Reviewerly/exHarmony huggingface dataset:
Features of gold-bearing quartz veins in an artisanal mining-dominated terrain, Batouri gold district, Eastern region of Cameroon

In a bid to identify features useful in discriminating barren from gold-bearing veins and thus assist in the training of locals on site suitability selection for shallow-depth gold exploitation from weathered veins, we investigated a major vein set in the Batouri gold district of eastern Cameroon hereafter referred to as the Mama vein system.The mineralized veins trend NE-SW and are foliated with sericitic, carbonate, sulphides and hematitic alterations.Quartz in the mineralized veins is brecciated, fibrous and smoky.Gold grains occur as inclusions in hematite mainly derived from the weathering of sulphides.Thus brecciated to stock work hematite-bearing veins in prospective mining sites is the main target for the local miners since they are the main host of primary mineralization in Batouri.The gold grains are rich in Ag and show low gold fineness (maximum 615).Bulk geochemistry reveals Au-As-Hg-Zn-Pb-Sb-Mo element association indicative of a low sulphidation environment.Fluid inclusion data affirm to moderate salinity brine as the ore-bearing fluids and mesothermal conditions of ore deposition consistent with the magmatic source of gold.The δ 34 S values for this system reflect a single homogenous and general light source for sulfur.
UTSP: User-Based Two-Step Recommendation With Popularity Normalization Towards Diversity and Novelty

Information technologies such as e-commerce and e-news bring overloaded information as well as convenience to users, cooperatives and companies. Recommender system is a significant technology in solving this information overload problem. Due to the outstanding accuracy performance in top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> recommendation tasks, two-step recommendation algorithms are suitable to generate recommendations. However, their recommendation lists are biased towards popular items. In this paper, we propose a user based two-step recommendation algorithm with popularity normalization to improve recommendation diversity and novelty, as well as two evaluation metrics to measure diverse and novel performance. Experimental results demonstrate that our proposed approach significantly improves the diversity and novelty performance while still inheriting the advantage of two-step recommendation approaches on accuracy metrics.
SAS: Painting Detection and Recognition via Smart Art System With Mobile Devices

Artwork recognition is an important research direction in the field of image processing. However, most of the current proposed methods are not designed for the demand of real-time analysis with mobile devices. Moreover, existing methods usually rely on high quality images and require large amounts of computing consumption. Based on the deep learning technology, in this paper, we propose a Smart Art System (SAS) with mobile devices. Our SAS mainly consists of two parts, i.e., painting detection unit and recognition unit. The detection module adopts a new painting detection algorithm called Single Shot Detection with Painting Landmark Location (SSD-PLL). SSD-PLL can effectively eliminate the influence of complex background factors on recognition. Considering the limited computing capacity of the mobile devices, our recognition module adopts a new ultra-light painting classifier. The classifier adopts MobileNet as the backbone and owns extra operation for Local Features Fusion (LFF). With our SAS, users can use mobile phone to take a photo of any paintings, then SAS would analyze the paintings and report the relevant information in real time. In order to validate the effectiveness of the proposed method, we have established two large scale image databases. The databases include 7,500 Traditional Chinese paintings (TCPs) and 8,800 Oil paintings (OPs), respectively. We evaluate our method and compare with the relevant algorithms, and our method achieves the highest performance and better real-time performance. Extensive experimental results on these databases show the effectiveness of the proposed algorithm.
Multi-turn Dialogue Response Generation in an Adversarial Learning Framework

We propose an adversarial learning approach for generating multi-turn dialogue responses.Our proposed framework, hredGAN, is based on conditional generative adversarial networks (GANs).The GAN's generator is a modified hierarchical recurrent encoder-decoder network (HRED) and the discriminator is a word-level bidirectional RNN that shares context and word embeddings with the generator.During inference, noise samples conditioned on the dialogue history are used to perturb the generator's latent space to generate several possible responses.The final response is the one ranked best by the discriminator.The hredGAN shows improved performance over existing methods: (1) it generalizes better than networks trained using only the log-likelihood criterion, and (2) it generates longer, more informative and more diverse responses with high utterance and topic relevance even with limited training data.This improvement is demonstrated on the Movie triples and Ubuntu dialogue datasets using both automatic and human evaluations.
Data-Driven Anomaly Detection of UAV based on Multimodal Regression Model

