Reviewerly/exHarmony huggingface dataset:
Diag2graph: Representing Deep Learning Diagrams In Research Papers As Knowledge Graphs

Which are the segmentation algorithms proposed during 2018-2019 in CVPR that have CNN architecture?’ Answering this question involves identifying and analyzing the deep learning architecture diagrams from several research papers. Retrieving such information poses significant challenge as most of the existing academic search engines are primarily based on only the text content. In this paper, we introduce Diag2Graph, an end-to-end framework for parsing deep learning diagram-figures, that enables powerful search and retrieval of architectural details in research papers. Our proposed approach automatically localizes figures from research papers, classifies them, and analyses the content of the diagram-figures. The key steps in analyzing the Figure content is the extraction of the different components data and finding their structural relation. Finally, the extracted components and their relations are represented in the form of a deep knowledge graph. A thorough evaluation on a real-word annotated dataset has been done to demonstrate the efficacy of our approach.
On the Relationship between Eye Tracking Resolution and Performance of Oculomotoric Biometric Identification

Distributional properties of fixations and saccades are known to constitute biometric characteristics. Additionally, high-frequency micro-movements of the eyes have recently been found to constitute biometric characteristics that allow for faster and more robust biometric identification than just macro-movements. Micro-movements of the eyes occur on scales that are very close to the precision of currently available eye trackers. This study therefore characterizes the relationship between the temporal and spatial resolution of eye tracking recordings on one hand and the performance of a biometric identification method that processes micro-and macro-movements via a deep convolutional network. We find that that the deteriorating effects of decreasing both, the temporal and spatial resolution are not cumulative. We observe that on low-resolution data, the network reaches performance levels above chance and outperforms statistical approaches.
Certifying Decision Trees Against Evasion Attacks by Program Analysis

Machine learning has proved invaluable for a range of different tasks, yet it also proved vulnerable to evasion attacks, i.e., maliciously crafted perturbations of input data designed to force mispredictions. In this paper we propose a novel technique to verify the security of decision tree models against evasion attacks with respect to an expressive threat model, where the attacker can be represented by an arbitrary imperative program. Our approach exploits the interpretability property of decision trees to transform them into imperative programs, which are amenable for traditional program analysis techniques. By leveraging the abstract interpretation framework, we are able to soundly verify the security guarantees of decision tree models trained over publicly available datasets. Our experiments show that our technique is both precise and efficient, yielding only a minimal number of false positives and scaling up to cases which are intractable for a competitor approach.
Self-modeling Networks Using Adaptive Internal Mental Models for Cognitive Analysis and Support Processes

In this paper, an adaptive network model is presented for cognitive analysis and support processes for the situation and states of a human, illustrated for a car driver. The adaptive network model makes use of first-order self-models for the internal mental models used for the cognitive analysis and support processes. To obtain adaptation of these first-order self-models, second-order self-models are included. The adaptive network model is illustrated for realistic scenarios for a car driver.
Amortized Mixture Prior for Variational Sequence Generation

Variational autoencoder (VAE) is a popular latent variable model for data generation. However, in natural language applications, VAE suffers from the posterior collapse in optimization procedure where the model posterior likely collapses to a standard Gaussian prior which disregards latent semantics from sequence data. The recurrent decoder accordingly generates du-plicate or noninformative sequence data. To tackle this issue, this paper adopts the Gaussian mixture prior for latent variable, and simultaneously fulfills the amortized regularization in encoder and skip connection in decoder. The noise robust prior, learned from the amortized encoder, becomes semantically meaningful. The prediction of sequence samples, due to skip connection, becomes contextually precise at each time. The amortized mixture prior (AMP) is then formulated in construction of variational recurrent autoencoder (VRAE) for sequence generation. Experiments on different tasks show that AMP-VRAE can avoid the posterior collapse, learn the meaningful latent features and improve the inference and generation for semantic representation.
Online Trajectory Planning for an Industrial Tractor Towing Multiple Full Trailers

This paper presents a novel solution for online trajectory planning of a full-size tractor-trailers vehicle composed of a car-like tractor and arbitrary number of passive full trailers. The motion planning problem for such systems was rarely addressed due to the complex nonlinear dynamics. A simulation-based prediction method is proposed to easily handle the complicated nonlinear dynamics and efficiently generate the obstacle-free and dynamically feasible trajectories. The vehicle dynamics model and a two-layer controller are used in the prediction. Implementation results on the real-world full-size industrial tractor-trailers vehicle are presented to validate the performance of the proposed methods.
Toward General AI: Consciousness Computational Modeling Under Uncertainty

