Reviewerly/exHarmony huggingface dataset:
Noise-mitigation strategies in physical feedforward neural networks

Physical neural networks are promising candidates for next generation artificial intelligence hardware. In such architectures, neurons and connections are physically realized and do not leverage digital concepts with their practically infinite signal-to-noise ratio to encode, transduce, and transform information. They, therefore, are prone to noise with a variety of statistical and architectural properties, and effective strategies leveraging network-inherent assets to mitigate noise in a hardware-efficient manner are important in the pursuit of next generation neural network hardware. Based on analytical derivations, we here introduce and analyze a variety of different noise-mitigation approaches. We analytically show that intra-layer connections in which the connection matrix's squared mean exceeds the mean of its square fully suppress uncorrelated noise. We go beyond and develop two synergistic strategies for noise that is uncorrelated and correlated across populations of neurons. First, we introduce the concept of ghost neurons, where each group of neurons perturbed by correlated noise has a negative connection to a single neuron, yet without receiving any input information. Second, we show that pooling of neuron populations is an efficient approach to suppress uncorrelated noise. As such, we developed a general noise-mitigation strategy leveraging the statistical properties of the different noise terms most relevant in analog hardware. Finally, we demonstrate the effectiveness of this combined approach for a trained neural network classifying the modified National Institute of Standards and Technology handwritten digits, for which we achieve a fourfold improvement of the output signal-to-noise ratio. Our noise mitigation lifts the 92.07% classification accuracy of the noisy neural network to 97.49%, which is essentially identical to the 97.54% of the noise-free network.
Visibility of points: Mining occlusion cues for monocular 3D object detection

Monocular 3D object detection aims at achieving prediction from two-dimensional image plane to three-dimensional physical world. It is an inevitable problem that occlusion phenomena limit the performance in practice. To solve the challenging problem that directly represents the spatial information of occlusion relation, we propose the visibility states of points to describe the spatial distance relationships of occlusion pairs and the implied orientation information. The visibility state introduction can better represent the level and direction of occlusion information and enhance the network's understanding of occlusion information. Furthermore, we redesign an end-to-end detector to encode features of visibility states to integrate occlusion ordering cues of the whole image to assist object localization in world space. Experiments on the KITTI3D dataset indicate that our method succeeds in establishing visibility states as occlusion cues and promoting the performance of the original detector. Our method is effective, and the performance is comparable with state-of-the-art approaches, especially outstanding in Moderate and Hard cases. Specifically, our method improves the accuracy of 3D moderate case detection to 42.75% and hard case to 37.03% in the KITTI3D dataset.
Edge preserving range image smoothing using hybrid locally kernel-based weighted least square

In this paper, we propose the hybrid locally kernel-based weighted least square (HKLS) method to reduce the noise of the range images captured from the Microsoft Kinect sensor. Although removing the noise of Kinect sensor is inevitable in mobile robot applications, preserving the main structures of an indoor scene is a key point of the smoothing methods for range data. Our method uses a situation estimation module to apply an appropriate method for denoising the input data. In this paper, situations are classified into two categories including flat regions and edge regions. The locally kernel-based weighted least square method is used for the edge regions that have significant changes in the surface normals and the weight of each point is assigned with the Gaussian mixture model. The Bilateral filtering is applied for the flat regions with no sharp change in surface normals. We provided a solution for the noise data issue and proof of uncertainty reduction in presence of noise data. There is a fusion module for assigning the proportion of each method according to the different situations. We evaluated our method on the NYUv2 dataset that contains different RGB-D indoor scenes with other state-of-the-art methods. We achieved the value of 49.48 for the Z-MAE and 0.502 for the N-MAE error metrics.
Marketing Strategy of Rural Tourism Based on Big Data and Artificial Intelligence

In recent years, with the rapid development of artificial intelligence technology and big data application and the continuous optimization of algorithms such as deep learning algorithms and reinforcement learning algorithms, tourist attractions using “smart tourism” technology can obtain more accurate and in-depth knowledge from tourism big data and mine the value behind the data. As an important branch of tourism, the rise of rural tourism has inspired rural areas to protect the local environment, inherit characteristic culture, develop tourism resources, develop tourism economy, and increase rural income, which has well responded to the call of the rural revitalization strategy. However, due to lack of information resources and technology, some rural scenic spots still adopt traditional marketing methods without the support of big data and artificial intelligence technology. This paper proposes a research work on the marketing strategy of rural tourist attractions based on big data and artificial intelligence, analyzes current application of big data and artificial intelligence in rural tourism, and discusses the transformation of rural tourism marketing to a new fusion model with the support of big data and artificial intelligence, aiming to study every step involved and supported deeply by big data and artificial intelligence, from the awakening of intentions before the travel to the analysis of behaviors and preferences during the travel and the collection of evaluation after the travel. The research results of this paper show that more sufficient and deep integration between rural tourism and artificial intelligence/big data are needed to make tourists get better tour experience and marketing strategy of rural scenic spots to get better effects and income.
Rethinking transition relationship between co-occurring items in graph neural networks for session-based recommendation

