Reviewerly/exHarmony huggingface dataset:
Encrypted Classification for Prevention of Adversarial Perturbation and Individual Identification in Health-Monitoring

Developments in sensing and analysis methods have significantly increased the scope of physiological monitoring for healthcare purposes. While the continuous monitoring of physiological measurements enables improved detection and management of many illnesses, accompanying cybersecurity concerns continue to evolve. The large amounts of individualized data necessary to enable learned models for analysis must be sufficiently protected. In addition, the analysis and classification methods themselves should not be vulnerable to attack. This work addresses adversarial individual identification with multiple forms of physiological data, as well as potential performance interruption attacks. The paper proposes a homomorphic encryption scheme to mitigate both of these threats.
Novel Rigid-Wing Bi-Directional Sailboat Design and Method of Sailing

This paper proposes a novel design of rigid-wing bi-directional sailboat and its method of sailing. The proposed design features a bi-directional hull, a rudder on both ends of the vessel and a freely rotating rigid wing sail whose angle of attack is controlled by a tail rudder. The proposed method of sailing involves reversing the vessel’s travel direction at every turn and is ideal for micro-scale autonomous robotic platforms as it overcomes the difficulty in turning the boat through the eye of the wind during an upwind sail. A simulation model using aerodynamic and hydrodynamic forces is developed to test the effectiveness of the proposed design and method of sailing and compared to the traditional method of sailing in station-keeping and sailing upwind scenarios. The effectiveness of the proposed method is demonstrated with a prototype model equipped with a rigid wing sail.
Solving Stochastic Inverse Problems with Stochastic BayesFlow

Normalizing flows have gained increasing attention in the area of probabilistic modeling. For solving inverse problems, BayesFlow is a state-of-the-art Bayesian inference method based on normalizing flows. However, BayesFlow suffers from overfitting in many real-world scenarios. Therefore, we put forward stochastic BayesFlow, enhancing BayesFlow through stochastic normalizing flows. Apart from being less prone to overfitting, stochastic BayesFlow performs more robustly in parameter identification from noisy observations. Moreover, we develop a stochastic BayesFlow algorithm to solve stochastic inverse problems and validate it using the inverse uncertainty quantification of a simulated vehicle dynamics model.
Visual Analysis of Campus Card Consumption Data based on Matplotlib

This paper selected the consumption data derived from the student campus card of the School of Management Science and Engineering of Anhui University of Finance and Economics as the research object. Initially, data cleansing and classification were conducted using Python and Excel. Subsequently, the Matplotlib library was utilized to visually analyze both overall data of student consumption and the dining data with its higher proportion. Through the in-depth study of the campus card data, a thorough understanding of students’ dining patterns was achieved. Furthermore, the study recorded fluctuations in cafeteria footfall at various times of the day, providing valuable reference for optimizing resources and informing management decision-making regarding the school canteen from multiple perspectives.
A Super-resolution Algorithm for Remote Sensing Images based on Data Enhancement and Generative Adversarial Networks

In this paper, a new algorithm is proposed to address the problem of excessive errors in current super-resolution algorithms for remote sensing images, which optimizes the processing effect of generative adversarial networks by increasing the type and size of training data with data enhancement techniques. First, the algorithm constructs a more reasonable degradation model for the problem of lacking data sets of paired images, and randomly mixes and washes the degraded prior knowledge (such as blur, noise, downsampling, etc.) in the imaging process to simulate the generation process of low-resolution remote sensing images in natural scenes, and generates realistic low-resolution images for training; meanwhile, the algorithm uses ResNet34 and CNN as the basis to enhance the texture details of remote sensing images, thus richer features can be extracted from the images. The results of experiments conducted on the Alsat2 B real dataset show that the method reduces the time and cost required for sample data acquisition, optimizes the processing speed, enhances the temporal and spatial resolution of remote sensing images, and improves the effect of super-resolution processing.
A Survey of Language Priors for Visual Question Answering

In recent years, with the development of deep learning technology, visual question answering tasks have gradually attracted the attention of scientific researchers. Due to the continuous improvement of relevant large-scale standard data sets, a large number of visual questions answering research results have been released one after another, and the accuracy rate of the visual question answering model based on deep learning on the data set has been continuously improved. Recent studies have found that the previously proposed visual question answering model has different degrees of data set language prior problems, that is, the model is overly dependent on the strong phase between the question and the answer in the training process. Many articles briefly describe various research methods, and look forward to the future development direction of alleviating the prior problem of visual question answering based on the existing research.
Automatic Play based on LR and SVM Algorithms

