GenAI4ELab/papercli-papers huggingface dataset:
Uncovering the Deceptions: An Analysis on Audio Spoofing Detection and Future Prospects
Authors: Rishabh Ranjan; Mayank Vatsa; Richa Singh
Venue: IJCAI 2023 (main)

Abstract: Audio has become an increasingly crucial biometric modality due to its ability to provide an intuitive way for humans to interact with machines. It is currently being used for a range of applications including person authentication to banking to virtual assistants. Research has shown that these systems are also susceptible to spoofing and attacks. Therefore, protecting audio processing systems against fraudulent activities such as identity theft, financial fraud, and spreading misinformation, is of paramount importance. This paper reviews the current state-of-the-art techniques for detecting audio spoofing and discusses the current challenges along with open research problems. The paper further highlights the importance of considering the ethical and privacy implications of audio spoofing detection systems. Lastly, the work aims to accentuate the need for building more robust and generalizable methods, the integration of automatic speaker verification and countermeasure systems, and better evaluation protocols.
Heuristic-Search Approaches for the Multi-Objective Shortest-Path Problem: Progress and Research Opportunities
Authors: Oren Salzman; Ariel Felner; Carlos Hernández; Han Zhang; Shao-Hung Chan; Sven Koenig
Venue: IJCAI 2023 (main)

Abstract: In the multi-objective shortest-path problem we are interested in computing a path, or a set of paths that simultaneously balance multiple cost functions. This problem is important for a diverse range of applications such as transporting hazardous materials considering travel distance and risk. This family of problems is not new with results dating back to the 1970's. Nevertheless, the significant progress made in the field of heuristic search resulted in a new and growing interest in the sub-field of multi-objective search. Consequently, in this paper we review the fundamental problems and techniques common to most algorithms and provide a general overview of the field. We then continue to describe recent work with an emphasis on new challenges that emerged and the resulting research opportunities.
A Survey of Federated Evaluation in Federated Learning
Authors: Behnaz Soltani; Yipeng Zhou; Venus Haghighi; John C. S. Lui
Venue: IJCAI 2023 (main)

Abstract: In traditional machine learning, it is trivial to conduct model evaluation since all data samples are managed centrally by a server. However, model evaluation becomes a challenging problem in federated learning (FL), which is called federated evaluation in this work. This is because clients do not expose their original data to preserve data privacy. Federated evaluation plays a vital role in client selection, incentive mechanism design, malicious attack detection, etc. In this paper, we provide the first comprehensive survey of existing federated evaluation methods. Moreover, we explore various applications of federated evaluation for enhancing FL performance and finally present future research directions by envisioning some challenges.
Transformers in Time Series: A Survey
Authors: Qingsong Wen; Tian Zhou; Chaoli Zhang; Weiqi Chen; Ziqing Ma; Junchi Yan; Liang Sun
Venue: IJCAI 2023 (main)

Abstract: Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community. Among multiple advantages of Transformers, the ability to capture long-range dependencies and interactions is especially attractive for time series modeling, leading to exciting progress in various time series applications. In this paper, we systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations. In particular, we examine the development of time series Transformers in two perspectives. From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis. From the perspective of applications, we categorize time series Transformers based on common tasks including forecasting, anomaly detection, and classification. Empirically, we perform robust analysis, model size analysis, and seasonal-trend decomposition analysis to study how Transformers perform in time series. Finally, we discuss and suggest future directions to provide useful research guidance.
A Systematic Survey of Chemical Pre-trained Models
Authors: Jun Xia; Yanqiao Zhu; Yuanqi Du; Stan Z. Li
Venue: IJCAI 2023 (main)

Abstract: Deep learning has achieved remarkable success in learning representations for molecules, which is crucial for various biochemical applications, ranging from property prediction to drug design. However, training Deep Neural Networks (DNNs) from scratch often requires abundant labeled molecules, which are expensive to acquire in the real world. To alleviate this issue, tremendous efforts have been devoted to Chemical Pre-trained Models (CPMs), where DNNs are pre-trained using large-scale unlabeled molecular databases and then fine-tuned over specific downstream tasks. Despite the prosperity, there lacks a systematic review of this fast-growing field. In this paper, we present the first survey that summarizes the current progress of CPMs. We first highlight the limitations of training molecular representation models from scratch to motivate CPM studies. Next, we systematically review recent advances on this topic from several key perspectives, including molecular descriptors, encoder architectures, pre-training strategies, and applications. We also highlight the challenges and promising avenues for future research, providing a useful resource for both machine learning and scientific communities.
Recent Advances in Direct Speech-to-text Translation
Authors: Chen Xu; Rong Ye; Qianqian Dong; Chengqi Zhao; Tom Ko; Mingxuan Wang; Tong Xiao; Jingbo Zhu
Venue: IJCAI 2023 (main)

