Reviewerly/exHarmony huggingface dataset:
Enhancing Post-Quantum Cryptography: Exploring Mathematical Foundations and Comparative Analysis of Different Cryptographic Algorithm

Abstract: This research paper surveys the landscape of cryptography, encompassing historical origins and modern applications. Beginning with foundational concepts, it explores encryption, decryption, ciphers, and keys, spanning symmetric and asymmetric cryptography. Historical context unfolds, tracing cryptography from ancient Egyptian hieroglyphs to Julius Caesar's cipher. The study then transitions to contemporary subjects. Elliptic Curves and Cryptography are investigated, showcasing their significance in secure communication, demonstrating ECC key exchange and AES-GCM encryption using python and Comparative analysis of ECC, RSA, and Diffie-Hellman sheds light on their performance. Zero-Knowledge Proofs are introduced as tools for privacy-preserving verification followed by an exploration of various Zero-Knowledge Proof (ZKP) protocols. By presenting practical implementation examples using Python, the paper illustrates how these proofs can be applied in real-world scenarios. Random Number Generation is examined and distinction between pseudorandom number generators (PRNGs) and cryptographically secure PRNGs (CSPRNGs) is emphasized conducting a thorough comparative analysis of PRNGs and CSPRNGs, considering factors like correlation, independence, periodicity, and entropy. Furthermore, the section evaluates the performance of different random number generation techniques. Fully Homomorphic Encryption emerges as a groundbreaking concept, discussing its mathematical properties, practical implementation, parameter selection, and optimization techniques enabling computation on encrypted data. Cryptographic Secret Sharing Schemes are explored for secure information distribution. The paper concludes by delving into the Chinese Remainder Theorem's applications within modern cryptographic protocols, particularly in RSA decryption and the integration factorization process of the RSA public key cryptosystem. It also provides a comprehensive overview of the theoretical foundations of primality testing, a pivotal aspect of the RSA algorithm. Overall, this research paper provides a comprehensive exploration of cryptography's historical context, core concepts, advanced techniques, and practical implementations, offering valuable insights into the realm of secure communication
Innovative navigation strategies based on multiple signals for performance improvement of drone-based operations

Abstract. The growing employment of Unmanned Aerial Systems (UASs, commonly referred to as drones) in civilian and military environments, along with the increasing accuracy of avionics components, has led to the definition of more stringent drone-based mission requirements. In this sense, the following work summarizes a series of Ph.D. research activities aimed at improving UAS performance under different aspects, in order to enhance their use in a variety of application contexts and environmental conditions while ensuring a satisfactory level of accuracy.
Artistic expression and data protection: Balancing aesthetics with data privacy in IoT

Integrating Internet of Things (IoT) technologies in art design has created new possibilities for artists to create immersive and interactive experiences. However, data collection, analysis, and utilization in IoT art installations raise significant security and privacy concerns. Additionally, incorporating differential privacy techniques in IoT art installations poses optimization challenges. This paper explores optimizing differential privacy budgets based on deep learning in IoT art installations. By leveraging deep learning models, privacy budgets can be dynamically allocated to preserve individual privacy while maintaining the aesthetic integrity of the artwork. In light of this, a deep learning-based differential privacy budget optimization strategy for IoT art installations is suggested. This method adaptively distributes various budgets by the iterative change law of parameters. A regularization term is provided to limit the disturbance term to avoid the issue of excessive noise. This stops the neural network from overfitting and also assists in learning the model's salient characteristics. The capacity of the model to generalize is effectively improved by the suggested strategy, according to experiments. The accuracy difference between the model trained with noise and the model trained with original data is less than 0.5% as the number of iterations increases. Therefore, the proposed method can protect the user's privacy, effectively ensure the model's availability, and achieve the balance between privacy and availability. This accuracy ensures that the installation functions as intended and delivers the desired aesthetic impact, enabling artists to convey their artistic message effectively.
Omnimatte3D: Associating Objects and Their Effects in Unconstrained Monocular Video

We propose a method to decompose a video into a background and a set of foreground layers, where the background captures stationary elements while the foreground layers capture moving objects along with their associated effects (e.g. shadows and reflections). Our approach is designed for unconstrained monocular videos, with an arbitrary camera and object motion. Prior work that tackles this problem assumes that the video can be mapped onto a fixed 2D canvas, severely limiting the possible space of camera motion. Instead, our method applies recent progress in monocular camera pose and depth estimation to create a full, RGBD video layer for the background, along with a video layer for each foreground object. To solve the underconstrained decomposition problem, we propose a new loss formulation based on multi-view consistency. We test our method on challenging videos with complex camera motion and show significant qualitative improvement over current approaches.
MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models

Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks. However, they often suffer from limitations such as difficulty in incorporating new knowledge, generating hallucinations, and explaining their reasoning process. To address these challenges, we propose a novel prompting pipeline, named \method, that leverages knowledge graphs (KGs) to enhance LLMs' inference and transparency. Our method enables LLMs to comprehend KG inputs and infer with a combination of implicit and external knowledge. Moreover, our method elicits the mind map of LLMs, which reveals their reasoning pathways based on the ontology of knowledge. We evaluate our method on diverse question \& answering tasks, especially in medical domains, and show significant improvements over baselines. We also introduce a new hallucination evaluation benchmark and analyze the effects of different components of our method. Our results demonstrate the effectiveness and robustness of our method in merging knowledge from LLMs and KGs for combined inference. To reproduce our results and extend the framework further, we make our codebase available at https://github.com/wyl-willing/MindMap.
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection

Lack of generalization to unseen domains/attacks is the Achilles heel of most face presentation attack detection (FacePAD) algorithms. Existing attempts to enhance the generalizability of FacePAD solutions assume that data from multiple source domains are available with a single entity to enable centralized training. In practice, data from different source domains may be collected by diverse entities, who are often unable to share their data due to legal and privacy constraints. While collaborative learning paradigms such as federated learning (FL) can overcome this problem, standard FL methods are ill-suited for domain generalization because they struggle to surmount the twin challenges of handling non-iid client data distributions during training and generalizing to unseen domains during inference. In this work, a novel framework called Federated Split learning with Intermediate representation Sampling (FedSIS) is introduced for privacy-preserving domain generalization. In FedSIS, a hybrid Vision Transformer (ViT) architecture is learned using a combination of FL and split learning to achieve robustness against statistical heterogeneity in the client data distributions without any sharing of raw data (thereby preserving privacy). To further improve generalization to unseen domains, a novel feature augmentation strategy called intermediate representation sampling is employed, and discriminative information from intermediate blocks of a ViT is distilled using a shared adapter network. The FedSIS approach has been evaluated on two well-known benchmarks for cross-domain FacePAD to demonstrate that it is possible to achieve state-of-the-art generalization performance without data sharing. Code: https://github.com/Naiftt/FedSIS
Normative Conditional Reasoning as a Fragment of HOL

We report some results regarding the mechanization of normative (preference-based) conditional reasoning. Our focus is on Aqvist's system E for conditional obligation (and its extensions). Our mechanization is achieved via a shallow semantical embedding in Isabelle/HOL. We consider two possible uses of the framework. The first one is as a tool for meta-reasoning about the considered logic. We employ it for the automated verification of deontic correspondences (broadly conceived) and related matters, analogous to what has been previously achieved for the modal logic cube. The second use is as a tool for assessing ethical arguments. We provide a computer encoding of a well-known paradox in population ethics, Parfit's repugnant conclusion. Whether the presented encoding increases or decreases the attractiveness and persuasiveness of the repugnant conclusion is a question we would like to pass on to philosophy and ethics.
Towards a knowledge leakage Mitigation framework for mobile Devices in knowledge-intensive Organizations

The use of mobile devices in knowledge-intensive organizations while effective and cost-efficient also pose a challenging management problem. Often employees whether deliberately or inadvertently are the cause of knowledge leakage in organizations and the use of mobile devices further exacerbates it. This problem is the result of overly focusing on technical controls while neglecting human factors. Knowledge leakage is a multidimensional problem, and in this paper, we highlight the different dimensions that constitute it. In this study, our contributions are threefold. First, we study knowledge leakage risk (KLR) within the context of mobile devices in knowledge-intensive organizations in Australia. Second, we present a conceptual framework to explain and categorize the mitigation strategies to combat KLR through the use of mobile devices grounded in the literature. And third, we apply the framework to the findings from interviews with security and knowledge managers. Keywords: Knowledge Leakage, Knowledge Risk, Knowledge intensive, Mobile device.
An Effective Method using Phrase Mechanism in Neural Machine Translation

Machine Translation is one of the essential tasks in Natural Language Processing (NLP), which has massive applications in real life as well as contributing to other tasks in the NLP research community. Recently, Transformer -based methods have attracted numerous researchers in this domain and achieved state-of-the-art results in most of the pair languages. In this paper, we report an effective method using a phrase mechanism, PhraseTransformer, to improve the strong baseline model Transformer in constructing a Neural Machine Translation (NMT) system for parallel corpora Vietnamese-Chinese. Our experiments on the MT dataset of the VLSP 2022 competition achieved the BLEU score of 35.3 on Vietnamese to Chinese and 33.2 BLEU scores on Chinese to Vietnamese data. Our code is available at https://github.com/phuongnm94/PhraseTransformer.
Algebraic relations over finite fields that preserve the endomorphism rings of CM $j$-invariants

