OpenRaiser/Intern-Atlas huggingface dataset:
On the strong metric dimension of composed graphs
Venue: arXiv cs.CC 2022
Year: 2022

Two vertices $u$ and $v$ of an undirected graph $G$ are strongly resolved by a vertex $w$ if there is a shortest path between $w$ and $u$ containing $v$ or a shortest path between $w$ and $v$ containing $u$. A vertex set $R$ is a strong resolving set for $G$ if for each pair of vertices there is a vertex in $R$ that strongly resolves them. The strong metric dimension of $G$ is the size of a minimum strong resolving set for $G$. We show that a minimum strong resolving set for an undirected graph $G$ can be computed efficiently if and only if a minimum strong resolving set for each biconnected component of $G$ can be computed efficiently.
HyperEnclave: An Open and Cross-platform Trusted Execution Environment
Venue: arXiv cs.CR 2022
Year: 2022

A number of trusted execution environments (TEEs) have been proposed by both academia and industry. However, most of them require specific hardware or firmware changes and are bound to specific hardware vendors (such as Intel, AMD, ARM, and IBM). In this paper, we propose HyperEnclave, an open and cross-platform process-based TEE that relies on the widely-available virtualization extension to create the isolated execution environment. In particular, HyperEnclave is designed to support the flexible enclave operation modes to fulfill the security and performance demands under various enclave workloads. We provide the enclave SDK to run existing SGX programs on HyperEnclave with little or no source code changes. We have implemented HyperEnclave on commodity AMD servers and deployed the system in a world-leading FinTech company to support real-world privacy-preserving computations. The evaluation on both micro-benchmarks and application benchmarks shows the design of HyperEnclave introduces only a small overhead.
Differentially-Private Bayes Consistency
Venue: arXiv cs.LG 2022
Year: 2022

We construct a universally Bayes consistent learning rule that satisfies differential privacy (DP). We first handle the setting of binary classification and then extend our rule to the more general setting of density estimation (with respect to the total variation metric). The existence of a universally consistent DP learner reveals a stark difference with the distribution-free PAC model. Indeed, in the latter DP learning is extremely limited: even one-dimensional linear classifiers are not privately learnable in this stringent model. Our result thus demonstrates that by allowing the learning rate to depend on the target distribution, one can circumvent the above-mentioned impossibility result and in fact, learn \emph{arbitrary} distributions by a single DP algorithm. As an application, we prove that any VC class can be privately learned in a semi-supervised setting with a near-optimal \emph{labeled} sample complexity of $\tilde{O}(d/\varepsilon)$ labeled examples (and with an unlabeled sample complexity that can depend on the target distribution).
Towards Accurate Ground Plane Normal Estimation from Ego-Motion
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles. In practice, the ground plane is dynamically changed due to braking and unstable road surface. As a result, the vehicle pose, especially the pitch angle, is oscillating from subtle to obvious. Thus, estimating ground plane normal is meaningful since it can be encoded to improve the robustness of various autonomous driving tasks (e.g., 3D object detection, road surface reconstruction, and trajectory planning). Our proposed method only uses odometry as input and estimates accurate ground plane normal vectors in real time. Particularly, it fully utilizes the underlying connection between the ego pose odometry (ego-motion) and its nearby ground plane. Built on that, an Invariant Extended Kalman Filter (IEKF) is designed to estimate the normal vector in the sensor's coordinate. Thus, our proposed method is simple yet efficient and supports both camera- and inertial-based odometry algorithms. Its usability and the marked improvement of robustness are validated through multiple experiments on public datasets. For instance, we achieve state-of-the-art accuracy on KITTI dataset with the estimated vector error of 0.39°. Our code is available at github.com/manymuch/ground_normal_filter.
Fruit Quality Assessment with Densely Connected Convolutional Neural Network
Venue: arXiv cs.CV 2022
Year: 2022

Accurate recognition of food items along with quality assessment is of paramount importance in the agricultural industry. Such automated systems can speed up the wheel of the food processing sector and save tons of manual labor. In this connection, the recent advancement of Deep learning-based architectures has introduced a wide variety of solutions offering remarkable performance in several classification tasks. In this work, we have exploited the concept of Densely Connected Convolutional Neural Networks (DenseNets) for fruit quality assessment. The feature propagation towards the deeper layers has enabled the network to tackle the vanishing gradient problems and ensured the reuse of features to learn meaningful insights. Evaluating on a dataset of 19,526 images containing six fruits having three quality grades for each, the proposed pipeline achieved a remarkable accuracy of 99.67%. The robustness of the model was further tested for fruit classification and quality assessment tasks where the model produced a similar performance, which makes it suitable for real-life applications.
Modularity and Architecture of PLC-based Software for Automated Production Systems: An analysis in industrial companies
Venue: arXiv cs.SE 2022
Year: 2022

