Reviewerly/exHarmony huggingface dataset:
Evaluation of mapping and path planning for non-holonomic mobile robot navigation in narrow pathway for agricultural application

This paper evaluates mapping and path planning methods for mobile robot with non-holonomic constraint in the narrow pathways. Selection of sensors such as depth camera or LiDAR sensor is complex problem as it depends on applications, demand for cost, robustness and data processing. Along with sensor selection map generation is essential task for mobile robot navigation. This paper presents experimental evaluation of laser-based mapping algorithm i.e., Gmapping and vision based mapping i.e., RTAB-Map. The platform used for autonomous navigation is mobile robot with nonholonomic constraint. The path planning for mobile robot with non-holonomic constraint is more complex as not all arbitrary trajectories are kinematically feasible. The application of mobile robot navigation is to transfer agriculture products in greenhouse from one place to another. Generally, the pathways of greenhouse are narrow, which often results in the planner failing to generate a traversable trajectory if the mobile robot is restricted to forward movement, hence the switchback (forward and backward) path planning is essential to navigate in such environments. In the following discussion, we implement the Reeds-Shepp curve based path planning for mobile robot with a non-holonomic constraint to navigate in narrow pathways. Reeds-Shepp curve can generate various combinations of such switch-back trajectories and it remains unmatched in terms of computation efficiency and reliability compared to other curves. Effectiveness of the proposed path planning method is validated experimentally.
Geometric Approach to<i>b</i>-Symbol Hamming Weights of Cyclic Codes

Symbol-pair codes were introduced by Cassuto and Blaum in 2010 to protect pair errors in symbol-pair read channels. Recently Yaakobi, Bruck and Siegel (2016) generalized this notion to b-symbol codes in order to consider consecutive b errors for a prescribed integer b ≥ 2, and they gave constructions and decoding algorithms. Cyclic codes were considered by various authors as candidates for symbol-pair codes and they established minimum distance bounds on (certain) cyclic codes. In this paper we use algebraic curves over finite fields in order to obtain tight lower and upper bounds on b-symbol Hamming weights of arbitrary cyclic codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> . Here b ≥ 2 is an arbitrary prescribed positive integer and \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> is an arbitrary finite field. We also present a stability theorem for an arbitrary cyclic code C of dimension k and length n: the b-symbol Hamming weight enumerator of C is the same as the k-symbol Hamming weight enumerator of C if k ≤ b ≤ n-1. Moreover, we give improved tight lower and upper bounds on b-symbol Hamming weights of some cyclic codes related to irreducible cyclic codes. Throughout the paper the length n is coprime to q.
Design Science Research Modes in Human-Computer Interaction Projects

In this editorial, we introduce the special issue on design science research in human-computer interaction with four papers extended from the 2020 European Conference on Information Systems and propose a conceptual model for such research projects.Research in the interdisciplinary human-computer interaction (HCI) discipline advances knowledge of how humans interact with technologies, systems, information, and work structures.Design science research (DSR) methods support three distinct modes in HCI projects.In the interior mode, researchers build and evaluate novel technical solutions with a focus on improved system interfaces to support effective human use.Next, in the exterior mode, researchers build and evaluate novel behavioral solutions with a process focus on interactions that increase human capabilities.Lastly, in the gestalt mode, researchers build and evaluate novel composite solutions that improve synergies between technologies and human behaviors.We pose a comprehensive model for identifying the DSR modes of HCI research with related artifacts, evaluation techniques, design theories, and research impacts.
CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities

The problem of inhomogeneous cluster densities has been a long-standing issue for distance-based and density-based algorithms in clustering and anomaly detection. These algorithms implicitly assume that all clusters have approximately the same density. As a result, they often exhibit a bias towards dense clusters in the presence of sparse clusters. Many remedies have been suggested; yet, we show that they are partial solutions which do not address the issue satisfactorily. To match the implicit assumption, we propose to transform a given dataset such that the transformed clusters have approximately the same density while all regions of locally low density become globally low density -- homogenising cluster density while preserving the cluster structure of the dataset. We show that this can be achieved by using a new multi-dimensional Cumulative Distribution Function in a transform-and-shift method. The method can be applied to every dataset, before the dataset is used in many existing algorithms to match their implicit assumption without algorithmic modification. We show that the proposed method performs better than existing remedies.
How to Better Distinguish Security Bug Reports (Using Dual Hyperparameter Optimization)