With the progress of society, people's quality of life significantly improved, this gives us more free time to do things we enjoy, so our life has become colorful, countless kinds of entertainment for people to choose, video games is one of the important, with the level of technology to improve the video game is also getting richer, when the video game into the artificial intelligence, but also to improve the fun of the game, the challenging. In this project, we will use logistic regression algorithm and support vector machine to automate the machine to play the dinosaur parkour game in Google Chrome, through the training of logistic regression and SVM, so that the machine can operate and control the dinosaur to jump and crouch to achieve high score.
DQDWA: Dynamic Weight Coefficients Based on Q-learning for Dynamic Window Approach Considering Environmental Situations

Autonomous mobile robots are used in a wide range of industrial application. Dynamic window approach (DWA) is one of effective local path planning methods considering collision avoidance and kinematic constraints. DWA selects the optical path from path candidates from velocity space by using an evaluation function with fixed weight coefficients. These fixed weight coefficients are designed for the specific environmental situation. Therefore, if the environmental situation such as congestion, road width, and obstacles changes, the evaluation function with fixed weight coefficients may select the inefficient path or path with the collision. To address this issue, this paper proposes the dynamic weight coefficients based on Q-learning for DWA considering environmental situations (DQDWA). Q-learning is one of reinforcement learning methods. The Q-table in DQDWA consists of states of robot and environmental situations, and actions of weight coefficients in DWA evaluation function. By using the learned Q-table, DQDWA dynamically selects weight coefficients and, the optimal path considering environmental situations is generated. The effectiveness of the proposed method was confirmed through simulations.
Cross-Domain Transformation for Outlier Detection on Tabular Datasets

The overwhelming success of Deep Learning approaches in recent years is often driven by the availability of large public datasets. However, in some domains like finance, creating and sharing realistic datasets is hindered by secrecy or privacy concerns. This can lead to a mismatch, where approaches that have proven to work well on public, research-oriented datasets end up underperforming when applied to real-world (private) datasets. In this work, we focus on the task of Outlier Detection (OD) and bridge the above gap by building an autoencoder based Deep Learning approach that can transform samples between two tabular datasets (e.g., a private and public one). The goal of our approach is that transformed samples become similar to the target dataset, while inliers remain inliers and outliers remain outliers. Among others, after successful transformation, this allows applying of proven methods on public datasets to internal datasets, even if they are of different dimensionality (rows and columns). To evaluate our approach, we introduce metrics to measure dataset similarity and the quality of transformed samples. Our experimental results show that combining public datasets with transformed samples of other datasets leads to higher dataset similarity while sustaining performance w.r.t. common OD algorithms.
Counterfactual Explanations of Neural Network-Generated Response Curves

Response curves exhibit the magnitude of the response of a sensitive system to a varying stimulus. However, response of such systems may be sensitive to multiple stimuli (i.e., input features) that are not necessarily independent. As a consequence, the shape of response curves generated for a selected input feature (referred to as “active feature”) might depend on the values of the other input features (referred to as “passive features”). In this work we consider the case of systems whose response is approximated using regression neural networks. We propose to use counterfactual explanations (CFEs) for the identification of the features with the highest relevance on the shape of response curves generated by neural network black boxes. CFEs are generated by a genetic algorithm-based approach that solves a multi-objective optimization problem. In particular, given a response curve generated for an active feature, a CFE finds the minimum combination of passive features that need to be modified to alter the shape of the response curve. We tested our method on a synthetic dataset with 1-D inputs and two crop yield prediction datasets with 2-D inputs. The relevance ranking of features and feature combinations obtained on the synthetic dataset coincided with the analysis of the equation that was used to generate the problem. Results obtained on the yield prediction datasets revealed that the impact on fertilizer responsivity of passive features depends on the terrain characteristics of each field.
Continual Learning with Pretrained Backbones by Tuning in the Input Space

The intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks. This issue is critical in practical supervised learning settings, such as the ones in which a pre-trained model computes projections toward a latent space where different task predictors are sequentially learned over time. As a matter of fact, incrementally fine-tuning the whole model to better adapt to new tasks usually results in catastrophic forgetting, with decreasing performance over the past experiences and losing valuable knowledge from the pretraining stage. In this paper, we propose a novel strategy to make the fine-tuning procedure more effective, by avoiding to update the pre-trained part of the network and learning not only the usual classification head, but also a set of newly-introduced learnable parameters that are responsible for transforming the input data. This process allows the network to effectively leverage the pre-training knowledge and find a good trade-off between plasticity and stability with modest computational efforts, thus especially suitable for on-the-edge settings. Our experiments on four image classification problems in a continual learning setting confirm the quality of the proposed approach when compared to several fine-tuning procedures and to popular continual learning methods.
Evaluating and Comparing Heterogeneous Ensemble Methods for Unsupervised Anomaly Detection