Adaptive and flexible production systems require modular and reusable software especially considering their long term life cycle of up to 50 years. SWMAT4aPS, an approach to measure Software Maturity for automated Production Systems is introduced. The approach identifies weaknesses and strengths of various companie's solutions for modularity of software in the design of automated Production Systems (aPS). At first, a self assessed questionnaire is used to evaluate a large number of companies concerning their software maturity. Secondly, we analyze PLC code, architectural levels, workflows and abilities to configure code automatically out of engineering information in four selected companies. In this paper, the questionnaire results from 16 German world leading companies in machine and plant manufacturing and four case studies validating the results from the detailed analyses are introduced to prove the applicability of the approach and give a survey of the state of the art in industry.
Encrypted machine learning of molecular quantum properties
Venue: arXiv cs.CR 2022
Year: 2022

Large machine learning models with improved predictions have become widely available in the chemical sciences. Unfortunately, these models do not protect the privacy necessary within commercial settings, prohibiting the use of potentially extremely valuable data by others. Encrypting the prediction process can solve this problem by double-blind model evaluation and prohibits the extraction of training or query data. However, contemporary ML models based on fully homomorphic encryption or federated learning are either too expensive for practical use or have to trade higher speed for weaker security. We have implemented secure and computationally feasible encrypted machine learning models using oblivious transfer enabling and secure predictions of molecular quantum properties across chemical compound space. However, we find that encrypted predictions using kernel ridge regression models are a million times more expensive than without encryption. This demonstrates a dire need for a compact machine learning model architecture, including molecular representation and kernel matrix size, that minimizes model evaluation costs.
Secure communication using low dimensional topological elements
Venue: arXiv cs.CR 2022
Year: 2022

Low-dimensional topological objects, such as knots and braids, have become prevalent in multiple areas of physics, such as fluid dynamics, optics, and quantum information processing. Such objects also now play a role in cryptography, where a framed knot can store encoded information using its braid representation for communications purposes. The greater resilience of low-dimensional topological elements under deformations allows them to be employed as a reliable framework for information exchange. Here, we introduce a challenge-response protocol as an application of this construction for authentication. We provide illustrative examples of both procedures showing how framed links and braids may help to enhance secure communication.
Increasing System Test Coverage in Production Automation Systems
Venue: arXiv cs.SE 2022
Year: 2022

An approach is introduced, which supports a testing technician in the identification of possibly untested behavior of control software of fully integrated automated production systems (aPS). Based on an approach for guided semi-automatic system testing, execution traces are recorded during testing, allowing a subsequent coverage assessment. As the behavior of an aPS is highly dependent on the software, omitted system behavior can be identified and assessed for criticality. Through close cooperation with industry, this approach represents the first coverage assessment approach for system testing in production automation to be applied on real industrial objects and evaluated by industrial experts.
Models Developed for Spiking Neural Networks
Venue: arXiv cs.NE 2022
Year: 2022

Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial neural networks (ANNs) once again. They have become the state-of-the-art models and have won different machine learning challenges. Although these networks are inspired by the brain, they lack biological plausibility, and they have structural differences compared to the brain. Spiking neural networks (SNNs) have been around for a long time, and they have been investigated to understand the dynamics of the brain. However, their application in real-world and complicated machine learning tasks were limited. Recently, they have shown great potential in solving such tasks. Due to their energy efficiency and temporal dynamics there are many promises in their future development. In this work, we reviewed the structures and performances of SNNs on image classification tasks. The comparisons illustrate that these networks show great capabilities for more complicated problems. Furthermore, the simple learning rules developed for SNNs, such as STDP and R-STDP, can be a potential alternative to replace the backpropagation algorithm used in DNNs.
Improving the Utilization of Digital Services - Evaluating Contest - Driven Open Data Development and the Adoption of Cloud Services
Venue: arXiv cs.DC 2022
Year: 2022

