Reviewerly/exHarmony huggingface dataset:
Entity recognition of Chinese medical text based on multi-head self-attention combined with BILSTM-CRF

Named entities are the main carriers of relevant medical knowledge in Electronic Medical Records (EMR). Clinical electronic medical records lead to problems such as word segmentation ambiguity and polysemy due to the specificity of Chinese language structure, so a Clinical Named Entity Recognition (CNER) model based on multi-head self-attention combined with BILSTM neural network and Conditional Random Fields is proposed. Firstly, the pre-trained language model organically combines char vectors and word vectors for the text sequences of the original dataset. The sequences are then fed into the parallel structure of the multi-head self-attention module and the BILSTM neural network module, respectively. By splicing the output of the neural network module to obtain multi-level information such as contextual information and feature association weights. Finally, entity annotation is performed by CRF. The results of the multiple comparison experiments show that the structure of the proposed model is very reasonable and robust, and it can effectively improve the Chinese CNER model. The model can extract multi-level and more comprehensive text features, compensate for the defect of long-distance dependency loss, with better applicability and recognition performance.
Brain tissue segmentation via non-local fuzzy c-means clustering combined with Markov random field

The segmentation and extraction of brain tissue in magnetic resonance imaging (MRI) is a meaningful task because it provides a diagnosis and treatment basis for observing brain tissue development, delineating lesions, and planning surgery. However, MRI images are often damaged by factors such as noise, low contrast and intensity brightness, which seriously affect the accuracy of segmentation. A non-local fuzzy c-means clustering framework incorporating the Markov random field for brain tissue segmentation is proposed in this paper. Firstly, according to the statistical characteristics that MRF can effectively describe the local spatial correlation of an image, a new distance metric with neighborhood constraints is constructed by combining probabilistic statistical information. Secondly, a non-local regularization term is integrated into the objective function to utilize the global structure feature of the image, so that both the local and global information of the image can be taken into account. In addition, a linear model of inhomogeneous intensity is also built to estimate the bias field in brain MRI, which has achieved the goal of overcoming the intensity inhomogeneity. The proposed model fully considers the randomness and fuzziness in the image segmentation problem, and obtains the prior knowledge of the image reasonably, which reduces the influence of low contrast in the MRI images. Then the experimental results demonstrate that the proposed method can eliminate the noise and intensity inhomogeneity of the MRI image and effectively improve the image segmentation accuracy.
Keep Calm and Do Not Carry-Forward: Toward Sensor-Data Driven AI Agent to Enhance Human Learning

The integration of Multimodal Data (MMD) and embodied learning systems (such as Motion Based Educational Games, MBEG), can help learning researchers to better understand the synergy between students' interactions and their learning experiences. Unfolding the dynamics behind this important synergy can lead to the design of intelligent agents which leverage students' movements and support their learning. However, real-time use of student-generated MMD derived from their interactions with embodied learning systems (MBEG in our case) is challenging and remains under-explored due to its complexity (e.g., handle sensor-data and enable an AI agent to use them). To bridge this gap, we conducted an in-situ study where 40 children, aged 9-12, played MBEG on maths and language development. We automatically, unobtrusively, and continuously monitored students' experiences using eye-tracking glasses, physiological wristbands, and Kinect, during game-play. This allowed us to understand the different cognitive and physiological dimensions of students' progress (right/wrong responses) during the three different stages of the MBEG problem-solving processes, namely the "see-solve-move-respond" (S2MR) cycle. We introduce the novel Carry Forward Effect (CFE); a phenomenon occurring in such games, whereby students propagate, or "carry forward," the cognitive and physiological effects derived from their MMD, to subsequent phases in the see-solve-move-respond cycle. By identifying moments when the Carry Forward Effect is congruent (or not) to students' learning performance, we uncover opportunities for feedback delivery to encourage or subdue the impact of the CFE. Our results demonstrate the importance of wristband and eye-tracking data as key indicators for prioritizing adaptive feedback to support students in MBEG and emphasize the significance of using MMD to support students' performance in real-time educational settings.
Data Augmentation for ASR Using TTS Via a Discrete Representation

While end-to-end automatic speech recognition (ASR) has achieved high performance, it requires a huge amount of paired speech and transcription data for training. Recently, data augmentation methods have actively been investigated. One method is to use a text-to-speech (TTS) system to gen-erate speech data from text-only data and use the generated speech for data augmentation, but it has been found that the synthesized log Mel-scale filterbank (lmfb) features could have a serious mismatch with the real speech features. In this study, we propose a data augmentation method via a discrete speech representation. The TTS model predicts discrete ID sequences instead of lmfb features, and the ASR also uses the ID sequences as training data. We expect that the use of a discrete representation based on vq-wav2vec not only makes TTS training easier but also mitigates the mismatch with real data. Experimental evaluations show that the pro-posed method outperforms the data augmentation method using the conventional TTS. We found that it reduces speaker dependency, and the generated features are distributed more closely to the real ones.
Realizing Persistent-Spin-Helix Lasing in the Regime of Rashba-Dresselhaus Spin-Orbit Coupling in a Dye-Filled Liquid-Crystal Optical Microcavity

