Reviewerly/exHarmony huggingface dataset:
Trajectory Guidance: Enhanced Remote Driving of highly-automated  Vehicles

Authors:
raw_author_name: Majstorovic, Domagoj
raw_author_name: Hoffmann, Simon
raw_author_name: Diermeyer, Frank

Abstract: Despite the rapid technological progress, autonomous vehicles still face a wide range of complex driving situations that require human intervention. Teleoperation technology offers a versatile and effective way to address these challenges. The following work puts existing ideas into a modern context and introduces a novel technical implementation of the trajectory guidance teleoperation concept. The presented system was developed within a high-fidelity simulation environment and experimentally validated, demonstrating a realistic ride-hailing mission with prototype autonomous vehicles and onboard passengers. The results indicate that the proposed concept can be a viable alternative to the existing remote driving options, offering a promising way to enhance teleoperation technology and improve overall operation safety.
Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion

Authors:
raw_author_name: Manor, Hila
raw_author_name: Michaeli, Tomer

Abstract: Editing signals using large pre-trained models, in a zero-shot manner, has recently seen rapid advancements in the image domain. However, this wave has yet to reach the audio domain. In this paper, we explore two zero-shot editing techniques for audio signals, which use DDPM inversion on pre-trained diffusion models. The first, adopted from the image domain, allows text-based editing. The second, is a novel approach for discovering semantically meaningful editing directions without supervision. When applied to music signals, this method exposes a range of musically interesting modifications, from controlling the participation of specific instruments to improvisations on the melody. Samples can be found on our examples page in https://hilamanor.github.io/AudioEditing/ and code can be found in https://github.com/hilamanor/AudioEditing/ .
Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning  under Distribution Shifts

Authors:
raw_author_name: Harrison, James
raw_author_name: Schiffer, Maximilian

Abstract: We study the robustness of deep reinforcement learning algorithms against distribution shifts within contextual multi-stage stochastic combinatorial optimization problems from the operations research domain. In this context, risk-sensitive algorithms promise to learn robust policies. While this field is of general interest to the reinforcement learning community, most studies up-to-date focus on theoretical results rather than real-world performance. With this work, we aim to bridge this gap by formally deriving a novel risk-sensitive deep reinforcement learning algorithm while providing numerical evidence for its efficacy. Specifically, we introduce discrete Soft Actor-Critic for the entropic risk measure by deriving a version of the Bellman equation for the respective Q-values. We establish a corresponding policy improvement result and infer a practical algorithm. We introduce an environment that represents typical contextual multi-stage stochastic combinatorial optimization problems and perform numerical experiments to empirically validate our algorithm's robustness against realistic distribution shifts, without compromising performance on the training distribution. We show that our algorithm is superior to risk-neutral Soft Actor-Critic as well as to two benchmark approaches for robust deep reinforcement learning. Thereby, we provide the first structured analysis on the robustness of reinforcement learning under distribution shifts in the realm of contextual multi-stage stochastic combinatorial optimization problems.
Privacy Attacks in Decentralized Learning

Authors:
raw_author_name: Cyffers, Edwige
raw_author_name: Bellet, Aurélien

Abstract: Decentralized Gradient Descent (D-GD) allows a set of users to perform collaborative learning without sharing their data by iteratively averaging local model updates with their neighbors in a network graph. The absence of direct communication between non-neighbor nodes might lead to the belief that users cannot infer precise information about the data of others. In this work, we demonstrate the opposite, by proposing the first attack against D-GD that enables a user (or set of users) to reconstruct the private data of other users outside their immediate neighborhood. Our approach is based on a reconstruction attack against the gossip averaging protocol, which we then extend to handle the additional challenges raised by D-GD. We validate the effectiveness of our attack on real graphs and datasets, showing that the number of users compromised by a single or a handful of attackers is often surprisingly large. We empirically investigate some of the factors that affect the performance of the attack, namely the graph topology, the number of attackers, and their position in the graph.
Why are Sensitive Functions Hard for Transformers?

Authors:
raw_author_name: Hahn, Michael

Abstract: Empirical studies have identified a range of learnability biases and limitations of transformers, such as a persistent difficulty in learning to compute simple formal languages such as PARITY, and a bias towards low-degree functions. However, theoretical understanding remains limited, with existing expressiveness theory either overpredicting or underpredicting realistic learning abilities. We prove that, under the transformer architecture, the loss landscape is constrained by the input-space sensitivity: Transformers whose output is sensitive to many parts of the input string inhabit isolated points in parameter space, leading to a low-sensitivity bias in generalization. We show theoretically and empirically that this theory unifies a broad array of empirical observations about the learning abilities and biases of transformers, such as their generalization bias towards low sensitivity and low degree, and difficulty in length generalization for PARITY. This shows that understanding transformers' inductive biases requires studying not just their in-principle expressivity, but also their loss landscape.
GraphCBAL: Class-Balanced Active Learning for Graph Neural Networks via  Reinforcement Learning

