Reviewerly/exHarmony huggingface dataset:
POI neural-rec model via graph embedding representation

With the booming of the Internet of Things (IoT) and the speedy advancement of Location-Based Social Networks (LBSNs), Point-Of-Interest (POI) recommendation has become a vital strategy for supporting people's ability to mine their POIs. However, classical recommendation models, such as collaborative filtering, are not effective for structuring POI recommendations due to the sparseness of user check-ins. Furthermore, LBSN recommendations are distinct from other recommendation scenarios. With respect to user data, a user's check-in record sequence requires rich social and geographic information. In this paper, we propose two different neural-network models, structural deep network Graph embedding Neural-network Recommendation system (SG-NeuRec) and Deepwalk on Graph Neural-network Recommendation system (DG-NeuRec) to improve POI recommendation. combined with embedding representation from social and geographical graph information (called SG-NeuRec and DG-NeuRec). Our model naturally combines the embedding representations of social and geographical graph information with user-POI interaction representation and captures the potential user-POI interactions under the framework of the neural network. Finally, we compare the performances of these two models and analyze the reasons for their differences. Results from comprehensive experiments on two real LBSNs datasets indicate the effective performance of our model.
Estimation of boundary conditions for patient-specific liver simulation during augmented surgery

Augmented reality can improve the outcome of hepatic surgeries, assuming an accurate liver model is available to estimate the position of internal structures. While researchers have proposed patient-specific liver simulations, very few have addressed the question of boundary conditions. Resulting mainly from ligaments attached to the liver, they are not visible in preoperative images, yet play a key role in the computation of the deformation. We propose to estimate both the location and stiffness of ligaments by using a combination of a statistical atlas, numerical simulation, and Bayesian inference. Ligaments are modeled as polynomial springs connected to a liver finite element model. They are initialized using an anatomical atlas and stiffness properties taken from the literature. These characteristics are then corrected using a reduced-order unscented Kalman filter based on observations taken from the laparoscopic image stream. Our approach is evaluated using synthetic data and phantom data. By relying on a simplified representation of the ligaments to speed up computation times, it is not estimating the true characteristics of ligaments. However, results show that our estimation of the boundary conditions still improves the accuracy of the simulation by 75% when compared to typical methods involving Dirichlet boundary conditions. By estimating patient-specific boundary conditions, using tracked liver motion from RGB-D data, our approach significantly improves the accuracy of the liver model. The method inherently handles noisy observations, a substantial feature in the context of augmented reality.
Artificial Intelligence for HCI: A Modern Approach

Artificial intelligence (AI) and Human Computer Interaction (HCI) share common roots and early work on conversational agents has laid the foundation for both fields. However, in subsequent decades the initial tight connection between the fields has become less pronounced. The recent rise of deep learning has revolutionized AI and has led to a raft of practical methods and tools that significantly impact areas outside of core-AI. In particular, modern AI techniques now power new ways for machines and humans to interact. Thus it is timely to investigate how modern AI can propel HCI research in new ways and how HCI research can help direct AI developments. This workshop offers a forum for researchers to discuss new opportunities that lie in bringing modern AI methods into HCI research, identifying important problems to investigate, showcasing computational and scientific methods that can be applied, and sharing datasets and tools that are already available or proposing those that should be further developed. The topics we are interested in including deep learning methods for understanding and modeling human behaviors and enabling new interaction modalities, hybrid intelligence that combine human and machine intelligence to solve difficult tasks, and tools and methods for interaction data curation and large-scale data-driven design. At the core of these topics, we want to start the conversation on how data-driven and data-centric approaches of modern AI can impact HCI.
A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications

Noise reduction is a perplexing undertaking for the researchers in digital image processing and has a wide range of applications in automation, IoT (Internet of Things), medicine, etc. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as a gathering of data and the existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Digital images are debased through noise through its transmission and procurement. Noisy image reduces the image contrast, edges, textures, object details, and resolution, thereby decreasing the performance of postprocessing algorithms. This paper mainly focuses on Gaussian noise, salt and pepper noise, uniform noise, speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as a reorganization of images. Here four types of noises have been undertaken and applied to process images. Besides linear and nonlinear filtering methods like Gaussian filter, median filter, mean filter and Weiner filter applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), average difference value (AD) and maximum difference value (MD) to diminish the noises without corrupting the medical image data.
Deconvolution of the composition of fine-grained pyrite in sedimentary matrix by regression of time-resolved LA-ICP-MS data