Session-based recommendation aims to recommend items based on anonymous behavior sequences. A session is generally modeled as a sequential structure or graph structure to extract its session-level representation. However, directly modeling a session as a sequence ignores the co-occurrence relationship between pairwise items, and current graph-based methods do not consider information transition directions sufficiently when calculating the similarity of co-occurring items. In this paper, we propose a novel approach, dubbed Item Transition Relationship Graph Neural Networks (ITR-GNN), to model different transition relationships between co-occurring items in an effective way, for better extracting user intents of current sessions. In ITR-GNN, session sequences are modeled as directed unweighted graphs, and two different transition relationships between a pair of co-occurring items are distinguished by two different concatenations of their latent vectors, according to the direction of the edge between them. In addition, we notice that the data used for session-based recommendation contain many noisy labels, degrading recommendation effects. Thereby, we introduce a label distillation strategy to learn improved soft labels and replace potential noisy labels through a teacher–student network. Experimental results on three benchmark datasets demonstrate that the ITR-GNN outperforms state-of-the-art methods. Source code is available at https://github.com/cyq002/ITR-GNN.
Detection of toxicity in social media based on Natural Language Processing methods

Comments on important websites, such as popular news portals or social media platforms, are among the main ways of virtual interaction. Unfortunately, the behavior of users on these websites often becomes rude or disrespectful, by spreading toxic comments which can muddle the proper functioning of these sites. The aim of this research is to detect these toxic comments, and to find parts, toxic spans, of these comments to which toxicity can be attributed. Thus, we explored and compared various classifiers belonging to three categories “Machine Learning, Ensemble Learning and Deep Learning” and using different text representations. For detecting toxic spans in the comments, we applied an unsupervised method, we apply the Local Interpretable Model-Agnostic Explanations (LIME).The measures we used to evaluate our methods are accuracy, recall, and Fl-score. Our experiments showed that deep learning models performed unquestionably in the task of detecting toxic comments. The LSTM models with the Globe representation and LSTM with FastText were able to produce a higher F1 and accuracy compared to the other models used. For Toxic spans detction, the higher scores were obtained when combining LIME with classifier LSTM(GloVe) with an accuracy of 98% to identify the toxic spans.
Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection

Multi-label aspect category detection allows a given review sentence to contain multiple aspect categories, which is shown to be more practical in sentiment analysis and attracting increasing attention. As annotating large amounts of data is time-consuming and labor-intensive, data scarcity occurs frequently in real-world scenarios, which motivates multi-label few-shot aspect category detection. However, research on this problem is still in infancy and few methods are available. In this paper, we propose a novel label-enhanced prototypical network (LPN) for multi-label few-shot aspect category detection. The highlights of LPN can be summarized as follows. First, it leverages label description as auxiliary knowledge to learn more discriminative prototypes, which can retain aspect-relevant information while eliminating the harmful effect caused by irrelevant aspects. Second, it integrates with contrastive learning, which encourages that the sentences with the same aspect label are pulled together in embedding space while simultaneously pushing apart the sentences with different aspect labels. In addition, it introduces an adaptive multi-label inference module to predict the aspect count in the sentence, which is simple yet effective. Extensive experimental results on three datasets demonstrate that our proposed model LPN can consistently achieve state-of-the-art performance.
Namespotting: Username toxicity and actual toxic behavior on Reddit

Without relying on any user reports, we use algorithmic detection and Bayesian statistical methods to analyse two large data streams (329 k users) of Reddit content to study the correlations between username toxicity (of various types, such as offensive or sexually explicit) and their online toxic behavior (personal attacks, sexual harassment among others). As it turns out, username toxicity (type) is a useful predictor in online profiling. Users with toxic usernames produce more toxic content than their neutral counterparts, with the difference in predicted mean increasing with activity (predicted 1.9 vs. 1.4 toxic comments a week for users with regular activity, and 5.6 vs. 4 for top 5% active users). More users with toxic usernames engage in toxic behavior than among neutral usernames (around 40% vs. 30%). They are also around 2.2 times more likely to have their account suspended by moderators (3.2% vs. 1.5% probability of suspension for regular and 4.5% vs. 2% for top 5% users)—detailed results vary depending on the username toxicity type and toxic behavior type. Thus, username toxicity can be used in the efforts of online communities to predict toxic behavior and to provide more safety to their users. • First large-scale comparative exploration of the toxic behavior of Reddit users with toxic usernames. • Our analysis employs algorithmic methods and Bayesian statistical models without relying on self-reported data. • Users with toxic usernames produce more toxic content (of various types) than those with neutral usernames. • Users with toxic usernames are more likely to have their account suspended than those with neutral usernames.
Teaching digital literacy: are teachers’ perspectives consistent with actual pedagogy?

