Reviewerly/exHarmony huggingface dataset:
Towards Human-Like RL: Taming Non-Naturalistic Behavior in Deep RL via Adaptive Behavioral Costs in 3D Games

In this paper, we propose a new approach called Adaptive Behavioral Costs in Reinforcement Learning (ABC-RL) for training a human-like agent with competitive strength. While deep reinforcement learning agents have recently achieved superhuman performance in various video games, some of these unconstrained agents may exhibit actions, such as shaking and spinning, that are not typically observed in human behavior, resulting in peculiar gameplay experiences. To behave like humans and retain similar performance, ABC-RL augments behavioral limitations as cost signals in reinforcement learning with dynamically adjusted weights. Unlike traditional constrained policy optimization, we propose a new formulation that minimizes the behavioral costs subject to a constraint of the value function. By leveraging the augmented Lagrangian, our approach is an approximation of the Lagrangian adjustment, which handles the trade-off between the performance and the human-like behavior. Through experiments conducted on 3D games in DMLab-30 and Unity ML-Agents Toolkit, we demonstrate that ABC-RL achieves the same performance level while significantly reducing instances of shaking and spinning. These findings underscore the effectiveness of our proposed approach in promoting more natural and human-like behavior during gameplay.
Finite Scalar Quantization: VQ-VAE Made Simple

We propose to replace vector quantization (VQ) in the latent representation of VQ-VAEs with a simple scheme termed finite scalar quantization (FSQ), where we project the VAE representation down to a few dimensions (typically less than 10). Each dimension is quantized to a small set of fixed values, leading to an (implicit) codebook given by the product of these sets. By appropriately choosing the number of dimensions and values each dimension can take, we obtain the same codebook size as in VQ. On top of such discrete representations, we can train the same models that have been trained on VQ-VAE representations. For example, autoregressive and masked transformer models for image generation, multimodal generation, and dense prediction computer vision tasks. Concretely, we employ FSQ with MaskGIT for image generation, and with UViM for depth estimation, colorization, and panoptic segmentation. Despite the much simpler design of FSQ, we obtain competitive performance in all these tasks. We emphasize that FSQ does not suffer from codebook collapse and does not need the complex machinery employed in VQ (commitment losses, codebook reseeding, code splitting, entropy penalties, etc.) to learn expressive discrete representations.
Grain-128PLE: Generic Physical-Layer Encryption for IoT Networks

Physical layer security (PLS) encompasses techniques proposed at the physical layer to achieve information security objectives while requiring a minimal resource footprint. The channel coding-based secrecy and signal modulation-based encryption approaches are reliant on certain channel conditions or a certain communications protocol stack to operate on, which prevents them from being a generic solution. This paper presents Grain-128PLE, a lightweight physical layer encryption (PLE) scheme that is derived from the Grain-128AEAD v2 stream cipher. The Grain-128PLE stream cipher performs encryption and decryption at the physical layer, in between the channel coding and signal modulation processes. This placement, like that of the A5 stream cipher that had been used in the GSM communications standard, makes it a generic solution for providing data confidentiality in IoT networks. The design of Grain-128PLE maintains the structure of the main building blocks of the original Grain-128AEAD v2 stream cipher, evaluated for its security strength during NIST's recent Lightweight Cryptography competition, and is therefore expected to achieve similar levels of security.
HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2V

To develop the next generation of intelligent LiDARs, we propose a novel framework of parallel LiDARs and construct a hardware prototype in our experimental platform, DAWN (Digital Artificial World for Natural). It emphasizes the tight integration of physical and digital space in LiDAR systems, with networking being one of its supported core features. In the context of autonomous driving, V2V (Vehicle-to-Vehicle) technology enables efficient information sharing between different agents which significantly promotes the development of LiDAR networks. However, current research operates under an ideal situation where all vehicles are equipped with identical LiDAR, ignoring the diversity of LiDAR categories and operating frequencies. In this paper, we first utilize OpenCDA and RLS (Realistic LiDAR Simulation) to construct a novel heterogeneous LiDAR dataset named OPV2V-HPL. Additionally, we present HPL-ViT, a pioneering architecture designed for robust feature fusion in heterogeneous and dynamic scenarios. It uses a graph-attention Transformer to extract domain-specific features for each agent, coupled with a cross-attention mechanism for the final fusion. Extensive experiments on OPV2V-HPL demonstrate that HPL-ViT achieves SOTA (state-of-the-art) performance in all settings and exhibits outstanding generalization capabilities.
Learning Spatial-Temporal Regularized Tensor Sparse RPCA for Background Subtraction

