Reviewerly/exHarmony huggingface dataset:
The Ecosystem of Trust (EoT): Enabling effective deployment of autonomous systems through collaborative and trusted ecosystems

Ecosystems are ubiquitous but trust within them is not guaranteed. Trust is paramount because stakeholders within an ecosystem must collaborate to achieve their objectives. With the twin transitions, digital transformation to go in parallel with green transition, accelerating the deployment of autonomous systems, trust has become even more critical to ensure that the deployed technology creates value. To address this need, we propose an ecosystem of trust approach to support deployment of technology by enabling trust among and between stakeholders, technologies and infrastructures, institutions and governance, and the artificial and natural environments in an ecosystem. The approach can help the stakeholders in the ecosystem to create, deliver, and receive value by addressing their concerns and aligning their objectives. We present an autonomous, zero emission ferry as a real world use case to demonstrate the approach from a stakeholder perspective. We argue that assurance, defined as grounds for justified confidence originated from evidence and knowledge, is a prerequisite to enable the approach. Assurance provides evidence and knowledge that are collected, analysed, and communicated in a systematic, targeted, and meaningful way. Assurance can enable the approach to help successfully deploy technology by ensuring that risk is managed, trust is shared, and value is created.
BCN: Batch Channel Normalization for Image Classification

Normalization techniques have been widely used in the field of deep learning due to their capability of enabling higher learning rates and are less careful in initialization. However, the effectiveness of popular normalization technologies is typically limited to specific areas. Unlike the standard Batch Normalization (BN) and Layer Normalization (LN), where BN computes the mean and variance along the (N,H,W) dimensions and LN computes the mean and variance along the (C,H,W) dimensions (N, C, H and W are the batch, channel, spatial height and width dimension, respectively), this paper presents a novel normalization technique called Batch Channel Normalization (BCN). To exploit both the channel and batch dependence and adaptively and combine the advantages of BN and LN based on specific datasets or tasks, BCN separately normalizes inputs along the (N, H, W) and (C, H, W) axes, then combines the normalized outputs based on adaptive parameters. As a basic block, BCN can be easily integrated into existing models for various applications in the field of computer vision. Empirical results show that the proposed technique can be seamlessly applied to various versions of CNN or Vision Transformer architecture. The code is publicly available at https://github.com/AfifaKhaled/BatchChannel-Normalization
Qubits, entangled states, and quantum gates realized on a set of classical pendulums

Here we show that the concepts behind such terms as entanglement, qubits, quantum gates, quantum error corrections, unitary time evolution etc., which are usually ascribed to quantum systems, can be adequately realized on a set of coupled classical pendulums.
A Recent Survey of Vision Transformers for Medical Image Segmentation

Medical image segmentation plays a crucial role in various healthcare applications, enabling accurate diagnosis, treatment planning, and disease monitoring. Traditionally, convolutional neural networks (CNNs) dominated this domain, excelling at local feature extraction. However, their limitations in capturing long-range dependencies across image regions pose challenges for segmenting complex, interconnected structures often encountered in medical data. In recent years, Vision Transformers (ViTs) have emerged as a promising technique for addressing the challenges in medical image segmentation. Their multi-scale attention mechanism enables effective modeling of long-range dependencies between distant structures, crucial for segmenting organs or lesions spanning the image. Additionally, ViTs' ability to discern subtle pattern heterogeneity allows for the precise delineation of intricate boundaries and edges, a critical aspect of accurate medical image segmentation. However, they do lack image-related inductive bias and translational invariance, potentially impacting their performance. Recently, researchers have come up with various ViT-based approaches that incorporate CNNs in their architectures, known as Hybrid Vision Transformers (HVTs) to capture local correlation in addition to the global information in the images. This survey paper provides a detailed review of the recent advancements in ViTs and HVTs for medical image segmentation. Along with the categorization of ViT and HVT-based medical image segmentation approaches, we also present a detailed overview of their real-time applications in several medical image modalities. This survey may serve as a valuable resource for researchers, healthcare practitioners, and students in understanding the state-of-the-art approaches for ViT-based medical image segmentation.
Hashmarks: Privacy-Preserving Benchmarks for High-Stakes AI Evaluation

There is a growing need to gain insight into language model capabilities that relate to sensitive topics, such as bioterrorism or cyberwarfare. However, traditional open source benchmarks are not fit for the task, due to the associated practice of publishing the correct answers in human-readable form. At the same time, enforcing mandatory closed-quarters evaluations might stifle development and erode trust. In this context, we propose hashmarking, a protocol for evaluating language models in the open without having to disclose the correct answers. In its simplest form, a hashmark is a benchmark whose reference solutions have been cryptographically hashed prior to publication. Following an overview of the proposed evaluation protocol, we go on to assess its resilience against traditional attack vectors (e.g. rainbow table attacks), as well as against failure modes unique to increasingly capable generative models.
PointBeV: A Sparse Approach to BeV Predictions

