Reviewerly/exHarmony huggingface dataset:
Multi-view ensemble learning method for microblog sentiment classification

With the rise of microblog services in recent years, microblog sentiment classification has been widely studied and applied in many fields like public opinion monitoring, commodity evaluation and market forecasting. Ensemble methods have been widely used in the feature construction and classification stages of microblog sentiment classification due to their excellent performance. In feature construction, most researchers use feature concatenation or ensemble methods to combine different features while the fusion of two methods is ignored. In classification, most ensemble classification methods combine classifiers based on majority voting or weighted averaging, and they do not fully consider the differences in the information contained in classifiers. In this paper, a novel multi-view ensemble learning method is proposed to fuse the information contained in different features for better microblog sentiment classification. This method consists of two stages: the local fusion stage and the global fusion stage. In the local fusion stage, the raw features and concatenation features are used to construct basic classifiers, and these basic classifiers are combined into five classifier groups to identify the microblog sentiment among all raw feature information. In the global fusion stage, these classifier groups with a global view are further integrated to make more accurate and comprehensive predictions. Two public microblog benchmark datasets provided by Sina Weibo are used in the experiment, and the experimental results show that our method outperforms other compared methods in identifying the polarities of microblog posts.
Visual Question Answering With Dense Inter- and Intra-Modality Interactions

Learning effective interactions between multi-modal features is at the heart of visual question answering (VQA). A common defect of the existing VQA approaches is that they only consider a very limited amount of inter-modality interactions, which may be not enough to model latent complex image-question relations that are necessary for accurately answering questions. Besides, most methods neglect the modeling of the intra-modality interactions that is also important to VQA. In this work, we propose a novel DenIII framework for modeling dense inter- and intra-modality interactions. It densely connects all pairwise layers of the network via the proposed Inter- and Intra-modality Attention Connectors, capturing fine-grained interplay across all hierarchical levels. The Inter-modality Attention Connector efficiently connects the multi-modality features at any two layers with bidirectional attention, capturing the inter-modality interactions. While the Intra-modality Attention Connector connects the features of the same modality with unidirectional attention, and models the intra-modality interactions. Extensive ablation studies and visualizations validate the effectiveness of our method, and DenIII achieves state-of-the-art or competitive performance on three publicly available datasets.
Solar radiation zoning and daily global radiation models for regions with only surface meteorological measurements in China

Daily global solar radiation is an indispensable variable in developing typical meteorological year and its data are important for constructing energy-efficient buildings. Solar radiation zoning and empirical models are essential for estimating the daily global solar radiation in regions that only have surface meteorological measurements. However, empirical models have become increasingly complicated without significant improvements in their accuracy. Thus, developing elaborate solar radiation zoning with minimal inputs and high accuracy daily global solar radiation models are key issues. In the present study, we develop an optimal criterion for solar radiation zoning by k-means clustering based on data obtained between 1970 and 2017 from 112 solar radiation measuring stations in China. According to the optimal criterion, solar radiation zoning is conducted based on data acquired by 1042 surface meteorological stations between 1970 and 2017. Zone models are established with linear relationships between the clearness index and relative sunshine duration based on data in each zone. Cross-validation is conducted to evaluate the performance of models by comparing individual models based on single station. The results show that the relative sunshine duration is the optimal criterion for solar radiation zoning and nine zones are identified. The zone models obtain good accuracy. Solar radiation zoning with zone models established in present study can be reliable and generalized to other regions with available sunshine duration data.
Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission

By deploying machine-learning algorithms at the network edge, edge learning can leverage the enormous real-time data generated by billions of mobile devices to train AI models, which enable intelligent mobile applications. In this emerging research area, one key direction is to efficiently utilize radio resources for wireless data acquisition to minimize the latency of executing a learning task at an edge server. Along this direction, we consider the specific problem of retransmission decision in each communication round to ensure both reliability and quantity of those training data for accelerating model convergence. To solve the problem, a new retransmission protocol called data-importance aware automatic-repeat-request (importance ARQ) is proposed. Unlike the classic ARQ focusing merely on reliability, importance ARQ selectively retransmits a data sample based on its uncertainty which helps learning and can be measured using the model under training. Underpinning the proposed protocol is a derived elegant communication-learning relation between two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data uncertainty. This relation facilitates the design of a simple threshold based policy for importance ARQ. The policy is first derived based on the classic classifier model of support vector machine (SVM), where the uncertainty of a data sample is measured by its distance to the decision boundary. The policy is then extended to the more complex model of convolutional neural networks (CNN) where data uncertainty is measured by entropy. Extensive experiments have been conducted for both the SVM and CNN using real datasets with balanced and imbalanced distributions. Experimental results demonstrate that importance ARQ effectively copes with channel fading and noise in wireless data acquisition to achieve faster model convergence than the conventional channel-aware ARQ. The gain is more significant when the dataset is imbalanced.
Mixed-precision quantized neural networks with progressively decreasing bitwidth

