Reviewerly/exHarmony huggingface dataset:
GeoDCF: Deep Collaborative Filtering with Multifaceted Contextual Information in Location-Based Social Networks

In this study we investigate the recommendation problem with multifaceted contextual information to overcome the scarcity of users’ check-in data in Location-based Social Networks. To generate accurate personalized Point-of-Interest (POI) recommendations in the presence of data scarcity, we account for both users’ and POIs’ contextual information such as the social influence of friends, as well as the geographical and sequential transition influence of POIs on user’s check-in behavior. We first propose a multi-view learning strategy to capture the multifaceted contextual information of users and POIs along with users’ check-in data. Then, we feed the learned user and POI latent vectors to a deep neural framework, to capture their non-linear correlations. Finally, we formulate the objective function of our geo-based deep collaborative filtering model (GeoDCF) as a Bayesian personalized ranking problem to focus on the top-k recommendation task and we learn the parameters of our model via backpropagation. Our experiments on real-world datasets confirm that GeoDCF achieves high recommendation accuracy, significantly outperforming other state-of-the-art methods. Furthermore, we confirm the influence of both users’ and POIs’ contextual information on our GeoDCF model. The evaluation datasets are publicly available at: http://snap.stanford.edu/data/loc-gowalla.html , https://sites.google.com/site/yangdingqi/home/foursquare-dataset .
All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification

Shift operation is an efficient alternative over depthwise separable convolution. However, it is still bottlenecked by its implementation manner, namely memory movement. To put this direction forward, a new and novel basic component named Sparse Shift Layer (SSL) is introduced in this paper to construct efficient convolutional neural networks. In this family of architectures, the basic block is only composed by 1x1 convolutional layers with only a few shift operations applied to the intermediate feature maps. To make this idea feasible, we introduce shift operation penalty during optimization and further propose a quantization-aware shift learning method to impose the learned displacement more friendly for inference. Extensive ablation studies indicate that only a few shift operations are sufficient to provide spatial information communication. Furthermore, to maximize the role of SSL, we redesign an improved network architecture to Fully Exploit the limited capacity of neural Network (FE-Net). Equipped with SSL, this network can achieve 75.0% top-1 accuracy on ImageNet with only 563M M-Adds. It surpasses other counterparts constructed by depthwise separable convolution and the networks searched by NAS in terms of accuracy and practical speed.
Learning to Calibrate Straight Lines for Fisheye Image Rectification

This paper presents a new deep-learning based method to simultaneously calibrate the intrinsic parameters of fisheye lens and rectify the distorted images. Assuming that the distorted lines generated by fisheye projection should be straight after rectification, we propose a novel deep neural network to impose explicit geometry constraints onto processes of the fisheye lens calibration and the distorted image rectification. In addition, considering the nonlinearity of distortion distribution in fisheye images, the proposed network fully exploits multi-scale perception to equalize the rectification effects on the whole image. To train and evaluate the proposed model, we also create a new largescale dataset labeled with corresponding distortion parameters and well-annotated distorted lines. Compared with the state-of-the-art methods, our model achieves the best published rectification quality and the most accurate estimation of distortion parameters on a large set of synthetic and real fisheye images.
Approximate Linear Dependence as a Design Method for Kernel Prototype-Based Classifiers

The approximate linear dependence (ALD) method is a sparsification procedure used to build a dictionary of samples extracted from a dataset. The extracted samples are approximately linearly independent in a high-dimensional kernel reproducing Hilbert space. In this paper, we argue that the ALD method itself can be used to select relevant prototypes from a training dataset and use them to classify new samples using kernelized distances. The results obtained from intensive experimentation with several datasets indicate that the proposed approach is viable to be used as a standalone classifier.
Novel framework for handwritten digit recognition through neural networks

The biggest challenge for natural language processing systems is to accurately identify and classify the hand-written characters.Accurate Handwritten Character recognition is a challenging task for humans too as the style, size and other handwriting parameters may vary from human to human.Though a relatively straightforward machine vision task but improved accuracy as compared to the existing implementations is still desirable.This manuscript aims to propose a novel neural network based framework for handwritten character recognition.The proposed neural network based framework, transforms the raw data set to a NumPy array to achieve image flattening and feeds the same into a pixel vector before feeding it into the network.In the neural network, the activation function is applied to transfer the resultant value to the hidden layer where it is further minimized through the use of minimized mean square and back propagation algorithms before applying a stochastic gradient on the resultant mini-batches.After a detailed study, the optimal algorithm for effective handwritten character recognition was proposed.Initially, the framework has been simulated only on digits through 50,000 training data samples, 10,000 validation data set and 10,000 test data set, the accuracy of 96.08.This manuscript aims to give the reader an insight into how the proposed neural network based framework has been applied for handwritten digit recognition.It highlights the successful applications of the same while laying down the directions for the enhancements possible.
Fast Single Shot Instance Segmentation

