Reviewerly/exHarmony huggingface dataset:
Considerations and Pitfalls in Controlled Experiments on Code Comprehension

Understanding program code is a complicated endeavor. As such, myriad different factors can influence the outcome. Investigations of program comprehension, and in particular those using controlled experiments, have to take these factors into account. In order to promote the development and use of sound experimental methodology, we discuss potential problems with regard to the experimental subjects, the code they work on, the tasks they are asked to perform, and the metrics for their performance.
An End-User Development Approach to Secure Smart Environments

Given the spread of the Internet of Things (IoT) technology, in several contexts there is a growing need for laypeople to configure their smart devices. Task Automation Systems (TASs) have emerged as tools to simplify the definition of rules for personalizing the behavior of such devices. However, one aspect often neglected by current TASs, which is instead typical of IoT technologies, relates to the security and privacy threats exposed by the “connected” devices. This paper tries to address this problem and illustrates a user-centered design that eventually led to identify a visual paradigm that facilitates the end users in understanding and controlling security and privacy threats.
Incentive mechanism for collaborative distributed learning in Artificial Intelligence of Things

Artificial Intelligence of Things (AIoT) is an emerging trend that integrates artificial intelligence into the Internet of Things, which enables intelligent IoT operations and smart industrial applications. AIoT can generate a large amount of data from the network edge. Due to the concerns of bandwidth and privacy, it is often impractical to move the collected data to the cloud. To address this issue, a collaborative distributed learning has been proposed to let the clients collaboratively train a machine learning model together with their local data in a distributed manner. In this paper, we study the free market incentive mechanism for collaborative distributed learning, where multiple parameter servers (PSs) compete with each other to motivate clients to contribute model training. Specifically, the Stackelberg game with multiple leaders and multiple followers has been designed to analyze the incentive mechanism. Different experiments have been performed to illustrate the efficiency of the proposed approach. In particular, compared with the state-of-the-art under the same budget constraint, the final average utility of the PSs can be increased by at least 94.3%.
Mutual Benefits: Interdisciplinary Education of Pre-Service Teachers and HCI Students in VR/AR Learning Environment Design

The successful development and classroom integration of Virtual (VR) and Augmented Reality (AR) learning environments requires competencies and content knowledge with respect to media didactics and the respective technologies. The paper discusses a pedagogical concept specifically aiming at the interdisciplinary education of pre-service teachers in collaboration with human-computer interaction students. The students’ overarching goal is the interdisciplinary realization and integration of VR/AR learning environments in teaching and learning concepts. To assist this approach, we developed a specific tutorial guiding the developmental process. We evaluate and validate the effectiveness of the overall pedagogical concept by analyzing the change in attitudes regarding 1) the use of VR/AR for educational purposes and in competencies and content knowledge regarding 2) media didactics and 3) technology. Our results indicate a significant improvement in the knowledge of media didactics and technology. We further report on four STEM learning environments that have been developed during the seminar.
Traffic Video Event Retrieval via Text Query using Vehicle Appearance and Motion Attributes

Traffic event retrieval is one of the important tasks for intelligent traffic system management. To find accurate candidate events in traffic videos corresponding to a specific text query, it is necessary to understand the text query’s attributes, represent the visual and motion attributes of vehicles in videos, and measure the similarity between them. Thus we propose a promising method for vehicle event retrieval from a natural-language-based specification. We utilize both appearance and motion attributes of a vehicle and adapt the COOT model to evaluate the semantic relationship between a query and a video track. Experiments with the test dataset of Track 5 in AI City Challenge 2021 show that our method is among the top 6 with a score of 0.1560.
Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance

Query focused summarization (QFS) models aim to generate summaries from source documents that can answer the given query.Most previous work on QFS only considers the query relevance criterion when producing the summary.However, studying the effect of answer relevance in the summary generating process is also important.In this paper, we propose QFS-BART, a model that incorporates the explicit answer relevance of the source documents given the query via a question answering model, to generate coherent and answerrelated summaries.Furthermore, our model can take advantage of large pre-trained models which improve the summarization performance significantly.Empirical results on the Debatepedia dataset show that the proposed model achieves the new state-of-the-art performance.
I Understand You: Blind 3D Human Attention Inference From the Perspective of Third-Person

