OpenRaiser/Intern-Atlas huggingface dataset:
Differentiable Surface Splatting for Point-based Geometry Processing
Venue: arXiv cs.GR 2019
Year: 2019

We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for point clouds. Gradients for point locations and normals are carefully designed to handle discontinuities of the rendering function. Regularization terms are introduced to ensure uniform distribution of the points on the underlying surface. We demonstrate applications of DSS to inverse rendering for geometry synthesis and denoising, where large scale topological changes, as well as small scale detail modifications, are accurately and robustly handled without requiring explicit connectivity, outperforming state-of-the-art techniques. The data and code are at https://github.com/yifita/DSS.
Human-Machine Collaboration for Fast Land Cover Mapping
Venue: arXiv cs.HC 2019
Year: 2019

We propose incorporating human labelers in a model fine-tuning system that provides immediate user feedback. In our framework, human labelers can interactively query model predictions on unlabeled data, choose which data to label, and see the resulting effect on the model's predictions. This bi-directional feedback loop allows humans to learn how the model responds to new data. Our hypothesis is that this rich feedback allows human labelers to create mental models that enable them to better choose which biases to introduce to the model. We compare human-selected points to points selected using standard active learning methods. We further investigate how the fine-tuning methodology impacts the human labelers' performance. We implement this framework for fine-tuning high-resolution land cover segmentation models. Specifically, we fine-tune a deep neural network -- trained to segment high-resolution aerial imagery into different land cover classes in Maryland, USA -- to a new spatial area in New York, USA. The tight loop turns the algorithm and the human operator into a hybrid system that can produce land cover maps of a large area much more efficiently than the traditional workflows. Our framework has applications in geospatial machine learning settings where there is a practically limitless supply of unlabeled data, of which only a small fraction can feasibly be labeled through human efforts.
Network-based Fake News Detection: A Pattern-driven Approach
Venue: arXiv cs.SI 2019
Year: 2019

Fake news gains has gained significant momentum, strongly motivating the need for fake news research. Many fake news detection approaches have thus been proposed, where most of them heavily rely on news content. However, network-based clues revealed when analyzing news propagation on social networks is an information that has hardly been comprehensively explored or used for fake news detection. We bridge this gap by proposing a network-based pattern-driven fake news detection approach. We aim to study the patterns of fake news in social networks, which refer to the news being spread, spreaders of the news and relationships among the spreaders. Empirical evidence and interpretations on the existence of such patterns are provided based on social psychological theories. These patterns are then represented at various network levels (i.e., node-level, ego-level, triad-level, community-level and the overall network) for being further utilized to detect fake news. The proposed approach enhances the explainability in fake news feature engineering. Experiments conducted on real-world data demonstrate that the proposed approach can outperform the state of the arts.
GSTR: Secure Multi-hop Message Dissemination in Connected Vehicles using Social Trust Model
Venue: arXiv cs.NI 2019
Year: 2019

The emergence of connected vehicles paradigm has made secure communication a key concern amongst the connected vehicles. Communication between the vehicles and Road Side Units (RSUs) is critical to disseminate message among the vehicles. We focus on secure message transmission in connected vehicles using multi_hop social networks environment to deliver the message with varying trustworthiness. We proposed a Geographic Social Trust Routing (GSTR) approach; messages are propagated using multiple hops and by considering the various available users in the vehicular network. GSTR is proposed in an application perspective with an assumption that the users are socially connected. The users are selected based on trustworthiness as defined by social connectivity. The route to send a message is calculated based on the highest trust level of each node by using the nodes social network connections along the path in the network. GSTR determines the shortest route using the trusted nodes along the route for message dissemination. GSTR is made delay tolerant by introducing message storage in the cloud if a trustworthy node is unavailable to deliver the message. We compared the proposed approach with Geographic and Traffic Load based Routing (GTLR), Greedy Perimeter Stateless Routing (GPSR), Trust-based GPSR (T_GPSR). The performance results obtained show that GSTR ensures efficient resource utilization, lower packet losses at high vehicle densities.
Analyzing the Structure of Attention in a Transformer Language Model
Venue: arXiv cs.CL 2019
Year: 2019

The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks. In this paper, we analyze the structure of attention in a Transformer language model, the GPT-2 small pretrained model. We visualize attention for individual instances and analyze the interaction between attention and syntax over a large corpus. We find that attention targets different parts of speech at different layer depths within the model, and that attention aligns with dependency relations most strongly in the middle layers. We also find that the deepest layers of the model capture the most distant relationships. Finally, we extract exemplar sentences that reveal highly specific patterns targeted by particular attention heads.
Chinese Embedding via Stroke and Glyph Information: A Dual-channel View
Venue: arXiv cs.CL 2019
Year: 2019

