Reviewerly/exHarmony huggingface dataset:
Self-E: Smartphone-Supported Guidance for Customizable Self-Experimentation

The ubiquity of self-tracking devices and smartphone apps has empowered people to collect data about themselves and try to self-improve. However, people with little to no personal analytics experience may not be able to analyze data or run experiments on their own (self-experiments). To lower the barrier to intervention-based self-experimentation, we developed an app called Self-E, which guides users through the experiment. We conducted a 2-week diary study with 16 participants from the local population and a second study with a more advanced group of users to investigate how they perceive and carry out self-experiments with the help of Self-E, and what challenges they face. We find that users are influenced by their preconceived notions of how healthy a given behavior is, making it difficult to follow Self-E's directions and trusting its results. We present suggestions to overcome this challenge, such as by incorporating empathy and scaffolding in the system.
Knowledge discovery from noisy imbalanced and incomplete binary class data

Class imbalance creates a considerable impact on the classification of instances using traditional classifiers. Class imbalance, along with other difficulties, creates a significant impact on recognizing instances of minority class. Researchers work in various directions to mitigate class imbalance effect along with noise as well as missing values in datasets. However, combined studies of noisy class imbalance along with incomplete datasets have not been performed yet. This article contains a detailed analysis of 84 different machine learning models to deal with noisy binary class imbalanced and incomplete data using AUC, G-Mean, and F1-score as performance metrics. This article contains a detailed experiment considering missing value imputation and oversampling techniques. The article contains three comparisons: first missing value imputation techniques in incomplete and binary class imbalanced data, second, resampling techniques in noisy binary class imbalanced data, and third, combined techniques in noisy binary class imbalanced and incomplete data. We conclude that MICE and KNN techniques perform well with an increase in the imbalanced dataset's missing value from the first comparison. In second comparison, the SMOTE-ENN technique performs better than state-of-art in noisy binary class imbalanced datasets, and in the third comparison, we conclude that MICE with SMOTE-ENN technique perform well compared to the rest of the techniques.
Wearing same- and opposite-sex virtual bodies and seeing them caressed in intimate areas

Immersive virtual reality enables people to undergo the experience of owning an artificial body and vicariously feeling tactile stimuli delivered to it. However, it is currently unknown how such experiences are modified by the sexual congruency between the human and the artificial agent. In two studies, heterosexual men (Experiment 1) and women (Experiment 2) embodied same-sex and opposite-sex avatars and were asked to evaluate the experience (e.g., pleasantness, erogeneity) of being touched on social or intimate areas of their virtual body by a male or female avatar. Electrocardiogram and galvanic skin response were also recorded. Moreover, participants' implicit and explicit gender biases were examined via a gender-potency implicit association test and the Ambivalent Sexism Inventory. When embodying a same-sex avatar, men and women rated caresses on intimate areas from an avatar of the opposite sex as more pleasant and erogenous. Conversely, body swap-that is, wearing an opposite-sex avatar-enhanced participants' perceptions of pleasantness and erogeneity for caresses on intimate areas from a same-sex toucher. This effect was stronger in men than in women. Furthermore, physiological correlates of enhanced processing of arousing stimuli predicted behavioural outcomes during the body swap illusion. Wearing an opposite-sex avatar affects one's own body representations and may have important implications on people's attitudes and implicit reactivity to touch-mediated interactions. Men seem more susceptible to this type of body swap illusion. Our paradigm may induce profound changes of cross-sex perspective-taking and provide novel tools for promoting empathy and comprehension of sex-related diversity.
Stochastic Dynamic Games in Belief Space

Information gathering while interacting with other agents under sensing and motion uncertainty is critical in domains such as driving, service robots, racing, or surveillance. The interests of agents may be at odds with others, resulting in a stochastic noncooperative dynamic game. Agents must predict others’ future actions without communication, incorporate their actions into these predictions, account for uncertainty and noise in information gathering, and consider what information their actions reveal. Our solution uses local iterative dynamic programming in Gaussian belief space to solve a game-theoretic continuous POMDP. Solving a quadratic game in the backward pass of a game-theoretic belief-space variant of iterative linear-quadratic Gaussian control (iLQG) achieves a runtime polynomial in the number of agents and linear in the planning horizon. Our algorithm yields linear feedback policies for our robot, and predicted feedback policies for other agents. We present three applications: Active surveillance, guiding eyes for a blind agent, and autonomous racing. Agents with game-theoretic belief-space planning win 44% more races than without game theory and 34% more than without belief-space planning.
Failure prediction in production line based on federated learning: an empirical study

