Reviewerly/exHarmony huggingface dataset:
A direct calibration method for line structured light measurement system based on parallel lines

In view of the complexity of the traditional calibration methods for the line structured light measurement system, this paper proposes a direct system calibration method and corresponds to a direct calibration model. This method adopts a planar target to create the feature lines and uses the moving distance of linear guide as constraints, which can determine the mathematical relationship between the pixel and world coordinates directly. Compared with traditional calibration models, the proposed model does not need to consider the spatial structure of this system and calibrate each component separately, which simplifies the calibration process and reduces accumulative errors. Furthermore, the target used in this approach can be an ordinary plane, which avoids the difficulty of manufacturing special targets. Experimental results show that the root mean square error (RMSE) is 0.0238 mm and the mean absolute error (MAE) is 0.0255 mm, which can be used for industrial measurement more effectively.
Machine learning-based prediction of trace element concentrations using data from the Karoo large igneous province and its application in prospectivity mapping

In this study, we present a machine learning-based method to predict trace element concentrations from major and minor element concentration data using a legacy lithogeochemical database of magmatic rocks from the Karoo large igneous province (Gondwana Supercontinent). Wedemonstrate that a variety of trace elements, including most of the lanthanides, chalcophile, lithophile, and siderophile elements, can be predicted with excellent accuracy. This finding reveals that there are reliable, high-dimensional elemental associations that can be used to predict trace elements in a range of plutonic and volcanic rocks. Since the major and minor elements are used as predictors, prediction performance can be used as a direct proxy for geochemical anomalies. As such, our proposed method is suitable for prospective exploration by identifying anomalous trace element concentrations. Compared to multivariate compositional data analysis methods, the new method does not rely on assumptions of stoichiometric combinations of elements in the data to discover geochemical anomalies. Because we do not use multivariate compositional data analysis techniques (e.g. principal component analysis and combined use of major, minor and trace elements data), we also show that log-ratio transforms do not increase the performance of the proposed approach and are unnecessary for algorithms that are not spatially aware in the feature space. Therefore, we demonstrate that high-dimensional elemental associations can be modelled in an automated manner through a data-driven approach and without assumptions of stoichiometry within the data. The approach proposed in this study can be used as a replacement method to the multivariate compositional data analysis technique that is used for prospectivity mapping, or be used as a pre-processor to reduce the detection of false geochemical anomalies, particularly where the data is of variable quality. • Machine learning can predict trace element concentrations from major and minor element geochemical data. • Various machine learning algorithms can accurately predict trace element concentrations. • Log-ratio transformation of geochemical data is not always necessary when using machine learning. • Prediction residuals (difference between predicted and actual quantities) can be used to map geochemical anomalies.
CROP: FPGA Implementation of High-Performance Polynomial Multiplication in Saber KEM based on Novel Cyclic-Row Oriented Processing Strategy

The rapid advancement in quantum technology has initiated a new round of post-quantum cryptography (PQC) related exploration. The key encapsulation mechanism (KEM) Saber is an important module lattice-based PQC, which has been selected as one of the PQC finalists in the ongoing National Institute of Standards and Technology (NIST) standardization process. On the other hand, however, efficient hardware implementation of KEM Saber has not been well covered in the literature. In this paper, therefore, we propose a novel cyclic-row oriented processing (CROP) strategy for efficient implementation of the key arithmetic operation of KEM Saber, i.e., the polynomial multiplication. The proposed work consists of three layers of interdependent efforts: (i) first of all, we have formulated the main operation of KEM Saber into desired mathematical forms to be further developed into CROP based algorithms, i.e., the basic version and the advanced higher-speed version; (ii) then, we have followed the proposed CROP strategy to innovatively transfer the derived two algorithms into desired polynomial multiplication structures with the help of a series of algorithm-architecture co-implementation techniques; (iii) finally, detailed complexity analysis and implementation results have shown that the proposed polynomial multiplication structures have better area-time complexities than the state-of-the-art solutions. Specifically, the field-programmable gate array (FPGA) implementation results show that the proposed design, e.g., the basic version has at least less 11.2% area-delay product (ADP) than the best competing one (Cyclone V device). The proposed high-performance polynomial multipliers offer not only efficient operation for output results delivery but also possess low-complexity feature brought by CROP strategy. The outcome of this work is expected to provide useful references for further development and standardization process of KEM Saber.
The Open Cloud Testbed (OCT): A Platform for Research into new Cloud Technologies

