bluuebunny/crossref_metadata_2025 huggingface dataset:
Features of the use of fiber-optic sensors for the study of tissue microcirculation
Authors: Pavlov V. S. , Zabolotna N. I. , Bezkrevnyy O. S. , Longyin Yang
Published: September 2023

The practical value of the work consists in the presented recommendations for developing a system for the study of tissue microcirculation based on one emitting and a group of receiving optical fibers, which made it possible to analyze the use of optical fibers of various parameters and characteristics. Schematic solutions for the construction of optoelectronic systems for the diagnosis of tissue microcirculation based on fiber-optic sensors that perform registration and processing of photoplethysmographic information in real-time are proposed to carry out a hardware and software implementation of a system for the study of peripheral blood circulation for the evaluation of biomedical signals based on the received photoplethysmograms.
Drop-out among ODL learners: A case study at the Open University of Mauritius
Authors: APPAVOO Perienen , GUNGEA Meera , SOHORAYE Mrinal
Published: September 2023

Dropout is a global and complex phenomenon affecting all universities. This study aimed at investigating the intrinsic and extrinsic factors causing dropout at the Open University of Mauritius (OU). Secondary data about 1885 learners were collected for five intakes and three levels of study - foundation, undergraduate and Master courses. The dropout rates were 46.08%, 39.14% and 17.31% respectively, showing that those with previous tertiary education were less likely to drop out of studies. Analysis of data from 96 completed questionnaires revealed that female learners were more persistent in their studies while mature students were less likely to drop out from university. Personal and career-related issues were the major causes leading to attrition at OU. Sub causes included wrong choice in programmes, inadequate tutorial support and lack of employer’s support. Corrective actions suggested include providing counselling sessions before registration and during studies, implementing strategies to help students develop time management skills, developing courses in line with industry requirements and improving tutor’s support. The low attrition rate at OU is probably because most of its learners are working adults who are able to shoulder learning with greater responsibility. This is also because OU has maximised on technology to reach out to learners, hence mitigating isolation.
The effectiveness of technology supported education to prevent sexual abuse in children with autism spectrum disorder
Authors: ÜNLÜ Özge
Published: September 2023

This study aimed to evaluate the effectiveness of technology-supported sexual abuse prevention training provided to children with Autism Spectrum Disorder (ASD). In addition, the social validity of the research was investigated based on the opinions of the mothers of the children participating in the research. The study was conducted with 4 male subjects with ASD between the ages of 8-13. A multiple probe design with probe conditions across subjects, one of the single-subject research models, was used in the study. Based on the findings regarding the effectiveness of the intervention it was concluded that technology-supported sexual abuse prevention training was effective in teaching sexual abuse prevention knowledge and skills to children with ASD. At the knowledge level, all subjects learnt the names of private body parts, the places where underwear can be removed, and the concepts of good and bad touch and at the skill level, they learnt to say "No" and leave the setting and to report the situation to a trusted person. It was also observed that all subjects maintained the target knowledge and skills and generalized them to different instruments, settings and people 2, 4 and 8 weeks after the end of instructional sessions. The social validity findings of the study showed that the mothers of the subjects expressed positive opinions about the study.
Obejście prawa w publicznym prawie gospodarczym – na granicy konfliktu wartości prawa prywatnego i publicznego
Authors: Bodanka Oskar
Published: September 2023

Tekst prezentuje ujęcie instytucji obejścia prawa w publicznym prawie gospodarczym. W artykule przedstawiono funkcjonowanie relacji sprzecznych wartości w publicznym prawie gospodarczym, czynności obejścia prawa oraz dokonywania czynności niestanowiących obejścia prawa. W celu dokładnego wyjaśnienia działania zmierzającego do obejścia prawa, dokonano również podziału na obejścia prawa w dobrej i złej wierze. W artykule wyrażono stanowisko, zgodnie z którym badanie obejścia prawa w prawie publicznym nie powinno ograniczać się jedynie do celu (zamiaru) podmiotu. W tym aspekcie obejście prawa pełni funkcję dodatkowej kontroli legalności działań podmiotów
Robust Federated Learning Method Against Data and Model Poisoning Attacks with Heterogeneous Data Distribution
Authors: Alharbi Ebtisaam , Marcolino Leandro Soriano , Gouglidis Antonios , Ni Qiang
Published: September 2023

