OpenRaiser/Intern-Atlas huggingface dataset:
Text-Based Detection of On-Hold Scripts in Contact Center Calls
Venue: arXiv cs.CL 2024
Year: 2024

Average hold time is a concern for call centers because it affects customer satisfaction. Contact centers should instruct their agents to use special on-hold scripts to maintain positive interactions with clients. This study presents a natural language processing model that detects on-hold phrases in customer service calls transcribed by automatic speech recognition technology. The task of finding hold scripts in dialogue was formulated as a multiclass text classification problem with three mutually exclusive classes: scripts for putting a client on hold, scripts for returning to a client, and phrases irrelevant to on-hold scripts. We collected an in-house dataset of calls and labeled each dialogue turn in each call. We fine-tuned RuBERT on the dataset by exploring various hyperparameter sets and achieved high model performance. The developed model can help agent monitoring by providing a way to check whether an agent follows predefined on-hold scripts.
FarFetched: Entity-centric Reasoning and Claim Validation for the Greek Language based on Textually Represented Environments
Venue: arXiv cs.CL 2024
Year: 2024

Our collective attention span is shortened by the flood of online information. With \textit{FarFetched}, we address the need for automated claim validation based on the aggregated evidence derived from multiple online news sources. We introduce an entity-centric reasoning framework in which latent connections between events, actions, or statements are revealed via entity mentions and represented in a graph database. Using entity linking and semantic similarity, we offer a way for collecting and combining information from diverse sources in order to generate evidence relevant to the user's claim. Then, we leverage textual entailment recognition to quantitatively determine whether this assertion is credible, based on the created evidence. Our approach tries to fill the gap in automated claim validation for less-resourced languages and is showcased on the Greek language, complemented by the training of relevant semantic textual similarity (STS) and natural language inference (NLI) models that are evaluated on translated versions of common benchmarks.
NamedCurves: Learned Image Enhancement via Color Naming
Venue: arXiv cs.CV 2024
Year: 2024

A popular method for enhancing images involves learning the style of a professional photo editor using pairs of training images comprised of the original input with the editor-enhanced version. When manipulating images, many editing tools offer a feature that allows the user to manipulate a limited selection of familiar colors. Editing by color name allows easy adjustment of elements like the "blue" of the sky or the "green" of trees. Inspired by this approach to color manipulation, we propose NamedCurves, a learning-based image enhancement technique that separates the image into a small set of named colors. Our method learns to globally adjust the image for each specific named color via tone curves and then combines the images using an attention-based fusion mechanism to mimic spatial editing. We demonstrate the effectiveness of our method against several competing methods on the well-known Adobe 5K dataset and the PPR10K dataset, showing notable improvements.
Learning a Mini-batch Graph Transformer via Two-stage Interaction Augmentation
Venue: arXiv cs.LG 2024
Year: 2024

Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robustness. However, existing methods for processing local information either rely on sampling or simple aggregation, which respectively result in the loss and squashing of critical neighbor information.Moreover, the limited number of nodes in each mini-batch restricts the model's capacity to capture the global characteristic of the graph. In this paper, we propose LGMformer, a novel MGT model that employs a two-stage augmented interaction strategy, transitioning from local to global perspectives, to address the aforementioned bottlenecks.The local interaction augmentation (LIA) presents a neighbor-target interaction Transformer (NTIformer) to acquire an insightful understanding of the co-interaction patterns between neighbors and the target node, resulting in a locally effective token list that serves as input for the MGT. In contrast, global interaction augmentation (GIA) adopts a cross-attention mechanism to incorporate entire graph prototypes into the target node epresentation, thereby compensating for the global graph information to ensure a more comprehensive perception. To this end, LGMformer achieves the enhancement of node representations under the MGT paradigm.Experimental results related to node classification on the ten benchmark datasets demonstrate the effectiveness of the proposed method. Our code is available at https://github.com/l-wd/LGMformer.
Resilience in PON-based data centre architectures with two-tier cascaded AWGRs
Venue: arXiv cs.NI 2024
Year: 2024

This paper investigates the performance of a two-tier AWGR-based Passive Optical Network (PON) data centre architecture against an AWGR-based PON data centre architecture by considering various scenarios involving link failures to evaluate the resilience of both designs. To optimize traffic routing under different failure scenarios, a Mixed Integer Linear Programming (MILP) model is developed and the power consumption and delay performance is assessed. The results demonstrate that the two-tier AWGR architecture reduced the power consumption and the delay compared to the AWGR-based architecture by up to 10% and 61%, respectively.
Metric Learning for Clifford Group Equivariant Neural Networks
Venue: arXiv cs.LG 2024
Year: 2024

