Reviewerly/exHarmony huggingface dataset:
CinemAirSim: A Camera-Realistic Robotics Simulator for Cinematographic Purposes

Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular in the film and entertainment industries, in part because of their maneuverability and perspectives they enable. While there exists methods for controlling the position and orientation of the drones for visibility, other artistic elements of the filming process, such as focal blur, remain unexplored in the robotics community. The lack of cinematographic robotics solutions is partly due to the cost associated with the cameras and devices used in the filming industry, but also because state-of-the-art photo-realistic robotics simulators only utilize a full in-focus pinhole camera model which does not incorporate these desired artistic attributes. To overcome this, the main contribution of this work is to endow the well-known drone simulator, AirSim, with a cinematic camera as well as extend its API to control all of its parameters in real time, including various filming lenses and common cinematographic properties. In this paper, we detail the implementation of our AirSim modification, CinemAirSim, present examples that illustrate the potential of the new tool, and highlight the new research opportunities that the use of cinematic cameras can bring to research in robotics and control.
A Proficient Load Balancing Using Priority Algorithm In Cloud Computing

Presently maintaining the load is the most challenging issue for a researcher in a cloud environment. In the IT sector, cloud computing is offering a variety of resources and free resources based on robust technology and Internet access in cloud computing. Users can also keep doing, gives a very reliable and inexpensive. Therefore, users of cloud computing are now evolving for every department of the organization, government and education for increased use of cloud services. Then, when they come in a variety of user demand for cloud resources, we can use load balancing technology to meet user needs. The use of the load balancing process is useful for distributing load over load to another node to adjust. The aim of load balancing is to balance virtual machines that should not be under load or overload. In this paper, propose two algorithms for the balance of load balancing algorithm including the workload of cloud system. In our work, there are basically low load systems and virtual machine demand will increase. The minimum finish time would be to put everything on one virtual machine and see the lowest number of high priority virtual machines. Improvements will be made to the importance of our research system, better use of resources and reduced full-time. Propose paper want the ClaudSim cloud platform to use a simulator. Also use unit response time in a VM as a metric, full implementation, and operation. Events demonstrate the features of the proposed algorithm running in a virtual machine to be used on experimental scale and for better application performance as possible.
Measure Students’ Contribution in Web Programming Projects by Exploring Source Code Repository

Large scale software systems are usually developed by multiple teams. Communication and collaboration skills are required as essential skills of qualified developers in the modern environment, in addition to the technical hard skills of software engineering. Therefore, group projects are included in almost courses to train students' professional skills and teamwork abilities. However, in their group projects, most of the students are only required to accomplish functional requirements with limited awareness about the quality of the software. This issue seriously affects the development speed, since it is difficult for team members to read others' code during the development process. Hence, we propose a quality-driven assessment system for group projects with the capability of checking the coding style after each submission and providing immediate feedback to help student teams maintain the code quality for their group projects. Besides, to objectively assess the contribution of all students in group projects, we propose a set of repository mining metrics including two major categories of productivity and quality. A case study about the usage of the system and the proposed contribution evaluation metrics are introduced to illustrate the feasibility of our approach.
A Framework for Automatic Failure Recovery in ICT Systems by Deep Reinforcement Learning

Because automatic recovery from failures is of great importance for future operations of ICT systems, we propose a framework for learning a recovery policy using deep reinforcement learning. In our framework, while iteratively trying various recovery actions and observing system metrics in a target system, an agent autonomously learns the optimal recovery policy, which indicates what recovery action should be executed on the basis of observations. By using failure injection tools designed for Chaos Engineering, we can reproduce many types of failures in the target system, thereby making the agent learn a recovery policy applicable to various failures. Once the recovery policy is obtained, we can automate failure recovery by executing recovery actions that the recovery policy returns. Unlike most previous methods, our framework does not require any historical documents of failure recovery or modeling of system behavior. To verify the feasibility of the framework, we conducted an experiment using a container-based environment built on a Kubernetes cluster, demonstrating that training converges in a few days and the obtained recovery policy can successfully recover from failures with a minimum number of recovery actions.
AdaPrune: An Accelerator-Aware Pruning Technique for Sustainable CNN Accelerators

