Reviewerly/exHarmony huggingface dataset:
Real-Time Multi-Task ADAS Implementation on Reconfigurable Heterogeneous MPSoC Architecture

The rapid adoption of Advanced Driver Assistance Systems (ADAS) in modern vehicles, aiming to elevate driving safety and experience, necessitates the real-time processing of high-definition video data. This requirement brings about considerable computational complexity and memory demands, highlighting a critical research void for a design integrating high FPS throughput with optimal Mean Average Precision (mAP) and Mean Intersection over Union (mIoU). Performance improvement at lower costs, multi-tasking ability on a single hardware platform, and flawless incorporation into memory-constrained devices are also essential for boosting ADAS performance. Addressing these challenges, this study proposes an ADAS multi-task learning hardware-software co-design approach underpinned by the Kria KV260 Multi-Processor System-on-Chip Field Programmable Gate Array (MPSoC-FPGA) platform. The approach facilitates efficient real-time execution of deep learning algorithms specific to ADAS applications. Utilizing the BDD100K, KITTI, and CityScapes datasets, our ADAS multi-task learning system endeavours to provide accurate and efficient multi-object detection, segmentation, and lane and drivable area detection in road images. The system deploys a segmentation-based object detection strategy, using a ResNet-18 backbone encoder and a Single Shot Detector architecture, coupled with quantization-aware training to augment inference performance without compromising accuracy. The ADAS multi-task learning offers customization options for various ADAS applications and can be further optimized for increased precision and reduced memory usage. Experimental results showcase the system’s capability to perform real-time multi-class object detection, segmentation, line detection, and drivable area detection on road images at approximately 25.4 FPS using a 1920x1080p Full HD camera. Impressively, the quantized model has demonstrated a 51% mAP for object detection, 56.62% mIoU for image segmentation, 43.86% mIoU for line detection, and 81.56% IoU for drivable area identification, reinforcing its high efficacy and precision. The findings underscore that the proposed ADAS multi-task learning system is a practical, reliable, and effective solution for real-world applications.
Predicting Label Distribution From Tie-Allowed Multi-Label Ranking

Label distribution offers more information about label polysemy than logical label. There are presently two approaches to obtaining label distributions: LDL (label distribution learning) and LE (label enhancement). In LDL, experts must annotate training instances with label distributions, and a predictive function is trained on this training set to obtain label distributions. In LE, experts must annotate instances with logical labels, and label distributions are recovered from them. However, LDL is limited by expensive annotations, and LE has no performance guarantee. Therefore, we investigate how to predict label distribution from TMLR (tie-allowed multi-label ranking) which is a compromise on annotation cost but has good performance guarantees. On the one hand, we theoretically dissect the relationship between TMLR and label distribution. We define EAE (expected approximation error) to quantify the quality of an annotation, provide EAE bounds for TMLR, and derive the optimal range of label distributions corresponding to a given TMLR annotation. On the other hand, we propose a framework for predicting label distribution from TMLR via conditional Dirichlet mixtures. This framework blends the procedures of recovering and learning label distributions end-to-end and allows us to effortlessly encode our knowledge by a semi-adaptive scoring function. Extensive experiments validate our proposal.
Monocular 3D Object Detection With Motion Feature Distillation

In the context of autonomous driving, environmental perception within a 360-degree field of view is extremely important.This can be achieved via the detection of three-dimensional (3D) objects in the surrounding scene with the inputs acquired by sensors such as LiDAR or RGB camera.The 3D perception generated is commonly represented as the bird's-eye-view (BEV) of the sensor.RGB camera has the advantages of low-cost and long-range acquisition.As the RGB images are two-dimensional (2D), the BEV generated from 2D images suffers from low accuracy due to limitations such as lack of temporal correlation.To address the problems, we propose a monocular 3D object detection method based on long short-term feature fusion and motion feature distillation.Long short-term temporal features are extracted with different feature map resolutions.The motion features and depth information are combined and encoded using an encoder based on the Transformer cross-correlation module, and further integrated into the BEV space of fused long short-term temporal features.Subsequently, a decoder with motion feature distillation is used to localize objects in 3D space.By combining BEV feature representations of different time steps, and supplemented with embedded motion features and depth information, our proposed method significantly improves the accuracy of monocular 3D object detection as demonstrated from experimental results obtained on nuScenes dataset.Our proposed method outperforms state-of-the-art methods, in particular the previous best art by 6.7% on mAP, and 8.3% on mATE.
Boundary-Aware Multiscale Learning Perception for Remote Sensing Image Segmentation

