Reviewerly/exHarmony huggingface dataset:
D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud Sequence

LiDAR sensors are widely used for 3D object detection in various mobile robotics applications. LiDAR sensors continuously generate point cloud data in real-time. Conventional 3D object detectors detect objects using a set of points acquired over a fixed duration. However, recent studies have shown that the performance of object detection can be further enhanced by utilizing spatio-temporal information obtained from point cloud sequences. In this paper, we propose a new 3D object detector, named D-Align, which can effectively produce strong bird's-eye-view (BEV) features by aligning and aggregating the features obtained from a sequence of point sets. The proposed method includes a novel dual-query co-attention network that uses two types of queries, including target query set (T-QS) and support query set (S-QS), to update the features of target and support frames, respectively. D-Align aligns S-QS to T-QS based on the temporal context features extracted from the adjacent feature maps and then aggregates S-QS with T-QS using a gated fusion mechanism. The dual queries are updated through multiple attention layers to progressively enhance the target frame features used to produce the detection results. Our experiments on the nuScenes dataset show that the proposed D-Align method greatly improved the performance of a single frame-based baseline method and significantly outperformed the latest 3D object detectors. Code is available at https://github.com/junhyung-SPALab/D-Align.
Linear Auto-calibration of Pan-Tilt-Zoom Cameras With Rotation Center Offset

This paper addresses the linear auto-calibration problem of a pan-tilt-zoom (PTZ) camera. Unlike existing methods, we take full advantage of the offset of the camera center from the rotation center, which is usually non-negligible in bullet-type PTZ cameras. Without any prior assumption, we propose a linear method to recover all intrinsic parameters. First, we successively acquired at least four images using the zoom and rotation capabilities of the PTZ camera. Second, using the homography of two images at the same location but different scales, the principal point and zoom scalar can be linearly recovered. Finally, based on the unknown offset of the camera center and rotation center, we propose a linear method to solve the scale factor in the Kruppa equation and recover the remaining camera intrinsic parameters, namely focal lengths and skew. Synthetic and real experiments demonstrate the feasibility of our approach.
Design and Evaluation of an Augmented Reality Head-Mounted Display User Interface for Controlling Legged Manipulators

Designing an intuitive User Interface (UI) for controlling assistive robots remains challenging. Most existing UIs leverage traditional control interfaces such as joysticks, hand-held controllers, and 2D UIs. Thus, users have limited availability to use their hands for other tasks. Furthermore, although there is extensive research regarding legged manipulators, comparatively little is on their UIs. Towards extending the state-of-art in this domain, we provide a user study comparing an Augmented Reality (AR) Head-Mounted Display (HMD) UI we developed for controlling a legged manipulator against off-the-shelf control methods for such robots. We made this comparison baseline across multiple factors relevant to a successful interaction. The results from our user study ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N=17$</tex> ) show that although the AR UI increases immersion, off-the-shelf control methods outperformed the AR UI in terms of time performance and cognitive workload. Nonetheless, a follow-up pilot study incorporating the lessons learned shows that AR UIs can outpace hand-held-based control methods and reduce the cognitive requirements when designers include hands-free interactions and cognitive offloading principles into the UI.
Semantics-aware Exploration and Inspection Path Planning

This paper contributes a novel strategy for semantics-aware autonomous exploration and inspection path planning. Attuned to the fact that environments that need to be explored often involve a sparse set of semantic entities of particular interest, the proposed method offers volumetric exploration combined with two new planning behaviors that together ensure that a complete mesh model is reconstructed for each semantic, while its surfaces are observed at appropriate resolution and through suitable viewing angles. Evaluated in extensive simulation studies and experimental results using a flying robot, the planner delivers efficient combined exploration and high-fidelity inspection planning that is focused on the semantics of interest. Comparisons against relevant methods of the state-of-the-art are further presented.
AMSwarm: An Alternating Minimization Approach for Safe Motion Planning of Quadrotor Swarms in Cluttered Environments

