OpenRaiser/Intern-Atlas huggingface dataset:
Crashworthiness design of 3D lattice-structure filled thin-walled tubes based on data mining
Venue: arXiv cs.DC 2021
Year: 2021

Lattice structures and thin-walled tubes are two types of energy-absorbers widely studied and applied in engineering practice. In this study, a new type of lattice-structure filled thin-walled tube (LFT) was proposed. In this new type of LFT, a BCC-Z lattice structure was filled into a square thin-walled tube. Then using data mining, a 3-D geometric design with five design variables was conducted on this new LFT. Using Latin Hypercubic sampling algorithm, 150 design cases were generated. Numerical models were then developed to simulate their crush behavior, and the simulation dataset was used for data mining. The results showed that (1) Filling the BBC-Z lattice structure into a thin-walled tube can significantly improve the energy absorption (EA) capacity of the structure. (2) The decision trees generated in the data mining process indicated that the rod diameter d of lattice structure is the key design variable that has most significant impact on EA, followed by m and n. (3) The design rules to build LFTs with high EA efficiency (SEA>=16 kJ/kg and CFE>=45%), high total EA (SEA>=16 kJ/kg and EA>=6 kJ), and lightweight (SEA>=16 kJ/kg and Mass<=0.45 kg) were obtained from decision trees. The ideal configurations of LFT corresponding to these three objectives are: d>2 mm, n>2 and m>3 for high EA efficiency; d>2 mm, n>2 and m>3 for high total EA; and d>2 mm, n>2, m<=4 and t<=1.7 mm for lightweight.
HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation
Venue: arXiv cs.LG 2021
Year: 2021

The personalized list continuation (PLC) task is to curate the next items to user-generated lists (ordered sequence of items) in a personalized way. The main challenge in this task is understanding the ternary relationships among the interacting entities (users, items, and lists) that the existing works do not consider. Further, they do not take into account the multi-hop relationships among entities of the same type. In addition, capturing the sequential information amongst the items already present in the list also plays a vital role in determining the next relevant items that get curated. In this work, we propose HyperTeNet -- a self-attention hypergraph and Transformer-based neural network architecture for the personalized list continuation task to address the challenges mentioned above. We use graph convolutions to learn the multi-hop relationship among the entities of the same type and leverage a self-attention-based hypergraph neural network to learn the ternary relationships among the interacting entities via hyperlink prediction in a 3-uniform hypergraph. Further, the entity embeddings are shared with a Transformer-based architecture and are learned through an alternating optimization procedure. As a result, this network also learns the sequential information needed to curate the next items to be added to the list. Experimental results demonstrate that HyperTeNet significantly outperforms the other state-of-the-art models on real-world datasets. Our implementation and datasets are available at https://github.com/mvijaikumar/HyperTeNet.
Implementation of Parallel Simplified Swarm Optimization in CUDA
Venue: arXiv cs.NE 2021
Year: 2021

As the acquisition cost of the graphics processing unit (GPU) has decreased, personal computers (PC) can handle optimization problems nowadays. In optimization computing, intelligent swarm algorithms (SIAs) method is suitable for parallelization. However, a GPU-based Simplified Swarm Optimization Algorithm has never been proposed. Accordingly, this paper proposed Parallel Simplified Swarm Optimization (PSSO) based on the CUDA platform considering computational ability and versatility. In PSSO, the theoretical value of time complexity of fitness function is O (tNm). There are t iterations and N fitness functions, each of which required pair comparisons m times. pBests and gBest have the resource preemption when updating in previous studies. As the experiment results showed, the time complexity has successfully reduced by an order of magnitude of N, and the problem of resource preemption was avoided entirely.
Traffic Flow Forecasting with Maintenance Downtime via Multi-Channel Attention-Based Spatio-Temporal Graph Convolutional Networks
Venue: arXiv cs.LG 2021
Year: 2021

Forecasting traffic flows is a central task in intelligent transportation system management. Graph structures have shown promise as a modeling framework, with recent advances in spatio-temporal modeling via graph convolution neural networks, improving the performance or extending the prediction horizon on traffic flows. However, a key shortcoming of state-of-the-art methods is their inability to take into account information of various modalities, for instance the impact of maintenance downtime on traffic flows. This is the issue we address in this paper. Specifically, we propose a novel model to predict traffic speed under the impact of construction work. The model is based on the powerful attention-based spatio-temporal graph convolution architecture but utilizes various channels to integrate different sources of information, explicitly builds spatio-temporal dependencies among traffic states, captures the relationships between heterogeneous roadway networks, and then predicts changes in traffic flow resulting from maintenance downtime events. The model is evaluated on two benchmark datasets and a novel dataset we have collected over the bustling Tyson's corner region in Northern Virginia. Extensive comparative experiments and ablation studies show that the proposed model can capture complex and nonlinear spatio-temporal relationships across a transportation corridor, outperforming baseline models.
Enhancing Voluntary Motion with Modular, Backdrivable, Powered Hip and Knee Orthoses
Venue: arXiv cs.RO 2021
Year: 2021