Recent studies have consistently given positive hints that morphology is helpful in enriching word embeddings. In this paper, we argue that Chinese word embeddings can be substantially enriched by the morphological information hidden in characters which is reflected not only in strokes order sequentially, but also in character glyphs spatially. Then, we propose a novel Dual-channel Word Embedding (DWE) model to realize the joint learning of sequential and spatial information of characters. Through the evaluation on both word similarity and word analogy tasks, our model shows its rationality and superiority in modelling the morphology of Chinese.
Federated Learning for Emoji Prediction in a Mobile Keyboard
Venue: arXiv cs.CL 2019
Year: 2019

We show that a word-level recurrent neural network can predict emoji from text typed on a mobile keyboard. We demonstrate the usefulness of transfer learning for predicting emoji by pretraining the model using a language modeling task. We also propose mechanisms to trigger emoji and tune the diversity of candidates. The model is trained using a distributed on-device learning framework called federated learning. The federated model is shown to achieve better performance than a server-trained model. This work demonstrates the feasibility of using federated learning to train production-quality models for natural language understanding tasks while keeping users' data on their devices.
FAMED-Net: A Fast and Accurate Multi-scale End-to-end Dehazing Network
Venue: arXiv cs.CV 2019
Year: 2019

Single image dehazing is a critical image pre-processing step for subsequent high-level computer vision tasks. However, it remains challenging due to its ill-posed nature. Existing dehazing models tend to suffer from model overcomplexity and computational inefficiency or have limited representation capacity. To tackle these challenges, here we propose a fast and accurate multi-scale end-to-end dehazing network called FAMED-Net, which comprises encoders at three scales and a fusion module to efficiently and directly learn the haze-free image. Each encoder consists of cascaded and densely connected point-wise convolutional layers and pooling layers. Since no larger convolutional kernels are used and features are reused layer-by-layer, FAMED-Net is lightweight and computationally efficient. Thorough empirical studies on public synthetic datasets (including RESIDE) and real-world hazy images demonstrate the superiority of FAMED-Net over other representative state-of-the-art models with respect to model complexity, computational efficiency, restoration accuracy, and cross-set generalization. The code will be made publicly available.
Secure Software-Defined Networking Based on Blockchain
Venue: arXiv cs.CR 2019
Year: 2019

Software-Defined Networking (SDN) separates the network control plane and data plane, which provides a network-wide view with centralized control (in the control plane) and programmable network configuration for data plane injected by SDN applications (in the application plane). With these features, a number of drawbacks of the traditional network architectures such as static configuration, non-scalability and low efficiency can be effectively avoided. However, SDN also brings with it some new security challenges, such as single-point failure of the control plane, malicious flows from applications, exposed network-wide resources and a vulnerable channel between the control plane and the data plane. In this paper, we design a monolithic security mechanism for SDN based on Blockchain. Our mechanism decentralizes the control plane to overcome single-point failure while maintaining a network-wide view. The mechanism also guarantees the authenticity, traceability, and accountability of application flows, and hence secures the programmable configuration. Moreover, the mechanism provides a fine-grained access control of network-wide resources and a secure controller-switch channel to further protect resources and communication in SDN.
Hybrid Function Sparse Representation towards Image Super Resolution
Venue: arXiv cs.CV 2019
Year: 2019

Sparse representation with training-based dictionary has been shown successful on super resolution(SR) but still have some limitations. Based on the idea of making the magnification of function curve without losing its fidelity, we proposed a function based dictionary on sparse representation for super resolution, called hybrid function sparse representation (HFSR). The dictionary we designed is directly generated by preset hybrid functions without additional training, which can be scaled to any size as is required due to its scalable property. We mixed approximated Heaviside function (AHF), sine function and DCT function as the dictionary. Multi-scale refinement is then proposed to utilize the scalable property of the dictionary to improve the results. In addition, a reconstruct strategy is adopted to deal with the overlaps. The experiments on Set14 SR dataset show that our method has an excellent performance particularly with regards to images containing rich details and contexts compared with non-learning based state-of-the art methods.
Classification of EEG Signals using Genetic Programming for Feature Construction
Venue: arXiv cs.NE 2019
Year: 2019

The analysis of electroencephalogram (EEG) waves is of critical importance for the diagnosis of sleep disorders, such as sleep apnea and insomnia, besides that, seizures, epilepsy, head injuries, dizziness, headaches and brain tumors. In this context, one important task is the identification of visible structures in the EEG signal, such as sleep spindles and K-complexes. The identification of these structures is usually performed by visual inspection from human experts, a process that can be error prone and susceptible to biases. Therefore there is interest in developing technologies for the automated analysis of EEG. In this paper, we propose a new Genetic Programming (GP) framework for feature construction and dimensionality reduction from EEG signals. We use these features to automatically identify spindles and K-complexes on data from the DREAMS project. Using 5 different classifiers, the set of attributes produced by GP obtained better AUC scores than those obtained from PCA or the full set of attributes. Also, the results obtained from the proposed framework obtained a better balance of Specificity and Recall than other models recently proposed in the literature. Analysis of the features most used by GP also suggested improvements for data acquisition protocols in future EEG examinations.
iProStruct2D: Identifying protein structural classes by deep learning via 2D representations
Venue: arXiv cs.CV 2019
Year: 2019

