Reviewerly/exHarmony huggingface dataset:
Empathetic creativity for frontline employees in the age of service robots: conceptualization and scale development

Purpose While robots have increasingly threatened frontline employees’ (FLEs) future employment by taking over more mechanical and analytical intelligence tasks, they are still unable to “experience” and “feel” to occupy empathetic intelligence tasks that can be handled better by FLEs. This study, therefore, aims to empirically develop and validate a scale measuring the new so-called empathetic creativity as being creative in practicing and performing empathetically intelligent skills during service encounters. Design/methodology/approach This study adopts a multistage design to develop the scale. Phase 1 combines a literature review with text mining from 3,737 service robots-related YouTube comments to generate 16 items capturing this new construct. Phase 2 assesses both face and content validity of those items, while Phase 3 recruits Prolific FLEs sample to evaluate construct validity. Phase 4 checks this construct’s nomological validity using PLS-SEM and Phase 5 experiments dedicated effort (vs natural talent) as an effective approach to foster FLEs’ perceived empathetic creativity. Findings The final scale is comprised of 13 refined items that capture three dimensions (social, interactive and emotional) of empathetic creativity. This research provides timely implications to help FLEs in high-contact services stay competitive. Originality/value This study introduces the new construct of empathetic creativity, which goes beyond the traditional definition of creativity in services and highlights the importance of empathetic intelligence for FLEs in future employment. This study also develops a multi-item scale to measure this construct, which can be applied to future service management research.
Internet Financial Risk Management in the Context of Big Data and Artificial Intelligence

In recent years, the emergence of big data and artificial intelligence technology has made Internet finance a brand new development model in the new era. As an emerging financial format, Internet finance plays an important role in providing people with convenient and efficient services. However, due to the late start in this regard and the imperfect related policies and regulations, China is currently still in the development stage, resulting in its risk management system not being mature and complete and lacking uniformity. There are also many regulatory deficiencies, which are not conducive to the healthy, stable, and continuous growth and progress of Internet finance. In the new situation, it is of great significance to strengthen the research on the security of China's Internet finance. Therefore, how to effectively manage Internet financial risks in the context of big data and artificial intelligence has become a topic of research. This study uses questionnaire analysis and data analysis to understand the distribution of risks and the importance of risk response measures through questionnaire surveys. According to the survey results, in the eyes of most interviewees, the ratios of operational risk, credit risk, platform operation risk, and lack of law and reputation risk in high-risk areas are 0.15, 0.3, 0.29, 0.51, and 0.1, respectively. The risks of these first-level indicators need to be particularly important and need to be effectively avoided to manage Internet financial risks. In addition, the most important risk response measures are the construction of information security, followed by the improvement of relevant laws and regulations. In their view, only from these aspects can we effectively control risks internally and externally.
Transfer Learning based Offline Handwritten Recognition System using Tamil Characters

As a subset of Machine Learning [ML], Transfer Learning (TL) is a method used to research-oriented challenges by dedicating a primary focus on preserving the collected knowledge. As an extended application of the transfer learning algorithm, this paper proposes a model for detecting Tamil characters by using a multilayer perceptron and offline handwriting. This method employs a number of methods, including preprocessing, image smoothing, thresholding, skeletonization, feature extraction, boundary tracing, and deployment. In the proposed methodology, algorithms such as transfer learning, Alex Net, and VGGNet19 are utilized to select out the Tamil letters and assist the user in identifying historical characters in a comprehensible and human readable style. The utilized Tamil historical dataset is downloaded from Kaggle.
High-resolution 3D shape measurement with extended depth of field using fast chromatic focus stacking

Close-range 3D sensors based on the structured light principle have a constrained measuring range due to their depth of field (DOF). Focus stacking is a method to extend the DOF. The additional time to change the focus is a drawback in high-speed measurements. In our research, the method of chromatic focus stacking was applied to a high-speed 3D sensor with 180 fps frame rate. The extended DOF was evaluated by the distance-dependent 3D resolution derived from the 3D-MTF of a tilted edge. The conventional DOF of 14 mm was extended to 21 mm by stacking two foci at 455 and 520 nm wavelength. The 3D sensor allowed shape measurements with extended DOF within 44 ms.
Enhancement of quantum excitation transport by photonic nonreciprocity

