Reviewerly/exHarmony huggingface dataset:
Deep Learning Method for Power Side-Channel Analysis on Chip Leakages

Power side channel analysis signal analysis is automated using deep learning. Signal processing and cryptanalytic techniques are necessary components of power side channel analysis. Chip leakages can be found using a classification approach called deep learning. In addition to this, we do this so that the deep learning network can automatically tackle signal processing difficulties such as re-alignment and noise reduction. We were able to break minimally protected Advanced Encryption Standard (AES), as well as masking-countermeasure AES and protected elliptic-curve cryptography (ECC). These results demonstrate that the attacker knowledge required for side channel analysis, which had previously placed a significant emphasis on human abilities, is decreasing. This research will appeal to individuals with a technical background who have an interest in deep learning, side channel analysis, and security.
Iterative Mask Filling: An Effective Text Augmentation Method Using Masked Language Modeling

Data augmentation is an effective technique for improving the performance of machine learning models. However, it has not been explored as extensively in natural language processing (NLP) as it has in computer vision. In this paper, we propose a novel text augmentation method that leverages the Fill-Mask feature of the transformer-based BERT model. Our method involves iteratively masking words in a sentence and replacing them with language model predictions. We have tested our proposed method on various NLP tasks and found it to be effective in many cases. Our results are presented along with a comparison to existing augmentation methods. Experimental results show that our proposed method significantly improves performance, especially on topic classification datasets.
Information Culture in the Library: Challenges Of Digitalization

The purpose of the article is to determine the main directions of information culture transformation in the space of modern libraries in the context of the challenges of the modern digital age. The methodology of the article is based on the combination of a systemic approach, which enabled the analysis of information culture as a multifunctional phenomenon, the involvement of logical, epistemological and ontological approaches in the systemic analysis contributed to the problem-theoretical understanding of the phenomenon under study. The basis of the work is the use of the following general scientific research methods: logical method - when building the research structure; analytical method - when understanding the interrelationships of different levels of information culture; semantic method - when clarifying the content of concepts; comparative analysis - to identify essential features of various scientific approaches to the analysis of the problem of digital competences of library workers; the methods of analysis, synthesis and generalization are used in the process of highlighting the most important aspects of information culture in the space of the library. The scientific novelty of the work is that: The work analyzes the essence of information culture as a dynamic multifunctional phenomenon, reveals its essential features in the context of the development of the modern digital age. In particular, 1) the competence features of information culture in the ethical, moral and legal plane are revealed; 2) the concepts of "information culture", "digital culture", "information literacy", "digital literacy" in relation to the library sphere are distinguished; 3) the content of digital competences of library workers in the conditions of modern challenges is clarified. Conclusions. Modern information culture in the space of the library can be presented as a system of standardized measures for digitization, preservation, popularization and communication to users of the set of material and spiritual values created by mankind, which appear as its knowledge potential, and is implemented by increasing the level of digital competencies of library workers. The information culture of the digital age is a dynamic phenomenon aimed at constant improvement and raising the level of digital competences - technological, educational, logical-intellectual, preservation of human dignity and human rights in ensuring equal opportunities for self-realization. &#x0D; Key words: information culture, digital culture, media culture, digital competences, library information space, digital literacy, information literacy, digital age, information security.
Internet Representative Offices of Libraries in the Period of Social Shocks in Ukraine: Theme Of Content

The purpose of the article is to highlight the online representation of Ukrainian libraries in the conditions of quarantine restrictions and martial law, to analyze the subject of the presented content. The methodological basis of the article was institutional, systemic, structural-functional and socio-communication approaches. Analysis, cognitive, synthesis, generalization and librarian methods are also applied. The scientific novelty of the study consists in arguing for the adaptation of the functioning of Ukrainian libraries in the conditions of quarantine restrictions and martial law of the library of Ukraine, when they reoriented their work to a remote form and offered online services and electronic products, actively started blogs, pages in social networks and created YouTube channels. Conclusions. On the website, blogs, pages of social networks, in a difficult period of social upheavals, the libraries of Ukraine cover both news of library life, as well as educational, entertainment, information, and local history materials. The bibliographic, factographic, and multimedia products created help to represent the cultural life of communities, creative, literary, and cultural achievements of prominent regions and present the best assets of Ukraine to the world. The epidemic, and then the full-scale war, put Ukrainian libraries in a situation where it is necessary to quickly respond to the urgent needs of society and do it mainly with the help of online services. This is what became for the majority a test of viability and stability, of professionalism and demand. During this time, most of the important and necessary resources were created, blogs, pages were launched in various social networks, YouTube channels were established, and new information technologies and products were actively introduced into library processes. Ukrainian libraries will be in demand and useful only through active development in the digital age.&#x0D; Keywords: libraries of Ukraine, library affair, information society, content, digitization, library website, digitization.
Decoherence limiting the cost to simulate an anharmonic oscillator

