Reviewerly/exHarmony huggingface dataset:
Hybrid fuzzy multiple SVM classifier through feature fusion based on convolution neural networks and its practical applications

• The convolution-base and composite kernel are used for alleviating overfitting. • The composite kernel could adjust the nonlinear fitting ability of the classifier. • Hybrid fuzzy multi-(HFM) SVM leads to better results with flexible feature fusion. • The effectiveness of the HFM-SVM is demonstrated by three practical applications. Lately, Convolutional Neural Networks (CNNs) have been introduced to extract features and further enhance the classification performance in different application areas. In this study, a hybrid fuzzy multiple (HFM)-SVM design with the convolution-base (which consists of a series of pooling and convolutional layers) and composite kernel function is proposed. The objective of the proposed classifier is to enhance the nonlinear fitting ability of the classifier and improve the classification performance in high-dimensional applications. The key points of the proposed HFM SVM are enumerated as follows: 1) The convolution-base of the proposed classifier extracts features. The extracted features exhibit flexibility and applicability in high-dimensional applications. 2) The proposed HFM SVM designed with the composite kernel could adjust the nonlinear fitting ability for improving classification performance. The procedure of the proposed HFM SVM is described as follows: Convolution-base is considered as a preprocessing unit for extracting features. The features are not always linearly separable especially when being extracted from high dimensional data. A composite kernel function is constructed by considering the complicated (nonlinear) classification boundary into several local linear boundaries. The structure of the extracted features is captured by FCM clustering and integrated into the composite kernel function for enhancing the nonlinear fitting ability of the proposed classifier. The nonlinearity of the composite function can fill the gap between linear and nonlinear kernel functions by adjusting the number of clusters obtained by the FCM clustering algorithm. The proposed HFM SVM classifier based on composite kernel could improve the classification performance on high dimensional datasets. The performance of the proposed HFM SVM is experimented with and demonstrated by using three high-dimensional applications to show the effectiveness as well as performance improvement.
Feminist Voices about Ecological Issues in HCI

Even though issues such as climate change, pollution, and declining biodiversity impact us all, people with historically disenfranchised and socio-politically marginalized (HDSM) identities often bear the harsher brunt of ecological crises and suffer disproportionately. There is a need for listening to the voices of people with intersecting HDSM identities in relation to feminist engagements with ecological issues as applicable to HCI and IxD research and practice. Building upon and braiding together two thriving HCI discourses on feminism and environmental sustainability, we invite submissions from researchers, designers, educators, and activists interested in the intersections of feminist and ecological issues with a priority towards the well-being of people with HDSM identities. Converging feminist concerns on power, voice, and public discourse through this online workshop distributed across three time-zones, we hope to provide a forum for contemporary feminist voices as agents of change while engaging with ecological issues through an intersectional feminist orientation.
Strategies for Fostering a Genuine Feeling of Connection in Technologically Mediated Systems

Human connection is essential for our personal well-being and a building block for a well-functioning society. There is a prominent interest in the potential of technology for mediating social connection, with a wealth of systems designed to foster the feeling of connection between strangers, friends, and family. By surveying this design landscape we present a transitional definition of mediated genuine connection and nine design strategies embodied within 50 design artifacts: affective self-disclosure, reflection on unity, shared embodied experience, transcendent emotions, embodied metaphors, interpersonal distance, touch, provocations, and play. In addition to drawing on design practice-based knowledge we also identify underlying psychological theories that can inform these strategies. We discuss design considerations pertaining to sensory modalities, vulnerability–comfort trade-offs, consent, situatedness in context, supporting diverse relationships, reciprocity, attention directedness, pursuing generalized knowledge, and questions of ethics. We hope to inspire and enrich designers’ understanding of the possibilities of technology to better support a mediated genuine feeling of connection.
Energy-efficient virtual-machine mapping algorithm (EViMA) for workflow tasks with deadlines in a cloud environment

Processing large scientific applications generates a huge amount of data, which makes running experiments in the cloud computing environment very expensive and energy-consuming. To find an optimal solution to the workflow scheduling problem, several approaches have been presented for scheduling workflow on cloud resources. However, more efficient approaches are needed to improve cloud service delivery. In this paper, an energy-efficient virtual machine mapping algorithm (EViMA) is proposed to improve resource management in the cloud computing environment to achieve effective scheduling that reduces cloud data center energy consumption, execution makespan, and execution cost. This ensures that the requirements of cloud users are met, and improves the quality of services offered by cloud providers. Our proposed mechanism considers the heterogeneity of scheduling from both cloud users’ and workflow applications’ perspectives. Through simulation experiments on real workflow datasets, the proposed EViMA can provide better solutions for both cloud users and cloud providers by reducing energy consumption, execution makespan, and execution cost better than the state-of-the-art.
Evaluating value mediation in patients with chronic low-back pain using virtual reality: contributions for empirical research in Value Sensitive Design

