OpenRaiser/Intern-Atlas huggingface dataset:
TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification
Venue: arXiv cs.LG 2024
Year: 2024

In time-series classification, understanding model decisions is crucial for their application in high-stakes domains such as healthcare and finance. Counterfactual explanations, which provide insights by presenting alternative inputs that change model predictions, offer a promising solution. However, existing methods for generating counterfactual explanations for time-series data often struggle with balancing key objectives like proximity, sparsity, and validity. In this paper, we introduce TX-Gen, a novel algorithm for generating counterfactual explanations based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). TX-Gen leverages evolutionary multi-objective optimization to find a diverse set of counterfactuals that are both sparse and valid, while maintaining minimal dissimilarity to the original time series. By incorporating a flexible reference-guided mechanism, our method improves the plausibility and interpretability of the counterfactuals without relying on predefined assumptions. Extensive experiments on benchmark datasets demonstrate that TX-Gen outperforms existing methods in generating high-quality counterfactuals, making time-series models more transparent and interpretable.
Pen-and-paper Rituals in Service Interaction: Combining High-touch and High-tech in Financial Advisory Encounters
Venue: arXiv cs.HC 2024
Year: 2024

Advisory services are ritualized encounters between an expert and an advisee. Empathetic, high-touch relationship between those two parties was identified as the key aspect of a successful advisory encounter. To facilitate the high-touch interaction, advisors established rituals which stress the unique, individual character of each client and each single encounter. Simultaneously, organizations like banks or insurances rolled out tools and technologies for use in advisory services to offer a uniform experience and consistent quality across branches and advisors. As a consequence, advisors were caught between the high-touch and high-tech aspects of an advisory service. This manuscript presents a system that accommodates for high-touch rituals and practices and combines them with high-tech collaboration. The proposed solution augments pen-and-paper practices with digital content and affords new material performances coherent with the existing rituals. The evaluation in realistic mortgage advisory services unveils the potential of mixed reality approaches for application in professional, institutional settings. The blow-by-blow analysis of the conversations reveals how an advisory service can become equally high-tech and high-touch thanks to a careful ritual-oriented system design. As a consequence, this paper presents a solution to the tension between the high-touch and high-tech tendencies in advisory services.
Scabbard: An Exploratory Study on Hardware Aware Design Choices of Learning with Rounding-based Key Encapsulation Mechanisms
Venue: arXiv cs.CR 2024
Year: 2024

Recently, the construction of cryptographic schemes based on hard lattice problems has gained immense popularity. Apart from being quantum resistant, lattice-based cryptography allows a wide range of variations in the underlying hard problem. As cryptographic schemes can work in different environments under different operational constraints such as memory footprint, silicon area, efficiency, power requirement, etc., such variations in the underlying hard problem are very useful for designers to construct different cryptographic schemes. In this work, we explore various design choices of lattice-based cryptography and their impact on performance in the real world. In particular, we propose a suite of key-encapsulation mechanisms based on the learning with rounding problem with a focus on improving different performance aspects of lattice-based cryptography. Our suite consists of three schemes. Our first scheme is Florete, which is designed for efficiency. The second scheme is Espada, which is aimed at improving parallelization, flexibility, and memory footprint. The last scheme is Sable, which can be considered an improved version in terms of key sizes and parameters of the Saber key-encapsulation mechanism, one of the finalists in the National Institute of Standards and Technology's post-quantum standardization procedure. In this work, we have described our design rationale behind each scheme. Further, to demonstrate the justification of our design decisions, we have provided software and hardware implementations. Our results show Florete is faster than most state-of-the-art KEMs on software and hardware platforms. The scheme Espada requires less memory and area than the implementation of most state-of-the-art schemes. The implementations of Sable maintain a trade-off between Florete and Espada regarding performance and memory requirements on the hardware and software platform.
Enumerating Minimal Unsatisfiable Cores of LTLf formulas
Venue: arXiv cs.AI 2024
Year: 2024

Linear Temporal Logic over finite traces ($\text{LTL}_f$) is a widely used formalism with applications in AI, process mining, model checking, and more. The primary reasoning task for $\text{LTL}_f$ is satisfiability checking; yet, the recent focus on explainable AI has increased interest in analyzing inconsistent formulas, making the enumeration of minimal explanations for infeasibility a relevant task also for $\text{LTL}_f$. This paper introduces a novel technique for enumerating minimal unsatisfiable cores (MUCs) of an $\text{LTL}_f$ specification. The main idea is to encode a $\text{LTL}_f$ formula into an Answer Set Programming (ASP) specification, such that the minimal unsatisfiable subsets (MUSes) of the ASP program directly correspond to the MUCs of the original $\text{LTL}_f$ specification. Leveraging recent advancements in ASP solving yields a MUC enumerator achieving good performance in experiments conducted on established benchmarks from the literature.
Planning Transformer: Long-Horizon Offline Reinforcement Learning with Planning Tokens
Venue: arXiv cs.LG 2024
Year: 2024