Abstract Pyrite is a common mineral in sedimentary rocks and is the major host for many chalcophile trace elements utilized as important tracers of the evolution of the ancient hydrosphere. Measurement of trace element composition of pyrite in sedimentary rocks is challenging due to fine-grain size and intergrowth with silicate matrix and other sulfide minerals. In this contribution, we describe a method for calculation of trace element composition of sedimentary pyrite from time-resolved LA-ICP-MS data. The method involves an analysis of both pyrite and pyrite-free sediment matrix, segmentation of LA-ICP-MS spectra, normalization to total, regression analysis of dependencies between the elements, and calculation of normalized composition of the mineral. Sulfur is chosen as an explanatory variable, relative to which all regressions are calculated. The S content value used for calculation of element concentrations from the regressions is calculated from the total, eliminating the need for independent constraints. The algorithm allows efficient measurement of concentrations of multiple chalcophile trace elements in pyrite in a wide range of samples, including quantification of detection limits and uncertainties while excluding operator bias. The data suggest that the main sources of uncertainties in pyrite composition are sample heterogeneity and counting statistics for elements of low abundance. The analysis of regression data of time-resolved LA-ICP-MS measurements could provide new insights into the geochemistry of the sedimentary rocks and minerals. It allows quantification of ratios of elements that do not have reference material available (such as Hg) and provides estimates on the content of non-sulfidic Fe in the silicate matrix. Regression analysis of the mixed LA-ICP-MS signal could be a powerful technique for deconvolution of phase compositions in complex multicomponent samples.
Residual network with detail perception loss for single image super-resolution

Recently, deep convolutional neural networks have demonstrated high-quality reconstruction for single image super-resolution. In this study, we present a network by using residual blocks with cascading simple blocks to improve the image resolution. Cascading simple blocks with a multi-layer perceptron are conducive to extract features and approximate a complex mapping with fewer parameters. Skip connections can help to alleviate the vanishing-gradient problem of deep networks. In addition, our network contains two pathways. One is to predict the high frequency information of the high resolution image and the other is to predict the low frequency information of the high resolution image. Then the information of two pathways is fused, and pixel-shuffle is used for upsampling. Moreover, to capture texture details of images, we introduce a novel loss function called detail perception loss, which is used to measure the difference of the wavelet coefficients from the reconstructed image and ground truth. By reducing detail perception loss, texture details of the reconstructed image are becoming more similar with texture details of ground truth. Extensive quantitative and qualitative experiments on four benchmark datasets show that our method achieves superior performance over typical single image super-resolution methods.
A Mixture of h - 1 Heads is Better than h Heads

Multi-head attentive neural architectures have achieved state-of-the-art results on a variety of natural language processing tasks.Evidence has shown that they are overparameterized; attention heads can be pruned without significant performance loss.In this work, we instead "reallocate" them-the model learns to activate different heads on different inputs.Drawing connections between multi-head attention and mixture of experts, we propose the mixture of attentive experts model (MAE).MAE is trained using a block coordinate descent algorithm that alternates between updating (1) the responsibilities of the experts and (2) their parameters.Experiments on machine translation and language modeling show that MAE outperforms strong baselines on both tasks.Particularly, on the WMT14 English to German translation dataset, MAE improves over "transformer-base" by 0.8 BLEU, with a comparable number of parameters.Our analysis shows that our model learns to specialize different experts to different inputs. 1
Detecting Text in Scene and Traffic Guide Panels With Attention Anchor Mechanism

Text detection in complex scene images is a challenging task for intelligent transportation. Recently, anchor mechanisms are widely utilized in scene text detection tasks. However, in existing methods, anchors are generally predefined empirically, degrading robustness to complex scenarios with various sizes and orientation variations. In this paper, we propose a novel Attention Anchor Mechanism (AAM), especially targeting at predicting appropriate anchors for each pixel. To be concrete, we regard a series of predefined anchors as basic anchors and utilize an attention model to predict weights corresponding to basic anchors. Consequently, the weighted sum of basic anchors in each pixel can obtain a predicted anchor. In this way, the gap between the predicted anchors and the corresponding ground truth boxes could be narrowed, making the network easier to regress. For facilitating the design of basic anchors, we adopt a dimension-decomposition mechanism to predict width, height, and angle of anchors, respectively. Extensive experiments on several public datasets demonstrate that our method achieves state-of-the-art performance.
The impact of blockchain technology on tourism intermediation

