Reviewerly/exHarmony huggingface dataset:
Spatially Covariant Lesion Segmentation

Compared to natural images, medical images usually show stronger visual patterns and therefore this adds flexibility and elasticity to resource-limited clinical applications by injecting proper priors into neural networks. In this paper, we propose spatially covariant pixel-aligned classifier (SCP) to improve the computational efficiency and meantime maintain or increase accuracy for lesion segmentation. SCP relaxes the spatial invariance constraint imposed by convolutional operations and optimizes an underlying implicit function that maps image coordinates to network weights, the parameters of which are obtained along with the backbone network training and later used for generating network weights to capture spatially covariant contextual information. We demonstrate the effectiveness and efficiency of the proposed SCP using two lesion segmentation tasks from different imaging modalities: white matter hyperintensity segmentation in magnetic resonance imaging and liver tumor segmentation in contrast-enhanced abdominal computerized tomography. The network using SCP has achieved 23.8, 64.9 and 74.7 reduction in GPU memory usage, FLOPs, and network size with similar or better accuracy for lesion segmentation.
Anti-unification and Generalization: A Survey

Anti-unification (AU) is a fundamental operation for generalization computation used for inductive inference. It is the dual operation to unification, an operation at the foundation of automated theorem proving. Interest in AU from the AI and related communities is growing, but without a systematic study of the concept nor surveys of existing work, investigations often resort to developing application-specific methods that existing approaches may cover. We provide the first survey of AU research and its applications and a general framework for categorizing existing and future developments.
U-Match: Two-view Correspondence Learning with Hierarchy-aware Local Context Aggregation

Local context capturing has become the core factor for achieving leading performance in two-view correspondence learning. Recent advances have devised various local context extractors whereas typically adopting explicit neighborhood relation modeling that is restricted and inflexible. To address this issue, we introduce U-Match, an attentional graph neural network that has the flexibility to enable implicit local context awareness at multiple levels. Specifically, a hierarchy-aware graph representation (HAGR) module is designed and fleshed out by local context pooling and unpooling operations. The former encodes local context by adaptively sampling a set of nodes to form a coarse-grained graph, while the latter decodes local context by recovering the coarsened graph back to its original size. Moreover, an orthogonal fusion module is proposed for the collaborative use of HAGR module, which integrates complementary local and global information into compact feature representations without redundancy. Extensive experiments on different visual tasks prove that our method significantly surpasses the state-of-the-arts. In particular, U-Match attains an AUC at 5 degree threshold of 60.53% on the challenging YFCC100M dataset without RANSAC, outperforming the strongest prior model by 8.61 absolute percentage points. Our code is publicly available at https://github.com/ZizhuoLi/U-Match.
Safe Reinforcement Learning via Probabilistic Logic Shields

Safe Reinforcement learning (Safe RL) aims at learning optimal policies while staying safe. A popular solution to Safe RL is shielding, which uses a logical safety specification to prevent an RL agent from taking unsafe actions. However, traditional shielding techniques are difficult to integrate with continuous, end-to-end deep RL methods. To this end, we introduce Probabilistic Logic Policy Gradient (PLPG). PLPG is a model-based Safe RL technique that uses probabilistic logic programming to model logical safety constraints as differentiable functions. Therefore, PLPG can be seamlessly applied to any policy gradient algorithm while still providing the same convergence guarantees. In our experiments, we show that PLPG learns safer and more rewarding policies compared to other state-of-the-art shielding techniques.
A Study on the Influence of Group Aggregation Perception on Marine Police Officials’ Job Enthusiasm

The purpose of this study is to verify the effect of collective cohesion has on marine police officials’ job enthusiasm. 150 maritime police officials were targeted for the study's progress. Group cohesion was defined as an effort for an organization's goal through intimacy and unity among members, focusing on the characteristics of personal needs, In addition, sub-variables were composed of task cohesion, social cohesion, and group attachment. Job enthusiasm is an important motivation for job performance, and sub-variables were composed of vitality, and commitment. As a result of the study, task cohesion and group attachment, which are sub-variables of group cohesion, had a positive (+) effect on vitality, which is a sub-variable of group cohesion, and a negative (-) effect on vitality, which is a sub-variable of job enthusiasm. In addition, it was found that only group attachment among the sub-variables of group cohesion had a positive (+) effect on immersion, which is a subvariable of job column. It was confirmed that maritime police officials clearly distinguished the public and private parts, such as performing their duties through high vitality.
Virtual Reality and Language Learning in Hospitality: An Investigation of Students' Perceptions

