Reviewerly/exHarmony huggingface dataset:
How to Train Your Guide Dog: Wayfinding and Safe Navigation with Human-Robot Modeling

A robot guide dog has the potential to enhance the independence and quality of life of individuals who are blind or visually impaired by providing accessible, automated, and intelligent guidance. However, developing effective robot guide dogs requires researchers not only to solve robotic perception and planning problems but also to understand complicated two-way interactions of the human-robot team. This work presents the formal definition of the wayfinding task of the robotic guide dog that is grounded by common practices in the real world. Given such a task, we train an effective policy for the robot guide dog while investigating two different human models, a rotating rod model and a rigid harness model. We show that our robot can safely guide a human user to avoid several obstacles in the real world. We also demonstrate that a proper human model is necessary to achieve collision-free navigation for both the human and the robot.
PT-FlowNet: Scene Flow Estimation on Point Clouds With Point Transformer

As a low-level task of 3D perception, scene flow is a fundamental representation of dynamic scenes and provides non-rigid motion descriptions for the objects in the 3D environment, which can strongly support many upper-level applications. Inspired by the revolutionary success of deep learning, many attention-based neural networks have recently been proposed to estimate scene flow from consecutive point clouds. However, extracting effective features and estimating accurate point motions for irregular and occluded point clouds remains a challenging task. In this letter, we propose PT-FlowNet, the first end-to-end scene flow estimation network embedding the point transformer (PT) into all functional stages of the task. In particular, we design novel PT-based modules for point feature extraction, iterative flow update, and flow refinement stage to encourage effective point-level feature aggregation. Experimental results on FlyingThings3D and KITTI datasets show that our PT-FlowNet achieves state-of-the-art performance. Trained on synthetic data only, our PT-FlowNet can generalize to real-world scans and outperforms the existing methods by at least 36.2% for the EPE3D metric on the KITTI dataset.
A Heuristic-Guided Dynamical Multi-Rover Motion Planning Framework for Planetary Surface Missions

We present a heuristic-guided multi-robot motion planning framework that solves the problem of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> dynamical agents visiting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> unlabeled targets in a partially known environment for planetary surface missions without solving the two-point boundary value problem (BVP). The framework design is motivated by typical planetary surface mission constraints of limited power, limited computation, and limited communication. The framework maintains a centralized, dynamically updated probabilistic roadmap (PRM) that incorporates new obstacle updates as the agents move in the environment. The dynamic roadmap captures the changing obstacle topology and provides updated cost-to-go heuristics to accelerate each agent's independent single-query motion-planning process. The agents use a feasible sampling-based motion planner without computing the BVP while leveraging the roadmap heuristics to quickly plan and visit their assigned target. The agents handle robot-robot and robot-obstacle collision avoidance in a decentralized fashion. We conduct multiple simulation experiments using robots with non-linear dynamics to show our planner performs better in overall planning time and mission time than approaches not using the roadmap heuristic. We also field our algorithm on prototype rovers and demonstrate the viability of implementing our algorithm on real-world hardware platforms.
Using Blockchain for Enabling Transparent, Traceable, and Trusted University Ranking Systems

Ranking systems have proven to improve the quality of education and help build the reputation of academic institutions. Each of the current academic ranking systems is based on different methodologies, criteria, and standards of measurement. Academic and employer reputations are subjective indicators of some rankings determined through surveying that is neither transparent nor traceable. The current academic ranking systems fall short of providing transparency and traceability features for both subjective and objective indicators that are used to calculate the ranking. Also, the ranking systems are managed and controlled in a centralized manner by specific entities. This raises concerns about fairness and trust. In this paper, we propose a blockchain-based solution to enable transparent, traceable, trusted, and decentralized academic ranking systems. We develop smart contracts to automatically govern entity interactions according to set policies. We leverage decentralized storage, oracles, and threshold encryption to securely fetch, store, and share institutions’ ranking data. We present algorithms along with their implementation and testing details, as well as validate smart contracts. The proposed solution is evaluated in terms of cost, throughput and latency, and security to show its affordability, resiliency against attacks, and superiority. All developed smart contract codes are made publicly available on GitHub.
A Rough Set Framework for Multihuman Tracking in Surveillance Video

