Reviewerly/exHarmony huggingface dataset:
Stable Learning via Differentiated Variable Decorrelation

Recently, as the applications of artificial intelligence gradually seeping into some risk-sensitive areas such as justice, healthcare and autonomous driving, an upsurge of research interest on model stability and robustness has arisen in the field of machine learning. Rather than purely fitting the observed training data, stable learning tries to learn a model with uniformly good performance under non-stationary and agnostic testing data. The key challenge of stable learning in practice is that we do not have any knowledge about the true model and test data distribution as a priori. Under such condition, we cannot expect a faithful estimation of model parameters and its stability over wild changing environments. Previous methods resort to a reweighting scheme to remove the correlations between all the variables through a set of new sample weights. However, we argue that such aggressive decorrelation between all the variables may cause the over-reduced sample size, which leads to the variance inflation and possible underperformance. In this paper, we incorporate the unlabled data from multiple environments into the variable decorrelation framework and propose a Differentiated Variable Decorrelation (DVD) algorithm based on the clustering of variables. Specifically, the variables are clustered according to the stability of their correlations and the variable decorrelation module learns a set of sample weights to remove the correlations merely between the variables of different clusters. Empirical studies on both synthetic and real world datasets clearly demonstrate the efficacy of our DVD algorithm on improving the model parameter estimation and the prediction stability over changing distributions.
ReliefNet: Fast Bas-relief Generation from 3D Scenes

Most previous methods of bas-relief generation run slow, or require tuning several important parameters. These issues seriously reduce the efficiency of bas-relief modeling. We introduce a fast generation method for high-quality bas-reliefs from 3D objects based on a deep learning technique. Unlike neural networks for image tasks, the proposed network for reliefs (ReliefNet) is elaborately designed to deal with a modeling problem in the field of graphics. We design our ReliefNet and equip it with a special loss function with the aim that the network can solve the essential problem of bas-relief modeling. Our network eliminates the height gaps and maintains the rich details simultaneously. The advantage over previous methods is that our method does not require parameter tuning and is a very efficient. Once the ReliefNet has been trained, a bas-relief can be produced by one feed-forward pass of the network instantly. To demonstrate the performance and effectiveness of our method, extensive experiments on a range of 3D scenes with high resolutions and comparisons to state-of-the-art methods are conducted.
Robust Learning-Enabled Intelligence for the Internet of Things: A Survey From the Perspectives of Noisy Data and Adversarial Examples

The Internet of Things (IoT) has been widely adopted in a range of verticals, e.g., automation, health, energy, and manufacturing. Many of the applications in these sectors, such as self-driving cars and remote surgery, are critical and high stakes applications, calling for advanced machine learning (ML) models for data analytics. Essentially, the training and testing data that are collected by massive IoT devices may contain noise (e.g., abnormal data, incorrect labels, and incomplete information) and adversarial examples. This requires high robustness of ML models to make reliable decisions for IoT applications. The research of robust ML has received tremendous attention from both academia and industry in recent years. This article will investigate the state of the art and representative works of robust ML models that can enable high resilience and reliability of IoT intelligence. Two aspects of robustness will be focused on, i.e., when the training data of ML models contain noises and adversarial examples, which may typically happen in many real-world IoT scenarios. In addition, the reliability of both neural networks and reinforcement learning framework will be investigated. Both of these two ML paradigms have been widely used in handling data in IoT scenarios. The potential research challenges and open issues will be discussed to provide future research directions.
Continuous User Authentication Featuring Keystroke Dynamics Based on Robust Recurrent Confidence Model and Ensemble Learning Approach

User authentication is considered to be an important aspect of any cyber security program. However, one-time validation of user's identity is not strong to provide resilient security throughout the user session. In this aspect, continuous monitoring of session is necessary to ensure that only legitimate user is accessing the system resources for entire session. In this paper, a true continuous user authentication system featuring keystroke dynamics behavioural biometric modality has been proposed and implemented. A novel method of authenticating the user on each action has been presented which decides the legitimacy of current user based on the confidence in the genuineness of each action. The 2-phase methodology, consisting of ensemble learning and robust recurrent confidence model(R-RCM), has been designed which employs a novel perception of two thresholds i.e., alert and final threshold. Proposed methodology classifies each action based on the probability score of ensemble classifier which is afterwards used along with hyper-parameters of R-RCM to compute the current confidence in genuineness of user. System decides if user can continue using the system or not based on new confidence value and final threshold. However, it tends to lock out imposter user more quickly if it reaches the alert threshold. Moreover, system has been validated with two different experimental settings and results are reported in terms of mean average number of genuine actions (ANGA) and average number of imposter actions(ANIA), whereby achieving the lowest mean ANIA with experimental setting II.
Asymmetric Gaussian Process multi-view learning for visual classification