In the presence of Rashba-Dresselhaus coupling, strong spin-orbit interactions in liquid-crystal optical cavities result in a distinctive spin-split entangled dispersion. Spin coherence between such modes gives rise to an optically persistent spin helix. In this paper, we introduce optical gain in such a system, by dispersing a molecular dye in a liquid-crystal microcavity, and demonstrate an optically persistent spin-helix lasing in the Rashba-Dresselhaus regime.
Deep Feature Prior Guided Face Deblurring

Most recent face deblurring methods have focused on utilizing facial shape priors such as face landmarks and parsing maps. While these priors can provide facial geometric cues effectively, they are insufficient to contain local texture details that act as important clues to solve face deblurring problem. To deal with this, we focus on estimating the deep features of pre-trained face recognition networks (e.g., VGGFace network) that include rich information about sharp faces as a prior, and adopt a generative adversarial network (GAN) to learn it. To this end, we propose a deep feature prior guided network (DFPGnet) that restores facial details using the estimated the deep feature prior from a blurred image. In our DFPGnet, the generator is divided into two streams including prior estimation and deblurring streams. Since the estimated deep features of the prior estimation stream are learned from the VGGFace network which is trained for face recognition not for deblurring, we need to alleviate the discrepancy of feature distributions between the two streams. Therefore, we present feature transform modules at the connecting points of the two streams. In addition, we propose a channel-attention feature discriminator and prior loss, which encourages the generator to focus on more important channels for deblurring among the deep feature prior during training. Experimental results show that our method achieves state-of-the-art performance both qualitatively and quantitatively.
Powerful knockoffs via minimizing reconstructability

Model-X knockoffs (J. R. Stat. Soc. Ser. B. Stat. Methodol. 80 (2018) 551–577) allows analysts to perform feature selection using almost any machine learning algorithm while provably controlling the expected proportion of false discoveries. This procedure involves constructing synthetic variables, called knockoffs, which effectively act as controls during feature selection. The gold standard for constructing knockoffs has been to minimize the mean absolute correlation (MAC) between features and their knockoffs, but, surprisingly, we prove this procedure can be powerless in extremely easy settings, including Gaussian linear models with correlated exchangeable features. The key problem is that minimizing the MAC creates joint dependencies between the features and knockoffs, which allow machine learning algorithms to reconstruct the effect of the features on the response using the knockoffs. To improve power, we propose generating knockoffs which minimize the reconstructability (MRC) of the features, and we demonstrate our proposal for Gaussian features by showing it is computationally efficient, robust, and powerful. We also prove that certain MRC knockoffs minimize a notion of estimation error in Gaussian linear models. Through extensive simulations, we show MRC knockoffs often dramatically outperform MAC-minimizing knockoffs, and we find no settings in which MAC-minimizing knockoffs outperform MRC knockoffs by more than a slight margin. We implement our methods and many others from the knockoffs literature in a new python package knockpy.
Weakly Supervised Text-based Person Re-Identification

The conventional text-based person re-identification methods heavily rely on identity annotations. However, this labeling process is costly and time-consuming. In this paper, we consider a more practical setting called weakly supervised text-based person re-identification, where only the text-image pairs are available without the requirement of annotating identities during the training phase. To this end, we propose a Cross-Modal Mutual Training (CMMT) framework. Specifically, to alleviate the intra-class variations, a clustering method is utilized to generate pseudo labels for both visual and textual instances. To further re-fine the clustering results, CMMT provides a Mutual Pseudo Label Refinement module, which leverages the clustering results in one modality to refine that in the other modality constrained by the text-image pairwise relationship. Mean-while, CMMT introduces a Text-IoU Guided Cross-Modal Projection Matching loss to resolve the cross-modal matching ambiguity problem. A Text-IoU Guided Hard Sample Mining method is also proposed for learning discriminative textual-visual joint embeddings. We conduct extensive experiments to demonstrate the effectiveness of the proposed CMMT, and the results show that CMMT performs favorably against existing text-based person re-identification methods. Our code will be available at https://github.com/X-BrainLab/WS_Text-ReID.
Effectiveness of virtual reality technologies in digital entrepreneurship: a comparative study of two types of virtual agents