Convolutional neural network (CNN) accelerators have achieved great success from cloud to edge scenarios. However, given the trend towards even larger and deeper neural network models, it remains a challenging problem to efficiently process these CNNs especially on edge devices with limited energy budget. Accordingly, reducing the energy consumption is of paramount importance for sustainable CNN accelerators. In this paper, we propose AdaPrune, a novel pruning technique that reduces model size and computation to achieve performance improvement and energy savings for CNN accelerators. Unlike previous pruning techniques that sacrifice either computational regularity or accuracy, AdaPrune maintains both by customizing CNN pruning for the underlying accelerators to maximally leverage the sparsity benefits. AdaPrune consists of two techniques: input channel group pruning and output channel group pruning. By analyzing the weight fetching patterns of sparse CNN accelerators, AdaPrune adaptively switches between the two techniques to guarantee that the zeros are evenly distributed in each fetching group. In doing so, the pruned network structure preserves customized computational regularity for the underlying accelerators, thereby boosting the performance and energy efficiency. We evaluate AdaPrune on three sparse CNN accelerators with different spatial tiling strategies. The experimental results show that AdaPrune achieves up to 1.6&#x00D7; performance speedup, and 1.5&#x00D7; energy savings compared to unstructured pruning.
Intelligent Target Visual Tracking and Control Strategy for Open Frame Underwater Vehicles

SUMMARY Visual tracking is an essential building block for target tracking and capture of the underwater vehicles. On the basis of remotely autonomous control architecture, this paper has proposed an improved kernelized correlation filter (KCF) tracker and a novel fuzzy controller. The model is trained to learn an online correlation filter from a plenty of positive and negative training samples. In order to overcome the influence from occlusion, the improved KCF tracker has been designed with an added self-discrimination mechanism based on system confidence uncertainty. The novel fuzzy logic tracking controller can automatically generate and optimize fuzzy rules. Through Q-learning algorithm, the fuzzy rules are acquired through the estimating value of each state action pairs. An S surface based fitness function has been designed for the improvement of learning based particle swarm optimization. Tank and channel experiments have been carried out to verify the proposed tracker and controller through pipe tracking and target grasp on the basis of designed open frame underwater vehicle.
Trust Model to Minimize the Influence of Malicious Attacks in Sharding Based Blockchain Networks

A sharding mechanism could potentially be the solution to enhance the scalability of blockchain networks and makes the distributed ledger technology more feasible. Despite the scalability improvement, it increases the influence of malicious attacks on blockchain networks. We develop a comprehensive trust model by enhancing the trust score of nodes to minimize the adversary influences of malicious attacks in sharding based blockchain networks. Firstly, a penalty factor is incorporated into this trust model to decrease the probability of malicious nodes becoming leaders in the shards. Then, we examine the leader selection probability for varying penalty factors. We also observe the influence of the global reputation on the trust score for a varying number of nodes. Secondly, we increase the trustworthiness of nodes by including penalty factors and reputation scores to nodes that could then identify the malicious influence. The fair node distribution among shards is achieved by distributing the nodes with the same aggregated trustworthiness scores. Finally, we develop a probability distribution model to identify the probabilities of clustering corrupted nodes into single shards and the existence of such corrupted shards in the entire network. Uncorrupted or honest shard probability is shown to be higher in the RapidChain than the Elastico and OmniLedger sharding protocols. This could be as a result of the shard resiliency of the RapidChain (υ/2) protocol being more significant than that of the Elastico (υ/3) and in OmniLedger (υ/3) protocols. Low message complexity of single intra-shard consensus of the RapidChain protocol O(υ) may contribute to perform security algorithms more efficiently than that of the Elastico O(υ2) and OmniLedger O(υ) sharding protocols. The probabilities of clustering corrupted nodes into single shards can be estimated, and the existence of such corrupted shards in entire networks can be identified using the proposed model.
On the Construction of a Post-Quantum Blockchain

Consensus and transaction are the main parts in a blockchain system. However, most cryptographic protocols used in these parts of current blockchains are vulnerable to rapid development of quantum computers. Besides, traditional proof of work (PoW) based consensus protocols such as Bitcoin can not supply memory mining. And the transaction capacity of each block in a blockchain is limited that need to be expanded. In this paper, a new post-quantum poof of work (PoW) consensus algorithm is proposed, which can be used to not only protect the blockchain under the quantum computing attack over existing classical hash based PoW algorithms but also can supply memory mining. Meanwhile, a identity-based post-quantum signature is embed into a transaction process so as to construct lightweight transactions. We thereafter give a detailed description on how the post-quantum lightweight transaction in a blockchain runs. All in all, this work can help to enrich the research on the future post-quantum blockchain (PQB).
Remote Sensing Image Captioning with SVM-Based Decoding

