Reviewerly/exHarmony huggingface dataset:
Bandit-Enhanced Graph Collaborative Filtering via Graph Reconstruction

The recommendation system’s overall goal is to provide personalized suggestions to users rather than recommend popular items. However, standard training paradigms assume that users have static preferences between users and items based on either implicit (click) or explicit (rating) feedback, favoring the model toward popular items. These lead to the effect where popular items are recommended more often and become more popular. The current work has dramatically improved the quality of recommendation by using a graph neural network. However, less attention has been paid to unbalanced recommendation results of hot and cold items. This paper explores the problem of cold and hot items recommendation in a graph-based recommendation system from a new perspective. We propose that the essence of the traditional graph convolution processing is to expand the graph into the tree structure, which loses the original interaction graph’s topological structure information. This loss of information leads to the lack of modeling of users’ preferences for unpopular items; therefore, the recommendation results favor popular items. For this reason, we first introduce Bandit algorithm into the existing graph neural network to capture the graph location information, extract and model the potential purchase tendency of users. It is worth noting that our model is not dependent on a particular graph training method, so it is easy to migrate to all graph-based recommendation systems. We verify the effectiveness of the method on three real datasets, exhibiting substantial improvements (about 8.0% relative improvement on average over the baseline models).
A Method for Generating Color Palettes with Deep Neural Networks Considering Human Perception

A method to generate color palettes from images is proposed. Here, deep neural networks (DNN) are utilized in order to consider human perception. Two aspects of human perception are considered; one is attention to image, and the other is human preference for colors. This method first extracts N regions with dominant color categories from the image considering human attention. Here, N is the number of colors in a color palette. Then, the representative color is obtained from each region considering the human preference for color. Two deep neural-net systems are adopted here, one is for estimating the image area which attracts human attention, and the other is for estimating human preferable colors from image regions to obtain representative colors. The former is trained with target images obtained by an eye tracker, and the latter is trained with dataset of color selection by human. Objective and subjective evaluation is performed to show high performance of the proposed system compared with conventional methods.
A Holistically-Guided Decoder for Deep Representation Learning with Applications to Semantic Segmentation and Object Detection

Both high-level and high-resolution feature representations are of great importance in various visual understanding tasks. To acquire high-resolution feature maps with high-level semantic information, one common strategy is to adopt dilated convolutions in the backbone networks to extract high-resolution feature maps, such as the dilatedFCN-based methods for semantic segmentation. However, due to many convolution operations are conducted on the high-resolution feature maps, such methods have large computational complexity and memory consumption. To balance the performance and efficiency, there also exist encoder-decoder structures that gradually recover the spatial information by combining multi-level feature maps from a feature encoder, such as the FPN architecture for object detection and the U-Net for semantic segmentation. Although being more efficient, the performances of existing encoder-decoder methods for semantic segmentation are far from comparable with the dilatedFCN-based methods. In this paper, we propose one novel holistically-guided decoder which is introduced to obtain the high-resolution semantic-rich feature maps via the multi-scale features from the encoder. The decoding is achieved via novel holistic codeword generation and codeword assembly operations, which take advantages of both the high-level and low-level features from the encoder features. With the proposed holistically-guided decoder, we implement the EfficientFCN architecture for semantic segmentation and HGD-FPN for object detection and instance segmentation. The EfficientFCN achieves comparable or even better performance than state-of-the-art methods with only 1/3 of their computational costs for semantic segmentation on PASCAL Context, PASCAL VOC, ADE20K datasets. Meanwhile, the proposed HGD-FPN achieves higher mean Average Precision (mAP) when integrated into several object detection frameworks with ResNet-50 encoding backbones.
A Tutorial on Quantum Approximate Optimization Algorithm for Maximum Independent Set Problem

In the past few years, quantum computers and quantum algorithms have shown advantages compared to their classical counterparts. In combinatorial optimization problems, quantum approximate optimization algorithm(QAOA) has good performance. This paper focuses on the application of QAOA to one combinational problem called maximum independent set(MIS). Two different Hamiltonian construction methods are employed in this algorithm using IBM Qiskit. It is shown that the key is to design proper Hamiltonians to map the solution to the feasible states of the Hilbert space.
Comparison of Self-Supervised Speech Pre-Training Methods on Flemish Dutch

