bluuebunny/crossref_metadata_2025 huggingface dataset:
Abductive Explanations of Classifiers Under Constraints: Complexity and Properties
Authors: Cooper Martin , Amgoud Leila
Published: September 2023

Abductive explanations (AXp’s) are widely used for understanding decisions of classifiers. Existing definitions are suitable when features are independent. However, we show that ignoring constraints when they exist between features may lead to an explosion in the number of redundant or superfluous AXp’s. We propose three new types of explanations that take into account constraints and that can be generated from the whole feature space or from a sample (such as a dataset). They are based on a key notion of coverage of an explanation, the set of instances it explains. We show that coverage is powerful enough to discard redundant and superfluous AXp’s. For each type, we analyse the complexity of finding an explanation and investigate its formal properties. The final result is a catalogue of different forms of AXp’s with different complexities and different formal guarantees.
Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness Under Unawareness Setting
Authors: Cornacchia Giandomenico , Anelli Vito Walter , Narducci Fedelucio , Ragone Azzurra , Di Sciascio Eugenio
Published: September 2023

Current AI regulations require discarding sensitive features (e.g., gender, race, religion) in the algorithm’s decision-making process to prevent unfair outcomes. However, even without sensitive features in the training set, algorithms can persist in discrimination. Indeed, when sensitive features are omitted (fairness under unawareness), they could be inferred through non-linear relations with the so-called proxy features. In this work, we propose a way to reveal the potential hidden bias of a machine learning model that can persist even when sensitive features are discarded. This study shows that it is possible to unveil whether the black-box predictor is still biased by exploiting counterfactual reasoning. In detail, when the predictor provides a negative classification outcome, our approach first builds counterfactual examples for a discriminated user category to obtain a positive outcome. Then, the same counterfactual samples feed an external classifier (that targets a sensitive feature) that reveals if the modifications to the user characteristics needed for a positive outcome moved the individual to the non-discriminated group. When this occurs, it could be a warning sign for discriminatory behavior in the decision process. Furthermore, we leverage the deviation of counterfactuals from the original sample to determine which features are proxies of specific sensitive information. Our experiments show that, even if the model is trained without sensitive features, it often suffers discriminatory biases.
Compilation of Tight ASP Programs
Authors: Dodaro Carmine , Mazzotta Giuseppe , Ricca Francesco
Published: September 2023

Answer Set Programming (ASP) is a well-known AI formalism. Traditional ASP systems, that follow the “ground&solve” approach, are intrinsically limited by the so-called grounding bottleneck. Basically, the grounding step (i.e., variable-elimination) can be computationally expensive, and even unfeasible in several cases of practical interest. Recent work demonstrated that the grounding bottleneck can be partially overcome by compiling in external propagators subprograms acting as constraints. In this paper a novel compilation technique is presented that can be applied to tight normal programs; thus, the class of ASP programs that can be compiled is extended beyond constraints. The approach is implemented in the new system PROASP. PROASP skips entirely the grounding phase and performs solving by injecting custom propagators in GLUCOSE. An experiment, conducted on grounding-intensive ASP benchmarks, shows that PROASP is capable of solving instances that are out of reach for state-of-the-art ASP systems.
SENA: Similarity-Based Error-Checking of Neural Activations
Authors: Ferreira Raul Sena , Guerin Joris , Guiochet Jeremie , Waeselynck Helene
Published: September 2023

In this work, we propose SENA, a run-time monitor focused on detecting unreliable predictions from machine learning (ML) classifiers. The main idea is that instead of trying to detect when an image is out-of-distribution (OOD), which will not always result in a wrong output, we focus on detecting if the prediction from the ML model is not reliable, which will most of the time result in a wrong output, independently of whether it is in-distribution (ID) or OOD. The verification is done by checking the similarity between the neural activations of an incoming input and a set of representative neural activations recorded during training. SENA uses information from true-positive and false-negative examples collected during training to verify if a prediction is reliable or not. Our approach achieves results comparable to state-of-the-art solutions without requiring any prior OOD information and without hyperparameter tuning. Besides, the code is publicly available for easy reproducibility at https://github.com/raulsenaferreira/SENA.
Feasible Action-Space Reduction as a Metric of Causal Responsibility in Multi-Agent Spatial Interactions
Authors: George Ashwin , Cavalcante Siebert Luciano , Abbink David , Zgonnikov Arkady
Published: September 2023