This research investigates the perceptions of students within the hospitality management program at Politeknik Negeri Lampung (Polinela) regarding the utilization of Virtual Reality (VR)-based learning in English for Hospitality, a specialized branch of English for Specific Purposes (ESP). With a qualitative approach, the study aims to uncover students’ perspectives on both the challenges encountered and the aspects appreciated in the context of VR-based language learning. The participants, comprising 23 individuals, responded to open-ended questions that aimed to delve into their experiences. The findings shed light on the feasibility and effectiveness of VR-based learning, with the majority of participants reporting minimal difficulties. Appreciation for the immersive learning experience, novelty, realism, and engagement factors were evident in their responses. This research contributes to the understanding of incorporating innovative technologies like VR in ESP contexts, highlighting the need to address technical challenges and optimize language support for a diverse learner population. The outcomes offer insights into the potential of VR-based learning to enhance language acquisition within specific vocational domains.
DiSProD: Differentiable Symbolic Propagation of Distributions for Planning

The paper introduces DiSProD, an online planner developed for environments with probabilistic transitions in continuous state and action spaces. DiSProD builds a symbolic graph that captures the distribution of future trajectories, conditioned on a given policy, using independence assumptions and approximate propagation of distributions. The symbolic graph provides a differentiable representation of the policy's value, enabling efficient gradient-based optimization for long-horizon search. The propagation of approximate distributions can be seen as an aggregation of many trajectories, making it well-suited for dealing with sparse rewards and stochastic environments. An extensive experimental evaluation compares DiSProD to state-of-the-art planners in discrete-time planning and real-time control of robotic systems. The proposed method improves over existing planners in handling stochastic environments, sensitivity to search depth, sparsity of rewards, and large action spaces. Additional real-world experiments demonstrate that DiSProD can control ground vehicles and surface vessels to successfully navigate around obstacles.
On the Compilability of Bounded Numeric Planning

Bounded numeric planning, where each numeric variable domain is bounded, is PSPACE-complete, but such a complexity result does not capture how hard it really is, since the same holds even for the practically much easier STRIPS fragment. A finer way to compare the difficulty of planning formalisms is through the notion of compilability, which has been however extensively studied only for classical planning by Nebel. This paper extends Nebel's framework to the setting of bounded numeric planning. First, we identify a variety of numeric fragments differing on the degree of the polynomials involved and the availability of features such as conditional effects and Boolean conditions; then we study the compilability of these fragments to each other and to the classical fragments. Surprisingly, numeric and classical planning with conditional effects and Boolean conditions can be compiled both ways preserving plan size exactly, while the same does not hold when targeting pure STRIPS. Our study reveals also that numeric fragments cluster into two equivalence classes separated by the availability of incomplete initial state specifications, a feature allowing to specify uncertainty in the initial state.
Reducing Communication for Split Learning by Randomized Top-k Sparsification

Split learning is a simple solution for Vertical Federated Learning (VFL), which has drawn substantial attention in both research and application due to its simplicity and efficiency. However, communication efficiency is still a crucial issue for split learning. In this paper, we investigate multiple communication reduction methods for split learning, including cut layer size reduction, top-k sparsification, quantization, and L1 regularization. Through analysis of the cut layer size reduction and top-k sparsification, we further propose randomized top-k sparsification, to make the model generalize and converge better. This is done by selecting top-k elements with a large probability while also having a small probability to select non-top-k elements. Empirical results show that compared with other communication-reduction methods, our proposed randomized top-k sparsification achieves a better model performance under the same compression level.
FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer

Federated Learning (FL) has been widely concerned for it enables decentralized learning while ensuring data privacy. However, most existing methods unrealistically assume that the classes encountered by local clients are fixed over time. After learning new classes, this impractical assumption will make the model's catastrophic forgetting of old classes significantly severe. Moreover, due to the limitation of communication cost, it is challenging to use large-scale models in FL, which will affect the prediction accuracy. To address these challenges, we propose a novel framework, Federated Enhanced Transformer (FedET), which simultaneously achieves high accuracy and low communication cost. Specifically, FedET uses Enhancer, a tiny module, to absorb and communicate new knowledge, and applies pre-trained Transformers combined with different Enhancers to ensure high precision on various tasks. To address local forgetting caused by new classes of new tasks and global forgetting brought by non-i.i.d class imbalance across different local clients, we proposed an Enhancer distillation method to modify the imbalance between old and new knowledge and repair the non-i.i.d. problem. Experimental results demonstrate that FedET's average accuracy on a representative benchmark dataset is 14.1% higher than the state-of-the-art method, while FedET saves 90% of the communication cost compared to the previous method.
Explaining Answer-Set Programs with Abstract Constraint Atoms