Federated Learning (FL) is essential for building global models across distributed environments. However, it is significantly vulnerable to data and model poisoning attacks that can critically compromise the accuracy and reliability of the global model. These vulnerabilities become more pronounced in heterogeneous environments, where clients’ data distributions vary broadly, creating a challenging setting for maintaining model integrity. Furthermore, malicious attacks can exploit this heterogeneity, manipulating the learning process to degrade the model or even induce it to learn incorrect patterns. In response to these challenges, we introduce RFCL, a novel Robust Federated aggregation method that leverages CLustering and cosine similarity to select similar cluster models, effectively defending against data and model poisoning attacks even amidst high data heterogeneity. Our experiments assess RFCL’s performance against various attacker numbers and Non-IID degrees. The findings reveal that RFCL outperforms existing robust aggregation methods and demonstrates the capability to defend against multiple attack types.
Resource-Constrained Knowledge Diffusion Processes Inspired by Human Peer Learning
Authors: Beikihassan Ehsan , Hoover Amy K. , Koutis Ioannis , Parviz Ali , Aghaieabiane Niloofar
Published: September 2023

We consider a setting where a population of artificial learners is given, and the objective is to optimize aggregate measures of performance, under constraints on training resources. The problem is motivated by the study of peer learning in human educational systems. In this context, we study natural knowledge diffusion processes in networks of interacting artificial learners. By ‘natural’, we mean processes that reflect human peer learning where the students’ internal state and learning process is mostly opaque, and the main degree of freedom lies in the formation of peer learning groups by a coordinator who can potentially evaluate the learners before assigning them to peer groups. Among else, we empirically show that such processes indeed make effective use of the training resources, and enable the design of modular neural models that have the capacity to generalize without being prone to overfitting noisy labels.
On Sustainable Ride Pooling Through Conditional Expected Value Decomposition
Authors: Bose Avinandan , Jiang Hao , Varakantham Pradeep , Ge Zichang
Published: September 2023

Centralized Multi-Agent Reinforcement Learning (MARL) presents itself as an ideal framework for aggregation companies (e.g., Uber, Lyft, Deliveroo) that have to take a sequential set of centralized decisions on assigning individual agents (typically resources like taxis, food delivery personnel) to customer requests online in the presence of demand uncertainty. However, centralized learning is especially challenging in such very large scale environments, with thousands of agents/resources and hundreds of thousands of requests coming in each day. In this paper, we provide a novel value decomposition mechanism that is able to tackle the scale and provide high quality (matching) decisions at each time step. We show that our value decomposition approach, Conditional Expectation based Value Decomposition (CEVD) is more sustainable (requires  9.9% fewer vehicles to serve equal number of requests) and more efficient (serves 9.76% more requests, while traveling 13.32% lesser distance) than the current best approach over two different city scale (New York and Chicago) benchmarks for ride pooling using taxis.
High-Multiplicity Fair Allocation Using Parametric Integer Linear Programming
Authors: Bredereck Robert , Kaczmarczyk Andrzej , Knop Dušan , Niedermeier Rolf
Published: September 2023

Using insights from parametric integer linear programming, we improve the work of Bredereck et al. [Proc. ACM EC 2019] on high-multiplicity fair allocation. Answering an open question from their work, we proved that the problem of finding envy-free Pareto-efficient allocations of indivisible items is fixed-parameter tractable with respect to the combined parameter “number of agents” plus “number of item types.” Our central improvement, compared to their result, is to break the condition that the corresponding utility and multiplicity values have to be encoded in unary, which is required there. Concretely, we show that, while preserving fixed-parameter tractability, these values can be encoded in binary. Thus, we substantially expand the range of feasible utility and multiplicity values.
Algorithmic Recognition of 2-Euclidean Preferences
Authors: Escoffier Bruno , Spanjaard Olivier , Tydrichová Magdaléna
Published: September 2023

A set of voters’ preferences on a set of candidates is 2-Euclidean if candidates and voters can be mapped to the plane so that the preferences of each voter decrease with the Euclidean distance between her position and the positions of candidates. Based on geometric properties, we propose a recognition algorithm, that returns either “yes” (together with a planar positioning of candidates and voters) if the preferences are 2-Euclidean, or “no” if it is able to find a concise certificate that they are not, or “unknown” if a time limit is reached. Our algorithm outperforms a quadratically constrained programming solver achieving the same task, both in running times and the percentage of instances it is able to recognize. In the numerical tests conducted on the PrefLib library of preferences, 91.5% (resp. 4.5%) of the available sets of complete strict orders are proven not to be (resp. to be) 2-Euclidean, and the status of only 4.5% of them could not be decided. Furthermore, for instances involving 5 (resp. 6, 7) candidates, we were able to find planar representations that are compatible with 87.4% (resp. 58.1%, 60.1%) of voters’ preferences.
Decidable Fragments of LTLf Modulo Theories
Authors: Geatti Luca , Gianola Alessandro , Gigante Nicola , Winkler Sarah
Published: September 2023