Supervised learning approaches to offline reinforcement learning, particularly those utilizing the Decision Transformer, have shown effectiveness in continuous environments and for sparse rewards. However, they often struggle with long-horizon tasks due to the high compounding error of auto-regressive models. To overcome this limitation, we go beyond next-token prediction and introduce Planning Tokens, which contain high-level, long time-scale information about the agent's future. Predicting dual time-scale tokens at regular intervals enables our model to use these long-horizon Planning Tokens as a form of implicit planning to guide its low-level policy and reduce compounding error. This architectural modification significantly enhances performance on long-horizon tasks, establishing a new state-of-the-art in complex D4RL environments. Additionally, we demonstrate that Planning Tokens improve the interpretability of the model's policy through the interpretable plan visualisations and attention map.
Deep Learning Under Siege: Identifying Security Vulnerabilities and Risk Mitigation Strategies
Venue: arXiv cs.CR 2024
Year: 2024

With the rise in the wholesale adoption of Deep Learning (DL) models in nearly all aspects of society, a unique set of challenges is imposed. Primarily centered around the architectures of these models, these risks pose a significant challenge, and addressing these challenges is key to their successful implementation and usage in the future. In this research, we present the security challenges associated with the current DL models deployed into production, as well as anticipate the challenges of future DL technologies based on the advancements in computing, AI, and hardware technologies. In addition, we propose risk mitigation techniques to inhibit these challenges and provide metrical evaluations to measure the effectiveness of these metrics.
Swarm Algorithms for Dynamic Task Allocation in Unknown Environments
Venue: arXiv cs.MA 2024
Year: 2024

Robot swarms, systems of many robots that operate in a distributed fashion, have many applications in areas such as search-and-rescue, natural disaster response, and self-assembly. Several of these applications can be abstracted to the general problem of task allocation in an environment, in which robots must assign themselves to and complete tasks. While several algorithms for task allocation have been proposed, most of them assume either prior knowledge of task locations or a static set of tasks. Operating under a discrete general model where tasks dynamically appear in unknown locations, we present three new swarm algorithms for task allocation. We demonstrate that when tasks appear slowly, our variant of a distributed algorithm based on propagating task information completes tasks more efficiently than a Levy random walk algorithm, which is a strategy used by many organisms in nature to efficiently search an environment. We also propose a division of labor algorithm where some agents are using our algorithm based on propagating task information while the remaining agents are using the Levy random walk algorithm. Finally, we introduce a hybrid algorithm where each agent dynamically switches between using propagated task information and following a Levy random walk. We show that our division of labor and hybrid algorithms can perform better than both our algorithm based on propagated task information and the Levy walk algorithm, especially at low and medium task rates. When tasks appear fast, we observe the Levy random walk strategy performs as well or better when compared to these novel approaches. Our work demonstrates the relative performance of these algorithms on a variety of task rates and also provide insight into optimizing our algorithms based on environment parameters.
CSQF-based Time-Sensitive Flow Scheduling in Long-distance Industrial IoT Networks
Venue: arXiv cs.NI 2024
Year: 2024

Booming time-critical services, such as automated manufacturing and remote operations, stipulate increasing demands for facilitating large-scale Industrial Internet of Things (IoT). Recently, a cycle specified queuing and forwarding (CSQF) scheme has been advocated to enhance the Ethernet. However, CSQF only outlines a foundational equipment-level primitive, while how to attain network-wide flow scheduling is not yet determined. Prior endeavors primarily focus on the range of a local area, rendering them unsuitable for long-distance factory interconnection. This paper devises the cycle tags planning (CTP) mechanism, the first integer programming model for the CSQF, which makes the CSQF practical for efficient global flow scheduling. In the CTP model, the per-hop cycle alignment problem is solved by decoupling the long-distance link delay from cyclic queuing time. To avoid queue overflows, we discretize the underlying network resources into cycle-related queue resource blocks and detail the core constraints within multiple periods. Then, two heuristic algorithms named flow offset and cycle shift (FO-CS) and Tabu FO-CS are designed to calculate the flows' cycle tags and maximize the number of schedulable flows, respectively. Evaluation results show that FO-CS increases the number of scheduled flows by 31.2%. The Tabu FO-CS algorithm can schedule 94.45% of flows at the level of 2000 flows.
On the effectiveness of enrollment speech augmentation for Target Speaker Extraction
Venue: arXiv cs.SD 2024
Year: 2024