Modelling causal responsibility in multi-agent spatial interactions is crucial for safety and efficiency of interactions of humans with autonomous agents. However, current formal metrics and models of responsibility either lack grounding in ethical and philosophical concepts of responsibility, or cannot be applied to spatial interactions. In this work we propose a metric of causal responsibility which is tailored to multi-agent spatial interactions, for instance interactions in traffic. In such interactions, a given agent can, by reducing another agent’s feasible action space, influence the latter. Therefore, we propose feasible action space reduction (FeAR) as a metric of causal responsibility among agents. Specifically, we look at ex-post causal responsibility for simultaneous actions. We propose the use of Moves de Rigueur (MdR) — a consistent set of prescribed actions for agents — to model the effect of norms on responsibility allocation. We apply the metric in a grid world simulation for spatial interactions and show how the actions, contexts, and norms affect the causal responsibility ascribed to agents. Finally, we demonstrate the application of this metric in complex multi-agent interactions. We argue that the FeAR metric is a step towards an interdisciplinary framework for quantifying responsibility that is needed to ensure safety and meaningful human control in human-AI systems.
Evolutionary Explainable Rule Extraction from (Modal) Random Forests
Authors: Ghiotti Michele , Manzella Federico , Pagliarini Giovanni , Sciavicco Guido , Stan Ionel Eduard
Published: September 2023

Symbolic learning is the subfield of machine learning concerned with learning predictive models with knowledge represented in logical form, such as decision tree and decision list models. Ensemble learning methods, such as random forests, are usually deployed to improve the performance of decision trees; unfortunately, interpreting tree ensembles is challenging. In order to deal with unstructured (e.g., temporal or spatial) data, moreover, decision trees and random forests have been recently generalized to the use of modal logics, which are harder to interpret than their propositional counterpart. Recently, a methodology for extracting simple rules from propositional random forests, based on a sequence of optimization steps, was proposed. In this work, we generalize this approach along two directions: from propositional to modal logic and from a sequence of optimization steps to a single multi-objective optimization problem. Even if confined to the temporal domain, our experimental results, based on open-source implementations and public data, show that our method is robust and able to extract small, accurate, and informative decision lists even for complex classification problems.
Take Expert Advice Judiciously: Combining Groupwise Calibrated Model Probabilities with Expert Predictions
Authors: Gupta Sumeet , Jain Shweta , Jha Shashi Shekhar , Hsiung Pao-Ann , Wang Ming-Hung
Published: September 2023

Training the machine learning (ML) models require a large amount of data, still the capacity of these models is limited. To enhance model performance, recent literature focuses on combining ML models’ predictions with that of human experts, a setting popularly known as the human-in-the-loop or human-AI teams. Human experts can complement the ML models as they are well-equipped with vast real-world experience and sometimes have access to private information that may not be accessible while training the ML model. Existing approaches for combining an expert and ML model either require end-to-end training of the combined model or require expert annotations for every task. End-to-end training further needs a custom loss function and human annotations, which is cumbersome, results in slower convergence, and may adversely impact the ML model’s accuracy. On the other hand, using expert annotations for every task is also cost-ineffective. We propose a novel technique that optimizes the cost of seeking the expert’s advice while utilizing the ML model’s predictions to improve accuracy. Our model considers two intrinsic parameters: the expert’s cost for each prediction and the misclassification cost of the combined human-AI model. Further, we present the impact of group-wise calibration on the combined model that improves the overall model’s performance. Experimental results on our combined model with group-wise calibration show a significant increase in accuracy with limited expert advice against different established ML models for the image classification task. In addition, the combined model’s accuracy is always greater than that of the ML model, irrespective of the expert’s accuracy, the expert’s cost, and the misclassification cost.
Exploring the Feasibility of Physical Adversarial Attacks: A Cybersecurity Study
Authors: Hamon Ronan , Junklewitz Henrik
Published: September 2023

Adversarial machine learning (AML), by designing attacks that intentionally break or misuse state-of-the-art machine learning models, has become the most prominent scientific field to explore the security aspects of Artificial Intelligence. A whole range of vulnerabilities, previously irrelevant in traditional ICT, have effectively emerged in these studies. In the light of upcoming legislations mandating security requirements for AI products and services, there is a need to understand how AML techniques connect with the broader field of cybersecurity, and how to articulate more tightly threat models with realistic cybersecurity procedures. This article aims to contribute to closing the gap between AML and cybersecurity by proposing an approach to study the feasibility of an attack in a cybersecurity risk assessment framework, illustrated with a specific use case of an evasion attack designed to fool traffic sign recognition systems in the physical world. The importance of considering the feasibility of carrying out such attacks under real conditions is emphasized through the analysis of two factors: the reproducibility of the attack according to a published description or existing code, and the applicability of the attack by a malicious actor operating in a real-world environment.
GraphSA: Smart Contract Vulnerability Detection Combining Graph Neural Networks and Static Analysis
Authors: He Long , Zhao Xiangfu , Wang Yichen , Yang Jiahui , Sun Xuelei
Published: September 2023