For remote sensing image segmentation, the boundaries of objects are difficult to distinguish, which is ignored by most methods. Therefore, it is challenging how to excavate and recover the boundaries of objects accurately. In this article, we propose a boundary-aware multi-scale network (BMNet) to solve this problem. The key components of BMNet include the scale attention module (SA-module) and boundary guidance module (BG-module). Specifically, SA-module is proposed to guide the refinement of multi-scale features in a context-aware way. It enhances the discriminability of multi-scale features by establishing contextual dependencies, which enables the refinement of the prediction of objects. Then, BG-module is proposed to enable networks to distinguish the boundary of objects. It utilizes manifold information of features to generate boundary guidance maps and forces the network to focus more on the boundary of objects. The effectiveness of the proposed BMNet is demonstrated on two public remote sensing datasets: ISPRS 2-D semantic labeling Potsdam dataset and Vaihingen dataset, where BMNet achieves better segmentation than prevalent methods. Finally, the experimental results indicate that BMNet can produce sharper boundaries of objects to reconstruct more detailed segmentation results.
FNPG-NH: A Reinforcement Learning Framework for Flexible Needle Path Generation With Nonholonomic Constraints

Path planning algorithms for minimally invasive neurosurgery involve avoiding critical structures such as blood vessels and ventricles while following needle kinematics. The majority of planning solutions proposed in the literature use sampling-based algorithms. This letter introduces a Flexible Needle Path Generation framework with Non-Holonomic constraints (FNPG-NH), an extension of our FNPG framework. FNPG-NH uses deep Reinforcement Learning (RL) based methods such as Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC) to obtain a kinematically feasible path for a bevel-tipped flexible needle using a nonholonomic model. RL algorithms presented in this work generate the control input for needle rotation based on the rewards generated by the environment. The deep RL algorithms are trained on an environment that consists of 1) ventricles segmented from T1 images of the healthy volunteers using atlas-based segmentation, 2) blood vessels segmented from MRA volumes of the same volunteer using thresholding, and 3) tumor volume from labeled BraTS 2020 dataset and placed at an anatomically relevant location. The paths generated by the reinforcement learning algorithm and the traditional sampling-based algorithm RRT are compared for various performance metrics. The reinforcement learning model was trained on 20 volumes and validated on 68 volumes, and RRT was evaluated on the same 68 validation volumes. The results show that the trajectories generated by the FNPG-NH framework are safer, shorter, and take less time than RRT while avoiding critical structures such as ventricles and blood vessels.
A Logic for Preference Lifting Under Uncertainty and Its Decidability

This paper explores the idea of the preference lifting under uncertainty. An attempt is proposed in the direction of “qualitative = qualitative + quantitative”. This leads to a novel lifting called the “pairwise lifting method”. It defines a $$\lambda $$ function to record the number of occurrences of “ $$\ge $$ -binary relation” and “ $$\le $$ -binary relation” between individuals, and the preference relation between sets of individuals can be defined. We consider the logic of preference lifting of arbitrary preference relations, and prove its decidability by two methods, namely reduction to Presburger arithmetic and reduction to linear integer arithmetic.
Deconstructing General References via Game Semantics

We investigate the game semantics of general references through the fully abstract game model of Abramsky, Honda and McCusker (AHM), which demonstrated that the visibility condition in games corresponds to the extra expressivity afforded by higher-order references with respect to integer references. First, we prove a stronger version of the visible factorisation result from AHM, by decomposing any strategy into a visible one and a single strategy corresponding to a reference cell of type $$\textsf{unit}\rightarrow \textsf{unit}$$ (AHM accounted only for finite strategies and its result involved unboundedly many cells). We show that the strengthened version of the theorem implies universality of the model and, consequently, we can rely upon it to provide semantic proofs of program transformation results. In particular, one can prove that any program with general references is equivalent to a purely functional program augmented with a single $$\textsf{unit}\rightarrow \textsf{unit}$$ reference cell and a single integer cell. We also propose a syntactic method of achieving such a transformation. Finally, we provide a type-theoretic characterisation of terms in which the use of general references can be simulated with an integer reference cell or through purely functional computation, without any changes to the underlying types.
A Structured Bipolar Argumentation Theory for Providing Explanations in Practical Reasoning