In order that the general public is not vulnerable to hackers, security bug reports need to be handled by small groups of engineers before being widely discussed. But learning how to distinguish the security bug reports from other bug reports is challenging since they may occur rarely. Data mining methods that can find such scarce targets require extensive optimization effort. The goal of this research is to aid practitioners as they struggle to optimize methods that try to distinguish between rare security bug reports and other bug reports. Our proposed method, called SWIFT, is a dual optimizer that optimizes both learner and pre-processor options. Since this is a large space of options, SWIFT uses a technique called 𝜖-dominance that learns how to avoid operations that do not significantly improve performance. When compared to recent state-of-the-art results (from FARSEC which is published in TSE’18), we find that the SWIFT’s dual optimization of both pre-processor and learner is more useful than optimizing each of them individually. For example, in a study of security bug reports from the Chromium dataset, the median recalls of FARSEC and SWIFT were 15.7% and 77.4%, respectively. For another example, in experiments with data from the Ambari project, the median recalls improved from 21.5% to 85.7% (FARSEC to SWIFT). Overall, our approach can quickly optimize models that achieve better recalls than the prior state-of-the-art. These increases in recall are associated with moderate increases in false positive rates (from 8% to 24%, median). For future work, these results suggest that dual optimization is both practical and useful.
Representation of $$k$$-mer Sets Using Spectrum-Preserving String Sets

Given the popularity and elegance of $$k$$-mer based tools, finding a space-efficient way to represent a set of $$k$$-mers is important for improving the scalability of bioinformatics analyses. One popular approach is to convert the set of $$k$$-mers into the more compact set of unitigs. We generalize this approach and formulate it as the problem of finding a smallest spectrum-preserving string set (SPSS) representation. We show that this problem is equivalent to finding a smallest path cover in a compacted de Bruijn graph. Using this reduction, we prove a lower bound on the size of the optimal SPSS and propose a greedy method called UST that results in a smaller representation than unitigs and is nearly optimal with respect to our lower bound. We demonstrate the usefulness of the SPSS formulation with two applications of UST. The first one is a compression algorithm, UST-Compress, which we show can store a set of $$k$$-mers using an order-of-magnitude less disk space than other lossless compression tools. The second one is an exact static $$k$$-mer membership index, UST-FM, which we show improves index size by 10–44% compared to other state-of-the-art low memory indices. Our tool is publicly available at: https://github.com/medvedevgroup/UST/ .
Research on Text Sentiment Analysis Based on Neural Network and Ensemble Learning

In view of the fact that a single sentiment classification model may be unstable in classification, this paper attempts to propose a joint neural network and ensemble learning sentiment analysis method. After data preprocessing such as word segmentation on the text, combined with document vectorization method for feature extraction, we then use four basic classifiers including long short-term memory network, convolutional neural network, a serial model combining convolutional neural network and long short-term memory network, and support vector machine to train model, respectively. Finally, the integration is carried out by stacking ensemble learning. The experimental results show that the integrated model significantly improves the accuracy of text sentiment analysis and it can effectively predict the sentiment polarity of the text.
Hidden Time-Reversal Symmetry, Quantum Detailed Balance and Exact Solutions of Driven-Dissipative Quantum Systems

Driven-dissipative quantum systems generically do not satisfy simple notions of detailed balance based on the time symmetry of correlation functions. We show that such systems can nonetheless exhibit a hidden time-reversal symmetry which most directly manifests itself in a doubled version of the original system prepared in an appropriate entangled thermofield double state. This hidden time-reversal symmetry has a direct operational utility: it provides a general method for finding exact solutions of non-trivial steady states. Special cases of this approach include the coherent quantum absorber and complex-$P$ function methods from quantum optics. We also show that hidden TRS has observable consequences even in single-system experiments, and can be broken by the non-trivial combination of nonlinearity, thermal fluctuations, and driving. To illustrate our ideas, we analyze concrete examples of driven qubits and nonlinear cavities. These systems exhibit hidden time-reversal symmetry but not conventional detailed balance.
Generation of <b>−</b>10.7 dB unbiased entangled states of light

In a continuous variable quantum key distribution (CV-QKD) system, strong Einstein–Podolsky–Rosen entangled states can significantly boost the robustness and distance for secure communication. However, an inevitable bias of two entanglement quadratures may degrade the secret key rate and distance during random quadrature base switching. The bias originates from several interdependent factors in the generation, propagation, and detection of entangled states, which faces a challenge to be completely eliminated. Here, we analyze in detail the origin of the bias effect and report on a scheme of generating unbiased entangled states, whereby a −10.7 ± 0.1 dB quadrature noise unbiased entanglement is first generated experimentally with two single-mode squeezed states. The unbiased quadrature correlations within the measurement bandwidth are expected to immensely enhance the key rate and secure distance for CV-QKD.
Batch Exploration With Examples for Scalable Robotic Reinforcement Learning