Unmanned Aerial Vehicles (UAVs) have been widely used in military and civilian applications. Meanwhile, anomaly detection as an essential part of UAV condition monitoring has become particularly critical for maintenance scheduling and mission re-planning in advance, especially for autonomous UAVs. Due to the issue of multiple flight modes dynamic switching in the actual operation, the adaptability of anomaly detection methods is always challenging when dealing with different flight trajectories. In this work, a data-driven method for flight data anomaly detection with enhanced adaptability is proposed based on multimodal regression model. Firstly, a complete flight trajectory is divided by flight mode recognition, and Relevance Vector Machine (RVM) regression is used as the basic anomaly detection method. Secondly, the dynamic input parameters of RVM models are automatically extracted by calculating the Pearson correlation coefficient between different flight parameters in each flight mode. Finally, RVM-based anomaly detection models in different flight modes are established. To deal with different flight trajectories, switching the anomaly detection model according to the flight mode can achieve adaptive anomaly detection. Experiments based on real flight data of UAV verify the effectiveness of the proposed method, and the adaptability of the anomaly detection approach can be improved.
Violent crowd behavior detection using deep learning and compressive sensing

In this paper, we propose a new crowd analysis method fusing deep learning network and compressive sensing for violent behavior detection. To this aim, a novel hybrid random matrix (HRM) is constructed and is proved to satisfy the restricted isometry property. The high-dimensional features can be projected to a low-dimensional space via the HRM. Furthermore, a deep neural network is developed for extracting crowd behaviour representations based on reduced dimension features. Finally, the learnt deep features are used for classification. Experimental results demonstrate that the proposed method is effective and efficient in violent behavior detection, and is on par with or better than the state-of-the-art methods.
SwipeVLock: A Supervised Unlocking Mechanism Based on Swipe Behavior on Smartphones

Smartphones have become a necessity in people’s daily lives, and changed the way of communication at any time and place. Nowadays, mobile devices especially smartphones have to store and process a large amount of sensitive information, i.e., from personal to financial and professional data. For this reason, there is an increasing need to protect the devices from unauthorized access. In comparison with the traditional textual password, behavioral authentication can verify current users in a continuous way, which can complement the existing authentication mechanisms. With the advanced capability provided by current smartphones, users can perform various touch actions to interact with their devices. In this work, we focus on swipe behavior and aim to design a machine learning-based unlock scheme called SwipeVLock, which verifies users based on their way of swiping the phone screen with a background image. In the evaluation, we measure several typical supervised learning algorithms and conduct a user study with 30 participants. Our experimental results indicate that participants could perform well with SwipeVLock, i.e., with a success rate of 98% in the best case.
Gendered Ambiguous Pronouns Shared Task: Boosting Model Confidence by Evidence Pooling

This paper presents a strong set of results for resolving gendered ambiguous pronouns on the Gendered Ambiguous Pronouns shared task. The model presented here draws upon the strengths of state-of-the-art language and coreference resolution models, and introduces a novel evidence-based deep learning architecture. Injecting evidence from the coreference models compliments the base architecture, and analysis shows that the model is not hindered by their weaknesses, specifically gender bias. The modularity and simplicity of the architecture make it very easy to extend for further improvement and applicable to other NLP problems. Evaluation on GAP test data results in a state-of-the-art performance at 92.5% F1 (gender bias of 0.97), edging closer to the human performance of 96.6%. The end-to-end solution presented here placed 1st in the Kaggle competition, winning by a significant lead.
Semantic Web Service Discovery Based on LDA Clustering

In recent years, with the exponential growth of Web services, how to find the best Web services quickly, accurately and efficiently from the large Web services becomes an urgent problem in Web service discovery. Based on the previous work, we propose a semantic Web service discovery method based on LDA clustering. Firstly, the OWL-S Web service documents are parsed to obtain the document word vectors. Then these vectors are extended to make the documents more abundant of semantic information. Moreover, these vectors are modeled, trained and inferred to get the Document-Topic distribution, and the Web service documents are clustered. Finally, we search the Web service request records or the Web services clusters to find Web services that meet the requirements. Based on the data sets of OWLS-TC4 and hRESTS-TC3_release2, the experimental results show that our method (LDA plus semantic) has higher accuracy (13.48% and 9.97%), recall (37.39% and 24.26%), F-value (30.46% and 23.58%) when compared with VSM method and LDA method.
Depth perception in shuffleboard: Depth cues effect on depth perception in virtual and augmented reality system

Abstract The virtual reality (VR) and augmented reality (AR) applications have been widely used in a variety of fields; one of the key requirements in a VR or AR system is to understand how users perceive depth in the virtual environment and AR. Three different graphics depth cues are designed in shuffleboard to explore what kind of graphics depth cues are beneficial for depth perception. We also conduct a depth‐matching experiment to compare performance in VR and AR systems using an optical see‐through head‐mounted display (HMD). The result shows that the absolute error increases as the distance becomes farther. Analysis from the inverse of distance shows that box depth cues have a significant effect on depth perception, while the points depth cues and line depth cues have no significant effect. The error in diopter in AR experiment is lower than that in VR experiment. Participants in the AR experiment under medium illuminance condition have less error than those under low and high illuminance conditions. Men have less error than women in certain display conditions, but the advantage disappears when there is a strong depth cue. Besides, there is no significant effect of completion time on depth perception.
Trust Evaluation and Covert Communication-Based Secure Content Delivery for D2D Networks: A Hierarchical Matching Approach

