Reviewerly/exHarmony huggingface dataset:
Mask removal isn’t always convenient in public! – The Impact of the Covid-19 Pandemic on Device Usage and User Authentication

The ongoing Covid-19 pandemic has impacted our everyday lives and demands everyone to take countermeasures such as wearing masks or disinfecting their hands. However, while previous work suggests that these countermeasures may profoundly impact biometric authentication, an investigation of the actual impact is still missing. Hence, in this work, we present our findings from an online survey (n=334) on experienced changes in device usage and failures of authentication. Our results show significant changes in personal and shared device usage, as well as a significant increase in experienced failures when comparing the present situation to before the Covid-19 pandemic. From our qualitative analysis of participants’ responses, we derive potential reasons for these changes in device usage and increases in authentication failures. Our findings suggest that making authentication contactless is only one of the aspects relevant to encounter the novel challenges caused by the pandemic.
Token Relation Aware Chinese Named Entity Recognition

Due to the lack of natural delimiters, most Chinese Named Entity Recognition (NER) approaches are character-based and utilize an external lexicon to leverage the word-level information. Although they have achieved promising results, the latent words they introduced are still non-contextualized. In this paper, we investigate three relations, i.e., adjacent relation between characters, character co-occurrence relation between latent words, and dependency relation among tokens, to address this issue. Specifically, we first establish the local context for latent words and then propose a masked self-attention mechanism to incorporate such local contextual information. Besides, since introducing external knowledge such as lexicon and dependency relation inevitably brings in some noises, we propose a gated information controller to handle this problem. Extensive experimental results show that the proposed approach surpasses most similar methods on public datasets and demonstrates its promising potential.
A Novel Martingale Based Model Using a Smartphone to Detect Gait Bout in Human Activity Recognition

Gait bout is when an individual performs certain physical activities such as walking or running. In the last few decades, the study of gait bout has led to substantial progress in treating gait impairment (neuropathic, myopathic, and parkinsonian) in a person. Recently, gait bout study has been improved by advancing smartphone technology. To perform gait bout tasks, two different human activity scenarios, such as walking upstairs and standing, are obtained using the axis orientation of a smartphone accelerometer. To capture the pattern of walking upstairs and standing, we utilize a smartphone device attached to the waist of 30 subjects within the age group from 19 to 48 years old. We propose a human activity recognition model known as the multivariate triple exponential weighted moving average of the martingale sequence using particle swarm optimization (MTMS(PSO)) in the experimental setup. MTMS(PSO) utilizes the martingale framework to capture gait bout in human activity recognition data. Firstly, MTMS(PSO) is an unsupervised learning method that uses smoothing techniques such as triple exponential smoothing to remove high-frequency noise from the processed activity times series, making the patterns more visible. Secondly, the activity recognition model involves computing a threshold for identifying gait bout. Thirdly, MTMS(PSO) uses logical precedent and particle swarm optimization to enhance accuracy and precision. As a result, the overall MTMS(PSO) accuracy and G-mean are <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mn>95.4</mn><mi>%</mi></math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><mn>96.1</mn><mi>%</mi></math> , respectively. In addition, MTMS(PSO) technique independently outperforms other traditional methods such as MRPM(PSO), MGM(PSO), and ELM.
‘SIGuiendo’ Each Other Steps

Connecting with each other on the basis of research interests, geographies or identity has shown to be an important aspect of community development within HCI. The advent of a variety of committees and Special Interest Groups (SIG) are a testament to this need of connecting with each other. As members of the nascent SIGCHI LATAM committee, we see an opportunity in learning together with different communities across the HCI field about how we are working to attract more practitioners, academics, and students to work together and grow research surrounding HCI. With this SIG, we want to promote an environment for discussing and exchanging practices among different collectives within SIGCHI worldwide. We propose inviting community leaders from different geographies, sub-fields and identities to discuss their strategies and experiences growing their communities. The event is open to all members interested in gaining insight into how to grow communities within HCI.
Identifying Temporal Corpus for Enhanced User Comments Analysis

User comments provide valuable information for requirements analysis. To effectively extract requirements from user comments, it is important to determine informative user comments. However, existing studies have mainly focused on NLP-based methods for analyzing user comments, rather than defining a set of user comments to be analyzed. If target user comments are not clearly determined, duplicate requirements can be discovered or new requirements cannot be discovered. To tackle this problem, we present a new method which defines a set of target corpora from user comments. Our method automatically defines a set of target corpora to be analyzed by identifying underlying temporal changes in user comments. We applied our method to real-world user comments collected from a mobile application store. We confirmed that our method successfully defined a set of corpora which aids the effective requirements elicitation, and facilitated discovering new requirements while avoiding the derivation of redundant requirements.
Variapsody: Creating Three Interactive Music Listening Experiences that Use Diversified Positive Emotion Regulation Strategies to Promote Subjective Well-being

