Reviewerly/exHarmony huggingface dataset:
Digitally mediated interaction as a resource for co-constructing multilingual identities in classrooms

The development of smartphones and mobile Internet have advanced tremendously since 2000 and have made the access to communication increasingly available in diverse settings, including classrooms. Today, smartphones are used in classrooms as part of both on- and off-task activities. For multilingual participants, this communication involves several languages. Previous research shows that classrooms are often oriented to and jointly constructed as monolingual settings in which participants orient to the language of instruction. In the research reported here, I focus on the ways that multilingual participants orient to and use mobile digital technology to co-construct multilingual identities in these classrooms, that is, how participants can, in-and-through the use of mobile communication, actively construct multilingual identities and bring them into the classroom. However, the mobile interactions also influence and contribute to classroom interactions and vice versa. Nevertheless, the digitally-local multilingual identity that is co-constructed and expressed in mobile interactions appears not to be problematized in the same manner as explicitly multilingual turns in the non-digital classroom may be oriented to as interactionally problematic. The verbal, non-digital, classroom becomes—in the interactional spaces afforded by the mobile communication—multilingual, as the participants co-construct multilingual identities in-and-through their mediated interactions.
Depth acquisition with the combination of structured light and deep learning stereo matching

The methods of structured light and deep learning are widely used in artificial vision to acquire a depth map of real-world scenes. In this paper, we propose a novel method of combining structured light and deep learning stereo matching to calculate the depth. To combat the problems with textureless areas of stereo matching, a pair of left and right side images with abundant structured light information is adopted to acquire a coarse depth map by unsupervised CNN networks. The coarse depth map is used for phase unwrapping, which is a bottleneck in the phase-based structured light method. Then, a fine and accurate depth map is obtained by phase matching. Compared with the traditional structured light method, the proposed method performs better on occluded and textureless areas. To evaluate the performance of our proposed method, an experimental platform is established and several experiments are conducted. Quantitative and qualitative experiments demonstrate that the proposed method can generate a high precision depth and relieve the occlusion in the structured light system.
A Classification of Decision Automation and Delegation in Digital Investment Management Systems

Digital investment management systems, commonly known as robo-advisors, provide new alternatives to traditional human services, offering competitive investment returns at lower cost and customer effort.However, users must give up control over their investments and rely on automated decision-making.Because humans display aversion to high levels of automation and delegation, it is important to understand the interplay of these two aspects.This study proposes a taxonomy of digital investment management systems based on their levels of decision automation and delegation along the investment management process.We find that the degree of automation depends on the frequency and urgency of decisions as well as the accuracy of algorithms.Notably, most providers only invest in a subset of funds pre-selected by humans, potentially limiting efficiency gains.Based on our taxonomy, we identify archetypical system designs, which facilitate further research on perception and adoption of digital investment management systems.
Implementation of fuzzy logic controller in power system applications

The natural activities and human thinking forms the basis for fuzzy logic which presents based on different application perspectives. The performance of various energy storage systems life time can be improved by utilizing fuzzy logic controllers and back up in a hybrid power system especiallywhil e usingrenewable sources. The Load Frequency Controllers (LFC) using classical control techniques are tuned based on trial and error methods.Also, when system complexity increases controller gives slow response, by considering the fuzzy intelligent control these system performances are improved. A soft computing fuzzy technique is employed to maximize the efficiency from solar panel to give maximumpower output.The various applications in power systems relating to energy storage system performance for energy management, controller for controlling the load-frequency in multi-area power system and for solar systems by considering the tracking efficiency which are utilized for synchronization into the grid.The fuzzy logic provides better improvement and efficiency when compared to conventional controllers. These controllers do not have any specific or particular procedure to implement in various applications. A brief review to fuzzy logic controllers (FLC) for energy storage systems LFC and PV solar MPPT is presented.
An Efficient Deniable Authenticated Encryption Scheme for Privacy Protection

In the field of social survey of misconduct and legal consultation, the features of confidentiality, integrity, deniable authentication, and non-repudiation are needed for the sake of preserving privacy. For this special kind of application scenario, we propose an efficient deniable authentication encryption scheme. Our scheme can achieve the four secure features in a single logical step. And compared with the latest scheme, our scheme reduces the computational cost of encryption by about 30%, reduces computational cost of decryption by about 50%, and reduces the length of ciphertext by about 33%. Its security is shown in the random oracle model.
Sparse Artificial Neural Networks Using a Novel Smoothed LASSO Penalization