Purpose The rapid progress of information and communication technologies enables business creators to access a wide variety of tools. These tools facilitate electronic exchanges and interactions with customers and companies. The purpose of this study is to test and compare the effectiveness of two virtual reality technologies, the avatar and anthropomorphic virtual agents, on consumers’ psychological states and perceived realism. Design/methodology/approach An experimental survey was conducted to measure the potential superiority of the anthropomorphic virtual agent over the avatar and to identify the determining characteristics of the anthropomorphic virtual agent’s effectiveness. An experimental website was designed for the purpose of the study. A total of 1,262 internet users participated in the experiment. Findings Results confirm the superiority of the anthropomorphic virtual agent over the avatar in affecting consumers’ flow state, telepresence experience and perceived realism. These findings can be explained by the humanized characteristics of this type of agent (i.e. verbal and nonverbal language). Originality/value The originality of this research lies in the study of different forms of social interactivity. This latter has been little studied and essentially treated with a dichotomous perspective (presence/absence of a virtual agent). New trends in digital marketing challenge entrepreneurs to be proactive and to anticipate customers’ behavior on their online stores. That is why, virtual reality technologies, namely, anthropomorphic agents, can be considered as a relevant tool to engage in efficient inbound marketing strategies. Today, the development of intelligent technologies encourages entrepreneurs operating online to design more interactive, realistic and humanized virtual merchant environments that are more adapted to the realities of the new consumption trends and environment.
Analysis of Job Failure and Prediction Model for Cloud Computing Using Machine Learning

Modern applications, such as smart cities, home automation, and eHealth, demand a new approach to improve cloud application dependability and availability. Due to the enormous scope and diversity of the cloud environment, most cloud services, including hardware and software, have encountered failures. In this study, we first analyze and characterize the behaviour of failed and completed jobs using publicly accessible traces. We have designed and developed a failure prediction model to determine failed jobs before they occur. The proposed model aims to enhance resource consumption and cloud application efficiency. Based on three publicly available traces: the Google cluster, Mustang, and Trinity, we evaluate the proposed model. In addition, the traces were also subjected to various machine learning models to find the most accurate one. Our results indicate a significant correlation between unsuccessful tasks and requested resources. The evaluation results also revealed that our model has high precision, recall, and F1-score. Several solutions, such as predicting job failure, developing scheduling algorithms, changing priority policies, or limiting re-submission of tasks, can improve the reliability and availability of cloud services.
A deep crowd density classification model for Hajj pilgrimage using fully convolutional neural network

This research enhances crowd analysis by focusing on excessive crowd analysis and crowd density predictions for Hajj and Umrah pilgrimages. Crowd analysis usually analyzes the number of objects within an image or a frame in the videos and is regularly solved by estimating the density generated from the object location annotations. However, it suffers from low accuracy when the crowd is far away from the surveillance camera. This research proposes an approach to overcome the problem of estimating crowd density taken by a surveillance camera at a distance. The proposed approach employs a fully convolutional neural network (FCNN)-based method to monitor crowd analysis, especially for the classification of crowd density. This study aims to address the current technological challenges faced in video analysis in a scenario where the movement of large numbers of pilgrims with densities ranging between 7 and 8 per square meter. To address this challenge, this study aims to develop a new dataset based on the Hajj pilgrimage scenario. To validate the proposed method, the proposed model is compared with existing models using existing datasets. The proposed FCNN based method achieved a final accuracy of 100%, 98%, and 98.16% on the proposed dataset, the UCSD dataset, and the JHU-CROWD dataset, respectively. Additionally, The ResNet based method obtained final accuracy of 97%, 89%, and 97% for the proposed dataset, UCSD dataset, and JHU-CROWD dataset, respectively. The proposed Hajj-Crowd-2021 crowd analysis dataset and the model outperformed the other state-of-the-art datasets and models in most cases.
Promotors or inhibitors? Role of task type on the effect of humanoid service robots on consumers’ use intention

Despite the pervasiveness of service robots in hospitality industry, it is unclear how highly human-like service robots elicit aversive effect on consumers’ use intention in addition to discomfort and when the aversive effect can be mitigated. Three experimental studies were conducted, showing that highly human-like service robots elicit greater consumer discomfort and decrease task attraction toward robots, in turn weakening consumers’ use interaction. Moreover, this research identified that emotional-social tasks (vs. mechanical tasks) mitigated the aversive effects of highly human-like service robots on consumers’ responses. The research extends the uncanny valley and mind perception theories and offers some guidelines for employing service robots with different degree of anthropomorphism.
Cross-cultural differences in information processing of chatbot journalism: chatbot news service as a cultural artifact

