OpenRaiser/Intern-Atlas huggingface dataset:
Build-A-Scene: Interactive 3D Layout Control for Diffusion-Based Image Generation
Venue: arXiv cs.CV 2024
Year: 2024

We propose a diffusion-based approach for Text-to-Image (T2I) generation with interactive 3D layout control. Layout control has been widely studied to alleviate the shortcomings of T2I diffusion models in understanding objects' placement and relationships from text descriptions. Nevertheless, existing approaches for layout control are limited to 2D layouts, require the user to provide a static layout beforehand, and fail to preserve generated images under layout changes. This makes these approaches unsuitable for applications that require 3D object-wise control and iterative refinements, e.g., interior design and complex scene generation. To this end, we leverage the recent advancements in depth-conditioned T2I models and propose a novel approach for interactive 3D layout control. We replace the traditional 2D boxes used in layout control with 3D boxes. Furthermore, we revamp the T2I task as a multi-stage generation process, where at each stage, the user can insert, change, and move an object in 3D while preserving objects from earlier stages. We achieve this through our proposed Dynamic Self-Attention (DSA) module and the consistent 3D object translation strategy. Experiments show that our approach can generate complicated scenes based on 3D layouts, boosting the object generation success rate over the standard depth-conditioned T2I methods by 2x. Moreover, it outperforms other methods in comparison in preserving objects under layout changes. Project Page: \url{https://abdo-eldesokey.github.io/build-a-scene/}
From Bias to Balance: Detecting Facial Expression Recognition Biases in Large Multimodal Foundation Models
Venue: arXiv cs.CV 2024
Year: 2024

This study addresses the racial biases in facial expression recognition (FER) systems within Large Multimodal Foundation Models (LMFMs). Despite advances in deep learning and the availability of diverse datasets, FER systems often exhibit higher error rates for individuals with darker skin tones. Existing research predominantly focuses on traditional FER models (CNNs, RNNs, ViTs), leaving a gap in understanding racial biases in LMFMs. We benchmark four leading LMFMs: GPT-4o, PaliGemma, Gemini, and CLIP to assess their performance in facial emotion detection across different racial demographics. A linear classifier trained on CLIP embeddings obtains accuracies of 95.9\% for RADIATE, 90.3\% for Tarr, and 99.5\% for Chicago Face. Furthermore, we identify that Anger is misclassified as Disgust 2.1 times more often in Black Females than White Females. This study highlights the need for fairer FER systems and establishes a foundation for developing unbiased, accurate FER technologies. Visit https://kvjvhub.github.io/FERRacialBias/ for further information regarding the biases within facial expression recognition.
Multimedia Traffic Anomaly Detection
Venue: arXiv cs.CR 2024
Year: 2024

Accuracy anomaly detection in user-level social multimedia traffic is crucial for privacy security. Compared with existing models that passively detect specific anomaly classes with large labeled training samples, user-level social multimedia traffic contains sizeable new anomaly classes with few labeled samples and has an imbalance, self-similar, and data-hungry nature. Recent advances, such as Generative Adversarial Networks (GAN), solve it by learning a sample generator only from seen class samples to synthesize new samples. However, if we detect many new classes, the number of synthesizing samples would be unfeasibly estimated, and this operation will drastically increase computational complexity and energy consumption. Motivation on these limitations, in this paper, we propose \textit{Meta-UAD}, a Meta-learning scheme for User-level social multimedia traffic Anomaly Detection. This scheme relies on the episodic training paradigm and learns from the collection of K-way-M-shot classification tasks, which can use the pre-trained model to adapt any new class with few samples by going through few iteration steps. Since user-level social multimedia traffic emerges from a complex interaction process of users and social applications, we further develop a feature extractor to improve scheme performance. It extracts statistical features using cumulative importance ranking and time-series features using an LSTM-based AutoEncoder. We evaluate our scheme on two public datasets and the results further demonstrate the superiority of Meta-UAD.
Unraveling the Airalo Ecosystem
Venue: arXiv cs.NI 2024
Year: 2024

