Reviewerly/exHarmony huggingface dataset:
Two Space-Time Obstacle Representations Based on Ellipsoids and Polytopes

When operating autonomous surface vessels in uncertain environments with dynamic obstacles, planning safe trajectories and evaluating collision risk is key to navigating safely. In order to perform these tasks, it is important to have a computationally efficient and adaptable obstacle representation to allow for quick and robust predictions of the obstacle trajectory. This paper presents a novel space-time obstacle representation, which is able to predict the reachable set for a dynamic obstacle under uncertainty. This is done by projecting the area occupied by the obstacle forward in time, using a set of velocities representing the possible maneuvers that the obstacle may take. Under some mild assumptions, we show how the space-time obstacle can be implemented in a computationally efficient way, using both convex polytopes and ellipsoids. Additionally, we show how the space-time obstacle representation can be used for risk assessment, collision avoidance and trajectory planning for autonomous surface vessels.
Learning Generalized Unsolvability Heuristics for Classical Planning

Recent work in classical planning has introduced dedicated techniques for detecting unsolvable states, i.e., states from which no goal state can be reached. We approach the problem from a generalized planning perspective and learn first-order-like formulas that characterize unsolvability for entire planning domains. We show how to cast the problem as a self-supervised classification task. Our training data is automatically generated and labeled by exhaustive exploration of small instances of each domain, and candidate features are automatically computed from the predicates used to define the domain. We investigate three learning algorithms with different properties and compare them to heuristics from the literature. Our empirical results show that our approach often captures important classes of unsolvable states with high classification accuracy. Additionally, the logical form of our heuristics makes them easy to interpret and reason about, and can be used to show that the characterizations learned in some domains capture exactly all unsolvable states of the domain.
A New Encoding Mechanism Embedded Evolutionary Algorithm for UAV Route Planning

Evolutionary algorithms (EAs) are often applied to deal with UAV route planning. The solution encoding is one of important factor in designing effective EAs. In a traditional encoding mechanism, each individual represents one route. The whole population then consists of a number of routes. We argue that such an encoding is less effective in route planning, and then proposed an alternative encoding mechanism in which one individual represents only one navigation point. The whole population then represents one route. This implicitly turns EAs into single-point based search with high exploitation ability. To further improve the exploration ability of algorithms using this new encoding, a slightly modified differential evolution operator is applied. Combining the modified DE operator and the new encoding mechanism, the performance of the derived algorithm is significantly improved, obtaining much better route planning results than DE with the traditional encoding mechanism.
MoBIE: A free and open-source platform for integration and cloud-based sharing of multi-modal correlative big image data

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Aircraft Targets Detection in Remote Sensing Images with Feature Optimization

Due to background interference, small size and dense arrangement in remote sensing images, the aircraft targets features are unconspicuous and easy to miss detection or misdetect. Aiming at this problem, a feature optimization algorithm based on YOLOv4 was proposed for aircraft targets detection in remote sensing images in this paper. In this algorithm, the feature fusion method of YOLOv4 was changed, and the multi-feature layers weighted fusion was used to reconstruct and optimize the different scale features. By expanding the scale range of the fusion features, the small targets detection ability is improved. Secondly, coordinate attention was used to effectively capture the relationship between feature-channels and the position information of feature-space. It can effectively reduce the interference of background and further enhance the feature information of the targets. The comparative trial results on the joint datasets of DIOR, RSOD and LEVIR show that the improved algorithm can effectively reduce the interference of complex background, significantly improve the detection effect of dense-small aircraft, and the performance is more superior.
A Paradigm Shift for Computational Excellence from Traditional Machine Learning to Modern Deep Learning-Based Image Steganalysis

Steganography is a method of hiding data in digital files in order to conceal its very existence, whereas steganalysis is the practice of detecting the presence of the secret (hidden) messages in the multimedia files (text, image, audio, and video files). The success of the steganalytic model highly depends upon its ability to extract effective feature representations present in the data set. This chapter spans across two comparative paradigms of feature extractions in the history of research timeline in steganalysis realms. The traditional paradigm, which is usually a two-step machine learning (ML) approach comprising feature extraction followed by a classification step. This approach relies on good quality; however, handcrafted features of high dimensionality based on enormously relevant statistical computations over image characteristics eventually leads to improved classification accuracies with respect to conventional steganalytic systems. The modern paradigm is a recent breakthrough in efficient feature representation and automated feature learning approach since 2015, coined as Deep Learning (DL) approaches. some noteworthy models to mention: Deep-Boltzmann Machines, Deep-autoencoders, and Convolution Neural Networks (CNNs) were introduced, leading to improved classification accuracies as against traditional ML-based steganalytic systems. These DL models unify the two steps, feature extraction and classification, under a single architecture, along with optimizing the dimensionality of extracted feature sets. This chapter discusses weight initialization methods for avoiding vanishing and exploding gradient problems, activation functions for introducing nonlinearity, pooling for reducing the dimensionality of features, optimizers for increasing training speed of the model, residual learning (ResL) for strengthening weak stego signals, batch normalization for faster convergence of the network, spatial pyramid pooling for handling arbitrary size images, and data augmentation and transfer learning for better performance of the steganalyzers.
Polynomial-Time in PDDL Input Size: Making the Delete Relaxation Feasible for Lifted Planning

