Reviewerly/exHarmony huggingface dataset:
Next-Day Prediction of Hourly Solar Irradiance Using Local Weather Forecasts and LSTM Trained with Non-Local Data

Solar irradiance prediction is significant for maximizing energy-saving effects in the predictive control of buildings. Several models for solar irradiance prediction have been developed; however, they require the collection of weather data over a long period in the predicted target region or evaluation of various weather data in real time. In this study, a long short-term memory algorithm–based model is proposed using limited input data and data from other regions. The proposed model can predict solar irradiance using next-day weather forecasts by the Korea Meteorological Administration and daily solar irradiance, and it is possible to build a model with one-time learning using national and international data. The model developed in this study showed excellent predictive performance with a coefficient of variation of the root mean square error of 12% per year even if the learning and forecast regions were different, assuming that the weather forecast was correct.
ROBOTS COME to RESCUE: How to reduce perceived risk of infectious disease in Covid19-stricken consumers?

• During Covid-19, people are more willing to visit a hotel/restaurant with robots. • People think that robots in hotels/restaurants can lower interpersonal interaction. • Reducing interpersonal interaction can lower perceived viral transmission. • Chinese are more likely to visit hotels/restaurants with robots than Americans.
Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network

In the field of multimedia, single image deraining is a basic pre-processing work, which can greatly improve the visual effect of subsequent high-level tasks in rainy conditions. In this paper, we propose an effective algorithm, called JDNet, to solve the single image deraining problem and conduct the segmentation and detection task for applications. Specifically, considering the important information on multi-scale features, we propose a Scale-Aggregation module to learn the features with different scales. Simultaneously, Self-Attention module is introduced to match or outperform their convolutional counterparts, which allows the feature aggregation to adapt to each channel. Furthermore, to improve the basic convolutional feature transformation process of Convolutional Neural Networks (CNNs), Self-Calibrated convolution is applied to build long-range spatial and inter-channel dependencies around each spatial location that explicitly expand fields-of-view of each convolutional layer through internal communications and hence enriches the output features. By designing the Scale-Aggregation and Self-Attention modules with Self-Calibrated convolution skillfully, the proposed model has better deraining results both on real-world and synthetic datasets. Extensive experiments are conducted to demonstrate the superiority of our method compared with state-of-the-art methods. The source code will be available at https://supercong94.wixsite.com/supercong94.
What Aspect Do You Like

Online micro-video recommender systems aim to address the information explosion of micro-videos and make the personalized recommendation for users. However, the existing methods still have some limitations in learning representative user interests, since the multi-scale time effects, user interest group modeling, and false positive interactions are not taken into consideration. In view of this, we propose an end-to-end Multi-scale Time-aware user Interest modeling Network (MTIN). In particular, we first present an interest group routing algorithm to generate fine-grained user interest groups based on user's interaction sequence. Afterwards, to explore multi-scale time effects on user interests, we design a time-aware mask network and distill multiple temporal information by several parallel temporal masks. And then an interest mask network is introduced to aggregate fine-grained interest groups and generate the final user interest representation. At last, in the prediction unit, the user representation and micro-video candidates are fed into a deep neural network (DNN) for predictions. To demonstrate the effectiveness of our method, we conduct experiments on two publicly available datasets, and the experimental results demonstrate that our proposed model achieves substantial gains over the state-of-the-art methods.
Multimodal Attention with Image Text Spatial Relationship for OCR-Based Image Captioning

OCR-based image captioning is the task of automatically describing images based on reading and understanding written text contained in images. Compared to conventional image captioning, this task is more challenging, especially when the image contains multiple text tokens and visual objects. The difficulties originate from how to make full use of the knowledge contained in the textual entities to facilitate sentence generation and how to predict a text token based on the limited information provided by the image. Such problems are not yet fully investigated in existing research. In this paper, we present a novel design - Multimodal Attention Captioner with OCR Spatial Relationship (dubbed as MMA-SR) architecture, which manages information from different modalities with a multimodal attention network and explores spatial relationships between text tokens for OCR-based image captioning. Specifically, the representations of text tokens and objects are fed into a three-layer LSTM captioner. Different attention scores for text tokens and objects are exploited through the multimodal attention network. Based on the attended features and the LSTM states, words are selected from the common vocabulary or from the image text by incorporating the learned spatial relationships between text tokens. Extensive experiments conducted on the TextCaps dataset verify the effectiveness of the proposed MMA-SR method. More remarkably, our MMA-SR increases CIDEr-D score from 93.7% to 98.0%.
A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments

Stance detection is a relatively new concept in data mining that aims to assign a stance label (favor, against, or none) to a social media post towards a specific pre-determined target. These targets may not be referred to in the post, and may not be the target of opinion in the post. In this paper, we propose a novel enhanced method for identifying the writer’s stance of a given tweet. This comprises a three-phase process for stance detection: (a) tweets preprocessing; here we clean and normalize tweets (e.g., remove stop-words) to generate words and stems lists, (b) features generation; in this step, we create and fuse two dictionaries for generating features vector, and lastly (c) classification; all the instances of the features are classified based on the list of targets. Our innovative feature selection proposes fusion of two ranked lists (top-k) of term frequency-inverse document frequency (tf-idf) scores and the sentiment information. We evaluate our method using six different classifiers: K nearest neighbor (K-NN), discernibility-based K-NN, weighted K-NN, class-based K-NN, exemplar-based K-NN, and Support Vector Machines. Furthermore, we investigate the use of Principal Component Analysis and study its effect on performance. The model is evaluated on the benchmark dataset (SemEval-2016 task 6), and the results significance is determined using t-test. We achieve our best performance of macro F-score (averaged across all topics) of 76.45% using the weighted K-NN classifier. This tops the current state-of-the-art score of 74.44% on the same dataset.
Mechanical fault time series prediction by using EFMSAE-LSTM neural network

The working state of mechanical parts plays an important role in the safe and reliable operation of equipment. Therefore, in order to enhance the dependability and security of mechanical equipment, it is very important to accurately predict the changing trend of mechanical components in advance. This paper proposes a method combining error fusion of multiple sparse auto-encoders with long short-term memory for predicting mechanical fault time series. First, error fusion of multiple sparse auto-encoders layer can extract multi-feature time series and fuse them into a square prediction error trend curve representing each channel and a threshold control line of bearing system health judgment. Then, long short-term memory layer predicts the irregular trend in the square prediction error trend curve, and sets a number of threshold control lines according to different machine fault variation trends for more accurate prediction. The experimental results prove the availability and superiority of this method in the prediction of mechanical fault time series.
Multiplex Graph Neural Networks for Multi-behavior Recommendation

This paper focuses on the multi-behavior recommendation problem, i.e., generating personalized recommendation based on multiple types of user behaviors. Methods proposed recently usually leverage the ordinal assumption, which means that users? different types of behaviors should take place in a fixed order. However, this assumption may be too strong in some scenarios. In this paper, a more general model named Multiplex Graph Neural Network (MGNN) is proposed as a remedy. MGNN tackles the multi-behavior recommendation problem from a novel perspective, i.e., the perspective of link prediction in multiplex networks. By taking advantage of both the multiplex network structure and graph representation learning techniques, MGNN learns shared embeddings and behavior-specific embeddings for users and items to model the collective effect of multiple types of behaviors. Experiments conducted on both ordinal-behavior datasets and generic-behavior datasets demonstrate the effectiveness of the proposed MGNN model.
CAPturAR: An Augmented Reality Tool for Authoring Human-Involved Context-Aware Applications

Recognition of human behavior plays an important role in context-aware applications. However, it is still a challenge for end-users to build personalized applications that accurately recognize their own activities. Therefore, we present CAPturAR, an in-situ programming tool that supports users to rapidly author context-aware applications by referring to their previous activities. We customize an AR head-mounted device with multiple camera systems that allow for non-intrusive capturing of user's daily activities. During authoring, we reconstruct the captured data in AR with an animated avatar and use virtual icons to represent the surrounding environment. With our visual programming interface, users create human-centered rules for the applications and experience them instantly in AR. We further demonstrate four use cases enabled by CAPturAR. Also, we verify the effectiveness of the AR-HMD and the authoring workflow with a system evaluation using our prototype. Moreover, we conduct a remote user study in an AR simulator to evaluate the usability.
zkRelay: Facilitating Sidechains using zkSNARK-based Chain-Relays

We facilitate trusted cross-blockchain state proofs by implementing a chain-relay that validates block headers from proof-of-work blockchains. While current approaches require proof sizes linear to the amount of blocks the state was built on, trusted intermediaries, or economic assumptions, we propose the utilization of off-chain computations through zkSNARKs to provide a cryptographically secure and highly scalable sidechain mechanism. Multiple block headers are included in batches and verified off-chain, while preserving light client support. Only the validity of the off-chain computation is verified on-chain, creating a sidechain mechanism that requires constant verification costs and releases the target ledger from processing and storing every single block header of the source blockchain. Efficient Merkle proofs enable including intermediary blocks for any batch that was submitted. We provide a prototypical implementation that facilitates the verification of 504 Bitcoin headers in a single proof on Ethereum using the ZoKrates framework. Hereby, the verification costs are reduced by a factor of 187 compared to current approaches such as BTC Relay.
Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items