In this work, we propose fast single shot instance segmentation framework (FSSI), which aims at jointly object detection, segmenting and distinguishing every individual instance (instance segmentation) in a flexible and fast way. In the pipeline of FSSI, the instance segmentation task is divided into three parallel sub-tasks: object detection, semantic segmentation, and direction prediction. The instance segmentation result is then generated from these three sub-tasks’ results by the post-process in parallel. In order to accelerate the process, the SSD-like detection structure and two-path architecture which can generate more accurate segmentation prediction without heavy calculation burden are adopted. Our experiments on the PASCAL VOC and the MSCOCO datasets demonstrate the benefits of our approach, which accelerate the instance segmentation process with competitive result compared to MaskRCNN. Code is public available ( https://github.com/lzx1413/FSSI ).
Extracting entity-specific substructures for RDF graph embeddings

Knowledge Graphs (KGs) have become useful sources of structured data for information retrieval and data analytics tasks. Enabling complex analytics, however, requires entities in KGs to be represented in a way that is suitable for Machine Learning tasks. Several approaches have been recently propos ed for obtaining vector representations of KGs based on identifying and extracting relevant graph substructures using both uniform and biased random walks. However, such approaches lead to representations comprising mostly popular, instead of relevant, entities in the KG. In KGs, in which different types of entities often exist (such as in Linked Open Data), a given target entity may have its own distinct set of most relevant nodes and edges. We propose specificity as an accurate measure of identifying most relevant, entity-specific, nodes and edges. We develop a scalable method based on bidirectional random walks to compute specificity. Our experimental evaluation results show that specificity-based biased random walks extract more meaningful (in terms of size and relevance) substructures compared to the state-of-the-art and the graph embedding learned from the extracted substructures perform well against existing methods in common data mining tasks.
Latency Adaptive Streaming of 8K 360 Degree Video to Mobile VR Headsets (demo)

In this demo, we showcase how to stream 8K 360° video to commod- ity mobile devices without pre-processing or viewpoint prediction using our Freedom system. Users can freely select the viewpoint, zoom in and zoom out to enjoy the full quality of the 8K resolu- tion using any Samsung GearVR compatible smartphone. We also present how our system works internally to dynamically adjust margin size to accommodate to network latency and how our ap- proach can save up to 80% bandwidth compared to streaming full video to mobile devices.
Ore resource estimation based on radial based functions - Case study on União Luiz and Morro do Carrapato Gold Deposits (Alta Floresta Gold Province)

Radial basis functions can be used as an alternative method for mineral resource estimation when ordinary kriging cannot be done because it is impossible to calculate an experimental variogram.Interpolation based on radial based functions is a method that is similar to ordinary kriging.In this context, this article presents the results of gold resource estimation and modeling of the União Luis and Morro do Carrapato gold deposits located in Alta Floresta Gold Province, Mato Grosso.Experimental variograms in the main direction (90-degree strike) resulted in a pure nugget effect, whereas in the orthogonal direction (180/85), they resulted in a structured variogram.However, the structured variogram for 180/85 is useless because three orthogonal variograms are needed to define the anisotropy ellipsoid for deriving a correlation model used in ordinary kriging.Thus, mineral resource estimation was done using multiquadric equations, a very popular radial based function kernel.The 3D model for gold grades showed a vertical distribution suggesting a structural conditioning for gold mineralization.The grade-tonnage curve with a simulated cutoff of 5.0 g/ton resulted in a gold mineral resource of 2.122 tons.Through the above, the gold estimation and multiquadric equation-based 3D model for the studied area can be considered effective in its objective to estimate ore resources from the available data.
Attributed Paths for Layout-Based Document Retrieval

A document is rich in its layout. The entities of interest can be scattered over the document page. Traditional layout matching has involved modeling layout structure as grids, graphs, and spatial histograms of patches. In this paper we propose a new way of representing layout, which we call attributed paths. This representation admits a string edit distance based match measure. Our experiments show that layout based retrieval using attributed paths is computationally efficient and more effective. It also offers flexibility in tuning the match criterion. We have demonstrated effectiveness of attributed paths in performing layout based retrieval tasks on datasets of floor plan images [14] and journal pages [1].
Storage Optimization Algorithm for Publication Blockchain

