pwc-archive/papers-with-abstracts huggingface dataset:
A New Class of Explanations for Classifiers with Non-Binary Features
Authors: Chunxi Ji, Adnan Darwiche
Date: 2023-04-28 00:00:00
Tasks: counterfactual, Counterfactual Explanation

Two types of explanations have been receiving increased attention in the literature when analyzing the decisions made by classifiers. The first type explains why a decision was made and is known as a sufficient reason for the decision, also an abductive explanation or a PI-explanation. The second type explains why some other decision was not made and is known as a necessary reason for the decision, also a contrastive or counterfactual explanation. These explanations were defined for classifiers with binary, discrete and, in some cases, continuous features. We show that these explanations can be significantly improved in the presence of non-binary features, leading to a new class of explanations that relay more information about decisions and the underlying classifiers. Necessary and sufficient reasons were also shown to be the prime implicates and implicants of the complete reason for a decision, which can be obtained using a quantification operator. We show that our improved notions of necessary and sufficient reasons are also prime implicates and implicants but for an improved notion of complete reason obtained by a new quantification operator that we also define and study.
Understanding accountability in algorithmic supply chains
Authors: Jennifer Cobbe, Michael Veale, Jatinder Singh
Date: 2023-04-28 00:00:00

Academic and policy proposals on algorithmic accountability often seek to understand algorithmic systems in their socio-technical context, recognising that they are produced by 'many hands'. Increasingly, however, algorithmic systems are also produced, deployed, and used within a supply chain comprising multiple actors tied together by flows of data between them. In such cases, it is the working together of an algorithmic supply chain of different actors who contribute to the production, deployment, use, and functionality that drives systems and produces particular outcomes. We argue that algorithmic accountability discussions must consider supply chains and the difficult implications they raise for the governance and accountability of algorithmic systems. In doing so, we explore algorithmic supply chains, locating them in their broader technical and political economic context and identifying some key features that should be understood in future work on algorithmic governance and accountability (particularly regarding general purpose AI services). To highlight ways forward and areas warranting attention, we further discuss some implications raised by supply chains: challenges for allocating accountability stemming from distributed responsibility for systems between actors, limited visibility due to the accountability horizon, service models of use and liability, and cross-border supply chains and regulatory arbitrage
FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systems
Authors: Liam Daly Manocchio, Siamak Layeghy, Wai Weng Lo, Gayan K. Kulatilleke, Mohanad Sarhan, Marius Portmann
Date: 2023-04-28 00:00:00
Tasks: Classification, Intrusion Detection, Network Intrusion Detection, text-classification, Text Classification

This paper presents the FlowTransformer framework, a novel approach for implementing transformer-based Network Intrusion Detection Systems (NIDSs). FlowTransformer leverages the strengths of transformer models in identifying the long-term behaviour and characteristics of networks, which are often overlooked by most existing NIDSs. By capturing these complex patterns in network traffic, FlowTransformer offers a flexible and efficient tool for researchers and practitioners in the cybersecurity community who are seeking to implement NIDSs using transformer-based models. FlowTransformer allows the direct substitution of various transformer components, including the input encoding, transformer, classification head, and the evaluation of these across any flow-based network dataset. To demonstrate the effectiveness and efficiency of the FlowTransformer framework, we utilise it to provide an extensive evaluation of various common transformer architectures, such as GPT 2.0 and BERT, on three commonly used public NIDS benchmark datasets. We provide results for accuracy, model size and speed. A key finding of our evaluation is that the choice of classification head has the most significant impact on the model performance. Surprisingly, Global Average Pooling, which is commonly used in text classification, performs very poorly in the context of NIDS. In addition, we show that model size can be reduced by over 50\%, and inference and training times improved, with no loss of accuracy, by making specific choices of input encoding and classification head instead of other commonly used alternatives.
Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain
Authors: Annerose Eichel, Helena Schlipf, Sabine Schulte im Walde
Date: 2023-04-28 00:00:00
Tasks: Domain Adaptation

We propose a novel approach to learn domain-specific plausible materials for components in the vehicle repair domain by probing Pretrained Language Models (PLMs) in a cloze task style setting to overcome the lack of annotated datasets. We devise a new method to aggregate salient predictions from a set of cloze query templates and show that domain-adaptation using either a small, high-quality or a customized Wikipedia corpus boosts performance. When exploring resource-lean alternatives, we find a distilled PLM clearly outperforming a classic pattern-based algorithm. Further, given that 98% of our domain-specific components are multiword expressions, we successfully exploit the compositionality assumption as a way to address data sparsity.
LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals
Authors: Caglar Demir, Michel Wiebesiek, Renzhong Lu, Axel-Cyrille Ngonga Ngomo, Stefan Heindorf
Date: 2023-04-28 00:00:00
Tasks: Knowledge Graphs, World Knowledge

