Reviewerly/exHarmony huggingface dataset:
Application of humanization to survey chatbots: Change in chatbot perception, interaction experience, and survey data quality

The use of chatbots as an online survey tool is becoming increasingly popular owing to their convenience, particularly when face-to-face interactions are difficult. However, with longer surveys, interaction experience and data quality can decrease due to several factors, such as increased fatigue. In this study, we compared how applying humanization techniques to survey chatbots can affect survey-taking experience in three aspects: respondents' perceptions of chatbots, interaction experience, and data quality. To address our research goal, two different versions of survey chatbots were compared: a humanization applied survey chatbot (HASbot) and a baseline chatbot (baselinebot). The HASbot simultaneously incorporates four humanization techniques: use of self-introduction, addressing by name, using adaptive response speed, and echoing respondents' answers. Our experimental study with 59 middle school-aged adolescents showed that compared to the baselinebot, respondents’ perceptions of the HASbot were more positive, with higher levels of anthropomorphism and social presence. In terms of interaction experience, the respondents spent more time interacting with the HASbot and showed a higher level of satisfaction. For data quality, the HASbot outperformed the baselinebot in terms of self-disclosure; however, the HASbot also elicited a higher social desirability bias. No difference was observed in the response differentiation between the two chatbots.
Understanding and Mitigating Annotation Bias in Facial Expression Recognition

The performance of a computer vision model depends on the size and quality of its training data. Recent studies have unveiled previously-unknown composition biases in common image datasets which then lead to skewed model outputs, and have proposed methods to mitigate these biases. However, most existing works assume that human-generated annotations can be considered gold-standard and unbiased. In this paper, we reveal that this assumption can be problematic, and that special care should be taken to prevent models from learning such annotation biases. We focus on facial expression recognition and compare the label biases between lab-controlled and in-the-wild datasets. We demonstrate that many expression datasets contain significant annotation biases between genders, especially when it comes to the happy and angry expressions, and that traditional methods cannot fully mitigate such biases in trained models. To remove expression annotation bias, we propose an AU-Calibrated Facial Expression Recognition (AUCFER) framework that utilizes facial action units (AUs) and incorporates the triplet loss into the objective function. Experimental results suggest that the proposed method is more effective in removing expression annotation bias than existing techniques.
Complementary Patch for Weakly Supervised Semantic Segmentation

Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels has been greatly advanced by exploiting the outputs of Class Activation Map (CAM) to generate the pseudo labels for semantic segmentation. However, CAM merely discovers seeds from a small number of regions, which may be insufficient to serve as pseudo masks for semantic segmentation. In this paper, we formulate the expansion of object regions in CAM as an increase in information. From the perspective of information theory, we propose a novel Complementary Patch (CP) Representation and prove that the information of the sum of the CAMs by a pair of input images with complementary hidden (patched) parts, namely CP Pair, is greater than or equal to the information of the baseline CAM. Therefore, a CAM with more information related to object seeds can be obtained by narrowing down the gap between the sum of CAMs generated by the CP Pair and the original CAM. We propose a CP Network (CPN) implemented by a triplet network and three regularization functions. To further improve the quality of the CAMs, we propose a Pixel-Region Correlation Module (PRCM) to augment the contextual in-formation by using object-region relations between the feature maps and the CAMs. Experimental results on the PAS-CAL VOC 2012 datasets show that our proposed method achieves a new state-of-the-art in WSSS, validating the effectiveness of our CP Representation and CPN.
A head mounted augmented reality design practice for maintenance assembly: Toward meeting perceptual and cognitive needs of AR users

Head Mounted Display (HMD) based Augmented Reality (AR) is being increasingly used in manufacturing and maintenance. However, limited research has been done to understand user interaction with AR interfaces, which may lead to poor usability, risk of occupational hazards, and low acceptance of AR systems. This paper uses a theoretically-driven approach to interaction design to investigate the impact of different AR modalities in terms of information mode (i.e. video vs. 3D animation) and interaction modality (i.e. hand-gesture vs. voice command) on user performance, workload, eye gaze behaviours, and usability during a maintenance assembly task. The results show that different information modes have distinct impacts compared to paper-based maintenance, in particular, 3D animation led to a 14% improvement over the video instructions in task completion time. Moreover, insights from eye gaze behaviours such as number of fixations and transition between Areas of Interest (AOIs) revealed the differences in attention switching and task comprehension difficulty with the choice of AR modalities. While, subjective user perceptions highlight some ergonomic issues such as misguidance and overreliance, which must be considered and addressed from the joint cognitive systems' (JCSs) perspective and in line with the predictions derived from the Multiple Resources Model.
Practical continuous-variable quantum key distribution with composable security