Over the past decade, initiatives to design for subjective well-being have gained increased attention and momentum in design research. These initiatives often draw from positive psychology to explore ways of making Positive Psychology Interventions (PPIs) more effective through technology. This paper explores how a mix of tangible and digital technology can realize activity-focused, diverse Emotion Regulation (ER) for its users. We propose that ER strategies can serve as a principle for designing technology that encourages users to savor, modify, reassess, or commemorate their experiences. By centering the design around music listening experiences, the paper explores how users can be supported to lower motivation hurdles that get in the way of frequent engagement with a PPI by developing three exploratory prototypes and reflecting on the development processes. Variapsody, a music listening device, integrates three features, each deploying a different set of ER strategies that make the experience more enjoyable and meaningful. Variapsody's regulatory diversity offers users the choice of how to approach music listening and expands their repertoire of ER strategies. The first feature, Reaction Tile, inscribes users' reactions to music onto a tangible, domino-sized tile to encourage them to savor the music. The second is Monofilter, which purposefully muffles the salience of background music while working on a cognitively demanding task. Vibelist is the third feature that helps users capture and revisit the context of music listening experiences in a digital collage. The paper discusses the lessons learned and future research opportunities.
MAM: Multiple Attention Mechanism Neural Networks for Cross-Age Face Recognition

Cross-age face recognition problem is of great challenge in practical applications because face features of the same person at different ages contain variant aging features in addition to the invariant identity features. To better extract the age-invariant identity features hiding beneath the age-variant aging features, a deep learning-based approach with multiple attention mechanisms is proposed in this paper. First, we propose the stepped local pooling strategy to improve the SE module. Then by incorporating the residual-attention mechanism, the self-attention mechanism, and the improved channel-attention mechanism to the backbone network, we proposed the Multiple Attention Mechanism Network (MAM-CNN) framework for the cross-age face recognition problem. The proposed framework can focus on essential face regions to highlight identity features and diminish the distractions caused by aging features. Experiments are carried out on two well-known public domain face aging datasets (MORPH and CACD-VS). The results yielded prove that the introduced multiple mechanisms jointly enhance the model performance by 0.96% and 0.52%, respectively, over the state-of-the-art algorithms.
Hanging on a Wire

How will we stay connected amidst a climate crisis? Conditions associated with climate change, such as sea level rise and increasing extreme weather events, can destabilize already vulnerable network and digital infrastructures. While existing infrastructures are in dire need of maintenance, additional infrastructures are simultaneously being built to address imbalances in network access and distribution. My dissertation project attends to these intersecting precarities as a way to reconsider how digital infrastructures can be reworked to address overlapping questions of environmental and digital inequities. My research is situated within marginalized coastal communities in south Louisiana, where legacies of petrochemical extraction has led to deep socioeconomic and ecological disparities, while increased intensity of storms and floods have begun to impede and damage an already sparse network infrastructure. In my project, I use archival, ethnographic, and design research methods to examine longer histories of environmental degradation, investigate current practices of maintaining and developing network infrastructures, and develop approaches for researchers in HCI and related computing fields to re-envision just and equitable network infrastructures.
Application of UAV path planning based on parameter optimization GA-PSO fusion algorithm

Abstract Aiming at the complexity of the unmanned aerial vehicle (UAV) path planning problem and the great influence of genetic algorithm parameters on the stability of the results, a fusion algorithm based on parameter optimization is proposed in this paper. In the iterative process, the GA-PSO fusion algorithm uses particle swarm optimization algorithm to search the optimal value of crossover rate and mutation rate in genetic algorithm, which makes the algorithm convergence speed is fast and search ability is strong. In addition, the core part of the algorithm fusion framework is realized by introducing the optimal adaptive value of the population after crossover and mutation as the adaptive value of the parameter particle. Finally, we design two groups of experiments and compare the proposed fusion algorithm with the classical particle swarm optimization algorithm (PSO), ant colony algorithm (ACA), genetic algorithm (GA), artificial fish swarm algorithm (AFSA), Wolf pack algorithm (WPA), artificial bee colony algorithm (ABC) and the improved algorithm through experimental simulation. The experimental results show that: In general, the path planned by the GA-PSO fusion algorithm in this paper is 10% shorter than that planned by other algorithms on average. Simulation results also show that the convergence speed of the fusion algorithm in this paper is faster, and the final search path is smoother.
A robust ensemble feature selection technique for high‐dimensional datasets based on minimum weight threshold method