Artificial neural networks (ANNs) are increasingly being used for a variety of machine learning problems. However, increased density of interconnections in ANNs leads to high computational and power requirements. One way to reduce the power is to reduce the number of interconnections which can be achieved using least absolute shrinkage and selection operator (LASSO) techniques. In this brief, we propose an alternative smoothing function to LASSO regularization and an incremental pruning algorithm on feedforward ANNs with an aim of achieving maximally sparse networks with minimal performance degradation. We compare the results obtained using the proposed smoothing function with the existing smoothing functions. Further, we also evaluate the performance of the proposed incremental pruning algorithm.
WAILS: Watershed Algorithm With Image-Level Supervision for Weakly Supervised Semantic Segmentation

Image semantic segmentation has great development in many fields, and the lack of fully supervised segmentation labels has always been a major problem in the development of image semantic segmentation.In this paper, we propose a WAILS method to solve this problem.First, the image is coarsely segmented through a weakly supervised network at the image level.Second, to further obtain the shape of the target in the image, the watershed algorithm is used to refine the result of the coarse segmentation.Third, this refined image is used as a label for the first round of training of a fully supervised image semantic segmentation network.At last, the results are refined through the watershed as the label of the second round of fully supervised training and then it iterates.This method achieves the pixel-level semantic segmentation only through image-level labels and watershed pre-segmentation.Our method achieved good performance on the PASCAL 2012 dataset and the COCO dataset, while its segmentation accuracy surpasses all current weakly supervised semantic segmentation models in the category of bird, airplane, sheep, and so on.
Knowledge Extraction of Adaptive Structural Learning of Deep Belief Network for Medical Examination Data

Deep learning has a hierarchical network structure to represent multiple features of input data. The adaptive structural learning method of Deep Belief Network (DBN) can reach the high classification capability while searching the optimal network structure during the training. The method can find the optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation–annihilation algorithm, and generate a new hidden layer in DBN by the extension of the algorithm. In this paper, the proposed adaptive structural learning of DBN (Adaptive DBN) was applied to the comprehensive medical examination data for cancer prediction. The developed prediction system showed higher classification accuracy for test data (99.5% for the lung cancer and 94.3% for the stomach cancer) than the several learning methods such as traditional RBM, DBN, Non-Linear Support Vector Machine (SVM), and Convolutional Neural Network (CNN). Moreover, the explicit knowledge that makes the inference process of the trained DBN is required in deep learning. The binary patterns of activated neurons for given input in RBM and the hierarchical structure of DBN can represent the relation between input and output signals. These binary patterns were classified by C4.5 for knowledge extraction. Although the extracted knowledge showed slightly lower classification accuracy than the trained DBN network, it was able to improve inference speed by about 1/40. We report that the extracted IF-THEN rules from the trained DBN for medical examination data showed some interesting features related to initial condition of cancer.
RKA Security of Identity-Based Homomorphic Signature Scheme

Recently, Lin et al. proposed a new primitive identity-based (IB) homomorphic signature scheme and presented an ingenious implement by using any IB-signature scheme as a building block.In this paper, we consider a new type of attack on their scheme: Related-key attack (RKA) is introduced by Bellare and Kohno in 2003 and widely considered for kinds of cryptographic primitives.Specifically, for the first time, we define the RKA security of IB-homomorphic signature scheme.By modifying the signing secret key as its linear form, we prove that Lin et al.'s IB-homomorphic signature scheme is not RKA secure.But a slight modification of it yields an RKA secure one under the original assumptions.We also present security proof in detail.However, we remark that the reason why RKA on Lin et al.'s scheme can be successful lies in that RKA is outside of its security model.Finally, the numerical analysis and experimental results demonstrate that our modified scheme does not distinctly decrease the computational efficiency of Lin et al.'s scheme.
SPENT: A Successive POI Recommendation Method Using Similarity-Based POI Embedding and Recurrent Neural Network with Temporal Influence

In recent years, successive Point-of-Interest (POI) recommendation has attracted more and more attention and many methods have been proposed to address the problem of successive POI recommendation. In this paper, we propose the SPENT method which uses similarity tree to organize all POIs and applies Word2Vec to perform POI embedding. Then, SPENT uses a recurrent neural network (RNN) to model users' successive transition behavior. We also propose to insert a bath normalization layer in front of the LSTM and a temporal distance gate in the back of the LSTM to improve the performance of SPENT. To compare the performance of SPENT and other prior successive POI recommendation methods, several experiments are conducted on two real datasets, Gowalla and Foursquare. Experimental results show that SPENT outperforms the other prior methods in terms of precision and recall.
Maneuvering Target Tracking Using Simultaneous Optimization and Feedback Learning Algorithm Based on Elman Neural Network

