OpenRaiser/Intern-Atlas huggingface dataset:
Encoding of direct 4D printing of isotropic single-material system for double-curvature and multimodal morphing
Venue: arXiv cs.GR 2022
Year: 2022

The ability to morph flat sheets into complex 3D shapes is extremely useful for fast manufacturing and saving materials while also allowing volumetrically efficient storage and shipment and a functional use. Direct 4D printing is a compelling method to morph complex 3D shapes out of as-printed 2D plates. However, most direct 4D printing methods require multi-material systems involving costly machines. Moreover, most works have used an open-cell design for shape shifting by encoding a collection of 1D rib deformations, which cannot remain structurally stable. Here, we demonstrate the direct 4D printing of an isotropic single-material system to morph 2D continuous bilayer plates into doubly curved and multimodal 3D complex shapes whose geometry can also be locked after deployment. We develop an inverse-design algorithm that integrates extrusion-based 3D printing of a single-material system to directly morph a raw printed sheet into complex 3D geometries such as a doubly curved surface with shape locking. Furthermore, our inverse-design tool encodes the localized shape-memory anisotropy during the process, providing the processing conditions for a target 3D morphed geometry. Our approach could be used for conventional extrusion-based 3D printing for various applications including biomedical devices, deployable structures, smart textiles, and pop-up Kirigami structures.
YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using Keypoint Regression
Venue: arXiv cs.CV 2022
Year: 2022

6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications. The state-of-the-art models for pose estimation are convolutional neural network (CNN)-based. Lately, Transformers, an architecture originally proposed for natural language processing, is achieving state-of-the-art results in many computer vision tasks as well. Equipped with the multi-head self-attention mechanism, Transformers enable simple single-stage end-to-end architectures for learning object detection and 6D object pose estimation jointly. In this work, we propose YOLOPose (short form for You Only Look Once Pose estimation), a Transformer-based multi-object 6D pose estimation method based on keypoint regression. In contrast to the standard heatmaps for predicting keypoints in an image, we directly regress the keypoints. Additionally, we employ a learnable orientation estimation module to predict the orientation from the keypoints. Along with a separate translation estimation module, our model is end-to-end differentiable. Our method is suitable for real-time applications and achieves results comparable to state-of-the-art methods.
One Size Does Not Fit All: The Case for Personalised Word Complexity Models
Venue: arXiv cs.CL 2022
Year: 2022

Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand. It has been shown that CWI systems can improve text simplification, readability prediction and vocabulary acquisition modelling. However, the difficulty of a word is a highly idiosyncratic notion that depends on a reader's first language, proficiency and reading experience. In this paper, we show that personal models are best when predicting word complexity for individual readers. We use a novel active learning framework that allows models to be tailored to individuals and release a dataset of complexity annotations and models as a benchmark for further research.
Study on the ERP Implementation Methodologies on SAP, Oracle NetSuite, and Microsoft Dynamics 365: A Review
Venue: arXiv cs.SE 2022
Year: 2022

There are Top three vendors in the ERP market: SAP, Oracle Net Suite and Microsoft dynamics 365 leading the Global ERP market.While analyzing the ERP selection and implementation trends, it is critical that any organization looking to implement an ERP system assesses the vendors through the lens of its own organization's specific requirements. When choosing the right ERP, a few things must be taken into consideration like the Time Budget and resources. The research paper analyses each phase and compares the methodologies of SAP, Oracle Net Suite and Microsoft Dynamics 365 and suggests the best methodologies to be practiced for any ERP projects. Like a poorly planned trip, if you don't have an effective methodology, you can expect a negative impact on your implementation, solution quality, and business satisfaction. Wrong choice of methodology may lead to poor decision-making, best practices may not be followed, and teams may be disjointed in the implementation, which can cause delays. Choosing the right ERP methodology is the key.Methodology is the lifeline for successful project implementation.
Applicability of Software Reliability Growth Models to Open Source Software
Venue: arXiv cs.SE 2022
Year: 2022

Software Reliability Growth Models (SRGMs) are based on underlying assumptions which make them typically more suited for quality evaluation of closed-source projects and their development lifecycles. Their usage in open-source software (OSS) projects is a subject of debate. Although the studies investigating the SRGMs applicability in OSS context do exist, they are limited by the number of models and projects considered which might lead to inconclusive results. In this paper, we present an experimental study of SRGMs applicability to a total of 88 OSS projects, comparing nine SRGMs, looking at the stability of the best models on the whole projects, on releases, on different domains, and according to different projects' attributes. With the aid of the STRAIT tool, we automated repository mining, data processing, and SRGM analysis for better reproducibility. Overall, we found good applicability of SRGMs to OSS, but with different performance when segmenting the dataset into releases and domains, highlighting the difficulty in generalizing the findings and in the search for one-fits-all models.
Very Pliable Index Coding
Venue: arXiv cs.IT 2022
Year: 2022

