Reviewerly/exHarmony huggingface dataset:
Towards an Online RRT-based Path Planning Algorithm for Ackermann-steering Vehicles

It is challenging to develop an online path planning algorithm for Ackermann-steering vehicles to find collision-free and kinematically-feasible paths, that is efficient for dense environments, adaptable to various environments, and suitable for environments with narrow passages. In this paper, we propose a kinematically constrained RRT-based path planning algorithm integrating with a trajectory parameter space (TP-space) with three novel improvements to meet the above requirements. In specific, we introduce a new way to choose candidate nodes to expand the tree for an RRT-based algorithm, which can significantly increase the success rate of the expansion and improve the efficiency of the algorithm. We also introduce a procedure to incrementally adjust the step size for the expansion, which enables the algorithm to automatically adapt to various environments. At last, we integrate rapidly-exploring random vines (RRV) with a TP-space to handle kinematic constraints and improve the performance of the algorithm to expand the tree through a narrow passage. We also prove that the algorithm is probabilistic complete and asymptotically near-optimal. An ablation study shows that all three improvements can notably improve the performance of the RRT-based path planning algorithm. We also evaluate the algorithm in various environments. The experimental results show that our algorithm achieves competitive performance compared with the state-of-the-art. The source code is available at https://github.com/PengJieb/fastbkrrt.
Implementation of a Multiple Target Tracking Filter on an Adiabatic Quantum Computer

Recent work at Fraunhofer FKIE shows that More-field&#x0027;s method for multiple target data association can in theory be solved on an adiabatic quantum computer. The present paper validates the theory and examines the significant limitations of currently available adiabatic quantum computers for solving the data association problem. The limitations of such architectures are both theoretical and practical in nature, and both are discussed. The data association problem is formulated as a quadratic unconstrained binary optimization (QUBO) problem; consequently, much of the discussion is relevant to other applications which are, or can be, posed as QUBO problems.
Novel Non-cryptographic Hash Functions for Networking and Security Applications on FPGA

This paper proposes the design and FPGA implementation of five novel non-cryptographic hash functions, that are suitable to be used in networking and security applications that require fast lookup and/or counting architectures. Our approach is inspired by the design of the existing non-cryptographic hash function Xoodoo-NC, which is constructed through the concatenation of several Xoodoo permutations. We similarly construct non-cryptographic hash functions based on the concatenation of several rounds of symmetric-key ciphers. The goal is to achieve high performance in combination with good avalanche properties, which are required in order to have a significant change in the output value as a result of a limited change in the input value. We simulate how many rounds are needed to achieve satisfactory avalanche scores and we implement the corresponding non-cryptographic hash functions on an FPGA to evaluate the occupied resources and the performance. One of the proposed non-cryptographic hash functions, namely GIFT-NC, outperforms all previously proposed non-cryptographic hash functions in terms of throughput and latency, in exchange for an acceptable increase in FPGA resources.
An adversarial learning framework with cross-domain loss for median filtered image restoration and anti-forensics

As an ill-posed problem, median filtered (MF) image restoration and anti-forensics still face a challenge in improving the visual quality and deceiving the forensic detectors simultaneously. MF image restoration mainly aims at image visual quality recovery, while MF image anti-forensics primarily focuses on deceiving the existing forensic algorithms. Median filtering is popularly used as an operation for image denoising and smoothing. It is also employed by image anti-forensics to conceal or remove the traces of other image manipulating operations, such as JPEG blocking artifacts and the periodicity introduced by image resampling. This work proposes an adversarial learning framework that introduces loss terms spanning three domains for MF image restoration and anti-forensics. The differences of high-frequency coefficients in the discrete wavelet transform (DWT) domain are introduced as a term of loss term to restore high-frequency components. Moreover, high-frequency subband variance loss in the discrete cosine transform (DCT) domain is proposed to make the statistical characteristics of the generated image closer to that of the original image. Together with the Huber loss in the spatial domain, a novel generator network is developed and trained to enhance information flow and feature reuse. A capsule network is applied as a discriminator for differentiating the generated image from the original version. We explore a solution through adversarial learning in an infinite set of possible solutions. Experimental results demonstrate that the proposed method is able to restore the visual quality of MF images with salt and pepper noise previously more effectively than the existing methods. What is more, our method achieves the best anti-forensic performance in comparison with the existing state-of-the-art MF image anti-forensic methods, while enhancing the quality of anti-forensically restored images.
Path Tracking Control of Tracked Paver Based on Improved Pure Pursuit Algorithm