In practical reasoning for decision-making, agents often need to provide reasonable explanations for taking a particular action. In some contexts, such as legal contexts, they also need to choose between potential explanation schemes. Arguably, an adequate explanation should not only provide the reason as to why an argument is defensible but also specify the necessary support relations, such as warrants. Some existing argumentation theories have considered this requirement and can deal with practical reasoning. However, most of them only provide attack relations explicitly in the argumentation framework for argument evaluation, or mainly consider support relations at an abstract level without giving the definition of how to obtain them. Therefore, aiming to provide explanations for the results of practical reasoning, we refer to critical questions in argumentation schemes for practical reasoning, existing structured argumentation systems, as well as bipolar argumentation frameworks. Inspired by these theories, the current paper presents an argumentation theory considering argument structures and obtaining abstract argumentation frameworks that explicitly show both support and attack relations among arguments. In addition, we discuss some criteria for selecting desired explanations, especially under legal contexts.
The Far Side of the Cube

Game-semantic models usually start from the core model of the prototypical language PCF, which is characterised by a range of combinatorial constraints on the shape of plays. Relaxing each such constraint usually corresponds to the introduction of a new language operation, a feature of game semantics commonly known as the Abramsky Cube. In this presentation we relax all such combinatorial constraints, resulting in the most general game model, in which all the other game models live. This is perhaps the simplest set up in which to understand game semantics, so it should serve as a portal to the other, more complex, game models in the literature. It might also be interesting in its own right, as an extremal instance of the game-semantic paradigm.
A Bimodal Extension of the Log-Normal Distribution on the Real Line with an Application to DNA Microarray Data

A bimodal double log-normal distribution on the real line is proposed using the random sign mixture transform. Its associated statistical inferences are derived. Its parameters are estimated by the maximum likelihood method. The performance of the estimators and the corresponding confidence intervals is checked by simulation studies. Application of the proposed distribution to a real data set from a DNA microarray is presented.
Graph-based pattern recognition on spectral reduced graphs

Graph-based pattern recognition – in particular in conjunction with large graphs – is often computationally expensive. This hampers, or makes it at least challenging, to employ graph-based representations for real-world data. To address this issue, we propose a method for reducing the size of the underlying graphs to their most important substructures using spectral graph clustering. The proposed method partitions the nodes of the graphs into clusters and then merges each cluster into supernodes. The motivation of this procedure is to reduce the computational cost of any graph comparison algorithm while maintaining the accuracy of the final classification. To assess the benefits and limitations of our method, we conduct thorough experiments on nine real-world datasets with different levels of graph reductions. The classification is obtained by four different graph classifiers (viz. a KNN based on graph edit distance, two SVMs based on a shortest path graph and a Weisfeiler-Lehman graph kernel, as well as a graph neural network). The results indicate that we can reduce computation time by up to two orders of magnitude without substantially degrading the classification accuracy.
Telepresence in the Recent Literature with a Focus on Robotic Platforms, Applications and Challenges

Three decades ago, telepresence was presented as an idea in the context of remote work and manipulation. Since then, it has evolved into a field combining different technologies and allowing users to have more or less realistic perceptions of immersion in remote environments. This paper reviews telepresence and its recent advances. While not covering all the work conducted in telepresence, this paper provides an array of applications for which telepresence can be envisioned, providing a clear view of the differences between components and functionalities of robotic platforms conceived for telepresence and pointing to the dependence of telepresence on several technological areas. Furthermore, challenges faced by telepresence technologies are shown, with consideration of user experiences. We consider telepresence from different perspectives, focusing on specific parts, making it possible to foresee future directions of research and applications. This review will be useful for researchers working in telepresence and related fields.
Evaluating Deep Learning Techniques for Blind Image Super-Resolution within a High-Scale Multi-Domain Perspective

Despite several solutions and experiments have been conducted recently addressing image super-resolution (SR), boosted by deep learning (DL), they do not usually design evaluations with high scaling factors. Moreover, the datasets are generally benchmarks which do not truly encompass significant diversity of domains to proper evaluate the techniques. It is also interesting to remark that blind SR is attractive for real-world scenarios since it is based on the idea that the degradation process is unknown, and, hence, techniques in this context rely basically on low-resolution (LR) images. In this article, we present a high-scale (8×) experiment which evaluates five recent DL techniques tailored for blind image SR: Adaptive Pseudo Augmentation (APA), Blind Image SR with Spatially Variant Degradations (BlindSR), Deep Alternating Network (DAN), FastGAN, and Mixture of Experts Super-Resolution (MoESR). We consider 14 datasets from five different broader domains (Aerial, Fauna, Flora, Medical, and Satellite), and another remark is that some of the DL approaches were designed for single-image SR but others not. Based on two no-reference metrics, NIQE and the transformer-based MANIQA score, MoESR can be regarded as the best solution although the perceptual quality of the created high-resolution (HR) images of all the techniques still needs to improve.
Cloud infrastructure availability optimization using Dragonfly and Grey Wolf optimization algorithms for health systems