The following topics are dealt with: neural nets; topology; learning (artificial intelligence); computer aided instruction; differential equations; set theory; computational complexity; teaching; educational institutions; feature extraction.
Progressive Point To Set Metric Learning For Semi-Supervised Few-Shot Classification

Few-shot learning aims to learn models that can generalize to unseen tasks from very few annotated samples of available tasks. The performance of few-shot learning is greatly affected by the number of samples per class. The massive unlabeled data can help to boost the performance of few shot learning models. In this paper, we propose a novel progressive point to set metric learning (PPSML) model for semisupervised few-shot classification. The distance metric is defined for an image of the query set to a class of the support set by point to set distance. A self-training strategy is designed to select the samples locally or globally with high confidence and use these samples to progressively update the point to set distance. Experiments on benchmark datasets show that our proposed PPSML significantly improves the accuracy of few shot classification and outperforms the state-of-the-art semisupervised few-shot learning methods.
FCN+RL: A Fully Convolutional Network followed by Refinement Layers to Offline Handwritten Signature Segmentation

Although secular, handwritten signature is one of the most reliable biometric method used by most countries. In the last ten years, the application of technology for verification of handwritten signatures has evolved strongly, including forensic aspects. Some factors, such as the complexity of the background and the small size of the region of interest - signature pixels - increase the difficulty of the targeting task. Other factors that make it challenging are the various variations present in handwritten signatures such as location, type of ink, color and type of pen, and the type of stroke. In this work, we propose an approach to locate and extract the pixels of handwritten signatures on identification documents, without any prior information on the location of the signatures. The technique used is based on a fully convolutional encoder-decoder network combined with a block of refinement layers for the alpha channel of the predicted image. The experimental results demonstrate that the technique outputs a clean signature with higher fidelity in the lines than the traditional approaches and preservation of the pertinent characteristics to the signer's spelling. To evaluate the quality of our proposal, we use the following image similarity metrics: SSIM, SIFT, and Dice Coefficient. The qualitative and quantitative results show a significant improvement in comparison with the baseline system.
Improving Deep Learning Approaches for Human Activity Recognition based on Natural Language Processing of Action Labels

Human activity recognition has always been an appealing research topic in computer vision due its theoretic interest and vast range of applications. In recent years, machine learning has dominated computer vision and human activity recognition research. Supervised learning methods and especially deep learning-based ones are considered to provide the best solutions for this task, achieving state-of-the art results. However, the performance of deep learning-based approaches depends greatly on the modelling capabilities of the spatio-temporal neural network architecture and the learning goals of the training process. Moreover, the design complexity is task-depended. In this paper, we show that we can exploit the information contained in the label description of action classes (action labels) to extract information regarding their similarity which can then be used to steer the learning process and improve the activity recognition performance. Moreover, we experimentally verify that the adopted strategy can be useful in both single and multi-stream architectures, providing better scalability on the training of the network in more complex datasets featuring activity classes with larger intra- and inter-class similarities.
Using Trust as a Measure to Derive Data Quality in Data Shared IoT Deployments

Recent developments in Internet of Things have heightened the need for data sharing across application domains to foster innovation. As most of these IoT deployments are based on heterogeneous sensor types, there is increased scope for sharing erroneous, inaccurate or inconsistent data. This in turn may lead to inaccurate models built from this data. It is important to evaluate this data as it is collected to establish its quality. This paper presents an analysis of data quality as it is represented in Internet of Things (IoT) systems and some of the limitations of this representation. The paper then introduces the use of trust as a heuristic to drive data quality measurements. Trust is a well-established metric that has been used to determine the validity of a piece or source of data in crowd sourced or other unreliable data collection techniques. The analysis extends to detail an appropriate framework for representing data quality within the big data model. To demonstrate the application of a trust backed framework, we used data collected from a IoT deployment of sensors to measure air quality in which a low cost sensor was co-located with a gold reference sensor. Using data streams modeled based on a dataset from an IoT deployment, our initial results show that the framework's trust score are consistent with the accuracy measure of the machine learning models.
A Novel Application of Educational Management Information System based on Micro Frontends