We study how decoherence increases the efficiency with which we can simulate the quantum dynamics of an anharmonic oscillator governed by the Kerr effect. As decoherence washes out the fine-grained sub-Planck structure associated with phase-space quantum interference in the closed quantum system, open quantum dynamics can be more efficiently simulated using a coarse-grained finite-difference numerical integration. We tie this to the way in which decoherence recovers the semiclassical truncated Wigner approximation, which strongly differs from the exact closed-system dynamics at times when quantum interference leads to cat states and more general superpositions of coherent states. The regression in quadrature measurement statistics to semiclassical dynamics becomes more pronounced as the initial amplitude of the oscillator grows, with implications for the quantum advantage that might be accessible as system size grows in noisy quantum devices. Lastly, we show that this regression does not have the form of a convex noise model, such as for a depolarizing noise channel. Instead, closed quantum system effects interact with the open-system effects, giving rise to distinct open-system behavior.
Perancangan Sistem Informasi Posyandu Aster XI Desa Saga Berbasis Java

Health is a need that must be met for all Indonesian people. A healthy body and mind are needed to support all activities. The Health service activities at Posyandu (Integrated health services post) Aster XI are still conducted manually or written by hand, from the collection to process of the data. This can hinder and cause many problems in the processing, searching and producing reports on Posyandu Aster XI activities. Thus, it is necessary to develop the conventional systems to become computerized. The research method used is Grounded Research, while the system development method used is waterfall. The aim of this research is to design and implement a Java-based information system at Posyandu so that it can help and speed up work in terms of processing data such as storing, changing and searching data to making reports at Posyandu Aster XI. The result of this research is the use of a computerized system, created with the Java programming language and MySQL database, that can help and make cadres process the data at the Posyandu more quickly, precisely and efficiently.
Design and build a hotel service reservation and verification system using web-based e-KTP

Hotel is a company that provides accommodation services and provides other facilities that meet the requirements of comfort and commercial purposes. The development of the times and technology, also affects the development of the hotel. The use of hotel reservation technology makes it easier for the hotel booking process. Service quality, trustworthiness, facilities and security affect hotel selection decisions. A common application of security technology is the use of duplicable RFID card lock technology. For this reason, it is necessary to add a double security system in the form of an OTP (One Time Password) code and limit access to visitors and visitors from visitors. The test results using NFC PN532 to read e-KTP cards obtained readings in under 1.00 seconds, with 100% accuracy within a distance of 0 - 2.5 cm. In testing with an additional OTP (One Time Password) code, the results obtained are good accuracy readings with an accuracy rate of 100% when the OTP code is appropriate.
Perencanaan Drainase di Dusun 1 Penfui Timur

Drainage functions as a series of waterworks that function to reduce or remove excess water from an area or land, so that the land can function optimally. Hamlet 1 is one of the hamlets in East Penfui Village, with an area of 49.47 ha. This hamlet is quite busy with students living because it is located between 2 major campuses in Kupang, namely Nusa Cendana University and Widya Mandira Catholic University Kupang. This hamlet has not been equipped with adequate drainage channels so that during the rainy season there are floods and result in standing water at several points which is quite disturbing to road users and the surrounding community. Therefore, it is necessary to know the magnitude of the planned discharge so that it can determine the dimensions of the channel to overcome the problem of inundation that occurs. From the results of planning the drainage system is divided into 2 primary canals with the notation SP1 and SP2, the P1 channel is 2300 m long and the P2 channel is 600 m long. Calculating the intensity of rainfall using the mononobe formula, with a concentration time of 3.16 hours, the rainfall intensity for the 5 year return period is 30.74 mm. By using the rational method, it is obtained that the planned debit is 2.67 m3/s, in the planning of the drainage system there must be different channel dimensions based on the water discharge flowing through the channel itself. With a planned flood discharge of 24.75 m3/s, the smallest canal has a width dimension of 0.4 m and a height of 0.8 m on the secondary channel while the largest is 0.6 m wide and 1.2 m high on the primary canal, and Infiltration wells have a diameter of 2 m with a depth of. Provided that Q plan &lt; channel discharge. Drainage planning uses a square-shaped channel with precast concrete materials, namely U-ditch channels and calvert box culverts.
NodeMixup: Tackling Under-Reaching for Graph Neural Networks