We study Linear Temporal Logic Modulo Theories over Finite Traces (LTLMTf), a recently introduced extension of LTL over finite traces (LTLf) where propositions are replaced by first-order formulas and where first-order variables referring to different time points can be compared. In general, LTLMTf was shown to be semi-decidable for any decidable first-order theory (e.g., linear arithmetics), with a tableau-based semi-decision procedure. In this paper we present a sound and complete pruning rule for the LTLMTf tableau. We show that for any LTLMTf formula that satisfies an abstract, semantic condition, that we call finite memory, the tableau augmented with the new rule is also guaranteed to terminate. Last but not least, this technique allows us to establish novel decidability results for the satisfiability of several fragments of LTLMTf, as well as to give new decidability proofs for classes that are already known.
Dynamic Causality
Authors: Gladyshev Maksim , Alechina Natasha , Dastani Mehdi , Doder Dragan , Logan Brian
Published: September 2023

There have been a number of attempts to develop a formal definition of causality that accords with our intuitions about what constitutes a cause. Perhaps the best known is the “modified” definition of actual causality, HPm, due to Halpern. In this paper, we argue that HPm gives counterintuitive results for some simple causal models. We propose Dynamic Causality (DC), an alternative semantics for causal models that leads to an alternative definition of causes. DC ascribes the same causes as HPm on the examples of causal models widely discussed in the literature and ascribes intuitive causes for the kinds of causal models we consider. Moreover, we show that the complexity of determining a cause under the DC definition is lower than for the HPm definition.
Exploration and Exploitation in Hierarchical Reinforcement Learning with Adaptive Scheduling
Authors: Huang Zhigang , Liu Quan
Published: September 2023

In hierarchical reinforcement learning (HRL), continuous options provide a knowledge carrier that is more aligned with human behavior, but reliable scheduling methods are not yet available. To design an available scheduling method for continuous options, in this paper, the hierarchical reinforcement learning with adaptive scheduling (HAS) algorithm is proposed. It focuses on achieving an adaptive balance between exploration and exploitation during the frequent scheduling of continuous options. It builds on multi-step static scheduling and makes switching decisions according to the relative advantages of the previous and the estimated options, enabling the agent to focus on different behaviors at different phases. The expected t-step distance is applied to demonstrate the superiority of adaptive scheduling in terms of exploration. Furthermore, an interruption incentive based on annealing is proposed to alleviate excessive exploration, accelerating the convergence rate. We develop a comprehensive experimental analysis scheme. The experimental results demonstrate the high performance and robustness of HAS. Moreover, it provides evidence that adaptive scheduling has a positive effect both on the representation and option policies.
Unsupervised Graph Structure-Assisted Personalized Federated Learning
Authors: Li Xiaoying , Chen Xiaojun , Tang Bisheng , Wang Shaopu , Xuan Yuexin , Zhao Zhendong
Published: September 2023

Non-IID data presents a significant challenge for federated learning(FL), and personalized FL is a natural solution to address this challenge. Recently, Graph Neural Network (GNN) has recently emerged to model the complex client relationship using a client graph to refine personalized models. However, this approach depends on an existing client relation graph on the server, making it impractical unless this prerequisite is satisfied. Furthermore, noisy and missing connections in the original graph structures can degrade personalization performance. In this work, we propose an unsupervised structure learning approach to improve personalized FL, where the server learns a dynamic client graph through self-supervision and generates structure-based client representations. These representations are then broadcasted to users, regulating local training using the learned knowledge as an inductive bias. Empirical studies on benchmark datasets demonstrate the significant effectiveness of our approach and the high quality of the client graphs. The code is available at https://github.com/lazyJane/FedSKA.
A Model-Theoretic Approach to Belief Revision in Multi-Agent Belief Logic and Its Syntactic Characterizations
Authors: Liang Aiting , Liu Yongmei
Published: September 2023

Belief change studies how an agent modifies her beliefs on receiving new information. However, so far most research on belief change works on beliefs represented in propositional logic. There have been many works on integrating belief revision with reasoning about actions, and some works extending belief change from propositional logic to epistemic logics. In this paper, we study revision on beliefs of a third person represented with the multi-agent KD45 logic. Our formal technique is analogous to that of distance-based belief revision in propositional logic: to revise a KB by a formula, select from models of the formula those that are closest to models of the KB. To this end, a challenge is that in modal logics, a formula may have infinitely many Kripke models. To tackle this, we propose a variant of Moss’ canonical formulas called alternating canonical formulas, treat them as models for formulas, and define a notion of distance between them, based on the Hausdorff distance between two sets. We show that our revision satisfies all of the AGM postulates. To give syntactic characterizations of our revision, we make use of a normal form for KD45n called alternating cover disjunctive formulas (ACDFs). We give syntactic characterizations firstly on fragments of ACDFs called proper ACDFs and alternating cover conjunctive formulas (ACCFs), and finally on the whole ACDFs.
Accelerating SAT-Based HTN Plan Verification by Exploiting Data Structures from HTN Planning
Authors: Lin Songtuan , Behnke Gregor , Bercher Pascal
Published: September 2023