Learning from diverse offline datasets is a promising path towards learning general purpose robotic agents. However, a core challenge in this paradigm lies in collecting large amounts of meaningful data, while not depending on a human in the loop for data collection. One way to address this challenge is through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">task-agnostic exploration</i> , where an agent attempts to explore without a task-specific reward function, and collect data that can be useful for any subsequent task. While these approaches have shown some promise in simple domains, they often struggle to explore the relevant regions of the state space in more challenging settings, such as vision-based robotic manipulation. This challenge stems from an objective that encourages exploring <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">everything</i> in a potentially vast state space. To mitigate this challenge, we propose to focus exploration on the important parts of the state space using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weak human supervision</i> . Concretely, we propose an exploration technique, Batch Exploration with Examples (BEE), that explores relevant regions of the state-space, guided by a modest number of human-provided images of important states. These human-provided images only need to be provided once at the beginning of data collection and can be acquired in a matter of minutes, allowing us to scalably collect diverse datasets, which can then be combined with any batch RL algorithm. We find that BEE is able to tackle challenging vision-based manipulation tasks both in simulation and on a real Franka Emika Panda robot, and observe that compared to task-agnostic and weakly-supervised exploration techniques, it (1) interacts more than twice as often with relevant objects, and (2) improves subsequent task performance when used in conjunction with offline RL.
Underwater 3D Scanner Model Using a Biaxial MEMS Mirror

Underwater 3D laser scanners are an essential type of sensor used by unmanned underwater vehicles (UUVs) for operations such as navigation, inspection and object recognition and manipulation. These sensors need to be able to provide highly accurate 3D data at fast refresh rates in order to accomplish these tasks. Usually, these scanners rely on a rotating mirror actuated by a galvanometer. However, the light planes steered by this type of mirrors are typically deformed into cones due to refraction. In order to produce accurate results, this distortion needs to be taken into account, which increases the computational cost of the 3D reconstruction. A novel approach consisting in using a biaxial MEMS mirror is proposed in this paper. The second rotational degree of freedom of the mirror can be used to project optimally curved light shapes, so that the refraction process transforms them into planes. Being able to model the light surfaces as planes rather than cones can significantly reduce the computation time of the 3D reconstruction. In order to do so, an exhaustive model of the complete light trajectories is presented. To the best of the authors' knowledge, this paper constitutes the first attempt to model and counteract the distortion in the scanning pattern introduced by a biaxial mirror and a double refraction process in the context of underwater robotics.
VideoGPT: Video Generation using VQ-VAE and Transformers

We present VideoGPT: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos. VideoGPT uses VQ-VAE that learns downsampled discrete latent representations of a raw video by employing 3D convolutions and axial self-attention. A simple GPT-like architecture is then used to autoregressively model the discrete latents using spatio-temporal position encodings. Despite the simplicity in formulation and ease of training, our architecture is able to generate samples competitive with state-of-the-art GAN models for video generation on the BAIR Robot dataset, and generate high fidelity natural videos from UCF-101 and Tumbler GIF Dataset (TGIF). We hope our proposed architecture serves as a reproducible reference for a minimalistic implementation of transformer based video generation models. Samples and code are available at https://wilson1yan.github.io/videogpt/index.html
SelectVisAR: Selective Visualisation of Virtual Environments in Augmented Reality

When establishing a visual connection between a virtual reality user and an augmented reality user, it is important to consider whether the augmented reality user faces a surplus of information. Augmented reality, compared to virtual reality, involves two – not one – planes of information: the physical and the virtual. We propose SelectVisAR, a selective visualisation system of virtual environments in augmented reality. Our system enables an augmented reality spectator to perceive a co-located virtual reality user in the context of four distinct visualisation conditions: Interactive, Proximity, Everything, and Dollhouse. We explore an additional two conditions, Context and Spotlight, in a follow-up study. Our design uses a human-centric approach to information filtering, selectively visualising only parts of the virtual environment related to the interactive possibilities of a virtual reality user. The research investigates how selective visualisations can be helpful or trivial for the augmented reality user when observing a virtual reality user.
Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference

In this research, we introduce a classification procedure based on rule induction and fuzzy reasoning. The classifier generalizes attribute information to handle uncertainty, which often occurs in real data. To induce fuzzy rules, we define the corresponding fuzzy information system. A transformation of the derived rules into interval type-2 fuzzy rules is provided as well. The fuzzification applied is optimized with respect to the footprint of uncertainty of the corresponding type-2 fuzzy sets. The classification process is related to a Mamdani type fuzzy inference. The method proposed was evaluated by the F-score measure on benchmark data.
NoiseQA: Challenge Set Evaluation for User-Centric Question Answering