Security incidents in smart contracts still occur frequently, as the underlying code is often vulnerable to attacks. However, traditional methods to detect vulnerabilities in smart contracts are limited by certain rigid rules, reducing accuracy and scalability. In this work, we propose GraphSA, which combines Graph neural networks (GNNs) and Static Analysis for smart contract vulnerability detection. First, we present the contract tree, which is obtained by converting the control flow graph (CFG) of a smart contract. Each node in the tree represents a crucial operation code (opcode) block, and each edge represents the control flow (execution order) between code blocks. Then, we propose an extended SAGConv and Topkpooling graph neural network (ST-GNN) to learn the features of each node in the tree. To enhance detection accuracy, we eliminate and merge some non-crucial nodes to highlight key nodes and execution orders. Finally, we evaluate our approach on 7,962 real-world smart contracts running on Ethereum and compare it with state-of-the-art approaches on six types of vulnerabilities. Experimental results show that our approach achieves higher detection accuracy than others.
Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning
Authors: Li Chuming , Jia Ruonan , Liu Jie , Zhang Yinmin , Niu Yazhe , Yang Yaodong , Liu Yu , Ouyang Wanli
Published: September 2023

Model-based reinforcement learning (RL) has demonstrated remarkable successes on a range of continuous control tasks due to its high sample efficiency. To save the computation cost of conducting planning online, recent practices tend to distill optimized action sequences into an RL policy during the training phase. Although the distillation can incorporate both the foresight of planning and the exploration ability of RL policies, the theoretical understanding of these methods is yet unclear. In this paper, we extend the policy improvement of Soft Actor-Critic (SAC) by developing an approach to distill from model-based planning to the policy. We then demonstrate that such an approach of policy improvement has a theoretical guarantee of monotonic improvement and convergence to the maximum value defined in SAC. We discuss effective design choices and implement our theory as a practical algorithm—Model-based Planning Distilled to Policy (MPDP)—that updates the policy jointly over multiple future time steps. Extensive experiments show that MPDP achieves better sample efficiency and asymptotic performance than both model-free and model-based planning algorithms on six continuous control benchmark tasks in MuJoCo.
Decentralized Local Updates with Dual-Slow Estimation and Momentum-Based Variance-Reduction for Non-Convex Optimization
Authors: Luo Kangyang , Zhang Kunkun , Zhang Shengbo , Li Xiang , Gao Ming
Published: September 2023

Decentralized learning (DL) has recently employed local updates to reduce the communication cost for general non-convex optimization problems. Specifically, local updates require each node to perform multiple update steps on the parameters of the local model before communicating with others. However, most existing methods could be highly sensitive to data heterogeneity (i.e., non-iid data distribution) and adversely affected by the stochastic gradient noise. In this paper, we propose DSE-MVR to address these problems. Specifically, DSE-MVR introduces a dual-slow estimation strategy that utilizes the gradient tracking technique to estimate the global accumulated update direction for handling the data heterogeneity problem; also for stochastic noise, the method uses the mini-batch momentum-based variance-reduction technique. We theoretically prove that DSE-MVR can achieve optimal convergence results for general non-convex optimization in both iid and non-iid data distribution settings. In particular, the leading terms in the convergence rates derived by DSE-MVR are independent of the stochastic noise for large-batches or large partial average intervals (i.e., the number of local update steps). Further, we put forward DSE-SGD and theoretically justify the importance of the dual-slow estimation strategy in the data heterogeneity setting. Finally, we conduct extensive experiments to show the superiority of DSE-MVR against other state-of-the-art approaches. We provide our code here: https://anonymous.4open.science/r/DSE-MVR-32B8/.
FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition
Authors: Mao Yuzhao , Lu Di , Zhang Yang , Wang Xiaojie
Published: September 2023

This paper concentrates on the understanding of interlocutors’ emotions evoked in conversational utterances. Previous studies in this literature mainly focus on more accurate emotional predictions, while ignoring model robustness when the local context is corrupted by adversarial attacks. To maintain robustness while ensuring accuracy, we propose an emotion recognizer augmented by a full-attention topic regularizer, which enables an emotion-related global view when modeling the local context in a conversation. A joint topic modeling strategy is introduced to implement regularization from both representation and loss perspectives. To avoid over-regularization, we drop the constraints on prior distributions that exist in traditional topic modeling and perform probabilistic approximations based entirely on attention alignment. Experiments show that our models obtain more favorable results than state-of-the-art models, and gain convincing robustness under three types of adversarial attacks. Code: https://github.com/ludybupt/FATRER.
Anticipating Responsibility in Multiagent Planning
Authors: Parker Timothy , Grandi Umberto , Lorini Emiliano
Published: September 2023

Responsibility anticipation is the process of determining if the actions of an individual agent may cause it to be responsible for a particular outcome. This can be used in a multi-agent planning setting to allow agents to anticipate responsibility in the plans they consider. The planning setting in this paper includes partial information regarding the initial state and considers formulas in linear temporal logic as positive or negative outcomes to be attained or avoided. We firstly define attribution for notions of active, passive and contributive responsibility, and consider their agentive variants. We then use these to define the notion of responsibility anticipation. We prove that our notions of anticipated responsibility can be used to coordinate agents in a planning setting and give complexity results for our model, discussing equivalence with classical planning. We also present an outline for solving some of our attribution and anticipation problems using PDDL solvers.
Instance-Aware Diffusion Implicit Process for Box-Based Instance Segmentation
Authors: Ren Hao , Liu Xingsong , Huang Junjian , Wan Ru , Pu Jian , Lu Hong
Published: September 2023