This paper investigates the high-precision path tracking control of tracked paver combined with global satellite navigation system. As the paver is performing paving operations, it requires high path tracking accuracy and good vehicle stability. Therefore, considering the influence of road curvature on path tracking accuracy and vehicle stability, and the situation that the vehicle can not move quickly to the expectation path when the lateral position of the vehicle deviates from the expectation path, this paper proposes a lateral path tracking control method based on improved Pure Pursuit algorithm. The control method is verified through experiments. The experimental results show that the maximum lateral tracking error of the improved algorithm is 0.04 m, which is 55.56% lower than that of the original algorithm, and the average lateral tracking error is 0.02 m, which is 60% lower than that of the original algorithm. The purpose of high-precision path tracking of the paver is realized.
Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast

This paper considers the problem of multi-modal future trajectory forecast with ranking. Here, multi-modality and ranking refer to the multiple plausible path predictions and the confidence in those predictions, respectively. We propose Social-STAGE, Social interaction-aware Spatio-Temporal multi-Attention Graph convolution network with novel Evaluation for multi-modality. Our main contributions include analysis and formulation of multi-modality with ranking using interaction and multi-attention, and introduction of new metrics to evaluate the diversity and associated confidence of multi-modal predictions. We evaluate our approach on existing public datasets ETH and UCY and show that the proposed algorithm outperforms the state of the arts on these datasets.
Collaborative Learning of Multiple-Discontinuous-Image Saliency Prediction for Drone Exploration

Most of the existing saliency prediction research focuses on either single images or videos (or more precisely multiple images in sequence). However, to apply saliency prediction to drone exploration that has to consider multiple images from different view angles or localizations to determine the direction to explore, saliency prediction over multiple discontinuous images is required. In this paper, we propose a deep relative saliency model (MS-Net) for such an application. MS-Net starts with a single-image saliency feature extraction network for each image separately and then integrate these images by using a GCN-based mechanism called multi-image saliency fusion that learns relative saliency information among all the images. Finally, it predicts the saliency of each image by considering the relative information. Because there are no existing saliency prediction datasets with such multiple discontinuous images, we randomly cropped a large number of sub-images from 360&#x00B0; images of the existing 360&#x00B0; image saliency datasets to build our own dataset for both training and evaluation. Experimental results showed that the proposed MSNet considerably outperformed both single-image and video saliency prediction methods and could achieve comparative performance to that of 360&#x00B0; image saliency prediction even with only limited field-of-views, i.e., five sub-images, considered.
Type-based analysis of logarithmic amortised complexity

We introduce a novel amortised resource analysis couched in a type-and-effect system. Our analysis is formulated in terms of the physicist's method of amortised analysis, and is potential-based. The type system makes use of logarithmic potential functions and is the first such system to exhibit *logarithmic amortised complexity*. With our approach we target the automated analysis of self-adjusting data structures, like splay trees, which so far have only manually been analysed in the literature. In particular, we have implemented a semi-automated prototype, which successfully analyses the zig-zig case of *splaying*, once the type annotations are fixed.
Influencing Factors on the Usefulness of an Earthquake Early Warning System during the 2017 Mexico City Earthquakes

The paper presents the results of an ongoing research project on the performance of the SASMEX system following the two earthquakes that hit Mexico City in 2017, (a) the 7 September, and (b) the 19 September. In an earlier work, some preliminary findings on the perceived usefulness of the system have been reported. The aim of the present study is the identification of the precursors leading to the outcome variable (‘Usefulness of the SASMEX system’). An ordinal logistic regression analysis has been conducted. The influential factors leading to (a) are the following: the 1985 earthquake experience, drills participation, current knowledge, knowledge on what to do, fear during the tremors, education level, earthquake severity impact. The identified influential factors leading to (b) are the following: knowledge vs. drills, fear of building collapsing, and warning time. The main conclusion of the study is that the outcome is warning time dependent following an event. When there is ‘enough time’ to take protective actions, such as in (a), the probability of the system being considered Useful is higher than otherwise. Conversely, if there is not enough warning time, such as in (b), the probability of the system being considered Not useful is higher than otherwise.
A survey on public blockchain-based networks: structural differences and address clustering methods