Value Sensitive Design (VSD) is the most well-known method to consider values in design. It consists of three iterative phases of investigation: conceptual, empirical, and technical. Although the approach is promising, the role of empirical research remains unclear. We address two opportunities for extending the role of empirical research in VSD. First, we argue that empirical research enables us to identify values in context. Second, we explain that empirical research enables us to anticipate how technology mediates the values of users. We make our point by means of an empirical study in a real-life controlled experimental context into the value mediation of virtual reality (VR) in patients with chronic low-back pain. Using value-oriented semi-structured interviews with twenty patients, we first analyze what values these patients consider important, and how the values are experienced. The second set of interviews held after all patients used VR four weeks at home, aims to provide insight into value changes as mediated by VR. We end the article by a comparison of our empirical results with previous, often speculative, literature into values in VR. We show that empirical research benefits the VSD process by providing in-depth insight into the effects of context and technology on values and the ability to translate these insights into recommendations for more responsible design and implementation of the technology.The online version contains supplementary material available at 10.1007/s12553-022-00671-w.
A New Dimensionality-Unbiased Score for Efficient and Effective Outlying Aspect Mining

Abstract The main aim of the outlying aspect mining algorithm is to automatically detect the subspace(s) (a.k.a. aspect(s)), where a given data point is dramatically different than the rest of the data in each of those subspace(s) (aspect(s)). To rank the subspaces for a given data point, a scoring measure is required to compute the outlying degree of the given data in each subspace. In this paper, we introduce a new measure to compute outlying degree, called Simple Isolation score using Nearest Neighbor Ensemble (SiNNE), which not only detects the outliers but also provides an explanation on why the selected point is an outlier. SiNNE is a dimensionally unbias measure in its raw form, which means the scores produced by SiNNE are compared directly with subspaces having different dimensions. Thus, it does not require any normalization to make the score unbiased. Our experimental results on synthetic and publicly available real-world datasets revealed that (i) SiNNE produces better or at least the same results as existing scores. (ii) It improves the run time of the existing outlying aspect mining algorithm based on beam search by at least two orders of magnitude. SiNNE allows the existing outlying aspect mining algorithm to run in datasets with hundreds of thousands of instances and thousands of dimensions which was not possible before.
Developing a service quality scale for artificial intelligence service agents

Purpose Service providers and consumers alike are increasingly adopting artificial intelligence service agents (AISA) for service. Yet, no service quality scale exists that can fully capture the key factors influencing AISA service quality. This study aims to address this shortcoming by developing a scale for measuring AISA service quality (AISAQUAL). Design/methodology/approach Based on extant service quality research and established scale development techniques, the study constructs, refines and validates a multidimensional AISAQUAL scale through a series of pilot and validation studies. Findings AISAQUAL contains 26 items across six dimensions: efficiency, security, availability, enjoyment, contact and anthropomorphism. The new scale demonstrates good psychometric properties and can be used to evaluate service quality across AISA, providing a means of examining the relationships between AISA service quality and satisfaction, perceived value as well as loyalty. Research limitations/implications Future research should validate AISAQUAL with other AISA types, as they diffuse throughout the service sector. Moderating factors related to services, the customer and the AISA can be investigated to uncover the boundary conditions under which AISAQUAL is likely to influence service outcomes. Longitudinal studies can be carried out to assess how ongoing use of AISA can change service outcomes. Practical implications Service managers can use AISAQUAL to effectively monitor, diagnose and improve services provided by AISA while enhancing their understanding of how AISA can deliver better service quality and customer loyalty outcomes. Originality/value Anthropomorphism is identified as a new service quality dimension. AISAQUAL facilitates theory development by providing a reliable scale to improve the current understanding of consumers’ perspectives concerning AISA services.
Perspectives of 360-Degree Cinematic Virtual Reality: Interview Study Among Health Care Professionals