Ensembles are one of the most promising research directions for unsupervised anomaly detection. But combining many different models into such an ensemble requires good combination procedures that are able to combine the strengths of many different submodels. To find, evaluate and understand these procedures, we create the biggest experiment to date, including multiple orders of magnitude more ensembles than each of our competitors. Using this high number of comparisons, we also study the effect different normalization methods have on the combination procedure and extract conditional performances of individual models. We use this, to develop a simple set of best practices to create good and reliable anomaly detection ensembles.
Deep Q-Network Updates for the Full Action-Space Utilizing Synthetic Experiences

Deep-Q-Networks are built in a way that, given a state, they predict the Q-values for the entire action-space. However, given an experience, the training update only incorporates the loss value for a single action-the one that has actually been executed. This is due to the rewards and follow-up states (required for computing the loss via the temporal-difference error) associated with the other actions being unknown. With these missing values at hand, or at least estimates of them, an update over the entire action-space would be possible. We present the Full-Update-DQN which is able to do just that. Sub-losses are weighted to compensate for uncertainty and noise and we are able to show in four different experiments in sparse reward settings, that our approach is able to solve these problems more consistently and even faster than the original approach.
NeuralHMM: A Deep Markov Network for Health Risk Prediction using Electronic Health Records

Health risk refers to the probability of the occurrence of a specific health outcome for a specific patient. Interest in health risk prediction has been increasing, especially with the availability of a large amount of electronic health records (EHR). An EHR contains multivariate time series data that records meaningful information associated with a chronological set of clinical events for each patient. Recurrent neural networks (RNN) and hidden Markov models (HMM) have been widely used as generative models of time series data. RNN-based models have strong prediction performance but lack transparency. HMMs have a simple functional form and the ability to provide an intuitive probabilistic interpretation, but their state dynamics are 'memoryless', making it difficult to thoroughly take into account the irregularity in patients' health trajectory. This paper proposes a novel deep Markov network for health risk prediction. The method integrates two modules, a GRU (Gated Recurrent Unit) with attention mechanism and a Neural HMM, into a single network. The GRU generates the inputs required for health risk predictions and uses an attention mechanism to create memorable state dynamics for the Neural HMM. The Neural HMM then provides interpretable structured representations through training. Mixture Density Networks are incorporated in the Neural HMM, which contribute to the modeling of complex patterns found in the transition process. Furthermore, an inference network is designed to embed hidden state representations of GRU and Neural HMM into the same space. The inference network enables the two types of representations to learn from each other during the decoding process of Neural HMM, thereby improving the quality of interpretable structured representations. Experimental results on MIMIC-III and eICU datasets demonstrate that our method can outperform state-of-the-art methods and provide transparency of the model decisions.
GCNH: A Simple Method For Representation Learning On Heterophilous Graphs

Graph Neural Networks (GNNs) are well-suited for learning on homophilous graphs, i.e., graphs in which edges tend to connect nodes of the same type. Yet, achievement of consistent GNN performance on heterophilous graphs remains an open research problem. Recent works have proposed extensions to standard GNN architectures to improve performance on heterophilous graphs, trading off model simplicity for prediction accuracy. However, these models fail to capture basic graph properties, such as neighborhood label distribution, which are fundamental for learning. In this work, we propose GCN for Heterophily (GCNH), a simple yet effective GNN architecture applicable to both heterophilous and homophilous scenarios. GCNH learns and combines separate representations for a node and its neighbors, using one learned importance coefficient per layer to balance the contributions of center nodes and neighborhoods. We conduct extensive experiments on eight real-world graphs and a set of synthetic graphs with varying degrees of heterophily to demonstrate how the design choices for GCNH lead to a sizable improvement over a vanilla GCN. Moreover, GCNH outperforms state-of-the-art models of much higher complexity on four out of eight benchmarks, while producing comparable results on the remaining datasets. Finally, we discuss and analyze the lower complexity of GCNH, which results in fewer trainable parameters and faster training times than other methods, and show how GCNH mitigates the oversmoothing problem.
Diversity-Enhanced Recommendation with Knowledge-Aware Devoted and Diverse Interest Learning