With the launch of the Education Informatization 2.0 action plan by the Ministry of Education, a large number of college information systems have been born in China. Most of these systems are single page web applications (SPA) based on traditional MVC structures. Due to the complex logic and high coupling between educational businesses, developers need to write a lot of code. The education information system has many businesses and high coupling between businesses that the system often face problems such as bloated frontend businesses, iterative system updates, and difficult incremental function developments. Combined with the idea of service-oriented architecture, this paper proposes a micro frontends solution and applies it to the new generation of graduate information platform of East China Normal University, which has better agile development capabilities. From the aspects of service separation, efficient development, and incremental upgrade, this paper verifies that the architecture can well adapt to the needs of future educational management information system. The design of the micro frontends provides a new idea for the development of a new generation of education information system.
Relative Pose Estimation For Stereo Rolling Shutter Cameras

In this paper, we present a novel linear algorithm to estimate the 6 DoF relative pose from consecutive frames of stereo rolling shutter (RS) cameras. Our method is derived based on the assumption that stereo cameras undergo motion with constant velocity around the center of the baseline, which needs 9 pairs of correspondences on both left and right consecutive frames. The stereo RS images enable the recovery of depth maps from the semi-global matching (SGM) algorithm. With the estimated camera motion and depth map, we can correct the RS images to get the undistorted images without any scene structure assumption. Experiments on both simulated points and synthetic RS images demonstrate the effectiveness of our algorithm in relative pose estimation.
Graph Convolutional Extreme Learning Machine

Extreme Learning Machine (ELM) has gained lots of research interest due to its universal approximation capability and fast learning speed. However, traditional ELMs are devised for regular Euclidean data, such as 2D grid and 1D sequence, and thus don't apply to non-Euclidean data, e.g., graph-structured data. To overcome this shortcoming, this paper presents a Graph Convolutional Extreme Learning Machine (termed as GCELM) for semi-supervised classification. Technically, a random graph convolutional layer is introduced to replace the random projection of original ELM, which endues ELM with the capability of dealing with graph-structured data directly. To generate a robust graph from the raw dataset, a self-representation model is adopted to construct a weighted graph. Extensive experiments on 27 UCI datasets demonstrate that GCELM outperforms many popular semi-supervised methods, and with faster learning speed. To the best of our knowledge, this is the first work that combines graph convolution with ELM.
Learning Light Field Synthesis with Multi-Plane Images: Scene Encoding as a Recurrent Segmentation Task

In this paper we address the problem of view synthesis from large baseline light fields, by turning a sparse set of input views into a Multi-plane Image (MPI). Because available datasets are scarce, we propose a lightweight network that does not require extensive training. Unlike latest approaches, our model does not learn to estimate RGB layers but only encodes the scene geometry within MPI alpha layers, which comes down to a segmentation task. A Learned Gradient Descent (LGD) framework is used to cascade the same convolutional network in a recurrent fashion in order to refine the volumetric representation obtained. Thanks to its low number of parameters, our model trains successfully on a small light field video dataset and provides visually appealing results. It also exhibits convenient generalization properties regarding both the number of input views, the number of depth planes in the MPI, and the number of refinement iterations.
An Adversarial Attack Detection Paradigm With Swarm Optimization

The rise of smart devices and applications has increased the dependence of human beings on machine learning (ML) based code-driven systems. While many of the pragmatic problems such as image classification, medical diagnosis, and statistical arbitrage have been addressed by extensive recent research in machine learning, it still lacks substantial work in the field of adversarial attacks on safety-critical networked systems. It is a matter of significant importance, as using the adversarial samples, attackers are now able to evade pre-trained systems and mount black-box attacks hence increasing the false positives. In this research, we are proposing a Random Neural Network-based Adversarial intrusion detection system (RNN-ADV). For adversarial attack generation, the Jacobian Saliency Map Attack (JSMA) algorithm has been used. Swarm optimization capabilities have been implemented by training the system with the Artificial Bee Colony (ABC) algorithm. Different scenarios have been designed and the proposed system is then evaluated with benchmark benign NSL-KDD dataset, adversarial data, and the performance is compared with deep neural networks (DNN) using several performance metrics. The results suggest that the proposed scheme outperforms DNN in terms of adversarial attack detection where it has successfully classified benign samples from crafted samples with better accuracy and high F1 scores.
Privacy-Preserving Selective Video Surveillance