Authors:
raw_author_name: Yu, Chengcheng
raw_author_name: Zhu, Jiapeng
raw_author_name: Li, Xiang

Abstract: Graph neural networks (GNNs) have recently demonstrated significant success. Active learning for GNNs aims to query the valuable samples from the unlabeled data for annotation to maximize the GNNs' performance at a low cost. However, most existing methods for reinforced active learning in GNNs may lead to a highly imbalanced class distribution, especially in highly skewed class scenarios. This further adversely affects the classification performance. To tackle this issue, in this paper, we propose a novel reinforced class-balanced active learning framework for GNNs, namely, GraphCBAL. It learns an optimal policy to acquire class-balanced and informative nodes for annotation, maximizing the performance of GNNs trained with selected labeled nodes. GraphCBAL designs class-balance-aware states, as well as a reward function that achieves trade-off between model performance and class balance. We further upgrade GraphCBAL to GraphCBAL++ by introducing a punishment mechanism to obtain a more class-balanced labeled set. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed approaches, achieving superior performance over state-of-the-art baselines. In particular, our methods can strike the balance between classification results and class balance.
Develop End-to-End Anomaly Detection System

Authors:
raw_author_name: Yao, Zhiyuan

Abstract: Anomaly detection plays a crucial role in ensuring network robustness. However, implementing intelligent alerting systems becomes a challenge when considering scenarios in which anomalies can be caused by both malicious and non-malicious events, leading to the difficulty of determining anomaly patterns. The lack of labeled data in the computer networking domain further exacerbates this issue, impeding the development of robust models capable of handling real-world scenarios. To address this challenge, in this paper, we propose an end-to-end anomaly detection model development pipeline. This framework makes it possible to consume user feedback and enable continuous user-centric model performance evaluation and optimization. We demonstrate the efficacy of the framework by way of introducing and bench-marking a new forecasting model -- named \emph{Lachesis} -- on a real-world networking problem. Experiments have demonstrated the robustness and effectiveness of the two proposed versions of \emph{Lachesis} compared with other models proposed in the literature. Our findings underscore the potential for improving the performance of data-driven products over their life cycles through a harmonized integration of user feedback and iterative development.
Reproducing, Extending, and Analyzing Naming Experiments

Authors:
raw_author_name: Feitelson, Dror G.

Abstract: Naming is very important in software development, as names are often the only vehicle of meaning about what the code is intended to do. A recent study on how developers choose names collected the names given by different developers for the same objects. This enabled a study of these names' diversity and structure, and the construction of a model of how names are created. We reproduce different parts of this study in three independent experiments. Importantly, we employ methodological variations rather than striving of an exact replication. When the same results are obtained this then boosts our confidence in their validity by demonstrating that they do not depend on the methodology. Our results indeed corroborate those of the original study in terms of the diversity of names, the low probability of two developers choosing the same name, and the finding that experienced developers tend to use slightly longer names than inexperienced students. We explain name diversity by performing a new analysis of the names, classifying the concepts represented in them as universal (agreed upon), alternative (reflecting divergent views on a topic), or optional (reflecting divergent opinions on whether to include this concept at all). This classification enables new research directions concerning the considerations involved in naming decisions. We also show that explicitly using the model proposed in the original study to guide naming leads to the creation of better names, whereas the simpler approach of just asking participants to use longer and more detailed names does not.
Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification

Authors:
raw_author_name: Sahoo, Alok Ranjan
raw_author_name: Chakraborty, Pavan

Abstract: Hyper spectral images have drawn the attention of the researchers for its complexity to classify. It has nonlinear relation between the materials and the spectral information provided by the HSI image. Deep learning methods have shown superiority in learning this nonlinearity in comparison to traditional machine learning methods. Use of 3-D CNN along with 2-D CNN have shown great success for learning spatial and spectral features. However, it uses comparatively large number of parameters. Moreover, it is not effective to learn inter layer information. Hence, this paper proposes a neural network combining 3-D CNN, 2-D CNN and Bi-LSTM. The performance of this model has been tested on Indian Pines(IP) University of Pavia(PU) and Salinas Scene(SA) data sets. The results are compared with the state of-the-art deep learning-based models. This model performed better in all three datasets. It could achieve 99.83, 99.98 and 100 percent accuracy using only 30 percent trainable parameters of the state-of-art model in IP, PU and SA datasets respectively.
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning  Attacks in Federated Learning

