OpenRaiser/Intern-Atlas huggingface dataset:
Towards Energy-Efficiency by Navigating the Trilemma of Energy, Latency, and Accuracy
Venue: arXiv cs.CV 2024
Year: 2024

Extended Reality (XR) enables immersive experiences through untethered headsets but suffers from stringent battery and resource constraints. Energy-efficient design is crucial to ensure both longevity and high performance in XR devices. However, latency and accuracy are often prioritized over energy, leading to a gap in achieving energy efficiency. This paper examines scene reconstruction, a key building block for immersive XR experiences, and demonstrates how energy efficiency can be achieved by navigating the trilemma of energy, latency, and accuracy. We explore three classes of energy-oriented optimizations, covering the algorithm, execution, and data, that reveal a broad design space through configurable parameters. Our resulting 72 designs expose a wide range of latency and energy trade-offs, with a smaller range of accuracy loss. We identify a Pareto-optimal curve and show that the designs on the curve are achievable only through synergistic co-optimization of all three optimization classes and by considering the latency and accuracy needs of downstream scene reconstruction consumers. Our analysis covering various use cases and measurements on an embedded class system shows that, relative to the baseline, our designs offer energy benefits of up to 60X with potential latency range of 4X slowdown to 2X speedup. Detailed exploration of a use case across representative data sequences from ScanNet showed about 25X energy savings with 1.5X latency reduction and negligible reconstruction quality loss.
Exploring User Privacy Awareness on GitHub: An Empirical Study
Venue: arXiv cs.CR 2024
Year: 2024

GitHub provides developers with a practical way to distribute source code and collaboratively work on common projects. To enhance account security and privacy, GitHub allows its users to manage access permissions, review audit logs, and enable two-factor authentication. However, despite the endless effort, the platform still faces various issues related to the privacy of its users. This paper presents an empirical study delving into the GitHub ecosystem. Our focus is on investigating the utilization of privacy settings on the platform and identifying various types of sensitive information disclosed by users. Leveraging a dataset comprising 6,132 developers, we report and analyze their activities by means of comments on pull requests. Our findings indicate an active engagement by users with the available privacy settings on GitHub. Notably, we observe the disclosure of different forms of private information within pull request comments. This observation has prompted our exploration into sensitivity detection using a large language model and BERT, to pave the way for a personalized privacy assistant. Our work provides insights into the utilization of existing privacy protection tools, such as privacy settings, along with their inherent limitations. Essentially, we aim to advance research in this field by providing both the motivation for creating such privacy protection tools and a proposed methodology for personalizing them.
An Argumentative Approach for Explaining Preemption in Soft-Constraint Based Norms
Venue: arXiv cs.AI 2024
Year: 2024

Although various aspects of soft-constraint based norms have been explored, it is still challenging to understand preemption. Preemption is a situation where higher-level norms override lower-level norms when new information emerges. To address this, we propose a derivation state argumentation framework (DSA-framework). DSA-framework incorporates derivation states to explain how preemption arises based on evolving situational knowledge. Based on DSA-framework, we present an argumentative approach for explaining preemption. We formally prove that, under local optimality, DSA-framework can provide explanations why one consequence is obligatory or forbidden by soft-constraint based norms represented as logical constraint hierarchies.
Site-Specific Color Features of Green Coffee Beans
Venue: arXiv cs.CV 2024
Year: 2024

Coffee is one of the most valuable primary commodities. Despite this, the common selection technique of green coffee beans relies on personnel visual inspection, which is labor-intensive and subjective. Therefore, an efficient way to evaluate the quality of beans is needed. In this paper, we demonstrate a site-independent approach to find site-specific color features of the seed coat in qualified green coffee beans. We then propose two evaluation schemes for green coffee beans based on this site-specific color feature of qualified beans. Due to the site-specific properties of these color features, machine learning classifiers indicate that compared with the existing evaluation schemes of beans, our evaluation schemes have the advantages of being simple, having less computational costs, and having universal applicability. Finally, this site-specific color feature can distinguish qualified beans from different growing sites. Moreover, this function can prevent cheating in the coffee business and is unique to our evaluation scheme of beans.
Do Android App Developers Accurately Report Collection of Privacy-Related Data?
Venue: arXiv cs.CR 2024
Year: 2024