Bitcoin is the most representative UTXO-based blockchain platform, and many studies have been conducted related to it. However, account-based blockchains such as Ethereum are not yet profoundly analyzed. There is an urgent need to track all cryptocurrency transactions involved with illegal activities to deanonymize and identify malicious users. To link users' accounts to real identities in both networks, we first need to examine the differences between Ethereum and Bitcoin to propose an efficient deanonymizing method. Therefore, this paper compares and analyzes the wallet address clustering method of Bitcoin and Ethereum.
Incremental k-Nearest Neighbors Using Reservoir Sampling for Data Streams

The online and potentially infinite nature of data streams leads to the inability to store the flow in its entirety and thus restricts the storage to a part of – and/or synopsis information from – the stream. To process these evolving data, we need efficient and accurate methodologies and systems, such as window models (e.g., sliding windows) and summarization techniques (e.g., sampling, sketching, dimensionality reduction). In this paper, we propose, RW-kNN, a k-Nearest Neighbors (kNN) algorithm that employs a practical way to store information about past instances using the biased reservoir sampling to sample the input instances along with a sliding window to maintain the most recent instances from the stream. We evaluate our proposal on a diverse set of synthetic and real datasets and compare against state-of-the-art algorithms in a traditional test-then-train evaluation. Results show how our proposed RW-kNN approach produces high-predictive performance for both real and synthetic datasets while using a feasible amount of resources.
Improvement of Detection Accuracy of Aircraft in Remote Sensing Images Based on YOLOV5 Model

Recently, object detection has experienced great progress. However, in the field of remote sensing, it is still under investigation for real-time detection of small objects because of the limited resolution and information. To detect a predefined object, such as aircraft, is important in some applications. This paper aims to improve the detection accuracy of the aircraft. Meanwhile, the speed of detection is another factor to be considered. At present, the model of You Only Look Once v5s (YOLOv5s) for object detection can achieve a good trade-off between accuracy and speed. So, YOLOv5s is adopted in the study. The model is trained with 600 remote-sensing images from UCAS-AOD dataset and tested with another 400 images from the same dataset. The experimental results show that YOLOv5 achieves 99.3% AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> in 74 seconds. It proves this model can effectively detect aircrafts in real time and achieve high accuracy.
Oxymoroning Education: A Poem about Actualizing Affect for Public Good

An oxymoron is a self-contradicting or incongruous word or group of words as in Lord Byron’s (1788–1824) line from his satirical epic poem Don Juan; “melancholy merriment”, An oxymoron is a rhetorical and epigrammatic device for effect, often revealing paradox. The effect I aim for here is the actualization of affect; affect made relevant and useful for education as a public good. Oxymoroning as an immediate edging of knowledge into experience, hence a way to access a proto subjective level of the affective power of X. The prefix proto indicating the first, original or earliest. I ask how we can become materially identifiable subjects for one another and what would it take to move from a mechanistic approach to education to a more machinic one. It is a view of change that does not steal my powers or affective force away. Furthermore, are the abstractions one attempts to move from imitation to imagination abstract enough? I aim for expansions in our educational rationales for social and natural sustainability. It implies an educational philosophy of multiplicity ready to support and join a creative pluralism of organization and pedagogies and simultaneously counteract predetermined and controlling pluralism of organization and pedagogies. The overarching contribution of this poem is political, pragmatic and ethical and concerns the constitution of subjectivity for education in inter- and intra-generational perspectives through taking part in polysemantic ambiguity, envisioning a modest view to the child as a knowledgeable and connectable collective. Ultimately, a view of the child is our primary measurement indicator for educational quality. The competence most important to develop for educators is impression tenderness in order to meet the expressions of the child.
Hyperspectral Imagery Super-Resolution Based on Self-Calibrated Attention Residual Network

Hyperspectral remote sensing images are well-known for their abundant spectral characteristics to discriminate different object materials. However, due to the constraints of sensor limitations and exceedingly high acquisition costs, it is difficult to obtain high spatial resolution hyperspectral imagery. Though many methods have been focusing on the restoration of the spatial structure information, spectral information may be over-smoothed during such spatial super-resolution. In this paper, a novel self-calibrated attention residual network (SCARN) is proposed to increase spatial resolution of hyperspectral images while retain spectral consistency. In particular, a self-calibrated attention residual block (SCARB) is elaborately designed to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data. Concretely, self-calibrated convolution, instead of standard convolution, is adopted to adaptively construct long-range spatial and spectral dependencies around each spatial location of hyperspectral imagery, and attention module is inserted to improve the representation ability of spectral information. Finally, global and local residual connections are designed to ease the network training difficulty and maintain a higher restoration accuracy. Experimental results over two benchmark hyperspectral datasets demonstrate the effectiveness and superiority of the proposed SCARN method against the state-of-the-art methods.
Placemaking for Urban Sustainability

