Reviewerly/exHarmony huggingface dataset:
Practical foundations of machine learning for addiction research. Part I. Methods and techniques

Machine learning assembles a broad set of methods and techniques to solve a wide range of problems, such as identifying individuals with substance use disorders (SUD), finding patterns in neuroimages, understanding SUD prognostic factors and their association, or determining addiction genetic underpinnings. However, the addiction research field underuses machine learning. This two-part narrative review focuses on machine learning tools and concepts, providing an introductory insight into their capabilities to facilitate their understanding and acquisition by addiction researchers. This first part presents supervised and unsupervised methods such as linear models, naive Bayes, support vector machines, artificial neural networks, and k-means. We illustrate each technique with examples of its use in current addiction research. We also present some open-source programming tools and methodological good practices that facilitate using these techniques. Throughout this work, we emphasize a continuum between applied statistics and machine learning, we show their commonalities, and provide sources for further reading to deepen the understanding of these methods. This two-part review is a primer for the next generation of addiction researchers incorporating machine learning in their projects. Researchers will find a bridge between applied statistics and machine learning, ways to expand their analytical toolkit, recommendations to incorporate well-established good practices in addiction data analysis (e.g., stating the rationale for using newer analytical tools, calculating sample size, improving reproducibility), and the vocabulary to enhance collaboration between researchers who do not conduct data analyses and those who do.
Another Look at DPR: Reproduction of Training and Replication of Retrieval

Text retrieval using learned dense representations has recently emerged as a promising alternative to "traditional" text retrieval using sparse bag-of-words representations. One foundational work that has garnered much attention is the dense passage retriever (DPR) proposed by Karpukhin et al. for end-to-end open-domain question answering. This work presents a reproduction and replication study of DPR. We first verify the reproducibility of the DPR model checkpoints by training passage and query encoders from scratch using two different implementations: the original code released by the authors and another independent codebase. After that, we conduct a detailed replication study of the retrieval stage, starting with model checkpoints provided by the authors but with an independent implementation from our group's Pyserini IR toolkit and PyGaggle neural text ranking library. Although our experimental results largely verify the claims of the original DPR paper, we arrive at two important additional findings: First, it appears that the original authors under-report the effectiveness of the BM25 baseline and hence also dense–sparse hybrid retrieval results. Second, by incorporating evidence from the retriever and improved answer span scoring, we manage to improve end-to-end question answering effectiveness using the same DPR models.
Prior Predictive Checks for the Method of Covariances in Bayesian Mediation Analysis

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Intelli-AR Preloading: A Learning Approach to Proactive Hologram Transmissions in Mobile AR

Mobile augmented reality (AR), which integrates virtual objects (i.e., holographic contents) with 3-D real environments in real time, has been rapidly gaining popularity in the last five years. The delivery mechanisms of these holographic contents to mobile AR devices, however, are rarely investigated. To combat bandwidth limitations that preclude providing holographic contents to user devices on-demand, in this article, we propose the intelligent AR (Intelli-AR) preloading algorithm to improve transmission efficiency in the edge-assisted network, in which edge servers proactively transmit holographic contents to the devices. Without user devices’ future motion trajectories, the Intelli-AR preloading algorithm models the user devices’ motion trajectories as Markov decision process (MDP) and adaptively learns the optimal preloading policy. The Intelli-AR preloading is decomposed into two parts and separately deployed on the edge server and the user devices to reduce the computation complexity. The Intelli-AR solution improves the ratio of successful preloading by 11.52% compared to the best baseline in the practical data set when the users’ motion trajectories tend to be more random, and by 21.97% compared to the best baseline in the data set which is synthesized from a real-life mobile AR environment.
Incremental PCA algorithm for fringe pattern demodulation

This work proposes a new algorithm for demodulating fringe patterns using principal component analysis (PCA). The algorithm is based on the incremental implantation of the singular value decomposition (SVD) technique for computing the principal values associated with a set of fringe patterns. Instead of processing an entire set of interferograms, the proposed algorithm proceeds in an incremental way, processing sequentially one (as minimum) interferogram at a given time. The advantages of this procedure are twofold. Firstly, it is not necessary to store the whole set of images in memory, and, secondly, by computing a phase quality parameter, it is possible to determine the minimum number of images necessary to accurately demodulate a given set of interferograms. The proposed algorithm has been tested for synthetic and experimental interferograms showing a good performance.
Conditional motion in-betweening