There is a growing interest in utilizing digital services, such as software apps and cloud-based software services. The utilization of digital services is increasing more rapidly than any other segment of world trade. The availability of open data unlocks the possibility of generating market possibilities in the public and private sectors. Digital service utilization can be improved by adopting cloud-based software services and open data innovation for service development. However, open data has no value unless utilized, and little is known about developing digital services using open data. Evaluation of digital service development processes to service deployment is indispensable. Despite this, existing evaluation models are not specifically designed to measure open data innovation contests. Additionally, existing cloud-based digital service implications are not used directly to adopt the technology, and empirical research needs to be included. The research question addressed in this thesis is: "How can contest-driven innovation of open data digital services be evaluated and the adoption of digital services be supported to improve the utilization of digital services?" The research approaches used are design science research, descriptive statistics, and case study. This thesis proposes Digital Innovation Contest Measurement Model (DICM-model) and Designing and Refining DICM (DRD-method) for designing and refining DICM-model to provide more agility. Additionally, a framework of barriers constraining developers of open data services from developing viable services is also presented. This framework enables requirement and cloud engineers to prioritize factors responsible for effective adoption. Future research possibilities are automation of idea generation, ex-post evaluation of the proposed artifacts, and expanding cloud-based digital service adoption from suppliers' perspectives.
VASR: Visual Analogies of Situation Recognition
Venue: arXiv cs.CV 2022
Year: 2022

A core process in human cognition is analogical mapping: the ability to identify a similar relational structure between different situations. We introduce a novel task, Visual Analogies of Situation Recognition, adapting the classical word-analogy task into the visual domain. Given a triplet of images, the task is to select an image candidate B' that completes the analogy (A to A' is like B to what?). Unlike previous work on visual analogy that focused on simple image transformations, we tackle complex analogies requiring understanding of scenes. We leverage situation recognition annotations and the CLIP model to generate a large set of 500k candidate analogies. Crowdsourced annotations for a sample of the data indicate that humans agree with the dataset label ~80% of the time (chance level 25%). Furthermore, we use human annotations to create a gold-standard dataset of 3,820 validated analogies. Our experiments demonstrate that state-of-the-art models do well when distractors are chosen randomly (~86%), but struggle with carefully chosen distractors (~53%, compared to 90% human accuracy). We hope our dataset will encourage the development of new analogy-making models. Website: https://vasr-dataset.github.io/
Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objects
Venue: arXiv cs.RO 2022
Year: 2022

Representing and reasoning about uncertainty is crucial for autonomous agents acting in partially observable environments with noisy sensors. Partially observable Markov decision processes (POMDPs) serve as a general framework for representing problems in which uncertainty is an important factor. Online sample-based POMDP methods have emerged as efficient approaches to solving large POMDPs and have been shown to extend to continuous domains. However, these solutions struggle to find long-horizon plans in problems with significant uncertainty. Exploration heuristics can help guide planning, but many real-world settings contain significant task-irrelevant uncertainty that might distract from the task objective. In this paper, we propose STRUG, an online POMDP solver capable of handling domains that require long-horizon planning with significant task-relevant and task-irrelevant uncertainty. We demonstrate our solution on several temporally extended versions of toy POMDP problems as well as robotic manipulation of articulated objects using a neural perception frontend to construct a distribution of possible models. Our results show that STRUG outperforms the current sample-based online POMDP solvers on several tasks.
An SLR on Edge Computing Security and possible threat protection
Venue: arXiv cs.CR 2022
Year: 2022

Mobile and Internet of Things devices are generating enormous amounts of multi-modal data due to their exponential growth and accessibility. As a result, these data sources must be directly analyzed in real time at the network edge rather than relying on the cloud. Significant processing power at the network's edge has made it possible to gather data and make decisions prior to data being sent to the cloud. Moreover, security problems have significantly towered as a result of the rapid expansion of mobile devices, Internet of Things (IoT) devices, and various network points. It's much harder than ever to guarantee the privacy of sensitive data, including customer information. This systematic literature review depicts the fact that new technologies are a great weapon to fight with the attack and threats to the edge computing security.
Localized Contrastive Learning on Graphs
Venue: arXiv cs.LG 2022
Year: 2022

Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data. However, its reliance on data augmentation and its quadratic computational complexity might lead to inconsistency and inefficiency problems. To mitigate these limitations, in this paper, we introduce a simple yet effective contrastive model named Localized Graph Contrastive Learning (Local-GCL in short). Local-GCL consists of two key designs: 1) We fabricate the positive examples for each node directly using its first-order neighbors, which frees our method from the reliance on carefully-designed graph augmentations; 2) To improve the efficiency of contrastive learning on graphs, we devise a kernelized contrastive loss, which could be approximately computed in linear time and space complexity with respect to the graph size. We provide theoretical analysis to justify the effectiveness and rationality of the proposed methods. Experiments on various datasets with different scales and properties demonstrate that in spite of its simplicity, Local-GCL achieves quite competitive performance in self-supervised node representation learning tasks on graphs with various scales and properties.
Transfer Learning Enhanced DeepONet for Long-Time Prediction of Evolution Equations
Venue: arXiv cs.LG 2022
Year: 2022