Tracking maneuvering targets is a challenging problem for sensors because of the unpredictability of the target's motion. Unlike classical statistical modeling of target maneuvers, a simultaneous optimization and feedback learning algorithm for maneuvering target tracking based on the Elman neural network (ENN) is proposed in this paper. In the feedback strategy, a scale factor is learnt to adaptively tune the dynamic model's error covariance matrix, and in the optimization strategy, a corrected component of the state vector is learnt to refine the final state estimation. These two strategies are integrated in an ENN-based unscented Kalman filter (UKF) model called ELM-UKF. This filter can be trained online by the filter residual, innovation and gain matrix of the UKF to simultaneously achieve maneuver feedback and an optimized estimation. Monte Carlo experiments on synthesized radar data showed that our algorithm had better performance on filtering precision compared with most maneuvering target tracking algorithms.
Asymmetric Physical Layer Encryption for Wireless Communications

In this paper, we establish a cryptographic primitive for wireless communications. An asymmetric physical layer encryption (PLE) scheme based on elliptic curve cryptography is proposed. Compared with the conventional symmetric PLE, asymmetric PLE avoids the need of key distribution on a private channel, and it has more tools available for processing complex-domain signals to confuse possible eavesdroppers when compared with upper-layer public key encryption. We use quantized information entropy to measure the constellation confusion degree. The numerical results show that the proposed scheme provides greater confusion to eavesdroppers and yet does not affect the bit error rate (BER) of the intended receiver (the information entropy of the constellation increases to 17.5 for 9-bit quantization length). The scheme also has low latency and complexity [O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.37</sup> ), where N is a fixed block size], which is particularly attractive for implementation.
Iodine: Fast Dynamic Taint Tracking Using Rollback-free Optimistic Hybrid Analysis

Dynamic information-flow tracking (DIFT) is useful for enforcing security policies, but rarely used in practice, as it can slow down a program by an order of magnitude.Static program analyses can be used to prove safe execution states and elide unnecessary DIFT monitors, but the performance improvement from these analyses is limited by their need to maintain soundness.In this paper, we present a novel optimistic hybrid analysis (OHA) to significantly reduce DIFT overhead while still guaranteeing sound results.It consists of a predicated whole-program static taint analysis, which assumes likely invariants gathered from profiles to dramatically improve precision.The optimized DIFT is sound for executions in which those invariants hold true, and recovers to a conservative DIFT for executions in which those invariants are false.We show how to overcome the main problem with using OHA to optimize live executions, which is the possibility of unbounded rollbacks.We eliminate the need for any rollback during recovery by tailoring our predicated static analysis to eliminate only safe elisions of noop monitors.Our tool, Iodine, reduces the overhead of DIFT for enforcing security policies to 9%, which is 4.4× lower than that with traditional hybrid analysis, while still being able to be run on live systems.
Learning Temporal Information from Spatial Information Using CapsNets for Human Action Recognition

Capsule Networks (CapsNets) are recently introduced to overcome some of the shortcomings of traditional Convolutional Neural Networks (CNNs). CapsNets replace neurons in CNNs with vectors to retain spatial relationships among the features. In this paper, we propose a CapsNet architecture that employs individual video frames for human action recognition without explicitly extracting motion information. We also propose weight pooling to reduce the computational complexity and improve the classification accuracy by appropriately removing some of the extracted features. We show how the capsules of the proposed architecture can encode temporal information by using the spatial features extracted from several video frames. Compared with a traditional CNN of the same complexity, the proposed CapsNet improves action recognition performance by 12.11% and 22.29% on the KTH and UCF-sports datasets, respectively.
An industrial missing values processing method based on generating model

The issue of missing values (MVs) has been found widely in real-world datasets and obstructed the use of many statistical or machine learning algorithms for data analytics due to their incompetence in processing incomplete datasets. Most of the current MVs filling methods are applied to the datasets with certain specific types or low missing rate. This paper proposes a method of missing values processing based on the combination of denoising autoencoder (DAE) and generative adversarial networks (GAN), aiming at the missing completely at random (MCAR) datasets with high missing rate and noise interference in industrial scenes. We execute the training process on a discrete dataset with missing values, in order to ensure the generated dataset is completely similar to the feature distribution of the original dataset. We conduct our experiments for different dimensional datasets to prove the feasibility and efficiency of this method, including three public authority datasets and an industrial production monitoring dataset. The results compared with traditional missing values imputation methods have shown when the missing rate is higher than 30%, our method performs better in robustness and accuracy.
Strategies and Best Practices for Model-Based Systems Engineering Adoption in Embedded Systems Industry