This paper presents a scalable online algorithm to generate safe and kinematically feasible trajectories for quadrotor swarms. Existing approaches rely on linearizing Euclidean distance-based collision constraints and on axis-wise decoupling of kinematic constraints to reduce the trajectory optimization problem for each quadrotor to a quadratic program (QP). This conservative approximation often fails to find a solution in cluttered environments. We present a novel alternative that handles collision constraints without linearization and kinematic constraints in their quadratic form while still retaining the QP form. We achieve this by reformulating the constraints in a polar form and applying an Alternating Minimization algorithm to the resulting problem. Through extensive simulation results, we demonstrate that, as compared to Sequential Convex Programming (SCP) baselines, our approach achieves on average, a 72% improvement in success rate, a 36% reduction in mission time, and a 42 times faster per-agent computation time. We also show that collision constraints derived from discrete-time barrier functions (BF) can be incorporated, leading to different safety behaviours without significant computational overhead. Moreover, our optimizer outperforms the state-of-the-art optimal control solver ACADO in handling BF constraints with a 31 times faster per-agent computation time and a 44% reduction in mission time on average. We experimentally validated our approach on a Crazyflie quadrotor swarm of up to 12 quadrotors. The code with supplementary material and video are released for reference.
Road Anomaly Segmentation Based on Pixel-wise Logit Variance with Iterative Background Highlighting

Anomaly segmentation on the urban landscape scene is an important task in autonomous driving. This process exploits a pre-trained semantic segmentation network to estimate anomalous regions. Anomaly segmentation approaches implemented with extra requirements such as out-of-domain data, extra network, or network retraining might increase the computational cost or degradation of segmentation performance. In this study, to exploit information from the segmentation network for more robust anomaly segmentation, we propose the use of pixel-wise logit variance, which tends to be small for anomalies as network outputs even logits without confidence. Additionally iterative background highlighting is proposed to robustly detect anomalous objects on the background, which is implemented by feeding the logits back into the linear classifier of the network. We achieved state-of-the-art performance among anomaly segmentation approaches without extra requirements, reaching relative average precision improvements of 21.7% on the Fishyscapes Lost&Found and 17.4% on the Fishyscapes Static compared to the state-of-the-art method. The code of this work is available at our Github repository (https://github.com/hagg30/LogitVar).
Shunted Collision Avoidance for Multi-UAV Motion Planning with Posture Constraints

This paper investigates the problem of fixed-wing unmanned aerial vehicles (UAV s) motion planning with posture constraints and the problem of the more general symmetrical situations where UAVs have more than one optimal solution. In this paper, the posture constraints are formulated in the 3D Dubins method, and the symmetrical situations are overcome by a more collaborative strategy called the shunted strategy. The effectiveness of the proposed method has been validated by conducting extensive simulation experiments. Meanwhile, we compared the proposed method with the other state-of-the-art methods, and the comparison results show that the proposed method advances the previous works. Finally, the practicability of the proposed algorithm was analyzed by the statistic in computational cost. The source code of our method can be available at https://github.com/wuuya1/SCA.
Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learning

The use of human demonstrations in reinforcement learning has proven to significantly improve agent performance. However, any requirement for a human to manually ‘teach’ the model is somewhat antithetical to the goals of reinforcement learning. This paper attempts to minimize human involvement in the learning process while retaining the performance advantages by using a single human example collected through a simple-to-use virtual reality simulation to assist with RL training. Our method augments a single demonstration to generate numerous human-like demonstrations that, when combined with Deep Deterministic Policy Gradients and Hindsight Experience Replay (DDPG + HER) significantly improve training time on simple tasks and allows the agent to solve a complex task (block stacking) that DDPG + HER alone cannot solve. The model achieves this significant training advantage using a single human example, requiring less than a minute of human input. Moreover, despite learning from a human example, the agent is not constrained to human-level performance, often learning a policy that is significantly different from the human demonstration.
CueCAn: Cue-driven Contextual Attention for Identifying Missing Traffic Signs on Unconstrained Roads

Unconstrained Asian roads often involve poor infrastructure, affecting overall road safety. Missing traffic signs are a regular part of such roads. Missing or non-existing object detection has been studied for locating missing curbs and estimating reasonable regions for pedestrians on road scene images. Such methods involve analyzing task-specific single object cues. In this paper, we present the first and most challenging video dataset for missing objects, with multiple types of traffic signs for which the cues are visible without the signs in the scenes. We refer to it as the Missing Traffic Signs Video Dataset (MTSVD). MTSVD is challenging compared to the previous works in two aspects i) The traffic signs are generally not present in the vicinity of their cues, ii) The traffic signs' cues are diverse and unique. Also, MTSVD is the first publicly available missing object dataset. To train the models for identifying missing signs, we complement our dataset with 10K traffic sign tracks, with 40% of the traffic signs having cues visible in the scenes. For identifying missing signs, we propose the Cue-driven Contextual Attention units (CueCAn), which we incorporate in our model's encoder. We first train the encoder to classify the presence of traffic sign cues and then train the entire segmentation model end-to-end to localize missing traffic signs. Quantitative and qualitative analysis shows that CueCAn significantly improves the performance of base models.
Deep Learning Based Digital Wireless Communication System