Bird's-eye View (BeV) representations have emerged as the de-facto shared space in driving applications, offering a unified space for sensor data fusion and supporting various downstream tasks. However, conventional models use grids with fixed resolution and range and face computational inefficiencies due to the uniform allocation of resources across all cells. To address this, we propose PointBeV, a novel sparse BeV segmentation model operating on sparse BeV cells instead of dense grids. This approach offers precise control over memory usage, enabling the use of long temporal contexts and accommodating memory-constrained platforms. PointBeV employs an efficient two-pass strategy for training, enabling focused computation on regions of interest. At inference time, it can be used with various memory/performance trade-offs and flexibly adjusts to new specific use cases. PointBeV achieves state-of-the-art results on the nuScenes dataset for vehicle, pedestrian, and lane segmentation, showcasing superior performance in static and temporal settings despite being trained solely with sparse signals. We will release our code along with two new efficient modules used in the architecture: Sparse Feature Pulling, designed for the effective extraction of features from images to BeV, and Submanifold Attention, which enables efficient temporal modeling. Our code is available at https://github.com/valeoai/PointBeV.
GIFT: Generative Interpretable Fine-Tuning Transformers

We present GIFT (Generative Interpretable Fine-tuning Transformers) for fine-tuning pretrained (often large) Transformer models at downstream tasks in a parameter-efficient way with built-in interpretability. Our GIFT is a deep parameter-residual learning method, which addresses two problems in fine-tuning a pretrained Transformer model: Where to apply the parameter-efficient fine-tuning (PEFT) to be extremely lightweight yet sufficiently expressive, and How to learn the PEFT to better exploit the knowledge of the pretrained model in a direct way? For the former, we select the final projection (linear) layer in the multi-head self-attention of a Transformer model, and verify its effectiveness. For the latter, in contrast to the prior art that directly introduce new model parameters (often in low-rank approximation form) to be learned in fine-tuning with downstream data, we propose a method for learning to generate the fine-tuning parameters. Our GIFT is a hyper-Transformer which take as input the pretrained parameters of the projection layer to generate its fine-tuning parameters using a proposed Parameter-to-Cluster Attention (PaCa). The PaCa results in a simple clustering-based forward explainer that plays the role of semantic segmentation in testing. In experiments, our proposed GIFT is tested on the VTAB benchmark and the fine-grained visual classification (FGVC) benchmark. It obtains significantly better performance than the prior art. Our code is available at https://github.com/savadikarc/gift
Generalized Quantum Singular Value Transformation

The quantum singular value transformation has revolutionised quantum algorithms. By applying a polynomial to an arbitrary matrix, it provides a unifying picture of quantum algorithms. However, polynomials are restricted to definite parity and real coefficients, and finding the circuit (the phase factors) has proven difficult in practice. Recent work has removed these restrictions and enabled faster computation of phase factors, yet only for unitary matrices. Here we propose two generalisations. The generalised quantum singular value transformation allows complex polynomials for arbitrary matrices. For Hermitian matrices, we propose the generalised quantum eigenvalue transformation that even allows polynomials of indefinite parity. While we find that the polynomial might have to be downscaled compared to the quantum singular value transformation, the higher expressivity of polynomials and faster computation of phase factors can sometimes result in advantages. The results are achieved with various block encoding (or projected unitary encoding) techniques, including qubitisation, Hermitianisation, and multiplication. We show how to multiply block-encoded matrices with only one extra qubit, and introduce measure-early multiplication to further avoid the extra qubit and decrease average circuit length.
Gaussian Grouping: Segment and Edit Anything in 3D Scenes

The recent Gaussian Splatting achieves high-quality and real-time novel-view synthesis of the 3D scenes. However, it is solely concentrated on the appearance and geometry modeling, while lacking in fine-grained object-level scene understanding. To address this issue, we propose Gaussian Grouping, which extends Gaussian Splatting to jointly reconstruct and segment anything in open-world 3D scenes. We augment each Gaussian with a compact Identity Encoding, allowing the Gaussians to be grouped according to their object instance or stuff membership in the 3D scene. Instead of resorting to expensive 3D labels, we supervise the Identity Encodings during the differentiable rendering by leveraging the 2D mask predictions by SAM, along with introduced 3D spatial consistency regularization. Comparing to the implicit NeRF representation, we show that the discrete and grouped 3D Gaussians can reconstruct, segment and edit anything in 3D with high visual quality, fine granularity and efficiency. Based on Gaussian Grouping, we further propose a local Gaussian Editing scheme, which shows efficacy in versatile scene editing applications, including 3D object removal, inpainting, colorization and scene recomposition. Our code and models will be at https://github.com/lkeab/gaussian-grouping.
SeaLLMs -- Large Language Models for Southeast Asia

