OpenRaiser/Intern-Atlas huggingface dataset:
Succinct Euler-Tour Trees
Venue: arXiv cs.DS 2021
Year: 2021

We show how a collection of Euler-tour trees for a forest on $n$ vertices can be stored in $2 n + o (n)$ bits such that simple queries take constant time, more complex queries take logarithmic time and updates take polylogarithmic amortized time.
Teachers' perspective on fostering computational thinking through educational robotics
Venue: arXiv cs.CY 2021
Year: 2021

With the introduction of educational robotics (ER) and computational thinking (CT) in classrooms, there is a rising need for operational models that help ensure that CT skills are adequately developed. One such model is the Creative Computational Problem Solving Model (CCPS) which can be employed to improve the design of ER learning activities. Following the first validation with students, the objective of the present study is to validate the model with teachers, specifically considering how they may employ the model in their own practices. The Utility, Usability and Acceptability framework was leveraged for the evaluation through a survey analysis with 334 teachers. Teachers found the CCPS model useful to foster transversal skills but could not recognise the impact of specific intervention methods on CT-related cognitive processes. Similarly, teachers perceived the model to be usable for activity design and intervention, although felt unsure about how to use it to assess student learning and adapt their teaching accordingly. Finally, the teachers accepted the model, as shown by their intent to replicate the activity in their classrooms, but were less willing to modify it or create their own activities, suggesting that they need time to appropriate the model and underlying tenets.
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research
Venue: arXiv cs.CV 2021
Year: 2021

The performances of Sign Language Recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories.
The Impact of Incomplete Information on Network Formation with Heterogeneous Agents
Venue: arXiv cs.SI 2021
Year: 2021

We propose an agent-based network formation model under uncertainty with the objective of relaxing the common assumption of complete information, calling attention to the role beliefs may play in segregation. We demonstrate that our model is capable of generating a set of networks that encompasses those of a complete information model. Further, we show that by allowing agents to be biased toward each other based on observable attributes, our model is able to generate homophilous equilibria with preferences that are indifferent to these attributes. We accompany our theoretical results with a simulation-based investigation of the relationship between beliefs and segregation and show that biased beliefs are an important driver of segregation under incomplete information.
Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions
Venue: arXiv cs.DC 2021
Year: 2021

Cloud computing has rapidly emerged as model for delivering Internet-based utility computing services. In cloud computing, Infrastructure as a Service (IaaS) is one of the most important and rapidly growing fields. Cloud providers provide users/machines resources such as virtual machines, raw (block) storage, firewalls, load balancers, and network devices in this service model. One of the most important aspects of cloud computing for IaaS is resource management. Scalability, quality of service, optimum utility, reduced overheads, increased throughput, reduced latency, specialised environment, cost effectiveness, and a streamlined interface are some of the advantages of resource management for IaaS in cloud computing. Traditionally, resource management has been done through static policies, which impose certain limitations in various dynamic scenarios, prompting cloud service providers to adopt data-driven, machine-learning-based approaches. Machine learning is being used to handle a variety of resource management tasks, including workload estimation, task scheduling, VM consolidation, resource optimization, and energy optimization, among others. This paper provides a detailed review of challenges in ML-based resource management in current research, as well as current approaches to resolve these challenges, as well as their advantages and limitations. Finally, we propose potential future research directions based on identified challenges and limitations in current research.
Research on Mosaic Image Data Enhancement for Overlapping Ship Targets
Venue: arXiv cs.CV 2021
Year: 2021

The problem of overlapping occlusion in target recognition has been a difficult research problem, and the situation of mutual occlusion of ship targets in narrow waters still exists. In this paper, an improved mosaic data enhancement method is proposed, which optimizes the reading method of the data set, strengthens the learning ability of the detection algorithm for local features, improves the recognition accuracy of overlapping targets while keeping the test speed unchanged, reduces the decay rate of recognition ability under different resolutions, and strengthens the robustness of the algorithm. The real test experiments prove that, relative to the original algorithm, the improved algorithm improves the recognition accuracy of overlapping targets by 2.5%, reduces the target loss time by 17%, and improves the recognition stability under different video resolutions by 27.01%.
Deja-Vu: A Glimpse on Radioactive Soft-Error Consequences on Classical and Quantum Computations
Venue: arXiv cs.CR 2021
Year: 2021