With the fast development of remote sensing (RS) technology we are now able to acquire high resolution images. To cope with the new challenges of analyzing such images, a recently introduced tool is RS image captioning (IC). With respect to conventional techniques such as scene classification, RSIC provides more information about an image. It aims to generate a description that summarizes the content of an image. Most of RSIC systems are based on deep learning frameworks (encoder-decoder). The performance of these frameworks strongly depend on the number of annotated samples used during training. In this paper, we propose an alternative RSIC system for a relatively small dataset based on support vector machines (SVMs). A pre-trained CNN is used to extract the image visual features and a network of SVMs is used to generate the descriptions. Experimental results on a RS image archive composed of images acquired by unmanned aerial vehicles (UAV), show that the proposed IC system could be an interesting alternative to deep learning frameworks when only small training samples are available.
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving

Extrinsic perturbation always exists in multiple sensors. In this paper, we focus on the extrinsic uncertainty in multi-LiDAR systems for 3D object detection. We first analyze the influence of extrinsic perturbation on geometric tasks with two basic examples. To minimize the detrimental effect of extrinsic perturbation, we propagate an uncertainty prior on each point of input point clouds, and use this information to boost an approach for 3D geometric tasks. Then we extend our findings to propose a multi-LiDAR 3D object detector called MLOD. MLOD is a two-stage network where the multi-LiDAR information is fused through various schemes in stage one, and the extrinsic perturbation is handled in stage two. We conduct extensive experiments on a real-world dataset, and demonstrate both the accuracy and robustness improvement of MLOD. The code, data and supplementary materials are available at: https://ram-lab.com/file/site/mlod.
Airborne geophysical characterization of Uruguayan basement

The integrated observation of geology, gammaspectrometry and magnetometry of southern Uruguay was made possible by the high-resolution aerogeophysical survey carried out during 2014 and 2015. This survey covers nearly the outcropping area of the Uruguayan basement. Previous studies focused on the Proterozoic and Mesozoic dike swarms present in the Uruguayan basement. In this work we address features previously undescribed and unstudied in the Uruguayan basement. Structures previously grouped with the dike swarm are separated (subcircular structures and a lava river) and the basement general patterns were studied. Distinctive magnetic and radiometric features characterize each structural block in the Uruguayan basement, supporting the main tectonic units previously described, but improving the location of their limits.
General-Sum Multi-Agent Continuous Inverse Optimal Control

Modeling possible future outcomes of robot-human interactions is of importance in the intelligent vehicle and mobile robotics domains. Knowing the reward function that explains the observed behavior of a human agent is advantageous for modeling the behavior with Markov Decision Processes (MDPs). However, learning the rewards that determine the observed actions from data is complicated by interactions. We present a novel inverse reinforcement learning (IRL) algorithm that can infer the reward function in multi-agent interactive scenarios. In particular, the agents may act boundedly rational (i.e., sub-optimal), a characteristic that is typical for human decision making. Additionally, every agent optimizes its own reward function which makes it possible to address non-cooperative setups. In contrast to other methods, the algorithm does not rely on reinforcement learning during inference of the parameters of the reward function. We demonstrate that our proposed method accurately infers the ground truth reward function in two-agent interactive experiments. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Leveraging auxiliary image descriptions for dense video captioning

Collecting textual descriptions is an especially costly task for dense video captioning, since each event in the video needs to be annotated separately and a long descriptive paragraph needs to be provided. In this paper, we investigate a way to mitigate this heavy burden and propose to leverage captions of visually similar images as auxiliary context. Our model successfully fetches visually relevant images and combines noun and verb phrases from their captions to generating coherent descriptions. To this end, we use a generator and discriminator design, together with an attention-based fusion technique, to incorporate image captions as context in the video caption generation process. The experiments on the challenging ActivityNet Captions dataset demonstrate that our proposed approach achieves more accurate and more diverse video descriptions compared to the strong baseline using METEOR, BLEU and CIDEr-D metrics and qualitative evaluations.
Multiplicative Controller Fusion: Leveraging Algorithmic Priors for Sample-efficient Reinforcement Learning and Safe Sim-To-Real Transfer

Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intractable, and transferring a learned policy from simulation to reality is still extremely challenging. We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment. During training, our gated fusion approach enables the prior to guide the initial stages of exploration, increasing sample-efficiency and enabling learning from sparse long-horizon reward signals. Importantly, the policy can learn to improve beyond the performance of the sub-optimal prior since the prior's influence is annealed gradually. During deployment, the policy's uncertainty provides a reliable strategy for transferring a simulation-trained policy to the real world by falling back to the prior controller in uncertain states. We show the efficacy of our Multiplicative Controller Fusion approach on the task of robot navigation and demonstrate safe transfer from simulation to the real world without any fine-tuning. The code for this project is made publicly available at https://sites.google.com/view/mcf-nav/home.
Privacy-Preserving Ride-Hailing with Verifiable Order-Linking in Vehicular Networks

Ride-hailing is a favored vehicular service model where drivers can deliver convenient rides to waiting riders via responding to a road-side unit or a ride-hailing service provider. However, previous works did not consider the order-linking function where a rider Cathy waving for a ride will be matched to a driver Bob in service with rider Alice whose destination is close to the start point of Cathy. Furthermore, a malicious matching executor could collude with an appointed driver to interfere with the matching process, which causes service unfairness and has not been addressed before. To mitigate these limitations, we first propose a privacy-preserving ride-hailing scheme OLink with the verifiable order-linking property. Specifically, we adopt road network partitioning and range query to achieve basic user matching. The user matching process supports range conditions and protects users' privacy. Next, a Proof-of-Linking protocol is designed based on the zero-knowledge succinct non-interactive argument of knowledge, zero-knowledge proof, and Bloom filters to enable the driver in service to generate three consecutive proofs for linking a current order to the next rider's order in advance; the proofs will be released such that anyone can verify the proofs and matching fairness is guaranteed. Finally, we formally prove the privacy and security of OLink, and then evaluate its performance with PySNARK to demonstrate feasibility and efficiency.
Do Data Almost Always Eventually Leak?

Shannon’s information theory states that there is a maximum rate for transmitting data reliably, implying that data almost always inevitably leak. Although there has been advancement in data protection technologies, a lack of understanding about data leaks and human error persists.
Post-hoc Explanation Options for XAI in Deep Learning: The Insight Centre for Data Analytics Perspective

This paper profiles the recent research work on eXplainable AI (XAI), at the Insight Centre for Data Analytics. This work concentrates on post-hoc explanation-by-example solutions to XAI as one approach to explaining black box deep-learning systems. Three different methods of post-hoc explanation are outlined for image and time-series datasets: that is, factual, counterfactual, and semi-factual methods). The future landscape for XAI solutions is discussed.
Regulatory Sandboxes and the Public Health

Recently, administrative agencies around the world have engaged in a grand experiment to regulate new technologies: regulatory sandboxes. Regulatory sandboxes allow developers, in cooperation with an agency, to conduct limited tests of new technologies in real-world settings for the purpose of generating and sharing information about them. Thus far, however, “regulatory sandboxes”—as named— appear almost exclusively in the context of financial technologies, or FinTech. Whether regulatory sandboxes, in fact, exist elsewhere in administrative law would be a significant finding for both regulators and scholars; it would blunt criticisms that agencies are slow to respond to new technologies, provide regulators with an additional tool for governing new technologies, and suggest that lessons learned from current regulatory sandboxes are applicable elsewhere. This Article is the first to explore this broader view of regulatory sandboxes and develop a synoptic theory of them. To do so, it uses one of the most radical programs to introduce new technologies in US history: the US Food and Drug Administration’s (FDA’s) Emergency Use Authorization (EUA) program for COVID-19 treatments and vaccines. EUAs—like regulatory sandboxes but in stark contrast to typical FDA approval processes—focus on real-world deployment as a means for information gathering. EUAs are also technologically flexible and crafted with close input from the developer, among other features. Generalizing FDA’s experience with EUAs also provides lessons about the intersection of regulatory sandboxes with public trust in the agency, political interference, and the maintenance of regulatory standards. At the same time, FDA’s COVID–19 EUAs are exceptional in two senses: they touch upon the public health, widely considered to be exceptional subject matter in administrative law; and arose in the context of unprecedented global pandemic. Nonetheless, FDA’s experience with EUAs suggest regulatory sandboxes may be an underexplored and undertheorized feature of administrative governance of new technologies. Future work in the area should assess whether regulatory sandboxes exist under the rubric identified here, which technologies they regulate, and how those sandboxes operate.