Reviewerly/exHarmony huggingface dataset:
LDW-RS Loss: Label Density-Weighted Loss with Ranking Similarity Regularization for Imbalanced Deep Fetal Brain Age Regression

Estimation of fetal brain age based on sulci by magnetic resonance imaging (MRI) is crucial in determining the normal development of the fetal brain. Deep learning provides a possible way for fetal brain age estimation using MRI. However, real-world MRI datasets often present imbalanced label distribution, resulting in the model tending to show undesirable bias towards the majority of labels. Thus, many methods have been designed for imbalanced regression. Nevertheless, most of them on handling imbalanced data focus on targets with discrete categorical indices, without considering the continuous and ordered nature of target values. To fill the research gap of fetal brain age estimation with imbalanced data, we propose a novel label density-weighted loss with a ranking similarity regularization (LDW-RS) for deep imbalanced regression of the fetal brain age. Label density-weighted loss is designed to capture information about the similarity between neighboring samples in the label space. Ranking similarity regularization is developed to establish a global constraint for calibrating the biased feature representations learned by the network. A total of 1327 MRI images from 157 healthy fetuses between 22 and 34 weeks were used in our experiments for the fetal brain age estimation regression task. In the random experiments, our LDW-RS achieved promising results with an average mean absolute error of 0.760 ± 0.066 weeks and an R-square ( $${\mathrm{R}}^{2}$$ ) coefficient of 0.914 ± 0.020. Our fetal brain age estimation algorithm might be useful for identifying abnormalities in brain development and reducing the risk of adverse development in clinical practice.
Comparative Study of Large Language Models as Emotion and Sentiment Analysis Systems: A Case-Specific Analysis of GPT vs. IBM Watson

Sentiment analysis and emotion-detection techniques have wide applications in diverse fields. Various systems such as IBM Watson NLU have been developed for this purpose. Separately, large language models (LLMs) like GPT-3.5 have shown promise for diverse natural language processing (NLP) applications. This study investigates whether an LLM without explicit training could perform sentiment and emotion classification comparably to customized systems. For this purpose, a comparative analysis was conducted between GPT-3.5 and IBM Watson's sentiment analysis, and emotion classification, using a dataset of 30,000 tweets related to the Covid-19 pandemic. The results revealed the versatility of LLMs, suggesting their potential transferability to diverse NLP tasks beyond their original training objective when properly prompted. GPT-3.5, despite not being explicitly trained for these tasks, achieves competitive performance with IBM Watson's emotion classification capabilities when provided with a suitable prompting context. Precisely, GPT-3.5 demonstrates surprising adaptability to detect nuanced sentiments, such as irony, compared to Watson's rigid emotion model. However, GPT-3.5 also struggles to fit textual expressions into the prescribed emotion classifications. Overall, this motivates expanded research into leveraging large pre-trained language models for affective computing applications by means of thoughtful prompt and evaluation design.
Dynamic Data Augmentation via Monte-Carlo Tree Search for Prostate MRI Segmentation

Medical image data are often limited due to the expensive acquisition and annotation process. Hence, training a deep-learning model with only raw data can easily lead to overfitting. One solution to this problem is to augment the raw data with various transformations, improving the model's ability to generalize to new data. However, manually configuring a generic augmentation combination and parameters for different datasets is non-trivial due to inconsistent acquisition approaches and data distributions. Therefore, automatic data augmentation is proposed to learn favorable augmentation strategies for different datasets while incurring large GPU overhead. To this end, we present a novel method, called Dynamic Data Augmentation (DDAug), which is efficient and has negligible computation cost. Our DDAug develops a hierarchical tree structure to represent various augmentations and utilizes an efficient Monte-Carlo tree searching algorithm to update, prune, and sample the tree. As a result, the augmentation pipeline can be optimized for each dataset automatically. Experiments on multiple Prostate MRI datasets show that our method outperforms the current state-of-the-art data augmentation strategies.
Towards an integrated risk analysis security framework according to a systematic analysis of existing proposals

Abstract The information society depends increasingly on risk assessment and management systems as means to adequately protect its key information assets. The availability of these systems is now vital for the protection and evolution of companies. However, several factors have led to an increasing need for more accurate risk analysis approaches. These are: the speed at which technologies evolve, their global impact and the growing requirement for companies to collaborate. Risk analysis processes must consequently adapt to these new circumstances and new technological paradigms. The objective of this paper is, therefore, to present the results of an exhaustive analysis of the techniques and methods offered by the scientific community with the aim of identifying their main weaknesses and providing a new risk assessment and management process. This analysis was carried out using the systematic review protocol and found that these proposals do not fully meet these new needs. The paper also presents a summary of MARISMA, the risk analysis and management framework designed by our research group. The basis of our framework is the main existing risk standards and proposals, and it seeks to address the weaknesses found in these proposals. MARISMA is in a process of continuous improvement, as is being applied by customers in several European and American countries. It consists of a risk data management module, a methodology for its systematic application and a tool that automates the process.
Entanglement-assisted quantum error-correcting codes from subfield subcodes of projective Reed–Solomon codes