Efficient model inference is an important and practical issue in the deployment of deep neural networks on resource constraint platforms. Network quantization addresses this problem effectively by leveraging low-bit representation and arithmetic that could be conducted on dedicated embedded systems. In the previous works, the parameter bitwidth is set homogeneously and there is a trade-off between superior performance and aggressive compression. Actually, the stacked network layers, which are generally regarded as hierarchical feature extractors, contribute diversely to the overall performance. For a well-trained neural network, the feature distributions of different categories are organized gradually as the network propagates forward. Hence the capability requirement on the subsequent feature extractors is reduced. It indicates that the neurons in posterior layers could be assigned with lower bitwidth for quantized neural networks. Based on this observation, a simple yet effective mixed-precision quantized neural network with progressively decreasing bitwidth is proposed to improve the trade-off between accuracy and compression. Extensive experiments on typical network architectures and benchmark datasets demonstrate that the proposed method could achieve better or comparable results while reducing the memory space for quantized parameters by more than 25% in comparison with the homogeneous counterparts. In addition, the results also demonstrate that the higher-precision bottom layers could boost the 1-bit network performance appreciably due to a better preservation of the original image information while the lower-precision posterior layers contribute to the regularization of k−bit networks.
Dual-regression model for visual tracking

Existing regression based tracking methods built on correlation filter model or convolution model do not take both accuracy and robustness into account at the same time. In this paper, we propose a dual-regression framework comprising a discriminative fully convolutional module and a fine-grained correlation filter component for visual tracking. The convolutional module trained in a classification manner with hard negative mining ensures the discriminative ability of the proposed tracker, which facilitates the handling of several challenging problems, such as drastic deformation, distractors, and complicated backgrounds. The correlation filter component built on the shallow features with fine-grained features enables accurate localization. By fusing these two branches in a coarse-to-fine manner, the proposed dual-regression tracking framework achieves a robust and accurate tracking performance. Extensive experiments on the OTB2013, OTB2015, and VOT2015 datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
Cross-Domain Palmprint Recognition via Regularized Adversarial Domain Adaptive Hashing

As an effective method of biometrics, palmprint recognition allows the safe identity recognition of humans without spatial and temporal limitations. To build a more robust palmprint recognition system, recent promising Convolutional Neural Networks (CNN) has been incorporated for better palmprint feature extraction and representation. However, the increasing number of palmprint datasets presents us with a cross-domain recognition problem where the upcoming images may come from different imaging conditions compared to the registered palmprints, which will undermine the recognition accuracy significantly. As a supervised approach, the performance of CNN-based model depends on the availability of data and labels from the same domain, which is hard for transferring recognition. To keep the outperforming recognition result of CNN-based models, we propose a novel Regularized Adversarial Domain Adaptative Hashing method (R-ADAH) for cross-domain palmprint recognition based on Deep Hashing Network (DHN). During training, the Maximum Mean Discrepancy (MMD) is incorporated for better adaptive performance. In this scenario, we only train a DHN on the source domain. With the adversarial training, the target network is becoming adaptive to the unlabeled palmprint images with more stable training, unbiased sample gradient and less sensitivity to the hyper-parameter tuning when only domain-specific label is provided. Extensive validation experiments are conducted on benchmark datasets and our self-collected palmprint datasets by mobile phones to test the performance of our model. The results show a promising increase of the recognition performance.
Bridging narrative and locality in mobile-based augmented reality educational activities: Effects of semantic coupling on students’ immersion and learning gains