Inferring substitutable and complementary items is an important and fundamental concern for recommendation in e-commerce websites. However, the item relationships in real-world are usually heterogeneous, posing great challenges to conventional methods that can only deal with homogeneous relationships. More specifically, for this problem, there is a lack of in-depth investigation on 1) decoupling item semantics for modeling heterogeneous item relationships, and at the same time, 2) incorporating mutual influence between different relationships. To fill this gap, we propose a novel solution, namely Decoupled Graph Convolutional Network (DecGCN), to solve the problem of inferring substitutable and complementary items. DecGCN is designed to model item substitutability and complementarity in separated embedding spaces, and is equipped with a two-step integration scheme,where inherent influences between 1) different graph structures and 2) different item semantics are captured. Our experiments on three real-world datasets demonstrate that DecGCN is more effective than the state-of-the-art baselines for the problem at hand. We also conduct offline and online A/B tests on large-scale industrial data, where the results show that DecGCN is effective to be deployed in real-world applications. We release the codes at https://github.com/liuyiding1993/CIKM2020_DecGCN.
Efficient Crowd Counting via Structured Knowledge Transfer

Crowd counting is an application-oriented task and its inference efficiency is crucial for real-world applications. However, most previous works relied on heavy backbone networks and required prohibitive run-time consumption, which would seriously restrict their deployment scopes and cause poor scalability. To liberate these crowd counting models, we propose a novel Structured Knowledge Transfer (SKT) framework, which fully exploits the structured knowledge of a well-trained teacher network to generate a lightweight but still highly effective student network. Specifically, it is integrated with two complementary transfer modules, including an Intra-Layer Pattern Transfer which sequentially distills the knowledge embedded in layer-wise features of the teacher network to guide feature learning of the student network and an Inter-Layer Relation Transfer which densely distills the cross-layer correlation knowledge of the teacher to regularize the student's feature evolution. Consequently, our student network can derive the layer-wise and cross-layer knowledge from the teacher network to learn compact yet effective features. Extensive evaluations on three benchmarks well demonstrate the effectiveness of our SKT for extensive crowd counting models. In particular, only using around $6%$ of the parameters and computation cost of original models, our distilled VGG-based models obtain at least 6.5× speed-up on an Nvidia 1080 GPU and even achieve state-of-the-art performance. Our code and models are available at https://github.com/HCPLab-SYSU/SKT.
University Students’ Readiness for Using Digital Media and Online Learning—Comparison between Germany and the USA

The year 2020 brought many changes to our everyday life but also our education system. Universities needed to change their teaching practices due to the COVID-19 pandemic. Words like “digital media”, “online teaching” and “online learning” were present in all of the discussions. The main issues here were the technical infrastructure of students and universities all over the world. However, to have good technical infrastructure does not mean that everybody is also ready to use it. Thus, the present study focused on the issue of university students’ readiness for online learning. The quantitative research goal was to evaluate German university students’ readiness for using digital media and online learning in their tertiary education and compare them with students from the United States. Overall, 72 students from the researchers’ university in Germany and 176 students from multiple universities in the United States completed the Student Readiness of Online Learning (SROL) questionnaire. Results show substantial differences between the two groups of students, with U.S. students being more ready for online learning. The results and limitations were discussed, and practical implications and further ideas were provided.
Speech-XLNet: Unsupervised Acoustic Model Pretraining for Self-Attention Networks

Self-attention network (SAN) can benefit significantly from the bi-directional representation learning through unsupervised pretraining paradigms such as BERT and XLNet. In this paper, we present an XLNet-like pretraining scheme for unsupervised acoustic model pretraining to learn speech representations with SAN. The pretrained SAN is finetuned under the hybrid SAN/HMM framework. We conjecture that by shuffling the speech frame orders, the permutation in Speech-XLNet serves as a strong regularizer to encourage the SAN to make inferences by focusing on global structures through its attention weights. In addition, Speech-XLNet also allows the model to explore the bi-directional contexts for effective speech representation learning. Experiments on TIMIT and WSJ demonstrate that Speech-XLNet greatly improves the SAN/HMM performance in terms of both convergence speed and recognition accuracy compared to the one trained from randomly initialized weights. Our best systems achieve a relative improvement of 11.9% and 8.3% on the TIMIT and WSJ tasks respectively. In particular, the best system achieves a phone error rate (PER) of 13.3% on the TIMIT test set, which to our best knowledge, is the lowest PER obtained from a single system.
Towards a physical-world adversarial patch for blinding object detection models