MultiHuman Tracking (MHT) has become a key focus area in video surveillance applications. Several research works have been carried out in MHT in the past two decades. The techniques such as the Kalman filter, particle filter, Markov chain process, blob detection, and so on have been used to detect and track humans. However, tracking human objects in consecutive frames is still a challenging problem due to spatial disorder, nonlinear motion, and occlusion of human objects. Also, human object labeling becomes difficult since most of the similarity measures used in the classification process do not consider the positional coordinates while computing the similarity. This article addresses the above challenges by introducing a rough set framework that identifies the human objects using the modified bounding box generation technique and by applying the rough set classifier. The framework was tested on the benchmark dataset PETS09, and the experimental results were compared with One-Class Extreme Learning Machine (OCELM), Multi-Object Detection and Tracking (MODT), and Multi-Person Tracking in Smart Surveillance System (MPTSSS) algorithms. The comparative analysis shows that the rough set framework outperforms the existing algorithms in terms of detection and tracking accuracy even in the cases of overlapped and hidden human objects.
A Survey of Data Pricing for Data Marketplaces

A data marketplace is an online venue that brings data owners, data brokers, and data consumers together and facilitates commoditisation of data amongst them. Data pricing, as a key function of a data marketplace, demands quantifying the monetary value of data. A considerable number of studies on data pricing can be found in literature. This article attempts to comprehensively review the state-of-the-art on existing data pricing studies to provide a general understanding of this emerging research area. Our key contribution lies in a new taxonomy of data pricing studies that unifies different attributes determining data prices. The basis of our framework categorises these studies by the kind of market structure, be it sell-side, buy-side, or two-sided. Then in a sell-side market, the studies are further divided by query type, which defines the way a data consumer accesses data, while in a buy-side market, the studies are divided according to privacy notion, which defines the way to quantify privacy of data owners. In a two-sided market, both privacy notion and query type are used as criteria. We systematically examine the studies falling into each category in our taxonomy. Lastly, we discuss gaps within the existing research and define future research directions.
Antiquantum Privacy Protection Scheme in Advanced Metering Infrastructure of Smart Grid Based on Consortium Blockchain and RLWE

Smart meters in advanced metering infrastructure (AMI) collect fine-grained, real-time power consumption data may result in user privacy leakage. Aiming at the problem that existing privacy protection schemes cannot resist quantum attacks, an antiquantum privacy protection scheme in AMI of smart grid based on consortium blockchain and ring learning with errors (RLWE) is proposed. The scheme has the following contributions. First, the RLWE-based secure communication scheme is designed in this article, which ensures the resistance of the scheme to quantum attacks. Second, all smart meters in AMI use anonymous identity to communicate with the control center, and the fine-grained power consumption data is also encrypted and digitally signed to prevent user privacy leakage and tampering of power consumption data. And the scheme does not use bilinear pairings and exponential operations and has efficient computational performance. Third, a two-tier consortium blockchain storage system consisted of regional gateways and data aggregators are designed to eliminate the single point of failure problems of the data aggregators. The practical byzantine fault tolerance consensus mechanism is improved to reduce the communication cost in the consensus process. The scheme's security is proven theoretically, and the experimental results show that the scheme has a low computational cost.
Poissonian Blurred Hyperspectral Imagery Denoising Based on Variable Splitting and Penalty Technique

Poisson noise is one of the significant sources of noise present in hyperspectral imagery (HSI). In most of the existing denoising methods, Poisson noise is first transformed into Gaussian noise through the Anscombe transform and then removed. However, the use of Anscombe transform can give rise to transform errors that affect the final denoising results. In addition, blurs often contaminate the HSI during the imaging procedure, which makes it more difficult to remove the Poisson noise. In view of the above problems, under the maximum a posteriori (MAP) model, we propose a Poissonian blurred HSI denoising based on variable splitting and penalty technique (named as VSPT) to directly remove the Poissonian blurred HSI noise without using the Anscombe transform. By finding the minimum value of the negative logarithmic Poisson log-likelihood combined with the total variation (TV), the proposed method transforms the problem into two sub-problems, which are easier to solve 1) a TV regularized deconvolution problem and 2) an ordinary convex optimization problem. The experimental results show that the proposed VSPT method can effectively remove Poisson noise in HSI contaminated by blurs during the imaging procedure.
Configuration and Placement of Serverless Applications Using Statistical Learning

In the last decade, serverless computing emerged as a new compelling paradigm for the deployment of cloud applications and services. It represents an evolution of cloud computing with a simplified programming model, that aims to abstract away most operational concerns. Running serverless applications requires users to configure multiple parameters, such as memory, CPU, cloud provider, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . While relatively simpler, configuring such parameters correctly while minimizing cost and meeting delay constraints is not trivial. In this paper, we present COSE, a framework that uses Bayesian Optimization to find the optimal resource configuration and placement for functions in a serverless application. COSE uses statistical learning techniques to intelligently collect samples and predict the cost and execution time of a serverless function across unseen configuration values. Our framework uses the predicted cost and execution time on available locations to select the “best” configuration parameters and placement for running a serverless application while satisfying customer objectives. We evaluate COSE on AWS Lambda with real-world applications consisting of multiple functions (both linear chains and service graphs), where we successfully found optimal/near-optimal configurations. We also evaluate COSE over a wide range of simulated distributed cloud environments that confirm the efficacy of our approach.
The Construction of Knowledge Graphs in the Aviation Assembly Domain Based on a Joint Knowledge Extraction Model