Abstract We study the subfield subcodes of projective Reed–Solomon codes and their duals: we provide bases for these codes and estimate their parameters. With this knowledge, we can construct symmetric and asymmetric entanglement-assisted quantum error-correcting codes, which in many cases have new or better parameters than the ones available in the literature.
Advanced all-optical classification using orbital-angular-momentum-encoded diffractive networks

As a successful case of combining deep learning with photonics, the research on optical machine learning has recently undergone rapid development. Among various optical classification frameworks, diffractive networks have been shown to have unique advantages in all-optical reasoning. As an important property of light, the orbital angular momentum (OAM) of light shows orthogonality and mode-infinity, which can enhance the ability of parallel classification in information processing. However, there have been few all-optical diffractive networks under the OAM mode encoding. Here, we report a strategy of OAM-encoded diffractive deep neural network (OAM-encoded D2NN) that encodes the spatial information of objects into the OAM spectrum of the diffracted light to perform all-optical object classification. We demonstrated three different OAM-encoded D2NNs to realize (1) single detector OAM-encoded D2NN for single task classification, (2) single detector OAM-encoded D2NN for multitask classification, and (3) multidetector OAM-encoded D2NN for repeatable multitask classification. We provide a feasible way to improve the performance of all-optical object classification and open up promising research directions for D2NN by proposing OAM-encoded D2NN.
Exploring the Naturalness of Cognitive Status-Informed Referring Form Selection Models

Language-capable robots must be able to efficiently and naturally communicate about objects in the environment. A key part of communication is Referring Form Selection (RFS): the process of selecting a form like it, that, or the N to use when referring to an object. Recent cognitive status-informed computational RFS models have been evaluated in terms of goodness-of-fit to human data. But it is as yet unclear whether these models actually select referring forms that are any more natural than baseline alternatives, regardless of goodness-of-fit. Through a human subject study designed to assess this question, we show that even though cognitive status-informed referring selection models achieve good fit to human data, they do not (yet) produce concrete benefits in terms of naturality. On the other hand, our results show that human utterances also had high variability in perceived naturality, demonstrating the challenges of evaluating RFS naturality.
Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy

Daphne Ippolito, Florian Tramer, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher Choquette Choo, Nicholas Carlini. Proceedings of the 16th International Natural Language Generation Conference. 2023.
Feature Importance for Clustering

The literature on cluster analysis methods evaluating the contribution of features to the emergence of the cluster structure for a given clustering partition is sparse. Despite advances in explainable supervised methods, explaining the outcomes of unsupervised algorithms is a less explored area. This paper proposes two post-hoc algorithms to determine feature importance for prototype-based clustering methods. The first approach assumes that the variation in the distance among cluster prototypes after marginalizing a feature can be used as a proxy for feature importance. The second approach, inspired by cooperative game theory, determines the contribution of each feature to the cluster structure by analyzing all possible feature coalitions. Multiple experiments using real-world datasets confirm the effectiveness of the proposed methods for both hard and fuzzy clustering settings.
Public-Key Encryption with Quantum Keys

In the framework of Impagliazzo's five worlds, a distinction is often made between two worlds, one where public-key encryption exists (Cryptomania), and one in which only one-way functions exist (MiniCrypt). However, the boundaries between these worlds can change when quantum information is taken into account. Recent work has shown that quantum variants of oblivious transfer and multi-party computation, both primitives that are classically in Cryptomania, can be constructed from one-way functions, placing them in the realm of quantum MiniCrypt (the so-called MiniQCrypt). This naturally raises the following question: Is it possible to construct a quantum variant of public-key encryption, which is at the heart of Cryptomania, from one-way functions or potentially weaker assumptions? In this work, we initiate the formal study of the notion of quantum public-key encryption (qPKE), i.e., public-key encryption where keys are allowed to be quantum states. We propose new definitions of security and several constructions of qPKE based on the existence of one-way functions (OWF), or even weaker assumptions, such as pseudorandom function-like states (PRFS) and pseudorandom function-like states with proof of destruction (PRFSPD). Finally, to give a tight characterization of this primitive, we show that computational assumptions are necessary to build quantum public-key encryption. That is, we give a self-contained proof that no quantum public-key encryption scheme can provide information-theoretic security.
Algebraic Group Model with Oblivious Sampling