Graph Neural Networks (GNNs) have become mainstream methods for solving the semi-supervised node classification problem. However, due to the uneven location distribution of labeled nodes in the graph, labeled nodes are only accessible to a small portion of unlabeled nodes, leading to the \emph{under-reaching} issue. In this study, we firstly reveal under-reaching by conducting an empirical investigation on various well-known graphs. Then, we demonstrate that under-reaching results in unsatisfactory distribution alignment between labeled and unlabeled nodes through systematic experimental analysis, significantly degrading GNNs' performance. To tackle under-reaching for GNNs, we propose an architecture-agnostic method dubbed NodeMixup. The fundamental idea is to (1) increase the reachability of labeled nodes by labeled-unlabeled pairs mixup, (2) leverage graph structures via fusing the neighbor connections of intra-class node pairs to improve performance gains of mixup, and (3) use neighbor label distribution similarity incorporating node degrees to determine sampling weights for node mixup. Extensive experiments demonstrate the efficacy of NodeMixup in assisting GNNs in handling under-reaching. The source code is available at \url{https://github.com/WeigangLu/NodeMixup}.
Generate E-commerce Product Background by Integrating Category Commonality and Personalized Style

The state-of-the-art methods for e-commerce product background generation suffer from the inefficiency of designing product-wise prompts when scaling up the production, as well as the ineffectiveness of describing fine-grained styles when customizing personalized backgrounds for some specific brands. To address these obstacles, we integrate the category commonality and personalized style into diffusion models. Concretely, we propose a Category-Wise Generator to enable large-scale background generation for the first time. A unique identifier in the prompt is assigned to each category, whose attention is located on the background by a mask-guided cross attention layer to learn the category-wise style. Furthermore, for products with specific and fine-grained requirements in layout, elements, etc, a Personality-Wise Generator is devised to learn such personalized style directly from a reference image to resolve textual ambiguities, and is trained in a self-supervised manner for more efficient training data usage. To advance research in this field, the first large-scale e-commerce product background generation dataset BG60k is constructed, which covers more than 60k product images from over 2k categories. Experiments demonstrate that our method could generate high-quality backgrounds for different categories, and maintain the personalized background style of reference images. The link to BG60k and codes will be available soon.
Towards Fair Graph Federated Learning via Incentive Mechanisms

Graph federated learning (FL) has emerged as a pivotal paradigm enabling multiple agents to collaboratively train a graph model while preserving local data privacy. Yet, current efforts overlook a key issue: agents are self-interested and would hesitant to share data without fair and satisfactory incentives. This paper is the first endeavor to address this issue by studying the incentive mechanism for graph federated learning. We identify a unique phenomenon in graph federated learning: the presence of agents posing potential harm to the federation and agents contributing with delays. This stands in contrast to previous FL incentive mechanisms that assume all agents contribute positively and in a timely manner. In view of this, this paper presents a novel incentive mechanism tailored for fair graph federated learning, integrating incentives derived from both model gradient and payoff. To achieve this, we first introduce an agent valuation function aimed at quantifying agent contributions through the introduction of two criteria: gradient alignment and graph diversity. Moreover, due to the high heterogeneity in graph federated learning, striking a balance between accuracy and fairness becomes particularly crucial. We introduce motif prototypes to enhance accuracy, communicated between the server and agents, enhancing global model aggregation and aiding agents in local model optimization. Extensive experiments show that our model achieves the best trade-off between accuracy and the fairness of model gradient, as well as superior payoff fairness.
Domain-Specific Code Language Models: Unraveling the Potential for HPC Codes and Tasks