Deep learning technologies have significantly advanced the performance of target speaker extraction (TSE) tasks. To enhance the generalization and robustness of these algorithms when training data is insufficient, data augmentation is a commonly adopted technique. Unlike typical data augmentation applied to speech mixtures, this work thoroughly investigates the effectiveness of augmenting the enrollment speech space. We found that for both pretrained and jointly optimized speaker encoders, directly augmenting the enrollment speech leads to consistent performance improvement. In addition to conventional methods such as noise and reverberation addition, we propose a novel augmentation method called self-estimated speech augmentation (SSA). Experimental results on the Libri2Mix test set show that our proposed method can achieve an improvement of up to 2.5 dB.
Open-World Test-Time Training: Self-Training with Contrast Learning
Venue: arXiv cs.LG 2024
Year: 2024

Traditional test-time training (TTT) methods, while addressing domain shifts, often assume a consistent class set, limiting their applicability in real-world scenarios characterized by infinite variety. Open-World Test-Time Training (OWTTT) addresses the challenge of generalizing deep learning models to unknown target domain distributions, especially in the presence of strong Out-of-Distribution (OOD) data. Existing TTT methods often struggle to maintain performance when confronted with strong OOD data. In OWTTT, the focus has predominantly been on distinguishing between overall strong and weak OOD data. However, during the early stages of TTT, initial feature extraction is hampered by interference from strong OOD and corruptions, resulting in diminished contrast and premature classification of certain classes as strong OOD. To address this, we introduce Open World Dynamic Contrastive Learning (OWDCL), an innovative approach that utilizes contrastive learning to augment positive sample pairs. This strategy not only bolsters contrast in the early stages but also significantly enhances model robustness in subsequent stages. In comparison datasets, our OWDCL model has produced the most advanced performance.
Programmable Cycle-Specified Queue for Long-Distance Industrial Deterministic Packet Scheduling
Venue: arXiv cs.NI 2024
Year: 2024

The time-critical industrial applications pose intense demands for enabling long-distance deterministic networks. However, previous priority-based and weight-based scheduling methods focus on probabilistically reducing average delay, which ignores strictly guaranteeing task-oriented on-time packet delivery with bounded worst-case delay and jitter. This paper proposes a new Programmable Cycle-Specified Queue (PCSQ) for long-distance industrial deterministic packet scheduling. By implementing the first high-precision rotation dequeuing, PCSQ enables microsecond-level time slot resource reservation (noted as T) and especially jitter control of up to 2T. Then, we propose the cycle tags computation to approximate cyclic scheduling algorithms, which allows packets to actively pick and lock their favorite queue in a sequence of nodes. Accordingly, PCSQ can precisely defer packets to any desired time. Further, the queue coordination and cycle mapping mechanisms are delicately designed to solve the cycle-queue mismatch problem. Evaluation results show that PCSQ can schedule tens of thousands of time-sensitive flows and strictly guarantee $ms$-level delay and us-level jitter.
One-Shot Learning for Pose-Guided Person Image Synthesis in the Wild
Venue: arXiv cs.CV 2024
Year: 2024

Current Pose-Guided Person Image Synthesis (PGPIS) methods depend heavily on large amounts of labeled triplet data to train the generator in a supervised manner. However, they often falter when applied to in-the-wild samples, primarily due to the distribution gap between the training datasets and real-world test samples. While some researchers aim to enhance model generalizability through sophisticated training procedures, advanced architectures, or by creating more diverse datasets, we adopt the test-time fine-tuning paradigm to customize a pre-trained Text2Image (T2I) model. However, naively applying test-time tuning results in inconsistencies in facial identities and appearance attributes. To address this, we introduce a Visual Consistency Module (VCM), which enhances appearance consistency by combining the face, text, and image embedding. Our approach, named OnePoseTrans, requires only a single source image to generate high-quality pose transfer results, offering greater stability than state-of-the-art data-driven methods. For each test case, OnePoseTrans customizes a model in around 48 seconds with an NVIDIA V100 GPU.
Improving Statistical Significance in Human Evaluation of Automatic Metrics via Soft Pairwise Accuracy
Venue: arXiv cs.CL 2024
Year: 2024

Selecting an automatic metric that best emulates human annotators is often non-trivial, because there is no clear definition of "best emulates." A meta-metric is required to compare the human judgments to the automatic metric scores, and metric rankings depend on the choice of meta-metric. We propose Soft Pairwise Accuracy (SPA), a new meta-metric that builds on Pairwise Accuracy (PA) but incorporates the statistical significance of both the human judgments and the metric scores. We show that SPA is more stable than PA with respect to changes in the number of systems/segments used for evaluation. We also show that PA can only assign a small set of distinct output values to metrics, and this results in many metrics being artificially assigned the exact same PA score. We demonstrate that SPA fixes this issue. Finally, we show that SPA is more discriminative than PA, producing more statistically significant comparisons between metrics. SPA was selected as the official system-level metric for the 2024 WMT Metrics Shared Task.
Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting
Venue: arXiv cs.CL 2024
Year: 2024

