Reviewerly/exHarmony huggingface dataset:
Analyzing Patterns in Student SQL Solutions via Levenshtein Edit Distance

Structured Query Language (SQL), the standard language for relational database management systems, is an essential skill for software developers, data scientists, and professionals who need to interact with databases. SQL is highly structured and presents diverse ways for learners to acquire this skill. However, despite the significance of SQL to other related fields, little research has been done to understand how students learn SQL as they work on homework assignments. In this paper, we analyze students' SQL submissions to homework problems of the Database Systems course offered at the University of Illinois at Urbana-Champaign. For each student, we compute the Levenshtein Edit Distances between every submission and their final submission to understand how students reached their final solution and how they overcame any obstacles in their learning process. Our system visualizes the edit distances between students' submissions to a SQL problem, enabling instructors to identify interesting learning patterns and approaches. These findings will help instructors target their instruction in difficult SQL areas for the future and help students learn SQL more effectively.
Characteristics based visual servo for 6DOF robot arm control

Visual servo is a method for robot arm motion control. It is controlled by the end effector velocity that is the result in calculating the internal Jacobi matrix and vector of feature error. In general, automatic robotic task require high quality sensor that can measure a 3-dimential distance, and do calibration in order to suit the sensor frame and robot frame in Euclidean space. In this paper, we only use RGB camera as the data collection, which not requiring the calibration in sensor frame. Thus, our method is simpler than any other automatic motion methods. Meanwhile, the proposed characteristics based visual servo method has varying the hyper parameter, and show the effectiveness for indicating the precision of pose error both simulation and actual environments.
Anonymization Methods of Structured Health Care Data: A Literature Review

Anonymization of health data is one of the most important technics of sharing data for secondary purposes such as research and statistics while preserving people's privacy. This article is a systematic literature review of current methods that are used for the anonymization of health data. This research includes the most recent methods, from the articles published in 2017–2020. In total 1349 records were found and, according to the selection criteria, 21 articles were chosen for the final review. This full paper aims to provide a literature review on existing methods of anonymization structured health care data and identify existing issues with those methods.
Digital forensic tool verification: An evaluation of options for establishing trustworthiness

Marshall and Paige (2018)2018) reported a reluctance, on the part of tool producers, to allow scrutiny of their internal testing processes and results. The absence of evidence of tool verification increases the work required for method validation to comply with regulatory and international standards requirements. The author presents a range of models for tool verification and evaluates them in terms of estimated risk and cost to end-user and provider. A preferred option is identified.
Multi-Label Prediction for Political Text-as-Data

Abstract Political scientists increasingly use supervised machine learning to code multiple relevant labels from a single set of texts. The current “best practice” of individually applying supervised machine learning to each label ignores information on inter-label association(s), and is likely to under-perform as a result. We introduce multi-label prediction as a solution to this problem. After reviewing the multi-label prediction framework, we apply it to code multiple features of (i) access to information requests made to the Mexican government and (ii) country-year human rights reports. We find that multi-label prediction outperforms standard supervised learning approaches, even in instances where the correlations among one’s multiple labels are low.
FLBench: A Benchmark Suite for Federated Learning

Federated learning is a new machine learning paradigm. The goal is to build a machine learning model from the data sets distributed on multiple devices–so-called an isolated data island–while keeping their data secure and private. Most existing federated learning benchmarks work manually splits commonly-used public datasets into partitions to simulate real-world isolated data island scenarios. Still, this simulation fails to capture real-world isolated data island’s intrinsic characteristics. This paper presents a federated learning (FL) benchmark suite named FLBench. FLBench contains three domains: medical, financial, and AIoT. By configuring various domains, FLBench is qualified to evaluate federated learning systems and algorithms’ essential aspects, like communication, scenario transformation, privacy-preserving, data distribution heterogeneity, and cooperation strategy. Hence, it becomes a promising platform for developing novel federated learning algorithms. Currently, FLBench is open-sourced and in fast-evolution. We package it as an automated deployment tool. The benchmark suite is available from https://www.benchcouncil.org/flbench.html .
FeatureTransfer: Unsupervised Domain Adaptation for Cross-Domain Deepfake Detection