Video background subtraction is one of the fundamental problems in computer vision that aims to segment all moving objects. Robust principal component analysis has been identified as a promising unsupervised paradigm for background subtraction tasks in the last decade thanks to its competitive performance in a number of benchmark datasets. Tensor robust principal component analysis variations have improved background subtraction performance further. However, because moving object pixels in the sparse component are treated independently and do not have to adhere to spatial-temporal structured-sparsity constraints, performance is reduced for sequences with dynamic backgrounds, camouflaged, and camera jitter problems. In this work, we present a spatial-temporal regularized tensor sparse RPCA algorithm for precise background subtraction. Within the sparse component, we impose spatial-temporal regularizations in the form of normalized graph-Laplacian matrices. To do this, we build two graphs, one across the input tensor spatial locations and the other across its frontal slices in the time domain. While maximizing the objective function, we compel the tensor sparse component to serve as the spatiotemporal eigenvectors of the graph-Laplacian matrices. The disconnected moving object pixels in the sparse component are preserved by the proposed graph-based regularizations since they both comprise of spatiotemporal subspace-based structure. Additionally, we propose a unique objective function that employs batch and online-based optimization methods to jointly maximize the background-foreground and spatial-temporal regularization components. Experiments are performed on six publicly available background subtraction datasets that demonstrate the superior performance of the proposed algorithm compared to several existing methods. Our source code will be available very soon.
Perception for Humanoid Robots

Purpose of Review: The field of humanoid robotics, perception plays a fundamental role in enabling robots to interact seamlessly with humans and their surroundings, leading to improved safety, efficiency, and user experience. This scientific study investigates various perception modalities and techniques employed in humanoid robots, including visual, auditory, and tactile sensing by exploring recent state-of-the-art approaches for perceiving and understanding the internal state, the environment, objects, and human activities. Recent Findings: Internal state estimation makes extensive use of Bayesian filtering methods and optimization techniques based on maximum a-posteriori formulation by utilizing proprioceptive sensing. In the area of external environment understanding, with an emphasis on robustness and adaptability to dynamic, unforeseen environmental changes, the new slew of research discussed in this study have focused largely on multi-sensor fusion and machine learning in contrast to the use of hand-crafted, rule-based systems. Human robot interaction methods have established the importance of contextual information representation and memory for understanding human intentions. Summary: This review summarizes the recent developments and trends in the field of perception in humanoid robots. Three main areas of application are identified, namely, internal state estimation, external environment estimation, and human robot interaction. The applications of diverse sensor modalities in each of these areas are considered and recent significant works are discussed.
Learning the Efficient Frontier

The efficient frontier (EF) is a fundamental resource allocation problem where one has to find an optimal portfolio maximizing a reward at a given level of risk. This optimal solution is traditionally found by solving a convex optimization problem. In this paper, we introduce NeuralEF: a fast neural approximation framework that robustly forecasts the result of the EF convex optimization problem with respect to heterogeneous linear constraints and variable number of optimization inputs. By reformulating an optimization problem as a sequence to sequence problem, we show that NeuralEF is a viable solution to accelerate large-scale simulation while handling discontinuous behavior.
ANNCRIPS: Artificial Neural Networks for Cancer Research In Prediction &amp; Survival

Prostate cancer is a prevalent malignancy among men aged 50 and older. Current diagnostic methods primarily rely on blood tests, PSA:Prostate-Specific Antigen levels, and Digital Rectal Examinations (DRE). However, these methods suffer from a significant rate of false positive results. This study focuses on the development and validation of an intelligent mathematical model utilizing Artificial Neural Networks (ANNs) to enhance the early detection of prostate cancer. The primary objective of this research paper is to present a novel mathematical model designed to aid in the early detection of prostate cancer, facilitating prompt intervention by healthcare professionals. The model's implementation demonstrates promising potential in reducing the incidence of false positives, thereby improving patient outcomes. Furthermore, we envision that, with further refinement, extensive testing, and validation, this model can evolve into a robust, marketable solution for prostate cancer detection. The long-term goal is to make this solution readily available for deployment in various screening centers, hospitals, and research institutions, ultimately contributing to more effective cancer screening and patient care.
Question answering using deep learning in low resource Indian language Marathi

Precise answers are extracted from a text for a given input question in a question answering system. Marathi question answering system is created in recent studies by using ontology, rule base and machine learning based approaches. Recently transformer models and transfer learning approaches are used to solve question answering challenges. In this paper we investigate different transformer models for creating a reading comprehension-based Marathi question answering system. We have experimented on different pretrained Marathi language multilingual and monolingual models like Multilingual Representations for Indian Languages (MuRIL), MahaBERT, Indic Bidirectional Encoder Representations from Transformers (IndicBERT) and fine-tuned it on a Marathi reading comprehension-based data set. We got the best accuracy in a MuRIL multilingual model with an EM score of 0.64 and F1 score of 0.74 by fine tuning the model on the Marathi dataset.
SHACIRA: Scalable HAsh-grid Compression for Implicit Neural Representations