The global market for medical education is projected to increase exponentially over the next 5 years. A mode of delivery expected to drive the growth of this market is virtual reality (VR). VR simulates real-world objects, events, locations, and interactions in 3D multimedia sensory environments. It has been used successfully in medical education for surgical training, learning anatomy, and advancing drug discovery. New VR research has been used to simulate role-playing and clinical encounters; however, most of this research has been conducted with health professions students and not current health care professionals. Thus, more research is needed to explore how health care professionals experience VR with role-playing and clinical encounters.The aim of this study was to explore health care professionals' experiences with a cinematic VR (cine-VR) training program focused on role-playing and clinical encounters addressing social determinants of health, Appalachian culture, and diabetes. Cine-VR leverages 360-degree video with the narrative storytelling of cinema to create an engaging educational experience.We conducted in-depth telephone interviews with health care professionals who participated in the cine-VR training. The interviews were audio recorded and transcribed verbatim. A multidisciplinary team coded and analyzed the data using content and thematic analyses with NVivo software.We conducted 24 in-depth interviews with health care professionals (age=45.3, SD 11.3, years; n=16, 67%, women; n=22, 92%, White; and n=4, 17%, physicians) to explore their experiences with the cine-VR training. Qualitative analysis revealed five themes: immersed in the virtual world: seeing a 360-degree sphere allowed participants to immerse themselves in the virtual world; facilitated knowledge acquisition: all the participants accurately recalled the culture of Appalachia and listed the social determinants of health presented in the training; empathized with multiple perspectives: the cine-VR provided a glimpse into the real life of the main character, and participants described thinking about, feeling, and empathizing with the character's frustrations and disappointments; perceived ease of use of cine-VR: 96% (23/24) of the participants described the cine-VR as easy to use, and they liked the 360-degree movement, image resolution, and sound quality but noted limitations with the buttons on the headsets and risk for motion sickness; and perceived utility of cine-VR as a teaching tool: participants described cine-VR as an effective teaching tool because it activated visual and affective learning for them.Participants emphasized the realism of the cine-VR training program. They attributed the utility of the cine-VR to visual learning in conjunction with the emotional connection to the VR characters. Furthermore, participants reported that the cine-VR increased their empathy for people. More research is needed to confirm an association between the level of immersion and empathy in cine-VR training for health care professionals.
DeepInteract: Multi-view features interactive learning for sequential recommendation

Deep learning models have been successfully applied in sequential recommendations. However, previous studies ignored the interaction between static and dynamic features of both items and users, thus fail to exactly capture users’ current preferences. To overcome this limitation, we first conducted feature representations from static, dynamic and interactive views and constructed corresponding feature mining modules. Then, based on the multi-view feature mining modules, we proposed a deep learning-based model, namely DeepInteract, to learn the interaction of multi-view features of both item profiles and user behaviors for sequential recommendation. Experimental results on three real-world datasets demonstrated that DeepInteract outperforms state-of-the-art methods not only on recommendation performance but also on stability and robustness. Furthermore, we used ablation experiments to investigate the importance of three feature mining modules on various measures of recommendation performance. It was demonstrated that interactive features play the most important role for sequential recommendation.
A Multi-Task Learning for 2D Phase Unwrapping in Fringe Projection

Phase unwrapping is a challenging task in signal processing, spanning its applications in optical metrology, SAR interferometry, and many other signal reconstruction tasks. Fringe Projection Profilometry is a popular active-sensing approach for generating high-resolution three-dimensional (3D) surface information in which phase unwrapping is a crucial step. This letter proposes a multi-task learning-based phase unwrapping method for simultaneous denoising and wrap-count prediction in fringe projection. The proposed network, referred to as TriNet, has nested pyramidal architecture with a single encoder and two decoders, all connected through skip connections. The proposed approach does not require any pre-processing for noise removal like the conventional methods or any post-processing such as smoothing, like in existing deep learning methods but results in a quite accurate phase unwrapping. The proposed method outperforms the existing and state-of-the-art methods for the 3D reconstruction task in Fringe Projection by a significant margin even in the presence of very high noise.
Emotionflow: Capture the Dialogue Level Emotion Transitions

Emotion recognition in conversations (ERC) has attracted increasing interests in recent years, due to its wide range of applications, such as customer service analysis, health-care consultation, etc. One key challenge of ERC is that users' emotions would change due to the impact of others' emotions. That is, the emotions within the conversation can spread among the communication participants. However, the spread impact of emotions in a conversation is rarely addressed in existing researches. To this end, we propose EmotionFlow for ERC with the consideration of the spread of participants' emotions during a conversation. EmotionFlow first encodes users' utterance by concatenating the context with an auxiliary question, which helps to learn user-specific features. Then, conditional random field is applied to capture the sequential information at emotional level. We conduct extensive experiments on a public dataset Multimodal EmotionLines Dataset (MELD), and the results demonstrate the effectiveness of our proposed model.
Deep attention ConvLSTM-based adaptive fusion of clear-sky physical prior knowledge and multivariable historical information for probabilistic prediction of photovoltaic power