The diffusion model has demonstrated impressive performance in image generation, but its potential for discriminative tasks such as instance segmentation remains unexplored. In this paper, we propose an Instance-aware Diffusion Implicit Process (IDIP) framework for instance segmentation based on boxes. During training, IDIP diffuses ground-truth boxes across various time steps, extracting corresponding Region of Interest (RoI) features. Dynamic convolution is then used to predict boxes and categories for each RoI, and the mask head generates masks from these predictions. During inference, IDIP iteratively refines randomly generated boxes with the denoising diffusion implicit model, while the mask head derives final masks from RoIs based on the refined boxes. Our method surpasses existing approaches on the COCO benchmark, requiring fewer training steps and less memory resources due to its dynamic design and instance-aware characteristic.
Synthesis of Procedural Models for Deterministic Transition Systems
Authors: Segovia-Aguas Javier , Ferrer-Mestres Jonathan , Jiménez Sergio
Published: September 2023

This paper introduces a general approach for synthesizing procedural models of the state-transitions of a given discrete system. The approach is general in that it accepts different target languages for modeling the state-transitions of a discrete system; different model acquisition tasks with different target languages, such as the synthesis of STRIPS action models, or the update rule of a cellular automaton, fit as particular instances of our general approach. We follow an inductive approach to synthesis meaning that a set of examples of state-transitions, represented as (pre-state, action, post-state) tuples, are given as input. The goal is to synthesize a structured program that, when executed on a given pre-state, outputs its associated post-state. Our synthesis method implements a combinatorial search in the space of well-structured terminating programs that can be built using a Random-Access Machine (RAM), with a minimalist instruction set, and a finite amount of memory. The combinatorial search is guided with functions that asses the complexity of the candidate programs, as well as their fitness to the given input set of examples.
BridgeHand2Vec Bridge Hand Representation
Authors: Sztyber-Betley Anna , Kołodziej Filip , Betley Jan , Duszak Piotr
Published: September 2023

Contract bridge is a game characterized by incomplete information, posing an exciting challenge for artificial intelligence methods. This paper proposes the BridgeHand2Vec approach, which leverages a neural network to embed a bridge player’s hand (consisting of 13 cards) into a vector space. The resulting representation reflects the strength of the hand in the game and enables interpretable distances to be determined between different hands. This representation is derived by training a neural network to estimate the number of tricks that a pair of players can take. In the remainder of this paper, we analyze the properties of the resulting vector space and provide examples of its application in reinforcement learning, and opening bid classification. Although this was not our main goal, the neural network used for the vectorization achieves SOTA results on the DDBP2 problem (estimating the number of tricks for two given hands).
Automatic Radiology Report Generation by Learning with Increasingly Hard Negatives
Authors: Voutharoja Bhanu Prakash , Wang Lei , Zhou Luping
Published: September 2023

Automatic radiology report generation is challenging as medical images or reports are usually similar to each other due to the common content of anatomy. This makes a model hard to capture the uniqueness of individual images and is prone to producing undesired generic or mismatched reports. This situation calls for learning more discriminative features that could capture even fine-grained mismatches between images and reports. To achieve this, this paper proposes a novel framework to learn discriminative image and report features by distinguishing them from their closest peers, i.e., hard negatives. Especially, to attain more discriminative features, we gradually raise the difficulty of such a learning task by creating increasingly hard negative reports for each image in the feature space during training, respectively. By treating the increasingly hard negatives as auxiliary variables, we formulate this process as a min-max alternating optimisation problem. At each iteration, conditioned on a given set of hard negative reports, image and report features are learned as usual by minimising the loss functions related to report generation. After that, a new set of harder negative reports will be created by maximising a loss reflecting image-report alignment. By solving this optimisation, we attain a model that can generate more specific and accurate reports. It is noteworthy that our framework enhances discriminative feature learning without introducing extra network weights. Also, in contrast to the existing way of generating hard negatives, our framework extends beyond the granularity of the dataset by generating harder samples out of the training set. Experimental study on benchmark datasets verifies the efficacy of our framework and shows that it can serve as a plug-in to readily improve existing medical report generation models. The code is publicly available at https://github.com/Bhanu068/ITHN.
BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization
Authors: Wang Junyi , Zhu Yuanyang , Wang Zhi , Zheng Yan , Hao Jianye , Chen Chunlin
Published: September 2023