Polynomial-time heuristic functions for planning are commonplace since 20 years. But polynomial-time in which input? Almost all existing approaches are based on a grounded task representation, not on the actual PDDL input which is exponentially smaller. This limits practical applicability to cases where the grounded representation is "small enough". Previous attempts to tackle this problem for the delete relaxation leveraged symmetries to reduce the blow-up. Here we take a more radical approach, applying an additional relaxation to obtain a heuristic function that runs in time polynomial in the size of the PDDL input. Our relaxation splits the predicates into smaller predicates of fixed arity K. We show that computing a relaxed plan is still NP-hard (in PDDL input size) for K&gt;=2, but is polynomial-time for K=1. We implement a heuristic function for K=1 and show that it can improve the state of the art on benchmarks whose grounded representation is large.
MotionViz: Artistic Visualization of Human Motion on Mobile Devices

We present MotionViz, an interactive iOS mobile app that enables users to amplify motion and dynamics in videos. MotionViz implements novel augmented reality and expressive rendering techniques in an end-to-end processing pipeline: multi-dimensional video data is captured, analyzed, and processed to render animated graphical elements that help express figures and actions. Through an easy-to-use graphical user interface, users can choose from a curated list of artistic motion visualization effects, including the overlay of animated silhouettes, halos, and contour lines. MotionViz is based on Apple’s LiDAR technology, accelerated image processing APIs, and dedicated Neural Engine for real-time on-device processing.
The Corpus for Idiolectal Research (CIDRE)

The Corpus for Idiolectal Research (CIDRE) is a collection of fiction works from 11 prolific 19th-century French authors (4 women, 7 men; 22–62 works/author; total of 37 million words). Every work is dated with the year it was written. Using programming scripts, the works have been gathered from open source platforms, for example La Bibliothèque électronique du Québec, and stripped of paratext (text not being part of the novel, e.g. prefaces). We distribute the text files, the dating, other metadata and the programming scripts under an open source license. CIDRE is the first resource of French for the study of style and idiolect in a diachronic manner (i.e. stylochronometry) on a larger scale.
Command Similarity Measurement Using NLP

Process invocations happen with almost every activity on a computer. To distinguish user input and potentially malicious activities, we need to better understand program invocations caused by commands. To achieve this, one must understand commands’ objectives, possible parameters, and valid syntax. In this work, we collected commands’ data by scrapping commands’ manual pages, including command description, syntax, and parameters. Then, we measured command similarity using two of these - description and parameters - based on commands' natural language documentation. We used Term Frequency-Inverse Document Frequency (TFIDF) of a word to compare the commands, followed by measuring cosine similarity to find a similarity of commands’ description. For parameters, after measuring TFIDF and cosine similarity, the Hungarian method is applied to solve the assignment of different parameters’ combinations. Finally, commands are clustered based on their similarity scores. The results show that these methods have efficiently clustered the commands in smaller groups (commands with aliases or close counterparts), and in a bigger group (commands belonging to a larger set of related commands, e.g., bitsadmin for Windows and systemd for Linux). To validate the clustering results, we applied topic modeling on the commands' data, which confirms that 84% of the Windows commands and 98% ofthe Linux commands are clustered correctly.
Solving Graph Homomorphism and Subgraph Isomorphism Problems Faster Through Clique Neighbourhood Constraints

Graph homomorphism problems involve finding adjacency-preserving mappings between two given graphs. Although theoretically hard, these problems can often be solved in practice using constraint programming algorithms. We show how techniques from the state-of-the-art in subgraph isomorphism solving can be applied to broader graph homomorphism problems, and introduce a new form of filtering based upon clique-finding. We demonstrate empirically that this filtering is effective for the locally injective graph homomorphism and subgraph isomorphism problems, and gives the first practical constraint programming approach to finding general graph homomorphisms.
Two-Photon Absorption in Molecules by time-frequency-entangled photon pairs: the roles of photon-number correlations and spectral correlations

Two-photon absorption is enhanced by time-frequency entanglement of photon pairs at extremely low optical flux, yet we find theoretically that for typical molecules TPA event rates are so low as to be unobservable.
ALaSca: an Automated approach for Large-Scale Lexical Substitution

