Reviewerly/exHarmony huggingface dataset:
Mitigate forgetting in few-shot class-incremental learning using different image views

In the few-shot class incremental learning (FSCIL) setting, new classes with few training examples become available incrementally, and deep learning models suffer from catastrophic forgetting of the previous classes when trained on new classes. Data augmentation techniques are generally used to increase the training data and improve the model performance. In this work, we demonstrate that differently augmented views of the same image obtained by applying data augmentations may not necessarily activate the same set of neurons in the model. Therefore, the information gained by a model regarding a class, when trained using data augmentation, may not necessarily be stored in the same set of neurons in the model. Consequently, during incremental training, even if some of the model weights that store the previously seen class information for a particular view get overwritten, the information of the previous classes for the other views may still remain intact in the other model weights. Therefore, the impact of catastrophic forgetting on the model predictions is different for different data augmentations used during training. Based on this, we present an Augmentation-based Prediction Rectification (APR) approach to reduce the impact of catastrophic forgetting in the FSCIL setting. APR can also augment other FSCIL approaches and significantly improve their performance. We also propose a novel feature synthesis module (FSM) for synthesizing features relevant to the previously seen classes without requiring training data from these classes. FSM outperforms other generative approaches in this setting. We experimentally show that our approach outperforms other methods on benchmark datasets.
Using Learning Curve Predictions to Learn from Incorrect Feedback

Robots can incorporate data from human teachers when learning new tasks. However, this data can often be noisy, which can cause robots to learn slowly or not at all. One method for learning from human teachers is Human-in-the-loop Reinforcement Learning (HRL), which can combine information from both an environmental reward and external feedback from human teachers. However, many HRL methods assume near-perfect information from teachers or must know the skill level of each teacher before starting the learning process. Our algorithm, Classification for Learning Erroneous Assessments using Rewards (CLEAR), is a feedback filter for Reinforcement Learning (RL) algorithms, enabling learning agents to learn from imperfect teachers without prior modeling. CLEAR is able to determine whether human feedback is correct based on observations of the RL learning curve. Our results suggest that CLEAR improves the quality of human feedback - from 57.5% to 65% correct in a human study - and performs more reliably than baselines by matching or outperforming RL without human teachers in all tested cases.
Dual-task attention-guided character image generation method

Human body pose transfer is to transform the character image from the source image pose to the target pose. In recent years, the research has achieved great success in transforming the human body pose from the source image to the target image, but it is still insufficient in the detailed texture of the generated image. To solve the above problems, a new two-stage TPIT network model is proposed to process the detailed texture of the pose-generated image. The first stage is the source image self-learning module, which extracts the source image features by learning the source image itself and further improves the appearance details of pose-generated image. The other stage is to change the pose of the figure gradually from the source image pose to the target pose. Then, by learning the feature correlation between source and target images through cross-modal attention, texture transmission between images is promoted to generate finer-grained details of the generated image. A large number of experiments show that the model has superior performance on the Market-1501 and DeepFashion datasets, especially in the quantitative and qualitative evaluation of Market-1501, which is superior to other advanced methods.
Serial dependence in perception across naturalistic generative adversarial network-generated mammogram

Human perception and decisions are biased toward previously seen stimuli. This phenomenon is known as serial dependence and has been extensively studied for the last decade. Recent evidence suggests that clinicians' judgments of mammograms might also be impacted by serial dependence. However, the stimuli used in previous psychophysical experiments on this question, consisting of artificial geometric shapes and healthy tissue backgrounds, were unrealistic. We utilized realistic and controlled generative adversarial network (GAN)-generated radiographs to mimic images that clinicians typically encounter.Mammograms from the digital database for screening mammography (DDSM) were utilized to train a GAN. This pretrained GAN was then adopted to generate a large set of authentic-looking simulated mammograms: 20 circular morph continuums, each with 147 images, for a total of 2940 images. Using these stimuli in a standard serial dependence experiment, participants viewed a random GAN-generated mammogram on each trial and subsequently matched the GAN-generated mammogram encountered using a continuous report. The characteristics of serial dependence from each continuum were analyzed.We found that serial dependence affected the perception of all naturalistic GAN-generated mammogram morph continuums. In all cases, the perceptual judgments of GAN-generated mammograms were biased toward previously encountered GAN-generated mammograms. On average, perceptual decisions had 7% categorization errors that were pulled in the direction of serial dependence.Serial dependence was found even in the perception of naturalistic GAN-generated mammograms created by a GAN. This supports the idea that serial dependence could, in principle, contribute to decision errors in medical image perception tasks.
Union label smoothing adversarial training: Recognize small perturbation attacks and reject larger perturbation attacks balanced

