Reviewerly/exHarmony huggingface dataset:
Aggregate Signatures with Versatile Randomization and Issuer-Hiding Multi-Authority Anonymous Credentials

Anonymous credentials (AC) offer privacy in user-centric identity management. They enable users to authenticate anonymously, revealing only necessary attributes. With the rise of decentralized systems like self-sovereign identity, the demand for efficient AC systems in a decentralized setting has grown. Relying on conventional AC systems, however, require users to present independent credentials when obtaining them from different issuers, leading to increased complexity. AC systems should ideally support being multi-authority for efficient presentation of multiple credentials from various issuers. Another vital property is issuer hiding, ensuring that the issuer's identity remains concealed, revealing only compliance with the verifier's policy. This prevents unique identification based on the sole combination of credential issuers. To date, there exists no AC scheme satisfying both properties simultaneously.
LedgerLocks: A Security Framework for Blockchain Protocols Based on Adaptor Signatures

The scalability and interoperability challenges in current cryptocurrencies have motivated the design of cryptographic protocols that enable efficient applications on top and across widely used cryptocurrencies such as Bitcoin or Ethereum. Examples of such protocols include (virtual) payment channels, atomic swaps, oracle-based contracts, deterministic wallets, and coin mixing services. Many of these protocols are built upon minimal core functionalities supported by a wide range of cryptocurrencies. Most prominently, adaptor signatures (AS) have emerged as a powerful tool for constructing blockchain protocols that are (mostly) agnostic to the specific logic of the underlying cryptocurrency. Even though AS-based protocols are built upon the same cryptographic principles, there exists no modular and faithful way for reasoning about their security. Instead, all the works analyzing such protocols focus on reproving how adaptor signatures are used to cryptographically link transactions while considering highly simplified blockchain models that do not capture security-relevant aspects of transaction execution in blockchain-based consensus.
Poster: Circumventing the GFW with TLS Record Fragmentation

State actors around the world censor the HTTPS protocol to block access to certain websites. While many circumvention strategies utilize the TCP layer only little emphasis has been placed on the analysis of TLS-a complex protocol and integral building block of HTTPS. In contrast to the TCP layer, circumvention methods on the TLS layer do not require root privileges since TLS operates on the application layer. With this proposal, we want to motivate a deeper analysis of TLS in regard to censorship circumvention techniques. To prove the existence of such techniques, we present TLS record fragmentation as a novel circumvention technique and circumvent the Great Firewall of China (GFW) using this technique. We hope that our research fosters collaboration between censorship and TLS researchers.
Poster: Membership Inference Attacks via Contrastive Learning

Since machine learning model is often trained on a limited data set, the model is trained multiple times on the same data sample, which causes the model to memorize most of the training set data. Membership Inference Attacks (MIAs) exploit this feature to determine whether a data sample is used for training a machine learning model. However, in realistic scenarios, it is difficult for the adversary to obtain enough qualified samples that mark accurate identity information, especially since most samples are non-members in real world applications. To address this limitation, in this paper, we propose a new attack method called CLMIA, which uses unsupervised contrastive learning to train an attack model. Meanwhile, in CLMIA, we require only a small amount of data with known membership status to fine-tune the attack model. We evaluated the performance of the attack using ROC curves showing a higher TPR at low FPR compared to other schemes.
3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) presents a critical global health challenge, and early detection is crucial for improving the 5-year survival rate. Recent medical imaging and computational algorithm advances offer potential solutions for early diagnosis. Deep learning, particularly in the form of convolutional neural networks (CNNs), has demonstrated success in medical image analysis tasks, including classification and segmentation. However, the limited availability of clinical data for training purposes continues to represent a significant obstacle. Data augmentation, generative adversarial networks (GANs), and cross-validation are potential techniques to address this limitation and improve model performance, but effective solutions are still rare for 3D PDAC, where the contrast is especially poor, owing to the high heterogeneity in both tumor and background tissues. In this study, we developed a new GAN-based model, named 3DGAUnet, for generating realistic 3D CT images of PDAC tumors and pancreatic tissue, which can generate the inter-slice connection data that the existing 2D CT image synthesis models lack. The transition to 3D models allowed the preservation of contextual information from adjacent slices, improving efficiency and accuracy, especially for the poor-contrast challenging case of PDAC. PDAC’s challenging characteristics, such as an iso-attenuating or hypodense appearance and lack of well-defined margins, make tumor shape and texture learning challenging. To overcome these challenges and improve the performance of 3D GAN models, our innovation was to develop a 3D U-Net architecture for the generator, to improve shape and texture learning for PDAC tumors and pancreatic tissue. Thorough examination and validation across many datasets were conducted on the developed 3D GAN model, to ascertain the efficacy and applicability of the model in clinical contexts. Our approach offers a promising path for tackling the urgent requirement for creative and synergistic methods to combat PDAC. The development of this GAN-based model has the potential to alleviate data scarcity issues, elevate the quality of synthesized data, and thereby facilitate the progression of deep learning models, to enhance the accuracy and early detection of PDAC tumors, which could profoundly impact patient outcomes. Furthermore, the model has the potential to be adapted to other types of solid tumors, hence making significant contributions to the field of medical imaging in terms of image processing models.
Lifelong Learning in the System of Higher Education: the State of the Problem and the Strategy of Integrating Educational Activities and Research