In recent years, we have witnessed myriad flavours of Mobile Network Aggregators (MNAs) which exploit the coverage footprint of a handful of base operators to provide global mobile connectivity. Under the MNA model, emerging operators reap the benefits of network softwarization and virtualization, including eSIM technology or control/data-plane separation. This paper investigates an emergent MNA type - a thick MNA - that relies on multiple (core) base operators from different economies to provision eSIM profiles, while employing gateway functions to the public internet located outside the respective base operators' home country. Specifically, our work is the first to capture the intricacies of Airalo - a thick MNA that operates in 219 countries. Unlike other MNAs that our community scrutinized, we show that Airalo often decouples the geographical location of the public internet gateway from the native country of the base operator via IPX Hub Breakout (IHBO). To map Airalo's underlying infrastructure, we ran web-based measurements that 14 volunteers performed while traveling and using an Airalo eSIM on their personal devices. We further dive into Airalo's performance by running device-based measurements (speedtest, traceroute, video streaming, etc.) in 10 countries with rooted Android devices. Finally, we examine Airalo's pricing by monitoring its marketplace.
From Chaos to Consistency: The Role of CSAF in Streamlining Security Advisories
Venue: arXiv cs.CR 2024
Year: 2024

Security advisories have become an important part of vulnerability management. They can be used to gather and distribute valuable information about vulnerabilities. Although there is a predefined broad format for advisories, it is not really standardized. As a result, their content and form vary greatly depending on the vendor. Thus, it is cumbersome and resource-intensive for security analysts to extract the relevant information. The Common Security Advisory Format (CSAF) aims to bring security advisories into a standardized format which is intended to solve existing problems and to enable automated processing of the advisories. However, a new standard only makes sense if it can benefit users. Hence the questions arise: Do security advisories cause issues in their current state? Which of these issues is CSAF able to resolve? What is the current state of automation? To investigate these questions, we interviewed three security experts, and then conducted an online survey with 197 participants. The results show that problems exist and can often be traced back to confusing and inconsistent structures and formats. CSAF attempts to solve precisely these problems. However, our results show that CSAF is currently rarely used. Although users perceive automation as necessary to improve the processing of security advisories, many are at the same time skeptical. One of the main reasons is that systems are not yet designed for automation and a migration would require vast amounts of resources.
Decentralized Unlabeled Multi-agent Pathfinding Via Target And Priority Swapping (With Supplementary)
Venue: arXiv cs.MA 2024
Year: 2024

In this paper we study a challenging variant of the multi-agent pathfinding problem (MAPF), when a set of agents must reach a set of goal locations, but it does not matter which agent reaches a specific goal - Anonymous MAPF (AMAPF). Current optimal and suboptimal AMAPF solvers rely on the existence of a centralized controller which is in charge of both target assignment and pathfinding. We extend the state of the art and present the first AMAPF solver capable of solving the problem at hand in a fully decentralized fashion, when each agent makes decisions individually and relies only on the local communication with the others. The core of our method is a priority and target swapping procedure tailored to produce consistent goal assignments (i.e. making sure that no two agents are heading towards the same goal). Coupled with an established rule-based path planning, we end up with a TP-SWAP, an efficient and flexible approach to solve decentralized AMAPF. On the theoretical side, we prove that TP-SWAP is complete (i.e. TP-SWAP guarantees that each target will be reached by some agent). Empirically, we evaluate TP-SWAP across a wide range of setups and compare it to both centralized and decentralized baselines. Indeed, TP-SWAP outperforms the fully-decentralized competitor and can even outperform the semi-decentralized one (i.e. the one relying on the initial consistent goal assignment) in terms of flowtime (a widespread cost objective in MAPF
Stochastic Geometry Based Modelling and Analysis of Uplink Cooperative Satellite-Aerial-Terrestrial Networks for Nomadic Communications with Weak Satellite Coverage
Venue: arXiv cs.NI 2024
Year: 2024