A quantum key distribution (QKD) system must fulfill the requirement of universal composability to ensure that any cryptographic application (using the QKD system) is also secure. Furthermore, the theoretical proof responsible for security analysis and key generation should cater to the number $N$ of the distributed quantum states being finite in practice. Continuous-variable (CV) QKD based on coherent states, despite being a suitable candidate for integration in the telecom infrastructure, has so far been unable to demonstrate composability as existing proofs require a rather large $N$ for successful key generation. Here we report the first Gaussian-modulated coherent state CVQKD system that is able to overcome these challenges and can generate composable keys secure against collective attacks with $N \lesssim 3.5\times10^8$ coherent states. With this advance, possible due to novel improvements to the security proof and a fast, yet low-noise and highly stable system operation, CVQKD implementations take a significant step towards their discrete-variable counterparts in practicality, performance, and security.
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media. Existing network embedding based methods have mostly focused on learning good node representations, whereas largely ignoring the subgraph structural changes related to the target nodes in a given time window. In this paper, we propose StrGNN, an end-to-end structural temporal Graph Neural Network model for detecting anomalous edges in dynamic graphs. In particular, we first extract the h-hop enclosing subgraph centered on the target edge and propose a node labeling function to identify the role of each node in the subgraph. Then, we leverage the graph convolution operation and Sortpooling layer to extract the fixed-size feature from each snapshot/timestamp. Based on the extracted features, we utilize the Gated Recurrent Units to capture the temporal information for anomaly detection. We fully implement StrGNN and deploy it into a real enterprise security system, and it greatly helps detect advanced threats and optimize the incident response. Extensive experiments on six benchmark datasets also demonstrate the effectiveness of StrGNN.
Quantum Key Distribution over 658 km Fiber with Distributed Vibration Sensing

Twin-field quantum key distribution (TFQKD) promises ultralong secure key distribution which surpasses the rate distance limit and can reduce the number of the trusted nodes in long-haul quantum network. Tremendous efforts have been made toward implementation of TFQKD, among which, the secure key with finite size analysis can distribute more than 500 km in the lab and in the field. Here, we demonstrate the sending-or-not-sending TFQKD experimentally, achieving a secure key distribution with finite size analysis over a 658 km ultra-low-loss optical fiber. Meanwhile, in a TFQKD system, any phase fluctuation due to temperature variation and ambient variation during the channel must be recorded and compensated, and all this phase information can then be utilized to sense the channel vibration perturbations. With our quantum key distribution system, we recovered the external vibrational perturbations generated by artificial vibroseis on both the quantum and frequency calibration link, and successfully located the perturbation position in the frequency calibration fiber with a resolution better than 1 km. Our results not only set a new distance record of quantum key distribution, but also demonstrate that the redundant information of TFQKD can be used for remote sensing of the channel vibration, which can find applications in earthquake detection and landslide monitoring besides secure communication.
PyTerrier: Declarative Experimentation in Python from BM25 to Dense Retrieval

PyTerrier is a Python-based retrieval framework for expressing simple and complex information retrieval (IR) pipelines in a declarative manner. While making use of the long-established Terrier IR platform for basic text indexing and retrieval, its salient utility comes from its expressive Python operators, which allow for individual IR operations to be pipelined and combined in different flexible manners as requested by the search application. Each operation applies a transformation upon a dataframe, while operators are defined with clear semantics in relational algebra. Going further, we have recently expanded the PyTerrier framework to include additional support for state-of-the-art BERT-based text re-rankers (such as EPIC) and dense retrieval implementations (such as ANCE and ColBERT). Transformer pipelines can be tuned and evaluated in a declarative manner. To increase the reusability of this framework as a resource for the IR community, PyTerrier provides easy access to a variety of standard benchmark datasets, including pre-built indices. Finally, we highlight the advantages of such a framework for information retrieval researchers and educators.
A self-adaptive weighted differential evolution approach for large-scale feature selection

