OpenRaiser/Intern-Atlas huggingface dataset:
UniRecSys: A Unified Framework for Personalized, Group, Package, and Package-to-Group Recommendations
Venue: arXiv cs.IR 2024
Year: 2024

Recommender systems aim to enhance the overall user experience by providing tailored recommendations for a variety of products and services. These systems help users make more informed decisions, leading to greater user engagement with the platform. However, the implementation of these systems largely depends on the context, which can vary from recommending an item or package to a user or a group. This requires careful exploration of several models during the deployment, as there is no comprehensive and unified approach that deals with recommendations at different levels. Furthermore, these individual models must be closely attuned to their generated recommendations depending on the context to prevent significant variation in their generated recommendations. In this paper, we propose a novel unified recommendation framework that addresses all four recommendation tasks, namely, personalized, group, package, and package-to-group recommendation, filling the gap in the current research landscape. The proposed framework can be integrated with most of the traditional matrix factorization-based collaborative filtering (CF) models. This research underscores the significance of including group and package information while learning latent representations of users and items for personalized recommendations. These components help in exploiting a rich latent representation of the user/item by enforcing them to align closely with their corresponding group/package representation. We consider two prominent CF techniques, namely Regularized Matrix Factorization and Maximum Margin Matrix factorization, as the baseline models and demonstrate their customization to various recommendation tasks. Experimental results on two publicly available datasets are reported, comparing them to other baseline approaches for various recommendation tasks.
Engineering LaCAM$^\ast$: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding
Venue: arXiv cs.AI 2024
Year: 2024

This paper addresses the challenges of real-time, large-scale, and near-optimal multi-agent pathfinding (MAPF) through enhancements to the recently proposed LaCAM* algorithm. LaCAM* is a scalable search-based algorithm that guarantees the eventual finding of optimal solutions for cumulative transition costs. While it has demonstrated remarkable planning success rates, surpassing various state-of-the-art MAPF methods, its initial solution quality is far from optimal, and its convergence speed to the optimum is slow. To overcome these limitations, this paper introduces several improvement techniques, partly drawing inspiration from other MAPF methods. We provide empirical evidence that the fusion of these techniques significantly improves the solution quality of LaCAM*, thus further pushing the boundaries of MAPF algorithms.
Finding a Sparse Connected Spanning Subgraph in a non-Uniform Failure Model
Venue: arXiv cs.DM 2024
Year: 2024

We study a generalization of the classic Spanning Tree problem that allows for a non-uniform failure model. More precisely, edges are either \emph{safe} or \emph{unsafe} and we assume that failures only affect unsafe edges. In Unweighted Flexible Graph Connectivity we are given an undirected graph $G = (V,E)$ in which the edge set $E$ is partitioned into a set $S$ of safe edges and a set $U$ of unsafe edges and the task is to find a set $T$ of at most $k$ edges such that $T - \{u\}$ is connected and spans $V$ for any unsafe edge $u \in T$. Unweighted Flexible Graph Connectivity generalizes both Spanning Tree and Hamiltonian Cycle. We study Unweighted Flexible Graph Connectivity in terms of fixed-parameter tractability (FPT). We show an almost complete dichotomy on which parameters lead to fixed-parameter tractability and which lead to hardness. To this end, we obtain FPT-time algorithms with respect to the vertex deletion distance to cluster graphs and with respect to the treewidth. By exploiting the close relationship to Hamiltonian Cycle, we show that FPT-time algorithms for many smaller parameters are unlikely under standard parameterized complexity assumptions. Regarding problem-specific parameters, we observe that Unweighted Flexible Graph Connectivity} admits an FPT-time algorithm when parameterized by the number of unsafe edges. Furthermore, we investigate a below-upper-bound parameter for the number of edges of a solution. We show that this parameter also leads to an FPT-time algorithm.
Fuzz4All: Universal Fuzzing with Large Language Models
Venue: arXiv cs.SE 2024
Year: 2024