Plan verification is the task of deciding whether a given plan is a solution to a planning problem. In this paper, we study the plan verification problem in the context of Hierarchical Task Network (HTN) planning, which has been proved to be NP-complete when partial order (PO) is involved. We will develop a novel SAT-based approach exploiting the data structures solution order graphs and path decomposition trees which encodes an HTN plan verification problem as a SAT one. We show in our experiments that this new approach outperforms the current state-of-the-art (SOTA) planning-based approach for verifying plans for POHTN problems.
POINE2: Improving Poincaré Embeddings for Hierarchy-Aware Complex Query Reasoning over Knowledge Graphs
Authors: Liu Junnan , Mao Qianren , Li Jianxin , Fu Xingcheng , Wang Zheng
Published: September 2023

Reasoning complex logical queries on incomplete and massive knowledge graphs (KGs) remains a significant challenge. The prevailing method for this problem is query embedding, which embeds KG units (i.e., entities and relations) and complex queries into low-dimensional space. Recent developments in the field show that embedding queries as geometric shapes is a viable means for modeling entity set and logical relationships between them. Despite being promising, current geometric-based methods face challenges in capturing hierarchical structures of complex queries, which leaves considerable room for improvement. This paper presents POINE2, a geometric-based query embedding framework based on hyperbolic geometry to handle complex queries on knowledge graphs. POINE2 maps entities and queries as geometric shapes on a Cartesian product space of Poincaré ball spaces. To capture the hierarchical structures of complex queries, we use the Poincaré radius to represent the different levels of the hierarchy, and we use the aperture of the shape to indicate semantic differences at the same level of the hierarchy. Additionally, POINE2 offers a flexible and expressive definition of logical operations. Experimental results show that POINE2 outperforms existing salient geometric-based embedding methods and significantly improves these methods on evaluation datasets.
How Should I Compute My Candidates? A Taxonomy and Classification of Diagnosis Computation Algorithms
Authors: Rodler Patrick
Published: September 2023

Model-based diagnosis is a powerful, versatile and well-founded approach to troubleshooting a wealth of different types of systems. Diagnosis algorithms are both numerous and highly heterogeneous. In this work, we propose a taxonomy that allows their standardized assessment, classification and comparison. The aim is to (i) give researchers and practitioners an impression of the diverse landscape of available techniques, (ii) allow them to easily retrieve and compare the main features as well as pros and cons, and (iii) facilitate the selection of the “right” algorithm to adopt for a particular problem case, e.g., in practical diagnostic settings, for comparison in experimental evaluations, or for reuse, modification, extension, or improvement in the course of research. Finally, we demonstrate the value and application of the taxonomy by assessing and categorizing a range of more than 30 important diagnostic methods, and we point out how using the taxonomy as a common guideline for algorithm analysis would benefit the research community in various regards.
Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Study
Authors: Silvestrin Luis Pedro , Yu Shujian , Hoogendoorn Mark
Published: September 2023

Coping with distributional shifts is an important part of transfer learning methods in order to perform well in real-life tasks. However, most of the existing approaches in this area either focus on an ideal scenario in which the data does not contain noises or employ a complicated training paradigm or model design to deal with distributional shifts. In this paper, we revisit the robustness of the minimum error entropy (MEE) criterion, a widely used objective in statistical signal processing to deal with non-Gaussian noises, and investigate its feasibility and usefulness in real-life transfer learning regression tasks, where distributional shifts are common. Specifically, we put forward a new theoretical result showing the robustness of MEE against covariate shift. We also show that by simply replacing the mean squared error (MSE) loss with the MEE on basic transfer learning algorithms such as fine-tuning and linear probing, we can achieve competitive performance with respect to state-of-the-art transfer learning algorithms. We justify our arguments on both synthetic data and 5 real-world time-series data.
Graduality in Probabilistic Argumentation Frameworks
Authors: Spaans Jeroen , Doder Dragan
Published: September 2023

Gradual semantics are methods that evaluate overall strengths of individual arguments in graphs. In this paper, we investigate gradual semantics for extended frameworks in which probabilities are used to quantify the uncertainty about arguments and attacks belonging to the graph. We define the likelihoods of an argument’s possible strengths when facing uncertainty about the topology of the argumentation framework. We also define an approach to compare the strengths of arguments in this probabilistic setting. Finally, we propose a method to calculate the overall strength of each argument in the framework, and we evaluate this method against a set of principles.
PLEASE: Generating Personalized Explanations in Human-Aware Planning
Authors: Vasileiou Stylianos Loukas , Yeoh William
Published: September 2023

