Reviewerly/exHarmony huggingface dataset:
Fast Trajectory Path Planning Inspired by Binary Searching Algorithm

A safe trajectory planning is a challenging problem due to the variety and complexity of driving environment. The sampling-based method of path-velocity decomposition method has the advantages of being intuitive and convenient, but it has a drawback that its real-time performance significantly declines as obstacle density increases. In this paper, we introduce a fast trajectory path planning method to address the issue. A sampling point selection approach based on the same-side method is proposed. This helps the planner to decrease the generation of collision paths. To increase the speed of collision checking, a fast collision checking method inspired by binary searching is designed. The effectiveness of the algorithm is verified by simulation experiments.
Delving into Shape-aware Zero-shot Semantic Segmentation

Thanks to the impressive progress of large-scale vision-language pretraining, recent recognition models can classify arbitrary objects in a zero-shot and open-set manner, with a surprisingly high accuracy. However, translating this success to semantic segmentation is not trivial, because this dense prediction task requires not only accurate semantic understanding but also fine shape delineation and existing vision-language models are trained with image-level language descriptions. To bridge this gap, we pursue shape-aware zero-shot semantic segmentation in this study. Inspired by classical spectral methods in the image segmentation literature, we propose to leverage the eigen vectors of Laplacian matrices constructed with self-supervised pixel-wise features to promote shape-awareness. Despite that this simple and effective technique does not make use of the masks of seen classes at all, we demonstrate that it out-performs a state-of-the-art shape-aware formulation that aligns ground truth and predicted edges during training. We also delve into the performance gains achieved on different datasets using different backbones and draw several interesting and conclusive observations: the benefits of promoting shape-awareness highly relates to mask compactness and language embedding locality. Finally, our method sets new state-of-the-art performance for zero-shot semantic segmentation on both Pascal and COCO, with significant margins. Code and models will be accessed at SAZS.
DRL-based path planning and obstacle avoidance of autonomous underwater vehicle

Both path planning and obstacle avoidance are important for the navigation safety of autonomous underwater vehicles (AUVs) in unknown environments. In this paper, in order to adjust to the complexity and flexibility of underwater environments, path planning and obstacle avoidance algorithms based on value iterative network (VIN) and deep deterministic policy gradient (DDPG) respectively are proposed to navigate the AUV through an unknown complex area. With a simulation multi-beam sonar equipped to detect obstacles of subsea surroundings, a grid map is constructed online as inputs of VIN and DDPG. Taking advantage of generalization of deep reinforcement learning, methods studied in this paper have demonstrated validity in simulation experiments implemented in Unity3D where dynamic and static obstacles are randomly placed and experiments are conducted.
LightedDepth: Video Depth Estimation in Light of Limited Inference View Angles

Video depth estimation infers the dense scene depth from immediate neighboring video frames. While recent works consider it a simplified structure-from-motion (SfM) problem, it still differs from the SfM in that significantly fewer view angels are available in inference. This setting, however, suits the mono-depth and optical flow estimation. This observation motivates us to decouple the video depth estimation into two components, a normalized pose estimation over af lowmap and a logged residual depth estimation over a mono-depth map. The two parts are unified with an efficient off-the-shelf scale alignment algorithm. Additionally, we stabilize the indoor two-view pose estimation by including additional projection constraints and ensuring sufficient camera translation. Though a two-view algorithm, we validate the benefit of the decoupling with the substantial performance improvement over multi-view iterative prior works on indoor and outdoor datasets. Codes and models are available at https://github.com/ShngJZ/LightedDepth.
Reduced-Complexity Erasure Decoding of Low-Rate Reed–Solomon Codes Based on LCH-FFT

This paper presents a new erasure decoding algorithm for low-rate Reed–Solomon codes (rate ≤ 0.5) based on a recently proposed FFT known as LCH-FFT. The algorithm requires O(n log k) finite field operations, where n and k are the code’s length and dimension, respectively. Experiments based on the Intel AVX2 Instructions show that notable improvements in the throughput are achieved compared with the best-known algorithm with complexity O(n log n) (also based on LCH-FFT), and new speed records are created.
Efficient Interpolation-Based Decoding of Reed-Solomon Codes