The lexical substitution task aims at finding suitable replacements for words in context. It has proved to be useful in several areas, such as word sense induction and text simplification, as well as in more practical applications such as writing-assistant tools. However, the paucity of annotated data has forced researchers to apply mainly unsupervised approaches, limiting the applicability of large pre-trained models and thus hampering the potential benefits of supervised approaches to the task. In this paper, we mitigate this issue by proposing ALaSca, a novel approach to automatically creating large-scale datasets for English lexical substitution. ALaSca allows examples to be produced for potentially any word in a language vocabulary and to cover most of the meanings it lists. Thanks to this, we can unleash the full potential of neural architectures and finetune them on the lexical substitution task. Indeed, when using our data, a transformer-based model performs substantially better than when using manually annotated data only. We release ALaSca at https://sapienzanlp.github.io/alasca/.
Real-Time Prediction of Future 3D Pose of Person Using RGB-D Camera for Personalized Services

This paper describes a method for prediction of 3D human pose using an RGB-D camera in real-time for personalized robot services. In input frame from RGB-D camera, 2D pose as a skeleton represented by 2D keypoints on an RGB image is estimated by OpenPose: an image-based human pose estimation technique. 3D pose represented by a set of 3D keypoints is estimated from the depth at the keypoints of 2D keypoints. Daily actions of a person, which are represented by a sequence of 3D keypoints, are measured by an RGB-D camera fixed to a daily environment. After the sequences of measured 3D keypoints are learned by a motion predictor of RNN, the future 3D keypoints are predicted by the RNN from the sequential input of 3D keypoints, which are measured in real-time by RGB-D camera. Experimental results show the predicted 3D pose and errors for standing from and siting on a chair in a room of daily environment.
On Equivalence of Neural Network Receivers

Neural Network (NN) based receivers have seen limited adoption in practical systems due to a lack of explainability and performance guarantees, despite their efficacy as a data-driven tool for physical layer signal processing. In order to bridge this gap in explainability, we present an equivalent NN-based receiver that performs the same optimizations used by classical receivers for symbol detection. Achieving equivalence is crucial to explaining how a NN-based receiver classifies symbols in high-dimensional channels and determining its structure that is robust to the underlying channel with minimum training. We realize this by deriving the risk function that guarantees equivalence, which also provides a measure of the disparity between NN-based and classical receivers. Consequently, this information allows us to derive mathematically tight data-dependent bounds on the bit error rate of NN-based receivers, and empirically determine its structure that achieves minimum error rate. Extensive simulation results show the efficacy of the derived bounds and structure of NN-based receivers for single and multi-antenna systems over a variety of channels.
Verifying Time Complexity of Binary Search using Dafny

Formal software verification techniques are widely used to specify and prove the functional correctness of programs. However, nonfunctional properties such as time complexity are usually carried out with pen and paper. Inefficient code in terms of time complexity may cause massive performance problems in large-scale complex systems. We present a proof of concept for using the Dafny verification tool to specify and verify the worst-case time complexity of binary search. This approach can also be used for academic purposes as a new way to teach algorithms and complexity.
Towards Human Haptic Gesture Interpretation for Robotic Systems

Physical human-robot interactions (pHRI) are less efficient and communicative than human-human interactions, and a key reason is a lack of informative sense of touch in robotic systems. Interpreting human touch gestures is a nuanced, challenging task with extreme gaps between human and robot capability. Among prior works that demonstrate human touch recognition capability, differences in sensors, gesture classes, feature sets, and classification algorithms yield a conglomerate of non-transferable results and a glaring lack of a standard. To address this gap, this work presents 1) four proposed touch gesture classes that cover an important subset of the gesture characteristics identified in the literature, 2) the collection of an extensive force dataset on a common pHRI robotic arm with only its internal wrist force-torque sensor, and 3) an exhaustive performance comparison of combinations of feature sets and classification algorithms on this dataset. We demonstrate high classification accuracies among our proposed gesture definitions on a test set, emphasizing that neural network classifiers on the raw data outperform other combinations of feature sets and algorithms. Accompanying video is here. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Empathetic Robot With Transformer-Based Dialogue Agent

Natural human-robot interaction (HRI) attracts considerable interest in letting robots understand users’ emotional states. This paper demonstrates a method to introduce the affection model to a robotic system’s conversational agent to provide natural and empathetic HRI. We use a large-scale pre-trained language model and fine-tune it on a dialogue dataset with empathetic characteristics. Based on recent progress in deep learning and natural language processing, we extend the current methods and enable our robotic agent to perform advanced sentiment analysis using our trained affection model. This dialogue agent will allow the robot to provide natural responses along with emotion classification with the estimations of arousal and valence levels. We evaluate our model using different metrics in comparison with recent studies and showing its emotion detection capability.
Me-to-We Design: How Can a Makerspace Nurture the Building of a Collaborative Community?