Abstract: Recently, speech-to-text translation has attracted more and more attention and many studies have emerged rapidly. In this paper, we present a comprehensive survey on direct speech translation aiming to summarize the current state-of-the-art techniques. First, we categorize the existing research work into three directions based on the main challenges --- modeling burden, data scarcity, and application issues. To tackle the problem of modeling burden, two main structures have been proposed, encoder-decoder framework (Transformer and the variants) and multitask frameworks. For the challenge of data scarcity, recent work resorts to many sophisticated techniques, such as data augmentation, pre-training, knowledge distillation, and multilingual modeling. We analyze and summarize the application issues, which include real-time, segmentation, named entity, gender bias, and code-switching. Finally, we discuss some promising directions for future work.
A Survey on Masked Autoencoder for Visual Self-supervised Learning
Authors: Chaoning Zhang; Chenshuang Zhang; Junha Song; John Seon Keun Yi; In So Kweon
Venue: IJCAI 2023 (main)

Abstract: With the increasing popularity of masked autoencoders, self-supervised learning (SSL) in vision undertakes a similar trajectory as in NLP. Specifically, generative pretext tasks with the masked prediction have become a de facto standard SSL practice in NLP (e.g., BERT). By contrast, early attempts at generative methods in vision have been outperformed by their discriminative counterparts (like contrastive learning). However, the success of masked image modeling has revived the autoencoder-based visual pretraining method. As a milestone to bridge the gap with BERT in NLP, masked autoencoder in vision has attracted unprecedented attention. This work conducts a survey on masked autoencoders for visual SSL.
State-wise Safe Reinforcement Learning: A Survey
Authors: Weiye Zhao; Tairan He; Rui Chen; Tianhao Wei; Changliu Liu
Venue: IJCAI 2023 (main)

Abstract: Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation environments, applying RL to real-world applications still faces many challenges. A major concern is safety, in another word, constraint satisfaction. State-wise constraints are one of the most common constraints in real-world applications and one of the most challenging constraints in Safe RL. Enforcing state-wise constraints is necessary and essential to many challenging tasks such as autonomous driving, robot manipulation. This paper provides a comprehensive review of existing approaches that address state-wise constraints in RL. Under the framework of State-wise Constrained Markov Decision Process (SCMDP), we will discuss the connections, differences, and trade-offs of existing approaches in terms of (i) safety guarantee and scalability, (ii) safety and reward performance, and (iii) safety after convergence and during training. We also summarize limitations of current methods and discuss potential future directions.
A Survey on Efficient Training of Transformers
Authors: Bohan Zhuang; Jing Liu; Zizheng Pan; Haoyu He; Yuetian Weng; Chunhua Shen
Venue: IJCAI 2023 (main)

Abstract: Recent advances in Transformers have come with a huge requirement on computing resources, highlighting the importance of developing efficient training techniques to make Transformer training faster, at lower cost, and to higher accuracy by the efficient use of computation and memory resources. This survey provides the first systematic overview of the efficient training of Transformers, covering the recent progress in acceleration arithmetic and hardware, with a focus on the former. We analyze and compare methods that save computation and memory costs for intermediate tensors during training, together with techniques on hardware/algorithm co-design. We finally discuss challenges and promising areas for future research.
Conjure: Automatic Generation of Constraint Models from Problem Specifications (Extended Abstract)
Authors: Özgür Akgün; Alan M. Frisch; Ian P. Gent; Christopher Jefferson; Ian Miguel; Peter Nightingale
Venue: IJCAI 2023 (main)

Abstract: When solving a combinatorial problem, the formulation or model of the problem is critical to the efficiency of the solver. Automating the modelling process has long been of interest given the expertise and time required to develop an effective model of a particular problem. We describe a method to automatically produce constraint models from a problem specification written in the abstract constraint specification language Essence. Our approach is to incrementally refine the specification into a concrete model by applying a chosen refinement rule at each step. Any non-trivial specification may be refined in multiple ways, creating a diverse space of models to choose from. The handling of symmetries is a particularly important aspect of automated modelling. We show how modelling symmetries may be broken automatically as they enter a model during refinement, removing the need for an expensive symmetry detection step following model formulation. Our approach is implemented in a system called Conjure. We compare the models produced by Conjure to constraint models from the literature that are known to be effective. Our empirical results confirm that Conjure can reproduce successfully the kernels of the constraint models of 42 benchmark problems found in the literature.
Survey and Evaluation of Causal Discovery Methods for Time Series (Extended Abstract)
Authors: Charles K. Assaad; Emilie Devijver; Eric Gaussier
Venue: IJCAI 2023 (main)

