Reviewerly/exHarmony huggingface dataset:
Surface Diffuse Solar Radiation Determined by Reanalysis and Satellite over East Asia: Evaluation and Comparison

Recently, surface diffuse solar radiation (Rdif) has been attracting a growing interest in view of its function in improving plant productivity, thus promoting global carbon uptake, and its impacts on solar energy utilization. To date, very few radiation products provide estimates of Rdif, and systematic validation and evaluation are even more scare. In this study, Rdif estimates from Reanalysis Fifth Generation (ERA5) of European Center for Medium-Range Weather Forecasts and satellite-based retrieval (called JiEA) are evaluated over East Asia using ground measurements at 39 stations from World Radiation Data Center (WRDC) and China Meteorological Administration (CMA). The results show that JiEA agrees well with measurements, while ERA5 underestimates Rdif significantly. Both datasets perform better at monthly mean scale than at daily mean and hourly scale. The mean bias error and root-mean-square error of daily mean estimates are −1.21 W/m2 and 20.06 W/m2 for JiEA and −17.18 W/m2 and 32.42 W/m2 for ERA5, respectively. Regardless of over- or underestimation, correlations of estimated time series of ERA5 and JiEA show high similarity. JiEA reveals a slight decreasing trend at regional scale, but ERA5 shows no significant trend, and neither of them reproduces temporal variability of ground measurements. Data accuracy of ERA5 is more robust than JiEA in time but less in space. Latitudinal dependency is noted for ERA5 while not for JiEA. In addition, spatial distributions of Rdif from ERA5 and JiEA show pronounced discrepancy. Neglect of adjacency effects caused by horizontal photon transport is the main cause for Rdif underestimation of ERA5. Spatial analysis calls for improvements to the representation of clouds, aerosols and water vapor for reproducing fine spatial distribution and seasonal variations of Rdif.
All Learning is Social and Emotional : Helping Students Develop Essential Skills for the Classroom and Beyond

Prikaz knjige
An improved A* algorithm for searching the minimum dose path in nuclear facilities

Nuclear power plant workers are inevitably exposed to radiation during routine maintenance work. Therefore, optimization of the walking-path is necessary to reduce the exposure radiation. However, the complex environment of nuclear power plants makes it hard to find the path with the minimum cumulative radiation dose. An improved A* algorithm is proposed in this paper, and it is more advantageous than the traditional A* algorithm. It can not only be suitable for more complicated environments but also obtain a more optimized path. Through the Unity 3D platform, which is an efficient game engine, the simulation experiments are performed based on the environment of a virtual factory. The simulation results show that the improved A* algorithm for searching the minimum dose path is more reliable and more effective. It provides a route with a lower cumulative dose, and the path length is appropriate. In the complex environment, the cumulative dose calculated by the improved algorithm is much smaller than that calculated by the traditional algorithm. Improved A* algorithm has a strong anti-interference ability and performs well in complex environments. It can provide path-planning for nuclear facility staff to reduce radiation exposure.
Nonlocal Stimulation of Three-Magnon Splitting in a Magnetic Vortex

We present a combined numerical, theoretical, and experimental study on stimulated three-magnon splitting in a magnetic disk in the vortex state. Our micromagnetic simulations and Brillouin-light-scattering results confirm that three-magnon splitting can be triggered even below threshold by exciting one of the secondary modes by magnons propagating in a waveguide next to the disk. The experiments show that stimulation is possible over an extended range of excitation powers and a wide range of frequencies around the eigenfrequencies of the secondary modes. Rate-equation calculations predict an instantaneous response to stimulation and the possibility to prematurely trigger three-magnon splitting even above threshold in a sustainable manner. These predictions are confirmed experimentally using time-resolved Brillouin-light-scattering measurements and are in a good qualitative agreement with the theoretical results. We believe that the controllable mechanism of stimulated three-magnon splitting could provide a possibility to utilize magnon-based nonlinear networks as hardware for neuromorphic computing.
Forecasting Cloud Application Workloads With<i>CloudInsight</i>for Predictive Resource Management

Predictive cloud resource management has been widely adopted to overcome the limitations of reactive cloud autoscaling. The predictive resource management is highly relying on workload predictors, which estimate short-/long-term fluctuations of cloud application workloads. These predictors tend to be pre-optimized for specific workload patterns. However, such predictors are still insufficient to handle real-world cloud workloads whose patterns may be unknown a priori, may dynamically change over time and may be irregular. As a result, these predictors often cause over-/under-provisioning of cloud resources. To address this problem, we have created CloudInsight, a novel cloud workload prediction framework, leveraging the combined power of multiple workload predictors. CloudInsight creates an ensemble model using multiple predictors to make accurate predictions for real workloads. The weights of the predictors in CloudInsight are determined at runtime with their accuracy for the current workload using multi-class regression. The ensemble model is periodically optimized to handle sudden changes in the workload. We evaluated CloudInsight with various real workload traces. The results show that CloudInsight has 13–27 percent higher accuracy than state-of-the-art predictors. Moreover, the results from trace-based simulations with a cloud resource manager show that CloudInsight has 15–20 percent less under-/over-provisioning periods, resulting in high cost-efficiency and low SLA violations.
ColluEagle: collusive review spammer detection using Markov random fields