Mobility disabilities are prominent in society with wide-ranging detriments to affected individuals. Addressing the specific deficits of individuals within this heterogeneous population requires modular, partial-assist, lower-limb exoskeletons. This paper introduces the Modular Backdrivable Lower-limb Unloading Exoskeleton (M-BLUE), which implements high torque, low mechanical impedance actuators on commercial orthoses with sheet metal modifications to produce a variety of hip- and/or knee-assisting configurations. Benchtop system identification verifies the desirable backdrive properties of the actuator, and allows for torque prediction within 0.4 Nm. An able-bodied human subject experiment demonstrates that three unilateral configurations of M-BLUE (hip only, knee only, and hip-knee) with a simple gravity compensation controller can reduce muscle EMG readings in a lifting and lowering task relative to the bare condition. Reductions in mean muscular effort and peak muscle activation were seen across the primary squat musculature (excluding biceps femoris), demonstrating the potential to reduce fatigue leading to poor lifting posture. These promising results motivate applications of M-BLUE to additional subject populations such as hip/knee osteoarthritis and geriatric frailty, and the expansion of M-BLUE to bilateral and ankle configurations.
Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients
Venue: arXiv cs.LG 2021
Year: 2021

There has been a growing need to provide Byzantine-resilience in distributed model training. Existing robust distributed learning algorithms focus on developing sophisticated robust aggregators at the parameter servers, but pay less attention to balancing the communication cost and robustness. In this paper, we propose Solon, an algorithmic framework that exploits gradient redundancy to provide communication efficiency and Byzantine robustness simultaneously. Our theoretical analysis shows a fundamental trade-off among computational load, communication cost, and Byzantine robustness. We also develop a concrete algorithm to achieve the optimal trade-off, borrowing ideas from coding theory and sparse recovery. Empirical experiments on various datasets demonstrate that Solon provides significant speedups over existing methods to achieve the same accuracy, over 10 times faster than Bulyan and 80% faster than Draco. We also show that carefully designed Byzantine attacks break Signum and Bulyan, but do not affect the successful convergence of Solon.
Sensitivity Study of Fiducial-Aided Navigation of Unmanned Aerial Vehicles
Venue: arXiv cs.RO 2021
Year: 2021

The possible applications and benefits of autonomous Unmanned Aerial Vehicle (UAV) use in urban areas are gaining considerable attention. Before these possibilities can be realized, it is essential that UAVs be able to navigate reliably and precisely in urban environments. The most common means of determining the location of a UAV is to utilize position measurements from Global Navigation Satellite Systems (GNSS). In urban environments, however, GNSS measurements are significantly degraded due to occlusions and multipath. This research analyzes the use of camera Line-of-Sight (LOS) measurements to self-describing fiducials as a replacement for conventional GNSS measurements. An extended Kalman filter (EKF) is developed and validated for the purpose of combining continuous measurements from an Inertial Measurement Unit (IMU) with the discrete LOS measurements to accurately estimate the states of a UAV. The sensitivity of the estimation error covariance to various system parameters is assessed, including IMU grade, fiducial placement, vehicle altitude, and image processing frequency.
Neural Implicit Surfaces for Efficient and Accurate Collisions in Physically Based Simulations
Venue: arXiv cs.GR 2021
Year: 2021

Current trends in the computer graphics community propose leveraging the massive parallel computational power of GPUs to accelerate physically based simulations. Collision detection and solving is a fundamental part of this process. It is also the most significant bottleneck on physically based simulations and it easily becomes intractable as the number of vertices in the scene increases. Brute force approaches carry a quadratic growth in both computational time and memory footprint. While their parallelization is trivial in GPUs, their complexity discourages from using such approaches. Acceleration structures -- such as BVH -- are often applied to increase performance, achieving logarithmic computational times for individual point queries. Nonetheless, their memory footprint also grows rapidly and their parallelization in a GPU is problematic due to their branching nature. We propose using implicit surface representations learnt through deep learning for collision handling in physically based simulations. Our proposed architecture has a complexity of O(n) -- or O(1) for a single point query -- and has no parallelization issues. We will show how this permits accurate and efficient collision handling in physically based simulations, more specifically, for cloth. In our experiments, we query up to 1M points in 300 milliseconds.
Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults
Venue: arXiv cs.CV 2021
Year: 2021