Deep operator network (DeepONet) has demonstrated great success in various learning tasks, including learning solution operators of partial differential equations. In particular, it provides an efficient approach to predict the evolution equations in a finite time horizon. Nevertheless, the vanilla DeepONet suffers from the issue of stability degradation in the long-time prediction. This paper proposes a {\em transfer-learning} aided DeepONet to enhance the stability. Our idea is to use transfer learning to sequentially update the DeepONets as the surrogates for propagators learned in different time frames. The evolving DeepONets can better track the varying complexities of the evolution equations, while only need to be updated by efficient training of a tiny fraction of the operator networks. Through systematic experiments, we show that the proposed method not only improves the long-time accuracy of DeepONet while maintaining similar computational cost but also substantially reduces the sample size of the training set.
Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds
Venue: arXiv cs.CV 2022
Year: 2022

Semantic segmentation in 3D indoor scenes has achieved remarkable performance under the supervision of large-scale annotated data. However, previous works rely on the assumption that the training and testing data are of the same distribution, which may suffer from performance degradation when evaluated on the out-of-distribution scenes. To alleviate the annotation cost and the performance degradation, this paper introduces the synthetic-to-real domain generalization setting to this task. Specifically, the domain gap between synthetic and real-world point cloud data mainly lies in the different layouts and point patterns. To address these problems, we first propose a clustering instance mix (CINMix) augmentation technique to diversify the layouts of the source data. In addition, we augment the point patterns of the source data and introduce non-parametric multi-prototypes to ameliorate the intra-class variance enlarged by the augmented point patterns. The multi-prototypes can model the intra-class variance and rectify the global classifier in both training and inference stages. Experiments on the synthetic-to-real benchmark demonstrate that both CINMix and multi-prototypes can narrow the distribution gap and thus improve the generalization ability on real-world datasets.
Comparative Study of Sentiment Analysis for Multi-Sourced Social Media Platforms
Venue: arXiv cs.CL 2022
Year: 2022

There is a vast amount of data generated every second due to the rapidly growing technology in the current world. This area of research attempts to determine the feelings or opinions of people on social media posts. The dataset we used was a multi-source dataset from the comment section of various social networking sites like Twitter, Reddit, etc. Natural Language Processing Techniques were employed to perform sentiment analysis on the obtained dataset. In this paper, we provide a comparative analysis using techniques of lexicon-based, machine learning and deep learning approaches. The Machine Learning algorithm used in this work is Naive Bayes, the Lexicon-based approach used in this work is TextBlob, and the deep-learning algorithm used in this work is LSTM.
The Platform for non-metallic pipes defects recognition. Design and Implementation
Venue: arXiv cs.LG 2022
Year: 2022

This paper describes a prototype software and hardware platform to provide support to field operators during the inspection of surface defects of non-metallic pipes. Inspection is carried out by video filming defects created on the same surface in real-time using a "smart" helmet device and other mobile devices. The work focuses on the detection and recognition of the defects which appears as colored iridescence of reflected light caused by the diffraction effect arising from the presence of internal stresses in the inspected material. The platform allows you to carry out preliminary analysis directly on the device in offline mode, and, if a connection to the network is established, the received data is transmitted to the server for post-processing to extract information about possible defects that were not detected at the previous stage. The paper presents a description of the stages of design, formal description, and implementation details of the platform. It also provides descriptions of the models used to recognize defects and examples of the result of the work.
Physically Plausible Animation of Human Upper Body from a Single Image
Venue: arXiv cs.CV 2022
Year: 2022

We present a new method for generating controllable, dynamically responsive, and photorealistic human animations. Given an image of a person, our system allows the user to generate Physically plausible Upper Body Animation (PUBA) using interaction in the image space, such as dragging their hand to various locations. We formulate a reinforcement learning problem to train a dynamic model that predicts the person's next 2D state (i.e., keypoints on the image) conditioned on a 3D action (i.e., joint torque), and a policy that outputs optimal actions to control the person to achieve desired goals. The dynamic model leverages the expressiveness of 3D simulation and the visual realism of 2D videos. PUBA generates 2D keypoint sequences that achieve task goals while being responsive to forceful perturbation. The sequences of keypoints are then translated by a pose-to-image generator to produce the final photorealistic video.