Abstract: We introduce in this survey the major concepts, models, and algorithms proposed so far to infer causal relations from observational time series, a task usually referred to as causal discovery in time series. To do so, after a description of the underlying concepts and modelling assumptions, we present different methods according to the family of approaches they belong to: Granger causality, constraint-based approaches, noise-based approaches, score-based approaches, logic-based approaches, topology-based approaches, and difference-based approaches. We then evaluate several representative methods to illustrate the behaviour of different families of approaches. This illustration is conducted on both artificial and real datasets, with different characteristics. The main conclusions one can draw from this survey is that causal discovery in times series is an active research field in which new methods (in every family of approaches) are regularly proposed, and that no family or method stands out in all situations. Indeed, they all rely on assumptions that may or may not be appropriate for a particular dataset.
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features (Extended Abstract)
Authors: Taha Belkhouja; Janardhan Rao Doppa
Venue: IJCAI 2023 (main)

Abstract: Time-series data arises in many real-world applications (e.g., mobile health) and deep neural networks (DNNs) have shown great success in solving them. Despite their success, little is known about their robustness to adversarial attacks. In this paper, we propose a novel adversarial framework referred to as Time-Series Attacks via STATistical Features (TSA-STAT). To address the unique challenges of time-series domain, TSA-STAT employs constraints on statistical features of the time-series data to construct adversarial examples. Optimized polynomial transformations are used to create attacks that are more effective (in terms of successfully fooling DNNs) than those based on additive perturbations. We also provide certified bounds on the norm of the statistical features for constructing adversarial examples. Our experiments on diverse real-world benchmark datasets show the effectiveness of TSA-STAT in fooling DNNs for time-series domain and in improving their robustness.
Constraint Solving Approaches to the Business-to-Business Meeting Scheduling Problem (Extended Abstract)
Authors: Miquel Bofill; Jordi Coll; Marc Garcia; Jesús Giráldez-Cru; Gilles Pesant; Josep Suy; Mateu Villaret
Venue: IJCAI 2023 (main)

Abstract: The B2B Meeting Scheduling Optimization Problem (B2BSP) consists of scheduling a set of meetings between given pairs of participants to an event, minimizing idle time periods in participants' schedules, while taking into account participants’ availability and accommodation capacity. Therefore, it constitutes a challenging combinatorial problem in many real-world B2B events. This work presents a comparative study of several approaches to solve this problem. They are based on Constraint Programming (CP), Mixed Integer Programming (MIP) and Maximum Satisfiability (MaxSAT). The CP approach relies on using global constraints and has been implemented in MiniZinc to be able to compare CP, Lazy Clause Generation and MIP as solving technologies in this setting. A pure MIP encoding is also presented. Finally, an alternative viewpoint is considered under MaxSAT, showing the best performance when considering some implied constraints. Experimental results on real world B2B instances, as well as on crafted ones, show that the MaxSAT approach is the one with the best performance for this problem, exhibiting better solving times, sometimes even orders of magnitude smaller than CP and MIP.
SAT Encodings for Pseudo-Boolean Constraints Together With At-Most-One Constraints (Extended Abstract)
Authors: Miquel Bofill; Jordi Coll; Peter Nightingale; Josep Suy; Felix Ulrich-Oltean; Mateu Villaret
Venue: IJCAI 2023 (main)

Abstract: When solving a combinatorial problem using propositional satisfiability (SAT), the encoding of the constraints is of vital importance. Pseudo-Boolean (PB) constraints appear frequently in a wide variety of problems. When PB constraints occur together with at-most-one (AMO) constraints over the same variables, they can be combined into PB(AMO) constraints. In this paper we present new encodings for PB(AMO) constraints. Our experiments show that these encodings can be substantially smaller than those of PB constraints and allow many more instances to be solved within a time limit. We also observed that there is no single overall winner among the considered encodings, but efficiency of each encoding may depend on PB(AMO) characteristics such as the magnitude of coefficient values.
A False Sense of Security (Extended Abstract)
Authors: Piero A. Bonatti
Venue: IJCAI 2023 (main)