The aviation assembly domain, which is a complex system, involves the multi-dimensional information of parts, processes, tools, plants and operation projects. In order to assist the knowledge management from natural language text in the aircraft manufacturing process, this paper proposes the corresponding ontology scheme and the joint knowledge extraction model, which is necessary for construct the knowledge graph in the aviation assembly domain. The model is able to automated end-to-end construct knowledge graph. The proposed model, which is based on reinforcement learning approach and a novel labeling scheme, takes the constraint relationships between entities and relations as an important identification basis. The model does not rely on manual feature setting, while it greatly reduces the training cost. The proposed joint knowledge extraction model was testified from the practical scenarios of the general assembly and component assembly. The experimental results showed that the proposed model showed an excellent performance in the aviation assembly domain, with the F1-score of 89.71% for entities, the F1-score of 91.27% for relations, and the overall average F1-score of 82.41%. Based on the superior performance of the model, the knowledge graph of the general assembly and component assembly, which included 1, 308 pairs of triples composed of five kinds of entities and three kinds of relations, was further constructed in the aviation assembly domain.
Toward Automated Right Ventricle Segmentation via Edge Feature-Induced Self-Attention Multiscale Feature Aggregation Full Convolution Network

In the field of cardiac magnetic resonance (MR) image analysis, the accurate segmentation of right ventricle (RV) regions plays an important role in the quantitative examination and medical diagnosis of various cardiovascular diseases. However, the automated RV segmentation in cardiac MR images is still challenging, due to its obscure and ill-defined boundaries, variably crescent-shaped structures, and extremely-unbalanced area ratio between the RV region and the background. In this work, an edge feature-induced self-attention multi-scale feature aggregation full convolutional neural network, called EFiSaMsFAFUnet, is proposed, to address the RV segmentation tasks. Specifically, EFiSaMsFAFUnet introduces an edge feature extraction module (EFEM) to mine the crescent-shaped boundary features of the RV area, and a kind of self-attention multi-scale feature expansion block (SaMsFEB) is proposed to mine the internal structure characteristics of the crescent-shaped RV internal regions. In addition, a kind of composite loss function is used to address the problem of model degradation caused by the proportion imbalance between RV region and background. The proposed EFiSaMsFAFUnet was evaluated on the MICCAI2017 Automatic Cardiac Diagnosis Challenge (ACDC) dataset. Extensive confirmatory and comparative experiments show that the EFiSaMsFAFUnet can achieve better segmentation results than the representatively state-of-the-art methods, and it can achieve comparable or the closest segmentation results to the manual segmentation of clinical specialist.
Fusion-based Music Recommender System Using Music Affective Space based on Serendipity

In recent years, as the popularity of music distribution services has increased, research on music recommendation has become more active. In particular, factors not only accuracies but also contextual information such as user’ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> emotion and “serendipity” are also considered necessary to improve the quality of music recommendations and attract attention. Serendipity is defined as novelty, unexpectedness, and preference; these items are considered important factors as evaluation criteria for recommender systems. In this paper, we propose a system that recommends music, named FUSION MUSIC, based on the affective information of two favorite music the user selects. The FUSION MUSIC is based on two concepts; one is a music affective space that reflects the affective information of each music, and another is a fusion-based music recommendation method that creates a partial affective space within that space and recommends serendipitous music. We finally developed FUSION MUSIC using these concepts and the Spotify API. The results of the evaluation experiment showed that FUSION MUSIC has the potential to recommend more serendipitous music compared to Spotify. For the proposed system, we will investigate more detailed validation methods in the future.
An Innovative Hashgraph-based Federated Learning Approach for Multi Domain 5G Network Protection

Federated Learning (FL) is a decentralized learning approach, meaning it learns from data housed locally on devices such as tablets, cellular phones, and more, and does not collect nor transfer user-sensitive data but merely learns from the data utilizing a shared model and sending periodical updates. Using federated learning throws out the problems associated with user privacy and the high bandwidth needed to transmit resource-intensive files to a central server for training. However, FL systems may be compromised to make a wrong decision or disclose private data once the attacker modifies the FL model and/or its paraments. The main contribution of this paper includes (1) introducing a comprehensive study that explores the FL and how it applies to different domains like healthcare and medicine, Insurance and Finance, Robotics and Autonomous Systems, Virtual Reality, and 5G. (2) Develop a Hashgraph-based federated learning Approach (HFLA) to protect the 5G network against poisoning and membership inherence attacks. The HFLA was evaluated using our Federated 5G testbed and proved its superiority compared to other existing FL approaches.
Efficient Transfer Learning in 6G