Product reviews are extremely valuable for online shoppers in providing purchase decisions. Driven by immense profit incentives, fraudsters deliberately fabricate untruthful reviews to distort the reputation of online products. As online reviews become more and more important, group spamming, i.e., a team of fraudsters working collaboratively to attack a set of target products, becomes a new fashion. Previous works use review network effects, i.e. the relationships among reviewers, reviews, and products, to detect fake reviews or review spammers, but ignore time effects, which are critical in characterizing group spamming. In this paper, we propose a novel Markov random field (MRF)-based method (ColluEagle) to detect collusive review spammers, as well as review spam campaigns, considering both network effects and time effects. First we identify co-review pairs, a review phenomenon that happens between two reviewers who review a common product in a similar way, and then model reviewers and their co-review pairs as a pairwise-MRF, and use loopy belief propagation to evaluate the suspiciousness of reviewers. We further design a high quality yet easy-to-compute node prior for ColluEagle, through which the review spammer groups can also be subsequently identified. Experiments show that ColluEagle can not only detect collusive spammers with high precision, significantly outperforming state-of-the-art baselines—FraudEagle and SpEagle, but also identify highly suspicious review spammer campaigns.
Optical patching scheme for optical convolutional neural networks based on wavelength-division multiplexing and optical delay lines

Recent progress on optical neural networks (ONNs) heralds a new future for efficient deep learning accelerators, and novel, to the best of our knowledge, architectures of optical convolutional neural networks (CNNs) provide potential solutions to the widely adopted convolutional models. So far in optical CNNs, the data patching (a necessary process in the convolutional layer) is mostly executed with electronics, resulting in a demand for large input modulator arrays. Here we experimentally demonstrate an optical patching scheme to release the burden of electronic data processing and to cut down the scale of the input modulator array for optical CNNs. Optical delay lines replace electronics to execute data processing, which can reduce the scale of the input modulator array. The adoption of wavelength-division multiplexing enables a single group of optical delay lines to simultaneously process multiple input data, reducing the system complexity. The optical patching scheme provides a new solution to the problem of data input, which is challenging and concerned with the field of ONNs.
Disentangled Feature Learning Network for Vehicle Re-Identification

Vehicle Re-Identification (ReID) has attracted lots of research efforts due to its great significance to the public security. In vehicle ReID, we aim to learn features that are powerful in discriminating subtle differences between vehicles which are visually similar, and also robust against different orientations of the same vehicle. However, these two characteristics are hard to be encapsulated into a single feature representation simultaneously with unified supervision. Here we propose a Disentangled Feature Learning Network (DFLNet) to learn orientation specific and common features concurrently, which are discriminative at details and invariant to orientations, respectively. Moreover, to effectively use these two types of features for ReID, we further design a feature metric alignment scheme to ensure the consistency of the metric scales. The experiments show the effectiveness of our method that achieves state-of-the-art performance on three challenging datasets.
Set and Rebase: Determining the Semantic Graph Connectivity for Unsupervised Cross-Modal Hashing

The label-free nature of unsupervised cross-modal hashing hinders models from exploiting the exact semantic data similarity. Existing research typically simulates the semantics by a heuristic geometric prior in the original feature space. However, this introduces heavy bias into the model as the original features are not fully representing the underlying multi-view data relations. To address the problem above, in this paper, we propose a novel unsupervised hashing method called Semantic-Rebased Cross-modal Hashing (SRCH). A novel ‘Set-and-Rebase’ process is defined to initialize and update the cross-modal similarity graph of training data. In particular, we set the graph according to the intra-modal feature geometric basis and then alternately rebase it to update the edges within according to the hashing results. We develop an alternating optimization routine to rebase the graph and train the hashing auto-encoders with closed-form solutions so that the overall framework is efficiently trained. Our experimental results on benchmarked datasets demonstrate the superiority of our model against state-of-the-art algorithms.
Intuitive, Interactive Beard and Hair Synthesis With Generative Models