The primary aim of a communication system is to transmit data reliably and efficiently. But the traditional communication systems that try to achieve this objective have their own limitations. Several research works are going on in this field that incorporate deep learning for communication systems to address the current problems. In this paper, we look into one such deep learning model called the autoencoders. Autoencoder based communication system using dense neural networks is constructed and its efficiency is evaluated by comparing the BER-SNR plots of our model to that of various theoretical, uncoded and coded traditional modulation schemes. The behaviour of the model is studied in detail to help pave the way for future researches.
Feature Extraction for Effective and Efficient Deep Reinforcement Learning on Real Robotic Platforms

Deep reinforcement learning (DRL) methods can solve complex continuous control tasks in simulated environments by taking actions based solely on state observations at each decision point. Because of the dynamics involved, individual snapshots of real-world sensor measurements afford only partial state observability, so it is typical to use a history of observations to improve training and policy performance. Such intertemporal information can be further exploited using a recurrent neural network (RNN) to reduce the dimensionality of the dynamic state representation. However, using RNNs as an internal part of a DRL network presents challenges of its own; and even then, the improvements in resulting policies are usually limited. To address these shortcomings, we propose using gated feature extraction to improve DRL training of real-world robots. Specifically, we use an untrained gated recurrent unit (GRU) to encode a low-dimension representation of the state observation sequence before passing it to the DRL training procedure. In addition to dimensionality reduction, this allows us to unroll the RNN by encoding the observations cumulatively as they are collected, thereby avoiding same-length input requirements, and train the RL network on the raw observations at the current step combined with the GRU-encoding of the preceding steps. Our simulation experiments employ gated feature extraction with the TD3 algorithm. Our results show that the GRU-encoded state observations improve the training speed and execution performance of the TD3 algorithm, improving the learned policies in all 19 test cases, exceeding the maximum achieved reward by over 38% in 8 and doubling the maximum achieved reward in three, while also outperforming a baseline implementation of SAC in 17 out of 19 environments. Moreover, the greatest improvement is seen in real-world experiments, where our approach successfully learns to balance a pendulum as well as a complex quadrupedal locomotion task. In contrast, the standard TD3 algorithm not only does not show any learning progress at all, but also repeatedly damages the hardware.
A Dimension Reduction Analysis of the Factors influencing the Adoption of Community Cloud Computing (ACCC) in HEI's of Dehradun, Uttarakhand

The purpose of this paper was to assess the various factors influencing the 'Adoption of Community Cloud Computing (ACCC)‘ in 'Higher Education Institutes (HEI's)’ located in Dehradun, Uttarakhand, India. The paper in the first phase was exploratory in nature as the researcher explored the various factors (attributes or variables - 21 were finally identified) through secondary data (research papers). In the second phase, an empirical study was conducted by collecting the primary data from 100 valid respondents - Professors, Associate & Assistant Professors of ten prominent HEI's of Dehradun. Convenience sampling was used for the same purpose and the demographic profile was not emphasized herewith. A five-point likert scale-based questionnaire (Google form) was used for collecting responses. Furthermore, the factor analysis technique through SPSS was applied for dimension reduction and reaching out to conclusions. Results revealed that a total of six (6) components (factors) had been extracted like as comparative cost advantage, competitive resource pooling, multitenancy, advanced technology, enhanced features etc. The research was practically significant because Dehradun is one of the most prominent higher education hubs in India. The study was unique being empirical in nature and there is no significant research had been conducted till date for adoption of ‘Community Cloud Computing’ in Indian context.
CNN based IDS Framework for Financial Cyber Security

The Cyber Financial domain has changed a lot over the past few years but with the digitalization of the same, the various gaps in the field of security have risen. In terms of Finance, although there has been the implementation of technology 4.0 there is a lack of proper regulations, and any countermeasures are still absent to make the system secure and prevent any attack on data integrity. This research builds a bridge in the security aspects of cyberspace as well as provides a comprehensive framework which is based on the blockchain network to improve cybersecurity. The various issues with the increasing number of intrusive attacks, data breaches, and system failures have made it important to adopt proper measures to curb the spread of such attacks. To make cyberspace more secure and understand the different types of intrusion attacks an IDS is developed to make use of self-learning to continually grow and thus be more efficient. The proposed VCM framework also makes use of a blockchain network for decentralization as well as the use of smart contracts are also discussed which gives an idea as to the depth of the cybersecurity which can be maintained with the help of this. All the proposed framework is centred around the vulnerabilities of cyberspace which are being continuously exploited by malicious attackers and thus the VCM acts in a comprehensive way to maintain the security, privacy as well as robustness of the system by enforcing certain regulations.
Memory-based Exploration-value Evaluation Model for Visual Navigation