Despite the remarkable achievements of large language models (LLMs) in various tasks, there remains a linguistic bias that favors high-resource languages, such as English, often at the expense of low-resource and regional languages. To address this imbalance, we introduce SeaLLMs, an innovative series of language models that specifically focuses on Southeast Asian (SEA) languages. SeaLLMs are built upon the Llama-2 model and further advanced through continued pre-training with an extended vocabulary, specialized instruction and alignment tuning to better capture the intricacies of regional languages. This allows them to respect and reflect local cultural norms, customs, stylistic preferences, and legal considerations. Our comprehensive evaluation demonstrates that SeaLLM-13b models exhibit superior performance across a wide spectrum of linguistic tasks and assistant-style instruction-following capabilities relative to comparable open-source models. Moreover, they outperform ChatGPT-3.5 in non-Latin languages, such as Thai, Khmer, Lao, and Burmese, by large margins while remaining lightweight and cost-effective to operate.
Crystal: Enhancing Blockchain Mining Transparency with Quorum Certificate

Researchers have discovered a series of theoretical attacks against Bitcoin's Nakamoto consensus; the most damaging ones are selfish mining, double-spending, and consistency delay attacks. These attacks have one common cause: block withholding. This paper proposes Crystal, which leverages quorum certificates to resist block withholding misbehavior. Crystal continuously elects committees from miners and requires each block to have a quorum certificate, i.e., a set of signatures issued by members of its committee. Consequently, an attacker has to publish its blocks to obtain quorum certificates, rendering block withholding impossible. To build Crystal, we design a novel two-round committee election in a Sybil-resistant, unpredictable and non-interactive way, and a reward mechanism to incentivize miners to follow the protocol. Our analysis and evaluations show that Crystal can significantly mitigate selfish mining and double-spending attacks. For example, in Bitcoin, an attacker with 30% of the total computation power will succeed in double-spending attacks with a probability of 15.6% to break the 6-confirmation rule; however, in Crystal, the success probability for the same attacker falls to 0.62%. We provide formal end-to-end safety proofs for Crystal, ensuring no unknown attacks will be introduced. To the best of our knowledge, Crystal is the first protocol that prevents selfish mining and double-spending attacks while providing safety proof.
VideoBooth: Diffusion-based Video Generation with Image Prompts

Text-driven video generation witnesses rapid progress. However, merely using text prompts is not enough to depict the desired subject appearance that accurately aligns with users' intents, especially for customized content creation. In this paper, we study the task of video generation with image prompts, which provide more accurate and direct content control beyond the text prompts. Specifically, we propose a feed-forward framework VideoBooth, with two dedicated designs: 1) We propose to embed image prompts in a coarse-to-fine manner. Coarse visual embeddings from image encoder provide high-level encodings of image prompts, while fine visual embeddings from the proposed attention injection module provide multi-scale and detailed encoding of image prompts. These two complementary embeddings can faithfully capture the desired appearance. 2) In the attention injection module at fine level, multi-scale image prompts are fed into different cross-frame attention layers as additional keys and values. This extra spatial information refines the details in the first frame and then it is propagated to the remaining frames, which maintains temporal consistency. Extensive experiments demonstrate that VideoBooth achieves state-of-the-art performance in generating customized high-quality videos with subjects specified in image prompts. Notably, VideoBooth is a generalizable framework where a single model works for a wide range of image prompts with feed-forward pass.
Hybrid Models for Predicting Cryptocurrency Price Using Financial and Non-Financial Indicators

Cryptocurrency has become very popular and widely used by major businesses as digital currency for online investments and services. However, the price prediction of such digital currencies as Bitcoin and Ethereum is challenging. It involves financial indicators and nonfinancial indicators, such as historical data and social media data, respectively. In this paper, we propose deep learning and hybrid models that effectively incorporate both types of indicators and introduce the optimal algorithms for long-term price prediction of Bitcoin and Ethereum. We conduct extensive experimental evaluations on real data we extracted from financial dataset comprising Yahoo Finance data, and non-financial data consisting of Google Trends data and approximately 30 million related Bitcoin and Ethereum. Our experimental results show that the hybrid models involving LSTM/1D-CNN with ARIMA/ARIMAX outperformed the individual models for the long-term prediction of cryptocurrency prices.
The historian of military libraries! (On A. M. Panchenko’s 65-th anniversary)