We present an interactive approach to synthesizing realistic variations in facial hair in images, ranging from subtle edits to existing hair to the addition of complex and challenging hair in images of clean-shaven subjects. To circumvent the tedious and computationally expensive tasks of modeling, rendering and compositing the 3D geometry of the target hairstyle using the traditional graphics pipeline, we employ a neural network pipeline that synthesizes realistic and detailed images of facial hair directly in the target image in under one second. The synthesis is controlled by simple and sparse guide strokes from the user defining the general structural and color properties of the target hairstyle. We qualitatively and quantitatively evaluate our chosen method compared to several alternative approaches. We show compelling interactive editing results with a prototype user interface that allows novice users to progressively refine the generated image to match their desired hairstyle, and demonstrate that our approach also allows for flexible and high-fidelity scalp hair synthesis.
Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs

One of the most crucial challenges in question answering (QA) is the scarcity of labeled data, since it is costly to obtain question-answer (QA) pairs for a target text domain with human annotation.An alternative approach to tackle the problem is to use automatically generated QA pairs from either the problem context or from large amount of unstructured texts (e.g.Wikipedia).In this work, we propose a hierarchical conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizing the mutual information between generated QA pairs to ensure their consistency.We validate our Information Maximizing Hierarchical Conditional Variational AutoEncoder (Info-HCVAE) on several benchmark datasets by evaluating the performance of the QA model (BERT-base) using only the generated QA pairs (QA-based evaluation) or by using both the generated and human-labeled pairs (semisupervised learning) for training, against stateof-the-art baseline models.The results show that our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training.
Learning Personalized Itemset Mapping for Cross-Domain Recommendation

Cross-domain recommendation methods usually transfer knowledge across different domains implicitly, by sharing model parameters or learning parameter mappings in the latent space. Differing from previous studies, this paper focuses on learning explicit mapping between a user's behaviors (i.e. interaction itemsets) in different domains during the same temporal period. In this paper, we propose a novel deep cross-domain recommendation model, called Cycle Generation Networks (CGN). Specifically, CGN employs two generators to construct the dual-direction personalized itemset mapping between a user's behaviors in two different domains over time. The generators are learned by optimizing the distance between the generated itemset and the real interacted itemset, as well as the cycle-consistent loss defined based on the dual-direction generation procedure. We have performed extensive experiments on real datasets to demonstrate the effectiveness of the proposed model, comparing with existing single-domain and cross-domain recommendation methods.
An Analytical Study and Review of open source Chatbot framework, Rasa

In the era of chatbots, besides imitating humans they can also perform complex tasks like booking tickets for movie etc.Out of various implementations, RASA is open source implementation for NLU and DIET model.It can interact with database, api, conversational flow, interactive learning with reinforcement Neural network.In this study, various features of rasa core are studied and upto much extent it can perform complex tasks.Implementation details are studied like interaction with database, API.Tracker Store has been examined with modifying the socket.iocore file adding metadata to the user message data, so that user ip and port can be captured.Furthermore, the action, interactive learning and implementation details are tested on windows Pycharm IDE.
When Low Resource NLP Meets Unsupervised Language Model: Meta-Pretraining then Meta-Learning for Few-Shot Text Classification (Student Abstract)

Text classification tends to be difficult when data are deficient or when it is required to adapt to unseen classes. In such challenging scenarios, recent studies have often used meta-learning to simulate the few-shot task, thus negating implicit common linguistic features across tasks. This paper addresses such problems using meta-learning and unsupervised language models. Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks. We show that our approach is not only simple but also produces a state-of-the-art performance on a well-studied sentiment classification dataset. It can thus be further suggested that pretraining could be a promising solution for few-shot learning of many other NLP tasks. The code and the dataset to replicate the experiments are made available at https://github.com/zxlzr/FewShotNLP.
Lightweight Privacy-Preserving Medical Diagnosis in Edge Computing

With the development of machine learning, it is popular that mobile users can submit individual symptoms at any time anywhere for medical diagnosis. Edge computing is frequently adopted to reduce transmission latency for real-time diagnosis service. However, the data-driven machine learning, which requires to build a diagnosis model over vast amounts of medical data, inevitably leaks the privacy of medical data. It is necessary to provide privacy preservation. To solve above challenging issues, in this article, we design a lightweight privacy-preserving medical diagnosis mechanism on edge, called LPME. Our LPME redesigns the extreme gradient boosting (XGBoost) model based on the edge-cloud model, which adopts encrypted model parameters instead of local data to remove amounts of ciphertext computation to plaintext computation, thus realizing lightweight privacy preservation on resource-limited edge. In addition, LPME provides secure diagnosis on edge with privacy preservation for private and timely diagnosis. Our security analysis and experimental evaluation indicates the security, effectiveness, and efficiency of LPME.
Challenging tough samples in unsupervised domain adaptation