Many Android applications collect data from users. The European Union's General Data Protection Regulation (GDPR) requires vendors to faithfully disclose which data their apps collect. This task is complicated because many apps use third-party code for which the same information is not readily available. Hence we ask: how accurately do current Android apps fulfill these requirements? In this work, we first expose a multi-layered definition of privacy-related data to correctly report data collection in Android apps. We further create a dataset of privacy-sensitive data classes that may be used as input by an Android app. This dataset takes into account data collected both through the user interface and system APIs. We manually examine the data safety sections of 70 Android apps to observe how data collection is reported, identifying instances of over- and under-reporting. Additionally, we develop a prototype to statically extract and label privacy-related data collected via app source code, user interfaces, and permissions. Comparing the prototype's results with the data safety sections of 20 apps reveals reporting discrepancies. Using the results from two Messaging and Social Media apps (Signal and Instagram), we discuss how app developers under-report and over-report data collection, respectively, and identify inaccurately reported data categories. Our results show that app developers struggle to accurately report data collection, either due to Google's abstract definition of collected data or insufficient existing tool support.
Towards Measuring Sell Side Outcomes in Buy Side Marketplace Experiments using In-Experiment Bipartite Graph
Venue: arXiv cs.LG 2024
Year: 2024

In this study, we evaluate causal inference estimators for online controlled bipartite graph experiments in a real marketplace setting. Our novel contribution is constructing a bipartite graph using in-experiment data, rather than relying on prior knowledge or historical data, the common approach in the literature published to date. We build the bipartite graph from various interactions between buyers and sellers in the marketplace, establishing a novel research direction at the intersection of bipartite experiments and mediation analysis. This approach is crucial for modern marketplaces aiming to evaluate seller-side causal effects in buyer-side experiments, or vice versa. We demonstrate our method using historical buyer-side experiments conducted at Vinted, the largest second-hand marketplace in Europe with over 80M users.
Introducing Gating and Context into Temporal Action Detection
Venue: arXiv cs.CV 2024
Year: 2024

Temporal Action Detection (TAD), the task of localizing and classifying actions in untrimmed video, remains challenging due to action overlaps and variable action durations. Recent findings suggest that TAD performance is dependent on the structural design of transformers rather than on the self-attention mechanism. Building on this insight, we propose a refined feature extraction process through lightweight, yet effective operations. First, we employ a local branch that employs parallel convolutions with varying window sizes to capture both fine-grained and coarse-grained temporal features. This branch incorporates a gating mechanism to select the most relevant features. Second, we introduce a context branch that uses boundary frames as key-value pairs to analyze their relationship with the central frame through cross-attention. The proposed method captures temporal dependencies and improves contextual understanding. Evaluations of the gating mechanism and context branch on challenging datasets (THUMOS14 and EPIC-KITCHEN 100) show a consistent improvement over the baseline and existing methods.
FPT Algorithms using Minimal Parameters for a Generalized Version of Maximin Shares
Venue: arXiv cs.DS 2024
Year: 2024

We study the computational complexity of fairly allocating indivisible, mixed-manna items. For basic measures of fairness, this problem is hard in general. Thus, research has flourished concerning input classes where efficient algorithms exist, both for the purpose of establishing theoretical boundaries and for the purpose of designing practical algorithms for real-world instances. Notably, the paradigm of fixed-parameter tractability (FPT) has lead to new insights and improved algorithms for a variety of fair allocation problems; see, for example, Bleim et al. (IJCAI 16), Aziz et al. (AAAI 17), Bredereck et al. (EC 19) and Kulkarni et al. (EC 21). Our focus is the fairness measure maximin shares (MMS). Motivated by the general non-existence of MMS allocations, Aziz et al. (AAAI 17) studied optimal MMS allocations, namely solutions that achieve the best $α$-approximation for the maximin share value of every agent. These allocations are guaranteed to exist, prompting the important open question of whether optimal MMS allocations can be computed efficiently. We answer this question affirmatively by providing FPT algorithms to output optimal MMS allocations. Furthermore, our techniques extend to find allocations that optimize alternative objectives, such as minimizing the additive approximation, and maximizing some variants of global welfare. In fact, all our algorithms are designed for a more general MMS problem in machine scheduling. Here, each mixed-manna item (job) must be assigned to an agent (machine) and has a processing time and a deadline. We develop efficient algorithms running in FPT time. Formally, we present polynomial time algorithms w.r.t. the input size times some function dependent on the parameters that yield optimal maximin-value approximations (among others) when parameterized by a set of natural parameters
RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning
Venue: arXiv cs.CV 2024
Year: 2024