A number of studies suggest bias of the face biometrics, i.e., face recognition and soft-biometric estimation methods, across gender, race, and age groups. There is a recent urge to investigate the bias of different biometric modalities toward the deployment of fair and trustworthy biometric solutions. Ocular biometrics has obtained increased attention from academia and industry due to its high accuracy, security, privacy, and ease of use in mobile devices. A recent study in $2020$ also suggested the fairness of ocular-based user recognition across males and females. This paper aims to evaluate the fairness of ocular biometrics in the visible spectrum among age groups; young, middle, and older adults. Thanks to the availability of the latest large-scale 2020 UFPR ocular biometric dataset, with subjects acquired in the age range 18 - 79 years, to facilitate this study. Experimental results suggest the overall equivalent performance of ocular biometrics across gender and age groups in user verification and gender classification. Performance difference for older adults at lower false match rate and young adults was noted at user verification and age classification, respectively. This could be attributed to inherent characteristics of the biometric data from these age groups impacting specific applications, which suggest a need for advancement in sensor technology and software solutions.
Voice Information Retrieval In Collaborative Information Seeking
Venue: arXiv cs.IR 2021
Year: 2021

Voice information retrieval is a technique that provides Information Retrieval System with the capacity to transcribe spoken queries and use the text output for information search. CIS is a field of research that involves studying the situation, motivations, and methods for people working in a collaborative group for information seeking projects, as well as building a system for supporting such activities. Humans find it easier to communicate and express ideas via speech. Existing voice search like Google and other mainstream voice search does not support collaborative search. The spoken speeches passed through the ASR for feature extraction using MFCC and HMM, Viterbi algorithm precisely for pattern matching. The result of the ASR is then passed as input into CIS System, results is then filtered to have an aggregate result. The result from the simulation shows that our model was able to achieve 81.25% transcription accuracy.
Compression, The Fermi Paradox and Artificial Super-Intelligence
Venue: arXiv cs.AI 2021
Year: 2021

The following briefly discusses possible difficulties in communication with and control of an AGI (artificial general intelligence), building upon an explanation of The Fermi Paradox and preceding work on symbol emergence and artificial general intelligence. The latter suggests that to infer what someone means, an agent constructs a rationale for the observed behaviour of others. Communication then requires two agents labour under similar compulsions and have similar experiences (construct similar solutions to similar tasks). Any non-human intelligence may construct solutions such that any rationale for their behaviour (and thus the meaning of their signals) is outside the scope of what a human is inclined to notice or comprehend. Further, the more compressed a signal, the closer it will appear to random noise. Another intelligence may possess the ability to compress information to the extent that, to us, their signals would appear indistinguishable from noise (an explanation for The Fermi Paradox). To facilitate predictive accuracy an AGI would tend to more compressed representations of the world, making any rationale for their behaviour more difficult to comprehend for the same reason. Communication with and control of an AGI may subsequently necessitate not only human-like compulsions and experiences, but imposed cognitive impairment.
Dataset: Large-scale Urban IoT Activity Data for DDoS Attack Emulation
Venue: arXiv cs.CR 2021
Year: 2021

As IoT deployments grow in scale for applications such as smart cities, they face increasing cyber-security threats. In particular, as evidenced by the famous Mirai incident and other ongoing threats, large-scale IoT device networks are particularly susceptible to being hijacked and used as botnets to launch distributed denial of service (DDoS) attacks. Real large-scale datasets are needed to train and evaluate the use of machine learning algorithms such as deep neural networks to detect and defend against such DDoS attacks. We present a dataset from an urban IoT deployment of 4060 nodes describing their spatio-temporal activity under benign conditions. We also provide a synthetic DDoS attack generator that injects attack activity into the dataset based on tunable parameters such as number of nodes attacked and duration of attack. We discuss some of the features of the dataset. We also demonstrate the utility of the dataset as well as our synthetic DDoS attack generator by using them for the training and evaluation of a simple multi-label feed-forward neural network that aims to identify which nodes are under attack and when.
Inverse Kinematics and Dexterous Workspace Formulation for 2-Segment Continuum Robots with Inextensible Segments
Venue: arXiv cs.RO 2021
Year: 2021