Knowledge graph (KG) is receiving increasing attention from researchers in recommender systems with the help of graph neural networks (GNN). Beyond accuracy, diversity of recommendation is also recognized as a key factor in broadening users’ horizons and boosting satisfaction for users. Considering diversity adapted to user demands can facilitate the recommendation performance for both accuracy and diversity. However, most approaches fail to (1) explore the help of KG for diversity, and (2) identify different kinds of latent interests from users for diversity. This paper proposes a diversity-enhanced recommendation with knowledge-aware interests. The user interest consists of devoted interest and diverse interest in our approach modeled by a dual-branch GNN-based learning structure with an adaptive trade-off. The devoted interest learning branch exploits the entity relations from KG to explore the potential patterns of users to improve accuracy, while the diverse interest learning branch additionally combines the category of the items with KG to achieve diversity. Attentive relational aggregation is designed to aggregate the information from KG and user-item interaction for the representations of users and items modeling. Extensive experiments on three real-world datasets show that our approach effectively improves the recommendation accuracy while obtaining impressive diversity. This work is available at https://github.com/ljf012/DERK.
RepEPnP: Weakly Supervised 3D Human Pose Estimation with EPnP Algorithm

This paper describes an end-to-end weakly super-vised framework for estimating 3D human pose from a single image. The model is trained by projecting 3D pose to 2D pose for matching ground-truth 2D pose for supervision. To obtain accu-rate projection from 3D pose to 2D pose, a mathematical camera model based on intrinsic and extrinsic camera parameters is used. Specifically, we use EPnP algorithm to estimate extrinsic transformation matrix to transform the estimated 3D pose to be reprojected back to 2D pose. The advantage of this projection is that it requires no training and it is robust to the diversity of training datasets. We further constrain the pose generation using an adversarial generative network, where a transformer is used as the 3D pose generator. Transformer can use self-attention mechanism to establish dependencies between each joint and predict pose based on important joints. Based on our reprojection method, our method achieves competitive results on Human3.6M and MPI-INF-3DHP among weakly supervised methods. The experiments also demonstrate our model's generalization ability for wild images.
General Adversarial Perturbation Simulating: Protect Unknown System by Detecting Unknown Adversarial Faces

Benefitting from the development of convolutional neural networks (CNNs), face recognition systems (FRSs) play a key role in many security-critical systems. However, FRSs have been proved to be vulnerable to adversarial faces (adv-faces). Adv-faces aim to change classification results by adding a subtle perturbation on real faces. The existence of adv-faces poses a significant threat to financial and privacy security. Previous detection methods require either training on pre-computed adv-faces or accessing to protected victim FRSs, bringing a dilemma in practical using. In this work, we heuristically propose an adversarial face detection method called General Adversarial Perturbation Simulating (GAPS) which is blind to both adversarial attacks and FRSs. Simulating noise patterns of several gradient-based adversarial perturbations, GAPS is able to generate simulated adversarial faces (sadv-faces) guiding detectors to learn general adversarial perturbation features and focus on classifying sensitive regions. Extensive experiments on LFW and CASIA-WebFace show that our method outperforms 9 state-of-the-art baseline methods and demonstrate the effectiveness of GAPS.
Gradient Sign Inversion: Making an Adversarial Attack a Good Defense

Deep neural networks have been proven vulnerable to deliberately crafted adversarial example, which cause serious safety and security concerns. Many defense approaches were proposed to resist such threats. However, existing defenses such as pre-compression or adversarial training would degrade the model performance on clean images or incur heavy computational costs. In this work, we propose a plug-and-play defensive module Gradient Sign Inversion (GSI) to defend gradient-based attack. Essentially, GSI attempts to inverse the direction of the backpropagated gradient for the victim model, disturbing the adversarial example generation of the attacking while retaining the performance of the vanilla network on genuine inputs. Specifically, an additive model based on periodic trigonometric function is established by investigating the necessary conditions that a suitable defensive module should have. By enforcing constraints on the defensive module, the parameters of GSI are determined, accompanied by a theoretical justification. Interestingly, we observe that the proposed GSI not only prevents the gradient-based adversarial attack, but can even improve the confidence of the ground-truth label when initiating an attack, making the attack betray as a defense. Source code is publicly available at https://github.com/JidaDiao/GSI.