Recent research in speech processing exhibits a growing interest in unsupervised and self-supervised representation learning from unlabelled data to alleviate the need for large amounts of annotated data. We investigate several popular pre-training methods and apply them to Flemish Dutch. We compare off-the-shelf English pre-trained models to models trained on an increasing amount of Flemish data. We find that the most important factors for positive transfer to downstream speech recognition tasks include a substantial amount of data and a matching pre-training domain. Ideally, we also finetune on an annotated subset in the target language. All pre-trained models improve linear phone separability in Flemish, but not all methods improve Automatic Speech Recognition. We experience superior performance with wav2vec 2.0 and we obtain a 30% WER improvement by finetuning the multilingually pre-trained XLSR-53 model on Flemish Dutch, after integration into an HMM-DNN acoustic model.
Research on the Application of Multi-scale &amp; Multi-sensor Fusion Algorithm in MEMS Gyroscope Data Processing

Abstract The application of multi-scale and multi-sensor data fusion algorithm in sensor data processing, especially the data fusion of MEMS gyroscope, can greatly improve the accuracy after fusion. Based on this, this paper first analyzes the concept of multi-scale multi-sensor data fusion, then studies the application status of multi-scale multi-sensor data fusion, and finally gives the application of multi-scale multi-sensor data fusion algorithm in MEMS gyroscope data processing.
Rich variety of DA approaches applied in social media research: A systematic scoping review

Social media is an endless source of texts and images about almost everything. Accordingly, the number of analyses based on this source increases daily. Among the numerous methods social media can be analysed by, our attention focusses on discourse analysis (DA). DA is a complex approach which makes it possible to capture not only the linguistic characteristics of given texts, but also their socially constructive and socially constructed features. Therefore, we carried out a systematic examination of the articles at one of the largest academic databases, EBSCO available in 2019 which used DA in social media research. Our investigation studies not only the geographical distribution of this corpus, but the different self-proclaimed DA approaches. Furthermore, we developed a three-level scale in order to capture the methodological complexity of the collected articles. At one end of the scale there are those research papers where discourse appears only as a label for the textual material gathered, without further indication of any DA theories or methodologies. The other end, however, refers to those research projects which applied DA both in their theoretical and methodological frameworks, providing complex discourse analytical investigations of social media texts. This way we were able to demonstrate the wide array of DA perspectives employed in social media research.
Siamese Networks with Distance-IoU Loss for Real-time Visual Tracking

The combination of siamese networks and region proposal networks (RPN) has drawn attention of a great many scholars in the field of visual object tracking with its balanced speed and accuracy. SiamRPN is composed of siamese subnetwork and RPN subnetwork. It abandons the traditional multi-scale testing and online tracking, which greatly meets real-time requirements. Aiming at the limited tracking accuracy of the SiamRPN, we propose the siamese networks with Distance-IoU Loss for real-time visual tracking. The first is to crop and fuse the multi-layer feature maps extracted by the feature extraction network to make full use of the multi-layer features. The weights of weighted fusion are obtained by network learning rather than the manual setting. For the loss function of the bounding box regression, we choose the Distance-IoU loss to replace the Smooth loss adopted by SiamRPN, which greatly improves the performance of tracking algorithm. We compare our algorithm with other state-of-the-art tracking algorithms on the VOT2016, VOT2018, and UAV123 datasets. The experimental results show that our algorithm shows better tracking accuracy and robustness in various scenarios and works at 154 frames per second, which verifies the effectiveness of the algorithm.
Toward Self-Aware Machines: Insights of Causal Reasoning in Artificial Intelligence

This study was focused on reviewing several research articles related to various perspectives and significant recent developments of Artificial Intelligence (AI). The main goal of this review was to gain insight into the implications of causal reasoning models in artificial intelligence. This review analyses state-of-the-art research articles and evaluations of applications, techniques algorithms, and trends in the field of Artificial Intelligence. By presenting recent results, this study has a strong emphasis on fundamental aspects of causal reasoning, logic, and computational structures of Strong AI agents. Findings of this study outline the importance of implementing causal reasoning methods in AI systems in order to achieve in the future, a truly intelligent machine. We can conclude that causal reasoning provides an important approach to advancing our understanding of artificial consciousness. Extensive research is needed in the future to validate and evaluate different causal tools for supporting causal reasoning in AI.
Enhancement of Non-Gaussianity and Nonclassicality of Photon Added Displaced Fock State: A Quantitative Approach