What do Apple, the FBI and a Belgian politician have in common? In 2003, in Belgium there was an election using electronic voting machines. Mysteriously one candidate summed an excess of 4096 votes. An accurate analysis led to the official explanation that a spontaneous creation of a bit in position 13 of the memory of the computer attributed 4096 extra votes to one candidate. One of the most credited answers to this event is attributed to cosmic rays i.e.(gamma), which can filter through the atmosphere. There are cases though, with classical computers, like forensic investigations, or system recovery where such soft-errors may be helpful to gain root privileges and recover data. In this paper we show preliminary results of using radioactive sources as a mean to generate bit-flips and exploit classical electronic computation devices. We used low radioactive emissions generated by Cobalt and Cesium and obtained bit-flips which made the program under attack crash. We also provide the first overview of the consequences of SEUs in quantum computers which are today used in production for protein folding optimization, showing potential impactful consequences. To the best of our knowledge we are the first to leverage SEUs for exploitation purposes which could be of great impact on classical and quantum computers.
kdehumor at semeval-2020 task 7: a neural network model for detecting funniness in dataset humicroedit
Venue: arXiv cs.CL 2021
Year: 2021

This paper describes our contribution to SemEval-2020 Task 7: Assessing Humor in Edited News Headlines. Here we present a method based on a deep neural network. In recent years, quite some attention has been devoted to humor production and perception. Our team KdeHumor employs recurrent neural network models including Bi-Directional LSTMs (BiLSTMs). Moreover, we utilize the state-of-the-art pre-trained sentence embedding techniques. We analyze the performance of our method and demonstrate the contribution of each component of our architecture.
An Efficient Bayes Coding Algorithm for the Non-Stationary Source in Which Context Tree Model Varies from Interval to Interval
Venue: arXiv cs.IT 2021
Year: 2021

The context tree source is a source model in which the occurrence probability of symbols is determined from a finite past sequence, and is a broader class of sources that includes i.i.d. and Markov sources. The proposed source model in this paper represents that a subsequence in each interval is generated from a different context tree model. The Bayes code for such sources requires weighting of the posterior probability distributions for the change patterns of the context tree source and for all possible context tree models. Therefore, the challenge is how to reduce this exponential order computational complexity. In this paper, we assume a special class of prior probability distribution of change patterns and context tree models, and propose an efficient Bayes coding algorithm whose computational complexity is the polynomial order.
Mandating Code Disclosure is Unnecessary -- Strict Model Verification Does Not Require Accessing Original Computer Code
Venue: arXiv cs.SE 2021
Year: 2021

Mandating public availability of computer code underlying computational simulation modeling research ends up doing a disservice to the cause of model verification when inconsistencies between the specifications in the publication text and specifications in the computer code go unchallenged. Conversely, a model is verified when an independent researcher undertakes the set of mental processing tasks necessary to convert natural language specifications in a publication text into computer code instructions that produce numerical or graphical outputs identical to the outputs found in the original publication. The effort towards obtaining convergence with the numerical or graphical outputs directs intensive consideration of the publication text. The original computer code has little role to play in determining the verification status - verified/ failed verification. An insight is obtained that skillful deployment of human intelligence is feasible when effort-directing feedback processes are in place to appropriately go around the human frailty of giving up in the absence of actionable feedback. This principle can be put to use to develop better organizational configurations in business, government and society.
The Role of Intent-Based Networking in ICT Supply Chains
Venue: arXiv cs.NI 2021
Year: 2021

The evolution towards Industry 4.0 is driving the need for innovative solutions in the area of network management, considering the complex, dynamic and heterogeneous nature of ICT supply chains. To this end, Intent-Based networking (IBN) which is already proven to evolve how network management is driven today, can be implemented as a solution to facilitate the management of large ICT supply chains. In this paper, we first present a comparison of the main architectural components of typical IBN systems and, then, we study the key engineering requirements when integrating IBN with ICT supply chain network systems while considering AI methods. We also propose a general architecture design that enables intent translation of ICT supply chain specifications into lower level policies, to finally show an example of how the access control is performed in a modeled ICT supply chain system.
Factoring Multidimensional Data to Create a Sophisticated Bayes Classifier
Venue: arXiv cs.LG 2021
Year: 2021

In this paper we derive an explicit formula for calculating the marginal likelihood of a given factorization of a categorical dataset. Since the marginal likelihood is proportional to the posterior probability of the factorization, these likelihoods can be used to order all possible factorizations and select the "best" way to factor the overall distribution from which the dataset is drawn. The best factorization can then be used to construct a Bayes classifier which benefits from factoring out mutually independent sets of variables.
PTeacher: a Computer-Aided Personalized Pronunciation Training System with Exaggerated Audio-Visual Corrective Feedback
Venue: arXiv cs.HC 2021
Year: 2021

