Reviewerly/exHarmony huggingface dataset:
Do Speech-Based Collaboration Analytics Generalize Across Task Contexts?

We investigated the generalizability of language-based analytics models across two collaborative problem solving (CPS) tasks: an educational physics game and a block programming challenge. We analyzed a dataset of 95 triads (N=285) who used videoconferencing to collaborate on both tasks for an hour. We trained supervised natural language processing classifiers on automatic speech recognition transcripts to predict the human-coded CPS facets (skills) of constructing shared knowledge, negotiation / coordination, and maintaining team function. We tested three methods for representing collaborative discourse: (1) deep transfer learning (using BERT), (2) n-grams (counts of words/phrases), and (3) word categories (using the Linguistic Inquiry Word Count [LIWC] dictionary). We found that the BERT and LIWC methods generalized across tasks with only a small degradation in performance (Transfer Ratio of .93 with 1 indicating perfect transfer), while the n-grams had limited generalizability (Transfer Ratio of .86), suggesting overfitting to task-specific language. We discuss the implications of our findings for deploying language-based collaboration analytics in authentic educational environments.
Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

Abstract Hate speech in social media is an increasing problem that can negatively affect individuals and society as a whole. Moderators on social media platforms need to be technologically supported to detect problematic content and react accordingly. In this article, we develop and discuss the design principles that are best suited for creating efficient user interfaces for decision support systems that use artificial intelligence (AI) to assist human moderators. We qualitatively and quantitatively evaluated various design options over three design cycles with a total of 641 participants. Besides measuring perceived ease of use, perceived usefulness, and intention to use, we also conducted an experiment to prove the significant influence of AI explainability on end users’ perceived cognitive efforts, perceived informativeness, mental model, and trustworthiness in AI. Finally, we tested the acquired design knowledge with software developers, who rated the reusability of the proposed design principles as high.
Self-calibrated hybrid weather forecasters for solar thermal and photovoltaic power plants

This study presents two novel ensemble forecasters that combine time delay and recurrent neural networks to predict the global horizontal irradiance (GHI), direct normal irradiance (DNI), and ambient temperature (TMP). The weights of these forecasters are optimized to minimize the summation of squared errors between the actual and the forecasted values of sub-models during the previous forecasting step. The developed models have been benchmarked against typical forecasting models for three forecasting horizons. Afterward, the impact of forecasting errors on the power estimation of photovoltaic (PV) and concentrated solar power (CSP) plants was examined. The two proposed forecasters consistently outperformed persistence ones by up to 88.653%. Compared to the aggregated model, the newly proposed forecasters enhanced the predictions of GHI, DNI, and TMP by up to 39.392, 27.081, and 79.598% in terms of mean absolute error, and up to 21.501, 13.645, and 78.961% in terms of root mean square error. The absolute and relative performances of the proposed forecasters also improved as the forecasting horizon decreased. Finally, the performances of the daily and the instantaneously calibrated models were comparable in both plants. Hence, the daily calibrated model is generally recommended as it requires significantly lower computational costs. • Daily and instantaneously auto-calibrated models are proposed and evaluated. • Both models reduce RMSE of a standard aggregated model by up to 78%. • The daily calibrated model is superior in predicting DNI and temperature. • All ensemble models easily outperformed baseline persistence models. • The proposed models perform even better for smaller forecasting horizons.
Resilient Penalty Function Method for Distributed Constrained Optimization under Byzantine Attack

Distributed optimization algorithms have the advantages of privacy protection and parallel computing. However, the distributed nature of these algorithms makes the system vulnerable to external attacks. This paper presents two penalty function based resilient algorithms for constrained distributed optimization under static and dynamic attacks. The objective function of the optimization problem is extended to nonsmooth ones and the convergence of the proposed algorithms in this case are proved under some mild conditions. Simulation experiments are performed and compared with some existing resilient primal-dual optimization algorithms using median-based mean estimator. For static attack, the proposed algorithm has better performance and faster convergence rate in the simulation experiments. For dynamic attack, the proposed algorithm has better performance and robustness in the simulation experiments, which illustrate that the proposed algorithms are more effective.
Efficient Large Scale NLP Feature Engineering with Apache Spark

Feature engineering is a computationally time-consuming process in the end-to-end machine learning pipeline. Large amounts of text data are being generated on many heterogeneous sources and platforms on the internet. The compute resources needed to extract valuable features from these big datasets are increasing significantly. In this research, we evaluate the runtime of the RDD and the Spark-SQL APIs of the Apache Spark framework to extract text features from the corpus of english Wikipedia. As a result, we demonstrate the significant runtime performance of the SparkSQL compared to RDD API.
GAIMMO: A Grade-Driven Auction-Based Incentive Mechanism With Multiple Objectives for Crowdsourcing Managed by Blockchain