Recently, many evolutionary computation methods have been developed to solve the feature selection problem. However, the studies focused mainly on small-scale issues, resulting in stagnation issues in local optima and numerical instability when dealing with large-scale feature selection dilemmas. To address these challenges, this paper proposes a novel weighted differential evolution algorithm based on self-adaptive mechanism, named SaWDE, to solve large-scale feature selection. First, a multi-population mechanism is adopted to enhance the diversity of the population. Then, we propose a new self-adaptive mechanism that selects several strategies from a strategy pool to capture the diverse characteristics of the datasets from the historical information. Finally, a weighted model is designed to identify the important features, which enables our model to generate the most suitable feature-selection solution. We demonstrate the effectiveness of our algorithm on twelve large-scale datasets. The performance of SaWDE is superior compared to six non-EC algorithms and six other EC algorithms, on both training and test datasets and on subset size, indicating that our algorithm is a favorable tool to solve the large-scale feature selection problem. Moreover, we have experimented SaWDE with six EC algorithms on twelve higher-dimensional data, which demonstrates that SaWDE is more robust and efficient compared to those state-of-the-art methods. SaWDE source code is available on Github at https://github.com/wangxb96/SaWDE.
Digital Banking in Northern India: The Risks on Customer Satisfaction

The widespread use of digital technologies and the current pandemic (COVID) have fueled the need and call for digital transformation in the banking sector. Although this has various benefits, it is a disruption to the norm to which a bank customer has to become accustomed. This variance means that customers would have to make some changes to their routine. This can constitute risks in terms of maintaining customer satisfaction at previous levels. These risks are associated with customer retention because a service or product needs to be aligned with customer expectations to avoid them switching to other service providers. Moreover, it can also have an effect on reputation. Offering digital account opening or remote deposits may not satisfy customers; competitive advantage depends on many aspects such as providing a hassle-free, personalized and cyber-secure experience, economic aspects and the needs of the society at large. Therefore, there is a need to understand the intensity of the risk factors that influence customer satisfaction for digitalized banking services and products. To do this, we carried out a structured survey, framed on the five dimensions of the SERVQUAL model, which was sent out to Northern Indian banking customers, to which we received 222 valid responses. We subjected the data received to Structural Equation Modelling using the SmartPLS version 3 application software. Results reveal that digital banking customers in Northern India are genuinely satisfied with the quality of services provided by digital banking. Moreover, ‘reliability’ has the strongest risk factor impact on customer satisfaction, followed by ‘tangibility’ and ‘responsiveness’.
Perceptual Quality Assessment of Low-light Image Enhancement

Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.
Usage of augmented reality (AR) and development of e-learning outcomes: An empirical evaluation of students’ e-learning experience

This study aims to examine students' experience of augmented reality learning applications (AR.LRP). Uses and gratifications theory was proposed as a theoretical foundation of the current study's conceptual model. Four dimensions of UGT benefits were proposed as key antecedences of the students' experience with AR.LRP: personal interactive benefits, social interactive benefits, affective benefits, and cognitive benefits. The model was also extended by considering the role of telepresence. Further, technostress was proposed on the current study to moderate the relationship between the aspects of UGT and e-learning experience. A sample size of 500 undergraduate students from the four largest Universities in Saudi Arabia was used in the current study survey. The statistical results largely support the significant impact of personal interactive benefits, affective benefits, and cognitive benefits, and telepresence on student experience with AR.LRP. Technostress was also found to significantly moderate the relationship between UGT dimensions and student experience with AR.LRP. Further, results support a significant relationship between student experience with AR.LRP and students' learning performance. This study has contributed to the perspectives of researchers and practitioners by highlighting the most important aspects and characteristics that ensure a distinctive and positive students' experience of the drum with applications with AR.LRP.
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part II—Generalization and Overfitting