Cooperative satellite-aerial-terrestrial networks (CSATNs), where unmanned aerial vehicles (UAVs) are utilized as nomadic aerial relays (A), are highly valuable for many important applications, such as post-disaster urban reconstruction. In this scenario, direct communication between terrestrial terminals (T) and satellites (S) is often unavailable due to poor propagation conditions for satellite signals, and users tend to congregate in regions of finite size. There is a current dearth in the open literature regarding the uplink performance analysis of CSATN operating under the above constraints, and the few contributions on the uplink model terrestrial terminals by a Poisson point process (PPP) relying on the unrealistic assumption of an infinite area. This paper aims to fill the above research gap. First, we propose a stochastic geometry based innovative model to characterize the impact of the finite-size distribution region of terrestrial terminals in the CSATN by jointly using a binomial point process (BPP) and a type-II Mat{é}rn hard-core point process (MHCPP). Then, we analyze the relationship between the spatial distribution of the coverage areas of aerial nodes and the finite-size distribution region of terrestrial terminals, thereby deriving the distance distribution of the T-A links. Furthermore, we consider the stochastic nature of the spatial distributions of terrestrial terminals and UAVs, and conduct a thorough analysis of the coverage probability and average ergodic rate of the T-A links under Nakagami fading and the A-S links under shadowed-Rician fading. Finally, the accuracy of our theoretical derivations are confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization for the realistic CSATNs involving nomadic aerial relays and terrestrial terminals confined in a finite-size region.
Deep Learning-based Average Shear Wave Velocity Prediction using Accelerometer Records
Venue: arXiv cs.CV 2024
Year: 2024

Assessing seismic hazards and thereby designing earthquake-resilient structures or evaluating structural damage that has been incurred after an earthquake are important objectives in earthquake engineering. Both tasks require critical evaluation of strong ground motion records, and the knowledge of site conditions at the earthquake stations plays a major role in achieving the aforementioned objectives. Site conditions are generally represented by the time-averaged shear wave velocity in the upper 30 meters of the geological materials (Vs30). Several strong motion stations lack Vs30 measurements resulting in potentially inaccurate assessment of seismic hazards and evaluation of ground motion records. In this study, we present a deep learning-based approach for predicting Vs30 at strong motion station locations using three-channel earthquake records. For this purpose, Convolutional Neural Networks (CNNs) with dilated and causal convolutional layers are used to extract deep features from accelerometer records collected from over 700 stations located in Turkey. In order to overcome the limited availability of labeled data, we propose a two-phase training approach. In the first phase, a CNN is trained to estimate the epicenters, for which ground truth is available for all records. After the CNN is trained, the pre-trained encoder is fine-tuned based on the Vs30 ground truth. The performance of the proposed method is compared with machine learning models that utilize hand-crafted features. The results demonstrate that the deep convolutional encoder based Vs30 prediction model outperforms the machine learning models that rely on hand-crafted features.
MRSE: An Efficient Multi-modality Retrieval System for Large Scale E-commerce
Venue: arXiv cs.IR 2024
Year: 2024

Providing high-quality item recall for text queries is crucial in large-scale e-commerce search systems. Current Embedding-based Retrieval Systems (ERS) embed queries and items into a shared low-dimensional space, but uni-modality ERS rely too heavily on textual features, making them unreliable in complex contexts. While multi-modality ERS incorporate various data sources, they often overlook individual preferences for different modalities, leading to suboptimal results. To address these issues, we propose MRSE, a Multi-modality Retrieval System that integrates text, item images, and user preferences through lightweight mixture-of-expert (LMoE) modules to better align features across and within modalities. MRSE also builds user profiles at a multi-modality level and introduces a novel hybrid loss function that enhances consistency and robustness using hard negative sampling. Experiments on a large-scale dataset from Shopee and online A/B testing show that MRSE achieves an 18.9% improvement in offline relevance and a 3.7% gain in online core metrics compared to Shopee's state-of-the-art uni-modality system.
Finding Convincing Views to Endorse a Claim
Venue: arXiv cs.DB 2024
Year: 2024