Methods of multi-view learning attain outstanding performance in different fields compared with the single-view based strategies. In this paper, the Gaussian Process Latent Variable Model (GPVLM), which is a generative and non-parametric model, is exploited to represent multiple views in a common subspace. Specifically, there exists a shared latent variable across various views that is assumed to be transformed to observations by using distinctive Gaussian Process projections. However, this assumption is only a generative strategy, being intractable to simply estimate the fused variable at the testing step. In order to tackle this problem, another projection from observed data to the shared variable is simultaneously learned by enjoying the view-shared and view-specific kernel parameters under the Gaussian Process structure. Furthermore, to achieve the classification task, label information is also introduced to be the generation from the latent variable through a Gaussian Process transformation. Extensive experimental results on multi-view datasets demonstrate the superiority and effectiveness of our model in comparison to state-of-the-art algorithms.
Locally Connected Network for Monocular 3D Human Pose Estimation

We present an approach for 3D human pose estimation from monocular images. The approach consists of two steps: it first estimates a 2D pose from an image and then estimates the corresponding 3D pose. This paper focuses on the second step. Graph convolutional network (GCN) has recently become the de facto standard for human pose related tasks such as action recognition. However, in this work, we show that GCN has critical limitations when it is used for 3D pose estimation due to the inherent weight sharing scheme. The limitations are clearly exposed through a novel reformulation of GCN, in which both GCN and Fully Connected Network (FCN) are its special cases. In addition, on top of the formulation, we present locally connected network (LCN) to overcome the limitations of GCN by allocating dedicated rather than shared filters for different joints. We jointly train the LCN network with a 2D pose estimator such that it can handle inaccurate 2D poses. We evaluate our approach on two benchmark datasets and observe that LCN outperforms GCN, FCN, and the state-of-the-art methods by a large margin. More importantly, it demonstrates strong cross-dataset generalization ability because of sparse connections among body joints.
Analysis of the determining factors of good teaching practices of mobile learning at the Spanish University. An explanatory model

The use of mobile devices in education is becoming standardized at the higher education stage. However, it is necessary to evaluate good teaching practices of mobile learning (m-learning) in order to set proper application of mobile devices and favourable learning outcomes for students. The purposes of this paper were, on the one hand, to determine the degree of implementation of m-learning and good teaching practices in Spanish universities; on the other hand, to know the causes that lead university professors not to integrate mobile devices and to determine the socio-demographic factors that influence the development of good teaching practices of m-learning. In relation to the researching method, it was used a quantitative approach focused on the implementation of an online survey. A total of 1544 university professors attached to the Faculties of Education throughout Spain, aged between 20 and 77, participated in the study (M = 45.29; SD = 10.45). The results obtained revealed that: 1) More than 70% of Spanish university professors use mobile devices in the classroom; 2) The main reasons whereby mobile devices are not used in the classroom are ignored, belief that they are a distraction, resistance to change and perceived uselessness; 3) Only 39.56% carried out good teaching practices; 4) The main and possible influential factors good teaching practices of m-learning were professional category, line of research in educational technology, other teaching innovations, believes in the adequacy of mobile devices and the expansion of m-learning in the coming years. Finally, the practical implications of this study are discussed, highlighting the wealth of collected data from this pioneering research on the evaluation of good teaching practices of m-learning.
Applying case-based reasoning and a learning-based adaptation strategy to irrigation scheduling in grape farming

As a key part of vineyard management, irrigation of grapevines puts forward the need of scheduling water resources in a more precise and efficient way. Typically, irrigation plans are generated through the use of mathematical models. However, the unwelcoming fact is that such models usually require a massive quantity of monitored data that is often unavailable or incomplete in most developing countries. As a consequence, this paper takes advantage of advanced artificial intelligence techniques, in particular, the case-based reasoning approach, to estimate the reference evapotranspiration, and therefore, to calculate the amount of irrigation water in grape farming. For improving the current case-based reasoning approach, especially the solution revision part, this paper proposes a learning-based adaptation strategy by fully making use of the hidden information in the case base. Inspired by the feature vector representation, a revision task could be also considered as a situation and action pair. The situation part attempts to capture the difference between the target case and past cases, while the action pair aims at adapting the solution to reflect the detected difference by learning adaptation knowledge from past experiences. Two retrieval tasks are involved in the revision process. On the one hand, the first one tries to retrieve an adaptation case and evaluate the difference between the new case. On the other hand, the second retrieval task should identify a collection of past cases that shares the similar difference as detected before. By learning from how the solutions of retrieved past cases were updated to solve the adaptation case, the solution of the adaptation case can be revised accordingly based on the obtained adaptation knowledge, and therefore to solve the new case. The experiment focuses on verifying the effectiveness of the case-based reasoning approach in irrigation scheduling, and evaluating the accuracy of the learning-based adaptation strategy. The experimental results demonstrate that the system is able to output a reasonable irrigation plan, while the deviation between the predicted and recorded values is around 5.42% and 7.94% for the relevance evapotranspiration and the amount of irrigation water respectively. In conclusion, the proposal in this paper has great potential for modeling irrigation scheduling system with promising advantages.
Enhancing session-based social recommendation through item graph embedding and contextual friendship modeling

