A Survey on the Real Power of ChatGPT

ChatGPT has changed the AI community and an active research line is the performance evaluation of ChatGPT. A key challenge for the evaluation is that ChatGPT is still closed-source and traditional benchmark datasets may have been used by ChatGPT as the training data. In this paper, (i) we survey recent studies which uncover the real performance levels of ChatGPT in seven categories of NLP tasks, (ii) review the social implications and safety issues of ChatGPT, and (iii) emphasize key challenges and opportunities for its evaluation. We hope our survey can shed some light on its blackbox manner, so that researchers are not misleaded by its surface generation.

Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance

Classification models are expected to perform equally well for different classes, yet in practice, there are often large gaps in their performance. This issue of class bias is widely studied in cases of datasets with sample imbalance, but is relatively overlooked in balanced datasets. In this work, we introduce the concept of spectral imbalance in features as a potential source for class disparities and study the connections between spectral imbalance and class bias in both theory and practice. To build the connection between spectral imbalance and class gap, we develop a theoretical framework for studying class disparities and derive exact expressions for the per-class error in a high-dimensional mixture model setting. We then study this phenomenon in 11 different state-of-the-art pretrained encoders and show how our proposed framework can be used to compare the quality of encoders, as well as evaluate and combine data augmentation strategies to mitigate the issue. Our work sheds light on the class-dependent effects of learning, and provides new insights into how state-of-the-art pretrained features may have unknown biases that can be diagnosed through their spectra.

UWB Radar SLAM: an Anchorless Approach in Vision Denied Indoor Environments

LiDAR and cameras are frequently used as sensors for simultaneous localization and mapping (SLAM). However, these sensors are prone to failure under low visibility (e.g. smoke) or places with reflective surfaces (e.g. mirrors). On the other hand, electromagnetic waves exhibit better penetration properties when the wavelength increases, thus are not affected by low visibility. Hence, this paper presents ultra-wideband (UWB) radar as an alternative to the existing sensors. UWB is generally known to be used in anchor-tag SLAM systems. One or more anchors are installed in the environment and the tags are attached to the robots. Although this method performs well under low visibility, modifying the existing infrastructure is not always feasible. UWB has also been used in peer-to-peer ranging collaborative SLAM systems. However, this requires more than a single robot and does not include mapping in the mentioned environment like smoke. Therefore, the presented approach in this paper solely depends on the UWB transceivers mounted on-board. In addition, an extended Kalman filter (EKF) SLAM is used to solve the SLAM problem at the back-end. Experiments were conducted and demonstrated that the proposed UWB-based radar SLAM is able to map natural point landmarks inside an indoor environment while improving robot localization.

Moving Object Localization based on the Fusion of Ultra-WideBand and LiDAR with a Mobile Robot

Localization of objects is vital for robot-object interaction. Light Detection and Ranging (LiDAR) application in robotics is an emerging and widely used object localization technique due to its accurate distance measurement, long-range, wide field of view, and robustness in different conditions. However, LiDAR is unable to identify the objects when they are obstructed by obstacles, resulting in inaccuracy and noise in localization. To address this issue, we present an approach incorporating LiDAR and Ultra-Wideband (UWB) ranging for object localization. The UWB is popular in sensor fusion localization algorithms due to its low weight and low power consumption. In addition, the UWB is able to return ranging measurements even when the object is not within line-of-sight. Our approach provides an efficient solution to combine an anonymous optical sensor (LiDAR) with an identity-based radio sensor (UWB) to improve the localization accuracy of the object. Our approach consists of three modules. The first module is an object-identification algorithm that compares successive scans from the LiDAR to detect a moving object in the environment and returns the position with the closest range to UWB ranging. The second module estimates the moving object's moving direction using the previous and current estimated position from our object-identification module. It removes the suspicious estimations through an outlier rejection criterion. Lastly, we fuse the LiDAR, UWB ranging, and odometry measurements in pose graph optimization (PGO) to recover the entire trajectory of the robot and object. Extensive experiments were performed to evaluate the performance of the proposed approach.

Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals

Leveraging multimodal information from biosignals is vital for building a comprehensive representation of people's physical and mental states. However, multimodal biosignals often exhibit substantial distributional shifts between pretraining and inference datasets, stemming from changes in task specification or variations in modality compositions. To achieve effective pretraining in the presence of potential distributional shifts, we propose a frequency-aware masked autoencoder ($\texttt{bio}$FAME) that learns to parameterize the representation of biosignals in the frequency space. $\texttt{bio}$FAME incorporates a frequency-aware transformer, which leverages a fixed-size Fourier-based operator for global token mixing, independent of the length and sampling rate of inputs. To maintain the frequency components within each input channel, we further employ a frequency-maintain pretraining strategy that performs masked autoencoding in the latent space. The resulting architecture effectively utilizes multimodal information during pretraining, and can be seamlessly adapted to diverse tasks and modalities at test time, regardless of input size and order. We evaluated our approach on a diverse set of transfer experiments on unimodal time series, achieving an average of $\uparrow$5.5% improvement in classification accuracy over the previous state-of-the-art. Furthermore, we demonstrated that our architecture is robust in modality mismatch scenarios, including unpredicted modality dropout or substitution, proving its practical utility in real-world applications. Code will be available soon.

LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration

Despite significant advances in deep learning, models often struggle to generalize well to new, unseen domains, especially when training data is limited. To address this challenge, we propose a novel approach for distribution-aware latent augmentation that leverages the relationships across samples to guide the augmentation procedure. Our approach first degrades the samples stochastically in the latent space, mapping them to augmented labels, and then restores the samples from their corrupted versions during training. This process confuses the classifier in the degradation step and restores the overall class distribution of the original samples, promoting diverse intra-class/cross-domain variability. We extensively evaluate our approach on a diverse set of datasets and tasks, including domain generalization benchmarks and medical imaging datasets with strong domain shift, where we show our approach achieves significant improvements over existing methods for latent space augmentation. We further show that our method can be flexibly adapted to long-tail recognition tasks, demonstrating its versatility in building more generalizable models. Code is available at https://github.com/nerdslab/LatentDR.

Half-Hop: A graph upsampling approach for slowing down message passing

Message passing neural networks have shown a lot of success on graph-structured data. However, there are many instances where message passing can lead to over-smoothing or fail when neighboring nodes belong to different classes. In this work, we introduce a simple yet general framework for improving learning in message passing neural networks. Our approach essentially upsamples edges in the original graph by adding "slow nodes" at each edge that can mediate communication between a source and a target node. Our method only modifies the input graph, making it plug-and-play and easy to use with existing models. To understand the benefits of slowing down message passing, we provide theoretical and empirical analyses. We report results on several supervised and self-supervised benchmarks, and show improvements across the board, notably in heterophilic conditions where adjacent nodes are more likely to have different labels. Finally, we show how our approach can be used to generate augmentations for self-supervised learning, where slow nodes are randomly introduced into different edges in the graph to generate multi-scale views with variable path lengths.

A Feature Set of Small Size for the Malware Detection

Machine learning (ML)-based malware detection systems are becoming increasingly important as malware threats increase and get more sophisticated. PDF files are often used as vectors for phishing attacks because they are widely regarded as trustworthy data resources, and are accessible across different platforms. Therefore, researchers have developed many different PDF malware detection methods. Performance in detecting PDF malware is greatly influenced by feature selection. In this research, we propose a small features set that don't require too much domain knowledge of the PDF file. We evaluate proposed features with six different machine learning models. We report the best accuracy of 99.75% when using Random Forest model. Our proposed feature set, which consists of just 12 features, is one of the most conciseness in the field of PDF malware detection. Despite its modest size, we obtain comparable results to state-of-the-art that employ a much larger set of features.

Exploiting Radio Fingerprints for Simultaneous Localization and Mapping

Simultaneous localization and mapping (SLAM) is paramount for unmanned systems to achieve self-localization and navigation. It is challenging to perform SLAM in large environments, due to sensor limitations, complexity of the environment, and computational resources. We propose a novel approach for localization and mapping of autonomous vehicles using radio fingerprints, for example WiFi (Wireless Fidelity) or LTE (Long Term Evolution) radio features, which are widely available in the existing infrastructure. In particular, we present two solutions to exploit the radio fingerprints for SLAM. In the first solution-namely Radio SLAM, the output is a radio fingerprint map generated using SLAM technique. In the second solution-namely Radio+LiDAR SLAM, we use radio fingerprint to assist conventional LiDAR-based SLAM to improve accuracy and speed, while generating the occupancy map. We demonstrate the effectiveness of our system in three different environments, namely outdoor, indoor building, and semi-indoor environment.

Can Feature Engineering Help Quantum Machine Learning for Malware Detection?

With the increasing number and sophistication of malware attacks, malware detection systems based on machine learning (ML) grow in importance. At the same time, many popular ML models used in malware classification are supervised solutions. These supervised classifiers often do not generalize well to novel malware. Therefore, they need to be re-trained frequently to detect new malware specimens, which can be time-consuming. Our work addresses this problem in a hybrid framework of theoretical Quantum ML, combined with feature selection strategies to reduce the data size and malware classifier training time. The preliminary results show that VQC with XGBoost selected features can get a 78.91% test accuracy on the simulator. The average accuracy for the model trained using the features selected with XGBoost was 74% (+- 11.35%) on the IBM 5 qubits machines.