We review the concept of overfitting, which is a well-known concern within the machine learning community, but less established in the clinical community. Overfitted models may lead to inadequate conclusions that may wrongly or even harmfully shape clinical decision-making. Overfitting can be defined as the difference among discriminatory training and testing performance, while it is normal that out-of-sample performance is equal to or ever so slightly worse than training performance for any adequately fitted model, a massively worse out-of-sample performance suggests relevant overfitting. We delve into resampling methods, specifically recommending k-fold cross-validation and bootstrapping to arrive at realistic estimates of out-of-sample error during training. Also, we encourage the use of regularization techniques such as L1 or L2 regularization, and to choose an appropriate level of algorithm complexity for the type of dataset used. Data leakage is addressed, and the importance of external validation to assess true out-of-sample performance and to-upon successful external validation-release the model into clinical practice is discussed. Finally, for highly dimensional datasets, the concepts of feature reduction using principal component analysis (PCA) as well as feature elimination using recursive feature elimination (RFE) are elucidated.
Evolution of YOLO-V5 Algorithm for Object Detection: Automated Detection of Library Books and Performace validation of Dataset

Object Detection, considered to be one of the basic fundamental and testing issues in Personal Computer vision, which is viewed as the extraordinary consideration in latest investigation. For the past 2 decades, it is been considered as an encapsulation of computer vision history. The objective of this paper is to look over the YOLOV5 and to evaluate the performance of YOLOV5 by various benchmarks and customized dataset. Motive of the study is to compare the performance of YOLOV5 by having the different data size, calculation speed and efficiency of detecting the objects, which are based on the dataset type. This work investigates and looks at the some of measurements for evaluating the YOLOV5 algorithm of detecting objects. Also audits the most utilized measurements for object detection and their disparities, applications, and primary ideas. It additionally proposes a standard execution that can be utilized as a benchmark among various datasets with least variation.
Strategic use of immersive media and narrative message in virtual marketing: Understanding the roles of telepresence and transportation

Abstract This study differentiates the roles of telepresence and transportation, the two widely cited processes underpinning an immersive media viewing experience, in the context of virtual marketing. In Study 1, college students viewed a real estate property tour in virtual reality (VR; high immersion) or as a 360° video (low immersion) in a research lab. The tour was accompanied by a voiceover presenting descriptive information about the apartment (descriptive evidence) or a narrative‐based introduction (narrative evidence). We found the enhanced telepresence under the VR viewing condition negatively impacted users' memory of the property; this effect was particularly pronounced with the narrative voiceover. Study 2 replicated key findings from Study 1 in an online experiment with a larger and more diverse sample. We also found that narrative‐induced transportation strengthened attitudinal outcomes, and enhanced telepresence boosted this effect. Our studies disentangle the impacts of VR‐induced telepresence on cognitive processing from the effects of narrative‐induced transportation on persuasive outcomes. The highly immersive media experience combined with deep transportation into the narrative can strengthen viewers' positive attitude towards the overall experience but hinder their ability to remember the details of the content. The findings point to a medium‐message matching strategy to achieve marketing goals.
Blockchain adoption in logistics and supply chain: a literature review and research agenda

The supply chain is an important source of knowledge that enables potential emerging technologies and, in this ecosystem, logistics are regarded as an intermediary for disseminating innovative solutions in a coordinated manner. As an emerging solution in the supply chain area, blockchain has recently been discussed as a disruptive technology and has received growing attention from academics and practitioners. Despite this interest, insufficient knowledge on the potential benefits and risks of blockchain technology causes vagueness for its successful implementation. Therefore, since effective management of logistics and supply chain operations through advanced solutions is of utmost importance, adopting an innovative approach rather than providing anectodal evidences or narrative expressions plays a critical role in blockchain adoption. Accordingly, this research aims to an in-depth analysis of the blockchain in logistics and supply chain by investigating enablers, barriers, and risks of this adoption. To this end, through employing both the Methodi Ordinatio and the narrative network analyses, the future direction of blockchain adoption is presented in light of the presented current state of knowledge. Consequently, the obtained findings offer academic and practical insights into the ambiguous, insufficiently explained, and conflicted areas in the relationship between blockchain technologies and logistics and supply chain contexts.