Implicit Neural Representations (INR) or neural fields have emerged as a popular framework to encode multimedia signals such as images and radiance fields while retaining high-quality. Recently, learnable feature grids proposed by Instant-NGP have allowed significant speed-up in the training as well as the sampling of INRs by replacing a large neural network with a multi-resolution look-up table of feature vectors and a much smaller neural network. However, these feature grids come at the expense of large memory consumption which can be a bottleneck for storage and streaming applications. In this work, we propose SHACIRA, a simple yet effective task-agnostic framework for compressing such feature grids with no additional post-hoc pruning/quantization stages. We reparameterize feature grids with quantized latent weights and apply entropy regularization in the latent space to achieve high levels of compression across various domains. Quantitative and qualitative results on diverse datasets consisting of images, videos, and radiance fields, show that our approach outperforms existing INR approaches without the need for any large datasets or domain-specific heuristics. Our project page is available at http://shacira.github.io .
Transformer-based approach to variable typing

The upsurge of multifarious endeavors across scientific fields propelled Big Data in the scientific domain. Despite the advancements in management systems, researchers find that mathematical knowledge remains one of the most challenging to manage due to the latter's inherent heterogeneity. One novel recourse being explored is variable typing where current works remain preliminary and, thus, provide a wide room for contribution. In this study, a primordial attempt to implement the end-to-end Entity Recognition (ER) and Relation Extraction (RE) approach to variable typing was made using the BERT (Bidirectional Encoder Representations from Transformers) model. A micro-dataset was developed for this process. According to our findings, the ER model and RE model, respectively, have Precision of 0.8142 and 0.4919, Recall of 0.7816 and 0.6030, and F1-Scores of 0.7975 and 0.5418. Despite the limited dataset, the models performed at par with values in the literature. This work also discusses the factors affecting this BERT-based approach, giving rise to suggestions for future implementations.
Comparing Object Detection Models for Water Trash Monitoring

If water trash exceeds the allowable load of a trash barrier, water trash barriers could be destroyed and the spilled waste negatively impacts the environment. Therefore, it is essential to measure the trash load in the water infrastructure to process collected water trash in a timely manner. However, there has been little investigation about how to monitor water trash in an automated way. To fill the knowledge gap, this study presents detailed investigation of water trash monitoring methods based on object detection models. To verify effective detection models and their performances, a new dataset is established, called the Foresys marine debris dataset. The dataset consists of a total of 6 water trash categories (Plastic, Vinyl, Styrofoam, Paper, Bottle, and Wood). State-of-the-art detection models were employed to test their performance, such as YOLOv3, YOLOv5, and YOLOv7 pretrained on the COCO dataset. The experiments showed that the detection models could achieve decent performance with proper amount of training image data; a number of training data required to secure decent performance varies by target class. The findings of this study will give a fresh insight for developing an automated water trash management system.
An Objective Improvement Approach to Solving Discounted Payoff Games

While discounted payoff games and classic games that reduce to them, like parity and mean-payoff games, are symmetric, their solutions are not.We have taken a fresh view on the constraints that optimal solutions need to satisfy, and devised a novel way to converge to them, which is entirely symmetric.It also challenges the gospel that methods for solving payoff games are either based on strategy improvement or on value iteration.
Large-scale precision micro-vibration stiffness regulation method based on spatial magnetic ring array

To solve the mutual constraint between the large load capacity and low stiffness of large-scale air-floating micro-vibration isolation (LAMI), a spatial magnetic ring array stiffness regulation method (SMRASRM) is proposed, in which multi-layer magnetic rings are axially arranged, and the adjacent layers are magnetized perpendicularly to each other to form a spatial magnetic ring array with high magnetic density, thus realizing low stiffness under large load capacity. The analytical magnetic force and stiffness model of SMRASRM is established to analyze its effect on the dynamics of LAMI, and the design constraints, characteristics, and laws for reducing the stiffness of LAMI and achieving low-frequency isolation under large load capacity are also investigated. An experimental setup is built, and experimental results show that the stiffness of LAMI decreases by 28.40%, and the peak transmissibility decreases from 15.61 dB to 3.94 dB, which combines low stiffness and low transmissibility under large load capacity.
On the Learning Dynamics of Attention Networks

Attention models are typically learned by optimizing one of three standard loss functions that are variously called – soft attention, hard attention, and latent variable marginal likelihood (LVML) attention. All three paradigms are motivated by the same goal of finding two models– a ‘focus’ model that ‘selects’ the right segment of the input and a ‘classification’ model that processes the selected segment into the target label. However, they differ significantly in the way the selected segments are aggregated, resulting in distinct dynamics and final results. We observe a unique signature of models learned using these paradigms and explain this as a consequence of the evolution of the classification model under gradient descent when the focus model is fixed. We also analyze these paradigms in a simple setting and derive closed-form expressions for the parameter trajectory under gradient flow. With the soft attention loss, the focus model improves quickly at initialization and splutters later on. On the other hand, hard attention loss behaves in the opposite fashion. Based on our observations, we propose a simple hybrid approach that combines the advantages of the different loss functions and demonstrates it on a collection of semi-synthetic and real-world datasets.
SCRAPS: Speech Contrastive Representations of Acoustic and Phonetic Spaces