Clifford Group Equivariant Neural Networks (CGENNs) leverage Clifford algebras and multivectors as an alternative approach to incorporating group equivariance to ensure symmetry constraints in neural representations. In principle, this formulation generalizes to orthogonal groups and preserves equivariance regardless of the metric signature. However, previous works have restricted internal network representations to Euclidean or Minkowski (pseudo-)metrics, handpicked depending on the problem at hand. In this work, we propose an alternative method that enables the metric to be learned in a data-driven fashion, allowing the CGENN network to learn more flexible representations. Specifically, we populate metric matrices fully, ensuring they are symmetric by construction, and leverage eigenvalue decomposition to integrate this additional learnable component into the original CGENN formulation in a principled manner. Additionally, we motivate our method using insights from category theory, which enables us to explain Clifford algebras as a categorical construction and guarantee the mathematical soundness of our approach. We validate our method in various tasks and showcase the advantages of learning more flexible latent metric representations. The code and data are available at https://github.com/rick-ali/Metric-Learning-for-CGENNs
MOAT: Securely Mitigating Rowhammer with Per-Row Activation Counters
Venue: arXiv cs.CR 2024
Year: 2024

The security vulnerabilities due to Rowhammer have worsened over the last decade, with existing in-DRAM solutions, such as TRR, getting broken with simple patterns. In response, the DDR5 specifications have been extended to support Per-Row Activation Counting (PRAC), with counters inlined with each row, and ALERT-Back-Off (ABO) to stop the memory controller if the DRAM needs more time to mitigate. Although PRAC+ABO represents a strong advance in Rowhammer protection, they are just a framework, and the actual security is dependent on the implementation. In this paper, we first show that a prior work, Panopticon (which formed the basis for PRAC+ABO), is insecure, as our Jailbreak pattern can cause 1150 activations on an attack row for Panopticon configured for a threshold of 128. We then propose MOAT, a provably secure design, which uses two internal thresholds: ETH, an "Eligibility Threshold" for mitigating a row, and ATH, an "ALERT Threshold" for initiating an ABO. As JEDEC specifications permit a few activations between consecutive ALERTs, we also study how an attacker can exploit such activations to inflict more activations than ATH on an attack row and thus increase the tolerated Rowhammer threshold. Our analysis shows that MOAT configured with ATH=64 can safely tolerate a Rowhammer threshold of 99. Finally, we also study performance attacks and denial-of-service due to ALERTs. Our evaluations, with SPEC and GAP workloads, show that MOAT with ATH=64 incurs an average slowdown of 0.28\% and 7 bytes of SRAM per bank.
Reproducibility of Issues Reported in Stack Overflow Questions: Challenges, Impact & Estimation
Venue: arXiv cs.SE 2024
Year: 2024

Software developers often submit questions to technical Q&A sites like Stack Overflow (SO) to resolve code-level problems. In practice, they include example code snippets with questions to explain the programming issues. Existing research suggests that users attempt to reproduce the reported issues using given code snippets when answering questions. Unfortunately, such code snippets could not always reproduce the issues due to several unmet challenges that prevent questions from receiving appropriate and prompt solutions. One previous study investigated reproducibility challenges and produced a catalog. However, how the practitioners perceive this challenge catalog is unknown. Practitioners' perspectives are inevitable in validating these challenges and estimating their severity. This study first surveyed 53 practitioners to understand their perspectives on reproducibility challenges. We attempt to (a) see whether they agree with these challenges, (b) determine the impact of each challenge on answering questions, and (c) identify the need for tools to promote reproducibility. Survey results show that - (a) about 90% of the participants agree with the challenges, (b) "missing an important part of code" most severely hurt reproducibility, and (c) participants strongly recommend introducing automated tool support to promote reproducibility. Second, we extract \emph{nine} code-based features (e.g., LOC, compilability) and build five Machine Learning (ML) models to predict issue reproducibility. Early detection might help users improve code snippets and their reproducibility. Our models achieve 84.5% precision, 83.0% recall, 82.8% F1-score, and 82.8% overall accuracy, which are highly promising. Third, we systematically interpret the ML model and explain how code snippets with reproducible issues differ from those with irreproducible issues.
What Appears Appealing May Not be Significant! -- A Clinical Perspective of Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

Various trending image generative techniques, such as diffusion models, have enabled visually appealing outcomes with just text-based descriptions. Unlike general images, where assessing the quality and alignment with text descriptions is trivial, establishing such a relation in a clinical setting proves challenging. This work investigates various strategies to evaluate the clinical significance of synthetic polyp images of different pathologies. We further explore if a relation could be established between qualitative results and their clinical relevance.
The weight distributions of some linear codes derived from Kloosterman sums
Venue: arXiv cs.IT 2024
Year: 2024