We characterise the integral affine plane curves over a finite field $k$ with the property that all but finitely many of their $\overline{k}$-points have coordinates that are $j$-invariants of elliptic curves with isomorphic endomorphism rings. This settles a finite field variant of the Andr\'e-Oort conjecture for $Y(1)^2_\mathbb{C}$, which is a theorem of Andr\'e. We use our result to solve the modular support problem for function fields of positive characteristic.
Switched auxiliary loss for robust training of transformer models for histopathological image segmentation

Functional tissue Units (FTUs) are cell population neighborhoods local to a particular organ performing its main function. The FTUs provide crucial information to the pathologist in understanding the disease affecting a particular organ by providing information at the cellular level. In our research, we have developed a model to segment multi-organ FTUs across 5 organs namely: the kidney, large intestine, lung, prostate and spleen by utilizing the HuBMAP + HPA - Hacking the Human Body competition dataset. We propose adding shifted auxiliary loss for training models like the transformers to overcome the diminishing gradient problem which poses a challenge towards optimal training of deep models. Overall, our model achieved a dice score of 0.793 on the public dataset and 0.778 on the private dataset and shows a 1% improvement with the use of the proposed method. The findings also bolster the use of transformers models for dense prediction tasks in the field of medical image analysis. The study assists in understanding the relationships between cell and tissue organization thereby providing a useful medium to look at the impact of cellular functions on human health.
Extreme Multilabel Classification for Specialist Doctor Recommendation with Implicit Feedback and Limited Patient Metadata

Recommendation Systems (RS) are often used to address the issue of medical doctor referrals. However, these systems require access to patient feedback and medical records, which may not always be available in real-world scenarios. Our research focuses on medical referrals and aims to predict recommendations in different specialties of physicians for both new patients and those with a consultation history. We use Extreme Multilabel Classification (XML), commonly employed in text-based classification tasks, to encode available features and explore different scenarios. While its potential for recommendation tasks has often been suggested, this has not been thoroughly explored in the literature. Motivated by the doctor referral case, we show how to recast a traditional recommender setting into a multilabel classification problem that current XML methods can solve. Further, we propose a unified model leveraging patient history across different specialties. Compared to state-of-the-art RS using the same features, our approach consistently improves standard recommendation metrics up to approximately $10\%$ for patients with a previous consultation history. For new patients, XML proves better at exploiting available features, outperforming the benchmark in favorable scenarios, with particular emphasis on recall metrics. Thus, our approach brings us one step closer to creating more effective and personalized doctor referral systems. Additionally, it highlights XML as a promising alternative to current hybrid or content-based RS, while identifying key aspects to take into account when using XML for recommendation tasks.
UnLoc: A Unified Framework for Video Localization Tasks

While large-scale image-text pretrained models such as CLIP have been used for multiple video-level tasks on trimmed videos, their use for temporal localization in untrimmed videos is still a relatively unexplored task. We design a new approach for this called UnLoc, which uses pretrained image and text towers, and feeds tokens to a video-text fusion model. The output of the fusion module are then used to construct a feature pyramid in which each level connects to a head to predict a per-frame relevancy score and start/end time displacements. Unlike previous works, our architecture enables Moment Retrieval, Temporal Localization, and Action Segmentation with a single stage model, without the need for action proposals, motion based pretrained features or representation masking. Unlike specialized models, we achieve state of the art results on all three different localization tasks with a unified approach. Code will be available at: \url{https://github.com/google-research/scenic}.
PrAIoritize: Learning to Prioritize Smart Contract Bugs and Vulnerabilities