Despite the rapid development of mobile-based Augmented Reality (AR) apps, little is yet known about how we can facilitate immersion and learning in AR contexts. This study investigated the hypothesis that greater coupling between physical space and the narrative of the AR learning activity can result in increased levels of immersion and students’ conceptual learning gains. Prior studies investigating the coupling effect are limited, inconclusive and primarily focused on adult populations. The present study investigated the effects of two types of coupling (strong/loose) on middle school students’ experienced immersion and conceptual learning gains, in the context of a narrative-based AR science education intervention. Forty-five middle school students participated in this study: Students in Condition 1 (n = 22) participated in an AR activity with strong coupling between narrative and the physical space, while students in Condition 2 (n = 23) participated in a loose coupling version of the activity. The data corpus consisted of baseline data, questionnaires investigating students’ immersion and conceptual learning gains, and post-activity interviews. Findings showed higher conceptual learning gains and increased immersion for the students participating in the strong coupling condition than for the students in the loose coupling condition. We discuss these findings and their implications, and we highlight questions for future research.
People Tracking System Using DeepSORT

The rapid development of image detection algorithm has led to its widespread application in security, such as facial recognition and crowd surveillance. However, real-time tracking is very challenging, especially in crowded places where the person might be in part or entirely occluded for some period. Hence, this paper objective is to create a people tracking system in crowd surveillance, using Deep SORT framework. Unlike object detection frameworks like CNN, this system does not just detect a person in real-time but on top of that, uses the information it has learned to track the trajectory of the person until they exit the frame of the camera. The system will use You Only Look Once (YOLO) for the person detection, and then use Deep SORT to process the detected person frame by frame to predict its movement path. The system was able to successfully detect and track the person movement path with average 2.59 frames per second (FPS).
Effects of Low and High Neuron Activation Gradients on the Dynamics of a Simple 3D Hopfield Neural Network

In this paper, the effects of low and fast response speeds of neuron activation gradient of a simple 3D Hopfield neural network are explored. It consists of analyzing the effects of low and high neuron activation gradients on the dynamics. By considering an imbalance of the neuron activation gradients, different electrical activities are induced in the network, which enable the occurrence of several nonlinear behaviors. The significant sensitivity of nontrivial equilibrium points to the activation gradients of the first and second neurons relative to that of the third neuron is reported. The dynamical analysis of the model is done in a wide range of the activation gradient of the second neuron. In this range, the model presents areas of periodic behavior, chaotic behavior and periodic window behavior through complex bifurcations. Interesting behaviors such as the coexistences of two, four, six and eight disconnected attractors, as well as the phenomenon of coexisting antimonotonicity, are reported. These singular results are obtained by using nonlinear dynamics analysis tools such as bifurcation diagrams and largest Lyapunov exponents, phase portraits, power spectra and basins of attraction. Finally, some analog results obtained from PSpice-based simulations further verify the numerical results.
Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?

For the past few years most published research on recommendation algorithms has been based on deep learning (DL) methods. Following common research practices in our field, these works usually demonstrate that a new DL method is outperforming other models not based on deep learning in offline experiments. This almost consistent success of DL based models is however not observed in recommendation-related machine learning competitions like the challenges that are held with the yearly ACM RecSys conference. Instead the winning solutions mostly consist of substantial feature engineering efforts and the use of gradient boosting or ensemble techniques. In this paper we investigate possible reasons for this surprising phenomenon. We consider multiple possible factors such as the characteristics and complexity of the problem settings, datasets, and DL methods; the background of the competition participants; or the particularities of the evaluation approach.
Hourly 5-km surface total and diffuse solar radiation in China, 2007–2018

Abstract Surface solar radiation is an indispensable parameter for numerical models, and the diffuse component contributes to the carbon uptake in ecosystems. We generated a 12-year (2007–2018) hourly dataset from Multi-functional Transport Satellite (MTSAT) satellite observations, including surface total solar radiation (R s ) and diffuse radiation (R dif ), with 5-km spatial resolution through deep learning techniques. The used deep network tacks the integration of spatial pattern and the simulation of complex radiation transfer by combining convolutional neural network and multi-layer perceptron. Validation against ground measurements shows the correlation coefficient, mean bias error and root mean square error are 0.94, 2.48 W/m 2 and 89.75 W/m 2 for hourly R s and 0.85, 8.63 W/m 2 and 66.14 W/m 2 for hourly R dif , respectively. The correlation coefficient of R s and R dif increases to 0.94 (0.96) and 0.89 (0.92) at daily (monthly) scales, respectively. The spatially continuous hourly maps accurately reflect regional differences and restore the diurnal cycles of solar radiation at fine resolution. This dataset can be valuable for studies on regional climate changes, terrestrial ecosystem simulations and photovoltaic applications.
DoS attack detection model of smart grid based on machine learning method