The pervasive and intrusive surveillance practices have raised a widespread concern amongst zillions of people about the invasion of their privacy. The privacy breaches in the existing mass-surveillance system are mainly attributed to the exploits of vulnerabilities by adversaries and abuse of cameras by people in charge of them. As a result, there has been a tremendously pressing demand from the public to make the surveillance system privacy-conscious. In this paper, we propose PriSev, a privacy-preserving selective video surveillance method, which enables selective-surveillance where only video frames containing aggressive and suspicious behavioral patterns, like gun brandishing or/and fist-raising, are made available for view by security personnel in the surveillance operation center and for storage. By introducing a lightweight dynamic chaotic image enciphering (DyCIE) scheme, the proposed PriSev method enables onsite object detection and frame encryption at the network edge where the video is created. At the fog/cloud layer, frame decryption is efficiently performed followed by deep-neural-network (DNN) based frame-filtering and selective storage that runs on a surveillance server. In addition, a multiagent system is introduced for the exchange of deciphering keys between the sending and receiving agents. Extensive experimental study and performance analyses corroborate that the proposed PriSev method is able to efficiently perform a privacy-preserving selective surveillance in real-time.
IntKB: A Verifiable Interactive Framework for Knowledge Base Completion

Knowledge bases (KBs) are essential for many downstream NLP tasks, yet their prime shortcoming is that they are often incomplete.State-of-the-art frameworks for KB completion often lack sufficient accuracy to work fully automated without human supervision.As a remedy, we propose IntKB: a novel interactive framework for KB completion from text based on a question answering pipeline.Our framework is tailored to the specific needs of a human-in-the-loop paradigm: (i) We generate facts that are aligned with text snippets and are thus immediately verifiable by humans.(ii) Our system is designed such that it continuously learns during the KB completion task and, therefore, significantly improves its performance upon initial zero-and few-shot relations over time.(iii) We only trigger human interactions when there is enough information for a correct prediction.Therefore, we train our system with negative examples and a fold-option if there is no answer.Our framework yields a favorable performance: it achieves a hit@1 ratio of 29.7% for initially unseen relations, upon which it gradually improves to 46.2%.
Tectonic discrimination and application based on convolution neural network and incomplete big data

Many classical discriminant diagrams have been employed by geologists on certain represented samples of different tectonic areas to distinguish the tectonic settings of igneous rocks in the last few decades. However, a high misjudgment rate was noted under the big data test and the classification results among the various diagrams are inconsistent. In order to avoid using a few indicators for judgment or typical sample data to discriminate, a new discriminant tool has emerged as a powerful platform to improve the accuracy and reliability of the classification of tectonic settings of igneous rocks using multi-element indicators and comprehensive big data. This paper explores the feasibility of using convolution neural network (CNN) in deep learning technology to synthesize multi-element indicators containing incomplete data. Thirty kinds of geochemical major and trace elements were extracted from GEOROC database, and the gray scale two-dimensional code of more than 1.09 million rock samples was constructed by using the incomplete data. The CNN model obtained in this study can effectively classify igneous rock samples in 11 tectonic settings and has good generalization ability. The model has a Top1 classification accuracy of 94.3% for the test dataset, which is substantially better than other algorithms such as decision trees, discriminant analysis, naive Bayes classifiers and support vector machines. An application based on this model is also provided, thus serving as a potentially important daily tool to assist the manual identification in tectonic discrimination.
Analyze Cloud Accounting Software Implementation and Security System for Accounting in MSMEs and Cloud Accounting Software Developer

This study aimed to analyze the factors of cloud accounting system implementation for accounting process in Micro, Small, Medium Enterprises (MSMEs) and the economic effects on the companies. The objects in this study are representative from MSME who use cloud accounting to assist their operational accounting processes, and cloud accounting system provider. This research is a descriptive qualitative study in the form of case study, which uses primary data from observation and interviews. The results of analysis show some factors that are discussed in the study: changes in accounting process, compliance, and security system. The discussion of the factors is useful for understanding how the effects of implementing cloud accounting on the company's operations and finance condition.
Task-based obstacle avoidance for uncertain targets based on semantic object matrix

Service robots meet numerous tasks-level commands with uncertain relationship between targets and obstacles, which introduces plenty of programming work involved with obstacle avoidance in robots’ operations for non-expert users. Hence an obstacle avoidance algorithm based on task semantic object matrix(SOM) was introduced to tell targets from obstacles in this paper. With this strategy, petri net was applied to decompose the robots’ complex operation tasks into sequential subtasks, which can be practical for finite state machine. And the SOM related to sequential subtasks was generated synchronously. Based on the SOM, the point cloud segmentation, collision geometry modeling and the transformation between targets and obstacles can be conducted for robots’ operations. Experimental results indicate that the SOM can be updated within 0.25s. And the experiments demonstrated that this algorithm is feasible to help non-expert users conduct robots’ task-based operations.