The inverse kinematics (IK) problem of continuum robots has been investigated in depth in the past decades. Under the constant-curvature bending assumption, closed-form IK solution has been obtained for continuum robots with variable segment lengths. Attempting to close the gap towards a complete solution, this paper presents an efficient solution for the IK problem of 2-segment continuum robots with one or two inextensible segments (a.k.a, constant segment lengths). Via representing the robot's shape as piecewise line segments, the configuration variables are separated from the IK formulation such that solving a one-variable nonlinear equation leads to the solution of the entire IK problem. Furthermore, an in-depth investigation of the boundaries of the dexterous workspace of the end effector caused by the configuration variables limits as well as the angular velocity singularities of the continuum robots was established. This dexterous workspace formulation, which is derived for the first time to the best of the authors' knowledge, is particularly useful to find the closest orientation to a target pose when the target orientation is out of the dexterous workspace. In the comparative simulation studies between the proposed method and the Jacobian-based IK method involving 500,000 cases, the proposed variable separation method solved 100% of the IK problems with much higher computational efficiency.
Emerging trends and collaboration patterns unveil the scientific production in blockchain technology: A bibliometric and network analysis from 2014-2020
Venue: arXiv cs.DL 2021
Year: 2021

Significant attention in the financial industry has paved the way for blockchain technology to spread across other industries, resulting in a plethora of literature on the subject. This study approaches the subject through bibliometrics and network analysis of 6790 records extracted from the Web of Science from 2014-2020 based on blockchain. This study asserts (i) the impact of open access publication on the growth and visibility of literature, (ii) the collaboration patterns and impact of team size on collaboration, (iii) the ranking of countries based on their national and international collaboration, and (iv) the major themes in the literature through thematic analysis. Based on the significant momentum gained by the blockchain, the trend of open access publications has increased 1.5 times than no open access in 2020. This analysis articulates the numerous potentials of blockchain literature and its adoption by various countries and their authors. China and the USA are the top leaders in the field and applied blockchain more with smart contracts, supply chain, and internet of things. Also, results show that blockchain has attracted the attention of less than 1% of authors who have contributed to multiple works on the blockchain and authors also preferred to work in teams smaller in size.
Local certification of MSO properties for bounded treedepth graphs
Venue: arXiv cs.DC 2021
Year: 2021

The graph model checking problem consists in testing whether an input graph satisfies a given logical formula. In this paper, we study this problem in a distributed setting, namely local certification. The goal is to assign labels to the nodes of a network to certify that some given property is satisfied, in such a way that the labels can be checked locally. We first investigate which properties can be locally certified with small certificates. Not surprisingly, this is almost never the case, except for not very expressive logic fragments. Following the steps of Courcelle-Grohe, we then look for meta-theorems explaining what happens when we parameterize the problem by some standard measures of how simple the graph classes are. In that direction, our main result states that any MSO formula can be locally certified on graphs with bounded treedepth with a logarithmic number of bits per node, which is the golden standard in certification.
AraCOVID19-SSD: Arabic COVID-19 Sentiment and Sarcasm Detection Dataset
Venue: arXiv cs.CL 2021
Year: 2021

Coronavirus disease (COVID-19) is an infectious respiratory disease that was first discovered in late December 2019, in Wuhan, China, and then spread worldwide causing a lot of panic and death. Users of social networking sites such as Facebook and Twitter have been focused on reading, publishing, and sharing novelties, tweets, and articles regarding the newly emerging pandemic. A lot of these users often employ sarcasm to convey their intended meaning in a humorous, funny, and indirect way making it hard for computer-based applications to automatically understand and identify their goal and the harm level that they can inflect. Motivated by the emerging need for annotated datasets that tackle these kinds of problems in the context of COVID-19, this paper builds and releases AraCOVID19-SSD a manually annotated Arabic COVID-19 sarcasm and sentiment detection dataset containing 5,162 tweets. To confirm the practical utility of the built dataset, it has been carefully analyzed and tested using several classification models.
SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation
Venue: arXiv cs.AI 2021
Year: 2021

We introduce SMProbLog, a generalization of the probabilistic logic programming language ProbLog. A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these probabilities are mutually independent. The semantics of ProbLog is given by the success probability of a query, which corresponds to the probability that the query succeeds in a randomly sampled program. It is well-defined when each random sample uniquely determines the truth values of all logical atoms. Argumentation problems, however, represent an interesting practical application where this is not always the case. SMProbLog generalizes the semantics of ProbLog to the setting where multiple truth assignments are possible for a randomly sampled program, and implements the corresponding algorithms for both inference and learning tasks. We then show how this novel framework can be used to reason about probabilistic argumentation problems. Therefore, the key contribution of this paper are: a more general semantics for ProbLog programs, its implementation into a probabilistic programming framework for both inference and parameter learning, and a novel approach to probabilistic argumentation problems based on such framework.