In recent years, smart grid has gradually become the common development trend of the world's power industry, and its security issues are increasingly valued by researchers. Smart grids have applied technologies such as physical control, data encryption, and authentication to improve their security, but there is still a lack of timely and effective detection methods to prevent the grid from being threatened by malicious intrusions. Aiming at this problem, a model based on machine learning to detect smart grid DoS attacks has been proposed. The model first collects network data, secondly selects features and uses PCA for data dimensionality reduction, and finally uses SVM algorithm for abnormality detection. By testing the SVM, Decision Tree and Naive Bayesian Network classification algorithms on the KDD99 dataset, it is found that the SVM model works best.
Hybrid Physical Education Teaching and Curriculum Design Based on a Voice Interactive Artificial Intelligence Educational Robot

In order to promote the development of individualized, accurate and intelligent physical education teaching, combined with artificial intelligence technology, the current physical education teaching mode has been improved. Through the establishment of an artificial intelligence educational robot based on voice interaction, a hybrid physical education teaching mode is constructed to realize personalized education for students. First, the speech recognition system is designed from three aspects of speech recognition, interaction management and speech synthesis, and the accuracy of recognition is improved by algorithm. Second, a new mode of hybrid physical education teaching is constructed. Through intelligent information technology, the advantages of traditional physical education teaching are combined to improve the classroom efficiency of physical education teaching and personalized education ability for students. Finally, the relevant experimental scheme and questionnaire are designed, and the actual situation of an educational robot introduced into physical education teaching is investigated and evaluated. The results show that the recognition accuracy of the artificial intelligence speech recognition system can reach more than 90%. It can communicate well with students and answer students’ questions. An educational robot is introduced into physical education teaching, and students’ learning attitude and interest are evaluated. The results show that before and after the introduction of an educational robot in physical education teaching, the average score of students’ learning interest increases by 21 points, and the average score of learning attitude increases by 9.8 points. Therefore, the introduction of an artificial intelligence educational robot based on voice interaction in physical education teaching can help to improve the classroom efficiency of physical education teaching and students’ interest. This study provides a reference for the development of artificial intelligence teaching and promoting the development of artificial intelligence.
JVM fuzzing for JIT-induced side-channel detection

Timing side channels arise in software when a program's execution time can be correlated with security-sensitive program input. Recent results on software side-channel detection focus on analysis of program's source code. However, runtime behavior, in particular optimizations introduced during just-in-time (JIT) compilation, can impact or even introduce timing side channels in programs. In this paper, we present a technique for automatically detecting such JIT-induced timing side channels in Java programs. We first introduce patterns to detect partitions of secret input potentially separable by side channels. Then we present an automated approach for exploring behaviors of the Java Virtual Machine (JVM) to identify states where timing channels separating these partitions arise. We evaluate our technique on three datasets used in recent work on side-channel detection. We find that many code variants labeled "safe" with respect to side-channel vulnerabilities are in fact vulnerable to JIT-induced timing side channels. Our results directly contradict the conclusions of four separate state-of-the-art program analysis tools for side-channel detection and demonstrate that JIT-induced side channels are prevalent and can be detected automatically.
Detection of hidden feature requests from massive chat messages via deep siamese network

Online chatting is gaining popularity and plays an increasingly significant role in software development. When discussing functionalities, developers might reveal their desired features to other developers. Automated mining techniques towards retrieving feature requests from massive chat messages can benefit the requirements gathering process. But it is quite challenging to perform such techniques because detecting feature requests from dialogues requires a thorough understanding of the contextual information, and it is also extremely expensive on annotating feature-request dialogues for learning. To bridge that gap, we recast the traditional text classification task of mapping single dialog to its class into the task of determining whether two dialogues are similar or not by incorporating few-shot learning. We propose a novel approach, named FRMiner, which can detect feature-request dialogues from chat messages via deep Siamese network. We design a BiLSTM-based dialog model that can learn the contextual information of a dialog in both forward and reverse directions. Evaluation on the real-world projects shows that our approach achieves average precision, recall and F1-score of 88.52%, 88.50% and 88.51%, which confirms that our approach could effectively detect hidden feature requests from chat messages, thus can facilitate gathering comprehensive requirements from the crowd in an automated way.