In the pliable variant of index coding, receivers are allowed to decode any new message not known a priori. Optimal code design for this variant involves identifying each receiver's choice of a new message that minimises the overall transmission rate. This paper proposes a formulation that further relaxes the decoding requirements of pliable index coding by allowing receivers to decode different new messages depending on message realisations. Such relaxation is shown to offer no rate benefit when linear codes are used, but can achieve strictly better rates in general. Scenarios are demonstrated for which the transmission rates are better when the message size is finite than when it is asymptotically large. This is in stark contrast to traditional communication setups.
GANimator: Neural Motion Synthesis from a Single Sequence
Venue: arXiv cs.GR 2022
Year: 2022

We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements of the original motion, while simultaneously synthesizing novel and diverse movements. Existing data-driven techniques for motion synthesis require a large motion dataset which contains the desired and specific skeletal structure. By contrast, GANimator only requires training on a single motion sequence, enabling novel motion synthesis for a variety of skeletal structures e.g., bipeds, quadropeds, hexapeds, and more. Our framework contains a series of generative and adversarial neural networks, each responsible for generating motions in a specific frame rate. The framework progressively learns to synthesize motion from random noise, enabling hierarchical control over the generated motion content across varying levels of detail. We show a number of applications, including crowd simulation, key-frame editing, style transfer, and interactive control, which all learn from a single input sequence. Code and data for this paper are at https://peizhuoli.github.io/ganimator.
Computing treedepth in polynomial space and linear fpt time
Venue: arXiv cs.DS 2022
Year: 2022

The treedepth of a graph $G$ is the least possible depth of an elimination forest of $G$: a rooted forest on the same vertex set where every pair of vertices adjacent in $G$ is bound by the ancestor/descendant relation. We propose an algorithm that given a graph $G$ and an integer $d$, either finds an elimination forest of $G$ of depth at most $d$ or concludes that no such forest exists; thus the algorithm decides whether the treedepth of $G$ is at most $d$. The running time is $2^{O(d^2)}\cdot n^{O(1)}$ and the space usage is polynomial in $n$. Further, by allowing randomization, the time and space complexities can be improved to $2^{O(d^2)}\cdot n$ and $d^{O(1)}\cdot n$, respectively. This improves upon the algorithm of Reidl et al. [ICALP 2014], which also has time complexity $2^{O(d^2)}\cdot n$, but uses exponential space.
Greedy Clustering-Based Algorithm for Improving Multi-point Robotic Manipulation Sequencing
Venue: arXiv cs.RO 2022
Year: 2022

The problem of optimizing a sequence of tasks for a robot, also known as multi-point manufacturing, is a well-studied problem. Many of these solutions use a variant of the Traveling Salesman Problem (TSP) and seek to find the minimum distance or time solution. Optimal solution methods struggle to run in real-time and scale for larger problems. In online planning applications where the tasks being executed are fast, the computational time to optimize the ordering can dominate the total execution time. The optimal solution in this application is defined as the computational time for planning plus the execution time. Therefore, the algorithm presented here balances the quality of the solution with the total execution time by finding a locally optimal sequence. The algorithm is comprised of waypoint generation, spatial clustering, and waypoint optimization. Significant improvements in time reduction were seen and validated against a base case algorithm in simulation and on a real UR5 robot.
Low-complexity Beam Selection algorithms based on SVD for MmWave Massive MIMO Systems
Venue: arXiv cs.IT 2022
Year: 2022

To realize mmWave massive MIMO systems in practice, Beamspace MIMO with beam selection provides an attractive solution at a considerably reduced number of radio frequency (RF) chains. We propose low-complexity beam selection algorithms based on singular value decomposition (SVD). We first diagonalize the channel matrix by SVD, and the appropriate beams are selected one-by-one in a decremental or incremental order based on the criterion of sum-rate maximization. To reduce the complexity of the proposed algorithms significantly, we make use of SVD in the last iteration to aviod SVD from scratch again. Meanwhile, our proposed algorithms naturally obtain the precoding matrix, which can eliminate the multiusers interference. Simulation results demonstrate that our proposed algorithms can outperform the competing algorithms, including the fully digital zero-precoding.
Coding IxD: Enabling Interdisciplinary Education by Sparking Reflection
Venue: arXiv cs.HC 2022
Year: 2022