Recently, various Deepfake detection methods have been proposed, and most of them are based on convolutional neural networks (CNNs). These detection methods suffer from overfitting on the source dataset and do not perform well on cross-domain datasets which have different distributions from the source dataset. To address these limitations, a new method named FeatureTransfer is proposed in this paper, which is a two-stage Deepfake detection method combining with transfer learning. Firstly, The CNN model pretrained on a third-party large-scale Deepfake dataset can be used to extract the more transferable feature vectors of Deepfake videos in the source and target domains. Secondly, these feature vectors are fed into the domain-adversarial neural network based on backpropagation (BP-DANN) for unsupervised domain adaptive training, where the videos in the source domain have real or fake labels, while the videos in the target domain are unlabelled. The experimental results indicate that the proposed method FeatureTransfer can effectively solve the overfitting problem in Deepfake detection and greatly improve the performance of cross-dataset evaluation.
Aspects of Nuclear Power Plant Digital Decommissioning

The paper considers a set of problems and trends related to the decommissioning of high-risk facilities, in particular nuclear power plants (NPP). Along with the traditional problems of ensuring nuclear and technological safety during the decommissioning of NPPs, new challenges have arisen related to ensuring cybersecurity. In accordance with the recommendations of regulatory documents, it is necessary to identify critical information assets at the design stage. The paper notes the need to create digital twins to develop technologies for decommissioning an object while ensuring its safety of all types. The paper discusses the areas of application of remote-control technologies and robotics during the decommissioning of nuclear power plants, and also highlights some of the problems of these technologies. The necessity of maintaining a knowledge base on the object, remote control of robots used for inspection and dismantling of the object, the use of artificial intelligence, ensuring cybersecurity, and the designing of digital twins is emphasized for the successful solution of the decommissioning problem.
Blockchain based Secure Data Sharing Model

There are mainly three traditional data sharing methods. The first is the most direct data copy, the second is to share data based on a data sharing protocol, and the third is to share data through a data center. These methods have a common feature, that is, the data requester will get the data of the data owner. This may cause serious problems in data security, such as data leakage and data abuse. As a data center is a centralized organization, there are risks such as data loss and data tampering. In addition, various countries have also issued a series of policies on data security issues, such as the GDPR implemented by the European Union in 2018. The blockchain technology using a decentralized model can be used as a new attempt to solve the above problems. This paper studies a data sharing scheme based on blockchain, and proposes a model that combines the Ethereum blockchain and federated learning ideas, and uses off-chain storage methods to share data. In this model, users can upload data description information to the blockchain through smart contracts, and can retrieve the required data through keywords, and then send the data identification and data processing model to the data owner in the form of transactions. The data owner can use this model to process the data, and finally return the result to the data requester. Because the data owner is in full control of his data and does not expose the source data to the outside, the use of this model for data sharing can effectively avoid problems such as data leakage, data loss, and data abuse.
ToF and Stereo Data Fusion Using Dynamic Search Range Stereo Matching

Time-of-Flight (ToF) sensors and stereo vision systems are both widely used for capturing depth data. They have some complementary strengths and limitations, which have been exploited in prior research to produce more accurate depth maps by fusing data from the two sources. However, among these diverse data fusion approaches, none of them provides an end-to-end neural network solution. In this work, we propose the first end-to-end ToF and stereo data fusion network using the coarse-to-fine matching framework, where the prior of ToF depth is integrated into the stereo matching process by constraining the search range of stereo matching within an interval around the ToF camera depth measurement. We adopt a dynamic search range for each pixel according to an estimated ToF error map, which is more efficient and effective than a constant one when handling various errors. The ToF error map is estimated by the ToF error estimator branching out from the stereo matching network. Both ToF error estimation and stereo matching are performed in a joint framework, with the two tasks assisting each other mutually. We also propose an upsampling module to replace the naive bilinear upsampling in the coarse-to-fine stereo matching network, which reduces the error caused by the upsampling. The proposed deep network is trained end-to-end on synthetic datasets and generalizable to real-world datasets without further fine-tuning. Experimental results show that our fusion method achieves higher accuracy than either ToF or stereo alone, and outperforms state-of-the-art fusion methods on both synthetic and real data.
Exploring the effects of different Clustering Methods on a News Recommender System