The traditional data annotation process is often labor-intensive, time-consuming, and susceptible to human bias, which complicates the management of increasingly complex datasets. This study explores the potential of large language models (LLMs) as automated data annotators to improve efficiency and consistency in annotation tasks. By employing rationale-driven collaborative few-shot prompting techniques, we aim to improve the performance of LLMs in text annotation. We conduct a rigorous evaluation of six LLMs across four benchmark datasets, comparing seven distinct methodologies. Our results demonstrate that collaborative methods consistently outperform traditional few-shot techniques and other baseline approaches, particularly in complex annotation tasks. Our work provides valuable insights and a robust framework for leveraging collaborative learning methods to tackle challenging text annotation tasks.
E-Commerce Inpainting with Mask Guidance in Controlnet for Reducing Overcompletion
Venue: arXiv cs.CV 2024
Year: 2024

E-commerce image generation has always been one of the core demands in the e-commerce field. The goal is to restore the missing background that matches the main product given. In the post-AIGC era, diffusion models are primarily used to generate product images, achieving impressive results. This paper systematically analyzes and addresses a core pain point in diffusion model generation: overcompletion, which refers to the difficulty in maintaining product features. We propose two solutions: 1. Using an instance mask fine-tuned inpainting model to mitigate this phenomenon; 2. Adopting a train-free mask guidance approach, which incorporates refined product masks as constraints when combining ControlNet and UNet to generate the main product, thereby avoiding overcompletion of the product. Our method has achieved promising results in practical applications and we hope it can serve as an inspiring technical report in this field.
Training Safe Neural Networks with Global SDP Bounds
Venue: arXiv cs.LG 2024
Year: 2024

This paper presents a novel approach to training neural networks with formal safety guarantees using semidefinite programming (SDP) for verification. Our method focuses on verifying safety over large, high-dimensional input regions, addressing limitations of existing techniques that focus on adversarial robustness bounds. We introduce an ADMM-based training scheme for an accurate neural network classifier on the Adversarial Spheres dataset, achieving provably perfect recall with input dimensions up to $d=40$. This work advances the development of reliable neural network verification methods for high-dimensional systems, with potential applications in safe RL policies.
Exploring Utility in a Real-World Warehouse Optimization Problem: Formulation Based on Quantum Annealers and Preliminary Results
Venue: arXiv cs.ET 2024
Year: 2024

In the current NISQ-era, one of the major challenges faced by researchers and practitioners lies in figuring out how to combine quantum and classical computing in the most efficient and innovative way. In this paper, we present a mechanism coined as Quantum Initialization for Warehouse Optimization Problem that resorts to D-Wave's Quantum Annealer. The module has been specifically designed to be embedded into already existing classical software dedicated to the optimization of a real-world industrial problem. We preliminary tested the implemented mechanism through a two-phase experiment against the classical version of the software.
Disentangling Visual Priors: Unsupervised Learning of Scene Interpretations with Compositional Autoencoder
Venue: arXiv cs.CV 2024
Year: 2024

Contemporary deep learning architectures lack principled means for capturing and handling fundamental visual concepts, like objects, shapes, geometric transforms, and other higher-level structures. We propose a neurosymbolic architecture that uses a domain-specific language to capture selected priors of image formation, including object shape, appearance, categorization, and geometric transforms. We express template programs in that language and learn their parameterization with features extracted from the scene by a convolutional neural network. When executed, the parameterized program produces geometric primitives which are rendered and assessed for correspondence with the scene content and trained via auto-association with gradient. We confront our approach with a baseline method on a synthetic benchmark and demonstrate its capacity to disentangle selected aspects of the image formation process, learn from small data, correct inference in the presence of noise, and out-of-sample generalization.
Measuring Recency Bias In Sequential Recommendation Systems
Venue: arXiv cs.IR 2024
Year: 2024

Recency bias in a sequential recommendation system refers to the overly high emphasis placed on recent items within a user session. This bias can diminish the serendipity of recommendations and hinder the system's ability to capture users' long-term interests, leading to user disengagement. We propose a simple yet effective novel metric specifically designed to quantify recency bias. Our findings also demonstrate that high recency bias measured in our proposed metric adversely impacts recommendation performance too, and mitigating it results in improved recommendation performances across all models evaluated in our experiments, thus highlighting the importance of measuring recency bias.