Numerous examples in the literature proved that deep learning models have the ability to work well with multimodal data. Recently, CLIP has enabled deep learning systems to learn shared latent spaces between images and text descriptions, with outstanding zero- or few-shot results in downstream tasks. In this paper we explore the same idea proposed by CLIP but applied to the speech domain, where the phonetic and acoustic spaces usually coexist. We train a CLIP-based model with the aim to learn shared representations of phonetic and acoustic spaces. The results show that the proposed model is sensible to phonetic changes, with a 91% of score drops when replacing 20% of the phonemes at random, while providing substantial robustness against different kinds of noise, with a 10% performance drop when mixing the audio with 75% of Gaussian noise. We also provide empirical evidence showing that the resulting embeddings are useful for a variety of downstream applications, such as intelligibility evaluation and the ability to leverage rich pre-trained phonetic embeddings in speech generation task. Finally, we discuss potential applications with interesting implications for the speech generation and recognition fields.
SUDS: Sanitizing Universal and Dependent Steganography

Steganography, or hiding messages in plain sight, is a form of information hiding that is most commonly used for covert communication. As modern steganographic mediums include images, text, audio, and video, this communication method is being increasingly used by bad actors to propagate malware, exfiltrate data, and discreetly communicate. Current protection mechanisms rely upon steganalysis, or the detection of steganography, but these approaches are dependent upon prior knowledge, such as steganographic signatures from publicly available tools and statistical knowledge about known hiding methods. These dependencies render steganalysis useless against new or unique hiding methods, which are becoming increasingly common with the application of deep learning models. To mitigate the shortcomings of steganalysis, this work focuses on a deep learning sanitization technique called SUDS that is not reliant upon knowledge of steganographic hiding techniques and is able to sanitize universal and dependent steganography. SUDS is tested using least significant bit method (LSB), dependent deep hiding (DDH), and universal deep hiding (UDH). We demonstrate the capabilities and limitations of SUDS by answering five research questions, including baseline comparisons and an ablation study. Additionally, we apply SUDS to a real-world scenario, where it is able to increase the resistance of a poisoned classifier against attacks by 1375%.
Data-Free Class-Incremental Learning with Implicit Representation of Prototypes

Class-incremental learning (CIL) has attracted much attention in deep learning due to the challenge problem of catastrophic forgetting. Various methods have been proposed for CIL, including exemplar-based class-incremental learning (EBCIL), non-exemplar class-incremental learning (NECIL) and data-free class-incremental learning (DFCIL). Without storing any information (such as examples and prototypes) about the old classes, DFCIL is obviously the most challenging one. To address the problem of lacking information in DFCIL and with the assumption that the learned representations are not linearly separable, we propose a method called IRP. We use the L2-similarity classifier instead of the FC classifier, where each weight vector represents a prototype that implicitly records information about the classes. We use representation-prototype distance minimization (RPDM) to solve the problem of loose representation caused by overfitting. To alleviate the excessive deviation of old prototypes under long-term CIL, we add prototype changing limitation (PCL) and prototype momentum updating (PMU) in incremental stages. In addition, we design a method for resampling around old prototypes (RAOP) to maintain the decision boundary of the old classes. Numerous experiments on three benchmarks have shown that IRP is significantly superior to other DFCIL methods and performs comparably to NECIL and partial EBCIL methods.
Argument Attribution Explanations in Quantitative Bipolar Argumentation Frameworks

Argumentative explainable AI has been advocated by several in recent years, with an increasing interest on explaining the reasoning outcomes of Argumentation Frameworks (AFs). While there is a considerable body of research on qualitatively explaining the reasoning outcomes of AFs with debates/disputes/dialogues in the spirit of extension-based semantics, explaining the quantitative reasoning outcomes of AFs under gradual semantics has not received much attention, despite widespread use in applications. In this paper, we contribute to filling this gap by proposing a novel theory of Argument Attribution Explanations (AAEs) by incorporating the spirit of feature attribution from machine learning in the context of Quantitative Bipolar Argumentation Frameworks (QBAFs): whereas feature attribution is used to determine the influence of features towards outputs of machine learning models, AAEs are used to determine the influence of arguments towards topic arguments of interest. We study desirable properties of AAEs, including some new ones and some partially adapted from the literature to our setting. To demonstrate the applicability of our AAEs in practice, we conclude by carrying out two case studies in the scenarios of fake news detection and movie recommender systems.