Enhanced interaction between two two-level emitters (e.g., atoms) by nonreciprocal photonic media can be of benefit to broad areas, from quantum information science to biological detection. Here we provide a detailed analysis on why nonreciprocal photon-mediated interaction enhances inter-atomic excitation transport efficiency. We investigate a system consisting of two two-level emitters embedded in a generic photonic environment. By comparing symmetric and asymmetric photon-exchange, we analytically show that breaking electromagnetic reciprocity makes it possible for the cooperative decay rate to exceed the spontaneous decay rate even in a translation-invariant homogeneous system. This means that the excitation of an emitter must decay mostly into the other emitter rather than leaking and dissipating into the reservoir photonic modes. We also provide an example where a chain of two-level emitters dominantly interact via the reciprocal modes of a plasmonic waveguide. We then show that breaking reciprocity in such a system via driving a DC current through the plasmonic material can drastically increase the probability of photon emission from one emitter to another, leading to an order-of-magnitude enhancement in quantum energy-transport efficiency.
Recentering Reframing as an RtD Contribution

Design produces valuable knowledge by offering new perspectives that reframe problematic situations. Research through Design (RtD) contributes new frames along with design work demonstrating a frame's value. Interestingly, RtD papers rarely describe how reframing happens. This gap in documentation unintentionally implies a romantic account of design, it implies that the first step of an RtD project is to have a brilliant idea. This is especially problematic in cases where the reframing causes a pivot that leads to a new research program. To help address this gap, we describe a case where through a series of three design experiments we experienced a research pivot. We describe how our work to improve web-table navigation for screen-reader users broke our frame. The break led to a new research program focused on constructing a conversational internet. This paper offers our case along with reflection on reporting design work that drives reframing.
Bivariate Effective Width Method to Improve the Normalization Capability for Subjective Speed-accuracy Biases in Rectangular-target Pointing

The effective width method of Fitts’ law can normalize speed-accuracy biases in 1D target pointing tasks. However, in graphical user interfaces, more meaningful target shapes are rectangular. To empirically determine the best way to normalize the subjective biases, we ran remote and crowdsourced user experiments with three speed-accuracy instructions. We propose to normalize the speed-accuracy biases by applying the effective sizes to existing Fitts’ law formulations including width W and height H. We call this target-size adjustment the bivariate effective width method. We found that, overall, Accot and Zhai’s weighted Euclidean model using the effective width and height independently showed the best fit to the data in which the three instruction conditions were mixed (i.e., the time data measured in all instructions were analyzed with a single regression expression). Our approach enables researchers to fairly compare two or more conditions (e.g., devices, input techniques, user groups) with the normalized throughputs.
On linear and super-linear convergence of Natural Policy Gradient algorithm

Markov Decision Processes are classically solved using Value Iteration and Policy Iteration algorithms. Recent interest in Reinforcement Learning has motivated the study of methods inspired by optimization, such as gradient ascent. Among these, a popular algorithm is the Natural Policy Gradient, which is a mirror descent variant for MDPs. This algorithm forms the basis of several popular RL algorithms such as Natural actor–critic, TRPO, PPO, etc., and so is being studied with growing interest. It has been shown that Natural Policy Gradient with constant step size converges with a sub-linear rate of O(1/k) to the global optimal. In this paper, we present improved finite time convergence bounds, and show that this algorithm has geometric (also known as linear) asymptotic convergence rate. We further improve this convergence result by introducing a variant of Natural Policy Gradient with adaptive step sizes. Finally, we compare different variants of policy gradient methods experimentally.
Attention-Based Noise Prior Network for Magnetic Resonance Image Denoising

Data obtained from magnitude magnetic resonance images (MRI) are corrupted by signal-dependent and spatially variant Rician noise. The contribution of this work can be discussed at three different levels. Firstly, Rician noise-level estimation map is fed as prior concatenated with the noisy input data; to handle spatially variant noise and achieve an optimal compromise between noise removal and detail preservation. Secondly, modified U-Net architecture is used to accommodate non-local multi-level and multi-scale features. Thirdly, to preserve the long-range dependencies lost in farther symmetric layers, features obtained from symmetric-group attention block is fed as input to the deconvolution layers for nonlocal high- and low-level feature mapping. Experimental results over synthetically corrupted MR images and real data obtained from MR scanners suggest the potential utility of our proposed technique over a wide-range of noise levels.
Inverse design assisted coherent optical lattices

We explore the use of inverse design methods for the generation of periodic optical patterns in photonic integrated circuits. A carefully selected objective function based on the integer lattice method, which is an algebraic technique for optical lattice generation, is shown to be key for successful device design. Furthermore, we present a polychromatic pattern generating device that switches between optical lattices with different symmetry and periodicity depending on the operating wavelength. Important links are drawn between optical coherent lattices and optical potentials, pointing towards practical applications in the fields of quantum simulations and computing, optical trapping, and bio-sensing.
Robust descriptor for key-point detection and matching in color images with radial distortion