Fuzzing has achieved tremendous success in discovering bugs and vulnerabilities in various software systems. Systems under test (SUTs) that take in programming or formal language as inputs, e.g., compilers, runtime engines, constraint solvers, and software libraries with accessible APIs, are especially important as they are fundamental building blocks of software development. However, existing fuzzers for such systems often target a specific language, and thus cannot be easily applied to other languages or even other versions of the same language. Moreover, the inputs generated by existing fuzzers are often limited to specific features of the input language, and thus can hardly reveal bugs related to other or new features. This paper presents Fuzz4All, the first fuzzer that is universal in the sense that it can target many different input languages and many different features of these languages. The key idea behind Fuzz4All is to leverage large language models (LLMs) as an input generation and mutation engine, which enables the approach to produce diverse and realistic inputs for any practically relevant language. To realize this potential, we present a novel autoprompting technique, which creates LLM prompts that are wellsuited for fuzzing, and a novel LLM-powered fuzzing loop, which iteratively updates the prompt to create new fuzzing inputs. We evaluate Fuzz4All on nine systems under test that take in six different languages (C, C++, Go, SMT2, Java and Python) as inputs. The evaluation shows, across all six languages, that universal fuzzing achieves higher coverage than existing, language-specific fuzzers. Furthermore, Fuzz4All has identified 98 bugs in widely used systems, such as GCC, Clang, Z3, CVC5, OpenJDK, and the Qiskit quantum computing platform, with 64 bugs already confirmed by developers as previously unknown.
BalanceVR: Balance Training to Increase Tolerance to Cybersickness in Immersive Virtual Reality
Venue: arXiv cs.HC 2024
Year: 2024

Cybersickness is a serious usability problem in virtual reality. Postural (or balance) instability theory has emerged as one of the major hypotheses for the cause of cybersickness. In this paper, we conducted a two-week-long experiment to observe the trends in user balance learning and sickness tolerance under different experimental conditions to analyze the potential inter-relationship between them. The experimental results have shown, aside from the obvious improvement in balance performance itself, that accompanying balance training had a stronger effect of increasing tolerance to cybersickness than mere exposure to VR. In addition, training in immersive VR was found to be more effective than using the 2D-based non-immersive medium, especially for the transfer effect to other non-training VR content.
Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
Venue: arXiv cs.AI 2024
Year: 2024

Ensuring alignment, which refers to making models behave in accordance with human intentions [1,2], has become a critical task before deploying large language models (LLMs) in real-world applications. For instance, OpenAI devoted six months to iteratively aligning GPT-4 before its release [3]. However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations. This obstacle hinders systematic iteration and deployment of LLMs. To address this issue, this paper presents a comprehensive survey of key dimensions that are crucial to consider when assessing LLM trustworthiness. The survey covers seven major categories of LLM trustworthiness: reliability, safety, fairness, resistance to misuse, explainability and reasoning, adherence to social norms, and robustness. Each major category is further divided into several sub-categories, resulting in a total of 29 sub-categories. Additionally, a subset of 8 sub-categories is selected for further investigation, where corresponding measurement studies are designed and conducted on several widely-used LLMs. The measurement results indicate that, in general, more aligned models tend to perform better in terms of overall trustworthiness. However, the effectiveness of alignment varies across the different trustworthiness categories considered. This highlights the importance of conducting more fine-grained analyses, testing, and making continuous improvements on LLM alignment. By shedding light on these key dimensions of LLM trustworthiness, this paper aims to provide valuable insights and guidance to practitioners in the field. Understanding and addressing these concerns will be crucial in achieving reliable and ethically sound deployment of LLMs in various applications.
Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification
Venue: arXiv cs.AI 2024
Year: 2024

Patent classification aims to assign multiple International Patent Classification (IPC) codes to a given patent. Recent methods for automatically classifying patents mainly focus on analyzing the text descriptions of patents. However, apart from the texts, each patent is also associated with some assignees, and the knowledge of their applied patents is often valuable for classification. Furthermore, the hierarchical taxonomy formulated by the IPC system provides important contextual information and enables models to leverage the correlations between IPC codes for more accurate classification. However, existing methods fail to incorporate the above aspects. In this paper, we propose an integrated framework that comprehensively considers the information on patents for patent classification. To be specific, we first present an IPC codes correlations learning module to derive their semantic representations via adaptively passing and aggregating messages within the same level and across different levels along the hierarchical taxonomy. Moreover, we design a historical application patterns learning component to incorporate the corresponding assignee's previous patents by a dual channel aggregation mechanism. Finally, we combine the contextual information of patent texts that contains the semantics of IPC codes, and assignees' sequential preferences to make predictions. Experiments on real-world datasets demonstrate the superiority of our approach over the existing methods. Besides, we present the model's ability to capture the temporal patterns of assignees and the semantic dependencies among IPC codes.
Breaking Language Barriers with MMTweets: Advancing Cross-Lingual Debunked Narrative Retrieval for Fact-Checking
Venue: arXiv cs.CL 2024
Year: 2024