In this paper we address the problem of protein classification starting from a multi-view 2D representation of proteins. From each 3D protein structure, a large set of 2D projections is generated using the protein visualization software Jmol. This set of multi-view 2D representations includes 13 different types of protein visualizations that emphasize specific properties of protein structure (e.g., a backbone visualization that displays the backbone structure of the protein as a trace of the Cα atom). Each type of representation is used to train a different Convolutional Neural Network (CNN), and the fusion of these CNNs is shown to be able to exploit the diversity of different types of representations to improve classification performance. In addition, several multi-view projections are obtained by uniformly rotating the protein structure around its central X, Y, and Z viewing axes to produce 125 images. This approach can be considered a data augmentation method for improving the performance of the classifier and can be used in both the training and the testing phases. Experimental evaluation of the proposed approach on two datasets demonstrates the strength of the proposed method with respect to the other state-of-the-art approaches. The MATLAB code used in this paper is available at https://github.com/LorisNanni.
Rate-Splitting Unifying SDMA, OMA, NOMA, and Multicasting in MISO Broadcast Channel: A Simple Two-User Rate Analysis
Venue: arXiv cs.IT 2019
Year: 2019

Considering a two-user multi-antenna Broadcast Channel, this paper shows that linearly precoded Rate-Splitting (RS) with Successive Interference Cancellation (SIC) receivers is a flexible framework for non-orthogonal transmission that generalizes, and subsumes as special cases, four seemingly different strategies, namely Space Division Multiple Access (SDMA) based on linear precoding, Orthogonal Multiple Access (OMA), Non- Orthogonal Multiple Access (NOMA) based on linearly precoded superposition coding with SIC, and physical-layer multicasting. The paper studies the sum-rate and shows analytically how RS unifies, outperforms, and specializes to SDMA, OMA, NOMA, and multicasting as a function of the disparity of the channel strengths and the angle between the user channel directions.
The Snippets Taxonomy in Web Search Engines
Venue: arXiv cs.IR 2019
Year: 2019

In this paper authors analyzed 50 000 keywords results collected from localized Polish Google search engine. We proposed a taxonomy for snippets displayed in search results as regular, rich, news, featured and entity types snippets. We observed some correlations between overlapping snippets in the same keywords. Results show that commercial keywords do not cause results having rich or entity types snippets, whereas keywords resulting with snippets are not commercial nature. We found that significant number of snippets are scholarly articles and rich cards carousel. We conclude our findings with conclusion and research limitations.
WikiDataSets: Standardized sub-graphs from Wikidata
Venue: arXiv cs.LG 2019
Year: 2019

Developing new ideas and algorithms in the fields of graph processing and relational learning requires public datasets. While Wikidata is the largest open source knowledge graph, involving more than fifty million entities, it is larger than needed in many cases and even too large to be processed easily. Still, it is a goldmine of relevant facts and relations. Using this knowledge graph is time consuming and prone to task specific tuning which can affect reproducibility of results. Providing a unified framework to extract topic-specific subgraphs solves this problem and allows researchers to evaluate algorithms on common datasets. This paper presents various topic-specific subgraphs of Wikidata along with the generic Python code used to extract them. These datasets can help develop new methods of knowledge graph processing and relational learning.
Learning robust visual representations using data augmentation invariance
Venue: arXiv cs.CV 2019
Year: 2019

Deep convolutional neural networks trained for image object categorization have shown remarkable similarities with representations found across the primate ventral visual stream. Yet, artificial and biological networks still exhibit important differences. Here we investigate one such property: increasing invariance to identity-preserving image transformations found along the ventral stream. Despite theoretical evidence that invariance should emerge naturally from the optimization process, we present empirical evidence that the activations of convolutional neural networks trained for object categorization are not robust to identity-preserving image transformations commonly used in data augmentation. As a solution, we propose data augmentation invariance, an unsupervised learning objective which improves the robustness of the learned representations by promoting the similarity between the activations of augmented image samples. Our results show that this approach is a simple, yet effective and efficient (10 % increase in training time) way of increasing the invariance of the models while obtaining similar categorization performance.
k-Nearest Neighbor Optimization via Randomized Hyperstructure Convex Hull
Venue: arXiv cs.LG 2019
Year: 2019