Inferring object-wise human attention in 3D space from the third-person perspective (e.g., a camera) is crucial to many visual tasks and applications, including human-robot collaboration, unmanned vehicle driving, etc. Challenges arise from classical human attention when human eyes are not visible to cameras, gaze point is outside the field of vision, or the gazed object is occluded by others in the 3D space. In this case, blind 3D human attention inference brings a new paradigm to the community. In this paper, we address these challenges by proposing a scene-behavior associated mechanism, in which both 3D scene and temporal behavior of human are adopted to infer object-wise human attention and its transition. Specifically, point cloud is reconstructed and used for the spatial representation of 3D scene, which is beneficial to handle the blind problem from the perspective of a camera. Based on this, in order to address the blind human attention inference without eye information, we propose a Sequential Skeleton Based Attention Network (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BAN) for behavior-based attention modeling. As is embedded in the scene-behavior associated mechanism, the proposed S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BAN is built under the temporal architecture of Long-Short-Term-Memory (LSTM). Our network employs human skeleton as behavior representation, and maps it to the attention direction frame by frame, which makes attention inference a temporal-correlated issue. With the help of S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BAN, 3D gaze spot and further the attended objects can be obtained frame by frame via intersection and segmentation on the previously reconstructed point cloud. Finally, we conduct experiments from various aspects to verify the object-wise attention localization accuracy, the angular error of attention direction calculation, as well as the subjective results. The experimental results show that the proposed outperforms other competitors.
Explicit Knowledge Incorporation for Visual Reasoning

Existing explainable and explicit visual reasoning methods only perform reasoning based on visual evidence but do not take into account knowledge beyond what is in the visual scene. To addresses the knowledge gap between visual reasoning methods and the semantic complexity of real-world images, we present the first explicit visual reasoning method that incorporates external knowledge and models high-order relational attention for improved generalizability and explainability. Specifically, we propose a knowledge incorporation network that explicitly creates and includes new graph nodes for entities and predicates from external knowledge bases to enrich the semantics of the scene graph used in explicit reasoning. We then create a novel Graph-Relate module to perform high-order relational attention on the enriched scene graph. By explicitly introducing structured external knowledge and high-order relational attention, our method demonstrates significant generalizability and explainability over the state-of-the-art visual reasoning approaches on the GQA and VQAv2 datasets.
“I’m Better Off on my Own”: Understanding How a Tutorial’s Medium Affects Physical Skill Development

The shift towards distance learning brought forth by the pandemic has highlighted the shortcomings of teaching physical skills at a distance. With the emergence of new augmented and connected mediums, new opportunities arise for transferring physical skills that have resisted traditional documentation methods. However, there lacks a framework that allows tutorial authors to capitalize on a new medium's unique affordances rather than remediating existing tutorial conventions. Our work analyzes a body of tutorials rendered in various mediums for centering clay on a pottery wheel — a foundational skill that exemplifies the difficulties of physical skill transfer. Through the lens of McLuhan's "The Medium is the Message" we synthesize a taxonomy of medium conventions and themes derived from analyzing a body of centering tutorials and observation of how a tutorial's medium affects how learners develop physical skills. We leverage our findings to motivate design recommendations to inform how new mediums can support material practices.
Improvement of the DPoS Consensus Mechanism in Blockchain Based on PLTS

Contemporarily, DPoS is the fastest, most effective, decentralized and flexible consensus mechanism among all consensus mechanisms. Similar to the vote of the board, the holders cast a certain number of delegates to perform verification and accounting on their behalf in DPoS. In order to select the most appropriate delegates to implement the operations in blockchain, the DPoS consensus mechanism needs to be improved. Therefore, an improved DPoS consensus mechanism based on Probabilistic Linguistic Term Set (PLTS) is proposed in this paper. Just like human elections, this method allows each node to vote “support” and “oppose” for the delegate node. The efficient and flexible method reduces the probability of malicious nodes becoming delegates and guarantees the security of block generation in the blockchain. The effectiveness of the improved DPoS was verified by examples.
Using AR/VR for Technical Subjects in Vocational Training – Of Substancial Benefit or Just Another Technical Gimmick?

Digitisation and Digitalisation are fundamentally changing many job profiles. Apprentices therefore need appropriate knowledge and experience with the relevant technical systems already during their vocational training. In the training companies, digital transformation varies for example according to the size and type of company. Here, vocational schools can build a bridge. Previous research shows that AR/VR offer new possibilities to improve the quality of vocational education and training, especially in technical subjects, and to balance different prior experiences of the apprentices. These findings are compiled in this paper, especially for the field of electrical engineering. It is shown how the implementation of AR/VR applications in vocational training can provide substantial didactic benefits. In order to enable teachers to make sensible decisions on the use of AR/VR applications, a didactically-driven approach is proposed based on an educational concern or educational problem.
AI authorship?