Linear codes with few weights have applications in data storage systems, secret sharing schemes, and authentication codes. In this paper, some kinds of p-ary linear codes with few weights are constructed by use of the given de ning set, where p is a prime. Their weight distributions are determined based on Kloosterman sums over nite elds. In addition, some linear codes we given is minimal.
Data Imputation using Large Language Model to Accelerate Recommendation System
Venue: arXiv cs.IR 2024
Year: 2024

This paper aims to address the challenge of sparse and missing data in recommendation systems, a significant hurdle in the age of big data. Traditional imputation methods struggle to capture complex relationships within the data. We propose a novel approach that fine-tune Large Language Model (LLM) and use it impute missing data for recommendation systems. LLM which is trained on vast amounts of text, is able to understand complex relationship among data and intelligently fill in missing information. This enriched data is then used by the recommendation system to generate more accurate and personalized suggestions, ultimately enhancing the user experience. We evaluate our LLM-based imputation method across various tasks within the recommendation system domain, including single classification, multi-classification, and regression compared to traditional data imputation methods. By demonstrating the superiority of LLM imputation over traditional methods, we establish its potential for improving recommendation system performance.
All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
Venue: arXiv cs.IR 2024
Year: 2024

Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users' diverse information needs. The emergence of large language models (LLMs) offers a new horizon for redefining recommender systems with vast general knowledge and reasoning capabilities. Standing across this LLM era, we aim to integrate recommender systems into a broader picture, and pave the way for more comprehensive solutions for future research. Therefore, we first offer a comprehensive overview of the technical progression of recommender systems, particularly focusing on language foundation models and their applications in recommendation. We identify two evolution paths of modern recommender systems -- via list-wise recommendation and conversational recommendation. These two paths finally converge at LLM agents with superior capabilities of long-term memory, reflection, and tool intelligence. Along these two paths, we point out that the information effectiveness of the recommendation is increased, while the user's acquisition cost is decreased. Technical features, research methodologies, and inherent challenges for each milestone along the path are carefully investigated -- from traditional list-wise recommendation to LLM-enhanced recommendation to recommendation with LLM agents. Finally, we highlight several unresolved challenges crucial for the development of future personalization technologies and interfaces and discuss the future prospects.
Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions
Venue: arXiv cs.IR 2024
Year: 2024

In recommendation systems, new items are continuously introduced, initially lacking interaction records but gradually accumulating them over time. Accurately predicting the click-through rate (CTR) for these items is crucial for enhancing both revenue and user experience. While existing methods focus on enhancing item ID embeddings for new items within general CTR models, they tend to adopt a global feature interaction approach, often overshadowing new items with sparse data by those with abundant interactions. Addressing this, our work introduces EmerG, a novel approach that warms up cold-start CTR prediction by learning item-specific feature interaction patterns. EmerG utilizes hypernetworks to generate an item-specific feature graph based on item characteristics, which is then processed by a Graph Neural Network (GNN). This GNN is specially tailored to provably capture feature interactions at any order through a customized message passing mechanism. We further design a meta learning strategy that optimizes parameters of hypernetworks and GNN across various item CTR prediction tasks, while only adjusting a minimal set of item-specific parameters within each task. This strategy effectively reduces the risk of overfitting when dealing with limited data. Extensive experiments on benchmark datasets validate that EmerG consistently performs the best given no, a few and sufficient instances of new items.
ChatLogic: Integrating Logic Programming with Large Language Models for Multi-Step Reasoning
Venue: arXiv cs.AI 2024
Year: 2024

Large language models (LLMs) such as ChatGPT and GPT-4 have demonstrated impressive capabilities in various generative tasks. However, their performance is often hampered by limitations in accessing and leveraging long-term memory, leading to specific vulnerabilities and biases, especially during long interactions. This paper introduces ChatLogic, an innovative framework specifically targeted at LLM reasoning tasks that can enhance the performance of LLMs in multi-step deductive reasoning tasks by integrating logic programming. In ChatLogic, the language model plays a central role, acting as a controller and participating in every system operation stage. We propose a novel method of converting logic problems into symbolic integration with an inference engine. This approach leverages large language models' situational understanding and imitation skills and uses symbolic memory to enhance multi-step deductive reasoning capabilities. Our results show that the ChatLogic framework significantly improves the multi-step reasoning capabilities of LLMs. The source code and data are available at \url{https://github.com/Strong-AI-Lab/ChatLogic}
Towards Enabling 5G-NTN Satellite Communications for Manned and Unmanned Rotary Wing Aircraft
Venue: arXiv cs.NI 2024
Year: 2024