[Context] Model-based Systems Engineering (MBSE) advocates the integrated use of models throughout all development phases of a system development life-cycle. It is also often suggested as a solution to cope with the challenges of engineering complex systems. However, MBSE adoption is no trivial task and companies, especially large ones, struggle to achieve it in a timely and effective way. [Goal] We aim to discover what are the best practices and strategies to implement MBSE in companies that develop embedded software systems. [Method] Using an inductive-deductive research approach, we conducted 14 semi-structured interviews with experts from 10 companies. Further, we analyzed the data and drew some conclusions which were validated by an on-line questionnaire in a triangulation fashion. [Results] Our findings are summarized in an empirically validated list of 18 best practices for MBSE adoption and through a prioritized list of the 5 most important best practices. [Conclusions] Raising engineers' awareness regarding MBSE advantages and acquiring experience through small projects are considered the most important practices to increase the success of MBSE adoption.
Investigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers

Given the wide use of machine learning approaches based on opaque prediction models, understanding the reasons behind decisions of black box decision systems is nowadays a crucial topic. We address the problem of providing meaningful explanations in the widely-applied image classification tasks. In particular, we explore the impact of changing the neighborhood generation function for a local interpretable model-agnostic explanator by proposing four different variants. All the proposed methods are based on a grid-based segmentation of the images, but each of them proposes a different strategy for generating the neighborhood of the image for which an explanation is required. A deep experimentation shows both improvements and weakness of each proposed approach.
Soft Measurement for VFA Concentration in Anaerobic Digestion for Treating Kitchen Waste Based on Improved DBN

Anaerobic digestion is a new method for treating kitchen waste, which can reduce waste, protect the environment, and create clean energy.Because the concentration of volatile fatty acids (VFA) in kitchen waste anaerobic digestion process cannot be measured in real time online, a soft measurement method based on deep belief network (DBN) is applied to the measurement of VFA.In this paper, the extreme learning machine (ELM) is applied to the training of DBN.The adaptive learning rate is introduced to increase the convergence speed of the network, which is different from traditional DBF.The data from a real plant is classified and decomposed using a Gaussian mixture model (GMM) and ensemble empirical mode analysis (EEMD) before training the network firstly.A DBN is used to perform numerical analysis of original data to extract features.Then, the extracted features are input into the extreme learning machine for training to obtain a soft measurement model.The experimental verification shows that this method is more precise than traditional methods and pure DBN model.
Eye-Gaze-Controlled Telepresence Robots for People with Motor Disabilities

Eye-gaze interaction is a common control mode for people with limited mobility of their hands. Mobile robotic telepresence systems are increasingly used to promote social interaction between geographically dispersed people. We are interested in how gaze interaction can be applied to such robotic systems, in order to provide new opportunities for people with physical challenges. However, few studies have implemented gaze-interaction into a telepresence robot and it is still unclear how gaze-interaction within these robotic systems impacts users and how to improve the systems. This paper introduces our research project, which takes a two-phase approach towards investigating a novel interaction-system we developed. Results of these two studies are discussed and future plans are described.
Watching Videos Together in Social Virtual Reality: An Experimental Study on User's QoE

In this paper, we describe a user study in which pairs of users watch a video trailer and interact with each other, using two social Virtual Reality (sVR) systems, as well as in a face-to-face condition. The sVR systems are: Facebook Spaces, based on puppet-like customized avatars, and a video-based sVR system using photo-realistic virtual user representations. We collect subjective and objective data to analyze users' Quality of Experience (QoE) and compare their interaction in VR to that observed during the real-life scenario. Our results show that the experience delivered by the video-based sVR system is comparable with real-life settings, while the puppet-based avatars limit the perceived quality of the interaction. Our protocol for QoE assessment is fully documented to allow replication in similar experiments.
A Universal Empirical Dynamic Programming Algorithm for Continuous State MDPs

We propose universal randomized function approximation-based empirical value learning (EVL) algorithms for Markov decision processes. The “empirical” nature comes from each iteration being done empirically from samples available from simulations of the next state. This makes the Bellman operator a random operator. A parametric and a nonparametric method for function approximation using a parametric function space and a reproducing kernel Hilbert space respectively are then combined with EVL. Both function spaces have the universal function approximation property. Basis functions are picked randomly. Convergence analysis is performed using a random operator framework with techniques from the theory of stochastic dominance. Finite time sample complexity bounds are derived for both universal approximate dynamic programming algorithms. Numerical experiments support the versatility and computational tractability of this approach.