Data protection across organizations is limiting the application of centralized learning (CL) techniques. Federated learning (FL) enables multiple participants to build a learning model without sharing data. Nevertheless, there is very few research works on FL in intelligent manufacturing. This paper presents the results of an empirical study on failure prediction in the production line based on FL. This paper (1) designs Federated Support Vector Machine and federated random forest algorithms for the horizontal FL and vertical FL scenarios, respectively; (2) proposes an experiment process for evaluating the effectiveness between the FL and CL algorithms; (3) finds that the performance of FL and CL are not significantly different on the global testing data, on the random partial testing data, and on the estimated unknown Bosch data, respectively. The fact that the testing data is heterogeneous enhances our findings. Our study reveals that FL can replace CL for failure prediction.
JPEG image steganography payload location based on optimal estimation of cover co-frequency sub-image

Abstract The excellent cover estimation is very important to the payload location of JPEG image steganography. But it is still hard to exactly estimate the quantized DCT coefficients in cover JPEG image. Therefore, this paper proposes a JPEG image steganography payload location method based on optimal estimation of cover co-frequency sub-image, which estimates the cover JPEG image based on the Markov model of co-frequency sub-image. The proposed method combines the coefficients of the same position in each 8 × 8 block in the JPEG image to obtain 64 co-frequency sub-images and then uses the maximum a posterior (MAP) probability algorithm to find the optimal estimations of cover co-frequency sub-images by the Markov model. Then, the residual of each DCT coefficient is obtained by computing the absolute difference between it and the estimated cover version of it, and the average residual over coefficients in the same position of multiple stego images embedded along the same path is used to estimate the stego position. The experimental results show that the proposed payload location method can significantly improve the locating accuracy of the stego positions in low frequencies.
Adaptive graph-based generalized regression model for unsupervised feature selection

Unsupervised feature selection is an important method to reduce dimensions of high-dimensional data without labels, which is beneficial to avoid “curse of dimensionality” and improve the performance of subsequent machine learning tasks, like clustering and retrieval. A lot of methods have been developed in recent years. However, most of them only focus on selecting discriminative features but ignore the redundancy of features. Besides, these methods construct the similarity structure of data in the original input space and remain fixed during feature selection. The similarity structure is easily affected by the noises and outliers. Then the reliability of the similarity structure will be reduced in the procedure of feature selection. In order to address these issues, we present a novel generalized regression model equipped with an uncorrelated constraint and the ℓ 2 , 1 -norm regularization. It can simultaneously select the uncorrelated and discriminative features as well as reduce the variance of these data points belonging to the same neighborhood, which is helpful for the clustering task. Furthermore, to obtain the similarity structure of data and alleviate the influence of noises and outliers, we propose to adaptively learn a similarity-induced graph matrix in the reduced space and integrate it into the generalized regression model. An alternative iterative optimization algorithm is developed to solve the final objective function. A series of experiments are carried out on nine real-world data sets to demonstrate the effectiveness of the proposed method in comparison with other competing approaches. • A generalized regression model coupled with a novel uncorrelated constraint and adaptive graph structure learning is proposed to select discriminative and uncorrelated features. • The similarity-induced graph matrix is adaptively learned in the reduced subspace. • An efficient alternative optimization algorithm is developed.
An Efficient Privacy-Preserving Aggregation Scheme for Multidimensional Data in IoT

Internet of Things (IoT) enables terminal devices connecting with the Internet and provides various intelligent applications by analyzing devices data. As a typical IoT technique, edge computing provides a three-tier architecture to reduce communications and improve efficiency. Specifically, edge nodes are responsible for collecting and aggregating device data, and then send processed results to the cloud for subsequent analysis. However, the data aggregation function will compromise the privacy of device data. In this article, we proposed an efficient privacy-preserving multidimensional data aggregation scheme for IoT, called PMDA. The scheme uses the Chinese remainder theorem to design a homomorphic encryption method that encryptes a multiple-dimensional small integer vector into one ciphertext and keeps linear homomorphic properties per dimension. Combining with the signature mechanism and the batch verification method, the scheme guarantees nonrepudiation of device data and enhance verification efficiency at edge nodes. Through theoretical analysis, we demonstrate that the proposed scheme can achieve correctness, privacy, authentication, and integrity. After performance evaluation, we demonstrate that our scheme is superior to other schemes in terms of computation and communication costs. In particular, as the message dimension increases, our scheme computation costs almost a tenth of others at the 80-bits security level.
Structure-Coherent Deep Feature Learning for Robust Face Alignment

