OpenRaiser/Intern-Atlas huggingface dataset:
Human-Robot Interface for Teleoperated Robotized Planetary Sample Collection and Assembly
Venue: arXiv cs.RO 2024
Year: 2024

As human space exploration evolves toward longer voyages farther from our home planet, in-situ resource utilization (ISRU) becomes increasingly important. Haptic teleoperations are one of the technologies by which such activities can be carried out remotely by humans, whose expertise is still necessary for complex activities. In order to perform precision tasks with effectiveness, the operator must experience ease of use and accuracy. The same features are demanded to reduce the complexity of the training procedures and the associated learning time for operators without a specific background in robotic teleoperations. Haptic teleoperation systems, that allow for a natural feeling of forces, need to cope with the trade-off between accurate movements and workspace extension. Clearly, both of them are required for typical ISRU tasks. In this work, we develop a new concept of operations and suitable human-robot interfaces to achieve sample collection and assembly with ease of use and accuracy. In the proposed operational concept, the teleoperation space is extended by executing automated trajectories, offline planned at the control station. In three different experimental scenarios, we validate the end-to-end system involving the control station and the robotic asset, by assessing the contribution of haptics to mission success, the system robustness to consistent delays, and the ease of training new operators.
Weaponizing Disinformation Against Critical Infrastructures
Venue: arXiv cs.CR 2024
Year: 2024

For nearly a decade, disinformation has dominated social debates, with its harmful impacts growing more evident. Episodes like the January 6 United States Capitol attack and the Rohingya genocide exemplify how this phenomenon has been weaponized. While considerable attention has been paid to its impact on societal discourse and minority persecution, there remains a gap in analyzing its role as a malicious hybrid tool targeting critical infrastructures. This article addresses this gap by presenting three case studies: a hypothetical scenario involving the electric grid, an attack on traffic management, and XZ Utils backdoor. Additionally, the study undertakes a criminological analysis to comprehend the criminal profiles driving such attacks, while also assessing their implications from a human rights perspective. The research findings highlight the necessity for comprehensive mitigation strategies encompassing technical solutions and crime prevention measures in order to safeguard critical infrastructures against these emerging threats.
The Behavior of Tree-Width and Path-Width under Graph Operations and Graph Transformations
Venue: arXiv cs.DS 2024
Year: 2024

Tree-width and path-width are well-known graph parameters. Many NP-hard graph problems allow polynomial-time solutions, when restricted to graphs of bounded tree-width or bounded path-width. In this work, we study the behavior of tree-width and path-width under various unary and binary graph transformations. Doing so, for considered transformations we provide upper and lower bounds for the tree-width and path-width of the resulting graph in terms of the tree-width and path-width of the initial graphs or argue why such bounds are impossible to specify. Among the studied, unary transformations are vertex addition, vertex deletion, edge addition, edge deletion, subgraphs, vertex identification, edge contraction, edge subdivision, minors, powers of graphs, line graphs, edge complements, local complements, Seidel switching, and Seidel complementation. Among the studied, binary transformations we consider the disjoint union, join, union, substitution, graph product, 1-sum, and corona of two graphs.
Introducing Brain-like Concepts to Embodied Hand-crafted Dialog Management System
Venue: arXiv cs.AI 2024
Year: 2024

Along with the development of chatbot, language models and speech technologies, there is a growing possibility and interest of creating systems able to interface with humans seamlessly through natural language or directly via speech. In this paper, we want to demonstrate that placing the research on dialog system in the broader context of embodied intelligence allows to introduce concepts taken from neurobiology and neuropsychology to define behavior architecture that reconcile hand-crafted design and artificial neural network and open the gate to future new learning approaches like imitation or learning by instruction. To do so, this paper presents a neural behavior engine that allows creation of mixed initiative dialog and action generation based on hand-crafted models using a graphical language. A demonstration of the usability of such brain-like inspired architecture together with a graphical dialog model is described through a virtual receptionist application running on a semi-public space.
A Passwordless MFA Utlizing Biometrics, Proximity and Contactless Communication
Venue: arXiv cs.CR 2024
Year: 2024