The article is timed to coincide with the anniversary of Reserve Colonel Anatoly Mikhailovich Panchenko, a prominent researcher of the history of military libraries, Doctor of Historical Sciences, Associate Professor, Chief Researcher of the Book Science Laboratory of the State Public Scientific and Technical Library of the Siberian Branch of the Russian Academy of Sciences (GPNTB SB RAS). The biography of the hero of the day is briefly reviewed, including the years of his military service, pedagogical and scientific activities in a military educational institution and work in the scientific institution. The author outlines and characterizes the range of Panchenko’s scientific interests reflected in more than 200 of his scientific publications: the history of military library science, scientific book publishing in the post-Soviet period, history of book culture, work with egodocuments, and cultural traditions of Russian officers. The key scientific works in the history of military libraries of the Russian army are characterized. Panchenko’s contribution to the personalization of the history of military librarianship is emphasized. The author focuses in particular on the interest Panchenko takes in the history of the library of the Military Headquarters and General Staff, its structure and development in the pre-revolutionary period, which is evidenced by the bulk of publications: several of these papers coincide with the Library’s various anniversaries. For many years, A. M. Panchenko has cooperated with the Military Historical Library of the General Staff of the Armed Forces of the Russian Federation. The journals that publish his works are named. The personal qualities that enable M. Panchenko to succeed in science are emphasized. The works by A. M. Panchenko have received high appraise among Russian historians of librarianship and librarians.
Utilizing Computational Music Analysis and AI for Enhanced Music Composition: Exploring Pre- and Post-Analysis

This research paper investigates the transformative potential of computational music analysis and artificial intelligence (AI) in advancing the field of music composition. Specifically, it explores the synergistic roles of pre-analysis and post-analysis techniques in leveraging AI-driven tools to enhance the creative process and quality of musical compositions. The study encompasses a historical overview of music composition, the evolution of computational music analysis, and contemporary AI applications. It delves into pre-analysis, focusing on its role in informing composition, and post-analysis, which evaluates and augments compositions. The paper underscores the significance of these technologies in fostering creativity while addressing challenges and ethical considerations. Through case studies, evaluations, and discussions, this research offers insights into the profound impact of computational music analysis and AI on music composition, paving the way for innovative and inclusive musical expressions.&#x0D; &#x0D;
Advancing object recognition in visual blur conditions: a benchmark study

Recent studies have shown that deep neural networks are sensitive to visual blur. This vulnerability raises concerns about the robustness of learning-based methods in safety-critical applications, such as autonomous driving and industrial inspection. Although many methods have demonstrated the potential to enhance the robustness of vision tasks in blurry scenes, a standard benchmark for comparing and validating their effectiveness is still lacking. To address this, we propose a new benchmark for the comprehensive evaluation of visual recognition tasks in blurry scenes. The benchmark dataset includes a large number of scenes with different blur types, enabling multi-dimensional evaluation and comparison of existing methods. The existing methods are integrated into a unified evaluation framework for comparison, and the mechanism of robustness improvement is roughly categorized as reducing image-level shift and distribution-level shift. Through this unified framework, we identify the strengths and weaknesses of current methods in visual recognition tasks under blurry conditions. Furthermore, we propose a prior knowledge-based regularization module that can be easily incorporated into existing methods, which consistently boosts performance on visual recognition tasks in blurry scenes.
A Comparative Study of Intent Classification Performance in Truncated Consumer Communication using GPT-Neo and GPT-2

This study presents a comparative analysis of intent classification performance using two widely used language models, GPT-Neo and GPT-2, in the context of truncated consumer communications. Generative Pre-trained Transformer (GPT) is a machine learning technique that has revolutionized the field of natural language processing (NLP). GPT uses a transformer-based neural network architecture that is pre-trained on large volumes of data to generate highly accurate and versatile NLP models capable of performing various tasks, such as language translation, question-answering, and text summarization. GPT's ability to generate natural language responses that closely resemble those of humans has greatly enhanced the potential of language processing in the future. The data used in this study was provided by Circana. This data is an essential resource as it includes real world consumer purchases, promotional and e-commerce activity from multiple retail markets, as well as related consumer messaging. The model is trained, evaluated and analyzed on their respective accuracies, precision, recall, and F1 scores. The results indicate that GPT- Neo with BCELoss function outperformed GPT-2 in terms of accuracy and F1 score, with a significant improvement in the reduction of false negative values. These findings demonstrate the potential of GPT-Neo for improving intent classification in consumer communication applications with limited text input.
Automatic System for Creating Educational Robotics Courses in the Repo-Educ System

Aiming to reduce the distance from the teachers of propaedeutic subjects to the use of educational robotics in the classroom, we propose the creation of a module in the Repo-Educ system, an automatic system for the formation of educational robotics courses. This module will be able to help teachers who wants to start working on a certain subject of the Common National Curricular Base, proposing a robotics course for the chosen subject and auxiliary subjects. Through these courses, teachers will have access to the lesson plans already registered in the system, which have a complete explanation of the robot’s assembly, programming, and use. It is hoped that the teacher will learn how to manage and use robotics in the classroom, adding value and knowledge to his or her students.