Recently, several adversarial training methods have been proposed for rejecting perturbation-based adversarial examples, which enhance the robustness of deep neural networks to larger perturbations. However, they often perform unsatisfactorily when dealing with examples with lower level perturbations. To address these issues, we introduce a novel adversarial training approach called the union label smoothing adversarial training (ULSAT), which employs a new label smoothing curve and a union strategy for adversarial training. The label smoothing curve assigns soft labels to perturbed examples, allowing for a more reasonable adjustment of calibration. The union strategy adds interpolation examples and combines adversarial examples generated under different perturbation tolerances into the training stage, which improves the rejection ability of the model and balances it with the classification ability. Through theoretical analysis and ablation study, we demonstrate the effectiveness of our proposed approach. Numerical experiments show that ULSAT can accurately classifies less disturbed examples while maintaining a good rejection ability for adversarial examples with higher levels of perturbation. Moreover, we introduce an evaluation index that comprehensively considers the classification ability and rejection ability of the model. Under this index, ULSAT achieves state-of-the-art results.
“A bundle of serendipity”: A crowdsourcing program for picture book information organization oriented toward preschool children

Mainstream taxonomy schemas in public libraries are not suitable for children's book organization or children's book search behavior. In this study, the researchers developed a picture book organization schema from preschool children's perspective through an online crowdsourcing and reading advocacy program. According to 691 valid descriptions from 67 children, a total of 1064 open codes, 30 selective codes, and 3 dimensions were generated. Preschool children frequently used search elements including animal, title, item, person, plot/theme, place, and front cover. Children's title use behavior is discussed. This study partially reinforces previous findings about preschool children's search strategies for known picture books. The findings could advance knowledge of preschool children's information behavior and be used in preschool-children-oriented picture book information organization.
Vehicle Make and Model Recognition as an Open-Set Recognition Problem and New Class Discovery

One of the main limitations of traditional neural-network-based classifiers is the assumption that all query data are well represented within their training set. Unfortunately, in real-life scenarios, this is often not the case, and unknown class data may appear during testing, which drastically weakens the robustness of the algorithms. For this type of problem, open-set recognition (OSR) proposes a new approach where it is assumed that the world knowledge of algorithms is incomplete, so they must be prepared to detect and reject objects of unknown classes. However, the goal of this approach does not include the detection of new classes hidden within the rejected instances, which would be beneficial to increase the model’s knowledge and classification capability, even after training. This paper proposes an OSR strategy with an extension for new class discovery aimed at vehicle make and model recognition. We use a neuroevolution technique and the contrastive loss function to design a domain-specific CNN that generates a consistent distribution of feature vectors belonging to the same class within the embedded space in terms of cosine similarity, maintaining this behavior in unknown classes, which serves as the main guide for a probabilistic model and a clustering algorithm to simultaneously detect objects of new classes and discover their classes. The results show that the presented strategy works effectively to address the VMMR problem as an OSR problem and furthermore is able to simultaneously recognize the new classes hidden within the rejected objects. OSR is focused on demonstrating its effectiveness with benchmark databases that are not domain-specific. VMMR is focused on improving its classification accuracy; however, since it is a real-world recognition problem, it should have strategies to deal with unknown data, which has not been extensively addressed and, to the best of our knowledge, has never been considered from an OSR perspective, so this work also contributes as a benchmark for future domain-specific OSR.
Task Parameter Planning Algorithm for UAV Area Complete Coverage in EO Sector Scanning Mode

When Unmanned Aerial Vehicles (UAVs) are used in search and rescue operations, electro-optical (EO) devices are usually used as the detection equipment, and area coverage is used as the main search method. However, the sector scanning mode of EO puts forward higher requirements for task parameter planning. First, to ensure there is no missing coverage, a method to determine the full coverage width of EO equipment in sector scanning mode is proposed. Second, the constraint of no interval missing and the model of the speed-to-high ratio constraint are established, and the constraints of other factors are addressed in the context of the problem situation. Third, a coverage efficiency index is proposed for the boustrophedon coverage of a rectangular area, and a comprehensive coverage index is established. Finally, task parameter planning algorithms are designed, based on Immune Algorithm (IA), Grey Wolf Optimization (GWO) and Variable Neighborhood Search (VNS), respectively. The simulation results showed that the designed algorithms, based on IA, GWO and VNS, can effectively solve task planning problems. In general, IA is more suitable for offline occasions, VNS is suitable for online real-time planning, and GWO has characteristics between the two. The coverage process, based on optimized parameters, meets all constraints, has higher search efficiency and does not miss areas, proving the correctness of these models and the effectiveness of the planning algorithm. The research presented in this paper provides a technical basis for efficient and fully automated target search and rescue.
Developing an Ethical Framework for Responsible Artificial Intelligence (AI) and Machine Learning (ML) Applications in Cryptocurrency Trading: A Consequentialism Ethics Analysis