Authors:
raw_author_name: Campos, Enrique Mármol
raw_author_name: Vidal, Aurora González
raw_author_name: Ramos, José Luis Hernández
raw_author_name: Skarmeta, Antonio

Abstract: Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is vulnerable to security attacks such as Byzantine behaviors and poisoning attacks, which can significantly degrade model performance and hinder convergence. The effectiveness of existing approaches to mitigate complex attacks, such as median, trimmed mean, or Krum aggregation functions, has been only partially demonstrated in the case of specific attacks. Our study introduces a novel robust aggregation mechanism utilizing the Fourier Transform (FT), which is able to effectively handling sophisticated attacks without prior knowledge of the number of attackers. Employing this data technique, weights generated by FL clients are projected into the frequency domain to ascertain their density function, selecting the one exhibiting the highest frequency. Consequently, malicious clients' weights are excluded. Our proposed approach was tested against various model poisoning attacks, demonstrating superior performance over state-of-the-art aggregation methods.
Quantized Embedding Vectors for Controllable Diffusion Language Models

Authors:
raw_author_name: Kang, Cheng
raw_author_name: Hu, Yong

Abstract: Improving the controllability, portability, and inference speed of diffusion language models (DLMs) is a key challenge in natural language generation. While recent research has shown significant success in complex text generation with language models, the memory and computational power are still very demanding and fall short of expectations, which naturally results in low portability and instability for the models. To mitigate these issues, numerous well-established methods were proposed for neural network quantization. To further enhance their portability of independent deployment as well as improve their stability evaluated by language perplexity, we propose a novel approach called the Quantized Embedding Controllable Diffusion Language Model (QE-CDLM). QE-CDLM builds upon the recent successful controllable DLMs by remodeling the task-specific embedding space via quantization. This leads to a gradient-based controller for the generation tasks, and more stable intermediate latent variables are obtained, which naturally brings in an accelerated convergence as well as better controllability. Additionally, the adaption fine-tuning method is employed to reduce tunable weights. Experimental results on five challenging fine-grained control tasks demonstrate that QE-CDLM compares favorably to existing methods in terms of quality and feasibility, achieving better perplexity and lightweight fine-tuning.
Text-Based Product Matching -- Semi-Supervised Clustering Approach

Authors:
raw_author_name: Łukasik, Szymon
raw_author_name: Gandomi, Amir H.

Abstract: Matching identical products present in multiple product feeds constitutes a crucial element of many tasks of e-commerce, such as comparing product offerings, dynamic price optimization, and selecting the assortment personalized for the client. It corresponds to the well-known machine learning task of entity matching, with its own specificity, like omnipresent unstructured data or inaccurate and inconsistent product descriptions. This paper aims to present a new philosophy to product matching utilizing a semi-supervised clustering approach. We study the properties of this method by experimenting with the IDEC algorithm on the real-world dataset using predominantly textual features and fuzzy string matching, with more standard approaches as a point of reference. Encouraging results show that unsupervised matching, enriched with a small annotated sample of product links, could be a possible alternative to the dominant supervised strategy, requiring extensive manual data labeling.
Transaction Capacity, Security and Latency in Blockchains

Authors:
raw_author_name: Doger, Mustafa
raw_author_name: Ulukus, Sennur

Abstract: We analyze how secure a block is after the block becomes k-deep, i.e., security-latency, for Nakamoto consensus under an exponential network delay model. We give parameter regimes for which transactions are safe when sufficiently deep in the chain. We compare our results for Nakamoto consensus under bounded network delay models and obtain analogous bounds for safety violation threshold. Next, modeling the blockchain system as a batch service queue with exponential network delay, we connect the security-latency analysis to sustainable transaction rate of the queue system. As our model assumes exponential network delay, batch service queue models give a meaningful trade-off between transaction capacity, security and latency. As adversary can attack the queue service to hamper the service process, we consider two different attacks for adversary. In an extreme scenario, we modify the selfish-mining attack for this purpose and consider its effect on the sustainable transaction rate of the queue.
A geometric approach to conjugation-invariant random permutations

Authors:
raw_author_name: Dubach, Victor

Abstract: We propose a new approach to conjugation-invariant random permutations. Namely, we explain how to construct uniform permutations in given conjugacy classes from certain point processes in the plane. This enables the use of geometric tools to study various statistics of such permutations. For their longest decreasing subsequences, we prove universality of the $2\sqrt n$ asymptotic. For Robinson--Schensted shapes, we prove universality of the Vershik--Kerov--Logan--Shepp limit curve, thus solving a conjecture of Kammoun. For the number of records, we establish a phase transition phenomenon. For pattern counts, we obtain an asymptotic normality result, partially answering a conjecture of Hamaker and Rhoades.
Textual Localization: Decomposing Multi-concept Images for  Subject-Driven Text-to-Image Generation