Evolutionary reinforcement learning (ERL) algorithms recently raise attention in tackling complex reinforcement learning (RL) problems due to high parallelism, while they are prone to insufficient exploration or model collapse without carefully tuning hyperparameters (aka meta-parameters). In the paper, we propose a general meta ERL framework via bilevel optimization (BiERL) to jointly update hyperparameters in parallel to training the ERL model within a single agent, which relieves the need for prior domain knowledge or costly optimization procedure before model deployment. We design an elegant meta-level architecture that embeds the inner-level’s evolving experience into an informative population representation and introduce a simple and feasible evaluation of the meta-level fitness function to facilitate learning efficiency. We perform extensive experiments in MuJoCo and Box2D tasks to verify that as a general framework, BiERL outperforms various baselines and consistently improves the learning performance for a diversity of ERL algorithms.
Towards Legal Judgment Summarization: A Structure-Enhanced Approach
Authors: Wang Qiqi , Wang Ruofan , Zhao Kaiqi , Amor Robert , Liu Benjamin , Zheng Xianda , Zhang Zeyu , Huang Zijian
Published: September 2023

Judgment summaries are beneficial for legal practitioners to comprehend and retrieve case law efficiently. Unlike summaries in general domains, e.g., news, judgment summaries often require a clear structure. Such a structure helps readers grasp the information contained in the summary and reduces information loss. To the best of our knowledge, none of the existing text summarizers can generate summaries aligned with the summary structure in the legal domain. Inspired by this observation, this paper introduces a Summary Structure-Enhanced (SSE) method to synthesize structured summaries for legal documents. SSE can easily be incorporated into the Encoder-Decoder framework, which is commonly adopted in state-of-the-art text summarizers. Experiments on the datasets of New Zealand and Chinese judgments show that the proposed method consistently improves the performance of state-of-the-art summarizers in terms of Rouge scores.
Multiplicative Sparse Tensor Factorization for Multi-View Multi-Task Learning
Authors: Wang Xinyi , Sun Lu , Nguyen Canh Hao , Mamitsuka Hiroshi
Published: September 2023

Multi-View Multi-Task Learning (MVMTL) aims to make predictions on dual-heterogeneous data. Such data contains features from multiple views, and multiple tasks in the data are related with each other through common views. Existing MVMTL methods usually face two major challenges: 1) to save the predictive information from full-order interactions between views efficiently. 2) to learn a parsimonious and highly interpretable model such that the target is related to the features through a subset of interactions. To deal with the challenges, we propose a novel MVMTL method based on multiplicative sparse tensor factorization. For 1), we represent full-order interactions between views as a tensor, that enables to capture the complex correlations in dual-heterogeneous data by a concise model. For 2), we decompose the interaction tensor into a product of two components: one being shared with all tasks and the other being specific to individual tasks. Moreover, tensor factorization is applied to control the model complexity and learn a consensus latent representation shared by multiple tasks. Theoretical analysis reveals the equivalence between our method and a family of models with a joint but more general form of regularizers. Experiments on both synthetic and real-world datasets prove its effectiveness.
WeaGAN: Weather-Aware Graph Attention Network for Traffic Prediction
Authors: Wang Yuxi , Luo Yuan
Published: September 2023

In recent years, traffic conditions in centralised cities have become more severe. To optimise public resources and reduce congestion, transportation departments rely on traffic prediction. However, unexpected events, for instance, rainfall can impact traffic conditions, which necessitates the introduction of weather elements to improve prediction results. Moreover, most of the existing works characterise the relationship between weather and traffic by simply combining these two issues together. Without carefully designing a structure that captures the inter-dependency between weather and traffic data, it is impossible to produce accurate predictions within a reasonable computational time. To address this issue, we propose a Weather-Aware Graph Attention Network (WeaGAN) that adapts an encoder-decoder architecture with weather attention mechanisms and a gate to model the complex spatial-temporal inter-dependency between weather and traffic adaptively. We further design a self-attention mechanism to improve prediction accuracy. Our experiments on a standard real-world dataset show that, compared to the state-of-the-art, WeaGAN: (i) can improve the prediction accuracy by up to 29%; and (ii) is efficient in terms of saving up to 61% of the computation time.
Read Key Points: Dialogue-Grounded Knowledge Points Generation with Multi-Level Salience-Aware Mixture
Authors: Zhang Sen , Li Baokui , Zhang Wangshu , Yang Changlin , Chen Yicheng , Hu Sen , Xu Teng , Li Jiwei
Published: September 2023

Knowledge-grounded dialogue (KGD) has become increasingly essential for online services, enabling individuals to obtain desired information. While KGD contains knowledge information, most knowledge points are fragmented and repeated in dialogues, making it difficult for users to quickly grasp complete and key information from a collection of sessions. In this paper, we propose a novel task of dialogue-grounded knowledge points generation (DialKPG) to condense a collection of sessions on a topic into succinct and complete knowledge points. To enable empirical study, we create TopicDial and OpenDial corpus based on two existing knowledge-grounded dialogue corpus FaithDial and OpenDialKG by a Three-Stage Annotation Framework, and establish a novel approach for DialKPG task, namely MSAM (Multi-Level Salience-Aware Mixture). MSAM explicitly incorporates salient information at the token-level, utterance-level, and session-level to better guide knowledge points generation. Extensive experiments have verified the effectiveness of our method over competitive baselines. Furthermore, our analysis shows that the proposed model is particularly effective at handling long inputs and multiple sessions due to its strong capability of duplicated elimination and knowledge integration.
Classifying Aircraft Categories from Magnetometry Data Using a Hypotheses-Based Multi-Task Framework
Authors: Vexler Julian , Kramer Stefan
Published: September 2023