Considering the role of humans in copyright protection of outputs produced by artificial intelligence.
A Novel Feature Selection Method Based on MRMR and Enhanced Flower Pollination Algorithm for High Dimensional Biomedical Data

Background: The massive amount of biomedical data accumulated in the past decades can be utilized for diagnosing disease. Objective: However, the high dimensionality, small sample sizes, and irrelevant features of data often have a negative influence on the accuracy and speed of disease prediction. Some existing machine learning models cannot capture the patterns on these datasets accurately without utilizing feature selection. Methods: Filter and wrapper are two prevailing feature selection methods. The filter method is fast but has low prediction accuracy, while the latter can obtain high accuracy but has a formidable computation cost. Given the drawbacks of using filter or wrapper individually, a novel feature selection method, called MRMR-EFPATS, is proposed, which hybridizes filter method Minimum Redundancy Maximum Relevance (MRMR) and wrapper method based on an improved Flower Pollination Algorithm (FPA). First, MRMR is employed to rank and screen out some important features quickly. These features are further chosen for individual populations following the wrapper method for faster convergence and less computational time. Then, due to its efficiency and flexibility, FPA is adopted to further discover an optimal feature subset. Result: FPA still has some drawbacks, such as slow convergence rate, inadequacy in terms of searching new solutions, and tends to be trapped in local optima. In our work, an elite strategy is adopted to improve the convergence speed of the FPA. Tabu search and Adaptive Gaussian Mutation are employed to improve the search capability of FPA and escape from local optima. Here, the KNN classifier with the 5-fold-CV is utilized to evaluate the classification accuracy. Conclusion: Extensive experimental results on six public high dimensional biomedical datasets show that the proposed MRMR-EFPATS has achieved superior performance compared to other state-of-theart methods.
Evolvability and complexity properties of the digital circuit genotype-phenotype map

Recent research on genotype-phenotype (G-P) maps in natural evolution has contributed significantly to our understanding of neutrality, redundancy, robustness, and evolvability. Here we investigate the properties of the digital logic gate G-P map and show that this map shares many of the common properties of natural G-P maps, with the exception of a positive relationship between evolvability and robustness. Our results show that in some cases robustness and evolvability may be negatively related as a result of the method used to approximate evolvability. We give two definitions of circuit complexity and empirically show that these two definitions are closely related. This study leads to a better understanding of the relationships between redundancy, robustness, and evolvability in genotype-phenotype maps. We further investigate these results in the context of complexity and show the relationships between phenotypic complexity and phenotypic redundancy, robustness and evolvability.
A multi-surrogate-assisted dual-layer ensemble feature selection algorithm

Ensemble feature selection has attracted much attention of scholars because of its good robustness. However, existing methods on dealing with high-dimensional or large-scale data still have some shortcomings, such as high computational cost or high feature redundancy. In view of it, this paper proposes a new ensemble feature selection algorithm for large-scale data, called Multi-surrogate-assisted Dual-layer Ensemble Feature Selection (MDEFS). In MDEFS, a filter ensemble feature selection method with fast search ability is firstly developed to remove irrelevant or weakly-relevant features in the first layer. In the second layer, a particle swarm-based ensemble method with global search ability is proposed to select optimal feature subset from those remaining features. Furthermore, a multi-surrogate-assisted search mechanism of swarm is developed to reduce the cost of MDEFS on processing large-scale data, where the whole original dataset is replaced by multiple types of representative samples. Finally, the proposed algorithm is applied to 13 datasets and compared with 7 feature selection algorithms. Experimental results show that the multi-surrogate-assisted search mechanism can obviously reduce the running time of MDEFS, and the proposed ensemble approach can make MDEFS obtain better feature subsets, whose average accuracy is 0.72% higher than the best comparison algorithm on each data set. All results indicate that MDEFS is a robust and competitive feature selection algorithm.
Non-fungible tokens: Stem cell transplantation in the blockchain