Educating students from diverse disciplinary backgrounds is challenging. In this article, we report on our interdisciplinary course coding interaction and design (Coding IxD), which is designed for computer science and design students alike. This course has been developed over several years by consciously deliberating on existing hurdles within the educational concept. First, we motivate the need for Coding IxD and introduce the teaching principles that helped shape the course's general structure. Our teaching principles materialize in four method-based phases derived from research through design. Each phase consists of several methods that emerged to be suitable in an interdisciplinary context. Then, based on two selected student projects, we exemplify how interdisciplinary teams can arrive at novel interactive prototypes. We conclude by reflecting on our teaching practice as essential for a meaningful learning experience.
Edge-enabled Metaverse: The Convergence of Metaverse and Mobile Edge Computing
Venue: arXiv cs.DC 2022
Year: 2022

The Metaverse is a virtual environment where users are represented by avatars to navigate a virtual world, which has strong links with the physical one. State-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering computation. Such centralized design is unfavorable as it suffers from several drawbacks caused by the long latency required for cloud access, such as low quality visualization. To solve this issue, in this paper, we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing paradigm, which makes use of edge devices computing power to fulfil the required computational cost for heavy tasks such as collision detection in virtual universe and computation of 3D physics in virtual simulation. The computational cost related to an entity in the Metaverse such as collision detection or physics emulation are performed at the end-device of the associated physical entity. To prove the effectiveness of the proposed architecture, we simulate a distributed social metaverse application. Simulation results shows that the proposed architecture can reduce the latency by 50% when compared with the legacy cloud-based Metaverse applications.
Finding Bipartite Components in Hypergraphs
Venue: arXiv cs.DS 2022
Year: 2022

Hypergraphs are important objects to model ternary or higher-order relations of objects, and have a number of applications in analysing many complex datasets occurring in practice. In this work we study a new heat diffusion process in hypergraphs, and employ this process to design a polynomial-time algorithm that approximately finds bipartite components in a hypergraph. We theoretically prove the performance of our proposed algorithm, and compare it against the previous state-of-the-art through extensive experimental analysis on both synthetic and real-world datasets. We find that our new algorithm consistently and significantly outperforms the previous state-of-the-art across a wide range of hypergraphs.
Computing Traffic Accident High-Risk Locations Using Graph Analytics
Venue: arXiv cs.SI 2022
Year: 2022

Analysis of the dynamic relationship between traffic accident events and road network topology based on connectivity and graph analytics offers a new approach to identifying, ranking and profiling traffic accident high risk-locations at different levels of space and time granularities. Previous studies on traffic accident hot spots have mostly adopted spatial statistics and Geographic Information Systems (GIS) where spatial point patterns are discovered based only on spatial dependence with no recognition of the temporal dependence of the events. A limitation arises from the fact that the results are either under or over-estimated because of the temporal aggregation of the events to an absolute time point. Furthermore, the existing methods apart from the Network Kernel Density Estimation (NETKDE), consider traffic accident events as events randomly on a 2-D geographic space. However, traffic accident events are network constrained events that happens majorly on the road network space. Therefore, in this paper, we adopt the connectivity of graph on a network space approach that identifies accident high risk-locations based on space-time-varying connectivity between traffic accident events and the road network geometry. A simple but extensible traffic accident space time-varying graph (STVG) model is developed and implemented for this study. Traffic accident high risk-locations are identified and ranked in space and time using time-dependent degree centrality and PageRank centrality graph metrics respectively through time-incremental graph queries. This study offers urban traffic accident analysts with a new and efficient approach to identify, rank and profile accident-prone areas in space and time at different scales.
Metaversal Learning Environments: Measuring, predicting and improving interpersonal effectiveness
Venue: arXiv cs.AI 2022
Year: 2022

Experiential learning has been known to be an engaging and effective modality for personal and professional development. The Metaverse provides ample opportunities for the creation of environments in which such experiential learning can occur. In this work, we introduce a novel architecture that combines Artificial intelligence and Virtual Reality to create a highly immersive and efficient learning experience using avatars. The framework allows us to measure the interpersonal effectiveness of an individual interacting with the avatar. We first present a small pilot study and its results which were used to enhance the framework. We then present a larger study using the enhanced framework to measure, assess, and predict the interpersonal effectiveness of individuals interacting with an avatar. Results reveal that individuals with deficits in their interpersonal effectiveness show a significant improvement in performance after multiple interactions with an avatar. The results also reveal that individuals interact naturally with avatars within this framework, and exhibit similar behavioral traits as they would in the real world. We use this as a basis to analyze the underlying audio and video data streams of individuals during these interactions. Finally, we extract relevant features from these data and present a machine-learning based approach to predict interpersonal effectiveness during human-avatar conversation. We conclude by discussing the implications of these findings to build beneficial applications for the real world.
Using network structure and community detection to discover important website features when distinguishing between phishing and legitimate ones
Venue: arXiv cs.SI 2022
Year: 2022