When Question-Answering (QA) systems are deployed in the real world, users query them through a variety of interfaces, such as speaking to voice assistants, typing questions into a search engine, or even translating questions to languages supported by the QA system.While there has been significant community attention devoted to identifying correct answers in passages assuming a perfectly formed question, we show that components in the pipeline that precede an answering engine can introduce varied and considerable sources of error, and performance can degrade substantially based on these upstream noise sources even for powerful pre-trained QA models.We conclude that there is substantial room for progress before QA systems can be effectively deployed, highlight the need for QA evaluation to expand to consider real-world use, and hope that our findings will spur greater community interest in the issues that arise when our systems actually need to be of utility to humans. 1
GCM: A Toolkit for Generating Synthetic Code-mixed Text

Code-mixing is common in multilingual communities around the world, and processing it is challenging due to the lack of labeled and unlabeled data.We describe a tool that can automatically generate code-mixed data given parallel data in two languages.We implement two linguistic theories of code-mixing, the Equivalence Constraint theory and the Matrix Language theory to generate all possible code-mixed sentences in the language-pair, followed by sampling of the generated data to generate natural code-mixed sentences.The toolkit provides three modes: a batch mode, an interactive library mode and a web-interface to address the needs of researchers, linguists and language experts.The toolkit can be used to generate unlabeled text data for pre-trained models, as well as visualize linguistic theories of code-mixing.We plan to release the toolkit as open source and extend it by adding more implementations of linguistic theories, visualization techniques and better sampling techniques.We expect that the release of this toolkit will help facilitate more research in code-mixing in diverse language pairs. 12
Balancing cybersecurity in a supply chain under direct and indirect cyber risks

Cybersecurity is an essential requirement for the sustainability of global supply chains. In this paper, a stochastic programming formulation is presented for optimisation of cybersecurity investment and selection of security controls to mitigate and balance the impact of direct and indirect (propagated) cyber risks in a multi-tier supply chain. Using a network transformation combined with the first-order Taylor series approximation of natural logarithm to linearise the nonlinear constraints, a nonlinear stochastic combinatorial optimisation model is approximated by its linear equivalent. The problem objective is to determine an optimal cybersecurity investment under limited budget and portfolio of security controls for each supply chain node to balance the cybersecurity over the entire supply chain. The minmax objective functions are applied to minimise either the maximum breach probability or the maximum loss of supply chain nodes. Alternatively, maxmin objectives are used to maximise either the minimum non-breach probability or the minimum saving of loss. The proposed integrated modelling approach is illustrated with results of computational study and a comparison of approximated and exact solution values is presented. The decision-making insights are provided and discussed.
Unification-based Reconstruction of Multi-hop Explanations for Science Questions

This paper presents a novel framework for reconstructing multi-hop explanations in science Question Answering (QA).While existing approaches for multi-hop reasoning build explanations considering each question in isolation, we propose a method to leverage explanatory patterns emerging in a corpus of scientific explanations.Specifically, the framework ranks a set of atomic facts by integrating lexical relevance with the notion of unification power, estimated analysing explanations for similar questions in the corpus.An extensive evaluation is performed on the Worldtree corpus, integrating k-NN clustering and Information Retrieval (IR) techniques.We present the following conclusions: (1) The proposed method achieves results competitive with Transformers, yet being orders of magnitude faster, a feature that makes it scalable to large explanatory corpora (2) The unificationbased mechanism has a key role in reducing semantic drift, contributing to the reconstruction of many hops explanations (6 or more facts) and the ranking of complex inference facts (+12.0Mean Average Precision) (3) Crucially, the constructed explanations can support downstream QA models, improving the accuracy of BERT by up to 10% overall.
A Novel Robust Kalman Filter With Non-stationary Heavy-tailed Measurement Noise

A novel robust Kalman filter based on Gaussian-Student’s t mixture (GSTM) distribution is proposed to address the filtering problem of a linear system with non-stationary heavy-tailed measurement noise. The mixing probability is recursively estimated by using its previous estimates as prior information, and the state vector, the auxiliary parameter, the Bernoulli random variable and the mixing probability are jointly estimated utilizing the variational Bayesian method. The excellent performance of the proposed robust Kalman filter, compared with the existing state-of-the-art filters, is illustrated by a target tracking simulation results under the case of non-stationary heavy-tailed measurement noise.