Smart contract vulnerabilities and bugs have become a key concern for software engineers, as they can lead to significant financial losses, reputational damage, and legal issues. Therefore, prioritizing bug fixing for smart contracts is critical to maintaining trust. Due to the lack of tracking tools, prioritizing smart contract-reported bugs is done manually, which is a tedious task, limits bug triaging, and needs specialized knowledge. Towards this end, we propose PrAIoritize; an automated approach for predicting smart contract bug priorities that assist software engineers in prioritizing highly urgent bug reports. PrAIoritize consists of two main phases: 1) automatic labeling, which involves the automatic construction of a smart contract keyword lexicon and the automatic assignment of priority levels to unlabeled bug reports; 2) model construction, which involves feature engineering and designs layers of feed-forward neural networks (FFNNs) and bidirectional long short-term memory (BiLSTM) with multi-class classification to better capture the features of the textual descriptions of bugs and predict their priority levels. The model then is trained using smart contract bug reports collected from two data sources: open-source software (OSS) projects available on GitHub and NVD vulnerability database. Our evaluation demonstrates significant improvement over state-of-the-art baselines and commonly used pre-trained models (e.g. BERT) for similar classification tasks, with 5.75%-35.29% increase in F-measure, precision, and recall.
Neural Amortized Inference for Nested Multi-agent Reasoning

Multi-agent interactions, such as communication, teaching, and bluffing, often rely on higher-order social inference, i.e., understanding how others infer oneself. Such intricate reasoning can be effectively modeled through nested multi-agent reasoning. Nonetheless, the computational complexity escalates exponentially with each level of reasoning, posing a significant challenge. However, humans effortlessly perform complex social inferences as part of their daily lives. To bridge the gap between human-like inference capabilities and computational limitations, we propose a novel approach: leveraging neural networks to amortize high-order social inference, thereby expediting nested multi-agent reasoning. We evaluate our method in two challenging multi-agent interaction domains. The experimental results demonstrate that our method is computationally efficient while exhibiting minimal degradation in accuracy.
CSM-H-R: A Context Modeling Framework in Supporting Reasoning Automation for Interoperable Intelligent Systems and Privacy Protection

The automation of High-Level Context (HLC) reasoning across intelligent systems at scale is imperative because of the unceasing accumulation of contextual data, the trend of the fusion of data from multiple sources (e.g., sensors, intelligent systems), and the intrinsic complexity and dynamism of context-based decision-making processes. To mitigate the challenges posed by these issues, we propose a novel Hierarchical Ontology-State Modeling (HOSM) framework CSM-H-R, which programmatically combines ontologies and states at the modeling phase and runtime phase for attaining the ability to recognize meaningful HLC. It builds on the model of our prior work on the Context State Machine (CSM) engine by incorporating the H (Hierarchy) and R (Relationship and tRansition) dimensions to take care of the dynamic aspects of context. The design of the framework supports the sharing and interoperation of context among intelligent systems and the components for handling CSMs and the management of hierarchy, relationship, and transition. Case studies are developed for IntellElevator and IntellRestaurant, two intelligent applications in a smart campus setting. The prototype implementation of the framework experiments on translating the HLC reasoning into vector and matrix computing and presents the potential of using advanced probabilistic models to reach the next level of automation in integrating intelligent systems; meanwhile, privacy protection support is achieved in the application domain by anonymization through indexing and reducing information correlation. An implementation of the framework is available at https://github.com/songhui01/CSM-H-R.
Spurious Correlations and Where to Find Them

Spurious correlations occur when a model learns unreliable features from the data and are a well-known drawback of data-driven learning. Although there are several algorithms proposed to mitigate it, we are yet to jointly derive the indicators of spurious correlations. As a result, the solutions built upon standalone hypotheses fail to beat simple ERM baselines. We collect some of the commonly studied hypotheses behind the occurrence of spurious correlations and investigate their influence on standard ERM baselines using synthetic datasets generated from causal graphs. Subsequently, we observe patterns connecting these hypotheses and model design choices.
Temporal-Distributed Backdoor Attack Against Video Based Action Recognition

Deep neural networks (DNNs) have achieved tremendous success in various applications including video action recognition, yet remain vulnerable to backdoor attacks (Trojans). The backdoor-compromised model will mis-classify to the target class chosen by the attacker when a test instance (from a non-target class) is embedded with a specific trigger, while maintaining high accuracy on attack-free instances. Although there are extensive studies on backdoor attacks against image data, the susceptibility of video-based systems under backdoor attacks remains largely unexplored. Current studies are direct extensions of approaches proposed for image data, e.g., the triggers are independently embedded within the frames, which tend to be detectable by existing defenses. In this paper, we introduce a simple yet effective backdoor attack against video data. Our proposed attack, adding perturbations in a transformed domain, plants an imperceptible, temporally distributed trigger across the video frames, and is shown to be resilient to existing defensive strategies. The effectiveness of the proposed attack is demonstrated by extensive experiments with various well-known models on two video recognition benchmarks, UCF101 and HMDB51, and a sign language recognition benchmark, Greek Sign Language (GSL) dataset. We delve into the impact of several influential factors on our proposed attack and identify an intriguing effect termed "collateral damage" through extensive studies.