In this paper, we uncover the essential features of websites that allow intelligent models to distinguish between phishing and legitimate sites. Phishing websites are those that are made with a similar user interface and a near similar address to trustworthy websites in order to persuade users to input their private data for potential future misuse by attackers. Detecting phishing websites with intelligent systems is an important goal to protect users, companies, and other online services that use the HTTP protocol. An intelligent model needs to distinguish features that are important as input to predict phishing sites. In this research, using correlation-based networks, we provide a novel network-based method to find features that are more important in phishing detection. The networks are trained and tested on an established phishing dataset. Three different networks are made by partitioning the dataset by its data instance labels. The important features are found by discovering the hubs of these networks, and the results are presented and analysed. This is the first time using a network-based approach for feature selection which is a fast and accurate way to do so.
Reconfigurable Heterogeneous Parallel Island Models
Venue: arXiv cs.NE 2022
Year: 2022

Heterogeneous Parallel Island Models (HePIMs) run different bio-inspired algorithms (BAs) in their islands. From a variety of communication topologies and migration policies fine-tuned for homogeneous PIMs (HoPIMs), which run the same BA in all their islands, previous work introduced HePIMs that provided competitive quality solutions regarding the best-adapted BA in HoPIMs. This work goes a step forward, maintaining the population diversity provided by HePIMs, and increasing their flexibility, allowing BA reconfiguration on islands during execution: according to their performance, islands may substitute their BAs dynamically during the evolutionary process. Experiments with the introduced architectures (RecHePIMs) were applied to the NP-hard problem of sorting permutations by reversals, using four different BAs, namely, simple Genetic Algorithm, Double-point crossover Genetic Algorithm, Differential Evolution, and self-adjusting Particle Swarm Optimization. The results showed that the new reconfigurable heterogeneous models compute better quality solutions than the HePIMs closing the gap with the HoPIM running the best-adapted BA.
CNN-Augmented Visual-Inertial SLAM with Planar Constraints
Venue: arXiv cs.RO 2022
Year: 2022

We present a robust visual-inertial SLAM system that combines the benefits of Convolutional Neural Networks (CNNs) and planar constraints. Our system leverages a CNN to predict the depth map and the corresponding uncertainty map for each image. The CNN depth effectively bootstraps the back-end optimization of SLAM and meanwhile the CNN uncertainty adaptively weighs the contribution of each feature point to the back-end optimization. Given the gravity direction from the inertial sensor, we further present a fast plane detection method that detects horizontal planes via one-point RANSAC and vertical planes via two-point RANSAC. Those stably detected planes are in turn used to regularize the back-end optimization of SLAM. We evaluate our system on a public dataset, \ie, EuRoC, and demonstrate improved results over a state-of-the-art SLAM system, \ie, ORB-SLAM3.
Public key cryptography based on skew dihedral group rings
Venue: arXiv cs.CR 2022
Year: 2022

In this paper, we propose to use a skew dihedral group ring given by the group $D_{2n}$ and the finite field $\mathbb{F}_{q^2}$ for public-key cryptography. Using the ambient space $\mathbb{F}_{q^{2}}^θ D_{2n}$ and a group homomorphism $θ: D_{2n} \rightarrow \mathrm{Aut}(\mathbb{F}_{q^2})$, we introduce a key exchange protocol and present an analysis of its security. Moreover, we explore the properties of the resulting skew group ring $\mathbb{F}_{q^{2}}^θ D_{2n}$, exploiting them to enhance our key exchange protocol. We also introduce a probabilistic public-key scheme derived from our key exchange protocol and obtain a key encapsulation mechanism (KEM) by applying a well-known generic transformation to our public-key scheme. Finally, we present a proof-of-concept implementation of our cryptographic constructions. To the best of our knowledge, this is the first paper that proposes a skew dihedral group ring for public-key cryptography.
Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design
Venue: arXiv cs.LG 2022
Year: 2022

Deep Generative Machine Learning Models have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. While early works are promising, further advancement will depend on addressing several critical considerations such as design quality, feasibility, novelty, and targeted inverse design. We propose the Design Target Achievement Index (DTAI), a differentiable, tunable metric that scores a design's ability to achieve designer-specified minimum performance targets. We demonstrate that DTAI can drastically improve the performance of generated designs when directly used as a training loss in Deep Generative Models. We apply the DTAI loss to a Performance-Augmented Diverse GAN (PaDGAN) and demonstrate superior generative performance compared to a set of baseline Deep Generative Models including a Multi-Objective PaDGAN and specialized tabular generation algorithms like the Conditional Tabular GAN (CTGAN). We further enhance PaDGAN with an auxiliary feasibility classifier to encourage feasible designs. To evaluate methods, we propose a comprehensive set of evaluation metrics for generative methods that focus on feasibility, diversity, and satisfaction of design performance targets. Methods are tested on a challenging benchmarking problem: the FRAMED bicycle frame design dataset featuring mixed-datatype parametric data, heavily skewed and multimodal distributions, and ten competing performance objectives.