Abstract Ensemble feature selection (EFS) is a valuable technique for developing accurate and robust machine‐learning (ML) models. Data variation plays a crucial role in the success of EFS models; however, it also causes some outliers in the ranked lists. In this study, we proposed the minimum weight threshold method‐based EFS (MWT‐EFS) to address the outlier problem and use the true power of EFS. The proposed method employs the support vector classifier to assign weights for features, and the MWT method handles outliers in the ranked feature lists while creating the ensemble list. First, a threshold value is determined. After that, the feature weights below the threshold are replaced with this value. This approach eliminates the negative effect of outliers. After the new feature weights are assigned, the average of the feature weights is calculated (mean aggregation) for all features, and the ensemble (final) feature list is created accordingly. The experiment results showed that the proposed method significantly improves gene selection stability while maintaining classification performance and reducing computational complexity. In conclusion, the proposed method led to an accurate and robust classification that can help domain experts to make more confident decisions with less effort, resources, and time.
Exploring Lecture Evaluation Tools Suitable for online classes at Universities

Purpose: To develop an evaluation tool suitable for online classes at universities due to the increased focus on the quality management of university classes because of the impact of COVID-19. Methods: Nineteen items relevant to evaluation of the teaching and learning at university were extracted from a review of previous studies and the content analysis by experts was undertaken. Based on the CIPP evaluation model, 16 items were selected from the analysis of learning evaluations of 1,000 students at the end of the second semester of 2021 at the researcher’s workplace. Results: First, following confirmatory exploratory factor analysis (CFA), 16 learning evaluation items were converted into a single factor related to learning and teaching. Second, as a result of confirmatory factor analysis (CFA) by the academic field, 16 learning evaluation items were converted into a single factor. Third, 16 learning evaluation items were found to have a significant positive effect on the learning evaluation score. Conclusion: The learning evaluation tools developed through this study were demonstrated to be meaningful in that they can be applied at the individual educator and university level to improve the quality of lectures in online classes in the future.
Triangulating Race, Capital, and Technology

This workshop transnationally triangulates race, capital, and technology to understand how colonialism and imperialism linger and mutate across various sites and scales. Furthermore, it brings together transnational HCI work that engages with critical ethnic studies as well as postcolonial and decolonial studies to intervene on the field’s long-standing epistemology and site focus on the West and fixation on the nation-state at large. Attention to colonial residual, geopolitical tensions, and historical specificities brings HCI in conversation with geopolitical shifts and their very real impacts on the practice and theory of technology design, while troubling the presumptions of who “gets to be human” in HCI. We invite papers and presentations that seek to: 1) triangulate sites of study; 2) draw from different disciplines, theoretical approaches, and methodologies; and 3) engage themes of transnational capital, race, and technology.
Temporal Sequence of Data Fluctuation-Based Approach for Tor Program Classification

With the continuous development of encryption technology, the share of encrypted traffic in the network is increasing, which brings great challenges to the traditional methods of rule-based traffic identification. Deep learning is becoming an inspiring methodology to solve the problem. Previous studies have confirmed that time characteristics play an important role in Tor traffic classification. We find that there is a similarity of time characteristics among different programs. This paper proposes an end-to-end classification framework: the temporal sequence of data fluctuation network (TSDFN). It first extracts the temporal sequence of data fluctuation in the original flow and then uses the GRU network to learn the hidden temporal features. Experiments on public data sets validate the effectiveness of our proposal over other methods.
Artificial Intelligence in Construction of English Classroom Situational Teaching Mode Based on Digital Twin Technology

It is combined the digital twin technology to construct the English classroom situational teaching mode. The system uses advanced virtual reality technology and computer image technology and combines with video and audio synchronization processing technology to provide a new set of methods for students’ language learning. The scene interactive teaching system’s graphics rendering server produce and create 3D virtual scenes or actual photos in real time. Furthermore, according to the English classroom teaching situation, this paper constructs the functional modules of the situational teaching system, conducts an in-depth analysis of the system implementation methods, expresses the system core algorithm flow in the form of diagrams and tables, and obtains the overall system framework. Finally, it is evaluated the effect of the English classroom situational teaching model proposed in this paper through experimental research. From the experimental results, it can be seen that the teaching model proposed in this paper is very effective.
The Qubit Fidelity Under Different Error Mechanisms Based on Error Correction Threshold