Purpose How does algorithmic information processing affect the thoughts and behavior of artificial intelligence (AI) users? In this study, the authors address this question by focusing on algorithm-based chatbots and examine the influence of culture on algorithms as a form of digital intermediation. Design/methodology/approach The authors conducted a study comparing the United States (US) and Japan to examine how users in the two countries perceive the features of chatbot services and how the perceived features affect user trust and emotion. Findings Clear differences emerged after comparing algorithmic information processes involved in using and interacting with chatbots. Major attitudes toward chatbots are similar between the two cultures, although the weights placed on qualities differ. Japanese users put more weight on the functional qualities of chatbots, and US users place greater emphasis on non-functional qualities of algorithms in chatbots. US users appear more likely to anthropomorphize and accept explanations of algorithmic features than Japanese users. Research limitations/implications Different patterns of chatbot news adoption reveal that the acceptance of chatbots involves a cultural dimension as the algorithms reflect the values and interests of their constituencies. How users perceive chatbots and how they consume and interact with the chatbots depends on the cultural context in which the experience is situated. Originality/value A comparative juxtaposition of cultural-algorithmic interactions offers a useful way to examine how cultural values influence user behaviors and identify factors that influence attitude and user acceptance. Results imply that chatbots can be a cultural artifact, and chatbot journalism (CJ) can be a socially contextualized practice that is driven by the user's input and behavior, which are reflections of cultural values and practices.
GazeDock: Gaze-Only Menu Selection in Virtual Reality using Auto-Triggering Peripheral Menu

Gaze-only input techniques in VR face the challenge of avoiding false triggering due to continuous eye tracking while maintaining interaction performance. In this paper, we proposed GazeDock, a technique for enabling fast and robust gaze-based menu selection in VR. GazeDock features a view-fixed peripheral menu layout that automatically triggers appearing and selection when the user&#x2019;s gaze approaches and leaves the menu zone, thus facilitating interaction speed and minimizing the false triggering rate. We built a dataset of 12 participants&#x2019; natural gaze movements in typical VR applications. By analyzing their gaze movement patterns, we designed the menu UI personalization and optimized selection detection algorithm of GazeDock. We also examined users&#x2019; gaze selection precision for targets on the peripheral menu and found that 4&#x2013;8 menu items yield the highest throughput when considering both speed and accuracy. Finally, we validated the usability of GazeDock in a VR navigation game that contains both scene exploration and menu selection. Results showed that GazeDock achieved an average selection time of 471ms and a false triggering rate of 3.6%. And it received higher user preference ratings compared with dwell-based and pursuit-based techniques.
Quantum Digital Signatures with Random Pairing

Digital signatures can guarantee the unforgeability and transferability of the message. Differently from classical digital signatures, whose security depends on computational complexity, quantum digital signatures (QDSs) can provide information-theoretic security. We propose a general method of random-pairing QDS, which can drastically increase QDS efficiency. In a way, our random-pairing method provides a tightened result for the security level of QDS. In the method, the parity value of each pair is used for the outcome-bit value. We present general formulas for the fraction of untagged bits and error rates of the outcome bits. Random pairing can be applied as a fundamental method to increase the QDS efficiency for all existing quantum key distribution (QKD) protocols. We take the QDS with sending-or-not-sending protocol and the QDS with side-channel-free protocol as examples to demonstrate the advantage of random pairing through numerical simulation. A similar advantage with random pairing is also found with decoy-state measurement-device-independent QKD and also the decoy-state BB84 protocol. We study the random-pairing QDS with sending-or-not-sending protocol considering effects of finite data size through optimization. The numerical simulation results show that the signature rate can be increased by more than $100\mathrm{%}$ under a noisy channel using our random-pairing method.
Exploring Situated &amp; Embodied Support for Youth’s Mental Health: Design Opportunities for Interactive Tangible Device

The ability to manage emotions effectively is critical to healthy psychological and social development in youth. Prior work has focused on investigating the design of mental health technologies for this population, yet it is still unclear how to help them cope with emotionally difficult situations in-the-moment. In this paper, we aim to explore the appropriation, naturally emerging engagement patterns, and perceived psychological impact of an exemplar interactive tangible device intervention designed to provide in-situ support, when deployed with n=109 youth for 1.5 months. Our findings from semi-structured interviews and co-design workshops with a subset of participants (n=44 and n=25, respectively) suggest the potential of using technology-enabled objects to aid with down-regulation and self-compassion in moments of heightened emotion, to facilitate the practice of cognitive strategies, and to act as emotional companions. Lastly, we discuss design opportunities for integrating situated and embodied support in mental health interventions for youth.