Satellite Communications (SatCom) are a backbone of worldwide development. In contrast with the past, when the GEO satellites were the only means for such connectivity, nowadays the multi-orbital connectivity is emerging, especially with the use of satellite constellations. Simultaneously, SatCom enabled the so-called In-Flight Connectivity, while with the advent of 5G-NTN, the development of this market is being accelerated. However, there are still various missing points before such a technology becomes mainstream, especially in the case of Rotary Wing Aircraft (RWA). Indeed, due to their particular characteristics, such as the low altitude flights and the blade interference, there are still open challenges. In this work, an End-to-End (E2E) analysis for the performance of SatCom under 5G-NTN for manned and unmanned RWA is performed. Various scenarios are examined, and related requirements are shown. The effects of blades and other characteristics of the RWA are established, and simulations for these cases are developed. Results along with related discussion are presented, while future directions for development are suggested. This work is part of the ESA ACROSS-AIR project.
Identity Chain
Venue: arXiv cs.GT 2024
Year: 2024

The first generation of cryptocurrencies introduced revolutionary concepts, yet faced challenges in privacy and regulatory compliance. While subsequent cryptocurrencies aimed to address privacy concerns (like Zcash and Monero), they often conflicted with regulatory frameworks, hindering broader adoption. In response, inspired by recent researches about privacy and accountability and incentive techniques in Blockchain, we propose IdentityChain as a novel framework that integrates privacy and accountability principles, leading to a robust system equipped with adaptable rules. IdentityChain is a KYC (Know Your Customer) service on top of a public Blockchain (e.g., Ethereum, Ton, Polygon). The goal is to maintain privacy while ensuring compliance with existing regulations. Privacy is one of the key characteristics of IdentityChain, it's crucial for preventing conflicts of interests further discussed how. Accountability is also one of the main characteristics of IdentityChain and prevents from misbehave of users. Privacy and accountability together wouldn't be possible unless advancements in cryptography.
Unexpected Benefits of Self-Modeling in Neural Systems
Venue: arXiv cs.LG 2024
Year: 2024

Self-models have been a topic of great interest for decades in studies of human cognition and more recently in machine learning. Yet what benefits do self-models confer? Here we show that when artificial networks learn to predict their internal states as an auxiliary task, they change in a fundamental way. To better perform the self-model task, the network learns to make itself simpler, more regularized, more parameter-efficient, and therefore more amenable to being predictively modeled. To test the hypothesis of self-regularizing through self-modeling, we used a range of network architectures performing three classification tasks across two modalities. In all cases, adding self-modeling caused a significant reduction in network complexity. The reduction was observed in two ways. First, the distribution of weights was narrower when self-modeling was present. Second, a measure of network complexity, the real log canonical threshold (RLCT), was smaller when self-modeling was present. Not only were measures of complexity reduced, but the reduction became more pronounced as greater training weight was placed on the auxiliary task of self-modeling. These results strongly support the hypothesis that self-modeling is more than simply a network learning to predict itself. The learning has a restructuring effect, reducing complexity and increasing parameter efficiency. This self-regularization may help explain some of the benefits of self-models reported in recent machine learning literature, as well as the adaptive value of self-models to biological systems. In particular, these findings may shed light on the possible interaction between the ability to model oneself and the ability to be more easily modeled by others in a social or cooperative context.
Vector Field Attention for Deformable Image Registration
Venue: arXiv cs.CV 2024
Year: 2024

Deformable image registration establishes non-linear spatial correspondences between fixed and moving images. Deep learning-based deformable registration methods have been widely studied in recent years due to their speed advantage over traditional algorithms as well as their better accuracy. Most existing deep learning-based methods require neural networks to encode location information in their feature maps and predict displacement or deformation fields though convolutional or fully connected layers from these high-dimensional feature maps. In this work, we present Vector Field Attention (VFA), a novel framework that enhances the efficiency of the existing network design by enabling direct retrieval of location correspondences. VFA uses neural networks to extract multi-resolution feature maps from the fixed and moving images and then retrieves pixel-level correspondences based on feature similarity. The retrieval is achieved with a novel attention module without the need of learnable parameters. VFA is trained end-to-end in either a supervised or unsupervised manner. We evaluated VFA for intra- and inter-modality registration and for unsupervised and semi-supervised registration using public datasets, and we also evaluated it on the Learn2Reg challenge. Experimental results demonstrate the superior performance of VFA compared to existing methods. The source code of VFA is publicly available at https://github.com/yihao6/vfa/.