News recommendations distinguishes from general content recommendations as it takes in consideration news freshness, sparsity, monotony and time. Recent works approach these features using hybrid Collaborative-Content-based Filtering methods, adapting clustering techniques to handle sparsity and monotony without considering the effects that different clustering methods may have over recommendation results. Such studies often evaluate the results of varying different parameters individually, ignoring possible interaction effects between them. They also base their results on metrics such as accuracy and recall that are sensitive to bias. To investigate the importance of clustering method selection to News Recommender System results we evaluated the effects of different traditional techniques in recommending news articles. We implemented an algorithm that used a hybrid Collaborative-Content-based Filtering method to incorporate user behavior, user interest, article popularity and time effect. The system uses an article selection method that built the recommendation set based on content features. With this algorithm, we examined the existence of interaction effects between the input parameters. We used a Gaussian regression process to explore the response surface while sequentially optimizing parameters. To avoid being misled by underlying biases we used Informedness, an accuracy metric that captures both positive and negative information from prediction results. Our results demonstrated that different clustering methods had a significant influence on the recommendation results. It was also found that a traditional hierarchical method outperformed optimization methods with important performance improvement. In addition, we demonstrated that parameters may interact with each other and that analyzing them separately may mislead interpretation.
A doubly stochastic block Gauss–Seidel algorithm for solving linear equations

We propose a doubly stochastic block Gauss–Seidel algorithm for solving linear systems of equations. By varying the row partition parameter and the column partition parameter for the coefficient matrix, we recover the Landweber algorithm, the randomized Kaczmarz algorithm, the randomized coordinate descent algorithm, and the doubly stochastic Gauss–Seidel algorithm. For arbitrary (consistent or inconsistent, full column rank or rank-deficient) linear systems, we prove the exponential convergence of the norm of the expected error via exact formulas. We also prove the exponential convergence of the expected norm of the error for consistent linear systems, and the exponential convergence of the expected norm of the residual for arbitrary linear systems. Numerical experiments for linear systems with synthetic and real-world coefficient matrices are given to demonstrate the efficiency of our algorithm.
Key Skills to Work With Agile Frameworks in Software Engineering: Chilean Perspectives

Agile frameworks continue to provide positive evidence regarding the benefits of their use in the software products. Since these methods develop professional skills in those who practice them, their knowledge and use will acquire greater demand in areas other than software development. For this reason, it is essential to recognize the key skills for agile team building. The goal of this paper is to identify the agile professional skills that the Chilean industry considers key to conform high-performance agile teams. A survey was applied to agile community professionals in Chile to validate the results of previous work and to identify relevant information regarding learning processes, techniques, and tools for working with agile frameworks. The results allowed to establish three key skills for high-performance teams with their respective levels of achievement.
Augmented reality for IT/OT failures in maintenance operations of digitized trains: current status, research challenges and future directions

The railway industry is moving towards a complex socio-technological system that relies on computer control and human-machine interfaces. Digitization and convergence of information technology (IT) with operational technology (OT) becomes increasingly important. Therefore, enabling innovations, lowering costs, increasing safety systems, enhancing performance, and increasing flexibility gains. In this scenario, opportunities arise for Augmented Reality (AR) capabilities to enhance maintenance operations of digitized trains. Initial research and tests have enabled training technicians and assistance of train drivers during specific activities by facilitating interactions with machines that go beyond traditional training manuals. Despite the positive results AR has shown, there are no specific indications available regarding the ‘deployability’ of the technology related to maintenance tasks. This position paper presents arguable boundaries and challenges for a structural appraisal of AR on maintenance operations of IT/OT converged trains. Potential AR solutions and specifications were based on current maintenance operations. Semi-structured interviews and surveys were conducted with maintenance providers to determine existing challenges. Moreover, the paper comprises an analysis to reveal the potential application AR has in maintenance procedures of digitized trains. Finally, this paper provides future research directions of AR technologies related to maintenance procedures of trains.
Mining Bytecode Features of Smart Contracts to Detect Ponzi Scheme on Blockchain

The emergence of smart contracts has increased the attention of industry and academia to blockchain technology, which is tamper-proofing, decentralized, autonomous, and enables decentralized applications to operate in untrustworthy environments. However, these features of this technology are also easily exploited by unscrupulous individuals, a typical example of which is the Ponzi scheme in Ethereum. The negative effect of unscrupulous individuals writing Ponzi scheme-type smart contracts in Ethereum and then using these contracts to scam large amounts of money has been significant. To solve this problem, we propose a detection model for detecting Ponzi schemes in smart contracts using bytecode. In this model, our innovation is shown in two aspects: We first propose to use two bytes as one characteristic, which can quickly transform the bytecode into a high-dimensional matrix, and this matrix contains all the implied characteristics in the bytecode. Then, We innovatively transformed the Ponzi schemes detection into an anomaly detection problem. Finally, an anomaly detection algorithm is used to identify Ponzi schemes in smart contracts. Experimental results show that the proposed detection model can greatly improve the accuracy of the detection of the Ponzi scheme contracts. Moreover, the F1-score of this model can reach 0.88, which is far better than those of other traditional detection models.
Do Institutions and Technologies Matter for Trade Openness? Empirical Evidence from African Countries