Engaging with urban sustainability requires not only changes in individual behavior, but also a collaborative process of decision making together with local constituents. In this paper, we argue for integrating principles of behavior change and gamification with urban participation strategies, to achieve long-term engagement of citizens. We present Smart Citizen, an app we designed by following a gradual process of engaging citizens through setting defaults, triggers and incentives, encouraging reflexivity, active participation and co-production. We evaluated Smart Citizen with 31 users who tested it for two weeks and found a positive attitude towards being more active and acting sustainably locally. Especially users who would be interested in the topic of sustainability but need more tools to establish behaviors had a positive attitude towards using Smart Citizen long-term.
Dual Adversarial Network Based on BERT for Cross-domain Sentiment Classification

Cross-domain sentiment classification uses useful information of the source domain to promote the classification accuracy of the target domain. Although previous approaches consider the effects of aspect information of the sentences, they lack the mechanism of syntactic constraints which may mistakenly assign irrelevant words to aspects. In this paper, we propose Dual Adversarial Network based on BERT (DAN-BERT), which can better transfer sentiment across domains by jointly learning the representation of sentences and aspect-based syntax. Specifically, DAN-BERT extracts the common features at the sentence level and aspect-based syntax level by adversarial training. We learn the features of aspect-based syntax by building Graph Convolutional Network over the dependency tree of a sentence. Experiments on the four datasets show that Dual Adversarial Network based on BERT outperforms state-of-the-art methods.
Homomorphic Secret Sharing for Multipartite and General Adversary Structures Supporting Parallel Evaluation of Low-Degree Polynomials

Homomorphic secret sharing (HSS) for a function f allows input parties to distribute shares for their private inputs and then locally compute output shares from which the value of f is recovered. HSS can be directly used to obtain a two-round multiparty computation (MPC) protocol for possibly non-threshold adversary structures whose communication complexity is independent of the size of f. In this paper, we propose two constructions of HSS schemes supporting parallel evaluation of a single low-degree polynomial and tolerating multipartite and general adversary structures. Our multipartite scheme tolerates a wider class of adversary structures than the previous multipartite one in the particular case of a single evaluation and has exponentially smaller share size than the general construction. While restricting the range of tolerable adversary structures (but still applicable to non-threshold ones), our schemes perform \(\ell \) parallel evaluations with communication complexity approximately \(\ell /\log \ell \) times smaller than simply using \(\ell \) independent instances. We also formalize two classes of adversary structures taking into account real-world situations to which the previous threshold schemes are inapplicable. Our schemes then perform O(m) parallel evaluations with almost the same communication cost as a single evaluation, where m is the number of parties.
Computation focusing

Focusing is a technique from proof theory that exploits type information to prune inessential nondeterminism from proof search procedures. Viewed through the lens of the Curry-Howard correspondence, a focused typing derivation yields terms in normal form. This paper explores how to exploit focusing for reasoning about contextual equivalences and full abstraction. We present a focused polymorphic call-by-push-value calculus and prove a computational completeness result: for every well-typed term, there exists a focused term that is βη-equivalent to it. This completeness result yields a powerful way to refine the context lemmas for establishing contextual equivalences, cutting down the set that must be considered to just focused contexts. The paper demonstrates the application of focusing to establish program equivalences, including free theorems. It also uses focusing to prove full abstraction of a translation of the pure, total call-by-push-value language into a language with divergence and simple effect types, yielding a novel solution to a simple-to-state, but hitherto difficult to solve problem.
Beyond Simple Images: Human Knowledge-Guided GANs for Clinical Data Generation

While Generative Adversarial Networks (GANs) have accelerated the use of generative modelling within the machine learning community, most of the adaptations of GANs are restricted to images. The use of GANs to generate clinical data has been rare due to the inability of GANs to faithfully capture the intrinsic relationships between features given a small amount of observational data. We hypothesize and verify that this challenge can be mitigated by incorporating rich domain knowledge in the form of expert advice in the generative process. Specifically, we propose human-allied GANs that uses correlation advice from humans to create synthetic clinical data. We construct a system that takes a symbolic representation of the expert advice and converts it into constraints on correlation of the features during the generative process. Our empirical evaluation demonstrates (a) the superiority of our approach over other GAN models, (b) the importance of incorporating advice over instance noise and (c) an initial framework for incorporation of privacy in our model while capturing the relationships between features.