Despite being more secure and strongly promoted, two-factor (2FA) or multi-factor (MFA) schemes either fail to protect against recent phishing threats such as real-time MITM, controls/relay MITM, malicious browser extension-based phishing attacks, and/or need the users to purchase and carry other hardware for additional account protection. Leveraging the unprecedented popularity of NFC and BLE-enabled smartphones, we explore a new horizon for designing an MFA scheme. This paper introduces an advanced authentication method for user verification that utilizes the user's real-time facial biometric identity, which serves as an inherent factor, together with BLE- NFC-enabled mobile devices, which operate as an ownership factor. We have implemented a prototype authentication system on a BLE-NFC-enabled Android device, and initial threat modeling suggests that it is safe against known phishing attacks. The scheme has been compared with other popular schemes using the Bonneau et al. assessment framework in terms of usability, deployability, and security.
Bayesian Statistical Modeling with Predictors from LLMs
Venue: arXiv cs.CL 2024
Year: 2024

State of the art large language models (LLMs) have shown impressive performance on a variety of benchmark tasks and are increasingly used as components in larger applications, where LLM-based predictions serve as proxies for human judgements or decision. This raises questions about the human-likeness of LLM-derived information, alignment with human intuition, and whether LLMs could possibly be considered (parts of) explanatory models of (aspects of) human cognition or language use. To shed more light on these issues, we here investigate the human-likeness of LLMs' predictions for multiple-choice decision tasks from the perspective of Bayesian statistical modeling. Using human data from a forced-choice experiment on pragmatic language use, we find that LLMs do not capture the variance in the human data at the item-level. We suggest different ways of deriving full distributional predictions from LLMs for aggregate, condition-level data, and find that some, but not all ways of obtaining condition-level predictions yield adequate fits to human data. These results suggests that assessment of LLM performance depends strongly on seemingly subtle choices in methodology, and that LLMs are at best predictors of human behavior at the aggregate, condition-level, for which they are, however, not designed to, or usually used to, make predictions in the first place.
How Decentralization Affects User Agency on Social Platforms
Venue: arXiv cs.CY 2024
Year: 2024

Mainstream social media platforms function as "walled garden" ecosystems that restrict user agency, control, and data portability. They have demonstrated a lack of transparency that contributes to a multitude of online harms. Our research investigates how decentralization might present promise as an alternative model to walled garden platforms. Specifically, we describe the user-driven content moderation through blocks as an expression of agency on Bluesky, a decentralized social platform. We examine the impact of providing users with more granular control over their online experiences, including what they post, who can see it, and whose content they are exposed to. We describe the patterns identified in user-driven content moderation and suggest directions for further research.
FacEnhance: Facial Expression Enhancing with Recurrent DDPMs
Venue: arXiv cs.CV 2024
Year: 2024

Facial expressions, vital in non-verbal human communication, have found applications in various computer vision fields like virtual reality, gaming, and emotional AI assistants. Despite advancements, many facial expression generation models encounter challenges such as low resolution (e.g., 32x32 or 64x64 pixels), poor quality, and the absence of background details. In this paper, we introduce FacEnhance, a novel diffusion-based approach addressing constraints in existing low-resolution facial expression generation models. FacEnhance enhances low-resolution facial expression videos (64x64 pixels) to higher resolutions (192x192 pixels), incorporating background details and improving overall quality. Leveraging conditional denoising within a diffusion framework, guided by a background-free low-resolution video and a single neutral expression high-resolution image, FacEnhance generates a video incorporating the facial expression from the low-resolution video performed by the individual with background from the neutral image. By complementing lightweight low-resolution models, FacEnhance strikes a balance between computational efficiency and desirable image resolution and quality. Extensive experiments on the MUG facial expression database demonstrate the efficacy of FacEnhance in enhancing low-resolution model outputs to state-of-the-art quality while preserving content and identity consistency. FacEnhance represents significant progress towards resource-efficient, high-fidelity facial expression generation, Renewing outdated low-resolution methods to up-to-date standards.
Engineering Digital Systems for Humanity: Challenges and Opportunities
Venue: arXiv cs.SE 2024
Year: 2024