We propose a new interpolation-based error decoding algorithm for (n,k) Reed-Solomon (RS) codes over a finite field of size q, where n = q − 1 is the length and k is the dimension. In particular, we employ the fast Fourier transform (FFT) together with properties of a circulant matrix associated with the error interpolation polynomial and some known results from elimination theory in the decoding process. The asymptotic computational complexity of the proposed algorithm for correcting any $t \leq \left\lfloor {\frac{{n - k}}{2}} \right\rfloor$ errors in an (n,k) RS code is of order ${\mathcal{O}}\left( {t{{\log }^2}t} \right)$ and ${\mathcal{O}}\left( {n{{\log }^2}n\log \log n} \right)$ over FFT-friendly and arbitrary finite fields, respectively, achieving the best currently known asymptotic decoding complexity, proposed for the same set of parameters.
Forensics Investigation on Social Media Apps and Web Apps Messaging in Android Smartphone

The widespread usage of smartphones in many domains such as education, finance, organizations, and gaming makes hackers covet them and raises the challenge of facing cybercrime and forensic investigation of these devices. The devices run apps that can be sophisticatedly linked to other devices or platforms, or Web Apps, which duplicate the forensic investigation problem. Each social media application has its feature to deal with various types of messages and has its method of receiving and storing the messages depending on their type, i.e., text, video, contact file, image, and record. This study works on two scenarios to track the recovered data by logical acquisition method in MOBILedit and FINALmobile forensic tools. The first scenario is social media massaging between two smartphone Apps, and the second one is between Web Apps and smartphone Apps. In the two scenarios, the acquired device is the receiver’s smartphone. This study finds that logical acquisition cannot recover any Facebook or Instagram data, but in WhatsApp and Telegram, logical acquisition can extract and analyze all data except text and contact files, without knowing which linked device or Web App sent the message. There is a need for more investigation on social media Apps and Web Apps in future works.
Efficient and Secure: Privacy-Preserving Federated Learning for Resource-Constrained Devices

Federated learning has gained popularity as a distributed machine learning approach that provides security and privacy for data trained on local devices. However, vulnerabilities still exist in this approach, and common solutions such as encryption and blockchain techniques often suffer from high computation and communication costs, making them impractical for resource-constrained devices. To solve this problem, we propose a privacy-preserving federated learning system that leverages compressive sensing and differential privacy, specifically designed for devices with limited computational resources. In this paper, we demonstrate the capabilities of our proposed system in resource-limited environments. We outline the features, infrastructure, and algorithm of our proposed system, and simulate its performance using image datasets on a Raspberry Pi 4 and an Android smartphone in a cloud environment. Our approach offers a practical solution for secure and privacy-preserving federated learning in resource-constrained scenarios, with potential applications in various domains such as healthcare, IoT, and edge computing.
Tackling Network Challenges in Context Aware Environments: Lightweight Context Management Architecture

Resilience in context-aware applications is especially important within challenged network and physical environments. This paper discusses the current methods for maintaining resilience in context management architectures. These methods largely focus on resolving contextual information loss, rather than maintaining the standard operation of the deployed system. The paper contends that this approach leads to loss of functionality and unwanted modifications to contextual information. Existing approaches for mitigating the effects of network instability have a high resource requirement and do not maintain the standard functionality of the system in real time. The paper proposes and discusses the Lightweight Context Management Architecture (LCMA) which addresses the lack of lightweight solutions for resilient context management systems. The functional requirements of the LCMA components are proposed and detailed. The proposed LCMA will be validated in mission-critical applications with potential adversarial actions.
The Specification of Blockchain Oracle System

Blockchain applications, especially DeFi, frequently require off-chain data through oracles. Distributed oracles have higher security, but the system is also more complex. This paper designs a unified formal description method for the communication process, security protocol, data aggregation and other related procedures and protocols of the oracle. This method has no ambiguity and has a high logical description ability. Therefore, it has good convertibility with Event-B and some other formal verification tools, as well as logic methods such as first-order logic, set theory, and CTL, which can provide a basis for technical personnel to establish formal models, protocol development, and implementation. The paper defines and analyzes this protocol description method and presents examples of some core protocols.
Upper bounds on the Natarajan dimensions of some function classes

The Natarajan dimension is a fundamental tool for characterizing multi-class PAC learnability, generalizing the Vapnik-Chervonenkis (VC) dimension from binary to multi-class classification problems. This work establishes upper bounds on Natarajan dimensions for certain function classes, including (i) multi-class decision tree and random forests, and (ii) multi-class neural networks with binary, linear and ReLU activations. These results may be relevant for describing the performance of certain multi-class learning algorithms.
A multi-view anomalous co-location detection framework considering both intra- and inter-feature couplings