The authors of this article consider the current situation of lifelong learning education in leading Russian universities and define some possible strategies for integrating scientific research with educational activities. The empirical bases were: 1) statistical data on the activities of educational institutions of higher education ( N = 1180), 2) dataset of monitoring the activities of universities participating in the federal academic leadership programme “Priority-2030”, 3) dataset of a survey of heads of centers for further vocational education in Russian universities ( N = 450) as part of the Project “Monitoring of education markets and organizations (MEMO)”. The authors identified strategies for combining scientific and educational activities for lifelong learning programs at universities in the interests of the development of individual consumers, enterprises and branches of the national economy. The authors also found that universities take into account the social needs of society and the preferences of employers, but to a very small extent focus on transforming their scientific results into a new educational product for mass consumers. The significant focus of universities on research activities to a certain extent hinders the development and launch of new education programs, however, it is a group of research universities that demonstrates a higher cost of programs sold. The authors consider, how universities can be focused on working with the population or industry enterprises, integrating the research results into the modules of education programs, and stimulating an increase in the number of teachers combining research and teaching activities, at realizing the third University’s mission and taking into account the research grant volume.
NISQ-compatible approximate quantum algorithm for unconstrained and constrained discrete optimization

Quantum algorithms are getting extremely popular due to their potential to significantly outperform classical algorithms. Yet, applying quantum algorithms to optimization problems meets challenges related to the efficiency of quantum algorithms training, the shape of their cost landscape, the accuracy of their output, and their ability to scale to large-size problems. Here, we present an approximate gradient-based quantum algorithm for hardware-efficient circuits with amplitude encoding. We show how simple linear constraints can be directly incorporated into the circuit without additional modification of the objective function with penalty terms. We employ numerical simulations to test it on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="monospace">MaxCut</mml:mtext></mml:mrow></mml:math> problems with complete weighted graphs with thousands of nodes and run the algorithm on a superconducting quantum processor. We find that for unconstrained <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="monospace">MaxCut</mml:mtext></mml:mrow></mml:math> problems with more than 1000 nodes, the hybrid approach combining our algorithm with a classical solver called CPLEX can find a better solution than CPLEX alone. This demonstrates that hybrid optimization is one of the leading use cases for modern quantum devices.
Detecting outdated code element references in software repository documentation

Abstract Outdated documentation is a pervasive problem in software development, preventing effective use of software, and misleading users and developers alike. We posit that one possible reason why documentation becomes out of sync so easily is that developers are unaware of when their source code modifications render the documentation obsolete. Ensuring that the documentation is always in sync with the source code takes considerable effort, especially for large codebases. To address this situation, we propose an approach that can automatically detect code element references that survive in the documentation after all source code instances have been deleted. In this work, we analysed over 3,000 GitHub projects and found that most projects contain at least one outdated code element reference at some point in their history. We submitted GitHub issues to real-world projects containing outdated references detected by our approach, some of which have already led to documentation fixes. As an initiative toward keeping documentation in software repositories up-to-date, we have made our implementation available for developers to scan their GitHub projects for outdated code element references.
Poster: Panacea --- Stateless and Non-Interactive Oblivious RAM

Oblivious RAM (ORAM) allows a client to outsource database storage to a remote server while hiding the data access pattern. Existing designs use non-linear data structures (e.g., trees or hierarchical structures) and follow a online-offline paradigm. Clients submit their queries in the online phase and then the queries are ''flushed'' in the offline (eviction) phase. Such designs are interactive, requiring more than one round of client-server communication, be it during the online, offline, or both phases. Moreover, the client has to maintain an internal state which depends on the database state.
Penyuluhan Kepada Remaja Putri Untuk Meningkatan Pengetahuan Dan Sikap Terhadap Konsumsi Tablet Tambah Darah