Model Reconciliation Problems (MRPs) and their variant, Logic-based MRPs (L-MRPs), have emerged as popular methods for explainable planning problems. Both MRP and L-MRP approaches assume that the explaining agent has access to an assumed model of the human user receiving the explanation, and it reconciles its own model with the human model to find the differences such that when they are provided as explanations to the human, they will understand them. However, in practical applications, the agent is likely to be fairly uncertain on the actual model of the human and wrong assumptions can lead to incoherent or unintelligible explanations. In this paper, we propose a less stringent requirement: The agent has access to a task-specific vocabulary known by the human and, if available, a human model capturing confidently-known information. Our goal is to find a personalized explanation, which is an explanation that is at an appropriate abstraction level with respect to the human’s vocabulary and model. Using a logic-based method called knowledge forgetting for generating abstractions, we propose a simple framework compatible with L-MRP approaches, and evaluate its efficacy through computational and human user experiments.
A Landmark-Cut Heuristic for Lifted Optimal Planning
Authors: Wichlacz Julia , Höller Daniel , Fišer Daniel , Hoffmann Jörg
Published: September 2023

Lifted planning – finding plans directly on the PDDL input model – has attracted renewed attention during the last years. This avoids the process of grounding, which can become computationally prohibitive very easily. However, the main focus of recent research in this area has been on satisficing, i.e., (potentially) suboptimal planning. We present a novel heuristic for optimal lifted planning. Our basic idea is inspired by the LM-cut heuristic, which has been very successful in grounded optimal planning. Like LM-cut, we generate cut-based landmarks via back-chaining from the goal, generating cuts of partially grounded actions. However, exactly mimicking the ground formulation is not feasible, this includes computing the hmax heuristic several times for one computation of the LM-cut heuristic (which is already NP-hard to compute). We show that our heuristic is admissible and evaluate it in a cost optimal setting.
Adaptive Self-Supervised Continual Learning
Authors: Wu Lilei , Wang Zhen , Liu Jie
Published: September 2023

Continual Learning (CL) studies the problem of developing a robust model that can learn new tasks while retaining previously learned knowledge. However, the current CL methods exclusively focus on data with annotations, disregarding that unlabelled data is the mainstream in real-world applications. To close this research gap, this study concentrates on continual self-supervised learning, which is plagued by challenges of memory over-fitting and class imbalance. Besides, these challenges are exacerbated throughout incremental training. Aimed at addressing these challenges from both loss and data perspectives, we introduce a framework, Adaptive Self-supervised Continual Learning (ASCL). Specifically, we devise an Adaptive Sharpness-Aware Minimization (ASAM) module responsible for identifying flatter local minima in the loss landscape with a smaller memory over-fitting risk. Additionally, we design an Adaptive Memory Enhancement (AME) module responsible for rebalancing self-supervised loss with new and old tasks from a data perspective. Finally, the adaptive mechanisms in AME and ASAM modules dynamically adjust the loss landscape sharpness and memory enhancement strength with the feedback of intermediate training results. The results of our extensive experiments demonstrate the state-of-the-art performance of our methods in continual self-supervised learning scenarios across multiple datasets.
Visualization Enhancement of Saliency Methods Based on the Sliding Window Mechanism
Authors: Xiang Xiaohong , Zhang Fuyuan , Deng Xin , Ding Xiaoyu
Published: September 2023

Deep neural networks are widely used in image classification tasks, but their internal decision-making mechanisms are often difficult to explain. While various algorithms have been developed to visualize these mechanisms, many of them produce coarse, noisy results that are not always convincing. To address this issue, we propose a method for enhancing saliency maps produced by saliency methods. Our method uses a fixed-size sliding window to upsample local regions of the input image and feed them into the selected visualization algorithm to generate class-specific saliency maps and probability scores. We then downsample the resulting saliency maps and multiply them by the probability scores to obtain maps with greater detail. We evaluate our method using different saliency methods and network architectures, and demonstrate its effectiveness through both quantitative metrics and intuitive evaluation. Our results show that our method significantly improves the performance of these saliency methods, providing a more valid and reliable means of visualizing the decision mechanisms of deep neural networks. Code is available at https://github.com/LuoLogic/Enhuncement-saliency.
Identifying Helpful Learnwares Without Examining the Whole Market
Authors: Xie Yi , Tan Zhi-Hao , Jiang Yuan , Zhou Zhi-Hua
Published: September 2023

The learnware paradigm aims to construct a market of numerous well-performing machine learning models, which enables users to leverage these models to accomplish specific tasks without having to build models from scratch. Each learnware in the market is a model associated with a specification, representing the model’s utility and enabling it to be identified according to future users’ requirements. In the learnware paradigm, due to the vast and ever-increasing number of models in the market, a significant challenge is to identify helpful learnwares efficiently for a specific user task without leaking data privacy. However, existing identification methods require examining the whole market, which is computationally unaffordable in a large market. In this paper, we propose a new framework for identifying helpful learnwares without examining the whole market. Specifically, using the Reduced Kernel Mean Embedding (RKME) specification, we derive a novel learnware scoring criterion for assessing the helpfulness of a learnware, based on which we design an anchor-based framework to identify helpful learnwares by examining only a small portion of learnwares in the market. Theoretical analyses are provided for both the criterion and the anchor-based method. Empirical studies on market containing thousands of learnwares from real-world datasets confirm the effectiveness of our proposed approach.
Micro-Expression Spotting Method Based on AU Prototype
Authors: He Yuhong , Wang Guangyu , Ma Lin , Li Haifeng
Published: September 2023