Abstract: The growing literature on confidentiality in knowledge representation and reasoning sometimes may cause a false sense of security, due to lack of details about the attacker, and some misconceptions about security-related concepts. This paper analyzes the vulnerabilities of some recent knowledge protection methods to increase the awareness about their actual effectiveness and their mutual differences.
Optimizing the Computation of Overriding in DLN (Extended Abstract)
Authors: Piero A. Bonatti; Iliana Petrova; Luigi Sauro
Venue: IJCAI 2023 (main)

Abstract: One of the factors that hinder the adoption of nonmonotonic description logics in applications is performance. Even when monotonic and nonmonotonic inferences have the same asymptotic complexity, the implementation of nonmonotonic reasoning may be significantly slower. The family of nonmonotonic logics DLN is no exception to this behavior. We address this issue by introducing two provably correct and complete optimizations for reasoning in DLN. The first optimization is a module extractor that has the purpose of focusing reasoning on a relevant subset of the knowledge base. The second, called optimistic evaluation, aims at exploiting incremental reasoning in a better way. Extensive experimental evaluation shows that the optimized DLN reasoning is often compatible with interactive query answering, thus bringing nonmonotonic description logics closer to practical applications.
SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Extended Abstract)
Authors: Radu Ciucanu; Pascal Lafourcade; Gael Marcadet; Marta Soare
Venue: IJCAI 2023 (main)

Abstract: We tackle the problem of secure cumulative reward maximization in multi-armed bandits in a cross-silo federated learning setting. Under the orchestration of a central server, each data owner participating at the cumulative reward computation has the guarantee that its raw data is not seen by some other participant. We rely on cryptographic schemes and propose SAMBA, a generic framework for Secure federAted Multi-armed BAndits. We show that SAMBA returns the same cumulative reward as the non-secure versions of bandit algorithms, while satisfying formally proven security properties. We also show that the overhead due to cryptographic primitives is linear in the size of the input, which is confirmed by our implementation.
Data-Driven Revision of Conditional Norms in Multi-Agent Systems (Extended Abstract)
Authors: Davide Dell'Anna; Natasha Alechina; Fabiano Dalpiaz; Mehdi Dastani; Brian Logan
Venue: IJCAI 2023 (main)

Abstract: In multi-agent systems, norm enforcement is a mechanism for steering the behavior of individual agents in order to achieve desired system-level objectives. Due to the dynamics of multi-agent systems, however, it is hard to design norms that guarantee the achievement of the objectives in every operating context. Also, these objectives may change over time, thereby making previously defined norms ineffective. In this paper, we investigate the use of system execution data to automatically synthesise and revise conditional prohibitions with deadlines, a type of norms aimed at preventing agents from exhibiting certain patterns of behaviors. We propose DDNR (Data-Driven Norm Revision), a data-driven approach to norm revision that synthesises revised norms with respect to a data set of traces describing the behavior of the agents in the system. We evaluate DDNR using a state-of-the-art, off-the-shelf urban traffic simulator. The results show that DDNR synthesises revised norms that are significantly more accurate than the original norms in distinguishing adequate and inadequate behaviors for the achievement of the system-level objectives.
Interpretable Local Concept-based Explanation with Human Feedback to Predict All-cause Mortality (Extended Abstract)
Authors: Radwa El Shawi; Mouaz Al-Mallah
Venue: IJCAI 2023 (main)

Abstract: Machine learning models are incorporated in different fields and disciplines, some of which require high accountability and transparency, for example, the healthcare sector. A widely used category of explanation techniques attempts to explain models' predictions by quantifying the importance score of each input feature. However, summarizing such scores to provide human-interpretable explanations is challenging. Another category of explanation techniques focuses on learning a domain representation in terms of high-level human-understandable concepts and then utilizing them to explain predictions. These explanations are hampered by how concepts are constructed, which is not intrinsically interpretable. To this end, we propose Concept-based Local Explanations with Feedback (CLEF), a novel local model agnostic explanation framework for learning a set of high-level transparent concept definitions in high-dimensional tabular data that uses clinician-labeled concepts rather than raw features.
Core Challenges in Embodied Vision-Language Planning (Extended Abstract)
Authors: Jonathan Francis; Nariaki Kitamura; Felix Labelle; Xiaopeng Lu; Ingrid Navarro; Jean Oh
Venue: IJCAI 2023 (main)

Abstract: Recent advances in the areas of Multimodal Machine Learning and Artificial Intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Robotics. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly leverage computer vision and natural language for interaction in physical environments. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulators, and datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalisability and furthers real-world deployment.