Co-location is one of the most fundamental spatial associations in spatial data. Previous anomalous co-location detections only aim to detect abnormal inter-feature co-locations on bivariate datasets, while the abnormal intra-feature co-locations are lost, which may have limits to applications. Thus, we propose a novel multi-view anomalous co-location detection framework (MACDF). Two kinds of relationships are captured by two proposed couplings, that is, intra-feature coupling and inter-feature coupling (intra-coupling and inter-coupling for short). Moreover, the neighboring concept in co-location relationships is redefined with a mixture-considered inter-coupling for point-like instances, inspired by the concept for polygonal instances, and a concreted algorithm, i.e. multi-view low-rank analysis with direction for anomalous co-location detection (MLAD-ACD), is designed to discover the anomalous co-locations, on not only bivariate datasets but also multivariate datasets. Experiments show the instantiated algorithm MLAD-ACD can detect anomalous co-locations effectively and efficiently on spatial datasets. Specifically, MLAD-ACD obtains at least a 71% higher AUC score than the state-of-the-art algorithms in an acceptable time.
BAEFormer: Bi-Directional and Early Interaction Transformers for Bird's Eye View Semantic Segmentation

Bird's Eye View (BEV) semantic segmentation is a critical task in autonomous driving. However, existing Transformer-based methods confront difficulties in transforming Perspective View (PV) to BEV due to their unidirectional and posterior interaction mechanisms. To address this issue, we propose a novel Bi-directional and Early Interaction Transformers framework named BAEFormer, consisting of (i) an early-interaction PV-BEV pipeline and (ii) a bi-directional cross-attention mechanism. Moreover, we find that the image feature maps' resolution in the cross-attention module has a limited effect on the final performance. Under this critical observation, we propose to enlarge the size of input images and downsample the multi-view image features for cross-interaction, further improving the accuracy while keeping the amount of computation controllable. Our proposed method for BEV semantic segmentation achieves state-of-the-art performance in real-time inference speed on the nuScenes dataset, i.e., 38.9 mIoU at 45 FPS on a single A100 GPU.
Training Debiased Subnetworks with Contrastive Weight Pruning

Neural networks are often biased to spuriously correlated features that provide misleading statistical evidence that does not generalize. This raises an interesting question: “Does an optimal unbiased functional subnetwork exist in a severely biased network? If so, how to extract such subnetwork?” While empirical evidence has been accumulated about the existence of such unbiased subnetworks, these observations are mainly based on the guidance of ground-truth unbiased samples. Thus, it is unexplored how to discover the optimal subnetworks with biased training datasets in practice. To address this, here we first present our theoretical insight that alerts potential limitations of existing algorithms in exploring unbiased subnetworks in the presence of strong spurious correlations. We then further elucidate the importance of bias-conflicting samples on structure learning. Motivated by these observations, we propose a Debiased Contrastive Weight Pruning (DCWP) algorithm, which probes unbiased subnetworks without expensive group annotations. Experimental results demonstrate that our approach significantly outperforms state-of-the-art debiasing methods despite its considerable reduction in the number of parameters.
GeoVLN: Learning Geometry-Enhanced Visual Representation with Slot Attention for Vision-and-Language Navigation

Most existing works solving Room-to-Room VLN problem only utilize RGB images and do not consider local context around candidate views, which lack sufficient visual cues about surrounding environment. Moreover, natural language contains complex semantic information thus its correlations with visual inputs are hard to model merely with cross attention. In this paper, we propose GeoVLN, which learns Geometry-enhanced visual representation based on slot attention for robust Visual-and-Language Navigation. The RGB images are compensated with the corresponding depth maps and normal maps predicted by Omnidata as visual inputs. Technically, we introduce a two-stage module that combine local slot attention and CLIP model to produce geometry-enhanced representation from such input. We employ V&L BERT to learn a cross-modal representation that incorporate both language and vision informations. Additionally, a novel multiway attention module is designed, encouraging different phrases of input instruction to exploit the most related features from visual input. Extensive experiments demonstrate the effectiveness of our newly designed modules and show the compelling performance of the proposed method.
CoralStyleCLIP: Co-optimized Region and Layer Selection for Image Editing

Edit fidelity is a significant issue in open-world controllable generative image editing. Recently, CLIP-based approaches have traded off simplicity to alleviate these problems by introducing spatial attention in a handpicked layer of a StyleGAN. In this paper, we propose CoralStyleCLIP, which incorporates a multi-layer attention-guided blending strategy in the feature space of StyleGAN2 for obtaining high-fidelity edits. We propose multiple forms of our co-optimized region and layer selection strategy to demonstrate the variation of time complexity with the quality of edits over different architectural intricacies while preserving simplicity. We conduct extensive experimental analysis and benchmark our method against state-of-the-art CLIP-based methods. Our findings suggest that CoralStyleCLIP results in high-quality edits while preserving the ease of use.
Coded Parallelism for Distributed Deep Learning