“We Media” is developing rapidly and there is a sharp increase in the number of various electronic publications. Meanwhile, copyright issues between author and publisher are becoming increasingly prominent. To solve this problem, storage optimization algorithm for publication blockchain is based on Pearson similarity algorithm and K-means algorithm. First, the author and publisher association table is built by cooperation record between author and publisher and familiarity calculation method is designed. Subsequently, clustering algorithm is established by using the maximum and minimum principle. On this basis, the prediction algorithm is established. These algorithms are used to adjust Merkle tree structure. Finally, effectiveness of the algorithm is verified by experiments.
Exploratory Visual Analysis of Anomalous Runtime Behavior in Streaming High Performance Computing Applications

Online analysis of runtime behavior is essential for performance tuning in streaming scientific workflows. Integration of anomaly detection and visualization is necessary to support human-centered analysis, such as verification of candidate anomalies utilizing domain knowledge. In this work, we propose an efficient and scalable visual analytics system for online performance analysis of scientific workflows toward the exascale scenario. Our approach uses a call stack tree representation to encode the structural and temporal information of the function executions. Based on the call stack tree features (e.g., execution time of the root function or vector representation of the tree structure), we employ online anomaly detection approaches to identify candidate anomalous function executions. We also present a set of visualization tools for verification and exploration in a level-of-detailed manner. General information, such as distribution of execution times, are provided in an overview visualization. The detailed structure (e.g., function invocation relations) and the temporal information (e.g., message communication) of the execution call stack of interest are also visualized. The usability and efficiency of our methods are verified in a real-world HPC application.
Analysis of Business English Information Mining Method Based on Task Cooperative Learning Model

In order to solve the problems such as the poor effect of business English learning information mining in China, this paper proposes a task-based learning model for business English information mining. Based on the investigation and classification of the current business English learning contents and characteristics, the key and difficult learning information is effectively mined. Combined with the task cooperative learning mode, the key and difficult learning element indexes are divided, and the mining and learning of business English teaching information are completed hierarchically according to the division results. Finally, empirical analysis proves that the business English information mining method based on task cooperative learning mode has higher application value than the traditional method.
Short Text Sentiment Analysis of Micro-blog Based on BERT

Micro-blog has become increasingly popular among the general public. It has brought a lot of comment text to researchers for its great convenience, timely updating, and a wide variety of self-focused topics. Identifying the emotions expressed in these comments has become a valuable topic in order to make inferences for focused contents in Micro-blog. In this paper, we report on the effectiveness of the language representation model BERT [1] with respect to the sentiment classification tasks. Experimental results show that the pre-training of deep bidirectional transformers can improve the accuracy, recall and F1 score on sentiment classification. The final evaluation index of this problem by using a Github data set increased by 2.3% on average.
Continuous-variable quantum key distribution with discretized modulations in the strong noise regime

We consider a general family of quantum key distribution (QKD) protocols utilizing displaced thermal states with discretized modulations. Separating the effects of the Gaussian channel and the non-Gaussian distribution, we study the dependence of the secret key generation rate on the magnitude of modulations (the strength of the modulated signal). We show that in the limit of a strong signal, QKD is impossible. In this case, from the perspective of an efficient eavesdropper, the ensemble of transmitted states is effectively classical and the amount of leaked information is limited only by the entropy of the distribution of transmitted states, while the mutual information between the legitimate parties is also subject to such limiting factors as detection and reconciliation efficiencies. We demonstrate that two regimes must be distinguished: weak and strong thermal noise. In the case of strong noise, the security boundary is mostly determined by the weak-signal limit, for which we obtain an explicit form of the condition where the secret key can be generated in the asymptotic limit. When the noise is weak, however, QKD may become possible only when the signal strength exceeds some critical value.
A Subgraph Query Method Based on Adjacent Node Features on Large-Scale Label Graphs

In the real world, various applications can be modeled as large-scale label graphs, which labels can be represented the feature of nodes. Subgraph query is a problem of finding isomorphism subgraphs that satisfy the query conditions, which has attracted much attention in recent years. Therefore, a subgraph query method based on adjacent node features on large-scale label graphs with fewer label categories was proposed in this paper. The method consists of two phases, which are offline index creation and online query. Firstly, we introduce a data structure to describe the information of nodes by scalar features. Secondly, we propose a three-level star structure features index include adjacent nodes’ label category, the degree of star-centered and the number of each label, which is named adjacent node scalar features index (ANSF index). After that, according to the structure of query graph is star structure or not, we proposed two different processing strategies on the phase of online query. Experimental results show the efficiency and the effectiveness of the proposed method in index creation time, space usage and subgraph query.
Range Shortest Unique Substring Queries