In this paper, we investigate the secure content delivery issue for device-to-device networks based on the trust evaluation mechanism and covert communication model.Specifically, in order to stimulate the lasting and rational cooperation among mobile devices, we propose a trust evaluation mechanism, where the trust degree between two mobile devices can be obtained through historical assessed values.Simultaneously, a covert communication model is introduced to guarantee the undetectability of content delivery, which helps to stop malicious wardens adopting some coming external attacks.In this way, the availability and dependability of content sharing, as well the confidentiality and integrity of transmitted contents cam be guaranteed.Then, combining security-aware metrics, in terms of trust degree and covert rate, with physical layer transmission performances, in terms of achievable rate and successful delivery probability, we propose the definition of secrecy-aware effective rate to serve as a guidance on the joint optimization issue of content delivery mode selection and resource management, which is formulated as a social welfare maximization problem.To solve the complex problem tactfully, it is decoupled into two subproblems, i.e., mode selection which is a many-to-one matching problem, and resource management which is a one-to-one matching problem.By analyzing the interaction between two issues, a novel hierarchical stable matching algorithm is proposed to obtain three-dimension stable matching results.Its properties, such as stability, convergence, optimality, and complexity, are theoretically analyzed and proved.Finally, extensive numerical results are provided to demonstrate the advantages of our proposed algorithms.
British Sign Language Recognition In The Wild Based On Multi-Class SVM

Developing assistive, cost-effective, non-invasive technologies to aid communication of people with hearing impairments is of prime importance in our society, in order to widen accessibility and inclusiveness.For this purpose, we have developed an intelligent vision system embedded on a smartphone and deployed in the wild.In particular, it integrates both computer vision methods involving Histogram of Oriented Gradients (HOG) and machine learning techniques such as multiclass Support Vector Machine (SVM) to detect and recognize British Visual Language (BSL) signs automatically.Our system was successfully tested on a real-world dataset containing 13,066 samples and shown an accuracy of over 99% with an average processing time of 170ms, thus appropriate for real-time visual signing.
Evaluation of Photovoltaic Power Generation by using Deep Learning in Solar Panels Installed in Buildings

Southern Taiwan has excellent solar energy resources that remain largely unused. This study incorporated a measure that aids in providing simple and effective power generation efficiency assessments of solar panel brands in the planning stage of installing these panels on roofs. The proposed methodology can be applied to evaluate photovoltaic (PV) power generation panels installed on building rooftops in Southern Taiwan. In the first phase, this study selected panels of the BP3 series, including BP350, BP365, BP380, and BP3125, to assess their PV output efficiency. BP Solar is a manufacturer and installer of photovoltaic solar cells. This study first derived ideal PV power generation and then determined the suitable tilt angle for the PV panels leading to direct sunlight that could be acquired to increase power output by panels installed on building rooftops. The potential annual power outputs for these solar panels were calculated. Climate data of 2016 were used to estimate the annual solar power output of the BP3 series per unit area. The results indicated that BP380 was the most efficient model for power generation (183.5 KWh/m2-y), followed by BP3125 (182.2 KWh/m2-y); by contrast, BP350 was the least efficient (164.2 KWh/m2-y). In the second phase, to simulate meteorological uncertainty during hourly PV power generation, a surface solar radiation prediction model was developed. This study used a deep learning–based deep neural network (DNN) for predicting hourly irradiation. The simulation results of the DNN were compared with those of a backpropagation neural network (BPN) and a linear regression (LR) model. In the final phase, the panel of module BP3125 was used as an example and demonstrated the hourly PV power output prediction at different lead times on a solar panel. The results demonstrated that the proposed method is useful for evaluating the power generation efficiency of the solar panels.
IMAGE-TO-IMAGE TRANSLATION FOR ENHANCED FEATURE MATCHING, IMAGE RETRIEVAL AND VISUAL LOCALIZATION

Abstract. The performance of machine learning and deep learning algorithms for image analysis depends significantly on the quantity and quality of the training data. The generation of annotated training data is often costly, time-consuming and laborious. Data augmentation is a powerful option to overcome these drawbacks. Therefore, we augment training data by rendering images with arbitrary poses from 3D models to increase the quantity of training images. These training images usually show artifacts and are of limited use for advanced image analysis. Therefore, we propose to use image-to-image translation to transform images from a rendered domain to a captured domain. We show that translated images in the captured domain are of higher quality than the rendered images. Moreover, we demonstrate that image-to-image translation based on rendered 3D models enhances the performance of common computer vision tasks, namely feature matching, image retrieval and visual localization. The experimental results clearly show the enhancement on translated images over rendered images for all investigated tasks. In addition to this, we present the advantages utilizing translated images over exclusively captured images for visual localization.
Enhancing Rolling Horizon Evolution with Policy and Value Networks