Recommender systems are designed to help users find matching items from plenty of candidates in online platforms. In many online platforms, such as Yelp and Epinions, users’ behaviors are constantly recorded over time, and the users also can build connections with others and share their interests. Previous recommendation methods have either modeled the dynamic interests or the dynamic social influences. A few studies have focused on the modeling of both factors, but they still have several limitations: 1) they fail to consider the complex items transitions among all session sequences, which can be used as a local factor to boost the performance of recommendation methods, and 2) they ignore that a user and their friends only share the same preferences in certain sessions, by keeping the friend vector unchanged for all target users at time t, and 3) they do not consider that a user’s long-term preference may change with the evolution of interests. To overcome the above issues, in this paper, we propose an approach to incorporate item graph embedding and contextual friendship modeling into the recommendation task. Specifically, 1) we construct a directed item graph based on all historical session sequences and utilize a graph neural network to capture the rich local dependency between items, and 2) take a session-level attention mechanism to get each friend’s representation according to the target user’s current interests, and 3) apply max-pooling on the target user’s historical session interests to learn the dynamics of his/her long-term interests. Extensive experiments on two real-world datasets show that our proposed model outperforms state-of-the-art methods consistently on various evaluation metrics.
Reducing the effect of sample bias for small data sets with double‐weighted support vector transfer regression

Abstract Small data sets are an extremely challenging problem in the machine learning (ML) realm, and in specific, in regression scenarios, as the lack of relevant data can lead to ML models that have large bias. However, there are many applications for which a purely data‐driven procedure would be advantageous, but a large amount of data are not available. This article proposes a novel regression‐based transfer learning (TL) model to address this challenge, where TL is defined as knowledge transfer from a large, relevant data set (source domain data) to a small data set (target domain data). The proposed TL model is termed double‐weighted support vector transfer regression (DW‐SVTR), which couples least squares support vector machines for regression (LS‐SVMR) with two weight functions. The first weight function uses kernel mean matching (KMM) to reweight the source domain data such that the mean values of the source and target domain data in a reproduced kernel Hilbert space (RKHS) are close. In this way, the source domain data points relevant to the target domain points have a larger weight than irrelevant source domain points. The second weight is a function of estimated residuals, which aims to further reduce the negative interference of irrelevant source domain points. The proposed approach is assessed and validated via simulated data and by enhanced shear strength prediction of nonductile columns based on limited availability of nonductile column data. Specifically, the results for the latter show that the proposed DW‐SVTR can reduce the root mean square error (RMSE) by 34% and enhance the coefficient of determination (R 2 ) by 229%. These numerical results demonstrate that the DW‐SVTR significantly reduces the effect of small sample bias and improves prediction performance compared to standard ML methods.
Those who control the code control the rules: How different perspectives of privacy are being written into the code of blockchain systems

Blockchain systems afford new privacy capabilities. This threatens to create conflict, as different social groups involved in blockchain development often disagree on which capabilities specific systems should enact. This article adopts a boundary object perspective to make sense of disagreements between collaborating social worlds. We perform a case study of privacy attitudes among collaborating actors in Monero, a cryptocurrency community that emphasises privacy and decentralisation alongside a set of values sometimes described as anti-establishment, crypto-anarchist, and/or cypherpunk. The case study performs a series of interviews with users, developers, cryptographic researchers, corporate architects, and government regulators. Three novel and important findings emerge. The first is that none of the social worlds express a desire to monitor routine transactions, despite the obvious business and tax-collection value of such data. The second is that regulators are happy to postpone active involvement, based on the flawed assumption they can impose privacy-related regulation later, once risks have become clear. Such regulation may not be possible as protocols and rulesets currently being coded into the system may be impossible to amend in the future (unless they can obtain either developer or network consensus). The third is that regulators assume methods for overseeing extraordinary transaction are necessary to avoid widespread, near-effortless money laundering. Yet, each of the other social worlds is operating under the assumption that this trade-off has already been accepted. These findings demonstrate subtle power transitions and changes in privacy attitudes that have implications for research on blockchain, management, and boundary objects in general.
Practical and Secure SVM Classification for Cloud-Based Remote Clinical Decision Services