Answer-Set Programming (ASP) is a popular declarative reasoning and problem solving formalism. Due to the increasing interest in explainabilty, several explanation approaches have been developed for ASP. However, support for commonly used advanced language features of ASP, as for example aggregates or choice rules, is still mostly lacking. We deal with explaining ASP programs containing Abstract Constraint Atoms, which encompass the above features and others. We provide justifications for the presence, or absence, of an atom in a given answer-set. To this end, we introduce several formal notions of justification in this setting based on the one hand on a semantic characterisation utilising minimal partial models, and on the other hand on a more ruled-guided approach. We provide complexity results for checking and computing such justifications, and discuss how the semantic and syntactic approaches relate and can be jointly used to offer more insight. Our results contribute to a basis for explaining commonly used language features and thus increase accessibility and usability of ASP as an AI tool.
Genetic Prompt Search via Exploiting Language Model Probabilities

Prompt tuning for large-scale pretrained language models (PLMs) has shown remarkable potential, especially in low-resource scenarios such as few-shot learning. Moreover, derivative-free optimisation (DFO) techniques make it possible to tune prompts for a black-box PLM to better fit downstream tasks. However, there are usually preconditions to apply existing DFO-based prompt tuning methods, e.g. the backbone PLM needs to provide extra APIs so that hidden states (and/or embedding vectors) can be injected into it as continuous prompts, or carefully designed (discrete) manual prompts need to be available beforehand, serving as the initial states of the tuning algorithm. To waive such preconditions and make DFO-based prompt tuning ready for general use, this paper introduces a novel genetic algorithm (GA) that evolves from empty prompts, and uses the predictive probabilities derived from the backbone PLM(s) on the basis of a (few-shot) training set to guide the token selection process during prompt mutations. Experimental results on diverse benchmark datasets show that the proposed precondition-free method significantly outperforms the existing DFO-style counterparts that require preconditions, including black-box tuning, genetic prompt search and gradient-free instructional prompt search.
Universal Adaptive Data Augmentation

Existing automatic data augmentation (DA) methods either ignore updating DA's parameters according to the target model's state during training or adopt update strategies that are not effective enough. In this work, we design a novel data augmentation strategy called ``Universal Adaptive Data Augmentation" (UADA). Different from existing methods, UADA would adaptively update DA's parameters according to the target model's gradient information during training: given a pre-defined set of DA operations, we randomly decide types and magnitudes of DA operations for every data batch during training, and adaptively update DA's parameters along the gradient direction of the loss concerning DA's parameters. In this way, UADA can increase the training loss of the target networks, and the target networks would learn features from harder samples to improve the generalization. Moreover, UADA is very general and can be utilized in numerous tasks, e.g., image classification, semantic segmentation and object detection. Extensive experiments with various models are conducted on CIFAR-10, CIFAR-100, ImageNet, tiny-ImageNet, Cityscapes, and VOC07+12 to prove the significant performance improvements brought by UADA.
Explainable Reinforcement Learning via a Causal World Model

Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future. In this paper, we develop a novel framework for explainable RL by learning a causal world model without prior knowledge of the causal structure of the environment. The model captures the influence of actions, allowing us to interpret the long-term effects of actions through causal chains, which present how actions influence environmental variables and finally lead to rewards. Different from most explanatory models which suffer from low accuracy, our model remains accurate while improving explainability, making it applicable in model-based learning. As a result, we demonstrate that our causal model can serve as the bridge between explainability and learning.
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator

Hyperparameter optimization (HPO) is a vital step in improving performance in deep learning (DL). Practitioners are often faced with the trade-off between multiple criteria, such as accuracy and latency. Given the high computational needs of DL and the growing demand for efficient HPO, the acceleration of multi-objective (MO) optimization becomes ever more important. Despite the significant body of work on meta-learning for HPO, existing methods are inapplicable to MO tree-structured Parzen estimator (MO-TPE), a simple yet powerful MO-HPO algorithm. In this paper, we extend TPE’s acquisition function to the meta-learning setting using a task similarity defined by the overlap of top domains between tasks. We also theoretically analyze and address the limitations of our task similarity. In the experiments, we demonstrate that our method speeds up MO-TPE on tabular HPO benchmarks and attains state-of-the-art performance. Our method was also validated externally by winning the AutoML 2022 competition on “Multiobjective Hyperparameter Optimization for Transformers”. See https://arxiv.org/abs/2212.06751 for the latest version with Appendix.
Microwave Quantum Illumination with Correlation-To-Displacement Conversion