Micro-expressions (MEs) are brief, involuntary facial expressions that reveal genuine emotions, making their accurate detection crucial in various applications, such as security, psychology, and human-computer interaction. Due to its small intensity and short duration, how accurately capturing the subtle movements of micro-expression is a challenging problem. This paper presents a novel AU prototype-based method for micro-expression spotting, which offers high accuracy and robustness. Action Units (AUs) are basic facial actions, such as brow lower and lip corner puller, that are widely used for micro-expression analysis, and an expression can be encoded as a sequence of AUs. Our approach involves designing AU prototypes that record representative dynamic information of AUs. We then calculate the prototype matching index between AU prototypes and the image sequence to construct time-domain prototype matching curves for ME spotting. In the experimental section, AU prototypes derived from CASMEII dataset enable a more intuitive analysis of AU within micro-expressions. Results on the CAS(ME)2 dataset demonstrate that our ME spotting method significantly outperforms existing approaches. This makes our method highly valuable for various application scenarios, potentially enhancing emotion recognition and analysis in real-world settings.
Information Bound and Its Applications in Bayesian Neural Networks
Authors: Zhang Jiaru , Hua Yang , Song Tao , Wang Hao , Xue Zhengui , Ma Ruhui , Guan Haibing
Published: September 2023

Bayesian neural networks have drawn extensive interest because of their distinctive probabilistic representation framework. However, despite its recent success, little work focuses on the information-theoretic understanding of Bayesian neural networks. In this paper, we propose Information Bound as a metric of the amount of information in Bayesian neural networks. Different from mutual information on deterministic neural networks where modification of network structure or specific input data is usually necessary, Information Bound can be easily estimated on current Bayesian neural networks without any modification of network structures or training processes. By observing the trend of Information Bound during training, we demonstrate the existence of the “critical period” in Bayesian neural networks. Besides, we show that the Information Bound can be used to judge the confidence of the model prediction and to detect out-of-distribution datasets. Based on these observations of model interpretation, we propose Information Bound regularization and Information Bound variance regularization methods. The Information Bound regularization encourages models to learn the minimum necessary information and improves the model generality and robustness. The Information Bound variance regularization encourages models to learn more about complex samples with low Information Bound. Extensive experiments on KMNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 verify the effectiveness of the proposed regularization methods.
Individual Fairness Under Uncertainty
Authors: Zhang Wenbin , Wang Zichong , Kim Juyong , Cheng Cheng , Oommen Thomas , Ravikumar Pradeep , Weiss Jeremy
Published: September 2023

Algorithmic fairness, the research field of making machine learning (ML) algorithms fair, is an established area in ML. As ML technologies expand their application domains, including ones with high societal impact, it becomes essential to take fairness into consideration during the building of ML systems. Yet, despite its wide range of socially sensitive applications, most work treats the issue of algorithmic bias as an intrinsic property of supervised learning, i.e., the class label is given as a precondition. Unlike prior studies in fairness, we propose an individual fairness measure and a corresponding algorithm that deal with the challenges of uncertainty arising from censorship in class labels, while enforcing similar individuals to be treated similarly from a ranking perspective, free of the Lipschitz condition in the conventional individual fairness definition. We argue that this perspective represents a more realistic model of fairness research for real-world application deployment and show how learning with such a relaxed precondition draws new insights that better explains algorithmic fairness. We conducted experiments on four real-world datasets to evaluate our proposed method compared to other fairness models, demonstrating its superiority in minimizing discrimination while maintaining predictive performance with uncertainty present.
Grafting Fine-Tuning and Reinforcement Learning for Empathetic Emotion Elicitation in Dialog Generation
Authors: Zhu Ying , Wang Bo , Zhao Dongming , Huang Kun , Jiang Zhuoxuan , He Ruifang , Hou Yuexian
Published: September 2023