Second language (L2) English learners often find it difficult to improve their pronunciations due to the lack of expressive and personalized corrective feedback. In this paper, we present Pronunciation Teacher (PTeacher), a Computer-Aided Pronunciation Training (CAPT) system that provides personalized exaggerated audio-visual corrective feedback for mispronunciations. Though the effectiveness of exaggerated feedback has been demonstrated, it is still unclear how to define the appropriate degrees of exaggeration when interacting with individual learners. To fill in this gap, we interview 100 L2 English learners and 22 professional native teachers to understand their needs and experiences. Three critical metrics are proposed for both learners and teachers to identify the best exaggeration levels in both audio and visual modalities. Additionally, we incorporate the personalized dynamic feedback mechanism given the English proficiency of learners. Based on the obtained insights, a comprehensive interactive pronunciation training course is designed to help L2 learners rectify mispronunciations in a more perceptible, understandable, and discriminative manner. Extensive user studies demonstrate that our system significantly promotes the learners' learning efficiency.
Digital Building Twins and Blockchain for Performance-Based (Smart) Contracts
Venue: arXiv cs.CR 2021
Year: 2021

Performance contracts used for servitized business models enable consideration of overall life-cycle costs rather than just production costs. However, practical implementation of performance contracts has been limited due to challenges with performance evaluation, accountability, and financial concepts. As a solution, this paper proposes the connection of the digital building twin with blockchain-based smart contracts to execute performance-based digital payments. First, we conceptualize a technical architecture to connect blockchain to digital building twins. The digital building twin stores and evaluates performance data in real-time while the blockchain ensures transparency and trusted execution of automatic performance evaluation and rewards through smart contracts. Next, we demonstrate the feasibility of both the concept and technical architecture by integrating the Ethereum blockchain with digital building models and sensors via the Siemens building twin platform. The resulting prototype is the first full-stack implementation of a performance-based smart contract in the built environment.
An Empirical Study of Compression-friendly Community Detection Methods
Venue: arXiv cs.SI 2021
Year: 2021

Real-world graphs are massive in size and we need a huge amount of space to store them. Graph compression allows us to compress a graph so that we need a lesser number of bits per link to store it. Of many techniques to compress a graph, a typical approach is to find clique-like caveman or traditional communities in a graph and encode those cliques to compress the graph. On the other side, an alternative approach is to consider graphs as a collection of hubs connecting spokes and exploit it to arrange the nodes such that the resulting adjacency matrix of the graph can be compressed more efficiently. We perform an empirical comparison of these two approaches and show that both methods can yield good results under favorable conditions. We perform our experiments on ten real-world graphs and define two cost functions to present our findings.
Online POMDP Planning via Simplification
Venue: arXiv cs.AI 2021
Year: 2021

In this paper, we consider online planning in partially observable domains. Solving the corresponding POMDP problem is a very challenging task, particularly in an online setting. Our key contribution is a novel algorithmic approach, Simplified Information Theoretic Belief Space Planning (SITH-BSP), which aims to speed-up POMDP planning considering belief-dependent rewards, without compromising on the solution's accuracy. We do so by mathematically relating the simplified elements of the problem to the corresponding counterparts of the original problem. Specifically, we focus on belief simplification and use it to formulate bounds on the corresponding original belief-dependent rewards. These bounds in turn are used to perform branch pruning over the belief tree, in the process of calculating the optimal policy. We further introduce the notion of adaptive simplification, while re-using calculations between different simplification levels and exploit it to prune, at each level in the belief tree, all branches but one. Therefore, our approach is guaranteed to find the optimal solution of the original problem but with substantial speedup. As a second key contribution, we derive novel analytical bounds for differential entropy, considering a sampling-based belief representation, which we believe are of interest on their own. We validate our approach in simulation using these bounds and where simplification corresponds to reducing the number of samples, exhibiting a significant computational speedup while yielding the optimal solution.
Collaborative Regression of Expressive Bodies using Moderation
Venue: arXiv cs.CV 2021
Year: 2021

