Reviewerly/exHarmony huggingface dataset:
AI in E-Commerce: Application of the Use and Gratification Model to The Acceptance of Chatbots

This study applies and builds on the Use and Gratification (U&amp;G) theory to explore consumer acceptance of applied artificial intelligence (AI) in the form of Chatbots in online shopping in China. Data were gathered via an anonymous online survey from 540 respondents who self-identified as frequent online shoppers and are familiar with Chatbots. The results of the data analysis provide empirical evidence indicating that utilitarian factors such as the “authenticity of conversation” and “convenience”, as well as hedonic factors such as “perceived enjoyment”, result in users having a positive attitude towards Chatbots. However, privacy issues and the immaturity of technology have had a negative impact on acceptance. This paper provides both theoretical and practical insights into Chinese attitudes toward Chatbots and may be of interest to e-commerce researchers, practitioners, and U&amp;G theorists.
Feature-Enhanced CenterNet for Small Object Detection in Remote Sensing Images

Compared with anchor-based detectors, anchor-free detectors have the advantage of flexibility and a lower calculation complexity. However, in complex remote sensing scenes, the limited geometric size, weak features of objects, and widely distributed environmental elements similar to the characteristics of objects make small object detection a challenging task. To solve these issues, we propose an anchor-free detector named FE-CenterNet, which can accurately detect small objects such as vehicles in complicated remote sensing scenes. First, we designed a feature enhancement module (FEM) composed of a feature aggregation structure (FAS) and an attention generation structure (AGS). This module contributes to suppressing the interference of false alarms in the scene by mining multiscale contextual information and combining a coordinate attention mechanism, thus improving the perception of small objects. Meanwhile, to meet the high positioning accuracy requirements of small objects, we proposed a new loss function without extra calculation and time cost during the inference process. Finally, to verify the algorithm performance and provide a foundation for subsequent research, we established a dim and small vehicle dataset (DSVD) containing various objects and complex scenes. The experiment results demonstrate that the proposed method performs better than mainstream object detectors. Specifically, the average precision (AP) metric of our method is 7.2% higher than that of the original CenterNet with only a decrease of 1.3 FPS.
Membership Inference Attacks by Exploiting Loss Trajectory

Machine learning models are vulnerable to membership inference attacks in which an adversary aims to predict whether or not a particular sample was contained in the target model's training dataset. Existing attack methods have commonly exploited the output information (mostly, losses) solely from the given target model. As a result, in practical scenarios where both the member and non-member samples yield similarly small losses, these methods are naturally unable to differentiate between them. To address this limitation, in this paper, we propose a new attack method, called TrajectoryMIA, which can exploit the membership information from the whole training process of the target model for improving the attack performance. To mount the attack in the common black-box setting, we leverage knowledge distillation, and represent the membership information by the losses evaluated on a sequence of intermediate models at different distillation epochs, namely distilled loss trajectory, together with the loss from the given target model. Experimental results over different datasets and model architectures demonstrate the great advantage of our attack in terms of different metrics. For example, on CINIC-10, our attack achieves at least 6 times higher true-positive rate at a low false-positive rate of 0.1% than existing methods. Further analysis demonstrates the general effectiveness of our attack in more strict scenarios.
A strategy learning framework for particle swarm optimization algorithm

Many variants with various strategies have been proposed to improve the efficiency of Particle Swarm Optimization (PSO) algorithm. These strategies are a precious resource waiting to be exploited. We conjecture that some new combinations of strategies selected from different PSO variants may better improve the performance of PSO. Inspired by this idea, this paper proposes a strategy learning framework to learn an optimal combination of strategies and thus derive a new PSO variant based on this combination. In this framework, a strategy pool with strategies selected from existing PSO variants is first constructed. Then, a training engine, implemented by an adaptive differential evolutionary algorithm, is employed to evaluate the performance of strategy combinations on training benchmark functions. Furthermore, a new PSO variant, named SLFPSO, is created based on the strategies learned from training results. This framework provides a novel method to design PSO variants by learning from existing algorithms through a learning mechanism. The performance and scalability of SLFPSO are compared with ten state-of-the-art PSO variants on 10/30/50/100-dimensional CEC2013/2014/2017 benchmark functions. The results verify that SLFPSO performs significantly better than the compared algorithms in most test scenarios.
The use of augmented reality in a gamified CLIL lesson and students’ achievements and attitudes: a quasi-experimental study