With the rapid development of deep learning, the parameters of modern neural network models, especially in the field of Natural Language Processing (NLP) are extremely huge. When the parameters of the model are larger even than the storage memory of a single device, it is necessary to split the original big learning model into different parts with each part assigned to one device, thus realizing joint model training over different devices (i.e., distributed training). In this paper, we aim to introduce the advanced coding scheme into the distributed parallel framework, which leads to the perfect combination of coding and the underlying calculation of neural networks. The proposed scheme is not only able to avoid the impact of poor computing power or low bandwidth and even dropped devices (stragglers) on system performance but also reduce the communication load between different devices, thereby greatly improving the performance of distributed parallel systems.
A Combined Model for Ultra-Short-Term PV Forecasting Based on SOM Clustering

A combined model for ultra-short-term PV forecasting based on SOM clustering is proposed to improve the accuracy of PV power prediction and reduce the impact of the randomness of PV power generation on the power system. At first, the key factors are first selected as inputs to the model by calculating the Pearson correlation coefficients between each factor and PV power. Second, to eliminate the influence of season on weather classification and the coupling relationship between many meteorological factors, the key factors are standardized and weighted summed month by month to obtain the classification index Sky Condition Factor (SCF). Then, the SCF is clustered unsupervised by self-organizing mapping (SOM) neural network, to classify the sample data into three weather types and construct CNN prediction models under different weather types respectively. The results show that the combined model proposed in this paper has obviously improve the accuracy of PV output power prediction for different weather conditions.
Optimum Noise Mechanism for Probabilistic Differentially Private Queries in Discrete Finite Sets

Most published work on differential privacy (DP) focuses exclusively on meeting privacy constraints by adding to the query noise with a pre-specified parametric distribution model, typically with one or two degrees of freedom. The accuracy of the response and its utility for the intended use are often not part of the design. Considering that several database queries are categorical in nature (e.g., label, color, etc.), or discrete numerical data (e.g., ranking, histogram, etc.), or can be quantized to discrete values, the parameters that define the randomized mechanism’s distribution are finite. Thus, it is reasonable to search through numerical optimization for the probability masses that meet the privacy constraints while minimizing the query distortion. Considering the modulo summation of random noise as the probabilistic DP mechanism, the goal of this paper is to introduce a tractable framework to design the optimum noise probability mass function (PMF) for database queries with a discrete and finite set, optimizing with an expected distortion metric for a given privacy requirement. This paper shows that the optimum PMF can be obtained by solving a mixed integer linear program (MILP) and that the proposed optimal mechanism significantly outperforms the state-of-the-art.
A Novel Blockchain Based Decentralized Data Management Model for Swarm UAV Systems

Blockchain technology offers a promising solution for decentralized data sharing and communication among IoT devices. The decentralization aspect of blockchain technology has made it a popular topic for integration with IoT systems, including UAVs. Therefore, the use of blockchain technology in UAV systems has led to the growing interest of researchers, particularly in swarm UAV systems, where decentralization is crucial for ensuring safety and continuity. This study proposes a new data management model that integrates blockchain technology into UAV systems, with a particular focus on swarm UAV systems. By leveraging the decentralized structure of blockchain, the proposed system offers robustness against tampering and irreversibility, ensuring secure data transfer between UAVs and decentralized processing. Especially for mission plan and command upload operations, blockchain enables the distribution of critical information to all UAVs in the system. A use case scenario is also presented to demonstrate the effectiveness of the proposed model in controlling and managing real-world problems.
BioEnvsense: A Usable Cybersecurity Framework for Critical Infrastructure

Cybersecurity solutions require a multidisciplinary approach, as around 95% of cybersecurity incidents stem from behavioral issues. Insider threats are a significant concern, with 90% of enterprises reporting experiencing such threats. Behavioral-based solutions have been proposed to deal with non-intentional insider threats, which include timely notifications and effective training materials. Biometric-based solutions such as face recognition and fingerprints have also been proposed to minimize such threats. However, there is still a need for new approaches to introduce controls on systems to deal with unpredictable user behavior and states to mitigate such malicious cybersecurity incidents. This work proposes a conceptual framework for determining the emotional states of users using soft biometrics and contextual working conditions using sensors to minimize non-intentional behavioral-based malicious cyber incidents. The framework will measure users' mental and emotional states and environmental conditions and take extra precautions when necessary, such as providing instructions, backups or limiting access. Overall, the proposed framework offers a usable contextual and biometric-based cybersecurity solution that could significantly improve the security of systems and users alike.