Motion in-betweening (MIB) is a process of generating intermediate skeletal movement between the given start and target poses while preserving the naturalness of the motion, such as periodic footstep motion while walking. Although state-of-the-art MIB methods are capable of producing plausible motions given sparse key-poses, they often lack the controllability to generate motions satisfying the semantic contexts required in practical applications. We focus on the method that can handle pose or semantic conditioned MIB tasks using a unified model. We also present a motion augmentation method to improve the quality of pose-conditioned motion generation via defining a distribution over smooth trajectories. Our proposed method outperforms the existing state-of-the-art MIB method in pose prediction errors while providing additional controllability.
Solar Energy Modeling and Mapping for the Sustainable Campus at Monash University

An increase in energy demands and positive public acceptance of clean energy resources have contributed to a growing need for using solar energy in cities. Solar photovoltaic (PV) deployment relies on suitable locations with high solar energy potential. In the urban context, building rooftops are often considered one of the most available locations for solar PV installation. This work demonstrates a new geospatial-method for spatiotemporal modeling and mapping solar energy potential based on a high-resolution (0.2 m) digital surface model (DSM) and solar radiation dataset. The proposed method identifies building rooftops with a high solar energy potential by using the Solar Analyst (SA) model. The results show that 93.5% of the rooftop area has high solar energy potential in the study area. The annual averaged sum of solar irradiation values is estimated to be 1.36 MWh/m 2 . In addition, the study showed that sloped rooftops facing to the north received up to 30% more incoming solar radiation than other rooftops with different geometry and orientation. The results are validated using recorded energy output data from four existing solar PV systems in the study area. The return on the initial investment of PV systems installation is estimated to be from four to five years.
CLSA-CapsNet: Dependency based concept level sentiment analysis for text

The refining of information from the immense amount of unstructured data on the internet can be a critical issue in identifying public opinion. It is difficult to extract relevant concepts from huge amounts of data. Concept level semantic parsing is improved over word-based investigation as it conserves the semantical data relevant to many-word articulations. The semantic proposals offer a superior comprehension of textual data and serve to altogether precision the exactness of numerous mining operations in text assignments. The extraction of concepts from textual data is a significant step forward in content analysis at the concept stage. We present a CLSA-CapsNet method that extracts concepts from natural language text. Then the extracted concepts are applied in Capsule networks (CapsNet). Moreover, the integration of Concept Level Sentiment Analysis (CLSA) and Capsule Networks (CapsNet) has not yet been implemented on the hotel review dataset. This is the first attempt, which we researched and embraced by the capsule network, to develop classification models for hotel reviews. The developed results demonstrated excellent performance with a prediction accuracy of 86.6% for CLSA-CapsNet models, respectively. Various similarities have also been made across our techniques and they are implemented by some other deep learning algorithms, such as rnn-lstm. Overall, the outstanding success obtained by CLSA-CapsNet in this investigation highlights its ability in the hotel review dataset. We likewise show exploratory outcomes, in which the proposed system outpaced the state-of-the-art CLSA-CapsNet model.
Posture Calibration Based Cross-View &amp; Hard-Sensitive Metric Learning for UAV-Based Vehicle Re-Identification

Machine vision based vehicle re-identification (ReID) plays an important role in some Intelligent Transportation Systems (ITS). Yet, most previous methods mainly focus on fixed surveillance cameras instead of Unmanned Aerial Vehicle (UAV). With high flexibility, the UAV-based vehicle ReID problem has some special challenges including complicated shooting angles, low discrimination of top-down features, and large variance in vehicle scales, etc. To overcome these challenges, we propose a novel structure for UAV-based vehicle ReID without license plates. Firstly, a triple-head segmentation net is proposed for segmenting UAV-captured vehicles under different heights and directions. Secondly, a posture calibration model is designed to uniform the vehicle postures based on the segmentation results, with the purpose of reducing the influence of different postures. Thirdly, the novel Cross-View & Hard-Sensitive Metric Learning (CHSML) method is proposed to train a ReID network with cross-view training constraint and hard sensitive principles; the mechanism of CHSML takes the cross-view samples of same ID as a training unit to learn the potential visual relationship in cross-view and builds a hard sensitive weight matrix to make learning more focus on hard samples, which improves the low ReID accuracy brought by cross-view or hard samples. Moreover, to facilitate the research of UAV-based vehicle ReID, a large-scale UAV-based vehicle ReID dataset called VeRi-UAV is released with 17 516 vehicles of 453 IDs. The experiments show that the proposed structure gains a better performance compared with the representative methods in the UAV-based vehicle ReID task.
On probability-raising causality in Markov decision processes