Entanglement is vulnerable to degradation in a noisy sensing scenario, but surprisingly, the quantum illumination protocol has demonstrated that its advantage can survive. However, designing a measurement system that realizes this advantage is challenging since the information is hidden in the weak correlation embedded in the noise at the receiver side. Recent progress in a correlation-to-displacement conversion module provides a route towards an optimal protocol for practical microwave quantum illumination. In this work, we extend the conversion module to accommodate experimental imperfections that are ubiquitous in microwave systems. To mitigate loss, we propose amplification of the return signals. In the case of ideal amplification, the entire six-decibel error-exponent advantage in target detection error can be maintained. However, in the case of noisy amplification, this advantage is reduced to three decibels. We analyze the quantum advantage under different scenarios with a Kennedy receiver in the final measurement. In the ideal case, the performance still achieves the optimal one over a fairly large range of error probability with only on-off detection. Empowered by photon-number-resolving detectors, the performance is further improved and also analyzed in terms of receiver operating characteristic curves. Our findings pave the way for the development of practical microwave quantum illumination systems.
Comparative Analysis of CPU Performance on FreeBSD 64-bit and RedHat 64-bit Operating System Against Denial of Service (DoS) Using Hping3

Denial of Services (Dos) attack is a type of attack against server traffic. DoS works by placing a heavy load on the server, so that the server cannot accommodate connections from other users and can cause system failure on the server. On the other hand, Denial of Services (DoS) attacks can be used to test server resilience. The servers to be tested are using RedHat Linux and FreeBSD operating systems. Testing is carried out by flooding the system with DoS attacks using the hping3 tool, then comparing the test results in the form of percentages of CPU consumption and memory consumption between operating systems to determine vulnerabilities in each operating system. Based on a comparison of test results between operating systems, the FreeBSD operating system has better CPU performance than the RedHat Linux operating system, but has a vulnerability in internal memory to DoS attacks. Meanwhile, the RedHat Linux operating system has better internal memory performance than the FreeBSD operating system, but has a vulnerability in CPU performance against DoS attacks.
The Effect of Social Skills Education on Social Skills and Interpersonal Relationship Levels in Student Nurses

Background: Social skills educations are effective in improving social skills in individuals and reducing social anxiety levels and yielding positive changes in interpersonal relations, psychological well-being, and happiness levels and in reducing verbal aggression. Aim:The study was conducted to determine the level of social skills of student nurses and to evaluate social skills education's effect on the social skills and interpersonal relationships of nursing students. Methods:The quasi-experimental study was conducted with a non-randomized control group (CG) using a pre-test, post-test, and follow-up design.The research was conducted with 78 undergraduate nursing 1 st year students at a nursing school in Türkiye.Eight weeks of social skills training program were applied to the experimental group (EG).The number, percentage, arithmetic mean and standard deviation, Chi-square, Kolmogorov-Smirnov test, Mann-Whitney U test, Friedman Test, and Wilcoxon Signed Ranks test were used for data analysis. Results:The mean age of the students in the EG was 19.23 ± 2.09 and the mean age of the CG was 18.94 ± 1.02.It was determined that there was no difference between the groups in terms of their sociodemographic variables.While there was a significant increase in the social skills scores of the students who participated in the social skills training group because of the follow-up after the training and in the 4 th month, no change was found in the CG.In the EG, the interpersonal relations scale showed a significant decrease in the toxic relationship style dimension in the 4 th month follow-up but no change was detected in the CG. Conclusion:As a result of the study, it was determined that the social skills training program had a positive effect on the social skill levels of the students and that the training program influenced the toxic relationship style in the 4 th month follow-up test.In future studies, the social skills training program may be included peer learning or peer mentoringbased experiences.
PromptPaint: Steering Text-to-Image Generation Through Paint Medium-like Interactions

While diffusion-based text-to-image (T2I) models provide a simple and powerful way to generate images, guiding this generation remains a challenge. For concepts that are difficult to describe through language, users may struggle to create prompts. Moreover, many of these models are built as end-to-end systems, lacking support for iterative shaping of the image. In response, we introduce PromptPaint, which combines T2I generation with interactions that model how we use colored paints. PromptPaint allows users to go beyond language to mix prompts that express challenging concepts. Just as we iteratively tune colors through layered placements of paint on a physical canvas, PromptPaint similarly allows users to apply different prompts to different canvas areas and times of the generative process. Through a set of studies, we characterize different approaches for mixing prompts, design trade-offs, and socio-technical challenges for generative models. With PromptPaint we provide insight into future steerable generative tools.