The rate of growth and interest surrounding data management information technology is astronomical, impacting financial markets, international modern art dealing, and sports memorabilia trading [ [1] What are NFTs, anyway? One just sold for $69 million. 2021https://www.nytimes.com/2021/03/11/arts/design/what-is-an-nft.html Google Scholar , [2] Why did someone pay $560,000 for a picture of my column?. 2021https://www.nytimes.com/2021/03/26/technology/nft-sale.html?name=styln-nfts&region=TOP_BANNER&block=storyline_menu_recirc&action=click&pgtype=Article&variant=show&is_new=false Google Scholar ]. This expansive growth is a result of the creation of Non-Fungible Tokens or NFTs, a non-replicable digital token that relies on a network of computers to store, modify, and prove that a digital item is both unique and authentic. The records of these transactions are recorded in a bitcoin or cryptocurrency [ [3] Kakarlapudi P.V. Mahmoud Q.H. A Systematic Review of Blockchain for Consent Management. Healthcare (Basel). 2021; 9: 137 Crossref PubMed Scopus (4) Google Scholar ], which is maintained in a blockchain [ [4] Moosavi J. Naeni L.M. Fathollahi-Fard A.M. Fiore U. Blockchain in supply chain management: a review, bibliometric, and network analysis. Environ Sci Pollut Res Int. 2021; (Epub ahead of print. PMID: 33638786)https://doi.org/10.1007/s11356-021-13094-3 Crossref PubMed Scopus (24) Google Scholar ], a series of peer-to-peer computers. The introduction of NFTs into healthcare has already began, including how personal electronic medical records will be safely stored, transmitted, and audited for security [ [5] Blockchain in healthcare: how blockchain can revolutionize the medical industry. 2021https://academy.ivanontech.com/blog/blockchain-in-healthcare-how-blockchain-can-revolutionize-the-medical-industry Google Scholar , [6] Roehrs A. da Costa C.A. Righi R.R. Mayer A.H. da Silva V.F. Goldim J.R. et al. Integrating multiple blockchains to support distributed personal health records. Health Informatics J. 2021; 2714604582211007546 Crossref Scopus (1) Google Scholar ].
BCNN: A Binary CNN With All Matrix Ops Quantized To 1 Bit Precision

This paper describes a CNN where all CNN style 2D convolution operations that lower to matrix matrix multiplication are fully binary. The network is derived from a common building block structure that is consistent with a constructive proof outline showing that binary neural networks are universal function approximators. 71.24% top 1 accuracy on the 2012 ImageNet validation set was achieved with a 2 step training procedure and implementation strategies optimized for binary operands are provided.
Spatio-temporal Predictive Network For Videos With Physical Properties

In this paper, we propose a spatio-temporal predictive network with attention weighting of multiple physical Deep Learning (DL) models for videos with various physical properties. Previous approaches have been models with multiple branches for difference properties in videos, but the outputs of branches have been simply summed even with properties that change in time and space. In addition, it is difficult to train previous models for sufficient representations of physical properties in videos. Therefore, we propose the design of the spatio-temporal prediction network and the training method for videos with multiple physical properties, motivated by the Mixtures of Experts framework. Multiple spatio-temporal DL branches/experts for multiple physical properties and pixel-wise and expert-wise attention mechanism for adaptively integrating outputs of experts, i.e., Spatial-Temporal Gating Networks (STGNs) are proposed. Experts are trained with a vast amount of synthetic image sequences by physical equations and noise models. Instead, the whole network including STGNs is allowed to be trained only with a limited number of real datasets. Experiments on various videos, i.e., traffic, pedestrian, Dynamic Texture videos, and radar images, show the superiority of our proposed approach compared with previous approaches.
Normal Integration via Inverse Plane Fitting with Minimum Point-to-Plane Distance

This paper presents a surface normal integration method that solves an inverse problem of local plane fitting. Surface reconstruction from normal maps is essential in photometric shape reconstruction. To this end, we formulate normal integration in the camera coordinates and jointly solve for 3D point positions and local plane displacements. Unlike existing methods that consider the vertical distances between 3D points, we minimize the sum of squared point-to-plane distances. Our method can deal with both orthographic or perspective normal maps with arbitrary boundaries. Compared to existing normal integration methods, our method avoids the checkerboard artifact and performs more robustly against natural boundaries, sharp features, and outliers. We further provide a geometric analysis of the source of artifacts that appear in previous methods based on our plane fitting formulation. Experimental results on analytically computed, synthetic, and real-world surfaces show that our method yields accurate and stable reconstruction for both orthographic and perspective normal maps <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification methods were established from different points of view. The present study is initiated in an attempt to review the state-of-the-art ensemble classification algorithms for dealing with imbalanced datasets, offering a comprehensive analysis for incorporating the dynamic selection of base classifiers in classification. By conducting 14 existing ensemble algorithms incorporating a dynamic selection on 56 datasets, the experimental results reveal that the classical algorithm with a dynamic selection strategy deliver a practical way to improve the classification performance for both a binary class and multi-class imbalanced datasets. In addition, by combining patch learning with a dynamic selection ensemble classification, a patch-ensemble classification method is designed, which utilizes the misclassified samples to train patch classifiers for increasing the diversity of base classifiers. The experiments' results indicate that the designed method has a certain potential for the performance of multi-class imbalanced classification.