In the algebraic group model (AGM), an adversary has to return with each group element a linear representation with respect to input group elements. In many groups, it is easy to sample group elements obliviously without knowing such linear representations. Since the AGM does not model this, it can be used to prove the security of spurious knowledge assumptions. We show several well-known zk-SNARKs use such assumptions. We propose AGM with oblivious sampling (AGMOS), an AGM variant where the adversary can access an oracle that allows sampling group elements obliviously from some distribution. We show that AGM and AGMOS are different by studying the family of "total knowledge-of-exponent" assumptions, showing that they are all secure in the AGM, but most are not secure in the AGMOS if the DL holds. We show an important separation in the case of the KZG commitment scheme. We show that many known AGM reductions go through also in the AGMOS, assuming a novel falsifiable assumption $$\textrm{TOFR}$$ .
Exploring the Integration of Large Language Models into Automatic Speech Recognition Systems: An Empirical Study

This paper explores the integration of Large Language Models (LLMs) into Automatic Speech Recognition (ASR) systems to improve transcription accuracy. The increasing sophistication of LLMs, with their in-context learning capabilities and instruction-following behavior, has drawn significant attention in the field of Natural Language Processing (NLP). Our primary focus is to investigate the potential of using an LLM's in-context learning capabilities to enhance the performance of ASR systems, which currently face challenges such as ambient noise, speaker accents, and complex linguistic contexts. We designed a study using the Aishell-1 and LibriSpeech datasets, with ChatGPT and GPT-4 serving as benchmarks for LLM capabilities. Unfortunately, our initial experiments did not yield promising results, indicating the complexity of leveraging LLM's in-context learning for ASR applications. Despite further exploration with varied settings and models, the corrected sentences from the LLMs frequently resulted in higher Word Error Rates (WER), demonstrating the limitations of LLMs in speech applications. This paper provides a detailed overview of these experiments, their results, and implications, establishing that using LLMs' in-context learning capabilities to correct potential errors in speech recognition transcriptions is still a challenging task at the current stage.
Limits in the Provable Security of ECDSA Signatures

Digital Signatures are ubiquitous in modern computing. One of the most widely used digital signature schemes is $$\textsf {ECDSA}$$ due to its use in TLS, various Blockchains such as Bitcoin and Etherum, and many other applications. Yet the formal analysis of $$\textsf {ECDSA}$$ is comparatively sparse. In particular, all known security results for $$\textsf {ECDSA}$$ rely on some idealized model such as the generic group model or the programmable (bijective) random oracle model. In this work, we study the question whether these strong idealized models are necessary for proving the security of $$\textsf {ECDSA}$$ . Specifically, we focus on the programmability of $$\textsf {ECDSA}$$ ’s “conversion function” which maps an elliptic curve point into its x-coordinate modulo the group order. Unfortunately, our main results are negative. We establish, by means of a meta reductions, that an algebraic security reduction for $$\textsf {ECDSA}$$ can only exist if the security reduction is allowed to program the conversion function. As a consequence, a meaningful security proof for $$\textsf {ECDSA}$$ is unlikely to exist without strong idealization.
On Time-Space Lower Bounds for Finding Short Collisions in Sponge Hash Functions

Sponge paradigm, used in the design of SHA-3, is an alternative hashing technique to the popular Merkle-Damgård paradigm. We revisit the problem of finding B-block-long collisions in sponge hash functions in the auxiliary-input random permutation model, in which an attacker gets a piece of S-bit advice about the random permutation and makes T (forward or inverse) oracle queries to the random permutation. Recently, significant progress has been made in the Merkle-Damgård setting and optimal bounds are known for a large range of parameters, including all constant values of B. However, the sponge setting is widely open: there exist significant gaps between known attacks and security bounds even for $$B=1$$ . Freitag, Ghoshal and Komargodski (CRYPTO 2022) showed a novel attack for $$B=1$$ that takes advantage of the inverse queries and achieves advantage $$\widetilde{\varOmega }(\min (S^2T^2/2^{2c}$$ , $$ (S^2T/2^{2c})^{2/3})+T^2/2^r)$$ , where r is bit-rate and c is the capacity of the random permutation. However, they only showed an $$\widetilde{O}(ST/2^c+T^2/2^r)$$ security bound, leaving open an intriguing quadratic gap. For $$B=2$$ , they beat the general security bound by Coretti, Dodis, Guo (CRYPTO 2018) for arbitrary values of B. However, their highly non-trivial argument is quite laborious, and no better (than the general) bounds are known for $$B\ge 3$$ . In this work, we study the possibility of proving better security bounds in the sponge setting. To this end, Overall, our results yield state-of-the-art security bounds for finding short collisions and fully characterize the power of the multi-instance technique in the sponge setting.
Chainable Functional Commitments for Unbounded-Depth Circuits

