Reviewerly/exHarmony huggingface dataset:
Garbage Detection using Advanced Object Detection Techniques

Developing nations today face a major hurdle of excessive waste generation due to overpopulation and rapid urbanization. Also, the waste management systems in such countries are ineffective and limited. Considering this issue, an effective and efficient waste management system would be of great societal benefit. Artificial Intelligence and Deep Learning has found its way into many diverse areas in recent years. This research work proposes a Garbage Detection System using object detection models to automatically detect and locate garbage in real-world images as well as video. The work comprises of a detailed review of previous research and proposes new method with different algorithms to detect garbage. Five different models used in this paper are EfficientDet-D1, SSD ResNet-50 V1, Faster R-CNN ResNet-101 V1, CenterNet ResNet-101 V1 and YOLOv5M. After hyper-parameter tuning and evaluation, YOLOv5M achieved the best results for the proposed system by achieving a Mean Average Precision (mAP@0.5) value of 0.613. This system directly engages citizens to join a national movement to help the authorities to maintain a clean and green environment.
HennaGAN: Henna Art Design Generation using Deep Convolutional Generative Adversarial Network (DCGAN)

The quest of imparting intelligence into human creations have led to the inception and progress of Artificial Intelligence (AI). One application of AI is the generation of things like text or images, using a class of neural networks known as Generative Adversarial Networks (GANs). Nowadays, AI can generate different sorts of artwork and arts using GANs. However, many traditional art design patterns that are tied historically to a particular segment of the demographics and geographic are left unexplored so far. Henna artwork is one of them. In this regard, this paper aims at the generation of Henna design patterns, which is a widely popular artwork involving of complex creative designs applied mostly on the hands as a type of temporary tattoo in the Indian sub-continent and parts of Asia. The HennaGan introduced in this paper shows that Deep Convolutional Neural Network (DCGANs) can be used to generate henna design images with variations effectively. It also creates the base for research into creative Henna art generation using DCGANs and provides insights into how the network parameters can be tuned to obtain a good result.
The structure of semantic representation shapes controlled semantic retrieval

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Virtual reality application to aid civil engineering laboratory course: A multicriteria comparative study

Abstract The social distancing measure imposed worldwide to combat the outbreak of the novel coronavirus has seen rapid adoption of technologies to support online education. Compared to traditional lecture‐based courses, online teaching is particularly more challenging for laboratory courses where hands‐on activities are essential to achieve the learning outcomes. To deal with this issue, the study presented in this paper was aimed to: (1) to develop a virtual reality (VR) application to aid a civil engineering laboratory course, and (2) to measure the potential of the VR‐aided learning (VRAL) application in civil engineering education by assessing the learners' preferences toward VRAL compared to instructor‐aided learning (IAL) and video‐aided learning (VAL). Using the Analytic Hierarchy Process, a comparison was conducted with 21 civil engineering students from both undergraduate and graduate levels. The results indicated that VRAL was more preferable than VAL by the participants based on the aspects of “interactivity” (the ability to provide theoretical and practical learning environments to the learners), “cognitive interest” (the ability to drive learners to engage in learning activity), “ease of understanding the content” (the ability to provide a clear lecture interpretation, i.e., easy to understand), and “support for learning” (the ability to help learners to achieve the learning goal). Although VRAL was comparatively less preferable than IAL by the participants overall, it was viewed as better in terms of cognitive interest and “accessibility” (the ability to provide learners with better access to the learning approach in terms of time and location). Complementing previous research, the findings supported the benefits of VR applications to aid civil engineering education.
FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation

Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF) techniques used in recommender systems due to its effectiveness and ability to deal with very large user-item rating matrix.However, when the rating matrix sparseness increases its performance deteriorates.Expanding MF to include side-information of users and items has been shown by many researchers both to improve general recommendation performance and to help alleviate the data-sparsity and cold-start issues in CF.In regard to item feature side-information, most schemes incorporate this information through a two stage process: intermediate results (e.g., on item similarity) are first computed based on item attributes; these are then combined with MF.In this paper, focussing on item side-information, we propose a model that directly incorporates item features into the MF framework in a single step process.The model, which we name FeatureMF, does this by projecting every available attribute datum in each of the item features into the same latent factor space with users and items, thereby in effect enriching item representation in MF.Results are presented of comparative performance experiments of the model against three state-of-the-art item information enriched models, as well as against four reference benchmark models, using two public real-world datasets, Douban and Yelp, with four training:test ratio scenarios each.It is shown to yield the best recommendation performance over all these models across all contexts including data-sparsity situations, in particular, achieving over 0.9% to over 6.5% MAE recommendation performance improvement over the next best model, HERec.FeatureMF is also found to alleviate cold start and to scale well, almost linearly, in regard to computational time, as a function of dataset size.
Developing a Simple and Cost-Effective Markerless Augmented Reality Tool for Chemistry Education