The rise in artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading has precipitated complex ethical considerations, demanding a thorough exploration of responsible regulatory approaches. This research expands upon this need by employing a consequentialist theoretical framework, emphasizing the outcomes of AI and ML’s deployment within the sector and its effects on stakeholders. Drawing on critical case studies, such as SBF and FTX, and conducting an extensive review of relevant literature, this study explores the ethical implications of AI and ML in the context of cryptocurrency trading. It investigates the necessity for novel regulatory methods that address the unique characteristics of digital assets alongside existing legalities, such as those about fraud and insider trading. The author proposes a typology framework for AI and ML trading by comparing consequentialism to other ethical theories applicable to AI and ML use in cryptocurrency trading. By applying a consequentialist lens, this study underscores the significance of balancing AI and ML’s transformative potential with ethical considerations to ensure market integrity, investor protection, and overall well-being in cryptocurrency trading.
Genetic algorithm with self adaptive immigrants for effective virtual machine placement in cloud environment

In cloud environments, optimization of resource utilizations is one among the predominant challenges. The two sub-research topics are cloud resource prediction and allocation. A few contributions to virtual machine (VM) placement techniques have been identified in the literature. In order to efficiently put up the virtual machine (VM) on the physical machine (PM), a Self Adaptive Immigrants with Genetic Algorithm (SAI-GA) is presented in this study. Based on CPU and memory usage, the proposed technique would forecast the best PM for each VM. The algorithm will adjust itself with the appropriate immigrant based on the history of past VM placement to find the best VM placement. In this paper, the VM live dataset from the CSAP lab at SNU in Korea has been used. For the purpose of demonstrating the significance of the findings, a number of non-parametric tests were used to evaluate how well the proposed SAI-GA performed. The outcomes demonstrate that the suggested approach makes a considerable contribution to the placement of VMs in cloud environments.
A Case Study of Liberal Arts Courses on ‘Leadership Education for Undergraduate Students’ －Applying a Flipped learning and Reflective journal Writing

The main purpose of this study is to propose a plan to apply flipped learning and reflection journal writing as an alternative to teaching leadership liberal arts courses for undergraduate students, and to extract components through the visualization of text network analysis centering on actual operation cases to enhance learners’ educational experiences. The subjects of the study were 68 students who took the liberal arts course required for the undergraduate students leadership education at the university located in A city. The class met for 15 weeks, from September 1 to December 14, 2022, and learners completed flip learning activities, as well as wrote reflection journals at each session except for the midterm/final exam period. Qualitative data such as documents including reflection journals, interviews, observations, and online activity data conducted on the Learning Management System(LMS) were collected and analyzed using MAXQDA. As a result of the study, an example of leadership liberal arts design and operation applying flipped learning and reflection journals was presented, and the components of learners’ experiences and perceptions were derived and visualized according to the code system analysis. Specifically, in the text network analysis, there were a total of 1,837 codes, and the frequency of ‘application plan’ and ‘motivation’ of leadership competency ranked the highest in the coding results. In the code visualization, ‘group practice among team members’ appeared the most, and finally, ‘learning how to learn’ was the phenomenon. Based on these results, suggestions for ways to cultivate and demonstrate leadership among undergraduate students in actual educational settings and suggestions for teaching methods are suggested.
H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions

The world is filled with articulated objects that are difficult to determine how to use from vision alone, e.g., a door might open inwards or outwards. Humans handle these objects with strategic trial-and-error: first pushing a door then pulling if that doesn't work. We enable these capabilities in autonomous agents by proposing “Hypothesize, Simulate, Act, Update, and Repeat” (H-SAUR), a probabilistic generative framework that simultaneously generates a distribution of hypotheses about how objects articulate given input observations, captures certainty over hypotheses over time, and infer plausible actions for exploration and goal-conditioned manipulation. We compare our model with existing work in manipulating objects after a handful of exploration actions, on the PartNet-Mobility dataset. We further propose a novel PuzzleBoxes benchmark that contains locked boxes that require multiple steps to solve. We show that the proposed model significantly outperforms the current state-of-the-art articulated object manipulation framework, despite using zero training data. We further improve the test-time efficiency of H-SAUR by integrating a learned prior from learning-based vision models.
A Deep Learning Human Activity Recognition Framework for Socially Assistive Robots to Support Reablement of Older Adults