Airport traffic surveillance requires reliable safety systems to prevent accidents in safety-critical areas. This paper examines airport aprons, where existing holding point protection systems have shown that they are sometimes not able to prevent accidents. One possible solution to this problem is the use of innovative sensor technology such as magnetometers. These sensors can be used to measure the distortion of the earth’s magnetic field by metallic objects. The main objective is to identify the geometrical pattern of a passing object by fusing coherent events, and classify it into a category based on its size. We propose a hypotheses-based multi-task framework for the classification of aircraft by making use of the estimated motion behaviour of a passing object. The framework includes statistical components, domain knowledge, and artificial intelligence solutions to infer the geometrical pattern and motion vector of an object from a predefined set of possible hypotheses. In future work, we aim to optimize the framework using synthetic and real-world data to increase its robustness and generalization ability to other airports.
Lithium, a Treatment Option for Alzheimer’s Disease? A Review of Existing Evidence and Discussion on Future Perspectives
Authors: Guilliot Solene , Gauthier Serge , Touchon Jacques , Soto Maria E.
Published: September 2023

After over 50 years of use, lithium-salts remain the first-line therapy for the management of bipolar disorder. Throughout this period, the potential for lithium salts has been extensively studied and numerous data favor its use in the treatment of neurodegenerative disorders such as Alzheimer’s disease (AD). We reviewed existing evidence gathered from clinical case reports and studies on the effect of lithium on neuropsychological symptoms of AD and as a disease-modifying treatment acting on cognitive symptoms. The review summarizes the molecular pathways, involving GSK-3β inhibition and neuroprotection, through which lithium is proposed to exert its effect. Limitations to its current use in AD are discussed and future perspectives as a potential treatment option for AD are considered in regard to ongoing clinical trials using different forms of lithium.
A Mediterranean Diet and Walking Intervention to Reduce Cognitive Decline and Dementia Risk in Independently Living Older Australians: The MedWalk Randomized Controlled Trial Experimental Protocol, Including COVID-19 Related Modifications and Baseline Characteristics
Authors: Pipingas Andrew , Murphy Karen J. , Davis Courtney R. , Itsiopoulos Catherine , Kingsley Michael , Scholey Andrew , Macpherson Helen , Segal Leonie , Breckon Jeff , Minihane Anne-Marie , Meyer Denny , Ogden Edward , Dyer Kathryn A. , Eversteyn Emily , Hardman Roy J. , Poorun Kaylass , Justice Keri , Hana Maher , Buckley Jonathan D. , White David , Davison Kade , Clark Jessie S. , Bracci Ella L. , Kennedy Greg ,   on behalf of MedWalk collaborative team
Published: September 2023

Background:

Several clinical trials have examined diet and physical activity lifestyle changes as mitigation strategies for risk factors linked to cognitive decline and dementias such as Alzheimer’s disease. However, the ability to modify these behaviors longer term, to impact cognitive health has remained elusive.

Objective:

The MedWalk trial’s primary aim is to investigate whether longer-term adherence to a Mediterranean-style diet and regular walking, delivered through motivational interviewing and cognitive-behavioral therapy (MI-CBT), can reduce age-associated cognitive decline and other dementia risk factors in older, independently living individuals without cognitive impairment.

Methods:

MedWalk, a one-year cluster-randomized controlled trial across two Australian states, recruited 60–90-year-old people from independent living retirement villages and the wider community. Participants were assigned to either the MedWalk intervention or a control group (maintaining their usual diet and physical activity). The primary outcome is 12-month change in visual memory and learning assessed from errors on the Paired Associates Learning Task of the Cambridge Neuropsychological Test Automated Battery. Secondary outcomes include cognition, mood, cardiovascular function, biomarkers related to nutrient status and cognitive decline, MI-CBT effectiveness, Mediterranean diet adherence, physical activity, quality of life, cost-effectiveness, and health economic evaluation.

Progress and Discussion:

Although COVID-19 impacts over two years necessitated a reduced timeline and sample size, MedWalk retains sufficient power to address its aims and hypotheses. Baseline testing has been completed with 157 participants, who will be followed over 12 months. If successful, MedWalk will inform interventions that could substantially reduce dementia incidence and ameliorate cognitive decline in the community.