As testified by new regulations like the European AI act, the worries about the societal impact of (autonomous) software technologies are becoming of public concern. Social and human values, besides the traditional software behaviour and quality, are increasingly recognized as important for sustainability and long-term well-being. In this paper, we identify the macro and technological challenges and opportunities of present and future digital systems that should be engineered for humanity. Our specific perspective in identifying the challenges is to focus on humans and on their role in their co-existence with digital systems. The first challenge considers humans in a proactive role when interacting with the digital systems, i.e., taking initiative in making things happening instead of reacting to events. The second concerns humans having an active role in the interaction with the digital systems i.e., on humans that interact with digital systems as a reaction to events. The third challenge focuses on humans that have a passive role i.e., they experience, enjoy or even suffer the decisions and/or actions of digital systems. Two further transversal challenges are considered: the duality of trust and trustworthiness and the compliance to legislation that both may seriously affect the deployment and use of digital systems.
ONNX-to-Hardware Design Flow for Adaptive Neural-Network Inference on FPGAs
Venue: arXiv cs.AR 2024
Year: 2024

The challenges involved in executing neural networks (NNs) at the edge include providing diversity, flexibility, and sustainability. That implies, for instance, supporting evolving applications and algorithms energy-efficiently. Using hardware or software accelerators can deliver fast and efficient computation of the NNs, while flexibility can be exploited to support long-term adaptivity. Nonetheless, handcrafting an NN for a specific device, despite the possibility of leading to an optimal solution, takes time and experience, and that's why frameworks for hardware accelerators are being developed. This work, starting from a preliminary semi-integrated ONNX-to-hardware toolchain [21], focuses on enabling approximate computing leveraging the distinctive ability of the original toolchain to favor adaptivity. The goal is to allow lightweight adaptable NN inference on FPGAs at the edge.
A Symbolic Computing Perspective on Software Systems
Venue: arXiv cs.SC 2024
Year: 2024

Symbolic mathematical computing systems have served as a canary in the coal mine of software systems for more than sixty years. They have introduced or have been early adopters of programming language ideas such ideas as dynamic memory management, arbitrary precision arithmetic and dependent types. These systems have the feature of being highly complex while at the same time operating in a domain where results are well-defined and clearly verifiable. These software systems span multiple layers of abstraction with concerns ranging from instruction scheduling and cache pressure up to algorithmic complexity of constructions in algebraic geometry. All of the major symbolic mathematical computing systems include low-level code for arithmetic, memory management and other primitives, a compiler or interpreter for a bespoke programming language, a library of high level mathematical algorithms, and some form of user interface. Each of these parts invokes multiple deep issues. We present some lessons learned from this environment and free flowing opinions on topics including: * Portability of software across architectures and decades; * Infrastructure to embrace and infrastructure to avoid; * Choosing base abstractions upon which to build; * How to get the most out of a small code base; * How developments in compilers both to optimise and to validate code have always been and remain of critical importance, with plenty of remaining challenges; * The way in which individuals including in particular Alan Mycroft who has been able to span from hand-crafting Z80 machine code up to the most abstruse high level code analysis techniques are needed, and * Why it is important to teach full-stack thinking to the next generation.
A nA-Range Area-Efficient Sub-100-ppm/°C Peaking Current Reference Using Forward Body Biasing in 0.11-$μ$m Bulk and 22-nm FD-SOI
Venue: arXiv cs.AR 2024
Year: 2024