Many older adults prefer to stay in their own homes and age-in-place. However, physical and cognitive limitations in independently completing activities of daily living (ADLs) requires older adults to receive assistive support, often necessitating transitioning to care centers. In this paper, we present the development of a novel deep learning human activity recognition and classification architecture capable of autonomously identifying ADLs in home environments to enable long-term deployment of socially assistive robots to aid older adults. Our deep learning architecture is the first to use multimodal inputs to create an embedding vector approach for classifying and monitoring multiple ADLs. It uses spatial mid-fusion to combine geometric, motion and semantic features of users, environments, and objects to classify and track ADLs. We leverage transfer learning to extract generic features using the early layers of deep networks trained on large datasets to apply our architecture to various ADLs. The embedding vector enables identification of unseen ADLs and determines intra-class variance for monitoring user ADL performance. Our proposed unique architecture can be used by socially assistive robots to promote reablement in the home via autonomously supporting the assistance of varying ADLs. Extensive experiments show improved classification accuracy compared to unimodal/dual-modal models and the ADL embedding space also incorporates the ability to distinctly identify and track seen and unseen ADLs.
VQA-based Robotic State Recognition Optimized with Genetic Algorithm

State recognition of objects and environment in robots has been conducted in various ways. In most cases, this is executed by processing point clouds, learning images with annotations, and using specialized sensors. In contrast, in this study, we propose a state recognition method that applies Visual Question Answering (VQA) in a Pre-Trained Vision-Language Model (PTVLM) trained from a large-scale dataset. By using VQA, it is possible to intuitively describe robotic state recognition in the spoken language. On the other hand, there are various possible ways to ask about the same event, and the performance of state recognition differs depending on the question. Therefore, in order to improve the performance of state recognition using VQA, we search for an appropriate combination of questions using a genetic algorithm. We show that our system can recognize not only the open/closed of a refrigerator door and the on/off of a display, but also the open/closed of a transparent door and the state of water, which have been difficult to recognize.
Can We Use Diffusion Probabilistic Models for 3D Motion Prediction?

After many researchers observed fruitfulness from the recent diffusion probabilistic model, its effectiveness in image generation is actively studied these days. In this paper, our objective is to evaluate the potential of diffusion probabilistic models for 3D human motion-related tasks. To this end, this pa-per presents a study of employing diffusion probabilistic models to predict future 3D human motion(s) from the previously observed motion. Based on the Human 3.6M and HumanEva-I datasets, our results show that diffusion probabilistic models are competitive for both single (deterministic) and multiple (stochastic) 3D motion prediction tasks, after finishing a single training process. In addition, we find out that diffusion probabilistic models can offer an attractive compromise, since they can strike the right balance between the likelihood and diversity of the predicted future motions. Our code is publicly available on the project website: https://sites.google.com/view/diffusion-motion-prediction.
Chance-Constrained Motion Planning with Event-Triggered Estimation

We consider the problem of motion and communication planning under uncertainty with limited information from a remote sensor network. Because the remote sensors are power and bandwidth limited, we use event-triggered (ET) estimation to manage communication costs. We introduce a fast and efficient sampling-based planner which computes motion plans coupled with ET communication strategies that minimize communication costs, while satisfying constraints on the probability of reaching the goal region and the point-wise probability of collision. We derive a novel method for offline propagation of the expected state distribution, and corresponding bounds on this distribution. These bounds are used to evaluate the chance constraints in the algorithm. Case studies establish the validity of our approach and demonstrate computational efficiency and asymptotic optimality of the planner.
GPF-BG: A Hierarchical Vision-Based Planning Framework for Safe Quadrupedal Navigation

Safe quadrupedal navigation through unknown environments is a challenging problem. This paper proposes a hierarchical vision-based planning framework (GPF-BG) integrating our previous Global Path Follower (GPF) navigation system and a gap-based local planner using Bézier curves, so called <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> ézier Gap (BG). This BG-based trajectory synthesis can generate smooth trajectories and guarantee safety for point-mass robots. With a gap analysis extension based on non-point, rectangular geometry, safety is guaranteed for an idealized quadrupedal motion model and significantly improved for an actual quadrupedal robot model. Stabilized perception space improves performance under oscillatory internal body motions that impact sensing. Simulation-based and real experiments under different benchmarking configurations test safe navigation performance. GPF-BG has the best safety outcomes across all experiments.