This study mapped instructional strategies that promote core digital literacies, as conceptualized by three theoretical frameworks: the digital competencies (DC) model (Eshet-Alkalai, 2004; 2012) the five core-competencies (5C) model (Hwang, Lai, & Wang, 2015) and the DigComp framework (Ferrari, 2013). Findings from a large qualitative sample of 65 Israeli elementary and middle-school teachers-experts in technology-enhanced pedagogy, demonstrated that their perspectives in semi-structured interviews were mostly consistent with their actual behavior observed in classrooms. Teachers over-emphasized certain competencies (searching for knowledge, photo-visual thinking, socio-emotional learning, constructing knowledge), while others competencies were significantly less common (real-time thinking, branching literacy and problem-solving skills). Based on bottom-up coding, we identified unique characteristics of digital literacy, suggested several modifications of the DC, 5C and DigComp frameworks, and mapped the level of instructional strategies (foundational, intermediate, or advanced) used to develop students' digital literacies. We discuss the implications of the findings for educational theory and practice.
An Adaptable UAV Sensor Data Anomaly Detection Method Based on TCN Model Transferring

Unmanned aerial vehicles play a critical role in both military and civilian applications, and their safety and reliability have also been paid more and more attention. UAV anomaly detection can detect and eliminate potential faults in a timely and effective manner, reducing the probability of accidents. Due to the influence of the complex and changeable operating environment, data shifting problems are inevitable in time series anomaly detection. Ignoring this issue may result in a significant drop in the accuracy of anomaly detection. Therefore, a UAV sensor data anomaly detection method based on Temporal Convolution Network (TCN) model transferring is proposed in this paper. First, the TCN model is pre-trained by using a large amount of data in the source domain. Then, parameters of the model are fine-tuned on the target domain. Finally, the threshold detection method is used to determine whether there is abnormality in the UAV sensor data. This work aims to address the multiple modes of UAV and improve the data-driven adaptivity for anomaly detection. In the experiments, the flight sensor data are used to verify the performance of the proposed model. The results show that the proposed method achieves high precision, high detection rate and low false detection rate in different domains.
Predicting earnings management through machine learning ensemble classifiers

Abstract In this paper, we utilize six novel ensemble classifiers to predict earnings management (EM) in both its forms, accrual‐based earnings management (AEM) and real earnings management (REM), and then compare the EM prediction accuracy of wrapper feature selection (FS) and filtering FS techniques in the context of EM. Specifically, we integrate three well‐known filtering FS techniques (information gain [IG], principal component analysis [PCA], and relief [Re]) and three popular wrapper FS techniques (particle swarm optimization [PSO], genetic algorithm [GA], and artificial bee colony [ABC]) with the support vector machine (SVM) to generate our ensemble classifiers. We then assess the performance of each of the six ensemble classifiers to predict AEM and REM based on three criteria: type Ι error, type ΙΙ error, and average accuracy. The results show that the ABC‐SVM ensemble classifier outperforms the others in predicting both AEM and REM. We also find that, overall, wrapper FS ensemble classifiers outperform filtering FS ensemble classifiers in predicting AEM and REM and that it is more difficult for our ensemble classifiers to predict REM than to predict AEM. This paper contributes to the literature on EM prediction by introducing six new ensemble classifiers. It is also the first work (to the best of our knowledge) in the domain of ensemble classifiers' applications (a) to consider both REM and AEM in one context and to show that REM is more difficult to predict than AEM and (b) to compare the performance of wrapper and filtering FS techniques in the EM prediction setting.
How the Outliers Influence the Quality of Clustering?