The Segment Anything Model 2 (SAM2) has recently demonstrated exceptional performance in zero-shot prompt segmentation for natural images and videos. However, when the propagation mechanism of SAM2 is applied to medical images, it often results in spatial inconsistencies, leading to significantly different segmentation outcomes for very similar images. In this paper, we introduce RevSAM2, a simple yet effective self-correction framework that enables SAM2 to achieve superior performance in unseen 3D medical image segmentation tasks without the need for fine-tuning. Specifically, to segment a 3D query volume using a limited number of support image-label pairs that define a new segmentation task, we propose reverse propagation strategy as a query information selection mechanism. Instead of simply maintaining a first-in-first-out (FIFO) queue of memories to predict query slices sequentially, reverse propagation selects high-quality query information by leveraging support images to evaluate the quality of each predicted query slice mask. The selected high-quality masks are then used as prompts to propagate across the entire query volume, thereby enhancing generalization to unseen tasks. Notably, we are the first to explore the potential of SAM2 in label-efficient medical image segmentation without fine-tuning. Compared to fine-tuning on large labeled datasets, the label-efficient scenario provides a cost-effective alternative for medical segmentation tasks, particularly for rare diseases or when dealing with unseen classes. Experiments on four public datasets demonstrate the superiority of RevSAM2 in scenarios with limited labels, surpassing state-of-the-arts by 12.18% in Dice. The code will be released.
Computer-Generated Sand Mixtures and Sand-based Images
Venue: arXiv cs.CV 2024
Year: 2024

This paper aims to verify the effectiveness of the software implementation of the proposed algorithm in creating computer-generated images of sand mixtures using a photograph of sand as an input and its effectiveness in converting digital pictures into sand-based images out of the mixtures it generated. The method of this paper is to visually compare the photographed image of the actual mixtures to its computer-generated counterpart to verify if the mixture generation produces results as expected and compare the computer-generated sand-based images with its source to verify image reproduction maintains same image content. The results of the mixture comparison shows that the actual and the computer-generated ones have similar overall shade and color. Still, the generated one has a rougher texture and higher contrast due to the method of inheriting visual features by pixel, not by individual sand particles. The comparison of the sand-based image and its source has demonstrated the software's ability to maintain the essence of its contents during conversion while replacing its texture with the visual properties of the generated sand mixture. The result have shown that the software implementation of the proposed algorithm can effectively use the images of sand to generate images of its mixtures and use those mixture images to convert a digital picture into a computer-generated sand-based image.
A naive aggregation algorithm for improving generalization in a class of learning problems
Venue: arXiv cs.LG 2024
Year: 2024

In this brief paper, we present a naive aggregation algorithm for a typical learning problem with expert advice setting, in which the task of improving generalization, i.e., model validation, is embedded in the learning process as a sequential decision-making problem. In particular, we consider a class of learning problem of point estimations for modeling high-dimensional nonlinear functions, where a group of experts update their parameter estimates using the discrete-time version of gradient systems, with small additive noise term, guided by the corresponding subsample datasets obtained from the original dataset. Here, our main objective is to provide conditions under which such an algorithm will sequentially determine a set of mixing distribution strategies used for aggregating the experts' estimates that ultimately leading to an optimal parameter estimate, i.e., as a consensus solution for all experts, which is better than any individual expert's estimate in terms of improved generalization or learning performances. Finally, as part of this work, we present some numerical results for a typical case of nonlinear regression problem.
Connectivity-Inspired Network for Context-Aware Recognition
Venue: arXiv cs.CV 2024
Year: 2024

The aim of this paper is threefold. We inform the AI practitioner about the human visual system with an extensive literature review; we propose a novel biologically motivated neural network for image classification; and, finally, we present a new plug-and-play module to model context awareness. We focus on the effect of incorporating circuit motifs found in biological brains to address visual recognition. Our convolutional architecture is inspired by the connectivity of human cortical and subcortical streams, and we implement bottom-up and top-down modulations that mimic the extensive afferent and efferent connections between visual and cognitive areas. Our Contextual Attention Block is simple and effective and can be integrated with any feed-forward neural network. It infers weights that multiply the feature maps according to their causal influence on the scene, modeling the co-occurrence of different objects in the image. We place our module at different bottlenecks to infuse a hierarchical context awareness into the model. We validated our proposals through image classification experiments on benchmark data and found a consistent improvement in performance and the robustness of the produced explanations via class activation. Our code is available at https://github.com/gianlucarloni/CoCoReco.
Hybrid Spiking Neural Networks for Low-Power Intra-Cortical Brain-Machine Interfaces
Venue: arXiv cs.LG 2024
Year: 2024