Non-Gaussian and nonclassical states and processes are already found to be important resources for performing various tasks related to quantum gravity and quantum information processing. Considering these facts, a quantitative analysis of the nonclassical and non-Gaussian features is performed here for photon added displaced Fock state, as a test case, using a set of measures like entanglement potential, Wigner Yanese skew information, Wigner logarithmic negativity and relative entropy of non-Gaussianity. It is observed that photon addition (Fock parameter) significantly increases the amount of nonclassicalty and non-Gaussianity for small (large) values of the displacement parameter, which decreases both the quantum features monotonically. In this respect, the role of Fock parameter is found to be more prominent and stronger compared to photon addition. Finally, the dynamics of Wigner function under the effect of photon loss channel is used to show that only highly efficient detectors are able to detect Wigner negativity.
Detecting and Finding the True Meaning of Jargons

While looking at the online bulletin boards, some obscure description could be found in the threads. In most cases, it is usually because that the authors are not willing to mention the true name of somebody or something directly, and in hence take the place of it with a substitute expression which has the same attributes, such as homophonic word or objects which appear similarly. These words and phrases, which are used irregularly in the context, could be called jargons. In this study, we propose a method to detect and understand jargons by considering the relevance of context words in word-embedding expressions. Specifically, we first calculate the topic correlation-coefficient of each word pair occurring in the text to find the jargon. Then, we combine a set of contextual information to find out a list of candidate words which probably deliver the true meaning of the jargon. By comparing the candidate words and the jargon word from 3 aspects (meaning, pronunciation and shape), the true meaning of the jargon could be determined based on the aggregative similarity. Finally, we have conducted an experiment to examine the usefulness of our approach.
Main Differences of DevOps on IoT Systems

IoT systems have barriers related to the different areas that involve their development. Hence, the scientific literature and industry practices investigate approaches that enable continuous interaction of these areas. Through semi-structured interviews with thirty-one professionals working in industry, this study investigated how DevOps is applied to make the development of IoT projects continuous and meet the demands of the industry. Through group discussions, we categorized the results of this study. As a preliminary contribution to this work, we investigate the contrasts between using DevOps in IoT system projects and using rigid, plan-oriented processes to develop embedded systems.
SA-GNN: Stereo Attention and Graph Neural Network for Stereo Image Super-Resolution

The goal of the stereoscopic image super-resolution (SR) is to reconstruct a pair of high-resolution (HR) images from corresponding low-resolution (LR) images. The existing stereo SR methods based on convolutional neural network (CNN) benefit from additional information from a different viewpoint to some extent. However, they cannot make good use of the complementary information from the different viewpoint, resulting in a lack of textures and details. The unevenly distributed features from left and right images were treated equally. To overcome the above difficulties, we put forward a stereo attention graph neural network (SA-GNN), which can extract reliable priors non-locally and fuse consistent contents adaptively cross different viewpoints. SA-GNN contains a series of stereo graph neural networks (SGNN), which alternate iteratively between in-view graph and cross-view graph under the aggregation and update mechanism of graph neural networks (GNN) to enhance SR performance. The comparison experiment results on four public datasets demonstrate that our SA-GNN outperforms the state-of-the-art methods.
A mobile energy trading scheme based on Lightning Network

Abstract Current blockchain‐based energy trading models raise serious concerns regarding the high and capped transaction latency and expensive service charges. To solve these problems, In this article, we combine Lightning Network (LN) and smart contract to present a mobile energy trading scheme based on LN. The focal point of the scheme lies in transfer of value occurs off‐blockchain, which addresses the problem of transaction latency. Next, to solve the security of proposed scheme, we design a mechanism to delivery secret R. Participants exchange some parameters to calculate R instead of delivery it directly. The found security is guaranteed by committing funds into a multi‐signature address in proposed scheme. Then, we conduct a comprehensive experiment to evaluate the proposed scheme. The simulation and analysis results show that the proposed scheme is efficient than traditional trading. In addition, we analyze our entire scheme and conclude that it defenses anti‐tampering attacks and replay attack capacity effectively.
Large-Scale Multi-granular Concept Extraction Based on Machine Reading Comprehension

