader implications of the proposed shift toward community stewardship and self‐determination as guiding practices for socio‐environmental data governance.
The effectiveness of augmented reality technology versus traditional teaching methods for undergraduate nursing education

The introduction of augmented reality (AR) technology is an innovative development in nursing education. The Manukau Institute of Technology (MIT) of Te Pūkenga Institute of Skills and Technology in New Zealand has recently implemented HoloLens, an AR technology, in their Bachelor of Nursing Program to improve the overall learning experience. In an experimental project, HoloLens was utilised for revision classes to compare to traditional classroom-based teaching. Pre-registration nursing students participated in either in-person revision sessions or through a HoloLens headset or with a HoloPatient mobile phone application. The results indicate that using HoloLens substantially improves the student learning experience; however, face-to-face interactions still yield better outcomes. The HoloPatient's 3D holographic health scenarios were highly valued by users and had great potential to improve nursing pedagogy. However, there were several challenges during the experiment, such as time constraints, the experiment being too close to the exam, problems with headset adjustment, and insufficient user training. Blending traditional face-to-face teaching with AR technology integration can significantly enhance the nursing education experience.
Real time anatomical landmarks and abnormalities detection in gastrointestinal tract

Gastrointestinal (GI) endoscopy is an active research field due to the lethal cancer diseases in the GI tract. Cancer treatments result better if diagnosed early and it increases the survival chances. There is a high miss rate in the detection of the abnormalities in the GI tract during endoscopy or colonoscopy due to the lack of attentiveness, tiring procedures, or the lack of required training. The procedure of the detection can be automated to the reduction of the risks by identifying and flagging the suspicious frames. A suspicious frame may have some of the abnormality or the information about anatomical landmark in the frame. The frame then can be analysed for the anatomical landmarks and the abnormalities for the detection of disease. In this research, a real-time endoscopic abnormalities detection system is presented that detects the abnormalities and the landmarks. The proposed system is based on a combination of handcrafted and deep features. Deep features are extracted from lightweight MobileNet convolutional neural network (CNN) architecture. There are some of the classes with a small inter-class difference and a higher intra-class differences, for such classes the same detection threshold is unable to distinguish. The threshold of such classes is learned from the training data using genetic algorithm. The system is evaluated on various benchmark datasets and resulted in an accuracy of 0.99 with the F1-score of 0.91 and Matthews correlation coefficient (MCC) of 0.91 on Kvasir datasets and F1-score of 0.93 on the dataset of DowPK. The system detects abnormalities in real-time with the detection speed of 41 frames per second.
Revisiting the Entropy Semiring for Neural Speech Recognition

In streaming settings, speech recognition models have to map sub-sequences of speech to text before the full audio stream becomes available. However, since alignment information between speech and text is rarely available during training, models need to learn it in a completely self-supervised way. In practice, the exponential number of possible alignments makes this extremely challenging, with models often learning peaky or sub-optimal alignments. Prima facie, the exponential nature of the alignment space makes it difficult to even quantify the uncertainty of a model's alignment distribution. Fortunately, it has been known for decades that the entropy of a probabilistic finite state transducer can be computed in time linear to the size of the transducer via a dynamic programming reduction based on semirings. In this work, we revisit the entropy semiring for neural speech recognition models, and show how alignment entropy can be used to supervise models through regularization or distillation. We also contribute an open-source implementation of CTC and RNN-T in the semiring framework that includes numerically stable and highly parallel variants of the entropy semiring. Empirically, we observe that the addition of alignment distillation improves the accuracy and latency of an already well-optimized teacher-student distillation model, achieving state-of-the-art performance on the Librispeech dataset in the streaming scenario.
Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning

Existing out-of-distribution (OOD) methods have shown great success on balanced datasets but become ineffective in long-tailed recognition (LTR) scenarios where 1) OOD samples are often wrongly classified into head classes and/or 2) tail-class samples are treated as OOD samples. To address these issues, current studies fit a prior distribution of auxiliary/pseudo OOD data to the long-tailed in-distribution (ID) data. However, it is difficult to obtain such an accurate prior distribution given the unknowingness of real OOD samples and heavy class imbalance in LTR. A straightforward solution to avoid the requirement of this prior is to learn an outlier class to encapsulate the OOD samples. The main challenge is then to tackle the aforementioned confusion between OOD samples and head/tail-class samples when learning the outlier class. To this end, we introduce a novel calibrated outlier class learning (COCL) approach, in which 1) a debiased large margin learning method is introduced in the outlier class learning to distinguish OOD samples from both head and tail classes in the representation space and 2) an outlier-class-aware logit calibration method is defined to enhance the long-tailed classification confidence. Extensive empirical results on three popular benchmarks CIFAR10-LT, CIFAR100-LT, and ImageNet-LT demonstrate that COCL substantially outperforms state-of-the-art OOD detection methods in LTR while being able to improve the classification accuracy on ID data. Code is available at https://github.com/mala-lab/COCL.
Approval-Based Committee Voting in Practice: A Case Study of (Over-)Representation in the Polkadot Blockchain

We provide the first large-scale data collection of real-world approval-based committee elections. These elections have been conducted on the Polkadot blockchain as part of their Nominated Proof-of-Stake mechanism and contain around one thousand candidates and tens of thousands of (weighted) voters each. We conduct an in-depth study of application-relevant questions, including a quantitative and qualitative analysis of the outcomes returned by different voting rules. Besides considering proportionality measures that are standard in the multiwinner voting literature, we pay particular attention to less-studied measures of overrepresentation, as these are closely related to the security of the Polkadot network. We also analyze how different design decisions such as the committee size affect the examined measures.
Unbiased Neural Networks for Parameter Estimation in Quantitative MRI