Intra-cortical brain-machine interfaces (iBMIs) have the potential to dramatically improve the lives of people with paraplegia by restoring their ability to perform daily activities. However, current iBMIs suffer from scalability and mobility limitations due to bulky hardware and wiring. Wireless iBMIs offer a solution but are constrained by a limited data rate. To overcome this challenge, we are investigating hybrid spiking neural networks for embedded neural decoding in wireless iBMIs. The networks consist of a temporal convolution-based compression followed by recurrent processing and a final interpolation back to the original sequence length. As recurrent units, we explore gated recurrent units (GRUs), leaky integrate-and-fire (LIF) neurons, and a combination of both - spiking GRUs (sGRUs) and analyze their differences in terms of accuracy, footprint, and activation sparsity. To that end, we train decoders on the "Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology" dataset and evaluate it using the NeuroBench framework, targeting both tracks of the IEEE BioCAS Grand Challenge on Neural Decoding. Our approach achieves high accuracy in predicting velocities of primate reaching movements from multichannel primary motor cortex recordings while maintaining a low number of synaptic operations, surpassing the current baseline models in the NeuroBench framework. This work highlights the potential of hybrid neural networks to facilitate wireless iBMIs with high decoding precision and a substantial increase in the number of monitored neurons, paving the way toward more advanced neuroprosthetic technologies.
ARSecure: A Novel End-to-End Encryption Messaging System Using Augmented Reality
Venue: arXiv cs.CR 2024
Year: 2024

End-to-End Encryption (E2EE) ensures that only the intended recipient(s) can read messages. Popular instant messaging (IM) applications such as Signal, WhatsApp, Apple's iMessage, and Telegram claim to offer E2EE. However, client-side scanning (CSS) undermines these claims by scanning all messages, including text, images, audio, and video files, on both sending and receiving ends. Industry and government parties support CSS to combat harmful content such as child pornography, terrorism, and other illegal activities. In this paper, we introduce ARSecure, a novel end-to-end encryption messaging solution utilizing augmented reality glasses. ARSecure allows users to encrypt and decrypt their messages before they reach their phone devices, effectively countering the CSS technology in E2EE systems.
Towards Understanding and Applying Security Assurance Cases for Automotive Systems
Venue: arXiv cs.CR 2024
Year: 2024

Security Assurance Cases (SAC) are structured bodies of arguments and evidence used to reason about security properties of a certain artefact. SAC are gaining focus in the automotive domain as the need for security assurance is growing due to software becoming a main part of vehicles. Market demands for new services and products in the domain require connectivity, and hence, raise security concerns. Regulators and standardisation bodies started recently to require a structured for security assurance of products in the automotive domain, and automotive companies started, hence, to study ways to create and maintain these cases, as well as adopting them in their current way of working. In order to facilitate the adoption of SAC in the automotive domain, we created CASCADE, an approach for creating SAC which have integrated quality assurance and are compliant with the requirements of ISO/SAE-21434, the upcoming cybersecurity standard for automotive systems. CASCADE was created by conducting design science research study in two iterative cycles. The design decisions of CASCADE are based on insights from a qualitative research study which includes a workshop, a survey, and one-to-one interviews, done in collaboration with our industrial partners about the needs and drivers of work in SAC in industry, and a systematic literature review in which we identified gaps between the industrial needs and the state of the art. The evaluation of CASCADE was done with help of security experts from a large automotive OEM. It showed that CASCADE is suitable for integration in industrial product development processes. Additionally, our results show that the elements of CASCADE align well with respect to the way of working at the company, and has the potential to scale to cover the requirements and needs of the company with its large organization and complex products
ConVer-G: Concurrent versioning of knowledge graphs
Venue: arXiv cs.DB 2024
Year: 2024

The multiplication of platforms offering open data has facilitated access to information that can be used for research, innovation, and decision-making. Providing transparency and availability, open data is regularly updated, allowing us to observe their evolution over time. We are particularly interested in the evolution of urban data that allows stakeholders to better understand dynamics and propose solutions to improve the quality of life of citizens. In this context, we are interested in the management of evolving data, especially urban data and the ability to query these data across the available versions. In order to have the ability to understand our urban heritage and propose new scenarios, we must be able to search for knowledge through concurrent versions of urban knowledge graphs. In this work, we present the ConVer-G (Concurrent Versioning of knowledge Graphs) system for storage and querying through multiple concurrent versions of graphs.
Chain-of-Translation Prompting (CoTR): A Novel Prompting Technique for Low Resource Languages
Venue: arXiv cs.CL 2024
Year: 2024

This paper introduces Chain of Translation Prompting (CoTR), a novel strategy designed to enhance the performance of language models in low-resource languages. CoTR restructures prompts to first translate the input context from a low-resource language into a higher-resource language, such as English. The specified task like generation, classification, or any other NLP function is then performed on the translated text, with the option to translate the output back to the original language if needed. All these steps are specified in a single prompt. We demonstrate the effectiveness of this method through a case study on the low-resource Indic language Marathi. The CoTR strategy is applied to various tasks, including sentiment analysis, hate speech classification, subject classification and text generation, and its efficacy is showcased by comparing it with regular prompting methods. Our results underscore the potential of translation-based prompting strategies to significantly improve multilingual LLM performance in low-resource languages, offering valuable insights for future research and applications. We specifically see the highest accuracy improvements with the hate speech detection task. The technique also has the potential to enhance the quality of synthetic data generation for underrepresented languages using LLMs.