Abstract The advancement of technology has provided new avenues for English language teachers to assist students in improving their language learning processes. Augmented reality is an emerging technology that can implement virtual objects into the physical learning environment. This quantitative study aimed to determine the impact of employing augmented reality in a CLIL lesson. The study also looks at EFL learners’ attitudes about the use of AR. For this purpose, 76 high school students (38 males and 38 females) have taken part in this study. The results showed that using an AR-based mobile application improved the language achievements of the learners. The learners who used AR have gathered better results in the CLIL lesson. Additionally, the learners had positive attitudes towards the use of augmented reality. They found it satisfying and engaging and were willing to use it in the future again. Based on the study findings, EFL teachers are encouraged to use AR in their classes to teach both content and the language.
A landscape-aware particle swarm optimization for parameter identification of photovoltaic models

Photovoltaic (PV) systems play a significant role in power systems since they can convert solar energy directly into electricity. Their conversion performance depends mainly on the unknown parameters of the PV model. Due to the multimodal, nonlinear, and multivariate characteristics of solar cells and modules, parameter identification of PV models is still a popular and challenging task in PV systems. However, most of the existing approaches face the problem of falling into local optima, high computational costs, and insufficient utilization of landscape information in the evolutionary process. In this study, a landscape-aware particle swarm optimization algorithm (LaPSO) is proposed to estimate the parameters of the PV system. LaPSO includes two main improvements: (i) A landscape-based adaptive operator selection mechanism is proposed to quantify the landscape modality and assign the most appropriate evolutionary strategy to the current population to improve population quality. (ii) The problem of getting trapped in local optima is alleviated by using the mirrored boundary handling method. The experimental results tested on various PV models demonstrate the excellence of LaPSO in terms of accuracy, stability, and convergence speed. Moreover, LaPSO has shown superior practicality and reliability at different temperatures and irradiances on other PV modules. Consequently, LaPSO is well-suited as an alternative for the parameter identification of solar cells and modules under various practical conditions.
Augmented Reality and Virtual Reality in Education: Public Perspectives, Sentiments, Attitudes, and Discourses

This study aims to understand the public’s perspectives, sentiments, attitudes, and discourses regarding the adoption, integration, and use of augmented reality and virtual reality in education and in general by analyzing social media data. Due to its nature, Twitter was the selected platform. Over 17 million tweets were retrieved from January 2010 to December 2020 and four datasets were created. Two of them referred to the general use of these technologies and two to their educational use. The data was analyzed using text mining, sentiment analysis (e.g., polarity and emotion detection), and topic modeling methods. TextBlob, Word-Emotion Association Lexicon (EmoLex), Valence Aware Dictionary for Sentiment Reasoning (VADER), and Latent Dirichlet Allocation (LDA) were some of the tools used. Based on the results, the majority of the public were positively disposed toward the general and the educational use of both augmented reality and virtual reality and mostly expressed positive emotions (e.g., anticipation, trust, and joy) when referring to them. In total, 11 topics emerged that were related to education, new technologies, digital and social media use, marketing and advertising, the industrial domain, the health domain, gaming, fitness and exercising, devices, the travel and tourism domain, and software development kits. The educational benefits of augmented reality and virtual reality, their ability to enrich both teaching and learning activities, and their role as effective educational means were evident.
A privacy preserving framework for federated learning in smart healthcare systems

Federated Learning (FL) is a platform for smart healthcare systems that use wearables and other Internet of Things enabled devices. However, source inference attacks (SIAs) can infer the connection between physiological data in training datasets with FL clients and reveal the identities of participants to the attackers. We propose a comprehensive smart healthcare framework for sharing physiological data, named FRESH, that is based on FL and ring signature defense from the attacks. In FRESH, physiological data are collected from individuals by wearable devices. These data are processed by edge computing devices (e.g., mobile phones, tablet PCs) that train ML models using local data. The model parameters are uploaded by edge computing devices to the central server for joint training of FL models of disease prediction. In this procedure, certificateless ring signature is used to hide the source of parameter updates during joint training for FL to effectively resist SIAs. In the proposed ring signature schema, an improved batch verification algorithm is designed to leverage additivity of linear operations on elliptic curves and to help reduce the computing workload of the server. Experimental results demonstrate that FRESH effectively reduces the success rate of SIAs and the batch verification method significantly improves the efficiency of signature verification. FRESH can be applied to large scale smart healthcare systems with FL involving large numbers of users.
YOLOv5 with ConvMixer Prediction Heads for Precise Object Detection in Drone Imagery