Quantum error correction is a crucial step to realize large-scale universal quantum computing, and the condition for realizing quantum error correction is that the error probability of each operation step must below some threshold. This requires that the qubits’ quality and the quantum gates precision can reach a certain level experimentally. We firstly discuss the mechanism of quantum errors: the precision of quantum gates corresponds to unitary operator errors, and the quality of qubits is attributed to decoherence. Then, according to the threshold of the surface code error correction, we proved the minimum of quantum gate fidelity should not be less than 1 − p with the error probability p , and found the natural decoherence time of qubits that can be used for error correction. This provides some kind of theoretical supports for qubits preparation and performing quantum operations experimentally.
Personal Dream Informatics: A Self-Information Systems Model of Dream Engagement

We present the research area of personal dream informatics: studying the self-information systems that support dream engagement and communication between the dreaming self and the wakeful self. Through a survey study of 281 individuals primarily recruited from an online community dedicated to dreaming, we develop a dream-information systems view of dreaming and dream tracking as a type of self-information system. While dream-information systems are characterized by diverse tracking processes, motivations, and outcomes, they are universally constrained by the ephemeral dreamset—the short period of time between waking up and rapid memory loss of dream experiences. By developing a system dynamics model of dreaming we highlight feedback loops that serve as high leverage points for technology designers, and suggest a variety of design considerations for crafting technology that best supports dream recall, dream tracking, and dreamwork for nightmare relief and personal development.
Pedestrian Fall Event Detection in Complex Scenes Based on Attention-Guided Neural Network

To address automatic detection of pedestrian fall events and provide feedback in emergency situations, this paper proposes an attention-guided real-time and robust method for pedestrian detection in complex scenes. First, the YOLOv3 network is used to effectively detect pedestrians in the videos. Then, an improved DeepSort algorithm is used to track by detection. After tracking, the authors extract effective features from the tracked bounding box, use the output of the last convolutional layer, and introduce the attention weight factor into the tracking module for final fall event prediction. Finally, the authors use the sliding window for storing feature maps and SVM classifier to redetect fall events. The experimental results on the CityPersons dataset, Montreal fall dataset, and self-built dataset indicate that this approach has good performance in complex scenes. The pedestrian detection rate is 87.05%, the accuracy of fall event detection reaches 98.55%, and the delay is within 120 ms.
Undoing Seamlessness: Exploring Seams for Critical Visualization

While seamful design has been part of discourses and work within HCI contexts for some time, it has not yet been fully explored in data visualization design. At the same time, critics of visualization have been arguing that the representation of data as contextual, contingent, relational, partial, heterogeneous, and situated is currently lacking in visualization. Seamful visualization promises a fresh perspective on visualization design as we seek to find more expressive encodings and novel approaches to representing data that acknowledge their wider qualities and limitations. By consulting seams in other realms and exploring existing seams and seamfulness in visualization, this paper offers a foundation for conceptualizing seamful visualization, points towards the value of seams and seamfulness in critical visualization, and proposes principles for engaging with seamful visualization in practice and research.
Python-Based Visual Classification Algorithm for Economic Text Big Data

In order to improve the classification accuracy and reduce the classification time of the economic text big data visualization classification algorithm, one based on the Pitton algorithm is proposed. The economic text big data are preprocessed by filtering out useless symbols, word segmentation processing, and removing stop words. According to the processing results, the most relevant features of the economic text big data classification process are selected, including Gini index, information gain, mutual information, etc., and the TF-IDF weighting algorithm is used to weight the economic text data features. Based on feature weighting, using Naive Bayesian algorithm, combining classification probability distribution and text probability distribution, Naive Bayesian classifier is constructed to obtain the optimal classification result through input vector, and the visual classification of economic text big data is completed through Python software programming. The simulation results show that the classification accuracy of the algorithm for the visual classification of economic text big data can reach 100%, and the classification time is less than 5 seconds. It has high accuracy and fast efficiency.
Voltage and temperature dependence of Random Telegraph Noise and their impacts on random number generator

With the continuous decrease in device dimensions, Random Telegraph Noise (RTN) has drawn increasing attention. From the intrinsic stochastic features, RTN signal is adopted as an entropy source for true random number generator (TRNG). In this study, RTN time constants are investigated under various gate voltages and temperatures. Their impacts on the TRNG are revealed by performing the National Institute of Standards and Technology (NIST) test. It is found that the random verification is highly dependent on the maximum frequency (fmax) of the clock signal in TRNG circuit, which shows linear dependence with the RTN time constant (τc + τe). Our findings may make a step towards generating truly random numbers and improving hardware or cyber security based on RTN phenomenon.