Let $$\mathsf {T}[1,n]$$ be a string of length n and $$\mathsf {T}[i,j]$$ be the substring of $$\mathsf {T}$$ starting at position i and ending at position j. A substring $$\mathsf {T}[i,j]$$ of $$\mathsf {T}$$ is a repeat if it occurs more than once in $$\mathsf {T}$$ ; otherwise, it is a unique substring of $$\mathsf {T}$$ . Repeats and unique substrings are of great interest in computational biology and in information retrieval. Given string $$\mathsf {T}$$ as input, the Shortest Unique Substring problem is to find a shortest substring of $$\mathsf {T}$$ that does not occur elsewhere in $$\mathsf {T}$$ . In this paper, we introduce the range variant of this problem, which we call the Range Shortest Unique Substring problem. The task is to construct a data structure over $$\mathsf {T}$$ answering the following type of online queries efficiently. Given a range $$[\alpha , \beta ]$$ , return a shortest substring $$\mathsf {T}[i,j]$$ of $$\mathsf {T}$$ with exactly one occurrence in $$[\alpha , \beta ]$$ . We present an $$\mathcal {O}(n\log n)$$ -word data structure with $$\mathcal {O}(\log _w n)$$ query time, where $$w=\varOmega (\log n)$$ is the word size. Our construction is based on a non-trivial reduction allowing us to apply a recently introduced optimal geometric data structure [Chan et al. ICALP 2018].
Verification and Repair of Neural Networks: A Progress Report on Convolutional Models

Recent public calls for the development of explainable and verifiable AI led to a growing interest in formal verification and repair of machine-learned models. Despite the impressive progress that the learning community has made, models such as deep neural networks remain vulnerable to adversarial attacks, and their sheer size represents a major obstacle to formal analysis and implementation. In this paper we present our current efforts to tackle repair of deep convolutional neural networks using ideas borrowed from Transfer Learning. With results obtained on popular MNIST and CIFAR10 datasets, we show that models of deep convolutional neural networks can be transformed into simpler ones preserving their accuracy, and we discuss how formal repair through convex programming techniques could benefit from this process.
Improved Particle Swarm Optimization Image Registration Based on Mutual Information

An improved particle swarm optimization (PSO) image registration algorithm based on mutual information is proposed. Firstly, the mutual information value of the image is calculated, and the mutual information is used as the fitness function of PSO to seek the global optimal value of the image and realize image registration. Then, the registration and the floating image are fused. The results show that the algorithm not only improves the ability of optimization, speeding up the speed and accuracy of the algorithm, but also makes the results of image registration more accurate.
Angular Detection System on Goal Frame using Image Processing with LabVIEW

The image processing technique is to transform the input image into another image so that the output has better quality than the input image quality. By utilizing the technology, it is hoped that there is an application aimed at capturing an object that is in front of the camera can identify the type of object. In the field of soccer robots, one of the problems that must be solved to make a robot system is that the camera in the robot can recognize the goal well. The camera is used as a detector sensor and a soccer robot can recognize the goal in the field. With these problems, it was designed as a goal frame detection system based on digital image processing techniques. The parameters used for this research are the distance and the position of the robot against the goal frame with the Harris Corner Detection method. Testing is done by conducting the processing and analysis of digital imagery. From the overall testing with the distance parameters and the position of the robot against the goal, for the shooting of the most accurate object is on the camera height half of the height of the object, the camera distance to the goal as far as 110 cm, and angles 45° to -45°.
Modified Artificial Potential Field Approaches for Mobile Robot Navigation in Unknown Environments

Singh, Ngangbam Herojit Devi, Salam Shuleenda Thongam, KhelchandraNavigation is the important task of any mobile robot. In this paper, a modified potential field method for mobile robot navigation has been proposed for unknown environments. Here, the shortest distance between the goal position and robot has been considered to establish a new repulsive potential functions. The developed repulsive potential functions assure that the goal is the global minimum of the total potential field. To demonstrate the efficiency of the proposed method, computer simulations have been carried out through MATLAB software.
Learning Domain Adaptive Features with Unlabeled Domain Bridges

Conventional cross-domain image-to-image translation or unsupervised domain adaptation methods assume that the source domain and target domain are closely related. This neglects a practical scenario where the domain discrepancy between the source and target is excessively large. In this paper, we propose a novel approach to learn domain adaptive features between the largely-gapped source and target domains with unlabeled domain bridges. Firstly, we introduce the framework of Cycle-consistency Flow Generative Adversarial Networks (CFGAN) that utilizes domain bridges to perform image-to-image translation between two distantly distributed domains. Secondly, we propose the Prototypical Adversarial Domain Adaptation (PADA) model which utilizes unlabeled bridge domains to align feature distribution between source and target with a large discrepancy. Extensive quantitative and qualitative experiments are conducted to demonstrate the effectiveness of our proposed models.