The NSF-funded Open Cloud Testbed (OCT) project is building and supporting a testbed for research and experimentation into new cloud platforms – the underlying software which provides cloud services to applications. Testbeds such as OCT are critical for enabling research into new cloud technologies – research that requires experiments which potentially change the operation of the cloud itself.This paper gives an overview of the Open Cloud Testbed, including an overview on the existing components OCT is based on and the description of new infrastructure and software extension. In addition, we present several use cases of OCT, including a description of FPGA-based research enabled by newly-deployed resources.
They See Me Rollin’: Inherent Vulnerability of the Rolling Shutter in CMOS Image Sensors

In this paper, we describe how the electronic rolling shutter in CMOS image sensors can be exploited using a bright, modulated light source (e.g., an inexpensive, off-the-shelf laser), to inject fine-grained image disruptions. We demonstrate the attack on seven different CMOS cameras, ranging from cheap IoT to semi-professional surveillance cameras, to highlight the wide applicability of the rolling shutter attack. We model the fundamental factors affecting a rolling shutter attack in an uncontrolled setting. We then perform an exhaustive evaluation of the attack's effect on the task of object detection, investigating the effect of attack parameters. We validate our model against empirical data collected on two separate cameras, showing that by simply using information from the camera's datasheet the adversary can accurately predict the injected distortion size and optimize their attack accordingly. We find that an adversary can hide up to 75% of objects perceived by state-of-the-art detectors by selecting appropriate attack parameters. We also investigate the stealthiness of the attack in comparison to a naïve camera blinding attack, showing that common image distortion metrics can not detect the attack presence. Therefore, we present a new, accurate and lightweight enhancement to the backbone network of an object detector to recognize rolling shutter attacks. Overall, our results indicate that rolling shutter attacks can substantially reduce the performance and reliability of vision-based intelligent systems.
Transfer learning based handwritten character recognition of tamil script using inception-V3 Model

The paper describes the excellent method to get first-rate accuracy and performance in the discipline of Tamil character recognition in a handwritten mode. However, the subject is still at a nascent stage and grossly lacks adequate accuracy in the Tamil language, even though several studies have been conducted within the discipline of handwritten character recognition. This paper draws the attention to the offline handwritten recognition for the Tamil language using the Inception-v3 based transfer learning method. The proposed work is conducted on the readily available HP Tamil handwritten character offline dataset (Hewlett-Packard Lab: hpl-tamil-iso-char-offline-1.0.). It reveals that with the suitable arrangement of transfer learning approach with Inception-v3, the pre-trained model can achieve the recognition accuracy of 93.1%, overtaking the former deep learning designs. The achieved accuracy is due to the use of a pre-trained version with transfer learning that regularly hastens the method of the training process on a new task. Overall, this results in higher accuracy and a more capable version.
ExtraNet

Both the frame rate and the latency are crucial to the performance of realtime rendering applications such as video games. Spatial supersampling methods, such as the Deep Learning SuperSampling (DLSS), have been proven successful at decreasing the rendering time of each frame by rendering at a lower resolution. But temporal supersampling methods that directly aim at producing more frames on the fly are still not practically available. This is mainly due to both its own computational cost and the latency introduced by interpolating frames from the future. In this paper, we present ExtraNet, an efficient neural network that predicts accurate shading results on an extrapolated frame, to minimize both the performance overhead and the latency. With the help of the rendered auxiliary geometry buffers of the extrapolated frame, and the temporally reliable motion vectors, we train our ExtraNet to perform two tasks simultaneously: irradiance in-painting for regions that cannot find historical correspondences, and accurate ghosting-free shading prediction for regions where temporal information is available. We present a robust hole-marking strategy to automate the classification of these tasks, as well as the data generation from a series of high-quality production-ready scenes. Finally, we use lightweight gated convolutions to enable fast inference. As a result, our ExtraNet is able to produce plausibly extrapolated frames without easily noticeable artifacts, delivering a 1.5× to near 2× increase in frame rates with minimized latency in practice.
Construction of sentimental knowledge graph of Chinese government policy comments

Social Media Networks have developed into an important channel and platform for the collection and dissemination of policy information. Various policy comments on them have fully demonstrated the basic characteristics of big data. This paper introduces knowledge graphs into the sentiment analysis, analyses and sorts out the policy comments of China's mainstream social media platforms from 2016 to 2019, build a sentiment analysis dictionary, and then use the policy comments evaluation system to form sentiment knowledge graphs of policy comments that includes seven sentiments and five themes. The process of the sentiment knowledge graph constructed in this paper helps to more accurately understand the changes of online public opinion, and provides a theoretical basis for local governments to adjust the implementation of various policies. Apart from being the prototype of the automated sentiment knowledge graph system for policy comments, it can also be applied to other related hot topics.
Portraying probabilistic relationships of continuous nodes in Bayesian networks with ranked nodes method