For human-like dialogue systems, it is significant to inject the empathetic ability or elicit the opposite’s positive emotions, while existing studies mostly only focus on either of the above two research lines. In this work, we propose a novel and grafted task named Empathetic Emotion Elicitation Dialog to make a dialog system able to possess both aspects of ability simultaneously. We do not train an empathetic dialog system and an emotion elicitation dialog system separately and then simply concatenate the responses generated by these two systems, which will cause illogical and repetitive responses. Instead, we propose a unified solution: (1) To generate empathetic responses and emotion elicitation responses within the same semantic space, we design a unified framework. (2) The unified framework has three stages which first retrieve the empathetic and emotion elicitation exemplars as external knowledge, then fine-tune the emotion/action prediction on a pre-trained language model to enhance the empathetic ability, and finally model the user feedback by reinforcement learning to enhance the emotion elicitation ability. Experiments show that our method outperforms the baselines in the response generation quality and simultaneously empathizes with the user and elicits their positive emotions.
Neuropsychological features evaluation of data related to Alzheimer’s disease progression using feature selection
Authors: Thabtah Fadi , Mohammad Heba , Lu Yonggang , Zhang Bo
Published: September 2023

BACKGROUND: Alzheimer’s Disease (AD) is normally assessed in clinical settings using neuropsychological tests and medical procedures such as neuroimaging techniques: Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) among others. The latter procedures are expensive and unavailable in most nations, so early diagnosis of AD does not occur, which heavily increases the subsequent treatment costs for the patients. AIMS: This research aims to evaluate cognitive features related to dementia progression based on neuropsychological tests’ data that are related to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) We utilise data related to two neuropsychological tests including the Clinical Dementia Rating Scale Sum of Boxes (CDR-SOB), and Mini-Mental State Examination (MMSE), to assess advancement of the AD. METHODS: To achieve the aim, we develop a data process called Neuropsychological Feature Assessment via Feature Selection (NFAFS) to identify impactful features using Information Gain (IG) and Pearson Correlation to assess class-feature and feature-feature correlations Later we will model a minimal subset of neuropsychological features using machine learning techniques to derive classification models. RESULTS AND IMPLICATIONS: Results obtained show key cognitive features of the MMSE are Time Orientation, Recall and Complex Attention, since they correlate with the progression class being ranked high in results of the feature selection techniques. For the CDR-SOB features, and aside from the memory feature it was difficult to identify other specific features that are signs of the dementia progression Clinicians can use specific features in a digital knowledge base to pay more attention to specific cognitive deficits related to Recall, Orientation and Complex Attention during dementia clinical evaluations in order to seek possible signs of the disease progression early.
Reading Difficulties in Parkinson’s Disease: A Stepped Care Model for Neurovisual Rehabilitation
Authors: van der Lijn Iris , de Haan Gera A. , van der Feen Fleur E. , Vrijling Anne C.L. , Stellingwerf Catharina , Fuermaier Anselm B.M. , Langenberg Pia , van Laar Teus , Heutink Joost
Published: September 2023

Background:

People with Parkinson’s disease (PD) frequently experience reading difficulties. Little is known about what functional impairments distinguish people with PD with and without reading difficulties and how these should guide rehabilitation.

Objective:

To provide concrete advice for an efficient stepped care model for reading difficulties in PD, based on extensive functional assessments.

Methods:

This study included 74 people with PD in a neurovisual rehabilitation setting who underwent assessment of visual, visuoperceptual, and cognitive functions. Outcomes were compared between those with frequent (RD+; N = 55) and infrequent reading difficulties (RD–; N = 19). Aids and advice provided during rehabilitation were registered.

Results:

Only a few functions appeared to distinguish RD+ and RD–. Visual functions (i.e., contrast sensitivity, g = 0.76; reading acuity, g = 0.66; visual acuity, g = 0.54) and visuoperceptual functions (i.e., visual attention, g = 0.58, visual motor speed, g = 0.56) showed significant worse scores in RD+ compared to RD–. Aids and advice applied consisted mainly of optimizing refraction, improving lighting, and optimizing text size and spacing.

Conclusion:

The test battery showed significant differences between RD+ and RD–on only a few tests on visual and visuoperceptual functions. The applied aids and advice matched well with these impairments. Therefore, we recommend a stepped care model, starting with a short test battery on these functions. If this battery indicates functional impairments, this can be followed by standard aids and advice to improve reading. Only in case of insufficient effect additional testing should take place.
PERLINDUNGAN HAK KONSUMEN TERHADAP MARAKNYA MONOPOLI BISNIS
Authors: Rahmadhani Desta Adinda , . Sakirman
Published: September 2023

In every company, consumer trust is a top priority. The Consumer Protection Law is one of the important things that must be known, so that in the future we will not be deceived or feel harmed by an item or thing that is purchased and consumed.