In this paper, we propose a structure-coherent deep feature learning method for face alignment. Unlike most existing face alignment methods which overlook the facial structure cues, we explicitly exploit the relation among facial landmarks to make the detector robust to hard cases such as occlusion and large pose. Specifically, we leverage a landmark-graph relational network to enforce the structural relationships among landmarks. We consider the facial landmarks as structural graph nodes and carefully design the neighborhood to passing features among the most related nodes. Our method dynamically adapts the weights of node neighborhood to eliminate distracted information from noisy nodes, such as occluded landmark point. Moreover, different from most previous works which only tend to penalize the landmarks absolute position during the training, we propose a relative location loss to enhance the information of relative location of landmarks. This relative location supervision further regularizes the facial structure. Our approach considers the interactions among facial landmarks and can be easily implemented on top of any convolutional backbone to boost the performance. Extensive experiments on three popular benchmarks, including WFLW, COFW and 300W, demonstrate the effectiveness of the proposed method. In particular, due to explicit structure modeling, our approach is especially robust to challenging cases resulting in impressive low failure rate on COFW and WFLW datasets. The model and code are publicly available at https://github.com/BeierZhu/Sturcture-Coherency-Face-Alignment.
Cloud Computing Adoption for Healthcare: An Empirical Study Using SEM Approach

Cloud computing has been adopted by many healthcare firms throughout the world especially in developed countries to enhance their service levels. The main objective of this study was to find the constructs from the prior literature review which influence the adoption of cloud computing from the prospering nation’s perspective. The main pillars of the theoretical framework are technology, organizational, environmental and ethical factors. The responses were collected from the employees or staff who were working in this sector with the help of a structured questionnaire. The analysis was performed utilizing SPSS 20.0 and AMOS 22.0. The hypothesis developed was tested utilizing a structural equation modelling approach. The findings indicated that the assumed variables each under technology, organization, environment and ethics have an impact on the dependent variable that is cloud computing adoption in the healthcare segment, thereby increasing the efficiency. The study is unique when compared to past studies as it considered ethical factors, thereby extending the cloud computing literature. It also provides a base for future studies and cloud technology development for a prospering nation perspective.
Optical multiple-image authentication and encryption based on phase retrieval and interference with sparsity constraints

An optical multiple-image authentication and encryption method based on phase retrieval algorithm (PRA) in the Fresnel domain and interference with sparsity constraints is proposed. In our proposed method, sparse phase and space multiplexing technique are respectively introduced to authentication and encryption for multiple-image. Each target image is respectively encrypted into two phase-only masks, which contain the phase data generated from three binary images by applying PRA in the Fresnel domain and need to be verified before used for decryption. That the target images are respectively reconstructed by using different decryption keys in the proposed method increases the security of the cryptosystem. Numerical simulation shows that the proposed method has high multiplexing capacity and security.
Adaptive Multifactorial Evolutionary Optimization for Multitask Reinforcement Learning

Evolutionary computation has largely exhibited its potential to complement conventional learning algorithms in a variety of machine learning tasks, especially those related to unsupervised (clustering) and supervised learning. It has not been until lately when the computational efficiency of evolutionary solvers has been put in prospective for training reinforcement learning models. However, most studies framed so far within this context have considered environments and tasks conceived in isolation, without any exchange of knowledge among related tasks. In this manuscript we present A-MFEA-RL, an adaptive version of the well-known MFEA algorithm whose search and inheritance operators are tailored for multitask reinforcement learning environments. Specifically, our approach includes crossover and inheritance mechanisms for refining the exchange of genetic material, which rely on the multilayered structure of modern deep-learning-based reinforcement learning models. In order to assess the performance of the proposed approach, we design an extensive experimental setup comprising multiple reinforcement learning environments of varying levels of complexity, over which the performance of A-MFEA-RL is compared to that furnished by alternative nonevolutionary multitask reinforcement learning approaches. As concluded from the discussion of the obtained results, A-MFEA-RL not only achieves competitive success rates over the simultaneously addressed tasks, but also fosters the exchange of knowledge among tasks that could be intuitively expected to keep a degree of synergistic relationship.
Interactivity in online chat: Conversational cues and visual cues in the service recovery process