Finding previously debunked narratives involves identifying claims that have already undergone fact-checking. The issue intensifies when similar false claims persist in multiple languages, despite the availability of debunks for several months in another language. Hence, automatically finding debunks (or fact-checks) in multiple languages is crucial to make the best use of scarce fact-checkers' resources. Mainly due to the lack of readily available data, this is an understudied problem, particularly when considering the cross-lingual scenario, i.e. the retrieval of debunks in a language different from the language of the online post being checked. This study introduces cross-lingual debunked narrative retrieval and addresses this research gap by: (i) creating Multilingual Misinformation Tweets (MMTweets): a dataset that stands out, featuring cross-lingual pairs, images, human annotations, and fine-grained labels, making it a comprehensive resource compared to its counterparts; (ii) conducting an extensive experiment to benchmark state-of-the-art cross-lingual retrieval models and introducing multistage retrieval methods tailored for the task; and (iii) comprehensively evaluating retrieval models for their cross-lingual and cross-dataset transfer capabilities within MMTweets, and conducting a retrieval latency analysis. We find that MMTweets presents challenges for cross-lingual debunked narrative retrieval, highlighting areas for improvement in retrieval models. Nonetheless, the study provides valuable insights for creating MMTweets datasets and optimising debunked narrative retrieval models to empower fact-checking endeavours. The dataset and annotation codebook are publicly available at https://doi.org/10.5281/zenodo.10637161.
Optimizing Performance of Feedforward and Convolutional Neural Networks through Dynamic Activation Functions
Venue: arXiv cs.LG 2024
Year: 2024

Deep learning training training algorithms are a huge success in recent years in many fields including speech, text,image video etc. Deeper and deeper layers are proposed with huge success with resnet structures having around 152 layers. Shallow convolution neural networks(CNN's) are still an active research, where some phenomena are still unexplained. Activation functions used in the network are of utmost importance, as they provide non linearity to the networks. Relu's are the most commonly used activation function.We show a complex piece-wise linear(PWL) activation in the hidden layer. We show that these PWL activations work much better than relu activations in our networks for convolution neural networks and multilayer perceptrons. Result comparison in PyTorch for shallow and deep CNNs are given to further strengthen our case.
MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction
Venue: arXiv cs.CV 2024
Year: 2024

High-definition (HD) map provides abundant and precise static environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomous driving system. In this paper, we present \textbf{Map} \textbf{TR}ansformer, an end-to-end framework for online vectorized HD map construction. We propose a unified permutation-equivalent modeling approach, \ie, modeling map element as a point set with a group of equivalent permutations, which accurately describes the shape of map element and stabilizes the learning process. We design a hierarchical query embedding scheme to flexibly encode structured map information and perform hierarchical bipartite matching for map element learning. To speed up convergence, we further introduce auxiliary one-to-many matching and dense supervision. The proposed method well copes with various map elements with arbitrary shapes. It runs at real-time inference speed and achieves state-of-the-art performance on both nuScenes and Argoverse2 datasets. Abundant qualitative results show stable and robust map construction quality in complex and various driving scenes. Code and more demos are available at \url{https://github.com/hustvl/MapTR} for facilitating further studies and applications.
Shared Memory-contention-aware Concurrent DNN Execution for Diversely Heterogeneous System-on-Chips
Venue: arXiv cs.DC 2024
Year: 2024

Two distinguishing features of state-of-the-art mobile and autonomous systems are 1) there are often multiple workloads, mainly deep neural network (DNN) inference, running concurrently and continuously; and 2) they operate on shared memory system-on-chips (SoC) that embed heterogeneous accelerators tailored for specific operations. State-of-the-art lacks efficient performance and resource management techniques necessary to either maximize total system throughput or minimize end-to-end workload latency. In this work, we propose HaX-CoNN, a novel scheme that characterizes and maps layers in concurrently executing DNN inference workloads to a diverse set of accelerators within a SoC. Our scheme uniquely takes per-layer execution characteristics, shared memory (SM) contention, and inter-accelerator transitions into account to find optimal schedules. We evaluate HaX-CoNN on NVIDIA Orin, NVIDIA Xavier, and Qualcomm Snapdragon 865 SoCs. Our experimental results indicate that HaX-CoNN minimizes memory contention by up to 45% and can improve latency and total throughput by up to 32% and 29%, respectively, compared to the state-of-the-art approaches.
Improving Ego-Cluster for Network Effect Measurement
Venue: arXiv cs.SI 2024
Year: 2024