Abstract The purpose of this paper is to introduce a notion of causality in Markov decision processes based on the probability-raising principle and to analyze its algorithmic properties. The latter includes algorithms for checking cause-effect relationships and the existence of probability-raising causes for given effect scenarios. Inspired by concepts of statistical analysis, we study quality measures (recall, coverage ratio and f-score) for causes and develop algorithms for their computation. Finally, the computational complexity for finding optimal causes with respect to these measures is analyzed.
Cycling in the Time of the Biodigital: Small Acts Towards a Conscious Uncoupling from Non-regenerative Digitised Economies

This chapter considers data creation and use within the context of a cycling group. It investigates how interaction with digital devices is ubiquitous in human activity and has become mundane. This makes the cyclist a member of the biodigital milieu where human activity and experience is collected, aggregated, shared and sold, classified as behaviours and purposed for a variety of uses that support both the social – communal and digital economy. Actor Network Theory is used to explore the extent of the cycling group's range of actors and to uncover the uses of data that are hidden or black-boxed to the advantage of tech companies. The chapter also looks at connections to the industrial which are removed from the digital product and the consciousness of the human actors as a result. The chapter calls for small acts of resistance that could reconfigure the biodigital connections that are now part of the everyday by realising how data flows can influence production and redress the acts of data trespass against the person that are commonplace. Rather like the political cycling groups of the industrial age, the chapter calls for change and an adopting, not of political class consciousness but of data consciousness, rights, and ownership.
Decentralized Blockchain-Based Framework for Securing Review System

The online review system in today's world has become more of a necessity than a feature when we talk about online platforms in industries such as hospitality, e-commerce, government portals, etc. But, the existing review systems are centralized and bring about a lot of shortcomings in terms of reliability, transparency, and trustworthiness. With this research work, we are trying to address some of the existing lacunae in the current review system with the help of blockchain technology. This research focuses on providing a secure, trustworthy, transparent, and reliable implementation of online review systems using Ethereum Blockchain, IPFS, and smart contracts. With the help of the decentralized system, ongoing issues related to tempering and deletion of data can be solved, which are the main issues occurring in the world. Besides, availability and non-repudiation are achieved due to the data being stored in IPFS. Furthermore, the dilemma related to the flooding of reviews can be cracked down with the designed contract on the blockchain. The proposed system tries to eliminate the security issues currently happening in the present review system with the help of suggested tools.
Hierarchical Attention Factorization Machine for CTR Prediction

Click-through rate (CTR) prediction is a crucial task in recommender systems and online advertising. The most critical step in this task is to perform feature interaction. Factorization machines are proposed to complete the second-order interaction of features to improve the prediction accuracy, but they are not competent for high-order feature interactions. In recent years, many state-of-the-art models employ shallow neural networks to capture high-order feature interactions to improve prediction accuracy. However, some studies have proven that the addictive feature interactions using feedforward neural networks are inefficient in capturing common high-order feature interactions. To solve this problem, we propose a new way, a hierarchical attention network, to capture high-order feature interactions. Through the hierarchical attention network, we can refine the feature representation of the previous layer at each layer, making it become a new feature representation containing the feature interaction information of the previous layer, and this representation will be refined again through the next layer. We also use shallow neural networks to perform higher-order non-linear interactions on feature interaction terms to further improve prediction accuracy. The experiment results on four real-world datasets demonstrate that our proposed HFM model outperforms state-of-the-art models.
How do developers deal with security issue reports on GitHub?

Security issue reports are the primary means of informing development teams of security risks in projects, but little is known about current practices. We aim to understand the characteristics of these reports in open-source projects and uncover opportunities to improve developer practices. We analysed 3,493 security issue reports in 182 different projects on GitHub and manually studied 333 reports, and their discussions and pull requests. We found that, the number of security issue reports has increased over time, they are resolved faster, and they are reported in earlier development stages compared to past years. Nevertheless, a tiny group of developers are involved frequently, security issues progress slowly, and a great number of them has been pending for a long time. We realized that only a small subset of security issue reports include reproducibility data, a potential fix is rarely suggested, and there is no hint regarding how a reporter spotted an issue. We noted that the resolution time of an issue is significantly shorter when the first reaction to a security report is fast and when a reference to a known vulnerability exists.
Drone-View Object Detection Based on the Improved YOLOv5

With the rapid development of computer vision and machine vision, deep learning-based methods have achieved good results in the fields of object detection, identification, and tracking. However, for the detection and identification of vehicles and pedestrians under drone views, the drone-view object detection algorithm detects poorly because the features of small size targets are difficult to be extracted and identified. In order to improve the accuracy and performance of drone view object detection, this paper proposes an improved YOLOv5 algorithm that adds a feature extraction layer to the network model and an image segmentation layer to the detection network. Specifically, a feature map for collecting small target features is formed by adding one upsampling to the neck portion of the network, and a new feature extraction layer is formed by concat. Experimental results show that the proposed method improves the detection effect of the YOLOV5 algorithm on small targets.
Affection Enhanced Relational Graph Attention Network for Sarcasm Detection