Constructing descriptors for key points in image matching is a crucial task in computer vision, visual tracking, and pattern recognition. Highly rated descriptors such as scale-invariant feature transform (SIFT) and speeded up robust features (SURF) utilize the grayscale gradient information of the square regions around the key points. Nevertheless, these famous descriptors fail to take advantage of the image color information. Furthermore, their performances will be severely degraded for distorted images and the error due to rotation of their square windows. To address the problems, we proposed Circular Coordinate Combining Shape–Color Descriptor Under Distortion Based SURF (CSCD-SURF) that achieves competitive performance for colorful images with distortion contamination and numerical similar structures. The proposed approach can generate scale-space by adaptive filter to rectify distortion and integrate the SURF descriptor and shape–color information into our own custom concentric circular coordinate with flying colors. Experiments with Institut National de Recherche en Infomatique et Automatique dataset and spherical radial distortion-SIFT distorted image datasets prove that the ability of distortion rectification as well as matching performance against rotation, scaling, viewpoints, and blurring is more competitive than the state-of-the-art methods and its computation cost is acceptable.
Segmentation of Multiple Myeloma Cells Using Feature Selection Pyramid Network and Semantic Cascade Mask RCNN

Multiple myeloma (MM) is a blood cancer that develops when plasma cells expand abnormally in the bone marrow. The early detection of MM is beneficial for accurate treatment in time and draws increasing recognition. There are several methods to detect myeloma cells in bone marrow, such as using microscopic analysis based on the aspirate slide images. In this paper, we propose a deep learning framework called the semantic cascade Mask RCNN for the detection and segmentation of myeloma cells. The framework is also integrated with the proposed feature selection pyramid network, which is a simple and effective module to improve the segmentation performance. The mask aggregation module refines and merges the high certainty instance masks into a single segmentation map and combines the results from the extra semantic segmentation branch to generate better predictions. The extensive experiments on the SegPC-2021 Challenge dataset demonstrate that the proposed method achieves a promising performance.
TaleBrush: Visual Sketching of Story Generation with Pretrained Language Models

Advancing text generation algorithms (e.g., GPT-3) have led to new kinds of human-AI story co-creation tools. However, it is difficult for authors to guide this generation and understand the relationship between input controls and generated output. In response, we introduce TaleBrush, a GPT-based tool that uses abstract visualizations and sketched inputs. The tool allows writers to draw out the protagonist's fortune with a simple and expressive interaction. The visualization of the fortune serves both as input control and representation of what the algorithm generated (a story with varying fortune levels). We hope this demonstration leads the community to consider novel controls and sensemaking interactions for human-AI co-creation.
PMD-Track: Portable Medical Devices’ Real-time Inventory and Tracking

The recent COVID pandemic challenged healthcare systems worldwide and highlighted not only a lack of sufficient resources in some cases, but also an overall inefficiency in managing available PMDs (Portable Medical Devices). Hospitals typically provide their staff with smartphones to facilitate internal communication and access to hospital services. The key contribution of PMD-Track lies in the use of smartphones replacing expensive stationary gateways scattered across a hospital, acting as mobile gateways associated with a front-end that allows staff to quickly find PMDs. Thus, employees walking nearby tagged PMDs — as they perform daily activities — constantly help to automatically update these PMDs’ locations in a live inventory tracker allowing to retrieve up-to-date information.PMDs equipped with traditional Bluetooth Low Energy (BLE) tags will update a backend service with the location of recently spotted tags and display information concerning their position in real-time. Different PMD types can issue alerts according to the type of their mobility (i.e., considering that portable devices can be more or less “dynamic”). Thus, it is expected that PMD-Track will enable hospitals to make efficient use of their PMDs in emergency situations, such as a pandemic or eventual natural disasters, where a sudden increase in demand can now be foreseen.
A large-scale analysis of Wi-Fi passwords