Recent studies investigated the challenge of assessing the strength of a given claim extracted from a dataset, particularly the claim's potential of being misleading and cherry-picked. We focus on claims that compare answers to an aggregate query posed on a view that selects tuples. The strength of a claim amounts to the question of how likely it is that the view is carefully chosen to support the claim, whereas less careful choices would lead to contradictory claims. We embark on the study of the reverse task that offers a complementary angle in the critical assessment of data-based claims: given a claim, find useful supporting views. The goal of this task is twofold. On the one hand, we aim to assist users in finding significant evidence of phenomena of interest. On the other hand, we wish to provide them with machinery to criticize or counter given claims by extracting evidence of opposing statements. To be effective, the supporting sub-population should be significant and defined by a ``natural'' view. We discuss several measures of naturalness and propose ways of extracting the best views under each measure (and combinations thereof). The main challenge is the computational cost, as naïve search is infeasible. We devise anytime algorithms that deploy two main steps: (1) a preliminary construction of a ranked list of attribute combinations that are assessed using fast-to-compute features, and (2) an efficient search for the actual views based on each attribute combination. We present a thorough experimental study that shows the effectiveness of our algorithms in terms of quality and execution cost. We also present a user study to assess the usefulness of the naturalness measures.
Once and for all: how to compose modules -- The composition calculus
Venue: arXiv cs.SE 2024
Year: 2024

Computability theory is traditionally conceived as the theoretical basis of informatics. Nevertheless, numerous proposals transcend computability theory, in particular by emphasizing interaction of modules, or components, parts, constituents, as a fundamental computing feature. In a technical framework, interaction requires composition of modules. Hence, a most abstract, comprehensive theory of modules and their composition is required. To this end, we suggest a minimal set of postulates to characterize systems in the digital world that consist of interacting modules. For such systems, we suggest a calculus with a simple, yet most general composition operator which exhibits important properties, in particular associativity. We claim that this composition calculus provides not just another conceptual, formal framework, but that essentially all settings of modules and their composition can be based on this calculus. This claim is supported by a rich body of theorems, properties, special classes of modules, and case studies.
MONAS: Efficient Zero-Shot Neural Architecture Search for MCUs
Venue: arXiv cs.LG 2024
Year: 2024

Neural Architecture Search (NAS) has proven effective in discovering new Convolutional Neural Network (CNN) architectures, particularly for scenarios with well-defined accuracy optimization goals. However, previous approaches often involve time-consuming training on super networks or intensive architecture sampling and evaluations. Although various zero-cost proxies correlated with CNN model accuracy have been proposed for efficient architecture search without training, their lack of hardware consideration makes it challenging to target highly resource-constrained edge devices such as microcontroller units (MCUs). To address these challenges, we introduce MONAS, a novel hardware-aware zero-shot NAS framework specifically designed for MCUs in edge computing. MONAS incorporates hardware optimality considerations into the search process through our proposed MCU hardware latency estimation model. By combining this with specialized performance indicators (proxies), MONAS identifies optimal neural architectures without incurring heavy training and evaluation costs, optimizing for both hardware latency and accuracy under resource constraints. MONAS achieves up to a 1104x improvement in search efficiency over previous work targeting MCUs and can discover CNN models with over 3.23x faster inference on MCUs while maintaining similar accuracy compared to more general NAS approaches.
Evidence-Enhanced Triplet Generation Framework for Hallucination Alleviation in Generative Question Answering
Venue: arXiv cs.CL 2024
Year: 2024