Iron deficiency anemia is a health problem experienced by many adolescent girl. Iron deficiency anemia will affect the learning progress of adolescent girls. Regularly consuming Blood Supplement Tablets (BST) can prevent and control iron deficiency anemia. The low coverage of BST consumption in the South Banjarbaru Public Health Center working area only reached 3.79%. Behavior change is implemented with counseling. Behavior change can be achieved by the intervention of behavioral predisposing factors. The purpose of counseling is to improve the knowledge and attitude of the target adolescent girls. Counseling was conducted online using the WhatsApp group platform and Zoom meeting. The application of the method through discussion with peer counselors, discussion, and question and answer. Participants of the counseling consisted of 30 adolescent girls from RT 003-005 RW 004 Guntung Paikat Subdistrict. Counseling media used PowerPoint slides and e-leaflet. The response of counseling participants was very enthusiastic about participating in the activity. The evaluation results showed a significant increase in knowledge (p-Value = 0,007) and attitude (p-Value = 0,0001) toward the consumption of Blood Supplement Tablets. Health promotion programs are expected to be carried out on a sustainable level to change behavior in consuming blood supplement tablets.
BIR: Biomedical Information Retrieval System for Cancer Treatment in Electronic Health Record Using Transformers

The rapid growth of electronic health records (EHRs) has led to unprecedented biomedical data. Clinician access to the latest patient information can improve the quality of healthcare. However, clinicians have difficulty finding information quickly and easily due to the sheer data mining volume. Biomedical information retrieval (BIR) systems can help clinicians find the information required by automatically searching EHRs and returning relevant results. However, traditional BIR systems cannot understand the complex relationships between EHR entities. Transformers are a new type of neural network that is very effective for natural language processing (NLP) tasks. As a result, transformers are well suited for tasks such as machine translation and text summarization. In this paper, we propose a new BIR system for EHRs that uses transformers for predicting cancer treatment from EHR. Our system can understand the complex relationships between the different entities in an EHR, which allows it to return more relevant results to clinicians. We evaluated our system on a dataset of EHRs and found that it outperformed state-of-the-art BIR systems on various tasks, including medical question answering and information extraction. Our results show that Transformers are a promising approach for BIR in EHRs, reaching an accuracy and an F1-score of 86.46%, and 0.8157, respectively. We believe that our system can help clinicians find the information they need more quickly and easily, leading to improved patient care.
A Survey on Blockchain Abnormal Transaction Detection

Blockchain technology has undergone rapid development in recent years, transactions on Blockchains, represented by prominent examples such as Bitcoin and Ethereum, are rapidly increasing in number. However, with the large volume of transactions, a variety of scams such as phishing and Ponzi schemes have become more prevalent and are often hidden among legitimate transactions. To combat these anomalies and fraudulent activities, it is necessary to adopt anomaly detection methods. In this article, we conduct an extensive survey of the current body of research in the area of blockchain anomaly transaction detection and analyze the current state of research by examining the key point involved in detection, including data imbalance, feature extraction, and classification algorithms. Additionally, we discuss the potential of applying graph convolutional networks (GCN) to the domain of blockchain anomaly detection and predict that GCN will likely become a mainstream approach in the near future.
“What do others think?”: Task-Oriented Conversational Modeling with Subjective Knowledge

Chao Zhao, Spandana Gella, Seokhwan Kim, Di Jin, Devamanyu Hazarika, Alexandros Papangelis, Behnam Hedayatnia, Mahdi Namazifar, Yang Liu, Dilek Hakkani-Tur. Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue. 2023.
Artificial Intelligence Applications and Innovations: Day-to-Day Life Impact

The idea of an intelligent machine has fascinated humans for centuries [...]
Secure And Adaptive Data Stream Mining For New Generation Big Data