Contemporary tourism has undergone many changes in the last decade, mainly due to expansion of the new technology. The use of new technology has had more positive than negative impacts on tourism intermediation, but none of them triggered a radical change. Blockchain technology has been one of the most discussed topics especially after 2016 due to its all opportunities, but also threats. Even though it is still somewhat unfamiliar, it is a form of technology which has a great potential to cause a revolution in traditional business activities, as it is predicted by futurists. For now, this phenomenon has only been recognized by bigger tourism intermediaries, technology-oriented companies. Such fast changes require even faster adaptations of all tourism intermediaries and this is challenging because most of stakeholders are not familiar with the use and the impact of blockchain. Therefore, in this paper the authors will try to provide answers by presenting a SWOT analysis of blockchain technology implications on activities undertaken by all participants in tourism intermediation. It implies to conclusion that everyone will face various challenges in its implementation which have to be considered seriously. The use and the general understanding of blockchain technology is still in an initial stage, so it is crucial that tourism research takes a proactive role in the exploration of this field of study. Furthermore, as trends are dynamic and evolve rapidly, the topic becomes particularly challenging for scientists in bridging practice into theory. Therefore, this theory-based paper on an overall assumption contributes to a better understanding of this phenomenon and raises the awareness for further research
Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks

Opinion role labeling (ORL) is a fine-grained opinion analysis task and aims to answer "who expressed what kind of sentiment towards what?".Due to the scarcity of labeled data, ORL remains challenging for data-driven methods.In this work, we try to enhance neural ORL models with syntactic knowledge by comparing and integrating different representations.We also propose dependency graph convolutional networks (DEPGCN) to encode parser information at different processing levels.In order to compensate for parser inaccuracy and reduce error propagation, we introduce multi-task learning (MTL) to train the parser and the ORL model simultaneously.We verify our methods on the benchmark MPQA corpus.The experimental results show that syntactic information is highly valuable for ORL, and our final MTL model effectively boosts the F1 score by 9.29 over the syntaxagnostic baseline.In addition, we find that the contributions from syntactic knowledge do not fully overlap with contextualized word representations (BERT).Our best model achieves 4.34 higher F1 score than the current state-ofthe-art.
Weakly Supervised Local-Global Relation Network for Facial Expression Recognition

To extract crucial local features and enhance the complementary relation between local and global features, this paper proposes a Weakly Supervised Local-Global Relation Network (WS-LGRN), which uses the attention mechanism to deal with part location and feature fusion problems. Firstly, the Attention Map Generator quickly finds the local regions-of-interest under the supervision of image-level labels. Secondly, bilinear attention pooling is employed to generate and refine local features. Thirdly, Relational Reasoning Unit is designed to model the relation among all features before making classification. The weighted fusion mechanism in the Relational Reasoning Unit makes the model benefit from the complementary advantages between different features. In addition, contrastive losses are introduced for local and global features to increase the inter-class dispersion and intra-class compactness at different granularities. Experiments on lab-controlled and real-world facial expression dataset show that WS-LGRN achieves state-of-the-art performance, which demonstrates its superiority in FER.
Validating and Verifying AI Systems

AI systems will only fulfill their promise for society if they can be relied upon. This means that the role and task of the system must be properly formulated; that the system must be bug free, be based on properly representative data, and can cope with anomalies and data quality issues; and that its output is sufficiently accurate for the task. AI systems will only fulfill their promise for society if they can be relied upon. This means that the role and task of the system must be properly formulated; that the system must be bug free, be based on properly representative data, and can cope with anomalies and data quality issues; and that its output is sufficiently accurate for the task.
BiRRE: Learning Bidirectional Residual Relation Embeddings for Supervised Hypernymy Detection

The hypernymy detection task has been addressed under various frameworks.Previously, the design of unsupervised hypernymy scores has been extensively studied.In contrast, supervised classifiers, especially distributional models, leverage the global contexts of terms to make predictions, but are more likely to suffer from "lexical memorization".In this work, we revisit supervised distributional models for hypernymy detection.Rather than taking embeddings of two terms as classification inputs, we introduce a representation learning framework named Bidirectional Residual Relation Embeddings (BiRRE).In this model, a term pair is represented by a BiRRE vector as features for hypernymy classification, which models the possibility of a term being mapped to another in the embedding space by hypernymy relations.A Latent Projection Model with Negative Regularization (LPMNR) is proposed to simulate how hypernyms and hyponyms are generated by neural language models, and to generate BiRRE vectors based on bidirectional residuals of projections.Experiments verify BiRRE outperforms strong baselines over various evaluation frameworks.
Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing

Many multi-domain neural machine translation (NMT) models achieve knowledge transfer by enforcing one encoder to learn shared embedding across domains. However, this design lacks adaptation to individual domains. To overcome this limitation, we propose a novel multi-domain NMT model using individual modules for each domain, on which we apply word-level, adaptive and layer-wise domain mixing. We first observe that words in a sentence are often related to multiple domains. Hence, we assume each word has a domain proportion, which indicates its domain preference. Then word representations are obtained by mixing their embedding in individual domains based on their domain proportions. We show this can be achieved by carefully designing multi-head dot-product attention modules for different domains, and eventually taking weighted averages of their parameters by word-level layer-wise domain proportions. Through this, we can achieve effective domain knowledge sharing and capture fine-grained domain-specific knowledge as well. Our experiments show that our proposed model outperforms existing ones in several NMT tasks.
Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection

Currently, the discussion about hate speech in Indonesia is warm, primarily through social media. Hate speech is communication that disparages a person or group based on characteristics such as (race, ethnicity, gender, citizenship, religion and organization). Twitter is one of the social media that someone uses to express their feelings and opinions through tweets, including tweets that contain expressions of hatred because Twitter has a significant influence on the success or destruction of one's image.This study aims to detect hate speech or not hate Indonesian speech tweets by using the Bidirectional Long Short Term Memory method and the word2vec feature extraction method with Continuous bag-of-word (CBOW) architecture. For testing the BiLSTM purpose with the calculation of the value of accuracy, precision, recall, and F-measure.The use of word2vec and the Bidirectional Long Short Term Memory method with CBOW architecture, with epoch 10, learning rate 0.001 and the number of neurons 200 on the hidden layer, produce an accuracy rate of 94.66%, with each precision value of 99.08%, recall 93, 74% and F-measure 96.29%. In contrast, the Bidirectional Long Short Term Memory with three layers has an accuracy of 96.93%. The addition of one layer to BiLSTM increased by 2.27%.
Trained Rank Pruning for Efficient Deep Neural Networks

To accelerate DNNs inference, low-rank approximation has been widely adopted because of its solid theoretical rationale and efficient implementations. Several previous works attempted to directly approximate a pre-trained model by low-rank decomposition; however, small approximation errors in parameters can ripple over a large prediction loss. Apparently, it is not optimal to separate low-rank approximation from training. Unlike previous works, this paper integrates low rank approximation and regularization into the training process. We propose Trained Rank Pruning (TRP), which alternates between low rank approximation and training. TRP maintains the capacity of the original network while imposing low-rank constraints during training. A nuclear regularization optimized by stochastic sub-gradient descent is utilized to further promote low rank in TRP. Networks trained with TRP has a low-rank structure in nature, and is approximated with negligible performance loss, thus eliminating fine-tuning after low rank approximation. The proposed method is comprehensively evaluated on CIFAR-10 and ImageNet, outperforming previous compression counterparts using low rank approximation. Our code is available at: https://github.com/yuhuixu1993/Trained-Rank-Pruning.
Domain Adaptive Classification on Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) are ubiquitous structures in that they can depict complex relational data. Due to their complexity, it is hard to obtain sufficient labeled data on HINs, hampering classification on HINs. While domain adaptation (DA) techniques have been widely utilized in images and texts, the heterogeneity and complex semantics pose specific challenges towards domain adaptive classification on HINs. On one hand, HINs involve multiple levels of semantics, making it demanding to do domain alignment among them. On the other hand, the trade-off between domain similarity and distinguishability must be elaborately chosen, in that domain invariant features have been shown to be homogeneous and uninformative for classification. In this paper, we propose Multi-space Domain Adaptive Classification (MuSDAC) to handle the problem of DA on HINs. Specifically, we utilize multi-channel shared weight GCNs, projecting nodes in HINs to multiple spaces where pairwise alignment is carried out. In addition, we propose a heuristic sampling algorithm that efficiently chooses the combination of channels featuring distinguishability, and moving-averaged weighted voting scheme to fuse the selected channels, minimizing both transfer and classification loss. Extensive experiments on pairwise datasets endorse not only our model's performance on domain adaptive classification on HINs and contributions by individual components.