Most real-world knowledge graphs, including Wikidata, DBpedia, and Yago are incomplete. Answering queries on such incomplete graphs is an important, but challenging problem. Recently, a number of approaches, including complex query decomposition (CQD), have been proposed to answer complex, multi-hop queries with conjunctions and disjunctions on such graphs. However, all state-of-the-art approaches only consider graphs consisting of entities and relations, neglecting literal values. In this paper, we propose LitCQD -- an approach to answer complex, multi-hop queries where both the query and the knowledge graph can contain numeric literal values: LitCQD can answer queries having numerical answers or having entity answers satisfying numerical constraints. For example, it allows to query (1)~persons living in New York having a certain age, and (2)~the average age of persons living in New York. We evaluate LitCQD on query types with and without literal values. To evaluate LitCQD, we generate complex, multi-hop queries and their expected answers on a version of the FB15k-237 dataset that was extended by literal values.
Benchmarking Automated Machine Learning Methods for Price Forecasting Applications
Authors: Horst Stühler, Marc-André Zöller, Dennis Klau, Alexandre Beiderwellen-Bedrikow, Christian Tutschku
Date: 2023-04-28 00:00:00
Tasks: AutoML, Benchmarking

Price forecasting for used construction equipment is a challenging task due to spatial and temporal price fluctuations. It is thus of high interest to automate the forecasting process based on current market data. Even though applying machine learning (ML) to these data represents a promising approach to predict the residual value of certain tools, it is hard to implement for small and medium-sized enterprises due to their insufficient ML expertise. To this end, we demonstrate the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions, which automatically generate the underlying pipelines. We combine AutoML methods with the domain knowledge of the companies. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. To take all complex industrial requirements into account and to demonstrate the applicability of our new approach, we designed a novel metric named method evaluation score, which incorporates the most important technical and non-technical metrics for quality and usability. Based on this metric, we show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts for innovative small and medium-sized enterprises which are interested in conducting such solutions.
Benchmark dataset and instance generator for Real-World Three-Dimensional Bin Packing Problems
Authors: Eneko Osaba, Esther Villar-Rodriguez, Sebastián V. Romero
Date: 2023-04-28 00:00:00
Tasks: Dataset Generation

In this article, a benchmark for real-world bin packing problems is proposed. This dataset consists of 12 instances of varying levels of complexity regarding size (with the number of packages ranging from 38 to 53) and user-defined requirements. In fact, several real-world-oriented restrictions were taken into account to build these instances: i) item and bin dimensions, ii) weight restrictions, iii) affinities among package categories iv) preferences for package ordering and v) load balancing. Besides the data, we also offer an own developed Python script for the dataset generation, coined Q4RealBPP-DataGen. The benchmark was initially proposed to evaluate the performance of quantum solvers. Therefore, the characteristics of this set of instances were designed according to the current limitations of quantum devices. Additionally, the dataset generator is included to allow the construction of general-purpose benchmarks. The data introduced in this article provides a baseline that will encourage quantum computing researchers to work on real-world bin packing problems.
Image-based Indian Sign Language Recognition: A Practical Review using Deep Neural Networks
Authors: Mallikharjuna Rao K, Harleen Kaur, Sanjam Kaur Bedi, M A Lekhana
Date: 2023-04-28 00:00:00
Tasks: Sign Language Recognition

People with vocal and hearing disabilities use sign language to express themselves using visual gestures and signs. Although sign language is a solution for communication difficulties faced by deaf people, there are still problems as most of the general population cannot understand this language, creating a communication barrier, especially in places such as banks, airports, supermarkets, etc. [1]. A sign language recognition(SLR) system is a must to solve this problem. The main focus of this model is to develop a real-time word-level sign language recognition system that would translate sign language to text. Much research has been done on ASL(American sign language). Thus, we have worked on ISL(Indian sign language) to cater to the needs of the deaf and hard-of-hearing community of India[2]. In this research, we provide an Indian Sign Language-based Sign Language recognition system. For this analysis, the user must be able to take pictures of hand movements using a web camera, and the system must anticipate and display the name of the taken picture. The acquired image goes through several processing phases, some of which use computer vision techniques, including grayscale conversion, dilatation, and masking. Our model is trained using a convolutional neural network (CNN), which is then utilized to recognize the images. Our best model has a 99% accuracy rate[3].
X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation
Authors: Guoliang He, Sean Parker, Eiko Yoneki
Date: 2023-04-28 00:00:00
Tasks: Decision Making, Graph Neural Network, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Tensor graph superoptimisation systems perform a sequence of subgraph substitution to neural networks, to find the optimal computation graph structure. Such a graph transformation process naturally falls into the framework of sequential decision-making, and existing systems typically employ a greedy search approach, which cannot explore the whole search space as it cannot tolerate a temporary loss of performance. In this paper, we address the tensor graph superoptimisation problem by exploring an alternative search approach, reinforcement learning (RL). Our proposed approach, X-RLflow, can learn to perform neural network dataflow graph rewriting, which substitutes a subgraph one at a time. X-RLflow is based on a model-free RL agent that uses a graph neural network (GNN) to encode the target computation graph and outputs a transformed computation graph iteratively. We show that our approach can outperform state-of-the-art superoptimisation systems over a range of deep learning models and achieve by up to 40% on those that are based on transformer-style architectures.
NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning
Authors: Wen Zhang, Zhen Yao, Mingyang Chen, Zhiwei Huang, Huajun Chen
Date: 2023-04-28 00:00:00
Tasks: Graph Representation Learning, Knowledge Graphs, Representation Learning

Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities. NeuralKG-ind is the first library of inductive KGRL as an important update of NeuralKG library. It includes standardized processes, rich existing methods, decoupled modules, and comprehensive evaluation metrics. With NeuralKG-ind, it is easy for researchers and engineers to reproduce, redevelop, and compare inductive KGRL methods. The library, experimental methodologies, and model re-implementing results of NeuralKG-ind are all publicly released at https://github.com/zjukg/NeuralKG/tree/ind .
Prompt Engineering for Healthcare: Methodologies and Applications
Authors: Jiaqi Wang, Enze Shi, Sigang Yu, Zihao Wu, Chong Ma, Haixing Dai, Qiushi Yang, Yanqing Kang, Jinru Wu, Huawen Hu, Chenxi Yue, Haiyang Zhang, Yiheng Liu, Yi Pan, Zhengliang Liu, Lichao Sun, Xiang Li, Bao Ge, Xi Jiang, Dajiang Zhu, Yixuan Yuan, Dinggang Shen, Tianming Liu, Shu Zhang
Date: 2023-04-28 00:00:00
Tasks: Machine Translation, Prompt Engineering, Question Answering, Text Summarization, Translation

Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks. With the recent advancements in large language models, prompt engineering has shown significant superiority across various domains and has become increasingly important in the healthcare domain. However, there is a lack of comprehensive reviews specifically focusing on prompt engineering in the medical field. This review will introduce the latest advances in prompt engineering in the field of natural language processing for the medical field. First, we will provide the development of prompt engineering and emphasize its significant contributions to healthcare natural language processing applications such as question-answering systems, text summarization, and machine translation. With the continuous improvement of general large language models, the importance of prompt engineering in the healthcare domain is becoming increasingly prominent. The aim of this article is to provide useful resources and bridges for healthcare natural language processing researchers to better explore the application of prompt engineering in this field. We hope that this review can provide new ideas and inspire for research and application in medical natural language processing.
Client Recruitment for Federated Learning in ICU Length of Stay Prediction
Authors: Vincent Scheltjens, Lyse Naomi Wamba Momo, Wouter Verbeke, Bart De Moor
Date: 2023-04-28 00:00:00
Tasks: Federated Learning, Length-of-Stay prediction

Machine and deep learning methods for medical and healthcare applications have shown significant progress and performance improvement in recent years. These methods require vast amounts of training data which are available in the medical sector, albeit decentralized. Medical institutions generate vast amounts of data for which sharing and centralizing remains a challenge as the result of data and privacy regulations. The federated learning technique is well-suited to tackle these challenges. However, federated learning comes with a new set of open problems related to communication overhead, efficient parameter aggregation, client selection strategies and more. In this work, we address the step prior to the initiation of a federated network for model training, client recruitment. By intelligently recruiting clients, communication overhead and overall cost of training can be reduced without sacrificing predictive performance. Client recruitment aims at pre-excluding potential clients from partaking in the federation based on a set of criteria indicative of their eventual contributions to the federation. In this work, we propose a client recruitment approach using only the output distribution and sample size at the client site. We show how a subset of clients can be recruited without sacrificing model performance whilst, at the same time, significantly improving computation time. By applying the recruitment approach to the training of federated models for accurate patient Length of Stay prediction using data from 189 Intensive Care Units, we show how the models trained in federations made up from recruited clients significantly outperform federated models trained with the standard procedure in terms of predictive power and training time.
MultiZenoTravel: a Tunable Benchmark for Multi-Objective Planning with Known Pareto Front
Authors: Alexandre Quemy, Marc Schoenauer, Johann Dreo
Date: 2023-04-28 00:00:00