This paper advances the use of the ranked nodes method (RNM) to portray probabilistic relationships of continuous quantities in Bayesian networks (BNs). In RNM, continuous quantities are represented by ranked nodes with discrete ordinal scales. The probabilistic relationships of the nodes are quantified in conditional probability tables (CPTs) generated with expert-elicited parameters. When ranked nodes are formed by discretizing continuous scales, ignorance about the functioning of RNM can lead to discretizations that make the generation of sensible CPTs impossible. While a guideline exists on this matter, it is limited by a requirement to define an equal number of ordinal states for all the nodes. This paper presents two novel discretization approaches that consider the functioning of RNM and allow the nodes to have non-equal numbers of ordinal states. In the first one, called the “static discretization approach”, the nodes can be given any desired discretizations that stay unchanged during the use of the BN. In the second one, called the “dynamic discretization approach”, the discretizations are algorithmically updated during the use of the BN to help manage the sizes of the generated CPTs. Both approaches are based on the original idea that, besides the RNM parameters, the nodes probabilistic relationship is defined by initial RNM-compatible discretizations elicited from the domain expert. Overall, the new approaches offer an easier and more versatile way of using RNM to depict the probabilistic relationships of continuous quantities. In doing so, they also facilitate the effective and diverse use of BNs in decision support systems.
A Reward Response Game in the Federated Learning System

The emergence of federated learning and the increasingly powerful mobile devices lead to a mobile-crowd machine learning paradigm. In this paper, we consider a mobile-crowd federated learning system that includes a central server and a set of mobile devices. As the model requester, the server motivates all devices to train an accurate model by paying them based on their individual contributions. Each participating device needs to balance between the training rewards and costs for profit maximization. A Stackelberg game is proposed to model interactions between the server and devices. To match with reality, our model takes the training deadline and the device-side upload time into consideration. Based on different definitions of individual contribution, two reward policies, i.e., the size-based policy and accuracy-based policy, are compared. The existence and uniqueness of Stackelberg equilibrium (SE) under both definitions are analyzed, according to which algorithms are proposed to achieve the corresponding SE(s). We show that there is a lower bound of 0.5 on the price of anarchy in the proposed game. We extend our model by considering the uncertainty in the upload time, where each device’s upload time is subject to a normal distribution due to its unstable channel. Numerical evaluations are presented to verify the proposed models.
Semisupervised Network Embedding With Differentiable Deep Quantization

Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many downstream network analytics tasks. For large networks, the trained embeddings often require a significant amount of space to store, making storage and processing a challenge. Building on our previous work on semisupervised network embedding, we develop d-SNEQ, a differentiable DNN-based quantization method for network embedding. d-SNEQ incorporates a rank loss to equip the learned quantization codes with rich high-order information and is able to substantially compress the size of trained embeddings, thus reducing storage footprint and accelerating retrieval speed. We also propose a new evaluation metric, path prediction, to fairly and more directly evaluate the model performance on the preservation of high-order information. Our evaluation on four real-world networks of diverse characteristics shows that d-SNEQ outperforms a number of state-of-the-art embedding methods in link prediction, path prediction, node classification, and node recommendation while being far more space- and time-efficient.
Knowledge Graphs and Linked Data for the Built Environment

The built environment in the Industry 4.0 is digitizing rapidly and producing great amounts of data. These data are of different kinds and follow specific structures and logics, depending on what they are used for and where they come from. This includes vendor-neutral and exchangeable files, such as the Industry Foundation Classes (IFC), that capture building and infrastructure data in neutral and exchangeable files; knowledge graphs and linked data that store building data in web-based graph databases; time-series databases that store sensor data values (big data; data lakes); diverse standard file formats to represent 2D and 3D geometry; and property servers as well as product databases. This chapter will particularly investigate knowledge graphs and linked data. These data technologies promise to represent any data on the web and link them all together. This chapter will explain the most basic parts of the technology, while omitting technical detail. It gives an overview of how these technologies are used in the AEC and smart buildings domains. The chapter ends with a discussion on applicability and limitations of the documented technologies and an outlook into the future.
Keyword Assisted Embedded Topic Model

By illuminating latent structures in a corpus of text, topic models are an essential tool for categorizing, summarizing, and exploring large collections of documents. Probabilistic topic models, such as latent Dirichlet allocation (LDA), describe how words in documents are generated via a set of latent distributions called topics. Recently, the Embedded Topic Model (ETM) has extended LDA to utilize the semantic information in word embeddings to derive semantically richer topics. As LDA and its extensions are unsupervised models, they aren't defined to make efficient use of a user's prior knowledge of the domain. To this end, we propose the Keyword Assisted Embedded Topic Model (KeyETM), which equips ETM with the ability to incorporate user knowledge in the form of informative topic-level priors over the vocabulary. Using both quantitative metrics and human responses on a topic intrusion task, we demonstrate that KeyETM produces better topics than other guided, generative models in the literature.
Feature space and label space selection based on Error-correcting output codes for partial label learning