In recent years, the development of the Internet of Things (IoT) has prompted the search for nA-range current references that are simultaneously constrained to a small area and robust to process, voltage and temperature variations. Yet, such references have remained elusive, as existing architectures fail to reach a low temperature coefficient (TC) while minimizing silicon area. In this work, we propose a nA-range constant-with-temperature (CWT) peaking current reference, in which a resistor is biased by the threshold voltage difference between two transistors in weak inversion. This bias voltage is lower than in conventional architectures to cut down the silicon area occupied by the resistor and is obtained by forward body biasing one of the two transistors with an ultra-low-power voltage reference so as to reduce its threshold voltage. In addition, the proposed reference includes a circuit to suppress the leakage of parasitic diodes at high temperature, and two simple trimming mechanisms for the reference current and its TC. As the proposed design relies on the body effect, it has been validated in both 0.11-$μ$m bulk and 22-nm fully-depleted silicon-on-insulator, to demonstrate feasibility across different technology types. In post-layout simulation, the 0.11-$μ$m design generates a 5-nA current with a 65-ppm/°C TC and a 2.84-%/V line sensitivity (LS), while in measurement, the 22-nm design achieves a 1.5-nA current with an 89-ppm/°C TC and a 0.51-%/V LS. As a result of the low resistor bias voltage, the proposed references occupy a silicon area of 0.00954 mm$^2$ in 0.11 $μ$m (resp. 0.00214 mm$^2$ in 22 nm) at least 1.8$\times$ (resp. 8.2$\times$) smaller than fabricated nA-range CWT references, but with a TC improved by 6.1$\times$ (resp. 4.4$\times$).
Optimal Control of Agent-Based Dynamics under Deep Galerkin Feedback Laws
Venue: arXiv cs.LG 2024
Year: 2024

Ever since the concepts of dynamic programming were introduced, one of the most difficult challenges has been to adequately address high-dimensional control problems. With growing dimensionality, the utilisation of Deep Neural Networks promises to circumvent the issue of an otherwise exponentially increasing complexity. The paper specifically investigates the sampling issues the Deep Galerkin Method is subjected to. It proposes a drift relaxation-based sampling approach to alleviate the symptoms of high-variance policy approximations. This is validated on mean-field control problems; namely, the variations of the opinion dynamics presented by the Sznajd and the Hegselmann-Krause model. The resulting policies induce a significant cost reduction over manually optimised control functions and show improvements on the Linear-Quadratic Regulator problem over the Deep FBSDE approach.
Beyond the Frontier: Predicting Unseen Walls from Occupancy Grids by Learning from Floor Plans
Venue: arXiv cs.RO 2024
Year: 2024

In this paper, we tackle the challenge of predicting the unseen walls of a partially observed environment as a set of 2D line segments, conditioned on occupancy grids integrated along the trajectory of a 360° LIDAR sensor. A dataset of such occupancy grids and their corresponding target wall segments is collected by navigating a virtual robot between a set of randomly sampled waypoints in a collection of office-scale floor plans from a university campus. The line segment prediction task is formulated as an autoregressive sequence prediction task, and an attention-based deep network is trained on the dataset. The sequence-based autoregressive formulation is evaluated through predicted information gain, as in frontier-based autonomous exploration, demonstrating significant improvements over both non-predictive estimation and convolution-based image prediction found in the literature. Ablations on key components are evaluated, as well as sensor range and the occupancy grid's metric area. Finally, model generality is validated by predicting walls in a novel floor plan reconstructed on-the-fly in a real-world office environment.
CLIP-Driven Cloth-Agnostic Feature Learning for Cloth-Changing Person Re-Identification
Venue: arXiv cs.CV 2024
Year: 2024