As the era of new-generation big data applications unfolds, the need for secure and adaptive data stream mining has become increasingly paramount. Evolving databases, characterized by ever-changing data streams and dynamic data distributions, present unique challenges and opportunities. This paper addresses the crucial intersection of security and adaptability in the context of data stream mining for new-generation big data. First, we delve into the evolving landscape of big data, where real-time data streams from diverse sources drive decision-making processes. Ensuring the privacy and security of sensitive information within these data streams is a fundamental concern. We explore cryptographic techniques, anonymization methods, and access control mechanisms that safeguard data while allowing for meaningful analysis. We present novel adaptive algorithms and model update strategies that can continuously learn and adjust to changing data distributions. These approaches enable data stream mining to remain effective and accurate over time. This paper offers insights into the fusion of security and adaptability in data stream mining, providing a foundation for the development of robust and privacy-conscious solutions for the evolving landscape of new-generation big data applications.
Spectral Hong-Ou-Mandel Effect between a Heralded Single-Photon State and a Thermal Field: Multiphoton Contamination and the Nonclassicality Threshold

The Hong-Ou-Mandel (HOM) effect is crucial for quantum information processing, and its visibility determines the system's quantum-classical characteristics. In an experimental and theoretical study of the spectral HOM effect between a thermal field and a heralded single-photon state, we demonstrate that the HOM visibility varies dependent on the relative photon statistics of the interacting fields. Our findings reveal that multiphoton components in a heralded state get engaged in quantum interference with a thermal field, resulting in improved visibilities at certain mean photon numbers. We derive a theoretical relationship for the HOM visibility as a function of the mean photon number of the thermal field and the thermal part of the heralded state. We show that the nonclassicality degree of a heralded state is reflected in its HOM visibility with a thermal field; our results establish a lower bound of 41.42% for the peak visibility, indicating the minimum assignable degree of nonclassicality to the heralded state. This research enhances our understanding of the HOM effect and its application to high-speed remote secret key sharing, addressing security concerns due to multiphoton contamination in heralded states.
Two-mode Raman quantum battery dependent on coupling strength

We investigate a series of two-mode quantum batteries (QBs) which exhibits better performance in both the stored energy and average charging power compared to the two-photon case. In both the model related to coupling strength and Raman cases, we find the QBs can store energy faster and have a higher efficiency than the original case. Among the derived models we have studied, the two-mode Raman QB dependent on coupling strength has the best performance. We also present the relationship between the ergotropy and battery-charger entanglement of incoherent QBs and define the resource utilization efficiency $k(t)$ and ${k}^{\mathrm{ins}}(t)$. We find that entanglement in the energy-extraction stage is a key resource. In addition, we consider a collection of two-level systems embedded in a two-mode microwave cavity with counterrotating terms, which shows that the counterrotating terms lead to a higher stored energy in the ultrastrong-coupling regime.
The Principal–Agent Problem in Liquid Staking

Proof-of-stake systems require stakers to lock up their funds in order to participate in consensus validation. This leads to capital inefficiency, as locked capital cannot be invested in Decentralized Finance (DeFi). Liquid staking rewards stakers with fungible tokens in return for staking their assets. These fungible tokens can in turn be reused in the DeFi economy. However, liquid staking introduces unexpected risks, as all delegated stake is now fungible. This exacerbates the already existing Principal–Agent problem faced during any delegation, in which the interests of the delegator (the Principal) are not aligned with the interests of the validator (the Agent). In this paper, we study the Principal–Agent problem in the context of liquid staking. We highlight the dilemma between the choice of proportional representation (having one’s stake delegated to one’s validator of choice) and fair punishment (being economically affected only when one’s choice is misinformed). We put forth an attack illustrating that these two notions are fundamentally incompatible in an adversarial setting. We then describe the mechanism of exempt delegations, used by some staking systems today, and devise a precise formula for quantifying the correct choice of exempt delegation which allows balancing the two conflicting virtues in the rational model.
Nine steps up to the pinnacle of science. (On the occasion of Yury Stolyarov’s 85-th anniversary)

The article is prepared on the occasion of the jubilee of Professor Yury N. Stolyarov, a prominent Russian scholar, pedagogue, science organizer, Doctor of Science in Pedagogy. The authors analyze the key vectors and products of his scholarly endeavors. His authority and status in the library theory and practice are high. The authors discuss Y. Stolyarov's contribution into modern library science, bibliology, documentology in the subject field of library science and practice. The authors emphasize his most significant achievements in methodology (structural and functional approach in library science, documentology development and institutionalization); terminology and its formation mechanisms; historical reconstruction of book culture origins; preservation and promotion of the legacy of famous librarians and library scientists of the past and present; theoretical and practice-oriented tools of library collection studies; scientific background of library policy; original approaches toward library profession studies and management; and science organization. The authors examine each group of achievements through the prism of the well-known and most significant theoretical and practical works by Yury N. Stolyarov.