Blockchain has been applied for decentralized crowdsourcing management by deploying a number of miners to reach a consensus on crowdsourced task allocation and payment decision. In a blockchain-based crowdsourcing system (BCS), incentive becomes essential to motivate the participation and cooperation of all system entities. However, existing literature scarcely investigates how to motivate heterogeneous crowdsourcers, workers, and miners simultaneously toward satisfying multiple objectives without the support of centralized management. In this article, we propose GAIMMO, a novel grade-driven auction-based incentive mechanism for BCS with multiple objectives in mind: crowdsourcer utility maximization, social welfare maximization, social grade maximization, and social cost minimization. Concretely, we propose a grade-based task sorting (GTS) algorithm to determine the service priority of heterogeneous crowdsourcers in order to motivate their cooperative behaviors, which consequently maximizes crowdsourcer utility when combining with the carefully designed utility functions of other system entities. We propose a grade-based utility function of workers and employ a hierarchical premium-based task assignment (PTA) algorithm to realize social welfare maximization, social grade maximization, and social cost minimization. We further propose a fixed-grade-sum and grade-based reward-sharing (FGSGRS) method to encourage fast block generation and motivate high-grade miners without damaging the profits of the crowdsourcers. We conduct simulation-based experiments to show the effectiveness and advance of our proposed incentive mechanism in stimulating the participation willingness of high-grade system entities and achieving the multiple objectives.
Fairness-Aware Federated Learning With Unreliable Links in Resource-Constrained Internet of Things

In order to make full use of the network data and guarantee user privacy simultaneously, federated learning (FL) is proposed to enable distributed intelligence for local nodes without sharing data with each other. However, in practice, due to resource limitations, traditional FL suffers from node scheduling and parameter transmission failure, which not only affects the final performance but also further reduces the fairness of the participating nodes. This article addresses the challenge and proposes an FL method to enhance the performance of FL on the basis of guaranteeing the fairness of the local nodes in a resource-constrained Internet of Things (IoT) network. Specifically, an analytical model is first constructed to characterize the performance of FL with joint considerations of node fairness, unreliable parameter transmissions as well as resource limitations. Thereafter, a statistically reweighted aggregation (SRA) scheme is proposed for parameter aggregation and the corresponding model is proved to be unbiased to that based on ideal parameter transmissions. With the knowledge of time dependency of the global model, we further extend SRA and propose a reliable SRA (RSRA) scheme. Additionally, we prove RSRA is able to achieve higher stability performance than SRA in model training. Furthermore, the convergence bound of the proposed RSRA is derived analytically, based on which an adaptive local training scheme is proposed under a given resource budget. Finally, extensive experiments are carried out with a public data set to validate the effectiveness of the proposed scheme with comparisons of other baseline schemes.
Reversible data hiding with pairwise PEE and 2D-PEH decomposition

Reversible data hiding (RDH) is an important topic of data hiding. In this paper, we exploit pixel-based pixel value ordering prediction (PPVO) and pairwise prediction-error expansion (PEE) to design a novel RDH with pairwise PEE and 2-dimensional prediction-error histogram (2D-PEH) decomposition. Specifically, for each pair of pixels, they are accurately predicted according to the maximum and minimum values of their neighbor pixels by using PPVO to generate a global 2D-PEH. After selecting negative 2D expansion bins, a series of sub 2D-PEHs can be obtained by decomposing the global 2D-PEH according to the errors between the maximum and minimum values of neighbor pixels. Finally, for each sub 2D-PEH, pairwise PEE mapping manner is selected to conduct data embedding according to the characteristic of the sub 2D-PEH. Experimental results demonstrate that the proposed method can achieve good capacity-distortion performance and outperforms some state-of-the-art RDH methods.
A class-specific mean vector-based weighted competitive and collaborative representation method for classification