The concepts in knowledge graphs (KGs) enable machines to understand natural language, and thus play an indispensable role in many applications. However, existing KGs have the poor coverage of concepts, especially fine-grained concepts. In order to supply existing KGs with more fine-grained and new concepts, we propose a novel concept extraction framework, namely MRC-CE, to extract large-scale multi-granular concepts from the descriptive texts of entities. Specifically, MRC-CE is built with a machine reading comprehension model based on BERT, which can extract more fine-grained concepts with a pointer network. Furthermore, a random forest and rule-based pruning are also adopted to enhance MRC-CE's precision and recall simultaneously. Our experiments evaluated upon multilingual KGs, i.e., English Probase and Chinese CN-DBpedia, justify MRC-CE's superiority over the state-of-the-art extraction models in KG completion. Particularly, after running MRC-CE for each entity in CN-DBpedia, more than 7,053,900 new concepts (instanceOf relations) are supplied into the KG. The code and datasets have been released at https://github.com/fcihraeipnusnacwh/MRC-CE
A Novel Multi-CPU/GPU Collaborative Computing Framework for SGD-based Matrix Factorization

This paper presents a heterogeneous collaborative computing framework for SGD-based Matrix Factorization, named HCC-MF. HCC-MF can train the feature matrix efficiently using multiple CPUs and GPUs. It performs collaborative computing with data parallelism, where a server CPU is in charge of management and synchronization and other heterogeneous worker CPUs and worker GPUs performs calculation with their data assignments. HCC-MF adopts two data partition strategies, “data partition with heterogeneous load balance” and “data partition with hidden synchronization.” We build a time cost model to guide the data distribution among multiple workers and we design several communication optimization techniques with consideration of datasets’ and processors’ characteristics. Experimental results indicate that HCC-MF can utilize more than 88% of the platform’s computing power, yielding a speedup of 2.9 compared with advanced SGD-based MF, CuMF_SGD, on large-scale data sets.
Cartoon Colorization with Gray Image Generated from Sketch

In this paper, we present a two stages approach to generate the color cartoon image from sketch by generating the grayscale image firstly, then the color cartoon is generated from the grayscale image with colorization methodology. The grayscale cartoon image from sketch is generated with conditional generative adversarial networks (cGAN), the conditions of cGAN will have some effects on the grayscale image such as edge and shade. The final color cartoon image from the grayscale image is generated with Encoder-Decoder UNet, and in this phase, both the prior global and local user hints information will be introduced to control the final color scheme. The benefit of this two phases approach is to generate the high-quality color cartoon image while allowing users to control final effect with more user guide involved. The two phases colorization could be done in almost real-time and user could take tuning for the generated color cartoon image interactively.
ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation

Understanding the 3D world without supervision is currently a major challenge in computer vision as the annotations required to supervise deep networks for tasks in this domain are expensive to obtain on a large scale. In this paper, we address the problem of unsupervised viewpoint estimation. We formulate this as a self-supervised learning task, where image reconstruction provides the supervision needed to predict the camera viewpoint. Specifically, we make use of pairs of images of the same object at training time, from unknown viewpoints, to self-supervise training by combining the viewpoint information from one image with the appearance information from the other. We demonstrate that using a perspective spatial transformer allows efficient viewpoint learning, outperforming existing unsupervised approaches on synthetic data, and obtains competitive results on the challenging PASCAL3D+ dataset.
FPD Net: Feature Pyramid DehazeNet

We propose an end-to-end dehazing model based on deep learning (CNN network) and uses the dehazing model re-proposed by AOD-Net based on the atmospheric scattering model for dehazing.Compare to the previously proposed dehazing network, the dehazing model proposed in this paper make use of the FPN network structure in the field of target detection, and uses five feature maps of different sizes to better obtain features of different proportions and different sub-regions.A large amount of experimental data proves that the dehazing model proposed in this paper is superior to previous dehazing technologies in terms of PSNR, SSIM, and subjective visual quality.In addition, it achieved a good performance in speed by using EfficientNet B0 as a feature extractor.We find that only using high-level semantic features can not effectively obtain all the information in the image.The FPN structure used in this paper can effectively integrate the high-level semantics and the low-level semantics, and can better take into account the global and local features.The five feature maps with different sizes are not simply weighted and fused.In order to keep all their information, we put them all together and get the final features through decode layers.At the same time, we have done a comparative experiment between ResNet with FPN and EfficientNet with BiFPN.It is proved that EfficientNet with BiFPN can obtain image features more efficiently.Therefore, EfficientNet with BiFPN is chosen as our network feature extraction.
Multi-agent Imitation Learning with Copulas

Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions, which is essential for understanding physical, social, and team-play systems. However, most existing works on modeling multi-agent interactions typically assume that agents make independent decisions based on their observations, ignoring the complex dependence among agents. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents. Extensive experiments on synthetic and real-world datasets show that our model outperforms state-of-the-art baselines across various scenarios in the action prediction task, and is able to generate new trajectories close to expert demonstrations.