In the k-nearest neighbor algorithm (k-NN), the determination of classes for test instances is usually performed via a majority vote system, which may ignore the similarities among data. In this research, the researcher proposes an approach to fine-tune the selection of neighbors to be passed to the majority vote system through the construction of a random n-dimensional hyperstructure around the test instance by introducing a new threshold parameter. The accuracy of the proposed k-NN algorithm is 85.71%, while the accuracy of the conventional k-NN algorithm is 80.95% when performed on the Haberman's Cancer Survival dataset, and 94.44% for the proposed k-NN algorithm, compared to the conventional's 88.89% accuracy score on the Seeds dataset. The proposed k-NN algorithm is also on par with the conventional support vector machine algorithm accuracy, even on the Banknote Authentication and Iris datasets, even surpassing the accuracy of support vector machine on the Seeds dataset.
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks
Venue: arXiv cs.LG 2019
Year: 2019

Deep neural networks (DNNs) have achieved state-of-the-art performances in many important domains, including medical diagnosis, security, and autonomous driving. In these domains where safety is highly critical, an erroneous decision can result in serious consequences. While a perfect prediction accuracy is not always achievable, recent work on Bayesian deep networks shows that it is possible to know when DNNs are more likely to make mistakes. Knowing what DNNs do not know is desirable to increase the safety of deep learning technology in sensitive applications. Bayesian neural networks attempt to address this challenge. However, traditional approaches are computationally intractable and do not scale well to large, complex neural network architectures. In this paper, we develop a theoretical framework to approximate Bayesian inference for DNNs by imposing a Bernoulli distribution on the model weights. This method, called MC-DropConnect, gives us a tool to represent the model uncertainty with little change in the overall model structure or computational cost. We extensively validate the proposed algorithm on multiple network architectures and datasets for classification and semantic segmentation tasks. We also propose new metrics to quantify the uncertainty estimates. This enables an objective comparison between MC-DropConnect and prior approaches. Our empirical results demonstrate that the proposed framework yields significant improvement in both prediction accuracy and uncertainty estimation quality compared to the state of the art.
Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos
Venue: arXiv cs.CV 2019
Year: 2019

Time-stamp aware anomaly detection in traffic videos is an essential task for the advancement of the intelligent transportation system. Anomaly detection in videos is a challenging problem due to sparse occurrence of anomalous events, inconsistent behavior of a different type of anomalies and imbalanced available data for normal and abnormal scenarios. In this paper, we present a three-stage pipeline to learn the motion patterns in videos to detect a visual anomaly. First, the background is estimated from recent history frames to identify the motionless objects. This background image is used to localize the normal/abnormal behavior within the frame. Further, we detect an object of interest in the estimated background and categorize it into anomaly based on a time-stamp aware anomaly detection algorithm. We also discuss the challenges faced in improving performance over the unseen test data for traffic anomaly detection. Experiments are conducted over Track 3 of NVIDIA AI city challenge 2019. The results show the effectiveness of the proposed method in detecting time-stamp aware anomalies in traffic/road videos.
Malware Detection with LSTM using Opcode Language
Venue: arXiv cs.CR 2019
Year: 2019

Nowadays, with the booming development of Internet and software industry, more and more malware variants are designed to perform various malicious activities. Traditional signature-based detection methods can not detect variants of malware. In addition, most behavior-based methods require a secure and isolated environment to perform malware detection, which is vulnerable to be contaminated. In this paper, similar to natural language processing, we propose a novel and efficient approach to perform static malware analysis, which can automatically learn the opcode sequence patterns of malware. We propose modeling malware as a language and assess the feasibility of this approach. First, We use the disassembly tool IDA Pro to obtain opcode sequence of malware. Then the word embedding technique is used to learn the feature vector representation of opcode. Finally, we propose a two-stage LSTM model for malware detection, which use two LSTM layers and one mean-pooling layer to obtain the feature representations of opcode sequences of malwares. We perform experiments on the dataset that includes 969 malware and 123 benign files. In terms of malware detection and malware classification, the evaluation results show our proposed method can achieve average AUC of 0.99 and average AUC of 0.987 in best case, respectively.
Journal Name Extraction from Japanese Scientific News Articles
Venue: arXiv cs.CL 2019
Year: 2019

In Japanese scientific news articles, although the research results are described clearly, the article's sources tend to be uncited. This makes it difficult for readers to know the details of the research. In this paper, we address the task of extracting journal names from Japanese scientific news articles. We hypothesize that a journal name is likely to occur in a specific context. To support the hypothesis, we construct a character-based method and extract journal names using this method. This method only uses the left and right context features of journal names. The results of the journal name extractions suggest that the distribution hypothesis plays an important role in identifying the journal names.