Sarcasm detection remains a challenge for numerous Natural Language Processing (NLP) tasks, such as sentiment classification or stance prediction. Existing sarcasm detection studies attempt to capture the subtle semantic incongruity patterns by using contextual information and graph information through Graph Convolutional Networks (GCN). However, direct application of dependence may inevitably introduce noisy information and inferiorly in modeling long-distance or disconnected words in the dependency tree. To better learn the sentiment inconsistencies between terms, we propose an Affection Enhanced Relational Graph Attention network (ARGAT) by jointly considering the affective information and the dependency information. Specifically, we use Relational Graph Attention Networks (RGAT) to integrate relation information guided by a trainable matrix of relation types and synchronously use GCNs to integrate affection information explicitly donated by affective adjacency matrixes. The employment of RGAT contributes to information interaction of structural relevant word pairs with a long distance. With the enhancement of affective information, the proposed model can capture complex forms of sarcastic expressions. Experimental results on six benchmark datasets show that our proposed approach outperforms state-of-the-art sarcasm detection methods. The best-improved results of accuracy and F1 are 4.19% and 4.33%, respectively.
Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems

Modulation signal classification in communication systems can be considered a pattern recognition problem.Earlier works have focused on several feature extraction approaches such as fractal feature, signal constellation reconstruction, etc.The recent advent of deep learning (DL) models makes it possible to proficiently classify the modulation signals.In this view, this study designs a chaotic oppositional satin bowerbird optimization (COSBO) with bidirectional long term memory (BiLSTM) model for modulation signal classification in communication systems.The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals.In addition, the fractal feature extraction process takes place by the use of Sevcik Fractal Dimension (SFD) approach.Moreover, the modulation signal classification process takes place using BiLSTM with fully convolutional network (BiLSTM-FCN).Furthermore, the optimal hyperparameter adjustment of the BiLSTM-FCN technique takes place by the use of COSBO algorithm.In order to ensure the enhanced classification performance of the COSBO-BiLSTM model, a wide range of simulations were carried out.The experimental results highlighted that the COSBO-BiLSTM technique has accomplished improved performance over the existing techniques.
A Coarse-to-Fine Deep Generative Model With Spatial Semantic Attention for High-Resolution Remote Sensing Image Inpainting

Large area array CCD splicing technology and hardware damage lead to the lack of information in high-resolution remote sensing images (HRSIs), which seriously restricts the application of remote sensing images. However, the complex spatial geographical relationship and the serious spatial heterogeneity will cause texture blur and incomplete structure, especially in the boundary and highly textured regions of HRSI. To deal with the challenge, this article proposes a coarse-to-fine deep generative model with a novel spatial semantic attention (SSA) mechanism. SSA is deployed in the encoder and decoder of the network to ensure the continuity of local features and the relevance of global semantic information and embedded in fine network. In the coarse network, a multilevel loss is proposed to provide more accurate information. Fine network performs more detailed image inpainting operations based on coarse network. Extensive experiments on three datasets, including GF2 satellite images, aerial images, and natural ones, demonstrate that our proposed model has better inpainting effect than the state-of-the-art algorithms.
Blockchain as a strategic enabler of agri-food sustainability

Motivated by the recent surge of interest in blockchain adoption across industries, this paper has theoretically examined the potential advantages and major barriers to blockchain adoption in the agri-food sector. Recent research and pilot studies suggest that blockchain may promote agri-food sustainability, however blockchain adoption is still in its infancy and further studies are needed to accurately assess the advantages and limitations of using lockchain-based technologies in the agri-food sector. We acknowledge the shortcomings of the present study due to its conceptual nature, but drawing on the existing theoretical and empirical literature, it may act as a springboard for future research directions.
Self-calibrated BFP-type apertometer for both dry and immersion lenses via grating projection reference

This work proposes a novel scheme for a back focal plane (BFP)-type apertometer. The basic concept is to generate “multi-references” on BFP via the grating projection, which shows superiorities in two aspects: (1) a wider measurement range for both dry and immersion objectives; (2) higher accuracy via self-calibration of multi-references. To the best of our knowledge, this is the first approach and setup to allow the measurement of objectives with NA across unity via a “virtual” Fourier plane. It is also the first time to demonstrate a self-calibrated BFP-type apertometer with multi-references. All the involved concepts are experimentally verified.