Passwords remain the most common method of authentication in computers and networks. Thus, passwords have been the prime targets of attackers, and the number of data breaches in the last few years proves the high value of passwords. A detailed analysis of such data can provide insight on password trends and patterns users follow when they create a password. While there is a wealth of research investigating online password choices, to the best of our knowledge, there are no studies specifically designed to capture user behavior towards Wi-Fi passwords. In this paper, we perform a large-scale analysis of Wi-Fi passwords categorizing them as public Wi-Fi passwords created for Wi-Fi hotspots and private Wi-Fi passwords created for private/home Wi-Fi installations. First, we analyze public Wi-Fi passwords by collecting and analyzing a corpus of more than one million passwords of Wi-Fi hotspots. The aim of the analysis of public Wi-Fi passwords is to reveal password characteristics and compare them against web account passwords, to discover similarities and differences between them. While comparing the collected dataset with a set of popular leaked web password databases, several similarities between them can be identified, despite the fact that these password categories serve different purposes. Secondly, we explore through an online survey, the characteristics of passwords in private/home Wi-Fi installations. The aim is to give greater insight into private Wi-Fi password selection of users and highlight password trends when it comes to context, length, strength and architecture. Results reveal that users follow several poor security practices when selecting their private Wi-Fi passwords and tend to underestimate their importance with regards to their security and privacy. To the best of the authors' knowledge, this is the first work to examine Wi-Fi passwords characteristics and shed light on how users choose them in terms of structure and composition.
HeadWind: Enhancing Teleportation Experience in VR by Simulating Air Drag during Rapid Motion

Teleportation, which instantly moves users from their current location to the target location, has become the most popular locomotion technique in VR games. It enables fast navigation with reduced VR sickness but results in significantly reduced immersion. We present HeadWind, a novel approach to improve the experience of teleportation by simulating the haptic sensation of air drag when rapidly moving through the air in real life. Specifically, HeadWind modulates bursts of compressed air to the face and uses multiple nozzles to provide directional cues. To design the wearable device and to model airflow speed and duration for teleportation, we conducted three formative studies and a design session. User experience evaluation with 24 participants showed that HeadWind significantly improved realism, immersion, and enjoyment of teleportation in VR (p<.01) with large effect sizes (r>0.5), and was preferred by 96% of participants.
Element: An Ambient Display System for Evoking Self-Reflections and Supporting Social-Interactions in a Workspace

In this paper, we present the design of Element, an ambient display system that displays the user's emotional engagement level through color and shape change on a light fixture. Given the increasing number of people who experience exhaustion or burnout in professional contexts, our goal is to design an interface that helps people keep track of their engagements through an ambient display of real-time physiological data. We conduct two qualitative studies to investigate its possible benefits and users' concerns from an individual perspective and social contexts with multiple users. The result shows that our design can not only aid users' self-reflections and discovery on the intrapersonal level, it also shows the potential to help participants understand others' emotional engagement and provide vital support when needed. Interestingly, our findings also highlighted the ethical concerns of displaying personal data in a public space. Reflecting on this crucial challenge, we also propose three social guidelines for helping users establish positive mindsets and cultures of using Element appropriately in sociotechnical environments.
Improving KNN Algorithm Efficiency Based on PCA and KD-tree

K-nearest neighbor (KNN) algorithm is a simple and widely used classification method in machine learning. This algorithm tries to search every object in the dataset to find the nearest several neighbors in order to classify and predict the target. Therefore, the runtime of KNN algorithm will become longer when encountering big datasets. There are several articles discussing the improved KNN algorithm based on the KD-Tree storage structure. However, the time complexity of KD-Tree grows rapidly with the increase of the dimensionality of datasets. Therefore, a new improved KNN algorithm is proposed in this paper. This new KNN algorithm is based on PCA analysis and KD-Tree data structure. By combining these two techniques, the efficiency of the classification process could be increased significantly. All features of PCA and KD-Tree related to the new proposed algorithm are discussed and the specific steps of the new method are stated. The new KNN algorithm is applied in two experiments through python. The result shows that the efficiency of the new KNN algorithm is improved greatly under certain situations and its accuracy also has a good performance. However, several potential drawbacks could be seen from the result of the experiments. To further improve the new proposed KNN algorithm, more advanced techniques and experiments are needed to be developed in the future.
BIGexplore: Bayesian Information Gain Framework for Information Exploration

The Bayesian information gain (BIG) framework has garnered significant interest as an interaction method for predicting a user’s intended target based on a user’s input. However, the BIG framework is constrained to goal-oriented cases, which renders it difficult to support changing goal-oriented cases such as design exploration. During the design exploration process, the design direction is often undefined and may vary over time. The designer’s mental model specifying the design direction is sequentially updated through the information-retrieval process. Therefore, tracking the change point of a user’s goal is crucial for supporting an information exploration. We introduce the BIGexplore framework for changing goal-oriented cases. BIGexplore detects transitions in a user’s browsing behavior as well as the user’s next target. Furthermore, a user study on BIGexplore confirms that the computational cost is significantly reduced compared with the existing BIG framework, and it plausibly detects the point where the user changes goals.