Purpose: To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cram\'er-Rao bound. Theory and Methods: We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications. Results: In simulations, the proposed strategy reduces the estimates' bias throughout parameter space and achieves a variance close to the Cram\'er-Rao bound. In vivo, we observe good concordance between parameter maps estimated with the proposed NNs and traditional estimators, such as non-linear least-squares fitting, while state-of-the-art NNs show larger deviations. Conclusion: The proposed NNs have greatly reduced bias compared to those trained using the mean squared error and offer significantly improved computational efficiency over traditional estimators with comparable or better accuracy.
Extracting Interpretable Local and Global Representations from Attention on Time Series

This paper targets two transformer attention based interpretability methods working with local abstraction and global representation, in the context of time series data. We distinguish local and global contexts, and provide a comprehensive framework for both general interpretation options. We discuss their specific instantiation via different methods in detail, also outlining their respective computational implementation and abstraction variants. Furthermore, we provide extensive experimentation demonstrating the efficacy of the presented approaches. In particular, we perform our experiments using a selection of univariate datasets from the UCR UEA time series repository where we both assess the performance of the proposed approaches, as well as their impact on explainability and interpretability/complexity. Here, with an extensive analysis of hyperparameters, the presented approaches demonstrate an significant improvement in interpretability/complexity, while capturing many core decisions of and maintaining a similar performance to the baseline model. Finally, we draw general conclusions outlining and guiding the application of the presented methods.
Explain To Decide: A Human-Centric Review on the Role of Explainable Artificial Intelligence in AI-assisted Decision Making

The unprecedented performance of machine learning models in recent years, particularly Deep Learning and transformer models, has resulted in their application in various domains such as finance, healthcare, and education. However, the models are error-prone and cannot be used autonomously, especially in decision-making scenarios where, technically or ethically, the cost of error is high. Moreover, because of the black-box nature of these models, it is frequently difficult for the end user to comprehend the models' outcomes and underlying processes to trust and use the model outcome to make a decision. Explainable Artificial Intelligence (XAI) aids end-user understanding of the model by utilizing approaches, including visualization techniques, to explain and interpret the inner workings of the model and how it arrives at a result. Although numerous research studies have been conducted recently focusing on the performance of models and the XAI approaches, less work has been done on the impact of explanations on human-AI team performance. This paper surveyed the recent empirical studies on XAI's impact on human-AI decision-making, identified the challenges, and proposed future research directions.
Fast Decision Boundary based Out-of-Distribution Detector

Efficient and effective Out-of-Distribution (OOD) detection is essential for the safe deployment of AI in latency-critical applications. Recently, studies have revealed that detecting OOD based on feature space information can be highly effective. Despite their effectiveness, however, exiting feature space OOD methods may incur non-negligible computational overhead, given their reliance on auxiliary models built from training features. In this paper, we aim to obviate auxiliary models to optimize computational efficiency while leveraging the rich information embedded in the feature space. We investigate from the novel perspective of decision boundaries and propose to detect OOD using the feature distance to decision boundaries. To minimize the cost of measuring the distance, we introduce an efficient closed-form estimation, analytically proven to tightly lower bound the distance. We observe that ID features tend to reside further from the decision boundaries than OOD features. Our observation aligns with the intuition that models tend to be more decisive on ID samples, considering that distance to decision boundaries quantifies model uncertainty. From our understanding, we propose a hyperparameter-free, auxiliary model-free OOD detector. Our OOD detector matches or surpasses the effectiveness of state-of-the-art methods across extensive experiments. Meanwhile, our OOD detector incurs practically negligible overhead in inference latency. Overall, we significantly enhance the efficiency-effectiveness trade-off in OOD detection.
Efficient and Scalable Graph Generation through Iterative Local Expansion

In the realm of generative models for graphs, extensive research has been conducted. However, most existing methods struggle with large graphs due to the complexity of representing the entire joint distribution across all node pairs and capturing both global and local graph structures simultaneously. To overcome these issues, we introduce a method that generates a graph by progressively expanding a single node to a target graph. In each step, nodes and edges are added in a localized manner through denoising diffusion, building first the global structure, and then refining the local details. The local generation avoids modeling the entire joint distribution over all node pairs, achieving substantial computational savings with subquadratic runtime relative to node count while maintaining high expressivity through multiscale generation. Our experiments show that our model achieves state-of-the-art performance on well-established benchmark datasets while successfully scaling to graphs with at least 5000 nodes. Our method is also the first to successfully extrapolate to graphs outside of the training distribution, showcasing a much better generalization capability over existing methods.
Cryptanalysis of PLWE based on zero-trace quadratic roots

We extend two of the attacks on the PLWE problem presented in (Y. Elias, K. E. Lauter, E. Ozman, and K. E. Stange, Ring-LWE Cryptography for the Number Theorist, in Directions in Number Theory, E. E. Eischen, L. Long, R. Pries, and K. E. Stange, eds., vol. 3 of Association for Women in Mathematics Series, Cham, 2016, Springer International Publishing, pp. 271-290) to a ring $R_q=\mathbb{F}_q[x]/(f(x))$ where the irreducible monic polynomial $f(x)\in\mathbb{Z}[x]$ has an irreducible quadratic factor over $\mathbb{F}_q[x]$ of the form $x^2+\rho$ with $\rho$ of suitable multiplicative order in $\mathbb{F}_q$. Our attack exploits the fact that the trace of the root is zero and has overwhelming success probability as a function of the number of samples taken as input. An implementation in Maple and some examples of our attack are also provided.