Use of both verbal and nonverbal cues in computer-mediated communication can influence customers' perceptions and their behavioral intentions. Drawing on the compensation effect theory, this study investigates how verbal and nonverbal cues used by customer service agents during online service recovery processes affect customers' perceptions of service chat agents' warmth and competence and their willingness to cooperate with a service agent to complete the service recovery process. A 2 (message interactivity cues: high vs. low) × 2 (visual cues: high vs. low) between-subjects experimental design is utilized to identify the main and interaction effects of verbal and visual nonverbal cues used in online communications on customers attitudes and behaviors. Results show that use of verbal cues leads to higher perception of a chat agent's competence, but lower perception of a chat agent's warmth while use of visual nonverbal cues results in higher warmth perceptions but lower competence perceptions. The interaction effect of verbal and visual nonverbal cues indicates that visual nonverbal cues have a compensatory effect on message interactivity and vice versa, such that customer-perceived warmth and competence are prone to trade-offs between verbal cues and visual nonverbal cues. Furthermore, perceived warmth and competence mediate the indirect effects of verbal and nonverbal cues on customers' cooperation intentions. Theoretical and practical implications of use of verbal and nonverbal cues during online communications in service recovery processes are discussed.
SymReg-GAN: Symmetric Image Registration with Generative Adversarial Networks

Symmetric image registration estimates bi-directional spatial transformations between images while enforcing an inverse-consistency. Its capability of eliminating bias introduced inevitably by generic single-directional image registration allows more precise analysis in different interdisciplinary applications of image registration, e.g., computational anatomy and shape analysis. However, most existing symmetric registration techniques especially for multimodal images are limited by low speed from the commonly-used iterative optimization, hardship in exploring inter-modality relations or high labor cost for labeling data. We propose SymReg-GAN to shatter these limits, which is a novel generative adversarial networks (GAN) based approach to symmetric image registration. We formulate symmetric registration of unimodal/multimodal images as a conditional GAN and train it with a semi-supervised strategy. The registration symmetry is realized by introducing a loss for encouraging that the cycle composed of the geometric transformation from one image to another and its reverse should bring an image back. The semi-supervised learning enables both the precious labeled data and large amounts of unlabeled data to be fully exploited. Experimental results from six public brain magnetic resonance imaging (MRI) datasets and 1 our own computed tomography (CT) and MRI dataset demonstrate the superiority of SymReg-GAN to several existing state-of-the-art methods.
Anomaly Detection and Anticipation in High Performance Computing Systems

In their quest toward Exascale, High Performance Computing (HPC) systems are rapidly becoming larger and more complex, together with the issues concerning their maintenance. Luckily, many current HPC systems are endowed with data monitoring infrastructures that characterize the system state, and whose data can be used to train Deep Learning (DL) anomaly detection models, a very popular research area. However, the lack of labels describing the state of the system is a wide-spread issue, as annotating data is a costly task, generally falling on human system administrators and thus does not scale toward exascale. In this article we investigate the possibility to extract labels from a service monitoring tool (Nagios) currently used by HPC system administrators to flag the nodes which undergo maintenance operations. This allows to automatically annotate data collected by a fine-grained monitoring infrastructure; this labelled data is then used to train and validate a DL model for anomaly detection. We conduct the experimental evaluation on a tier-0 production supercomputer hosted at CINECA, Bologna, Italy. The results reveal that the DL model can accurately detect the real failures, and, moreover, it can <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">predict</i> the insurgency of anomalies, by systematically anticipating the actual labels (i.e., the moment when system administrators realize when an anomalous event happened); the average advance time computed on historical traces is around 45 minutes. The proposed technology can be easily scaled toward exascale systems to easy their maintenance.
External features enriched model for biomedical question answering

Abstract Background Biomedical question answering (QA) is a sub-task of natural language processing in a specific domain, which aims to answer a question in the biomedical field based on one or more related passages and can provide people with accurate healthcare-related information. Recently, a lot of approaches based on the neural network and large scale pre-trained language model have largely improved its performance. However, considering the lexical characteristics of biomedical corpus and its small scale dataset, there is still much improvement room for biomedical QA tasks. Results Inspired by the importance of syntactic and lexical features in the biomedical corpus, we proposed a new framework to extract external features, such as part-of-speech and named-entity recognition, and fused them with the original text representation encoded by pre-trained language model, to enhance the biomedical question answering performance. Our model achieves an overall improvement of all three metrics on BioASQ 6b, 7b, and 8b factoid question answering tasks. Conclusions The experiments on BioASQ question answering dataset demonstrated the effectiveness of our external feature-enriched framework. It is proven by the experiments conducted that external lexical and syntactic features can improve Pre-trained Language Model’s performance in biomedical domain question answering task.