We propose a hierarchical visual navigation solution, called Memory-based Exploration-value Evaluation Model (MEEM), to improve the agent's navigation performance. MEEM employs a hierarchical policy to tackle the challenge of sparse rewards, holds an episodic memory to store the historical information of the agent, and applies an Exploration-value Evaluation Model to calculate an exploration-value for action planning at each location in the observable area. We experimentally verify MEEM by navigation performance comparison on two datasets including the grid-map dataset and the 3D scenes Gibson dataset, where our approach achieves state-of-the-art performance on both. Specifically, the overall success rate of MEEM is 95% on the grid-map dataset while the best competitor reaches 68% only. As for the Gibson dataset, the success rate of ours and the best competitor SemExp are 69.8% and 54.4%, respectively. Ablation analysis on the tile-map dataset indicates that all three components of MEEM have positive effects.
Study of Cryptographic Techniques Adopted in Blockchain

This study investigates how blockchain, a decentralized ledger technology that has garnered popularity due to its potential to revolutionize industries, uses cryptography technology. In the article, the main ideas of cryptography-such as encryption, hashing, digital signatures, and public-key cryptography-as well as their applications in blockchain technology are discussed. The significance of cryptography in the blockchain is examined in the paper, with a focus on protecting user data privacy, maintaining data integrity, and authenticating transactions. By using encryption algorithms to scramble data so that only authorized parties can decrypt it, cryptography guarantees that data is protected from unauthorized access. Data integrity is protected by hashing algorithms because any alterations to the data would produce a new hash value. Transactions are authenticated using digital signatures and public-key encryption, ensuring that only authorized parties carry them out. The study also looks at the cryptographic limitations of blockchain, like the possibility of quantum attacks on public-key cryptography, which might jeopardize the security of blockchain systems. The paper's discussion of future research paths and potential areas for advancement in cryptography technology to maintain the security and robustness of blockchain systems comes to a close.
Neural network based approach for Ethereum fraud detection

Over the years with the advent of technology 4.0, there have been an increased inclination towards the blockchain technology and the cryptocurrencies. Thus it is becoming imperative to implement fraud detection techniques over the Ethereum network which nowadays is a popular platform for the developers to create smart contracts and deploy decentralized apps. Over the years the various machine learning algorithms have been growing for the process of fraud detection and in particular neural networks have shown promising results. As such in this paper, a neural-network based approach has been presented for Ethereum fraud detection and to validate the effects of the performance this proposed model has been compared with its peers. In contrast to the various models such as Logistic Regression, SVM, Gaussian Naive Bayes, K-nearest neighbour, the neural network perform the best providing an accuracy of about 97.09% which is higher than the rest. It is then seen that neural networks are relatively effective in learning complex patterns of the dataset and thus classifying the resultant transaction as genuine or fraudulent. Thus this work contribute in the development of effective solutions for fraud detection in the Ethereum and other blockchain platforms, enhancing their security and reliability.
Machine Learning And Blockchain: A Promising Future

Currently, the two developments that are most obvious to the public are machine learning (ML) and blockchain technology. The first is the development of AI and big data, and the financial sector has been substantially impacted by the second. Recently, the advancement of blockchain technology has converted a spectacular, frequently challenging, and moving innovation. The blockchain's decentralized database emphasizes information security and protection. The agreement tool also makes sure that data is reliable and true. In any event, it brings up a few security concerns; to address these concerns, data analytics on blockchain-based safe information is required. Analysis of this data highlights the importance of the newly-emerging invention, machine learning (ML). The substantial amount of data used by machine learning technology allows it to provide precise conclusions. In ML, reliability and data exchange are crucial to enhancing the accuracy of results. ML and blockchain technology combined can produce incredibly precise results. Since the two innovations are information-driven, there is a growing interest in implementing them for more secure and useful information analysis and sharing. In this article, research is done to demonstrate the viability and productivity of merging blockchain with machine learning advances. First, examine the fundamental language and structure of blockchain and machine learning. Different ML techniques that can be applied to blockchain framework are presented in the next section. Additionally, consider how the fusion of these two concepts advances data security and cybersecurity. The study is ended in the final section, which discusses future prospects for research and challenges brought on by the ongoing and possible integration of ML and blockchain.