The network effect, wherein one user's activity impacts another user, is common in social network platforms. Many new features in social networks are specifically designed to create a network effect, enhancing user engagement. For instance, content creators tend to produce more when their articles and posts receive positive feedback from followers. This paper discusses a new cluster-level experimentation methodology for measuring creator-side metrics in the context of A/B experiments. The methodology is designed to address cases where the experiment randomization unit and the metric measurement unit differ. It is a crucial part of LinkedIn's overall strategy to foster a robust creator community and ecosystem. The method is developed based on widely-cited research at LinkedIn but significantly improves the efficiency and flexibility of the clustering algorithm. This improvement results in a stronger capability for measuring creator-side metrics and an increased velocity for creator-related experiments.
Large Language Models for Telecom: Forthcoming Impact on the Industry
Venue: arXiv cs.IT 2024
Year: 2024

Large Language Models (LLMs), AI-driven models that can achieve general-purpose language understanding and generation, have emerged as a transformative force, revolutionizing fields well beyond Natural Language Processing (NLP) and garnering unprecedented attention. As LLM technology continues to progress, the telecom industry is facing the prospect of its impact on its landscape. To elucidate these implications, we delve into the inner workings of LLMs, providing insights into their current capabilities and limitations. We also examine the use cases that can be readily implemented in the telecom industry, streamlining tasks, such as anomalies resolutions and technical specifications comprehension, which currently hinder operational efficiency and demand significant manpower and expertise. Furthermore, we uncover essential research directions that deal with the distinctive challenges of utilizing the LLMs within the telecom domain. Addressing them represents a significant stride towards fully harnessing the potential of LLMs and unlocking their capabilities to the fullest extent within the telecom domain.
Large Language Models in Cryptocurrency Securities Cases: Can a GPT Model Meaningfully Assist Lawyers?
Venue: arXiv cs.AI 2024
Year: 2024

Large Language Models (LLMs) could be a useful tool for lawyers. However, empirical research on their effectiveness in conducting legal tasks is scant. We study securities cases involving cryptocurrencies as one of numerous contexts where AI could support the legal process, studying GPT-3.5's legal reasoning and ChatGPT's legal drafting capabilities. We examine whether a) GPT-3.5 can accurately determine which laws are potentially being violated from a fact pattern, and b) whether there is a difference in juror decision-making based on complaints written by a lawyer compared to ChatGPT. We feed fact patterns from real-life cases to GPT-3.5 and evaluate its ability to determine correct potential violations from the scenario and exclude spurious violations. Second, we had mock jurors assess complaints written by ChatGPT and lawyers. GPT-3.5's legal reasoning skills proved weak, though we expect improvement in future models, particularly given the violations it suggested tended to be correct (it merely missed additional, correct violations). ChatGPT performed better at legal drafting, and jurors' decisions were not statistically significantly associated with the author of the document upon which they based their decisions. Because GPT-3.5 cannot satisfactorily conduct legal reasoning tasks, it would be unlikely to be able to help lawyers in a meaningful way at this stage. However, ChatGPT's drafting skills (though, perhaps, still inferior to lawyers) could assist lawyers in providing legal services. Our research is the first to systematically study an LLM's legal drafting and reasoning capabilities in litigation, as well as in securities law and cryptocurrency-related misconduct.
Joint Beamforming Optimization for Active STAR-RIS-Assisted ISAC Systems
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, we investigate an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted integrated sensing and communications (ISAC) system, where the dual-function base station (DFBS) operates in full-duplex (FD) mode to provide communication services and performs targets sensing simultaneously. Meanwhile, we consider multiple targets and multiple users scenario as well as the self-interference at the FD DFBS. Through jointly optimizing the DFBS and active STAR-RIS beamforming under different work modes, our purpose is to achieve the maximum communication sum-rate, while satisfying the minimum radar signal-to-interference-plus-noise ratio (SINR) constraint, the active STAR-RIS hardware constraints and the total power constraint of DFBS and active STAR-RIS. To tackle the complex non-convex optimization problem formulated, an efficient alternating optimization algorithm is proposed. Specifically, the fractional programming method is first leveraged to turn the original problem into a more tractable one, and subsequently the transformed problem is decomposed into several sub-problems. Next, we develop a derivation method to obtain the closed-form expression of the radar receiving beamforming, and then the DFBS transmit beamforming is optimized under the radar SINR requirement and total power constraints. After that, the active STAR-RIS reflection and transmission beamforming are optimized by majorization minimization, complex circle manifold and convex optimization techniques. Finally, the proposed schemes are conducted through numerical simulations to show their benefits and efficiency.