Existing domain adaptation approaches focus on taking advantage of easy samples, i.e, target samples which are easier for adaptation. In previous work, tough, or hard, target samples are generally regarded as outliers or just being left to chance. As a result, the adaptation of tough target samples remains as a challenging problem in the community. In this paper, we report three novel ideas for domain adaptation: 1) splitting target samples into easy and tough ones; 2) deploying different strategies for samples with different adaptation difficulties; 3) leveraging easy samples to facilitate tough ones. Furthermore, we present a novel approach, named challenging tough sample networks (CTSN), to practice the three ideas and tame tough samples. Specifically, in our approach, a CNN with domain adaptation layers is first used to rapidly handle the easy samples and identify the tough ones. Then, a GAN with two classifiers is tailored to adapt the tough samples. The GAN leverages classification discrepancy and easy samples to tame the tough ones. Extensive experiments on both classic and large-scale benchmarks verify that both easy and tough samples do exist in real-world datasets and our approach is able to handle them.
A Novel CapsNet based Image Reconstruction and Regression Analysis

In the field of image processing, all types of computation models are almost evolved to solve the issues through encoded neurons. However, compared with decoding orientation and regression analysis, still the doors are open due to its complexity. At present technologies uses two steps such as, decoding the intermediate terms and reconstruction using decoded information. The performance in terms of regression analysis is lagging due to the decoded intermediate terms. Conventional neural network models perform better in feature classification and representation, though the performance is reduced while handling high level features. Considering these issues in image classification and regression, the proposed model is designed with capsule network as an innovative method which is suitable to handle high level features. The experimental results of the proposed model are compared with conventional neural network models such as BPNN and CNN to validate the superior performance. The proposed model achieves better retrieval efficiency of 95.4% which is much better than other neural network models.
Language Models as Fact Checkers?

Recent work has suggested that language models (LMs) store both common-sense and factual knowledge learned from pre-training data.In this paper, we leverage this implicit knowledge to create an effective end-to-end fact checker using a solely a language model, without any external knowledge or explicit retrieval components.While previous work on extracting knowledge from LMs have focused on the task of open-domain question answering, to the best of our knowledge, this is the first work to examine the use of language models as fact checkers.In a closed-book setting, we show that our zero-shot LM approach outperforms a random baseline on the standard FEVER task, and that our finetuned LM compares favorably with standard baselines.Though we do not ultimately outperform methods which use explicit knowledge bases, we believe our exploration shows that this method is viable and has much room for exploration.
Wireless Device Identification Based on Radio Frequency Fingerprint Features

With the development of the Internet of Things (IoT) technology and the rapid deployment of 5G wireless, more and more radiation devices are appearing in the increasingly complex electromagnetic environment. To be able to manage these devices in a unified manner, accurate identification of the devices has become a top priority. Specific emitter identification (SEI) is to effectively solve this problem. In this paper, both power spectral density (PSD) and fractional Fourier transform (FrFT) methods are used to extract the characteristics of transient signals. The characteristics of steady-state signals are analyzed by the bispectrum method. The SEI system model in this paper is constructed based on these techniques. Our experiments results show that when the SNR is 16dB, the SEI system can achieve a recognition accuracy of over 97% by exploiting the characteristics of the transient signal. Since the characteristics of the steady-state signal can better suppress noise, the SEI system can achieve a nearly 90% classification recognition accuracy under extremely low SNR.
Accelerating truss decomposition on heterogeneous processors

Truss decomposition is to divide a graph into a hierarchy of subgraphs, or trusses. A subgraph is a k -truss ( k ≥ 2) if each edge is in at least k --- 2 triangles in the subgraph. Existing algorithms work by first counting the number of triangles each edge is in and then iteratively incrementing k to peel off the edges that will not appear in ( k + 1)-truss. Due to the data and computation intensity, truss decomposition on billion-edge graphs takes hours to complete on a commodity computer. We propose to accelerate in-memory truss decomposition by (1) compacting intermediate results to optimize memory access, (2) dynamically adjusting the computation based on data characteristics, and (3) parallelizing the algorithm on both the multicore CPU and the GPU. In particular, we optimize the triangle enumeration with data skew handling, and determine at runtime whether to pursue peeling or direct triangle counting to obtain a certain k -truss. We further develop a CPU-GPU co-processing strategy in which the CPU first computes intermediate results and sends the compacted results to the GPU for further computation. Our experiments on real-world datasets show that our implementations outperform the state of the art by up to an order of magnitude. Our source code is publicly available at https://github.com/RapidsAtHKUST/AccTrussDecomposition.