Cloud infrastructure provides a real time computing environment to customers and had wide applicability in healthcare, medical facilities, business, and several other areas. Most of the health data recorded and saved on the cloud. But the cloud infrastructure is configured using several components and that makes it a complex structure. And the high value of availability and reliability is essential for satisfactory operation of such systems. So, the present study is conducted with the prominent objective of assessing the optimum availability of the cloud infrastructure. For this purpose, a novel stochastic model is proposed and optimized using dragonfly algorithm (DA) and Grey Wolf optimization (GWO) algorithms. The Markovian approach is employed to develop the Chapman-Kolmogorov differential difference equations associate with the system. It is considered that all failure and repair rates are exponentially distributed. The repairs are perfect. The numerical results are derived to highlight the importance of the study and identify the best algorithm. The system attains its optimum availability 0.9998649 at population size 120 with iteration 700 by GWO. It is revealed that grey wolf optimization algorithm performed better than the Dragonfly algorithm in assessing the availability, best fitted parametric values and execution time.
Functional content and architecture of software laboratory for ontological data analysis

The article presents the functionality and architecture of the software laboratory for ontological analysis and through this prism the methodology of this analysis. The methodological complex of ontological analysis and the procedure for its application provide extraction from the data of multidimensional observations and measurements of the knowledge domain of its semantic model in the form of a formal ontology a set of formal concepts, each of which is determined by extent and intent. In the set of formal concepts, a partial order is revealed (a binary relation of generalization) and intensional relations are revealed that reflect the connections between the elements of the concepts extents. The developed software laboratory differs from the well-known tools for constructing formal ontologies based on empirical object-feature data (i.e., based on data presented in the generally recognized form of a measurement results registration protocol), first of all, by taking into account the realities of accumulating information about the knowledge domain under study. In the general case, they cause the incompleteness and inconsistency of the initial data, for the processing of which the apparatus of multi-valued vector logic is involved. Another unique difference of the software laboratory is the consideration of a priori known (i.e., known before measurements) constraints on the properties existence during the primary processing of empirical data binary relations of conditionality and incompatibility of objects properties of the studied knowledge domain. The presented software laboratory is implemented on the Excel table processor platform and the programming language Visual Basic for Application. The main motive for this choice was to ensure the availability and facilitate the familiarization of ontological data analysis technology by a very wide range of users who use Excel in their professional work.
An efficient approach for data-imbalanced hate speech detection in Arabic social media

The use of hateful language in public debates and forums is becoming more common. However, this might result in antagonism and conflicts among individuals, which is undesirable in an online environment. Countries, businesses, and educational institutions are exerting their greatest efforts to develop effective solutions to manage this issue. In addition, recognizing such content is difficult, particularly in Arabic, due to a variety of challenges and constraints. Long-tailed data distribution is often one of the most significant issues in actual Arabic hate speech datasets. Pre-trained models, such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformers (GPT), have become more popular in numerous natural language processing (NLP) applications in recent years. We conduct extensive experiments to address data imbalance issues by utilizing oversampling methods and a focal loss function in addition to traditional loss functions. Quasi-recurrent neural networks (QRNN) are employed to fine-tune the cutting-edge transformer-based models, MARBERTv2, MARBERTv1, and ARBERT. In this context, we suggest a new approach using ensemble learning that incorporates best-performing models for both original and oversampled datasets. Experiments proved that our proposed approach achieves superior performance compared to the most advanced methods described in the literature.
Design and FPGA implementation of JPEG-LS image decompression

As the resolution of images processed in real-time continues to increase, it is necessary to compress the transmitted image data, and then transmit it to the terminal to decompress and restore the image. JPEG-LS is an algorithm that supports lossless and near-lossless compression. However, the decompression of JPEG-LS images is mostly implemented in a software environment. When decompressing multiple high-resolution images, the problems of decoding speed and resource consumption are more prominent. Therefore, the implementation of the JPEG-LS image decompression algorithm on FPGA is proposed in this paper, which divides the image into blocks and adopts a parallel processing structure. After the experiment on the hardware decoder, for the 1024*2048 test image, the designed hardware decoder can improve the original software decoding time by approximately 55%.