With easier access to powerful compute resources, there is a growing trend in AI for software development to develop larger language models (LLMs) to address a variety of programming tasks. Even LLMs applied to tasks from the high-performance computing (HPC) domain are huge in size and demand expensive compute resources for training. This is partly because these LLMs for HPC tasks are obtained by finetuning existing LLMs that support several natural and/or programming languages. We found this design choice confusing - why do we need large LMs trained on natural languages and programming languages unrelated to HPC for HPC-specific tasks? In this line of work, we aim to question choices made by existing LLMs by developing smaller LMs for specific domains - we call them domain-specific LMs. Specifically, we start off with HPC as a domain and build an HPC-specific LM, named MonoCoder, that is orders of magnitude smaller than existing LMs but delivers similar, if not better performance, on non-HPC and HPC tasks. Specifically, we pre-trained MonoCoder on an HPC-specific dataset (named HPCorpus) of C and C++ programs mined from GitHub. We evaluated the performance of MonoCoder against conventional multi-lingual LLMs. Results demonstrate that MonoCoder, although much smaller than existing LMs, achieves similar results on normalized-perplexity tests and much better ones in CodeBLEU competence for high-performance and parallel code generations. Furthermore, fine-tuning the base model for the specific task of parallel code generation (OpenMP parallel for pragmas) demonstrates outstanding results compared to GPT, especially when local misleading semantics are removed by our novel pre-processor Tokompiler, showcasing the ability of domain-specific models to assist in HPC-relevant tasks.
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity

Motivated by high resource costs of centralized machine learning schemes as well as data privacy concerns, federated learning (FL) emerged as an efficient alternative that relies on aggregating locally trained models rather than collecting clients' potentially private data. In practice, available resources and data distributions vary from one client to another, creating an inherent system heterogeneity that leads to deterioration of the performance of conventional FL algorithms. In this work, we present a federated quantization-based self-supervised learning scheme (Fed-QSSL) designed to address heterogeneity in FL systems. At clients' side, to tackle data heterogeneity we leverage distributed self-supervised learning while utilizing low-bit quantization to satisfy constraints imposed by local infrastructure and limited communication resources. At server's side, Fed-QSSL deploys de-quantization, weighted aggregation and re-quantization, ultimately creating models personalized to both data distribution as well as specific infrastructure of each client's device. We validated the proposed algorithm on real world datasets, demonstrating its efficacy, and theoretically analyzed impact of low-bit training on the convergence and robustness of the learned models.
Time is Encoded in the Weights of Finetuned Language Models

We present time vectors, a simple tool to customize language models to new time periods. Time vectors are created by finetuning a language model on data from a single time (e.g., a year or month), and then subtracting the weights of the original pretrained model. This vector specifies a direction in weight space that, as our experiments show, improves performance on text from that time period. Time vectors specialized to adjacent time periods appear to be positioned closer together in a manifold. Using this structure, we interpolate between time vectors to induce new models that perform better on intervening and future time periods, without any additional training. We demonstrate the consistency of our findings across different tasks, domains, model sizes, and time scales. Our results suggest that time is encoded in the weight space of finetuned models.
Texture Matching GAN for CT Image Enhancement

Deep neural networks (DNN) are commonly used to denoise and sharpen X-ray computed tomography (CT) images with the goal of reducing patient X-ray dosage while maintaining reconstruction quality. However, naive application of DNN-based methods can result in image texture that is undesirable in clinical applications. Alternatively, generative adversarial network (GAN) based methods can produce appropriate texture, but naive application of GANs can introduce inaccurate or even unreal image detail. In this paper, we propose a texture matching generative adversarial network (TMGAN) that enhances CT images while generating an image texture that can be matched to a target texture. We use parallel generators to separate anatomical features from the generated texture, which allows the GAN to be trained to match the desired texture without directly affecting the underlying CT image. We demonstrate that TMGAN generates enhanced image quality while also producing image texture that is desirable for clinical application.
VADIS -- a VAriable Detection, Interlinking and Summarization system

The VADIS system addresses the demand of providing enhanced information access in the domain of the social sciences. This is achieved by allowing users to search and use survey variables in context of their underlying research data and scholarly publications which have been interlinked with each other.
How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry'' Benchmark

Pruning large language models (LLMs) from the BERT family has emerged as a standard compression benchmark, and several pruning methods have been proposed for this task. The recent ``Sparsity May Cry'' (SMC) benchmark put into question the validity of all existing methods, exhibiting a more complex setup where many known pruning methods appear to fail. We revisit the question of accurate BERT-pruning during fine-tuning on downstream datasets, and propose a set of general guidelines for successful pruning, even on the challenging SMC benchmark. First, we perform a cost-vs-benefits analysis of pruning model components, such as the embeddings and the classification head; second, we provide a simple-yet-general way of scaling training, sparsification and learning rate schedules relative to the desired target sparsity; finally, we investigate the importance of proper parametrization for Knowledge Distillation in the context of LLMs. Our simple insights lead to state-of-the-art results, both on classic BERT-pruning benchmarks, as well as on the SMC benchmark, showing that even classic gradual magnitude pruning (GMP) can yield competitive results, with the right approach.