This study examines the influence of institutions, the Internet, and mobile phone usage on African countries' trade openness. Applying various estimations for a balanced panel of data for 47 African economies over the period of 2003 to 2017, our empirical analysis arrives at the following interesting conclusions. Improvement of the institutional framework has mixed impacts on the trade relationships of African countries within the continent. Notably, internet and mobile phone usage have greater impacts on trade openness in countries that have a better institutional framework.
Weighted Random Forest Algorithm Based on Bayesian Algorithm

Abstract The random forest(RF) algorithm is a very efficient and excellent ensemble classification algorithm. In this paper, we improve the random forest algorithm and propose an algorithm called ‘Bayesian Weighted Random Forest’(B-RF), focus on the problem that inaccurate decision tree classification caused by the same voting weights in the traditional random forest model. The main idea underlying the proposed model is to replace the supermajority voting of random forests into weighted voting, fully consider the difference of classification ability of each decision tree, using the Bayesian formula to dynamically update the weight value for each tree, so that the strong classifier has higher voting power and effectively improves the overall performance of classification. Through the verification of UCI database, the results show that the classification accuracy of the proposed weighted random forest model is higher. This illustrate the outperformance of the proposed model in comparison with the general random forest algorithm.
AutoKG - An Automotive Domain Knowledge Graph for Software Testing: A position paper

Industries have a significant amount of data in semi-structured and unstructured formats which are typically captured in text documents, spreadsheets, images, etc. This is especially the case with the software description documents used by domain experts in the automotive domain to perform tasks at various phases of the Software Development Life Cycle (SDLC). In this paper, we propose an end-to-end pipeline to extract an Automotive Knowledge Graph (AutoKG) from textual data using Natural Language Processing (NLP) techniques with the application of automatic test case generation. The proposed pipeline primarily consists of the following components: 1) AutoOntology, an ontology that has been derived by analyzing several industry scale automotive domain software systems, 2) AutoRE, a Relation Extraction (RE) model to extract triplets from various sentence types typically found in the automotive domain, and 3) AutoVec, a neural embedding based algorithm for triplet matching and context-based search. We demonstrate the pipeline with an application of automatic test case generation from requirements using AutoKG.
Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow

The Coarse-To-Fine (CTF) matching scheme has been widely applied to reduce computational complexity and matching ambiguity in stereo matching and optical flow tasks by converting image pairs into multi-scale representations and performing matching from coarse to fine levels. Despite its efficiency, it suffers from several weaknesses, such as tending to blur the edges and miss small structures like thin bars and holes. We find that the pixels of small structures and edges are often assigned with wrong disparity/flow in the upsampling process of the CTF framework, introducing errors to the fine levels and leading to such weaknesses. We observe that these wrong disparity/flow values can be avoided if we select the best-matched value among their neighborhood, which inspires us to propose a novel differentiable Neighbor-Search Upsampling (NSU) module. The NSU module first estimates the matching scores and then selects the best-matched disparity/flow for each pixel from its neighbors. It effectively preserves finer structure details by exploiting the information from the finer level while upsampling the disparity/flow. The proposed module can be a drop-in replacement of the naive upsampling in the CTF matching framework and allows the neural networks to be trained end-to-end. By integrating the proposed NSU module into a baseline CTF matching network, we design our Detail Preserving Coarse-To-Fine (DPCTF) matching network. Comprehensive experiments demonstrate that our DPCTF can boost performances for both stereo matching and optical flow tasks. Notably, our DPCTF achieves new state-of-the-art performances for both tasks - it outperforms the competitive baseline (Bi3D) by 28.8% (from 0.73 to 0.52) on EPE of the FlyingThings3D stereo dataset, and ranks first in KITTI flow 2012 benchmark. The code is available at https://github.com/Deng-Y/DPCTF.
A Systematic Framework to Identify Violations of Scenario-dependent Driving Rules in Autonomous Vehicle Software

Safety compliance is paramount to the safe deployment of autonomous vehicle (AV) technologies in real-world transportation systems. As AVs will share road infrastructures with human drivers and pedestrians, it is an important requirement for AVs to obey standard driving rules. Existing AV software testing methods, including simulation and road testing, only check fundamental safety rules such as collision avoidance and safety distance. Scenario-dependent driving rules, including crosswalk and intersection rules, are more complicated because the expected driving behavior heavily depends on the surrounding circumstances. However, a testing framework is missing for checking scenario-dependent driving rules on various AV software.