Photovoltaic (PV) power is an important way to utilize solar energy. Accurate PV power forecast is crucial to the large-scale application of PV power and the stability of electricity grid. This paper proposes a novel method for short-term photovoltaic power forecast using deep attention convolutional long short-term memory (ConvLSTM) network and kernel density estimation (KDE). ConvLSTM has strong ability to automatically extract the information from multiple variables related to PV power and the temporal information from historical PV data. Considering that clear-sky solar radiation is an important physical prior knowledge in PV power generation, attention mechanism is innovatively introduced into ConvLSTM to dynamically and adaptively adjust the weights of the physical prior knowledge and the historical PV data in forecast. KDE is used for estimating the joint probabilistic density function and giving the probabilistic confidence interval. Experiments in the three-year actual photovoltaic power data of two provinces in Belgium verified the effectiveness of the proposed method. Comparison experiments were made with naive persistence and other eight conventional methods including autoregressive integrated moving average with exogenous variable (ARIMAX) model, convolutional neural network (CNN), long short-term memory (LSTM) network, multilayer perceptron (MLP), support vector regression (SVR), extreme learning machine (ELM), classification and regression tree (CART) and gradient boosting decision tree (GBDT). Experiments show that the proposed method can realize the optimal fusion of the historical data and clear-sky prior knowledge, and significantly improve the PV power forecast accuracy in all seasons of a year.
Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities

Multimodal sentiment analysis has been studied under the assumption that all modalities are available. However, such a strong assumption does not always hold in practice, and most of multimodal fusion models may fail when partial modalities are missing. Several works have addressed the missing modality problem; but most of them only considered the single modality missing case, and ignored the practically more general cases of multiple modalities missing. To this end, in this paper, we propose a Tag-Assisted Transformer Encoder (TATE) network to handle the problem of missing uncertain modalities. Specifically, we design a tag encoding module to cover both the single modality and multiple modalities missing cases, so as to guide the network's attention to those missing modalities. Besides, we adopt a new space projection pattern to align common vectors. Then, a Transformer encoder-decoder network is utilized to learn the missing modality features. At last, the outputs of the Transformer encoder are used for the final sentiment classification. Extensive experiments are conducted on CMU-MOSI and IEMOCAP datasets, showing that our method can achieve significant improvements compared with several baselines.
12 Plagues of AI in Healthcare: A Practical Guide to Current Issues With Using Machine Learning in a Medical Context

The healthcare field has long been promised a number of exciting and powerful applications of Artificial Intelligence (AI) to improve the quality and delivery of health care services. AI techniques, such as machine learning (ML), have proven the ability to model enormous amounts of complex data and biological phenomena in ways only imaginable with human abilities alone. As such, medical professionals, data scientists, and Big Tech companies alike have all invested substantial time, effort, and funding into these technologies with hopes that AI systems will provide rigorous and systematic interpretations of large amounts of data that can be leveraged to augment clinical judgments in real time. However, despite not being newly introduced, AI-based medical devices have more than often been limited in their true clinical impact that was originally promised or that which is likely capable, such as during the current COVID-19 pandemic. There are several common pitfalls for these technologies that if not prospectively managed or adjusted in real-time, will continue to hinder their performance in high stakes environments outside of the lab in which they were created. To address these concerns, we outline and discuss many of the problems that future developers will likely face that contribute to these failures. Specifically, we examine the field under four lenses: approach, data, method and operation. If we continue to prospectively address and manage these concerns with reliable solutions and appropriate system processes in place, then we as a field may further optimize the clinical applicability and adoption of medical based AI technology moving forward.
Improving The Latency And Quality Of Cascaded Encoders

In this paper, we explore reducing computational latency of the 2-pass cascaded encoder model [1]. Specifically, we experiment with reducing the size of the causal 1st-pass and adding capacity to the non-causal 2nd-pass, such that the overall latency can be reduced without loss of quality. In addition, we explore using a confidence model for deciding to stop 2nd-pass recognition if we are confident in the 1st-pass hypothesis. Overall, we are able to reduce latency by a factor of 1.7X, compared to the baseline cascaded encoder from [1]. Secondly, with the added capacity in the non-causal 2nd-pass, we find that we can improve WER by up to 7% relative using wav2vec and minimum word-error-rate (MWER) training.