Trial registration:

Registered on the Australia New Zealand Clinical Trials Registry ANZCTR 12620000978965 ( https://www.anzctr.org.au ).
An interval type-3 fuzzy PID control system design and its application in solid oxide fuel cells power plant
Authors: Shi Jianzhong
Published: September 2023

Compared with type-2 fuzzy sets, the secondary membership degree of interval type-3 fuzzy sets is an interval rather than crisp value, which makes interval type-3 fuzzy sets can obtain more degree of freedoms. This article studies an interval type-3 fuzzy PID controller based on interval type-3 fuzz sets. The framework of interval type-3 fuzzy PID controller is identical with type-2 fuzzy PID controller, but it contains more adjustment controller parameters and its type reduction procedure is more complex. In this paper, type reduction of interval type-3 fuzzy sets is derived from general type-2 fuzzy sets represented by α-plane and a direct NT type reduction algorithm is applied. The control effects of interval type-3 fuzzy PID controller are firstly tested by 2 nonlinear plants, the simulation results show that interval type-3 fuzzy PID controller has better control performance indexes than PID controller, type-1 fuzzy PID controller, interval type-2 fuzzy PID controller and general type-2 fuzzy PID controller. Furthermore, the interval type-3 fuzzy PID controller will be applied in rated voltage control of solid oxide fuel cells (SOFC) power plant. The output voltage control of SOFC is quite challenging because of the strong nonlinearity, limited fuel flow, and rapid variation of the load disturbance. The simulation results demonstrate the advantages and robustness of proposed interval type-3 fuzzy PID controller.
3D-Memory efficient listless set partitioning in hierarchical trees for hyperspectral image sensors
Authors: Chandra Harshit , Bajpai Shrish , Alam Monauwer , Chandel Vishal Singh , Pandey Amit Kumar , Pandey Digvijay
Published: September 2023

Hyperspectral (HS) images contain rich spatial and spectral information. Due to its large size, it is difficult to store, process, analyze, or transmit the critical information contained in it. The compression of hyperspectral images is inevitable. Many transform based Hyper Spectral Image Compression Algorithms (HSICAs) have been proposed in the past that work for both lossy and lossless compression processes. The transform based HSICA uses linked lists or dedicated markers or array structure to keep track of significant and insignificant sets or coefficients of a transformed HS image. However, these algorithms either suffered from low coding efficiency, high memory requirements, or high coding complexity. This work proposes a transform based HSICA using a curvelet transform to improve the directional elements and the ability to represent edges and other singularities along curves. The proposed HSICA aims to provide superior quality compressed HS images by representing HS images at different scales and directions and to achieve a high compression ratio. Experimental results show that the proposed algorithm has a low coding memory requirement with a 2% to 5% increase in coding gain compared to the other state of art compression algorithms.
Two new limited-memory preconditioned conjugate gradient algorithms for nonlinear optimization problems
Authors: Khatab Hussein Ageel , Shareef Salah Gazi
Published: September 2023

The conjugate gradient (CG) techniques are a class of unconstrained optimization algorithms with strong local and global convergence qualities and minimal memory needs. While the quasi-Newton methods are reliable and efficient on a wide range of problems and these methods are converge faster than the conjugate gradient methods and require fewer function evaluations, however, they are request substantially more storage, and if the problem is ill-conditioned, they may require several iterations. There is another class, termed preconditioned conjugate gradient method, it is a technique that combines two methods conjugate gradient with quasi-Newton. In this work, we proposed a new two limited memory preconditioned conjugate gradient methods (New1 and New2), to solve nonlinear unconstrained minimization problems, by using new modified symmetric rank one (NMSR1) and new modified Davidon, Fletcher, Powell (NMDFP), and also using projected vectors. We proved that these modifications fulfill some conditions. Also, the descent condition of the new technique has been proved. The numerical results showed the efficiency of the proposed new algorithms compared with some standard nonlinear, unconstrained problems.
RETRACTED: Deep learning aided prostate cancer detection for early diagnosis & treatment using MR with TRUS images
Authors: Sucharitha G. , sankardass Veeramalai , Rani R. , Bhat Nagaraj , Rajaram A.
Published: September 2023

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Effects of flow rate accuracy in two-day anticancer drug infusion with disposable pumps on plasma drug concentrations
Authors: Lee Kyoung Jin , Lee Jung Chan
Published: September 2023

BACKGROUND: Elastomeric pumps have a curved infusion rate profile over infusion time. Chemically driven pumps can overcome such limitations of elastomeric pumps and infuse constantly. However, studies on the pharmacokinetic benefit of chemically-driven pumps are insufficient. OBJECTIVE: This study aimed to determine effects of constant infusion with a chemically-driven pump on plasma drug concentrations compared to elastomeric pumps. METHODS: Infusion rate profiles of a chemically driven pump and two elastomeric pumps were measured in vitro tests under three height conditions of drug reservoir. Plasma drug concentrations were estimated using a pharmacokinetic model of 5-fluorouracil (5FU). RESULTS: The chemically-driven pump was more accurate than elastomeric pumps during the total infusion time (Root-mean-square-error (RMSE): 3% vs. 13%) which thus reduced its deviation of plasma 5FU concentration over time to one-fifth of that with an elastomeric pump. The chemically-driven pump had less than 5% of RMSE despite the influence of height difference. CONCLUSION: Although chemically-driven pumps maintained plasma 5FU concentration successfully and elastomeric pumps did not, both pumps were proper for 5FU infusion because the time-dependent changes in infusion rate did not affect the area under the curve. Chemically driven pumps would be more advantageous for drugs that are sensitive to their plasma concentrations.
Relationship between demographic characteristics and return-to-work for loss of income claimants at the Motor Vehicle Accident Fund, Botswana
Authors: Modise Gofaone Lady , Uys Kitty , Masenge Andries , du Plooy Eileen
Published: September 2023