Contrastive Language-Image Pre-Training (CLIP) has shown impressive performance in short-term Person Re-Identification (ReID) due to its ability to extract high-level semantic features of pedestrians, yet its direct application to Cloth-Changing Person Re-Identification (CC-ReID) faces challenges due to CLIP's image encoder overly focusing on clothes clues. To address this, we propose a novel framework called CLIP-Driven Cloth-Agnostic Feature Learning (CCAF) for CC-ReID. Accordingly, two modules were custom-designed: the Invariant Feature Prompting (IFP) and the Clothes Feature Minimization (CFM). These modules guide the model to extract cloth-agnostic features positively and attenuate clothes-related features negatively. Specifically, IFP is designed to extract fine-grained semantic features unrelated to clothes from the raw image, guided by the cloth-agnostic text prompts. This module first covers the clothes in the raw image at the pixel level to obtain the shielding image and then utilizes CLIP's knowledge to generate cloth-agnostic text prompts. Subsequently, it aligns the raw image-text and the raw image-shielding image in the feature space, emphasizing discriminative clues related to identity but unrelated to clothes. Furthermore, CFM is designed to examine and weaken the image encoder's ability to extract clothes features. It first generates text prompts corresponding to clothes pixels. Then, guided by these clothes text prompts, it iteratively examines and disentangles clothes features from pedestrian features, ultimately retaining inherent discriminative features. Extensive experiments have demonstrated the effectiveness of the proposed CCAF, achieving new state-of-the-art performance on several popular CC-ReID benchmarks without any additional inference time.
Python-based DSL for generating Verilog model of Synchronous Digital Circuits
Venue: arXiv cs.AR 2024
Year: 2024

We have designed a Python-based Domain Specific Language (DSL) for modeling synchronous digital circuits. In this DSL, hardware is modeled as a collection of transactions -- running in series, parallel, and loops. When the model is executed by a Python interpreter, synthesizable and behavioural Verilog is generated as output, which can be integrated with other RTL designs or directly used for FPGA and ASIC flows. In this paper, we describe - 1) the language (DSL), which allows users to express computation in series/parallel/loop constructs, with explicit cycle boundaries, 2) the internals of a simple Python implementation to produce synthesizable Verilog, and 3) several design examples and case studies for applications in post-quantum cryptography, stereo-vision, digital signal processing and optimization techniques. In the end, we list ideas to extend this framework.
Applying Multi-Agent Negotiation to Solve the Production Routing Problem With Privacy Preserving
Venue: arXiv cs.AI 2024
Year: 2024

This paper presents a novel approach to address the Production Routing Problem with Privacy Preserving (PRPPP) in supply chain optimization. The integrated optimization of production, inventory, distribution, and routing decisions in real-world industry applications poses several challenges, including increased complexity, discrepancies between planning and execution, and constraints on information sharing. To mitigate these challenges, this paper proposes the use of intelligent agent negotiation within a hybrid Multi-Agent System (MAS) integrated with optimization algorithms. The MAS facilitates communication and coordination among entities, encapsulates private information, and enables negotiation. This, along with optimization algorithms, makes it a compelling framework for establishing optimal solutions. The approach is supported by real-world applications and synergies between MAS and optimization methods, demonstrating its effectiveness in addressing complex supply chain optimization problems.
MGRQ: Post-Training Quantization For Vision Transformer With Mixed Granularity Reconstruction
Venue: arXiv cs.CV 2024
Year: 2024

Post-training quantization (PTQ) efficiently compresses vision models, but unfortunately, it accompanies a certain degree of accuracy degradation. Reconstruction methods aim to enhance model performance by narrowing the gap between the quantized model and the full-precision model, often yielding promising results. However, efforts to significantly improve the performance of PTQ through reconstruction in the Vision Transformer (ViT) have shown limited efficacy. In this paper, we conduct a thorough analysis of the reasons for this limited effectiveness and propose MGRQ (Mixed Granularity Reconstruction Quantization) as a solution to address this issue. Unlike previous reconstruction schemes, MGRQ introduces a mixed granularity reconstruction approach. Specifically, MGRQ enhances the performance of PTQ by introducing Extra-Block Global Supervision and Intra-Block Local Supervision, building upon Optimized Block-wise Reconstruction. Extra-Block Global Supervision considers the relationship between block outputs and the model's output, aiding block-wise reconstruction through global supervision. Meanwhile, Intra-Block Local Supervision reduces generalization errors by aligning the distribution of outputs at each layer within a block. Subsequently, MGRQ is further optimized for reconstruction through Mixed Granularity Loss Fusion. Extensive experiments conducted on various ViT models illustrate the effectiveness of MGRQ. Notably, MGRQ demonstrates robust performance in low-bit quantization, thereby enhancing the practicality of the quantized model.