To address the hallucination in generative question answering (GQA) where the answer can not be derived from the document, we propose a novel evidence-enhanced triplet generation framework, EATQA, encouraging the model to predict all the combinations of (Question, Evidence, Answer) triplet by flipping the source pair and the target label to understand their logical relationships, i.e., predict Answer(A), Question(Q), and Evidence(E) given a QE, EA, and QA pairs, respectively. Furthermore, we bridge the distribution gap to distill the knowledge from evidence in inference stage. Our framework ensures the model to learn the logical relation between query, evidence and answer, which simultaneously improves the evidence generation and query answering. In this paper, we apply EATQA to LLama and it outperforms other LLMs-based methods and hallucination mitigation approaches on two challenging GQA benchmarks. Further analysis shows that our method not only keeps prior knowledge within LLM, but also mitigates hallucination and generates faithful answers.
A Survey of Large Language Models for European Languages
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) have gained significant attention due to their high performance on a wide range of natural language tasks since the release of ChatGPT. The LLMs learn to understand and generate language by training billions of model parameters on vast volumes of text data. Despite being a relatively new field, LLM research is rapidly advancing in various directions. In this paper, we present an overview of LLM families, including LLaMA, PaLM, GPT, and MoE, and the methods developed to create and enhance LLMs for official European Union (EU) languages. We provide a comprehensive summary of common monolingual and multilingual datasets used for pretraining large language models.
Distributed Planning for Rigid Robot Formations with Probabilistic Collision Avoidance
Venue: arXiv cs.RO 2024
Year: 2024

This paper presents a distributed method for robots moving in rigid formations while ensuring probabilistic collision avoidance between the robots. The formation is parametrised through the transformation of a base configuration. The robots map their desired velocities into a corresponding desired change in the formation parameters and apply a consensus step to reach agreement on the desired formation and a constraint satisfaction step to ensure collision avoidance within the formation. The constraint set is found such that the probability of collision remains below an upper bound. The method was demonstrated in a manual teleoperation scenario both in simulation and a real-world experiment.
On Controlling Knockout Tournaments Without Perfect Information
Venue: arXiv cs.DS 2024
Year: 2024

Over the last decade, extensive research has been conducted on the algorithmic aspects of designing single-elimination (SE) tournaments. Addressing natural questions of algorithmic tractability, we identify key properties of input instances that enable the tournament designer to efficiently schedule the tournament in a way that maximizes the chances of a preferred player winning. Much of the prior algorithmic work on this topic focuses on the perfect (complete and deterministic) information scenario, especially in the context of fixed-parameter algorithm design. Our contributions constitute the first fixed-parameter tractability results applicable to more general settings of SE tournament design with potential imperfect information.
Delay as Payoff in MAB
Venue: arXiv cs.LG 2024
Year: 2024

In this paper, we investigate a variant of the classical stochastic Multi-armed Bandit (MAB) problem, where the payoff received by an agent (either cost or reward) is both delayed, and directly corresponds to the magnitude of the delay. This setting models faithfully many real world scenarios such as the time it takes for a data packet to traverse a network given a choice of route (where delay serves as the agent's cost); or a user's time spent on a web page given a choice of content (where delay serves as the agent's reward). Our main contributions are tight upper and lower bounds for both the cost and reward settings. For the case that delays serve as costs, which we are the first to consider, we prove optimal regret that scales as $\sum_{i:Δ_i > 0}\frac{\log T}{Δ_i} + d^*$, where $T$ is the maximal number of steps, $Δ_i$ are the sub-optimality gaps and $d^*$ is the minimal expected delay amongst arms. For the case that delays serves as rewards, we show optimal regret of $\sum_{i:Δ_i > 0}\frac{\log T}{Δ_i} + \bar{d}$, where $\bar d$ is the second maximal expected delay. These improve over the regret in the general delay-dependent payoff setting, which scales as $\sum_{i:Δ_i > 0}\frac{\log T}{Δ_i} + D$, where $D$ is the maximum possible delay. Our regret bounds highlight the difference between the cost and reward scenarios, showing that the improvement in the cost scenario is more significant than for the reward. Finally, we accompany our theoretical results with an empirical evaluation.