A functional commitment (FC) scheme allows one to commit to a vector $$\boldsymbol{x}$$ and later produce a short opening proof of $$(f, f(\boldsymbol{x}))$$ for any admissible function f. Since their inception, FC schemes supporting ever more expressive classes of functions have been proposed. In this work, we introduce a novel primitive that we call chainable functional commitment (CFC), which extends the functionality of FCs by allowing one to 1) open to functions of multiple inputs $$f(\boldsymbol{x}_1, \ldots , \boldsymbol{x}_m)$$ that are committed independently, 2) while preserving the output also in committed form. We show that CFCs for quadratic polynomial maps generically imply FCs for circuits. Then, we efficiently realize CFCs for quadratic polynomials over pairing groups and lattices, resulting in the first FC schemes for circuits of unbounded depth based on either pairing-based or lattice-based falsifiable assumptions. Our FCs require fixing a-priori only the maximal width of the circuit to be evaluated, and have opening proof size depending only on the circuit depth. Additionally, our FCs feature other nice properties such as being additively homomorphic and supporting sublinear-time verification after offline preprocessing. Using a recent transformation that constructs homomorphic signatures (HS) from FCs, we obtain the first pairing- and lattice-based realisations of HS for bounded-width, but unbounded-depth, circuits. Prior to this work, the only HS for general circuits is lattice-based and requires bounding the circuit depth at setup time.
Revisiting Updatable Encryption: Controlled Forward Security, Constructions and a Puncturable Perspective

Updatable encryption (UE) allows a third party to periodically rotate encryption keys from one epoch to another without the need to download, decrypt, re-encrypt and upload already encrypted data by a client. Updating those outsourced ciphertexts is carried out via the use of so-called update tokens which in turn are generated during key rotation and can be sent (publicly) to the third party. The arguably most efficient variant of UE is ciphertext-independent UE as the key rotation does not depend on the outsourced ciphertexts which makes it particularly interesting in scenarios where access to (information of the) ciphertexts is not possible during key rotation. Available security notions for UE cannot guarantee any form of forward security (i.e., old ciphertexts are in danger after key leakage). Counter-intuitively, forward security would violate correctness, as ciphertexts should be updatable ad-infinitum given the update token. In this work, we investigate if we can have at least some form of "controlled" forward security to mitigate the following shortcoming: an adversary would record available information (i.e., some ciphertexts, all update tokens) and simply would wait for a single key leakage to decrypt all data ever encrypted. Our threefold contribution is as follows:
Hybrid Intelligent Systems

Dive into the foundations of intelligent systems, machine learning, and control with this hands-on, project-based introductory textbook. Precise, clear introductions to core topics in fuzzy logic, neural networks, optimization, deep learning, and machine learning, avoid the use of complex mathematical proofs, and are supported by over 70 examples. Modular chapters built around a consistent learning framework enable tailored course offerings to suit different learning paths. Over 180 open-ended review questions support self-review and class discussion, over 120 end-of-chapter problems cement student understanding, and over 20 hands-on Arduino assignments connect theory to practice, supported by downloadable Matlab and Simulink code. Comprehensive appendices review the fundamentals of modern control, and contain practical information on implementing hands-on assignments using Matlab, Simulink, and Arduino. Accompanied by solutions for instructors, this is the ideal guide for senior undergraduate and graduate engineering students, and professional engineers, looking for an engaging and practical introduction to the field.
Artificial Neural Networks

Dive into the foundations of intelligent systems, machine learning, and control with this hands-on, project-based introductory textbook. Precise, clear introductions to core topics in fuzzy logic, neural networks, optimization, deep learning, and machine learning, avoid the use of complex mathematical proofs, and are supported by over 70 examples. Modular chapters built around a consistent learning framework enable tailored course offerings to suit different learning paths. Over 180 open-ended review questions support self-review and class discussion, over 120 end-of-chapter problems cement student understanding, and over 20 hands-on Arduino assignments connect theory to practice, supported by downloadable Matlab and Simulink code. Comprehensive appendices review the fundamentals of modern control, and contain practical information on implementing hands-on assignments using Matlab, Simulink, and Arduino. Accompanied by solutions for instructors, this is the ideal guide for senior undergraduate and graduate engineering students, and professional engineers, looking for an engaging and practical introduction to the field.