Recovering expressive humans from images is essential for understanding human behavior. Methods that estimate 3D bodies, faces, or hands have progressed significantly, yet separately. Face methods recover accurate 3D shape and geometric details, but need a tight crop and struggle with extreme views and low resolution. Whole-body methods are robust to a wide range of poses and resolutions, but provide only a rough 3D face shape without details like wrinkles. To get the best of both worlds, we introduce PIXIE, which produces animatable, whole-body 3D avatars with realistic facial detail, from a single image. For this, PIXIE uses two key observations. First, existing work combines independent estimates from body, face, and hand experts, by trusting them equally. PIXIE introduces a novel moderator that merges the features of the experts, weighted by their confidence. All part experts can contribute to the whole, using SMPL-X's shared shape space across all body parts. Second, human shape is highly correlated with gender, but existing work ignores this. We label training images as male, female, or non-binary, and train PIXIE to infer "gendered" 3D body shapes with a novel shape loss. In addition to 3D body pose and shape parameters, PIXIE estimates expression, illumination, albedo and 3D facial surface displacements. Quantitative and qualitative evaluation shows that PIXIE estimates more accurate whole-body shape and detailed face shape than the state of the art. Models and code are available at https://pixie.is.tue.mpg.de.
A Computational Framework for Modeling Complex Sensor Network Data Using Graph Signal Processing and Graph Neural Networks in Structural Health Monitoring
Venue: arXiv cs.LG 2021
Year: 2021

Complex networks lend themselves to the modeling of multidimensional data, such as relational and/or temporal data. In particular, when such complex data and their inherent relationships need to be formalized, complex network modeling and its resulting graph representations enable a wide range of powerful options. In this paper, we target this - connected to specific machine learning approaches on graphs for structural health monitoring on an analysis and predictive (maintenance) perspective. Specifically, we present a framework based on Complex Network Modeling, integrating Graph Signal Processing (GSP) and Graph Neural Network (GNN) approaches. We demonstrate this framework in our targeted application domain of Structural Health Monitoring (SHM). In particular, we focus on a prominent real-world structural health monitoring use case, i.e., modeling and analyzing sensor data (strain, vibration) of a large bridge in the Netherlands. In our experiments, we show that GSP enables the identification of the most important sensors, for which we investigate a set of search and optimization approaches. Furthermore, GSP enables the detection of specific graph signal patterns (mode shapes), capturing physical functional properties of the sensors in the applied complex network. In addition, we show the efficacy of applying GNNs for strain prediction on this kind of data.
Seeing All From a Few: Nodes Selection Using Graph Pooling for Graph Clustering
Venue: arXiv cs.SI 2021
Year: 2021

Recently, there has been considerable research interest in graph clustering aimed at data partition using the graph information. However, one limitation of the most of graph-based methods is that they assume the graph structure to operate is fixed and reliable. And there are inevitably some edges in the graph that are not conducive to graph clustering, which we call spurious edges. This paper is the first attempt to employ graph pooling technique for node clustering and we propose a novel dual graph embedding network (DGEN), which is designed as a two-step graph encoder connected by a graph pooling layer to learn the graph embedding. In our model, it is assumed that if a node and its nearest neighboring node are close to the same clustering center, this node is an informative node and this edge can be considered as a cluster-friendly edge. Based on this assumption, the neighbor cluster pooling (NCPool) is devised to select the most informative subset of nodes and the corresponding edges based on the distance of nodes and their nearest neighbors to the cluster centers. This can effectively alleviate the impact of the spurious edges on the clustering. Finally, to obtain the clustering assignment of all nodes, a classifier is trained using the clustering results of the selected nodes. Experiments on five benchmark graph datasets demonstrate the superiority of the proposed method over state-of-the-art algorithms.
Trimmed Minimum Error Entropy for Robust Online Regression
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, online linear regression in environments corrupted by non-Gaussian noise (especially heavy-tailed noise) is addressed. In such environments, the error between the system output and the label also does not follow a Gaussian distribution and there might exist abnormally large error samples (or outliers) which mislead the learning process. The main challenge is how to keep the supervised learning problem least affected by these unwanted and misleading outliers. In recent years, an information theoretic algorithm based on Renyi's entropy, called minimum error entropy (MEE), has been employed to take on this issue. However, this minimization might not result in a desired estimator inasmuch as entropy is shift-invariant, i.e., by minimizing the error entropy, error samples may not be necessarily concentrated around zero. In this paper, a quantization technique is proposed by which not only aforementioned need of setting errors around the origin in MEE is addressed, but also major outliers are rejected from MEE-based learning and MEE performance is improved from convergence rate, steady state misalignment, and testing error points of view.