Multi-objective AI planning suffers from a lack of benchmarks exhibiting known Pareto Fronts. In this work, we propose a tunable benchmark generator, together with a dedicated solver that provably computes the true Pareto front of the resulting instances. First, we prove a proposition allowing us to characterize the optimal plans for a constrained version of the problem, and then show how to reduce the general problem to the constrained one. Second, we provide a constructive way to find all the Pareto-optimal plans and discuss the complexity of the algorithm. We provide an implementation that allows the solver to handle realistic instances in a reasonable time. Finally, as a practical demonstration, we used this solver to find all Pareto-optimal plans between the two largest airports in the world, considering the routes between the 50 largest airports, spherical distances between airports and a made-up risk.
From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARL
Authors: Dapeng Li, Zhiwei Xu, Bin Zhang, Guoliang Fan
Date: 2023-04-28 00:00:00

Centralized training with decentralized execution (CTDE) is a widely-used learning paradigm that has achieved significant success in complex tasks. However, partial observability issues and the absence of effectively shared signals between agents often limit its effectiveness in fostering cooperation. While communication can address this challenge, it simultaneously reduces the algorithm's practicality. Drawing inspiration from human team cooperative learning, we propose a novel paradigm that facilitates a gradual shift from explicit communication to tacit cooperation. In the initial training stage, we promote cooperation by sharing relevant information among agents and concurrently reconstructing this information using each agent's local trajectory. We then combine the explicitly communicated information with the reconstructed information to obtain mixed information. Throughout the training process, we progressively reduce the proportion of explicitly communicated information, facilitating a seamless transition to fully decentralized execution without communication. Experimental results in various scenarios demonstrate that the performance of our method without communication can approaches or even surpasses that of QMIX and communication-based methods.
Imbalanced Node Classification Beyond Homophilic Assumption
Authors: Jie Liu, Mengting He, Guangtao Wang, Nguyen Quoc Viet Hung, Xuequn Shang, Hongzhi Yin
Date: 2023-04-28 00:00:00
Tasks: Classification, Node Classification

Imbalanced node classification widely exists in real-world networks where graph neural networks (GNNs) are usually highly inclined to majority classes and suffer from severe performance degradation on classifying minority class nodes. Various imbalanced node classification methods have been proposed recently which construct synthetic nodes and edges w.r.t. minority classes to balance the label and topology distribution. However, they are all based on the homophilic assumption that nodes of the same label tend to connect despite the wide existence of heterophilic edges in real-world graphs. Thus, they uniformly aggregate features from both homophilic and heterophilic neighbors and rely on feature similarity to generate synthetic edges, which cannot be applied to imbalanced graphs in high heterophily. To address this problem, we propose a novel GraphSANN for imbalanced node classification on both homophilic and heterophilic graphs. Firstly, we propose a unified feature mixer to generate synthetic nodes with both homophilic and heterophilic interpolation in a unified way. Next, by randomly sampling edges between synthetic nodes and existing nodes as candidate edges, we design an adaptive subgraph extractor to adaptively extract the contextual subgraphs of candidate edges with flexible ranges. Finally, we develop a multi-filter subgraph encoder that constructs different filter channels to discriminatively aggregate neighbor's information along the homophilic and heterophilic edges. Extensive experiments on eight datasets demonstrate the superiority of our model for imbalanced node classification on both homophilic and heterophilic graphs.
CVRecon: Rethinking 3D Geometric Feature Learning For Neural Reconstruction
Authors: Ziyue Feng, Liang Yang, Pengsheng Guo, Bing Li
Date: 2023-04-28 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D geometry

Recent advances in neural reconstruction using posed image sequences have made remarkable progress. However, due to the lack of depth information, existing volumetric-based techniques simply duplicate 2D image features of the object surface along the entire camera ray. We contend this duplication introduces noise in empty and occluded spaces, posing challenges for producing high-quality 3D geometry. Drawing inspiration from traditional multi-view stereo methods, we propose an end-to-end 3D neural reconstruction framework CVRecon, designed to exploit the rich geometric embedding in the cost volumes to facilitate 3D geometric feature learning. Furthermore, we present Ray-contextual Compensated Cost Volume (RCCV), a novel 3D geometric feature representation that encodes view-dependent information with improved integrity and robustness. Through comprehensive experiments, we demonstrate that our approach significantly improves the reconstruction quality in various metrics and recovers clear fine details of the 3D geometries. Our extensive ablation studies provide insights into the development of effective 3D geometric feature learning schemes. Project page: https://cvrecon.ziyue.cool/