Traditional visualization methods have a limited capacity to enhance students’ understanding of 3D molecular structure and reactivity. Studies have shown that 3D visualization tools can play an essential role in improving students’ learning. Augmented reality (AR) is a technology that merges virtual objects with real-world images seamlessly. We have previously developed a “marker-based” AR app (ARchemy) for Android devices to help students visualize molecular structures in 3D. Using recent technological advancements, to avoid the limitation of using a printed marker, we have successfully developed a simple and low-cost “markerless” AR app, which can be used for both Android and iOS devices. Students who used the AR app saw a significant increase in understanding of the complexity of molecular structures compared to those who used traditional molecular modeling kits. This unique technology will not only help teachers create more interactive and engaging lessons but also benefit students by making it more accessible and cost-effective to access the resources from any place and at any time.
Fairness among New Items in Cold Start Recommender Systems

This paper investigates recommendation fairness among new items. While previous efforts have studied fairness in recommender systems and shown success in improving fairness, they mainly focus on scenarios where unfairness arises due to biased prior user-feedback history (like clicks or views). Yet, it is unknown whether new items without any feedback history can be recommended fairly, and if unfairness does exist, how can we provide fair recommendations among these new items in such a cold-start scenario. In detail, we first formalize fairness among new items with the well-known concepts of equal opportunity and Rawlsian Max-Min fairness. We empirically show the prevalence of unfairness in cold start recommender systems. Then we propose a novel learnable post-processing framework as a model blueprint for enhancing fairness, with which we propose two concrete models: a joint-learning generative model, and a score scaling model. Extensive experiments over four public datasets show the effectiveness of the proposed models for enhancing fairness while also preserving recommendation utility.
EDITOR: An Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints

Abstract We introduce an Edit-Based TransfOrmer with Repositioning (EDITOR), which makes sequence generation flexible by seamlessly allowing users to specify preferences in output lexical choice. Building on recent models for non-autoregressive sequence generation (Gu et al., 2019), EDITOR generates new sequences by iteratively editing hypotheses. It relies on a novel reposition operation designed to disentangle lexical choice from word positioning decisions, while enabling efficient oracles for imitation learning and parallel edits at decoding time. Empirically, EDITOR uses soft lexical constraints more effectively than the Levenshtein Transformer (Gu et al., 2019) while speeding up decoding dramatically compared to constrained beam search (Post and Vilar, 2018). EDITOR also achieves comparable or better translation quality with faster decoding speed than the Levenshtein Transformer on standard Romanian-English, English-German, and English-Japanese machine translation tasks.
Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction

Conversion Rate (CVR) prediction in modern industrial e-commerce platforms is becoming increasingly important, which directly contributes to the final revenue. In order to address the well-known sample selection bias (SSB) and data sparsity (DS) issues encountered during CVR modeling, the abundant labeled macro behaviors (i.e., user's interactions with items) are used. Nonetheless, we observe that several purchase-related micro behaviors (i.e., user's interactions with specific components on the item detail page) can supplement fine-grained cues for CVR prediction. Motivated by this observation, we propose a novel CVR prediction method by Hierarchically Modeling both Micro and Macro behaviors (HM3). Specifically, we first construct a complete user sequential behavior graph to hierarchically represent micro behaviors and macro behaviors as one-hop and two-hop post-click nodes. Then, we embody HM3 as a multi-head deep neural network, which predicts six probability variables corresponding to explicit sub-paths in the graph. They are further combined into the prediction targets of four auxiliary tasks as well as the final CVR according to the conditional probability rule defined on the graph. By employing multi-task learning and leveraging the abundant supervisory labels from micro and macro behaviors, HM3 can be trained end-to-end and address the SSB and DS issues. Extensive experiments on both offline and online settings demonstrate the superiority of the proposed HM3 over representative state-of-the-art methods.
Understanding Selfish Mining in Imperfect Bitcoin and Ethereum Networks With Extended Forks