The potency of object detection techniques using Unmanned Aerial Vehicles (UAVs) is unprecedented due to their mobility. This potency has stimulated the use of UAVs with object detection functionality in numerous crucial real-life applications. Additionally, more efficient and accurate object detection techniques are being researched and developed for usage in UAV applications. However, object detection in UAVs presents challenges that are not common to general object detection. First, as UAVs fly at varying altitudes, the objects imaged via UAVs vary vastly in size, making the task at hand more challenging. Second due to the motion of the UAVs, there could be a presence of blur in the captured images. To deal with these challenges, we present a You Only Look Once v5 (YOLOv5)-like architecture with ConvMixers in its prediction heads and an additional prediction head to deal with minutely-small objects. The proposed architecture has been trained and tested on the VisDrone 2021 dataset, and the acquired results are comparable with the existing state-of-the-art methods.
Quantum Commitments and Signatures Without One-Way Functions

In the classical world, the existence of commitments is equivalent to the existence of one-way functions. In the quantum setting, on the other hand, commitments are not known to imply one-way functions, but all known constructions of quantum commitments use at least one-way functions. Are one-way functions really necessary for commitments in the quantum world? In this work, we show that non-interactive quantum commitments (for classical messages) with computational hiding and statistical binding exist if pseudorandom quantum states exist. Pseudorandom quantum states are sets of quantum states that are efficiently generated but their polynomially many copies are computationally indistinguishable from the same number of copies of Haar random states [Ji, Liu, and Song, CRYPTO 2018]. It is known that pseudorandom quantum states exist even if $\BQP=\QMA$ (relative to a quantum oracle) [Kretschmer, TQC 2021], which means that pseudorandom quantum states can exist even if no quantum-secure classical cryptographic primitive exists. Our result therefore shows that quantum commitments can exist even if no quantum-secure classical cryptographic primitive exists. In particular, quantum commitments can exist even if no quantum-secure one-way function exists. In this work, we also consider digital signatures, which are other fundamental primitives in cryptography. We show that one-time secure digital signatures with quantum public keys exist if pseudorandom quantum states exist. In the classical setting, the existence of digital signatures is equivalent to the existence of one-way functions. Our result, on the other hand, shows that quantum signatures can exist even if no quantum-secure classical cryptographic primitive (including quantum-secure one-way functions) exists.
An ensemble deep federated learning cyber-threat hunting model for Industrial Internet of Things

Industrial Internet of Things (IIoT) is an emerging technology with prompt evolution in diverse applications, including critical infrastructure. While the increasing number of IIoT devices in today’s critical infrastructure enhances their efficiency and reliability, it also increases their vulnerability towards cyber-attacks. Ambient Intelligence (AmI), including machine learning techniques, is a way to handle such challenges with minimizing the human role. Although using machine learning-based techniques is increased in some applications these days, they are not widely used in IIoT environments due to the privacy issues of transferring all the data into a single machine to train the models. This paper proposes an ensemble-based deep federated learning cyber-threat hunting model to hunt the attack samples without data sharing. The proposed hunting model consists of two parallel federated-based components, one analyzes the IIoT status based on the normal situation of the network, and the other analyzes it with considering the threat situation. This model used an ensemble of classifiers to make the final decision. The proposed cyber-threat hunting model is evaluated using two test cases and compared with some works in the literature and outperformed them in the f1-score metric. Moreover, evaluations show that the proposed model acts stable in facing different numbers of clients, and its training time is faster than the centralized models with the same computational complexity.
Suitability of rooftop solar photovoltaic at educational building towards energy sustainability in Malaysia

To maintain the sustainability of the country's energy resources and less carbon, the Malaysian Government has set several strategies for achieving this target. One of them is using renewable energy (RE) sources. Solar energy is one of the most prospective sources because it is abundant and easy to implement. With increased interest and existing limitations, the application of rooftop solar photovoltaic (PV) systems is experiencing a significant increase as an alternative energy provider in Malaysia. However, further investigation is necessary to explore the suitability of rooftop solar PV based on its potential. The present work studies the potential of installing solar PV on the rooftop of educational buildings with various roof tilted angles. To this end, results show that the potential PV generation is significantly high even for small rooftop areas at certain education buildings and can accommodate the institute's energy load demand. Positive high savings for energy and electricity bills also show that installing rooftop solar PV benefits education institutes. Emission savings could reach approximately 26,260 tCO2 or 21,358 tCO2 annually for c-Si and mc-Si, respectively. These results show that rooftop solar PV can be suitable for an educational building. This study also could be used as support information by the decision maker and education institution administration in implementing solar PV to achieve national aspiration toward energy sustainability and, simultaneously, decarbonise the energy supply.
XGBoost, A Novel Explainable AI Technique, in the Prediction of Myocardial Infarction: A UK Biobank Cohort Study