To protect consumers from fraudulent competition, consumers need to get legal protection. Legal protection that can be provided are: preventive legal protection and repressive legal protection. The purpose of this paper is to determine the form of legal protection for consumers due to unfair competition. The conclusion obtained is that the form of preventive legal protection has been established by the government, namely with the establishment of Law Number 8 of 1999 concerning Consumer Protection and forms of repressive legal protection, carried out by litigation and non-litigation. Litigation settlement is carried out in court and non-litigation with the National Consumer Protection Agency, Non-Governmental Consumer Protection Agency, and Consumer Dispute Settlement Agency.
Exploration and extraction of deep minerals without mines and quarries with the help of nuclear autonomous thermal drills-geoprodes
Authors: Vashchenko Volodymyr M. , Korduba Iryna B.
Published: September 2023

The Earth's interior at 10–15 km depth horizons is a geoastrophysical space within which direct contact access of research instruments and other devices is not yet possible. At the same time, the list of fundamental and applied tasks that can be solved with the help of direct contact deep research of deep matter with the delivery of its samples to the surface of the Earth is a rather voluminous process and has an extraordinary scientific and technical significance, physical significance. Extraction of deep minerals without mines and quarries with the help of nuclear geosondes, for this it is necessary to consider the hydrodynamic problem of the vertical melt flow from the front part of the geosonde in the opposite direction to the movement of the geosonde vector. The need for a comprehensive consideration of thermophysical and hydrodynamic problems is justified by the presence of a common parameter – the speed of the geosonde as a heat source. The paper examines aspects of the theory and construction of autonomous nuclear deep thermal drills-geoprobes, which are used to solve fundamental scientific and applied industrial tasks, as well as significantly reduce the ecological burden on the environment with the help of geotechnical methods and means of exploration and extraction of deep minerals. The purpose of this work is a detailed theoretical study of the most fundamental possibility of creating ultra-deep nuclear autonomous geoprobes-thermodrills and an assessment of the main engineering parameters of the heat and mass transfer process in real conditions of contact melting of deep rocks. The results of theoretical studies show that the possibility of deep thermal penetration is theoretically substantiated quite correctly. Moreover, even today the problem of creating an ultra-deep geosonde is a fundamentally solvable scientific-technical and engineering-physical task. New deep industrial geotechnologies are a fundamentally new concept of greening the mining process and are suitable for the development of poor and deep ore horizons for which the construction of mines or quarries is impractical or impossible.
Development of materials with a multilayer structure of the gradient type based on liquid compositions for the shielding of electromagnetic fields
Authors: Burdeina Nataliia , Biruk Yana I. , Nikolaiev Kyrylo D.
Published: September 2023

Nowadays, for shielding and absorption of electromagnetic fields of a wide frequency range, composite materials or solid metal structures are offered and widely used. The article deals with research and development of multilayer protective liquid materials for shielding electric, magnetic and electromagnetic fields of a wide frequency range. A series of liquid shielding mixtures based on standard certified paints and finely dispersed magnetite was produced. Testing of the protective properties of these mixtures was carried out by sequentially applying 2–3 layers of mixtures to the surface, each of which had a certain concentration of the shielding substance. The shielding and reflection coefficients for these multilayer structures are given. It is shown that the advantage of the developed multilayer protective liquid materials lies in obtaining a gradient of electrophysical and magnetic properties of the protective structure with the possibility of calculating dielectric and magnetic permeabilities, taking into account the morphology of the shielding particles. A method of obtaining protective materials of the gradient type, which reduce the influence of electromagnetic fields, is proposed, which is possible for the treatment of surfaces of large areas and complex configurations.
The problem of flooding and analysis of the ways of its solution
Authors: Petrochenko Oleksiy V.
Published: September 2023

An analysis of literary sources and informational materials on flood situations that occurred in different historical periods on different continents and in different countries was carried out. It has been established that floods accompany mankind from the earliest times to the present day. Information about catastrophic floods has been preserved since the third millennium BC, in connection with which they should be considered as a historical category. According to the World Meteorological Organization (WMO), floods account for more than 45% of natural disasters related to water (hydrological disasters), which account for about 70% of all natural disasters on the planet. Taking into account the fact that floods are among the most dangerous natural phenomena in terms of the number of victims and the amount of damage, the problem of floods should be considered global. The global nature of the flood problem is confirmed by its following features: the problem concerns all humanity, affects the interests and fate of all countries and peoples; leads to significant social, ecological and economic losses, which, if they increase, will threaten the existence of human civilization; contributes to the aggravation of other internationally recognized global problems, such as environmental, food, social (in terms of population protection), land degradation. Taking into account the fact that in recent years the incidence of catastrophic floods has increased, their social, ecological and economic consequences have increased, as well as the number of human victims from floods, the article focuses on the need to develop a highly effective scientific and methodological toolkit for solving the problem of floods and its wide use not only in Ukraine, but also in many countries of the world. It has been established that among the many areas of systemic research of the problem of floods, the main and most important area is the fundamental improvement of the conceptual foundations of flood protection. The author's version of the universal concept of solving the flood problem is proposed, which is based on a universal algorithm for achieving the target indicator of flood protection. The maximum value of the ratio of the flood protection effect to the costs of achieving this effect was chosen as the target indicator of flood protection. The protective anti-flood effect is represented by the total damage averted by anti-flood measures, which consists of averted social, ecological and economic damages.