Rolling Horizon Evolutionary Algorithm (RHEA) is an online planning method for real-time game playing; its performance is closely related to the planning horizon and the search cost allowed. In this paper, we propose to learn a prior for RHEA in an offline manner by training a value network and a policy network. The value network is used to reduce the planning horizon by providing an estimation of future rewards, and the policy network is used to initialize the population, which helps to narrow down the search scope. The proposed algorithm, named prior-based RHEA (p-RHEA), trains policy and value networks by performing planning and learning iteratively. In the planning stage, the horizon-limited search is performed to improve the policies and collect training samples with the help of the learned networks. In the learning stage, the policy network and value network are trained with the collected samples to learn better prior knowledge. Experimental results on OpenAI MuJoCo tasks show that the performance of the proposed p- RHEA is significantly improved compared to that of RHEA.
MESOPOROUS HKUST-1 SYNTHESIZED USING SOLVOTHERMAL METHOD

Metal-organic frameworks type HKUST-1 were synthesized using a solvothermal method with reactants of copper(II) nitrate trihydrate and benzene-1,3,5-tricarboxylic acid in a solvent mixture of ethanol, DMF and water with a volume ratio of 1:1:1.The synthesis of HKUST-1 was carried out by adding cetyltrimethylammonium bromide (CTAB) surfactant and acetic acid as a modulator.The process of synthesis was done in a vial bottle at a temperature of 100 °C for 10 h.The obtained solids, in the form of a light blue colored powder, were characterized by X-Ray Powder Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and nitrogen adsorption-desorption methods.The diffraction patterns of the obtained solids show the characteristic peaks of the HKUST-1 at 2θ of 6.7, 9.5, 11.5 and 13.4°, respectively.The surface morphology of the synthesized HKUST-1 was octahedral forms with a particle size of between 16.4-17.9µm.The HKUST-1 obtained with the addition of CTAB surfactant had a nitrogen adsorption-desorption isotherm of type IV, characteristic to a mesoporous material, with a specific surface area as high as 1010 m 2 /g and pore diameter of 9.2 nm.
Latent Space Expanded Variational Autoencoder for Sentence Generation

Sentence generation is a key task in many natural language processing systems. Models based on a variational autoencoder (VAE) can generate plausible sentences from a continuous latent space. However, the VAE forces the latent distribution of each input sentence to match the same prior, which results in a large overlap among the latent subspaces of different sentences and a limited informative latent space. Therefore, the sentences generated by sampling from a subspace may have little correlation with the corresponding input, and the latent space cannot capture rich useful information from the input sentences, which leads to the failure of the model to generate diverse sentences from the latent space. Additionally, the Kullback-Leibler (KL) divergence collapse problem makes the VAE notoriously difficult to train. In this paper, a latent space expanded VAE (LSE-VAE) model is presented for sentence generation. The model maps each sentence to a continuous latent subspace under the constraint of its own prior distribution, and constrains nearby sentences to map to nearby subspaces. Sentences are dispersed to a large continuous latent space according to sentence similarity, where the latent subspaces of different sentences may be relatively far away from each other and arranged in an orderly manner. The experimental results show that the LSE-VAE improves the reconstruction ability of the VAE, generates plausible and more diverse sentences, and learns a larger informative latent space than the VAE with the properties of continuity and smoothness. The LSE-VAE does not suffer from the KL collapse problem, and it is robust to hyperparameters and much easier to train.
Real-Time Video Saliency Prediction Via 3D Residual Convolutional Neural Network

Attention is a fundamental attribute of human visual system that plays important roles in many visual perception tasks. The key issue of video saliency lies in how to efficiently exploit the temporal information. Instead of singling out the temporal saliency maps, we propose a real-time end-to-end video saliency prediction model via 3D residual convolutional neural network (3D-ResNet), which incorporates the prediction of spatial and temporal saliency maps into one single process. In particular, a multi-scale feature representation scheme is employed to further boost the model performance. Besides, a frame skipping strategy is proposed for speeding up the saliency map inference process. Moreover, a new challenging eye tracking database with 220 video clips is established to facilitate the research of video saliency prediction. Extensive experimental results show our model outperforms the state-of-the-art methods over the eye fixation datasets in terms of both prediction accuracy and inference speed.
A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents

How should conversational agents respond to verbal abuse through the user?To answer this question, we conduct a large-scale crowdsourced evaluation of abuse response strategies employed by current state-of-the-art systems.Our results show that some strategies, such as "polite refusal" score highly across the board, while for other strategies demographic factors, such as age, as well as the severity of the preceding abuse influence the user's perception of which response is appropriate.In addition, we find that most data-driven models lag behind rule-based or commercial systems in terms of their perceived appropriateness.