We wanted to assess if "Explainable AI" in the form of extreme gradient boosting (XGBoost) could outperform traditional logistic regression in predicting myocardial infarction (MI) in a large cohort. Two machine learning methods, XGBoost and logistic regression, were compared in predicting risk of MI. The UK Biobank is a population-based prospective cohort including 502 506 volunteers with active consent, aged 40 to 69 years at recruitment from 2006 to 2010. These subjects were followed until end of 2019 and the primary outcome was myocardial infarction. Both models were trained using 90% of the cohort. The remaining 10% was used as a test set. Both models were equally precise, but the regression model classified more of the healthy class correctly. XGBoost was more accurate in identifying individuals who later suffered a myocardial infarction. Receiver operator characteristic (ROC) scores are class size invariant. In this metric XGBoost outperformed the logistic regression model, with ROC scores of 0.86 (accuracy 0.75 (CI ±0.00379) and 0.77 (accuracy 0.77 (CI ± 0.00369) respectively. Secondly, we demonstrate how SHAPley values can be used to visualize and interpret the predictions made by XGBoost models, both for the cohort test set and for individuals. The XGBoost machine learning model shows very promising results in evaluating risk of MI in a large and diverse population. This model can be used, and visualized, both for individual assessments and in larger cohorts. The predictions made by the XGBoost models, points toward a future where "Explainable AI" may help to bridge the gap between medicine and data science.
Privacy Research with Marginalized Groups

People who are marginalized experience disproportionate harms when their privacy is violated. Meeting their needs is vital for developing equitable and privacy-protective technologies. In response, research at the intersection of privacy and marginalization has acquired newfound urgency in the HCI and social computing community. In this literature review, we set out to understand how researchers have investigated this area of study. What topics have been examined, and how? What are the key findings and recommendations? And, crucially, where do we go from here? Based on a review of papers on privacy and marginalization published between 2010-2020 across HCI, Communication, and Privacy-focused venues, we make three main contributions: (1) we identify key themes in existing work and introduce the Privacy Responses and Costs framework to describe the tensions around protecting privacy in marginalized contexts, (2) we identify understudied research topics (e.g., race) and other avenues for future work, and (3) we characterize trends in research practices, including the under-reporting of important methodological choices, and provide suggestions to establish shared best practices for this growing research area.
The Algorithmic Crystal: Conceptualizing the Self through Algorithmic Personalization on TikTok

This research examines how TikTok users conceptualize and engage with personalized algorithms on the TikTok platform. Using qualitative methods, we analyzed 24 interviews with TikTok users to explore how algorithmic personalization processes inform people's understanding of their identities as well as shape their orientation to others. Building on insights from our qualitative data and previous scholarship on algorithms and identity, we propose a novel conceptual model to understand how people think about and interact with personalized algorithmic systems. Drawing on the metaphor of crystals and their properties, the algorithmic crystal framework is an analytic frame that captures user understandings of how personalized algorithms (1) interact with user identity by reflecting user self-concepts that are both multifaceted and dynamic and (2) shape perspectives on others encountered through the algorithm, by orienting users to recognize parts of themselves refracted in other users and to experience ephemeral, diffracted connections with groups of similar others. We describe how the algorithmic crystal framework can extend theory and inform new lines of research around the implications of algorithms in self-concept development and social life.
Incorporating anticipation embedding into reinforcement learning framework for multi-hop knowledge graph question answering

Multi-hop knowledge graph question answering (KGQA) aims to pinpoint answer entities by reasoning across multiple triples in knowledge graphs (KGs). To enhance model interpretability, reinforcement learning (RL) based methods are proposed. However, RL-based solutions suffer from two major issues. The first is aimless exploration due to the limited observation information. The second is delayed and sparse rewards caused by weak supervision. To bridge the gap, we propose an interpretable model based on RL for multi-hop KGQA with weak supervision, which we refer to as Anticipation Reasoning Network (ARN) that incorporates KG embeddings as anticipation information into RL framework to avoid aimlessly traversing in KG. ARN achieves the goal via three steps. (1) KG embedding models represent the entities and relations as low-dimensional vectors. (2) We propose a knowledge embedding based question answering (KEQA) framework to measure the plausibility of the “triple” <topic entity, question, answer entity> in embedding space. (3) A policy-guided agent is designed to perform effective path reasoning by selecting the most promising action at each step. Moreover, we utilize reward shaping strategy and actor-critic optimization to alleviate the problem of low-quality rewards. Extensive experiments well demonstrate the effectiveness of our method footnoteFor reproducibility, we publicly release the code at https://github.com/iDylanCui/ARN.