6G networks will greatly expand the support for data-oriented, autonomous applications for over the top (OTT) and networking use cases. The success of these use cases will depend on the availability of big data sets which is not practical in many real scenarios due to the highly dynamic behavior of systems and the cost of data collection procedures. Transfer learning (TL) is a promising approach to deal with these challenges through the sharing of knowledge among diverse learning algorithms. with TL, the learning rate and learning accuracy can be considerably improved. There are implementation challenges to efficiently deploy and utilize TL in 6G. Here, we initiate this discussion by providing some performance metrics to measure the TL success. We show how infrastructure, application, management, and training planes of 6G can be adapted to handle TL. We provide examples of TL in 6G and highlight the spatio-temporal features of data in 6G that can lead to efficient TL. By simulations, we demonstrate how transferring the quantized neural network weights between two use cases can make a trade-off between overheads and performance and attain more efficient TL in 6G.
A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks

With the rapid development and integration of artificial intelligence (AI) methods in next-generation networks (NextG), AI algorithms have provided significant advantages for NextG in terms of frequency spectrum usage, bandwidth, latency, and security. A key feature of NextG is the integration of AI, i.e., self-learning architecture based on self-supervised algorithms, to improve the performance of the network. A secure AI-powered structure is also expected to protect N extG networks against cyber-attacks. However, AI itself may be attacked, i.e., model poisoning targeted by attackers, and it results in cybersecurity violations. This paper proposes an AI trust platform using Streamlit for N extG networks that allows researchers to evaluate, defend, certify, and verify their AI models and applications against adversarial threats of evasion, poisoning, extraction, and interference.
QUIC Bitcoin: Fast and Secure Peer-to-Peer Payments and Payment Channels

Blockchain technologies allow for records to be publicly secured, without the need of a trusted authority. The Bitcoin white paper introduced blockchain technology in 2008, and Bitcoin is currently the most widely adopted protocol for this type of technology. One of the most common use-cases for Bitcoin is to use the underlying data structure as a public ledger for an electronic cash system. Using this public ledger, Bitcoin protocol allows direct transfer of value between peers (users or machines). The transfer of value occurs via transactions published on the ledger. Alternatively, transactions can be exchanged directly, peer-to-peer, and published on the ledger at a later stage. The latter approach improves transfer speed and system efficiency (e.g. bandwidth and data storage). Due to its vulnerability to man-in-the-middle attacks, however, the direct exchange of unpublished transactions was abandoned in the early days. In this paper, we propose a new method to send and receive unpublished Bitcoin transactions (i.e. peer-to-peer) using a recently introduced network protocol called QUIC, an alternative to TCP. QUIC allows for fast handshakes, secure and authenticated sessions, and it supports network switches (e.g. Wi-Fi to 5G). Using this protocol, we introduce a new type of peer-to-peer Bitcoin transactions, called QUIC Bitcoin Transactions. QUIC Bitcoin Transactions provide a secure and fast way to exchange unpublished transactions between users and machines. Finally, we introduce QUIC Bitcoin Channels, fast and encrypted Bitcoin payment channels. QUIC Bitcoin Transactions and QUIC Bitcoin Channels guarantee secure and encrypted peer-to-peer transactions, and they can be widely adopted without new hardware requirements, as they are implemented in TCP/IP application space.
UniPseudo: A universal pseudoword generator

Pseudowords are letter strings that look like words but are not words. They are used in psycholinguistic research, particularly in tasks such as lexical decision. In this context, it is essential that the pseudowords respect the orthographic statistics of the target language. Pseudowords that violate them would be too easy to reject in a lexical decision and would not enforce word recognition on real words. We propose a new pseudoword generator, UniPseudo, using an algorithm based on Markov chains of orthographic n-grams. It generates pseudowords from a customizable database, which allows one to control the characteristics of the items. It can produce pseudowords in any language, in orthographic or phonological form. It is possible to generate pseudowords with specific characteristics, such as frequency of letters, bigrams, trigrams, or quadrigrams, number of syllables, frequency of biphones, and number of morphemes. Thus, from a list of words composed of verbs, nouns, adjectives, or adverbs, UniPseudo can create pseudowords resembling verbs, nouns, adjectives, or adverbs in any language using an alphabetic or syllabic system.