Partial label learning (PLL) is a type of weakly supervised learning. This paper proposes a new heuristic algorithm for Partial Label learning through the combination of Feature space And Label space using Error-correcting output codes (PL-FALE for short). In the proposed framework, the feature space of different training sample subsets is exploited to generate diverse positive group and negative group pairs by the divide-and-conquer strategy. Then, the training sample label space is exploited to deal with the partial label overlap in every group pair. A set of experiments are conducted on nine controlled UCI datasets and five real-world datasets, and the experimental results show that PL-FALE can improve the classification performance by utilizing the information embedded in the feature space and label space in most cases. Also, the proposed algorithm achieves more stable performance than other ECOC-based PLL algorithms. The source code of PL-FALE is publicly available for non-commercial and research use at: https://github.com/xiaoziyang1/plfale1.
Selective Hole Filling of Red Blood Cells for Improved Marker-Controlled Watershed Segmentation

Manual counting and evaluation of red blood cells with the presence of malaria parasites is a tiresome, time-consuming process that can be altered by environmental conditions and human error. Many algorithms were presented to segment red blood cells for subsequent parasitemia evaluation by machine learning algorithms. However, the segmentation of overlapping red blood cells always has been a challenge. Marker-controlled watershed segmentation is one of the methods that was implemented to separate overlapping red blood cells. However, a high number of overlapped red blood cells were still an issue. We propose a novel approach to improve the segmentation efficiency of marker-controlled watershed segmentation. Local minimum histogram background segmentation with a selective hole filling algorithm was introduced to improve segmentation efficiency of marker-controlled watershed segmentation on a high number of overlapping red blood cells. The local minimum was selected on the smoothed histogram for background segmentation. The combination of selective filling, convex hull, and Hough circle detection algorithms was utilized for the intact segmentation of red blood cells. The markers were computed from the resulted mask, and finally, marker-controlled watershed segmentation was applied to separate overlapping red blood cells. As a result, the proposed algorithm achieved higher background segmentation accuracy compared to popular background segmentation algorithms, and the inclusion of corner details improved watershed segmentation efficiency.
An Efficient Non-Profiled Side-Channel Attack on the CRYSTALS-Dilithium Post-Quantum Signature

Post-quantum digital signature is a critical primitive of computer security in the era of quantum hegemony. As a finalist of the post-quantum cryptography standardization process, the theoretical security of the CRYSTALS-Dilithium (Dilithium) signature scheme has been quantified to withstand classical and quantum cryptanalysis. However, there is an inherent power side-channel information leakage in its implementation instance due to the physical characteristics of hardware.This work proposes an efficient non-profiled Correlation Power Analysis (CPA) strategy on Dilithium to recover the secret key by targeting the underlying polynomial multiplication arithmetic. We first develop a conservative scheme with a reduced key guess space, which can extract a secret key coefficient with a 99.99% confidence using 157 power traces of the reference Dilithium implementation. However, this scheme suffers from the computational overhead caused by the large modulus in Dilithium signature. To further accelerate the CPA run-time, we propose a fast two-stage scheme that selects a smaller search space and then resolves false positives. We finally construct a hybrid scheme that combines the advantages of both schemes. Real-world experiment on the power measurement data shows that our hybrid scheme improves the attack’s execution time by 7.77×.
Blockchain-based Personal Data Trading System using Decentralized Identifiers and Verifiable Credentials

As the needs for personal data increase due to the advent of the AI era, many companies are collecting their users' data and using it to advance the service. As the use of personal data increases, the value of personal data also increases. Although these valuable personal data are generated by individuals, only centralized service providers get profit from the data. In this paper, we propose a blockchain-based personal data trading system using DID (Decentralized Identifiers) and VC (Verifiable Credentials). Our proposed system allows users to collect personal data in their own data storage provided by the system. DID and VC are used to authenticate the user's identity and to prove ownership of the data without any centralized systems, respectively. The integrity of the traded data and the history of the transactions are ensured by Hyperledger Fabric, which is a decentralized infrastructure composed of consortium blockchain nodes. We show how our system works by implementing the monitoring system that provides the current status of the user's data and trading. We verify that two end entities including a seller and a buyer can complete personal data trading by using our proposed system without centralized service providers.