Authors:
raw_author_name: Moubayed, Noura Al

Abstract: Subject-driven text-to-image diffusion models empower users to tailor the model to new concepts absent in the pre-training dataset using a few sample images. However, prevalent subject-driven models primarily rely on single-concept input images, facing challenges in specifying the target concept when dealing with multi-concept input images. To this end, we introduce a textual localized text-to-image model (Texual Localization) to handle multi-concept input images. During fine-tuning, our method incorporates a novel cross-attention guidance to decompose multiple concepts, establishing distinct connections between the visual representation of the target concept and the identifier token in the text prompt. Experimental results reveal that our method outperforms or performs comparably to the baseline models in terms of image fidelity and image-text alignment on multi-concept input images. In comparison to Custom Diffusion, our method with hard guidance achieves CLIP-I scores that are 7.04%, 8.13% higher and CLIP-T scores that are 2.22%, 5.85% higher in single-concept and multi-concept generation, respectively. Notably, our method generates cross-attention maps consistent with the target concept in the generated images, a capability absent in existing models.
Case Study: Testing Model Capabilities in Some Reasoning Tasks

Authors:
raw_author_name: Takumi, Sato
raw_author_name: Zhang, Jack
raw_author_name: Wang, Jun

Abstract: Large Language Models (LLMs) excel in generating personalized content and facilitating interactive dialogues, showcasing their remarkable aptitude for a myriad of applications. However, their capabilities in reasoning and providing explainable outputs, especially within the context of reasoning abilities, remain areas for improvement. In this study, we delve into the reasoning abilities of LLMs, highlighting the current challenges and limitations that hinder their effectiveness in complex reasoning scenarios.
Mitigating subjectivity and bias in AI development indices: A robust  approach to redefining country rankings

Authors:
raw_author_name: Pelegrina, Guilherme Dean
raw_author_name: Pelissari, Renata
raw_author_name: Suyama, Ricardo
raw_author_name: Duarte, Leonardo Tomazeli

Abstract: Countries worldwide have been implementing different actions national strategies for Artificial Intelligence (AI) to shape policy priorities and guide their development concerning AI. Several AI indices have emerged to assess countries' progress in AI development, aiding decision-making on investments and policy choices. Typically, these indices combine multiple indicators using linear additive methods such as weighted sums, although they are limited in their ability to account for interactions among indicators. Another limitation concerns the use of deterministic weights, which can be perceived as subjective and vulnerable to debate and scrutiny, especially by nations that feel disadvantaged. Aiming at mitigating these problems, we conduct a methodological analysis to derive AI indices based on multiple criteria decision analysis. Initially, we assess correlations between different AI dimensions and employ the Choquet integral to model them. Thus, we apply the Stochastic Multicriteria Acceptability Analysis (SMAA) to conduct a sensitivity analysis using both weighted sum and Choquet integral in order to evaluate the stability of the indices with regard the weights. Finally, we introduce a novel ranking methodology based on SMAA, which considers several sets of weights to derive the ranking of countries. As a result, instead of using predefined weights, in the proposed approach, the ranking is achieved based on the probabilities of countries in occupying a specific position. In the computational analysis, we utilize the data employed in The Global AI Index proposed by Tortoise. Results reveal correlations in the data, and our approach effectively mitigates bias. In the sensitivity analysis, we scrutinize changes in the ranking resulting from weight adjustments. We demonstrate that our proposal rankings closely align with those derived from weight variations, proving to be more robust.
Chain-of-Thought Reasoning Without Prompting

Authors:
raw_author_name: Wang, Xuezhi
raw_author_name: Zhou, Denny

Abstract: In enhancing the reasoning capabilities of large language models (LLMs), prior research primarily focuses on specific prompting techniques such as few-shot or zero-shot chain-of-thought (CoT) prompting. These methods, while effective, often involve manually intensive prompt engineering. Our study takes a novel approach by asking: Can LLMs reason effectively without prompting? Our findings reveal that, intriguingly, CoT reasoning paths can be elicited from pre-trained LLMs by simply altering the \textit{decoding} process. Rather than conventional greedy decoding, we investigate the top-$k$ alternative tokens, uncovering that CoT paths are frequently inherent in these sequences. This approach not only bypasses the confounders of prompting but also allows us to assess the LLMs' \textit{intrinsic} reasoning abilities. Moreover, we observe that the presence of a CoT in the decoding path correlates with a higher confidence in the model's decoded answer. This confidence metric effectively differentiates between CoT and non-CoT paths. Extensive empirical studies on various reasoning benchmarks show that the proposed CoT-decoding substantially outperforms the standard greedy decoding.