BACKGROUND: The Motor Vehicle Accident (MVA) Fund Botswana compensates claimants who lose their incomes due to road traffic accidents. In Botswana, road traffic accidents are becoming more frequent, and the MVA Fund is experiencing escalating claims. We describe the demographic characteristics of loss of income (LOI) claimants of the MVA Fund Botswana. We assess whether demographic characteristics are related to return to work (RTW). OBJECTIVE: We retrospectively reviewed records of MVA Fund claimants and extracted demographic information. We investigated the demographic profile and the relationship between demographic information and RTW. METHODS: We reviewed 432 LOI claims received by MVA Fund from January 1, 2015 to December 31, 2020. We descriptively analysed the demographic profiles of claimants. We used a univariate analysis and multivariate logistic regression to determine the association between independent demographic variables and the dependent variable, RTW. RESULTS: MVA Fund claimants were on average 37-years-old. Claimants were mostly from low-income socio-economic backgrounds. RTW was significantly associated with injury severity, type of injury, and having a RTW plan offer. The final predictors of RTW, using logistic regression, were time away from work and severity of injury. CONCLUSION: In Botswana, claimants who had severe injuries and who stayed away from work for longer were less likely to RTW. The MVA Fund Botswana must recognise the demographic profiles of claimants which are likely to influence RTW.
Stroke survivors’ long-term participation in paid employment
Authors: van Meijeren-Pont Winke ,   on behalf of the SCORE-study group, van Velzen Judith M. , Volker Gerard , Arwert Henk J. , Meesters Jorit J.L. , de Kloet Arend J. , van Bennekom Coen A.M. , Vliet Vlieland Thea P.M. , Tamminga Sietske J. , Oosterveer Daniëlla M.
Published: September 2023

BACKGROUND: Knowledge on long-term participation is scarce for patients with paid employment at the time of stroke. OBJECTIVE: Describe the characteristics and the course of participation (paid employment and overall participation) in patients who did and did not remain in paid employment. METHODS: Patients with paid employment at the time of stroke completed questions on work up to 30 months after starting rehabilitation, and the Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P, Frequency, Restrictions and Satisfaction scales) up to 24 months. Baseline characteristics of patients with and without paid employment at 30 months were compared using Fisher’s Exact Tests and Mann-Whitney U Tests. USER-P scores over time were analysed using Linear Mixed Models. RESULTS: Of the 170 included patients (median age 54.2 interquartile range 11.2 years; 40% women) 50.6% reported paid employment at 30 months. Those returning to work reported at baseline more working hours, better quality of life and communication, were more often self-employed and in an office job. The USER-P scores did not change statistically significantly over time. CONCLUSION: About half of the stroke patients remained in paid employment. Optimizing interventions for returning to work and achieving meaningful participation outside of employment seem desirable.
COVID-19 and the Americans with Disabilities Act: Implications for employment and higher education
Authors: Garcia-Torres Mariana , Condon Olivia , Harris Sarah Parker , Gould Robert , Harniss Mark
Published: September 2023

BACKGROUND: The Americans with Disabilities Act (ADA) is a federal law that protects individuals with disabilities from discrimination in all areas of public life. The ADA contributes to equal opportunity across policy areas, including the interconnected domains of higher education and employment. Since the onset of the COVID-19 pandemic in 2020, emerging research has begun to document the disparities in impact on people with disabilities, among other marginalized groups. However, no research to date has reviewed and synthesized literature that addresses disability discrimination related to COVID-19 that has implications for application of the ADA. OBJECTIVE: This rapid evidence review aims to increase understanding about how COVID-19 has resulted in challenges for people with disabilities in the domains of employment and higher education that may be resolved through application of the ADA. METHODS: Keyword searches were conducted in five electronic databases. Title, abstract, and full text screening was conducted followed by a thematic analysis of key ADA themes. RESULTS: Twelve final articles were included in this review, eight categorized within higher education and four within employment. In relation to the ADA and COVID-19, five studies revealed findings related to web accessibility, eight related to effective communication, and four related to reasonable accommodations. CONCLUSION: The findings provide a broad overview of the current research on how COVID-19 has affected accessibility, communications and accommodations in employment and higher education and identify gaps in the literature within these policy domains.