In this article, we evaluate the efficiency and performance of two clustering algorithms: AHC (Agglomerative Hierarchical Clustering) and K-Means. We are aware that there are various linkage options and distance measures that influence the clustering results. We assess the quality of clustering using the Davies-Bouldin and Dunn cluster validity indexes. The main contribution of this research is to verify whether the quality of clusters without outliers is higher than those with outliers in the data. To do this, we compare and analyze outlier detection algorithms depending on the applied clustering algorithm. In our research, we use and compare the LOF (Local Outlier Factor) and COF (Connectivity-based Outlier Factor) algorithms for detecting outliers before and after removing 1%, 5%, and 10% of outliers. Next, we analyze how the quality of clustering has improved. In the experiments, three real data sets were used with a different number of instances.
A multi-resource scheduling scheme of Kubernetes for IIoT

With the rapid development of data applications in the scene of Industrial Internet of Things (IIoT), how to schedule resources in IIoT environment has become an urgent problem to be solved.Due to benefit of its strong scalability and compatibility, Kubernetes has been applied to resource scheduling in IIoT scenarios.However, the limited types of resources, the default scheduling scoring strategy, and the lack of delay control module limit its resource scheduling performance.To address these problems, this paper proposes a multi-resource scheduling (MRS) scheme of Kubernetes for IIoT.The MRS scheme dynamically balances resource utilization by taking both requirements of tasks and the current system state into consideration.Furthermore, the experiments demonstrate the effectiveness of the MRS scheme in terms of delay control and resource utilization.
Bayesian networks for risk analysis and decision support

Risk AnalysisVolume 42, Issue 6 p. 1149-1154 EDITORIAL Bayesian networks for risk analysis and decision support Anca M. Hanea, Corresponding Author Anca M. Hanea anca.hanea@unimelb.edu.au Centre of Excellence for Biosecurity Risk Analysis, University of Melbourne, Melbourne, Victoria, Australia Correspondence Anca M. Hanea, Centre of Excellence for Biosecurity Risk Analysis, University of Melbourne, Australia. Email: anca.hanea@unimelb.edu.auSearch for more papers by this authorAnnemarie Christophersen, Annemarie Christophersen GNS Science, Lower Hutt, Wellington, New ZealandSearch for more papers by this authorSandra Alday, Sandra Alday University of Sydney, Sydney, New South Wales, AustraliaSearch for more papers by this author Anca M. Hanea, Corresponding Author Anca M. Hanea anca.hanea@unimelb.edu.au Centre of Excellence for Biosecurity Risk Analysis, University of Melbourne, Melbourne, Victoria, Australia Correspondence Anca M. Hanea, Centre of Excellence for Biosecurity Risk Analysis, University of Melbourne, Australia. Email: anca.hanea@unimelb.edu.auSearch for more papers by this authorAnnemarie Christophersen, Annemarie Christophersen GNS Science, Lower Hutt, Wellington, New ZealandSearch for more papers by this authorSandra Alday, Sandra Alday University of Sydney, Sydney, New South Wales, AustraliaSearch for more papers by this author First published: 30 June 2022 https://doi.org/10.1111/risa.13938 Funding informationNew Zealand's Ministry of Business, Innovation & Employment Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume42, Issue6Special Issue: Bayesian Networks for Risk Analysis and Decision SupportJune 2022Pages 1149-1154 RelatedInformation
Do we need to pay technical debt in blockchain software systems?

For blockchain software systems, framework developers may introduce technical debts that application developers are not aware of. Because these technical debts can have a negative impact on software projects, we need to investigate the issue of technical debt in blockchain software systems. We wanted to investigate what types of self-introduced technical debt exist in open-source blockchain software systems, and how these technical debts are distributed. We have selected six most popular blockchain software projects from GitHub. Then the code comments from these software projects were extracted and manually labelled. Finally, the code comments were statistically analysed. We propose a new type of technical debt, resource debt, which is explicitly identified by the framework developers and requires special attention in subsequent production systems. Six types of technical debt are prevalent and there is not any algorithm debt. In addition, we find that the code comments containing technical debt are not entirely determined by task tags. SATD is prevalent in blockchain projects. There is more significant variability between different application software projects for different technical debts. The results of the study imply that for detecting SATD, deep semantic discovery models should be used, such as pre-trained models.
Simulating behavior to help researchers build experiments

Testing that an experiment works as intended is critical for identifying design problems and catching technical errors that could invalidate the results. Testing is also time-consuming because of the need to manually run the experiment. This makes testing the experiment costly for researchers, and therefore testing is less comprehensive than in other kinds of software development where tools to automate and speed up the testing process are widely used. In this paper, we describe an approach that substantially reduces the time required to test behavioral experiments: automated simulation of participant behavior. We describe how software that is used to build experiments can use information contained in the experiment's code to automatically generate plausible participant behavior. We demonstrate this through an implementation using jsPsych. We then describe four potential scenarios where automated simulation of participant behavior can improve the way researchers build experiments. Each scenario includes a demo and accompanying code. The full set of examples can be found at https://jspsych.github.io/simulation-examples/ .