Support vector machine (SVM) classification techniques have been widely adopted for building clinical decision models. In cloud-based remote clinical decision services, a healthcare center outsources the clinical decision model to a cloud server, which then provides remote clinical decision services to end users. In this article, we propose a practical and secure SVM classification scheme ( <inline-formula><tex-math notation="LaTeX">${\sf SSVMC}$</tex-math></inline-formula> ) for cloud-based remote clinical decision services. Specifically, we first extract SVM decision rules from an SVM classifier. Then, we leverage symmetric key encryption to protect the confidentiality of medical data and prevent the cloud service provider from misusing intellectual property of the outsourced clinical model. Finally, we build encrypted indexes to achieve efficient SVM classification. We define a leakage function, formulate a security definition, and provide a simulation-based security proof for <inline-formula><tex-math notation="LaTeX">${\sf SSVMC}$</tex-math></inline-formula> . The performance analysis demonstrates that <inline-formula><tex-math notation="LaTeX">${\sf SSVMC}$</tex-math></inline-formula> achieves linear computational complexity when an SVM classifier (a.k.a., the clinical decision model) is pre-trained. The simulations evaluate the impact of several parameters on time costs. The experimental evaluations show the performance differences between <inline-formula><tex-math notation="LaTeX">${\sf SSVMC}$</tex-math></inline-formula> and several existing schemes in terms of time costs, storage costs, communication costs, and precisions in a real-world clinical dataset, which demonstrate that <inline-formula><tex-math notation="LaTeX">${\sf SSVMC}$</tex-math></inline-formula> is computationally efficient with high decision accuracy.
Design and implementation of a new memristive chaotic system with application in touchless fingerprint encryption

In this paper, a new memristive chaotic system was constructed from three-dimensional Lorenz-type system. The system has infinitely many equilibria and exhibits coexisting attractors. The dynamic evolution corresponds to the parameters and the coupling strength indicate that the system is easy to generate chaos. Also the bifurcation diagrams from different initial conditions determine the coexistence of multiple attractors. An electronic circuit is done for verifying the physically existence of the system. Based on this system, chaos-based random number generator and the corresponding randomness tests are studied. An algorithm for touchless fingerprint encryption is established. Some comparative tests illustrate the effectiveness of the algorithm.
Organizational Adoption of Information Security Solutions

Information systems literature has cast organizational information security practices as a form of innovation. Using the notions of innovation adoption and diffusion of innovations, this paper develops an integrative model grounded in two theoretical perspectives- diffusion of innovation theory and the technologyorganization- environment framework-to examine the adoption of information security solutions (ISS) in organizations. We specify four innovation characteristics that are specific to ISS (compatibility, complexity, costs, and perceived gain), two organizational factors (organizational readiness and top management support), and two environmental factors (external pressure and visibility) as influential toward ISS adoption. We tested our model using data collected through a survey of 368 information systems managers in North American organizations. Our findings are insightful and have important theoretical and practical implications. Overall, the results suggest that organizational and environmental factors contribute to the extent of ISS adoption above and beyond characteristics of ISS themselves. The results are consistent across two measures of ISS adoption- perceived and (self-reported) actual-thereby supporting the robustness of our findings.
A collaborative LSHADE algorithm with comprehensive learning mechanism

In this study, a novel L-SHADE variant with collaborative scheme and comprehensive learning mechanism, named LSHADE-CLM, was proposed to improve the exploration and exploitation capabilities of the L-SHADE algorithm. In LSHADE-CLM, a novel cooperative mutation mechanism including "DE∕current−to−pbetter∕r" and "DE∕current−to−pbest−w∕1" is proposed in the mutation operation. In the "DE∕current−to−pbetter∕r" strategy with comprehensive learning mechanism, the population covariance matrix is utilized to generate candidate solutions and guide the search direction. Meanwhile, a competitive reward mechanism is implemented to control the mutation factor F to generate a trial vector for the cooperative mechanism. Moreover, the dimensional reset strategy is applied to enhance the diversity of the population at the dimensional level when stagnation is identified at certain dimension. The proposed LSHADE-CLM is tested on the CEC2017 benchmark functions and compared with the other four state-of-the-art variants of L-SHADE. The experimental results demonstrated that the efficiency and effectiveness of the LSHADE-CLM algorithm for the non-separable optimization problem.