Makerspaces provide communal access to resources such as 3D printers, laser cutters, electronics equipment, and sewing machines. This way, makerspaces aspire to facilitate their users – the so-called makers – in acquiring craft skills, creating products, learning about technology, and meeting other makers. The collaborative qualities of the makerspace community are key to this aspiration. Yet, just like many non-makerspace initiatives, makerspaces often struggle to foster and sustain a collaborative community. In this study, we use the model of me-to-we design to analyze a makerspace that has succeeded in nurturing a collaborative community. We disentangle the makerspace activities into the five stages of the model and, on that basis, arrive at six principles for nurturing collaborative makerspace communities: (1) accept diverse entry and end points, (2) plan for transitions, (3) help makers devise meaningful projects, (4) encourage sharing and lightweight documentation, (5) collaborate toward communal goals, and (6) attend to the social.
Numerical simulations of agent navigation via Half-Sweep Modified Two-Parameter Over-Relaxation (HSMTOR)

Abstract The research on the efficiency of route navigation has been continuously developing. Especially, the capability of the generated route to provide a collision-free route for an agent to move in a particular environment. Thus, this study attempts to solve the route navigational problem iteratively via a numerical method. A new method called Half-Sweep Modified Two-Parameter Over-Relaxation (HSMTOR) is used to solve the navigational problems. For numerical simulation purposes, HSMTOR is used to obtain Laplace’s equation solutions called harmonic functions. A gradient descent search algorithm then utilizes the harmonic functions to provide a smooth and collision-free route for an agent to commute inside the environment. In addition, the formulation of the HSMTOR iterative method is presented. Several numerical experiments and simulations are conducted in order to verify the efficiency of the proposed method. The result shows that the proposed method performed better than the existing methods such as full-, half-sweep for Modified Successive Over-Relaxation, Modified Accelerated Over-Relaxation and Modified Two-Parameter Over-Relaxation respectively (FSMSOR, HSMSOR, FSMAOR, HSMAOR and FSMTOR).
Semantic-Aware Network for Aerial-To-Ground Image Synthesis

Aerial-to-ground image synthesis is an emerging and challenging problem that aims to synthesize a ground image from an aerial image. Due to the highly different layout and object representation between the aerial and ground images, existing approaches usually fail to transfer the components of the aerial scene into the ground scene. In this paper, we propose a novel framework to explore the challenges by imposing enhanced structural alignment and semantic awareness. We introduce a novel semantic-attentive feature transformation module that allows to reconstruct the complex geographic structures by aligning the aerial feature to the ground layout. Furthermore, we propose semantic-aware loss functions by leveraging a pre-trained segmentation network. The network is enforced to synthesize realistic objects across various classes by separately calculating losses for different classes and balancing them. Extensive experiments including comparisons with previous methods and ablation studies show the effectiveness of the proposed framework both qualitatively and quantitatively.
Analysing Student Expectation on Mobile Technologies to Enhance Student Learning Experience

This research explores the student expectation on using Mobile Devices (MD) such as smartphones, tablets, and iPad for their learning activities which can enhance the learning experience. The data obtained through survey questionnaires were analysed and presented in this paper. The responses were analysed to identify how comfortable they are using MD in learning, level of impact on their learning and difficulties in introducing MD to Teaching and Learning (T&L). The results indicated that, overall 66.1% of the respondents strongly agreed/agreed that they are comfortable with using MD for learning while 33.8% disagreed with the statement. Students in the age group 30 years+ strongly disagreed with the statement. Furthermore, 89.6% postgraduate, 94.1% undergraduate in all age groups clearly indicated that there is a positive/ high impact on using MD. We further analysed the responses and observed that most of the students; 52.1% postgraduate and 76.5% undergraduate are not satisfied with the features in the current mobile Apps. This study indicates that students are willing and comfortable to use MD and they understand that there is a positive/ high impact on using MD for learning and conversely, they are not satisfied with the existing Apps. However, about 3.1% of the students are using only MD for assessment preparation. Some of the reasons contributing to not using MD for learning may be; (1) the lack of awareness of existing Apps among students, (2) many courses did not offer opportunities/ tasks to use MD for assessments, and (3) lack of suitable Apps / existing Apps do not provide support to use. The study demonstrates the need to adopt mobile learning (ML) tasks into course curriculum; develop suitable Apps and improve awareness among students. The study concluded with a suggestion to future developers to consider an appropriate course development framework to adopt ML by contemplating learners’ behaviour and their cultures.