Selfish mining, as a serious threat to blockchain, has been attracting attentions from academic and industry. Stochastic modeling has been explored to quantitatively investigate selfish mining in imperfect blockchain networks. However, prior modeling-based analysis approaches have some of the following issues: (1) only focus on Bitcoin or Ethereum, or (2) ignore extended forks and just consider natural forks, or (3) only compute the mining revenue without assessing the performance and security of the blockchain system when the system suffers from selfish mining. In this paper, we aim to address these issues. We build a Markov chain to make quantitative analysis of selfish mining in imperfect Bitcoin and Ethereum networks with natural and extended forks. Formulas are derived to calculate the mining revenue for the selfish pool (comprising selfish miners) and honest miners, respectively. Moreover, we derive the formulas of performance metrics (namely, transactions per second and stale block ratio) and the formula of security metric (namely, the probability of double-spending success) of the system. These quantitative results can help understand the impact of selfish mining on imperfect blockchain networks and then help the detection of selfish mining.
A Typology of Chatbots in Public Service Delivery

Chatbots are being used in an increasingly wide range of public services.Chatbots present several benefits for public service delivery (e.g.cost efficiency for public organizations and better service accessibility for service applicants).However, obtaining effective service outcomes from a chatbot-mediated service delivery requires designing a suitable type of chatbot for specific types of public services.We (a) classify different types of public services in terms of complexity of their delivery process; (b) classify chatbots based on their technical properties and capabilities; and (c) propose a typology that supports public organizations with deciding what type of chatbot is suitable for what type of public services.Our typology contributes to effective chatbot-mediated service delivery in the public service context.
Spatial–temporal pooling for action recognition in videos

Recently, deep convolutional neural networks have demonstrated great effectiveness in action recognition with both RGB and optical flow in the past decade. However, existing studies generally treat all frames and pixels equally, potentially leading to poor robustness of models. In this paper, we propose a novel parameter-free spatial–temporal pooling block (referred to as STP) for action recognition in videos to address this challenge. STP is proposed to learn spatial and temporal weights, which are further used to guide information compression. Different from other temporal pooling layers, STP is more efficient as it discards the non-informative frames in a certain clip. In addition, STP applies a novel loss function that forces the model to learn information from sparse and discriminative frames. Moreover, we introduce a dataset for ferry action classification, named Ferryboat-4, which includes four categories: Inshore, Offshore, Traffic, and Negative. This designed dataset can be used for the identification of ferries with abnormal behaviors, providing the essential information to support the supervision, management, and monitoring of ships. All the videos are acquired via real-world cameras. We perform extensive experiments on publicly available datasets as well as Ferryboat-4 and find that the proposed method outperforms several state-of-the-art methods in action classification. Source code and datasets are available at https://github.com/jiaming-wang/STP.
Data Sharing in Biomedical Sciences: A Systematic Review of Incentives

Background: The lack of incentives has been described as the rate-limiting step for data sharing. Currently, the evaluation of scientific productivity by academic institutions and funders has been heavily reliant upon the number of publications and citations, raising questions about the adequacy of such mechanisms to reward data generation and sharing. This article provides a systematic review of the current and proposed incentive mechanisms for researchers in biomedical sciences and discusses their strengths and weaknesses. Methods: PubMed, Web of Science, and Google Scholar were queried for original research articles, editorials, and opinion articles on incentives for data sharing. Articles were included if they discussed incentive mechanisms for data sharing, were applicable to biomedical sciences, and were written in English. Results: Although coauthorship in return for the sharing of data is common, this might be incompatible with authorship guidelines and raise concerns over the ability of secondary analysts to contest the proposed research methods or conclusions that are drawn. Data publication, citation, and altmetrics have been proposed as alternative routes to credit data generators, which could address these disadvantages. Their primary downsides are that they are not well-established, it is difficult to acquire evidence to support their implementation, and that they could be gamed or give rise to novel forms of research misconduct. Conclusions: Alternative recognition mechanisms need to be more commonly used to generate evidence on their power to stimulate data sharing, and to assess where they fall short. There is ample discussion in policy documents on alternative crediting systems to work toward Open Science, which indicates that that there is an interest in working out more elaborate metascience programs.
A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts

Despite substantial progress in applying neural networks (NN) to a wide variety of areas, they still largely suffer from a lack of transparency and interpretability. While recent developments in explainable artificial intelligence attempt to bridge this gap (e.g., by visualizing the correlation between input pixels and final outputs), these approaches are limited to explaining low-level relationships, and crucially, do not provide insights on error correction. In this work, we propose a framework (VRX) to interpret classification NNs with intuitive structural visual concepts. Given a trained classification model, the proposed VRX extracts relevant class-specific visual concepts and organizes them using structural concept graphs (SCG) based on pairwise concept relationships. By means of knowledge distillation, we show VRX can take a step towards mimicking the reasoning process of NNs and provide logical, concept-level explanations for final model decisions. With extensive experiments, we empirically show VRX can meaningfully answer "why" and "why not" questions about the prediction, providing easy-to-understand insights about the reasoning process. We also show that these insights can potentially provide guidance on improving NN’s performance.
From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning

The applicability of sensor-based human activity recognition in sports has been repeatedly shown for laboratory settings. However, the transferability to real-world scenarios cannot be granted due to limitations on data and evaluation methods. On the example of football shot and pass detection against a null class we explore the influence of those factors for real-world event classification in field sports. For this purpose we compare the performance of an established Support Vector Machine (SVM) for laboratory settings from literature to the performance in three evaluation scenarios gradually evolving from laboratory settings to real-world scenarios. In addition, three different types of neural networks, namely a convolutional neural net (CNN), a long short term memory net (LSTM) and a convolutional LSTM (convLSTM) are compared. Results indicate that the SVM is not able to reliably solve the investigated three-class problem. In contrast, all deep learning models reach high classification scores showing the general feasibility of event detection in real-world sports scenarios using deep learning. The maximum performance with a weighted f1-score of 0.93 was reported by the CNN. The study provides valuable insights for sports assessment under practically relevant conditions. In particular, it shows that (1) the discriminative power of established features needs to be reevaluated when real-world conditions are assessed, (2) the selection of an appropriate dataset and evaluation method are both required to evaluate real-world applicability and (3) deep learning-based methods yield promising results for real-world HAR in sports despite high variations in the execution of activities.
Protection Motivation Theory in Information Systems Security Research

Protection motivation theory (PMT) is one of the most commonly used theories to examine information security behaviors. Our systematic review of the application of PMT in information systems (IS) security and the comparison with its application for decades in psychology identified five categories of important issues that have not yet been examined in IS security research. Discussing these issues in terms of why they are relevant and important for IS security, and to what extent IS research has not considered them, offers new research opportunities associated with the study of PMT and IS security threats. We suggest how future studies can approach each of the open issues to provide a new road map for quantitative and qualitative IS scholars.
Implementation of a volunteer contact tracing program for COVID-19 in the United States: A qualitative focus group study

Background Contact tracing is an important tool for suppressing COVID-19 but has been difficult to adapt to the conditions of a public health emergency. This study explored the experiences and perspectives of volunteer contact tracers in order to identify facilitators, challenges, and novel solutions for implementing COVID-19 contact tracing. Methods As part of a study to evaluate an emergently established volunteer contact tracing program for COVID-19 in New Haven, Connecticut, April-June 2020, we conducted focus groups with 36 volunteer contact tracers, thematically analyzed the data, and synthesized the findings using the RE-AIM implementation framework. Results To successfully reach cases and contacts, participants recommended identifying clients’ outreach preferences, engaging clients authentically, and addressing sources of mistrust. Participants felt that the effectiveness of successful isolation and quarantine was contingent on minimizing delays in reaching clients and on systematically assessing and addressing their nutritional, financial, and housing needs. They felt that successful adoption of a volunteer-driven contact tracing model depended on the ability to recruit self-motivated contact tracers and provide rapid training and consistent, supportive supervision. Participants noted that implementation could be enhanced with better management tools, such as more engaging interview scripts, user-friendly data management software, and protocols for special situations and populations. They also emphasized the value of coordinating outreach efforts with other involved providers and agencies. Finally, they believed that long-term maintenance of a volunteer-driven program requires monetary or educational incentives to sustain participation. Conclusions This is one of the first studies to qualitatively examine implementation of a volunteer-run COVID-19 contact tracing program. Participants identified facilitators, barriers, and potential solutions for improving implementation of COVID-19 contact tracing in this context. These included standardized communication skills training, supportive supervision, and peer networking to improve implementation, as well as greater cooperation with outside agencies, flexible scheduling, and volunteer incentives to promote sustainability.