Collaborative representation-based classification (CRC), as a typical kind of linear representation-based classification, has attracted more attention due to the effective and efficient pattern classification performance. However, the existing class-specific representations are not competitively learned from collaborative representation for achieving more informative pattern discrimination among all the classes. With the purpose of enhancing the power of competitive and discriminant representations among all the classes for favorable classification, we propose a novel CRC method called the class-specific mean vector-based weighted competitive and collaborative representation (CMWCCR). The CMWCCR mainly contains three discriminative constraints including the competitive, mean vector and weighted constraints that fully employ the discrimination information in different ways. In the competitive constraint, the representations from any one class and the other classes are adapted for learning competitive representations among all the classes. In the newly designed mean vector constraint, the mean vectors of all the class-specific training samples with the corresponding class-specific representations are taken into account to further enhance the competitive representations. In the devised weighted constraint, the class-specific weights are constrained on the representation coefficients to make the similar classes have more representation contributions to strengthening the discrimination among all the class-specific representations. Thus, these three constraints in the unified CMWCCR model can complement each other for competitively learning the discriminative class-specific representations. To verify the CMWCCR classification performance, the extensive experiments are conducted on twenty-eight data sets in comparisons with the state-of-the-art representation-based classification methods. The experimental results show that the proposed CMWCCR is an effective and robust CRC method with satisfactory performance.
Decoupled Low-Light Image Enhancement

The visual quality of photographs taken under imperfect lightness conditions can be degenerated by multiple factors, e.g., low lightness, imaging noise, color distortion, and so on. Current low-light image enhancement models focus on the improvement of low lightness only, or simply deal with all the degeneration factors as a whole, therefore leading to sub-optimal results. In this article, we propose to decouple the enhancement model into two sequential stages. The first stage focuses on improving the scene visibility based on a pixel-wise non-linear mapping. The second stage focuses on improving the appearance fidelity by suppressing the rest degeneration factors. The decoupled model facilitates the enhancement in two aspects. On the one hand, the whole low-light enhancement can be divided into two easier subtasks. The first one only aims to enhance the visibility. It also helps to bridge the large intensity gap between the low-light and normal-light images. In this way, the second subtask can be described as the local appearance adjustment. On the other hand, since the parameter matrix learned from the first stage is aware of the lightness distribution and the scene structure, it can be incorporated into the second stage as the complementary information. In the experiments, our model demonstrates the state-of-the-art performance in both qualitative and quantitative comparisons, compared with other low-light image enhancement models. In addition, the ablation studies also validate the effectiveness of our model in multiple aspects, such as model structure and loss function.
Privacy-Preserving Federated Learning Model for Healthcare Data

Federated Machine Learning (FL) can be used effectively in distributed datasets, where data owners hesitate to share their raw data, as a reliable approach to train an ML algorithm. However, in the case of sensitive healthcare datasets, additional privacy measures before feeding into machine learning mechanisms are also necessary. Our approach uses the federated learning framework, which removes the necessity of sharing patients' sensitive data in a raw format outside the premise. First, the data owners agree on a list of features selected by the correlation; then, after training the local models, the obtained local models are transmitted to the central server for aggregation. The differential privacy (DP) approach is adopted to perturb the local models before transmission to add an extra privacy layer. As a result, our framework achieves improved utility as the feature selection reduces the data dimension. Finally, based on the patient's genomic data, the framework establishes a practical healthcare application to privacy-predict certain heart failure/cancer diseases. application to predict certain heart failure diseases in a private manner.
Classification of Underwater Target Based on S-ResNet and Modified DCGAN Models

Underwater target classification has been an important topic driven by its general applications. Convolutional neural network (CNN) has been shown to exhibit excellent performance on classifications especially in the field of image processing. However, when applying CNN and related deep learning models to underwater target classifications, the problems, including small sample size of underwater target and low complexity requirement, impose a great challenge. In this paper, we have proposed the modified DCGAN model to augment data for targets with small sample size. The data generated from the proposed model help to improve classification performance under imbalanced category conditions. Furthermore, we have proposed the S-ResNet model to obtain good classification accuracy while significantly reducing complexity of the model, and achieve a good tradeoff between classification accuracy and model complexity. The effectiveness of proposed models is verified through measured data from sea trial and lake tests.
Anthropomorphize service robots: the role of human nature traits

Empowered by artificial intelligence (AI), human-like service robots are prevalent, but they may have negative effects. Limited research has studied suitable strategies for anthropomorphizing service robots. In contrast to ordinary nonhuman objects, one of the robots' most essential features is that they have logic and are rational when they are empowered by AI, yet such rationality may destroy consumers' perceived identity uniqueness as human beings, eliciting bad outcomes. Therefore, we explore how to anthropomorphize service robots through maintaining consumers' perceived identity uniqueness. Through four studies, we find that consumers' attitudes about service robots will improve via a decreased identity threat if service robots are anthropomorphized with external human nature traits (that can be shared by animals) rather than uniquely human traits (that only humans have). In addition, this effect is mitigated by robots' servant communication style. The results indicate what anthropomorphic type of service robot is suitable for managers.
Low rank label subspace transformation for multi-label learning with missing labels