As one of the core components of the computer vision, the object detection model plays a vital role in various security-sensitive systems. However, it has been proved that the object detection model is vulnerable to the adversarial attack. In this paper, we propose a novel adversarial patch attack against object detection models. Our attack can make the object of a specific class invisible to object detection models. We design the detection score to measure the detection model’s output and generate the adversarial patch by minimizing the detection score. We successfully suppress the model’s inference and fool several state-of-the-art object detection models. We triumphantly achieve a minimum recall of 11.02% and a maximum fooling rate of 81.00% and demonstrates the high transferability of adversarial patch between different architecture and datasets. Finally, we successfully fool a real-time object detection system in the physical world, demonstrating the feasibility of transferring the digital adversarial patch to the physical world. Our work illustrates the vulnerability of the object detection model against the adversarial patch attack in both the digital and physical world.
Perceived teacher support and students’ acceptance of mobile‐assisted language learning: Evidence from Vietnamese higher education context

Abstract Teachers play key roles in advancing the use of mobile devices for language learning in both formal and informal settings. However, in contexts where top‐down educational policies are prevalent, the roles of teachers are usually overemphasized while learners–the end‐users of educational technologies remain largely ignored. Less understood is what roles students expect teachers to play in facilitating their acceptance of mobile‐assisted language learning. This study was conducted in an attempt to fill this gap using the extended technology acceptance model (TAM). Survey data from 293 higher education learners of English in Vietnam were analyzed by the Rasch‐based path model. Results indicated that students showed stronger desire for teachers’ orientation toward appropriate use of mobile resources for language learning both inside and outside the classroom than teachers’ demonstration of mobile‐assisted language learning activities in the classroom. The findings offer useful implications for teachers, researchers, and language education policy makers in fostering the use of mobile devices for language learning. What is already known about this topic? Teachers’ role in promoting learners’ adoption of mobile‐assisted language learning is under‐researched. In contexts dominated by top‐down educational policies, the voice of learners is largely ignored. Learners’ expectation of teachers’ roles in promoting their adoption of mobile‐assisted language learning needs more empirical evidence. What this paper adds? This paper highlighted the important roles of teachers in orienting students toward appropriate mobile learning resources for out‐of‐class learning. Teachers were considered by learners as “guide on the side” rather than “sage on the stage” in enhancing their adoption of mobile‐assisted language learning. Implications for practice/policy Teacher training programs on mobile‐assisted language learning can be redesigned to enable teachers to better support mobile‐assisted language learning in and outside the classroom. Teachers should be better able to enrich their knowledge of various mobile learning resources to support their students.
Face Super-Resolution Guided by 3D Facial Priors

State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high-resolution facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and struggle to deal with facial images that exhibit large pose variations. In this paper, we propose a novel face super-resolution method that explicitly incorporates 3D facial priors which grasp the sharp facial structures. Our work is the first to explore 3D morphable knowledge based on the fusion of parametric descriptions of face attributes (e.g., identity, facial expression, texture, illumination, and face pose). Furthermore, the priors can easily be incorporated into any network and are extremely efficient in improving the performance and accelerating the convergence speed. Firstly, a 3D face rendering branch is set up to obtain 3D priors of salient facial structures and identity knowledge. Secondly, the Spatial Attention Module is used to better exploit this hierarchical information (i.e., intensity similarity, 3D facial structure, and identity content) for the super-resolution problem. Extensive experiments demonstrate that the proposed 3D priors achieve superior face super-resolution results over the state-of-the-arts.
Optimal Coding and the Origins of Zipfian Laws

The problem of compression in standard information theory consists of assigning codes as short as possible to numbers. Here we consider the problem of optimal coding – under an arbitrary coding scheme – and show that it predicts Zipf’s law of abbreviation, namely a tendency in natural languages for more frequent words to be shorter. We apply this result to investigate optimal coding also under so-called non-singular coding, a scheme where unique segmentation is not warranted but codes stand for a distinct number. Optimal non-singular coding predicts that the length of a word should grow approximately as the logarithm of its frequency rank, which is again consistent with Zipf’s law of abbreviation. Optimal non-singular coding in combination with the maximum entropy principle also predicts Zipf’s rank-frequency distribution. Furthermore, our findings on optimal non-singular coding challenge common beliefs about random typing. It turns out that random typing is in fact an optimal coding process, in stark contrast with the common assumption that it is detached from cost cutting considerations. Finally, we discuss the implications of optimal coding for the construction of a compact theory of Zipfian laws more generally as well as other linguistic laws.