Multi-label datasets often contain label information with missing values and recovering them is a non-trivial challenge. Several methods augment the observed label matrix by constructing auxiliary labels and learning high order label correlations. Some other techniques exploit the low rank of the label matrix to capture a mix of label correlations. Both these approaches rely on label correlations, however in different ways. In this paper, we propose a unified framework that captures the label correlations utilizing both auxiliary label matrix and the low rank constraints on estimated labels. Our model also enforces maximal separation among different label subspaces for better label differentiation. The proposed method captures local and global correlations using Low Rank label subspace transformation for Multi-label learning with Missing Labels (LRMML). The model considers an auxiliary label matrix which facilitates the missing label information recovery. Low rank on predictions ensures that local label structures are captured and the maximal inter-label subspace separation helps identify discriminatory label correlations. The proposed method builds a multi-label classification model by solving a multivariate difference of convex objective function using surrogate optimization technique and alternating minimization. Empirical results on several benchmark datasets validate the effectiveness of the proposed method against state-of-the-art multi-label learning approaches.
Jiffy

Stateful serverless analytics can be enabled using a remote memory system for inter-task communication, and for storing and exchanging intermediate data. However, existing systems allocate memory resources at job granularity---jobs specify their memory demands at the time of the submission; and, the system allocates memory equal to the job's demand for the entirety of its lifetime. This leads to resource underutilization and/or performance degradation when intermediate data sizes vary during job execution.
NDSRGAN: A Novel Dense Generative Adversarial Network for Real Aerial Imagery Super-Resolution Reconstruction

In recent years, more and more researchers have used deep learning methods for super-resolution reconstruction and have made good progress. However, most of the existing super-resolution reconstruction models generate low-resolution images for training by downsampling high-resolution images through bicubic interpolation, and the models trained from these data have poor reconstruction results on real-world low-resolution images. In the field of unmanned aerial vehicle (UAV) aerial photography, the use of existing super-resolution reconstruction models in reconstructing real-world low-resolution aerial images captured by UAVs is prone to producing some artifacts, texture detail distortion and other problems, due to compression and fusion processing of the aerial images, thereby resulting in serious loss of texture detail in the obtained low-resolution aerial images. To address this problem, this paper proposes a novel dense generative adversarial network for real aerial imagery super-resolution reconstruction (NDSRGAN), and we produce image datasets with paired high- and low-resolution real aerial remote sensing images. In the generative network, we use a multilevel dense network to connect the dense connections in a residual dense block. In the discriminative network, we use a matrix mean discriminator that can discriminate the generated images locally, no longer discriminating the whole input image using a single value but instead in chunks of regions. We also use smoothL1 loss instead of the L1 loss used in most existing super-resolution models, to accelerate the model convergence and reach the global optimum faster. Compared with traditional models, our model can better utilise the feature information in the original image and discriminate the image in patches. A series of experiments is conducted with real aerial imagery datasets, and the results show that our model achieves good performance on quantitative metrics and visual perception.
Application of Machine Learning and Internet of Things Techniques in Evaluation of English Teaching Effect in Colleges

College English classroom teaching evaluation is one of the key issues discussed by all schools at present. First of all, teachers and students are highly concerned about English learning in the compulsory course of English examination, but there are also many problems. The distance network teaching with computer technology as the main body develops rapidly, a large number of video and audio teaching resources network through the network transmission to present in front of learners, through the network transmission of video and audio aided, expand the audience of network teaching, is conducive to the realization of digitalization, information, lifelong, new education goals. Firstly, this article summarizes the problems of poor effect, uneven performance, and mismatch between evaluation and teaching in college English at present. Then, based on the previous works, the author designed a machine learning-based Internet of things technology and verifies its feasibility. The data of teaching experiments show that the writing performance of the students in the lower section of the experimental class is better than that of the students in the middle and high sections. Among them, significant progresses have been made in grammar, length, expression, and structure, which has optimized students' preclass preview efficiency, autonomous learning motivation, quality of homework completion, and afterclass reflection behavior. Finally, the author summarized the shortcomings of this research and puts forward the prospect of relevant research, in order to provide reference value for the national college English blended teaching and promotes the efficient implementation and vigorous development of college English teaching. In the future, the evaluation index system under the pressure of performance can be further studied.